{"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005899997,0.9925619,0.0007471825,0.00075875066,0.00055515807,0.0016312259,0.00047649842,0.00019279307,0.003017506],"genre_scores_gemma":[0.000113348375,0.9954554,0.0007439482,0.0006973192,0.0013400611,0.0005969645,0.0006480599,0.00013616662,0.0002687512],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99535924,0.00059301674,0.0011741702,0.0013142509,0.00067405845,0.00088524853],"domain_scores_gemma":[0.99652845,0.0017065003,0.00038293877,0.00083974976,0.00013279269,0.00040959392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012263025,0.0006508727,0.00376081,0.0005533526,0.000072789604,0.000015397252,0.00060901087,0.0009278058,0.00084693334],"category_scores_gemma":[0.005183987,0.00054478704,0.00078915653,0.00059314066,0.0007523166,0.000057890054,0.0002264668,0.0011788023,0.00012367775],"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":[0.0002686882,0.0007760617,0.001361105,0.011809576,0.0004835622,0.00085348525,0.00017138266,0.0000019326321,8.495192e-7,0.0021289636,0.014451366,0.96769303],"study_design_scores_gemma":[0.0016876127,0.00020685192,0.00113314,0.002626576,0.000265159,0.00021018912,0.00008454044,0.00007679593,8.739674e-8,0.00024271935,0.99302727,0.00043908178],"about_ca_topic_score_codex":0.000117386095,"about_ca_topic_score_gemma":0.00035585812,"teacher_disagreement_score":0.9785759,"about_ca_system_score_codex":0.00046561274,"about_ca_system_score_gemma":0.0010868564,"threshold_uncertainty_score":0.99970037},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9114544,0.00004936782,0.084685035,0.0027843837,0.0005487515,0.0001853591,0.0000065811287,0.00003240116,0.00025372198],"genre_scores_gemma":[0.9965986,0.0014305793,0.0005290514,0.0011680848,0.0002307971,0.000003201681,0.000006541658,0.000014321275,0.000018811359],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961313,0.00025609572,0.0013039346,0.00007784627,0.001846844,0.00038396474],"domain_scores_gemma":[0.996567,0.00015945685,0.0024461173,0.0002019214,0.00031173296,0.00031376668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039660134,0.00013648305,0.0005680967,0.00030168582,0.00022259538,0.000027496433,0.00021568796,0.00008635085,0.000011323804],"category_scores_gemma":[0.0022369982,0.00010054407,0.00017008043,0.0010146513,0.0001004827,0.0004882233,0.000051948824,0.0008315377,0.000008002563],"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":[0.00046979298,0.000508935,0.82115865,0.00036486154,0.0004894201,0.000028544386,0.0051584947,0.008876279,0.00015423882,0.000018082219,0.0013559522,0.16141675],"study_design_scores_gemma":[0.0019123738,0.00044107813,0.27885452,0.00023948225,0.000017002842,0.00037947786,0.0014982956,0.7158002,0.00004212542,0.00004211149,0.0006263046,0.00014697839],"about_ca_topic_score_codex":0.00005994587,"about_ca_topic_score_gemma":0.00010535493,"teacher_disagreement_score":0.70692396,"about_ca_system_score_codex":0.003582073,"about_ca_system_score_gemma":0.00086795376,"threshold_uncertainty_score":0.9366996},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9827099,0.010669291,0.00051018933,0.0024937529,0.001049857,0.0004953958,0.0008942645,0.000015969648,0.0011613941],"genre_scores_gemma":[0.9947997,0.00037057375,0.00009747302,0.0043832706,0.00018359257,0.0000023840498,0.00011710826,0.000019655725,0.000026266938],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975691,0.00015158551,0.00080949446,0.00026591326,0.00019517088,0.0010087496],"domain_scores_gemma":[0.9953982,0.0003603008,0.00020204377,0.00029529253,0.0002617222,0.0034823942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088555715,0.00021771496,0.0006572073,0.0004491652,0.00012615042,0.000022318978,0.00032205574,0.00010119568,0.0007717679],"category_scores_gemma":[0.0017380886,0.0001958112,0.00010072653,0.00036140336,0.00029156744,0.000046209178,0.0001382284,0.00042794083,0.000015376758],"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":[0.00023662772,0.00011132672,0.95814395,0.00014607352,0.00022035984,0.0060380185,0.000039990882,0.00004522535,0.000069063,0.00012994143,0.015683088,0.019136334],"study_design_scores_gemma":[0.0025730699,0.00042790573,0.92748964,0.0012791025,0.0002782264,0.002395911,0.000809634,0.000032714237,0.00006288037,0.00011016547,0.06413447,0.0004062639],"about_ca_topic_score_codex":0.8472399,"about_ca_topic_score_gemma":0.98836195,"teacher_disagreement_score":0.141122,"about_ca_system_score_codex":0.0013301722,"about_ca_system_score_gemma":0.005799599,"threshold_uncertainty_score":0.9998366},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003058974,0.0007470525,0.0016511463,0.000024576495,0.00009698811,0.0008133537,0.0004677412,0.00019944795,0.9956938],"genre_scores_gemma":[0.02402169,0.00030735554,0.000086976775,0.00020482019,0.00024192705,0.000005205799,0.0003715582,0.000065069034,0.9746954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99826056,0.00010160877,0.0002513553,0.00063428737,0.0004254298,0.00032678412],"domain_scores_gemma":[0.99848557,0.000118027805,0.00014142891,0.0009474814,0.000059095753,0.00024838664],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007390828,0.00040076073,0.0006362083,0.00031776953,0.00012679062,0.000026872702,0.00047292915,0.00020956024,0.000005131991],"category_scores_gemma":[0.00003497694,0.00036717302,0.00036242267,0.000020202657,0.0001775483,0.000058760983,0.00024436825,0.00077086984,0.000011809946],"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":[0.0015372017,0.00096157077,0.0008528492,0.0019361075,0.0009533064,0.008302314,0.0009910493,0.00082208205,0.000011632277,0.8386676,0.13624868,0.008715622],"study_design_scores_gemma":[0.0021501686,0.00011831049,0.00023652104,0.00035745144,0.0007657836,0.00013438007,0.000060079412,0.017696539,4.8164924e-7,0.000029203056,0.9778552,0.00059587724],"about_ca_topic_score_codex":0.00016809803,"about_ca_topic_score_gemma":0.0000021113324,"teacher_disagreement_score":0.84160656,"about_ca_system_score_codex":0.00062773813,"about_ca_system_score_gemma":0.00012844404,"threshold_uncertainty_score":0.99987805},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2707178,0.0014603438,0.72623235,0.00023297372,0.00024606808,0.00052247505,0.00023976037,0.0000687087,0.00027950146],"genre_scores_gemma":[0.9829724,0.00003202279,0.015978476,0.000627702,0.00033462545,0.0000028071327,0.000011257386,0.000030068692,0.000010668997],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99757016,0.000044045562,0.00043094318,0.0010257172,0.00045000637,0.00047912367],"domain_scores_gemma":[0.99800414,0.00084724557,0.0002575636,0.0004443113,0.00014221003,0.00030450273],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041442915,0.0004426398,0.0007669747,0.00028435397,0.00026957772,0.00027675056,0.00022023534,0.00016069683,0.0000124764865],"category_scores_gemma":[0.00028309438,0.00037314728,0.000051123214,0.00018164914,0.0008797475,0.00024345436,0.0002051214,0.00051267137,8.532396e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002795303,0.000023640336,0.75801027,0.0005108589,0.00006864656,0.0039554555,0.00033533538,0.00040826085,0.00030368924,0.00010672818,0.0000021545977,0.23624703],"study_design_scores_gemma":[0.0049729203,0.0010564572,0.6676929,0.01138389,0.0006023324,0.012223092,0.000013389909,0.28144297,0.00024465902,0.016913833,0.00038171638,0.003071843],"about_ca_topic_score_codex":0.00015418752,"about_ca_topic_score_gemma":0.00015494403,"teacher_disagreement_score":0.7122546,"about_ca_system_score_codex":0.00010853021,"about_ca_system_score_gemma":0.00019607705,"threshold_uncertainty_score":0.999872},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77853906,0.023965443,0.0006253775,0.049198955,0.004663532,0.0040981527,0.0087294495,0.0007443713,0.12943569],"genre_scores_gemma":[0.9948535,0.00023009346,0.0011875569,0.0023790214,0.00019358292,0.000005261203,0.0007917397,0.000017715307,0.00034155525],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991328,0.000032443306,0.0001527916,0.00023990814,0.00012631927,0.00031571288],"domain_scores_gemma":[0.99851656,0.000017164562,0.00005553933,0.0009145586,0.000038757753,0.00045741306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032378855,0.0000912557,0.00014534904,0.000041800777,0.0003068394,0.000024650419,0.00025179633,0.000040322535,0.0001842086],"category_scores_gemma":[0.00011675019,0.00006445731,0.000014238383,0.00010320179,0.00009565888,0.000075260745,0.0001577865,0.00014922571,0.000037380472],"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":[0.000039800856,0.000017463002,0.5004627,0.00006287554,0.00010787389,0.000074581294,0.0011003395,2.9967995e-7,0.000008903315,0.0011580018,0.2990719,0.19789524],"study_design_scores_gemma":[0.0001959625,0.000016636803,0.703974,0.000020860209,0.0000065866902,0.000017747203,0.00006701237,0.0001260604,0.000001574305,0.00014015903,0.29538324,0.000050110182],"about_ca_topic_score_codex":0.18665028,"about_ca_topic_score_gemma":0.6496244,"teacher_disagreement_score":0.46297413,"about_ca_system_score_codex":0.00007948665,"about_ca_system_score_gemma":0.0007426109,"threshold_uncertainty_score":0.8187659},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060988637,0.004368649,0.8907735,0.00015933615,0.0011022165,0.0031604453,0.074752256,0.00051626033,0.019068517],"genre_scores_gemma":[0.012772473,0.0026407798,0.95951635,0.00031652456,0.00048138833,0.000095210984,0.009679048,0.0005713129,0.01392691],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974028,0.00018980486,0.00077018625,0.00076620176,0.00044672505,0.00042427747],"domain_scores_gemma":[0.9983189,0.00019302165,0.00033527936,0.00073719054,0.00018824426,0.00022734601],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003904322,0.0004686911,0.0008955517,0.00018595661,0.00010620905,0.00006013482,0.00015827235,0.00030738584,0.0007345568],"category_scores_gemma":[0.00092041976,0.00050447974,0.000045724115,0.00019393273,0.0006334242,0.00012021987,0.00015122427,0.00042044712,0.000032176133],"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":[0.0044950643,0.0031808983,0.23430838,0.044658702,0.0010056053,0.0018372622,0.002807779,0.026414447,0.00013552318,0.12075627,0.46464884,0.095751226],"study_design_scores_gemma":[0.0035720365,0.0007830884,0.012467851,0.0065367813,0.00053344993,0.00019761211,0.00023427926,0.72247046,0.000008093085,0.0690393,0.1817526,0.0024044446],"about_ca_topic_score_codex":0.0018988521,"about_ca_topic_score_gemma":0.008021699,"teacher_disagreement_score":0.696056,"about_ca_system_score_codex":0.0001938941,"about_ca_system_score_gemma":0.00048498577,"threshold_uncertainty_score":0.99974066},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76623,0.026411477,0.0032787612,0.17711361,0.0007649472,0.00496252,0.0003585802,0.00042864552,0.020451445],"genre_scores_gemma":[0.9835715,0.0036790904,0.0008998324,0.011421756,0.00014386096,0.00007284802,0.00012391423,0.000029158013,0.00005802546],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99471724,0.0023707633,0.0009295524,0.00039084748,0.0008749444,0.00071663695],"domain_scores_gemma":[0.9937554,0.0037794067,0.0006856282,0.0012661177,0.0003149418,0.00019851851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018048448,0.00029553458,0.00089352444,0.000045977296,0.00021294452,0.00009374428,0.000494775,0.00010586814,0.00002502224],"category_scores_gemma":[0.0048593986,0.00017278471,0.00026675433,0.001193748,0.00087702496,0.0003004323,0.00016681138,0.00057701557,0.00002275703],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006945249,0.00044983602,0.8674884,0.0012500441,0.00032470722,0.00000589976,0.025489131,0.00003960635,0.000011286776,0.0043160594,0.05651678,0.043413766],"study_design_scores_gemma":[0.004694122,0.00012504979,0.82884604,0.00011904355,0.000012683045,0.00005397013,0.004709861,0.0008047532,0.000001389683,0.00017329105,0.1602321,0.00022772791],"about_ca_topic_score_codex":0.00025808252,"about_ca_topic_score_gemma":0.00031399066,"teacher_disagreement_score":0.21734151,"about_ca_system_score_codex":0.000117502605,"about_ca_system_score_gemma":0.0003528796,"threshold_uncertainty_score":0.7045956},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97421616,0.005499195,0.00026677793,0.01515112,0.00011049558,0.00017289148,0.000010475578,0.000017710943,0.004555168],"genre_scores_gemma":[0.99613154,0.0012210908,0.0017856578,0.00067435304,0.00004928998,0.000005811526,0.00007863111,0.000012350964,0.000041284882],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99887776,0.00039350742,0.00039602534,0.00005697297,0.00012139922,0.00015430312],"domain_scores_gemma":[0.99818575,0.00010172511,0.00029205,0.00088304374,0.00014733506,0.00039012014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011304651,0.000065450564,0.00021847586,0.00031465475,0.00010645984,0.000024080711,0.0003411299,0.00002684762,0.000051153096],"category_scores_gemma":[0.00034578715,0.00006832236,0.00004773408,0.00023090084,0.00005973795,0.0003384383,0.00001957564,0.000338279,0.000015981352],"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":[0.00022379782,0.00021630496,0.21483132,0.00024132042,0.000118944314,0.00004512337,0.017412541,0.0004890117,0.0006158103,0.0091382675,0.012886235,0.7437813],"study_design_scores_gemma":[0.0017222291,0.00018311244,0.8430989,0.0010893844,0.000021779182,0.00017128924,0.0027597658,0.0005133602,0.00016748137,0.0003967459,0.14972375,0.00015224707],"about_ca_topic_score_codex":0.0070567066,"about_ca_topic_score_gemma":0.09689782,"teacher_disagreement_score":0.7436291,"about_ca_system_score_codex":0.00041232043,"about_ca_system_score_gemma":0.00074058067,"threshold_uncertainty_score":0.9995554},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.4577093e-7,0.013300034,0.004872936,0.008262663,0.0008030426,0.00082357094,0.00015709348,0.0005822793,0.97119766],"genre_scores_gemma":[0.00017960125,0.003768604,0.0017373011,0.0060946844,0.0013955876,0.000046734865,0.00036303504,0.00020850818,0.98620594],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99829125,0.000107089996,0.0002822447,0.00035157532,0.00034528517,0.0006225303],"domain_scores_gemma":[0.9981283,0.000056765246,0.00020873058,0.0010693736,0.00005398881,0.00048284695],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00035181863,0.00023824685,0.00041406747,0.000028191063,0.00008140086,0.000077021825,0.00038551903,0.00013113637,0.010481828],"category_scores_gemma":[0.00013468976,0.00013890362,0.00010211375,0.00030143483,0.00016160718,0.000024205356,0.00015159635,0.00018721946,0.004935614],"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":[0.0000045568418,0.000025586898,0.0026289055,0.00005085203,0.0000956302,0.000004487096,0.0000021785816,3.2235738e-7,4.42554e-9,0.01552364,0.88373685,0.097927004],"study_design_scores_gemma":[0.000084055035,0.000045088753,0.0021706175,0.000135407,0.000008998639,0.000011091509,0.000019131397,0.00006452152,3.008284e-8,0.0002643835,0.9970701,0.00012658496],"about_ca_topic_score_codex":0.00090626033,"about_ca_topic_score_gemma":0.0017619296,"teacher_disagreement_score":0.11333326,"about_ca_system_score_codex":0.00017832895,"about_ca_system_score_gemma":0.00056635356,"threshold_uncertainty_score":0.9958392},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67071444,0.24964547,0.00029518874,0.019137923,0.006333879,0.0026839166,0.0007832087,0.00023370147,0.050172254],"genre_scores_gemma":[0.989906,0.0037346112,0.0001674127,0.00082151813,0.0009921604,0.000024046029,0.00016341606,0.000046718003,0.0041440967],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968548,0.000106972206,0.001372163,0.00022451351,0.0006997893,0.0007417766],"domain_scores_gemma":[0.9965122,0.0002784091,0.001289643,0.00047019855,0.0007832063,0.0006663872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062293997,0.00024322588,0.00051515864,0.00021871473,0.000876287,0.00028142155,0.00053333514,0.00007775979,0.000016650496],"category_scores_gemma":[0.0005934716,0.00015593953,0.000107692176,0.00019190373,0.00012647679,0.00025000455,0.0002535883,0.00063187437,0.000028190707],"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":[0.0007661373,0.00015552489,0.6328298,0.0013220088,0.0014265782,0.000038895243,0.01372984,0.00078324566,0.000022802096,0.009519879,0.14777143,0.19163385],"study_design_scores_gemma":[0.0024449453,0.00043050945,0.059862815,0.0020860916,0.000029770394,0.0014479883,0.003423581,0.02506788,0.000009446344,0.0006539132,0.90416396,0.0003790939],"about_ca_topic_score_codex":0.012704435,"about_ca_topic_score_gemma":0.28111485,"teacher_disagreement_score":0.75639254,"about_ca_system_score_codex":0.0025687579,"about_ca_system_score_gemma":0.003967454,"threshold_uncertainty_score":0.9938701},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9361909,0.00078270317,0.058940172,0.00014082767,0.0001629107,0.0023260475,0.00018662165,0.00011929095,0.0011505259],"genre_scores_gemma":[0.99852717,0.000054389868,0.00026666347,0.000092675764,0.000084570216,0.00068209413,0.00011442689,0.000011649293,0.00016634283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986794,0.000044846227,0.00040649058,0.00027433687,0.00020182454,0.00039308018],"domain_scores_gemma":[0.999153,0.000058209473,0.00015936403,0.00039555732,0.000112590176,0.00012128351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007441012,0.00012020286,0.0005593877,0.00029073222,0.00002805904,0.000010334089,0.000095394134,0.0000640718,0.0000017912956],"category_scores_gemma":[0.00014029768,0.00011513491,0.00014580127,0.0012334959,0.00002251013,0.00008806043,0.00001162277,0.00007399462,6.8386197e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087311736,0.00013803518,0.5316819,0.00020863782,0.0003676432,0.000003356484,0.00004381842,0.36030975,0.00003134045,0.00004607473,0.00021279034,0.10608354],"study_design_scores_gemma":[0.00043391704,0.000021607508,0.5211369,0.000011122542,0.00010708593,8.23642e-7,0.0000028582756,0.47810113,0.000013413261,0.000010704636,0.00010339232,0.000057057376],"about_ca_topic_score_codex":0.000022127713,"about_ca_topic_score_gemma":0.00026532638,"teacher_disagreement_score":0.117791384,"about_ca_system_score_codex":0.00010840655,"about_ca_system_score_gemma":0.000032231925,"threshold_uncertainty_score":0.46950656},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97721845,0.00034033725,0.02066895,0.00021460676,0.00007048725,0.00089709996,0.00054772815,0.000013558324,0.000028803153],"genre_scores_gemma":[0.9971293,0.000024682513,0.0011873042,0.0004896815,0.00010494076,0.00003351862,0.0010020748,0.000023768796,0.000004703356],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984814,0.0002369836,0.00050211884,0.00019053297,0.00029338434,0.00029559937],"domain_scores_gemma":[0.99855983,0.0003737533,0.0003989844,0.00019153247,0.0002696833,0.00020620294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010651418,0.00012849829,0.00030754748,0.00012737968,0.00015200648,0.000029609862,0.00007730965,0.000058894926,0.000018592156],"category_scores_gemma":[0.00035220393,0.00011206274,0.00003356544,0.0001834083,0.00008489318,0.0002845763,0.000031821295,0.00009642475,3.0418948e-7],"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":[0.0002904501,0.0009605279,0.9265764,0.0011213516,0.00008000298,0.0000035532644,0.0025591166,0.00005273423,0.0046936395,0.005384824,0.00011114472,0.058166254],"study_design_scores_gemma":[0.0026698515,0.0015839939,0.9601236,0.00018681979,0.00007666827,0.000024898154,0.0013560597,0.027658077,0.0014441804,0.0006019475,0.003955277,0.00031865717],"about_ca_topic_score_codex":0.15212798,"about_ca_topic_score_gemma":0.10905064,"teacher_disagreement_score":0.057847597,"about_ca_system_score_codex":0.00017493375,"about_ca_system_score_gemma":0.00076558057,"threshold_uncertainty_score":0.90720683},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99803275,0.00013460114,0.0000043762775,0.0006859382,0.000017351136,0.00029486662,0.00020133705,0.000014895412,0.0006138743],"genre_scores_gemma":[0.99904794,0.000056895013,0.00010216109,0.00046272782,0.000044835884,0.000015978963,0.000099976896,0.000009025652,0.0001604439],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988446,0.0000773551,0.0003499511,0.0001499023,0.00042565982,0.00015250561],"domain_scores_gemma":[0.99874884,0.00016481348,0.00015523263,0.00078238413,0.00010764808,0.000041084993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004562876,0.000091242946,0.00046397152,0.00013661452,0.000021093741,0.000005761453,0.0002589077,0.000044315846,0.00006140662],"category_scores_gemma":[0.00035203574,0.000051700456,0.00012645843,0.0004882915,0.00012801107,0.000029472534,0.000060527203,0.00024760433,0.000005123399],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009768517,0.00085225067,0.7904735,0.00010226821,0.001086476,0.000021945838,0.0006673062,0.000004220271,0.20651054,0.000013620527,0.00007979805,0.000090330075],"study_design_scores_gemma":[0.00068687485,0.000034572684,0.99004,0.00007180374,0.0012864156,0.0000013897615,0.00019280768,0.00018630996,0.007314872,0.000011137549,0.000120375975,0.00005342256],"about_ca_topic_score_codex":0.0002482985,"about_ca_topic_score_gemma":0.0009689248,"teacher_disagreement_score":0.19956647,"about_ca_system_score_codex":0.0000133527665,"about_ca_system_score_gemma":0.00002386531,"threshold_uncertainty_score":0.21082833},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000071482486,0.004625986,0.000053756805,0.00019279533,0.0001102285,0.00034941835,0.0001390062,0.00010000611,0.99435735],"genre_scores_gemma":[0.000789072,0.0047467384,0.0011979482,0.00024588153,0.0001598389,0.0000012865312,0.00010941823,0.0001645138,0.9925853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994489,0.000015102347,0.000113781745,0.0001511803,0.00015393131,0.00011710154],"domain_scores_gemma":[0.99935347,0.00004379986,0.00008899696,0.00041090496,0.00003496242,0.00006789223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060966508,0.000107867636,0.00025854234,0.000040055125,0.000027546168,0.0000049365367,0.000043285185,0.00009109924,0.0005147004],"category_scores_gemma":[0.000060097667,0.00006460945,0.000034513807,0.000060489827,0.00014483093,0.0000067988008,0.000040198298,0.000098130615,0.000045359546],"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":[0.000021180596,0.000008578842,0.0007470436,0.00009938473,0.00006011056,0.00003234304,0.000009325995,8.189306e-8,0.000017066395,0.0000619713,0.9948709,0.0040720226],"study_design_scores_gemma":[0.0003435302,0.00001667825,0.00082079147,0.00027045715,0.000032096432,0.000048252554,0.000021986612,0.00003944681,0.00002822771,0.000006716221,0.99830455,0.000067255],"about_ca_topic_score_codex":0.00024538834,"about_ca_topic_score_gemma":0.00024256235,"teacher_disagreement_score":0.0040047676,"about_ca_system_score_codex":0.000010421844,"about_ca_system_score_gemma":0.00006843564,"threshold_uncertainty_score":0.56356084},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13962361,0.009883852,0.000029252591,0.71740294,0.0021529472,0.009870267,0.00415814,0.0012724325,0.11560655],"genre_scores_gemma":[0.12627265,0.00042881555,0.002212236,0.7374492,0.018501015,0.00584995,0.009453793,0.000617231,0.09921509],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9977,0.000085188854,0.0003842208,0.0006641464,0.00042692063,0.00073957647],"domain_scores_gemma":[0.99866885,0.0002987481,0.00015795183,0.00059549615,0.000063631625,0.00021529963],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002452888,0.00035183813,0.00064798794,0.00026255607,0.00003608304,0.000081278675,0.00019403368,0.00038990026,0.00008230585],"category_scores_gemma":[0.0009027205,0.00033005417,0.00006633057,0.00022515535,0.00012279276,0.00012940897,0.00015870357,0.0012385419,0.00008767209],"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":[0.000033154774,0.00004142666,0.3042678,0.00070120534,0.00006612271,0.0028695613,0.000050789182,4.875891e-7,6.053105e-7,9.31864e-7,0.6275736,0.06439434],"study_design_scores_gemma":[0.0012527844,0.000012636512,0.56592333,0.00018385956,0.00006383945,0.00012604024,0.0000023790126,0.000058191534,8.2691594e-7,0.000013890711,0.4321093,0.000252944],"about_ca_topic_score_codex":0.00031209498,"about_ca_topic_score_gemma":0.00027755968,"teacher_disagreement_score":0.26165554,"about_ca_system_score_codex":0.00024190143,"about_ca_system_score_gemma":0.000054532946,"threshold_uncertainty_score":0.9999151},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9787719,0.010394491,0.00041499743,0.007452041,0.0016849092,0.00064564217,0.00039161017,0.000054705506,0.00018967222],"genre_scores_gemma":[0.9857616,0.01034545,0.002273303,0.00025782763,0.0005057674,0.000007828052,0.00073005457,0.000037686896,0.000080477635],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99639726,0.00078307674,0.0016783216,0.00026198698,0.00039802777,0.00048134977],"domain_scores_gemma":[0.9956414,0.00006554569,0.0012562422,0.00026836465,0.0008650299,0.0019033859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035479215,0.00020389419,0.0009050368,0.0005663147,0.00026547172,0.0000028621469,0.00013553626,0.00006120735,0.0006574763],"category_scores_gemma":[0.00038647506,0.0001805002,0.000045170415,0.0006514196,0.00014675247,0.00023713872,0.000015730891,0.00016337434,0.0000016905246],"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":[0.00007986779,0.000058626927,0.9273524,0.0012979783,0.00042004706,0.00020569257,0.0056614294,0.000011326236,0.0007954583,0.00048626188,0.056988653,0.006642252],"study_design_scores_gemma":[0.0026762295,0.0021326556,0.982388,0.0002884488,0.000052836975,0.0008963871,0.0035126973,0.0005833211,0.000015249788,0.000035493587,0.0072310045,0.00018762791],"about_ca_topic_score_codex":0.022406356,"about_ca_topic_score_gemma":0.053352863,"teacher_disagreement_score":0.055035643,"about_ca_system_score_codex":0.00031680602,"about_ca_system_score_gemma":0.0041559157,"threshold_uncertainty_score":0.9841035},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95359206,0.00024605836,0.020271776,0.024280824,0.0001599972,0.00022932969,0.00018047502,0.000012850687,0.0010266446],"genre_scores_gemma":[0.9919303,0.00007992906,0.0015050434,0.0062163547,0.00014404285,3.7519268e-7,0.00011095556,0.000007798004,0.0000051715706],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99388,0.004145296,0.0012490172,0.000011415279,0.00029335223,0.0004209423],"domain_scores_gemma":[0.995456,0.00243825,0.0008986868,0.0007301501,0.00015540497,0.00032150513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026769498,0.00010704646,0.0005522242,0.0001798728,0.0002469448,0.000011971033,0.0008495374,0.000047089925,0.000020371199],"category_scores_gemma":[0.004197836,0.000072160416,0.00004648509,0.00021279289,0.00015403786,0.00066392886,0.0005438233,0.0011322239,0.000017629573],"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":[0.00024155571,0.00025087703,0.005287373,0.00017999733,0.00012274373,3.597357e-7,0.9704137,0.000034586337,0.0000025894492,0.0006369681,0.015810514,0.007018732],"study_design_scores_gemma":[0.0014456921,0.00034421502,0.1234263,0.00013210592,0.00004176376,0.00013998698,0.8602256,0.0002542751,0.0000021211367,0.00072879967,0.013145308,0.000113831775],"about_ca_topic_score_codex":0.0005573918,"about_ca_topic_score_gemma":0.0005548159,"teacher_disagreement_score":0.11813893,"about_ca_system_score_codex":0.00030479717,"about_ca_system_score_gemma":0.0003823665,"threshold_uncertainty_score":0.9277827},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97653115,0.0014284129,0.020004325,0.0001783326,0.00029257976,0.0009366889,0.00029536636,0.00005541936,0.00027773372],"genre_scores_gemma":[0.99770445,0.00009208147,0.0018061675,0.00006538767,0.000060732258,0.000074962365,0.000115064744,0.000018529292,0.00006260294],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99892694,0.00016903538,0.00036112897,0.00025772897,0.000110081564,0.00017509089],"domain_scores_gemma":[0.9991465,0.00009067716,0.00017346223,0.0002856122,0.00014488708,0.00015881743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008109472,0.000118656106,0.00035551668,0.00012528566,0.000026033169,0.000009319409,0.000057173598,0.000058234302,0.000014775191],"category_scores_gemma":[0.00037441935,0.00011252285,0.000071181305,0.00010987132,0.000062203995,0.00011573559,0.000025327256,0.00005850238,0.0000030678507],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034436437,0.0003513954,0.89578277,0.0012325944,0.00013372482,0.000030486968,0.00047045294,0.0000056483073,0.0050349142,0.00011130267,0.0006938862,0.092709176],"study_design_scores_gemma":[0.00097192783,0.0002740191,0.98740906,0.00012920536,0.000027365397,0.000009243108,0.00004978197,0.0070960307,0.0024026306,0.00027292597,0.0012194848,0.00013834667],"about_ca_topic_score_codex":0.001973871,"about_ca_topic_score_gemma":0.00046376314,"teacher_disagreement_score":0.092570834,"about_ca_system_score_codex":0.00001478152,"about_ca_system_score_gemma":0.000049404873,"threshold_uncertainty_score":0.45885485},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026407759,0.014853454,0.0001307037,0.94518787,0.00042806115,0.0034804633,0.033214603,0.000009135969,0.000054907214],"genre_scores_gemma":[0.031650934,0.002660158,0.00064529164,0.9562089,0.0018194198,0.00016195195,0.006182584,0.00015087363,0.0005198939],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9872492,0.0054457444,0.0021965837,0.00054709206,0.0014415012,0.0031198536],"domain_scores_gemma":[0.9782943,0.005584136,0.0018923709,0.0020103515,0.0067777494,0.0054410943],"candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.038974922,0.0003824843,0.0016669891,0.0012750028,0.00273748,0.00038681895,0.0014903415,0.00032862448,0.00011176151],"category_scores_gemma":[0.008216636,0.00031122926,0.00024627754,0.00093746337,0.0010359639,0.00036806444,0.00011257197,0.0056392034,0.0000012555448],"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":true,"study_design_scores_codex":[0.000057085515,0.000038647424,0.00385442,0.002990377,0.0001711208,0.00001592047,0.0021543861,2.301796e-7,4.3585064e-7,0.00026388097,0.9806018,0.009851737],"study_design_scores_gemma":[0.0010431681,0.0020585328,0.029113282,0.0014559432,0.000012615068,0.00026332788,0.0049771178,0.000011810006,4.1072911e-7,0.0005040489,0.96037537,0.00018438659],"about_ca_topic_score_codex":0.82360387,"about_ca_topic_score_gemma":0.98807865,"teacher_disagreement_score":0.19341832,"about_ca_system_score_codex":0.0064952685,"about_ca_system_score_gemma":0.19991359,"threshold_uncertainty_score":0.99993396},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97840995,0.0004623972,0.000033727418,0.00845286,0.00029349534,0.002505927,0.0003717642,0.00030394117,0.00916594],"genre_scores_gemma":[0.9950518,0.000039574647,0.000055201723,0.0008460328,0.00020956676,0.0016555036,0.00033618775,0.000041505773,0.0017646282],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99816495,0.00018181338,0.0003838828,0.00036881203,0.0002420801,0.0006584443],"domain_scores_gemma":[0.9979283,0.0011401866,0.00012409026,0.0005588725,0.000108997025,0.00013954437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016392573,0.00019488698,0.0004043733,0.000101550235,0.00012250886,0.000029503062,0.00022414635,0.0001260059,0.000055767836],"category_scores_gemma":[0.0015474997,0.00013271089,0.00017589505,0.00024533708,0.00017952296,0.00006436919,0.000046749134,0.00026669822,0.00008850694],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011551201,0.00041610748,0.7562301,0.00022451284,0.00041404247,0.00008165701,0.000057799665,0.00021383626,0.00047390838,0.0066089123,0.17283547,0.061288536],"study_design_scores_gemma":[0.0014805652,0.000043688247,0.9651282,0.000022314265,0.000029486124,0.000029682247,0.0000148223,0.0006090358,0.00015243962,0.00089409435,0.03144279,0.00015287483],"about_ca_topic_score_codex":0.00004104913,"about_ca_topic_score_gemma":0.000064876214,"teacher_disagreement_score":0.2088981,"about_ca_system_score_codex":0.00013450476,"about_ca_system_score_gemma":0.00008787487,"threshold_uncertainty_score":0.5411793},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049410585,0.0014215518,0.85909784,0.00019760967,0.002443491,0.0134379165,0.06378072,0.0014220297,0.008788229],"genre_scores_gemma":[0.07124163,0.0018806838,0.8493617,0.0006064444,0.0016527912,0.00038984392,0.0108036855,0.0019621956,0.062100973],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969269,0.00039495048,0.0008292781,0.000947449,0.00031899934,0.00058241305],"domain_scores_gemma":[0.9976279,0.00067919045,0.00050937827,0.0007526288,0.00020296636,0.00022793113],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000946643,0.0005403324,0.0013442265,0.00022168594,0.00012980281,0.000076304634,0.00016974143,0.00036205014,0.00029849348],"category_scores_gemma":[0.0015146864,0.0005578864,0.00007672416,0.00023118278,0.0005853467,0.0000647202,0.00014436814,0.00031391883,0.00002096638],"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":[0.036845934,0.0031120523,0.11276413,0.12172786,0.003127502,0.0006681363,0.0023033519,0.0024576525,0.0018195164,0.0211405,0.55627567,0.1377577],"study_design_scores_gemma":[0.01759432,0.0048211394,0.042763907,0.010168025,0.001113319,0.00064911065,0.00013487863,0.70036876,0.0000356391,0.047290333,0.17050043,0.004560131],"about_ca_topic_score_codex":0.0006919327,"about_ca_topic_score_gemma":0.0050463006,"teacher_disagreement_score":0.6979111,"about_ca_system_score_codex":0.00034058152,"about_ca_system_score_gemma":0.0003259798,"threshold_uncertainty_score":0.99968725},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99742305,0.0012068573,0.00011183053,0.00017025069,0.000049870956,0.00014434692,0.00037114162,0.000005976034,0.0005166443],"genre_scores_gemma":[0.99688244,0.000080314974,0.0023443236,0.00006840148,0.00009385146,0.0000040641207,0.0005093737,0.00000385496,0.000013390658],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998785,0.00003824802,0.00039461296,0.00011483475,0.0005214161,0.00014587505],"domain_scores_gemma":[0.99903053,0.000063243846,0.00023558323,0.00009181128,0.0003796867,0.00019913753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033590308,0.0001032556,0.0002525912,0.00009850062,0.000023923518,0.00001930808,0.000110567176,0.000032186243,0.000011987441],"category_scores_gemma":[0.00014308938,0.00006222446,0.000017703258,0.0002642675,0.000051824598,0.00045810116,0.00005304036,0.00010924128,0.0000010666714],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011145769,0.0023041517,0.9812321,0.00015347484,0.00019391057,0.000030320009,0.0022056175,0.000001657262,0.0013710448,0.000040873238,0.008422923,0.0029293238],"study_design_scores_gemma":[0.0024036544,0.0002144262,0.976371,0.0002932087,0.000008206776,0.00035691477,0.0016443339,8.491203e-7,0.001270949,0.000004462711,0.017324785,0.000107239626],"about_ca_topic_score_codex":0.0000017998924,"about_ca_topic_score_gemma":0.000019319537,"teacher_disagreement_score":0.008901861,"about_ca_system_score_codex":0.00006256912,"about_ca_system_score_gemma":0.00016684941,"threshold_uncertainty_score":0.25374398},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99027157,0.0013571815,0.0005580766,0.005180324,0.00007542994,0.0019569055,0.00040154936,0.00015631207,0.000042656513],"genre_scores_gemma":[0.9967462,0.00006252846,0.0009467205,0.0016083108,0.00011423335,0.0003843872,0.00009783874,0.00003334533,0.000006470942],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99636275,0.0012911886,0.00071915064,0.0006542091,0.0005931044,0.00037960403],"domain_scores_gemma":[0.99509054,0.0003896916,0.00045839118,0.0011252059,0.00030188813,0.0026343006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010337337,0.00027606226,0.0006442459,0.00017495922,0.00021388609,0.000046490808,0.00028063147,0.000045033277,0.00008079365],"category_scores_gemma":[0.0046363645,0.0001854908,0.000099818084,0.0005954738,0.0008518836,0.00032520623,0.00019495496,0.00016058532,8.092643e-7],"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":[0.0005953773,0.0005964648,0.9678974,0.00021852383,0.00007939036,0.00010412119,0.02526214,2.5570958e-7,0.00008573649,0.0011222988,0.0009404101,0.0030978709],"study_design_scores_gemma":[0.0056730337,0.00149661,0.88681626,0.00039454043,0.0001246149,0.000041460182,0.09943797,0.0012673066,0.0001351273,0.0015676599,0.0026016396,0.00044379142],"about_ca_topic_score_codex":0.009275478,"about_ca_topic_score_gemma":0.0010695156,"teacher_disagreement_score":0.08108117,"about_ca_system_score_codex":0.00019871927,"about_ca_system_score_gemma":0.0018726955,"threshold_uncertainty_score":0.99732184},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92985624,0.0013023508,0.00001726158,0.0064877886,0.0003168317,0.0019485465,0.0009455237,0.00023371981,0.058891762],"genre_scores_gemma":[0.9895854,0.000036715308,0.00004545897,0.0034139398,0.00023651565,0.0010715858,0.00066334906,0.00003309172,0.0049139406],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843806,0.00004765204,0.00030130416,0.0004003655,0.00030622224,0.0005064194],"domain_scores_gemma":[0.9988583,0.000036275287,0.00006382112,0.0005836691,0.00005057642,0.00040738753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002099411,0.00017081999,0.00029860766,0.00013335045,0.00003161498,0.00003456291,0.00018260012,0.000047913116,0.00013301375],"category_scores_gemma":[0.00018603004,0.00015463265,0.00009505159,0.00031428714,0.000055564527,0.0001562376,0.00009385763,0.00012101883,0.00018303267],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042238313,0.0006047959,0.8004602,0.00014742972,0.000031950323,0.0008562144,0.000042791475,0.000019796542,0.000019914483,0.0001504616,0.12870672,0.06853732],"study_design_scores_gemma":[0.0015217309,0.0000034199934,0.8554665,0.000028960232,0.000024518058,0.0000018866027,0.000007706338,0.000067264074,0.00008530332,0.00010893876,0.14252898,0.00015482598],"about_ca_topic_score_codex":0.00017043437,"about_ca_topic_score_gemma":0.00014851714,"teacher_disagreement_score":0.06838249,"about_ca_system_score_codex":0.00012192801,"about_ca_system_score_gemma":0.00006398317,"threshold_uncertainty_score":0.63057363},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9686946,0.029659176,0.0000026877094,0.00008386477,0.000021191945,0.0002422834,0.00025327774,0.00001710775,0.0010257917],"genre_scores_gemma":[0.9636688,0.03432461,0.00052731024,0.0012952896,0.0000048991574,0.000012394464,0.000047675494,0.000013866239,0.00010510779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99907863,0.00013405667,0.00034116756,0.00014582944,0.00015213141,0.00014817867],"domain_scores_gemma":[0.99901545,0.000058999573,0.0001664912,0.00056981907,0.00007959586,0.00010964557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002837807,0.000098685836,0.00032556313,0.000036063797,0.00002120171,0.0000015401037,0.00014473854,0.000035269462,0.00006444961],"category_scores_gemma":[0.00017435233,0.000093765564,0.000023315974,0.00016997282,0.0000801661,0.000057289784,0.000047029367,0.00018638247,0.0000041208914],"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":[0.00006932539,0.00006957471,0.9739822,0.0005468148,0.000035706198,0.000010870815,0.00037106045,0.000001620599,0.00007291005,0.00017931798,0.001964624,0.022695947],"study_design_scores_gemma":[0.00097180955,0.000024627274,0.9848098,0.00048597384,0.00003794457,0.0000057119937,0.00019915252,0.00002678081,0.00015080516,0.00010649367,0.0130903395,0.00009051823],"about_ca_topic_score_codex":0.8122298,"about_ca_topic_score_gemma":0.8533133,"teacher_disagreement_score":0.0410835,"about_ca_system_score_codex":0.00007295099,"about_ca_system_score_gemma":0.0006189681,"threshold_uncertainty_score":0.38236487},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6577228,0.00022257384,0.33939317,0.00019680805,0.0001456371,0.0018624708,0.000083824074,0.000045857407,0.000326902],"genre_scores_gemma":[0.9167511,0.0000086125765,0.08231064,0.00009413577,0.000066223976,0.0005921737,0.000027199392,0.00001929339,0.00013063279],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905807,0.000071522256,0.0002860033,0.00019310092,0.0001739326,0.00021738501],"domain_scores_gemma":[0.9991218,0.00010912838,0.0001306409,0.00030089405,0.00019140894,0.0001461675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033252937,0.000095818854,0.00031508796,0.00009886557,0.000023170247,0.0000047871217,0.00007152865,0.00008535967,0.000015164649],"category_scores_gemma":[0.0005380297,0.00007864561,0.00012156482,0.00011408127,0.00006275617,0.0000960374,0.000051783292,0.00006955489,5.681704e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005021714,0.00020707588,0.0018542625,0.0003707292,0.0000779833,8.7485427e-7,0.00024392345,0.00025897071,0.18809922,0.000036746354,0.0006701018,0.8076779],"study_design_scores_gemma":[0.0027262513,0.000085714004,0.1806138,0.000075317505,0.00018468441,0.000017606939,0.00008172298,0.65121764,0.16146581,0.0022735377,0.0010198285,0.0002380877],"about_ca_topic_score_codex":0.00007120375,"about_ca_topic_score_gemma":0.000007892939,"teacher_disagreement_score":0.80743986,"about_ca_system_score_codex":0.0000409739,"about_ca_system_score_gemma":0.000037026875,"threshold_uncertainty_score":0.32070747},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9722022,0.00003775579,0.023599498,0.0024853721,0.0005653198,0.00068972923,0.00038975812,0.000013463496,0.000016899647],"genre_scores_gemma":[0.9186592,0.000016115237,0.07400133,0.006840545,0.0003238339,0.000037475667,0.00007674885,0.000026829453,0.000017909397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99736995,0.00023616244,0.0016059736,0.0002462651,0.00023911474,0.00030253374],"domain_scores_gemma":[0.9943955,0.0014152387,0.0014682747,0.00036730582,0.0017847705,0.0005688884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011876761,0.00014464979,0.0010830635,0.00015593144,0.000029374767,0.000010092952,0.00022965584,0.00006489796,0.000007866295],"category_scores_gemma":[0.0025907783,0.00012267774,0.00028753642,0.0002494227,0.0001662669,0.00014201441,0.00008997377,0.00016719814,0.0000060836105],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016300578,0.00249194,0.5843321,0.0006537188,0.0007101368,0.00022047192,0.0035414144,0.0013679382,0.0025155493,0.000032747404,0.008955164,0.37887824],"study_design_scores_gemma":[0.022236114,0.050833374,0.64595735,0.0013350104,0.0012411026,0.00088643876,0.01013619,0.008510592,0.0007625339,0.00035686733,0.25687158,0.00087285385],"about_ca_topic_score_codex":0.00010136864,"about_ca_topic_score_gemma":0.0001256766,"teacher_disagreement_score":0.3780054,"about_ca_system_score_codex":0.000040303716,"about_ca_system_score_gemma":0.0003632436,"threshold_uncertainty_score":0.5002653},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9326177,0.026047088,0.021620397,0.018150736,0.0007207747,0.00067404925,0.000066692664,0.00004514638,0.00005736899],"genre_scores_gemma":[0.995503,0.0022854414,0.00013548289,0.001884447,0.000111659334,0.000017819371,0.000041729472,0.000010364778,0.000010030642],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978492,0.0008508912,0.00056808675,0.00015712983,0.00014960092,0.00042506086],"domain_scores_gemma":[0.99865186,0.00024199247,0.0005093813,0.00027666605,0.00012099033,0.00019910422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036814155,0.00009880403,0.00058970106,0.000064038126,0.00020185648,0.0000014095178,0.00009872171,0.000105682644,0.000012384328],"category_scores_gemma":[0.00039567592,0.000068665664,0.00007204754,0.00010869434,0.0017958119,0.00006866386,0.00006551982,0.00023963086,0.000002900866],"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":[0.00089667307,0.00046356337,0.17362176,0.002073521,0.0002454956,0.0000010226028,0.026153293,0.000006800654,0.00031758487,0.013512225,0.00042793524,0.78228015],"study_design_scores_gemma":[0.004566455,0.001091755,0.95881414,0.00017525279,0.00007804027,0.00018968167,0.017450018,0.00048566086,0.00046776264,0.0061781853,0.010186026,0.00031704587],"about_ca_topic_score_codex":0.00038956245,"about_ca_topic_score_gemma":0.000028938262,"teacher_disagreement_score":0.7851924,"about_ca_system_score_codex":0.00007415193,"about_ca_system_score_gemma":0.0004170795,"threshold_uncertainty_score":0.6616744},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95141655,0.009821466,0.0004554255,0.021399673,0.00066627975,0.0028565337,0.010203579,0.00010604378,0.0030744427],"genre_scores_gemma":[0.99688387,0.0003071948,0.00078977906,0.00089089,0.0000909707,0.000087313965,0.00086275104,0.000022140826,0.00006510757],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957302,0.00095793407,0.001111973,0.00063596765,0.0010786947,0.00048522517],"domain_scores_gemma":[0.9947071,0.00072994444,0.0007452435,0.001508079,0.0014273636,0.00088224467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004630179,0.00023718427,0.0008581529,0.000090926624,0.00025173536,0.000059642145,0.00048606764,0.000078425845,0.000008767958],"category_scores_gemma":[0.0020785842,0.00015148414,0.000059525548,0.00061432115,0.0004399021,0.00024202772,0.00051496644,0.00019974243,0.0000017983436],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016659417,0.00012785276,0.96778214,0.00038025941,0.00018087121,0.00000508375,0.000238749,0.0000026979312,0.000037032783,0.0007142323,0.026843004,0.0035214638],"study_design_scores_gemma":[0.0012494287,0.00014036671,0.9378212,0.00009938581,0.0000043955833,0.000059369293,0.0006589672,0.0020240897,9.303506e-7,0.000031822874,0.05776673,0.00014332499],"about_ca_topic_score_codex":0.0018557934,"about_ca_topic_score_gemma":0.0026453044,"teacher_disagreement_score":0.045467295,"about_ca_system_score_codex":0.00023589129,"about_ca_system_score_gemma":0.0035970598,"threshold_uncertainty_score":0.63810295},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.961077,0.0016331838,0.00005748566,0.023163872,0.00064451224,0.0032757514,0.0029692864,0.00022504498,0.0069539007],"genre_scores_gemma":[0.98983645,0.0001525324,0.0014284062,0.0062860055,0.0003540772,0.000526049,0.001199456,0.00012306735,0.000093944196],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929257,0.00029715223,0.0010828724,0.0011214431,0.00074933463,0.0038234736],"domain_scores_gemma":[0.98802525,0.00067224377,0.00027711052,0.0013100707,0.0012325746,0.008482741],"candidate_categories":["metaepi_narrow","bibliometrics","sts"],"consensus_categories":[],"category_scores_codex":[0.0026828877,0.0005955667,0.0014558026,0.013626388,0.0016131212,0.00024127364,0.00094698474,0.00023164992,0.000086433196],"category_scores_gemma":[0.00068515306,0.0006480582,0.0004171749,0.007820047,0.0014354711,0.00023368344,0.000118495314,0.00053032034,0.000017371733],"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":[0.00011193515,0.00007983931,0.86534595,0.0013012262,0.0004336636,0.000024026811,0.006646314,0.00005368733,0.00007056119,0.02887967,0.06677446,0.030278651],"study_design_scores_gemma":[0.0015409607,0.0007601488,0.24240799,0.00042137166,0.000077810706,0.00002406904,0.023872524,0.00032592914,0.000046711742,0.0015630418,0.72810495,0.00085451413],"about_ca_topic_score_codex":0.9532609,"about_ca_topic_score_gemma":0.9996977,"teacher_disagreement_score":0.66133046,"about_ca_system_score_codex":0.0007674335,"about_ca_system_score_gemma":0.0029497477,"threshold_uncertainty_score":0.99968666},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031468593,0.9559344,0.00007599084,0.0025492767,0.00053384854,0.005474734,0.0027617242,0.00009648366,0.0011049666],"genre_scores_gemma":[0.38642108,0.6098152,0.0000051856614,0.0008456636,0.00015486368,0.0019221472,0.00040011597,0.000065968634,0.0003697824],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948267,0.0024665722,0.0007159766,0.0007497242,0.0004926459,0.00074843055],"domain_scores_gemma":[0.98928624,0.008298399,0.00045545056,0.0013125872,0.00019613064,0.0004511725],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038910012,0.00051200564,0.0020783902,0.00018121314,0.000063100815,0.00005484095,0.00079185824,0.0002929585,0.00004980806],"category_scores_gemma":[0.017110683,0.00027538624,0.000268187,0.00026571253,0.0009753224,0.00013378864,0.00051314326,0.0010443887,0.000056285546],"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":[0.00015348056,0.00018165717,0.00033523972,0.002943143,0.0005048127,0.0001231037,0.00004340291,0.0000036120211,9.0551154e-8,0.00011748311,0.027152408,0.96844155],"study_design_scores_gemma":[0.00070914446,0.0003765265,0.044439606,0.0037450362,0.0007030893,0.00076850207,0.000009426668,0.00009732737,7.2036704e-7,0.00015096583,0.9486111,0.00038859525],"about_ca_topic_score_codex":0.00014116174,"about_ca_topic_score_gemma":0.000041756062,"teacher_disagreement_score":0.968053,"about_ca_system_score_codex":0.00026902952,"about_ca_system_score_gemma":0.00027687923,"threshold_uncertainty_score":0.99996984},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033181258,0.9947101,0.000021672247,0.0000107901715,0.0007450281,0.000670844,0.0002736385,0.00006989305,0.00017991177],"genre_scores_gemma":[0.00063919305,0.99672,0.000092719594,0.00017167229,0.00025191615,0.00003101262,0.0019299723,0.000026329782,0.00013719352],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972361,0.0008048081,0.0012035964,0.0005509341,0.000022916407,0.0001817014],"domain_scores_gemma":[0.99818605,0.00038394963,0.0006328847,0.0005307136,0.00011531608,0.00015109475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007244621,0.00028536303,0.0023519709,0.000113461334,0.000020299116,0.000008518683,0.00010619309,0.0007813277,0.00036004675],"category_scores_gemma":[0.0004167773,0.00020593971,0.00047257083,0.00015348796,0.00046208737,0.000065802975,0.000097903,0.0005051395,0.00026508106],"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":[0.000030116682,0.0002917399,0.01509801,0.012143181,0.0003107755,0.0000028636275,0.0000023424122,1.0644311e-8,0.000031353506,0.000070110545,0.004471489,0.967548],"study_design_scores_gemma":[0.00038305816,0.0008025528,0.0072895223,0.0046850704,0.00037963592,0.00020542576,3.950002e-7,0.0000020912319,0.000002849652,0.000014176785,0.9860534,0.00018179529],"about_ca_topic_score_codex":0.000042197382,"about_ca_topic_score_gemma":0.000017969043,"teacher_disagreement_score":0.9815819,"about_ca_system_score_codex":0.000026469224,"about_ca_system_score_gemma":0.00012264038,"threshold_uncertainty_score":0.83979774},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99575204,0.00008442152,0.00087602745,0.00073109916,0.00045166534,0.0006919179,0.00074387237,0.000026355012,0.00064261013],"genre_scores_gemma":[0.9981791,0.000040573308,0.0008034654,0.00012319403,0.00003192831,0.00012705398,0.000028536266,0.000015872763,0.00065028016],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99917614,0.000052304582,0.00030495005,0.00013987561,0.00017820763,0.00014853322],"domain_scores_gemma":[0.9989479,0.00013209553,0.00009641172,0.00070698914,0.00008890211,0.000027688344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023896781,0.00010046816,0.0002031409,0.000045867044,0.0001684921,0.00000973904,0.00030331546,0.00002807173,0.00038008593],"category_scores_gemma":[0.000013154156,0.00005193021,0.00013865839,0.00022203349,0.0003532072,0.0001225373,0.000014527253,0.0001941906,0.0000028360894],"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":[0.010424172,0.007452493,0.4474176,0.0058372235,0.003100499,0.000013785698,0.37290558,0.0006163525,0.013911574,0.007676714,0.0028541028,0.12778994],"study_design_scores_gemma":[0.0016774673,0.0002537397,0.54189646,0.0003712162,0.0004192159,0.0000072312378,0.0049055414,0.00027863204,0.008588465,0.0006268234,0.44080064,0.00017456684],"about_ca_topic_score_codex":0.077726975,"about_ca_topic_score_gemma":0.38832822,"teacher_disagreement_score":0.43794653,"about_ca_system_score_codex":0.00006699268,"about_ca_system_score_gemma":0.00030990434,"threshold_uncertainty_score":0.9284145},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42697442,0.00022167877,0.4363198,0.12740833,0.0006204544,0.001938629,0.0062792907,0.00011860143,0.00011880781],"genre_scores_gemma":[0.8602928,0.000075400196,0.105776176,0.023925776,0.0007056379,0.000018040442,0.009123904,0.000037800128,0.000044483953],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962547,0.00028022725,0.0019319316,0.00016388361,0.00093427405,0.00043497994],"domain_scores_gemma":[0.9916499,0.000068631154,0.0011243742,0.0011337557,0.001849491,0.004173817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005606316,0.00019122931,0.0005701109,0.0004199756,0.00006347779,0.000109299355,0.0006843413,0.00005459915,0.00002211856],"category_scores_gemma":[0.0044675847,0.00015960274,0.000045872654,0.0007776567,0.000029680219,0.0013963375,0.00012613679,0.00031655465,0.00006270871],"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":[0.0031037733,0.002976122,0.0898998,0.0009931686,0.0003109859,0.000042297957,0.0050017415,0.004762112,0.0001264418,0.00056887267,0.24646144,0.64575326],"study_design_scores_gemma":[0.006347297,0.00183335,0.29724038,0.0002760198,0.00005274359,0.00013199949,0.0034823748,0.12779161,0.0000029404562,0.00006246755,0.5624435,0.00033533742],"about_ca_topic_score_codex":0.00005674764,"about_ca_topic_score_gemma":0.0000908496,"teacher_disagreement_score":0.6454179,"about_ca_system_score_codex":0.0005313998,"about_ca_system_score_gemma":0.0046728114,"threshold_uncertainty_score":0.8289367},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9725271,0.012587419,0.00083118636,0.0064334525,0.002809308,0.0016403554,0.00045390165,0.00036109923,0.0023561683],"genre_scores_gemma":[0.9957069,0.0010208602,0.0010986459,0.0006581737,0.000560407,0.000049150254,0.00012694272,0.000047183188,0.00073170813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99683887,0.00038981234,0.00065759337,0.0005079799,0.0010324306,0.00057332614],"domain_scores_gemma":[0.99725556,0.0003119963,0.00045628418,0.0010000274,0.00075202744,0.0002240736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022359618,0.00031533508,0.0007910297,0.000021374239,0.00019747445,0.00002558223,0.0003442372,0.000105055784,0.000020022764],"category_scores_gemma":[0.00078055845,0.00022349259,0.00049056154,0.0007538752,0.0004534783,0.00012980601,0.00034980633,0.0002724232,0.000019855184],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018504946,0.00010171834,0.8988001,0.00021027474,0.0004832961,0.0000037155326,0.0019105709,0.00092378014,0.00020018719,0.000112464295,0.096389495,0.0006793598],"study_design_scores_gemma":[0.0027698518,0.00015466924,0.937656,0.00018822459,0.000053297415,0.000009800009,0.0036163288,0.00065447966,0.00021399888,0.00047297048,0.05386281,0.0003475566],"about_ca_topic_score_codex":0.00006344019,"about_ca_topic_score_gemma":0.00023374855,"teacher_disagreement_score":0.04252669,"about_ca_system_score_codex":0.00042114264,"about_ca_system_score_gemma":0.00031912094,"threshold_uncertainty_score":0.91137636},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990983,0.0010451775,0.00002928821,0.0071584824,0.0003365499,0.00026414866,0.00004818658,0.0000011089732,0.00013407419],"genre_scores_gemma":[0.9983312,0.000025109599,0.00075432,0.0007927117,0.0000743305,0.0000015736954,0.0000017745896,0.0000073590522,0.000011648225],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985107,0.00015293239,0.0006118932,0.00008415106,0.00037853257,0.00026179504],"domain_scores_gemma":[0.9988398,0.00018475088,0.000318026,0.0003259828,0.00011793802,0.0002134944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019042386,0.00008323745,0.00020230918,0.00022524812,0.00004122467,0.000012831962,0.00034416735,0.00003419642,0.000048392463],"category_scores_gemma":[0.00021146977,0.00005149196,0.0000543393,0.0006115076,0.000079985584,0.00009591663,0.0000033611198,0.00038332478,6.8043073e-7],"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":[0.000073870906,0.000027859525,0.9965213,0.00019844667,0.00001857005,0.00008975381,0.00045650708,0.00009536574,0.000010306088,0.0014726076,0.0009446434,0.00009079219],"study_design_scores_gemma":[0.000536456,0.00004418995,0.99629,0.00048166496,0.00004008391,0.00012803369,0.00023990899,0.000043876844,0.0000018143737,0.002034425,0.000109571374,0.00004997341],"about_ca_topic_score_codex":0.8876576,"about_ca_topic_score_gemma":0.99985445,"teacher_disagreement_score":0.11219685,"about_ca_system_score_codex":0.00031391197,"about_ca_system_score_gemma":0.0075796237,"threshold_uncertainty_score":0.99804646},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72790664,0.03501769,0.19232032,0.0046958625,0.018749392,0.01111546,0.00063433335,0.0018088667,0.0077514322],"genre_scores_gemma":[0.9853295,0.00028876038,0.013172587,0.00064895937,0.0003815662,0.000015091147,0.000057829184,0.00005909684,0.000046586403],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9962223,0.00003292053,0.00058695895,0.0011552341,0.0010105496,0.0009919936],"domain_scores_gemma":[0.9979458,0.00017861895,0.00037807194,0.0009187609,0.00021133233,0.00036745655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000597812,0.0004904796,0.0008172486,0.005276774,0.0001261966,0.000070702314,0.0008344706,0.0003447462,0.000022140126],"category_scores_gemma":[0.00039387183,0.0004735702,0.00011051335,0.002278691,0.0005247371,0.00023897219,0.0007788909,0.0010568221,0.00007992112],"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":[0.000038871767,0.0001343902,0.367916,0.00017458531,0.000025365665,0.0001292689,0.00054950744,0.0001057294,0.00005325746,0.000060968072,0.000008890255,0.63080317],"study_design_scores_gemma":[0.0049859514,0.0017998707,0.971007,0.008982132,0.000059785085,0.00017296254,0.000010086095,0.0019922857,0.001834835,0.0031301584,0.0039881943,0.0020367312],"about_ca_topic_score_codex":0.000083321276,"about_ca_topic_score_gemma":0.000014930344,"teacher_disagreement_score":0.6287664,"about_ca_system_score_codex":0.0015086526,"about_ca_system_score_gemma":0.00061743864,"threshold_uncertainty_score":0.9997716},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93737376,0.006814097,0.000031262785,0.054495025,0.0002392795,0.00015068513,0.00007508994,0.0000069354996,0.00081388006],"genre_scores_gemma":[0.9929988,0.0001834289,0.00002060752,0.0063651507,0.0003900079,0.0000019391439,0.000013329364,0.0000044190347,0.000022325694],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989321,0.0003087541,0.00024808923,0.00009621997,0.000108107815,0.0003066821],"domain_scores_gemma":[0.9990169,0.00010698748,0.00012818529,0.00020428718,0.0000864531,0.0004571954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072920765,0.00009706941,0.00022989024,0.00006879474,0.00025259316,0.00002239361,0.00018831412,0.00006447243,0.00010665122],"category_scores_gemma":[0.0003169565,0.00005921403,0.0000885734,0.00017606535,0.00042593203,0.00005939499,0.000009672159,0.00037487622,0.000004795849],"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":[0.000076694894,0.000032656968,0.9692597,0.0000057372167,0.00008395209,0.00043039356,0.0009127631,0.00012820953,0.0000031959069,0.001959516,0.021352252,0.005754955],"study_design_scores_gemma":[0.0005656057,0.00010517786,0.959935,0.000034631023,0.00008211228,0.00036830557,0.00021123016,0.00006400739,1.7271637e-7,0.008262328,0.030308913,0.00006248636],"about_ca_topic_score_codex":0.0026069612,"about_ca_topic_score_gemma":0.032422747,"teacher_disagreement_score":0.055625048,"about_ca_system_score_codex":0.00020466707,"about_ca_system_score_gemma":0.00092555414,"threshold_uncertainty_score":0.985233},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977674,0.0003481054,0.0000031212435,0.00002818363,0.000021685675,0.00080454454,0.0008942996,0.000004344846,0.00012830088],"genre_scores_gemma":[0.99869233,0.00001948184,0.00035063206,0.000029693034,0.0000024687806,0.000022221388,0.0008560236,0.000005336524,0.000021837248],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985914,0.0002441881,0.00055651314,0.0002444572,0.00016684334,0.00019659096],"domain_scores_gemma":[0.998878,0.00024374783,0.0003393682,0.0003640907,0.00007830436,0.00009649469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005486288,0.00009877348,0.0006258006,0.00013287472,0.0000069826497,0.000003852813,0.00011262163,0.000035782865,0.0000043372256],"category_scores_gemma":[0.00047635156,0.00009792706,0.000020071897,0.00018583878,0.00008576877,0.0001529409,0.000060690265,0.00012377453,8.397513e-8],"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":[0.0006767326,0.00027620682,0.9754137,0.00015543326,0.000022117441,0.000009213175,0.0024904793,0.000006291404,0.000010605132,0.0000020519,0.0000908207,0.020846395],"study_design_scores_gemma":[0.0013228543,0.00033047484,0.99668276,0.00016043289,0.000012835462,0.0000010458386,0.0008110098,0.000047646558,0.0005286622,0.0000029089679,0.000037989186,0.0000613863],"about_ca_topic_score_codex":0.02917946,"about_ca_topic_score_gemma":0.5530599,"teacher_disagreement_score":0.5238804,"about_ca_system_score_codex":0.00011732477,"about_ca_system_score_gemma":0.00034486808,"threshold_uncertainty_score":0.9772853},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7390122,0.0005186692,0.1381532,0.09979099,0.0042794095,0.00050345797,0.00034848796,0.00013045387,0.017263155],"genre_scores_gemma":[0.99122876,0.00018962372,0.0022200507,0.0058534257,0.00029315465,0.0000013518408,0.00017692253,0.000004529306,0.000032195312],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99854016,0.000044714885,0.00059465755,0.00004568582,0.000638729,0.00013603452],"domain_scores_gemma":[0.99811715,0.00004912678,0.0006232923,0.00010802539,0.00086686184,0.00023551549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007691083,0.000079270576,0.0001903536,0.0003481898,0.000041751122,0.00011818126,0.00016521,0.0000333996,0.00010472683],"category_scores_gemma":[0.0007626854,0.00006688028,0.000068572255,0.000075376076,0.00003866944,0.0021016288,0.00004583646,0.00014663064,0.00007751569],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003805172,0.00013048733,0.053371597,0.000114041104,0.0003288217,0.000011382392,0.0012472299,0.00004432319,0.0000688968,0.006626413,0.043073602,0.8946027],"study_design_scores_gemma":[0.0026121594,0.00032088195,0.24063982,0.00014624921,0.00001559338,0.0006768057,0.00029395326,0.0023724937,0.000041517935,0.0003737905,0.75240684,0.000099903344],"about_ca_topic_score_codex":0.00002812083,"about_ca_topic_score_gemma":0.000004483962,"teacher_disagreement_score":0.89450276,"about_ca_system_score_codex":0.0001452471,"about_ca_system_score_gemma":0.00021105197,"threshold_uncertainty_score":0.27272987},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953951,0.0001337295,0.00002791994,0.000722863,0.0003469228,0.00046648286,0.000026545245,0.00007111351,0.0028092891],"genre_scores_gemma":[0.9981066,0.000022431133,0.001350274,0.000126789,0.00017937493,0.00001942015,0.0000059156223,0.000019408792,0.00016979438],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988023,0.000003946973,0.00027169863,0.00027296954,0.00037980248,0.00026925243],"domain_scores_gemma":[0.99925935,0.000027045651,0.0002135904,0.00016458998,0.0001988991,0.00013653691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004011913,0.00014282341,0.00024418495,0.000060745107,0.000117914155,0.00004691757,0.00020702262,0.000051625953,0.000049222388],"category_scores_gemma":[0.00052376895,0.00010271234,0.00008702921,0.00014368689,0.00013419008,0.00012770646,0.00035221825,0.00026318783,0.000007993111],"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":[0.000071259696,0.00008893235,0.4319229,0.00024496776,0.00006165592,0.000002125876,0.00031405734,1.4878927e-7,0.5555875,0.0012190281,0.0012163165,0.009271135],"study_design_scores_gemma":[0.002216688,0.00011521828,0.53886795,0.0006188931,0.00021908722,0.0001386382,0.00035492607,0.00024622914,0.45116684,0.0008454765,0.0049518733,0.00025820252],"about_ca_topic_score_codex":0.00013341222,"about_ca_topic_score_gemma":0.000022352353,"teacher_disagreement_score":0.106945,"about_ca_system_score_codex":0.00003790813,"about_ca_system_score_gemma":0.000033211258,"threshold_uncertainty_score":0.41884878},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.948858,0.016940273,0.005059105,0.016008206,0.000269201,0.00505505,0.007602028,0.0001730312,0.00003506543],"genre_scores_gemma":[0.97018576,0.00027300563,0.0088216765,0.0007654461,0.00007928517,0.000023082614,0.019818403,0.000028815984,0.000004542141],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99505484,0.0006446625,0.0020537826,0.00081771676,0.00079402205,0.0006349886],"domain_scores_gemma":[0.99382764,0.00014327408,0.002283848,0.0025147325,0.00020467355,0.0010258338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016830353,0.00023640314,0.0012199947,0.0001750805,0.00017265626,0.000036655787,0.0003750706,0.00004822367,0.00005314978],"category_scores_gemma":[0.00048654253,0.00021351666,0.000042261538,0.0003552643,0.0001100474,0.0006328931,0.0005350389,0.00015318327,0.000002627579],"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":[0.000044490916,0.0022403488,0.9103139,0.0041000787,0.00013464941,0.0000030032104,0.00086151605,0.000014435663,0.0000042479637,0.00012880491,0.013972523,0.06818201],"study_design_scores_gemma":[0.0012275368,0.0015547645,0.9443028,0.00043632145,0.000030783252,0.000040968673,0.0013981428,0.04016893,0.0000016104445,0.000021989934,0.010650511,0.00016566018],"about_ca_topic_score_codex":0.013753777,"about_ca_topic_score_gemma":0.0015901611,"teacher_disagreement_score":0.06801634,"about_ca_system_score_codex":0.0010359602,"about_ca_system_score_gemma":0.010876909,"threshold_uncertainty_score":0.9947305},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98908883,0.0074923676,0.00014921356,0.00043023648,0.00021978682,0.0013186933,0.00037519072,0.00045995458,0.00046570518],"genre_scores_gemma":[0.99560714,0.0023764062,0.00024270317,0.00025606176,0.0003524007,0.00017681564,0.0003654955,0.00007833854,0.0005446634],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99725926,0.00012713084,0.0006432835,0.00087056856,0.0005793393,0.0005204128],"domain_scores_gemma":[0.9982852,0.00042602842,0.00026349904,0.00043519418,0.00023405577,0.00035606584],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076544075,0.00043367647,0.00071673794,0.0001950867,0.00017946993,0.00013406108,0.0003625689,0.00019446906,0.000039831444],"category_scores_gemma":[0.0007245761,0.00032958252,0.000106097454,0.0005989936,0.00040780488,0.0004134515,0.00023078542,0.00033657983,0.000016943579],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066774274,0.00012386542,0.967356,0.00031539163,0.00003888721,0.00010378819,0.00020449702,0.0000029990172,0.00017082176,0.0001457841,0.030776186,0.00009400165],"study_design_scores_gemma":[0.0014056127,0.000063809515,0.9797531,0.0010221449,0.000032141954,0.00015265263,0.00024299188,0.0011792644,0.000078097604,0.000048566853,0.015591442,0.00043020348],"about_ca_topic_score_codex":0.00042643884,"about_ca_topic_score_gemma":0.0006176522,"teacher_disagreement_score":0.015184745,"about_ca_system_score_codex":0.0001146394,"about_ca_system_score_gemma":0.00016102502,"threshold_uncertainty_score":0.9999156},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94689065,0.010172572,0.0021989099,0.017059969,0.003025039,0.0038363477,0.008640413,0.0010507809,0.007125316],"genre_scores_gemma":[0.9945797,0.00013733347,0.0017500194,0.0016893101,0.0001677079,0.000020893463,0.0014318428,0.000048007292,0.00017520897],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982222,0.00014656778,0.0005586255,0.00039941666,0.00033704878,0.00033614313],"domain_scores_gemma":[0.9970642,0.000031405623,0.00023639928,0.001940021,0.00046132327,0.00026668925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002785524,0.00017801885,0.00041703082,0.00012951842,0.000056308898,0.000008981234,0.00033540442,0.00010943351,0.00017975023],"category_scores_gemma":[0.00008612012,0.0001687475,0.000043195414,0.00029713206,0.00008814588,0.00015330238,0.00005380743,0.00019609157,0.00010842001],"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":[0.0011867371,0.0008544546,0.27333978,0.0036513947,0.0003024295,0.00015328536,0.04111666,5.8176266e-7,0.0008396618,0.0024452885,0.006421542,0.6696882],"study_design_scores_gemma":[0.0041655805,0.0035762424,0.87577707,0.0010791457,0.00017536663,0.000051691433,0.0066559003,0.00032244012,0.00023301425,0.0008330539,0.10647553,0.00065496797],"about_ca_topic_score_codex":0.002804486,"about_ca_topic_score_gemma":0.004573656,"teacher_disagreement_score":0.6690332,"about_ca_system_score_codex":0.000057892015,"about_ca_system_score_gemma":0.0005405612,"threshold_uncertainty_score":0.68813235},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9969526,0.0006330565,0.00006600205,0.00014360335,0.0001300546,0.0017517626,0.00014864368,0.00014636583,0.000027888582],"genre_scores_gemma":[0.9990366,0.00005685287,0.000016149523,0.00008155053,0.00019462113,0.000037970018,0.00027774353,0.000024226169,0.00027429897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99864197,0.00011971662,0.00029231032,0.00045979323,0.00022407415,0.00026213774],"domain_scores_gemma":[0.99869776,0.00007603345,0.00010906583,0.0008411221,0.00013015735,0.000145889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007212288,0.00016977516,0.00029992894,0.00014518587,0.00021034064,0.00006687188,0.00017551963,0.000024177963,0.00006434726],"category_scores_gemma":[0.00024676687,0.00012754818,0.000013961301,0.00030486027,0.000006445779,0.00022487952,0.00020361353,0.00027933953,0.00003552883],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000521671,0.00023773385,0.9858455,0.0000992139,0.000109934044,0.000009505249,0.00028858613,0.00006742886,0.007678928,0.0000049533355,0.0033601036,0.0017764074],"study_design_scores_gemma":[0.0026496327,0.00022837303,0.983523,0.00003241667,0.00011721895,0.00018174523,0.0002028492,0.0109442305,0.00007390784,0.000005389637,0.0019075667,0.00013368299],"about_ca_topic_score_codex":0.00024859727,"about_ca_topic_score_gemma":0.0011440841,"teacher_disagreement_score":0.010876802,"about_ca_system_score_codex":0.000085345026,"about_ca_system_score_gemma":0.00005577678,"threshold_uncertainty_score":0.52012634},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27587983,0.014144325,0.0045950804,0.6953989,0.00037973747,0.007143368,0.00086060434,0.00023239401,0.0013657466],"genre_scores_gemma":[0.8928983,0.0020540275,0.003827973,0.08649167,0.0011945029,0.0002521462,0.0010617541,0.00010997642,0.012109662],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963773,0.00068172166,0.000567991,0.00055223017,0.0006296626,0.0011911073],"domain_scores_gemma":[0.99737334,0.0009896819,0.0002355859,0.00056381203,0.00003353942,0.000804038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004573031,0.00019235852,0.00043585376,0.00017885822,0.00052408286,0.00006637902,0.00017994424,0.00005342143,0.000100635014],"category_scores_gemma":[0.00044303318,0.00015094492,0.000055426135,0.00025551685,0.00014065775,0.00008735826,0.00011301112,0.00049484713,0.000100046425],"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":[0.00039657328,0.00036321735,0.0060466067,0.0016872445,0.0000257538,0.000007301008,0.001401023,0.0000045118636,0.00001759731,0.0030048497,0.9211942,0.0658511],"study_design_scores_gemma":[0.0022753489,0.0015555375,0.096441664,0.0004625382,0.0000055825526,0.00005315453,0.0019924056,0.0011289947,0.000004414086,0.00012053906,0.8958031,0.00015669597],"about_ca_topic_score_codex":0.0022811014,"about_ca_topic_score_gemma":0.00066399534,"teacher_disagreement_score":0.61701846,"about_ca_system_score_codex":0.001151136,"about_ca_system_score_gemma":0.0022545853,"threshold_uncertainty_score":0.61553556},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000047020844,0.99127746,0.0000069304156,0.0034500835,0.0016884628,0.0010739557,0.0011729777,0.00006130821,0.0012641527],"genre_scores_gemma":[0.00002689191,0.99444336,0.00007464228,0.000038734313,0.0020322704,0.000047620804,0.0007742845,0.000039284307,0.0025228965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966885,0.00065002724,0.00072735973,0.0005853333,0.00065515947,0.000693664],"domain_scores_gemma":[0.9966989,0.00026662648,0.00069584267,0.0015397234,0.00020609074,0.000592819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038219215,0.00030306997,0.0013946953,0.00016675146,0.0002862806,0.000119768425,0.00047860603,0.00012657083,0.00008026634],"category_scores_gemma":[0.00008334725,0.00017396738,0.0002974864,0.00086293166,0.0006232034,0.00008901386,0.00025214851,0.0003153812,0.000083567174],"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":[0.0000031430625,0.00006497934,0.00030115352,0.009710065,0.00008602253,0.0000017511986,0.000026185293,2.2103166e-9,1.3325636e-7,0.0001701285,0.06777762,0.92185885],"study_design_scores_gemma":[0.0001355051,0.000025347426,0.002482078,0.0007657393,0.00008026979,0.000033738612,0.00002237761,0.0000012095519,5.262463e-8,0.000005210351,0.99631035,0.00013814041],"about_ca_topic_score_codex":0.000008790046,"about_ca_topic_score_gemma":0.00001567282,"teacher_disagreement_score":0.9285327,"about_ca_system_score_codex":0.00007897543,"about_ca_system_score_gemma":0.00083132146,"threshold_uncertainty_score":0.70941836},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031639138,0.98184174,0.009043933,0.0013250088,0.0005461875,0.0038673065,0.0031147362,0.00010106509,0.00015683101],"genre_scores_gemma":[0.0000038512762,0.9474015,0.034677412,0.0013115427,0.0006105033,0.0005418678,0.015010859,0.0001041407,0.0003383155],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99247867,0.0029402787,0.0026256535,0.0011689359,0.00014116662,0.00064526795],"domain_scores_gemma":[0.9898863,0.0052737617,0.0023038376,0.0013302045,0.00041450316,0.0007914159],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00653521,0.00080702134,0.008566046,0.00022379575,0.000085716914,0.0000061747837,0.00034832573,0.00063980935,0.00041510383],"category_scores_gemma":[0.022812238,0.00059764786,0.0014135508,0.00029912917,0.0005329979,0.000063850784,0.000221253,0.00068411336,0.000051731746],"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":[0.00016553831,0.000076014876,0.003178094,0.18974856,0.00019828364,0.0000066197435,0.0000019580814,8.830954e-8,5.0358756e-7,0.0002458999,0.012542536,0.7938359],"study_design_scores_gemma":[0.0005004911,0.00025489173,0.0008286678,0.043363344,0.0012196656,0.000031667696,3.077505e-7,0.00031638422,2.2526507e-7,0.00051346683,0.9524836,0.00048729096],"about_ca_topic_score_codex":0.00038440194,"about_ca_topic_score_gemma":0.00010259044,"teacher_disagreement_score":0.93994105,"about_ca_system_score_codex":0.00006181595,"about_ca_system_score_gemma":0.0010062014,"threshold_uncertainty_score":0.9996475},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8634101,0.0068348646,0.036312,0.056802634,0.009067956,0.0022966072,0.022467818,0.00026675125,0.0025412645],"genre_scores_gemma":[0.99384147,0.0003342684,0.00048141467,0.003773023,0.0011218635,0.0000059088798,0.00034348245,0.000015560721,0.000083002175],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986254,0.000077456614,0.00045693366,0.00017388652,0.0005411379,0.00012521607],"domain_scores_gemma":[0.997943,0.0005253947,0.00051978906,0.00020460019,0.0004987834,0.00030843652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003089085,0.000112438145,0.0003116691,0.00007064162,0.000058326234,0.000049058937,0.0001558002,0.000023919622,0.000039296556],"category_scores_gemma":[0.0037101698,0.000084019644,0.00007165761,0.000038260623,0.000081515514,0.00014741614,0.000062975116,0.000072995485,0.000002127121],"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":[0.016964788,0.0016255103,0.45975456,0.00042530723,0.0025630426,0.0002182275,0.00021623728,0.00016793744,0.000591943,0.0048172716,0.3495551,0.1631001],"study_design_scores_gemma":[0.029691974,0.0053972453,0.18674773,0.00090948824,0.0005528146,0.0022957078,0.0003577275,0.0059682117,0.0000779414,0.0017287651,0.7658531,0.0004192797],"about_ca_topic_score_codex":0.000022971013,"about_ca_topic_score_gemma":0.000032812317,"teacher_disagreement_score":0.41629803,"about_ca_system_score_codex":0.00022828973,"about_ca_system_score_gemma":0.0002674245,"threshold_uncertainty_score":0.44416878},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5400628,0.0021709022,0.00035266875,0.43696406,0.00005491549,0.0029135644,0.0003813309,0.00028696776,0.016812813],"genre_scores_gemma":[0.9918946,0.00032038568,0.005433872,0.0007553507,0.00009892331,0.000024351388,0.00023314168,0.000012685326,0.0012266984],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9979411,0.00032266197,0.00019236124,0.00054354,0.00064723386,0.00035312446],"domain_scores_gemma":[0.99786013,0.00025950174,0.000027614396,0.0014740592,0.00013844726,0.00024026944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020822755,0.00011024715,0.00024105422,0.00023068344,0.00007403872,0.00010142589,0.0003588936,0.00015956389,0.00017531184],"category_scores_gemma":[0.0025671402,0.00008930939,0.000018873441,0.00064520026,0.00016210794,0.0002397703,0.00029534972,0.00079065596,0.00006848536],"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":[0.0016545891,0.0018795023,0.12285662,0.000785772,0.00014246005,0.00097476854,0.001345904,0.0000045849356,0.037065055,0.048526537,0.5171629,0.26760128],"study_design_scores_gemma":[0.003849949,0.0006903124,0.7099159,0.00066427165,0.000021722817,0.00007016105,0.00033254788,0.0019901898,0.0021786827,0.009865733,0.27007216,0.0003483993],"about_ca_topic_score_codex":0.000059659975,"about_ca_topic_score_gemma":0.00017153662,"teacher_disagreement_score":0.58705926,"about_ca_system_score_codex":0.000031490075,"about_ca_system_score_gemma":0.00021966326,"threshold_uncertainty_score":0.36419314},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881084,0.000038798113,0.0070235194,0.0012592119,0.0007754859,0.00077129423,0.0016715769,0.00010182506,0.00024991456],"genre_scores_gemma":[0.9748929,0.0000075275407,0.010636365,0.00055865006,0.0009214663,0.000017374654,0.012919427,0.000021883034,0.000024396915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809766,0.000153603,0.00026967152,0.00060629053,0.00067300576,0.00019979173],"domain_scores_gemma":[0.9980214,0.000025817277,0.00016826415,0.000999987,0.00052699127,0.00025749835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014426854,0.00013852672,0.00017560623,0.00013801025,0.00013039411,0.000085627944,0.00024052072,0.000048989117,0.00025495977],"category_scores_gemma":[0.00064251496,0.00014206662,0.000017907132,0.00011717272,0.000080134254,0.0011829108,0.00031125656,0.0002727914,0.000017078051],"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":[0.002041899,0.0026800213,0.12171291,0.00033384297,0.0007914587,0.00032939875,0.0047136056,0.0004400029,0.16654506,0.0020584525,0.0029397372,0.6954136],"study_design_scores_gemma":[0.0020511139,0.00019246452,0.19814003,0.0001313476,0.00008190047,0.00073724356,0.00026085606,0.77628624,0.0007826121,0.00017770822,0.02090494,0.00025352978],"about_ca_topic_score_codex":0.0006334732,"about_ca_topic_score_gemma":0.0007341188,"teacher_disagreement_score":0.77584624,"about_ca_system_score_codex":0.000100870675,"about_ca_system_score_gemma":0.0004928204,"threshold_uncertainty_score":0.57933086},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906805,0.00040631046,0.001184633,0.0056845136,0.001377074,0.0002678027,0.00027722644,0.000029427398,0.00009251424],"genre_scores_gemma":[0.9912517,0.00044702954,0.006440556,0.0013587143,0.00039601926,0.0000050426743,0.000069792026,0.000022283317,0.000008855234],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99729395,0.00009532152,0.0010669437,0.00018608138,0.0011059964,0.00025168285],"domain_scores_gemma":[0.99601257,0.00022097283,0.0009785597,0.00038437734,0.0018547494,0.0005487705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014007785,0.00014875324,0.0004092996,0.0012670796,0.000070658745,0.000021431873,0.00057156745,0.00007940271,0.00013017349],"category_scores_gemma":[0.0010937623,0.00013364684,0.00027080125,0.0005416705,0.00010024205,0.00023434617,0.000048920443,0.00054744794,0.000008998322],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022972064,0.00063235534,0.94483936,0.000049857525,0.00022017746,0.000052494677,0.00010115634,0.00003551246,0.0045768134,0.00019302712,0.0005166185,0.048552927],"study_design_scores_gemma":[0.0012383736,0.0011006196,0.9857201,0.00030401003,0.000038639242,0.00037957294,0.000053041487,0.00008019093,0.000335589,0.0008503642,0.009788578,0.00011094574],"about_ca_topic_score_codex":0.00015742688,"about_ca_topic_score_gemma":0.00030131376,"teacher_disagreement_score":0.04844198,"about_ca_system_score_codex":0.000039868053,"about_ca_system_score_gemma":0.00058920897,"threshold_uncertainty_score":0.544996},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98074645,0.002407285,0.0035213663,0.0119782705,0.00053087133,0.0002522065,0.0005073634,0.000025896312,0.000030269939],"genre_scores_gemma":[0.9856864,0.0037412809,0.004518406,0.0052027185,0.00032441661,0.0000040760597,0.0005025701,0.0000091215115,0.000011009192],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99861217,0.000018580487,0.0006680341,0.00008511598,0.0004735431,0.00014257348],"domain_scores_gemma":[0.9981266,0.000058952905,0.0005854236,0.000102318205,0.0008549666,0.00027173012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043899543,0.00008567634,0.0002163206,0.00045936092,0.000094371215,0.00002926445,0.00017599638,0.000026665039,0.000009734205],"category_scores_gemma":[0.00014560216,0.00007674247,0.00011045599,0.00011868512,0.00004866915,0.0005335686,0.00005340777,0.00014240974,0.000001378306],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007160036,0.00024014893,0.9688497,0.00064977445,0.00025070092,0.00006877791,0.0006062962,0.000057544894,0.000007721734,0.00035145378,0.00084433873,0.027357511],"study_design_scores_gemma":[0.0016879747,0.00011961645,0.9392545,0.00048470378,0.000019152363,0.0003049926,0.00017090193,0.0023408793,0.0000012733614,0.00043674972,0.055112373,0.000066838],"about_ca_topic_score_codex":0.000046410863,"about_ca_topic_score_gemma":0.000012434807,"teacher_disagreement_score":0.054268032,"about_ca_system_score_codex":0.000051825085,"about_ca_system_score_gemma":0.0002501195,"threshold_uncertainty_score":0.3129467},"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":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17341815,0.013915602,0.24724294,0.44585547,0.10564834,0.01076545,0.001887804,0.00016493109,0.0011012988],"genre_scores_gemma":[0.19523862,0.06522385,0.3230379,0.0178812,0.38826844,0.0008546147,0.0075537683,0.00042353032,0.0015180852],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9894038,0.0037040627,0.0023300906,0.0002943999,0.0036013692,0.0006662745],"domain_scores_gemma":[0.98003334,0.012038798,0.001349447,0.0010591117,0.004009085,0.0015102328],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.056704514,0.00026691818,0.0012097582,0.001174248,0.000094018105,0.00021049462,0.0016599894,0.00088915793,0.00013700692],"category_scores_gemma":[0.09481916,0.00017798699,0.00014542529,0.0005950944,0.0007049226,0.0002843955,0.0007430329,0.0049165483,0.0000034342254],"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":[0.0005046471,0.00017638937,0.0013128584,0.00044745766,0.00018443103,0.0000040302148,0.0030104092,7.3594464e-7,0.0000029191601,0.0019667754,0.7931123,0.19927707],"study_design_scores_gemma":[0.0012717866,0.005549931,0.004823325,0.002459699,0.000057290636,0.00006957213,0.0016628694,0.003363834,0.000015672518,0.00071170845,0.97983664,0.0001776421],"about_ca_topic_score_codex":0.0002640402,"about_ca_topic_score_gemma":0.00010364124,"teacher_disagreement_score":0.42797428,"about_ca_system_score_codex":0.0003894677,"about_ca_system_score_gemma":0.0022285583,"threshold_uncertainty_score":0.9973791},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053258454,0.00034375544,0.989675,0.00019646519,0.0011899864,0.000834334,0.002053949,0.00013499353,0.0002456886],"genre_scores_gemma":[0.06770364,0.005018342,0.9208631,0.00009581435,0.0005814075,0.000020127389,0.005567918,0.00004735765,0.0001023174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998562,0.000017885499,0.0004808693,0.00035736614,0.0004283173,0.00015358735],"domain_scores_gemma":[0.9985548,0.00016895843,0.00023652217,0.0006719576,0.00020618999,0.0001615574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005525329,0.00016094447,0.00022544306,0.00034932725,0.000030014955,0.00005540936,0.0006658476,0.000056279976,0.000030119689],"category_scores_gemma":[0.00036423418,0.00015500386,0.000027978966,0.0000938139,0.00003012727,0.0007193486,0.0007326631,0.00009545394,0.0000049377804],"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":[0.0037221166,0.0021843244,0.017824156,0.0033782753,0.014771848,0.0012508288,0.0021989131,0.014511461,0.0008928785,0.0083029475,0.035403714,0.89555854],"study_design_scores_gemma":[0.002296044,0.00008064348,0.041964587,0.00046877476,0.0003508627,0.000032448672,0.000051246654,0.92914003,0.000014306612,0.00009434597,0.02536867,0.00013805924],"about_ca_topic_score_codex":0.000007417593,"about_ca_topic_score_gemma":7.1700134e-7,"teacher_disagreement_score":0.91462857,"about_ca_system_score_codex":0.000047516725,"about_ca_system_score_gemma":0.000014493343,"threshold_uncertainty_score":0.6320874},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06875688,0.0024565144,0.85040015,0.0037459107,0.0024063606,0.0022540765,0.007513586,0.00054134766,0.061925195],"genre_scores_gemma":[0.92884624,0.00008448976,0.06441231,0.00074373075,0.00095856434,0.000039000734,0.0033184579,0.000057482164,0.0015397546],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9977361,0.00008977747,0.0006439203,0.0004050407,0.0006724796,0.000452708],"domain_scores_gemma":[0.99855673,0.00042966357,0.00013538188,0.0004963399,0.0001638592,0.00021802129],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041496908,0.0002386054,0.00061264023,0.00025358197,0.000047904985,0.000010701199,0.00013665861,0.000066955115,0.0022613148],"category_scores_gemma":[0.001250059,0.00020683582,0.000026856396,0.0004040952,0.00040508,0.000036304005,0.00004407301,0.00029700404,0.0001329779],"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":[0.0007743803,0.0005727906,0.31438068,0.0005751446,0.000082761966,0.0046063093,0.00024343141,0.00009870694,0.00066438416,0.034971897,0.4542191,0.18881042],"study_design_scores_gemma":[0.0060215946,0.0005044218,0.92995834,0.00063893915,0.00015297913,0.00006723111,0.000070924914,0.006842421,0.000026846474,0.030794328,0.02460032,0.0003216228],"about_ca_topic_score_codex":0.0034343959,"about_ca_topic_score_gemma":0.0036284653,"teacher_disagreement_score":0.8600893,"about_ca_system_score_codex":0.00018664969,"about_ca_system_score_gemma":0.0001890327,"threshold_uncertainty_score":0.9986507},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9695646,0.00026652555,0.0006618335,0.004579052,0.00018605274,0.0011612227,0.00095590425,0.00022087818,0.022403918],"genre_scores_gemma":[0.9921929,0.00007288182,0.0019392584,0.005232839,0.000031565996,0.000083893756,0.00040903827,0.000023851555,0.000013742286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982975,0.00023644372,0.00045272606,0.00028040857,0.0002979533,0.00043495552],"domain_scores_gemma":[0.99818355,0.0004525598,0.00021467399,0.0006714613,0.00008602922,0.0003917097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058584544,0.0001361206,0.0004283799,0.00032482864,0.000045609835,0.000008985806,0.00015853059,0.000049466667,0.00027231305],"category_scores_gemma":[0.0009602225,0.00012567408,0.000088338915,0.0005873594,0.00008171775,0.00015976047,0.0000312554,0.00017244197,0.00012801503],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019476759,0.0058892793,0.9462113,0.0013071069,0.00003317619,0.0001231473,0.00060813257,0.000009825685,0.000014990449,0.011858607,0.008202244,0.025547432],"study_design_scores_gemma":[0.001391155,0.00048766888,0.99315524,0.00028785883,0.0000054184025,0.0000074093573,0.00011436617,0.00024785456,0.00001744916,0.00033182453,0.0038481024,0.00010565047],"about_ca_topic_score_codex":0.00057586224,"about_ca_topic_score_gemma":0.00048030517,"teacher_disagreement_score":0.04694395,"about_ca_system_score_codex":0.00016501009,"about_ca_system_score_gemma":0.0019823653,"threshold_uncertainty_score":0.512484},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9871515,0.0013330509,0.0055900626,0.0005837544,0.00048572838,0.0012366223,0.0002528795,0.00032339623,0.0030429808],"genre_scores_gemma":[0.99507403,0.0001407319,0.0027591288,0.00050577486,0.00039154,0.00014831427,0.0002815133,0.00007775517,0.0006212128],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99646753,0.0005343942,0.00082823983,0.0007414573,0.00081323145,0.0006151746],"domain_scores_gemma":[0.99644697,0.0010418022,0.00047861796,0.0010281062,0.000470067,0.0005344671],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012016553,0.0004614205,0.0009815872,0.00016944847,0.00013520251,0.000034117173,0.00025537988,0.00009713126,0.00015884264],"category_scores_gemma":[0.0038872662,0.0003506639,0.00006227976,0.00057924207,0.00070934655,0.00051639974,0.00015626218,0.00034248637,0.000053747022],"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":[0.005036452,0.001839787,0.5435202,0.0036410226,0.00086679234,0.0050412724,0.0022940002,0.000058120822,0.37169138,0.00039108153,0.06351485,0.0021050156],"study_design_scores_gemma":[0.031072322,0.028575394,0.21527314,0.015468516,0.00080073654,0.018765228,0.0038506712,0.018626437,0.0020546352,0.000046347162,0.6624408,0.0030258126],"about_ca_topic_score_codex":0.00028053176,"about_ca_topic_score_gemma":0.000013478391,"teacher_disagreement_score":0.5989259,"about_ca_system_score_codex":0.00036236978,"about_ca_system_score_gemma":0.00044777297,"threshold_uncertainty_score":0.99989456},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029913193,0.99650633,0.00053843565,0.000029086916,0.0010366159,0.0009056843,0.0004117906,0.000050584265,0.00022231463],"genre_scores_gemma":[0.00066268025,0.9982102,0.0006183946,0.00013688217,0.00022777072,0.000008590906,0.00008042291,0.000033853834,0.000021204705],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99235237,0.0003182751,0.00539229,0.00010763417,0.001546927,0.00028249],"domain_scores_gemma":[0.98959965,0.0002936203,0.008410663,0.0006818447,0.00082707603,0.00018714517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018549808,0.0003914809,0.0036175526,0.0003335223,0.00004934562,0.000012126711,0.00053110154,0.00043398477,0.000035077035],"category_scores_gemma":[0.0018982444,0.0002166304,0.0013932083,0.0013760497,0.00040164794,0.00023298085,0.00015643233,0.00075400254,0.0000116205565],"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":[0.000030202598,0.00015963598,0.000061496736,0.26863816,0.00025993143,0.000014834615,0.000035380697,2.567704e-7,0.000005283654,3.566835e-7,0.003670367,0.7271241],"study_design_scores_gemma":[0.0007069068,0.00042009167,0.0005159038,0.19759278,0.0014782802,0.001894299,0.0000211192,0.00034954745,0.000056835215,0.0000059258987,0.7967714,0.00018690445],"about_ca_topic_score_codex":0.000017549324,"about_ca_topic_score_gemma":0.000011648814,"teacher_disagreement_score":0.793101,"about_ca_system_score_codex":0.00025628856,"about_ca_system_score_gemma":0.0010254065,"threshold_uncertainty_score":0.8833931},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99524814,0.0007675009,0.00057620165,0.000043542263,0.000022445036,0.0032380652,0.00007347354,0.0000136134595,0.000017040416],"genre_scores_gemma":[0.999215,0.000021071919,0.0001604309,0.000027285294,0.000025251742,0.00050033047,0.000039324153,0.000007958385,0.0000033257068],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99785143,0.00033703764,0.0009937882,0.00015413343,0.00054340914,0.000120221855],"domain_scores_gemma":[0.99714917,0.00054968934,0.0011267774,0.00030739396,0.0008482215,0.000018758841],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006275451,0.00007803109,0.0002838819,0.00008065718,0.00003348554,0.000009116371,0.00009267756,0.00003951826,0.000003600099],"category_scores_gemma":[0.015502782,0.00004514453,0.00003575762,0.0002791552,0.00004554249,0.000105284926,0.00002287178,0.000116960524,0.0000013191758],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030852435,0.00077445054,0.6848944,0.0019250966,0.00063921977,0.0000011193082,0.0029349993,0.00034730404,0.23695241,0.000010598279,0.000063958614,0.07114794],"study_design_scores_gemma":[0.00076486694,0.00031601396,0.69624543,0.0023209814,0.0007737412,0.00000808704,0.002422165,0.26684913,0.030026065,0.00015659037,0.000013136038,0.00010376627],"about_ca_topic_score_codex":0.0027997396,"about_ca_topic_score_gemma":0.00010158926,"teacher_disagreement_score":0.26650184,"about_ca_system_score_codex":0.00005500198,"about_ca_system_score_gemma":0.000097818614,"threshold_uncertainty_score":0.99279004},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99483275,0.0007046791,0.00121505,0.0020332723,0.0002879673,0.0003585059,0.0002875548,0.000007542335,0.0002726753],"genre_scores_gemma":[0.99716645,0.00015155415,0.001856238,0.00023065173,0.00045430652,0.0000037327804,0.000026848465,0.000016512213,0.00009368435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99768996,0.00026967912,0.0010739472,0.00019789056,0.0005824122,0.00018610875],"domain_scores_gemma":[0.99741006,0.00028379887,0.00076455623,0.00095650123,0.00040039473,0.00018468314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015571426,0.00015708114,0.0007100683,0.00022472526,0.00003183443,0.0000041575568,0.00053892756,0.00005085368,0.00013638978],"category_scores_gemma":[0.002051559,0.00009874822,0.000059795304,0.00037367365,0.0002669138,0.000213265,0.00030672326,0.00027676093,0.0000011231882],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.021988558,0.0029934647,0.74924767,0.023745134,0.0005706611,0.021313036,0.0032369117,0.002294162,0.0736721,0.00035746617,0.031945046,0.06863576],"study_design_scores_gemma":[0.012171777,0.019666076,0.8772378,0.022347203,0.0007760931,0.04597143,0.0005719109,0.00924969,0.0018136901,0.0004343881,0.009360013,0.00039994705],"about_ca_topic_score_codex":0.001498127,"about_ca_topic_score_gemma":0.00014089225,"teacher_disagreement_score":0.12799008,"about_ca_system_score_codex":0.00004537015,"about_ca_system_score_gemma":0.00020024211,"threshold_uncertainty_score":0.40268356},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3412401,0.004429438,0.6382742,0.013121896,0.0010038064,0.0010443424,0.0006307552,0.00017959926,0.000075892414],"genre_scores_gemma":[0.97933793,0.007733855,0.004608864,0.0049624853,0.0003179454,0.000013180806,0.002980923,0.000029922441,0.000014864361],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99724746,0.00014123031,0.0009553099,0.00031115563,0.00093810057,0.00040671733],"domain_scores_gemma":[0.99721545,0.0002057705,0.00074896525,0.0004449714,0.0008794955,0.0005053716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015790026,0.00026750122,0.00038377903,0.0009766198,0.0009420571,0.00061744975,0.0004853985,0.0001343442,0.000025185494],"category_scores_gemma":[0.0009928271,0.00021302624,0.00019016396,0.0008723141,0.00030643432,0.0018043924,0.000227821,0.00033129004,0.0000012413839],"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":[0.0015697769,0.0007494001,0.608252,0.00083137397,0.0059395093,0.000050577255,0.023432905,0.00089442346,0.00056137267,0.04621889,0.049411055,0.26208875],"study_design_scores_gemma":[0.0070408913,0.0007209612,0.047261387,0.0013756533,0.0008272337,0.0063564437,0.0062254067,0.34233248,0.000050788156,0.0063278247,0.5802544,0.0012265495],"about_ca_topic_score_codex":0.0002696829,"about_ca_topic_score_gemma":0.000017709406,"teacher_disagreement_score":0.6380979,"about_ca_system_score_codex":0.00007140966,"about_ca_system_score_gemma":0.00045107835,"threshold_uncertainty_score":0.8686958},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9824171,0.0014958003,0.009721397,0.0006275364,0.0007318248,0.0010529138,0.0035977701,0.000050674484,0.00030499257],"genre_scores_gemma":[0.9875985,0.00008050858,0.0014009443,0.00018262747,0.00039368996,0.000048129994,0.010250216,0.000033412332,0.000011994304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9954999,0.0011339244,0.0015477746,0.00052267284,0.0002618668,0.0010338291],"domain_scores_gemma":[0.9972274,0.0004189299,0.0007357409,0.0005022521,0.00019812303,0.000917584],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032039003,0.00036728926,0.0014576056,0.00027854354,0.00014229563,0.000010631499,0.00014196585,0.00036452114,0.00009147719],"category_scores_gemma":[0.0013159398,0.00033719884,0.00015316995,0.00033295972,0.00026238683,0.00020611443,0.00009836874,0.0003822567,0.0000058499654],"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":[0.00022494957,0.00014465467,0.9928564,0.00023484186,0.00011171939,0.000008975065,0.0000684188,0.0004324516,0.000016676335,0.0037400813,0.00022985438,0.0019309701],"study_design_scores_gemma":[0.001481583,0.0007286147,0.94828826,0.00017194913,0.00008636856,0.000034883764,0.00006413606,0.03948971,0.000018077828,0.00023214037,0.009106217,0.00029807654],"about_ca_topic_score_codex":0.72104645,"about_ca_topic_score_gemma":0.6114263,"teacher_disagreement_score":0.10962013,"about_ca_system_score_codex":0.00070230843,"about_ca_system_score_gemma":0.00087309466,"threshold_uncertainty_score":0.99990803},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35037348,0.0008335302,0.6471711,0.00012372527,0.00021583831,0.00046984508,0.0007447679,0.000046064655,0.000021679243],"genre_scores_gemma":[0.9379033,0.00003863906,0.059438597,0.000046298846,0.00027299926,0.000016668197,0.002204429,0.000015356552,0.000063710606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991287,0.00004341074,0.0002620292,0.00019075141,0.00019776875,0.00017733907],"domain_scores_gemma":[0.99913406,0.00012340023,0.00014254959,0.00009841773,0.00015967422,0.0003419028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014591157,0.00012223954,0.0002203627,0.00014104292,0.000088512934,0.000010127242,0.000022414228,0.000044334876,0.000022205995],"category_scores_gemma":[0.00013345259,0.00012401672,0.000041259977,0.00011091847,0.000071178816,0.00018633263,0.0000322709,0.000050671195,0.0000031717996],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0066885822,0.00046985783,0.69914,0.0010704601,0.00018820916,0.000005419258,0.0003698291,0.0022514854,0.00022792492,0.0005477566,0.0088807745,0.2801597],"study_design_scores_gemma":[0.002890117,0.00020894555,0.85721004,0.000044756907,0.00016668497,0.00001429977,0.000034328776,0.1367256,0.00004434854,0.0017298717,0.00079669757,0.00013432323],"about_ca_topic_score_codex":0.0003363269,"about_ca_topic_score_gemma":0.000029533423,"teacher_disagreement_score":0.5877325,"about_ca_system_score_codex":0.000059165668,"about_ca_system_score_gemma":0.00014609787,"threshold_uncertainty_score":0.5057255},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996493,0.00018500304,0.0007596397,0.0021179563,0.00004975986,0.00021673247,0.0001639498,0.000008196013,0.0000057787215],"genre_scores_gemma":[0.9785522,0.0004576614,0.019487718,0.0013531066,0.000056482833,9.737371e-7,0.00008352221,0.0000074071363,9.2571565e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978087,0.0002450307,0.00140637,0.0000485445,0.00032284838,0.0001685235],"domain_scores_gemma":[0.9976007,0.00004650554,0.0013284408,0.00022139713,0.0004574691,0.00034552175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020091145,0.00009519247,0.0003465256,0.00036597147,0.000035440986,0.00001912143,0.0000999979,0.000061915256,0.0000023317446],"category_scores_gemma":[0.0008870205,0.00006541065,0.000027842261,0.00028029678,0.00013198284,0.0015342926,0.000028373199,0.0002590559,9.1961354e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025371925,0.0011109944,0.60889244,0.0017812509,0.000054227716,0.0000070220317,0.022010026,0.000016351698,0.00016445242,0.00021635903,0.00009911448,0.365394],"study_design_scores_gemma":[0.0014252976,0.002330626,0.957696,0.0005149325,0.000024965868,0.00034659365,0.0078055523,0.028353952,0.000019952811,0.0001713687,0.0012433466,0.00006737713],"about_ca_topic_score_codex":0.00020469617,"about_ca_topic_score_gemma":0.0018657597,"teacher_disagreement_score":0.36532664,"about_ca_system_score_codex":0.000050709587,"about_ca_system_score_gemma":0.0007388077,"threshold_uncertainty_score":0.2667369},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98124593,0.0026831399,0.013668555,0.00065579254,0.0006137535,0.00031425076,0.00003432402,0.000015362113,0.0007689086],"genre_scores_gemma":[0.98791564,0.0005803777,0.010832636,0.00038095948,0.00018391582,0.000035077654,0.000007991055,0.000035441088,0.000027968863],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99617046,0.00088251324,0.0012750033,0.00040046909,0.00015012045,0.0011214585],"domain_scores_gemma":[0.9952863,0.0014782263,0.0008023938,0.00040444377,0.0007451471,0.0012835104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017122618,0.00036153398,0.0012227069,0.0007200798,0.00011786174,0.000030534204,0.00045673884,0.00033529673,0.0002033422],"category_scores_gemma":[0.004288925,0.00036724014,0.00019050403,0.00014851314,0.0009341433,0.00053493865,0.00006726082,0.00092168164,0.0000029577197],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031407364,0.0006038483,0.055149663,0.018822955,0.0007000179,0.0043855268,0.083384,0.0028910309,0.021459075,0.0053785574,0.00074355985,0.80334103],"study_design_scores_gemma":[0.002274936,0.0016832291,0.7146584,0.0011397584,0.007390094,0.004747144,0.25434983,0.0050889323,0.0071056834,0.00067866704,0.00037522332,0.0005081047],"about_ca_topic_score_codex":0.17296681,"about_ca_topic_score_gemma":0.43132374,"teacher_disagreement_score":0.8028329,"about_ca_system_score_codex":0.0012469194,"about_ca_system_score_gemma":0.002360877,"threshold_uncertainty_score":0.9998779},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95411867,0.0035862788,0.000546237,0.0014911421,0.0012393297,0.0016907477,0.0034673954,0.0009341951,0.032925997],"genre_scores_gemma":[0.9944425,0.00008747291,0.00004654124,0.0039189104,0.00055639475,0.0001854993,0.00023726028,0.000036966732,0.000488454],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988021,0.00010180572,0.00018396546,0.00025109082,0.00023484114,0.00042615406],"domain_scores_gemma":[0.9986527,0.0003201885,0.000092001406,0.00060176663,0.00010906517,0.00022430114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016239639,0.00017517431,0.00038840817,0.000031030097,0.00024294067,0.000032701766,0.00013758069,0.0000293403,0.0003626774],"category_scores_gemma":[0.001143017,0.00012606084,0.00010913753,0.0001751556,0.000072084884,0.000059741953,0.000028186814,0.00012369613,0.000047036694],"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":[0.00029141837,0.00027209814,0.7203068,0.00027608225,0.0002468794,0.000058883503,0.000049868457,0.0002358778,0.00023455925,0.0022302326,0.2727558,0.0030415074],"study_design_scores_gemma":[0.0028921333,0.0001332201,0.31753293,0.00006937936,0.00029365782,0.000059324288,0.000060048427,0.00012834891,0.000360113,0.0009156628,0.6772716,0.00028354579],"about_ca_topic_score_codex":0.038485635,"about_ca_topic_score_gemma":0.17885071,"teacher_disagreement_score":0.40451583,"about_ca_system_score_codex":0.00017632838,"about_ca_system_score_gemma":0.000748372,"threshold_uncertainty_score":0.96791714},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44608527,0.00023537579,0.5525225,0.000026180984,0.000044472217,0.00027931604,0.0000370959,0.000032373307,0.00073741085],"genre_scores_gemma":[0.37183598,0.00004132784,0.6269034,0.0011120008,0.000046849007,0.000014138814,0.000030636616,0.000010915778,0.0000047361837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975809,0.00006543039,0.0010408189,0.00015962328,0.00092937634,0.00022384086],"domain_scores_gemma":[0.996807,0.0018860005,0.00034525432,0.0003574823,0.00023080871,0.00037344778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010532954,0.00017873627,0.00052655383,0.00017020981,0.000071274866,0.00006147822,0.0001461415,0.0001946668,0.0005974708],"category_scores_gemma":[0.0018589117,0.00013321626,0.00007322692,0.00014674154,0.0001686085,0.0003557368,0.00020033387,0.00019700454,0.000037385777],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006263839,0.000046173413,0.09683778,0.0007528102,0.000022664151,0.0000053447056,0.00067704177,0.000010236205,0.000013628081,0.00008570489,0.0015318565,0.89995414],"study_design_scores_gemma":[0.003849982,0.00020368696,0.21097136,0.001423147,0.00010758306,0.000046194098,0.0004414108,0.77591896,0.000030859923,0.0025334992,0.004220371,0.0002529542],"about_ca_topic_score_codex":0.000052217172,"about_ca_topic_score_gemma":0.000023455232,"teacher_disagreement_score":0.8997012,"about_ca_system_score_codex":0.000023421273,"about_ca_system_score_gemma":0.00034223014,"threshold_uncertainty_score":0.6541886},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0200328,0.9734897,0.000010686485,0.00031993986,0.0039506746,0.0009827161,0.00022374916,0.00001515838,0.00097457255],"genre_scores_gemma":[0.0031749378,0.9943732,0.00010221658,0.000026619759,0.00044114626,0.0000038277863,0.000243789,0.00011778626,0.001516493],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99205756,0.0015176886,0.004167181,0.00047398423,0.0012268618,0.00055671146],"domain_scores_gemma":[0.994922,0.00013457591,0.0030356878,0.00084063073,0.00045177588,0.0006153763],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027043088,0.000645074,0.0032794229,0.0003939356,0.00008087159,0.0000042006236,0.0005916042,0.00007814577,0.003078491],"category_scores_gemma":[0.0008830336,0.0003959386,0.00042345718,0.0006892066,0.00013688803,0.0001345766,0.00015621992,0.0010796689,0.000014792527],"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":[0.0019060302,0.0022575103,0.11734058,0.08405077,0.009040867,0.06831689,0.001547966,0.00016915076,0.00011056773,0.000060513114,0.48329777,0.23190139],"study_design_scores_gemma":[0.0016437228,0.0010133612,0.24306032,0.014298852,0.0011145077,0.0017721974,0.000024752257,0.0000023318707,8.445419e-8,0.000006896479,0.7367118,0.0003511874],"about_ca_topic_score_codex":0.3446542,"about_ca_topic_score_gemma":0.4575803,"teacher_disagreement_score":0.253414,"about_ca_system_score_codex":0.0007571458,"about_ca_system_score_gemma":0.0040241485,"threshold_uncertainty_score":0.99984926},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49537823,0.0000862572,0.50373846,0.000030683328,0.00020230681,0.0002508725,0.00009939987,0.000018100529,0.00019566719],"genre_scores_gemma":[0.47061953,0.00010458478,0.52833945,0.00069485937,0.000071019116,0.00002901344,0.00012660683,0.000011348284,0.000003612168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799293,0.000040390398,0.00085853616,0.00018527234,0.00068383425,0.00023901218],"domain_scores_gemma":[0.99597514,0.0028927193,0.0002937067,0.00042843932,0.00010090173,0.00030909872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017834554,0.00013266901,0.000329043,0.0001997908,0.00008596442,0.00006380433,0.00018710892,0.000103930484,0.00012607723],"category_scores_gemma":[0.002708056,0.000109539345,0.00006257411,0.00020946338,0.00003842942,0.000106642074,0.0001215041,0.00035077526,0.0000047563776],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020257337,0.000024519355,0.17796236,0.00017050229,0.00002221285,0.000015683947,0.00013596432,0.00007809059,0.0000075517537,0.00019431366,0.00075705256,0.82042915],"study_design_scores_gemma":[0.0014364949,0.000012692813,0.20781282,0.00047842786,0.00004451328,0.00001341251,0.0006246449,0.7805899,0.0000065190525,0.006495139,0.002356772,0.00012869757],"about_ca_topic_score_codex":0.083953224,"about_ca_topic_score_gemma":0.8204992,"teacher_disagreement_score":0.82030046,"about_ca_system_score_codex":0.000053033054,"about_ca_system_score_gemma":0.0007457496,"threshold_uncertainty_score":0.9221468},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9745008,0.00070594536,0.00047335515,0.0014940253,0.000061590334,0.00024106301,0.000023450366,0.000063717496,0.022436058],"genre_scores_gemma":[0.9912981,0.00038201496,0.008112003,0.00011043647,0.000007678533,0.0000020227824,0.000034821576,0.000004990904,0.000047960337],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990502,0.00003306455,0.00019910252,0.00017221074,0.00040447328,0.00014095707],"domain_scores_gemma":[0.997995,0.00005494308,0.00012438207,0.001504087,0.00022728837,0.00009428289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067750533,0.00005839154,0.00013614829,0.00009512697,0.000106087035,0.000015243003,0.00057728123,0.00002039966,0.00001617469],"category_scores_gemma":[0.0007721282,0.00005330459,0.000029342304,0.00066302076,0.00085216074,0.00021884001,0.00018305621,0.000090550806,0.000035784702],"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":[0.0005385363,0.001927154,0.20765871,0.0006314809,0.00005311712,0.000026147498,0.0058140685,0.0005831771,0.56643206,0.17006068,0.0007261969,0.045548666],"study_design_scores_gemma":[0.0007664462,0.000032041244,0.95483655,0.0003514932,0.000012364808,0.0000044626154,0.00010248243,0.00004264937,0.039051622,0.0034414465,0.0012724524,0.00008600352],"about_ca_topic_score_codex":0.000028569433,"about_ca_topic_score_gemma":0.00009737444,"teacher_disagreement_score":0.74717784,"about_ca_system_score_codex":0.00013649241,"about_ca_system_score_gemma":0.00038769905,"threshold_uncertainty_score":0.31398216},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42440528,0.024254387,0.00033720923,0.51497895,0.0010804964,0.003604394,0.0246129,0.00026616128,0.006460233],"genre_scores_gemma":[0.7618744,0.00040884703,0.000012757821,0.23469341,0.0025682906,0.000014893746,0.0003966857,0.00002231898,0.00000843247],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784744,0.00060511887,0.0004166505,0.00033556475,0.0004312097,0.00036402987],"domain_scores_gemma":[0.99751353,0.00018069694,0.00044785684,0.0017523085,0.00004646348,0.000059142803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008772281,0.00019666059,0.00077971,0.00005862293,0.000056432775,0.000014257743,0.0005649658,0.00012373323,0.000005381873],"category_scores_gemma":[0.00016668443,0.000118065545,0.000055260625,0.0002881098,0.0001745873,0.000039908973,0.00017411231,0.0005221197,0.000005492758],"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":[0.0006966847,0.00020139731,0.40877515,0.0038842058,0.00029448557,0.0000329012,0.00058213435,0.0000056277863,0.00006701398,0.00021499183,0.43983483,0.14541057],"study_design_scores_gemma":[0.0012713635,0.00027365415,0.49279153,0.0005406091,0.0001285589,0.00005676714,0.000053763408,0.00012285059,0.0000038243397,0.0010341139,0.5035811,0.00014184313],"about_ca_topic_score_codex":0.0062144487,"about_ca_topic_score_gemma":0.0016355466,"teacher_disagreement_score":0.3374691,"about_ca_system_score_codex":0.00008861964,"about_ca_system_score_gemma":0.00020615374,"threshold_uncertainty_score":0.9394429},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810048,0.00015259715,0.010863002,0.0012069419,0.00024988153,0.0010233242,0.00082305726,0.00064192683,0.004034473],"genre_scores_gemma":[0.99668986,0.000025143278,0.00004056214,0.0017591445,0.00070003857,0.00025155305,0.00007238795,0.000039061848,0.0004222329],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998689,0.00015603568,0.00020901566,0.00037998357,0.00019531403,0.00037067052],"domain_scores_gemma":[0.99795276,0.0008719871,0.00008577014,0.0006521065,0.00015036859,0.0002870203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044293428,0.0002028592,0.00044764663,0.00008317234,0.00019209438,0.000115721195,0.00012669088,0.00004652906,0.000015144163],"category_scores_gemma":[0.0033293418,0.00014939548,0.000109289096,0.00021547641,0.00011098459,0.0001249983,0.000082149425,0.000093880895,0.00006895166],"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":[0.030757539,0.00056833116,0.38770536,0.00054023636,0.00037440908,0.000011183604,0.00036825906,0.00018072642,0.03643126,0.00040082587,0.008210587,0.5344513],"study_design_scores_gemma":[0.0040557473,0.0010194906,0.85773283,0.00007432336,0.00009645391,0.000037343998,0.000034201083,0.00041690425,0.0015112511,0.00089990237,0.13377857,0.00034298803],"about_ca_topic_score_codex":0.000011934612,"about_ca_topic_score_gemma":0.00006610107,"teacher_disagreement_score":0.5341083,"about_ca_system_score_codex":0.000072059396,"about_ca_system_score_gemma":0.00005394366,"threshold_uncertainty_score":0.60921705},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920791,0.00009406559,0.0054234713,0.0006488252,0.0008166945,0.00017516073,0.00025004538,0.0000061426367,0.0005065049],"genre_scores_gemma":[0.9990114,0.00005421281,0.00041268044,0.00017624606,0.00015432991,0.000003100392,0.00018049093,0.0000043635264,0.0000031590982],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977053,0.000060057435,0.0009918438,0.00006552423,0.0010611938,0.00011610438],"domain_scores_gemma":[0.9971835,0.00011469713,0.001086531,0.00015548879,0.001330809,0.00012899644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041660952,0.000101364996,0.00025905124,0.00046300283,0.000016263413,0.000014087445,0.00028239246,0.000044786415,0.00005405687],"category_scores_gemma":[0.0008496236,0.000094740746,0.00009976741,0.00019715306,0.00006137714,0.0013325837,0.000054296754,0.00018381425,0.0000033663057],"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":[0.0029228437,0.00031691583,0.9078757,0.00043286098,0.0003025882,0.000085841435,0.0004166933,0.008956914,0.00096721353,0.0034041717,0.00051807874,0.07380016],"study_design_scores_gemma":[0.0009947427,0.00008591651,0.98228526,0.0003145239,0.00004001067,0.000060059523,0.00009164746,0.013542462,0.00021123265,0.00057430216,0.0017258436,0.00007400376],"about_ca_topic_score_codex":0.030858954,"about_ca_topic_score_gemma":0.013755929,"teacher_disagreement_score":0.07440954,"about_ca_system_score_codex":0.00050310465,"about_ca_system_score_gemma":0.0009422448,"threshold_uncertainty_score":0.97559464},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961461,0.0012999278,0.0006242059,0.00080557435,0.000099579476,0.00043016134,0.000012946519,0.00004304637,0.0005384805],"genre_scores_gemma":[0.99691063,0.0016016886,0.00016638977,0.0005939476,0.000074979886,0.00002122579,0.00015531167,0.000011566593,0.00046423095],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990558,0.00005419574,0.0003394875,0.00019876451,0.00025286808,0.00009891033],"domain_scores_gemma":[0.99939024,0.000048863778,0.00023328495,0.00022965358,0.000019782576,0.00007817803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042124858,0.000118175216,0.0002814058,0.000060017606,0.00007577778,0.0000024521194,0.00003869017,0.00004995833,0.0001483446],"category_scores_gemma":[0.000118934055,0.000078644116,0.000034926576,0.00010509016,0.00047648934,0.00014911206,0.000034054883,0.00008571466,0.0000067535316],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006654351,0.000065547174,0.9894915,0.000049292234,0.000066828965,0.0000015919827,0.0018989134,3.941279e-7,0.00356121,0.0007458499,0.00055594346,0.0028974507],"study_design_scores_gemma":[0.0071699442,0.00026017346,0.98832124,0.0001052974,0.00019719894,0.000027104228,0.0011642568,0.00011208897,0.0003653375,0.00066643243,0.0015382437,0.00007266777],"about_ca_topic_score_codex":0.00023520831,"about_ca_topic_score_gemma":0.000034615263,"teacher_disagreement_score":0.006504509,"about_ca_system_score_codex":0.000025542682,"about_ca_system_score_gemma":0.000012593474,"threshold_uncertainty_score":0.3207014},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4037052,0.0024570648,0.5928723,0.00007328789,0.000121437486,0.0007127547,0.000005360998,0.000048239028,0.0000043352597],"genre_scores_gemma":[0.8841808,0.0002847207,0.11483077,0.00012406691,0.00026438487,0.000014084168,0.00027647224,0.000019076128,0.000005632679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984956,0.00032822086,0.00047154515,0.00034876275,0.0001579288,0.00019797699],"domain_scores_gemma":[0.9988893,0.00037356207,0.00019208883,0.00020169695,0.0001310064,0.00021230397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063358713,0.0001921221,0.00060338376,0.00019839716,0.00004373049,0.000046058456,0.000039579667,0.000088875764,0.000018483968],"category_scores_gemma":[0.00014386408,0.0001555399,0.000032162436,0.00047165592,0.0002496982,0.00015407159,0.00009657091,0.000085249965,1.6539938e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013688767,0.00005474309,0.46792755,0.0002002708,0.000033447384,0.000003868957,0.00014093729,0.00055779,0.000052303396,0.000009256814,0.00003914852,0.5308438],"study_design_scores_gemma":[0.004288222,0.00036721467,0.34283647,0.000266189,0.000050420353,0.00001773468,0.000019183524,0.65100294,0.000003735997,0.00028847973,0.00073263573,0.000126757],"about_ca_topic_score_codex":0.0004121281,"about_ca_topic_score_gemma":0.000021776043,"teacher_disagreement_score":0.65044516,"about_ca_system_score_codex":0.000021322936,"about_ca_system_score_gemma":0.000037679165,"threshold_uncertainty_score":0.63427335},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000065299296,0.9868736,0.00013780127,0.0016889181,0.000668802,0.0028957971,0.0010849697,0.00018652217,0.006457006],"genre_scores_gemma":[0.00006825312,0.9902654,0.0012517156,0.003230517,0.0012944526,0.00023994393,0.0024217684,0.00012938016,0.001098524],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99156165,0.003292338,0.0017885289,0.001094233,0.0005596675,0.0017035608],"domain_scores_gemma":[0.9949958,0.00071507524,0.0010518184,0.0024775353,0.00015212946,0.00060768146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0072735543,0.0006987594,0.0033983758,0.00030945317,0.00020181145,0.00011934091,0.0012187496,0.00036707052,0.00017205058],"category_scores_gemma":[0.0009682794,0.00047861796,0.00060329185,0.0021176902,0.00020139813,0.00014734948,0.00020036986,0.0011623921,0.00038319698],"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":[0.0000541537,0.00036991073,0.005322962,0.054073498,0.0001007609,0.0000996285,0.00016252717,2.4643595e-7,4.1689208e-9,0.0015184957,0.50254744,0.43575037],"study_design_scores_gemma":[0.00052121334,0.00004532767,0.009203794,0.008443752,0.000040425482,0.0001020204,0.000011887242,0.0000013062064,1.525275e-9,0.000019536348,0.98123044,0.00038032292],"about_ca_topic_score_codex":0.00032797456,"about_ca_topic_score_gemma":0.0064442176,"teacher_disagreement_score":0.47868297,"about_ca_system_score_codex":0.00067188294,"about_ca_system_score_gemma":0.00858328,"threshold_uncertainty_score":0.9997665},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949696,0.0018374351,0.0006026672,0.00059506745,0.0002151693,0.0012671984,0.000094349154,0.0001552801,0.00026324266],"genre_scores_gemma":[0.998507,0.00070353784,0.00023153723,0.00007437425,0.00019302449,0.00016882186,0.00009888561,0.000013989925,0.000008835576],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975764,0.00076132856,0.00071159186,0.00026155752,0.00016643511,0.00052265363],"domain_scores_gemma":[0.99755776,0.0014234761,0.0002834254,0.0002183703,0.00007038654,0.00044657761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007135967,0.00015936513,0.0005212585,0.00008413357,0.00039758178,0.000051560626,0.00006995101,0.0001299399,3.9141403e-7],"category_scores_gemma":[0.00069322163,0.00011069749,0.000038073857,0.00014057614,0.00007226779,0.00014194344,0.00003046065,0.00025512592,0.0000033565605],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014526269,0.00012587993,0.77582467,0.015696501,0.000047593523,0.0000033382644,0.00090517115,0.000059042457,0.00017065169,0.0047964226,0.0012861599,0.19963194],"study_design_scores_gemma":[0.002233882,0.00052768236,0.96904343,0.0005855133,0.00002557945,0.000052533433,0.000903075,0.018946199,0.0000023903588,0.000025907404,0.007486854,0.00016692084],"about_ca_topic_score_codex":0.00088557333,"about_ca_topic_score_gemma":0.000034079294,"teacher_disagreement_score":0.19946502,"about_ca_system_score_codex":0.00014267323,"about_ca_system_score_gemma":0.00008465373,"threshold_uncertainty_score":0.45141125},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898692,0.0000724169,0.00041931035,0.002612502,0.00040990615,0.0019031694,0.004676998,0.000025014,0.000011477184],"genre_scores_gemma":[0.9877069,0.000019337598,0.00006151605,0.0008471267,0.00032449182,0.00007958875,0.010927651,0.000025553472,0.00000785449],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943407,0.0028137125,0.0010638142,0.0010394619,0.00037339368,0.00036888794],"domain_scores_gemma":[0.97765356,0.017786352,0.00075729674,0.0035527784,0.000075385185,0.00017461952],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0060504,0.00035784906,0.00096979237,0.000076303644,0.00018117306,0.000020318706,0.00076064124,0.0001862229,0.0006719474],"category_scores_gemma":[0.015230857,0.00020366866,0.00006774947,0.000117803786,0.0004222496,0.00013577427,0.0005167625,0.0008012387,0.000021669204],"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":[0.00074344216,0.00047287962,0.9925123,0.000027683265,0.00013768743,0.00034718902,0.00017369812,0.00004910771,0.00011003848,0.000025868107,0.0028982735,0.0025018281],"study_design_scores_gemma":[0.0016155749,0.0007302772,0.93178564,0.00010372238,0.00020559732,0.00004309191,0.00008499757,0.06409116,0.000025572035,0.000121716184,0.0010061391,0.00018652015],"about_ca_topic_score_codex":0.895217,"about_ca_topic_score_gemma":0.9708244,"teacher_disagreement_score":0.07560739,"about_ca_system_score_codex":0.00009026315,"about_ca_system_score_gemma":0.000293206,"threshold_uncertainty_score":0.9930643},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957261,0.0023384108,0.0000018655402,0.00043152613,0.00009668513,0.001017919,0.0001750863,0.000016705417,0.00019566677],"genre_scores_gemma":[0.99903274,0.00052644353,0.000015670936,0.00023605181,0.000023454688,0.00008709671,0.000036155023,0.000018544291,0.00002384565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977454,0.00026270383,0.00060419785,0.0004560004,0.00035975326,0.0005719525],"domain_scores_gemma":[0.9981904,0.00018034778,0.00015400352,0.0006612322,0.00013846648,0.00067553844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004025808,0.0002693791,0.0005792159,0.0022081225,0.00009381184,0.000022531241,0.00017425156,0.000095041985,0.000053057724],"category_scores_gemma":[0.00018806478,0.00023438137,0.000125656,0.0023919316,0.002200054,0.0002041235,0.00003088091,0.0003214851,6.5783104e-7],"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":[0.0001590278,0.0004004648,0.9788481,0.000085989595,0.00013254091,0.0007542706,0.017971512,0.0000127758585,0.0000059291397,0.00018790316,0.00029367715,0.0011478148],"study_design_scores_gemma":[0.0013860802,0.0005448092,0.922174,0.00009317711,0.00014271458,0.00003413669,0.07230529,0.00007602967,0.0000034703512,0.00006241902,0.0029818255,0.00019608505],"about_ca_topic_score_codex":0.9616954,"about_ca_topic_score_gemma":0.99989456,"teacher_disagreement_score":0.056674138,"about_ca_system_score_codex":0.00013034546,"about_ca_system_score_gemma":0.00018743887,"threshold_uncertainty_score":0.95577943},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4033889,0.00047939474,0.0042759273,0.55465263,0.00096836133,0.009933422,0.026082652,0.00016772875,0.000050977633],"genre_scores_gemma":[0.19184293,0.000057682802,0.009177918,0.6895303,0.0006381861,0.00015663872,0.1085103,0.00007950763,0.000006549613],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9900742,0.0043254737,0.0018073345,0.00052129105,0.0023951044,0.00087659666],"domain_scores_gemma":[0.9941959,0.00042358675,0.0033317297,0.0011343302,0.00056442444,0.0003500694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030762854,0.0006024854,0.0013861624,0.00027812037,0.00049150223,0.00005727616,0.00049908535,0.0002826334,0.00006560452],"category_scores_gemma":[0.00013699422,0.00048719972,0.00020439927,0.00036053054,0.00010588957,0.00031036447,0.00039082734,0.0019605886,0.000008958625],"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":[0.0010783577,0.001969445,0.11672672,0.011975113,0.0009154652,0.0000045625297,0.0060993736,0.0008356286,0.00035926173,0.000042958505,0.4318916,0.42810154],"study_design_scores_gemma":[0.009487685,0.0019877471,0.6528921,0.009616935,0.0005928389,0.000028209734,0.00015005194,0.04491176,0.00018567524,0.00024194962,0.2787018,0.001203244],"about_ca_topic_score_codex":0.017550763,"about_ca_topic_score_gemma":0.00036582365,"teacher_disagreement_score":0.53616536,"about_ca_system_score_codex":0.0017027888,"about_ca_system_score_gemma":0.0014328324,"threshold_uncertainty_score":0.99975795},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98999596,0.0023783525,0.0039901533,0.00022959337,0.00057926454,0.0012609771,0.0012437849,0.00011661627,0.00020531646],"genre_scores_gemma":[0.9957576,0.00014340573,0.0014947818,0.00007367204,0.00037895984,0.00015359824,0.0015263144,0.000050768864,0.00042091048],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99681306,0.00063900143,0.00088897324,0.00073109334,0.0004279586,0.000499918],"domain_scores_gemma":[0.99740785,0.00040592987,0.00042513994,0.0012243263,0.00029378102,0.00024297657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003185699,0.00033609278,0.0014280272,0.0007442647,0.000061199244,0.000005200295,0.00029960522,0.00012583678,0.00016648539],"category_scores_gemma":[0.0018689743,0.00031083095,0.00017850737,0.0015043315,0.00019182463,0.0006507458,0.0003708369,0.00022748057,0.00001011575],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.020709729,0.0021955261,0.70400476,0.002221013,0.004901593,0.00017764728,0.0017026867,0.00005574871,0.25020143,0.000019308063,0.006931636,0.006878922],"study_design_scores_gemma":[0.003677002,0.004284328,0.9614369,0.00046579723,0.0015370946,0.0001676445,0.00010384883,0.0067474237,0.00041960375,0.000014980211,0.020742565,0.00040276107],"about_ca_topic_score_codex":0.000118052834,"about_ca_topic_score_gemma":0.000056009747,"teacher_disagreement_score":0.2574322,"about_ca_system_score_codex":0.0009154613,"about_ca_system_score_gemma":0.000082870494,"threshold_uncertainty_score":0.9999344},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9834589,0.0022933325,0.0024991056,0.0015815968,0.00026045163,0.00017590358,0.000010596004,0.0000246231,0.0096954955],"genre_scores_gemma":[0.99287736,0.00078347494,0.004326569,0.0014823868,0.0002872278,0.0000022861393,0.0000045190027,0.000014812343,0.00022134806],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986025,0.00017033445,0.0006094526,0.00015372456,0.0002529017,0.0002110822],"domain_scores_gemma":[0.9981509,0.00016873566,0.0007906745,0.0002291441,0.00032568115,0.0003348825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073662377,0.00015074406,0.0008434677,0.00019962082,0.000024245934,0.0000016757551,0.00013619401,0.000026474438,0.00033143815],"category_scores_gemma":[0.0009828015,0.00010251767,0.000083953724,0.00026135583,0.0012092771,0.00010535845,0.00005372227,0.00026289473,0.000011330396],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013931096,0.00014211787,0.8438669,0.00005828092,0.0004226581,0.0004053654,0.002351989,2.9473728e-7,0.0009038183,0.00043951848,0.0054101814,0.14460579],"study_design_scores_gemma":[0.00222966,0.0046635647,0.9749958,0.00037385622,0.00019664313,0.0013607275,0.0011610204,0.000008579483,0.000092064525,0.000679292,0.014123565,0.00011522465],"about_ca_topic_score_codex":0.000058205285,"about_ca_topic_score_gemma":0.0000044609806,"teacher_disagreement_score":0.14449055,"about_ca_system_score_codex":0.000036220423,"about_ca_system_score_gemma":0.000104914405,"threshold_uncertainty_score":0.4455632},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98488575,0.0017160862,0.003865048,0.008168429,0.00010446726,0.0009851195,0.00007715634,0.000016858554,0.00018110893],"genre_scores_gemma":[0.98923,0.000022581808,0.0094299195,0.0012330865,0.00006054341,0.000008130377,0.000005963592,0.000008131075,0.0000016474651],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99722844,0.00013380393,0.00086424395,0.0002776484,0.0007534184,0.0007424178],"domain_scores_gemma":[0.99770993,0.000015030079,0.0005692663,0.0003152765,0.00033972907,0.0010507976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003221375,0.00013531922,0.0005589602,0.00032138743,0.00009686958,0.00002262637,0.00033299148,0.00003194944,0.000006374493],"category_scores_gemma":[0.00021249651,0.000106969135,0.000030492634,0.002105123,0.00020932635,0.00050842704,0.000045010664,0.00022914061,0.000007770742],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016878634,0.00031174647,0.98615956,0.00021104343,0.0000018560039,0.000009214151,0.0018187865,0.00038107802,0.00015079034,0.000021127851,0.00020065877,0.010565321],"study_design_scores_gemma":[0.0011567812,0.0016414551,0.9933283,0.000546318,0.0000033889207,0.00026155208,0.0021234883,0.00032805654,0.000015162288,0.000009523325,0.00049225666,0.000093741204],"about_ca_topic_score_codex":0.00041015455,"about_ca_topic_score_gemma":0.0010153408,"teacher_disagreement_score":0.01047158,"about_ca_system_score_codex":0.0014735978,"about_ca_system_score_gemma":0.003695208,"threshold_uncertainty_score":0.65551406},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2770417,0.0022545725,0.6936013,0.022701278,0.002396111,0.00055414333,0.0012750566,0.00008647037,0.000089354115],"genre_scores_gemma":[0.9520974,0.00032055314,0.04321665,0.0030162772,0.0007687945,0.000009261484,0.000534687,0.000027526712,0.000008851225],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974769,0.000105581035,0.0008193913,0.00028782958,0.0010339587,0.00027637594],"domain_scores_gemma":[0.9961846,0.00041218978,0.0010351513,0.00057196943,0.0014379555,0.00035814114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009947979,0.00016828426,0.00040849898,0.00047156276,0.00011685556,0.00009553821,0.0007437357,0.00007678344,0.00017368185],"category_scores_gemma":[0.00059291994,0.00015315648,0.00016677454,0.00029456342,0.000110550565,0.0005891222,0.00014944881,0.00057621114,0.000034214136],"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":[0.00029689545,0.0020120533,0.13202219,0.00008756428,0.0011697845,0.00010236555,0.00013056779,0.0002839975,0.0012962556,0.00007007181,0.0060470584,0.8564812],"study_design_scores_gemma":[0.004980367,0.000363243,0.87726957,0.0007090282,0.00010755675,0.0002525705,0.00015837373,0.09602349,0.0002474872,0.0006113596,0.019002251,0.00027468702],"about_ca_topic_score_codex":0.0024216084,"about_ca_topic_score_gemma":0.00016300537,"teacher_disagreement_score":0.85620654,"about_ca_system_score_codex":0.00017342957,"about_ca_system_score_gemma":0.0011204868,"threshold_uncertainty_score":0.624554},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99696,0.00014542934,0.0008989937,0.0008585035,0.00017847291,0.00072274194,0.00020658415,0.0000063971343,0.000022916383],"genre_scores_gemma":[0.9978902,0.00029929273,0.000023439574,0.00024277007,0.000045451266,0.000031527175,0.0014629273,0.0000041178346,3.2690434e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978764,0.00022820775,0.0008974558,0.00013546456,0.0007113611,0.00015113116],"domain_scores_gemma":[0.9981132,0.0001534041,0.00048205617,0.0004467856,0.0007266348,0.00007789511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002864427,0.00011084148,0.00020943655,0.0013462735,0.0002474135,0.00014209445,0.00063821394,0.000054874952,0.000007507635],"category_scores_gemma":[0.00042261704,0.00006547746,0.00010179081,0.0015395514,0.00013588993,0.0010509904,0.000056778375,0.00016795407,1.4406571e-7],"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":[0.00021924752,0.00016075,0.98877615,0.000014300556,0.00024609518,0.0000015647347,0.0043126214,0.0007829233,0.000002376981,0.000791601,0.000040191524,0.0046521914],"study_design_scores_gemma":[0.0012353654,0.00018501971,0.9715151,0.000039080405,0.00019755482,0.000009672133,0.009765145,0.015741808,0.000003070571,0.00081147667,0.0004318449,0.00006486652],"about_ca_topic_score_codex":0.04634019,"about_ca_topic_score_gemma":0.4707793,"teacher_disagreement_score":0.4244391,"about_ca_system_score_codex":0.000037163514,"about_ca_system_score_gemma":0.00016931871,"threshold_uncertainty_score":0.9600103},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8203954,0.00027645327,0.17790577,0.000084784304,0.00041627532,0.00026951387,0.00040120276,0.00010499747,0.00014561931],"genre_scores_gemma":[0.99741054,0.000007717052,0.0005775288,0.000059871614,0.000424062,0.000004467009,0.001411986,0.000016874232,0.00008698199],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988377,0.00004548263,0.00026061598,0.00023533395,0.0003933428,0.00022750148],"domain_scores_gemma":[0.9993265,0.000052664484,0.00011759028,0.00025227823,0.00015038955,0.00010052599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022252112,0.00012087517,0.00018993675,0.0005049151,0.000081943144,0.000034257802,0.000056547917,0.00008666365,0.000031804146],"category_scores_gemma":[0.00023578056,0.000116329975,0.00008962626,0.002304667,0.000041573436,0.000076449665,0.000048543803,0.00008441751,0.00005610032],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016533323,0.0028468904,0.5710337,0.0006195179,0.00022565048,0.0002954176,0.000038110917,0.0004354746,0.18159606,0.00014387812,0.013229213,0.22788274],"study_design_scores_gemma":[0.0019655344,0.00018473106,0.90672225,0.000044276352,0.00011625448,0.000087786844,0.00000819946,0.027423173,0.0069910176,0.000055773435,0.056177687,0.00022332513],"about_ca_topic_score_codex":0.001204956,"about_ca_topic_score_gemma":0.000085585554,"teacher_disagreement_score":0.3356885,"about_ca_system_score_codex":0.00047082626,"about_ca_system_score_gemma":0.000049303,"threshold_uncertainty_score":0.47437987},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.4634255e-7,0.984876,0.005493994,0.0022089656,0.0028145332,0.0027363654,0.0012066659,0.00008647119,0.0005763108],"genre_scores_gemma":[0.00018118648,0.9912173,0.000275423,0.0040357136,0.00056041195,0.00013157219,0.0034896806,0.000042136886,0.00006654591],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.98985726,0.0010717391,0.0058178995,0.00020965296,0.0026024536,0.00044100478],"domain_scores_gemma":[0.98369724,0.0004939986,0.010782112,0.00079478347,0.0036894158,0.0005424437],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0062570586,0.0005857549,0.0050414912,0.0013946341,0.000081153776,0.00038661217,0.001254093,0.00022978277,0.000103255355],"category_scores_gemma":[0.0049035735,0.0004296321,0.0013371939,0.0005279011,0.000065939574,0.0016648271,0.00010202096,0.0007377688,0.0007247276],"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":[0.000029679893,0.00007504072,0.000020090269,0.88538486,0.0010817253,0.000029202321,0.00002400524,0.000011214858,1.4887418e-8,0.00018753248,0.027982796,0.08517387],"study_design_scores_gemma":[0.00064591656,0.000107048734,0.000010046061,0.40404457,0.0004120806,0.0010114152,0.000012922628,0.000332775,1.2791955e-8,0.0000012623925,0.59323,0.00019194439],"about_ca_topic_score_codex":0.000019401416,"about_ca_topic_score_gemma":0.0000042423912,"teacher_disagreement_score":0.5652472,"about_ca_system_score_codex":0.0016618539,"about_ca_system_score_gemma":0.0044658817,"threshold_uncertainty_score":0.9998155},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9346328,0.0010153755,0.06216983,0.0000362902,0.00015780056,0.0012424209,0.00038068314,0.00004284273,0.00032198356],"genre_scores_gemma":[0.98117024,0.0074326354,0.010076332,0.000003243153,0.00035870186,0.00015373128,0.00016663013,0.000047196845,0.0005913167],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9973633,0.00008396218,0.00036848392,0.000629673,0.00083612977,0.0007184376],"domain_scores_gemma":[0.99884546,0.00017751488,0.00009906435,0.0003017333,0.000013901387,0.00056232215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014363565,0.00023880615,0.00028900983,0.00021604364,0.0005147526,0.00003194501,0.000090042246,0.000095824806,0.000117407035],"category_scores_gemma":[0.00006132601,0.0002215099,0.00007008466,0.00011491241,0.0003185678,0.00013458553,0.00026070082,0.00035444065,0.000021995913],"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":[0.0005193738,0.00064733846,0.16328846,0.00011735332,0.00025979022,0.00005519034,0.00024307703,0.000024679755,0.32050335,0.000009504345,0.00005691116,0.51427495],"study_design_scores_gemma":[0.0017205027,0.001307596,0.98630613,0.000050619336,0.00007086331,0.000042984568,0.0015111759,0.0050500054,0.0029297066,0.00023709015,0.00051532936,0.00025801055],"about_ca_topic_score_codex":0.00008424924,"about_ca_topic_score_gemma":0.000006080227,"teacher_disagreement_score":0.82301766,"about_ca_system_score_codex":0.00058685744,"about_ca_system_score_gemma":0.00003930865,"threshold_uncertainty_score":0.90329117},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98040646,0.013209019,0.000061154875,0.00010663252,0.00043238967,0.0003251861,0.000086339154,0.000061452425,0.005311372],"genre_scores_gemma":[0.9953706,0.00022034119,0.0002569665,0.0023884033,0.0016251903,0.000014086168,0.000046617923,0.00005068167,0.000027092457],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964646,0.00037506307,0.00089490967,0.00035641686,0.0014147232,0.00049428025],"domain_scores_gemma":[0.9971067,0.00023153101,0.00046643615,0.0005112106,0.00034369197,0.0013404663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002239606,0.0002973362,0.00071279454,0.0002783254,0.000189624,0.00003071736,0.00018220107,0.0002120533,0.00019090307],"category_scores_gemma":[0.00041243614,0.00024821082,0.00013106158,0.00017244708,0.00024370685,0.00028170654,0.00020693112,0.0006714598,0.00001251332],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019248987,0.00015618777,0.83939093,0.00046293993,0.00032611247,0.00074462895,0.00038814254,2.2264513e-7,0.00060197484,0.000016490687,0.00170732,0.15601256],"study_design_scores_gemma":[0.0026201592,0.0016036137,0.82801974,0.00055469794,0.0003345116,0.00096000015,0.0002276062,7.2031827e-7,0.00068444497,0.0000104874225,0.16464578,0.00033822848],"about_ca_topic_score_codex":0.000004497618,"about_ca_topic_score_gemma":0.0000027685217,"teacher_disagreement_score":0.16293846,"about_ca_system_score_codex":0.00038591918,"about_ca_system_score_gemma":0.0009560886,"threshold_uncertainty_score":0.999997},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43502378,0.00020909506,0.5556716,0.000036855774,0.00026946797,0.0007434417,0.0003972094,0.00060672866,0.007041851],"genre_scores_gemma":[0.78569156,0.00022348447,0.21216823,0.0011848699,0.0002569132,0.00001690164,0.000351484,0.00003769444,0.00006887154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693716,0.000031086096,0.0011632984,0.00023368401,0.0011547402,0.00048002397],"domain_scores_gemma":[0.9947058,0.0032712633,0.00027943234,0.00048770284,0.00023393065,0.0010218844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002529941,0.00023047169,0.0005422575,0.0001755821,0.00015502253,0.00007922178,0.00019124319,0.00016602055,0.00027629244],"category_scores_gemma":[0.0024646218,0.00018060603,0.00013648225,0.00023823476,0.00021775861,0.00015670538,0.00018186255,0.0001709919,0.00009559802],"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":[0.009091861,0.00041288545,0.115350395,0.0016597183,0.00012315394,0.00034180074,0.00034117172,0.00007074278,0.00004501573,0.003183926,0.055686865,0.81369245],"study_design_scores_gemma":[0.0035446554,0.000101211284,0.097554855,0.00091237045,0.00004010209,0.00007857162,0.00020716066,0.8880274,0.000004800442,0.00085381296,0.008360795,0.00031424232],"about_ca_topic_score_codex":0.0000033715903,"about_ca_topic_score_gemma":0.00003137114,"teacher_disagreement_score":0.8879567,"about_ca_system_score_codex":0.00006563562,"about_ca_system_score_gemma":0.0003713496,"threshold_uncertainty_score":0.73649},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9985878,0.00003953955,0.00016235294,0.000048889306,0.000166508,0.00024114393,0.000020778669,0.000039529128,0.0006934823],"genre_scores_gemma":[0.9985074,0.0000334721,0.0011554074,0.0000379252,0.000029073419,0.0000023116047,0.00007954329,0.000004269225,0.00015061349],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99876094,0.000030518335,0.00016831809,0.0005184579,0.0003962936,0.00012548888],"domain_scores_gemma":[0.99907684,0.000026888978,0.00007155528,0.00060115044,0.00007727319,0.00014628994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094591186,0.00007002475,0.00011871889,0.00023597259,0.00017960362,0.000071897375,0.00014868006,0.000022907716,0.000056015175],"category_scores_gemma":[0.0002794939,0.000055215645,0.000007644273,0.00065222714,0.00015997936,0.0013207111,0.00025331345,0.00006450348,0.000015615442],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009193404,0.00013918917,0.9464992,0.000016940177,0.000004256521,0.000007021963,0.00026469296,2.3359519e-7,0.0037674846,0.000007718591,0.000050895047,0.04915039],"study_design_scores_gemma":[0.00045530178,0.00016088075,0.99691445,0.00002372738,0.000019710385,0.000011599437,0.0004623337,0.0010826923,0.00066686986,0.000024874715,0.00011395222,0.00006361198],"about_ca_topic_score_codex":0.00019374203,"about_ca_topic_score_gemma":0.0000047680423,"teacher_disagreement_score":0.0504152,"about_ca_system_score_codex":0.000014177503,"about_ca_system_score_gemma":0.000018301618,"threshold_uncertainty_score":0.22516286},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8974282,0.0002687394,0.011876654,0.088884436,0.00043212197,0.000687668,0.00019716215,0.000030406189,0.00019461023],"genre_scores_gemma":[0.9896648,0.0001171323,0.0035849495,0.0021115688,0.0021314207,0.00004282397,0.00012896508,0.000052092684,0.0021662645],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9912722,0.0011422656,0.0012030841,0.00040265417,0.0052697617,0.00071005133],"domain_scores_gemma":[0.988745,0.0044768183,0.00031022186,0.0007207083,0.0034167697,0.0023304906],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.021394616,0.00018876296,0.0007114495,0.0005332574,0.00012464737,0.00010573393,0.0011216236,0.0001338374,0.00042823152],"category_scores_gemma":[0.08238273,0.00014230599,0.00025023887,0.00056058087,0.00027390695,0.00016063852,0.00048332082,0.0012950994,0.000036043915],"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":[0.017996063,0.0020535742,0.12323241,0.0015597013,0.0005227188,0.00095996115,0.000889485,0.0002028657,0.0037854176,0.0021941494,0.63352513,0.21307854],"study_design_scores_gemma":[0.008941875,0.0041511776,0.4139489,0.0066232025,0.00009607376,0.00016131627,0.0005120232,0.27246484,0.00038625786,0.0007855107,0.2915021,0.00042671035],"about_ca_topic_score_codex":0.00013465712,"about_ca_topic_score_gemma":0.00030395665,"teacher_disagreement_score":0.34202302,"about_ca_system_score_codex":0.0002798309,"about_ca_system_score_gemma":0.0010508635,"threshold_uncertainty_score":0.92534673},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99751645,0.00037108737,0.000028311535,0.0014114854,0.000049122253,0.00055545726,0.000035586312,0.000008740404,0.000023762812],"genre_scores_gemma":[0.9944012,0.00018410146,0.00429192,0.0010358747,0.000010408902,0.000004881219,0.0000520057,0.000008574868,0.000011006496],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99600875,0.002129392,0.00071118213,0.00026342153,0.0004841002,0.0004031214],"domain_scores_gemma":[0.9972158,0.001749456,0.00024554433,0.00020838554,0.00029547483,0.00028530296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002062991,0.00012284305,0.0009089145,0.0002963888,0.00007173197,0.0000033928081,0.0000913472,0.0001056551,0.000009948463],"category_scores_gemma":[0.00050076126,0.00009863122,0.000024782485,0.00038329375,0.0000834039,0.00005907895,0.00009188506,0.00025980576,1.6983668e-7],"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":[0.00071815297,0.00017172161,0.98315185,0.001392204,0.00004126264,0.000001642344,0.0014624402,0.0000036576657,0.00015614484,0.000007044273,0.000010166628,0.01288371],"study_design_scores_gemma":[0.0020220662,0.0007440879,0.9933478,0.00017291683,0.000015535736,0.0000023884634,0.00042926378,0.0026390336,0.0002803139,0.000050604045,0.00023750392,0.000058438345],"about_ca_topic_score_codex":0.6901561,"about_ca_topic_score_gemma":0.7603893,"teacher_disagreement_score":0.07023321,"about_ca_system_score_codex":0.00014703078,"about_ca_system_score_gemma":0.0004628969,"threshold_uncertainty_score":0.40220645},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43905312,0.02250046,0.0000039928395,0.5371126,0.00044212275,0.0003370543,0.00034633724,0.000008930544,0.00019538825],"genre_scores_gemma":[0.9232344,0.022508543,0.00057389354,0.05319675,0.0001917373,0.0000017973343,0.000273187,0.000011876517,0.0000078201365],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978825,0.00015659296,0.00086056686,0.00013937624,0.000725181,0.00023581216],"domain_scores_gemma":[0.9986522,0.000065194836,0.0007160512,0.00015132297,0.0001583049,0.00025692474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000787971,0.00015061266,0.00047115708,0.0002710113,0.00006047152,0.000012521284,0.00020820375,0.000029595802,0.000019498297],"category_scores_gemma":[0.000026180185,0.00011150777,0.000098265016,0.000059316404,0.00017520302,0.00017972109,0.00006379371,0.00022930713,5.8147947e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013437357,0.0005739695,0.006714302,0.000074553594,0.0002288644,0.000059382717,0.0058956086,0.0000018359522,0.00017515085,0.0011872551,0.064022824,0.9197225],"study_design_scores_gemma":[0.019730398,0.010743101,0.6235611,0.0064736814,0.00006452651,0.004526861,0.04092129,0.0010523204,0.002041857,0.011394399,0.27892798,0.0005625022],"about_ca_topic_score_codex":0.00027646756,"about_ca_topic_score_gemma":0.00041651487,"teacher_disagreement_score":0.91916,"about_ca_system_score_codex":0.00043851166,"about_ca_system_score_gemma":0.00014577381,"threshold_uncertainty_score":0.45471546},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900351,0.0011665347,0.004347545,0.0011347062,0.00031381793,0.0025050014,0.00037758084,0.0000941565,0.000025554828],"genre_scores_gemma":[0.9970203,0.00009187289,0.0019540796,0.00038575786,0.00016959455,0.00020661623,0.000027204667,0.00003772282,0.000106842424],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960613,0.0013873068,0.00082344556,0.00031553904,0.0005600691,0.00085234357],"domain_scores_gemma":[0.9874398,0.0103587145,0.0005048121,0.0010581317,0.00029307016,0.00034545266],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008572358,0.00018900931,0.0005294682,0.00009440645,0.00048608228,0.000071920906,0.00033796226,0.00004586498,0.00002001382],"category_scores_gemma":[0.010886826,0.000105360705,0.00014315807,0.00041901116,0.00015140595,0.00030991982,0.00015884585,0.00020377195,0.000013255885],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056125195,0.00058248616,0.96105915,0.00031419497,0.00015969003,0.0000013774934,0.017885176,0.0000080468735,0.000010508419,0.0004206484,0.0036724044,0.015325071],"study_design_scores_gemma":[0.0013547402,0.0002238402,0.95892364,0.00013524498,0.000012488667,0.00003277425,0.013913212,0.0002335087,6.730349e-7,0.000053098553,0.024988625,0.00012813191],"about_ca_topic_score_codex":0.0001508019,"about_ca_topic_score_gemma":0.0010182759,"teacher_disagreement_score":0.021316221,"about_ca_system_score_codex":0.0003178187,"about_ca_system_score_gemma":0.00091841625,"threshold_uncertainty_score":0.99744487},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9618908,0.0001106962,0.017241556,0.01929073,0.00034877428,0.00024112522,0.00015542033,0.000011451541,0.0007094437],"genre_scores_gemma":[0.99703807,0.00035906813,0.0006016978,0.0017631369,0.00013782953,0.0000013422481,0.00009382699,0.0000018789076,0.0000031417662],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969686,0.0000962777,0.0013103173,0.00003498764,0.001468717,0.00012105301],"domain_scores_gemma":[0.9981767,0.0002706238,0.0009294057,0.00010049719,0.0003594104,0.0001633838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015194364,0.000094640774,0.0003532673,0.00024126588,0.000026540823,0.00004920089,0.00033807263,0.000061047,0.0000477153],"category_scores_gemma":[0.00083448767,0.000055664947,0.00006503241,0.00016268647,0.00011201014,0.0005309138,0.00003590385,0.0003403309,0.0000030423016],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024221648,0.00044814378,0.80625784,0.00017252014,0.00019423617,0.000031690295,0.0073204795,0.000023942177,0.000002662831,0.0005321391,0.003177461,0.18159667],"study_design_scores_gemma":[0.0020224643,0.0011300442,0.985103,0.00054279814,0.000013822757,0.00032293177,0.0016540285,0.0029796257,0.000015567757,0.00028193797,0.0058560497,0.00007770085],"about_ca_topic_score_codex":0.00012093452,"about_ca_topic_score_gemma":0.00006240229,"teacher_disagreement_score":0.18151897,"about_ca_system_score_codex":0.00006458807,"about_ca_system_score_gemma":0.00051001203,"threshold_uncertainty_score":0.22699507},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88439584,0.0009549037,0.09611633,0.010740758,0.0001300713,0.0017541265,0.0011985012,0.0008842319,0.003825249],"genre_scores_gemma":[0.98090273,0.00011085228,0.012761662,0.0012098525,0.00019274023,0.0001839173,0.0041176784,0.000044713124,0.00047587266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991727,0.00002520332,0.00020605713,0.00015195525,0.00022678835,0.00021732028],"domain_scores_gemma":[0.9990642,0.0003509158,0.00007127824,0.00032529942,0.00012547085,0.00006281956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020397898,0.00011725174,0.00022312823,0.00006599169,0.00011847093,0.000030196627,0.00010728187,0.000036144593,0.000091732676],"category_scores_gemma":[0.00030346014,0.0000806554,0.000058825168,0.00022481574,0.00005455071,0.00017013693,0.000015265454,0.0000831411,0.00003436043],"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":[0.0055961316,0.012752569,0.036267918,0.013275551,0.007285782,0.000016214115,0.02123878,0.0022202402,0.22608036,0.011869943,0.035492726,0.6279038],"study_design_scores_gemma":[0.004665733,0.00078548817,0.018437453,0.0004197934,0.001544023,0.00000416792,0.00041326162,0.8842851,0.05779251,0.0010300025,0.030249335,0.0003731658],"about_ca_topic_score_codex":0.0001397115,"about_ca_topic_score_gemma":0.000028173634,"teacher_disagreement_score":0.8820648,"about_ca_system_score_codex":0.000030694795,"about_ca_system_score_gemma":0.000018859728,"threshold_uncertainty_score":0.32890317},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043290246,0.0001487177,0.95230675,0.00093490334,0.00003272052,0.0009903549,0.0021855314,0.000080713806,0.000030076457],"genre_scores_gemma":[0.9608384,0.00014955242,0.026237614,0.00025531233,0.00019180341,0.00022450276,0.012049463,0.000022977956,0.000030387324],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984464,0.00005610712,0.00030203213,0.0005560401,0.00032931415,0.00031008906],"domain_scores_gemma":[0.9977349,0.0003406129,0.00012559546,0.0014140104,0.00020149723,0.00018338667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055381615,0.00016346648,0.0004067481,0.000245267,0.00019748278,0.00005973685,0.00037474016,0.000051763385,0.00012997282],"category_scores_gemma":[0.0002504122,0.00010195669,0.00019644872,0.001211427,0.00012984588,0.000160678,0.00006619484,0.000091089976,0.0000029440423],"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":[0.00890937,0.0011762645,0.7109175,0.0005553991,0.009214572,0.000055167886,0.00015226785,0.009019911,0.00020814971,0.00051777624,0.0014652136,0.25780842],"study_design_scores_gemma":[0.0017436725,0.00026333632,0.26512325,0.000016926277,0.0064849346,0.0000074672903,0.000054645447,0.7220857,0.000003126541,0.0001852627,0.0038801886,0.0001514482],"about_ca_topic_score_codex":0.0005051617,"about_ca_topic_score_gemma":0.0056133373,"teacher_disagreement_score":0.92606914,"about_ca_system_score_codex":0.000012658746,"about_ca_system_score_gemma":0.000039037688,"threshold_uncertainty_score":0.4157673},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002922341,0.0000024537123,0.03679124,0.0010255467,0.0000067761493,0.9607504,0.00044230267,0.0001079293,0.0005810928],"genre_scores_gemma":[0.0014874937,2.0897447e-7,0.057643108,0.00011997719,0.00013587638,0.9401038,0.00018599509,0.00008188088,0.00024162087],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9938539,0.0021815298,0.0011433836,0.0006752923,0.0010904475,0.0010554017],"domain_scores_gemma":[0.9926358,0.0019403495,0.0006440387,0.0011689077,0.0031125601,0.0004983831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016015332,0.00025946109,0.000985861,0.00067390205,0.00018696285,0.000061615945,0.00046867915,0.00016288966,0.00012857992],"category_scores_gemma":[0.004877615,0.00022453254,0.00034519995,0.0011998392,0.0004503843,0.00019357202,0.0001347006,0.00035765252,0.000006138235],"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":[0.03301028,0.007982242,0.08016731,0.09228194,0.0005140335,0.000003953875,0.0025887648,0.000013733756,0.10923992,0.004350491,0.1974718,0.47237554],"study_design_scores_gemma":[0.018783867,0.010098893,0.021384751,0.0020558997,0.0000030944693,0.000005218421,0.0002163713,0.0020580317,0.017417116,0.0011093287,0.92657936,0.00028805636],"about_ca_topic_score_codex":0.00007567234,"about_ca_topic_score_gemma":0.00003835508,"teacher_disagreement_score":0.72910756,"about_ca_system_score_codex":0.00012686977,"about_ca_system_score_gemma":0.0014377686,"threshold_uncertainty_score":0.91561717},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9892919,0.000903776,0.0024290653,0.0010426051,0.00029677482,0.0008522579,0.00012963203,0.000049007907,0.0050050016],"genre_scores_gemma":[0.99367994,0.0000061405017,0.005172941,0.00009909475,0.00020105838,0.000019710338,0.000060037746,0.00000966404,0.0007514161],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995376,0.000012397745,0.00013807003,0.00006642723,0.00010885771,0.00013667885],"domain_scores_gemma":[0.99955046,0.00010833147,0.000047389443,0.0002088704,0.00002257268,0.00006236577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001383348,0.00005304332,0.00013758369,0.000022943424,0.000014480152,0.000003304154,0.000048136942,0.000022305787,0.0004094766],"category_scores_gemma":[0.00024351601,0.000035610505,0.00005031879,0.00010476912,0.000017212222,0.00006724575,0.000017671935,0.000036161626,0.00004029455],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011227539,0.00004829301,0.98205215,0.000051715062,0.000017849112,6.115017e-7,0.00015146178,0.0000022137658,0.00018875307,0.0021142757,0.010230915,0.005029462],"study_design_scores_gemma":[0.00066946354,0.000010291338,0.9799722,0.000043188422,0.00002959296,0.000005746662,0.00004491562,0.00017765445,0.00008885597,0.00027817214,0.018642414,0.000037531325],"about_ca_topic_score_codex":0.00019431395,"about_ca_topic_score_gemma":0.000046341163,"teacher_disagreement_score":0.008411501,"about_ca_system_score_codex":0.000025914092,"about_ca_system_score_gemma":0.000035311285,"threshold_uncertainty_score":0.44834816},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59128577,0.00006198009,0.37882063,0.0015759569,0.0008227956,0.0022972552,0.0004345682,0.00043246962,0.024268605],"genre_scores_gemma":[0.4165381,0.0000059239223,0.57575613,0.0004119917,0.00021404466,0.00016529187,0.00026199777,0.000025879768,0.006620628],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990478,0.00009696056,0.00023585204,0.0002726561,0.00019770469,0.00014898634],"domain_scores_gemma":[0.99866676,0.00037812846,0.00011459837,0.0005294139,0.00021298004,0.00009810126],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002293026,0.00007859899,0.00014950619,0.000051147617,0.00006211902,0.00002450719,0.00008020048,0.000050858675,0.0013987832],"category_scores_gemma":[0.0024068158,0.00006428776,0.00008890443,0.00008441867,0.00002038831,0.000072436116,0.000027249298,0.00013169949,0.00013173484],"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":[0.0005658534,0.00014072261,0.044259924,0.0001590509,0.00012356676,0.0000026478147,0.00012460814,0.000003158516,0.25066885,0.00040549188,0.040554736,0.6629914],"study_design_scores_gemma":[0.0032526576,0.00018728076,0.7633014,0.00003765834,0.0004298162,0.000018027466,0.0001321498,0.1284943,0.015485396,0.0021585692,0.08621588,0.0002868674],"about_ca_topic_score_codex":0.00004079895,"about_ca_topic_score_gemma":0.00010444394,"teacher_disagreement_score":0.71904147,"about_ca_system_score_codex":0.000015016106,"about_ca_system_score_gemma":0.000046336587,"threshold_uncertainty_score":0.9995141},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95741916,0.0035666523,0.0018883438,0.029448124,0.00081564015,0.0022629208,0.00022918482,0.0006237169,0.0037462576],"genre_scores_gemma":[0.99154544,0.0010267739,0.0016216601,0.004579277,0.00022754865,0.00007968912,0.0006018553,0.00005757995,0.0002601497],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950057,0.00088537723,0.0011353156,0.00092410535,0.00072838383,0.0013210801],"domain_scores_gemma":[0.9970359,0.00022688066,0.0005736653,0.0010052057,0.00018658407,0.0009717294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003837882,0.00031351854,0.00086825783,0.00036154952,0.00022934531,0.000038659655,0.00025272495,0.000153824,0.00028107423],"category_scores_gemma":[0.0006951568,0.0003248814,0.00011427547,0.0010361165,0.00014400689,0.00028224743,0.000077763245,0.0006692792,0.000095225616],"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":[0.0009259001,0.0005060975,0.97004354,0.0005901062,0.000029725577,0.000070254304,0.002270165,0.000009346268,0.000011044141,0.0003697883,0.017060336,0.008113719],"study_design_scores_gemma":[0.00246306,0.00036624583,0.966922,0.00023709911,0.0000032628304,0.000055164004,0.00022565332,0.0017768316,0.000009131821,0.00004525442,0.027631033,0.0002652636],"about_ca_topic_score_codex":0.008315302,"about_ca_topic_score_gemma":0.028685058,"teacher_disagreement_score":0.034126308,"about_ca_system_score_codex":0.00049172156,"about_ca_system_score_gemma":0.0016305462,"threshold_uncertainty_score":0.9999203},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920638,0.0007230344,0.00037393413,0.000016422435,0.000032937463,0.0011250849,0.00043991703,0.00009547968,0.0051293494],"genre_scores_gemma":[0.9870755,0.0000521922,0.011332412,0.000029358156,0.000048648228,0.00012055537,0.0011608254,0.000030874,0.00014964712],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977062,0.00021032759,0.00034230822,0.00035856376,0.0011807167,0.0002018871],"domain_scores_gemma":[0.99791485,0.000043979813,0.00026925345,0.0007723941,0.00071989425,0.0002796288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008060152,0.00014932016,0.00037934058,0.00024622615,0.00003192812,0.000006009288,0.00013992604,0.00005173602,0.0005380621],"category_scores_gemma":[0.00252553,0.00014081266,0.000097459444,0.0005381768,0.0000897684,0.00011087293,0.000055688426,0.00014043963,0.00012964771],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096964394,0.009477564,0.98264194,0.0006131085,0.0005523994,0.000008482268,0.00029103403,0.000013765827,0.0001908835,0.000028026572,0.00017281451,0.0050403303],"study_design_scores_gemma":[0.0018305195,0.0001304106,0.8775734,0.00013241699,0.0018527776,4.691908e-7,0.000028648674,0.117729045,0.0003031099,0.00027281418,0.0000084873345,0.00013794916],"about_ca_topic_score_codex":0.00017096676,"about_ca_topic_score_gemma":0.000023502536,"teacher_disagreement_score":0.11771528,"about_ca_system_score_codex":0.00012242,"about_ca_system_score_gemma":0.00026791365,"threshold_uncertainty_score":0.58914024},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7651566,0.00031034095,0.2325784,0.0010703602,0.0005939327,0.0001290159,0.0001276545,0.000009795928,0.000023868362],"genre_scores_gemma":[0.79340494,0.000061703904,0.20522454,0.001097428,0.00014352528,5.838709e-7,0.000056029756,0.000010252997,9.835487e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975,0.0001618055,0.0014051764,0.00016496993,0.0005546869,0.00021334052],"domain_scores_gemma":[0.996729,0.00038697943,0.00150908,0.00018919696,0.0009208876,0.00026486628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040338454,0.00012333051,0.0005679228,0.002193133,0.000050981183,0.000021639089,0.00027784662,0.000067714485,0.00009336741],"category_scores_gemma":[0.00041365446,0.000118759286,0.00036096145,0.00056921254,0.000119774195,0.00013166618,0.000042123036,0.00019818831,0.0000016607104],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006214818,0.0001449499,0.95769584,0.000060314338,0.0018472937,0.000031659016,0.00020547547,0.0019233677,0.0019310444,0.0002681239,0.00004906296,0.035221364],"study_design_scores_gemma":[0.0019229096,0.00024310281,0.98747987,0.00021445916,0.00043130692,0.00008172815,0.00029835096,0.006300546,0.0013451808,0.00028569374,0.0012602055,0.00013667185],"about_ca_topic_score_codex":0.00035727065,"about_ca_topic_score_gemma":0.00009832121,"teacher_disagreement_score":0.03508469,"about_ca_system_score_codex":0.00028879213,"about_ca_system_score_gemma":0.0006716458,"threshold_uncertainty_score":0.4842863},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9917563,0.000225943,0.00047173354,0.005835463,0.00023777038,0.000432925,0.00095682836,0.000025271493,0.00005774883],"genre_scores_gemma":[0.996462,0.00014008972,0.00029032058,0.0004830386,0.000110798755,0.000012246855,0.0024741127,0.000012347655,0.000015069867],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99689335,0.0011111434,0.0008274767,0.00058571255,0.00017617983,0.00040616194],"domain_scores_gemma":[0.99758625,0.0006066005,0.00059626007,0.00087272935,0.000086959895,0.0002511938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004013064,0.00020428625,0.001139021,0.00037573228,0.00010295089,0.000014377611,0.00023226389,0.00014694987,0.00007924465],"category_scores_gemma":[0.003799503,0.0001327769,0.00012243853,0.0005968878,0.00035266316,0.00017318684,0.00024478172,0.0004122713,0.0000013571326],"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":[0.00031450196,0.00009462123,0.9928491,0.000046373254,0.00018876475,0.000004098247,0.000105001986,0.00007587842,0.00012841847,0.000241016,0.00071752036,0.005234712],"study_design_scores_gemma":[0.000701826,0.00020753681,0.9633167,0.00003248992,0.000053809323,0.0000072934995,0.000022104821,0.03480162,0.0000046925666,0.00020698752,0.0005391103,0.00010583685],"about_ca_topic_score_codex":0.044976156,"about_ca_topic_score_gemma":0.13642083,"teacher_disagreement_score":0.09144467,"about_ca_system_score_codex":0.00005027124,"about_ca_system_score_gemma":0.00025824035,"threshold_uncertainty_score":0.9613834},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68266106,0.00039644196,0.31145528,0.0047076833,0.000078689955,0.00046565683,0.00016677071,0.00002138085,0.00004702016],"genre_scores_gemma":[0.9836677,0.0003363549,0.015498073,0.00018772329,0.000075694785,0.000033353175,0.000013455647,0.00001276186,0.00017490108],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99861693,0.00006092509,0.00048970676,0.00028686476,0.0003329995,0.0002125741],"domain_scores_gemma":[0.9988399,0.00022814446,0.00015396887,0.00048372717,0.00011228261,0.00018196943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014203963,0.00015441256,0.00075726386,0.00023410309,0.00016617599,0.0000016298782,0.00017717194,0.00009352687,0.0000780199],"category_scores_gemma":[0.00033993166,0.00010563929,0.0005638213,0.0013795664,0.00044600666,0.000026111784,0.00001695299,0.00023030728,6.925477e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0061379997,0.003074216,0.7410893,0.0016432616,0.010543771,0.000015944355,0.008295672,0.044501416,0.0026752609,0.0003297613,0.020855078,0.1608383],"study_design_scores_gemma":[0.0032354216,0.000863175,0.9853731,0.00009462524,0.0020088141,0.000004015294,0.00011691054,0.006994262,0.0005222529,0.00015060314,0.000527172,0.00010967667],"about_ca_topic_score_codex":0.005521263,"about_ca_topic_score_gemma":0.00070412306,"teacher_disagreement_score":0.30100662,"about_ca_system_score_codex":0.00006329668,"about_ca_system_score_gemma":0.00007281749,"threshold_uncertainty_score":0.8346535},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99372,0.00053171895,0.00034951567,0.0003397377,0.00024282034,0.00021128074,0.0003582441,0.00007856795,0.0041681244],"genre_scores_gemma":[0.99572885,0.000042703326,0.00015323833,0.00013605964,0.00016677147,0.000027007829,0.00042663747,0.000014731988,0.003304029],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986979,0.00017850166,0.00035765665,0.00017895715,0.00035019388,0.00023681963],"domain_scores_gemma":[0.99885285,0.000045037486,0.00021870733,0.00067789276,0.00008489375,0.000120592566],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00070053834,0.00012370759,0.0003549455,0.00035638793,0.000047204154,0.000009312443,0.00014174092,0.00006361518,0.0012243689],"category_scores_gemma":[0.00010463025,0.0000889287,0.0003993017,0.0012001141,0.00007358551,0.00021233667,0.00006742956,0.00011569244,0.000068931135],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069341826,0.0010276114,0.6805869,0.0000877801,0.0034650865,0.000001929187,0.00034783446,0.000008261033,0.014196423,0.00079943385,0.018756473,0.28002888],"study_design_scores_gemma":[0.0003062717,0.00015474642,0.9716135,0.00003914753,0.0031142423,0.0000020488246,0.00005855952,0.00020256013,0.0031671522,0.00008985817,0.02113538,0.000116513045],"about_ca_topic_score_codex":0.000023371083,"about_ca_topic_score_gemma":0.000017598057,"teacher_disagreement_score":0.29102665,"about_ca_system_score_codex":0.000049248276,"about_ca_system_score_gemma":0.00003294861,"threshold_uncertainty_score":0.9996886},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026193818,0.97014534,0.0002585073,0.000099341254,0.0003394471,0.0012711937,0.0011581846,0.000040992938,0.00049316447],"genre_scores_gemma":[0.065430105,0.93263084,0.00009368942,0.000017359514,0.00029301728,0.00009695503,0.0012364237,0.00007249893,0.00012908306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952364,0.00090262166,0.0012469618,0.00088992243,0.0010944869,0.00062958687],"domain_scores_gemma":[0.996981,0.0007370113,0.00038310714,0.0012972156,0.00023504143,0.00036657308],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017107967,0.00042023067,0.0034132167,0.0018012705,0.00007139944,0.000041426352,0.00041546058,0.0003574536,0.0005881123],"category_scores_gemma":[0.00081237766,0.00034736143,0.0006539717,0.0018088487,0.00032684804,0.00005564321,0.00040063256,0.0012108213,0.00003849631],"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":[0.00009880844,0.00018100848,0.73847234,0.016887778,0.0018801058,0.000019467663,0.00003827522,0.0000040028563,5.676993e-7,0.000005210258,0.00021918076,0.24219327],"study_design_scores_gemma":[0.0010854675,0.00030886743,0.8826013,0.013113352,0.0053544957,0.000015740176,0.00004009038,0.005172255,1.9282653e-7,0.000024068806,0.091727585,0.00055658905],"about_ca_topic_score_codex":0.06871249,"about_ca_topic_score_gemma":0.3401008,"teacher_disagreement_score":0.2713883,"about_ca_system_score_codex":0.00022978938,"about_ca_system_score_gemma":0.0016202773,"threshold_uncertainty_score":0.99989784},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9079212,0.030850101,0.0028154214,0.035342745,0.004124953,0.00046309424,0.0166101,0.000019644525,0.0018527154],"genre_scores_gemma":[0.98325676,0.002570891,0.0056826016,0.0057929587,0.00073517795,0.000004621344,0.001250547,0.000059164217,0.0006472605],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99658346,0.00092368486,0.0009398906,0.00032312775,0.00036606827,0.0008637878],"domain_scores_gemma":[0.99662036,0.00019871071,0.0006441301,0.00045137497,0.00041114382,0.0016742732],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00089495484,0.00037850993,0.00090150634,0.0005795206,0.00028647517,0.00003966883,0.00038971298,0.00030962742,0.0019714367],"category_scores_gemma":[0.00034591576,0.000367304,0.00023319697,0.00023941115,0.0009632858,0.0002444481,0.000047508725,0.00082571147,0.00005854934],"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":[0.00023995215,0.00018760074,0.4808939,0.00024759522,0.00061965815,0.07698526,0.0041576503,0.0019081968,0.0005898071,0.0014644285,0.43033165,0.002374301],"study_design_scores_gemma":[0.002877545,0.0004753857,0.6239865,0.00057277415,0.0003010995,0.019778915,0.00043947162,0.0002830872,0.00023648773,0.00067114254,0.34998083,0.00039676443],"about_ca_topic_score_codex":0.00714354,"about_ca_topic_score_gemma":0.10268342,"teacher_disagreement_score":0.1430926,"about_ca_system_score_codex":0.00068333343,"about_ca_system_score_gemma":0.00660194,"threshold_uncertainty_score":0.99987787},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98596865,0.011970881,0.000050380615,0.0014833377,0.00009236901,0.00015309772,0.000017007987,0.000011334192,0.00025296776],"genre_scores_gemma":[0.99703145,0.0010720331,0.000932607,0.000557828,0.00034782907,0.0000015315327,0.0000071886784,0.000014859592,0.00003466435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99044895,0.006565297,0.0018315776,0.00034299018,0.0002189599,0.0005922494],"domain_scores_gemma":[0.99507916,0.00338928,0.00067382574,0.00026984792,0.00027130317,0.0003165641],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.020873273,0.0001726337,0.001306583,0.00031725885,0.000023980327,0.0000045069714,0.00024293928,0.00005734718,0.0000338582],"category_scores_gemma":[0.03465514,0.00012192112,0.0001283839,0.00020620806,0.0006167819,0.00008605564,0.00018691432,0.000794627,0.000013063981],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011853963,0.00012192047,0.98845905,0.000025412315,0.00022349652,0.00077779294,0.00062492734,0.000038972667,0.00012430844,0.000049485585,0.004448651,0.003920592],"study_design_scores_gemma":[0.0011999907,0.0020143825,0.99430925,0.00065962423,0.00005598803,0.00024283315,0.00019187531,0.00004816864,0.000011424598,0.00017997403,0.0010022871,0.000084191066],"about_ca_topic_score_codex":0.000011715028,"about_ca_topic_score_gemma":0.00002192507,"teacher_disagreement_score":0.013781867,"about_ca_system_score_codex":0.00005977393,"about_ca_system_score_gemma":0.00004158104,"threshold_uncertainty_score":0.97347635},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99624884,0.0015647823,0.00008266928,0.00021886262,0.00003885765,0.00034870347,0.000053513304,0.00006149899,0.0013822471],"genre_scores_gemma":[0.99881464,0.000100512814,0.0006804062,0.00019350596,0.0000733666,0.00002258756,0.00006844571,0.0000067460824,0.000039780025],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925894,0.00003538123,0.00015901323,0.00007867036,0.0003514138,0.00011658627],"domain_scores_gemma":[0.99928373,0.00006345901,0.00010018985,0.00019334906,0.00020310444,0.0001561661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002142507,0.0000711315,0.00024974436,0.00003339022,0.000019421303,0.000012290242,0.0000409687,0.00003259587,0.000009297672],"category_scores_gemma":[0.0007018123,0.0000670978,0.000021589609,0.000108528504,0.000030237885,0.00030913405,0.0000572333,0.000070463204,0.000006023633],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017758453,0.00017138876,0.986113,0.0023909393,0.00028491628,0.0000048199263,0.00047294586,0.0000025937497,0.004579877,0.000053247495,0.0011758211,0.0045728274],"study_design_scores_gemma":[0.005106961,0.00018530631,0.95047617,0.004172448,0.00086755387,0.0000051677484,0.00016443775,0.0066609057,0.031087313,0.000057079884,0.00095641817,0.00026026278],"about_ca_topic_score_codex":0.00009672058,"about_ca_topic_score_gemma":0.000011944399,"teacher_disagreement_score":0.035636894,"about_ca_system_score_codex":0.00008532807,"about_ca_system_score_gemma":0.00009643129,"threshold_uncertainty_score":0.27361688},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9044086,0.0064870734,0.015855378,0.010198907,0.016778728,0.00136069,0.00075174903,0.00016466175,0.043994263],"genre_scores_gemma":[0.9956725,0.00055379106,0.0013499563,0.001783277,0.00051617133,0.0000031860632,0.000091205235,0.000004218159,0.00002565414],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975254,0.000029285144,0.0015058708,0.000019236026,0.0007448594,0.00017533916],"domain_scores_gemma":[0.99703294,0.000047880032,0.0010876751,0.00011425354,0.0013937746,0.0003234944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087152916,0.000098693105,0.00026517594,0.0003355363,0.000039962706,0.000101134676,0.00012814775,0.0000684223,0.000015714695],"category_scores_gemma":[0.00020876281,0.00007617781,0.00007262113,0.00008558353,0.000030590225,0.004358401,0.000041792722,0.000191427,0.00007331651],"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":[0.0011604783,0.0002696113,0.63405365,0.0032803519,0.0009445927,0.000024716768,0.026356215,0.0002236883,0.000023044411,0.07630952,0.046405423,0.21094869],"study_design_scores_gemma":[0.0015994145,0.00018900947,0.071078636,0.00052874937,0.000026701307,0.0032298702,0.005713502,0.001444319,0.000023136588,0.000015351148,0.9160247,0.0001266155],"about_ca_topic_score_codex":0.000039007104,"about_ca_topic_score_gemma":6.7394063e-7,"teacher_disagreement_score":0.86961925,"about_ca_system_score_codex":0.00013906349,"about_ca_system_score_gemma":0.00017698988,"threshold_uncertainty_score":0.31597355},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9805749,0.00033785123,0.00008775508,0.01853702,0.000081905615,0.00017664014,0.00009702617,0.000015180302,0.00009169939],"genre_scores_gemma":[0.99672335,0.00094912446,0.00034664007,0.0007705056,0.00024296876,5.6219477e-7,0.00008842136,0.000013936194,0.00086446194],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970023,0.00055043446,0.0010650492,0.0003139823,0.0005391155,0.00052909995],"domain_scores_gemma":[0.99632084,0.00009530223,0.0008061449,0.00042272083,0.000275404,0.00207961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00331955,0.0002093425,0.0012387768,0.0011513617,0.00004455315,0.000041908028,0.00019220547,0.00011671971,0.00017112149],"category_scores_gemma":[0.00034376045,0.0001649991,0.00026436636,0.0014269039,0.00012569377,0.00034568843,0.000030356388,0.00050028466,0.0000013513251],"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":[0.00014768579,0.00033619197,0.9786382,0.000042752603,0.0007116314,0.000025635043,0.0013962181,0.000015804308,0.00003416235,0.00020666151,0.009562016,0.00888304],"study_design_scores_gemma":[0.0029251568,0.0011364052,0.9765087,0.00008919591,0.0003160066,0.000055154473,0.00045615493,0.00009157251,0.000006723704,0.0003224934,0.017959049,0.0001333906],"about_ca_topic_score_codex":0.05417337,"about_ca_topic_score_gemma":0.023081828,"teacher_disagreement_score":0.031091543,"about_ca_system_score_codex":0.00013384386,"about_ca_system_score_gemma":0.0015726169,"threshold_uncertainty_score":0.99474436},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987297,0.00015652775,0.0027076004,0.008682466,0.0005445639,0.00040142008,0.00017380231,0.000009183208,0.000027466825],"genre_scores_gemma":[0.9957868,0.00008282843,0.0023750938,0.0014404235,0.0002038234,0.000026512365,0.00007472916,0.000006125698,0.0000036962615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984653,0.00013554927,0.00045052983,0.000101944854,0.0007427429,0.0001039416],"domain_scores_gemma":[0.99698627,0.0010426285,0.00040091496,0.00013385982,0.0013340843,0.00010223886],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001628823,0.000072011266,0.00018295777,0.00018762628,0.000029707087,0.000065082764,0.00011398927,0.000029961073,0.00007511278],"category_scores_gemma":[0.0149337845,0.000050765342,0.00007946559,0.00007934166,0.000045299155,0.00040622245,0.000033691653,0.00017127774,6.4062743e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041655998,0.00092568353,0.913851,0.000045920533,0.00022715202,0.000034712088,0.00018023084,0.000008420895,0.000074407166,0.0004831831,0.010514438,0.073238246],"study_design_scores_gemma":[0.003320123,0.00038946007,0.98442686,0.00013429485,0.000096627366,0.0006904353,0.00009543986,0.0002952853,0.000038529957,0.0010415195,0.009398726,0.000072715084],"about_ca_topic_score_codex":0.00002490955,"about_ca_topic_score_gemma":0.00014700372,"teacher_disagreement_score":0.07316553,"about_ca_system_score_codex":0.00010049743,"about_ca_system_score_gemma":0.0011552903,"threshold_uncertainty_score":0.99336386},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17259473,0.0013721589,0.7942876,0.00042279213,0.00078872533,0.00059466396,0.029208919,0.00000653498,0.00072382565],"genre_scores_gemma":[0.9653646,0.000014709079,0.034255497,0.00007562712,0.00014409263,3.320498e-7,0.00011841259,0.000015061945,0.000011667422],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998936,0.000017183986,0.00054295966,0.00008259529,0.00021676041,0.0002044757],"domain_scores_gemma":[0.9981585,0.00016951219,0.00027825162,0.00030034047,0.0006492022,0.00044416674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066992943,0.000075457036,0.00031072856,0.00012886843,0.000024629233,0.000007770929,0.00022891695,0.000034842367,0.00010867276],"category_scores_gemma":[0.002236855,0.00006209678,0.000030022507,0.00005960328,0.00037310988,0.000051448704,0.000023474242,0.0000984589,7.814121e-7],"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":[0.010399136,0.00080108264,0.308771,0.012086258,0.0022361113,0.0054021026,0.0043313797,0.00050544477,0.029257828,0.17363535,0.14325859,0.30931568],"study_design_scores_gemma":[0.01870955,0.0077630784,0.767166,0.008685646,0.002778618,0.0024615745,0.0027602753,0.08162098,0.017773505,0.01245736,0.07637042,0.0014529827],"about_ca_topic_score_codex":0.0012780089,"about_ca_topic_score_gemma":0.025626495,"teacher_disagreement_score":0.79276985,"about_ca_system_score_codex":0.00007566823,"about_ca_system_score_gemma":0.0016181575,"threshold_uncertainty_score":0.9921533},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002268119,0.00042265162,0.00011691957,0.9839539,0.000375443,0.0018952911,0.0004398702,0.00009109308,0.010436731],"genre_scores_gemma":[0.026050273,0.000048252004,0.00015571939,0.95889074,0.012041954,0.00015861704,0.0012110039,0.000058992606,0.001384437],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99480563,0.00019628748,0.00064132764,0.00088330545,0.0026095312,0.0008639011],"domain_scores_gemma":[0.9977321,0.00016315642,0.00037306684,0.0009516344,0.00023202643,0.00054798496],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0016185065,0.00041714002,0.0009384906,0.00031693323,0.00043608085,0.000033418124,0.0013212713,0.0001914998,0.0003232266],"category_scores_gemma":[0.0003314932,0.0002624677,0.00020491745,0.0015610047,0.004112185,0.00015280653,0.0003544675,0.0010374396,0.000027854276],"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":[0.0001579589,0.00009028193,0.0037504055,0.0003657381,0.00006047973,0.00018630481,0.0036710973,0.000001138128,0.000023001581,0.000035986002,0.9804104,0.011247189],"study_design_scores_gemma":[0.001079463,0.0002656754,0.0091941785,0.0007020422,0.0003926177,0.000012654156,0.00053831,0.000027223576,0.000004389009,0.00022283992,0.9872851,0.00027555772],"about_ca_topic_score_codex":0.00069495843,"about_ca_topic_score_gemma":0.0000544414,"teacher_disagreement_score":0.025063131,"about_ca_system_score_codex":0.00038475366,"about_ca_system_score_gemma":0.0007115432,"threshold_uncertainty_score":0.9999828},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004785041,0.12215596,0.0009258644,0.858855,0.008768678,0.0020093382,0.0020885197,0.00021216687,0.00019940977],"genre_scores_gemma":[0.56544703,0.042903725,0.0021063683,0.36191723,0.013827754,0.0006329534,0.009012106,0.00031664467,0.0038362131],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945232,0.0011808998,0.0009801962,0.00075496733,0.0012000877,0.0013606513],"domain_scores_gemma":[0.993051,0.0009893683,0.000909155,0.0023575053,0.0009729374,0.001720014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008475928,0.0004017885,0.0010492811,0.00012502888,0.0003589095,0.00015597513,0.001006477,0.0002221285,0.0001586857],"category_scores_gemma":[0.0011403318,0.0002429689,0.00027248584,0.00062335207,0.00043083847,0.00068222644,0.00019671941,0.00076209445,0.000024932502],"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":[0.00008701257,0.0002884929,0.011672483,0.00019307586,0.00017061642,0.0000026994178,0.005442323,0.0000019453043,0.0000020496623,0.002203146,0.8574008,0.12253536],"study_design_scores_gemma":[0.0019457948,0.00032344353,0.07993362,0.000071892464,0.000013226841,0.000013820531,0.010973918,0.00035106656,2.286401e-7,0.00008584935,0.90609753,0.00018957691],"about_ca_topic_score_codex":0.0023591374,"about_ca_topic_score_gemma":0.00907118,"teacher_disagreement_score":0.560662,"about_ca_system_score_codex":0.0022641912,"about_ca_system_score_gemma":0.018909516,"threshold_uncertainty_score":0.9907984},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030180631,0.00075085636,0.92092633,0.04378425,0.0015346406,0.0020556194,0.00056744425,0.00014277657,0.000057431313],"genre_scores_gemma":[0.92244416,0.0043556346,0.025825316,0.038250558,0.00092409935,0.00013858447,0.007936379,0.000037632624,0.000087658875],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791205,0.000070932016,0.0008379106,0.0001684071,0.000678731,0.00033196708],"domain_scores_gemma":[0.9967963,0.000253691,0.0005736699,0.00020393616,0.0018829672,0.0002894224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018422748,0.00019515755,0.00025225995,0.0009457656,0.00054418255,0.00044953026,0.00029722415,0.00009753909,0.000021571737],"category_scores_gemma":[0.00072235597,0.0001437862,0.00029589602,0.0004432047,0.00009844073,0.0009539364,0.000030214474,0.0001775078,0.0000027323276],"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":[0.0036853065,0.00076658226,0.014118302,0.00018610847,0.000876494,0.0000050308035,0.0016860549,0.0009868323,0.00009627443,0.053766683,0.102427416,0.8213989],"study_design_scores_gemma":[0.0070657237,0.001424052,0.069887534,0.00025689832,0.00011049059,0.00035987093,0.0009512437,0.03976495,0.000022285212,0.015983034,0.86383563,0.00033830982],"about_ca_topic_score_codex":0.000006270143,"about_ca_topic_score_gemma":0.000010854172,"teacher_disagreement_score":0.895101,"about_ca_system_score_codex":0.000064767715,"about_ca_system_score_gemma":0.0002475449,"threshold_uncertainty_score":0.5863431},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860907,0.00069107057,0.008734833,0.0026239478,0.00012024291,0.00068534445,0.000058882288,0.00008732198,0.000907635],"genre_scores_gemma":[0.9958139,0.00017467282,0.0032455006,0.0002756823,0.0001423714,0.00003333154,0.00023687123,0.000025560596,0.000052107476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970136,0.00092830823,0.00090634066,0.00058186305,0.00015472443,0.0004151572],"domain_scores_gemma":[0.99695575,0.0018761967,0.00044211603,0.00036449856,0.00013609002,0.00022533457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022365435,0.00025882418,0.0012505649,0.0001727946,0.0000636635,0.000004383479,0.00010468696,0.0002015702,0.0001625575],"category_scores_gemma":[0.0060127564,0.00021455229,0.00013249993,0.00025823672,0.00022378915,0.00010571732,0.00006573588,0.00028580762,0.000020060725],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073342415,0.00008971268,0.9267377,0.00017693639,0.000036387653,0.000007139631,0.00012566592,0.00006442822,0.0013610475,0.0001737338,0.00016318655,0.07033058],"study_design_scores_gemma":[0.0018912025,0.0005798727,0.95599806,0.00017379927,0.000024341498,0.000017492535,0.00001592024,0.035640854,0.00079789746,0.0018343312,0.0027979387,0.00022827208],"about_ca_topic_score_codex":0.006028869,"about_ca_topic_score_gemma":0.0019490345,"teacher_disagreement_score":0.07010231,"about_ca_system_score_codex":0.000058165184,"about_ca_system_score_gemma":0.00008972738,"threshold_uncertainty_score":0.9113887},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961929,0.000022185217,0.00022922769,0.0018713082,0.00013567692,0.00022275657,0.000012690853,0.000014682534,0.0012986228],"genre_scores_gemma":[0.99625814,0.00000981941,0.0024196138,0.0011713167,0.00005903781,0.000018953591,0.000043969678,0.000008139858,0.000011004397],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999372,0.000025086636,0.00032826632,0.00013150902,0.000033930053,0.0001091739],"domain_scores_gemma":[0.99941254,0.000051427938,0.00021726615,0.00026472675,0.000018039642,0.00003597897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023008708,0.00005885979,0.000115159586,0.00002047198,0.000028163142,0.000008837558,0.0000795734,0.00004715003,0.00003096334],"category_scores_gemma":[0.00009897156,0.000052434967,0.000025264346,0.000045988345,0.00008014934,0.000114884366,0.000012412089,0.00013617313,0.000010180763],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006318151,0.000027800445,0.9911076,0.000016252205,0.000018497907,0.0000031456009,0.00028214222,0.00017332924,0.0037342098,0.0015143468,0.0009949993,0.0020645172],"study_design_scores_gemma":[0.00058301154,0.00001674207,0.9722988,0.000028474291,0.000010063496,0.000003374766,0.00014369737,0.024575593,0.0016999239,0.00014884854,0.00041518328,0.00007630713],"about_ca_topic_score_codex":0.00001613954,"about_ca_topic_score_gemma":0.000051522948,"teacher_disagreement_score":0.024402263,"about_ca_system_score_codex":0.000083829946,"about_ca_system_score_gemma":0.00006092736,"threshold_uncertainty_score":0.21382359},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82130957,0.0022771633,0.1684316,0.004896819,0.00021422758,0.0012933486,0.00026386764,0.0002238188,0.001089582],"genre_scores_gemma":[0.9890505,0.000041344872,0.009551257,0.0007580295,0.00004594243,0.000027415515,0.00049741124,0.00002159357,0.000006487618],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996992,0.00089806784,0.00092094165,0.00041064777,0.00032479464,0.0004535506],"domain_scores_gemma":[0.9975981,0.00037723538,0.0004826098,0.00065095274,0.000058070345,0.000833029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045708013,0.00015697675,0.00063305214,0.00024336188,0.00007465827,0.000030195322,0.000095178846,0.000041412633,0.000020827565],"category_scores_gemma":[0.0032010076,0.00014866359,0.000051020947,0.00033738496,0.00008158583,0.00014297925,0.00013702562,0.00019062111,0.0000030052463],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000492659,0.0004780495,0.9429472,0.0011271501,0.000025520765,0.000002873384,0.00018006643,0.00008569012,0.000034253473,0.0010024374,0.0005442077,0.053523306],"study_design_scores_gemma":[0.001668505,0.00015965215,0.97340953,0.00022051462,0.000019462741,0.000012424138,0.00038986243,0.012422947,0.000013974797,0.00035778148,0.011177552,0.0001477761],"about_ca_topic_score_codex":0.0007840672,"about_ca_topic_score_gemma":0.0009879672,"teacher_disagreement_score":0.16774094,"about_ca_system_score_codex":0.00034559055,"about_ca_system_score_gemma":0.0010894465,"threshold_uncertainty_score":0.6062325},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9804723,0.0015719312,0.007565941,0.008104212,0.00018731083,0.001170805,0.00055075117,0.000230401,0.00014629796],"genre_scores_gemma":[0.98916256,0.00020089254,0.0025112391,0.007353092,0.00011758593,0.000003503531,0.0005013991,0.000021532429,0.00012817468],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982368,0.00015811795,0.00033127962,0.0003864832,0.00037005835,0.00051729695],"domain_scores_gemma":[0.99812835,0.000055230223,0.00015816958,0.0004841633,0.000063459665,0.0011106442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049509545,0.00016507362,0.00035883585,0.00018648767,0.00025134863,0.00002783717,0.00007460346,0.000044291595,0.00004599075],"category_scores_gemma":[0.00022560247,0.00016607641,0.000076096076,0.00039019005,0.000020741929,0.00012936212,0.000026809987,0.00016464818,0.000043587905],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002758411,0.0003661422,0.95593464,0.00049433473,0.00005025751,0.00009777974,0.00041732378,0.010830702,0.000035479385,0.00020649923,0.020341903,0.008466539],"study_design_scores_gemma":[0.0013894584,0.00026424156,0.9825145,0.00028800435,0.00006504704,0.000011639292,0.00012828097,0.009343808,0.0000016515571,0.00023590552,0.005562527,0.00019492189],"about_ca_topic_score_codex":0.0012963978,"about_ca_topic_score_gemma":0.000309366,"teacher_disagreement_score":0.026579887,"about_ca_system_score_codex":0.0004781111,"about_ca_system_score_gemma":0.00032329521,"threshold_uncertainty_score":0.67723995},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3697453,0.11134247,0.117940366,0.29606313,0.007909148,0.008217024,0.06624248,0.00038764128,0.022152422],"genre_scores_gemma":[0.96606284,0.009847374,0.009070178,0.0051244083,0.0006659069,0.00000860567,0.008256258,0.00004153489,0.0009229177],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99742925,0.0006538964,0.00090219587,0.00021008746,0.00055606203,0.00024851618],"domain_scores_gemma":[0.996806,0.0006716868,0.0014251047,0.000493776,0.00029468865,0.00030870317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071631316,0.00012410681,0.00042950822,0.00025530424,0.00007909435,0.00011994958,0.00016338011,0.000027848408,0.000026611664],"category_scores_gemma":[0.0010518343,0.000106829924,0.000022899909,0.00017016471,0.00008061088,0.0011419621,0.00014002733,0.00017143448,0.000002163865],"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":[0.0031103715,0.0015950925,0.09810402,0.012302147,0.0012662004,0.0008182549,0.00009415139,0.000023980756,0.000036172376,0.0051059667,0.79282063,0.08472303],"study_design_scores_gemma":[0.00185736,0.00020104209,0.18153426,0.00019233362,0.00014637804,0.00019011252,0.00019217705,0.00074239075,2.7041696e-7,0.000024979548,0.8148431,0.00007556455],"about_ca_topic_score_codex":0.0003097117,"about_ca_topic_score_gemma":0.00001655645,"teacher_disagreement_score":0.59631747,"about_ca_system_score_codex":0.00009764619,"about_ca_system_score_gemma":0.0007083957,"threshold_uncertainty_score":0.4356398},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.925195,0.016853021,0.012075535,0.03362128,0.0009647521,0.0042219497,0.00021244519,0.00084533915,0.0060106926],"genre_scores_gemma":[0.99185026,0.0019704374,0.0001803474,0.0047051585,0.0005066116,0.0001427038,0.00015633216,0.000048400958,0.00043972186],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973546,0.00032706937,0.0005969038,0.00057347445,0.0006883753,0.00045955952],"domain_scores_gemma":[0.9976633,0.0006529605,0.00021077579,0.0007983706,0.00021405779,0.0004605342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006866512,0.0003301801,0.00078281306,0.00021406153,0.00019078147,0.000011250977,0.00022960131,0.000056325393,0.00026052239],"category_scores_gemma":[0.0026321504,0.00020245543,0.00010344848,0.00047848708,0.0012847325,0.000088444984,0.000095311865,0.00038352705,0.000054368775],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0058780033,0.000717051,0.9368414,0.0015753362,0.00041992505,0.0009743334,0.0051426673,0.000058400048,0.00011660839,0.0005520031,0.038635723,0.009088508],"study_design_scores_gemma":[0.025016867,0.0018116961,0.8233347,0.0037471023,0.0006753507,0.00046270574,0.0012627693,0.07252119,0.00030397243,0.00045399473,0.06921691,0.0011927601],"about_ca_topic_score_codex":0.00062823744,"about_ca_topic_score_gemma":0.00031673777,"teacher_disagreement_score":0.11350676,"about_ca_system_score_codex":0.00010419359,"about_ca_system_score_gemma":0.00036753403,"threshold_uncertainty_score":0.82558924},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8015148,0.0010281658,0.18775849,0.0031182624,0.0020505025,0.0003997457,0.00088574225,0.0000066367234,0.0032376575],"genre_scores_gemma":[0.9836849,0.0010594598,0.014676158,0.00011709986,0.0003051711,0.0000062126583,0.00009080346,0.0000090861495,0.000051140327],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99218607,0.0022374513,0.00116578,0.00012226812,0.0038920727,0.0003963636],"domain_scores_gemma":[0.9920085,0.005746376,0.00027424694,0.00014820289,0.0013719228,0.00045073283],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.035042148,0.000080335296,0.00042277886,0.0006128223,0.000011930028,0.000010947937,0.00055036764,0.00010489523,0.0015098696],"category_scores_gemma":[0.04417478,0.00006506558,0.000053574997,0.00029480032,0.00035884927,0.00011008289,0.00019555792,0.0012820661,0.000015281968],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110676745,0.000850483,0.9255007,0.00005968567,0.000072092196,0.0023155783,0.000046373072,0.000014947707,0.00009522268,0.004368385,0.004584775,0.061981067],"study_design_scores_gemma":[0.0016383434,0.00013032797,0.99225914,0.0005250166,0.0000050607496,0.0002502878,0.0001070603,0.0016192085,0.000033022206,0.0018203127,0.0015652496,0.000046994388],"about_ca_topic_score_codex":0.00017321346,"about_ca_topic_score_gemma":0.00013844293,"teacher_disagreement_score":0.18217008,"about_ca_system_score_codex":0.000514491,"about_ca_system_score_gemma":0.0010460585,"threshold_uncertainty_score":0.9994029},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99792534,0.00018699719,0.00023690834,0.00031192903,0.00030808587,0.00024256883,0.00014136457,0.000038068796,0.00060875085],"genre_scores_gemma":[0.99862015,0.000005510795,0.0010187625,0.00011895747,0.00012465214,0.000027509135,0.000009623612,0.0000040536943,0.000070770984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991837,0.00001088551,0.00011558434,0.00017472931,0.00031862987,0.00019643271],"domain_scores_gemma":[0.99932015,0.000117159354,0.000054045246,0.00017076689,0.0002702139,0.0000676703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080310204,0.00004768359,0.00017201579,0.00005430269,0.00011160861,0.000004937811,0.00015443047,0.000013552069,0.00001458101],"category_scores_gemma":[0.0020830615,0.00003782806,0.000040613697,0.00023516447,0.00048287062,0.000075156044,0.0000721119,0.000027151631,0.000011754844],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042542917,0.0015964112,0.48622775,0.0010516646,0.00010465277,0.000019220422,0.013694571,0.000001061437,0.2453014,0.058987413,0.042092558,0.1504979],"study_design_scores_gemma":[0.0015105526,0.00015712343,0.9639397,0.00004509697,0.000082015,0.0000025856295,0.00071289035,0.00013401189,0.009936659,0.00090644346,0.022450626,0.00012228238],"about_ca_topic_score_codex":6.541318e-7,"about_ca_topic_score_gemma":0.000013969974,"teacher_disagreement_score":0.47771198,"about_ca_system_score_codex":0.000023611814,"about_ca_system_score_gemma":0.000106028,"threshold_uncertainty_score":0.24937697},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9729436,0.0008227093,0.000059641054,0.0037862863,0.0001646738,0.00040408687,0.000013417558,0.00001251776,0.021793049],"genre_scores_gemma":[0.9993659,0.000049400194,0.00003747829,0.00038317795,0.00010478143,0.000013084883,0.0000044386275,0.0000038221347,0.000037869388],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981728,0.00017341842,0.00033601042,0.00017717252,0.00091935176,0.00022123524],"domain_scores_gemma":[0.99913776,0.00013371951,0.0001758164,0.00031015425,0.00019226888,0.00005030026],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002652244,0.00008724363,0.00028129335,0.00013484058,0.00015130224,0.00000550799,0.0003947403,0.000031080242,0.00009481021],"category_scores_gemma":[0.0009322844,0.000043643453,0.00007755617,0.0017782593,0.003675081,0.00009701627,0.000024129822,0.00012870626,0.0000014684542],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013441744,0.00045735738,0.8880857,0.00016817969,0.000046962276,0.000010659732,0.02264505,9.883472e-7,0.0032230446,0.04258302,0.003170575,0.039474018],"study_design_scores_gemma":[0.0010117656,0.00065943634,0.9791339,0.00016870657,0.00006896089,0.0000057347856,0.0064060586,0.000002465826,0.00038217232,0.006723776,0.005365984,0.00007102248],"about_ca_topic_score_codex":0.00017347492,"about_ca_topic_score_gemma":0.00008266614,"teacher_disagreement_score":0.091048196,"about_ca_system_score_codex":0.000018008464,"about_ca_system_score_gemma":0.00025615332,"threshold_uncertainty_score":0.9990364},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28304246,0.13778333,0.124554366,0.44402072,0.0016944519,0.006865779,0.0005861829,0.00066534267,0.00078737125],"genre_scores_gemma":[0.9842878,0.0007006945,0.001558115,0.012658814,0.0002606862,0.000057294692,0.00013206476,0.000054367178,0.00029014854],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960904,0.0014367513,0.0011338315,0.00014947372,0.0006833995,0.0005061448],"domain_scores_gemma":[0.99424773,0.0013989977,0.0025925303,0.0007674463,0.0006857154,0.00030759146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00604636,0.00019913533,0.0005884182,0.00020319992,0.001145396,0.00007813279,0.00047033676,0.00006640051,0.000026204354],"category_scores_gemma":[0.001691073,0.00012702907,0.00015440321,0.00072552095,0.000057012112,0.0004288965,0.00011852658,0.0006375685,0.0000061625246],"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":[0.0006243549,0.0017212927,0.7101334,0.0015724386,0.00097420596,0.0000014925863,0.009126006,0.003287471,0.00015393464,0.007966054,0.22495013,0.039489225],"study_design_scores_gemma":[0.008781219,0.0010979789,0.35833585,0.004316493,0.0012320199,0.0002970448,0.007904161,0.18497707,0.000048806614,0.0006456048,0.43137717,0.0009865684],"about_ca_topic_score_codex":0.00019422002,"about_ca_topic_score_gemma":0.00062464294,"teacher_disagreement_score":0.70124537,"about_ca_system_score_codex":0.0024331105,"about_ca_system_score_gemma":0.00064883556,"threshold_uncertainty_score":0.8809576},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9750408,0.0072169565,0.012093236,0.00028321144,0.0013342766,0.0012068287,0.0004444506,0.0014119846,0.0009682847],"genre_scores_gemma":[0.99698824,0.00011552754,0.00025412667,0.0003044226,0.0012881714,0.00034811665,0.0004051406,0.00009078167,0.00020549879],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99777275,0.00010563229,0.00034991163,0.0003954055,0.00034810213,0.0010281813],"domain_scores_gemma":[0.99828273,0.00031001915,0.00018507254,0.0005509179,0.00017265395,0.0004986043],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021011941,0.00031135802,0.00045741443,0.00018143095,0.00023377326,0.0000321752,0.00012564973,0.00005983061,0.00020301361],"category_scores_gemma":[0.0008297215,0.00031613227,0.00032316914,0.00044702526,0.000073013434,0.00021045326,0.00004687867,0.00017545209,0.000098681805],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013936928,0.000544179,0.98627865,0.00021450427,0.00015792677,0.000004582086,0.00004186508,0.0005509033,0.00042272525,0.00006775011,0.0031341156,0.008443421],"study_design_scores_gemma":[0.0022647697,0.00020630802,0.91394454,0.00011504104,0.00021424926,0.00000865111,0.00004497334,0.0015679715,0.00047190432,0.00006628502,0.08064617,0.00044915456],"about_ca_topic_score_codex":0.0000594124,"about_ca_topic_score_gemma":0.00001157986,"teacher_disagreement_score":0.077512056,"about_ca_system_score_codex":0.000303071,"about_ca_system_score_gemma":0.00024527183,"threshold_uncertainty_score":0.9999291},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99765444,0.00053996773,0.000437078,0.000086174296,0.000042285275,0.0005126402,0.0005896621,0.000066012486,0.000071759525],"genre_scores_gemma":[0.98580956,0.00019772536,0.012749039,0.00007009015,0.000046687303,0.000012076962,0.0010504506,0.000025153253,0.00003918364],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989768,0.000089509296,0.00024348304,0.00027752132,0.0002764111,0.00013621949],"domain_scores_gemma":[0.99881953,0.000058100755,0.00014110471,0.0005919656,0.0002934454,0.000095832795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021089682,0.000107623826,0.00032944744,0.000084708845,0.000035475223,0.000016678354,0.00010898854,0.00005325244,0.000075866956],"category_scores_gemma":[0.00018769005,0.00011188627,0.000012631048,0.00018565025,0.000085036605,0.00039195787,0.00014348922,0.000072272975,0.000026488891],"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":[0.00021838315,0.005019244,0.4240399,0.0029349804,0.00095298706,0.00009520703,0.00016907843,0.000015956362,0.541813,0.000027886608,0.0040657865,0.020647556],"study_design_scores_gemma":[0.003080624,0.0003902531,0.7897234,0.0008146248,0.00021690472,0.000036069556,0.000047403348,0.19874877,0.006364199,0.000039453498,0.00020891675,0.00032936328],"about_ca_topic_score_codex":0.00007565871,"about_ca_topic_score_gemma":0.0000027691317,"teacher_disagreement_score":0.53544885,"about_ca_system_score_codex":0.000043405453,"about_ca_system_score_gemma":0.000069592286,"threshold_uncertainty_score":0.45625895},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9942596,0.0005673956,0.000016805845,0.00025720583,0.00010643832,0.00022883137,0.00003543017,0.000040469702,0.004487805],"genre_scores_gemma":[0.99941397,0.00003699604,0.00003345945,0.00034709086,0.00007107533,0.000013118307,0.0000366073,0.0000074193135,0.00004026833],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99749255,0.00012558095,0.00040683598,0.0004853812,0.0010185736,0.00047104998],"domain_scores_gemma":[0.9987332,0.00008079946,0.00016775506,0.00044112027,0.0002784839,0.00029859538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002027333,0.0001444848,0.0006314448,0.0010074861,0.00012256623,0.000009145607,0.00034383114,0.000055037868,0.00045762953],"category_scores_gemma":[0.0013684507,0.00011539814,0.00010785072,0.0075005945,0.0023769408,0.0002477609,0.000028122435,0.00014433754,0.0000021636702],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001162252,0.00013863444,0.986937,0.000049014576,0.000053184136,0.0000063242933,0.0016993566,0.0000032795197,0.009102805,0.000968088,0.000086937354,0.00094374537],"study_design_scores_gemma":[0.0006471932,0.0001851165,0.99699306,0.00009611857,0.00049511634,3.4756098e-7,0.00043641985,0.0003232074,0.00024229249,0.00015771038,0.00031420545,0.00010920244],"about_ca_topic_score_codex":0.00008370707,"about_ca_topic_score_gemma":0.00010535224,"teacher_disagreement_score":0.010056061,"about_ca_system_score_codex":0.00011690108,"about_ca_system_score_gemma":0.00024580533,"threshold_uncertainty_score":0.87579376},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99726385,0.00051467266,0.0006737991,0.00034756502,0.000023676148,0.0008245994,0.00014944047,0.00014916019,0.000053257005],"genre_scores_gemma":[0.9632797,0.00008092112,0.035631783,0.00008904651,0.00002098494,0.00029351946,0.00056524813,0.000028263667,0.000010523327],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99836403,0.00022783231,0.00069414795,0.00030187936,0.00023972806,0.0001724057],"domain_scores_gemma":[0.9976191,0.00067694945,0.0003473807,0.00063239946,0.00060458557,0.00011957631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007628522,0.00015028614,0.0009588059,0.00073182676,0.000017697479,0.0000030995711,0.00015476855,0.00009116317,0.00018422923],"category_scores_gemma":[0.0021111644,0.00013914377,0.00007866317,0.0017162461,0.0002063266,0.00016347022,0.000041807834,0.00009359538,0.000010588611],"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":[0.00013849941,0.0016601569,0.077836834,0.0011392699,0.0014319442,0.0000029866237,0.00040804868,0.000008062995,0.9157583,0.00002669127,0.0005054688,0.0010837342],"study_design_scores_gemma":[0.0021254653,0.0010344714,0.8802522,0.00068205455,0.0024989469,6.073509e-7,0.00068832724,0.101107284,0.011290031,0.00006826556,0.000067401044,0.00018498335],"about_ca_topic_score_codex":0.00014821785,"about_ca_topic_score_gemma":0.00002716379,"teacher_disagreement_score":0.9044683,"about_ca_system_score_codex":0.00010818085,"about_ca_system_score_gemma":0.00011822403,"threshold_uncertainty_score":0.56741184},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83434397,0.0014232454,0.00013399008,0.11148849,0.0017903749,0.00034739022,0.00036045472,0.00016049767,0.049951565],"genre_scores_gemma":[0.9641369,0.00088234607,0.00027177084,0.027747858,0.0026419507,0.00000555253,0.00013827911,0.000040602103,0.0041347705],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99674493,0.00014766016,0.00044364468,0.0002020127,0.0017697399,0.0006920181],"domain_scores_gemma":[0.99513507,0.00012300971,0.00018365223,0.00020918834,0.0005311684,0.003817933],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010339234,0.00014790325,0.00034952175,0.00021810376,0.00036626123,0.00003551343,0.00020535334,0.00031100173,0.0069633536],"category_scores_gemma":[0.0039768345,0.00013335446,0.00017529784,0.00030005942,0.00009115701,0.00013286129,0.00001645121,0.0010343866,0.00073035824],"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":[0.000022197773,0.000029280893,0.55922633,0.000004919703,0.00013907405,0.005284472,0.00010845165,0.0000017907593,0.000010656771,0.00008161028,0.42611414,0.008977069],"study_design_scores_gemma":[0.0015035337,0.00005860155,0.6547269,0.00009985469,0.000039494615,0.0030075237,0.00010491487,0.00008916231,0.000011109667,0.00006345878,0.34014758,0.00014782666],"about_ca_topic_score_codex":0.003970664,"about_ca_topic_score_gemma":0.020284472,"teacher_disagreement_score":0.12979288,"about_ca_system_score_codex":0.0015013212,"about_ca_system_score_gemma":0.0060736863,"threshold_uncertainty_score":0.99956095},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94256026,0.00041057353,0.053773623,0.0011754826,0.00035843623,0.0011296502,0.0005486282,0.000014538459,0.000028825089],"genre_scores_gemma":[0.996716,0.00012047966,0.0019823052,0.00025547374,0.00008837876,0.0001697139,0.00062302,0.000014952944,0.000029633196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975856,0.0007230791,0.0009777653,0.00035938655,0.0001232137,0.00023094444],"domain_scores_gemma":[0.99699074,0.0016292203,0.00071544916,0.00037037468,0.00016945279,0.00012477218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017554968,0.00016452774,0.00062810123,0.00009680996,0.00005533168,0.0000023268396,0.00016146005,0.000086142565,0.00004031324],"category_scores_gemma":[0.003586603,0.00012231404,0.00007165622,0.00010226304,0.00022693245,0.00010086446,0.000059624777,0.00010174887,2.206552e-7],"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":[0.0016903734,0.00014078584,0.9032938,0.000358571,0.00010478729,0.000003431104,0.0014843291,0.0019807771,0.00016013859,0.00021527438,0.000114652634,0.0904531],"study_design_scores_gemma":[0.00083575817,0.00018138386,0.9140704,0.00005696365,0.00009561452,0.0000011701935,0.0002193724,0.0836467,0.00025994302,0.00044748143,0.000078623096,0.000106539876],"about_ca_topic_score_codex":0.90149254,"about_ca_topic_score_gemma":0.9467345,"teacher_disagreement_score":0.09034657,"about_ca_system_score_codex":0.00006999046,"about_ca_system_score_gemma":0.0003849435,"threshold_uncertainty_score":0.4987822},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82718194,0.01414151,0.028978184,0.12159096,0.002847741,0.0017971256,0.001783533,0.0002340913,0.0014449235],"genre_scores_gemma":[0.98857915,0.0031071648,0.00251702,0.0051773987,0.00022256534,0.0000058419814,0.0003641651,0.000020627562,0.0000060449884],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957948,0.0003706908,0.0019405107,0.00025795243,0.0012472902,0.00038878137],"domain_scores_gemma":[0.99546874,0.00021497678,0.002189979,0.0003578651,0.0015082826,0.00026017043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023755594,0.00020729628,0.0007762024,0.0004683018,0.000099399964,0.000023501525,0.00037664323,0.00009778595,0.00006467622],"category_scores_gemma":[0.00033755947,0.00018757048,0.00033540188,0.00043388354,0.000170314,0.00019124449,0.000028909853,0.0004721771,0.000006099592],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028173025,0.002384205,0.79210865,0.00067973154,0.0005084118,0.00006058487,0.00065153884,0.0012092624,0.0002630823,0.004907707,0.0033134467,0.19109604],"study_design_scores_gemma":[0.0064098937,0.0021036193,0.9608978,0.00081642315,0.000027229462,0.00021125613,0.00014104148,0.0030976466,0.000060599534,0.0023434847,0.023663282,0.00022770153],"about_ca_topic_score_codex":0.00051488203,"about_ca_topic_score_gemma":0.00016480159,"teacher_disagreement_score":0.19086835,"about_ca_system_score_codex":0.00025891783,"about_ca_system_score_gemma":0.0015933971,"threshold_uncertainty_score":0.7648902},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18007106,0.008224408,0.000016455175,0.7921892,0.013647683,0.0016583483,0.00041726048,0.00027380476,0.0035018192],"genre_scores_gemma":[0.072079144,0.0033803089,0.00010098309,0.8976374,0.01222285,0.00015236723,0.0010133758,0.000119038246,0.013294527],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944618,0.0012760358,0.0014857352,0.0011835777,0.0003930755,0.0011997966],"domain_scores_gemma":[0.9942551,0.0029296633,0.0006694931,0.0014912576,0.00016340028,0.00049108546],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0016089478,0.00064383243,0.001851693,0.0001447551,0.00011282136,0.00012243217,0.0007555925,0.0023805476,0.0015517334],"category_scores_gemma":[0.0021005634,0.00043473954,0.00048162238,0.00017597376,0.001642529,0.00013899295,0.00083918433,0.0048579127,0.00041301223],"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":[0.00014848412,0.00023253677,0.06685074,0.00016039767,0.00042737505,0.0010923978,0.00009228356,3.5082326e-7,0.000015175964,0.000024187011,0.9115405,0.019415548],"study_design_scores_gemma":[0.0010757362,0.0007044494,0.14644755,0.0001764389,0.00048442732,0.0002845203,0.000037715523,0.000031843683,0.000010121153,0.00007239148,0.85029405,0.00038073497],"about_ca_topic_score_codex":0.00008909364,"about_ca_topic_score_gemma":0.00047661725,"teacher_disagreement_score":0.10799191,"about_ca_system_score_codex":0.0001949618,"about_ca_system_score_gemma":0.000421228,"threshold_uncertainty_score":0.99981046},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7804757,0.0012222155,0.098107755,0.00049448735,0.00050903537,0.022971494,0.09562349,0.00038816797,0.00020770142],"genre_scores_gemma":[0.9747992,0.0000040211776,0.010745695,0.0006092009,0.00014637991,0.00048845017,0.013062708,0.000045236917,0.00009908457],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772805,0.000033111763,0.0006194021,0.00063814374,0.0005888126,0.0003925065],"domain_scores_gemma":[0.99834746,0.000033474847,0.00026480213,0.00073350075,0.00027971927,0.0003410342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004152402,0.00020155477,0.0004959486,0.00017809402,0.0000661896,0.000016966764,0.00046854003,0.000047245136,0.000020097164],"category_scores_gemma":[0.00070496375,0.00020375292,0.00005648102,0.00035017927,0.000016901146,0.000255842,0.00030726969,0.00010437342,0.000012613883],"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":[0.0017051791,0.0015850975,0.7960738,0.0020952334,0.0003511713,0.0000031081127,0.0015118854,0.0039895773,0.00030797033,0.00024134987,0.16011819,0.03201742],"study_design_scores_gemma":[0.0071672713,0.00075172534,0.4574801,0.00242681,0.00013951733,0.000013629738,0.00006483302,0.52130365,0.00044021243,0.00071352965,0.008517164,0.0009815345],"about_ca_topic_score_codex":0.00048332935,"about_ca_topic_score_gemma":0.00013743472,"teacher_disagreement_score":0.5173141,"about_ca_system_score_codex":0.00022325656,"about_ca_system_score_gemma":0.00061989506,"threshold_uncertainty_score":0.8308803},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99479353,0.0008589243,0.0009863918,0.0019894259,0.00010648779,0.00088234514,0.00023034887,0.00004087258,0.000111659414],"genre_scores_gemma":[0.998305,0.00017939409,0.000313181,0.0009945543,0.00007110569,0.000041050116,0.00004112598,0.000015484657,0.000039083494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99679226,0.00035067878,0.001065162,0.00023150326,0.0009406587,0.0006197206],"domain_scores_gemma":[0.98680246,0.008120449,0.0025041644,0.00085338217,0.0015168631,0.00020266583],"candidate_categories":["metaresearch","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.010421384,0.0002707424,0.0006025591,0.0005716925,0.001414741,0.00026519407,0.0010453179,0.00013048289,2.1468172e-7],"category_scores_gemma":[0.034844838,0.00012054916,0.000282938,0.006164107,0.005499744,0.0021336316,0.00026632182,0.0010291265,9.534772e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024023237,0.0002002189,0.9253983,0.00017252924,0.00039124987,0.000006485267,0.059453044,0.0017228699,0.00053736795,0.0033409658,0.0063120625,0.0024408952],"study_design_scores_gemma":[0.0053745066,0.0008349079,0.526879,0.00085809594,0.0014863623,0.0011897332,0.3563793,0.092460014,0.0005130995,0.0066646785,0.0072485446,0.00011175876],"about_ca_topic_score_codex":0.000042725664,"about_ca_topic_score_gemma":0.000018880633,"teacher_disagreement_score":0.39851928,"about_ca_system_score_codex":0.00011867682,"about_ca_system_score_gemma":0.0004674469,"threshold_uncertainty_score":0.99988526},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31984583,0.00003245109,0.6768658,0.0003395192,0.001677554,0.00018698369,0.00005465967,0.000022040858,0.0009751378],"genre_scores_gemma":[0.98501456,0.000014735191,0.014065843,0.00028507202,0.000388796,0.0000041322196,0.00020874351,0.000007582936,0.0000105432255],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9976096,0.00015976603,0.00096373353,0.000068613794,0.0010871823,0.00011108273],"domain_scores_gemma":[0.99544984,0.00006440068,0.0011181846,0.00018622118,0.0029979458,0.00018341748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020100742,0.00011647883,0.00024409582,0.00055027346,0.000024448696,0.000055064163,0.00025872653,0.00008308935,0.000069128044],"category_scores_gemma":[0.0009248769,0.000102259815,0.00012996745,0.00013077357,0.000041194868,0.0031108274,0.000045373105,0.00018062946,0.000034095767],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016446094,0.00017843938,0.0045852056,0.000042615015,0.00021656955,0.0000075170465,0.0011181437,0.002112352,0.0021683124,0.00013292463,0.0004924336,0.9873009],"study_design_scores_gemma":[0.009841113,0.0021549705,0.8007759,0.00032847957,0.00024365839,0.0008402688,0.0011208074,0.124039665,0.029164381,0.0011975425,0.029965758,0.00032749155],"about_ca_topic_score_codex":0.00008126754,"about_ca_topic_score_gemma":0.00003901921,"teacher_disagreement_score":0.9869734,"about_ca_system_score_codex":0.00033061844,"about_ca_system_score_gemma":0.00021233881,"threshold_uncertainty_score":0.41700345},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934972,0.002389536,0.00009152025,0.001961936,0.00020907765,0.0005618735,0.0002967042,0.000107425425,0.0008847241],"genre_scores_gemma":[0.99450076,0.00281158,0.0012660004,0.0011891894,0.000095375464,0.00001719713,0.000035639212,0.000013553818,0.00007068561],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99775404,0.00008375533,0.0003535107,0.00083662366,0.00043963795,0.0005324381],"domain_scores_gemma":[0.9982582,0.00028073613,0.0002019343,0.00045172786,0.00036295014,0.00044450196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001296134,0.0002664206,0.00041052088,0.00018065196,0.0008471652,0.00013879161,0.0001766518,0.000160028,0.000009601888],"category_scores_gemma":[0.0009989884,0.00022426757,0.00006973043,0.0005387071,0.001919614,0.0009918854,0.00013223442,0.00024245968,0.000013285682],"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":[0.020725502,0.00580386,0.30889013,0.0053371927,0.0010602626,0.005704966,0.02287613,0.000012344713,0.45082107,0.03202865,0.015659323,0.13108058],"study_design_scores_gemma":[0.0093968995,0.0035195742,0.73054296,0.00033126152,0.0003398984,0.013694184,0.0064578154,0.002558872,0.01416917,0.0009674158,0.21553217,0.0024897696],"about_ca_topic_score_codex":0.0001792846,"about_ca_topic_score_gemma":0.000026880973,"teacher_disagreement_score":0.4366519,"about_ca_system_score_codex":0.00003547997,"about_ca_system_score_gemma":0.000464467,"threshold_uncertainty_score":0.9145366},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023259604,0.989735,0.0007275658,0.00002792013,0.0015145354,0.004294735,0.0007896908,0.00019827191,0.00038628708],"genre_scores_gemma":[0.003048726,0.9887864,0.0015150511,0.00004007322,0.0007307709,0.0013506935,0.0040581697,0.00019326808,0.00027686835],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99516195,0.00070245663,0.0015520848,0.0013941744,0.00043554045,0.0007538136],"domain_scores_gemma":[0.9970439,0.0004498175,0.0008744187,0.0008278399,0.00025599185,0.00054805033],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011369229,0.0011330298,0.0039643077,0.00058713515,0.00019538152,0.000020234693,0.00041048886,0.00029472448,0.00037260196],"category_scores_gemma":[0.00042003172,0.0008907401,0.0005326531,0.00033056887,0.00057151617,0.0002084053,0.0006200635,0.00058941153,0.000077017045],"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":[0.0006522466,0.00036018717,0.000051438674,0.06851049,0.0011011525,0.00054503075,0.00028580747,1.9577767e-8,0.000015396643,0.000061966864,0.0010637736,0.9273525],"study_design_scores_gemma":[0.0023462658,0.0036720717,0.0036476965,0.040815815,0.0013170239,0.0025282789,0.000033580473,0.0000050507715,8.145426e-7,0.000045653636,0.94480956,0.00077819463],"about_ca_topic_score_codex":0.0000058907613,"about_ca_topic_score_gemma":6.971239e-7,"teacher_disagreement_score":0.9437458,"about_ca_system_score_codex":0.00036009942,"about_ca_system_score_gemma":0.0006605383,"threshold_uncertainty_score":0.9993543},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957419,0.0005300314,0.00002379916,0.00052849675,0.00007693435,0.00093719835,0.0012846037,0.000071626484,0.0008054046],"genre_scores_gemma":[0.9928521,0.00011803517,0.0022856805,0.00044595107,0.00014287537,0.000038392816,0.003963665,0.00004112657,0.00011215683],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99645585,0.0007817989,0.00068110414,0.0003899677,0.0010116423,0.0006796186],"domain_scores_gemma":[0.99772865,0.00029999105,0.00024885286,0.00087865186,0.00048379827,0.0003600781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016177682,0.000168752,0.000591153,0.00027637254,0.00027992454,0.000023978257,0.00061623345,0.00012604591,0.0006423422],"category_scores_gemma":[0.0000450325,0.00015372754,0.000078339,0.00051532435,0.00014738546,0.00013891955,0.00044642147,0.00041958896,0.00012342243],"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":[0.0009048141,0.00023150432,0.9908934,0.0015230624,0.00005968671,0.0000036727058,0.0033479906,6.337149e-8,0.00017127628,0.00014123564,0.0006969223,0.0020263284],"study_design_scores_gemma":[0.0017965361,0.00019113452,0.99266374,0.00046481,0.000013762436,9.247558e-7,0.00082201674,0.00026744534,0.00012331083,0.00067483296,0.0028743835,0.00010707244],"about_ca_topic_score_codex":0.053633634,"about_ca_topic_score_gemma":0.040666625,"teacher_disagreement_score":0.01296701,"about_ca_system_score_codex":0.00018498448,"about_ca_system_score_gemma":0.00037445378,"threshold_uncertainty_score":0.9768387},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882027,0.00027744082,0.009885533,0.00033719226,0.000053537755,0.0002637971,0.00003582352,0.000019967438,0.0009240083],"genre_scores_gemma":[0.9168905,0.00014209237,0.08211219,0.0006458201,0.000069981834,0.000005187718,0.00006286395,0.000020543146,0.000050803035],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979117,0.00064103585,0.0007098563,0.000079709265,0.00046308793,0.00019462703],"domain_scores_gemma":[0.99657184,0.00031756726,0.0011694019,0.00085412804,0.0008747462,0.00021233046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007599712,0.0001722982,0.00060753425,0.0004374567,0.000104575796,0.000029046869,0.00062241533,0.000028556335,0.000067883695],"category_scores_gemma":[0.00040738875,0.00012190951,0.00036014104,0.0010592293,0.00027927346,0.0000636711,0.00015352368,0.0007378067,0.0000035153932],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035192008,0.00083961524,0.5480385,0.000014578584,0.0013636632,0.0000032488153,0.0010504569,0.0001291414,0.0000695168,0.0000052670907,0.000538995,0.44759512],"study_design_scores_gemma":[0.00040298147,0.0016236091,0.98178613,0.00016127664,0.0015484185,0.000016666769,0.0027058849,0.0014613076,0.00035760543,0.000007268041,0.009832518,0.00009631859],"about_ca_topic_score_codex":0.00085335283,"about_ca_topic_score_gemma":0.0018063963,"teacher_disagreement_score":0.4474988,"about_ca_system_score_codex":0.000046099256,"about_ca_system_score_gemma":0.00018450672,"threshold_uncertainty_score":0.49713254},"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":"protocol","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06222188,0.0009171236,0.009149588,0.0055990354,0.00019543193,0.9146519,0.0037301865,0.00045449098,0.0030803632],"genre_scores_gemma":[0.49794072,0.000007506468,0.011220213,0.00035318473,0.0010916215,0.4881052,0.00021676773,0.000063806234,0.0010009669],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989588,0.00003112056,0.00026391874,0.00016273385,0.000278529,0.00030486545],"domain_scores_gemma":[0.99834913,0.00052377506,0.00019926768,0.0005315658,0.00030349928,0.0000927322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005582837,0.00012112627,0.00035592422,0.000028880437,0.00007820863,0.000010539616,0.00015386684,0.00005748161,0.000052833337],"category_scores_gemma":[0.001070024,0.000082532235,0.000106742315,0.00011609355,0.000096841686,0.000076280616,0.00004374878,0.00008424269,0.000006399163],"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":[0.008749452,0.006310937,0.8899586,0.022435723,0.0023601868,0.0000028233608,0.0014866224,0.000004567681,0.014027466,0.0034349675,0.04115632,0.010072341],"study_design_scores_gemma":[0.020392993,0.0018897612,0.39539582,0.003057636,0.0009473149,0.000012466504,0.00037444348,0.002582094,0.08879711,0.0009181119,0.4847538,0.0008784575],"about_ca_topic_score_codex":0.000005503163,"about_ca_topic_score_gemma":0.000012554011,"teacher_disagreement_score":0.49456277,"about_ca_system_score_codex":0.000028002305,"about_ca_system_score_gemma":0.00008805481,"threshold_uncertainty_score":0.33655667},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8970155,0.00001914313,0.10111582,0.00053624745,0.00008681074,0.0011917683,0.000011535709,0.000010780399,0.000012399319],"genre_scores_gemma":[0.99470776,0.000005043634,0.00376099,0.0012571316,0.000028583248,0.00021973312,0.000010298678,0.000010195132,2.3657357e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99601567,0.0026075,0.0006797545,0.0003378694,0.00017170915,0.00018750345],"domain_scores_gemma":[0.99583435,0.0031245477,0.0002829282,0.00061521697,0.000095423224,0.000047538077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034888047,0.000109535766,0.00046854254,0.00021297457,0.000021570597,0.0000014581456,0.00022224986,0.000057759058,0.0000021921737],"category_scores_gemma":[0.006417487,0.00008716823,0.000039750033,0.0005953377,0.000041535724,0.000036983885,0.00006568847,0.00018472948,0.0000010789129],"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":[0.00021708508,0.00014753523,0.94344264,0.000026937918,0.000010351851,0.0000011741591,0.000113617614,0.000925113,0.018736413,0.000021128528,0.000013208792,0.03634481],"study_design_scores_gemma":[0.0010123629,0.00032551496,0.9791297,0.000015239238,0.000026023048,0.0000026234395,0.000051925872,0.018519504,0.00055475923,0.00023674036,0.00007090499,0.000054735836],"about_ca_topic_score_codex":0.5086454,"about_ca_topic_score_gemma":0.91991705,"teacher_disagreement_score":0.41127163,"about_ca_system_score_codex":0.000060299888,"about_ca_system_score_gemma":0.000052583695,"threshold_uncertainty_score":0.7682795},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63379997,0.0069818352,0.21252596,0.13362725,0.0013908963,0.0055677323,0.002653998,0.00030220236,0.0031501427],"genre_scores_gemma":[0.8280835,0.0007465206,0.12249217,0.046792664,0.0008831908,0.000039996587,0.00066054636,0.00007667295,0.0002247278],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99636674,0.00016423938,0.0023149895,0.00006379611,0.00052447134,0.00056574226],"domain_scores_gemma":[0.994394,0.00027621456,0.0016619298,0.00059468707,0.0011290347,0.0019441004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030384045,0.0001778387,0.0007124758,0.00032947,0.00016397612,0.000107514556,0.00036290486,0.00006439289,0.00016040013],"category_scores_gemma":[0.0023641405,0.00014141145,0.0002164955,0.00024242619,0.000056165216,0.0013186317,0.000066531604,0.0004841768,0.000021738233],"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":[0.0005664437,0.003355897,0.083454646,0.009897009,0.00064419,0.00004295142,0.0040176013,0.00021652208,0.000051874293,0.0012199924,0.39577264,0.50076026],"study_design_scores_gemma":[0.00651664,0.0017408204,0.11104394,0.0010942185,0.00007049761,0.00011564042,0.0031120756,0.033566758,0.000004176697,0.00022087058,0.84223086,0.00028347864],"about_ca_topic_score_codex":0.000019251891,"about_ca_topic_score_gemma":0.000013604988,"teacher_disagreement_score":0.5004768,"about_ca_system_score_codex":0.0003261967,"about_ca_system_score_gemma":0.0035412915,"threshold_uncertainty_score":0.6282099},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016174169,0.00043463774,0.946495,0.026972255,0.00038651313,0.0018016888,0.000088318506,0.000088261324,0.022115914],"genre_scores_gemma":[0.020949902,0.0001655478,0.9706609,0.007546079,0.00005757076,0.00009818395,0.00046986906,0.0000121164085,0.000039798393],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977631,0.0002547874,0.0011812574,0.00012487052,0.0003922393,0.00028374777],"domain_scores_gemma":[0.99757975,0.00074410863,0.0005011831,0.00032358506,0.00060331787,0.00024808053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00819534,0.0001369668,0.0005522093,0.00045671768,0.000039286944,0.0000028174165,0.00010598014,0.0000660268,0.0003195633],"category_scores_gemma":[0.008330431,0.00010546049,0.00007177869,0.000374607,0.00014101827,0.0010830892,0.000021642063,0.00016641452,0.000011813441],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002140016,0.0005267068,0.0013258961,0.0017813755,0.00014937992,0.0000041560643,0.026392581,0.000007920443,0.0003834981,0.01093876,0.015838606,0.9405111],"study_design_scores_gemma":[0.020629395,0.003489616,0.054471023,0.0010337482,0.00012687901,0.00015251571,0.014289689,0.0062316298,0.004190447,0.0022246419,0.89276433,0.00039607074],"about_ca_topic_score_codex":0.00019748954,"about_ca_topic_score_gemma":0.000007563883,"teacher_disagreement_score":0.94011503,"about_ca_system_score_codex":0.00011228417,"about_ca_system_score_gemma":0.00034167955,"threshold_uncertainty_score":0.99729055},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.958362,0.0010561157,0.0005743793,0.0020341957,0.0006614308,0.00070439366,0.000062300845,0.00041415732,0.036131065],"genre_scores_gemma":[0.99554974,0.0000051151965,0.0014266379,0.000610942,0.0003433889,0.0000064589067,0.00003197568,0.000008065919,0.0020176803],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985779,0.000014858131,0.00012828513,0.00035248857,0.0005395711,0.0003869298],"domain_scores_gemma":[0.9990075,0.000016241027,0.000038300306,0.0005043915,0.00010650681,0.0003270154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034225662,0.000082972074,0.00011410527,0.000051254923,0.00015802603,0.000039329978,0.000235529,0.000012282744,0.00013151577],"category_scores_gemma":[0.00016795835,0.00006644404,0.00004053031,0.000774494,0.0005995398,0.00017355886,0.00008272607,0.00005887795,0.00028180043],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022727675,0.0003146476,0.9215436,0.00007375375,0.000006888255,0.00034156692,0.000019785013,0.00050803524,0.0100015085,0.0036970575,0.054493587,0.008772317],"study_design_scores_gemma":[0.00031624176,0.000026032398,0.9588594,0.000043064294,0.000012689774,0.000005412847,0.0000031345508,0.0011707206,0.00030551438,0.0010060626,0.03815541,0.000096312804],"about_ca_topic_score_codex":0.00001964314,"about_ca_topic_score_gemma":0.000010332327,"teacher_disagreement_score":0.037315827,"about_ca_system_score_codex":0.000057630994,"about_ca_system_score_gemma":0.0005105861,"threshold_uncertainty_score":0.36220685},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9700473,0.0006862373,0.00009087336,0.026403248,0.0006693085,0.00065754825,0.0014019359,0.000013378677,0.000030195524],"genre_scores_gemma":[0.99647486,0.00028081576,0.00042327304,0.00257691,0.0001373612,0.0000051779957,0.00008500583,0.000011974686,0.0000046173805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962036,0.0009902583,0.0012041929,0.00029284557,0.00093128945,0.0003778378],"domain_scores_gemma":[0.9949354,0.00026505016,0.001359119,0.00049417524,0.0012170998,0.001729144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008603865,0.00016600791,0.00048425226,0.0004793654,0.00035978347,0.00005442696,0.00047965237,0.00006274955,0.00017859238],"category_scores_gemma":[0.002187344,0.00011456316,0.00014811064,0.00033140206,0.0003664029,0.00020318861,0.00012900423,0.0009482388,0.0000011681116],"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":[0.00026115178,0.001381516,0.9871186,0.00004349745,0.00017710049,0.000052162144,0.00068866793,0.0000023099858,0.0000017205872,0.0003087291,0.00013073036,0.009833824],"study_design_scores_gemma":[0.0017252406,0.00056769524,0.993434,0.00004579304,0.000025634621,0.00011299257,0.0009375826,0.0003732826,2.3887281e-7,0.0006198755,0.0020796987,0.00007797468],"about_ca_topic_score_codex":0.02751783,"about_ca_topic_score_gemma":0.40933457,"teacher_disagreement_score":0.38181674,"about_ca_system_score_codex":0.00020648578,"about_ca_system_score_gemma":0.0048090983,"threshold_uncertainty_score":0.978958},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9821636,0.0006108402,0.000009338628,0.00050778786,0.0006724083,0.000121829515,0.00011331154,0.0000037169307,0.015797153],"genre_scores_gemma":[0.9975214,0.0000029296116,0.0015200537,0.000019697532,0.00010347546,0.000001801878,0.000036241177,0.000020227579,0.0007741886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986559,0.00015141694,0.00051683665,0.00018825414,0.00016240888,0.00032515335],"domain_scores_gemma":[0.99798477,0.0001492968,0.00034019482,0.00021564397,0.0004384569,0.00087162957],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006495962,0.00014396891,0.00046977063,0.00038917467,0.000038485337,0.000012681996,0.00019749456,0.00023478626,0.0045366306],"category_scores_gemma":[0.0009990593,0.00013366662,0.00010242397,0.00019051116,0.0004960288,0.00012391225,0.000025692543,0.0006419578,0.00001716471],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011185955,0.00006406645,0.9335278,0.00007120369,0.0002753333,0.05473001,0.00029862358,0.000002616136,0.008633187,0.0013104737,0.00039988768,0.00057489314],"study_design_scores_gemma":[0.00063628086,0.0003630342,0.62175053,0.000041800406,0.000045643086,0.37545004,0.00002163168,0.000040801548,0.00011743843,0.0013876528,0.000074015596,0.00007113708],"about_ca_topic_score_codex":0.0029227498,"about_ca_topic_score_gemma":0.00011916487,"teacher_disagreement_score":0.32072005,"about_ca_system_score_codex":0.000071029535,"about_ca_system_score_gemma":0.001725787,"threshold_uncertainty_score":0.99637336},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953439,0.0010191692,0.0003370899,0.0021941252,0.00026203162,0.0006524074,0.000070800124,0.000041007774,0.000079467456],"genre_scores_gemma":[0.9986181,0.00033705268,0.0004159673,0.00017331795,0.00020105208,0.000065762055,0.00013850736,0.000025086723,0.000025144058],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99740505,0.0005619322,0.0009595603,0.00043107703,0.00020937133,0.00043301718],"domain_scores_gemma":[0.99800485,0.0006766833,0.00046851006,0.0004325161,0.00014669377,0.00027072197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011855981,0.00026939507,0.0011108047,0.0001268592,0.000114432354,0.000010539696,0.00013967372,0.00014944495,0.00010028058],"category_scores_gemma":[0.0014509362,0.00018302903,0.00019891936,0.00016385075,0.00042585007,0.0002438363,0.00015904878,0.0002721297,0.000007845167],"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":[0.00029107794,0.00006808046,0.9649097,0.00019346815,0.0001376088,0.00002057921,0.0002984079,0.00001785671,0.021714361,0.00010788659,0.00011788031,0.012123124],"study_design_scores_gemma":[0.0010380034,0.00074083795,0.9951386,0.00013175662,0.00009054265,0.000035727728,0.00019079473,0.0014053334,0.000475355,0.00042244678,0.00016802152,0.00016261895],"about_ca_topic_score_codex":0.010872729,"about_ca_topic_score_gemma":0.00062584237,"teacher_disagreement_score":0.030228892,"about_ca_system_score_codex":0.000015981186,"about_ca_system_score_gemma":0.00010002733,"threshold_uncertainty_score":0.99571395},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014293721,0.00089069246,0.7188408,0.2622884,0.0008362353,0.0012928452,0.00018699092,0.0000750957,0.0012952553],"genre_scores_gemma":[0.7174303,0.0032221987,0.18890317,0.076752126,0.011827574,0.0003693108,0.0009731794,0.000083781284,0.00043839833],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99696904,0.00004229621,0.0009842431,0.00005456209,0.0017239387,0.00022592327],"domain_scores_gemma":[0.9977364,0.00027138856,0.0006103315,0.00019518004,0.0007513529,0.00043536013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151339,0.00009442571,0.0002053005,0.00017119994,0.00011827518,0.00012070624,0.0007930307,0.00005641865,0.00004478035],"category_scores_gemma":[0.0008617183,0.00005855032,0.00012525936,0.000105907005,0.000108940214,0.00035115084,0.0000722041,0.00038871818,0.000009996967],"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":[0.0000971056,0.00022019465,0.0003297011,0.000052510302,0.00022070877,0.0000052102055,0.0015047957,0.000056929992,0.0000028719035,0.003139029,0.027165921,0.96720505],"study_design_scores_gemma":[0.0015010841,0.00015054276,0.0004784012,0.000105148996,0.000012162272,0.0006839534,0.00082730193,0.014489235,0.000009135077,0.0002949167,0.9813896,0.00005852176],"about_ca_topic_score_codex":0.000004860375,"about_ca_topic_score_gemma":0.000025264655,"teacher_disagreement_score":0.9671465,"about_ca_system_score_codex":0.00018993452,"about_ca_system_score_gemma":0.0015927808,"threshold_uncertainty_score":0.28255248},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889228,0.0002888666,0.0064187194,0.0015314417,0.00002170534,0.0021975352,0.00059039256,0.000018594934,0.00000995491],"genre_scores_gemma":[0.99485934,0.0010830708,0.0016896909,0.00044272578,0.00003624673,0.00046229415,0.0013363011,0.000027299471,0.00006301656],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975359,0.00051037915,0.00032203752,0.00045842605,0.00049432064,0.0006789616],"domain_scores_gemma":[0.99785256,0.0015624933,0.000061088715,0.00033847,0.000008256484,0.00017710945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036687148,0.00014412876,0.0002120479,0.0003240011,0.00032074095,0.00008062671,0.00020779207,0.000061282204,0.000023219887],"category_scores_gemma":[0.00059623766,0.00012398924,0.000041136387,0.0002303932,0.00029462573,0.00022392819,0.0001617421,0.00035977122,0.0000028757463],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012699893,0.0004245683,0.9837235,0.00027390016,0.000019689589,0.00000463626,0.0012922036,0.00006825777,0.00021271019,0.000090575995,0.00008334548,0.013679593],"study_design_scores_gemma":[0.0016835709,0.00032274632,0.9721916,0.00016584712,0.0000067858946,0.00000993533,0.0017615763,0.014167085,0.000059695252,0.00022734271,0.009289699,0.00011413735],"about_ca_topic_score_codex":0.00016298078,"about_ca_topic_score_gemma":0.00033698927,"teacher_disagreement_score":0.014098828,"about_ca_system_score_codex":0.000788893,"about_ca_system_score_gemma":0.000077303186,"threshold_uncertainty_score":0.50561345},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78233415,0.077764615,0.09271161,0.037761405,0.0024220524,0.0018333613,0.0007537789,0.0001325395,0.004286488],"genre_scores_gemma":[0.99130404,0.0038352946,0.004346848,0.0003403075,0.00008502355,0.000012169797,0.00006052163,0.000006710932,0.000009070097],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988029,0.000102843645,0.00058341795,0.000069581416,0.00035207154,0.000089199915],"domain_scores_gemma":[0.99819374,0.00022348919,0.0004875306,0.0002063188,0.00053120806,0.00035772927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006800482,0.000058066737,0.00022680097,0.00014885904,0.00008616038,0.0000070222454,0.00023522688,0.00003704213,0.000024359726],"category_scores_gemma":[0.00097464083,0.000049189137,0.000061286075,0.000060896004,0.00015092778,0.00012041124,0.000076471784,0.000127245,4.133215e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017026792,0.00095856417,0.058116816,0.0013462204,0.00070232165,0.0000146038565,0.0024039054,0.0000010303122,0.000664901,0.011100487,0.001287963,0.92170054],"study_design_scores_gemma":[0.02937951,0.0063352464,0.7098199,0.0142019335,0.0010537788,0.0034688928,0.019259693,0.024376255,0.0027827818,0.007064428,0.18146776,0.0007898247],"about_ca_topic_score_codex":0.000045086173,"about_ca_topic_score_gemma":0.000022028873,"teacher_disagreement_score":0.92091066,"about_ca_system_score_codex":0.000058048554,"about_ca_system_score_gemma":0.00036520738,"threshold_uncertainty_score":0.20058748},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965786,0.00022239315,0.00010584802,0.0018291144,0.0007145871,0.0003898227,0.000054143842,0.00001773423,0.00008778649],"genre_scores_gemma":[0.9959149,0.000776813,0.0022509538,0.0006472565,0.00026055702,0.000013136727,0.000038368507,0.000014909648,0.00008314787],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971165,0.0001598009,0.0011693899,0.00005795593,0.0011649769,0.00033138116],"domain_scores_gemma":[0.9965685,0.00021210666,0.0023316157,0.00019364862,0.00037410116,0.00032001155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038736197,0.00013223058,0.0005676658,0.00010136584,0.00006190413,0.00001962399,0.00015109651,0.00009788415,0.00003151074],"category_scores_gemma":[0.0042872457,0.00008797911,0.0001377487,0.00043456836,0.000037470127,0.0003012162,0.000047324796,0.00038070072,0.0000028368852],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010021628,0.00021501414,0.9761634,0.000113101654,0.00044707904,0.000003384787,0.00054156734,0.0000062347476,0.00012298615,0.000006244216,0.021102084,0.0011786715],"study_design_scores_gemma":[0.0024613428,0.00018295605,0.9790915,0.00013600301,0.0002099881,0.00016136676,0.00036954187,0.0018657208,0.000086215994,0.000035039946,0.015249079,0.00015120968],"about_ca_topic_score_codex":0.000021289787,"about_ca_topic_score_gemma":0.00004318295,"teacher_disagreement_score":0.0058530048,"about_ca_system_score_codex":0.00064343045,"about_ca_system_score_gemma":0.00040981336,"threshold_uncertainty_score":0.5132543},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877877,0.00035043733,0.004035965,0.00033169647,0.00016063036,0.00045346143,0.00079492264,0.00002462332,0.0060605654],"genre_scores_gemma":[0.97275585,0.000017854914,0.02660098,0.00020962457,0.0002844399,0.0000052479636,0.00008606815,0.000010543243,0.000029376588],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885315,0.000036551686,0.0004417777,0.000112021335,0.0003464861,0.00021004018],"domain_scores_gemma":[0.9987895,0.00014101417,0.0002926337,0.00018747842,0.00020968496,0.0003797223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081250834,0.00010993728,0.0003701658,0.000042359374,0.000042409065,0.000015660591,0.0000637505,0.000045759276,0.00019151063],"category_scores_gemma":[0.0002253562,0.00008502423,0.00005239825,0.00006729654,0.00022097792,0.00012654775,0.00004081387,0.00021802359,0.000006602753],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027818282,0.0012551062,0.88027436,0.0006220648,0.00036756264,0.000035820114,0.00046684162,0.000015432146,0.0025150236,0.057971288,0.032075107,0.021619592],"study_design_scores_gemma":[0.0020564643,0.00019699062,0.95207024,0.000039554674,0.00016836746,0.00021987026,0.00009402529,0.000029055878,0.00038903594,0.02693439,0.017652072,0.00014994915],"about_ca_topic_score_codex":0.0000018038868,"about_ca_topic_score_gemma":0.0000023462394,"teacher_disagreement_score":0.07179589,"about_ca_system_score_codex":0.00008480235,"about_ca_system_score_gemma":0.00012055934,"threshold_uncertainty_score":0.34671873},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8014637,0.00039281297,0.19719762,0.00005791324,0.00034037704,0.0003357305,0.000050859602,0.00003205139,0.00012894042],"genre_scores_gemma":[0.9943982,0.000045554905,0.0035928518,0.00009108009,0.0015380953,0.0000059581075,0.00029955004,0.000021515334,0.0000071891286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974014,0.0004139962,0.00094758766,0.00018587815,0.0008470425,0.0002041015],"domain_scores_gemma":[0.99810386,0.00010712056,0.00073249865,0.00018132952,0.0005645643,0.00031061622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014753684,0.00015297961,0.0005308323,0.00049916265,0.00010645564,0.00010895737,0.00011657991,0.0001178631,0.000054514123],"category_scores_gemma":[0.00029372575,0.00012670583,0.00023334983,0.00040607882,0.00008656572,0.0006558654,0.000018293362,0.00029887687,0.000005071677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012075593,0.0029601052,0.87349015,0.0011486139,0.0014195362,0.00032802924,0.000762812,0.06749787,0.022601157,0.0018406189,0.000868656,0.015006853],"study_design_scores_gemma":[0.021168226,0.00042310453,0.75293756,0.00069156906,0.0005704207,0.0032526397,0.00045507084,0.21795584,0.000091459755,0.00025218396,0.0018031833,0.00039874157],"about_ca_topic_score_codex":0.000112726324,"about_ca_topic_score_gemma":0.000010605781,"teacher_disagreement_score":0.19360475,"about_ca_system_score_codex":0.000106060135,"about_ca_system_score_gemma":0.00014371517,"threshold_uncertainty_score":0.5166913},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6043956,0.000317224,0.38437012,0.005897517,0.00039870647,0.00076836074,0.00044741214,0.000045479926,0.0033595306],"genre_scores_gemma":[0.95147896,0.00012933173,0.046628933,0.0016256084,0.000025831736,0.000009735646,0.000033289376,0.00001530138,0.000052996897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963568,0.002069342,0.00069730036,0.00023645117,0.00030050767,0.00033962177],"domain_scores_gemma":[0.99666095,0.0015670406,0.001056668,0.00036420036,0.00025206176,0.00009910013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031192377,0.00013007331,0.00071389286,0.00014227285,0.000028031918,0.0000025416382,0.00014897269,0.00007241318,0.00007908478],"category_scores_gemma":[0.005341634,0.00010613286,0.00025805287,0.00054325594,0.00031057184,0.000039569717,0.0000404728,0.0001927202,0.00000275708],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014148478,0.00015408642,0.9773194,0.000024633782,0.00018558088,4.4641212e-7,0.00009353982,0.0054435604,0.000083261744,0.0004008356,0.0014856673,0.014667508],"study_design_scores_gemma":[0.0008386724,0.00021171133,0.98622537,0.000041756324,0.000086151136,8.73128e-7,0.00014499688,0.007168857,0.0006489984,0.0020781488,0.002426718,0.00012772711],"about_ca_topic_score_codex":0.004799094,"about_ca_topic_score_gemma":0.0002673814,"teacher_disagreement_score":0.34708333,"about_ca_system_score_codex":0.000044676177,"about_ca_system_score_gemma":0.00010861223,"threshold_uncertainty_score":0.72548264},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9897516,0.00057824,0.0036991907,0.0005224774,0.0014781109,0.0008670804,0.0014001293,0.0006993838,0.0010037884],"genre_scores_gemma":[0.9779866,0.00015426431,0.0010388459,0.016679972,0.0033409453,0.00002397855,0.0005991931,0.00009750825,0.000078719044],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952092,0.00090298976,0.0014727133,0.0012158911,0.0005346366,0.00066456996],"domain_scores_gemma":[0.9913388,0.0030309784,0.00025113358,0.003222229,0.0003895896,0.0017672639],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022439081,0.00034444616,0.0011044829,0.00018779187,0.00018677197,0.00008166218,0.00068210973,0.00024329642,0.00015676388],"category_scores_gemma":[0.023496358,0.00032346215,0.00036524024,0.0005329411,0.0003798782,0.0003185022,0.0010002999,0.00060663716,0.0009862868],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025802283,0.0010081569,0.94303787,0.00013257917,0.00018456033,0.00003906895,0.000009682789,0.000272007,0.000028936645,0.00007317911,0.02411098,0.03084498],"study_design_scores_gemma":[0.0022949674,0.00060349586,0.9108258,0.00029911648,0.00052901584,0.000011912534,0.000029889587,0.012862318,0.000008798627,0.00028896518,0.07179599,0.0004497221],"about_ca_topic_score_codex":0.00015123998,"about_ca_topic_score_gemma":0.000043071832,"teacher_disagreement_score":0.047685016,"about_ca_system_score_codex":0.00012052456,"about_ca_system_score_gemma":0.00042709062,"threshold_uncertainty_score":0.99992174},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98866326,0.0051517696,0.00078142295,0.004706738,0.00020638414,0.00028916687,0.00007491838,0.000027255166,0.00009906951],"genre_scores_gemma":[0.9595914,0.0016849525,0.033489868,0.0048946883,0.00016382361,0.000019858251,0.00002010535,0.000024965684,0.000110374414],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99487436,0.0020958076,0.002034779,0.00031337,0.00012847781,0.0005532197],"domain_scores_gemma":[0.9605625,0.03687342,0.0016248762,0.00036521733,0.00034615042,0.0002278288],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0058004805,0.00024695825,0.002962259,0.00045295476,0.000036324982,0.0000021564013,0.00018477088,0.000057236462,0.00009248915],"category_scores_gemma":[0.056392025,0.00018088179,0.00042133144,0.000619653,0.0011408539,0.00013792001,0.000020379412,0.00034962504,0.0000035721005],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003254264,0.00015805838,0.94249016,0.00010973938,0.000299942,0.00007610259,0.00009274561,0.003850618,0.0002728197,0.00010022931,0.0070026126,0.042292684],"study_design_scores_gemma":[0.0030965675,0.0035161818,0.9487843,0.0010191842,0.00027116665,0.000111386136,0.00031751068,0.0014139336,0.00029087078,0.0018524291,0.03901039,0.00031608227],"about_ca_topic_score_codex":0.00018650475,"about_ca_topic_score_gemma":0.00032938403,"teacher_disagreement_score":0.050591547,"about_ca_system_score_codex":0.00016182664,"about_ca_system_score_gemma":0.00028045644,"threshold_uncertainty_score":0.9515564},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002519011,0.85559136,0.13081707,0.000082995786,0.00036341598,0.0015828778,0.0114297345,0.000012900302,0.000117153235],"genre_scores_gemma":[0.00016157264,0.9638061,0.029282678,0.000026440555,0.00025941388,0.00010642853,0.0061825803,0.000047650065,0.00012715427],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976101,0.00011953893,0.0013033235,0.0003043784,0.0004585958,0.00020407584],"domain_scores_gemma":[0.99630046,0.0017569888,0.0008714436,0.0006078178,0.00032205088,0.00014123837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058314233,0.00029244207,0.001985451,0.00024713087,0.000030280928,0.0000028140107,0.00019586053,0.0000966375,0.00018983059],"category_scores_gemma":[0.0014871926,0.00018395594,0.0001501465,0.0003369954,0.0006813498,0.000022624494,0.00003584917,0.00026161852,0.0000033047252],"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":[0.0003530454,0.00015805181,0.0009806433,0.1011754,0.00041946396,0.00004763827,0.00011542641,0.00020175998,1.1277476e-7,0.011084989,0.017899433,0.867564],"study_design_scores_gemma":[0.0024897954,0.00040207515,0.00012026421,0.04631603,0.005077187,0.000017841934,0.00011876797,0.028825555,5.267665e-7,0.005943966,0.9103649,0.00032308834],"about_ca_topic_score_codex":0.00008645314,"about_ca_topic_score_gemma":0.00001699813,"teacher_disagreement_score":0.8924655,"about_ca_system_score_codex":0.00007199892,"about_ca_system_score_gemma":0.00033350248,"threshold_uncertainty_score":0.75015056},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21282437,0.0005879516,0.78483564,0.00037871,0.00007611549,0.00040410494,0.00017124135,0.000030314222,0.0006915315],"genre_scores_gemma":[0.27155817,0.00030472525,0.7265904,0.00031793432,0.00002998402,0.000015847323,0.0011696122,0.000005333343,0.000008010346],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981534,0.00021144058,0.0011150244,0.00014941224,0.00022314291,0.00014759907],"domain_scores_gemma":[0.9968213,0.0019019664,0.0004907447,0.00052034855,0.00017755669,0.00008812218],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006012824,0.0001087486,0.0010119168,0.00046093823,0.000032847587,0.0000017975032,0.00019353417,0.00006891627,0.000048436617],"category_scores_gemma":[0.012842812,0.00008069367,0.000060264796,0.0006823636,0.00025403115,0.0003841617,0.00011784346,0.00011473765,2.8416719e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012267017,0.00018219903,0.4905043,0.001775733,0.0018208948,0.00001125458,0.010953838,0.0026423528,0.0047308486,0.0008878634,0.0019639377,0.4833001],"study_design_scores_gemma":[0.0033536586,0.00037757686,0.51162887,0.00038042772,0.00060750515,0.000014687484,0.0015296037,0.47836664,0.0004698773,0.00020091813,0.0029456557,0.00012459015],"about_ca_topic_score_codex":0.00011780679,"about_ca_topic_score_gemma":0.000004625395,"teacher_disagreement_score":0.48317552,"about_ca_system_score_codex":0.000022370512,"about_ca_system_score_gemma":0.000042592925,"threshold_uncertainty_score":0.99547243},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98650163,0.0008847608,0.00012853002,0.00077869825,0.006358476,0.00010250078,0.0009310092,0.00012657042,0.004187818],"genre_scores_gemma":[0.99681556,0.000071787406,0.00017618483,0.00091194577,0.0018744889,0.0000042718602,0.00005611047,0.000017973036,0.000071693285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984215,0.00005124482,0.0004361699,0.00015965491,0.00076518726,0.00016624457],"domain_scores_gemma":[0.99735045,0.00008879542,0.00036156373,0.00022394462,0.0015575566,0.00041765583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001606054,0.00015649061,0.00028146838,0.00017621552,0.000039601236,0.000067351044,0.0002965032,0.0000616911,0.00072976353],"category_scores_gemma":[0.0009686786,0.00013617639,0.0002694398,0.00014904779,0.00013058491,0.00030183585,0.00006659879,0.00029214198,0.00012339828],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042920487,0.0007786821,0.9536374,0.000027006334,0.0005727797,0.000980352,0.000015148789,0.000012054193,0.003463174,0.00062735507,0.03272904,0.0067277905],"study_design_scores_gemma":[0.0025023369,0.0001765751,0.888691,0.00011688942,0.00017963834,0.00076105597,0.000019194986,0.000019323039,0.0003682892,0.001185122,0.105804056,0.00017653941],"about_ca_topic_score_codex":0.000021045182,"about_ca_topic_score_gemma":0.000047519618,"teacher_disagreement_score":0.07307501,"about_ca_system_score_codex":0.00021672276,"about_ca_system_score_gemma":0.0006322953,"threshold_uncertainty_score":0.7990399},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99140406,0.0004807037,0.0015360722,0.0047606714,0.00045544503,0.00089600467,0.00033342416,0.000020236057,0.00011337705],"genre_scores_gemma":[0.9945316,0.000023069655,0.0030539818,0.001472146,0.00017634583,0.000051803232,0.0004644135,0.000021454702,0.000205214],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969545,0.0005261125,0.0013241395,0.00049352704,0.00028094134,0.00042077992],"domain_scores_gemma":[0.99759364,0.00062848534,0.0006238898,0.00063832913,0.00021855852,0.00029709417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065688195,0.00027119706,0.0011474531,0.00012447042,0.000051157695,0.0000059188833,0.00029750977,0.00016262222,0.00056440086],"category_scores_gemma":[0.0023936797,0.00020258185,0.00009954197,0.00028985238,0.00026203375,0.00013722274,0.00029195572,0.00027053946,0.000013876184],"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":[0.0005095868,0.000046628764,0.98325926,0.00009276589,0.000036089994,0.000010297282,0.00010708684,0.0006041451,0.00017449001,0.000042959586,0.009285482,0.0058311825],"study_design_scores_gemma":[0.0010709126,0.00011533602,0.98640347,0.00026322578,0.000023644609,0.000003178818,0.00005686452,0.00852886,0.0001961254,0.0005548861,0.002593157,0.00019034088],"about_ca_topic_score_codex":0.99521214,"about_ca_topic_score_gemma":0.9975802,"teacher_disagreement_score":0.007924715,"about_ca_system_score_codex":0.00017007916,"about_ca_system_score_gemma":0.0009571368,"threshold_uncertainty_score":0.8261048},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8045062,0.045462366,0.0010286297,0.029915834,0.0031006553,0.0028147667,0.019479364,0.00090438046,0.09278785],"genre_scores_gemma":[0.9785708,0.00007940741,0.008103546,0.008224271,0.0001554541,0.0000014365755,0.0043949448,0.00001626158,0.00045386364],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999092,0.000037142665,0.00027324943,0.000100550285,0.00035876813,0.00013829177],"domain_scores_gemma":[0.9983961,0.00002440219,0.00014647117,0.0011007455,0.00014869343,0.00018359278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060254487,0.00007200805,0.00025303298,0.000049291604,0.000012166312,0.0000050854565,0.00016770992,0.00023147406,0.000023936402],"category_scores_gemma":[0.000623899,0.0000571594,0.00002199382,0.00014693712,0.00003325872,0.000093752744,0.00017071213,0.0006475653,0.000062312996],"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":[0.0001807529,0.00012232632,0.015391208,0.0005968607,0.00007116119,0.00003649924,0.0006979121,0.0000045408665,0.000022833292,0.00027826318,0.94074714,0.04185051],"study_design_scores_gemma":[0.0020152507,0.00014351422,0.04269618,0.0001878922,0.000029665855,0.00008518958,0.00060493586,0.0010827809,0.000059917056,0.00009656967,0.95288634,0.000111745234],"about_ca_topic_score_codex":0.000021456115,"about_ca_topic_score_gemma":0.000023468416,"teacher_disagreement_score":0.17406465,"about_ca_system_score_codex":0.00003554572,"about_ca_system_score_gemma":0.00041651787,"threshold_uncertainty_score":0.2813385},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.8599845e-7,0.852016,0.0028849344,0.11750491,0.00056044693,0.025482029,0.0009456821,0.0004128692,0.00019298939],"genre_scores_gemma":[0.000004403932,0.9615237,0.002242822,0.017622428,0.00037683407,0.0026109088,0.015442326,0.00009021109,0.00008634153],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9878329,0.0045751217,0.004150308,0.0009691382,0.001123245,0.0013492987],"domain_scores_gemma":[0.9870697,0.0005535742,0.008329778,0.0012191017,0.0011470118,0.0016808208],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.014838307,0.00077546586,0.0043198005,0.0008697159,0.0006571901,0.00024471976,0.00024492224,0.00036664758,0.000024423336],"category_scores_gemma":[0.0056691086,0.0006864893,0.00044047108,0.0014483444,0.0000775817,0.0027352886,0.00008089525,0.0011154026,0.0001405932],"study_design_candidate":"not_applicable","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":[0.000016034768,0.00060365663,0.000003552598,0.38579246,0.00009278989,0.0000021734718,0.00005319726,5.1248577e-8,1.3798345e-9,0.00042949393,0.022089634,0.59091693],"study_design_scores_gemma":[0.0008192805,0.0035705348,0.000055428736,0.14411129,0.00035450794,0.0013223544,0.00004191968,0.00003850191,1.3246501e-9,0.000009317253,0.8493393,0.00033755382],"about_ca_topic_score_codex":0.0002907896,"about_ca_topic_score_gemma":0.00002664031,"teacher_disagreement_score":0.82724965,"about_ca_system_score_codex":0.0015615975,"about_ca_system_score_gemma":0.009518566,"threshold_uncertainty_score":0.9995586},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9297809,0.0011483869,0.06351562,0.0050345208,0.00004061027,0.00034468752,0.00010811143,0.000010545431,0.000016635297],"genre_scores_gemma":[0.99230134,0.0001702952,0.0071148816,0.00027580542,0.00008545231,0.0000062980266,0.00002709348,0.0000046377186,0.000014175605],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99910545,0.000093487855,0.00031102463,0.00008866695,0.0003225568,0.00007878812],"domain_scores_gemma":[0.9991326,0.0002110393,0.00016760394,0.00014029184,0.00021363924,0.00013484768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006627689,0.000055776673,0.00016752182,0.00053448265,0.00004853441,0.000007962436,0.000073875286,0.000024544177,0.000036276837],"category_scores_gemma":[0.0009310619,0.000037895115,0.00004654918,0.0012197249,0.00007534102,0.00006622402,0.00004599785,0.00008832688,0.00001017388],"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":[0.005540458,0.0007926399,0.042096246,0.00018918142,0.00011989811,0.000011015823,0.0002624251,0.00014637213,0.09528313,0.000021012831,0.011337298,0.8442003],"study_design_scores_gemma":[0.0007479559,0.0005466578,0.9853041,0.00012215166,0.000072042305,0.00019411402,0.0000869177,0.0037027209,0.0060269414,0.0000122864985,0.0031266215,0.000057482535],"about_ca_topic_score_codex":0.000024288365,"about_ca_topic_score_gemma":0.000002992242,"teacher_disagreement_score":0.94320786,"about_ca_system_score_codex":0.00005606213,"about_ca_system_score_gemma":0.000064967004,"threshold_uncertainty_score":0.15453179},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955523,0.00002149223,0.00001632141,0.0014362024,0.000067490764,0.00030619896,0.00013570343,0.000029546049,0.0024347536],"genre_scores_gemma":[0.99824506,0.000003345496,0.001325177,0.00022568437,0.0001483857,0.000021173513,4.1856842e-7,0.0000028903196,0.000027846752],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987432,0.000002532281,0.0002037027,0.00020047207,0.00072437734,0.00012575038],"domain_scores_gemma":[0.9991938,0.000053885808,0.00025102656,0.00000973866,0.00040824033,0.00008331699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076365686,0.000070804366,0.00012208745,0.00007939523,0.00016589969,0.000012941528,0.000266177,0.00004732001,0.00000996371],"category_scores_gemma":[0.0023049684,0.00004962997,0.00009968061,0.0005668909,0.0005580046,0.00028338595,0.000060728573,0.00011158497,8.632567e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020102708,0.00018346531,0.43814525,0.000408764,0.00004467105,1.1220122e-8,0.000050589806,0.000054214106,0.51231825,0.039759047,0.0012166114,0.0076180957],"study_design_scores_gemma":[0.00041765007,0.000047907328,0.9006276,0.00005347711,0.000039653398,0.000010969951,0.00001835584,0.0019979265,0.04790151,0.047183152,0.0016272961,0.00007446697],"about_ca_topic_score_codex":0.000008850806,"about_ca_topic_score_gemma":0.0000010074466,"teacher_disagreement_score":0.46441674,"about_ca_system_score_codex":0.000038062266,"about_ca_system_score_gemma":0.00005227659,"threshold_uncertainty_score":0.27594292},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81492203,0.0002779599,0.1822284,0.0009978989,0.0012340687,0.00014847975,0.0001514364,0.000024672187,0.000015069768],"genre_scores_gemma":[0.9966223,0.00041350117,0.0011817625,0.00075096474,0.0007298561,0.0000018605483,0.00027958592,0.000015021869,0.000005094621],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977108,0.0001660788,0.0008224302,0.00014813556,0.0009220927,0.00023041792],"domain_scores_gemma":[0.99781525,0.000031973206,0.0007643155,0.0001547724,0.00096431945,0.00026938933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008840688,0.00012355637,0.0002798677,0.0003008277,0.00015196031,0.000021717471,0.00018250837,0.000065134984,0.000032338692],"category_scores_gemma":[0.0001350275,0.00011278438,0.00019200207,0.00017823649,0.000091396534,0.0003545789,0.000043177435,0.00032834607,0.0000034034606],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003887453,0.002238292,0.87116855,0.00018691494,0.0006724265,0.00071429496,0.0005426242,0.0031890196,0.00094230496,0.00011060749,0.0005395722,0.11580793],"study_design_scores_gemma":[0.003643438,0.0008942227,0.94615906,0.00026473973,0.000058373804,0.006866875,0.00008270764,0.033878647,0.000360498,0.00015424483,0.007446142,0.00019102251],"about_ca_topic_score_codex":0.00035237873,"about_ca_topic_score_gemma":0.00007494255,"teacher_disagreement_score":0.18170033,"about_ca_system_score_codex":0.00027245533,"about_ca_system_score_gemma":0.00043916432,"threshold_uncertainty_score":0.45992133},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46728462,0.000028578173,0.49801654,0.00002314232,0.0000670395,0.03444578,0.000010062976,0.000050320134,0.00007391981],"genre_scores_gemma":[0.828996,0.0000017312075,0.1617198,0.00038023954,0.000049578444,0.00880174,0.00003007425,0.000018875458,0.0000019343804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947403,0.0004007266,0.0030800719,0.00014758314,0.0013263802,0.0003049507],"domain_scores_gemma":[0.9947069,0.0009989057,0.0029719272,0.00059517956,0.00035338476,0.0003736511],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008606936,0.0002132314,0.0005760761,0.0002993478,0.00020346929,0.00016730944,0.00017341675,0.000064265114,0.00016283372],"category_scores_gemma":[0.010927819,0.00012221746,0.00020867732,0.0003412312,0.00007124067,0.0006635163,0.00022012457,0.00024409726,0.0000105482095],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007382871,0.0004791621,0.029295636,0.002763221,0.000054227996,0.0000041837006,0.00092611695,0.0025860074,0.0000033479287,0.000007032355,0.00053008075,0.9626127],"study_design_scores_gemma":[0.0030558896,0.0005837517,0.098488145,0.004509945,0.000033296546,0.000014407286,0.0021897666,0.8907138,0.000007720478,0.0001895217,0.00008395229,0.00012981879],"about_ca_topic_score_codex":0.00022161547,"about_ca_topic_score_gemma":0.000014644927,"teacher_disagreement_score":0.96248287,"about_ca_system_score_codex":0.00016788726,"about_ca_system_score_gemma":0.00087231415,"threshold_uncertainty_score":0.99740356},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98687845,0.0013744886,0.000044031938,0.00010380555,0.009961836,0.00077153597,0.00046865112,0.0002966968,0.000100511876],"genre_scores_gemma":[0.9937786,0.0000040978034,0.0013736199,0.0000062877,0.00051146565,0.000014311474,0.003723713,0.00003713125,0.0005508086],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9948617,0.0002122691,0.00079467223,0.002141818,0.0015077462,0.00048174374],"domain_scores_gemma":[0.9926359,0.00003730909,0.00040052886,0.0055237426,0.00039100504,0.0010115003],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028809712,0.00027647687,0.00046372323,0.00024978883,0.0002489598,0.00037331946,0.0003666093,0.000054047134,0.00006957457],"category_scores_gemma":[0.0013620342,0.0002552262,0.00010919078,0.0009525883,0.0003105835,0.0008134707,0.0005421935,0.00018603301,0.00004482419],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032843478,0.0021785938,0.975604,0.000027414932,0.00008888337,0.005433466,0.0005345336,0.00019387415,0.0009064791,2.631268e-7,0.006095136,0.008608953],"study_design_scores_gemma":[0.001100058,0.00011033252,0.9909515,0.00013385393,0.00036473482,0.000034914712,0.0006526127,0.003316047,0.00011717862,0.00016587888,0.0027245975,0.00032829226],"about_ca_topic_score_codex":0.00037113446,"about_ca_topic_score_gemma":0.000053449043,"teacher_disagreement_score":0.015347531,"about_ca_system_score_codex":0.00038966007,"about_ca_system_score_gemma":0.0007291377,"threshold_uncertainty_score":0.99999},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7751907,0.069463916,0.0064426735,0.025353959,0.0012835539,0.0023831958,0.10050499,0.0012351098,0.018141901],"genre_scores_gemma":[0.985813,0.0035878904,0.005029009,0.0004913386,0.00019173334,0.000008925834,0.004632716,0.000028517496,0.00021684114],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9969103,0.000045013185,0.00023774014,0.0013058946,0.0010361413,0.00046492164],"domain_scores_gemma":[0.9932521,0.00011419568,0.00012070002,0.005026563,0.0002629769,0.0012234664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023880336,0.00021335695,0.00031232668,0.00012678417,0.00014035183,0.00012418593,0.0019979987,0.00002402518,0.000017483306],"category_scores_gemma":[0.0018665441,0.00016473018,0.000008689292,0.00026508307,0.0013182465,0.0028066842,0.002764277,0.00012630646,0.00001750232],"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":[0.0043061776,0.0032975096,0.25161475,0.002801885,0.0004747644,0.003965703,0.004473478,0.00004233699,0.010009386,0.0139105655,0.04613005,0.6589734],"study_design_scores_gemma":[0.0059644324,0.00063913397,0.6634562,0.0011665807,0.00022450346,0.00046277742,0.0072525553,0.024772577,0.0003060361,0.00015756779,0.29399985,0.0015977732],"about_ca_topic_score_codex":0.000078221216,"about_ca_topic_score_gemma":0.0004962185,"teacher_disagreement_score":0.65737563,"about_ca_system_score_codex":0.00005098374,"about_ca_system_score_gemma":0.001901258,"threshold_uncertainty_score":0.6717502},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96273535,0.014284938,0.00010023294,0.016272726,0.0008354717,0.0005119005,0.0034385412,0.000024917224,0.0017959272],"genre_scores_gemma":[0.9956643,0.0020045387,0.00025023843,0.0011118216,0.00029818914,0.0000048762186,0.00045548638,0.000021584743,0.00018896678],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976788,0.00038923963,0.0007930323,0.00023140889,0.00025153568,0.00065597316],"domain_scores_gemma":[0.99637085,0.000046791138,0.0003583465,0.00069575483,0.00012883088,0.0023994248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028164815,0.00014597573,0.00040329184,0.0005274239,0.00012209364,0.000028699991,0.0005007889,0.000059342365,0.0008059446],"category_scores_gemma":[0.00078951893,0.0001031444,0.00004865302,0.0004892861,0.000104730134,0.00033249054,0.000046641344,0.0003760881,0.000006367758],"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":[0.000040705218,0.00007503741,0.74695474,0.000044438504,0.00008897131,0.00025313455,0.0009314685,1.662981e-7,0.000002270886,0.000069160174,0.12005037,0.13148952],"study_design_scores_gemma":[0.0011366915,0.00023931573,0.84341854,0.00005736331,0.000021669563,0.00025287314,0.0005818418,0.00006448446,4.2213568e-7,0.00005415479,0.15409859,0.000074045005],"about_ca_topic_score_codex":0.022282654,"about_ca_topic_score_gemma":0.45220017,"teacher_disagreement_score":0.42991754,"about_ca_system_score_codex":0.00040435127,"about_ca_system_score_gemma":0.005368274,"threshold_uncertainty_score":0.9842281},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97743404,0.004104469,0.009629125,0.004303331,0.0009341632,0.0019502325,0.00024017008,0.00049954414,0.00090493384],"genre_scores_gemma":[0.98243463,0.0003709029,0.012825441,0.0036379024,0.00010496499,0.000012834174,0.0005425244,0.000043595785,0.000027229426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996818,0.00046179252,0.0009792516,0.00044297436,0.00055624166,0.00074171304],"domain_scores_gemma":[0.9979169,0.000111927904,0.0005260073,0.00055597455,0.00017636929,0.00071284466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026194155,0.00018784388,0.0006317391,0.0012402234,0.00013539367,0.00001923519,0.00013066828,0.00008366564,0.00011321685],"category_scores_gemma":[0.0014116586,0.00019503452,0.00006993776,0.0034046946,0.000028538903,0.00023803046,0.000051881794,0.0002475453,0.000053002525],"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":[0.000044106684,0.00024468577,0.97156173,0.0002963951,0.000009861725,0.000007396219,0.00028618405,0.00002113511,0.0000012916324,0.00066304114,0.00041709276,0.026447097],"study_design_scores_gemma":[0.00091895123,0.00013051182,0.9899522,0.000064634565,0.000002382317,0.000013249961,0.00005607571,0.0043661753,9.637455e-7,0.00014824631,0.0042035463,0.00014307628],"about_ca_topic_score_codex":0.007896841,"about_ca_topic_score_gemma":0.0012171854,"teacher_disagreement_score":0.02630402,"about_ca_system_score_codex":0.0010611285,"about_ca_system_score_gemma":0.0007795441,"threshold_uncertainty_score":0.9987097},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23767662,0.0009247857,0.7451801,0.010272139,0.0016816375,0.0012113694,0.002837217,0.000042849533,0.00017330269],"genre_scores_gemma":[0.84501404,0.00010090969,0.1527524,0.001380151,0.00034528304,0.000013494283,0.00037270086,0.000018130302,0.000002924545],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99748725,0.00019396305,0.0011044628,0.00031695404,0.0005782946,0.00031905848],"domain_scores_gemma":[0.9975422,0.0005824698,0.0004701154,0.0003936789,0.00048266017,0.0005288831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052393307,0.00013388367,0.00040899657,0.0005227986,0.00005496355,0.000026103826,0.00055356254,0.000041081992,0.000029806675],"category_scores_gemma":[0.00499705,0.00012550787,0.00010630202,0.00025957933,0.00006935987,0.00022724763,0.00019501499,0.00030402045,0.0000025859806],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021650237,0.0009958695,0.8936049,0.0005254367,0.00023880233,0.00018033656,0.00062410353,0.0001522606,0.0000051831307,0.0076234397,0.00070349517,0.09318118],"study_design_scores_gemma":[0.0014472613,0.00015418585,0.97944987,0.00066982966,0.00004471563,0.000068427056,0.00022297188,0.0059449896,0.000001295392,0.0058040726,0.0060595814,0.00013278992],"about_ca_topic_score_codex":0.00012132519,"about_ca_topic_score_gemma":0.000054200485,"teacher_disagreement_score":0.6073374,"about_ca_system_score_codex":0.00013792401,"about_ca_system_score_gemma":0.0007104396,"threshold_uncertainty_score":0.59822965},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1608569,0.0010216985,0.019214591,0.80756015,0.0034959957,0.0064648963,0.0011921141,0.000069428075,0.00012421457],"genre_scores_gemma":[0.2619571,0.000050348084,0.010334451,0.7178608,0.008459567,0.0003151833,0.000062339466,0.00014296835,0.00081720104],"study_design_codex":"not_applicable","study_design_gemma":"case_report","domain_scores_codex":[0.96534026,0.02873459,0.0032677695,0.0010230208,0.0005682499,0.0010660985],"domain_scores_gemma":[0.94985455,0.04202525,0.004769618,0.001477164,0.0012714015,0.0006020098],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.017067898,0.00081232825,0.005442512,0.0007125373,0.00019825227,0.000020813937,0.00095463166,0.00031833153,0.0022951842],"category_scores_gemma":[0.036020894,0.00046317698,0.0008678545,0.0005733208,0.0011676304,0.00012181679,0.0002671923,0.0043298565,0.00022384984],"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":[0.0021111385,0.0005118895,0.044036064,0.000028122991,0.0055756723,0.26919416,0.000556692,0.00008793384,0.0000021820233,0.0000065192135,0.6630229,0.014866727],"study_design_scores_gemma":[0.015012643,0.06157075,0.071518816,0.00034487052,0.00795185,0.43590802,0.026691666,0.00029749182,0.0000012511605,0.000795275,0.3785483,0.0013590894],"about_ca_topic_score_codex":0.002786275,"about_ca_topic_score_gemma":0.0005749788,"teacher_disagreement_score":0.2844746,"about_ca_system_score_codex":0.00036200986,"about_ca_system_score_gemma":0.0005920051,"threshold_uncertainty_score":0.99978197},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57696366,0.0011507864,0.4198687,0.000457412,0.000029883298,0.0009290248,0.00023611292,0.00003584474,0.0003285848],"genre_scores_gemma":[0.98282576,0.00013433938,0.015466339,0.00060680177,0.00023826577,0.00016565072,0.00048340342,0.000010217278,0.00006924769],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989174,0.00006858928,0.00035549945,0.0003723838,0.000033239296,0.00025289136],"domain_scores_gemma":[0.9988723,0.0006199613,0.00012999079,0.00017365592,0.00006402866,0.00014008353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000823974,0.00013229986,0.00064103195,0.00022226527,0.00010882389,0.0000014701882,0.000053405536,0.00014072654,0.000043134987],"category_scores_gemma":[0.0010418621,0.000095014875,0.00006822798,0.00022201047,0.00038924732,0.000017588116,0.000040714145,0.00009725511,5.831431e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029489532,0.00005928875,0.97942597,0.00013484097,0.0002649425,0.0000016703422,0.00043060436,0.0000063069965,0.0038028175,0.00091990305,0.00005770309,0.014601049],"study_design_scores_gemma":[0.0067821913,0.00095409394,0.8703403,0.00022313862,0.0018045786,0.000028965096,0.0012905224,0.0761319,0.0001299497,0.004816243,0.0371634,0.00033470697],"about_ca_topic_score_codex":0.00019699498,"about_ca_topic_score_gemma":0.00024986966,"teacher_disagreement_score":0.4058621,"about_ca_system_score_codex":0.000018379287,"about_ca_system_score_gemma":0.000028898288,"threshold_uncertainty_score":0.3874594},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003326109,0.98830813,0.0005439579,0.0030199846,0.0017577491,0.0014025745,0.004910352,0.00002914099,0.000024795469],"genre_scores_gemma":[0.0000029980274,0.99135727,0.003639931,0.0020016385,0.0008386258,0.000039383638,0.0019821038,0.00007795774,0.000060076403],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947931,0.0003409492,0.0029120883,0.00033881157,0.0012597817,0.00035523783],"domain_scores_gemma":[0.98832244,0.00085232884,0.005342974,0.0006144086,0.0042272178,0.00064062653],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003166722,0.00042377916,0.0034397442,0.00082051294,0.000052563464,0.000023167795,0.0009164523,0.00019823213,0.00014367914],"category_scores_gemma":[0.005276954,0.0003332645,0.0020343673,0.0005331788,0.0001724653,0.00011534381,0.00011176738,0.00091139594,0.00001647673],"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":[0.00014923215,0.0002668589,0.0012291613,0.20052381,0.0007663142,0.000066225795,0.0000037618706,2.5716352e-7,7.2589586e-8,0.000075929594,0.026948307,0.76997006],"study_design_scores_gemma":[0.0005636177,0.0001704424,0.00067173893,0.20548452,0.00045077084,0.00025789536,7.492724e-7,0.000002067582,3.389525e-8,0.00007882411,0.7921487,0.0001706279],"about_ca_topic_score_codex":0.000009757372,"about_ca_topic_score_gemma":0.000005120522,"teacher_disagreement_score":0.7697995,"about_ca_system_score_codex":0.00016248239,"about_ca_system_score_gemma":0.0050429297,"threshold_uncertainty_score":0.99991196},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98720217,0.0000645084,0.011161898,0.0006847112,0.0004154015,0.0002533114,0.000026156567,0.000024225365,0.00016764534],"genre_scores_gemma":[0.99168086,0.00030001233,0.006635638,0.000927793,0.00037957155,0.0000073770843,0.000005516119,0.00001705867,0.00004614442],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997526,0.00013350717,0.0011553378,0.00008012339,0.00080668414,0.00029836452],"domain_scores_gemma":[0.9953475,0.0005293247,0.0032045434,0.00019571278,0.00043015127,0.0002927645],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0034153352,0.00014163504,0.0006223615,0.000116123316,0.00014411069,0.000020021831,0.00021736814,0.00009260672,0.000012111261],"category_scores_gemma":[0.010352074,0.00009857526,0.00021349838,0.00028118983,0.00012814667,0.00025695798,0.0000918638,0.000483263,0.0000036014603],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004541797,0.00009183335,0.89913607,0.00034596986,0.00041991472,0.000007682061,0.0023618923,0.000024017632,0.0010533516,0.0000032519267,0.0012523563,0.09484947],"study_design_scores_gemma":[0.0017627619,0.0003012692,0.968346,0.00027964194,0.00014452376,0.00021254118,0.0011301172,0.026545033,0.00062865455,0.0000630713,0.00044341976,0.00014295669],"about_ca_topic_score_codex":0.000057921465,"about_ca_topic_score_gemma":0.000029653153,"teacher_disagreement_score":0.094706506,"about_ca_system_score_codex":0.00045593857,"about_ca_system_score_gemma":0.00020690603,"threshold_uncertainty_score":0.9979842},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957702,0.00018988614,0.00015047775,0.0006368202,0.0002727205,0.0028723737,0.000047981204,0.000036902115,0.000022601891],"genre_scores_gemma":[0.99625534,0.0000024838046,0.0018210375,0.0008994359,0.000036666293,0.00077915314,0.00016647913,0.000031186522,0.000008220114],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974667,0.00083910004,0.0006089414,0.0003108965,0.00047282712,0.00030155168],"domain_scores_gemma":[0.99822515,0.0006194533,0.00024970007,0.00049643416,0.00024272493,0.00016655018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021788725,0.00017600153,0.0004717607,0.00022773643,0.00011967027,0.000024599005,0.00011414501,0.00006127269,0.00008350526],"category_scores_gemma":[0.000391824,0.00013600619,0.00009559607,0.00025491705,0.000028893262,0.00012595116,0.000018901233,0.00024824665,0.000011541141],"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":[0.00017244316,0.0018971958,0.9291138,0.0007122509,0.000026559766,7.5672756e-7,0.001819987,0.004960351,0.0067831925,0.000012266352,0.00001679457,0.05448441],"study_design_scores_gemma":[0.0010532017,0.00029397328,0.9781208,0.0011785881,0.000110261775,0.0000027166668,0.0022323558,0.01634353,0.0005380919,0.000012260132,0.0000013747634,0.000112812304],"about_ca_topic_score_codex":0.009489079,"about_ca_topic_score_gemma":0.00023306858,"teacher_disagreement_score":0.054371595,"about_ca_system_score_codex":0.00045108167,"about_ca_system_score_gemma":0.0027156943,"threshold_uncertainty_score":0.9971068},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92177427,0.019801727,0.00056678354,0.055597678,0.00028011587,0.0016990128,0.00006293924,0.000016226988,0.00020125772],"genre_scores_gemma":[0.9878506,0.0100835245,0.00019477605,0.0009155432,0.0006922399,0.00006473369,0.000072591116,0.00002217246,0.00010381545],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99570906,0.00050811924,0.0009864522,0.0002049504,0.0022354436,0.00035598362],"domain_scores_gemma":[0.99446374,0.0018270042,0.00028384698,0.00020196925,0.0028271002,0.00039635983],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007249435,0.000111814654,0.0006706412,0.00044911096,0.00004086566,0.00002707293,0.00033394515,0.00012441869,0.0014489273],"category_scores_gemma":[0.017738339,0.000080264814,0.00009221834,0.00045802662,0.0007212911,0.000118738724,0.0001898195,0.00078077597,0.000019124001],"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":[0.006688116,0.0014805814,0.05385024,0.088983595,0.0007062606,0.000505651,0.0014242763,6.0774954e-7,0.0014626895,0.008601675,0.4618101,0.3744862],"study_design_scores_gemma":[0.021578906,0.008861309,0.5776148,0.35190025,0.00022160316,0.0005770021,0.0016419644,0.0029846237,0.002270552,0.003733142,0.027737467,0.0008784063],"about_ca_topic_score_codex":0.00003769956,"about_ca_topic_score_gemma":0.000030219111,"teacher_disagreement_score":0.52376455,"about_ca_system_score_codex":0.00010250344,"about_ca_system_score_gemma":0.0014549325,"threshold_uncertainty_score":0.99946386},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9929007,0.004547484,0.000083694154,0.0003989768,0.000021483882,0.0008931303,0.00030540407,0.00005180848,0.0007973067],"genre_scores_gemma":[0.9980262,0.001257193,0.000112897265,0.000110023044,0.000047438924,0.00020209349,0.00015473604,0.000007660512,0.00008176291],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982475,0.00023490573,0.00035431955,0.00043440127,0.00047047978,0.0002584003],"domain_scores_gemma":[0.998929,0.00015097726,0.00014368574,0.00054338877,0.00015050736,0.00008244306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004424957,0.00015534127,0.0005683632,0.00012225941,0.00003302976,0.000054851997,0.00017339853,0.0000627076,0.000025489482],"category_scores_gemma":[0.0004413486,0.000113296956,0.000042610176,0.0011894556,0.00009302973,0.00013653829,0.00003884764,0.00017411786,0.000029113515],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049341565,0.00049301214,0.99710274,0.000105008236,0.00014582486,0.000007772291,0.00011687855,0.000012524898,0.0006775353,0.00008693124,0.000022941626,0.0011794887],"study_design_scores_gemma":[0.0005952407,0.000053711996,0.994068,0.00015005401,0.00012290388,0.0000027548356,0.0001701138,0.0045170444,0.000012891524,0.00016321935,0.00002294103,0.00012107013],"about_ca_topic_score_codex":0.0006905897,"about_ca_topic_score_gemma":0.004224802,"teacher_disagreement_score":0.0051254835,"about_ca_system_score_codex":0.00018304752,"about_ca_system_score_gemma":0.00003133405,"threshold_uncertainty_score":0.46201158},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9497004,0.0042396993,0.009034561,0.016984161,0.00078725547,0.00725949,0.009182651,0.0014636177,0.0013481786],"genre_scores_gemma":[0.96767545,0.0002244348,0.010312335,0.01691159,0.00064631994,0.00029243797,0.0037244537,0.00014362336,0.000069339825],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99434745,0.0005061179,0.001333144,0.0012189373,0.00069806696,0.0018962531],"domain_scores_gemma":[0.9936134,0.00017632265,0.0009440299,0.0029771507,0.000282541,0.0020065338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020802468,0.00044108104,0.001054491,0.00031042774,0.00050203275,0.00017099215,0.0010253933,0.00010796672,0.000085576685],"category_scores_gemma":[0.0016319053,0.00039886785,0.00016524918,0.0004495677,0.00018088099,0.001227292,0.00090503966,0.00036384322,0.00005041405],"study_design_candidate":"observational","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":[0.00039450979,0.0020718386,0.728631,0.0040093316,0.0003079265,0.000011763465,0.011596897,9.850642e-7,0.000052099083,0.0015393713,0.032018505,0.21936576],"study_design_scores_gemma":[0.0041267527,0.0007093515,0.48284686,0.0004106013,0.000027226668,0.00003332137,0.001385213,0.00044402576,0.000010849726,0.00016377438,0.5093517,0.00049032166],"about_ca_topic_score_codex":0.001615791,"about_ca_topic_score_gemma":0.00086192985,"teacher_disagreement_score":0.4773332,"about_ca_system_score_codex":0.0010773632,"about_ca_system_score_gemma":0.007249786,"threshold_uncertainty_score":0.99984634},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930651,0.0013039652,0.00054523954,0.0014073671,0.0003011313,0.0006381634,0.0022367672,0.00008824642,0.0004139727],"genre_scores_gemma":[0.9966285,0.0006076203,0.0005238427,0.0015004519,0.00022332752,0.00004259951,0.0004382413,0.000027302822,0.000008089298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980661,0.00029097882,0.0005706499,0.00023088655,0.00027626986,0.0005650691],"domain_scores_gemma":[0.9976471,0.0006216106,0.0004515977,0.0006204586,0.00017694925,0.00048227914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010992816,0.00016916271,0.00043317841,0.000094295625,0.00021509647,0.000034567263,0.00009916569,0.000047692076,0.000030935007],"category_scores_gemma":[0.0013533938,0.00012245718,0.000071610564,0.00029660566,0.00017459212,0.0002923519,0.000091897724,0.00019440464,0.000021021386],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007456134,0.00095662265,0.94778377,0.0013367595,0.00010651098,0.000063265295,0.0018584239,9.59781e-7,0.00009240626,0.0016219945,0.0051127775,0.040991962],"study_design_scores_gemma":[0.0009437638,0.00027220283,0.9556423,0.00020961116,0.00003054246,0.0017247507,0.0008524828,0.0007415899,0.000013414004,0.000035973102,0.039309364,0.00022403729],"about_ca_topic_score_codex":0.00023130879,"about_ca_topic_score_gemma":0.00008512394,"teacher_disagreement_score":0.040767923,"about_ca_system_score_codex":0.00011107767,"about_ca_system_score_gemma":0.00073161646,"threshold_uncertainty_score":0.4993659},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015438634,0.97741556,0.015198833,0.002020897,0.0024918758,0.0011171139,0.0016548275,0.000034885,0.000050564635],"genre_scores_gemma":[0.00019444789,0.99105376,0.004296204,0.0013777282,0.0011598766,0.00003837555,0.0016966917,0.000077074044,0.000105853745],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.995409,0.00019236043,0.0023262126,0.00032276948,0.0013162892,0.00043338494],"domain_scores_gemma":[0.99372613,0.0007509436,0.0028031631,0.0003279993,0.0016310504,0.0007606957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021868208,0.00037192972,0.0016604123,0.00198102,0.00013401863,0.000083524596,0.0006696688,0.00017571762,0.000047987534],"category_scores_gemma":[0.00095893175,0.00031803455,0.0017087203,0.0003927709,0.00011573523,0.00020824393,0.00007826619,0.0006974966,0.000010974289],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030904243,0.00026137882,0.00052629935,0.0072714374,0.0013053963,0.00024722845,0.000046348712,0.0000060933144,4.011788e-8,0.00048695432,0.0032004525,0.98633933],"study_design_scores_gemma":[0.0012103919,0.00020925258,0.0022114401,0.019214602,0.00079886813,0.00035333828,0.000031305626,0.00010805487,9.282127e-9,0.00019283422,0.9754538,0.00021611182],"about_ca_topic_score_codex":0.000013381122,"about_ca_topic_score_gemma":0.000019167397,"teacher_disagreement_score":0.9861232,"about_ca_system_score_codex":0.00045471085,"about_ca_system_score_gemma":0.0034635938,"threshold_uncertainty_score":0.99992716},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937196,0.0016041232,0.00045226497,0.0030097347,0.00023191323,0.00013091009,0.00013008951,0.000002986171,0.0007184136],"genre_scores_gemma":[0.9983971,0.000031665648,0.00043936007,0.0008116758,0.0001030655,0.0000011423664,0.000036597783,0.0000083765735,0.00017100202],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998051,0.00021108687,0.00070119224,0.00009915286,0.00030275545,0.00063485233],"domain_scores_gemma":[0.9956998,0.000050548428,0.00044698213,0.00026952743,0.00025932243,0.003273789],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002721219,0.0000904076,0.000586387,0.0014514922,0.000030692983,0.000013963608,0.00016945797,0.000046872876,0.0015315249],"category_scores_gemma":[0.0007255292,0.00008367548,0.00015712065,0.00090004003,0.00007177641,0.00025945218,0.000009195251,0.00031599804,0.0000057868965],"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":[0.000011822693,0.00006704203,0.98427767,0.00007106883,0.00021577263,0.000041719853,0.0017453777,0.000007266725,0.0000023460439,0.00009081557,0.0011529129,0.01231617],"study_design_scores_gemma":[0.0004561908,0.00014840116,0.9559439,0.0000865161,0.00007130762,0.000049278355,0.00013881252,0.000046775574,0.0000011045078,0.0000035095072,0.04300047,0.000053778633],"about_ca_topic_score_codex":0.6560693,"about_ca_topic_score_gemma":0.98530453,"teacher_disagreement_score":0.32923523,"about_ca_system_score_codex":0.0017634089,"about_ca_system_score_gemma":0.021457706,"threshold_uncertainty_score":0.9993812},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9914162,0.00009284364,0.0065923585,0.00078289787,0.0006515265,0.00016048584,0.000014411925,0.0000581052,0.00023114089],"genre_scores_gemma":[0.99797004,0.00028767262,0.0009249775,0.0005563437,0.00022023905,0.0000011586479,0.000025100138,0.000008040807,0.000006430933],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99665755,0.00011353638,0.0014655883,0.0000633468,0.0015154072,0.00018457971],"domain_scores_gemma":[0.99385476,0.0002802274,0.0047385776,0.00018984395,0.00060136244,0.00033525587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00234055,0.00010440894,0.00040550923,0.00008276104,0.00004328997,0.00002140377,0.00011115749,0.00006977813,0.000006714522],"category_scores_gemma":[0.008114773,0.00007489091,0.00010698512,0.00055917125,0.00013668758,0.00028778004,0.00006275082,0.00022096823,0.000001372854],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014236216,0.00012051657,0.9883667,0.00012463881,0.00012191736,0.000018272185,0.0007756286,0.000015498037,0.0000393711,0.00009610878,0.0023898524,0.007789095],"study_design_scores_gemma":[0.000585298,0.00012482134,0.99021214,0.000364058,0.000117406715,0.00011971964,0.0004926508,0.007155003,0.000081576065,0.00017641262,0.00050665054,0.0000642812],"about_ca_topic_score_codex":0.00006677956,"about_ca_topic_score_gemma":0.000011944929,"teacher_disagreement_score":0.007724814,"about_ca_system_score_codex":0.00077920355,"about_ca_system_score_gemma":0.0004318085,"threshold_uncertainty_score":0.97147274},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98396665,0.0001985799,0.005967426,0.0095133195,0.000056000772,0.0002046282,0.0000150419855,0.0000035714747,0.00007476881],"genre_scores_gemma":[0.99230707,0.0005307957,0.005814366,0.0012395548,0.00005764542,0.00003801161,0.000004557727,0.000006495982,0.0000014899123],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975979,0.0010274425,0.0009782055,0.0001104917,0.000106777894,0.00017919944],"domain_scores_gemma":[0.9908872,0.006824665,0.0014318725,0.0004574016,0.00032962507,0.00006921721],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0029524802,0.000080920305,0.0005891194,0.00011696117,0.000028403281,0.0000018653182,0.00036688615,0.000018426896,0.000031120664],"category_scores_gemma":[0.010852654,0.000043659216,0.00012756337,0.00041210387,0.00044758568,0.00012250437,0.000042507738,0.00022484401,0.0000039067663],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008664692,0.00010380797,0.95463264,0.000013948795,0.0000717054,0.0000012013924,0.00091440405,0.00054678426,0.007897151,0.00046300198,0.00047315357,0.034795526],"study_design_scores_gemma":[0.00027932646,0.00033146242,0.99706507,0.00009569559,0.00004094449,0.00004733007,0.0007721038,0.00040620528,0.000062327585,0.0007374588,0.00012700718,0.000035086843],"about_ca_topic_score_codex":0.0019366263,"about_ca_topic_score_gemma":0.00009330316,"teacher_disagreement_score":0.042432386,"about_ca_system_score_codex":0.00003906375,"about_ca_system_score_gemma":0.000093317416,"threshold_uncertainty_score":0.9974794},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9988143,0.00016922574,0.000011473957,0.000063831445,0.00018890496,0.00011519304,0.000296894,0.000011947105,0.0003282354],"genre_scores_gemma":[0.9994071,0.00005477301,0.00003510961,0.000023293289,0.00007252542,0.0000027770589,0.00024685627,0.000010433322,0.00014712145],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99881315,0.000056843888,0.00052265916,0.00015797433,0.00023492343,0.00021442871],"domain_scores_gemma":[0.9992414,0.000033696873,0.00023859546,0.0002319581,0.000107306136,0.00014702072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008468687,0.00010336813,0.00029775343,0.00018320879,0.000037526468,0.000018164483,0.000065292385,0.000080332975,0.00057605526],"category_scores_gemma":[0.0001449397,0.000092241404,0.000064382344,0.00021202519,0.00011469912,0.00008691088,0.000028068556,0.0006484467,0.0000068282498],"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":[0.00017623587,0.0001719466,0.992919,0.000016347289,0.000022233095,0.00046827734,0.00026404767,0.0000024914857,0.0043295035,0.0000079684805,0.0002967577,0.0013251452],"study_design_scores_gemma":[0.0015921701,0.00006446707,0.9945499,0.00010749041,0.000024423134,0.00023355789,0.00011823022,0.000015790863,0.0000984928,0.00012413102,0.002986853,0.00008450295],"about_ca_topic_score_codex":0.034537856,"about_ca_topic_score_gemma":0.4421575,"teacher_disagreement_score":0.40761963,"about_ca_system_score_codex":0.00006026519,"about_ca_system_score_gemma":0.00022449881,"threshold_uncertainty_score":0.9718912},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98167783,0.0010438754,0.00009117001,0.0011221976,0.0007313784,0.0015205012,0.0023204288,0.0011707235,0.010321886],"genre_scores_gemma":[0.99276805,0.00004359081,0.00007346085,0.002796192,0.0005429776,0.0001317657,0.001142914,0.00006896133,0.0024321028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99743235,0.00009691797,0.00049857394,0.0007222843,0.00055379956,0.00069609133],"domain_scores_gemma":[0.9972887,0.00018230407,0.00019851672,0.00093367556,0.00027581805,0.001120988],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017540512,0.00041432117,0.0006201042,0.00014855976,0.00029965775,0.000052685187,0.00019307173,0.000058876292,0.00070664386],"category_scores_gemma":[0.0007187798,0.0004222705,0.00029756848,0.0002986897,0.00007294745,0.00017150532,0.000046526937,0.00015701822,0.00004692846],"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":[0.0007551905,0.0004900499,0.80377346,0.00014515978,0.00014848527,0.00024791845,0.000040557094,0.00021332555,0.00034394927,0.00013534479,0.19042706,0.003279489],"study_design_scores_gemma":[0.00201965,0.00022519646,0.864471,0.00009500951,0.0001806675,0.0000068365093,0.000008858632,0.00016172498,0.000020729572,0.00021372837,0.13215141,0.0004451502],"about_ca_topic_score_codex":0.3670192,"about_ca_topic_score_gemma":0.6184438,"teacher_disagreement_score":0.2514246,"about_ca_system_score_codex":0.0010725864,"about_ca_system_score_gemma":0.0033771521,"threshold_uncertainty_score":0.9998229},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.1218647e-7,0.9758155,0.0007315982,0.007904268,0.00025720522,0.0121390745,0.0025045755,0.00017236885,0.0004751345],"genre_scores_gemma":[0.0000068184618,0.97226477,0.0014406629,0.01496032,0.00017616895,0.000769396,0.010110223,0.00015498686,0.00011666628],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9831925,0.0044021592,0.0068412977,0.0021411611,0.0014751018,0.0019477826],"domain_scores_gemma":[0.9812155,0.00063063664,0.00343259,0.008957969,0.00031708332,0.005446245],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.034626886,0.0007585275,0.012327196,0.001320373,0.00010730071,0.00010848643,0.003343407,0.00022493921,0.00030672227],"category_scores_gemma":[0.042110126,0.00060526666,0.0005250754,0.0031049086,0.0000744637,0.00035953976,0.001586781,0.0007950734,0.00025975014],"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":[0.000001687877,0.00009640337,0.00013384357,0.8971197,0.0001330621,0.000005476373,0.000051531664,1.366922e-8,7.6177853e-10,0.00030230082,0.0263021,0.0758539],"study_design_scores_gemma":[0.00019883247,0.00007960599,0.000032385255,0.4915759,0.000101906626,0.0000458479,0.000023709918,0.00002462468,2.4714938e-10,0.0000011697355,0.5077199,0.00019615852],"about_ca_topic_score_codex":0.00047094197,"about_ca_topic_score_gemma":0.00055587065,"teacher_disagreement_score":0.48141775,"about_ca_system_score_codex":0.0022138956,"about_ca_system_score_gemma":0.04170174,"threshold_uncertainty_score":0.99963987},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000458568,0.9961655,0.0001501445,0.0010619402,0.00090583233,0.00044162327,0.00033072056,0.000021127471,0.00046454556],"genre_scores_gemma":[0.0030296843,0.9922843,0.000028704024,0.0016379416,0.0024190235,0.000007644302,0.00010753185,0.000045185054,0.00043996534],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963598,0.0005237128,0.0009399446,0.0002104114,0.0015443565,0.000421776],"domain_scores_gemma":[0.9957515,0.00019686474,0.00096698134,0.0002835246,0.0003737668,0.002427375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015938439,0.0002457758,0.0010560942,0.0003147821,0.00011882639,0.00006847134,0.00035440802,0.000474469,0.0007874765],"category_scores_gemma":[0.0032648102,0.00016727007,0.000621012,0.00038477944,0.000064591266,0.000078959034,0.000024581115,0.0015914807,0.000073069314],"study_design_candidate":"not_applicable","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":[0.0000123741265,0.000025364281,0.0008297087,0.00060800754,0.00034012977,0.0003641995,0.000033070137,2.4018263e-7,1.1097333e-7,0.000009053981,0.0074811773,0.99029654],"study_design_scores_gemma":[0.00031398554,0.000046241395,0.004218997,0.006420222,0.0009925303,0.00033047365,0.000013559457,0.000064202635,1.1798146e-7,0.000016374117,0.98743933,0.00014393487],"about_ca_topic_score_codex":0.0018791002,"about_ca_topic_score_gemma":0.003822179,"teacher_disagreement_score":0.99015266,"about_ca_system_score_codex":0.002095015,"about_ca_system_score_gemma":0.0077501247,"threshold_uncertainty_score":0.99787503},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9714513,0.00018940697,0.0037544314,0.0170501,0.0015153087,0.0009918932,0.003603979,0.0001580421,0.0012855574],"genre_scores_gemma":[0.36485323,0.0041345223,0.59697247,0.021492613,0.0073473747,0.000045680288,0.00398524,0.0002698408,0.00089901296],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99619764,0.000061994615,0.002325851,0.00008953393,0.00067633775,0.0006486251],"domain_scores_gemma":[0.9939797,0.0003435773,0.0020886248,0.00051379396,0.0018157301,0.0012585727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056796824,0.00021564912,0.00085129624,0.00037060925,0.00011789549,0.00007464759,0.00034226826,0.00014459377,0.00012324023],"category_scores_gemma":[0.00386235,0.00017267272,0.00019035462,0.00045238386,0.00010035556,0.00086692884,0.000057974794,0.0006952718,0.000034556826],"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":[0.0010663572,0.003287137,0.08298041,0.0065729553,0.00082280376,0.00008691823,0.004656589,0.000036158508,0.0018646625,0.002286688,0.4601134,0.43622592],"study_design_scores_gemma":[0.0043399213,0.001112199,0.05716838,0.00015130169,0.000022127042,0.00038888995,0.00044413083,0.004958717,0.000019427409,0.000075733326,0.93107736,0.00024183994],"about_ca_topic_score_codex":0.000010252526,"about_ca_topic_score_gemma":0.000060010614,"teacher_disagreement_score":0.6065981,"about_ca_system_score_codex":0.0001922432,"about_ca_system_score_gemma":0.0052298675,"threshold_uncertainty_score":0.927756},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83798903,0.0057013202,0.026322141,0.00012756039,0.0003095417,0.00043869365,0.12841627,0.00015418013,0.000541231],"genre_scores_gemma":[0.8692943,0.00021685656,0.07419091,0.000043264128,0.00027160338,0.0000106750085,0.055814832,0.00003184353,0.0001257291],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978525,0.000064600536,0.0005134819,0.0007179988,0.00059063226,0.00026079398],"domain_scores_gemma":[0.9961397,0.0011945996,0.0003231319,0.001953397,0.00021890788,0.00017028493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018756006,0.0002372848,0.00051575765,0.00045179352,0.00004597927,0.00003140664,0.00066840614,0.00007925057,0.0005571183],"category_scores_gemma":[0.0023616168,0.00021406435,0.000060047554,0.0020172163,0.00025895008,0.0002800098,0.00042082858,0.000109246044,0.000037955528],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009980603,0.0025283447,0.78135777,0.0008021631,0.0005474941,0.00028216152,0.00003326162,0.00009116763,0.135605,0.0000686522,0.06933739,0.008348507],"study_design_scores_gemma":[0.0015402761,0.0000942415,0.95257163,0.00027032912,0.00037645327,0.0000036894999,0.00006314839,0.011048741,0.029705686,0.000028315495,0.004016234,0.00028126052],"about_ca_topic_score_codex":0.0010430644,"about_ca_topic_score_gemma":0.00010606411,"teacher_disagreement_score":0.17121384,"about_ca_system_score_codex":0.00004943451,"about_ca_system_score_gemma":0.00025850022,"threshold_uncertainty_score":0.87292904},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9826951,0.0005795899,0.00008392428,0.015631847,0.00063523115,0.00013720102,0.0001522533,0.000049731592,0.00003512299],"genre_scores_gemma":[0.99653894,0.0003538249,0.0015089287,0.001234724,0.00031179027,0.000001638333,0.000029966122,0.000017072798,0.0000031163179],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980183,0.00009166293,0.0007818171,0.00018345981,0.00065132737,0.00027339906],"domain_scores_gemma":[0.9984486,0.00010951168,0.0007294686,0.00013473854,0.00048784865,0.000089833455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014871842,0.00014114808,0.0004075671,0.00074030686,0.00008623448,0.000042627755,0.0003004677,0.00013703377,0.000015708585],"category_scores_gemma":[0.0004661547,0.00013117639,0.000108696986,0.00027891502,0.00024783236,0.00014730782,0.00012736898,0.0009053704,0.0000011190906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004690867,0.0002023342,0.93652785,0.00011025095,0.0001099422,0.00014632569,0.00022987934,0.000010428371,0.001739796,0.00026337072,0.00087437034,0.059316378],"study_design_scores_gemma":[0.0034769755,0.00030038127,0.94182014,0.0005497293,0.000018855164,0.0008331947,0.0007002989,0.0019403368,0.00013023101,0.0006205929,0.049326375,0.00028289302],"about_ca_topic_score_codex":0.00007935681,"about_ca_topic_score_gemma":0.0002517649,"teacher_disagreement_score":0.059033483,"about_ca_system_score_codex":0.00007498854,"about_ca_system_score_gemma":0.0004733126,"threshold_uncertainty_score":0.5349218},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31712475,0.00042482963,0.65860504,0.022500109,0.0005403305,0.00057079276,0.000057509285,0.00006774239,0.000108879176],"genre_scores_gemma":[0.48878992,0.00012090594,0.50566334,0.0045105256,0.0007853234,0.000008766357,0.000065242544,0.000034206903,0.00002178297],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99647516,0.0012799597,0.0011379187,0.00042109608,0.00016374291,0.0005221223],"domain_scores_gemma":[0.9944266,0.0011370152,0.0026558056,0.001036645,0.00026529428,0.00047862067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00856581,0.00019615513,0.0014213346,0.00024916904,0.00009111743,0.0000070615206,0.00044461305,0.00005857073,0.000041091294],"category_scores_gemma":[0.008072001,0.0001354564,0.000111110654,0.0003888577,0.00039647485,0.00022747375,0.00015005865,0.0011343063,0.0000059826434],"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":[0.0021522848,0.00021557428,0.1622755,0.0001502338,0.000641561,0.000121387246,0.00037546243,0.000058995673,0.01636453,0.00015178374,0.0031188396,0.81437385],"study_design_scores_gemma":[0.004389716,0.008486978,0.67599547,0.0005747307,0.00019472562,0.013079474,0.001023312,0.010491278,0.00016150367,0.00075834285,0.28428805,0.00055642915],"about_ca_topic_score_codex":0.0008076683,"about_ca_topic_score_gemma":0.00009906261,"teacher_disagreement_score":0.81381744,"about_ca_system_score_codex":0.000091010894,"about_ca_system_score_gemma":0.00067469757,"threshold_uncertainty_score":0.9663523},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7362233,0.0001628331,0.25396714,0.0015931631,0.00027053984,0.00057496666,0.007035934,0.0000017481301,0.00017038861],"genre_scores_gemma":[0.9486313,0.00008317518,0.04847575,0.000045825887,0.00018048107,0.000007804168,0.002566665,0.0000068241725,0.0000021672258],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98967224,0.002765006,0.0023972006,0.00029094415,0.0046629133,0.00021169384],"domain_scores_gemma":[0.98702717,0.00785818,0.0009120884,0.00056260935,0.0034641004,0.00017586628],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.035659604,0.00010535541,0.000763664,0.000742783,0.000012461283,0.000013855655,0.0012504156,0.000118376505,0.00014818703],"category_scores_gemma":[0.061512463,0.00007977736,0.00007119019,0.0008726826,0.00024858763,0.00013961263,0.0001769243,0.00053954986,4.034903e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.01232815,0.002833334,0.7827012,0.0001558823,0.0013562522,0.000068206995,0.002223053,0.0042278185,0.00044932117,0.0009439327,0.0045447554,0.18816808],"study_design_scores_gemma":[0.005883591,0.0003414468,0.55752856,0.00025471143,0.00017458448,0.0000037885914,0.00037326102,0.43313697,0.00006388139,0.0020620688,0.00014107663,0.000036082456],"about_ca_topic_score_codex":0.0012593349,"about_ca_topic_score_gemma":0.005314279,"teacher_disagreement_score":0.42890915,"about_ca_system_score_codex":0.00026180968,"about_ca_system_score_gemma":0.0011893667,"threshold_uncertainty_score":0.9929914},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987355,0.0037434765,0.0010883192,0.0040086117,0.000952759,0.00009208497,0.000025799078,0.000032010437,0.0027019358],"genre_scores_gemma":[0.9932196,0.00040483737,0.00022854506,0.00030964654,0.0035160293,0.0000061085348,0.000022686567,0.00003007733,0.002262473],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9934808,0.0007947329,0.00082542887,0.00021107247,0.003985494,0.0007024721],"domain_scores_gemma":[0.9960756,0.0009609894,0.00022065453,0.00040078396,0.0005395088,0.0018025148],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008218962,0.00014960501,0.00053500355,0.0006035555,0.0000418083,0.000046017823,0.00064283324,0.00028552132,0.008578294],"category_scores_gemma":[0.0062879985,0.00010770581,0.00025667401,0.00050014857,0.0003018602,0.00034593302,0.00026475912,0.0022846912,0.0006243779],"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":[0.0021059148,0.0010620741,0.149143,0.00007501065,0.00058841234,0.0009279601,0.00086344883,4.7448597e-7,0.008444089,0.000084452804,0.2179099,0.6187953],"study_design_scores_gemma":[0.004182539,0.0016546375,0.6347395,0.0012036575,0.00012358245,0.0011873264,0.00030938585,0.0014782011,0.008699842,0.00026114137,0.34593248,0.00022766794],"about_ca_topic_score_codex":0.0009862412,"about_ca_topic_score_gemma":0.00008625959,"teacher_disagreement_score":0.6185676,"about_ca_system_score_codex":0.00032292237,"about_ca_system_score_gemma":0.00027940702,"threshold_uncertainty_score":0.9925974},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939737,0.00019665384,0.004477163,0.000009624777,0.00009800166,0.0009285717,0.00017696015,0.00010940473,0.00002997158],"genre_scores_gemma":[0.9995998,0.000019694353,0.000085735905,0.000022149577,0.000052845855,0.00009930763,0.00006964288,0.000035418045,0.000015385498],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980354,0.00014580932,0.0008256851,0.00034020288,0.0003689106,0.0002840234],"domain_scores_gemma":[0.9979371,0.0006635969,0.0004914557,0.00049374846,0.0002767449,0.00013739636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048486362,0.00021809475,0.000863936,0.00033788546,0.000023066716,0.000010704346,0.0001709979,0.00009947335,0.0000069016714],"category_scores_gemma":[0.0016187197,0.00019940826,0.00012094171,0.00038117243,0.00012364941,0.00014330108,0.00003881625,0.00014889003,0.000005713067],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038966147,0.0005913472,0.9618372,0.0031465767,0.000115210794,5.13366e-7,0.0001552392,0.00063445885,0.032264665,0.000009770243,0.0000016234711,0.0008537492],"study_design_scores_gemma":[0.0010038256,0.0009442964,0.7972294,0.0018665257,0.00009929107,4.825239e-7,0.000039349794,0.061968487,0.13665663,0.000011323602,0.000005568956,0.00017478848],"about_ca_topic_score_codex":0.0007487065,"about_ca_topic_score_gemma":0.0002147799,"teacher_disagreement_score":0.16460775,"about_ca_system_score_codex":0.00014577774,"about_ca_system_score_gemma":0.00008421381,"threshold_uncertainty_score":0.8131633},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99580854,0.0006231808,0.000037483227,0.000114297734,0.00013939338,0.0027186212,0.000067263274,0.00017348921,0.00031775146],"genre_scores_gemma":[0.99855965,0.0002835792,0.00021628509,0.000059555354,0.0002957972,0.00036709555,0.000033856715,0.000048634687,0.00013556538],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956059,0.00035766413,0.001963506,0.00067699485,0.0006102426,0.00078570197],"domain_scores_gemma":[0.99528134,0.0001652989,0.0017805552,0.0009941863,0.0003019211,0.0014766884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002406321,0.00026543075,0.0011739752,0.00021394396,0.00013168724,0.000011772338,0.0001523453,0.00004183009,0.000014622533],"category_scores_gemma":[0.0007370252,0.0002457701,0.000063948515,0.00046919484,0.000046044755,0.000073016985,0.00009189688,0.00026327683,0.00001869911],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033299609,0.0017757253,0.9742621,0.011293862,0.00006147358,0.000029388946,0.0005845365,0.000042685977,0.0030934627,0.000056896733,0.00044724433,0.00502264],"study_design_scores_gemma":[0.0015488195,0.007591211,0.98832965,0.00081313116,0.00007327828,0.000013649121,0.000759122,0.00032308354,0.00011132516,0.000007550394,0.00024479034,0.00018439382],"about_ca_topic_score_codex":0.001161465,"about_ca_topic_score_gemma":0.00005382599,"teacher_disagreement_score":0.014067523,"about_ca_system_score_codex":0.00033089778,"about_ca_system_score_gemma":0.0010350989,"threshold_uncertainty_score":0.99999946},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7215491,0.0075498726,0.010057864,0.0014150614,0.00007368629,0.0013662882,0.25793773,0.00004383647,0.000006602894],"genre_scores_gemma":[0.9884618,0.0006347378,0.0018607858,0.00016752753,0.000060521477,0.000050831586,0.0087362295,0.000017689012,0.000009856548],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986712,0.000066252585,0.00039727864,0.00046526417,0.00016951816,0.00023052862],"domain_scores_gemma":[0.9980301,0.0003017087,0.00014758007,0.0011362949,0.00016898735,0.0002153168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006400914,0.00013888473,0.00047324784,0.00008758204,0.00028148436,0.000021093832,0.00030713872,0.000028376753,0.00001805227],"category_scores_gemma":[0.0001871819,0.00011364719,0.000070970316,0.0004920545,0.0002595747,0.0002504349,0.0001139364,0.000039236165,1.7713529e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016059777,0.00080763403,0.92930406,0.004386868,0.0043020747,0.0000048899487,0.008316547,0.000056238074,0.0014162828,0.00021028706,0.013507068,0.021628298],"study_design_scores_gemma":[0.0015832654,0.0005249093,0.9551119,0.00007197635,0.0013439893,0.0000011407016,0.0006411928,0.008254586,0.000032014632,0.000021485504,0.032267295,0.00014622576],"about_ca_topic_score_codex":0.0002217049,"about_ca_topic_score_gemma":0.00085426634,"teacher_disagreement_score":0.26691276,"about_ca_system_score_codex":0.00000979032,"about_ca_system_score_gemma":0.0003011358,"threshold_uncertainty_score":0.4634398},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977465,0.0020271533,0.0011154518,0.0075985305,0.0028565917,0.0012335549,0.0066150627,0.00018296672,0.0009057212],"genre_scores_gemma":[0.9958535,0.00044642994,0.0015525676,0.00066467363,0.0005909106,0.000032645898,0.0007267252,0.00006556902,0.00006701498],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99488753,0.00034797346,0.0020056753,0.00037919715,0.0018187029,0.0005609154],"domain_scores_gemma":[0.9939341,0.0006679766,0.0015049813,0.00025372356,0.0033423419,0.0002968608],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023145096,0.00031422757,0.00092443987,0.00086752867,0.00010355659,0.00005750432,0.0005847826,0.00008481525,0.00007319248],"category_scores_gemma":[0.0004935512,0.00029480355,0.00022261917,0.00072567567,0.00009976811,0.00019214362,0.000056625588,0.00043368593,0.000014296925],"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":[0.0018471814,0.0004748169,0.96976686,0.0003002169,0.00028537036,0.00029286483,0.0000231424,0.0009982317,0.00066590967,0.0008447745,0.023335971,0.0011646738],"study_design_scores_gemma":[0.0043859724,0.00042375788,0.9668908,0.0006277473,0.000011037408,0.000449831,0.00007986723,0.0032100456,0.000039422375,0.00012666482,0.023477135,0.00027768261],"about_ca_topic_score_codex":0.5575938,"about_ca_topic_score_gemma":0.90043336,"teacher_disagreement_score":0.34283954,"about_ca_system_score_codex":0.0028244823,"about_ca_system_score_gemma":0.008318462,"threshold_uncertainty_score":0.9999504},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8807765,0.031150848,0.00026802695,0.07188738,0.009329599,0.0030365286,0.0016320943,0.00008518933,0.0018337995],"genre_scores_gemma":[0.96723694,0.0018137172,0.00026025795,0.014553027,0.0034729282,0.00017381496,0.00088376907,0.00023755807,0.011367966],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931723,0.00042278055,0.0028553093,0.0006784604,0.0012362221,0.0016349675],"domain_scores_gemma":[0.98936224,0.0000759297,0.0014863422,0.0009412233,0.0016668974,0.006467379],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030459594,0.000567245,0.0015545043,0.0007845062,0.0006180069,0.0000082677525,0.0006123985,0.00020130069,0.0060450416],"category_scores_gemma":[0.0023446016,0.0005108606,0.00038234776,0.0008717144,0.0002993256,0.00024614643,0.000028123164,0.0007726772,0.000026938169],"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":[0.0001250721,0.00009904011,0.43477675,0.00017061211,0.00018027007,0.00026513494,0.0014119929,0.0000018281783,0.00012323595,0.000028909217,0.56230044,0.0005167357],"study_design_scores_gemma":[0.002762244,0.0034886154,0.21245019,0.00014890326,0.00005521797,0.000097590506,0.00015836216,0.00000848775,0.000011525376,0.000034565226,0.78045326,0.00033105418],"about_ca_topic_score_codex":0.1315761,"about_ca_topic_score_gemma":0.77803165,"teacher_disagreement_score":0.6464555,"about_ca_system_score_codex":0.002876492,"about_ca_system_score_gemma":0.027359687,"threshold_uncertainty_score":0.9997343},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021483753,0.00089914637,0.9257008,0.005961943,0.0014962989,0.0046676723,0.03565661,0.00020967223,0.003924144],"genre_scores_gemma":[0.8008389,0.00013351608,0.16413383,0.0016979913,0.00083352404,0.00018015478,0.031301837,0.000082725404,0.00079753256],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9977118,0.000078229226,0.00064530014,0.00056671037,0.000535294,0.0004626749],"domain_scores_gemma":[0.99691224,0.0010805125,0.0001666678,0.0012036205,0.00030482106,0.00033212884],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00059776736,0.00023826487,0.0006753731,0.00018272827,0.000061244755,0.000021463047,0.0003990104,0.00006263595,0.0022785894],"category_scores_gemma":[0.00427478,0.00020127436,0.000018331059,0.00020943648,0.00037008262,0.000109266875,0.00015400126,0.00022970342,0.00013543111],"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":[0.00040343523,0.00029259443,0.03202844,0.0008527241,0.00012484768,0.00029003122,0.00029938552,0.0000042957245,0.0006353179,0.006724166,0.82859176,0.12975298],"study_design_scores_gemma":[0.016121557,0.0017744312,0.7170585,0.001090314,0.00045971433,0.00009964527,0.0005516162,0.13542452,0.000023683138,0.033949748,0.092781805,0.00066448306],"about_ca_topic_score_codex":0.004071804,"about_ca_topic_score_gemma":0.0018504662,"teacher_disagreement_score":0.7793551,"about_ca_system_score_codex":0.00012982228,"about_ca_system_score_gemma":0.00021900066,"threshold_uncertainty_score":0.99863344},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.640315e-8,0.9346812,0.038614407,0.0006954585,0.00017804976,0.016506853,0.009114268,0.00012732946,0.00008241702],"genre_scores_gemma":[5.843282e-7,0.93925315,0.012392023,0.002066289,0.0005397542,0.0011843173,0.04438151,0.00009090167,0.00009145304],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99424404,0.00059026707,0.0027742381,0.0011227513,0.0006339339,0.0006347962],"domain_scores_gemma":[0.9927949,0.0003663467,0.0024872525,0.0035151457,0.00038336156,0.00045301605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025900328,0.00058543956,0.006937187,0.00046180427,0.000083890336,0.000019489333,0.00085253007,0.00024045468,0.0004000281],"category_scores_gemma":[0.0008872983,0.0004613231,0.0012148224,0.0008083978,0.00007933189,0.00013742373,0.00066141685,0.00035484464,0.000047812682],"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":[0.000012746128,0.00007615052,0.0000070337805,0.45057297,0.0004701768,0.000001447318,0.0000036677186,3.9276937e-8,5.7874567e-9,0.0000021512642,0.014042723,0.5348109],"study_design_scores_gemma":[0.00020902036,0.00023263125,0.000008874897,0.4790137,0.0043622707,0.000008762589,8.959151e-7,0.00010816853,1.8990345e-7,0.0000027917722,0.51583296,0.00021972973],"about_ca_topic_score_codex":0.0034634916,"about_ca_topic_score_gemma":0.0018991374,"teacher_disagreement_score":0.53459114,"about_ca_system_score_codex":0.0002958863,"about_ca_system_score_gemma":0.0038448004,"threshold_uncertainty_score":0.9997839},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99637806,0.00003133252,0.0011987336,0.00076681876,0.000100881196,0.0009516207,0.00011837138,0.00004081775,0.00041337183],"genre_scores_gemma":[0.9963431,0.000008560726,0.00055034953,0.0007094209,0.00005120581,0.000051987925,0.0012467377,0.000018781468,0.0010198845],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980966,0.0001538731,0.0006498747,0.00032165716,0.00035389696,0.00042412456],"domain_scores_gemma":[0.9989441,0.00003276061,0.00022154918,0.0004240057,0.00015020109,0.00022739821],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041206213,0.00016410729,0.00071952934,0.00030693127,0.000040726725,0.000012712793,0.00010881372,0.00007883998,0.0026644345],"category_scores_gemma":[0.000034142475,0.00013020949,0.00012866962,0.00039667462,0.00005210135,0.00008220704,0.000060983257,0.0001896479,0.00030231974],"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":[0.00028653504,0.00039433944,0.97938454,0.00016631566,0.00030387167,0.000008956425,0.0075935093,0.00011610886,0.00090036937,0.000020135674,0.00030902505,0.010516292],"study_design_scores_gemma":[0.0015482353,0.00029516383,0.9895376,0.00005244445,0.0001494939,4.586114e-7,0.00018572807,0.006219506,0.00023168433,0.000029612675,0.0016249823,0.00012511162],"about_ca_topic_score_codex":0.6886787,"about_ca_topic_score_gemma":0.7414538,"teacher_disagreement_score":0.052775133,"about_ca_system_score_codex":0.00039742276,"about_ca_system_score_gemma":0.00037503458,"threshold_uncertainty_score":0.99824727},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97733486,0.000821754,0.00065230904,0.02085458,0.00009930953,0.00010060513,0.000010748239,0.000005042415,0.00012082386],"genre_scores_gemma":[0.9953703,0.0003575881,0.0010995353,0.0023761545,0.0007726493,0.0000027469803,0.000008722958,0.0000074440504,0.0000048356114],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843425,0.0002543905,0.0006051132,0.00011599838,0.0003337976,0.00025644375],"domain_scores_gemma":[0.9985078,0.00061077095,0.0004013878,0.00013179054,0.00008243219,0.0002658223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005983473,0.00012889662,0.00077565986,0.00013011623,0.000035969537,0.000005600619,0.00011057694,0.000045780107,0.000045669534],"category_scores_gemma":[0.0013201591,0.00006820265,0.00006522395,0.00018360653,0.0006917586,0.00013586917,0.000033929748,0.00034949576,0.0000011040116],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003017443,0.00015039495,0.85612875,0.00003573281,0.00003948729,0.00003848063,0.0030387223,0.0000011008718,0.00015156971,0.00021246442,0.0011125513,0.13878903],"study_design_scores_gemma":[0.001533098,0.0013171184,0.9933848,0.00015204758,0.00010790612,0.00020419311,0.0017805741,0.000037116915,0.000012286349,0.00014091127,0.0012680943,0.000061873616],"about_ca_topic_score_codex":0.00040572646,"about_ca_topic_score_gemma":0.000019643017,"teacher_disagreement_score":0.13872716,"about_ca_system_score_codex":0.000021765523,"about_ca_system_score_gemma":0.000032268767,"threshold_uncertainty_score":0.27812234},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41155505,0.01200578,0.003081638,0.5700207,0.0011950051,0.000939387,0.00095667987,0.00010101794,0.00014475871],"genre_scores_gemma":[0.51516724,0.17367727,0.13336457,0.16245887,0.005002374,0.0000273918,0.006774604,0.00027043867,0.0032572679],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949122,0.00053114013,0.002661858,0.0001346791,0.0009488022,0.0008113017],"domain_scores_gemma":[0.9949333,0.00033861946,0.001540239,0.0011997861,0.0005486219,0.0014394195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016231231,0.00021126853,0.0008707994,0.00030803235,0.00021935419,0.00025148437,0.0007290523,0.00008291582,0.00009122934],"category_scores_gemma":[0.0024900956,0.00011614731,0.000056410652,0.00046156193,0.00015803652,0.0021292334,0.00035637457,0.0009132139,0.000019238754],"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":[0.000075323886,0.00043531606,0.0024758584,0.0024947426,0.00023705939,0.00000893032,0.016161954,0.00003471578,0.0000018562773,0.00020894251,0.08532528,0.89254004],"study_design_scores_gemma":[0.0022969139,0.0010894464,0.010791461,0.0008293171,0.000016290576,0.0006218132,0.016777549,0.021372888,9.727041e-7,0.00011236391,0.9459433,0.00014766253],"about_ca_topic_score_codex":0.00005876952,"about_ca_topic_score_gemma":0.000045123754,"teacher_disagreement_score":0.89239234,"about_ca_system_score_codex":0.00038894947,"about_ca_system_score_gemma":0.0023819252,"threshold_uncertainty_score":0.56254536},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906851,0.000008832092,0.0008214589,0.007708484,0.00047895755,0.00010044156,0.000043275668,0.000013789135,0.00013967871],"genre_scores_gemma":[0.98765194,0.000095227864,0.0075167655,0.004623465,0.00008278308,0.0000017181643,0.000012474414,0.000007672009,0.000007932341],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99802107,0.00008471623,0.0006186789,0.00003963801,0.0010829766,0.00015294526],"domain_scores_gemma":[0.9968935,0.0008812255,0.001492288,0.00016890325,0.00031606134,0.0002480152],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0010712482,0.000087433065,0.0003114612,0.00011418102,0.000082090744,0.000040608793,0.00012800013,0.000047576163,0.00002198614],"category_scores_gemma":[0.010198481,0.000056568493,0.000101387704,0.00025123244,0.00015016568,0.00009300883,0.000041723662,0.0007375343,0.000010361896],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030701366,0.0005242473,0.8980846,0.0002467586,0.00041596947,0.00009124102,0.0005609873,0.000087963264,0.00014033024,0.001688795,0.03092948,0.066922635],"study_design_scores_gemma":[0.0016115801,0.00024970784,0.97045225,0.00055867736,0.00015451007,0.00044549152,0.00016105102,0.013247707,0.000032887805,0.00025703164,0.01269289,0.00013621266],"about_ca_topic_score_codex":0.000006677413,"about_ca_topic_score_gemma":0.000008984997,"teacher_disagreement_score":0.072367676,"about_ca_system_score_codex":0.00020969726,"about_ca_system_score_gemma":0.0006770972,"threshold_uncertainty_score":0.998139},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073234916,0.028769543,0.038666762,0.85438603,0.0026045658,0.0013326212,0.00045897017,0.0001060403,0.00044055315],"genre_scores_gemma":[0.79959995,0.010764832,0.03384589,0.15310189,0.0020064956,0.000015147588,0.0005160938,0.00005131249,0.00009839483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99479115,0.0011381655,0.0016247192,0.00028741476,0.0016887758,0.00046978233],"domain_scores_gemma":[0.99493116,0.00083527656,0.0020781744,0.00040435712,0.0009901932,0.0007608536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007211339,0.00019590967,0.00053240015,0.00043303272,0.00034309682,0.00010816551,0.00053865794,0.00005702025,0.00003471778],"category_scores_gemma":[0.0018407986,0.00014773586,0.0002374299,0.0003859266,0.00007464778,0.0003783025,0.00008531726,0.0006365528,0.000039368544],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030108688,0.0011503624,0.056154765,0.00007997162,0.00085123186,0.00004201314,0.0019020113,0.00039164003,0.0000030740273,0.011206637,0.053660855,0.87154657],"study_design_scores_gemma":[0.0012800718,0.0006775691,0.45554763,0.0002971264,0.000020491923,0.00046566094,0.00042845987,0.00016606365,9.021528e-7,0.001635643,0.53937304,0.000107360276],"about_ca_topic_score_codex":0.003453354,"about_ca_topic_score_gemma":0.0004745356,"teacher_disagreement_score":0.8714392,"about_ca_system_score_codex":0.0006882368,"about_ca_system_score_gemma":0.0018016396,"threshold_uncertainty_score":0.60244936},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906969,0.0014644541,0.0004954209,0.0016854609,0.0014992448,0.0014437198,0.001710467,0.00030153582,0.00070277555],"genre_scores_gemma":[0.9907697,0.000039405928,0.00014820784,0.008096372,0.00045288404,0.00011129206,0.00012517557,0.00008600918,0.00017094795],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99577767,0.00056780444,0.0011301329,0.0009414634,0.00083208847,0.0007508285],"domain_scores_gemma":[0.9959573,0.00051185326,0.00045494092,0.0020555428,0.00036768467,0.0006526684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024300804,0.00047462588,0.00079335156,0.00035991508,0.00013149975,0.000044584354,0.0006219693,0.0001675531,0.00035156248],"category_scores_gemma":[0.0038168915,0.00038981682,0.0002486026,0.0014034741,0.00030577552,0.00014412898,0.00014651242,0.0007471581,0.00030146734],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015303786,0.0003095506,0.96984196,0.00012199023,0.000028157408,0.000074959724,0.000055842545,0.0029092217,0.010827152,0.00006403048,0.012645271,0.0015914803],"study_design_scores_gemma":[0.0022389728,0.00019707729,0.81310755,0.000115008486,0.000015036245,0.000007755446,0.000011316884,0.001025396,0.0010477711,0.000030015864,0.18184899,0.00035510873],"about_ca_topic_score_codex":0.00014811788,"about_ca_topic_score_gemma":0.002166872,"teacher_disagreement_score":0.16920371,"about_ca_system_score_codex":0.00009965412,"about_ca_system_score_gemma":0.00056391023,"threshold_uncertainty_score":0.9998554},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8666367,0.000056543387,0.1324145,0.000006150346,0.0002641985,0.0002256501,0.00001713986,0.000047761765,0.000331366],"genre_scores_gemma":[0.8390286,0.000010884192,0.16002192,0.0007667793,0.00014049186,0.0000033229178,0.000010023282,0.000016148553,0.0000018241736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973145,0.000052494423,0.001018825,0.00015154794,0.0011824967,0.00028010109],"domain_scores_gemma":[0.9974566,0.0010187696,0.00040003637,0.00059306755,0.00018074064,0.00035077892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016801072,0.00017237436,0.0003776793,0.00016152568,0.00021167612,0.00009283045,0.00025686892,0.00015638792,0.0002771672],"category_scores_gemma":[0.0019342796,0.00011221673,0.00008615829,0.00020910994,0.00016812085,0.0003672336,0.00033889443,0.00045574727,0.000008576922],"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":[0.0007082064,0.00016180136,0.02655516,0.000660259,0.000075655975,0.0000671679,0.0025805659,0.00032387415,0.0024494403,0.0004096322,0.000115416246,0.9658928],"study_design_scores_gemma":[0.0015169631,0.000072384435,0.0106363185,0.000883228,0.000048354752,0.00021424388,0.00076010183,0.9838624,0.00025621438,0.0005308027,0.0010475804,0.00017142185],"about_ca_topic_score_codex":0.00001685462,"about_ca_topic_score_gemma":0.00009509882,"teacher_disagreement_score":0.9835385,"about_ca_system_score_codex":0.000016615979,"about_ca_system_score_gemma":0.00029700814,"threshold_uncertainty_score":0.45760655},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4736162,0.029894294,0.022496723,0.022604898,0.008887025,0.003819375,0.41613397,0.0027062641,0.019841233],"genre_scores_gemma":[0.91528636,0.0064965333,0.0045677405,0.0062660635,0.0027333647,0.000024368657,0.06382701,0.000077251054,0.00072130724],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706656,0.00023681164,0.0004391488,0.0012120453,0.0005074677,0.0005379958],"domain_scores_gemma":[0.99423623,0.00038387103,0.00015037044,0.004657909,0.0001741736,0.0003974632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017400462,0.0003228618,0.0006172615,0.000035167814,0.00020394477,0.00011689455,0.0011741698,0.00018070062,0.000060524846],"category_scores_gemma":[0.0012307981,0.0002946821,0.00007747648,0.0002762832,0.0013810797,0.0004435492,0.0015188216,0.0003642167,0.000056517252],"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":[0.000057790614,0.00011678084,0.045084894,0.0005010053,0.00022985452,0.00004098087,0.0002639195,2.0580045e-7,0.00096265407,0.0003314924,0.90821755,0.044192854],"study_design_scores_gemma":[0.0016656074,0.00007179078,0.05163749,0.00014735643,0.000093740055,0.00013814759,0.00063181936,0.0009612994,0.000067818386,0.00019415158,0.9439583,0.00043252046],"about_ca_topic_score_codex":0.001072314,"about_ca_topic_score_gemma":0.0013185282,"teacher_disagreement_score":0.44167015,"about_ca_system_score_codex":0.0000641977,"about_ca_system_score_gemma":0.0004930328,"threshold_uncertainty_score":0.9999505},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9831899,0.0022610505,0.00008441264,0.013178505,0.000026506734,0.00087533955,0.00032324003,0.000013275767,0.00004781022],"genre_scores_gemma":[0.99804246,0.0008791167,0.000107442844,0.0006918284,0.000017130322,0.000021545537,0.00021663624,0.000013112035,0.000010728406],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961049,0.0018847735,0.000607907,0.00030055616,0.0005765387,0.0005253128],"domain_scores_gemma":[0.9974788,0.00062140456,0.00040872078,0.00060945103,0.00045013498,0.00043152898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005771109,0.00014664074,0.00043929636,0.00014571531,0.0002165117,0.000025854984,0.00021060293,0.000032421773,0.000012865858],"category_scores_gemma":[0.0032664207,0.00008901104,0.000034600256,0.00063058326,0.00030815697,0.00012075676,0.000046407167,0.000155133,2.4057186e-7],"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":[0.00008643289,0.00017739214,0.99177194,0.00021301801,0.000028210317,0.0000013312274,0.0024328835,0.0000019594797,0.000006335601,0.00004925023,0.0035890646,0.0016421586],"study_design_scores_gemma":[0.0016468643,0.00035538533,0.9950184,0.0000531158,0.000002129384,0.000013464511,0.0016845232,0.0006507488,2.7233986e-7,0.0000030826438,0.0005092507,0.00006275607],"about_ca_topic_score_codex":0.74813175,"about_ca_topic_score_gemma":0.82610655,"teacher_disagreement_score":0.0779748,"about_ca_system_score_codex":0.00089553045,"about_ca_system_score_gemma":0.033225305,"threshold_uncertainty_score":0.9722554},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044625696,0.92996496,0.000034264798,0.06748716,0.00026062498,0.0013374458,0.000845759,0.0000013301952,0.00002384748],"genre_scores_gemma":[0.00018270033,0.9955303,0.00011884069,0.0027055203,0.0011105569,0.00008584208,0.00009018398,0.000017800807,0.00015824883],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710244,0.0005772977,0.0007899718,0.00016360873,0.0011373932,0.00022931235],"domain_scores_gemma":[0.99771357,0.00072972185,0.00075452647,0.00023831498,0.00022247725,0.00034137652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037757524,0.00013627103,0.00069549144,0.00014574647,0.00012207258,0.00004629214,0.00046867,0.00006199613,0.00010190222],"category_scores_gemma":[0.0007682906,0.00006383365,0.00020210717,0.000117843956,0.000500666,0.00010280218,0.00023447283,0.00057670666,0.0000017577474],"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":[0.00002193385,0.00010275803,0.00096747605,0.023615886,0.0003131165,0.0000014789642,0.000024186886,2.8850806e-8,3.119042e-7,0.00015082878,0.022229558,0.95257246],"study_design_scores_gemma":[0.0003487071,0.00017727626,0.0018848327,0.030913236,0.000061345825,0.00017868364,0.000040557738,0.000004656062,1.0883601e-7,0.00007999143,0.966266,0.00004460829],"about_ca_topic_score_codex":0.000014456233,"about_ca_topic_score_gemma":0.000013745992,"teacher_disagreement_score":0.9525278,"about_ca_system_score_codex":0.00032205397,"about_ca_system_score_gemma":0.001470014,"threshold_uncertainty_score":0.26077417},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98689497,0.000098669116,0.010158231,0.0019422069,0.00035055313,0.00033281767,0.000062060724,0.000012741196,0.00014776213],"genre_scores_gemma":[0.9946714,0.0001361734,0.0012987076,0.003504692,0.00031262747,0.00001738304,0.000031511987,0.00000925045,0.00001825777],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996436,0.00012870764,0.0012830703,0.000052176376,0.0018355148,0.00026452166],"domain_scores_gemma":[0.9951078,0.00011101883,0.0034106376,0.00033606563,0.00055829925,0.00047615467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022376203,0.00011877963,0.0006052231,0.0002666229,0.00005343967,0.000008331156,0.00030130678,0.00005781862,0.00007153857],"category_scores_gemma":[0.0017041971,0.00007992026,0.0003296672,0.0015082348,0.000036560967,0.00033252247,0.00011649489,0.00023234589,0.000026626753],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011561968,0.00028294197,0.95500267,0.000056923083,0.0013585667,6.362024e-7,0.0027169667,0.000077288394,0.000103588485,0.00002666367,0.0068256366,0.033432484],"study_design_scores_gemma":[0.0004900601,0.0001479687,0.9828478,0.000047639125,0.0010455257,0.000008310875,0.0006828894,0.0025858674,0.00014464221,0.000013892141,0.011881309,0.000104090555],"about_ca_topic_score_codex":0.00005402915,"about_ca_topic_score_gemma":0.000018140308,"teacher_disagreement_score":0.033328395,"about_ca_system_score_codex":0.0004721069,"about_ca_system_score_gemma":0.0001141481,"threshold_uncertainty_score":0.32590535},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33958247,0.00008048082,0.2906196,0.03355702,0.0012578209,0.33088633,0.0033015106,0.00012765234,0.0005870912],"genre_scores_gemma":[0.90051323,0.000032676486,0.06359811,0.0055397,0.0016174881,0.028248874,0.00019964247,0.0001049029,0.00014534943],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99707174,0.00025124048,0.0011072991,0.000082185106,0.0012549902,0.00023255714],"domain_scores_gemma":[0.9929505,0.0008017275,0.004826121,0.0005772471,0.0006400998,0.00020430525],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0062627555,0.00011876048,0.00044880528,0.000053431122,0.00018196095,0.000040991286,0.0008879995,0.000043598193,0.000013588641],"category_scores_gemma":[0.025505105,0.00006333542,0.0002341812,0.00023158832,0.00017538284,0.0005599092,0.0002229235,0.0004101842,6.3586435e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011220415,0.0018129308,0.7005271,0.008151988,0.002916083,0.000009545184,0.016573152,0.000059541388,0.00020203849,0.00055515236,0.16673133,0.091240786],"study_design_scores_gemma":[0.021220768,0.002879674,0.6673876,0.0052508176,0.0017637917,0.00009195268,0.011226024,0.18841438,0.0005548888,0.010490167,0.089805275,0.000914688],"about_ca_topic_score_codex":0.000039339102,"about_ca_topic_score_gemma":0.000041764146,"teacher_disagreement_score":0.5609308,"about_ca_system_score_codex":0.00021151145,"about_ca_system_score_gemma":0.000819665,"threshold_uncertainty_score":0.9827035},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99817634,0.00036834288,0.00001457971,0.00032989396,0.000091605485,0.00016046719,0.0007210507,0.000003021771,0.0001346874],"genre_scores_gemma":[0.9982646,0.00074227003,0.00047376717,0.0002889428,0.00009264101,0.00000962556,0.000031528918,0.000014912149,0.000081695005],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99670875,0.000255827,0.0008020634,0.00023463122,0.0016744836,0.00032424636],"domain_scores_gemma":[0.99898964,0.00014414416,0.00037288462,0.00018266712,0.000041098236,0.0002695759],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001281792,0.00012788405,0.00041316097,0.0011894817,0.000054813667,0.000014508326,0.0003564055,0.000053879598,0.002516008],"category_scores_gemma":[0.00003855996,0.00010882941,0.00020247571,0.0002491118,0.0002905341,0.00013356392,0.00015306362,0.0005609718,0.000013833728],"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":[0.0016107217,0.0009992865,0.9743585,0.000018608118,0.00071321,0.00017208196,0.0027327493,0.0008137642,0.003353703,0.0000039360493,0.000016000437,0.015207425],"study_design_scores_gemma":[0.00093901553,0.0003943448,0.9957527,0.00022453033,0.000048015085,0.00007866468,0.0012440471,0.0010472646,0.00009517892,0.000014708303,0.00009056964,0.00007094667],"about_ca_topic_score_codex":0.017126033,"about_ca_topic_score_gemma":0.017219724,"teacher_disagreement_score":0.0213942,"about_ca_system_score_codex":0.0012205282,"about_ca_system_score_gemma":0.00023373647,"threshold_uncertainty_score":0.9983958},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9757787,0.0012617786,0.0005923514,0.00012092689,0.0030004825,0.001447443,0.0014008621,0.004508757,0.011888689],"genre_scores_gemma":[0.99626863,0.00006379756,0.00006515025,0.0006614146,0.0010175473,0.0005211266,0.0010257235,0.00012815479,0.00024845314],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964866,0.00021475261,0.0006600611,0.0010031916,0.00077410234,0.00086128566],"domain_scores_gemma":[0.9958824,0.00024230298,0.00031547731,0.0016757313,0.00041833165,0.0014657819],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003116397,0.0006214747,0.00084896723,0.00043392935,0.00044713152,0.00013697277,0.0002913381,0.00015176849,0.0009413998],"category_scores_gemma":[0.0009768398,0.00060821575,0.00053238036,0.0008513606,0.00028543407,0.00026745512,0.00013779328,0.00057475007,0.000880315],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004469233,0.0011779862,0.9859225,0.0011167707,0.00021306737,0.00049749657,0.00010620954,0.0009425138,0.0010850871,0.00024611133,0.0055298693,0.0027154302],"study_design_scores_gemma":[0.008208948,0.0005190235,0.84916466,0.0004837969,0.0007361243,0.00018271488,0.00017692674,0.008313593,0.00030049408,0.000106169915,0.13012248,0.0016850698],"about_ca_topic_score_codex":0.00024001862,"about_ca_topic_score_gemma":0.00021749747,"teacher_disagreement_score":0.13675788,"about_ca_system_score_codex":0.00024613776,"about_ca_system_score_gemma":0.0006376392,"threshold_uncertainty_score":0.99997187},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99206495,0.0024707504,0.0015469701,0.0021703301,0.000641913,0.0008435195,0.00014921489,0.0000066840967,0.00010563578],"genre_scores_gemma":[0.99909264,0.000022386535,0.00011976693,0.0004019297,0.0001344264,0.000011950737,0.000007681503,0.00002061622,0.00018860862],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975761,0.0004857761,0.0010265281,0.00013928668,0.00031653614,0.00045574486],"domain_scores_gemma":[0.9958266,0.00033998265,0.0014238679,0.000404692,0.0012415529,0.0007632953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032024705,0.00013768407,0.0005723819,0.00023059383,0.00018976949,0.00008580863,0.0003222849,0.00008773018,0.000037181668],"category_scores_gemma":[0.0016736297,0.00008390883,0.0001688403,0.00036733047,0.00019796393,0.0005411617,0.000014853484,0.00041922487,0.0000029378803],"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":[0.000020115396,0.000027458325,0.98559207,0.00030571892,0.00020385087,0.000003864784,0.001816824,0.00007245378,0.00024679335,0.00008951244,0.001979416,0.009641901],"study_design_scores_gemma":[0.0007858858,0.00021477145,0.9867587,0.00040641072,0.000023125482,0.00018007556,0.0005593391,0.00035411384,0.000013919987,0.00007945218,0.010549137,0.00007507162],"about_ca_topic_score_codex":0.29061264,"about_ca_topic_score_gemma":0.30638337,"teacher_disagreement_score":0.017434034,"about_ca_system_score_codex":0.0014402563,"about_ca_system_score_gemma":0.018874291,"threshold_uncertainty_score":0.9866878},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3266954,0.008667042,0.053837057,0.60255843,0.0010028861,0.0023598284,0.0006627444,0.00030427848,0.0039123124],"genre_scores_gemma":[0.5542859,0.039496753,0.1288322,0.27472952,0.0014587916,0.000008301921,0.00084370165,0.0000660601,0.00027880483],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9924878,0.00037168866,0.0048803296,0.000075558586,0.001196424,0.0009882217],"domain_scores_gemma":[0.99218947,0.00027838684,0.0041749915,0.00058585434,0.00089465553,0.0018766406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013278612,0.00028555977,0.00119444,0.000544091,0.00028747425,0.00022793954,0.00047026752,0.00011261253,0.00003664071],"category_scores_gemma":[0.002766221,0.00017544639,0.00019159552,0.0007068151,0.0002545305,0.0015313607,0.00010563694,0.0010964922,0.000016192098],"study_design_candidate":"not_applicable","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":[0.00015324529,0.0005763122,0.011212256,0.0012310852,0.00019415138,0.000007091403,0.012952821,0.000027777736,4.8619546e-7,0.0025890095,0.0471874,0.92386836],"study_design_scores_gemma":[0.012184376,0.0019823897,0.057332266,0.0004963439,0.000040211024,0.0027316583,0.008337443,0.019387133,2.9751226e-7,0.0004238118,0.89680374,0.00028031104],"about_ca_topic_score_codex":0.000012187463,"about_ca_topic_score_gemma":0.000020823898,"teacher_disagreement_score":0.92358804,"about_ca_system_score_codex":0.00047621498,"about_ca_system_score_gemma":0.006282401,"threshold_uncertainty_score":0.9993511},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010925721,0.98987263,0.00027641456,0.0003916516,0.0009319765,0.0058971406,0.001320703,0.00016919104,0.000047693426],"genre_scores_gemma":[0.00067857123,0.9945685,0.000019137598,0.0006206982,0.0003554743,0.0016723956,0.0020280636,0.000051819938,0.0000053293124],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99321294,0.0016316215,0.0030875686,0.0006016218,0.00075115764,0.0007150691],"domain_scores_gemma":[0.99442744,0.0008776505,0.0011761282,0.0010907107,0.00091227685,0.0015157617],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0049269754,0.0005654446,0.0043949857,0.0008684379,0.00014044564,0.000037479233,0.00035743718,0.0005442945,0.000032365646],"category_scores_gemma":[0.011590965,0.00039410088,0.00015743429,0.0005801411,0.000610511,0.000477442,0.0002332409,0.0005959214,0.0000032842681],"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":[0.00023122584,0.000064702595,0.000148432,0.6450426,0.00007472647,0.000005255884,0.00672783,4.139156e-9,1.8568064e-8,0.0003966565,0.00088561664,0.34642297],"study_design_scores_gemma":[0.0016401684,0.000633873,0.000025323337,0.27832535,0.00040696937,0.00051276933,0.009422027,0.000012298514,2.0925242e-9,0.0000025590514,0.70880574,0.00021290623],"about_ca_topic_score_codex":0.0001050206,"about_ca_topic_score_gemma":0.000007659436,"teacher_disagreement_score":0.70792013,"about_ca_system_score_codex":0.00028477586,"about_ca_system_score_gemma":0.0037903334,"threshold_uncertainty_score":0.9998511},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006735171,0.99522704,0.0008084376,0.00025795432,0.00016929628,0.0033926007,0.000049578124,0.000033322456,0.000055045206],"genre_scores_gemma":[0.00000721607,0.9963971,0.0024309587,0.0002255684,0.000050713592,0.00038511696,0.00047889416,0.000018969316,0.000005501863],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99440354,0.00026518185,0.0034326182,0.00016542371,0.0012926329,0.00044060504],"domain_scores_gemma":[0.99648637,0.0012614045,0.0010191445,0.00038100584,0.00037596026,0.00047614003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009304757,0.00029428708,0.0029537762,0.00059953716,0.000078132674,0.000032299708,0.00023207995,0.00040135867,0.0000062047025],"category_scores_gemma":[0.004851019,0.00021391117,0.000094715106,0.0004986528,0.0004026587,0.0001633547,0.00033674572,0.0013260184,0.000018226865],"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":[0.0000053973718,0.000027275599,0.00001549293,0.47859806,0.00012310104,6.948946e-7,0.00045503498,5.1476878e-8,1.6452796e-10,0.0024060926,0.0020568254,0.516312],"study_design_scores_gemma":[0.0003839381,0.00011887186,0.000008263818,0.2812264,0.00010652347,0.000035133377,0.00017177872,0.00013509986,6.706879e-9,0.00009056331,0.71760094,0.00012247369],"about_ca_topic_score_codex":0.000020415811,"about_ca_topic_score_gemma":0.0000045052006,"teacher_disagreement_score":0.7155441,"about_ca_system_score_codex":0.00019648095,"about_ca_system_score_gemma":0.0015582272,"threshold_uncertainty_score":0.8723045},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99679255,0.000019514744,0.0010960657,0.0010994308,0.00041421741,0.00031158837,0.000050376402,0.000022837981,0.00019344313],"genre_scores_gemma":[0.9961746,0.00029538258,0.0024197008,0.000782461,0.00019926413,0.000004528677,0.000031423,0.000013039159,0.00007961149],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99481976,0.00030456617,0.0017178893,0.00007926137,0.00286718,0.00021133047],"domain_scores_gemma":[0.9918974,0.000594208,0.0061141183,0.0003682626,0.0007402288,0.0002857838],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003757165,0.00012845325,0.0005563627,0.0002360079,0.00013582953,0.000018126839,0.00033874143,0.00007665269,0.000044231907],"category_scores_gemma":[0.022854388,0.00009201537,0.00028125625,0.00086510065,0.00011058538,0.00027369361,0.00008854906,0.00044434977,0.00000975624],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013252991,0.000107312546,0.98800725,0.000051335235,0.00027576188,0.0000026574928,0.0009663063,0.0005195841,0.0001374089,0.0000033312976,0.0033967178,0.006399821],"study_design_scores_gemma":[0.0007769598,0.00027978816,0.9847516,0.0002219161,0.00028534033,0.00002822493,0.0004945692,0.0125287045,0.000090039735,0.000061221814,0.00039825242,0.00008338612],"about_ca_topic_score_codex":0.0001951274,"about_ca_topic_score_gemma":0.00002871505,"teacher_disagreement_score":0.019097222,"about_ca_system_score_codex":0.0005568926,"about_ca_system_score_gemma":0.00019123986,"threshold_uncertainty_score":0.98537654},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038457674,0.980456,0.000016754933,0.015078252,0.001181254,0.0014175877,0.0013412759,0.000049336482,0.000074982105],"genre_scores_gemma":[0.0005357891,0.99077386,0.0013172688,0.0031573551,0.0012266196,0.000016958737,0.0028287913,0.00010584819,0.00003752809],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98486674,0.0030508519,0.005013318,0.0010267936,0.0047995313,0.0012427472],"domain_scores_gemma":[0.98935235,0.0013169944,0.004909249,0.001858387,0.0016659136,0.0008971172],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02216776,0.0006816617,0.0037998604,0.007962891,0.0002568585,0.00029139922,0.0038797094,0.00038409783,0.000062066196],"category_scores_gemma":[0.0037439342,0.0005701681,0.0007670691,0.0025569294,0.00039116442,0.00058567704,0.0008929934,0.004409199,0.000032496362],"study_design_candidate":"design_other","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":[0.00011189943,0.0009578049,0.010398652,0.008966292,0.0007123054,0.00051252433,0.00023990838,0.0000033869303,2.947365e-8,0.0004956696,0.011349081,0.96625245],"study_design_scores_gemma":[0.0011132166,0.00077372306,0.017288981,0.1218747,0.00008315733,0.0004859058,0.00005454832,0.000013572058,2.666644e-8,0.00062269304,0.8574042,0.00028532534],"about_ca_topic_score_codex":0.0009222086,"about_ca_topic_score_gemma":0.00019667156,"teacher_disagreement_score":0.9659671,"about_ca_system_score_codex":0.000934262,"about_ca_system_score_gemma":0.006287411,"threshold_uncertainty_score":0.999675},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09151675,0.00050272176,0.89624935,0.008766179,0.0009548902,0.0014268378,0.00048365231,0.000044904482,0.000054718435],"genre_scores_gemma":[0.87825525,0.00012653031,0.11794066,0.0024284746,0.0005625898,0.00006854528,0.00058426283,0.000024953802,0.000008761984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977155,0.00005895577,0.0011409941,0.00013121575,0.0006693343,0.0002840212],"domain_scores_gemma":[0.99657416,0.00021268698,0.0012017357,0.0001780062,0.0016232468,0.00021013356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011639984,0.00014409522,0.00035125436,0.0004275693,0.0001323041,0.00008787869,0.00030925867,0.000061597595,0.000008160813],"category_scores_gemma":[0.0011989294,0.00012641368,0.00028059297,0.00007728714,0.000043651566,0.0006145527,0.00008765504,0.00023057817,8.316531e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017957044,0.00035497747,0.01990149,0.0009539598,0.0003695732,0.0000027723486,0.0020436516,0.0029639264,0.000028100534,0.0006615663,0.0042699007,0.96665436],"study_design_scores_gemma":[0.00547751,0.0016658057,0.011357585,0.0010070364,0.00008548608,0.00009551721,0.0001767218,0.6313628,0.000059677524,0.003900329,0.34458792,0.00022361864],"about_ca_topic_score_codex":0.00007002028,"about_ca_topic_score_gemma":0.00004139677,"teacher_disagreement_score":0.9664308,"about_ca_system_score_codex":0.000115087074,"about_ca_system_score_gemma":0.000596138,"threshold_uncertainty_score":0.5155},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081948104,0.98068464,0.0014353577,0.000042033498,0.0015762631,0.0045975926,0.009498576,0.0012052235,0.00014081872],"genre_scores_gemma":[0.037051257,0.9461076,0.0002087819,0.00038982264,0.0008781781,0.00086661254,0.014048414,0.000288297,0.00016098037],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942719,0.0004311849,0.0020504596,0.0015200296,0.00084242213,0.0008840498],"domain_scores_gemma":[0.99337494,0.00056321424,0.0020415343,0.0021797528,0.0006851093,0.0011554568],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028633128,0.0012514694,0.00394382,0.00048493047,0.00023105384,0.00011473686,0.00025841978,0.00034776906,0.00011398874],"category_scores_gemma":[0.003928171,0.0011497135,0.0029009902,0.0013244024,0.000258933,0.0002463016,0.000112484166,0.00033840974,0.000013472072],"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":[0.00042688785,0.0016778469,0.17218919,0.05487236,0.0015797722,0.0009850392,0.000005282983,0.0013000246,5.927314e-7,0.00015566152,0.012833809,0.75397354],"study_design_scores_gemma":[0.0015605757,0.00038567494,0.0342127,0.0113004,0.005678212,0.0002564747,0.0000013334966,0.00020417785,6.2058547e-7,0.00042028274,0.9447522,0.001227305],"about_ca_topic_score_codex":0.00008945701,"about_ca_topic_score_gemma":0.00007723389,"teacher_disagreement_score":0.93191844,"about_ca_system_score_codex":0.00077069155,"about_ca_system_score_gemma":0.0029294358,"threshold_uncertainty_score":0.99909526},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88304436,0.00033920986,0.081806146,0.024391282,0.000788585,0.0022243364,0.0048190774,0.00031537755,0.0022716087],"genre_scores_gemma":[0.31255275,0.000228102,0.67828614,0.0060920487,0.0003047277,0.000015712838,0.0024038726,0.000048358437,0.00006829056],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99691397,0.000056370725,0.0019738476,0.000115607356,0.0004329061,0.00050729234],"domain_scores_gemma":[0.9955419,0.000034671953,0.0017069009,0.0010057471,0.0012034121,0.00050741347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028363117,0.00017281828,0.0005527856,0.0010722534,0.00009857456,0.00002808503,0.0007864461,0.00008814546,0.000047186135],"category_scores_gemma":[0.00073957734,0.00014130872,0.000051069845,0.00093258556,0.0000921153,0.0010341026,0.00023566671,0.0003673363,0.0000069264406],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012706162,0.0063517275,0.10093346,0.011513435,0.0013115921,0.00018372195,0.0033501906,0.000035795885,0.0000449302,0.0052651623,0.03270763,0.8370317],"study_design_scores_gemma":[0.021403572,0.0043101856,0.22111075,0.0011630537,0.00014453602,0.00065229955,0.011503507,0.031630535,0.00006573586,0.00032220394,0.70714575,0.00054787786],"about_ca_topic_score_codex":0.000003765451,"about_ca_topic_score_gemma":0.00001612801,"teacher_disagreement_score":0.83648384,"about_ca_system_score_codex":0.00022419273,"about_ca_system_score_gemma":0.0019718246,"threshold_uncertainty_score":0.57624024},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97022444,0.003652687,0.008931252,0.01571267,0.00048708878,0.0005640588,0.00036853028,0.000014030696,0.00004525697],"genre_scores_gemma":[0.99403024,0.0010093077,0.0041333116,0.00061977294,0.00011152105,0.0000054322895,0.00007020163,0.000011358139,0.000008863053],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978034,0.000054548844,0.0010722859,0.00012734265,0.0007855481,0.0001568951],"domain_scores_gemma":[0.9929718,0.00029419153,0.0017542873,0.00017041892,0.004669832,0.00013948033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011573486,0.00009927534,0.0003805595,0.00042440946,0.00003563174,0.000010367079,0.00030320362,0.000051509644,0.000014119397],"category_scores_gemma":[0.0027944688,0.00008861125,0.00016792839,0.00034034005,0.00014992311,0.00022003544,0.000039799448,0.00018030817,9.840791e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0077996138,0.0024229723,0.93754447,0.0023616364,0.0011087209,0.00010729098,0.0011163492,0.0012490272,0.0030618904,0.017547773,0.0022357558,0.023444505],"study_design_scores_gemma":[0.008933578,0.0035693983,0.94995683,0.0054372414,0.00011795988,0.0007317773,0.00047528572,0.00025858625,0.0071762265,0.019461278,0.0036742366,0.00020758888],"about_ca_topic_score_codex":0.00035451312,"about_ca_topic_score_gemma":0.000062013525,"teacher_disagreement_score":0.023805806,"about_ca_system_score_codex":0.00011196837,"about_ca_system_score_gemma":0.0018371589,"threshold_uncertainty_score":0.36134616},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55198246,0.0635611,0.28356847,0.091135316,0.0051424634,0.002119879,0.0022931702,0.00015014494,0.00004700738],"genre_scores_gemma":[0.98693305,0.00534234,0.0038916147,0.0029841657,0.00064965786,0.000007558312,0.00016786193,0.000019098128,0.0000046320847],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972811,0.00017656159,0.0010898677,0.00020521686,0.0009395891,0.0003077106],"domain_scores_gemma":[0.9954335,0.00053254527,0.001168508,0.0004624194,0.002016645,0.00038638368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018079776,0.00018330768,0.00049186184,0.00050416286,0.00010553586,0.00003997444,0.00073280593,0.00007581049,0.000045078243],"category_scores_gemma":[0.00089344237,0.00012606653,0.00044875202,0.00048582064,0.00020027056,0.00017701676,0.000021911823,0.00046993655,0.0000011594325],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046576406,0.0020678027,0.6800663,0.00025652625,0.0010702207,0.000105285166,0.0001640594,0.0010876317,0.00009316491,0.013981368,0.0023077158,0.29414228],"study_design_scores_gemma":[0.0016592566,0.0016838553,0.9647823,0.00063076743,0.000066153705,0.000072861665,0.00007684226,0.0020806254,0.000011339321,0.007068412,0.02174088,0.00012671918],"about_ca_topic_score_codex":0.000053840857,"about_ca_topic_score_gemma":0.00003992079,"teacher_disagreement_score":0.43495062,"about_ca_system_score_codex":0.00011121193,"about_ca_system_score_gemma":0.0011285454,"threshold_uncertainty_score":0.5140844},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97232234,0.00049173826,0.00022399047,0.024113396,0.002548137,0.00013778993,0.00008906416,0.000024244811,0.00004931329],"genre_scores_gemma":[0.9927972,0.00083350326,0.002320507,0.0018778642,0.0018334677,8.7555856e-7,0.00013297788,0.000028435594,0.0001751847],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99754834,0.000059281396,0.00087233447,0.0001900099,0.0009878513,0.00034217778],"domain_scores_gemma":[0.9976118,0.00014006389,0.00033829655,0.00037196183,0.00023591593,0.0013019376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052882853,0.00017639046,0.00055132515,0.00037228828,0.000025733785,0.00006086466,0.00028644578,0.00014616895,0.00026812748],"category_scores_gemma":[0.0015181694,0.00013059977,0.00021231259,0.00045150155,0.00030690126,0.0003694939,0.00010313803,0.000944517,0.000040269206],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035269891,0.013491802,0.6522635,0.0009177335,0.0004921437,0.026270859,0.0004852539,0.000008276885,0.027203161,0.0004492461,0.057472058,0.21741895],"study_design_scores_gemma":[0.0059852977,0.0005781197,0.80992556,0.0010639755,0.0002741493,0.0007441943,0.00060580595,0.0013376069,0.0001390256,0.00062713225,0.17841983,0.00029933057],"about_ca_topic_score_codex":0.00001322048,"about_ca_topic_score_gemma":0.000095525655,"teacher_disagreement_score":0.21711963,"about_ca_system_score_codex":0.00005214721,"about_ca_system_score_gemma":0.00050891336,"threshold_uncertainty_score":0.5325704},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99710953,0.0005874402,0.00001659881,0.00048327618,0.00002441665,0.0005090426,0.00006119315,0.00011681252,0.0010916602],"genre_scores_gemma":[0.9911055,0.00010812919,0.007866922,0.00028160546,0.00030037863,0.0000774058,0.000063442836,0.000038874987,0.00015772846],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985581,0.00007799926,0.0002561456,0.0003063944,0.00046811355,0.00033323438],"domain_scores_gemma":[0.99892205,0.00013856674,0.000050199757,0.0005501129,0.000106989144,0.00023209638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018466357,0.00014461784,0.00039783152,0.000102605285,0.000027876318,0.000023359573,0.00014153525,0.00006769589,0.0002715021],"category_scores_gemma":[0.000292468,0.00012315932,0.000028048798,0.00023287092,0.00005720501,0.00022663527,0.00008474654,0.000362737,0.00052713987],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001224104,0.0006289418,0.98605824,0.00010775946,0.00015382816,0.00004640651,0.00020699923,0.0000141197,0.010990487,0.00000513798,0.00013941614,0.0015262475],"study_design_scores_gemma":[0.0011595224,0.00008629294,0.9888674,0.001058823,0.00005068225,0.0000011330916,0.000072764065,0.00075648224,0.007672902,0.000086444554,0.000047662586,0.00013989846],"about_ca_topic_score_codex":0.0029422299,"about_ca_topic_score_gemma":0.00006319895,"teacher_disagreement_score":0.007850323,"about_ca_system_score_codex":0.00011110075,"about_ca_system_score_gemma":0.00009162217,"threshold_uncertainty_score":0.67754924},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99175686,0.00027517864,0.00017341832,0.004604805,0.00034809264,0.002077191,0.0003584155,0.00021691904,0.00018910672],"genre_scores_gemma":[0.99453753,0.000012674458,0.0005265087,0.003707219,0.0005471202,0.00020165948,0.00039698053,0.000057474135,0.000012857781],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9948849,0.0007525315,0.000997911,0.0010464706,0.0011679655,0.0011501845],"domain_scores_gemma":[0.99546796,0.0001983139,0.00030919365,0.00063737377,0.0010443769,0.0023427657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0041188877,0.00042349374,0.0011800188,0.00037913863,0.00025821198,0.0001404633,0.00030063718,0.00017382263,0.000035602006],"category_scores_gemma":[0.0024869263,0.00041702093,0.00007057059,0.001645542,0.00022395319,0.0006453689,0.00022864898,0.00054070476,0.00001581148],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047969611,0.001034799,0.98046094,0.000079770856,0.00002806493,0.000037231734,0.007996605,0.000012141277,0.0000027125009,0.00010572038,0.0018305163,0.0079318015],"study_design_scores_gemma":[0.003944788,0.0011997392,0.98140496,0.000019706016,0.0000010198561,0.000012373894,0.0041577322,0.0018697169,1.772881e-7,0.00006877873,0.0069064694,0.00041456378],"about_ca_topic_score_codex":0.0006593902,"about_ca_topic_score_gemma":0.007917293,"teacher_disagreement_score":0.0075172377,"about_ca_system_score_codex":0.001326708,"about_ca_system_score_gemma":0.0030570482,"threshold_uncertainty_score":0.99982816},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9121133,0.00037566037,0.08463798,0.0003254547,0.000064723674,0.00041875654,0.00096010225,0.00012922013,0.00097481307],"genre_scores_gemma":[0.9940867,0.000007627949,0.004753419,0.0004202986,0.0002755593,0.000039612445,0.00039485114,0.000016773734,0.0000051704756],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974048,0.0002017966,0.00048327865,0.0007135608,0.0004492797,0.00074726844],"domain_scores_gemma":[0.9975525,0.00009097656,0.00009362716,0.0014760669,0.000060544957,0.0007262549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011139705,0.00023610755,0.00072420546,0.00067184394,0.00009722001,0.00004720811,0.0003715988,0.000112904185,0.00011069452],"category_scores_gemma":[0.00048306762,0.00021138575,0.00032197105,0.004569628,0.000068859554,0.00020144456,0.00046774631,0.0002517014,0.000025115134],"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":[0.00025917176,0.00030599604,0.9452107,0.000041339394,0.0011365099,0.0002293517,0.000041203817,0.00041932738,0.000013533863,0.00000883689,0.00003940572,0.052294616],"study_design_scores_gemma":[0.0018016148,0.00004179557,0.88186383,0.000018825227,0.00279985,0.0002564363,0.00008317682,0.11249479,0.0000025943991,0.000007987338,0.00037450044,0.0002545913],"about_ca_topic_score_codex":0.007164634,"about_ca_topic_score_gemma":0.016791679,"teacher_disagreement_score":0.112075455,"about_ca_system_score_codex":0.00007735879,"about_ca_system_score_gemma":0.000032113887,"threshold_uncertainty_score":0.99944675},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997033,0.0010838392,0.000057668945,0.00002281274,0.00006235613,0.0007940843,0.0007520809,0.000016933474,0.00017722645],"genre_scores_gemma":[0.997335,0.000049002956,0.0014531849,9.822315e-7,0.000056604138,0.00007257064,0.0009454896,0.000018200408,0.00006893529],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99789184,0.00033732163,0.00057551265,0.00033280574,0.00050937437,0.00035311835],"domain_scores_gemma":[0.99715805,0.0010380002,0.00015078131,0.0010668972,0.00035525108,0.00023101477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020798806,0.00010170162,0.0004002218,0.00024653735,0.000056768185,0.000007833349,0.00025224173,0.000042136347,0.000041285373],"category_scores_gemma":[0.0012773777,0.00009841664,0.00005049538,0.0002728834,0.00024134133,0.00045943665,0.00033441195,0.00015296535,0.0000021560797],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019802987,0.00066972343,0.9867848,0.0017308807,0.00003333605,0.000015630414,0.0007281264,0.000006253778,0.0051474837,0.00012170841,0.000065151835,0.0027165818],"study_design_scores_gemma":[0.0009772306,0.0011812604,0.9937543,0.00038521277,0.000023701585,0.00002206616,0.0013375576,0.00023150195,0.00020790772,0.000014295086,0.0017896721,0.00007527912],"about_ca_topic_score_codex":0.003913151,"about_ca_topic_score_gemma":0.0029219256,"teacher_disagreement_score":0.00696949,"about_ca_system_score_codex":0.0001174475,"about_ca_system_score_gemma":0.00035371122,"threshold_uncertainty_score":0.591554},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9019505,0.058619544,0.0000040754417,0.035599295,0.000759628,0.0009294448,0.00076406635,0.00037836676,0.0009950935],"genre_scores_gemma":[0.90289253,0.08800728,0.0002518269,0.007364058,0.00057721563,0.0001866431,0.0004797525,0.000101637685,0.00013908539],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99527824,0.00012978392,0.0007145163,0.001136655,0.0007902155,0.0019506072],"domain_scores_gemma":[0.99208164,0.0005754713,0.00021482543,0.0010991569,0.00035407473,0.005674823],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004743493,0.0007348755,0.0009824467,0.005330232,0.0006623904,0.00030731698,0.0005561057,0.00041395632,0.00067875336],"category_scores_gemma":[0.0010116034,0.000729449,0.00050209335,0.0045373887,0.0029057723,0.00074500823,0.00012545304,0.00060170906,0.000034977696],"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":[0.000051804924,0.00007658675,0.8605639,0.0002646823,0.0005375617,0.0014433826,0.0004896443,0.00001394611,0.0000032830164,0.00084244437,0.031286675,0.10442609],"study_design_scores_gemma":[0.0010505301,0.00011838614,0.5519778,0.0009563684,0.00015286305,0.00043765557,0.005983383,0.00008959216,0.0000027690278,0.0003556204,0.43818286,0.00069218397],"about_ca_topic_score_codex":0.25099593,"about_ca_topic_score_gemma":0.98059225,"teacher_disagreement_score":0.7295963,"about_ca_system_score_codex":0.00016841544,"about_ca_system_score_gemma":0.00059186347,"threshold_uncertainty_score":0.9998078},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4577117,0.001973515,0.52469456,0.011268388,0.0012426868,0.0006381799,0.00234218,0.00007979589,0.00004899309],"genre_scores_gemma":[0.9439768,0.0002196435,0.051239643,0.0010814554,0.0010892359,0.000027509252,0.002326886,0.000027650194,0.000011175836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99752533,0.00022328494,0.0010407026,0.00026292625,0.0006958737,0.00025185774],"domain_scores_gemma":[0.9966364,0.00024353569,0.00077507924,0.0003157635,0.0016139967,0.00041523424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011293456,0.00016346558,0.0003208648,0.0005864448,0.00017210584,0.000075599506,0.00027068576,0.00006726572,0.000044272936],"category_scores_gemma":[0.00023487945,0.00015809092,0.00023595974,0.00024628543,0.00007093081,0.00040062924,0.000021330778,0.00025530127,0.0000050967756],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00461557,0.0043317163,0.849459,0.00048806015,0.0009163701,0.002007546,0.00033429332,0.009825159,0.001613423,0.022972854,0.010181751,0.09325428],"study_design_scores_gemma":[0.0051761623,0.00041346945,0.8574723,0.0005423227,0.0002487695,0.0010201514,0.00028699497,0.09371015,0.000046293604,0.008394381,0.032395873,0.00029311556],"about_ca_topic_score_codex":0.0014132579,"about_ca_topic_score_gemma":0.00031709115,"teacher_disagreement_score":0.48626512,"about_ca_system_score_codex":0.0002801697,"about_ca_system_score_gemma":0.0009412121,"threshold_uncertainty_score":0.6446761},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7430392,0.1849718,0.00058464264,0.05216717,0.0032902882,0.008416169,0.0025947425,0.0005752783,0.004360711],"genre_scores_gemma":[0.92202324,0.07466124,0.0006762091,0.00089255284,0.00065287657,0.00020593153,0.0008276333,0.000021977932,0.00003833354],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971478,0.00022851767,0.0007635205,0.00009293191,0.00094036537,0.0008268904],"domain_scores_gemma":[0.99749345,0.00012195829,0.0001535586,0.0003700705,0.000619242,0.0012417077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030736213,0.0001435465,0.00032204582,0.0006300893,0.00044161387,0.00013902728,0.00007416556,0.000079188794,0.000008748715],"category_scores_gemma":[0.00013663052,0.00012074947,0.000031948188,0.0006734449,0.00017567865,0.0011049377,0.00015924092,0.00084534445,0.00008470675],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009294953,0.00009165449,0.14412148,0.009177951,0.00003520542,8.213063e-7,0.008648073,9.502214e-7,2.9224222e-7,0.0031729967,0.011042938,0.8236147],"study_design_scores_gemma":[0.00046346863,0.00017362082,0.3029646,0.00024746134,0.000004070613,0.000093923976,0.002061204,0.0012432351,6.1619994e-7,0.000030456875,0.6926234,0.00009399636],"about_ca_topic_score_codex":0.00057835336,"about_ca_topic_score_gemma":0.000072910385,"teacher_disagreement_score":0.8235207,"about_ca_system_score_codex":0.00019347113,"about_ca_system_score_gemma":0.0004290435,"threshold_uncertainty_score":0.49240205},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9729175,0.00058251905,0.00034739502,0.022038125,0.00028176824,0.0005356441,0.0002517083,0.00007648015,0.0029688634],"genre_scores_gemma":[0.99644756,0.00008282915,0.00014356662,0.002782796,0.00013708163,0.000018885083,0.00026244466,0.000017838698,0.00010699944],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984834,0.00018820842,0.00028370542,0.00028866107,0.00027787,0.00047814372],"domain_scores_gemma":[0.99890494,0.000089245215,0.0000788224,0.00027696724,0.000024580037,0.000625424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057672756,0.00012062236,0.00027741477,0.00011286512,0.000056732373,0.000020177764,0.000089508736,0.00003637826,0.00006610202],"category_scores_gemma":[0.0004786977,0.00011333854,0.000019993027,0.00018093057,0.000026201053,0.00008644258,0.000046498575,0.00013458454,0.000008839762],"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":[0.00003917147,0.00012135397,0.89171064,0.0004042498,0.00001004493,0.0000071591935,0.00010103403,0.0000023911152,0.000002792592,0.00052985834,0.02368306,0.08338822],"study_design_scores_gemma":[0.0012129764,0.000067614,0.7972838,0.00002573546,0.000001871562,0.0000057192465,0.00009881902,0.0017433297,5.818368e-7,0.000018929602,0.19945455,0.00008604687],"about_ca_topic_score_codex":0.6477731,"about_ca_topic_score_gemma":0.99765533,"teacher_disagreement_score":0.34988225,"about_ca_system_score_codex":0.00033172496,"about_ca_system_score_gemma":0.0025694529,"threshold_uncertainty_score":0.46218115},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6122518,0.012058627,0.35207224,0.01994046,0.0012231732,0.0015408032,0.0008049548,0.0000873818,0.000020566269],"genre_scores_gemma":[0.98642033,0.0008564797,0.009477378,0.0024174547,0.0005028122,0.00005416228,0.00019061958,0.00004244386,0.00003833568],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968017,0.00030783645,0.0011063948,0.0004115884,0.0009983362,0.00037418268],"domain_scores_gemma":[0.995444,0.00048595187,0.0011729829,0.00028642203,0.0018114076,0.000799207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017782192,0.0002870021,0.0005617695,0.0009490153,0.00017832474,0.00022876762,0.0003147693,0.00006081056,0.00003426254],"category_scores_gemma":[0.00063134125,0.0002296602,0.00031143788,0.0002790007,0.0002130654,0.00051651313,0.00008097851,0.00033453145,0.0000018299016],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010683394,0.00061707286,0.9618151,0.0010054751,0.0009367854,0.00042557495,0.000158398,0.00028371022,0.00013922376,0.0024404544,0.0015896478,0.019905154],"study_design_scores_gemma":[0.0049656504,0.00053428323,0.95603323,0.0018719891,0.00033698042,0.0003175745,0.00012233052,0.0014086317,0.000050878036,0.03110005,0.0029851024,0.00027330898],"about_ca_topic_score_codex":0.00047001528,"about_ca_topic_score_gemma":0.0002753784,"teacher_disagreement_score":0.37416852,"about_ca_system_score_codex":0.00010633721,"about_ca_system_score_gemma":0.0005660599,"threshold_uncertainty_score":0.93652713},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11815536,0.000053474574,0.8559894,0.02384243,0.0009654028,0.0005783772,0.0002841205,0.000041049127,0.00009039335],"genre_scores_gemma":[0.6491962,0.00004304396,0.33787066,0.011592243,0.0005095543,0.000010105558,0.0007418612,0.000016176797,0.000020166066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713767,0.00007266068,0.0018819248,0.000064315456,0.00055032945,0.00029312473],"domain_scores_gemma":[0.9947471,0.00037956084,0.0022067393,0.00027564628,0.0018645338,0.00052641996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031636802,0.00013348406,0.00043763244,0.00031190555,0.0000720315,0.0000839808,0.00016256337,0.00013931618,0.000063288346],"category_scores_gemma":[0.008228194,0.0001116478,0.0001383264,0.00024816365,0.000059705304,0.0007629722,0.000014704582,0.0005975827,0.000008135779],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016197605,0.0023824917,0.031946756,0.0064681736,0.00046185608,0.000010735678,0.020216785,0.000778995,0.0004325805,0.059691656,0.03360124,0.842389],"study_design_scores_gemma":[0.008473964,0.0033985358,0.050865132,0.0014648827,0.00012554617,0.00026387698,0.0039564455,0.5488471,0.000116851996,0.0052243304,0.3768418,0.0004215165],"about_ca_topic_score_codex":0.0000065559566,"about_ca_topic_score_gemma":0.00005575477,"teacher_disagreement_score":0.84196746,"about_ca_system_score_codex":0.00019906847,"about_ca_system_score_gemma":0.0037287076,"threshold_uncertainty_score":0.9850511},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5636864,0.00012333586,0.43462765,0.00013269254,0.00009226708,0.00060078414,0.00018156467,0.000043727003,0.0005115874],"genre_scores_gemma":[0.15673238,0.00041224962,0.8407809,0.0015968475,0.00011780406,0.000026981075,0.00031310206,0.00001292948,0.0000067835276],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99616057,0.000068329515,0.002004486,0.00037682778,0.0011284387,0.00026136078],"domain_scores_gemma":[0.99435127,0.0024924395,0.00055704516,0.0011003724,0.00037320887,0.0011256504],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0034366825,0.00019137753,0.00057857967,0.000066121036,0.00010035446,0.00007592701,0.0003669633,0.00013479916,0.000076623466],"category_scores_gemma":[0.01036886,0.00015037652,0.000046658166,0.00028800912,0.00015110771,0.0003796215,0.0011878205,0.00015846043,0.00016188346],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008972835,0.00037357974,0.06985315,0.0003068342,0.000077627534,0.000007020845,0.00009872626,0.0000137615,0.00001717972,0.000120079094,0.005119376,0.9231154],"study_design_scores_gemma":[0.0022899476,0.000072653056,0.51462275,0.0005718688,0.00006388847,0.00004172533,0.00016398427,0.4654892,9.381502e-7,0.00008739134,0.016364828,0.00023083654],"about_ca_topic_score_codex":0.000018607057,"about_ca_topic_score_gemma":0.000056876266,"teacher_disagreement_score":0.9228845,"about_ca_system_score_codex":0.000036483245,"about_ca_system_score_gemma":0.0006412478,"threshold_uncertainty_score":0.99796724},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9292705,0.0014847761,0.044742443,0.02089133,0.00045205213,0.000859252,0.002176782,0.000034153774,0.00008870581],"genre_scores_gemma":[0.8218925,0.000368298,0.17478943,0.0020021342,0.00026537688,0.000005155526,0.0006419645,0.000024218514,0.000010900695],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947596,0.00029597923,0.0033922626,0.0001024252,0.0009929509,0.00045676064],"domain_scores_gemma":[0.99416554,0.00019867979,0.0029255657,0.00087709073,0.00096973986,0.0008633554],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013856846,0.00016548517,0.0010686242,0.00055787264,0.000029084069,0.000033535678,0.00071193796,0.00012239948,0.000007971086],"category_scores_gemma":[0.01818282,0.00012273197,0.000091620364,0.00074719824,0.0001208845,0.0008164724,0.00020003441,0.00049680495,0.0000018966183],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001142025,0.0028552273,0.7213959,0.009970884,0.0003744401,0.000011021067,0.014656646,0.00006933766,0.000010825969,0.0018636388,0.018165693,0.22948438],"study_design_scores_gemma":[0.019852519,0.003915355,0.50418794,0.004241684,0.00006337673,0.00025146248,0.023437837,0.0226124,0.00006445914,0.0012981896,0.41929168,0.0007831321],"about_ca_topic_score_codex":0.00007915187,"about_ca_topic_score_gemma":0.000116536095,"teacher_disagreement_score":0.40112597,"about_ca_system_score_codex":0.00031143407,"about_ca_system_score_gemma":0.008592678,"threshold_uncertainty_score":0.9970277},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99299544,0.0001959941,0.004300511,0.00091012253,0.0008640176,0.00030820761,0.0003355676,0.000016397782,0.00007372071],"genre_scores_gemma":[0.9936617,0.00049419195,0.002392971,0.0026647404,0.0005128777,0.000004294274,0.00021661406,0.000019536456,0.000033054046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965938,0.0002532106,0.0011227189,0.00012040245,0.0015915756,0.00031830656],"domain_scores_gemma":[0.9955138,0.00018711777,0.002820761,0.0005311425,0.0004034639,0.000543733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029274377,0.00015454939,0.00056841923,0.0000979964,0.0000783228,0.000045992223,0.00047864695,0.000068464615,0.000023968612],"category_scores_gemma":[0.0063387426,0.00010816265,0.00009019382,0.00046467944,0.00006453726,0.0004333322,0.0003251894,0.00042661084,0.000009170235],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014389771,0.00031289784,0.90604085,0.00008228993,0.00092858524,0.000031164414,0.0012982773,0.00015458491,0.00036575447,0.000016209367,0.048506953,0.040823463],"study_design_scores_gemma":[0.0050097737,0.0009999424,0.9284571,0.000375217,0.00051171624,0.00007495144,0.0010808392,0.033107545,0.000091286616,0.00046449792,0.02939084,0.00043632428],"about_ca_topic_score_codex":0.0002204116,"about_ca_topic_score_gemma":0.00018160617,"teacher_disagreement_score":0.04038714,"about_ca_system_score_codex":0.0009556467,"about_ca_system_score_gemma":0.0007172194,"threshold_uncertainty_score":0.7588525},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.970043,0.0021621608,0.0029687872,0.0017771384,0.00051743747,0.0010419746,0.00086093834,0.00034702272,0.020281553],"genre_scores_gemma":[0.97892493,0.0000057533352,0.012352367,0.004783941,0.00038880375,0.000033940687,0.0015467572,0.000072445415,0.0018910896],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983061,0.00007760241,0.00037369644,0.00049733906,0.0003635145,0.00038173687],"domain_scores_gemma":[0.9983846,0.0000754476,0.00009858072,0.00053611316,0.0003074658,0.0005978182],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041205465,0.00025270492,0.00042389956,0.00013357973,0.00005718258,0.000047102716,0.00023470279,0.00009220342,0.00014610995],"category_scores_gemma":[0.00013913577,0.00024261682,0.00009361083,0.00040213324,0.00009657702,0.00009528924,0.0001968022,0.0001698798,0.0021736783],"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":[0.0015839869,0.0015572124,0.32626066,0.00062724325,0.00021570356,0.00024382563,0.0020837488,0.009937697,0.02022286,0.00025489548,0.6305207,0.0064914967],"study_design_scores_gemma":[0.0089007,0.00042954535,0.21714033,0.00013303368,0.000100101126,0.00014000956,0.0005270243,0.0034887278,0.008291388,0.00035501752,0.7592823,0.0012118403],"about_ca_topic_score_codex":0.00003074683,"about_ca_topic_score_gemma":0.000013134765,"teacher_disagreement_score":0.12876159,"about_ca_system_score_codex":0.0001270993,"about_ca_system_score_gemma":0.00036806086,"threshold_uncertainty_score":0.9986032},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97528726,0.00011005719,0.024122508,0.00009344206,0.00015669031,0.00008262276,0.0000050275635,0.000011322285,0.00013105905],"genre_scores_gemma":[0.9975442,0.00021715659,0.0019467154,0.00015816875,0.000111511035,4.5702976e-7,0.000006349026,0.0000069842613,0.000008449356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984219,0.000055572804,0.0008759961,0.000037044007,0.00048318063,0.00012627617],"domain_scores_gemma":[0.99848753,0.0002718228,0.00082708534,0.00012885514,0.00017974574,0.000104933395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010454366,0.00008892302,0.00031032535,0.00010182582,0.00008504283,0.000016843174,0.00009360641,0.00006528855,0.000011670349],"category_scores_gemma":[0.00095761626,0.00005168118,0.00006599193,0.00017124055,0.00017265689,0.0003546419,0.00005096643,0.00030661805,0.0000032662126],"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":[0.0005320608,0.00013689838,0.29025233,0.0015480199,0.000118900796,0.000007304982,0.0033985516,0.002298706,0.0009025612,0.000012946795,0.0000691786,0.7007226],"study_design_scores_gemma":[0.001338124,0.00088083785,0.09231079,0.00041754337,0.000098038196,0.00027971438,0.0003645,0.88373286,0.0011084331,0.000017910548,0.019376243,0.00007498227],"about_ca_topic_score_codex":0.0000042466872,"about_ca_topic_score_gemma":0.000003134674,"teacher_disagreement_score":0.8814342,"about_ca_system_score_codex":0.00002370701,"about_ca_system_score_gemma":0.00004317323,"threshold_uncertainty_score":0.21074975},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988213,0.00018024155,0.00001219714,0.00020564595,0.00021640159,0.0016066876,0.00007307489,0.00008744647,0.00940533],"genre_scores_gemma":[0.9931482,0.000032427386,0.000037064587,0.00024638115,0.000097298565,0.0020403182,0.00016144001,0.000024038172,0.00421283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99867076,0.000052366337,0.00032594337,0.00032827177,0.000160764,0.00046192357],"domain_scores_gemma":[0.9993615,0.00005220671,0.000073433126,0.00032866627,0.000043665397,0.00014051009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043015654,0.00014028726,0.00031974592,0.00020204672,0.000034141307,0.000018855302,0.0000772727,0.00007926856,0.0004286333],"category_scores_gemma":[0.00022179523,0.00014695032,0.00006676431,0.00027329754,0.000037119626,0.00012777056,0.000029360968,0.00020315753,0.00006406964],"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":[0.00007134264,0.00033193926,0.9800459,0.000041853524,0.000012114243,0.000026918991,0.00011033295,0.000022833718,0.000034341167,0.000014425101,0.0053595984,0.013928405],"study_design_scores_gemma":[0.0024666067,0.00003584249,0.95151293,0.000017616832,0.0000059625363,0.0000052566566,0.0000029205878,0.00007566702,0.000045582325,0.00003972664,0.04565103,0.0001408689],"about_ca_topic_score_codex":0.07733979,"about_ca_topic_score_gemma":0.87239337,"teacher_disagreement_score":0.7950536,"about_ca_system_score_codex":0.0007545458,"about_ca_system_score_gemma":0.00004389728,"threshold_uncertainty_score":0.9288043},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6058945,0.0009295671,0.0002960463,0.010735449,0.0029799775,0.31442648,0.050936278,0.0011694096,0.012632315],"genre_scores_gemma":[0.900473,0.000014591751,0.00059791043,0.0011417854,0.0008512551,0.08881489,0.00733383,0.00015410407,0.00061861495],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960102,0.00008015041,0.00083740463,0.00088505057,0.0005089004,0.0016783283],"domain_scores_gemma":[0.99626255,0.00017192874,0.0003246275,0.00086417765,0.00067116984,0.0017055178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022010572,0.0006591094,0.00079313724,0.0020800442,0.00065174355,0.00012383053,0.00037322182,0.00023982866,0.00025023794],"category_scores_gemma":[0.00019732285,0.0006843702,0.0004246909,0.0021034882,0.0007925006,0.00016550148,0.000036396275,0.00024293289,0.000004070633],"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":[0.0005622434,0.00008910867,0.6000086,0.0007660729,0.00031266132,0.00054597046,0.00008663239,0.000041079482,0.000067949004,0.0013691433,0.38659295,0.009557546],"study_design_scores_gemma":[0.0022341274,0.00019111628,0.40632463,0.00015864488,0.00010001214,0.000055747474,0.00040222504,0.00019120303,0.00006450403,0.00040481988,0.5892363,0.00063666835],"about_ca_topic_score_codex":0.9994526,"about_ca_topic_score_gemma":0.9999929,"teacher_disagreement_score":0.29457852,"about_ca_system_score_codex":0.00042889058,"about_ca_system_score_gemma":0.002788167,"threshold_uncertainty_score":0.9995608},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013919421,0.97833675,0.000020234336,0.00053167285,0.000588376,0.016605126,0.003202326,0.00059868343,0.00010288559],"genre_scores_gemma":[0.00015481003,0.98812044,0.00012237113,0.0024556422,0.00024612484,0.004695834,0.0040937564,0.000066442306,0.000044569708],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949582,0.0005415018,0.0021424482,0.000857491,0.0005294282,0.0009708928],"domain_scores_gemma":[0.99532884,0.00011554471,0.0015821373,0.001329361,0.00038874493,0.0012553898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013829835,0.0007807521,0.0047204066,0.00091441366,0.0002966168,0.00019137075,0.00026649458,0.0005916465,0.0000011714208],"category_scores_gemma":[0.00021060264,0.00057389366,0.00022582954,0.001173356,0.00010207194,0.00025479496,0.00020084673,0.0010623705,0.000041527266],"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":[0.000014248684,0.000013626647,0.000020194906,0.5160576,0.000026163147,0.0000042820243,0.00014341527,4.482504e-8,1.912192e-9,0.000121667836,0.0077373064,0.47586146],"study_design_scores_gemma":[0.00034606797,0.00104783,0.000042894746,0.24667917,0.0003200289,0.00028260247,0.00037196532,0.0000058589417,1.7666061e-9,8.048894e-7,0.75061,0.0002928042],"about_ca_topic_score_codex":0.00016302527,"about_ca_topic_score_gemma":0.000016307413,"teacher_disagreement_score":0.74287266,"about_ca_system_score_codex":0.0004849303,"about_ca_system_score_gemma":0.0018484988,"threshold_uncertainty_score":0.9996713},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9907236,0.00012918552,0.00819721,0.00026778955,0.00016642475,0.00018571717,0.0000284171,0.00000570554,0.00029593942],"genre_scores_gemma":[0.99871486,0.000096104246,0.0008569918,0.00017900848,0.00013380079,0.0000016093628,7.7352263e-7,0.000006296941,0.000010533897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843514,0.000032516527,0.00030967244,0.00017356203,0.0008614586,0.00018765136],"domain_scores_gemma":[0.998654,0.0002561179,0.00029632944,0.00010144095,0.00044395254,0.00024814703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045021006,0.000100084755,0.00029251832,0.0001572812,0.00012756158,0.000016679445,0.0001605826,0.000020959404,0.0000684978],"category_scores_gemma":[0.0022440294,0.00007049729,0.00003389769,0.00017869333,0.0005827971,0.00020021392,0.000054716587,0.00015979809,0.0000030063416],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003652926,0.0019058412,0.777137,0.0023584564,0.0003170584,0.004731187,0.006702394,0.0003354274,0.090169445,0.0004324345,0.0040887683,0.10816909],"study_design_scores_gemma":[0.0015609678,0.0011327297,0.96216774,0.000559001,0.000032477936,0.000422992,0.00003330096,0.00068541226,0.03296152,0.00009851606,0.000247054,0.00009831701],"about_ca_topic_score_codex":0.0001298091,"about_ca_topic_score_gemma":0.00015444116,"teacher_disagreement_score":0.18503074,"about_ca_system_score_codex":0.00006451921,"about_ca_system_score_gemma":0.0003327294,"threshold_uncertainty_score":0.2874796},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8262747,0.00023210315,0.00012423783,0.00026488956,0.00012752129,0.17169143,0.00007068004,0.0004825587,0.00073185016],"genre_scores_gemma":[0.9241097,0.000007895753,0.0076720887,0.0013573974,0.0014617188,0.063158326,0.0005217296,0.00023766054,0.0014734969],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98708904,0.0035606923,0.0019417887,0.0015400968,0.0031678493,0.0027005174],"domain_scores_gemma":[0.99385107,0.00025417135,0.0006793157,0.001947296,0.0010802107,0.0021879529],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.009298423,0.00050601625,0.0014359729,0.00089644734,0.0015227972,0.0002309664,0.0007171655,0.000178294,0.00023687191],"category_scores_gemma":[0.000044457352,0.00046968996,0.00012456963,0.001945187,0.00012188677,0.00046761127,0.00044138826,0.0015257933,0.00034749618],"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":true,"study_design_scores_codex":[0.003363289,0.0037965209,0.9567214,0.026720442,0.00005538831,0.00023209717,0.0063266777,0.00014520138,0.00008565501,0.00008192203,0.00021748127,0.0022539194],"study_design_scores_gemma":[0.008284359,0.00797818,0.95015603,0.002890864,0.000016584407,0.00012892415,0.010714922,0.0058394745,0.000020161344,0.000030739335,0.013526527,0.0004132583],"about_ca_topic_score_codex":0.77188885,"about_ca_topic_score_gemma":0.53968674,"teacher_disagreement_score":0.23220213,"about_ca_system_score_codex":0.0048892135,"about_ca_system_score_gemma":0.014455974,"threshold_uncertainty_score":0.9997771},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9607034,0.0007857584,0.0078047877,0.023794213,0.0003283218,0.00049557985,0.0002487153,0.00011744077,0.0057218103],"genre_scores_gemma":[0.5241198,0.00063535565,0.42106503,0.05032112,0.0018276912,0.000020848564,0.0007271806,0.0001341167,0.001148874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99639195,0.000076392855,0.0020974535,0.00006710141,0.00076726533,0.00059984426],"domain_scores_gemma":[0.99524724,0.00009437961,0.0019613863,0.00035313278,0.001131585,0.0012122841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016589302,0.00019632636,0.0006496625,0.0005665737,0.0000982001,0.000105167186,0.00024398722,0.000068285866,0.00036706583],"category_scores_gemma":[0.00083964644,0.00013198568,0.000110665984,0.00068123377,0.00007509923,0.0014852663,0.00006180448,0.0005414675,0.00004569662],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064395,0.00049547525,0.050136082,0.0008180349,0.0001933678,0.000024289146,0.0018723197,0.00011013633,0.0000036411018,0.00007276545,0.049319673,0.8968898],"study_design_scores_gemma":[0.0051668114,0.0025948011,0.13399309,0.00076797075,0.00004759548,0.0021182,0.0030061926,0.024442365,0.0000036681927,0.000032464643,0.8275543,0.00027255775],"about_ca_topic_score_codex":0.000029034507,"about_ca_topic_score_gemma":0.00002011924,"teacher_disagreement_score":0.8966173,"about_ca_system_score_codex":0.0002613355,"about_ca_system_score_gemma":0.001969049,"threshold_uncertainty_score":0.538222},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99691343,0.00022992596,0.00037356117,0.0015334802,0.00023182378,0.0005762567,0.0000525678,0.000028464816,0.00006050051],"genre_scores_gemma":[0.99868447,0.000008693299,0.0002792201,0.00018088309,0.00072233763,0.000035929577,0.000025879126,0.000025604164,0.000036958085],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99482733,0.0007395888,0.00075515604,0.00018679418,0.0031451234,0.00034603372],"domain_scores_gemma":[0.9963478,0.0002046706,0.0018224358,0.00023732573,0.0011228316,0.00026493816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046740035,0.00013990648,0.00053948205,0.00014363798,0.00009000122,0.000018526041,0.00028351558,0.00005650067,0.000035275934],"category_scores_gemma":[0.0036110946,0.00010423207,0.00025569228,0.00044214,0.000057107074,0.00019856013,0.000056460463,0.00026456872,0.000005724485],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017026454,0.0029427821,0.68223596,0.0010404523,0.0027690562,0.00022459419,0.0014944696,0.0021621503,0.0908129,0.00023403762,0.014601466,0.18445566],"study_design_scores_gemma":[0.0029568183,0.0086266585,0.97512436,0.00063773995,0.0011033918,0.00020135619,0.00048245318,0.007170537,0.0002469563,0.00012025236,0.0031551057,0.00017438826],"about_ca_topic_score_codex":0.00020925912,"about_ca_topic_score_gemma":0.000013765442,"teacher_disagreement_score":0.29288837,"about_ca_system_score_codex":0.0023683957,"about_ca_system_score_gemma":0.0013763995,"threshold_uncertainty_score":0.61932725},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99602956,0.00013408155,0.0016833093,0.0011991204,0.00028306906,0.00027502756,0.00006153769,0.000020993852,0.00031328163],"genre_scores_gemma":[0.9840737,0.0006341958,0.013640037,0.0013441046,0.00018989213,0.0000026651746,0.000030349114,0.00001461493,0.000070439426],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99661005,0.000067347704,0.0023198486,0.00004717969,0.0005861256,0.00036947016],"domain_scores_gemma":[0.9956171,0.000055682493,0.0022675272,0.00030219133,0.001144301,0.0006131815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016929503,0.00013316511,0.00059969054,0.00038002917,0.00005662667,0.000027566008,0.00020043972,0.00006933238,0.00011630024],"category_scores_gemma":[0.0007726201,0.00010088707,0.000113848335,0.00033164595,0.00006607051,0.0011616963,0.000052836887,0.00033232855,0.000042339725],"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":[0.00003594959,0.00035369257,0.16067767,0.0017108358,0.000045271066,0.0000028523261,0.0007366593,0.000016485998,0.000044270964,0.0000035576422,0.0019455068,0.83442724],"study_design_scores_gemma":[0.002384028,0.0021431441,0.8988913,0.00065108883,0.000029256036,0.00096204766,0.0014107375,0.0382366,0.00027427412,0.000013979001,0.05484311,0.00016038862],"about_ca_topic_score_codex":0.000028897186,"about_ca_topic_score_gemma":0.000018464425,"teacher_disagreement_score":0.83426684,"about_ca_system_score_codex":0.00017591473,"about_ca_system_score_gemma":0.0012762435,"threshold_uncertainty_score":0.4114055},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9179735,0.0028863356,0.00013729236,0.0001981506,0.0010093558,0.000406306,0.0032379183,0.00035677006,0.07379433],"genre_scores_gemma":[0.9986379,0.00010391941,0.00018433869,0.00015800992,0.00025077694,0.000036836893,0.0005089508,0.00002589056,0.00009335686],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985521,0.00007118722,0.00031751106,0.00019364804,0.00052911835,0.00033645364],"domain_scores_gemma":[0.99838775,0.000098693694,0.00017859925,0.0008991581,0.000070195216,0.0003656086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008858461,0.00019571661,0.00030993507,0.000035396075,0.00012186979,0.000012330244,0.00021504026,0.000044078253,0.00042403763],"category_scores_gemma":[0.00024145389,0.00019832747,0.00020236276,0.00027478996,0.0002059986,0.00025405732,0.0003313561,0.0000893718,0.00015729756],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003497088,0.0017817465,0.98635435,0.0000777325,0.0002763767,0.0000039207907,0.000039411876,0.0000065097124,0.00020784297,0.0007641113,0.005535977,0.0046022944],"study_design_scores_gemma":[0.0037603986,0.00016722907,0.97134125,0.00007682379,0.0005227447,0.000026593138,0.0003878713,0.000249784,0.00017430662,0.000014132885,0.023014506,0.00026432824],"about_ca_topic_score_codex":0.00020478525,"about_ca_topic_score_gemma":0.000052789965,"teacher_disagreement_score":0.08066438,"about_ca_system_score_codex":0.0007880427,"about_ca_system_score_gemma":0.00008522952,"threshold_uncertainty_score":0.80875593},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99749583,0.00031487236,0.000055061748,0.0011907129,0.00025790485,0.00023410063,0.000019602827,0.000015596352,0.00041629394],"genre_scores_gemma":[0.9989726,0.00018268915,0.00022245178,0.00033262552,0.00007429743,0.0000032859684,0.00000627142,0.000018893397,0.00018687364],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99735653,0.0004634388,0.001090074,0.00016387501,0.00047412844,0.0004519773],"domain_scores_gemma":[0.9978925,0.00011649188,0.00086836104,0.0004281937,0.0002866197,0.00040784912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037433149,0.00014161445,0.00072119484,0.0002778592,0.000057830064,0.000013534989,0.0003476877,0.00006286934,0.00042472745],"category_scores_gemma":[0.0004636373,0.00009501444,0.00014784114,0.00029919864,0.000107586064,0.00023576058,0.00005810643,0.00067663373,0.0000082267725],"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":[0.0001562502,0.00024287713,0.9940528,0.00015851157,0.00006453031,0.0000885487,0.0034144786,0.0000025544696,0.0001882668,0.000021101689,0.00026129585,0.001348748],"study_design_scores_gemma":[0.0018429887,0.00023916517,0.9947341,0.00020724784,0.0000068393556,0.0007772161,0.0004642886,0.000010991394,0.00001263483,0.000029112045,0.0015986419,0.00007676284],"about_ca_topic_score_codex":0.014219082,"about_ca_topic_score_gemma":0.07753774,"teacher_disagreement_score":0.063318655,"about_ca_system_score_codex":0.0009158239,"about_ca_system_score_gemma":0.0034776523,"threshold_uncertainty_score":0.99234533},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99833405,0.00022855787,0.000089994915,0.00044874693,0.000059997295,0.00029414758,0.00033530057,0.000017769673,0.00019142509],"genre_scores_gemma":[0.99721426,0.00004701606,0.00035561132,0.0010812981,0.00001412916,0.0000094927045,0.0011575543,0.000009055927,0.00011158388],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986998,0.00005239178,0.00048206848,0.00022891106,0.00029892803,0.00023792133],"domain_scores_gemma":[0.99913895,0.00003263627,0.0001888499,0.0002947506,0.000097561424,0.0002472747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013329058,0.000109319444,0.00052842515,0.00022516362,0.00007103829,0.000002894263,0.000055412238,0.000034714256,0.00017017717],"category_scores_gemma":[0.00006344513,0.00011177515,0.000044368466,0.0006899908,0.000058356713,0.000054152075,0.00003324705,0.00011403374,7.9959955e-7],"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":[0.000054365133,0.00007407635,0.9968982,0.000044311986,0.00010284877,0.000033571574,0.0007045157,0.00034191285,0.0000011805582,0.000022093878,0.0012796419,0.000443331],"study_design_scores_gemma":[0.0006326613,0.00011930313,0.9925115,0.00003193247,0.000069778,0.000004113004,0.000036270852,0.0014380553,0.000002189089,0.0000058792216,0.0050656637,0.00008264497],"about_ca_topic_score_codex":0.99811965,"about_ca_topic_score_gemma":0.99987185,"teacher_disagreement_score":0.0043866416,"about_ca_system_score_codex":0.00049837,"about_ca_system_score_gemma":0.004477529,"threshold_uncertainty_score":0.79429436},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7214704,0.0027388416,0.2542441,0.006647846,0.0014683792,0.0010363503,0.0045581358,0.000036704947,0.0077992594],"genre_scores_gemma":[0.9885272,0.000027800124,0.010095259,0.00043181257,0.00015357461,6.2436936e-7,0.00009835057,0.000018720077,0.00064665254],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989982,0.00002197561,0.00046568844,0.00008593198,0.00018952267,0.00023866704],"domain_scores_gemma":[0.99835575,0.000121720295,0.00022309697,0.00013545809,0.00053764216,0.0006263558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006324396,0.00007681457,0.00019537189,0.00036546105,0.000029678704,0.000030161618,0.00009309351,0.00003776244,0.00024681227],"category_scores_gemma":[0.0017217458,0.00007419602,0.000021774584,0.00026383772,0.00007106554,0.0001083126,0.000004530056,0.00016153857,0.00007291669],"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":[0.0007192799,0.00023724379,0.5019042,0.001275021,0.00021373041,0.0150232315,0.0030771235,0.00020749893,0.0021143693,0.0026474914,0.4301744,0.04240638],"study_design_scores_gemma":[0.0018829738,0.00020389888,0.9549041,0.00044083057,0.00009519783,0.0007016878,0.00037942096,0.0002724463,0.0005909351,0.0017862022,0.038525328,0.00021702106],"about_ca_topic_score_codex":0.0007009376,"about_ca_topic_score_gemma":0.04283233,"teacher_disagreement_score":0.45299986,"about_ca_system_score_codex":0.0002670035,"about_ca_system_score_gemma":0.0019174017,"threshold_uncertainty_score":0.97463346},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036215775,0.014258778,0.0060179364,0.93425256,0.0012028078,0.0023126088,0.00027042718,0.00076666725,0.0047024316],"genre_scores_gemma":[0.94555897,0.0010076098,0.007991804,0.043224867,0.00036895188,0.000014696702,0.0016816579,0.00005698828,0.00009446156],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9920891,0.0021199936,0.0018097376,0.0008058272,0.0012282039,0.0019471496],"domain_scores_gemma":[0.9937397,0.000357132,0.0018431726,0.0011030474,0.0006539514,0.0023029943],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008061616,0.000357439,0.0013691332,0.0005820898,0.00045959593,0.00010840389,0.00027875055,0.000116361996,0.000058071746],"category_scores_gemma":[0.0037183808,0.0003685027,0.0001558567,0.0015765808,0.00015023502,0.0010298552,0.000023571105,0.00048716992,0.00002626211],"study_design_candidate":"design_other","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":[0.00010369791,0.0018500312,0.038706526,0.0005268371,0.000040586565,0.000004905578,0.0022449677,0.000005922619,0.0000069656544,0.004252683,0.029048538,0.92320836],"study_design_scores_gemma":[0.0015490388,0.0014309405,0.7608424,0.00069882674,0.0000060107755,0.00014412198,0.001055055,0.0004023835,0.0000015618053,0.00020190729,0.23340538,0.00026237956],"about_ca_topic_score_codex":0.0016274195,"about_ca_topic_score_gemma":0.00012808216,"teacher_disagreement_score":0.922946,"about_ca_system_score_codex":0.002526981,"about_ca_system_score_gemma":0.0056868135,"threshold_uncertainty_score":0.99995},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9495966,0.00014516384,0.049290057,0.00059290504,0.00013868701,0.00018654315,0.000005874215,0.00003176885,0.000012432935],"genre_scores_gemma":[0.9984436,0.00033438354,0.00076425966,0.0002780264,0.000084363586,0.000022106968,0.00006365084,0.000007790797,0.0000018026482],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99777967,0.0012210442,0.0005173803,0.00022139799,0.000096422744,0.00016407907],"domain_scores_gemma":[0.9972574,0.0015857874,0.0008809119,0.00016043105,0.000050295253,0.00006514519],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0016150604,0.00009983301,0.0003869948,0.000040425843,0.000083213235,0.000004686654,0.00002780331,0.00012814214,0.000030291922],"category_scores_gemma":[0.009024918,0.000074474636,0.000034885245,0.00009744389,0.00026021327,0.00027621715,0.00005771265,0.00023515902,0.000001077392],"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":[0.00024762732,0.000030669467,0.9860874,0.00010261539,0.000064736094,6.0615207e-7,0.00012372254,0.000064168154,0.0029685774,0.00009836088,0.000030519994,0.010181014],"study_design_scores_gemma":[0.00042821464,0.00075627945,0.87744635,0.00013454435,0.00008514766,0.00006467264,0.00000929579,0.11876245,0.0005113207,0.0006704264,0.0010777484,0.000053539054],"about_ca_topic_score_codex":0.009088101,"about_ca_topic_score_gemma":0.0026720462,"teacher_disagreement_score":0.118698284,"about_ca_system_score_codex":0.00002507844,"about_ca_system_score_gemma":0.000030262338,"threshold_uncertainty_score":0.9993225},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9744616,0.0018004283,0.0000012609341,0.000036493755,0.00053033396,0.0025615084,0.01850599,0.00021463439,0.0018877325],"genre_scores_gemma":[0.9464742,0.00092476077,0.014021907,0.0017824861,0.0003903818,0.00017935487,0.034916725,0.00045916953,0.00085102],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942186,0.0005720615,0.0014917317,0.0017348205,0.0011766942,0.0008060758],"domain_scores_gemma":[0.99244666,0.0017754211,0.0014540203,0.0031798405,0.00084506517,0.00029898126],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029523014,0.0008396487,0.0014342661,0.00029298716,0.0009697967,0.00016431426,0.0010377566,0.0004340099,0.000019927447],"category_scores_gemma":[0.0052395165,0.0007156181,0.00032433504,0.0006450962,0.00015893209,0.0009243244,0.00032016556,0.0011709814,0.00001972487],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018901123,0.00028270733,0.00060123083,0.0019301432,0.00057027815,0.0000868963,0.000024138675,0.00020589399,0.0657778,0.0019311735,0.00012122284,0.9265784],"study_design_scores_gemma":[0.011800145,0.0021297296,0.21042252,0.0098074535,0.0024509532,0.00009141924,0.001044898,0.0066970293,0.016976273,0.010988184,0.7229191,0.004672315],"about_ca_topic_score_codex":0.00014035561,"about_ca_topic_score_gemma":0.0007008325,"teacher_disagreement_score":0.9219061,"about_ca_system_score_codex":0.00024255551,"about_ca_system_score_gemma":0.00013987132,"threshold_uncertainty_score":0.9995295},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891226,0.001031586,0.000011398584,0.001501076,0.00027758564,0.0022655327,0.0029104277,0.0000799336,0.0027998332],"genre_scores_gemma":[0.99733573,0.00009108139,0.000083982224,0.00047706248,0.0006017434,0.00093203015,0.00011043775,0.000030241416,0.00033767833],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99801004,0.000055726723,0.00044932435,0.0003440443,0.00066709594,0.00047378676],"domain_scores_gemma":[0.99878055,0.000062521605,0.0001484415,0.0004009177,0.00040911947,0.00019842995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005606109,0.00011440918,0.00033696045,0.000044314624,0.00006701029,0.000108106484,0.00041147176,0.000057032164,0.000120131124],"category_scores_gemma":[0.0017838875,0.00012861264,0.00007602282,0.0003735605,0.00004795823,0.0001831622,0.00017590856,0.00016445569,0.0000064529013],"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":[0.000034344826,0.00009083092,0.6891546,0.00011018505,0.000020663298,0.00002514114,0.00024020327,0.000037509784,0.0000064305664,1.5651412e-7,0.020553865,0.28972605],"study_design_scores_gemma":[0.0006925869,0.000033929195,0.9034775,0.000058482474,0.000030582636,0.000004472859,0.00016556944,0.000028985187,0.000033067692,0.0000071673844,0.09535096,0.00011671211],"about_ca_topic_score_codex":0.012139836,"about_ca_topic_score_gemma":0.7850954,"teacher_disagreement_score":0.77295554,"about_ca_system_score_codex":0.0002796737,"about_ca_system_score_gemma":0.00024421376,"threshold_uncertainty_score":0.9944384},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99346316,0.004140512,0.00010779609,0.00058607955,0.00053265423,0.0007146247,0.00039661053,0.000007135964,0.00005140895],"genre_scores_gemma":[0.9989418,0.00010752612,0.000009324036,0.0005774462,0.00019569483,0.000053771422,0.00006732406,0.00001278769,0.00003435517],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986278,0.00011456497,0.0006580333,0.00017972726,0.00010743509,0.0003124486],"domain_scores_gemma":[0.99798167,0.00013614871,0.00027852395,0.00017194585,0.00026908063,0.0011626175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030744387,0.00013707388,0.00036191862,0.00016227143,0.00008393982,0.000010780061,0.00011333306,0.00008702482,0.00060342374],"category_scores_gemma":[0.0013118478,0.00009658833,0.0001551619,0.00009769328,0.00024223284,0.000072829374,0.000020924132,0.00020665773,0.000004063421],"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":[0.0001302231,0.0000892284,0.99380726,0.0001158104,0.000118043215,0.00009732507,0.000094879,0.00001427888,0.0002795143,0.000029652261,0.0016806386,0.003543166],"study_design_scores_gemma":[0.0018085678,0.00050886674,0.98592216,0.00018438799,0.00008345829,0.00017934882,0.000040260187,0.00014358501,0.0000047546678,0.00022441252,0.010817836,0.00008234113],"about_ca_topic_score_codex":0.02905436,"about_ca_topic_score_gemma":0.122082695,"teacher_disagreement_score":0.09302834,"about_ca_system_score_codex":0.0002486685,"about_ca_system_score_gemma":0.003133526,"threshold_uncertainty_score":0.97741127},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13223866,0.0010031539,0.10062666,0.72533315,0.00065059256,0.008676759,0.0066856374,0.0033474194,0.02143798],"genre_scores_gemma":[0.6729636,0.00047417753,0.21955886,0.0031125194,0.0002535203,0.000005455074,0.015172303,0.000175022,0.088284515],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810165,0.00024177863,0.00024908822,0.0007279027,0.00023379854,0.00044576367],"domain_scores_gemma":[0.9979548,0.0002899242,0.00019282768,0.001135006,0.000043543365,0.00038391314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030312233,0.00022765757,0.0005908027,0.00032743072,0.00014033512,0.00002662471,0.0005230801,0.00009742403,0.00007721899],"category_scores_gemma":[0.00030904016,0.00024296585,0.00009157225,0.0005838992,0.000099405355,0.00091316045,0.00049882353,0.00024584113,0.000020670816],"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":[0.0018162589,0.00034458868,0.106035456,0.0011719072,0.00026884337,0.00012865012,0.0004926701,0.00031974394,0.000006496446,0.0032450536,0.8839702,0.0022001315],"study_design_scores_gemma":[0.017738074,0.0008764033,0.07045035,0.0009854191,0.00022661372,0.0000034200787,0.0033654247,0.010295925,0.0001586071,0.0003640967,0.8945466,0.0009890329],"about_ca_topic_score_codex":0.0019920582,"about_ca_topic_score_gemma":0.00007596694,"teacher_disagreement_score":0.7222206,"about_ca_system_score_codex":0.00016450878,"about_ca_system_score_gemma":0.0007404428,"threshold_uncertainty_score":0.99078596},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9858514,0.0110092005,0.0004237916,0.00020767147,0.00022891004,0.00038138364,0.00089175196,0.000012926759,0.0009929836],"genre_scores_gemma":[0.9972454,0.00011800654,0.0025236167,0.000065903776,0.00001483148,0.0000020342395,0.000021014213,0.0000060682974,0.000003179041],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950621,0.0011518955,0.0015867265,0.00023387244,0.0015245551,0.00044081578],"domain_scores_gemma":[0.9949121,0.0002495398,0.0017215541,0.0006015917,0.0018589906,0.0006562375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015090722,0.0001477712,0.0012562571,0.00021005345,0.000055676785,0.00001539464,0.000833964,0.000036531103,0.0000019245597],"category_scores_gemma":[0.002588728,0.00011741708,0.00009382575,0.0021374011,0.00093436707,0.00033079824,0.00016793754,0.00017320433,0.0000021696303],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055389124,0.00042299478,0.99531865,0.0005994374,0.000022802724,0.00001557177,0.0007624049,0.00023425157,0.000015691818,0.00027137506,0.0008273413,0.0009555693],"study_design_scores_gemma":[0.0018645667,0.0007011196,0.9904116,0.0005916974,0.000007955113,0.00010545638,0.005578642,0.00056280335,0.00001080722,0.00004984889,0.000027144984,0.000088355766],"about_ca_topic_score_codex":0.0036434566,"about_ca_topic_score_gemma":0.00018232888,"teacher_disagreement_score":0.012501994,"about_ca_system_score_codex":0.0007199421,"about_ca_system_score_gemma":0.004321143,"threshold_uncertainty_score":0.7665522},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99253577,0.00013508914,0.000023028351,0.0031707636,0.00004184717,0.00020025454,0.000079017125,0.000035557387,0.0037786495],"genre_scores_gemma":[0.99387157,0.000018486728,0.0029755032,0.0007739549,0.000029344474,3.5707927e-8,0.00028076966,0.000017463793,0.0020328974],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991354,0.000060797905,0.00010054742,0.00018329805,0.00018805092,0.0003319141],"domain_scores_gemma":[0.9986654,0.000045665278,0.000053375967,0.00027733893,0.000032392516,0.0009258557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083159684,0.00008730672,0.00021733531,0.00026319953,0.00012048634,0.0000112431335,0.00018004936,0.00004589931,0.00031666836],"category_scores_gemma":[0.000024346891,0.00010343264,0.000055245328,0.00029441877,0.00011528512,0.0004202875,0.00009934578,0.00010671802,0.000047264697],"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":[0.00030784658,0.000114320595,0.9243184,0.00015810983,0.0001345623,0.0006078876,0.04094603,0.00029337793,0.00007331344,0.00011817725,0.029813234,0.0031147788],"study_design_scores_gemma":[0.008109577,0.0010406509,0.79068893,0.0010316987,0.00019722081,0.00030977855,0.06818315,0.012126651,0.0012866916,0.00017551953,0.11577481,0.0010753182],"about_ca_topic_score_codex":0.37708592,"about_ca_topic_score_gemma":0.043832596,"teacher_disagreement_score":0.33325332,"about_ca_system_score_codex":0.00018541409,"about_ca_system_score_gemma":0.0025978324,"threshold_uncertainty_score":0.973615},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98602647,0.007556499,0.000037651564,0.001319977,0.00009005431,0.00047763868,0.0040993984,0.000008446888,0.0003838663],"genre_scores_gemma":[0.9987593,0.00014291439,0.000087727945,0.00082119496,0.000048791233,0.0000028531178,0.00012577767,0.000006573134,0.000004843616],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99900746,0.000036540598,0.00023195514,0.00026939917,0.0002385483,0.00021607979],"domain_scores_gemma":[0.9988939,0.00007451169,0.00006369749,0.000692309,0.00003373858,0.00024184452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074538155,0.0001235528,0.00023634212,0.000045654153,0.000075346645,0.000016702648,0.00016914347,0.000014759564,0.00006496256],"category_scores_gemma":[0.00007593929,0.00009135637,0.000026583855,0.00020036216,0.00015202134,0.000081835555,0.00011411425,0.00006744325,1.4654383e-7],"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":[0.0035538792,0.001089082,0.7304405,0.0016323503,0.0011582244,0.011410181,0.0010950252,0.0008186438,0.00023513638,0.0035513414,0.11516514,0.12985048],"study_design_scores_gemma":[0.0021629788,0.0009207786,0.9642513,0.0005264914,0.00047049375,0.00022995472,0.008227194,0.0028730684,0.00011167106,0.00048255135,0.019283451,0.00046010496],"about_ca_topic_score_codex":0.2898092,"about_ca_topic_score_gemma":0.6045736,"teacher_disagreement_score":0.3147644,"about_ca_system_score_codex":0.00052597676,"about_ca_system_score_gemma":0.0035337808,"threshold_uncertainty_score":0.71492004},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90249574,0.002876853,0.004980795,0.012388016,0.00037535714,0.003315539,0.0018878519,0.000953058,0.070726804],"genre_scores_gemma":[0.964306,0.0011343341,0.0021535046,0.005270222,0.00053738395,0.000023181401,0.0013611435,0.00015780696,0.02505642],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957487,0.00029320628,0.00047279158,0.0011294469,0.000597147,0.0017587133],"domain_scores_gemma":[0.99522436,0.00065584836,0.00044277665,0.0010589809,0.000360251,0.0022577662],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005882194,0.0009070041,0.0012063488,0.00093725236,0.0014223703,0.00064511487,0.0019975917,0.00052997534,0.00045383227],"category_scores_gemma":[0.0004271172,0.00082288886,0.00068874465,0.0017426627,0.00083647337,0.0018750733,0.0030354073,0.0005485287,0.000091691094],"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":[0.041447986,0.0014158507,0.33024275,0.001578804,0.0033401994,0.0015378823,0.17864706,0.00021275425,0.001098754,0.32532728,0.08981644,0.025334245],"study_design_scores_gemma":[0.0076390756,0.00052348024,0.05788502,0.00020390181,0.000684744,0.00031809436,0.009083693,0.0006287503,0.00032449668,0.0011315905,0.92035115,0.0012259855],"about_ca_topic_score_codex":0.0011749667,"about_ca_topic_score_gemma":0.000117704105,"teacher_disagreement_score":0.8305347,"about_ca_system_score_codex":0.0011959984,"about_ca_system_score_gemma":0.0010804941,"threshold_uncertainty_score":0.99987763},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22765018,0.011440791,0.003213445,0.024335237,0.018930474,0.02335088,0.011980672,0.0021626432,0.6769357],"genre_scores_gemma":[0.9864183,0.0031006036,0.0010419659,0.0028432058,0.0004718823,0.00042355648,0.0009099912,0.00009470613,0.00469579],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9925582,0.0008546834,0.0022870086,0.0011365269,0.0018898363,0.0012737385],"domain_scores_gemma":[0.9943358,0.0002943877,0.0018141257,0.0019195153,0.0011931962,0.0004430245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023908566,0.00084135536,0.0015332251,0.0003676117,0.00030487598,0.000114318434,0.0017579976,0.000292799,0.0007410303],"category_scores_gemma":[0.00021710139,0.0006991884,0.00043865416,0.0008080127,0.00053131307,0.0004881276,0.0006424314,0.0007724292,0.0003901754],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009020337,0.0014942562,0.7401797,0.0040540486,0.0007159301,0.00008764534,0.0005094075,0.00025689675,0.00027049799,0.05512292,0.17838651,0.009901849],"study_design_scores_gemma":[0.0020655768,0.0010737735,0.7577638,0.0010121812,0.000013560879,0.00011023557,0.00008819185,0.0021410608,0.000008087002,0.00010930303,0.23501547,0.00059876544],"about_ca_topic_score_codex":0.002214803,"about_ca_topic_score_gemma":0.0023032804,"teacher_disagreement_score":0.75876814,"about_ca_system_score_codex":0.00043188082,"about_ca_system_score_gemma":0.0007790691,"threshold_uncertainty_score":0.99954593},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019446506,0.83756065,0.1585728,0.0011129976,0.00009245868,0.00256679,0.000046789246,0.000020446043,0.000007610125],"genre_scores_gemma":[0.0017037076,0.8974563,0.099801674,0.00051823136,0.00011067822,0.0003359559,0.00005994387,0.000013194818,3.297094e-7],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9985605,0.00008783435,0.0008618022,0.00011278863,0.00017657624,0.00020052867],"domain_scores_gemma":[0.9974297,0.00146918,0.00048376754,0.0004790709,0.00008541317,0.000052837757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012748637,0.00020784106,0.0011104224,0.00013588712,0.00036160494,0.00004233321,0.00078992604,0.00012512771,1.8821436e-7],"category_scores_gemma":[0.00090705534,0.00012153848,0.00016238025,0.0007462301,0.00032227277,0.00015792926,0.00044229432,0.00031204653,6.008145e-7],"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":[0.0000034870673,0.000044398646,0.000024947245,0.81917894,0.00020901048,1.0513836e-7,0.0055618403,0.007602502,2.1302091e-7,0.0031494007,0.00011095685,0.16411418],"study_design_scores_gemma":[0.0008349614,0.00015774088,0.0008436497,0.68167835,0.006410969,0.00023686505,0.00015695252,0.10722769,9.842755e-7,0.00028455633,0.2007099,0.0014573911],"about_ca_topic_score_codex":0.000018318362,"about_ca_topic_score_gemma":0.00011268997,"teacher_disagreement_score":0.20059893,"about_ca_system_score_codex":0.00010221354,"about_ca_system_score_gemma":0.00018458358,"threshold_uncertainty_score":0.4956195},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048591744,0.0033236516,0.6996749,0.21200906,0.0010586281,0.005705647,0.02852237,0.0009875741,0.00012644581],"genre_scores_gemma":[0.87280476,0.0020625275,0.06513168,0.011410395,0.0013931255,0.00046817292,0.045881853,0.00028599813,0.0005614876],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99287134,0.0006185962,0.0015057601,0.0024400295,0.00074483495,0.0018194413],"domain_scores_gemma":[0.99188745,0.0012632495,0.00088177336,0.0036073842,0.00056651415,0.0017936437],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.013129308,0.0004795553,0.0012068375,0.00048203627,0.00042294123,0.00028092563,0.0014796557,0.00019469332,0.000061167615],"category_scores_gemma":[0.010458861,0.00039754523,0.0000623995,0.0011168157,0.00014022007,0.001936279,0.0013411868,0.00038309302,0.000018206218],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022209834,0.0005204658,0.6784855,0.0014873367,0.00007434385,0.000004583814,0.00024178177,0.0000017247045,0.00049767905,0.0005363564,0.017867025,0.30006108],"study_design_scores_gemma":[0.0077731907,0.00022663487,0.5463305,0.00014828451,0.0000017489533,0.000092439026,0.0002496209,0.04135986,0.0000026199589,0.00003487321,0.40315834,0.00062184857],"about_ca_topic_score_codex":0.000479109,"about_ca_topic_score_gemma":0.0036468045,"teacher_disagreement_score":0.824213,"about_ca_system_score_codex":0.00072382705,"about_ca_system_score_gemma":0.0047227433,"threshold_uncertainty_score":0.99984765},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.980848,0.0011926481,0.0007050062,0.013597218,0.00019575475,0.0004516964,0.002551754,0.00003590323,0.00042199413],"genre_scores_gemma":[0.9961942,0.00025209767,0.0008436347,0.0021391588,0.00007854386,0.00001172899,0.00024350388,0.000020264208,0.00021689382],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980684,0.00022632559,0.00054004823,0.00035482828,0.00033986292,0.00047053868],"domain_scores_gemma":[0.9979573,0.00014754677,0.00031122947,0.00046598143,0.00017847455,0.00093948],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007121724,0.00015533206,0.0005056256,0.00020142089,0.00003217796,0.000012024292,0.00011745831,0.0000488582,0.0009752086],"category_scores_gemma":[0.0013950429,0.000108710155,0.000039393748,0.000431771,0.0000968962,0.000118473676,0.000059431146,0.00010932102,0.0000052106025],"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":[0.000053078747,0.000072923576,0.9499984,0.00024962908,0.000056530545,0.000007495171,0.00004428072,0.0000018233142,0.000026893817,0.000015691765,0.04200661,0.0074666664],"study_design_scores_gemma":[0.0019144505,0.00006616146,0.9718755,0.00022072645,0.000017769355,0.0000040999807,0.000050550923,0.00019354877,0.000041324744,0.00000753539,0.025488155,0.00012018379],"about_ca_topic_score_codex":0.968344,"about_ca_topic_score_gemma":0.9937816,"teacher_disagreement_score":0.02543761,"about_ca_system_score_codex":0.0005237073,"about_ca_system_score_gemma":0.016739719,"threshold_uncertainty_score":0.999938},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99170125,0.00008247245,0.0004815553,0.0022979437,0.00012386407,0.0003056468,0.00013326929,0.00008967985,0.004784305],"genre_scores_gemma":[0.9976041,0.00007311668,0.00083615957,0.000108163855,0.00009918099,0.0000051335724,0.000757396,0.000009167952,0.0005076],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9991771,0.000071660856,0.00024806507,0.00013988814,0.00027215004,0.00009116442],"domain_scores_gemma":[0.9994253,0.00003400452,0.00014470973,0.00015550616,0.00013932369,0.0001011561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040498524,0.00005478936,0.00018572163,0.00002995812,0.000023893108,0.0000045334245,0.00003674476,0.00002941579,0.00007425284],"category_scores_gemma":[0.00021505258,0.000049296144,0.000021260374,0.000100977624,0.00003764957,0.00013116804,0.000024201447,0.000027882184,0.00007085954],"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":[0.0014393668,0.0016831213,0.21632506,0.0013136087,0.0003883587,0.000012480684,0.028748082,0.00000715105,0.060378954,0.010185377,0.62535876,0.054159682],"study_design_scores_gemma":[0.004085711,0.0001860933,0.9743101,0.00009017843,0.00008577399,0.000010913182,0.001002835,0.00023708398,0.010825715,0.005708773,0.0032495596,0.00020729215],"about_ca_topic_score_codex":0.000115671246,"about_ca_topic_score_gemma":0.000014547558,"teacher_disagreement_score":0.757985,"about_ca_system_score_codex":0.00005260824,"about_ca_system_score_gemma":0.00014568315,"threshold_uncertainty_score":0.20102383},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9873283,0.0038940792,0.00044881165,0.0009641188,0.00022165764,0.0002448233,0.0063217273,0.000046401572,0.0005301005],"genre_scores_gemma":[0.99252856,0.0001871695,0.000095251744,0.00036246842,0.00008256846,0.000010780272,0.006687831,0.000014349245,0.00003105213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979305,0.0004970096,0.0005448557,0.000620266,0.00013540911,0.00027191994],"domain_scores_gemma":[0.99731356,0.00085381366,0.00032701687,0.0010448168,0.00012781136,0.000333003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012780204,0.00018674192,0.000745318,0.00012791087,0.00006076592,0.000008550867,0.00014977629,0.00006666083,0.000010744003],"category_scores_gemma":[0.0028916537,0.0001602023,0.00001538455,0.0002889951,0.00035510273,0.0004425118,0.00020501568,0.0002164034,0.000004685725],"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":[0.0005095576,0.000110048655,0.9450922,0.000050532024,0.000052554773,0.000009890307,0.00002550275,0.000126911,0.000016085029,0.000041576914,0.042687617,0.011277537],"study_design_scores_gemma":[0.001431412,0.00016272458,0.9712763,0.000038988073,0.00003049029,0.000007892883,0.000049728587,0.007408817,0.000003325803,0.00004830765,0.019384759,0.00015725536],"about_ca_topic_score_codex":0.18290047,"about_ca_topic_score_gemma":0.76591235,"teacher_disagreement_score":0.58301187,"about_ca_system_score_codex":0.00010457847,"about_ca_system_score_gemma":0.00073735765,"threshold_uncertainty_score":0.82254064},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94710386,0.02226295,0.0066337795,0.011648146,0.0005714406,0.010978776,0.0002524313,0.0003795773,0.00016904584],"genre_scores_gemma":[0.9848126,0.009687338,0.00024932603,0.0031921759,0.00019761451,0.0015152735,0.00017001701,0.00003429421,0.00014133271],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955141,0.001904446,0.001162825,0.00054422766,0.0003397964,0.0005345881],"domain_scores_gemma":[0.99408996,0.0034559937,0.00043721648,0.0013513605,0.00018979383,0.0004756879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00568113,0.00025297684,0.0010085644,0.00011775464,0.0001717922,0.000031045238,0.0003783585,0.00015715818,0.000035685716],"category_scores_gemma":[0.0014084739,0.00016211155,0.00005224744,0.00038868465,0.00007333005,0.00011500583,0.00015193826,0.0003908729,0.00002042628],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005153989,0.0046069548,0.85692406,0.00064684165,0.00018736957,0.000021422235,0.0005756219,0.00022039383,0.000018520403,0.0004889341,0.04220911,0.08894681],"study_design_scores_gemma":[0.008289901,0.0070234966,0.892468,0.0013865223,0.000012255339,0.000037244827,0.0003285756,0.011210462,2.1601667e-7,0.00001840851,0.07891934,0.00030558207],"about_ca_topic_score_codex":0.0009312284,"about_ca_topic_score_gemma":0.0025608647,"teacher_disagreement_score":0.08864123,"about_ca_system_score_codex":0.00021215386,"about_ca_system_score_gemma":0.00020776398,"threshold_uncertainty_score":0.6610717},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889797,0.002225156,0.0000822672,0.0016134856,0.00009344088,0.0010975917,0.0005085695,0.00011355473,0.0052862307],"genre_scores_gemma":[0.99661237,0.000058926576,0.00018572214,0.0019811075,0.00021793963,0.00019965944,0.0007006603,0.000020874895,0.000022724118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99859506,0.0001701578,0.0003261183,0.0002702277,0.00028851518,0.00034993052],"domain_scores_gemma":[0.99865574,0.00010164551,0.0002240448,0.0004013422,0.00029193712,0.00032527008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005159342,0.00012483816,0.00047143069,0.0000951693,0.000051949013,0.000025358515,0.00008716599,0.00005010442,0.0000017117353],"category_scores_gemma":[0.00032529744,0.00012048887,0.000038270762,0.00037700674,0.00010396838,0.00016105761,0.000058820813,0.00008516376,0.0000042969177],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027994975,0.000029189992,0.6394257,0.0005250326,0.000033523833,4.133312e-7,0.00014980887,4.4350273e-7,0.000018539491,0.00006419793,0.0007859555,0.3589392],"study_design_scores_gemma":[0.0010379006,0.00006545748,0.94362867,0.000021737857,0.000010785344,5.746312e-7,0.00010950311,0.0000042181273,0.000057501355,0.000020696236,0.054949634,0.000093344606],"about_ca_topic_score_codex":0.000049195452,"about_ca_topic_score_gemma":0.00008149955,"teacher_disagreement_score":0.35884586,"about_ca_system_score_codex":0.00014134383,"about_ca_system_score_gemma":0.00018670075,"threshold_uncertainty_score":0.49133936},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934928,0.00029168313,0.004947857,0.00002006863,0.00004233049,0.00060744514,0.0005157535,0.000013553987,0.00006854893],"genre_scores_gemma":[0.99607456,0.00012329468,0.0031577358,0.0004246376,0.000041311443,0.000007922086,0.00015684795,0.000012887206,8.1017333e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997433,0.00013449776,0.00070409005,0.00028101323,0.0010480303,0.0003993428],"domain_scores_gemma":[0.995908,0.0015409173,0.0002142894,0.001155356,0.00011592553,0.0010654731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020462486,0.00016541657,0.00044890918,0.0001287703,0.00012071174,0.000022279974,0.0004278475,0.0000872091,0.0001560693],"category_scores_gemma":[0.0008117828,0.00008915093,0.000028518523,0.00009833108,0.00022369846,0.00014643648,0.00033903716,0.00019085455,0.0000018453449],"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":[0.0006565655,0.0001059526,0.62923104,0.00036839885,0.000018246568,0.000013378378,0.00019206518,0.0000030061765,1.6601209e-7,0.0000101496325,0.000030752486,0.3693703],"study_design_scores_gemma":[0.0026850048,0.0007656796,0.8691946,0.0022203794,0.000081034574,0.000007697817,0.0006851793,0.12370711,0.00000198631,0.000057098234,0.00045858152,0.00013561247],"about_ca_topic_score_codex":0.0016188844,"about_ca_topic_score_gemma":0.12986572,"teacher_disagreement_score":0.36923468,"about_ca_system_score_codex":0.00016316782,"about_ca_system_score_gemma":0.0046612504,"threshold_uncertainty_score":0.88601196},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97704494,0.007228572,0.000077468256,0.010879393,0.00020090526,0.003790185,0.00062759535,0.00011871497,0.000032254997],"genre_scores_gemma":[0.9921708,0.006018378,0.000055005148,0.0012381669,0.00003860632,0.00013609148,0.00029307563,0.000029408508,0.000020434763],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99242955,0.0022860188,0.00223128,0.00070832187,0.0007374462,0.0016073985],"domain_scores_gemma":[0.9949182,0.00060661347,0.0013856903,0.0010367332,0.0006937326,0.0013590099],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009297548,0.00036494626,0.0017821614,0.00076523685,0.00013548059,0.0000815579,0.00020667464,0.00012263897,0.000011049002],"category_scores_gemma":[0.0021092568,0.00028336197,0.00007958746,0.0016381684,0.00027836673,0.0009435075,0.00013201812,0.00031473718,0.0000029261994],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023090657,0.0006243335,0.9417321,0.0022627902,0.00005981945,7.722299e-7,0.0006166493,6.842315e-7,0.000004027095,0.00020919445,0.00073042,0.05352831],"study_design_scores_gemma":[0.002886675,0.0032636402,0.9633143,0.00018622684,6.450191e-7,0.000008874812,0.00044231716,0.0002499393,1.16072904e-7,0.000012989036,0.02942118,0.00021308682],"about_ca_topic_score_codex":0.0026784812,"about_ca_topic_score_gemma":0.0044294707,"teacher_disagreement_score":0.053315222,"about_ca_system_score_codex":0.00057497056,"about_ca_system_score_gemma":0.004958657,"threshold_uncertainty_score":0.99996185},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73851484,0.0051952866,0.00037853632,0.25246733,0.00009573498,0.0026099011,0.0006562322,0.000022999655,0.000059130278],"genre_scores_gemma":[0.9862099,0.00059145165,0.00064704596,0.0122486595,0.00002525058,0.00012399457,0.000084262465,0.000018859098,0.00005053473],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99591655,0.0016411651,0.0012533036,0.00024697676,0.00040334827,0.00053867383],"domain_scores_gemma":[0.995257,0.0020444083,0.0010299747,0.000952174,0.00022264387,0.0004937631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007101848,0.00019543305,0.00094372634,0.00007526904,0.00032303334,0.00001255811,0.0004145228,0.000061844636,0.0000075967623],"category_scores_gemma":[0.0008965694,0.000089200104,0.00007906341,0.0005225751,0.00044511497,0.000055581913,0.00013879327,0.00023806469,3.907424e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022718268,0.0005587865,0.80774075,0.007689403,0.00011092122,2.6845788e-7,0.011069201,0.0000017746231,0.000045797045,0.0010591609,0.015005459,0.15444663],"study_design_scores_gemma":[0.011659927,0.00041592112,0.82830715,0.00064252707,0.0000017095982,0.000005518592,0.0024049268,0.0002935196,0.000019185582,0.000074715215,0.15604103,0.0001338541],"about_ca_topic_score_codex":0.0024476782,"about_ca_topic_score_gemma":0.0129798725,"teacher_disagreement_score":0.2476951,"about_ca_system_score_codex":0.00013200718,"about_ca_system_score_gemma":0.0107985,"threshold_uncertainty_score":0.9948093},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81043154,0.0001285228,0.177353,0.00026007943,0.00052709196,0.0030202589,0.00017365812,0.0010960114,0.007009817],"genre_scores_gemma":[0.99564207,0.000010495898,0.002270606,0.00005379642,0.0005064262,0.00054091035,0.0008869607,0.000059550224,0.000029172337],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99785054,0.00038509548,0.0004974565,0.0004745668,0.00041289945,0.0003794152],"domain_scores_gemma":[0.99824685,0.00028691147,0.00022822333,0.00037056507,0.0006837189,0.00018375916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000781324,0.0002640816,0.0004894349,0.00014436671,0.00023058361,0.000025270996,0.00011507526,0.00017613407,0.000014463371],"category_scores_gemma":[0.00017166548,0.00027771253,0.00012472835,0.0013504409,0.00011965953,0.00030167063,0.000031446172,0.0003400758,0.00005069683],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011168073,0.008355776,0.83180195,0.0022124443,0.0013205921,0.000043620865,0.005723574,0.0024972924,0.03383566,0.001770742,0.0015542998,0.10976726],"study_design_scores_gemma":[0.01047533,0.0015704795,0.9514239,0.00057326874,0.00089929666,0.00024642094,0.00038392004,0.008419981,0.0024417914,0.0021704016,0.0205954,0.0007997987],"about_ca_topic_score_codex":0.0001917174,"about_ca_topic_score_gemma":0.00007261254,"teacher_disagreement_score":0.18521051,"about_ca_system_score_codex":0.00026974684,"about_ca_system_score_gemma":0.00041191775,"threshold_uncertainty_score":0.9999675},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6546143,0.0015590627,0.000003248567,0.33985892,0.0007302569,0.00030863553,0.0000565998,0.000012506024,0.0028564322],"genre_scores_gemma":[0.5718226,0.00049724465,0.00016424533,0.4260172,0.0013178596,0.0000054218995,0.0000037942991,0.000026713196,0.0001449343],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9907775,0.0023298028,0.0017065954,0.001064659,0.0016475345,0.0024739406],"domain_scores_gemma":[0.994474,0.0014776782,0.0013574968,0.00048444117,0.00047461002,0.0017317943],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.0072287098,0.00069532014,0.0014740186,0.0021558278,0.0014440413,0.0011294029,0.0039908537,0.0006980316,0.00044006392],"category_scores_gemma":[0.0037302081,0.00040573624,0.00048323924,0.0024838138,0.011869551,0.0007414797,0.00014285723,0.0069290474,0.0000248578],"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":[0.00009730913,0.00002843365,0.7892094,0.00003211116,0.000016864587,0.042691484,0.00033723432,0.00046085397,0.0000031801758,0.000031462678,0.16595621,0.0011354357],"study_design_scores_gemma":[0.0012137429,0.032032605,0.4158903,0.00049687165,0.00010379843,0.04495223,0.00013739723,0.0015098593,0.0000130394865,0.0066009117,0.49623927,0.000809942],"about_ca_topic_score_codex":0.007628766,"about_ca_topic_score_gemma":0.25325185,"teacher_disagreement_score":0.3733191,"about_ca_system_score_codex":0.00056052796,"about_ca_system_score_gemma":0.006979799,"threshold_uncertainty_score":0.9999075},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34654114,0.012525773,0.0009964921,0.1144667,0.01209596,0.0015212289,0.00021680757,0.0011177935,0.51051813],"genre_scores_gemma":[0.88064337,0.008996592,0.0018965508,0.0042829616,0.029988326,0.00008830669,0.0038224342,0.00019411162,0.070087366],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958781,0.00010823637,0.0013728268,0.00062163273,0.0014607961,0.00055843306],"domain_scores_gemma":[0.9978669,0.00008056874,0.00024246545,0.00094619143,0.00023371066,0.0006301234],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007488245,0.00031157106,0.0006318393,0.00016063768,0.00009532901,0.0000034247664,0.00030885445,0.00018870473,0.011382731],"category_scores_gemma":[0.0030057617,0.00023790033,0.00016194684,0.00054961536,0.0001567825,0.00008099558,0.00010249827,0.000430096,0.00026231798],"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":[0.00030996933,0.00024512547,0.043292552,0.00012341302,0.000057149173,0.00029397194,0.0002196133,0.0000061953638,0.0005165344,0.0004654132,0.94660187,0.0078682],"study_design_scores_gemma":[0.0038495671,0.00028671656,0.08126767,0.00036075225,0.00012515746,0.000059631893,0.00017453515,0.00044322215,0.0001522415,0.0003659729,0.9126425,0.0002720821],"about_ca_topic_score_codex":0.0014110293,"about_ca_topic_score_gemma":0.00151677,"teacher_disagreement_score":0.5341022,"about_ca_system_score_codex":0.000100982754,"about_ca_system_score_gemma":0.00021922025,"threshold_uncertainty_score":0.989521},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958668,0.000014388371,0.0010470985,0.0014249093,0.00023109124,0.00041884938,0.00008768716,0.00024451126,0.0006646731],"genre_scores_gemma":[0.993457,0.000003961267,0.0019223668,0.0029559897,0.00024082306,0.000007840634,0.0011102782,0.00003896206,0.00026278046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985462,0.000067292385,0.0002503874,0.00041592706,0.00041021255,0.00030998327],"domain_scores_gemma":[0.9989285,0.000042759217,0.00010368271,0.0005623212,0.000075135555,0.00028759826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015731945,0.00019845819,0.00034800626,0.00030556548,0.00009704492,0.00004717151,0.00011126189,0.000070673785,0.00039477844],"category_scores_gemma":[0.00014111737,0.00016809964,0.00009824595,0.0004855436,0.000020498912,0.00014440344,0.0000609785,0.00011259632,0.000041202427],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005830617,0.000017722807,0.9787228,0.00003713809,0.000030186537,0.0000014782432,0.00004717928,0.000028141563,0.0021314113,0.00032875274,0.0013127284,0.017284155],"study_design_scores_gemma":[0.0005105029,0.000097535674,0.9960303,0.000047977035,0.00006509756,0.0000059378517,0.000065751265,0.00085762783,0.00041416712,0.00059811183,0.001104211,0.00020281052],"about_ca_topic_score_codex":0.003128501,"about_ca_topic_score_gemma":0.0050522294,"teacher_disagreement_score":0.01730747,"about_ca_system_score_codex":0.000084128675,"about_ca_system_score_gemma":0.000011028565,"threshold_uncertainty_score":0.6854904},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98478866,0.00013561685,0.0033786867,0.00017789204,0.000051298415,0.0005842276,0.010531586,0.000065632914,0.00028640294],"genre_scores_gemma":[0.9944409,0.000061373095,0.0006527002,0.00007591503,0.0000128123975,0.00008324742,0.0045446944,0.000028032804,0.00010031427],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99750906,0.00029771225,0.00090730895,0.000489584,0.00045956692,0.0003367709],"domain_scores_gemma":[0.9977742,0.00036879478,0.0004175253,0.0007659981,0.00038905483,0.00028442199],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002549813,0.0002766614,0.0010600319,0.0020857153,0.00015612917,0.000015986028,0.000118726304,0.00010977558,0.0006580074],"category_scores_gemma":[0.00022011354,0.00027922055,0.00029934724,0.007238927,0.00015504698,0.00015026167,0.000024414869,0.0003063445,0.0000024736032],"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":[0.00006218245,0.0005375549,0.87778234,0.000031177366,0.004997422,0.0000053290764,0.0002513781,0.114517346,0.00031818225,0.000015493559,0.00031509183,0.0011664764],"study_design_scores_gemma":[0.001635983,0.00010061189,0.8494812,0.000026997423,0.021829143,0.0000010677131,0.00013345593,0.12639579,0.00005586259,0.000009415205,0.00013036751,0.00020007172],"about_ca_topic_score_codex":0.8740249,"about_ca_topic_score_gemma":0.98345846,"teacher_disagreement_score":0.10943358,"about_ca_system_score_codex":0.00028218728,"about_ca_system_score_gemma":0.00062040007,"threshold_uncertainty_score":0.999966},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66579074,0.00064628554,0.322935,0.0014473979,0.0007458393,0.0008673704,0.006631896,0.000059735587,0.00087576825],"genre_scores_gemma":[0.98682,0.000043053795,0.011771814,0.00031314677,0.000070096095,0.00001394048,0.00012516072,0.000021447593,0.0008213751],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896884,0.0000412944,0.00034996792,0.00026752957,0.00015742565,0.00021491991],"domain_scores_gemma":[0.99855775,0.0004854457,0.00012980663,0.00024413603,0.0004043923,0.00017845514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002516076,0.000149148,0.000345499,0.00004756347,0.000041950352,0.000031078747,0.00011140632,0.000071500486,0.00045200388],"category_scores_gemma":[0.0010307175,0.00012235377,0.000042003056,0.000057909183,0.00014826121,0.00006190776,0.000060931077,0.00018784394,0.000027378273],"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":[0.0041176975,0.0013121357,0.040761773,0.0038667317,0.0010162753,0.00008590902,0.00284666,0.00003431903,0.39639416,0.24740085,0.28962103,0.012542488],"study_design_scores_gemma":[0.009916447,0.0041685104,0.23436603,0.0012725478,0.00078171486,0.0001638055,0.0008497698,0.048196714,0.55171037,0.0752306,0.07184217,0.0015012972],"about_ca_topic_score_codex":0.00000800381,"about_ca_topic_score_gemma":0.00011241617,"teacher_disagreement_score":0.32102925,"about_ca_system_score_codex":0.0000120776285,"about_ca_system_score_gemma":0.00010692176,"threshold_uncertainty_score":0.49894416},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032255452,0.057109393,0.10038136,0.62666625,0.003018657,0.0076530073,0.0014902817,0.00018531295,0.17124031],"genre_scores_gemma":[0.7481413,0.04967783,0.1605348,0.03644797,0.0008087939,0.00005403181,0.0004809923,0.00007466721,0.0037796053],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99750704,0.000733662,0.0006875726,0.000266565,0.0004471258,0.00035805107],"domain_scores_gemma":[0.9965374,0.0011653756,0.0008990625,0.00051617186,0.0004291998,0.00045279547],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010819688,0.00013134145,0.00041170444,0.00014490697,0.00012825395,0.00013752784,0.00020399982,0.000035174213,0.00010815241],"category_scores_gemma":[0.010753769,0.000115319795,0.000052621563,0.0002354247,0.00005102742,0.0012844293,0.00007912158,0.00020571334,0.0000113819015],"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":[0.0018095535,0.0012826415,0.01220512,0.0013782738,0.0013618978,0.0008155412,0.0004239952,8.394711e-7,0.0000053231647,0.040395588,0.37113827,0.56918293],"study_design_scores_gemma":[0.0027959403,0.0009128738,0.01591036,0.00009319896,0.000048858226,0.0010788162,0.0011245406,0.000021390388,8.0640444e-7,0.00024738663,0.977664,0.000101862795],"about_ca_topic_score_codex":0.0000088016905,"about_ca_topic_score_gemma":0.000015885995,"teacher_disagreement_score":0.7158859,"about_ca_system_score_codex":0.00007624149,"about_ca_system_score_gemma":0.0008840885,"threshold_uncertainty_score":0.9975791},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018353239,0.004574171,0.00017293176,0.00022358981,0.0045564547,0.0029540665,0.0035368132,0.00078000006,0.98136663],"genre_scores_gemma":[0.015613082,0.00045463484,0.0007588683,0.0053381342,0.0031937608,0.00023408735,0.003192328,0.00070802454,0.9705071],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99015915,0.0006444276,0.0020888716,0.0026383025,0.0021537219,0.002315506],"domain_scores_gemma":[0.99109125,0.00051498157,0.0011624403,0.00467858,0.00066384225,0.0018889147],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0011726887,0.0021641946,0.0031768412,0.00079991115,0.00044393138,0.00021568735,0.0014331031,0.0016668447,0.008303183],"category_scores_gemma":[0.00068832026,0.0022287709,0.0013301326,0.00022382916,0.0011537005,0.0001461041,0.00055798877,0.002556486,0.018745257],"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":[0.00094629964,0.00058988156,0.0047692223,0.0033577417,0.0028095953,0.0054368554,0.00060920353,0.000004214077,0.00070322666,0.013276157,0.9010364,0.06646119],"study_design_scores_gemma":[0.0043613412,0.0005365259,0.0028141886,0.002588815,0.0015383314,0.00026766834,0.00008438165,0.000030857133,0.0004771213,0.0026375847,0.98249453,0.0021686633],"about_ca_topic_score_codex":0.000074139076,"about_ca_topic_score_gemma":0.000074340605,"teacher_disagreement_score":0.08145811,"about_ca_system_score_codex":0.0011644835,"about_ca_system_score_gemma":0.004865111,"threshold_uncertainty_score":0.99974465},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8924474,0.00013044602,0.00032987967,0.0039443444,0.0004367274,0.00059837464,0.00012499727,0.0001373884,0.10185042],"genre_scores_gemma":[0.9922512,0.00011772131,0.0021125667,0.0014948267,0.0013372094,0.00004878608,0.00012847486,0.00008246864,0.002426703],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99745965,0.00020609648,0.00065895496,0.0006491996,0.0002128564,0.0008132219],"domain_scores_gemma":[0.9977714,0.0005577806,0.00014973221,0.0009877253,0.00011569756,0.0004176618],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021222706,0.00025319532,0.0006242176,0.0003299839,0.00012890444,0.00008590261,0.0003806863,0.00016992226,0.00046080732],"category_scores_gemma":[0.0007617573,0.00027763596,0.00012996468,0.0002603831,0.00017321121,0.00021312093,0.00026596893,0.00064619695,0.001366038],"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":[0.0016687402,0.0019795918,0.22158417,0.0012692785,0.00024999116,0.0010753229,0.0022909453,0.0049300725,0.0043495353,0.004019027,0.5130611,0.2435222],"study_design_scores_gemma":[0.008638375,0.00048282798,0.12985201,0.00050691684,0.000081585844,0.0001177972,0.0006794302,0.061286204,0.0027355705,0.0021009182,0.79231435,0.0012040333],"about_ca_topic_score_codex":0.0003357857,"about_ca_topic_score_gemma":0.00038252564,"teacher_disagreement_score":0.2792532,"about_ca_system_score_codex":0.0003493908,"about_ca_system_score_gemma":0.00026594964,"threshold_uncertainty_score":0.9999676},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003096852,0.9455975,0.020121658,0.0012311307,0.00038716826,0.014905283,0.011216514,0.00007589348,0.0033679798],"genre_scores_gemma":[0.0051851543,0.98222184,0.005744396,0.0013633963,0.0002467845,0.0001844945,0.005010813,0.0000392608,0.000003851598],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962461,0.00032746486,0.0014806,0.00021571154,0.0014709809,0.00025914964],"domain_scores_gemma":[0.9965031,0.00058605435,0.0008393959,0.0017327913,0.00007404235,0.00026462125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030406744,0.00027189715,0.0014654831,0.00020212795,0.00005040569,0.000036330828,0.00097482774,0.00022319949,0.000016395026],"category_scores_gemma":[0.00035820316,0.00014669407,0.000096145406,0.00028813546,0.000081764185,0.000080625636,0.00075300207,0.0006644645,0.0000043315313],"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":[0.0000066803036,0.00008992317,0.00024442986,0.03289868,0.00009162097,0.000020884925,0.0025344254,0.0000064737296,2.0182438e-9,0.00028282538,0.001830871,0.96199316],"study_design_scores_gemma":[0.001574804,0.00026946352,0.0027979154,0.014587169,0.00053398067,0.00048091472,0.0039047734,0.003571545,4.255738e-8,0.0000029077364,0.9719806,0.00029592938],"about_ca_topic_score_codex":0.00019899283,"about_ca_topic_score_gemma":0.00031473784,"teacher_disagreement_score":0.9701497,"about_ca_system_score_codex":0.00010790218,"about_ca_system_score_gemma":0.0008821768,"threshold_uncertainty_score":0.5982011},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97762,0.00010669362,0.00075686665,0.0012730152,0.001672811,0.0001882892,0.00003717716,0.000028144274,0.018316979],"genre_scores_gemma":[0.9951408,0.00004931188,0.0034801534,0.00074248953,0.00029007188,0.0000020753903,0.000012845506,0.0000075627195,0.0002746764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99716884,0.000047311933,0.0003966121,0.00018135576,0.001977041,0.00022885116],"domain_scores_gemma":[0.99732476,0.000029156998,0.00029683672,0.00022357816,0.0017530267,0.00037265348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029014777,0.000093966686,0.00016520711,0.000815757,0.00003845476,0.00011154354,0.0008926866,0.00001531106,0.00003097361],"category_scores_gemma":[0.00034539867,0.0000770703,0.000083929786,0.0005870082,0.00025785956,0.00048326477,0.00025208338,0.000120938545,0.00005326677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001134187,0.0009317039,0.9187849,0.00005360681,0.0007714546,0.00085421366,0.00077352015,0.00077576714,0.0026948696,0.007465904,0.012910046,0.05284984],"study_design_scores_gemma":[0.0016091845,0.00013854988,0.9864838,0.000112988215,0.000042973934,0.00020687199,0.00048729812,0.00033994252,0.0006221584,0.0004252559,0.009418366,0.00011257568],"about_ca_topic_score_codex":0.0003128541,"about_ca_topic_score_gemma":0.00004133679,"teacher_disagreement_score":0.06769895,"about_ca_system_score_codex":0.00024603392,"about_ca_system_score_gemma":0.00033470074,"threshold_uncertainty_score":0.31428355},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89394,0.01672845,0.00013195607,0.021614587,0.0013424236,0.006769414,0.0579585,0.0006032831,0.0009114398],"genre_scores_gemma":[0.98593944,0.0010864979,0.00261615,0.0025089558,0.00014272233,0.00039820632,0.007005431,0.00015814093,0.00014443208],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99363774,0.00021423466,0.0011358613,0.0017566218,0.00031535522,0.0029402138],"domain_scores_gemma":[0.9912179,0.0002654045,0.0003036013,0.0027285423,0.00050374895,0.0049808207],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014328785,0.00073082413,0.0012451215,0.007829188,0.00076182524,0.000103177896,0.0012184556,0.00030464874,0.000059157333],"category_scores_gemma":[0.0009345584,0.0006332584,0.00029959148,0.004490377,0.0016120917,0.0006840604,0.000082946455,0.0003101405,0.000004527238],"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":[0.00059833337,0.00027396527,0.9066699,0.0016132938,0.0016562656,0.00071875093,0.0014696183,0.00006078354,0.0003644208,0.0028123695,0.031615075,0.052147187],"study_design_scores_gemma":[0.016350232,0.0027418553,0.6896841,0.0021695923,0.00056096906,0.00036836762,0.026172528,0.008792284,0.000048642603,0.0013763442,0.2472253,0.0045097712],"about_ca_topic_score_codex":0.95169175,"about_ca_topic_score_gemma":0.99972034,"teacher_disagreement_score":0.21698582,"about_ca_system_score_codex":0.00095090596,"about_ca_system_score_gemma":0.0038364031,"threshold_uncertainty_score":0.99961185},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46305197,0.00094826246,0.007470226,0.0126272,0.00033869338,0.0012276347,0.0005566658,0.000408694,0.51337063],"genre_scores_gemma":[0.9869006,0.000004449682,0.0004584456,0.0005432737,0.000042822554,0.000004774041,0.000010102244,0.0000066504076,0.012028832],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995676,0.000011857628,0.0001133553,0.000100371064,0.00011972218,0.00008703976],"domain_scores_gemma":[0.9995271,0.00003683852,0.000027851644,0.00027302591,0.000064316475,0.000070859875],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004658696,0.000044140885,0.0001305429,0.000015556632,0.0000047400467,8.716908e-7,0.00004371607,0.000018883682,0.005673002],"category_scores_gemma":[0.00011673407,0.000022332591,0.00003696104,0.000042008323,0.00007662655,0.000051852865,0.000023116321,0.000015104362,0.000087901986],"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":[0.00039645287,0.0004872828,0.14111544,0.00015334463,0.00018189737,0.00005247282,0.00012488748,4.8192396e-7,0.1496856,0.00792103,0.27564523,0.42423588],"study_design_scores_gemma":[0.004947115,0.00038961828,0.2453869,0.00027324873,0.000055556047,0.000017756947,0.000051842046,0.000011737965,0.32363692,0.0005209856,0.42450762,0.00020068654],"about_ca_topic_score_codex":0.000007256312,"about_ca_topic_score_gemma":0.000005169247,"teacher_disagreement_score":0.52384865,"about_ca_system_score_codex":0.0000103762295,"about_ca_system_score_gemma":0.000043566302,"threshold_uncertainty_score":0.9952359},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9835284,0.00022748517,0.000071294424,0.015034723,0.00019525521,0.00025145774,0.00047374217,0.000008173071,0.00020950414],"genre_scores_gemma":[0.9929726,0.0005890727,0.0030643274,0.0019481106,0.0007077052,0.0000044515336,0.000016484795,0.00001476941,0.0006824788],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978284,0.00008974917,0.00065972854,0.00018107567,0.0010534142,0.00018760489],"domain_scores_gemma":[0.9980825,0.00040169613,0.00046007565,0.00025424268,0.00050189,0.00029956538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001397562,0.000120471224,0.00037023347,0.00014283856,0.000056802342,0.000045258796,0.00039454643,0.00010359143,0.000077689154],"category_scores_gemma":[0.0012523028,0.00006263921,0.00024394537,0.00038158926,0.0003862458,0.0001622999,0.00041351366,0.00041899784,0.000003815699],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050235714,0.00014539958,0.88813776,0.000016663682,0.00062687823,0.000010161686,0.0009816878,1.9474047e-7,0.00020830733,0.000095313,0.05519375,0.05408151],"study_design_scores_gemma":[0.0026457876,0.0004810528,0.8414518,0.0008019559,0.00008220608,0.00010338691,0.0005006465,0.000014728179,0.0002064179,0.0015920496,0.15201868,0.00010127956],"about_ca_topic_score_codex":0.00004475579,"about_ca_topic_score_gemma":0.000015691772,"teacher_disagreement_score":0.09682494,"about_ca_system_score_codex":0.00013729073,"about_ca_system_score_gemma":0.00040914674,"threshold_uncertainty_score":0.25543526},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943172,0.0001726509,0.0003229572,0.0037533094,0.00004095451,0.00020649066,0.0010255359,0.000009783554,0.0001511723],"genre_scores_gemma":[0.97280335,0.003548052,0.022599345,0.0004749541,0.00006492979,8.6375366e-7,0.0003990918,0.000019827783,0.00008956289],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972564,0.00010688528,0.0016161379,0.000097414486,0.0005760138,0.00034712726],"domain_scores_gemma":[0.99722636,0.00015148117,0.0013962211,0.00060244696,0.00010242621,0.0005210962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028243428,0.00014890594,0.0006556603,0.0003649841,0.000027040736,0.0000140159755,0.0003055873,0.000054334956,0.00012822622],"category_scores_gemma":[0.00025518678,0.00008929896,0.000037154325,0.00019104886,0.00018074366,0.0008223486,0.00014295428,0.00026089014,0.0000042262127],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015808933,0.00039199964,0.9594433,0.00022762474,0.00008252842,0.000017619272,0.0004899061,0.000001023048,0.00011604043,0.000010727534,0.0007227241,0.038338464],"study_design_scores_gemma":[0.003472344,0.0008054376,0.97416717,0.0004242065,0.000011727043,0.0003817968,0.00026073202,0.00024830727,0.0000019770857,0.000009072351,0.020117354,0.00009986442],"about_ca_topic_score_codex":0.00061079446,"about_ca_topic_score_gemma":0.008988958,"teacher_disagreement_score":0.0382386,"about_ca_system_score_codex":0.00027090524,"about_ca_system_score_gemma":0.0019397711,"threshold_uncertainty_score":0.501605},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9290337,0.0017458216,0.0004070018,0.0013407542,0.0015380284,0.0018173817,0.0021427209,0.0006462096,0.061328392],"genre_scores_gemma":[0.98383015,0.00014452239,0.0045700306,0.0006169303,0.0014990952,0.000450517,0.0007469262,0.00026577944,0.0078760525],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953166,0.0002116907,0.00094717817,0.0016514781,0.00077195215,0.0011010872],"domain_scores_gemma":[0.99539745,0.0004586361,0.00068973046,0.0021715409,0.0003792264,0.0009034228],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00079556525,0.0009126306,0.0015526628,0.00043983286,0.00020444432,0.00011437001,0.00062131626,0.00046183172,0.00258733],"category_scores_gemma":[0.00042853146,0.0007131814,0.0004611767,0.00018460337,0.000911311,0.00019728462,0.0010349562,0.0011478659,0.0009441037],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004224869,0.00026396092,0.9684962,0.00076232856,0.00083812803,0.00056864554,0.00015884348,0.000024534953,0.000110989764,0.00019800694,0.023470549,0.0046853437],"study_design_scores_gemma":[0.018657926,0.0022046433,0.6814052,0.020127797,0.0021411192,0.0032712314,0.0006632103,0.0028607212,0.003953006,0.0026255928,0.2552378,0.006851757],"about_ca_topic_score_codex":0.00008725049,"about_ca_topic_score_gemma":0.00019419815,"teacher_disagreement_score":0.287091,"about_ca_system_score_codex":0.0011052615,"about_ca_system_score_gemma":0.0026319232,"threshold_uncertainty_score":0.99983376},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99235594,0.0020958274,0.000012383434,0.0020850825,0.000023564577,0.0010062697,0.0022083388,0.00014812463,0.00006446768],"genre_scores_gemma":[0.9975511,0.000034528963,0.00011411178,0.00084081484,0.000019333103,0.0007434658,0.0006161209,0.000039010363,0.000041523857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965412,0.0015661386,0.00061142904,0.00036092688,0.0003391078,0.0005811729],"domain_scores_gemma":[0.9980892,0.000533315,0.00016021845,0.00049477234,0.000055202,0.0006672746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012545146,0.00023912243,0.00086181285,0.00033865284,0.00019593495,0.000022369255,0.00010699633,0.00010793885,0.000017143704],"category_scores_gemma":[0.0001670339,0.0002158474,0.000029168881,0.000782012,0.00023729671,0.00008799736,0.000075161515,0.0003766223,0.0000050334666],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008974668,0.0017774059,0.9862357,0.0007342022,0.00007863511,0.000013721707,0.0007378852,0.0000021650103,0.0029744077,0.000009350522,0.0044636275,0.002075482],"study_design_scores_gemma":[0.010114497,0.0012221434,0.98178625,0.0026837788,0.00002703642,0.000003295702,0.0025007355,0.00062721624,0.00058984535,0.0000036806596,0.00010067456,0.000340838],"about_ca_topic_score_codex":0.0039751255,"about_ca_topic_score_gemma":0.009554335,"teacher_disagreement_score":0.00921703,"about_ca_system_score_codex":0.00059764495,"about_ca_system_score_gemma":0.00032951502,"threshold_uncertainty_score":0.88020015},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7670681,0.0008781798,0.22787882,0.00065795844,0.0013099841,0.0005974576,0.0015369502,0.000019505285,0.000053059903],"genre_scores_gemma":[0.9898729,0.000051906423,0.008178766,0.00090744445,0.0008122151,0.0000055118016,0.00013626966,0.000026218224,0.000008784743],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99802756,0.00007845434,0.00062417315,0.00016293667,0.00077400106,0.00033288228],"domain_scores_gemma":[0.997631,0.00014514064,0.0003570021,0.00031433816,0.00041175782,0.0011407281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005102774,0.00015893286,0.00031304787,0.0013139696,0.00004688503,0.00003067402,0.0001697893,0.000053883596,0.0000248563],"category_scores_gemma":[0.0014134486,0.00014703418,0.000051430343,0.0022854991,0.00002733329,0.0001767397,0.000051305684,0.00021926693,0.000017942091],"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":[0.00084279676,0.0014417691,0.54301685,0.00013321014,0.0000832288,0.00028283746,0.0001414136,0.00006634316,0.0009218505,0.000043554937,0.007276445,0.44574973],"study_design_scores_gemma":[0.001084016,0.00031963925,0.9698969,0.000038971484,0.000069119116,0.00010109113,0.00009198844,0.0006171643,0.0002492264,0.000026966705,0.02734247,0.00016241231],"about_ca_topic_score_codex":0.022870151,"about_ca_topic_score_gemma":0.118121244,"teacher_disagreement_score":0.4455873,"about_ca_system_score_codex":0.0006475869,"about_ca_system_score_gemma":0.00057627267,"threshold_uncertainty_score":0.9836366},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9806149,0.000120027966,0.000058953887,0.01631829,0.00009415273,0.0010125622,0.0017578233,0.000018637846,0.0000046229857],"genre_scores_gemma":[0.9955291,0.00049673155,0.00011314484,0.0018558586,0.00017298081,0.00025304672,0.0005615608,0.000017875192,0.0009996565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998506,0.00023263553,0.0003812588,0.00026425032,0.00026976602,0.00034609478],"domain_scores_gemma":[0.9983753,0.00070921006,0.00012775508,0.00038102243,0.00021232502,0.00019437069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048249884,0.0001662192,0.0003972426,0.000111692585,0.000046490673,0.0000126893165,0.00024882136,0.000066303764,0.00018777116],"category_scores_gemma":[0.00016638706,0.00007257679,0.000043847922,0.0003762507,0.0003192885,0.000052598607,0.000086937325,0.00022713265,3.2317408e-7],"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":[0.0025368915,0.00013331181,0.95898825,0.00013008802,0.00018435459,0.000013199914,0.0005197787,0.0015226654,0.00043520762,0.00060712546,0.026314253,0.008614883],"study_design_scores_gemma":[0.006589315,0.00019445606,0.8740976,0.00038828014,0.000050592836,0.0000029453508,0.00017788728,0.0066914763,0.00007350503,0.0001639941,0.11142931,0.00014067719],"about_ca_topic_score_codex":0.89063096,"about_ca_topic_score_gemma":0.90806407,"teacher_disagreement_score":0.08511506,"about_ca_system_score_codex":0.0003574132,"about_ca_system_score_gemma":0.0011790667,"threshold_uncertainty_score":0.29595956},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5766844,0.008431183,0.3356494,0.036214158,0.001007139,0.009037317,0.032091897,0.0005796563,0.00030483547],"genre_scores_gemma":[0.87031895,0.0006847916,0.116443664,0.0019350505,0.00081031193,0.0001567204,0.009232396,0.00012949288,0.0002886316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99481004,0.0012103614,0.0011553038,0.0009882692,0.0009557948,0.00088024593],"domain_scores_gemma":[0.99272585,0.00289961,0.0007108062,0.002245125,0.0006053265,0.00081327563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0084904935,0.00022772857,0.00086539803,0.00017589639,0.00038446527,0.000022853543,0.00041372023,0.00011400109,0.0000990719],"category_scores_gemma":[0.0048891627,0.0001901132,0.0000745121,0.0004041325,0.00026338577,0.0003165319,0.00016145044,0.0002555742,0.000010491271],"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":[0.022209989,0.0049932715,0.15822425,0.02107091,0.00059061276,0.000057811045,0.012280843,0.00039265183,0.006653013,0.0003949128,0.37512907,0.39800268],"study_design_scores_gemma":[0.051536977,0.00847893,0.25110343,0.016893692,0.0003903631,0.000024268598,0.01000369,0.45077214,0.0071832095,0.0008930727,0.2003582,0.002362027],"about_ca_topic_score_codex":0.015098536,"about_ca_topic_score_gemma":0.001212391,"teacher_disagreement_score":0.4503795,"about_ca_system_score_codex":0.0018102789,"about_ca_system_score_gemma":0.007913488,"threshold_uncertainty_score":0.9977107},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9151509,0.00036685952,0.05956289,0.0013377579,0.00091536,0.0036948018,0.0026176197,0.0012949466,0.0150588965],"genre_scores_gemma":[0.9927151,0.0000025644777,0.004321864,0.0004066119,0.00042736396,0.00009385379,0.0014822092,0.000041931984,0.0005085056],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9980494,0.00020427331,0.00045106775,0.0004739549,0.00036597182,0.00045535158],"domain_scores_gemma":[0.9983858,0.00023813538,0.00015496672,0.00069223624,0.00024527984,0.00028357247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008756999,0.00024930577,0.0004812949,0.00012790688,0.00011533921,0.00005715172,0.00017086614,0.00008238484,0.00013308773],"category_scores_gemma":[0.00016705366,0.000234825,0.00017707766,0.00026064445,0.000035491496,0.00019509974,0.000010613973,0.000118698976,0.00020496659],"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":[0.0045535876,0.0024896958,0.22730033,0.002813966,0.0009940374,0.0005210654,0.0044772336,0.00016342456,0.066876024,0.02914672,0.05008672,0.61057717],"study_design_scores_gemma":[0.01564966,0.004048979,0.94067526,0.0006745575,0.00049380556,0.00019791894,0.0023432977,0.0073240143,0.0044370163,0.0015051969,0.02139005,0.0012602257],"about_ca_topic_score_codex":0.00033964746,"about_ca_topic_score_gemma":0.00026509256,"teacher_disagreement_score":0.713375,"about_ca_system_score_codex":0.00024321719,"about_ca_system_score_gemma":0.0001282242,"threshold_uncertainty_score":0.95758855},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9453227,0.0002587108,0.0014842137,0.0031577528,0.0002863287,0.0014414719,0.00018405162,0.00017093022,0.04769386],"genre_scores_gemma":[0.97566986,0.00030921513,0.022233406,0.0010937656,0.00008083261,0.000086029446,0.00021237193,0.000019337418,0.00029519072],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99832046,0.00067184167,0.00025787557,0.00025477112,0.0003125926,0.00018246692],"domain_scores_gemma":[0.99781334,0.00061458326,0.00016664597,0.0012956556,0.00004921491,0.000060530092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00194469,0.00013171865,0.00018476392,0.000117245814,0.00017524684,0.000050295843,0.00024824048,0.000049833954,0.00014187867],"category_scores_gemma":[0.00053772103,0.000096409334,0.00006865911,0.00022407727,0.00006413381,0.000093529285,0.00007372751,0.00063089526,0.00024207855],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005163198,0.0012390979,0.57736653,0.00017444517,0.000328074,0.00020508123,0.0059460616,0.00042666178,0.0008154193,0.026697272,0.08324659,0.30303848],"study_design_scores_gemma":[0.001675381,0.00004850139,0.9414148,0.000108712906,0.00017473818,0.000017571743,0.00088492874,0.0020743085,0.00046167115,0.0032412496,0.049701456,0.00019670435],"about_ca_topic_score_codex":0.00039426357,"about_ca_topic_score_gemma":0.0014353633,"teacher_disagreement_score":0.36404827,"about_ca_system_score_codex":0.000033981705,"about_ca_system_score_gemma":0.000023351642,"threshold_uncertainty_score":0.39314583},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930951,0.0036326244,0.000035596873,0.00019100384,0.00013520489,0.00062740844,0.0020974432,0.000088837776,0.0000967596],"genre_scores_gemma":[0.9983152,0.00014619756,0.00052434317,0.00011275334,0.00005271605,0.000036043,0.00019629687,0.000028492317,0.0005879528],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983959,0.00014794145,0.00048619913,0.00031327986,0.00024179342,0.00041485188],"domain_scores_gemma":[0.9986178,0.0003066188,0.00007458456,0.0005517577,0.000065316526,0.00038388127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025650635,0.00020980218,0.0005054206,0.00030002592,0.000045433404,0.000010974149,0.00010028543,0.000109171844,0.00007644126],"category_scores_gemma":[0.0007920017,0.00019902014,0.000092112925,0.00038258632,0.00014907082,0.0005632668,0.00009790867,0.00016620626,0.00005769814],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002385002,0.00013148476,0.99524814,0.00021777328,0.00006249859,0.00002421363,0.0007911916,0.000040728108,0.0009785332,0.00022657811,0.0011471005,0.0008932619],"study_design_scores_gemma":[0.0006853175,0.00013236649,0.9968518,0.00016055405,0.000033618926,0.000019852392,0.00018119012,0.00013496484,0.00013782976,0.00023377498,0.0012244582,0.00020427836],"about_ca_topic_score_codex":0.0011719852,"about_ca_topic_score_gemma":0.0002621472,"teacher_disagreement_score":0.005220084,"about_ca_system_score_codex":0.000081502694,"about_ca_system_score_gemma":0.00006939537,"threshold_uncertainty_score":0.81158054},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932481,0.00017031323,0.00024673174,0.0014603822,0.00010769466,0.0001583199,0.00004224082,0.000027148628,0.004539075],"genre_scores_gemma":[0.9979387,0.00026309246,0.0005640206,0.00008263684,0.00011068476,1.2892986e-7,0.000011727117,0.000010782645,0.0010182287],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99760664,0.00041068465,0.0003057919,0.000227524,0.0011071747,0.00034220872],"domain_scores_gemma":[0.99684215,0.0004931137,0.0002388943,0.00046157895,0.001450843,0.0005134231],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001434645,0.00012201336,0.0005859417,0.011089542,0.00012596018,0.0000411656,0.0003981328,0.00009108229,0.0010007503],"category_scores_gemma":[0.00074398087,0.000105735286,0.000401053,0.016600464,0.0001555015,0.00031228884,0.000111543704,0.0006682617,0.00035932177],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011701806,0.0008899337,0.89409286,0.00005560054,0.0025665753,0.0021553477,0.00012282752,0.00017568265,0.00085747807,0.00025341057,0.092215545,0.005444564],"study_design_scores_gemma":[0.0013893618,0.00055319065,0.9816253,0.00003733611,0.00023833569,0.00008055232,0.00023718242,0.00029802343,0.000048968483,0.000050552168,0.015334759,0.00010642124],"about_ca_topic_score_codex":0.00016206656,"about_ca_topic_score_gemma":0.00001853901,"teacher_disagreement_score":0.08753247,"about_ca_system_score_codex":0.0004021105,"about_ca_system_score_gemma":0.00022609429,"threshold_uncertainty_score":0.99991244},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9186212,0.00047556753,0.0000029419632,0.00015759261,0.00038669334,0.0011148441,0.00027607603,0.00022291063,0.07874218],"genre_scores_gemma":[0.9940378,0.00007690268,0.00022875497,0.0034455517,0.0001367802,0.00019247862,0.00019310095,0.0000898308,0.0015987948],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99634033,0.00022728338,0.00080397836,0.0008302252,0.000591594,0.0012065829],"domain_scores_gemma":[0.9978593,0.00018446322,0.00020633602,0.0011433917,0.00007898598,0.00052753725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009768907,0.00044804657,0.00079890364,0.0006278477,0.000051632127,0.000034214383,0.0004138117,0.00023563017,0.0008210642],"category_scores_gemma":[0.0011773531,0.00044058738,0.00015348876,0.0008830589,0.00015637698,0.000272295,0.0001430109,0.0006564123,0.00075509393],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027730966,0.0011269037,0.97179484,0.00012823274,0.00008841121,0.0010568238,0.0010466983,0.000057512883,0.0007669442,0.00025772137,0.009158941,0.011743884],"study_design_scores_gemma":[0.0047525032,0.0001565203,0.9618242,0.00029544078,0.000028853974,0.000018908144,0.0001231753,0.00006458486,0.0005875948,0.000104150844,0.031603076,0.00044101378],"about_ca_topic_score_codex":0.0013657749,"about_ca_topic_score_gemma":0.0026729873,"teacher_disagreement_score":0.07714338,"about_ca_system_score_codex":0.00043559275,"about_ca_system_score_gemma":0.00025664855,"threshold_uncertainty_score":0.9998046},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9981788,0.0000772763,0.00096354145,0.00006652385,0.000026185231,0.000046500507,0.00009869436,0.0000020012983,0.0005404846],"genre_scores_gemma":[0.99913967,0.0001556324,0.0006179408,0.00003259137,0.0000059991944,1.9804167e-8,0.000015000526,0.0000020561793,0.000031077696],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99936515,0.0000545409,0.00024981343,0.00007726359,0.00017586668,0.00007735602],"domain_scores_gemma":[0.9994153,0.000032900338,0.00024439825,0.000094040224,0.00012458717,0.00008881059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003037426,0.00004849543,0.00030290507,0.00051217224,0.000009971801,8.9262295e-7,0.0000612896,0.000030106265,0.000025340916],"category_scores_gemma":[0.00008476539,0.000049903105,0.00005090719,0.00049035676,0.000022238692,0.00019348189,0.000020344998,0.00013074433,1.00538884e-7],"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":[0.00042715372,0.00006498038,0.99310017,0.000014131969,0.00015713222,0.00012905672,0.0002724144,0.0044846726,0.00018320183,0.000014075632,0.0005346163,0.00061837304],"study_design_scores_gemma":[0.00087010977,0.000038671747,0.93252873,0.000041773772,0.00037665645,0.0000059407153,0.00036566422,0.06564589,0.000032041975,0.000031000814,0.000031945823,0.00003155699],"about_ca_topic_score_codex":0.19790815,"about_ca_topic_score_gemma":0.62182677,"teacher_disagreement_score":0.4239186,"about_ca_system_score_codex":0.0005169582,"about_ca_system_score_gemma":0.0006242745,"threshold_uncertainty_score":0.80743307},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9452563,0.0033338738,0.0001331595,0.005433883,0.018802475,0.0010299095,0.000378798,0.00002284013,0.025608746],"genre_scores_gemma":[0.9983664,0.00037658817,0.00007087702,0.000105074185,0.0006361446,0.0000015993733,0.00012280929,0.00001687171,0.00030362335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970746,0.00012567478,0.0014836856,0.00013232492,0.00075773,0.00042595484],"domain_scores_gemma":[0.995872,0.000020305957,0.00088231487,0.00048826405,0.0011386267,0.0015984508],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00072258135,0.0001967157,0.0005078,0.00052537705,0.00048334966,0.0000033824952,0.00038695987,0.000087250286,0.002679607],"category_scores_gemma":[0.0010883776,0.00013556251,0.00022516809,0.0006692902,0.0001538924,0.00028552304,0.000026374473,0.00031192042,0.000031204774],"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":[0.00007015646,0.000016432698,0.43853784,0.0006023606,0.00015808528,0.00024853193,0.0016166933,0.00008741756,0.000082422615,0.0002577561,0.55752975,0.00079255295],"study_design_scores_gemma":[0.0013278204,0.000295046,0.3902205,0.0015660569,0.00026210406,0.00039347692,0.0010555352,0.0003618398,0.0000678202,0.000011903344,0.60425293,0.00018492696],"about_ca_topic_score_codex":0.19075993,"about_ca_topic_score_gemma":0.3827093,"teacher_disagreement_score":0.19194935,"about_ca_system_score_codex":0.0008007047,"about_ca_system_score_gemma":0.0023593218,"threshold_uncertainty_score":0.99823207},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64056855,0.0022610384,0.33650312,0.0063068457,0.0007212173,0.0071185892,0.0047060573,0.0004944739,0.0013201187],"genre_scores_gemma":[0.9756372,0.0000041717594,0.019489987,0.0022170127,0.000056107463,0.0011469334,0.0012563808,0.000023528462,0.00016870754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99836606,0.00016980825,0.00042657636,0.00023208889,0.0002661092,0.0005393309],"domain_scores_gemma":[0.9982031,0.00026933174,0.00024753384,0.00067740976,0.00011583698,0.00048680845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002195485,0.00013215115,0.0008402179,0.00034656873,0.000035691824,0.0000061022442,0.00011608037,0.000045091765,0.00005875617],"category_scores_gemma":[0.0005835809,0.00010764251,0.0003175731,0.00062332256,0.00003324752,0.00010853684,0.000019656394,0.000053628602,0.0000025094382],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013285577,0.00050496816,0.8480271,0.00014551509,0.0009930669,5.5255174e-7,0.00007000642,0.00005758829,0.0000068573127,0.00007829567,0.0009690857,0.14901409],"study_design_scores_gemma":[0.0011558558,0.000036057172,0.9883106,0.000004600994,0.0010166144,4.2787227e-7,0.000041278978,0.00219947,0.00008373996,0.000011755802,0.007043041,0.00009658762],"about_ca_topic_score_codex":0.00018423364,"about_ca_topic_score_gemma":0.000085221065,"teacher_disagreement_score":0.33506864,"about_ca_system_score_codex":0.000093710376,"about_ca_system_score_gemma":0.00008695721,"threshold_uncertainty_score":0.4389534},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027799432,0.0038928015,0.0000072801477,0.98263294,0.0008419596,0.0011254135,0.0052183797,0.00013903114,0.0033622456],"genre_scores_gemma":[0.037692912,0.0019634054,0.000042144977,0.9361014,0.016243301,0.00024880518,0.0017890941,0.000111843125,0.0058070924],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963924,0.0007141116,0.00055963255,0.0006135724,0.0006714358,0.0010488018],"domain_scores_gemma":[0.9966096,0.00029810786,0.00033014212,0.0026133065,0.00004921847,0.00009960354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012636044,0.0004529006,0.0013247251,0.00007786257,0.00012522798,0.000058708018,0.0009177249,0.00026788775,0.0005900812],"category_scores_gemma":[0.00008464233,0.00021221925,0.00020461806,0.00021580311,0.00035842907,0.000058010242,0.0002643734,0.0017768565,0.000693476],"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":[0.00046524522,0.000019533007,0.072918005,0.00022069835,0.00008682008,0.00020254396,0.00022436475,1.2232609e-7,0.0000032754458,0.000002349254,0.92376643,0.00209061],"study_design_scores_gemma":[0.0013983046,0.00005295318,0.15458554,0.0004551445,0.000013457583,0.000060612554,0.000017904189,0.0000055507157,0.0000013875938,0.000043601907,0.8431384,0.00022716173],"about_ca_topic_score_codex":0.00031806057,"about_ca_topic_score_gemma":0.00056187174,"teacher_disagreement_score":0.08166754,"about_ca_system_score_codex":0.00035412,"about_ca_system_score_gemma":0.00026004307,"threshold_uncertainty_score":0.8913462},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762037,0.0002077021,0.019995691,0.0011234432,0.00015331779,0.00043768864,0.00037513764,0.00018612917,0.0013171955],"genre_scores_gemma":[0.99456465,0.0000226507,0.0028267188,0.0013350473,0.0001664131,0.000020799378,0.00007679818,0.000019136429,0.0009677831],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99919283,0.000020890555,0.00015703672,0.0003496831,0.00010504329,0.00017452151],"domain_scores_gemma":[0.99882424,0.0002105751,0.00004487733,0.0007438844,0.00006901699,0.000107419684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028531236,0.00009087346,0.00016900738,0.000041496904,0.000036157715,0.000014111229,0.00010496716,0.00003774734,0.000094028095],"category_scores_gemma":[0.0005684279,0.00005637693,0.000020032267,0.000057266778,0.000051722713,0.00016848517,0.00012867137,0.00002392965,0.000031023206],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077601493,0.00004988343,0.68393177,0.0001564985,0.00010699133,0.0000095600935,0.000045451034,1.347281e-7,0.043374453,0.000027526079,0.033287678,0.23823403],"study_design_scores_gemma":[0.0037446134,0.00015731923,0.5944673,0.00007646182,0.000025880727,0.00003387337,0.000048910348,0.0006181717,0.002043457,0.00006130738,0.39851776,0.00020498347],"about_ca_topic_score_codex":0.000012335624,"about_ca_topic_score_gemma":0.00008709269,"teacher_disagreement_score":0.36523008,"about_ca_system_score_codex":0.000018603954,"about_ca_system_score_gemma":0.000027288563,"threshold_uncertainty_score":0.22989845},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9579073,0.0009553009,0.000032422977,0.013994903,0.000100738354,0.0006812949,0.00024637085,0.00006956016,0.026012098],"genre_scores_gemma":[0.9976997,0.000029400679,0.00026777215,0.00035741477,0.000067742396,0.000014829164,0.00006608342,0.000010627547,0.0014864272],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992403,0.000034673933,0.0002088526,0.00012250249,0.00029709656,0.00009659531],"domain_scores_gemma":[0.9989892,0.000033212484,0.00014174331,0.0004878295,0.00024566252,0.000102357226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039660925,0.0000575214,0.0001509538,0.000044778677,0.000014940944,0.0000064200926,0.00018882127,0.000059771126,0.00010281758],"category_scores_gemma":[0.00094045873,0.000039187504,0.00004459572,0.00022119141,0.00008210947,0.00011367479,0.000058601698,0.00016262324,0.00009759212],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016883822,0.00018393331,0.80293614,0.00021389223,0.0000917834,0.000004934263,0.0017242986,0.0000010270185,0.00236555,0.0010061663,0.1878214,0.0034820423],"study_design_scores_gemma":[0.0013580859,0.00011104692,0.5774461,0.00017112379,0.00006142348,0.000006440212,0.00023855554,0.00011446789,0.004704601,0.00037433134,0.41533622,0.00007761018],"about_ca_topic_score_codex":0.000028605962,"about_ca_topic_score_gemma":0.0000046319833,"teacher_disagreement_score":0.22751483,"about_ca_system_score_codex":0.000025463924,"about_ca_system_score_gemma":0.00011865201,"threshold_uncertainty_score":0.159802},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83304864,0.00054950005,0.15683343,0.0017098044,0.0002344876,0.0003960961,0.007081086,0.000009159586,0.0001377728],"genre_scores_gemma":[0.96404785,0.0002310513,0.03540969,0.00008044688,0.00009206747,0.0000065174327,0.000067133275,0.0000067900246,0.000058466896],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99920154,0.000018454039,0.00034165842,0.00012090286,0.0002320791,0.00008536875],"domain_scores_gemma":[0.99808407,0.00024528764,0.00021855709,0.000059735536,0.0012802517,0.00011212184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003673751,0.000073426796,0.0001845094,0.000055003944,0.000032838987,0.000029427978,0.000065668246,0.00002980621,0.000029279045],"category_scores_gemma":[0.00043387047,0.000050386978,0.00003353942,0.000018973351,0.000050448318,0.0001259777,0.000030146954,0.000043808686,5.0313463e-7],"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":[0.025610156,0.0018386034,0.39503795,0.0019718814,0.0017614461,0.00017519562,0.00062118494,0.0002513157,0.048595544,0.27726033,0.021322228,0.22555414],"study_design_scores_gemma":[0.037613135,0.0022305788,0.36497772,0.0019117418,0.00057774596,0.00068170525,0.000081485836,0.059442073,0.0057935053,0.5147188,0.011287802,0.0006837522],"about_ca_topic_score_codex":0.000005732303,"about_ca_topic_score_gemma":0.000021136759,"teacher_disagreement_score":0.23745842,"about_ca_system_score_codex":0.00006561726,"about_ca_system_score_gemma":0.00011220535,"threshold_uncertainty_score":0.20547214},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62132466,0.00020753912,0.3638698,0.0009446582,0.00018199261,0.00078335154,0.012320042,0.0003240006,0.000043986274],"genre_scores_gemma":[0.92295176,0.000011099991,0.07337559,0.0004385113,0.00019076573,0.00007115639,0.0027989817,0.000057423782,0.00010473181],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953569,0.0015031197,0.00077813445,0.0007966086,0.0006105534,0.0009546871],"domain_scores_gemma":[0.99656105,0.0005765859,0.00056368165,0.0010778524,0.00019391497,0.0010269257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025896505,0.00031631265,0.00060237054,0.00018657219,0.00024426618,0.00008131564,0.0003093281,0.00012850986,0.00075181935],"category_scores_gemma":[0.0025730175,0.000231604,0.0001590035,0.00033227922,0.00005822628,0.0003396533,0.00008024765,0.0002477368,0.00007007382],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004467036,0.00039801316,0.9821193,0.00032936304,0.00002371449,0.000003579396,0.00009842645,0.000004560949,0.000041836636,0.000009736026,0.00055583636,0.016370913],"study_design_scores_gemma":[0.0024530734,0.000112101516,0.981699,0.00024790448,0.000023156672,0.0000054126426,0.000027416123,0.014891955,0.0000058053993,0.000020096604,0.00026339668,0.000250712],"about_ca_topic_score_codex":0.0038152377,"about_ca_topic_score_gemma":0.0011229435,"teacher_disagreement_score":0.3016271,"about_ca_system_score_codex":0.0005984771,"about_ca_system_score_gemma":0.0017533177,"threshold_uncertainty_score":0.9444537},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91253155,0.0017778293,0.009324217,0.052618407,0.0001641142,0.0029408776,0.020083904,0.0002736627,0.00028540843],"genre_scores_gemma":[0.99233377,0.0011213671,0.00120016,0.00053382706,0.00014866504,0.00013791982,0.00407943,0.000043929253,0.00040091216],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99758637,0.0002478736,0.0006103436,0.000558038,0.00042627292,0.0005710889],"domain_scores_gemma":[0.9969754,0.0005912221,0.00032753506,0.00092516973,0.00017831643,0.00100231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014051552,0.00021799555,0.0005046948,0.000086065134,0.00039380172,0.00006322472,0.00032742773,0.00007373232,0.00007205249],"category_scores_gemma":[0.00065390667,0.00013672592,0.00008074111,0.00019497023,0.00029436342,0.00038409088,0.000095369345,0.000098521436,0.00001069258],"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":[0.006295056,0.0012936055,0.40166613,0.006220357,0.0003071495,0.000029067469,0.0008259895,0.00004312077,0.0018308056,0.0016038656,0.044376474,0.5355084],"study_design_scores_gemma":[0.0035057394,0.00022521551,0.86593765,0.0002999968,0.000007766061,0.000020919515,0.0000665145,0.07248615,0.000006215516,0.00013265945,0.05704371,0.00026745014],"about_ca_topic_score_codex":0.00010479053,"about_ca_topic_score_gemma":0.00010729615,"teacher_disagreement_score":0.53524095,"about_ca_system_score_codex":0.00015586519,"about_ca_system_score_gemma":0.0014940486,"threshold_uncertainty_score":0.55755216},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99629974,0.00021804687,0.00018667168,0.0023333926,0.000029484054,0.0005379691,0.000061543215,0.00012248766,0.00021063749],"genre_scores_gemma":[0.99772537,0.000012467081,0.0010597887,0.0008626163,0.00006509619,0.00009558567,0.000027925027,0.000027928983,0.00012325212],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976898,0.00019345681,0.0008778289,0.00038658365,0.0004530683,0.00039926887],"domain_scores_gemma":[0.99826705,0.00027613,0.00041049573,0.00059408206,0.00013357117,0.0003186449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012076331,0.00017919025,0.0006272194,0.00009313916,0.00005352488,0.000050981198,0.00008446378,0.000053018,0.000065723296],"category_scores_gemma":[0.002339694,0.00013532405,0.000044690132,0.00017338176,0.00008069843,0.0001833506,0.00010017036,0.0001156104,0.000041055726],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015957812,0.0006517469,0.9546639,0.00019549813,0.000061213046,0.00012746744,0.0000623361,0.0000015039865,0.037892755,0.0000017460793,0.00011193422,0.0060703615],"study_design_scores_gemma":[0.0018322989,0.00009717758,0.9888674,0.0017244287,0.000042586893,0.0000037158816,0.00005415959,0.00018537151,0.006851443,0.000013028449,0.00014178784,0.00018661354],"about_ca_topic_score_codex":0.000098966026,"about_ca_topic_score_gemma":0.00043964162,"teacher_disagreement_score":0.03420353,"about_ca_system_score_codex":0.00017643085,"about_ca_system_score_gemma":0.00014314505,"threshold_uncertainty_score":0.5518355},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97565734,0.00030775144,0.0017893855,0.001719812,0.010385515,0.0010031702,0.0010521917,0.0003882782,0.0076965443],"genre_scores_gemma":[0.98341817,0.0000089361165,0.00055628695,0.00007506872,0.00062594947,0.00008972445,0.006567104,0.000045469653,0.0086133],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99583584,0.00009341195,0.00090116216,0.0017628024,0.00094762706,0.0004591484],"domain_scores_gemma":[0.9944195,0.000039255436,0.0008331845,0.0032257168,0.00041740955,0.0010649],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014308715,0.00037379208,0.000856947,0.0005854599,0.00059313234,0.0010672942,0.00026135606,0.00022329434,0.00006444865],"category_scores_gemma":[0.0016725401,0.000365978,0.0002810268,0.00025973935,0.00015519239,0.00020017868,0.0010888139,0.00048976555,0.000022069671],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010909122,0.000093639,0.97130454,0.00014793566,0.0001360421,0.0007241191,0.00014765667,0.00065600564,0.000028973298,0.000008962678,0.01293339,0.0138078425],"study_design_scores_gemma":[0.00042538327,0.000031515876,0.9589287,0.00053300656,0.00067451934,0.000041011714,0.000020978106,0.0027127187,0.00002136045,0.007544978,0.028388428,0.00067742163],"about_ca_topic_score_codex":0.0008013314,"about_ca_topic_score_gemma":0.00048234029,"teacher_disagreement_score":0.015455037,"about_ca_system_score_codex":0.00026572676,"about_ca_system_score_gemma":0.00091663137,"threshold_uncertainty_score":0.9999697},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902189,0.00036582505,0.001198078,0.0013187528,0.00044564717,0.0036936824,0.0023791431,0.00019500998,0.00018496938],"genre_scores_gemma":[0.9937853,0.0000022260797,0.00021779748,0.00039617886,0.000583131,0.003266565,0.0015400362,0.0000321339,0.00017658193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980032,0.00005500165,0.00058597693,0.00036797553,0.00049918296,0.0004886886],"domain_scores_gemma":[0.9978231,0.00012738253,0.00039735192,0.00024990216,0.0002166246,0.0011856456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009547807,0.00016173076,0.00024863618,0.00010551521,0.00016744228,0.000037855865,0.0000536618,0.00003272614,0.00002461316],"category_scores_gemma":[0.0041120015,0.00015877787,0.00009720724,0.00011723811,0.000030707914,0.0002529959,0.00005816636,0.0000495807,0.000020235873],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0051372536,0.00021820952,0.92351043,0.0006885122,0.00016493062,0.000038008136,0.00025886708,0.000023398785,0.0006975383,0.00042222888,0.0030046855,0.065835945],"study_design_scores_gemma":[0.0021859913,0.000010760178,0.98504764,0.000050865085,0.00023746179,0.000021204294,0.00013679494,0.0012285977,0.00022404855,0.0000150413825,0.010665878,0.00017572443],"about_ca_topic_score_codex":0.000018302571,"about_ca_topic_score_gemma":0.000020100097,"teacher_disagreement_score":0.06566022,"about_ca_system_score_codex":0.0006488882,"about_ca_system_score_gemma":0.00009148315,"threshold_uncertainty_score":0.6474773},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9567882,0.000112149726,0.007206488,0.031992517,0.0001774222,0.0022528153,0.00062265905,0.00023778992,0.0006099474],"genre_scores_gemma":[0.98804474,0.000028642584,0.010316126,0.0003578404,0.00027027246,0.00032719257,0.000072992574,0.00005629768,0.00052588276],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9954854,0.0005540312,0.00042213438,0.00078493444,0.0016146862,0.0011387873],"domain_scores_gemma":[0.99334484,0.003780539,0.00006503215,0.0013756471,0.000723908,0.0007100069],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0032268385,0.0002257181,0.0004317244,0.00031125802,0.00014644096,0.000083487335,0.00032992422,0.000083792,0.0013408407],"category_scores_gemma":[0.022339266,0.00015286109,0.0001419165,0.00062060304,0.00020719954,0.00017217366,0.00034849945,0.00027336564,0.0033173847],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019922678,0.00089813775,0.8622614,0.00032616893,0.00009983693,0.0005296789,0.000045952915,0.000003888229,0.020488624,0.0010529754,0.095404096,0.016896965],"study_design_scores_gemma":[0.0037386552,0.0016193896,0.91403747,0.00038942584,0.000014193222,0.00003182816,0.00003730564,0.00016119919,0.0073737525,0.00023348726,0.07194899,0.00041430982],"about_ca_topic_score_codex":0.0001624018,"about_ca_topic_score_gemma":0.00023468203,"teacher_disagreement_score":0.051776055,"about_ca_system_score_codex":0.00040182748,"about_ca_system_score_gemma":0.0014531248,"threshold_uncertainty_score":0.9995721},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34019008,0.001641537,0.63497514,0.014298494,0.00070384576,0.0017321298,0.005923994,0.000031877615,0.00050286733],"genre_scores_gemma":[0.9905269,0.000059904323,0.008755547,0.00023548996,0.00022693865,0.000008954257,0.00013202452,0.000022203441,0.000031996973],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978868,0.00027633723,0.0008492652,0.00024160245,0.00027717193,0.00046883084],"domain_scores_gemma":[0.9967272,0.000068923684,0.00070603366,0.0005417676,0.00038171338,0.0015743355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017742104,0.00012599386,0.00046821823,0.00080382073,0.000068751506,0.000020738322,0.0003490937,0.00006146697,0.00006125134],"category_scores_gemma":[0.00038029815,0.00009668046,0.00007449967,0.0005080272,0.00012250428,0.0005180511,0.00003513974,0.00027544197,0.000004890483],"study_design_candidate":"design_other","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":[0.000059895687,0.00042937507,0.11315006,0.0002627428,0.00025093116,0.00010051314,0.000669647,0.000048776117,0.00068714266,0.00009796691,0.0032596043,0.88098335],"study_design_scores_gemma":[0.010097949,0.001144709,0.38226876,0.0018757,0.0001315378,0.0023410742,0.0038451825,0.077653915,0.00010073327,0.00021196574,0.51961184,0.0007166514],"about_ca_topic_score_codex":0.009756503,"about_ca_topic_score_gemma":0.05098776,"teacher_disagreement_score":0.88026667,"about_ca_system_score_codex":0.0016007358,"about_ca_system_score_gemma":0.01277255,"threshold_uncertainty_score":0.9968376},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91521555,0.00029926212,0.040298842,0.032940276,0.0006138063,0.001026428,0.0037816742,0.0008312447,0.0049928892],"genre_scores_gemma":[0.9940227,0.000010732924,0.00052027014,0.0028603815,0.0005625648,0.000025038513,0.0018636808,0.00002041276,0.00011417307],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987466,0.00016439123,0.00023937649,0.0003824739,0.00018369293,0.000283496],"domain_scores_gemma":[0.99878514,0.00055521284,0.0000934901,0.00018483092,0.00017232387,0.00020901297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009961427,0.00015762307,0.0003013785,0.00007121687,0.00010053425,0.0000074065692,0.000120618824,0.0000652449,0.00050767424],"category_scores_gemma":[0.00022290144,0.000108830995,0.00009940878,0.00011871755,0.00023552343,0.000030717012,0.000060902577,0.00008232319,0.000424604],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003386586,0.0011633779,0.85139126,0.00007613526,0.0005042117,0.00013161458,0.00002956476,0.0021010928,0.0020290883,0.04773642,0.05207097,0.03937966],"study_design_scores_gemma":[0.0016042734,0.00014423048,0.9757335,0.00003185692,0.000016943164,0.000004644882,0.0000014427414,0.009143281,0.0000049087744,0.0038660944,0.009279584,0.00016920386],"about_ca_topic_score_codex":0.000001877963,"about_ca_topic_score_gemma":0.000002254517,"teacher_disagreement_score":0.12434226,"about_ca_system_score_codex":0.00007390169,"about_ca_system_score_gemma":0.00010518028,"threshold_uncertainty_score":0.55586773},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9735252,0.00029044674,0.0019076186,0.014180827,0.0005043407,0.0030543287,0.00048330153,0.0001646606,0.005889244],"genre_scores_gemma":[0.9944124,0.0000039478837,0.00028928815,0.0020401867,0.00017833865,0.0016266517,0.00012805959,0.000024877698,0.0012962138],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887455,0.000013369581,0.00015806373,0.0002660826,0.0003103588,0.0003775916],"domain_scores_gemma":[0.9991977,0.000039773757,0.00006156507,0.0003037346,0.00012225429,0.00027495198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016845169,0.00011678762,0.00019497184,0.000058479844,0.000033585005,0.000019327854,0.000101442994,0.00004256016,0.000043486503],"category_scores_gemma":[0.00043163527,0.000109226,0.00006625287,0.00010295324,0.00003201879,0.000059377082,0.000027287064,0.00009157579,0.000012703649],"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":[0.0017180181,0.0006203231,0.52388746,0.0008129848,0.00066970516,0.0003721207,0.00072657445,0.0007452716,0.00013025703,0.0019146593,0.030257564,0.43814504],"study_design_scores_gemma":[0.0024708274,0.00001371984,0.93092495,0.000018079092,0.000030144727,0.000014562079,0.000059246126,0.00006787774,0.0002294803,0.00012362254,0.06592534,0.0001221685],"about_ca_topic_score_codex":0.04008284,"about_ca_topic_score_gemma":0.16675062,"teacher_disagreement_score":0.43802288,"about_ca_system_score_codex":0.000451317,"about_ca_system_score_gemma":0.00047078665,"threshold_uncertainty_score":0.9663093},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99492353,0.00027819615,0.0009880953,0.0008268226,0.00037351472,0.00028722495,0.0021656277,0.000016999156,0.0001400133],"genre_scores_gemma":[0.9992006,0.00013627892,0.00005387121,0.0000993025,0.00029676573,0.0000029772864,0.00014909121,0.00001098679,0.000050114195],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975674,0.000063106454,0.00055191404,0.00052300893,0.0010319244,0.00026260514],"domain_scores_gemma":[0.9984179,0.0001287527,0.00034908368,0.0005647255,0.0003532337,0.00018631527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012612522,0.000089989444,0.00048174654,0.000046099714,0.0001661582,0.000023683513,0.0004195607,0.00004487528,0.00022051066],"category_scores_gemma":[0.0011967415,0.0000938775,0.000020587298,0.00040791713,0.0017306732,0.00025377204,0.00023120973,0.00005909674,6.124168e-7],"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":[0.000020518793,0.000022825016,0.8843076,0.000049540256,0.000030353773,0.000008192508,0.00012998018,2.8325985e-7,0.0949009,0.0000010738312,0.0030004326,0.01752829],"study_design_scores_gemma":[0.00082564057,0.000028666327,0.9967592,0.00026914853,0.000070392794,0.000008369146,0.00011075741,0.0010711215,0.00029140737,0.00002600517,0.0004389652,0.00010035575],"about_ca_topic_score_codex":0.9679508,"about_ca_topic_score_gemma":0.9576896,"teacher_disagreement_score":0.11245156,"about_ca_system_score_codex":0.00020158866,"about_ca_system_score_gemma":0.0007950532,"threshold_uncertainty_score":0.63767374},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9566804,0.00018722755,0.031963494,0.0071479552,0.0007723531,0.0005662018,0.002518159,0.000024519908,0.00013969425],"genre_scores_gemma":[0.99455124,0.00061492034,0.0021117022,0.0011809487,0.00018479767,0.000014400005,0.0012998618,0.000022479826,0.000019675055],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996225,0.00026751586,0.0015437723,0.00041384148,0.0012724919,0.00027738707],"domain_scores_gemma":[0.9957864,0.0003639371,0.0011243065,0.0005876281,0.0018004896,0.00033718083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035606609,0.00018258681,0.0004653962,0.0010921682,0.000030673957,0.000055641176,0.0013304637,0.000079982034,0.000062508436],"category_scores_gemma":[0.0015626181,0.00014663709,0.00009453192,0.000595873,0.00008612867,0.0006284178,0.0003088867,0.00023628639,0.000017382887],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007104031,0.00040208083,0.9358538,0.000046148754,0.0001497907,0.000018571161,0.00018712583,0.00005062463,0.00058913627,0.00039613416,0.0011662396,0.060429946],"study_design_scores_gemma":[0.0014783561,0.00024571756,0.99265975,0.00096576515,0.000014315359,0.000037732414,0.00012911446,0.0005354095,0.00007315064,0.0003926484,0.003339634,0.00012842823],"about_ca_topic_score_codex":0.0007842781,"about_ca_topic_score_gemma":0.0011562776,"teacher_disagreement_score":0.060301516,"about_ca_system_score_codex":0.0002625823,"about_ca_system_score_gemma":0.00049761444,"threshold_uncertainty_score":0.5979687},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022300645,0.0005184509,0.0010862404,0.0008831989,0.00043175867,0.00085332646,0.00060054695,0.0011587733,0.9944454],"genre_scores_gemma":[0.0145308245,0.00006512684,0.0020299412,0.0025086682,0.0013621781,0.000084551575,0.0024636786,0.0004230462,0.976532],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850756,0.00008312649,0.000428457,0.00023342973,0.0004603653,0.0002870876],"domain_scores_gemma":[0.9984436,0.000057078913,0.00038552086,0.0009425301,0.0000838486,0.000087430366],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037463283,0.00029228316,0.000456498,0.00016782412,0.000056265453,0.00006284568,0.00025250134,0.00021955765,0.0007171291],"category_scores_gemma":[0.00009280941,0.00018008842,0.00013828732,0.00023517906,0.000072286966,0.00007422576,0.00007898855,0.00019904261,0.0018291338],"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":[0.000012772338,0.00001123396,0.004114762,0.0006816208,0.00008897448,0.000010207627,0.000021533195,0.0000022336997,0.0000023390778,0.00032079805,0.98828995,0.0064435485],"study_design_scores_gemma":[0.0004983583,0.000017834554,0.0033264637,0.0005907872,0.000044710738,0.00003544372,0.00011938658,0.00021197191,0.0000063935977,0.000002031687,0.99496615,0.00018049001],"about_ca_topic_score_codex":0.0009152548,"about_ca_topic_score_gemma":0.0004163354,"teacher_disagreement_score":0.01791342,"about_ca_system_score_codex":0.00011055637,"about_ca_system_score_gemma":0.00010822246,"threshold_uncertainty_score":0.99894804},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009084332,0.007442229,0.000107666725,0.000047433987,0.00018931647,0.0014399358,0.0022234197,0.00010676065,0.9793589],"genre_scores_gemma":[0.46614102,0.009248307,0.00008434104,0.00013613499,0.00019483163,0.00001475663,0.0009096346,0.000060631253,0.52321035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982823,0.00014310495,0.0002878844,0.0005692734,0.00043719984,0.00028021823],"domain_scores_gemma":[0.9986595,0.00011972746,0.0002557926,0.0006832924,0.00013680078,0.0001448897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039324493,0.00032596668,0.0006030692,0.00011911305,0.000052154835,0.00003739907,0.00041438002,0.00025882904,0.0000033556985],"category_scores_gemma":[0.000045778772,0.00028383508,0.000065687665,0.00003266521,0.00023356902,0.000082969345,0.00018133328,0.00039707377,0.0000010880163],"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":[0.0053452686,0.0002984939,0.32568625,0.0027848342,0.0003248865,0.0024893815,0.0013535236,0.00015390493,0.000041576517,0.49731317,0.0036980433,0.16051064],"study_design_scores_gemma":[0.0015331014,0.000069980124,0.048146557,0.0005359818,0.000055286328,0.00004465649,0.00013114008,0.00019315405,3.3905192e-7,0.00032748547,0.94865394,0.0003084045],"about_ca_topic_score_codex":0.00022580926,"about_ca_topic_score_gemma":0.0018295815,"teacher_disagreement_score":0.9449559,"about_ca_system_score_codex":0.00034700768,"about_ca_system_score_gemma":0.00015739955,"threshold_uncertainty_score":0.9999614},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932504,0.0003037967,0.000051687828,0.0027968045,0.00020702773,0.0011377693,0.0018918468,0.000027036811,0.0003335949],"genre_scores_gemma":[0.99611413,0.00019729006,0.00009534918,0.00042523324,0.00008148134,0.0000051641114,0.001519797,0.000039708062,0.001521827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974237,0.000355127,0.0004510562,0.00049824046,0.00088535034,0.0003865411],"domain_scores_gemma":[0.99704194,0.00016258823,0.00060923,0.001535644,0.00044235607,0.00020825335],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006894513,0.00033097214,0.0007239435,0.00024230118,0.00016341842,0.00001839841,0.0010050206,0.000113845745,0.0004596705],"category_scores_gemma":[0.000120284094,0.00024992006,0.00015240023,0.00043720892,0.00008942364,0.0007804723,0.00022209235,0.00027784854,0.00004606224],"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":[0.031550728,0.0007607496,0.634029,0.004602942,0.0031030355,0.00047832465,0.041511744,0.0017187799,0.16927092,0.0005974889,0.10698951,0.0053868107],"study_design_scores_gemma":[0.0020879642,0.0001327599,0.94992435,0.00032866574,0.00023202531,0.000004062179,0.0147307385,0.000034699067,0.0006278904,0.000048034624,0.031520013,0.0003287873],"about_ca_topic_score_codex":0.7185428,"about_ca_topic_score_gemma":0.9473614,"teacher_disagreement_score":0.3158954,"about_ca_system_score_codex":0.0005704804,"about_ca_system_score_gemma":0.001473884,"threshold_uncertainty_score":0.9999953},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001076616,0.0039744074,0.0016736084,0.96366936,0.0037678431,0.0025373574,0.008104964,0.00028632267,0.014909505],"genre_scores_gemma":[0.0019769545,0.0041961307,0.032508295,0.93644977,0.0045831073,0.00015208253,0.01640498,0.00019198631,0.0035366847],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99454457,0.0012676633,0.0012548099,0.0009907378,0.0011097195,0.00083248614],"domain_scores_gemma":[0.9951943,0.0028675343,0.00037978205,0.0012109474,0.00014614066,0.00020127457],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018441423,0.00066505023,0.0023898748,0.00084849744,0.000032437663,0.000009914617,0.00049287383,0.0007774439,0.002112056],"category_scores_gemma":[0.0064353026,0.00051274616,0.000076263466,0.00045248505,0.00065919134,0.00004075664,0.00015168043,0.0033211596,0.00028548168],"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":[0.00037922323,0.00018856868,0.018928982,0.00066409673,0.00008671409,0.0149757285,0.00017882566,6.451551e-7,0.000011625226,0.0010821284,0.95967853,0.003824939],"study_design_scores_gemma":[0.004679311,0.0014490503,0.11050883,0.0024080372,0.00010087783,0.000060990456,0.000045962697,0.000053705982,0.000010122157,0.01752473,0.86256933,0.0005890585],"about_ca_topic_score_codex":0.000759004,"about_ca_topic_score_gemma":0.0001451086,"teacher_disagreement_score":0.0971092,"about_ca_system_score_codex":0.00034790306,"about_ca_system_score_gemma":0.00012650983,"threshold_uncertainty_score":0.99973243},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9317429,0.01060412,0.0009078952,0.012888086,0.0005127423,0.0024625356,0.040505037,0.00019741521,0.00017929026],"genre_scores_gemma":[0.96803766,0.006350899,0.01839101,0.0026864326,0.00049451145,0.0006588868,0.0028666107,0.00013970931,0.00037427119],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961783,0.000051503095,0.00088859996,0.0011936009,0.00025695146,0.0014310072],"domain_scores_gemma":[0.9943521,0.0012947468,0.00023604694,0.0018029786,0.00089300337,0.0014211165],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008137972,0.00050344405,0.0010743734,0.0059023495,0.00044947924,0.00012527978,0.000674248,0.00026063484,0.00006684631],"category_scores_gemma":[0.0044290107,0.00041333685,0.0003887171,0.004991887,0.0012522409,0.00041610733,0.00012068258,0.00012919448,0.0000018737992],"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":[0.00024026966,0.00002095276,0.9821017,0.0008245603,0.0015500094,0.00006232724,0.00021141328,0.0000060192824,0.0000071141303,0.004136075,0.006968217,0.003871336],"study_design_scores_gemma":[0.0028350528,0.00032422246,0.8182368,0.0032850883,0.0019325429,0.00003487773,0.0025695763,0.0005641232,0.000014217584,0.0038936082,0.16510779,0.0012020624],"about_ca_topic_score_codex":0.2351264,"about_ca_topic_score_gemma":0.9843807,"teacher_disagreement_score":0.74925435,"about_ca_system_score_codex":0.0001305269,"about_ca_system_score_gemma":0.00042850248,"threshold_uncertainty_score":0.99983186},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99149674,0.00049555826,0.0031591242,0.0013061743,0.00008417512,0.0029989567,0.00030304605,0.00009322757,0.00006302106],"genre_scores_gemma":[0.9904792,0.00005034858,0.007238938,0.0016522242,0.00006523856,0.00021044517,0.00022340193,0.000046625628,0.000033615528],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99535197,0.0007680616,0.0013125465,0.0005633548,0.0012985814,0.0007054598],"domain_scores_gemma":[0.99589366,0.00041671022,0.00094621925,0.00085744564,0.0013909539,0.0004950271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012132957,0.00024603735,0.0010320224,0.00034941512,0.00006253713,0.000014105243,0.0001924187,0.00008106202,0.00014864597],"category_scores_gemma":[0.0021877482,0.00017482934,0.000079665864,0.00055238494,0.00008926237,0.0003824801,0.000043846427,0.00013393891,0.0000068449176],"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":[0.0012664418,0.001043767,0.9795508,0.00088235596,0.00008748704,0.0000010389576,0.00088121457,0.00004354314,0.00034325742,0.00017985146,0.00029280805,0.015427432],"study_design_scores_gemma":[0.013700624,0.0024795658,0.97560185,0.0005327529,0.000020073978,0.00001189232,0.00012820578,0.0019875912,0.00007605297,0.00006883283,0.0051964605,0.00019608738],"about_ca_topic_score_codex":0.05578585,"about_ca_topic_score_gemma":0.47250983,"teacher_disagreement_score":0.41672397,"about_ca_system_score_codex":0.0020136458,"about_ca_system_score_gemma":0.033704616,"threshold_uncertainty_score":0.9717734},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986996,0.0019821438,0.004987392,0.00022430587,0.001262591,0.0006436914,0.0032843105,0.00050962745,0.00010995816],"genre_scores_gemma":[0.97801435,0.00015313155,0.02098671,0.0001360343,0.00038260096,0.000052088766,0.0000040680397,0.00024473525,0.000026252133],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99596417,0.00018112369,0.0011800603,0.0012355905,0.0006249138,0.0008141688],"domain_scores_gemma":[0.9949655,0.0001794361,0.001218201,0.0023500393,0.0007716797,0.0005151397],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096110726,0.0007277441,0.0015459748,0.0006491628,0.00010362075,0.000048976555,0.0005748471,0.00061094045,0.00013304292],"category_scores_gemma":[0.0010764812,0.00068827334,0.0004324405,0.0007115313,0.00027393803,0.00017113854,0.00080853654,0.0007374401,0.000035225297],"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":[0.00032865262,0.00043333627,0.1108209,0.0031935747,0.00097425585,0.00038575107,0.0000128152615,0.00025082304,0.88223207,0.00029659554,0.0009387014,0.00013250028],"study_design_scores_gemma":[0.007939547,0.00045621174,0.6527952,0.02052476,0.003061563,0.000001146255,0.0000115046,0.12343121,0.17049333,0.000037493628,0.01620484,0.0050432174],"about_ca_topic_score_codex":0.000046665184,"about_ca_topic_score_gemma":0.000003591115,"teacher_disagreement_score":0.71173877,"about_ca_system_score_codex":0.00070803467,"about_ca_system_score_gemma":0.0010773339,"threshold_uncertainty_score":0.99955684},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98663914,0.0030853779,0.000064179694,0.0050637643,0.00011108802,0.0029708876,0.0020356954,0.000026852864,0.0000030037795],"genre_scores_gemma":[0.9977682,0.0009675215,0.00026216937,0.00028135497,0.00012747146,0.0004213531,0.00010425042,0.000025151288,0.000042478285],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973216,0.0004347978,0.00041437874,0.00063167576,0.00057250645,0.0006250586],"domain_scores_gemma":[0.9978226,0.00048374492,0.00018256699,0.00019475026,0.00026594198,0.0010504238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020013887,0.0002165575,0.00057746033,0.0001630942,0.00028074053,0.00008847644,0.00004916976,0.000038078753,0.0000056725194],"category_scores_gemma":[0.0006360223,0.0001816755,0.000018102064,0.00023966002,0.00011101774,0.0004664262,0.00008403916,0.00014354796,5.4619573e-8],"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":[0.00048797217,0.00009624827,0.9706505,0.00031676044,0.000040011448,0.000004409277,0.000446362,0.000002535577,0.000029697852,0.00002489803,0.00024586855,0.027654732],"study_design_scores_gemma":[0.0038417547,0.0003607112,0.99097747,0.00011354305,0.0000034404106,0.0000052445007,0.0013992877,0.002826597,0.0000011174675,0.000013960241,0.00028969123,0.00016717055],"about_ca_topic_score_codex":0.21769427,"about_ca_topic_score_gemma":0.48027167,"teacher_disagreement_score":0.2625774,"about_ca_system_score_codex":0.002056993,"about_ca_system_score_gemma":0.008693304,"threshold_uncertainty_score":0.9969265},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7270571,0.00077033084,0.00085581484,0.0076702056,0.00025388304,0.001851364,0.25892895,0.00007883186,0.0025335373],"genre_scores_gemma":[0.92739147,0.00021356341,0.0023667766,0.00040080937,0.00005292219,0.000013847006,0.06942189,0.000026953845,0.000111755835],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99592686,0.0007063984,0.0009662402,0.0010482407,0.00075829844,0.00059393595],"domain_scores_gemma":[0.9905122,0.0021656456,0.00022552354,0.006807772,0.000053801334,0.00023504086],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003188392,0.00024505184,0.0006960598,0.00014616591,0.000036607184,0.000021828857,0.0031231833,0.00009136608,0.00064675615],"category_scores_gemma":[0.009080376,0.0001413147,0.0000089598925,0.0006250214,0.00041151323,0.0008546962,0.0025609634,0.0007501327,0.00003366588],"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":[0.0004940337,0.0001479037,0.8980165,0.00007499505,0.0000398803,0.0008936145,0.000053700493,0.000010628112,0.000044377404,0.00043855383,0.09567225,0.004113547],"study_design_scores_gemma":[0.0029538455,0.00007008808,0.9646696,0.0005569337,0.00004074804,0.000039842693,0.00021033254,0.00838043,0.0000014904836,0.00006914091,0.022788906,0.00021863061],"about_ca_topic_score_codex":0.7123034,"about_ca_topic_score_gemma":0.9714566,"teacher_disagreement_score":0.25915316,"about_ca_system_score_codex":0.00038991848,"about_ca_system_score_gemma":0.0034317793,"threshold_uncertainty_score":0.99926656},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97552216,0.004229076,0.008646076,0.007053157,0.00011250893,0.0027919987,0.0011900362,0.00022333016,0.00023164107],"genre_scores_gemma":[0.9890932,0.00046149865,0.0027794265,0.006089098,0.0003184965,0.00055753556,0.00056509534,0.000041145984,0.00009450202],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9945706,0.0009003159,0.001203596,0.0013251409,0.0007039952,0.0012963348],"domain_scores_gemma":[0.9943373,0.0014852606,0.00078175426,0.0009716759,0.0007044843,0.0017195034],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003917203,0.00053837465,0.0021941245,0.0009728897,0.0003873964,0.0002707644,0.00021774984,0.0001703543,0.000097242206],"category_scores_gemma":[0.0018545754,0.00041054547,0.00037343588,0.0028350858,0.00028899874,0.0007789618,0.00016047746,0.00020427191,0.000009380451],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053553033,0.00014093662,0.95427895,0.0003732659,0.0020313999,0.000009273273,0.0014928558,0.0000075639337,0.00009727552,0.00011392805,0.00047526843,0.04044373],"study_design_scores_gemma":[0.004208549,0.0007102382,0.9570471,0.00007145967,0.00014625832,0.00002743592,0.0006339667,0.019071551,0.0000023400746,0.00006413122,0.017399779,0.0006171963],"about_ca_topic_score_codex":0.00040635752,"about_ca_topic_score_gemma":0.0012906243,"teacher_disagreement_score":0.039826535,"about_ca_system_score_codex":0.00028834475,"about_ca_system_score_gemma":0.00085730804,"threshold_uncertainty_score":0.99983466},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99755734,0.00007570621,0.0006943418,0.0007224059,0.00017113202,0.00017015381,0.000033028842,0.00047841307,0.00009748239],"genre_scores_gemma":[0.9991674,0.0003093735,0.00033469326,0.000007434311,0.00004102891,0.000013084528,0.0000067151022,0.000006417774,0.00011389214],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993994,0.00002759021,0.00015029333,0.00022763883,0.000079969366,0.00011506759],"domain_scores_gemma":[0.9996399,0.000055654033,0.000048565045,0.00016917604,0.000042000218,0.00004469685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001618469,0.00007991207,0.00013877015,0.000060643506,0.000049296934,0.00002765323,0.000028720815,0.00007442932,0.000023284165],"category_scores_gemma":[0.00030716162,0.00004627843,0.0000130663675,0.000051219726,0.00014645948,0.00009294825,0.000067892666,0.00003692858,0.000010875402],"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":[0.00027066976,0.000020190622,0.10772509,0.000020493124,0.00001795524,0.000008912202,0.000017464095,2.6092719e-8,0.8282939,0.0001274097,0.000059632213,0.06343823],"study_design_scores_gemma":[0.0011804705,0.00019363372,0.47798732,0.00006517876,0.000017203878,0.000056863315,0.00047319633,0.000017323984,0.5186631,0.0005834347,0.000653949,0.00010830829],"about_ca_topic_score_codex":0.0000072142348,"about_ca_topic_score_gemma":0.0000013250311,"teacher_disagreement_score":0.37026224,"about_ca_system_score_codex":0.000028164239,"about_ca_system_score_gemma":0.0000058044134,"threshold_uncertainty_score":0.18871796},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000040708546,0.9931984,0.00020650035,0.00008741874,0.002430363,0.0014973328,0.0019145149,0.00010333237,0.00052146404],"genre_scores_gemma":[0.0030218707,0.9946922,0.000011603209,0.0003363223,0.0012780069,0.00011928299,0.00022253527,0.00009844069,0.0002197126],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9944707,0.0005907485,0.0025629622,0.00044560258,0.0016293615,0.0003006019],"domain_scores_gemma":[0.9906951,0.0005846652,0.0040545743,0.0007453795,0.0031366523,0.0007836047],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046829937,0.0006928117,0.0037365614,0.0012447363,0.00005922711,0.00010335171,0.00069016765,0.0002120989,0.0003341312],"category_scores_gemma":[0.0040212725,0.00045418058,0.0031503516,0.00064076047,0.00018449992,0.00046827673,0.00018342889,0.0005733262,0.00013668879],"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":[0.00021326765,0.00091303216,0.0021274188,0.7399787,0.006648108,0.0029927448,0.000026494949,0.0000019870179,0.0000014862719,0.00006556219,0.003311676,0.24371952],"study_design_scores_gemma":[0.0012304484,0.00023840206,0.00046803447,0.75876546,0.007911898,0.0040121116,0.0000034498978,0.0000012439033,7.4709885e-7,0.00007998475,0.2269354,0.0003528494],"about_ca_topic_score_codex":0.0000028787854,"about_ca_topic_score_gemma":0.0000030773467,"teacher_disagreement_score":0.24336667,"about_ca_system_score_codex":0.00082560943,"about_ca_system_score_gemma":0.0013652779,"threshold_uncertainty_score":0.99979097},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9957537,0.00030752478,0.00003851071,0.0007106094,0.000021457883,0.00038638484,0.0022070555,0.00018521155,0.0003895487],"genre_scores_gemma":[0.99491155,0.0005237344,0.0029221058,0.0008481371,0.00019384579,0.000037416456,0.000364908,0.00005112453,0.0001471731],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980962,0.000116757394,0.00034586206,0.00044480388,0.0005824416,0.0004139072],"domain_scores_gemma":[0.99826205,0.00028539915,0.00017963043,0.00079131953,0.0001757804,0.0003058148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013653588,0.00021852132,0.00058796006,0.0001453135,0.000030123221,0.000009018,0.00018791211,0.0000526219,0.00027977175],"category_scores_gemma":[0.0010358568,0.00014027738,0.00004354252,0.00055607036,0.00019546495,0.00012421786,0.0000677798,0.00021464916,0.00041656883],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006328104,0.0017080017,0.98663735,0.00006853257,0.00017941356,0.00012362665,0.00012401056,0.0000021548497,0.0008194799,0.0000017910257,0.00017649453,0.00952636],"study_design_scores_gemma":[0.0031207255,0.00024267253,0.9935971,0.0004018686,0.00006749745,0.0000029619277,0.000028040822,0.000044557004,0.00022723997,0.0000107465285,0.0020300574,0.00022655266],"about_ca_topic_score_codex":0.00020568591,"about_ca_topic_score_gemma":0.009714365,"teacher_disagreement_score":0.00950868,"about_ca_system_score_codex":0.00010389587,"about_ca_system_score_gemma":0.0001353283,"threshold_uncertainty_score":0.57203454},"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":"empirical","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7036747,0.0025094915,0.000027783797,0.28887427,0.0014633238,0.0004892714,0.0003591715,0.00012854188,0.002473457],"genre_scores_gemma":[0.05864114,0.001454397,0.00009111464,0.9251958,0.0057713133,0.00017394917,0.0008427256,0.000096284035,0.007733259],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99629056,0.0012134811,0.00090742664,0.00067828223,0.00015115216,0.00075907214],"domain_scores_gemma":[0.9969833,0.001651595,0.00053465646,0.0006070107,0.000060957587,0.0001624785],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015847887,0.00040032843,0.0010119537,0.0002694707,0.00017632148,0.000007788529,0.00013228582,0.00078388234,0.0005367049],"category_scores_gemma":[0.002779272,0.00026400475,0.00012339636,0.00026376793,0.00036198046,0.00013915803,0.000116034644,0.0022602053,0.000086870336],"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":[0.00002258985,0.0000036510253,0.5888536,0.00013540455,0.000054352702,0.000032213567,0.000015475216,0.000010274902,0.00007014318,0.0000034787236,0.40739566,0.0034031402],"study_design_scores_gemma":[0.0006299267,0.000036395937,0.598779,0.00029953185,0.0000460806,0.00014769488,0.0000031859106,0.00005967002,0.00000809256,0.00017463288,0.3996297,0.00018608362],"about_ca_topic_score_codex":0.43788958,"about_ca_topic_score_gemma":0.476955,"teacher_disagreement_score":0.64503354,"about_ca_system_score_codex":0.0007187205,"about_ca_system_score_gemma":0.0007273878,"threshold_uncertainty_score":0.9999812},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8379907,0.0004459146,0.0013182423,0.15548216,0.0007216644,0.00035771838,0.0033998135,0.000011989896,0.00027178222],"genre_scores_gemma":[0.9927425,0.0000053487056,0.000813254,0.0060262787,0.00018484016,0.000002101163,0.00017442902,0.000023974628,0.000027289867],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99727154,0.0002696256,0.0007686876,0.00027220868,0.0005271339,0.0008908256],"domain_scores_gemma":[0.99349695,0.0001866432,0.00039197932,0.00038723066,0.00046047295,0.0050767288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015799681,0.00016871162,0.00045881845,0.0009545119,0.00007921962,0.000046950998,0.00027910247,0.00007369159,0.0003713331],"category_scores_gemma":[0.0014200851,0.00013676968,0.00008586771,0.0006631939,0.000078942794,0.00017195432,0.000008786628,0.00027840625,0.00001383175],"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":[0.00003794098,0.000038618717,0.91688067,0.000030922136,0.000043736225,0.00040131854,0.00011845573,0.000011358864,0.00020584169,0.00007509176,0.052930266,0.029225772],"study_design_scores_gemma":[0.0014886888,0.00014424315,0.8762833,0.00038594333,0.000007773986,0.000028467408,0.00025197028,0.000030907537,0.000024832407,0.000012318659,0.12120063,0.00014093115],"about_ca_topic_score_codex":0.95486057,"about_ca_topic_score_gemma":0.99840766,"teacher_disagreement_score":0.15475176,"about_ca_system_score_codex":0.003276589,"about_ca_system_score_gemma":0.049340546,"threshold_uncertainty_score":0.9560488},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000025536326,0.9726908,0.000061042,0.013246578,0.0004568534,0.007865296,0.005298688,0.000088070614,0.0002900838],"genre_scores_gemma":[0.000022263244,0.9921072,0.00007402282,0.0029789368,0.00021686402,0.0006419736,0.0038120756,0.000092602895,0.00005405339],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9807303,0.010648007,0.004296172,0.0013168856,0.0015565563,0.0014521058],"domain_scores_gemma":[0.9901707,0.0011374078,0.0035918192,0.0016723973,0.001967375,0.0014602726],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032144777,0.00074131583,0.006790422,0.0006490446,0.00009512612,0.00007855481,0.00045726812,0.00021601735,0.00012728553],"category_scores_gemma":[0.010647063,0.00047666204,0.00037190955,0.0017199421,0.00019442127,0.0011904663,0.00020177811,0.00052984327,0.00004790552],"study_design_candidate":"not_applicable","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":[0.0000022965942,0.0001223946,0.0002669243,0.3010551,0.000059867267,0.000001866674,0.000052481522,1.0401702e-8,1.3603987e-9,0.00018016688,0.0066830963,0.6915758],"study_design_scores_gemma":[0.00044472647,0.00021856332,0.00079385535,0.23636696,0.000029573872,0.000058856338,0.00005455453,0.0000029960884,9.385135e-10,0.0000046286823,0.7617695,0.00025583585],"about_ca_topic_score_codex":0.00017282703,"about_ca_topic_score_gemma":0.00024053735,"teacher_disagreement_score":0.75508636,"about_ca_system_score_codex":0.0018501714,"about_ca_system_score_gemma":0.017331868,"threshold_uncertainty_score":0.9997685},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87298787,0.016966956,0.06631481,0.0133347325,0.00087441225,0.019866478,0.007408711,0.0008623276,0.0013837017],"genre_scores_gemma":[0.9845321,0.0005714499,0.013583663,0.00020544506,0.00016498881,0.0004126638,0.00045871007,0.000017859331,0.000053080803],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992061,0.00001740397,0.00026170805,0.00019734116,0.00007264107,0.00024479636],"domain_scores_gemma":[0.999123,0.00003237686,0.00028866687,0.0003032308,0.00006789668,0.0001848423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016747577,0.00012925158,0.00023402792,0.0000941581,0.00060178456,0.00021255031,0.000070116555,0.000047140133,6.0231423e-7],"category_scores_gemma":[0.00012020011,0.00011216428,0.000039234317,0.000028071885,0.000085717234,0.00047668637,0.00006603697,0.000052045256,6.4095383e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016767168,0.000023742188,0.258323,0.004895472,0.000031905158,3.0278798e-7,0.000186455,1.3556675e-7,0.0000029557802,0.0028312965,0.00006757274,0.7334695],"study_design_scores_gemma":[0.0053690085,0.00047944573,0.9719254,0.0032058933,0.000052883635,0.00007280983,0.00015974659,0.0011342535,0.000023524963,0.00027581063,0.017121144,0.00018011247],"about_ca_topic_score_codex":0.00021134857,"about_ca_topic_score_gemma":0.00012632868,"teacher_disagreement_score":0.73328936,"about_ca_system_score_codex":0.00002467368,"about_ca_system_score_gemma":0.0001234097,"threshold_uncertainty_score":0.46285012},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97441095,0.00018137535,0.0000074490495,0.000017761908,0.000079600155,0.0009035447,0.0002842709,0.00001893649,0.024096131],"genre_scores_gemma":[0.990951,0.00002195622,0.00014146022,0.0000012731089,0.000015089801,0.0000023773198,0.0013025394,0.000024143981,0.007540167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962257,0.00083725736,0.00026363632,0.00052833336,0.0018189156,0.0003261477],"domain_scores_gemma":[0.99741304,0.0000764564,0.0002355785,0.000621731,0.0014292671,0.00022394395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031187073,0.00018498168,0.00062710594,0.00078653655,0.00008387207,0.00001507054,0.0003086134,0.00022365546,0.00027463507],"category_scores_gemma":[0.00015749663,0.00022742173,0.00008985887,0.00040782732,0.00019218659,0.00018992303,0.00005676828,0.00048880203,0.000020804382],"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":[0.00035037007,0.00023034615,0.98630625,0.0025730764,0.00011228301,0.00013454966,0.0037756546,0.000011725956,0.0005210689,0.00028776907,0.00006502456,0.005631903],"study_design_scores_gemma":[0.0027681426,0.00035238988,0.9726972,0.00083517155,0.00006793187,0.00001208376,0.019582754,0.0024319505,0.00004515913,0.000011661694,0.0010262451,0.00016928479],"about_ca_topic_score_codex":0.013733177,"about_ca_topic_score_gemma":0.39972618,"teacher_disagreement_score":0.385993,"about_ca_system_score_codex":0.00047505618,"about_ca_system_score_gemma":0.001336989,"threshold_uncertainty_score":0.99283445},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7296181,0.0013502775,0.17116304,0.09646943,0.0005690084,0.0005891963,0.00012213535,0.000103089784,0.000015756703],"genre_scores_gemma":[0.9519653,0.0021478375,0.04423325,0.0009366948,0.0006014412,0.00001474319,0.000017586444,0.00003110912,0.000052074225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99650633,0.00010732716,0.0020740689,0.00010168384,0.0004745635,0.0007360175],"domain_scores_gemma":[0.99543744,0.00016405828,0.0021872136,0.00028154723,0.00086424215,0.0010654718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004458218,0.00017358235,0.0006558778,0.00094647106,0.00019542828,0.000061786006,0.00018528882,0.00011539224,0.000006363583],"category_scores_gemma":[0.0031665333,0.00011920424,0.000104168466,0.00047013068,0.00007978859,0.0009573797,0.000080518665,0.0003926752,0.0000036172808],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004021924,0.00017220232,0.053212296,0.0007294103,0.00008448104,0.0000024797714,0.0008280595,0.0000013477506,0.00006331578,0.00008912036,0.00046547037,0.9443116],"study_design_scores_gemma":[0.009883267,0.007380405,0.21913971,0.0022595744,0.000036559974,0.0015078919,0.0048801736,0.0016512446,0.00010313766,0.0002797096,0.7524997,0.0003786136],"about_ca_topic_score_codex":0.000009976314,"about_ca_topic_score_gemma":0.000010880406,"teacher_disagreement_score":0.943933,"about_ca_system_score_codex":0.0007082499,"about_ca_system_score_gemma":0.0025075346,"threshold_uncertainty_score":0.48610076},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99210167,0.00011757795,0.0007919669,0.000066032255,0.000018713015,0.00022994455,0.004153766,0.000013873392,0.0025064682],"genre_scores_gemma":[0.9962365,0.00058705773,0.0022732029,0.0000020519174,0.00003135342,8.061479e-8,0.0004313144,0.000013584572,0.00042485676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983638,0.00018686184,0.00013095637,0.00041542997,0.0006178751,0.0002850565],"domain_scores_gemma":[0.9975624,0.0001094025,0.00022256812,0.00181658,0.00008380521,0.0002052144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013040104,0.00009808055,0.0003219012,0.0001726814,0.00075116305,0.000022246606,0.00087783963,0.000065137145,0.0002361948],"category_scores_gemma":[0.00016283497,0.00011706745,0.000048649963,0.00007444357,0.001203896,0.00040822895,0.0013452041,0.0002799811,0.000014482548],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055224762,0.00020287422,0.9831191,0.00015847593,0.00012886767,0.00014540859,0.00016243926,0.00030813768,0.013156381,0.00011867749,0.00088241,0.0015620142],"study_design_scores_gemma":[0.0018861415,0.00015581204,0.86221343,0.000099471705,0.00005603982,0.000008570366,0.0008758287,0.12764873,0.00031273146,0.00006535776,0.0065236893,0.00015422501],"about_ca_topic_score_codex":0.0053047454,"about_ca_topic_score_gemma":0.0020559614,"teacher_disagreement_score":0.12734058,"about_ca_system_score_codex":0.00006715442,"about_ca_system_score_gemma":0.00007908757,"threshold_uncertainty_score":0.8019223},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95497787,0.004208118,0.027566064,0.012422413,0.00022111712,0.00019980194,0.00016127691,0.000051763505,0.0001915459],"genre_scores_gemma":[0.9828496,0.0001401847,0.013140855,0.0032334235,0.0004902773,0.00000413037,0.00008727425,0.000028874561,0.000025401947],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968442,0.0010379277,0.0011482049,0.00032359138,0.0001862859,0.00045983877],"domain_scores_gemma":[0.9964476,0.0010187655,0.0013429798,0.00040321206,0.00020634111,0.0005810652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002074636,0.00022607694,0.0012223626,0.0002650115,0.000058679816,0.0000066827674,0.00015986447,0.000049761355,0.000066259454],"category_scores_gemma":[0.0026732655,0.0001889119,0.00036390894,0.00031552554,0.0007675092,0.00008618496,0.000040196162,0.0003119546,0.0000084974745],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009780383,0.000093622366,0.9746853,0.000040861454,0.000104608094,0.00017562383,0.000008248814,0.00013061888,0.00014010978,0.00012214523,0.0062960894,0.017224718],"study_design_scores_gemma":[0.0010011554,0.00069071725,0.9844933,0.000088164685,0.00007851237,0.00018710006,0.000031950538,0.001237534,0.000010151696,0.001055822,0.010937227,0.00018836882],"about_ca_topic_score_codex":0.0002712004,"about_ca_topic_score_gemma":0.000033004482,"teacher_disagreement_score":0.02787168,"about_ca_system_score_codex":0.00009638309,"about_ca_system_score_gemma":0.00037861455,"threshold_uncertainty_score":0.77036035},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93637395,0.0028663976,0.009948196,0.04834207,0.000827078,0.0006211989,0.0007463839,0.000053491618,0.00022125339],"genre_scores_gemma":[0.83495885,0.0005810989,0.09681016,0.065069295,0.0011105454,0.00006386812,0.00089539454,0.000110982815,0.00039979393],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.991659,0.0030462234,0.0032090202,0.0006681341,0.00018582119,0.001231839],"domain_scores_gemma":[0.9857833,0.009653532,0.002776373,0.00074893114,0.00046657107,0.00057131035],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0064656055,0.0004693286,0.0035530468,0.00053874135,0.00011696921,0.0000069528655,0.00041160797,0.00027274623,0.000034157816],"category_scores_gemma":[0.008666163,0.00040064778,0.0006230729,0.00046148457,0.001602393,0.00013907345,0.00010734859,0.0008816867,0.000014718111],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033788187,0.00053313223,0.8292897,0.00012386558,0.00038747975,0.0007597595,0.00032719676,0.001410789,0.0016687089,0.00099576,0.14531602,0.015808776],"study_design_scores_gemma":[0.0048724622,0.002286748,0.8770702,0.00027493807,0.00023590322,0.0032380982,0.00082566554,0.0009931982,0.000043157772,0.0035277493,0.106099576,0.0005323231],"about_ca_topic_score_codex":0.0034524563,"about_ca_topic_score_gemma":0.0013877433,"teacher_disagreement_score":0.10141507,"about_ca_system_score_codex":0.00037822477,"about_ca_system_score_gemma":0.00044472725,"threshold_uncertainty_score":0.99984455},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97171605,0.0006799486,0.00026770643,0.014990454,0.00050463236,0.0008889895,0.0000072229477,0.000011306056,0.010933701],"genre_scores_gemma":[0.998118,0.00033028165,0.00069446437,0.00059190847,0.00012419546,0.000008302367,0.000020233307,0.000009061726,0.00010358251],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969741,0.0008729857,0.000799159,0.00019340328,0.00094017386,0.00022015306],"domain_scores_gemma":[0.9955019,0.00012705503,0.0025191333,0.0005825316,0.00079451234,0.00047486566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02045907,0.00009915249,0.00034538528,0.0002536157,0.00013403373,0.00012543633,0.00012184036,0.000029323828,0.00006366075],"category_scores_gemma":[0.006269263,0.00008858953,0.00004691979,0.00008924244,0.000056641416,0.0017614516,0.00006564883,0.00024646625,9.4526837e-7],"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":[0.0005130078,0.00070109835,0.89362115,0.00027718613,0.00009740175,0.0004642166,0.0002807055,0.000017224578,0.0000021463327,0.00016281211,0.0011619765,0.10270107],"study_design_scores_gemma":[0.0018494821,0.00034245223,0.92915064,0.00009646611,0.000097125194,0.00016598689,0.00014005712,0.00017726874,1.9249369e-7,0.00020025711,0.06771535,0.00006470017],"about_ca_topic_score_codex":0.0072607873,"about_ca_topic_score_gemma":0.032477856,"teacher_disagreement_score":0.10263637,"about_ca_system_score_codex":0.00047010148,"about_ca_system_score_gemma":0.0023797771,"threshold_uncertainty_score":0.99934995},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99539685,0.0011602469,0.0015964735,0.00084166735,0.00013680346,0.0003779113,0.00041399963,0.000013922957,0.00006214656],"genre_scores_gemma":[0.99641603,0.00014521969,0.0007544938,0.0022772974,0.0002222878,0.000007380658,0.0001357862,0.000019154642,0.000022372265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946204,0.0024250993,0.0018071566,0.00046798604,0.00019642268,0.00048295184],"domain_scores_gemma":[0.9942852,0.002830305,0.0017104407,0.00033004052,0.00023848194,0.000605516],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0030566351,0.00019500377,0.002036922,0.00009647103,0.00008876841,0.00000647078,0.00014621862,0.0000679366,0.000054874043],"category_scores_gemma":[0.01097851,0.00021221348,0.00012952188,0.0002102605,0.0010673162,0.000055476037,0.00007608117,0.0003024925,3.1337922e-7],"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":[0.00022996901,0.00041411121,0.99377894,0.00002981415,0.00019603134,0.00043674963,0.000058094043,0.00011794235,0.0000026211496,0.000026752212,0.0013534314,0.003355533],"study_design_scores_gemma":[0.0012564582,0.002142134,0.9941353,0.000111655536,0.0002433324,0.00017022589,0.0006531716,0.00022866888,1.2920168e-7,0.0006935305,0.00019693142,0.00016842003],"about_ca_topic_score_codex":0.88085884,"about_ca_topic_score_gemma":0.7559352,"teacher_disagreement_score":0.124923654,"about_ca_system_score_codex":0.00017952366,"about_ca_system_score_gemma":0.00060964766,"threshold_uncertainty_score":0.9973524},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98485845,0.0052976916,0.00012628757,0.008981312,0.000045937424,0.00020257657,0.00017307689,0.0000056957438,0.00030896673],"genre_scores_gemma":[0.9899677,0.0017327819,0.007599191,0.00049699016,0.00011507211,0.000012480516,0.000056398836,0.000009903228,0.000009476412],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.992376,0.0047943867,0.0018243156,0.00038257943,0.0001702918,0.00045241666],"domain_scores_gemma":[0.9629896,0.034873616,0.0011165058,0.00051907456,0.0003380881,0.00016309801],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015541168,0.00014379786,0.0016901047,0.0004950259,0.000034044024,0.0000037034729,0.00023070388,0.00007300495,0.000044951295],"category_scores_gemma":[0.0481702,0.00011299238,0.00006188857,0.000574744,0.0022608808,0.00012767408,0.00013801565,0.00061005354,0.0000021225592],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026064046,0.00025160483,0.9907667,0.00002217696,0.00003786691,0.00024760858,0.00005310176,0.00016214336,0.000053546544,0.0017795822,0.0045196735,0.0018453496],"study_design_scores_gemma":[0.0007811994,0.0014148765,0.9820309,0.00012186015,0.000008563083,0.0003213444,0.000469787,0.0001454015,0.00002077489,0.013513214,0.0010883929,0.00008364292],"about_ca_topic_score_codex":0.0024283612,"about_ca_topic_score_gemma":0.0031126733,"teacher_disagreement_score":0.03262903,"about_ca_system_score_codex":0.00007880017,"about_ca_system_score_gemma":0.00034477538,"threshold_uncertainty_score":0.95984745},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9698261,0.007866692,0.0019441438,0.005264368,0.0073602023,0.00059518573,0.0006008448,0.00008885523,0.006453586],"genre_scores_gemma":[0.99732184,0.00015287347,0.000421416,0.000061409875,0.0017585071,0.0000033094334,0.000053351818,0.000026081394,0.00020117819],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99523354,0.00032865768,0.001349497,0.00030464682,0.002426391,0.00035728398],"domain_scores_gemma":[0.99556744,0.0002991554,0.0012621231,0.0018794097,0.00023123875,0.0007606147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008125487,0.00017938757,0.0010861241,0.0001717436,0.00010552191,0.0001460254,0.0021905573,0.00020312384,0.00006871728],"category_scores_gemma":[0.006420131,0.00013055357,0.00011132662,0.0001105438,0.00015496973,0.00037354956,0.00047189413,0.0005536045,0.00005947285],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033948678,0.00019355501,0.93430394,0.0017000114,0.00023035737,0.008337578,0.00009326701,0.000009222328,0.00006273061,0.000274508,0.04706571,0.0073896525],"study_design_scores_gemma":[0.012892243,0.0004937228,0.76623684,0.022969132,0.0001401785,0.0100435065,0.003007474,0.027618863,0.000009063556,0.000004489176,0.15610044,0.00048407872],"about_ca_topic_score_codex":0.00013802582,"about_ca_topic_score_gemma":0.0000666854,"teacher_disagreement_score":0.16806711,"about_ca_system_score_codex":0.00020022914,"about_ca_system_score_gemma":0.00063822186,"threshold_uncertainty_score":0.76859605},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94681937,0.00032744175,0.00028529274,0.03775622,0.0004531799,0.0055379197,0.008760662,0.000024026627,0.000035876077],"genre_scores_gemma":[0.996513,0.000006555972,0.00035211735,0.002576496,0.000049503153,0.00035823727,0.00009794307,0.000031200398,0.000014920038],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99100006,0.0036174029,0.0015179542,0.00075644953,0.0021078533,0.0010003033],"domain_scores_gemma":[0.9925108,0.0006531976,0.0010616926,0.0027928432,0.0016050943,0.0013763477],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0321997,0.00021380254,0.0010066581,0.0002316967,0.0002471447,0.00004535837,0.00061759056,0.000049808685,0.000051466992],"category_scores_gemma":[0.0458498,0.00017503698,0.00013880209,0.0004213966,0.00012423661,0.00019977658,0.00022791384,0.00013575633,0.0000012804509],"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":true,"study_design_scores_codex":[0.0020512221,0.00030289748,0.9452649,0.0011574421,0.000042826123,0.0000015688822,0.00027835937,0.000030838004,0.00036091957,0.000041148167,0.011999462,0.038468435],"study_design_scores_gemma":[0.0033322535,0.00048851286,0.97886026,0.00012716476,0.000009723693,0.0000015888352,0.00015934937,0.0002342533,0.000032894917,0.000031336498,0.016593697,0.00012897742],"about_ca_topic_score_codex":0.2973852,"about_ca_topic_score_gemma":0.82862985,"teacher_disagreement_score":0.53124464,"about_ca_system_score_codex":0.006275387,"about_ca_system_score_gemma":0.15198861,"threshold_uncertainty_score":0.99753934},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.933602,0.0024019429,0.045883168,0.012616963,0.0042113108,0.000512312,0.0006712214,0.00001679513,0.0000843384],"genre_scores_gemma":[0.9337246,0.00081993215,0.06191851,0.0014638294,0.0019569537,8.5726515e-7,0.000072473114,0.000017104905,0.00002578462],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99575424,0.0006615497,0.0027656935,0.00036841212,0.0000628762,0.00038720996],"domain_scores_gemma":[0.9759731,0.016659725,0.0048604463,0.0017299198,0.00057971646,0.00019706486],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0105810175,0.0001704759,0.0016498461,0.00003117551,0.000300529,0.000026833086,0.0018570112,0.00033729337,0.00001730569],"category_scores_gemma":[0.08680645,0.000097499375,0.00053531217,0.000035377125,0.0014650964,0.00009172387,0.00068843155,0.0009073319,0.000008397709],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015340746,0.0009246447,0.72913015,0.0002914345,0.001662396,0.00014315081,0.00004086348,0.000019526766,0.04484551,0.008217741,0.0744945,0.12488935],"study_design_scores_gemma":[0.0077295206,0.0030049926,0.5946804,0.0015525334,0.00045916543,0.001198405,0.000066041044,0.00090383756,0.0008952577,0.007887905,0.38127646,0.00034545767],"about_ca_topic_score_codex":0.0000063431553,"about_ca_topic_score_gemma":0.000013089598,"teacher_disagreement_score":0.30678195,"about_ca_system_score_codex":0.000021739268,"about_ca_system_score_gemma":0.00036655474,"threshold_uncertainty_score":0.92088574},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9983611,0.000109006076,0.0002023545,0.00014287914,0.000025656842,0.0008189856,0.0002477116,0.000013203061,0.00007911443],"genre_scores_gemma":[0.9991215,0.00007143875,0.0004830415,0.000060902854,0.000024689076,0.000022643757,0.00019120006,0.00001403221,0.000010558176],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987476,0.00011080337,0.00034484602,0.00030873725,0.00020933304,0.00027872142],"domain_scores_gemma":[0.99897945,0.000052185653,0.00026382535,0.00040962055,0.00007308844,0.00022181078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057548436,0.000128569,0.00044435367,0.00018128808,0.00015448284,0.00007376012,0.00009494734,0.000037060938,0.000032891145],"category_scores_gemma":[0.000058877442,0.00011239117,0.0000095634205,0.00009848999,0.00014460964,0.00014585035,0.00007533043,0.00015159615,8.3915296e-7],"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":[0.00015611712,0.0006073415,0.98402333,0.00012494085,0.000020667028,0.000012007945,0.0009894299,0.0000028050251,0.00002451515,0.00007087614,0.000009773109,0.01395822],"study_design_scores_gemma":[0.0025253037,0.000756876,0.99450696,0.00008284759,0.000013964308,0.0000036334243,0.0004514858,0.0011274685,6.238016e-7,0.000023771461,0.00040853248,0.000098560624],"about_ca_topic_score_codex":0.82461494,"about_ca_topic_score_gemma":0.9657314,"teacher_disagreement_score":0.14111647,"about_ca_system_score_codex":0.00011356998,"about_ca_system_score_gemma":0.0007072327,"threshold_uncertainty_score":0.45831788},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8641414,0.0001985635,0.00019423035,0.13096148,0.000291377,0.0031175998,0.0008190352,0.000056450375,0.00021982842],"genre_scores_gemma":[0.9941954,0.000023121129,0.00022891499,0.0049873237,0.00023179592,0.00012038433,0.000076433906,0.000032660457,0.000103941886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960544,0.0012171591,0.0006161007,0.00066454895,0.0006430515,0.00080474885],"domain_scores_gemma":[0.9934174,0.00051450776,0.00037928455,0.0043921135,0.0004019452,0.0008947371],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0075141117,0.00023414362,0.00047376083,0.00009277385,0.0026287357,0.00031409587,0.0014754115,0.000057229405,0.000037653026],"category_scores_gemma":[0.00410414,0.00012628916,0.000093584036,0.0005706682,0.00025310836,0.0003103439,0.0012765491,0.00047618727,0.000009608372],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001143051,0.0005959695,0.9526824,0.00018881263,0.0001436531,0.000057598263,0.001522834,0.000012295842,0.000071988936,0.00007986656,0.012803944,0.03172637],"study_design_scores_gemma":[0.0019093397,0.00026256096,0.89169586,0.0000567498,0.000008065323,0.00026077247,0.004465144,0.006462813,0.0000015991224,0.0000060858006,0.09466467,0.00020633258],"about_ca_topic_score_codex":0.0015046063,"about_ca_topic_score_gemma":0.00444917,"teacher_disagreement_score":0.130054,"about_ca_system_score_codex":0.00015289383,"about_ca_system_score_gemma":0.0025608253,"threshold_uncertainty_score":0.9986697},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9962714,0.0007043647,0.00002680729,0.00093212596,0.00021263694,0.0010660772,0.00067011,0.000052425818,0.00006405223],"genre_scores_gemma":[0.998952,0.00039875676,0.00003485562,0.00012765707,0.00004695555,0.00018066897,0.0001675351,0.00003095139,0.00006066592],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965673,0.0006367426,0.0010413019,0.0006152932,0.0004136148,0.0007257155],"domain_scores_gemma":[0.99629724,0.0001620518,0.0009916151,0.0011354568,0.00027176828,0.0011418489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021044281,0.0002544066,0.0011691414,0.0001390433,0.00023592691,0.0000804121,0.0002226987,0.00008675608,0.000009710515],"category_scores_gemma":[0.0014422322,0.0002383355,0.000074265685,0.00021715766,0.00053078274,0.0003682739,0.00018052715,0.00013725883,0.000002405653],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008276715,0.00016604348,0.9847498,0.0033640678,0.00002482747,0.000043046828,0.00072509865,4.0199032e-7,0.0003175642,0.000059687125,0.000027257838,0.0096945325],"study_design_scores_gemma":[0.0027963864,0.00016153241,0.9921753,0.00032601334,0.0000029758994,0.0000071830173,0.00021640491,0.00309445,0.000028075123,0.000004918483,0.0010007371,0.00018603078],"about_ca_topic_score_codex":0.00038233082,"about_ca_topic_score_gemma":0.0005013033,"teacher_disagreement_score":0.009508502,"about_ca_system_score_codex":0.00007737123,"about_ca_system_score_gemma":0.0009794151,"threshold_uncertainty_score":0.9719039},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97701216,0.0014916937,0.000003517025,0.02079769,0.00030501542,0.0002161931,0.000045974964,0.00003162789,0.000096157775],"genre_scores_gemma":[0.98809487,0.00067099894,0.00001824775,0.01074227,0.0000907024,0.000006414933,0.000006433608,0.00002167998,0.00034836525],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974076,0.00067282986,0.00066657504,0.00073962566,0.0001955362,0.0003177916],"domain_scores_gemma":[0.9951089,0.0033941385,0.00024622682,0.0009033588,0.00008268312,0.00026469716],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0016681859,0.00018486936,0.00072655664,0.000066428365,0.00007853355,0.00003692313,0.0002632751,0.00021543416,0.00007703981],"category_scores_gemma":[0.008495863,0.00016657435,0.00011498814,0.00008860542,0.0015396463,0.00020711133,0.00046179674,0.00076718256,0.000013313123],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020539812,0.00019593538,0.9809956,0.00035630952,0.000009723526,0.0019193159,0.000020932663,1.9736595e-7,0.0005071523,0.00002258348,0.001545744,0.012372512],"study_design_scores_gemma":[0.00086597697,0.0006693673,0.9958388,0.00006231853,0.00002832309,0.00027775913,0.0000027334859,0.0013170557,0.00020777619,0.00007419661,0.00054476893,0.000110892404],"about_ca_topic_score_codex":0.000053836797,"about_ca_topic_score_gemma":0.00002921466,"teacher_disagreement_score":0.01484322,"about_ca_system_score_codex":0.0000025883714,"about_ca_system_score_gemma":0.000092417424,"threshold_uncertainty_score":0.999856},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9057251,0.0005503816,0.000117200725,0.09016741,0.00023440334,0.0012423889,0.0016101652,0.00012594298,0.00022701101],"genre_scores_gemma":[0.9901802,0.00015707892,0.00048825896,0.008094353,0.0002622086,0.00016654795,0.00052008394,0.00006248649,0.00006875881],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952484,0.000582961,0.0009541709,0.0013566335,0.00047979693,0.0013780473],"domain_scores_gemma":[0.9947913,0.00012333499,0.00042400236,0.002790537,0.00018188679,0.0016888852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033482667,0.00040455826,0.0010989095,0.00085929513,0.00042778288,0.00053970155,0.0011747445,0.00011963101,0.00014367257],"category_scores_gemma":[0.00092969276,0.0003904337,0.000053293516,0.0009928404,0.00015299294,0.0012104493,0.0012591819,0.00056272454,0.00000960958],"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":[0.00032703188,0.00048756032,0.8265551,0.00013111407,0.000039300565,0.000098023236,0.0003602868,0.0000017599114,0.0000135573555,0.00002148747,0.004670536,0.16729425],"study_design_scores_gemma":[0.0040200055,0.00017905892,0.96804345,0.0001638253,0.000001550018,0.000058608963,0.00038866952,0.0032705013,1.6134237e-7,0.000002511435,0.02352755,0.00034411417],"about_ca_topic_score_codex":0.015632277,"about_ca_topic_score_gemma":0.11003492,"teacher_disagreement_score":0.16695012,"about_ca_system_score_codex":0.00036445056,"about_ca_system_score_gemma":0.0014166953,"threshold_uncertainty_score":0.99985474},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9733729,0.00068629987,0.00088294374,0.004699152,0.00027811705,0.00028026217,0.0026211462,0.0001668261,0.017012391],"genre_scores_gemma":[0.995243,0.0002367426,0.0011735687,0.001114122,0.0001231352,0.00002275248,0.0007868731,0.00002113274,0.001278675],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978805,0.00009861273,0.00058561825,0.0003788089,0.0006182048,0.00043822438],"domain_scores_gemma":[0.99738455,0.000073913274,0.0002788798,0.0017286714,0.00021277263,0.00032121802],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001765141,0.00025666918,0.00094510574,0.00027720002,0.0002161352,0.0000956963,0.00050886226,0.00012481729,0.0023499655],"category_scores_gemma":[0.00024258153,0.000224274,0.00036171143,0.00078670046,0.0002335913,0.00031470548,0.00022131244,0.00015587632,0.00014337858],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011262507,0.00016554761,0.91321665,0.00038921693,0.001799472,0.0005740069,0.00017582392,0.00004071609,0.000011965764,0.00058473065,0.024023607,0.059006985],"study_design_scores_gemma":[0.0014079512,0.00005730927,0.9706591,0.00011861504,0.0010163338,0.00008086133,0.0000362432,0.0031147832,0.00025831145,0.00003555987,0.0230097,0.00020524407],"about_ca_topic_score_codex":0.00083064626,"about_ca_topic_score_gemma":0.0021490343,"teacher_disagreement_score":0.05880174,"about_ca_system_score_codex":0.0001547931,"about_ca_system_score_gemma":0.0001720653,"threshold_uncertainty_score":0.99856204},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.58693e-7,0.91037875,0.00020287221,0.07386229,0.0004983711,0.0123847425,0.0014612261,0.00037019092,0.00084128923],"genre_scores_gemma":[0.00001857875,0.9773492,0.0008556235,0.012494225,0.0004431963,0.0020676102,0.005623166,0.00021928694,0.00092910894],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9447491,0.03946229,0.0077119474,0.0023347721,0.0025722156,0.0031696751],"domain_scores_gemma":[0.9815823,0.0032829242,0.006066223,0.0049707703,0.00076658226,0.003331231],"candidate_categories":["metaresearch","metaepi_narrow","metaepi_broad","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","metaepi_narrow"],"category_scores_codex":[0.06980396,0.0013053984,0.01770046,0.0011315197,0.00040602434,0.00037292278,0.0016898254,0.0009726455,0.00024082528],"category_scores_gemma":[0.04824703,0.0009962795,0.0013232458,0.0017857422,0.00039769593,0.00037985476,0.00046348918,0.003542056,0.0007878171],"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":true,"study_design_scores_codex":[0.000004097739,0.00027238866,0.000092104994,0.7889882,0.00019110377,0.00003406234,0.000767549,6.293215e-9,2.7298155e-10,0.0011819513,0.04579627,0.16267231],"study_design_scores_gemma":[0.000478373,0.00016761993,0.00017693122,0.3785348,0.000055517863,0.00013209623,0.00018706094,0.000003877918,1.4176442e-10,0.000016173977,0.61982197,0.00042560406],"about_ca_topic_score_codex":0.0010498187,"about_ca_topic_score_gemma":0.0018567506,"teacher_disagreement_score":0.5740257,"about_ca_system_score_codex":0.0038688101,"about_ca_system_score_gemma":0.04891098,"threshold_uncertainty_score":0.99999017},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42570326,0.015081572,0.027296044,0.00847545,0.006327945,0.0014807535,0.0032715376,0.0008381197,0.51152533],"genre_scores_gemma":[0.91993386,0.0016431423,0.0077240705,0.00023284846,0.0016267921,0.00010105642,0.00034225037,0.00008004223,0.06831597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9917334,0.0021630167,0.0011158639,0.0014827717,0.0022623113,0.0012426429],"domain_scores_gemma":[0.99152535,0.0040781815,0.00040942573,0.00075156864,0.0019141935,0.0013212948],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0054686214,0.00078704365,0.0007055955,0.00088406727,0.0009714855,0.0010898471,0.0007415855,0.0010467977,0.00021754122],"category_scores_gemma":[0.019396326,0.00068230374,0.0008072013,0.0020532117,0.009854097,0.003390269,0.00029030224,0.0009833282,0.00054876064],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0016783387,0.005620594,0.27423632,0.0015381131,0.0026095312,0.0048381337,0.0018857532,0.005019039,0.06432833,0.39960292,0.07592173,0.16272119],"study_design_scores_gemma":[0.003772228,0.000111711735,0.21275923,0.0050515877,0.0003958262,0.0021298141,0.0002718574,0.0013105486,0.009992236,0.021384116,0.74143577,0.0013850946],"about_ca_topic_score_codex":0.00020570922,"about_ca_topic_score_gemma":0.00034143525,"teacher_disagreement_score":0.66551405,"about_ca_system_score_codex":0.00554955,"about_ca_system_score_gemma":0.0072576636,"threshold_uncertainty_score":0.99994713},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97115403,0.001954863,0.0002254762,0.023786407,0.00015003748,0.00088047533,0.0002650553,0.00006223515,0.0015214257],"genre_scores_gemma":[0.99733007,0.00058669643,0.00040157314,0.0010781147,0.00007080494,0.00005538435,0.00027084173,0.000013580124,0.00019294562],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848133,0.00016751763,0.00045692924,0.00032669687,0.00020705242,0.00036046375],"domain_scores_gemma":[0.9981516,0.00012187226,0.00034933403,0.00067269796,0.0001378057,0.00056671834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093569316,0.00013692635,0.00049462315,0.0001358732,0.00020862496,0.00007402174,0.00014167785,0.0000719644,0.000030188037],"category_scores_gemma":[0.0008281539,0.00012747908,0.000029020901,0.00013073499,0.00025459225,0.00024570667,0.00008923275,0.0001531543,0.000003967405],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003934796,0.00010735272,0.97962487,0.00028021753,0.000013107815,0.0000043208966,0.00037175344,8.260906e-8,0.000011614478,0.00062228803,0.0029611094,0.01596394],"study_design_scores_gemma":[0.0013596878,0.00010286433,0.92653656,0.000040432304,5.293289e-7,0.000010481371,0.00011334769,0.00021306702,7.331023e-7,0.0000474666,0.07147639,0.00009842798],"about_ca_topic_score_codex":0.0006035384,"about_ca_topic_score_gemma":0.00076480285,"teacher_disagreement_score":0.068515286,"about_ca_system_score_codex":0.00005135627,"about_ca_system_score_gemma":0.0008599084,"threshold_uncertainty_score":0.5198446},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6265134,0.016335525,0.0004178873,0.35061556,0.00047571614,0.004865968,0.00034816345,0.00016502391,0.0002627279],"genre_scores_gemma":[0.9883076,0.00020264779,0.00017236719,0.010012555,0.00030148582,0.0004157814,0.00037669353,0.000040776107,0.00017007542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9939382,0.0020817062,0.0010588454,0.00080723094,0.001287228,0.000826769],"domain_scores_gemma":[0.9941316,0.00097652513,0.0013313923,0.0016227154,0.0008298588,0.0011078839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005661986,0.0003681417,0.0011482125,0.00023516473,0.0007187399,0.00013078845,0.00065787794,0.0001101495,0.000058726833],"category_scores_gemma":[0.003812127,0.00024336777,0.00012209934,0.0003595062,0.0004935339,0.00031510965,0.00018730911,0.000472889,0.00000788614],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012272964,0.0008531407,0.92669785,0.0027933463,0.00009368971,0.000014047544,0.004623307,0.0000013291385,0.000008090585,0.00006839569,0.029293843,0.03432564],"study_design_scores_gemma":[0.0040963576,0.0023302957,0.976329,0.0014435367,0.0000058054193,0.0000067686333,0.001898509,0.0014680942,0.0000011819102,0.00000556664,0.012218603,0.00019622392],"about_ca_topic_score_codex":0.0017497202,"about_ca_topic_score_gemma":0.0016793294,"teacher_disagreement_score":0.36179417,"about_ca_system_score_codex":0.00038104295,"about_ca_system_score_gemma":0.0073402617,"threshold_uncertainty_score":0.9982872},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995041,0.0001347174,0.0033246,0.00033510706,0.00014122404,0.0001066868,0.00084995676,0.0000011294088,0.00006554739],"genre_scores_gemma":[0.9780721,0.00003120604,0.021810906,0.000015750486,0.00005375902,0.0000013614427,0.000009998533,0.000002698633,0.000002230562],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987777,0.00008345646,0.0005932458,0.000081457954,0.00040113454,0.000063046355],"domain_scores_gemma":[0.9968476,0.0008572584,0.0014227744,0.00022384325,0.00059086294,0.000057640245],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0013166718,0.000053645883,0.00019037942,0.00003704655,0.0000393439,0.000029800658,0.00022841181,0.0000117168,0.000011968921],"category_scores_gemma":[0.020215034,0.000035792422,0.000041902516,0.000015309693,0.00024285575,0.00010447341,0.00006692207,0.000112950984,8.581961e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013441363,0.00015517391,0.9861175,0.00017158756,0.00006788705,0.00011771336,0.00015820314,0.00023147983,0.000055201832,0.00074348284,0.00005618279,0.011991207],"study_design_scores_gemma":[0.00038114723,0.000079922684,0.95985603,0.00029618794,0.000057473724,0.000022590197,0.000028301023,0.007902888,0.000033987973,0.03129506,0.000017586393,0.000028805161],"about_ca_topic_score_codex":0.00012666492,"about_ca_topic_score_gemma":0.00005160189,"teacher_disagreement_score":0.030551579,"about_ca_system_score_codex":0.00001951713,"about_ca_system_score_gemma":0.00013459729,"threshold_uncertainty_score":0.9880381},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000029934788,0.9930808,0.000046109482,0.000010530488,0.00010019061,0.00556249,0.0005249922,0.0002504345,0.00042147716],"genre_scores_gemma":[0.0000144183205,0.99459934,0.0041988553,0.000017832002,0.00006316026,0.00048920815,0.00037270214,0.00007393661,0.0001705461],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99606246,0.0008952342,0.001905091,0.00038523847,0.0005517809,0.00020021798],"domain_scores_gemma":[0.99336505,0.00031880804,0.0034155361,0.002206447,0.0005530058,0.00014113824],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016280514,0.0003901488,0.008741799,0.0002035139,0.000028792958,0.00000910023,0.000428752,0.00022178314,0.000048702233],"category_scores_gemma":[0.008724055,0.00028952156,0.00085191056,0.00027605458,0.00010223176,0.00006802493,0.00013094008,0.00025666892,0.000089746034],"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":[0.0000044914464,0.00079813105,0.0000019950282,0.939002,0.0028653005,0.000009333791,0.0000069106172,1.1796741e-9,0.000018702918,0.0000014172139,0.000072770454,0.057218984],"study_design_scores_gemma":[0.00018979075,0.00023503712,0.000010771632,0.9599435,0.031775028,0.000013842133,0.0000019183267,0.000034187124,0.000064793065,0.0000011370487,0.007520431,0.00020961859],"about_ca_topic_score_codex":0.0000151841305,"about_ca_topic_score_gemma":9.927254e-7,"teacher_disagreement_score":0.057009365,"about_ca_system_score_codex":0.00011847116,"about_ca_system_score_gemma":0.00031636056,"threshold_uncertainty_score":0.9999557},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97464424,0.000217117,0.000014982374,0.00040893877,0.000011841809,0.0002506712,0.000045450124,0.00021306805,0.024193667],"genre_scores_gemma":[0.9874873,0.0000074887457,0.007285609,0.0010765061,0.00018442453,0.000096690586,0.00012643187,0.000042477393,0.0036930614],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99893016,0.000019567016,0.00017777036,0.0002349321,0.00036041543,0.00027714184],"domain_scores_gemma":[0.99908656,0.000043452026,0.00007018166,0.0004313866,0.00015160114,0.00021683378],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006451866,0.00013103223,0.00029052387,0.00009288241,0.00004443206,0.000025963805,0.000090115434,0.00003899856,0.0016858573],"category_scores_gemma":[0.00014536019,0.00010222799,0.0000353088,0.00025727015,0.000055467794,0.00017334225,0.000047799233,0.00012850924,0.0007475337],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004124645,0.0033432092,0.8857616,0.0006248883,0.0011705382,0.00013888623,0.00035534866,0.000009383042,0.025623335,0.00007318042,0.014673222,0.06781393],"study_design_scores_gemma":[0.0061308607,0.0012834761,0.9581803,0.0016599122,0.00074327324,0.000046540503,0.00013190438,0.006967355,0.012609636,0.000109007946,0.011448602,0.0006891497],"about_ca_topic_score_codex":0.000072099414,"about_ca_topic_score_gemma":0.000022529975,"teacher_disagreement_score":0.07241867,"about_ca_system_score_codex":0.000039707575,"about_ca_system_score_gemma":0.000041969684,"threshold_uncertainty_score":0.99922675},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1640905,0.007017378,0.11367468,0.6873529,0.0060191164,0.0030402804,0.018070498,0.00010809384,0.0006265813],"genre_scores_gemma":[0.73711395,0.032401234,0.21063744,0.015325519,0.0016039596,0.000008747729,0.0026664562,0.000051332183,0.00019134309],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959727,0.000066169625,0.0018453173,0.00024805017,0.001152253,0.0007154887],"domain_scores_gemma":[0.99221677,0.00009771567,0.002853063,0.0012239489,0.0015951309,0.0020133823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014482556,0.00021106648,0.00080465886,0.0003029023,0.00041298216,0.00040838582,0.0017067549,0.000057764842,0.000010624393],"category_scores_gemma":[0.0058366996,0.00017931528,0.0001044005,0.0002524546,0.0005102147,0.0031313046,0.0005767691,0.00044495836,0.0000012077056],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017629236,0.00032541927,0.46457157,0.0010957355,0.000098925644,0.0000058062496,0.0011782243,0.0000052668033,8.444793e-7,0.00020281503,0.35051405,0.18182504],"study_design_scores_gemma":[0.004533273,0.00024004748,0.61873573,0.00045169116,0.0000064244937,0.000050508344,0.0009035995,0.083120435,1.9289492e-7,0.00026312572,0.29150733,0.00018766221],"about_ca_topic_score_codex":0.00013511047,"about_ca_topic_score_gemma":0.00028293268,"teacher_disagreement_score":0.67202735,"about_ca_system_score_codex":0.00075770746,"about_ca_system_score_gemma":0.029906785,"threshold_uncertainty_score":0.97559273},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024088129,0.9920865,0.0003791326,0.0005282958,0.0009781278,0.003275703,0.0024953387,0.0000415145,0.00019124759],"genre_scores_gemma":[0.00001796097,0.9957689,0.0013644715,0.00068994024,0.0004394734,0.000033135908,0.0015550044,0.0000730899,0.000058015525],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9862383,0.00086527586,0.010443221,0.00019309386,0.001473037,0.0007870831],"domain_scores_gemma":[0.97113436,0.0003079697,0.023441976,0.0016185391,0.001641263,0.0018558947],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010035944,0.0006290904,0.007519606,0.00086766895,0.00015611984,0.00012799379,0.0009832885,0.00019637919,0.00005266663],"category_scores_gemma":[0.008667518,0.00047376202,0.0010542441,0.000531502,0.00013656584,0.00077173696,0.000253652,0.0010758535,0.000037048645],"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":[0.0000067914925,0.000226066,0.00046679672,0.59942096,0.00039955796,0.00004103846,0.00002806416,0.0000013092001,1.074647e-9,0.000010101344,0.00414369,0.39525563],"study_design_scores_gemma":[0.00060788606,0.00021680618,0.00011470505,0.46377724,0.00035828433,0.0007804109,0.000014785379,0.00005731516,7.093013e-10,7.7324484e-7,0.53389543,0.00017635505],"about_ca_topic_score_codex":0.000016519498,"about_ca_topic_score_gemma":0.000014448385,"teacher_disagreement_score":0.5297517,"about_ca_system_score_codex":0.0009214757,"about_ca_system_score_gemma":0.024490643,"threshold_uncertainty_score":0.9997714},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75081205,0.00077056553,0.23209864,0.010938185,0.0006234392,0.00053067657,0.0036326023,0.000106278865,0.00048756402],"genre_scores_gemma":[0.96882665,0.0007730026,0.025470596,0.00069257454,0.00024810372,7.5108056e-7,0.0039332346,0.000016388965,0.000038707414],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967519,0.00019056993,0.0020035985,0.00011220454,0.00062956807,0.00031216216],"domain_scores_gemma":[0.9932465,0.000069971305,0.0037828072,0.0012200078,0.0009899274,0.000690811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005332556,0.00016299404,0.000876303,0.0005883975,0.00026079774,0.00028546175,0.0005627401,0.00007714383,0.000021122987],"category_scores_gemma":[0.0024774459,0.00012558061,0.00008677104,0.0004708818,0.000110221925,0.001582792,0.00020755558,0.00028421966,0.0000046396353],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007824743,0.00028801724,0.85074586,0.0019434835,0.0010811281,0.000028503,0.00060196844,0.000044755532,0.0000016073298,0.000306572,0.0074899555,0.13738993],"study_design_scores_gemma":[0.0015975295,0.00026714648,0.7435351,0.0002902831,0.00016675744,0.00018801159,0.0011903399,0.20449509,9.835488e-7,0.000003929767,0.04812911,0.00013569086],"about_ca_topic_score_codex":0.00019400995,"about_ca_topic_score_gemma":0.0003822095,"teacher_disagreement_score":0.21801458,"about_ca_system_score_codex":0.00021846658,"about_ca_system_score_gemma":0.0017318445,"threshold_uncertainty_score":0.5121029},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58081806,0.0021944093,0.08973995,0.31567332,0.0033914952,0.0013142647,0.0023331095,0.0002821585,0.004253209],"genre_scores_gemma":[0.81188303,0.006512554,0.15700817,0.021207731,0.0018354283,0.000008865176,0.0010758914,0.00008119434,0.00038713004],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99342084,0.00042170144,0.0034802426,0.00015984374,0.0013917246,0.001125656],"domain_scores_gemma":[0.9887321,0.00025038517,0.0066938363,0.0011803702,0.0015474005,0.0015958945],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010072369,0.0003039178,0.0012163918,0.0003430453,0.0008302307,0.00041997945,0.00074238674,0.00012424384,0.00008810374],"category_scores_gemma":[0.010634704,0.0002535321,0.00025237785,0.0003064982,0.00021060235,0.0034686357,0.00019154967,0.00097414455,0.00003828627],"study_design_candidate":"design_other","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":[0.00007744704,0.0008874353,0.07594817,0.0032061501,0.00029503403,0.000022733728,0.008043053,0.000032723845,0.000006032893,0.00047139495,0.05088242,0.8601274],"study_design_scores_gemma":[0.0033348156,0.0011773409,0.20386888,0.00043133588,0.000009214166,0.00054799876,0.0075875064,0.0038876375,0.0000016064923,0.00008351223,0.7788119,0.0002582646],"about_ca_topic_score_codex":0.00013478419,"about_ca_topic_score_gemma":0.00022342807,"teacher_disagreement_score":0.8598691,"about_ca_system_score_codex":0.000985019,"about_ca_system_score_gemma":0.008186555,"threshold_uncertainty_score":0.9999917},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8256959,0.0106281955,0.007036324,0.12776892,0.0013906191,0.012093528,0.0031251593,0.0007479102,0.0115135],"genre_scores_gemma":[0.99281377,0.0004894225,0.00044664418,0.0021998899,0.0003133367,0.0011202939,0.00031682604,0.00006735221,0.0022324808],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99652827,0.00027785002,0.0008811891,0.00076870556,0.00050087704,0.0010431225],"domain_scores_gemma":[0.9948522,0.0008167936,0.001010223,0.002062213,0.00034786062,0.000910754],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030424744,0.00036315052,0.0009539358,0.00016845854,0.0011723466,0.00013998969,0.0005871567,0.000090154586,0.00007694406],"category_scores_gemma":[0.0009657714,0.0002880236,0.00017679595,0.00031602528,0.0005363985,0.00024418483,0.00023236335,0.0002629971,0.000039183516],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040225565,0.0001244617,0.9359146,0.0010786818,0.000066079374,0.0000026917687,0.0001763564,0.000004956439,0.00010448964,0.0004738034,0.04240279,0.019248888],"study_design_scores_gemma":[0.0013493919,0.00009072636,0.5667058,0.000044128028,0.0000020113637,0.0000058223804,0.000022731527,0.0017709,0.000011544164,0.00003225172,0.42978027,0.00018441692],"about_ca_topic_score_codex":0.00025989942,"about_ca_topic_score_gemma":0.01342517,"teacher_disagreement_score":0.38737747,"about_ca_system_score_codex":0.0002577201,"about_ca_system_score_gemma":0.00061291846,"threshold_uncertainty_score":0.9999572},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98323673,0.0016146777,0.010334205,0.0015491464,0.00060191646,0.0018888689,0.0006541664,0.0000288138,0.00009148959],"genre_scores_gemma":[0.9977521,0.00036644223,0.00058419426,0.00008856411,0.0010029298,0.00006564232,0.00008647318,0.000029045807,0.000024642515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973309,0.00020634913,0.0007970963,0.0004300683,0.0008047474,0.000430857],"domain_scores_gemma":[0.9961933,0.00017568003,0.0009513864,0.0011633759,0.00085186324,0.000664427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024445697,0.00020014464,0.00075022224,0.00045021286,0.00044141445,0.0005857945,0.0012265866,0.00014178244,0.000018784633],"category_scores_gemma":[0.0016341031,0.000156491,0.00019135964,0.00021608682,0.0001787767,0.00071821397,0.0003647595,0.00038324305,8.322309e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002991358,0.00022438698,0.9728548,0.0008438229,0.0009904434,0.00007813745,0.000044376226,0.00004939629,0.006448914,0.0004341864,0.0018095033,0.013230678],"study_design_scores_gemma":[0.007245701,0.0013945743,0.96957463,0.0019565446,0.0003718067,0.00004858268,0.00004311648,0.00066246913,0.00005512291,0.00063538324,0.017750928,0.00026114605],"about_ca_topic_score_codex":0.0016651877,"about_ca_topic_score_gemma":0.0015475597,"teacher_disagreement_score":0.015941424,"about_ca_system_score_codex":0.000045097295,"about_ca_system_score_gemma":0.00022140001,"threshold_uncertainty_score":0.6381518},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57190907,0.024203492,0.0017401903,0.40042183,0.00032572812,0.00034829826,0.00042299816,0.000040835293,0.0005875179],"genre_scores_gemma":[0.945229,0.013147759,0.004079991,0.03685893,0.00022108863,0.0000026382359,0.0004277414,0.000015088356,0.000017784483],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99325544,0.0035224475,0.0013331939,0.0008447552,0.00007725723,0.00096690963],"domain_scores_gemma":[0.98381096,0.011988994,0.0012092562,0.0022561136,0.00015066241,0.0005840366],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.037636686,0.00017534713,0.0017163475,0.0001619881,0.0001933804,0.0000058871015,0.00050499476,0.00026845027,0.000009809687],"category_scores_gemma":[0.2107376,0.00015275813,0.000046828933,0.000058341368,0.0014092935,0.00014175437,0.0011474539,0.0006308682,0.0000050895615],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052830976,0.000053966072,0.8769506,0.0002302876,0.000053671105,0.000010754701,0.00009498774,0.0000013664367,0.0000055207834,0.00404952,0.011039372,0.107457094],"study_design_scores_gemma":[0.0006218879,0.00019560085,0.94637704,0.00016667445,0.000006851332,0.000048084075,0.000051814994,0.0024998786,0.0000011458861,0.005279475,0.04464921,0.000102365324],"about_ca_topic_score_codex":0.0028553999,"about_ca_topic_score_gemma":0.0011749647,"teacher_disagreement_score":0.3733199,"about_ca_system_score_codex":0.0000114259365,"about_ca_system_score_gemma":0.00029588368,"threshold_uncertainty_score":0.99095553},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9460328,0.00035726078,0.011951889,0.040185675,0.000104862156,0.0007446105,0.0005306091,0.000019644871,0.000072646515],"genre_scores_gemma":[0.9947421,0.00023087014,0.0028554671,0.0019415873,0.00012907588,0.000011973566,0.00005178016,0.000024127163,0.000013000232],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767464,0.00017758204,0.0010981445,0.00020425922,0.00037972513,0.0004656686],"domain_scores_gemma":[0.9946138,0.00018444002,0.0034244983,0.00070998556,0.00037704987,0.00069018756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002495851,0.00013504735,0.00095247955,0.0002715967,0.00011099659,0.00008725274,0.00033400947,0.000023402916,0.00000415659],"category_scores_gemma":[0.0024825283,0.0001181499,0.00013962339,0.00026093144,0.00031074288,0.0007113428,0.000051349107,0.00021691251,0.0000019350207],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017724007,0.00022975438,0.5959931,0.00013168518,0.000035337096,0.0000026974478,0.0002348703,0.0000065612403,0.000010626805,0.00011916426,0.0010607275,0.4019982],"study_design_scores_gemma":[0.001409778,0.001530464,0.9410581,0.00011843601,0.0000025577942,0.000049093982,0.00014954284,0.00019026532,0.0000018291861,0.00005590171,0.05535263,0.000081384496],"about_ca_topic_score_codex":0.0012573667,"about_ca_topic_score_gemma":0.00024064266,"teacher_disagreement_score":0.40191683,"about_ca_system_score_codex":0.00032048236,"about_ca_system_score_gemma":0.0029329509,"threshold_uncertainty_score":0.5202929},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899405,0.0062448406,0.00000837728,0.0018276173,0.0006936013,0.00019641689,0.0010296793,0.0000037233685,0.00005521082],"genre_scores_gemma":[0.9981744,0.00038127427,0.0000067238657,0.0010107448,0.00021986736,0.0000053850677,0.000056335677,0.000013162543,0.00013205723],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985482,0.00020108509,0.000539563,0.00019092792,0.00016684932,0.00035337845],"domain_scores_gemma":[0.997216,0.00018469185,0.00043973548,0.0003427021,0.00032954855,0.0014873523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055846636,0.00010667736,0.00050852756,0.00006582704,0.00024371994,0.00007775336,0.0003170628,0.000075834025,0.0006656914],"category_scores_gemma":[0.004783612,0.00009865801,0.00010541287,0.00007669493,0.0007039172,0.00007688005,0.000055466997,0.00026891634,0.000001175823],"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":[0.000057452,0.00005115879,0.96357393,0.00009725047,0.00007252013,0.0011856036,0.000010936019,0.0000050731246,0.00007196318,0.0000013599823,0.008903178,0.025969554],"study_design_scores_gemma":[0.0017987012,0.00018670573,0.9895926,0.00043816242,0.0000726387,0.0010622769,0.000029088105,0.0000727168,7.1106206e-7,0.000041286512,0.0066021546,0.00010294572],"about_ca_topic_score_codex":0.90361106,"about_ca_topic_score_gemma":0.9978345,"teacher_disagreement_score":0.094223425,"about_ca_system_score_codex":0.00018218765,"about_ca_system_score_gemma":0.009224538,"threshold_uncertainty_score":0.99639225},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9727111,0.00084625935,0.00003354548,0.022244671,0.00065628614,0.00013095996,0.000040226376,0.000016301967,0.003320672],"genre_scores_gemma":[0.9927292,0.000037233665,0.0025386391,0.0039292383,0.0007076735,0.0000019644049,9.2871505e-7,0.000013532175,0.000041595733],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952439,0.0009173417,0.000925223,0.0005729416,0.00095091463,0.0013896634],"domain_scores_gemma":[0.995761,0.00041643117,0.0012795583,0.00041665856,0.00042496578,0.0017013951],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.006021507,0.0003270816,0.00058777985,0.0008915928,0.005741296,0.0014497951,0.002794077,0.0001536286,0.00022476962],"category_scores_gemma":[0.006019065,0.00020272995,0.00027614943,0.0011603537,0.0066432874,0.0014262545,0.00014922602,0.0015963021,0.000003385311],"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":[0.000090776484,0.000023320248,0.96814156,0.000013820111,0.0000063444595,0.014152892,0.00012752858,0.0015313243,0.00005933709,0.00041149167,0.000117935364,0.015323667],"study_design_scores_gemma":[0.00050890434,0.007988212,0.92031443,0.000037638558,0.00007950485,0.046746965,0.00012979051,0.020973576,0.000019342411,0.001933081,0.0010051165,0.0002634156],"about_ca_topic_score_codex":0.0025309238,"about_ca_topic_score_gemma":0.033611413,"teacher_disagreement_score":0.04782711,"about_ca_system_score_codex":0.0003226098,"about_ca_system_score_gemma":0.004157597,"threshold_uncertainty_score":0.9995868},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98910457,0.0013829421,0.00065225974,0.0024057853,0.00043587014,0.0011553954,0.000045718065,0.00031431313,0.0045031216],"genre_scores_gemma":[0.9926685,0.00096742244,0.0030739286,0.00005711812,0.0012006824,0.000019091705,0.00006114873,0.00008814743,0.0018639443],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9944658,0.000921757,0.00070838566,0.0007158879,0.0022475133,0.0009406455],"domain_scores_gemma":[0.9952061,0.0016770642,0.00010688147,0.00073276454,0.0007085575,0.0015685906],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004062804,0.0002756626,0.0005084985,0.00030800543,0.0005624833,0.00006880163,0.00043574898,0.00010520774,0.00041735097],"category_scores_gemma":[0.02418532,0.00022149633,0.00015268427,0.0003795693,0.0005611443,0.0001797927,0.0007922715,0.0011294414,0.00023927007],"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":[0.0016821629,0.001074213,0.4514194,0.0009006878,0.0002080012,0.008808969,0.0100266235,0.000007770856,0.0035414542,0.00024480734,0.027491614,0.49459428],"study_design_scores_gemma":[0.0035378207,0.00052149256,0.8132283,0.0029605438,0.00004277689,0.00049121305,0.0016577691,0.002040554,0.006231252,0.00009647175,0.16859384,0.00059793115],"about_ca_topic_score_codex":0.000050013743,"about_ca_topic_score_gemma":0.0000063183975,"teacher_disagreement_score":0.49399635,"about_ca_system_score_codex":0.0001376588,"about_ca_system_score_gemma":0.0004691924,"threshold_uncertainty_score":0.98403436},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97395194,0.00012511868,0.0012807435,0.002447029,0.0005090472,0.001361827,0.005033834,0.00022391177,0.015066532],"genre_scores_gemma":[0.9955872,0.00001618732,0.0010904043,0.0003837994,0.0007032587,0.000024494144,0.0021028346,0.000012714163,0.00007905813],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988319,0.000045637124,0.00018064858,0.00043936216,0.00025669258,0.00024570397],"domain_scores_gemma":[0.99823594,0.000029787063,0.00006826492,0.0014449139,0.000049722945,0.00017137507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000367109,0.00010562071,0.00024967905,0.00009888744,0.00010838169,0.000030269413,0.00048602256,0.000065699925,0.000101198326],"category_scores_gemma":[0.0012148559,0.00009260251,0.000029697841,0.00016795484,0.000052453837,0.00024375819,0.00053016277,0.00013999308,0.000065473185],"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":[0.0007009482,0.0019240817,0.19977646,0.00019484707,0.000047834463,0.000054595723,0.0007873822,8.70288e-8,0.00025269695,0.00005433224,0.008196524,0.78801024],"study_design_scores_gemma":[0.0010247488,0.000015007418,0.98155874,0.000012419551,0.000019921148,0.000003603387,0.00013036423,0.0031254,0.00003615491,0.000014900727,0.01396581,0.00009294842],"about_ca_topic_score_codex":0.0004975153,"about_ca_topic_score_gemma":0.0070497124,"teacher_disagreement_score":0.78791726,"about_ca_system_score_codex":0.000061725295,"about_ca_system_score_gemma":0.00004827549,"threshold_uncertainty_score":0.39339048},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95107377,0.00079346146,0.04709696,0.00008212168,0.00008942968,0.0005754514,0.00022010745,0.000019416511,0.000049287144],"genre_scores_gemma":[0.96430224,0.000055997054,0.034973767,0.00055425684,0.000025606943,0.000013200811,0.00006167295,0.000010182896,0.0000030662472],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963965,0.000034615932,0.0022158194,0.00020006244,0.0009261328,0.00022691274],"domain_scores_gemma":[0.9953229,0.00036241248,0.0025738247,0.00068572024,0.00035498085,0.00070017995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025625732,0.00014550671,0.00081476424,0.000097795826,0.00019847298,0.00006600128,0.00009434261,0.0000625144,0.000017618455],"category_scores_gemma":[0.0073228604,0.000111520196,0.0000370651,0.0000916237,0.00016967903,0.0001607025,0.0003912877,0.00010772182,5.985687e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096380175,0.00023349535,0.95367867,0.0034614583,0.00003373195,0.00009261495,0.00036217732,0.00002663138,0.000008962167,0.00009933526,0.002147878,0.03889126],"study_design_scores_gemma":[0.002129154,0.0002513306,0.73966336,0.0042726225,0.000072725365,0.0000663044,0.0005694509,0.24782978,0.000023732051,0.00023251136,0.00468507,0.0002039462],"about_ca_topic_score_codex":0.000021574338,"about_ca_topic_score_gemma":0.00006300345,"teacher_disagreement_score":0.24780314,"about_ca_system_score_codex":0.000022489954,"about_ca_system_score_gemma":0.00067319255,"threshold_uncertainty_score":0.87666774},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014230899,0.9852599,0.0061793835,0.002013436,0.0007447772,0.005187915,0.00028651,0.000018608273,0.0002952592],"genre_scores_gemma":[0.0000612348,0.99412125,0.0030679596,0.0020595202,0.00009985972,0.00005301415,0.0005033588,0.000024332283,0.000009493022],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.96419644,0.0012074494,0.0311545,0.000090444984,0.0025792406,0.00077190896],"domain_scores_gemma":[0.9601381,0.0012061342,0.034635425,0.0010987937,0.002672777,0.0002487805],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.046176977,0.00038950896,0.0037160187,0.0052330443,0.00024872564,0.0002258654,0.0007430138,0.00031382265,0.0000074778573],"category_scores_gemma":[0.015176999,0.000258228,0.00026951643,0.00584863,0.00027650202,0.0027315351,0.00018876814,0.003048225,0.000012832025],"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":[0.00006319744,0.00009506506,0.00049218244,0.678459,0.00012017253,0.000004940074,0.0020163346,0.0002816737,1.5019268e-9,0.0067186872,0.024145706,0.28760305],"study_design_scores_gemma":[0.00060560124,0.00027752586,0.000090969545,0.28188366,0.000032174245,0.00021260358,0.00087609695,0.0011154424,3.6668606e-9,0.00003793506,0.7147491,0.00011888671],"about_ca_topic_score_codex":0.00014436436,"about_ca_topic_score_gemma":0.000022185264,"teacher_disagreement_score":0.6906034,"about_ca_system_score_codex":0.0025285208,"about_ca_system_score_gemma":0.009228653,"threshold_uncertainty_score":0.999987},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9987701,0.0005254456,0.000019905632,0.00005261807,0.00004096796,0.00022725944,0.00014002026,0.000009879408,0.00021380397],"genre_scores_gemma":[0.9992173,0.00031274286,0.00027192693,0.000012219274,0.00014146969,0.00000897082,0.000020280162,0.000011238541,0.0000038886133],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99900866,0.00007462913,0.00022881699,0.00017596823,0.00036002594,0.00015188903],"domain_scores_gemma":[0.99894375,0.00008005526,0.00020169544,0.0006500599,0.00006634261,0.0000580986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030842825,0.00009840843,0.00045216348,0.000035801968,0.000055576722,0.000019204068,0.0001628069,0.00002363752,0.000032750388],"category_scores_gemma":[0.00032505582,0.00007827654,0.00005702102,0.000024652218,0.000059919148,0.00025803834,0.000097084114,0.00007578962,4.5339806e-7],"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":[0.00012144262,0.0002639564,0.9368446,0.00027412485,0.00017759435,0.00001757501,0.0008182008,0.0000014825723,0.06043152,0.000003587577,0.0000018338659,0.0010440773],"study_design_scores_gemma":[0.00045649422,0.000044099885,0.9646154,0.00075611996,0.00024115069,6.274817e-7,0.00047954678,0.00023643278,0.033091392,0.0000038956896,0.0000044939343,0.00007034781],"about_ca_topic_score_codex":0.058884162,"about_ca_topic_score_gemma":0.005666486,"teacher_disagreement_score":0.053217676,"about_ca_system_score_codex":0.000037133803,"about_ca_system_score_gemma":0.000032295542,"threshold_uncertainty_score":0.9473828},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84608454,0.000033877517,0.14528053,0.0016950516,0.00055195676,0.0030500062,0.00027762356,0.00033312684,0.0026932945],"genre_scores_gemma":[0.91153777,0.0000590004,0.0839834,0.0014049006,0.00073801185,0.00058143644,0.0015343006,0.00005656258,0.0001046055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970339,0.0000553274,0.0010917957,0.00017642532,0.0011757073,0.00046683347],"domain_scores_gemma":[0.9966612,0.00021941736,0.00073492376,0.001384148,0.000289876,0.0007104293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015880822,0.00021013091,0.00059729593,0.00012531066,0.00016449418,0.00005381706,0.0009878102,0.00042147332,0.0003187272],"category_scores_gemma":[0.0027470984,0.00017732379,0.0001498473,0.00009964934,0.00032569782,0.00047596716,0.00030910198,0.000420606,0.000009663476],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014246447,0.0020101303,0.0060048723,0.004709542,0.0005233117,0.00006771872,0.002219494,0.0000019319245,0.0020107292,0.0008894937,0.037455175,0.942683],"study_design_scores_gemma":[0.032955058,0.009892792,0.0501287,0.0053395354,0.0005028307,0.0009995218,0.0012991136,0.7968202,0.029368328,0.001337619,0.06963885,0.001717407],"about_ca_topic_score_codex":0.00002484209,"about_ca_topic_score_gemma":0.0000410714,"teacher_disagreement_score":0.94096553,"about_ca_system_score_codex":0.00010228465,"about_ca_system_score_gemma":0.0011226891,"threshold_uncertainty_score":0.72310543},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9741675,0.00027000398,0.0000016721601,0.0003777065,0.0076891114,0.0001653766,0.00010009642,0.000076850665,0.017151639],"genre_scores_gemma":[0.9808083,0.000005368632,0.00016761366,0.00002701947,0.000112098154,0.0000073964893,0.00030897674,0.000019766585,0.01854343],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979319,0.000029533008,0.00046441308,0.00068495143,0.0005409895,0.0003481868],"domain_scores_gemma":[0.99546176,0.000023949726,0.0006881855,0.0031824328,0.00041840656,0.00022525525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011504629,0.00014660813,0.00041137915,0.00014525515,0.0002933081,0.00015479118,0.00024851403,0.000056232835,0.00031540135],"category_scores_gemma":[0.0011910236,0.00012530577,0.00012789386,0.0002551632,0.0006081131,0.00013838774,0.00016660722,0.00009503055,0.000026442302],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029362287,0.00012459392,0.8602166,0.00010908715,0.00006166544,0.00034423894,0.000062482504,0.0000020847654,0.07010595,0.000017876715,0.046540767,0.0223853],"study_design_scores_gemma":[0.00042028222,0.00011283008,0.8638389,0.00009098654,0.000041252624,0.000043427153,0.00001691044,0.00014369549,0.034469124,0.00048511857,0.10010718,0.00023028144],"about_ca_topic_score_codex":0.0001026073,"about_ca_topic_score_gemma":0.00018345186,"teacher_disagreement_score":0.053566415,"about_ca_system_score_codex":0.00004177156,"about_ca_system_score_gemma":0.00026249545,"threshold_uncertainty_score":0.5109821},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087990647,0.40928686,0.0038725405,0.013871343,0.0057537653,0.030844765,0.060691744,0.00085966435,0.47393942],"genre_scores_gemma":[0.27732152,0.32056752,0.003290716,0.0029126771,0.000910939,0.0024015948,0.040925622,0.00016873245,0.35150066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981231,0.000021565154,0.00092236063,0.0003571838,0.00033594514,0.00023983947],"domain_scores_gemma":[0.99800354,0.00008007499,0.000759021,0.0005915369,0.0002593319,0.00030649945],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031589947,0.00032375232,0.00057373504,0.00017205799,0.00026890772,0.00018229643,0.00008620909,0.00010086802,0.000010032041],"category_scores_gemma":[0.00008790977,0.00031378315,0.000052011157,0.000016039374,0.0001834505,0.0034222112,0.000067941924,0.00014006344,0.000009255723],"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":[0.00265107,0.000035883946,0.022062987,0.039510973,0.000089722555,0.000079103826,0.00013990805,0.000036574194,2.8132894e-7,0.8948351,0.0025734655,0.037984896],"study_design_scores_gemma":[0.0007449571,0.00022445347,0.0062473016,0.0017211685,0.000025722928,0.00004043098,0.000117376425,0.00011721746,0.0000010413959,0.0005392679,0.9899272,0.00029386097],"about_ca_topic_score_codex":0.00003470538,"about_ca_topic_score_gemma":0.00035888457,"teacher_disagreement_score":0.98735374,"about_ca_system_score_codex":0.000089733556,"about_ca_system_score_gemma":0.0002594881,"threshold_uncertainty_score":0.99993145},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9868657,0.000010966344,0.00017331573,0.0048139994,0.0006222057,0.00034329708,0.00015566347,0.00000643859,0.0070084366],"genre_scores_gemma":[0.9983858,0.0000015141189,0.0007996907,0.00031255296,0.000051625466,0.000010847713,0.000016081252,0.0000045276092,0.00041737308],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883246,0.000011774203,0.00020624933,0.00022450116,0.0005184488,0.00020654543],"domain_scores_gemma":[0.99915606,0.00007292953,0.000117510186,0.00036044884,0.00023412253,0.000058919133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074720546,0.00006127413,0.00013969056,0.00019823517,0.0001198787,0.000057175228,0.0006293444,0.000020895652,0.00004844505],"category_scores_gemma":[0.0011640636,0.00004640163,0.000060244613,0.0001317166,0.0009721185,0.00020310757,0.00016639345,0.00009358308,0.000003785111],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009007577,0.000057202866,0.9706481,0.000023603612,0.000010932267,0.000015812891,0.0012055044,0.000015047626,0.011064029,0.0023820712,0.0013262517,0.013161359],"study_design_scores_gemma":[0.0011112813,0.000028665663,0.98325324,0.00017898504,0.000002506569,0.000016027489,0.00020922336,0.004255628,0.007000388,0.0002664722,0.0036245969,0.0000530013],"about_ca_topic_score_codex":0.00021180137,"about_ca_topic_score_gemma":0.00027328476,"teacher_disagreement_score":0.013108358,"about_ca_system_score_codex":0.00013359805,"about_ca_system_score_gemma":0.0004099164,"threshold_uncertainty_score":0.35818112},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033878163,0.9849637,0.00050281675,0.008680249,0.001330272,0.000931145,0.00012598152,0.000015702306,0.00006227324],"genre_scores_gemma":[0.013387564,0.9776333,0.004934687,0.00030975632,0.0036227684,0.00000497403,0.00003233735,0.0000673749,0.0000072177704],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99547803,0.0010078254,0.0022744562,0.00035168222,0.00047026615,0.00041771558],"domain_scores_gemma":[0.9922005,0.0025361895,0.0041750567,0.0002917434,0.00029289396,0.00050362543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050739404,0.00029524986,0.003768671,0.00017092904,0.0004503256,0.00012328086,0.00021756315,0.00022467485,0.000004542651],"category_scores_gemma":[0.0034883542,0.00018042001,0.00038920672,0.00013070872,0.0016694472,0.00014147267,0.000031050706,0.0008896362,1.9471499e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002487397,0.00013253346,0.018565437,0.0028558576,0.00036491817,0.000003815754,0.00021171676,4.1425345e-8,1.225666e-8,0.00007312185,0.0010340996,0.9765097],"study_design_scores_gemma":[0.0051879343,0.0048417887,0.15860827,0.119274564,0.0013871903,0.0033690915,0.0018882846,0.000022853917,4.9623928e-8,0.00017071312,0.7047339,0.0005153228],"about_ca_topic_score_codex":0.0000084819085,"about_ca_topic_score_gemma":0.000004304204,"teacher_disagreement_score":0.9759944,"about_ca_system_score_codex":0.00013430625,"about_ca_system_score_gemma":0.0020525306,"threshold_uncertainty_score":0.7357315},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956613,0.00026261676,0.0003225426,0.0014553044,0.00011859495,0.0015302497,0.00023984251,0.00005894378,0.00035061437],"genre_scores_gemma":[0.99897957,0.000109096974,0.00011400601,0.00016989035,0.00007461286,0.0003703627,0.00006201975,0.000025561982,0.00009489591],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972671,0.0006752843,0.00039010087,0.00051848456,0.0003077885,0.0008412051],"domain_scores_gemma":[0.9972049,0.0007074866,0.000334116,0.0008186334,0.00021403629,0.0007208576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005573835,0.00021210422,0.00090129254,0.00018685579,0.00021773207,0.00010970641,0.00026009494,0.00011043478,0.000011674651],"category_scores_gemma":[0.0031988982,0.00019690205,0.00007288062,0.00016117438,0.0003242832,0.0004175145,0.00021152293,0.0002216025,0.0000013093075],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011011385,0.00022816447,0.9465149,0.0018459635,0.00002612812,0.0000091483835,0.00072040077,6.377871e-7,0.00013197299,0.000046231864,0.00018525127,0.049190048],"study_design_scores_gemma":[0.0022391365,0.00035422167,0.99311584,0.0001508267,5.076968e-7,0.000007669997,0.00014508615,0.0013515793,0.000059249433,0.000017972014,0.00239791,0.00016001433],"about_ca_topic_score_codex":0.00481564,"about_ca_topic_score_gemma":0.0026635295,"teacher_disagreement_score":0.049030032,"about_ca_system_score_codex":0.00028490653,"about_ca_system_score_gemma":0.0011681317,"threshold_uncertainty_score":0.8029433},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97188056,0.002388409,0.0004224582,0.01804131,0.00079363526,0.0017699689,0.00072713377,0.00081596297,0.0031605617],"genre_scores_gemma":[0.9906162,0.00007061198,0.00050228025,0.0072031934,0.0005966657,0.00035971784,0.0004507437,0.0000901067,0.000110482084],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947641,0.00075289625,0.0010067633,0.0012776398,0.00087135297,0.0013272787],"domain_scores_gemma":[0.99286157,0.00042926127,0.0006234311,0.002493867,0.00040733544,0.0031845449],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001607795,0.0005375505,0.001404255,0.00026852766,0.0011242734,0.00050218235,0.0006820916,0.00022330745,0.000038308357],"category_scores_gemma":[0.0016336056,0.00049263204,0.00017451395,0.0003423344,0.00026150528,0.0005823093,0.00026746618,0.00046167514,0.00019260355],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004087655,0.00020909446,0.97005033,0.00095792115,0.00010009741,0.00016836193,0.00038515436,0.000004136197,0.00029562242,0.00070184283,0.011184454,0.01553424],"study_design_scores_gemma":[0.00282837,0.0002525866,0.7919391,0.00024114267,0.000002511861,0.000016338117,0.00022645654,0.0029757256,0.000010497776,0.000014167363,0.20102367,0.0004694426],"about_ca_topic_score_codex":0.0005607249,"about_ca_topic_score_gemma":0.0010851151,"teacher_disagreement_score":0.18983921,"about_ca_system_score_codex":0.00039994912,"about_ca_system_score_gemma":0.002229125,"threshold_uncertainty_score":0.9997525},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000039834954,0.8657346,0.034793634,0.09089292,0.00013889192,0.006802674,0.0014002527,0.00011851158,0.00011455801],"genre_scores_gemma":[0.000013142102,0.9108939,0.07748071,0.009259717,0.00010081962,0.00017151456,0.0020310846,0.000041833566,0.0000072764765],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9818289,0.00092092383,0.014272564,0.00016028505,0.0015067161,0.0013106035],"domain_scores_gemma":[0.96443146,0.0005482804,0.027452078,0.003710485,0.0023063244,0.0015513502],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.020046348,0.000644522,0.0047642854,0.0022975472,0.0010992636,0.0005418989,0.002994045,0.00030033948,0.000011177071],"category_scores_gemma":[0.0077838264,0.00044813947,0.00050886057,0.0020311845,0.0005594639,0.0034532854,0.0014202361,0.0018980305,0.000015880792],"study_design_candidate":"not_applicable","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":[0.0000012823666,0.0001461052,0.000031357995,0.22260083,0.0001450165,1.8263938e-7,0.0014163341,3.9401402e-7,2.1727147e-9,0.00034361507,0.0028313727,0.7724835],"study_design_scores_gemma":[0.00044044384,0.000316306,0.000028899012,0.16126451,0.00011232065,0.00058464677,0.002194134,0.00031673402,5.915502e-9,0.000010185676,0.8344929,0.0002389157],"about_ca_topic_score_codex":0.000043341195,"about_ca_topic_score_gemma":0.000078723446,"teacher_disagreement_score":0.8316615,"about_ca_system_score_codex":0.0015269669,"about_ca_system_score_gemma":0.03984124,"threshold_uncertainty_score":0.99979705},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024896426,0.0013716437,0.95200825,0.018851712,0.0005228619,0.0010445508,0.0010892813,0.000044258428,0.00017104308],"genre_scores_gemma":[0.6396449,0.0026780567,0.34958774,0.0021071471,0.0009279858,0.00011630979,0.0043491567,0.000042239728,0.0005464264],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9980268,0.00023925108,0.00084783364,0.00046223457,0.00016486413,0.0002590363],"domain_scores_gemma":[0.9948427,0.0023209322,0.0013967803,0.00064274593,0.0006082233,0.00018858304],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0035470577,0.00015842252,0.00056892174,0.00013774885,0.00029265127,0.000074534044,0.0011838115,0.00012782586,0.000026026157],"category_scores_gemma":[0.036216013,0.00012972485,0.000066941124,0.000034428544,0.00032946854,0.000649189,0.0013562824,0.00019351114,0.0000040241034],"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":[0.0010023346,0.00021377482,0.44283348,0.000112881185,0.00055435376,0.000029989122,0.000030879757,0.00003602532,0.00033706945,0.008355702,0.044971395,0.5015221],"study_design_scores_gemma":[0.0019910664,0.00020117057,0.9102842,0.00033719014,0.00021849976,0.00052438653,0.00004265858,0.010235606,0.000013366489,0.008767622,0.06720975,0.00017444996],"about_ca_topic_score_codex":0.000026477614,"about_ca_topic_score_gemma":0.000011746877,"teacher_disagreement_score":0.6147485,"about_ca_system_score_codex":0.00007665238,"about_ca_system_score_gemma":0.000049675484,"threshold_uncertainty_score":0.9719024},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001969681,0.012279809,0.9664426,0.00499558,0.0017206831,0.0034164216,0.004729102,0.000251098,0.005967735],"genre_scores_gemma":[0.9420934,0.0005716192,0.007826074,0.002234608,0.008011607,0.000057623307,0.038444396,0.0003346821,0.0004260384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956644,0.00020823386,0.0011244396,0.0013469146,0.0005420003,0.0011139946],"domain_scores_gemma":[0.9957347,0.00089552446,0.0014694844,0.0014138778,0.00018605484,0.00030035753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019319786,0.00074738415,0.0018064355,0.0003039476,0.0006629112,0.00020672395,0.000999157,0.001179932,0.000065039174],"category_scores_gemma":[0.0006891676,0.0008116439,0.00026543855,0.00015044956,0.0004794701,0.00025541466,0.0004196721,0.0019299784,0.0000018822892],"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":[0.0018540915,0.0005947354,0.10586249,0.002053415,0.0015094725,0.0007836431,0.0008887507,0.18510227,0.000005683458,0.0113360295,0.008787457,0.68122196],"study_design_scores_gemma":[0.0028337473,0.00013143019,0.018957192,0.001731762,0.00007567536,0.000044253567,0.000007639731,0.93793195,5.243382e-8,0.019914953,0.017165562,0.0012057753],"about_ca_topic_score_codex":0.00016057905,"about_ca_topic_score_gemma":0.0152958175,"teacher_disagreement_score":0.95861655,"about_ca_system_score_codex":0.0009592962,"about_ca_system_score_gemma":0.0012818688,"threshold_uncertainty_score":0.99943346},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000087522,0.12834886,0.39427438,0.09145251,0.0046689846,0.0065188133,0.00044784395,0.00060797605,0.3735931],"genre_scores_gemma":[0.8713404,0.009331129,0.00060804194,0.027928732,0.015817935,0.00015442164,0.0028379292,0.00030408788,0.07167734],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99781626,0.0002849993,0.0004672246,0.00053351186,0.00033585922,0.0005621208],"domain_scores_gemma":[0.99784446,0.0006983397,0.0005087135,0.0006602058,0.000100234785,0.0001880725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010786658,0.0003877601,0.0010858866,0.000091357,0.00026527562,0.00015951548,0.00051384635,0.00061231066,0.00011500095],"category_scores_gemma":[0.00094180694,0.00027563513,0.00020513462,0.00010109578,0.0012067159,0.000055485736,0.00033898663,0.0018135429,0.000016992059],"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":[0.0004828581,0.00005727023,0.0018477351,0.0003045585,0.00030261275,0.00008622432,0.0022284158,0.00010494937,3.524605e-8,0.0077253496,0.10926519,0.87759477],"study_design_scores_gemma":[0.002905574,0.00020457075,0.006172712,0.0016208126,0.00016525206,0.00006610209,0.000016248443,0.008148405,5.5930116e-7,0.070053056,0.9097844,0.00086233224],"about_ca_topic_score_codex":0.00012087663,"about_ca_topic_score_gemma":0.0010976285,"teacher_disagreement_score":0.87673247,"about_ca_system_score_codex":0.00037210787,"about_ca_system_score_gemma":0.0008004399,"threshold_uncertainty_score":0.9999696},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99065006,0.0004165208,0.00042479203,0.00012706674,0.0003444249,0.00066150376,0.00016330926,0.0007577134,0.0064546266],"genre_scores_gemma":[0.9740848,0.00008017015,0.023427203,0.0013120167,0.0007124778,0.000025672789,0.0001419503,0.00010128016,0.00011440889],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973097,0.000014985496,0.0006542028,0.0009303867,0.0005061493,0.00058458315],"domain_scores_gemma":[0.99613655,0.00008082645,0.0008406231,0.0023099028,0.00036498724,0.00026713143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009916922,0.00032112555,0.0005988719,0.00007564397,0.0007476709,0.00022465945,0.0010965114,0.00018238054,0.000059267848],"category_scores_gemma":[0.0036827978,0.0003142894,0.000101095604,0.00017764949,0.00017206461,0.0018851685,0.0012172704,0.00042563005,0.00008657807],"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":[0.00033379192,0.00036751866,0.9334008,0.0009843395,0.001035351,0.00034835096,0.0018751087,0.00002701872,0.03214756,0.00012877601,0.014111189,0.015240195],"study_design_scores_gemma":[0.00999541,0.00044917385,0.17756788,0.002111204,0.0033012347,0.0024415378,0.0028604325,0.07854838,0.016042594,0.0015694153,0.7023508,0.002761972],"about_ca_topic_score_codex":0.00064649427,"about_ca_topic_score_gemma":0.000012964373,"teacher_disagreement_score":0.7558329,"about_ca_system_score_codex":0.0001675432,"about_ca_system_score_gemma":0.00021998455,"threshold_uncertainty_score":0.9999309},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9626829,0.009490767,0.000022780321,0.02485264,0.00036363205,0.0013033535,0.00017186257,0.00025215605,0.0008599269],"genre_scores_gemma":[0.9934529,0.0029276584,0.00026017628,0.0021812448,0.0002532235,0.00029405652,0.00012994035,0.0000622356,0.00043856128],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99447066,0.0011527769,0.001566519,0.0007744593,0.00056300167,0.00147256],"domain_scores_gemma":[0.9927623,0.00043554654,0.0014416621,0.0018052084,0.00019647028,0.0033588104],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.008972986,0.0004041261,0.0012823103,0.0006260966,0.0014267678,0.00041300806,0.00034112183,0.00008789444,0.00001567467],"category_scores_gemma":[0.005149818,0.00039834357,0.00007909363,0.0004332852,0.00031720375,0.00041958116,0.0004304283,0.00058233005,0.000007325164],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003103371,0.000121423545,0.9447587,0.0008037693,0.000022640746,0.00009753296,0.0027663002,0.0000012506669,0.000008154216,0.00014383567,0.0010195421,0.0499465],"study_design_scores_gemma":[0.0012927225,0.00021183606,0.96118355,0.00039690884,8.2328444e-7,0.000027540336,0.0012862834,0.00016977305,0.0000011966703,0.000040759813,0.03506078,0.00032782505],"about_ca_topic_score_codex":0.0012354068,"about_ca_topic_score_gemma":0.0017639154,"teacher_disagreement_score":0.049618676,"about_ca_system_score_codex":0.00035168946,"about_ca_system_score_gemma":0.0017758784,"threshold_uncertainty_score":0.9998732},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0120071815,0.0040496173,0.000121948986,0.0007586088,0.00010832221,0.001253916,0.004035733,0.000023815808,0.97764087],"genre_scores_gemma":[0.99416035,0.0006610005,0.000010799166,0.00008300092,0.000020821226,0.000044887696,0.0007289284,0.000011560974,0.004278638],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863994,0.00002954046,0.0007381194,0.0000905624,0.00037872122,0.00012310593],"domain_scores_gemma":[0.9972066,0.000114903625,0.0010054763,0.0009242551,0.00066807057,0.00008067207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033992057,0.00015900315,0.00053925795,0.00021814786,0.00007701192,0.000028496239,0.0002650735,0.00018019046,0.000010393276],"category_scores_gemma":[0.00003914356,0.000110482244,0.00004342214,0.000042643296,0.0001583257,0.0002790995,0.000096667325,0.00044060042,0.0000020009302],"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":[0.009266201,0.0005257622,0.39797384,0.017835895,0.016561253,0.000118904565,0.0330156,0.12035216,0.000012682586,0.16986121,0.13048366,0.10399284],"study_design_scores_gemma":[0.006258628,0.0013086754,0.08849524,0.023317516,0.0006884518,0.00015187085,0.025164202,0.49895716,0.000026015612,0.00017453486,0.35412765,0.0013300786],"about_ca_topic_score_codex":0.064206615,"about_ca_topic_score_gemma":0.09285305,"teacher_disagreement_score":0.9821532,"about_ca_system_score_codex":0.000069606634,"about_ca_system_score_gemma":0.00086524774,"threshold_uncertainty_score":0.9420249},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9765136,0.0035110398,0.00064814975,0.009703874,0.0003811032,0.0034157708,0.0054422105,0.00019228154,0.00019194033],"genre_scores_gemma":[0.9959161,0.00075544923,0.0002462776,0.0007690178,0.00033803348,0.0008676922,0.0009956352,0.000040518746,0.00007125132],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99713016,0.00035351256,0.00077814056,0.0006149021,0.00040601037,0.00071726396],"domain_scores_gemma":[0.99613273,0.00070022076,0.00089591666,0.0012853562,0.00037947128,0.00060628296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023575986,0.00026945266,0.00084965536,0.00006563767,0.0007123376,0.00014742573,0.00048211383,0.00011004802,0.000024965308],"category_scores_gemma":[0.0006003487,0.00020244176,0.00014569768,0.0001376563,0.00024137647,0.00021532943,0.00015163525,0.0001950416,0.000007643309],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012443143,0.00005637948,0.91801816,0.00061834545,0.00008001713,0.0000022168153,0.000497993,5.118004e-7,0.000081545855,0.0003660593,0.00532094,0.07483337],"study_design_scores_gemma":[0.0028233225,0.00020292446,0.92189914,0.00006760861,0.0000030757003,0.000016810927,0.0019462896,0.00027928778,0.000008852992,0.000031987307,0.072536185,0.00018453845],"about_ca_topic_score_codex":0.00279692,"about_ca_topic_score_gemma":0.0050629936,"teacher_disagreement_score":0.074648835,"about_ca_system_score_codex":0.00017237551,"about_ca_system_score_gemma":0.00096817344,"threshold_uncertainty_score":0.82553357},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881582,0.00008888906,0.0061355857,0.0043663676,0.00004381208,0.0009849296,0.00014997578,0.000025249134,0.000046963163],"genre_scores_gemma":[0.9991994,0.000121956815,0.00032974075,0.00006861374,0.00005393891,0.00014830306,0.000057179972,0.000015946107,0.000004957466],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99819434,0.00023330864,0.000620682,0.00031172176,0.00034008504,0.00029986707],"domain_scores_gemma":[0.9987144,0.00022830936,0.00027303703,0.00040023724,0.00020852825,0.00017549015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006009333,0.00014938487,0.00044330413,0.00036622368,0.00006897142,0.000013576153,0.00014418154,0.000028778313,0.000020060446],"category_scores_gemma":[0.00028777367,0.00008585523,0.00002409423,0.00030425962,0.00022190365,0.00027327545,0.00007636301,0.00015260967,8.11726e-7],"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":[0.0003657179,0.00103724,0.9358135,0.0001565501,0.00007154591,0.000013593123,0.017598197,0.00005487717,0.000041011655,0.0024086141,0.000014136766,0.04242502],"study_design_scores_gemma":[0.0026443019,0.0006419024,0.9903205,0.00037903263,0.00000938332,0.000004856416,0.0040817135,0.0015692386,0.000022521404,0.00015582568,0.000073420866,0.00009732824],"about_ca_topic_score_codex":0.0076848776,"about_ca_topic_score_gemma":0.04475243,"teacher_disagreement_score":0.05450698,"about_ca_system_score_codex":0.00016292676,"about_ca_system_score_gemma":0.0010224696,"threshold_uncertainty_score":0.99892306},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9696683,0.00065554294,0.0000873435,0.026590407,0.00017385487,0.00086408725,0.0006898937,0.00024160926,0.0010289476],"genre_scores_gemma":[0.9941056,0.001082697,0.00011412067,0.0013614898,0.0001414103,0.00013437157,0.0013462774,0.00003278001,0.0016812512],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976025,0.00029730884,0.00039976754,0.0005262203,0.0005140443,0.00066011416],"domain_scores_gemma":[0.99690807,0.00017861272,0.00034413542,0.00096804166,0.0003625513,0.0012385739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010818731,0.00023105105,0.0005558838,0.00011938946,0.00071437215,0.00015968326,0.00018391633,0.00010792267,0.00025610224],"category_scores_gemma":[0.0020449357,0.00019854492,0.000057345245,0.00013582737,0.0003344336,0.0004404461,0.00009401442,0.00033576414,0.00008475362],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022783747,0.00015027201,0.98005646,0.00017252672,0.000113722985,0.000050396477,0.00038093468,0.0000014677913,0.000017284552,0.000117985364,0.0076003796,0.011110708],"study_design_scores_gemma":[0.0017103157,0.00024965918,0.91046023,0.0001198382,0.000002548046,0.000022660437,0.00022447066,0.00018038791,0.0000015205512,0.000009118447,0.08683747,0.00018179555],"about_ca_topic_score_codex":0.003549175,"about_ca_topic_score_gemma":0.005494652,"teacher_disagreement_score":0.07923709,"about_ca_system_score_codex":0.00030211164,"about_ca_system_score_gemma":0.0016764549,"threshold_uncertainty_score":0.8096427},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98751616,0.0024204098,0.0017525366,0.00445825,0.000116527925,0.001063277,0.000118716285,0.00019773844,0.002356356],"genre_scores_gemma":[0.99683505,0.00017753715,0.00087151997,0.0015360061,0.00018232325,0.00020065013,0.000092356546,0.000061152736,0.000043397868],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966053,0.00025639788,0.0006609175,0.000891514,0.0005280255,0.001057866],"domain_scores_gemma":[0.9962837,0.00023708017,0.0006117893,0.0011236101,0.00027238022,0.0014714058],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00228709,0.00040357877,0.0009908477,0.00013598001,0.0009589681,0.00036857676,0.00030062278,0.00011143628,0.000008607462],"category_scores_gemma":[0.0011305641,0.00033362635,0.000059161637,0.00014621037,0.00051957817,0.0005278689,0.00027137098,0.000412788,0.000004840455],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003766768,0.00009765496,0.9558293,0.0004991297,0.000090297144,0.000031317184,0.0005658694,0.00005012569,0.0000023981008,0.0006251879,0.000115984,0.04171606],"study_design_scores_gemma":[0.0042782933,0.00052062026,0.8381362,0.00014513057,0.0000026465245,0.000103863844,0.00035427877,0.14900927,0.0000010328681,0.00017462147,0.006768976,0.0005050526],"about_ca_topic_score_codex":0.00033510517,"about_ca_topic_score_gemma":0.0013248966,"teacher_disagreement_score":0.14895916,"about_ca_system_score_codex":0.000094880335,"about_ca_system_score_gemma":0.00075353164,"threshold_uncertainty_score":0.9999116},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8697525,0.09590637,0.00041140532,0.011932546,0.0022306964,0.0006809922,0.00047966494,0.0005457068,0.018060077],"genre_scores_gemma":[0.93768543,0.038546316,0.0014894034,0.000857859,0.002078937,0.00013049578,0.00013376876,0.00025115805,0.018826637],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950029,0.00050076767,0.0008056237,0.0011256882,0.00063876243,0.0019262618],"domain_scores_gemma":[0.9938994,0.00062175014,0.0005872125,0.0028263691,0.00034948328,0.0017157594],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0013610182,0.0009269164,0.0011888511,0.0008132615,0.0023393428,0.000560601,0.0014481824,0.00085461134,0.00037479456],"category_scores_gemma":[0.0028168347,0.0009844395,0.0011698776,0.00084937835,0.045916643,0.00084347307,0.00029672735,0.0012994464,0.00041677814],"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":[0.00085402856,0.0010518238,0.60513896,0.001560873,0.0021113555,0.0044460963,0.0037693765,0.00004094323,0.0014335478,0.017489765,0.075376295,0.28672695],"study_design_scores_gemma":[0.002672818,0.00023675706,0.6441658,0.0010118991,0.00087788136,0.00043588833,0.000678701,0.000074220894,0.0002279039,0.0030648473,0.34567887,0.00087440066],"about_ca_topic_score_codex":0.06819256,"about_ca_topic_score_gemma":0.16919217,"teacher_disagreement_score":0.28585255,"about_ca_system_score_codex":0.0007097786,"about_ca_system_score_gemma":0.0011171203,"threshold_uncertainty_score":0.9992606},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9338772,0.0007402613,0.00023861845,0.049757574,0.00031079908,0.0005805164,0.0063863155,0.00008823241,0.008020465],"genre_scores_gemma":[0.9853558,0.00050518307,0.00031102507,0.0129785435,0.00016704321,0.000009542591,0.000455003,0.000010621611,0.00020723068],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99769855,0.000116227944,0.00059658784,0.0005225313,0.0005676762,0.0004984399],"domain_scores_gemma":[0.9971962,0.000058566333,0.0006273168,0.0010354478,0.00040917387,0.0006733108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082499103,0.00020394026,0.00066554826,0.00011022293,0.000489674,0.000094758274,0.00023485463,0.000104907944,0.00019998342],"category_scores_gemma":[0.00034259752,0.00019295615,0.00023872335,0.0003053848,0.00034360046,0.00018723204,0.000106495834,0.0001577851,0.000081043305],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045328846,0.00024167144,0.9549973,0.00015526424,0.00045245007,0.000012306766,0.00003682085,0.000021521939,0.0000012066987,0.0035548904,0.03885324,0.0012200597],"study_design_scores_gemma":[0.0011577286,0.0004223476,0.9889159,0.00006757851,0.00008112889,0.0000136511435,0.000029141893,0.0002428524,0.000005432888,0.00053507776,0.008405015,0.0001241046],"about_ca_topic_score_codex":0.0039933603,"about_ca_topic_score_gemma":0.00091915886,"teacher_disagreement_score":0.051478587,"about_ca_system_score_codex":0.00015493395,"about_ca_system_score_gemma":0.0007853543,"threshold_uncertainty_score":0.78685236},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877835,0.0007282337,0.000034893797,0.009156289,0.00009411482,0.0015516113,0.00046960608,0.00002453877,0.00015724027],"genre_scores_gemma":[0.99812543,0.00030124042,0.00041850706,0.00048736762,0.00012692268,0.00011754993,0.00037584172,0.00002128371,0.000025826974],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99796635,0.0003189941,0.000602829,0.0003804933,0.00033590067,0.00039543485],"domain_scores_gemma":[0.99649113,0.00042271957,0.0006542032,0.0009614376,0.0012792252,0.00019127439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026899506,0.00018238417,0.0005283463,0.00015329315,0.0002880439,0.000085173924,0.00029809747,0.00006709673,0.0000016490129],"category_scores_gemma":[0.0026996913,0.000114868555,0.000042820528,0.00037711373,0.00040052674,0.00023213135,0.000094567724,0.00024158078,1.3766764e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005244988,0.000135145,0.9813504,0.00048756404,0.00005155953,0.000019875446,0.012058925,0.0000015373167,0.000075674536,0.0006185166,0.00031007917,0.0043662284],"study_design_scores_gemma":[0.0016428083,0.00044324636,0.9678979,0.00005662666,0.0000018583041,0.000037510737,0.01719971,0.0001624101,0.00002831606,0.0000729706,0.0123433275,0.00011331444],"about_ca_topic_score_codex":0.0014169903,"about_ca_topic_score_gemma":0.00027215315,"teacher_disagreement_score":0.013452495,"about_ca_system_score_codex":0.00007775652,"about_ca_system_score_gemma":0.00074419443,"threshold_uncertainty_score":0.46842036},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99015546,0.0018948679,0.0002277361,0.0029177733,0.0002530202,0.0028104684,0.00045087523,0.00021880024,0.0010710207],"genre_scores_gemma":[0.9926608,0.0002478822,0.0009204612,0.0042693913,0.00016413162,0.00087680644,0.0006340721,0.00009474802,0.00013168027],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9931822,0.0009624001,0.0014553512,0.0017738893,0.000714701,0.001911455],"domain_scores_gemma":[0.99290586,0.0004223379,0.0007175029,0.00401437,0.00017075425,0.0017692049],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0051596817,0.00055689283,0.0016624915,0.00047867122,0.00047955403,0.00035009382,0.0013589667,0.00019174803,0.000028148377],"category_scores_gemma":[0.003162708,0.00055032474,0.00007450987,0.0011705788,0.0003978802,0.0009659492,0.0011295204,0.0007172127,0.00005329122],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043901103,0.00051528885,0.9647639,0.0007214932,0.000028189399,0.00006110797,0.001637626,0.00008580366,0.0000078286575,0.00002492966,0.0023354916,0.029379334],"study_design_scores_gemma":[0.0023757825,0.00017641837,0.87315434,0.00019905518,9.996246e-7,0.000024783609,0.0005770687,0.0026609486,2.1283618e-7,0.0000053239446,0.12029588,0.00052917877],"about_ca_topic_score_codex":0.0012814763,"about_ca_topic_score_gemma":0.0015255745,"teacher_disagreement_score":0.117960386,"about_ca_system_score_codex":0.00034137376,"about_ca_system_score_gemma":0.0011583581,"threshold_uncertainty_score":0.9996948},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895213,0.0050676437,0.000111489615,0.002589861,0.00015828767,0.0006363707,0.001428132,0.0000576025,0.0004292843],"genre_scores_gemma":[0.9976677,0.0006085705,0.0005489002,0.0007949534,0.00010846566,0.000018239778,0.0001663395,0.000046632467,0.00004025671],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99649596,0.00049186405,0.00092276634,0.00088228687,0.00033334031,0.00087375945],"domain_scores_gemma":[0.9953675,0.00028088022,0.00076330215,0.0026507613,0.00018944746,0.00074806967],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003364866,0.00035418393,0.0010643407,0.0002935487,0.00061230536,0.00022370838,0.00059851946,0.000142081,0.000014258424],"category_scores_gemma":[0.0008254429,0.00028574027,0.00006272324,0.00043291683,0.0006197287,0.0010126459,0.00070805405,0.00037972856,0.0000011842349],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022955154,0.00016878353,0.9546175,0.00080761564,0.000073214134,0.0005720328,0.00041564633,0.00007009761,0.000087731256,0.00021850302,0.00066311867,0.04207621],"study_design_scores_gemma":[0.0041062315,0.00017655252,0.5930667,0.00017787471,0.0000058208416,0.0038011705,0.00039714252,0.38989514,7.522751e-7,0.00008132245,0.0078036883,0.00048760016],"about_ca_topic_score_codex":0.004236778,"about_ca_topic_score_gemma":0.0070443656,"teacher_disagreement_score":0.38982505,"about_ca_system_score_codex":0.00015079764,"about_ca_system_score_gemma":0.001279458,"threshold_uncertainty_score":0.99995947},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96856767,0.0041436614,0.000038330567,0.023966957,0.00021146165,0.0016585218,0.0003651406,0.000057437934,0.0009908255],"genre_scores_gemma":[0.99636966,0.0020711531,0.000072542054,0.00083407963,0.00011313284,0.00015755714,0.00013038292,0.0000130106455,0.00023849757],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767214,0.00034720977,0.0005910493,0.00043368788,0.00045527934,0.0005006529],"domain_scores_gemma":[0.99700606,0.0005207394,0.00049951544,0.0006891873,0.00032976532,0.0009547599],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002026876,0.00018988842,0.00061663915,0.000058571284,0.0017191167,0.00016027607,0.00022032327,0.000087463784,0.000048446367],"category_scores_gemma":[0.0012740417,0.00013152469,0.000058785878,0.00010434696,0.00075040956,0.00020040954,0.00016267356,0.0002860357,0.000006586429],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045535612,0.00042400067,0.970708,0.00013020735,0.00003506524,0.000009719277,0.00031053356,2.3857788e-7,4.377915e-7,0.0008883633,0.0031937305,0.023844324],"study_design_scores_gemma":[0.0023648958,0.00052082184,0.93859565,0.000029276207,0.0000020470845,0.000024224073,0.00022616018,0.00055972784,3.1523598e-8,0.000047519978,0.057522994,0.000106668405],"about_ca_topic_score_codex":0.00012350766,"about_ca_topic_score_gemma":0.0005671466,"teacher_disagreement_score":0.054329265,"about_ca_system_score_codex":0.00006926468,"about_ca_system_score_gemma":0.0012324242,"threshold_uncertainty_score":0.9995805},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6817345,0.004480598,0.23176585,0.070563,0.00069931254,0.0011326842,0.004206614,0.001148864,0.0042685787],"genre_scores_gemma":[0.94273376,0.039820056,0.011306231,0.0011210166,0.0005604556,0.0001525951,0.0040005487,0.00011089486,0.00019442174],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964731,0.00058644573,0.0008307334,0.0007171755,0.0006407378,0.00075184274],"domain_scores_gemma":[0.9956931,0.0008662473,0.00068257656,0.0017875177,0.00070440443,0.00026617327],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0016400723,0.00053845014,0.0005046449,0.0016292377,0.0022734138,0.003333118,0.0013744731,0.00031704287,0.0009698972],"category_scores_gemma":[0.0030529606,0.0005784247,0.00010667866,0.0024854396,0.0009655071,0.014514359,0.0003981356,0.00040498632,0.00019237705],"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":[0.00021655513,0.0011208651,0.69930553,0.0017044919,0.0007861636,0.000092781294,0.18530846,0.0013110024,0.032515142,0.014967645,0.01750668,0.045164663],"study_design_scores_gemma":[0.0025918277,0.00020963045,0.8661001,0.0009735417,0.0007655247,0.0001221718,0.08225691,0.004972162,0.018207287,0.00832597,0.014738119,0.000736767],"about_ca_topic_score_codex":0.035233665,"about_ca_topic_score_gemma":0.110113494,"teacher_disagreement_score":0.2609993,"about_ca_system_score_codex":0.0010524027,"about_ca_system_score_gemma":0.0020385494,"threshold_uncertainty_score":0.9999434},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9576942,0.0064980546,0.00097109436,0.030736763,0.000032730706,0.00069598446,0.0030600664,0.000027927896,0.00028323132],"genre_scores_gemma":[0.99754286,0.0007729891,0.0005364044,0.00034392453,0.000038700684,0.0001158266,0.0005033462,0.000014679496,0.00013127875],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99742097,0.0007084671,0.00065687747,0.0005033047,0.00017167276,0.0005387375],"domain_scores_gemma":[0.9914075,0.006951987,0.0005368622,0.0005377614,0.00011647088,0.00044938925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037170965,0.00019272062,0.0011792395,0.0003565743,0.0003731713,0.00008716493,0.00016910993,0.00007609177,0.000004238825],"category_scores_gemma":[0.0063247816,0.00013433976,0.00010189303,0.00035397394,0.0009385325,0.0003594885,0.00018340444,0.00020469562,3.5149253e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042087008,0.000057804147,0.9143983,0.00031473557,0.00039179093,0.0000018712441,0.006735778,0.0000010970724,0.0000024250317,0.0016677061,0.00036170715,0.0756459],"study_design_scores_gemma":[0.0010090844,0.000658107,0.9831231,0.000035213867,0.000018129515,0.000002588412,0.0048693684,0.003937047,1.5572442e-7,0.000285346,0.005938522,0.00012330362],"about_ca_topic_score_codex":0.0006981414,"about_ca_topic_score_gemma":0.0015913614,"teacher_disagreement_score":0.075522594,"about_ca_system_score_codex":0.000045567296,"about_ca_system_score_gemma":0.00024075035,"threshold_uncertainty_score":0.7571811},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88467616,0.0036547107,0.0022500663,0.07748225,0.00053781184,0.0060578114,0.024736905,0.00027760607,0.00032666975],"genre_scores_gemma":[0.9851804,0.00034727526,0.00085438386,0.004946407,0.00027937983,0.0013144829,0.006811954,0.00008480337,0.00018095027],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9920877,0.0020555626,0.0017840812,0.001321193,0.0010564947,0.0016949343],"domain_scores_gemma":[0.99177223,0.0011125788,0.001082091,0.0020388938,0.0010216171,0.0029726143],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0090670725,0.000615647,0.001722353,0.00068151735,0.00090315455,0.0003368257,0.00089050445,0.00014504559,0.000050296127],"category_scores_gemma":[0.011099841,0.00066396955,0.00017119129,0.0012117852,0.00035989567,0.00074167486,0.00058866886,0.00087041286,0.000026350142],"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":[0.0025018775,0.0017897043,0.95424026,0.0013972968,0.00006625623,0.000025295296,0.0010330123,0.000008751729,0.0000117630725,0.00048784626,0.008221997,0.030215953],"study_design_scores_gemma":[0.0067898873,0.00037137372,0.9566563,0.00042560822,8.6413786e-7,0.000002109721,0.00023382223,0.00078848324,3.960644e-7,0.000678358,0.033510607,0.0005422213],"about_ca_topic_score_codex":0.004039437,"about_ca_topic_score_gemma":0.025592342,"teacher_disagreement_score":0.1005042,"about_ca_system_score_codex":0.0011928222,"about_ca_system_score_gemma":0.0039988398,"threshold_uncertainty_score":0.99958116},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77431226,0.0024794221,0.00017654413,0.22085337,0.00032457578,0.0012537772,0.00022825536,0.00007158194,0.00030022423],"genre_scores_gemma":[0.9867823,0.0022650622,0.00037655153,0.008065392,0.00011295417,0.000040185703,0.000077945086,0.000028681008,0.0022509254],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99823445,0.00019718264,0.00040271188,0.00033590567,0.00033304046,0.00049673725],"domain_scores_gemma":[0.99729717,0.0001640892,0.0003272626,0.0013167514,0.00014861302,0.0007461409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135086,0.00015227946,0.00042308526,0.000093485934,0.00017436357,0.00039428473,0.00036521105,0.000044903583,0.00011847538],"category_scores_gemma":[0.001735269,0.00010654177,0.000060299146,0.00009615943,0.00019174795,0.0005315946,0.00030737184,0.0001920984,0.000029644842],"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":[0.00013008717,0.00046681816,0.25481355,0.00084237475,0.00016196039,0.000021273692,0.005384968,0.0000042725574,0.000056684483,0.0012679448,0.07821021,0.65863985],"study_design_scores_gemma":[0.00052445976,0.00028812326,0.5295709,0.0004932906,0.000012185445,0.000018840987,0.00039779436,0.00017540129,0.000051273157,0.000049282826,0.46830449,0.00011400405],"about_ca_topic_score_codex":0.0072203744,"about_ca_topic_score_gemma":0.005719038,"teacher_disagreement_score":0.6585258,"about_ca_system_score_codex":0.00014286347,"about_ca_system_score_gemma":0.00069905823,"threshold_uncertainty_score":0.9993906},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98087543,0.0008923658,0.00007394348,0.013362881,0.00093139964,0.0014859939,0.0019137779,0.00014149102,0.0003227121],"genre_scores_gemma":[0.99343956,0.0000458156,0.00049654004,0.0023838629,0.0007895676,0.00014819339,0.0022086625,0.000032417865,0.00045536636],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99759305,0.00040748742,0.00055069703,0.00044969373,0.0004214998,0.0005775806],"domain_scores_gemma":[0.9961902,0.00026287395,0.00025419504,0.0014121145,0.00035975955,0.0015208457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012763691,0.0002157941,0.0005028524,0.000059817296,0.0005679802,0.000048752972,0.00028555584,0.00006805217,0.000049441795],"category_scores_gemma":[0.0039058665,0.00019044646,0.00007877969,0.0001463122,0.00023848264,0.00009696659,0.00028600622,0.00016854363,0.0000019038706],"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":[0.01617986,0.0006836259,0.6500752,0.0003152126,0.0002753073,0.0019671428,0.00007921046,3.4805893e-7,0.00022137356,0.00016424437,0.32395548,0.0060830014],"study_design_scores_gemma":[0.0015051961,0.00036429992,0.34208277,0.00007002674,0.00002109167,0.00016097078,0.0000608018,0.00007168476,0.000008332508,0.00005736489,0.6554091,0.00018838487],"about_ca_topic_score_codex":0.090085514,"about_ca_topic_score_gemma":0.2586579,"teacher_disagreement_score":0.3314536,"about_ca_system_score_codex":0.00059877295,"about_ca_system_score_gemma":0.015054008,"threshold_uncertainty_score":0.9905297},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95546067,0.004462819,0.000013545182,0.0009111597,0.0006527638,0.0008588306,0.0019919341,0.00032431306,0.03532398],"genre_scores_gemma":[0.99144304,0.00009056477,0.00006116293,0.004253038,0.00021776742,0.000064089785,0.0014614403,0.00006895758,0.0023399454],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.996346,0.00021369717,0.0008948586,0.0006530045,0.0011168808,0.0007755344],"domain_scores_gemma":[0.99258155,0.00016610471,0.000384789,0.004203009,0.0010453375,0.0016192092],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004952925,0.00039107923,0.0007294847,0.000057273162,0.0003546936,0.000047161273,0.00070800405,0.00007459183,0.00055491604],"category_scores_gemma":[0.0014572873,0.0003909209,0.000099971025,0.0004702058,0.0003193385,0.00014489237,0.00036170808,0.00034698864,0.000012060091],"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":[0.0003610597,0.00009633113,0.6735973,0.00015222823,0.0002088079,0.0066317674,0.000017159933,0.000058383343,0.000049334598,0.00004052522,0.31787938,0.0009077744],"study_design_scores_gemma":[0.0006361328,0.00003155378,0.4484788,0.00010571615,0.00006824833,0.0003261381,0.00011350381,0.0002779222,0.000016016036,0.000020513602,0.5496141,0.00031137798],"about_ca_topic_score_codex":0.9955663,"about_ca_topic_score_gemma":0.99968237,"teacher_disagreement_score":0.23173472,"about_ca_system_score_codex":0.0019437396,"about_ca_system_score_gemma":0.08551802,"threshold_uncertainty_score":0.99985427},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961503,0.001165363,0.00015312042,0.0007173176,0.00015656446,0.00125424,0.00022168538,0.00006487491,0.000116511386],"genre_scores_gemma":[0.9976896,0.00005615585,0.00067887816,0.0010085657,0.00040741026,0.000044222146,0.00007669195,0.000028602337,0.000009882558],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99697334,0.00056490576,0.0006736475,0.00071671343,0.0003659529,0.00070543215],"domain_scores_gemma":[0.9974606,0.00047152344,0.00028255445,0.0010659666,0.00017595306,0.00054340565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042683166,0.0002549676,0.0005439728,0.00022452313,0.00025031526,0.00021998538,0.00032206476,0.00006309029,0.000007743696],"category_scores_gemma":[0.0017548348,0.00019058172,0.000022667542,0.0006615661,0.00019904226,0.0004961236,0.00047025137,0.00032617996,0.0000016218856],"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":[0.00013648237,0.00025247296,0.9660045,0.00018971758,0.000035927467,0.00026353725,0.0030020166,4.192593e-7,0.0000089697405,0.000008798612,0.00021883944,0.029878335],"study_design_scores_gemma":[0.0039888094,0.0007621744,0.93873495,0.00013151483,0.0000052858695,0.0027402919,0.023000535,0.013555068,7.118628e-7,0.000003004,0.016749464,0.00032819947],"about_ca_topic_score_codex":0.006756946,"about_ca_topic_score_gemma":0.004212336,"teacher_disagreement_score":0.029550135,"about_ca_system_score_codex":0.0001367802,"about_ca_system_score_gemma":0.00051342027,"threshold_uncertainty_score":0.9998571},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9883376,0.00055043626,0.009513238,0.00012482416,0.00031227455,0.00049869507,0.0005144529,0.000009030863,0.00013946097],"genre_scores_gemma":[0.9981045,0.000055956534,0.00061333826,0.000035350065,0.00020652506,0.000020225738,0.00046669756,0.000021268888,0.00047615435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99802375,0.00010997067,0.0006208445,0.00019541172,0.00075287354,0.00029713404],"domain_scores_gemma":[0.998955,0.00006424986,0.00033826864,0.0001449213,0.00021675062,0.00028079952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010436015,0.00015896186,0.000443425,0.00017114719,0.000043963413,0.000054772347,0.00020815218,0.000064037384,0.000025157507],"category_scores_gemma":[0.00014497721,0.000115496056,0.00012467064,0.000035332643,0.00012248772,0.00020380509,0.000086337895,0.00012827205,0.0000026470154],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0075562303,0.0017679589,0.9591378,0.00078527245,0.0015600853,0.0010835328,0.001538715,0.014212353,0.0011127449,0.00014395421,0.0042294874,0.006871866],"study_design_scores_gemma":[0.041121967,0.003532232,0.7840422,0.00050428236,0.0006243034,0.0028040658,0.02674423,0.0799953,0.0016375013,0.00039697747,0.057544306,0.0010526406],"about_ca_topic_score_codex":0.00018993777,"about_ca_topic_score_gemma":0.0000015112113,"teacher_disagreement_score":0.1750956,"about_ca_system_score_codex":0.00034793315,"about_ca_system_score_gemma":0.00008256844,"threshold_uncertainty_score":0.47097924},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8803216,0.0010842646,0.11123426,0.0024747418,0.00008813033,0.001944985,0.00130593,0.00032243738,0.0012236095],"genre_scores_gemma":[0.99359393,0.0024451842,0.0023626613,0.00005436525,0.0002708083,0.0009469273,0.00011621545,0.00013720336,0.00007271961],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98191833,0.014249599,0.0008851966,0.0009556085,0.00047763623,0.001513613],"domain_scores_gemma":[0.9877461,0.009740774,0.00062508264,0.00074480876,0.0002916242,0.00085160485],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008631476,0.0008609815,0.0012681516,0.00047751874,0.00088069105,0.0002105757,0.0005341385,0.0008500225,0.000021361933],"category_scores_gemma":[0.0026898338,0.000797771,0.00036909865,0.0005341598,0.00087944383,0.0019512988,0.0002181056,0.0015471311,0.0000048884444],"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":[0.0031220356,0.0029289993,0.36133385,0.0026703305,0.0017458335,0.000099914825,0.33002928,0.0036936605,0.016590213,0.004716672,0.000044330267,0.2730249],"study_design_scores_gemma":[0.0013725603,0.00034924,0.9196571,0.0027129732,0.0006587443,0.00062164984,0.043723613,0.022944504,0.006988048,0.00020984982,0.00008835734,0.0006733594],"about_ca_topic_score_codex":0.0084446,"about_ca_topic_score_gemma":0.005844926,"teacher_disagreement_score":0.55832326,"about_ca_system_score_codex":0.0028279524,"about_ca_system_score_gemma":0.0010552373,"threshold_uncertainty_score":0.99944735},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43303466,0.4125426,0.00041277372,0.13680167,0.00027615324,0.009590014,0.006104689,0.00020571558,0.0010317034],"genre_scores_gemma":[0.7548982,0.24005334,0.0018678023,0.0016131187,0.00015462431,0.00009581215,0.0012433681,0.000035259163,0.000038460563],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981444,0.000658087,0.00042054185,0.00041477286,0.0001174721,0.0002447159],"domain_scores_gemma":[0.99626344,0.0023771995,0.00016333425,0.00091397297,0.00015340991,0.00012866326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013281461,0.0001456214,0.00045850346,0.000054731692,0.0005843341,0.000050224364,0.00025258432,0.0000696113,0.000029656887],"category_scores_gemma":[0.0029268372,0.00009797613,0.000031834432,0.00015538838,0.0013776494,0.00011579633,0.0005232558,0.00023195376,0.0000017171803],"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":[0.0035688672,0.0021062607,0.29906255,0.0107971355,0.004709351,0.000023418883,0.013081278,5.8369676e-7,0.00057126937,0.015563305,0.26366258,0.3868534],"study_design_scores_gemma":[0.006496745,0.0009143534,0.788611,0.024778154,0.0008227334,0.0003480347,0.00031699415,0.0028367806,0.00003729517,0.00093520165,0.17328711,0.00061559724],"about_ca_topic_score_codex":0.000038060534,"about_ca_topic_score_gemma":0.0007414614,"teacher_disagreement_score":0.48954844,"about_ca_system_score_codex":0.000018226094,"about_ca_system_score_gemma":0.000036234545,"threshold_uncertainty_score":0.50760067},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918192,0.0012424912,0.0039113997,0.00062316627,0.0005229469,0.00007668423,0.000013118727,0.000021625141,0.0017693592],"genre_scores_gemma":[0.982681,0.0011850875,0.015419344,0.0006221607,0.000035808338,2.6406283e-7,0.000007359091,0.000023011744,0.00002600496],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99666226,0.0008948331,0.0018355271,0.000119491,0.00011423052,0.00037367007],"domain_scores_gemma":[0.9967875,0.0006290479,0.0015943306,0.0003267237,0.000379071,0.00028332157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003557638,0.00016309514,0.0012331159,0.00028114198,0.000044889228,0.0000028650702,0.00023517564,0.00006362603,0.000099279794],"category_scores_gemma":[0.0039531454,0.00012954541,0.00012189267,0.0001588612,0.0008908955,0.00012361193,0.00016382698,0.0003208494,0.000076865595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004201237,0.00005078621,0.98415554,0.00022477924,0.00024716323,0.00025410988,0.000578987,0.000009521848,0.00012610735,0.0009978544,0.007148927,0.0057860846],"study_design_scores_gemma":[0.0029868414,0.0023030946,0.9064864,0.0005415489,0.000115900184,0.061504643,0.00028691525,0.00043918888,0.00007773359,0.00018651149,0.024895946,0.00017528885],"about_ca_topic_score_codex":0.00000863733,"about_ca_topic_score_gemma":0.0000017665506,"teacher_disagreement_score":0.077669166,"about_ca_system_score_codex":0.000020945909,"about_ca_system_score_gemma":0.00012347271,"threshold_uncertainty_score":0.52827084},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9694711,0.0051189265,0.000032156586,0.020984232,0.00033047103,0.0032204993,0.00010141036,0.00007455643,0.0006666463],"genre_scores_gemma":[0.99188197,0.00028395822,0.00016469196,0.0068883672,0.00032960897,0.0002972486,0.00012390867,0.000023468363,0.0000067963388],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9917393,0.0044773985,0.0015206942,0.0005997034,0.0005391159,0.0011237946],"domain_scores_gemma":[0.9970231,0.00065268076,0.00065667764,0.00096849597,0.00024142057,0.00045762522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009256567,0.00028709954,0.0011653696,0.00028786474,0.00035017575,0.000055641118,0.00046370656,0.000091093796,0.0000040015548],"category_scores_gemma":[0.0007907513,0.00017959217,0.000057762405,0.0011644518,0.00017768833,0.00013382538,0.00010212416,0.0007296032,0.0000049811056],"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":[0.000109220964,0.00032360537,0.9353068,0.0010660163,0.000012684533,0.000012722246,0.03477465,0.000001329233,4.25007e-7,0.00016384391,0.0019729044,0.026255783],"study_design_scores_gemma":[0.0017433895,0.0025714156,0.90540534,0.00020927182,3.88571e-7,0.00006738603,0.07055936,0.00015660198,4.595157e-8,0.000002990009,0.019166816,0.000116966876],"about_ca_topic_score_codex":0.00994357,"about_ca_topic_score_gemma":0.026580932,"teacher_disagreement_score":0.03578471,"about_ca_system_score_codex":0.0005381072,"about_ca_system_score_gemma":0.0024008804,"threshold_uncertainty_score":0.9966493},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7793255,0.00034730678,0.00016106392,0.21925308,0.00025124854,0.00031996,0.0002460069,0.00000383712,0.00009198147],"genre_scores_gemma":[0.963118,0.000052487314,0.00016363195,0.03645393,0.00015944132,0.0000030438168,0.000021324982,0.000009638215,0.000018462933],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99786407,0.0005079384,0.00076420966,0.0000838579,0.00026364415,0.00051630655],"domain_scores_gemma":[0.9967274,0.00013564256,0.0005321294,0.00031776083,0.0003085826,0.0019784705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067308364,0.000095816366,0.00033777585,0.0005468134,0.00023435059,0.00007850495,0.00025197325,0.000033776985,0.000043743523],"category_scores_gemma":[0.0015895847,0.00006254488,0.00004338819,0.00045391542,0.00014800992,0.00046410644,0.000023360644,0.00031645727,0.00000673122],"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":[0.0008280176,0.000070388436,0.6333287,0.00015055617,0.000087863475,0.00007101301,0.11009572,0.0000021752842,0.000097362536,0.0008262478,0.027888028,0.22655396],"study_design_scores_gemma":[0.00043515125,0.0005561401,0.7189798,0.00017089455,0.0000033010049,0.00015127075,0.0026622978,0.000012594023,0.0000017438718,0.000003882597,0.27698234,0.000040614144],"about_ca_topic_score_codex":0.10700393,"about_ca_topic_score_gemma":0.49749777,"teacher_disagreement_score":0.39049384,"about_ca_system_score_codex":0.00077756314,"about_ca_system_score_gemma":0.018677471,"threshold_uncertainty_score":0.9868857},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936967,0.0010198307,0.00001704374,0.0030347672,0.00009289225,0.000329832,0.0009025726,0.00005236651,0.0008539993],"genre_scores_gemma":[0.9956099,0.00041022743,0.00012054267,0.0003182348,0.000051363466,0.000023222545,0.0029948892,0.000017483328,0.00045414356],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997494,0.0003756847,0.00069867296,0.0007104517,0.00022377195,0.00049743144],"domain_scores_gemma":[0.99793774,0.00009237256,0.00021677557,0.0009658539,0.00009786643,0.00068940775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020163697,0.0001786987,0.00084048917,0.00085721887,0.00006334988,0.000043946817,0.00027697373,0.00011361639,0.0003819191],"category_scores_gemma":[0.00015923606,0.00016221123,0.000049485243,0.0011759967,0.00028969717,0.00026102923,0.00035059376,0.00022123025,0.000012490238],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013265398,0.000179146,0.92596155,0.000114313785,0.00010882338,0.000007925573,0.0004276793,3.3543571e-7,0.000015473828,0.00003466623,0.00283531,0.070182115],"study_design_scores_gemma":[0.0011555166,0.0003231291,0.97386086,0.000029489767,0.000011740775,0.000010278993,0.00014777762,0.008180094,0.0000015910362,0.0000022487268,0.016144121,0.00013313022],"about_ca_topic_score_codex":0.00084908184,"about_ca_topic_score_gemma":0.0061803893,"teacher_disagreement_score":0.07004898,"about_ca_system_score_codex":0.00013253452,"about_ca_system_score_gemma":0.00052143843,"threshold_uncertainty_score":0.66147816},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99254686,0.0020449995,0.00004957246,0.0039213756,0.00013840136,0.0007519813,0.00014597701,0.0000801953,0.0003206447],"genre_scores_gemma":[0.99712175,0.00094094995,0.0004339039,0.0010075492,0.0001634855,0.00002970125,0.00008504928,0.000015204146,0.00020243003],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997607,0.00036093363,0.00044633605,0.0005586508,0.0003588669,0.0006682338],"domain_scores_gemma":[0.9985343,0.00008436838,0.00013089973,0.00054692716,0.0000855124,0.00061796064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014047594,0.00018823124,0.00046681537,0.00044743417,0.00008413265,0.000046576683,0.00020683785,0.00009149772,0.00021459587],"category_scores_gemma":[0.0004408837,0.00016189991,0.000040903404,0.00066175836,0.00030437892,0.00027330234,0.000121789395,0.00026708268,0.000042366406],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032213662,0.000330121,0.99515134,0.00031608925,0.000007224061,0.0000045128713,0.00029219902,1.4786755e-8,0.0000022325275,0.00015048837,0.001450584,0.0022629958],"study_design_scores_gemma":[0.0012097314,0.00023461993,0.9965286,0.00020362272,0.0000036269682,0.000021043876,0.00004347619,0.00015052655,0.0000010687005,0.000085965505,0.001373261,0.00014444205],"about_ca_topic_score_codex":0.0021021136,"about_ca_topic_score_gemma":0.012879493,"teacher_disagreement_score":0.010777379,"about_ca_system_score_codex":0.00013639109,"about_ca_system_score_gemma":0.0009240528,"threshold_uncertainty_score":0.71870595},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97843766,0.00065014814,0.018284317,0.00076763006,0.00035064798,0.00065326004,0.00032023047,0.00014790801,0.0003881776],"genre_scores_gemma":[0.9976814,0.0000927545,0.0010182428,0.0005138388,0.00024405471,0.000035045934,0.0003475191,0.00003050673,0.000036640355],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973452,0.00023233292,0.0008386146,0.0006066509,0.00041424672,0.0005629549],"domain_scores_gemma":[0.9970029,0.00013424981,0.0008578298,0.0005195503,0.0008522617,0.00063319015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013797999,0.00022787112,0.0008921919,0.00047373824,0.00019131434,0.000062998355,0.000084428226,0.00008122976,0.00014424688],"category_scores_gemma":[0.0005726463,0.00021324662,0.000117979456,0.0016755661,0.00020516079,0.00032445198,0.00010395636,0.00012586727,0.0000018904972],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120763645,0.000114924966,0.98230994,0.0005616855,0.0003072065,0.00001562729,0.0008418964,0.000008358607,0.00029778446,0.000024212606,0.00010995334,0.015287639],"study_design_scores_gemma":[0.00056147814,0.00011726627,0.9599719,0.00010607314,0.000018699258,0.00009067305,0.0006352616,0.036785264,0.000012537414,0.000005976109,0.0014783936,0.00021646796],"about_ca_topic_score_codex":0.0016730474,"about_ca_topic_score_gemma":0.0006210132,"teacher_disagreement_score":0.036776904,"about_ca_system_score_codex":0.00024358337,"about_ca_system_score_gemma":0.0011598495,"threshold_uncertainty_score":0.86959445},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996006,0.0018740175,0.00029296323,0.00004090933,0.000100276084,0.00078716216,0.00037508877,0.00008076939,0.00044278946],"genre_scores_gemma":[0.9964903,0.000033053304,0.0028877503,0.000026121397,0.00014507031,0.00008761891,0.00029404715,0.000029712082,0.0000063401117],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99835783,0.00013290154,0.00047751988,0.0004109491,0.00026076176,0.00036000655],"domain_scores_gemma":[0.9984336,0.00021783319,0.0002604143,0.00047189847,0.00050870056,0.000107547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009759434,0.0001895503,0.00046613242,0.0003121124,0.0001439174,0.000060511906,0.0001703059,0.00008413424,0.000025480567],"category_scores_gemma":[0.00035915544,0.00018177205,0.00008080959,0.00041259854,0.0008379195,0.00012703643,0.00011597638,0.00012497994,8.319022e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054254953,0.00018161297,0.9867655,0.0005058666,0.00007692671,0.0000075129487,0.00019011136,2.617916e-7,0.00005597545,0.00023250873,0.00015959905,0.011281598],"study_design_scores_gemma":[0.0017365516,0.00060332665,0.99536866,0.00019943138,0.000030534822,0.0000036291362,0.000056290224,0.00017063251,0.000118607044,0.00090796227,0.0006295264,0.00017486543],"about_ca_topic_score_codex":0.0005680749,"about_ca_topic_score_gemma":0.0016656037,"teacher_disagreement_score":0.011106733,"about_ca_system_score_codex":0.000013160178,"about_ca_system_score_gemma":0.00009190143,"threshold_uncertainty_score":0.7412449},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948096,0.0002655123,0.00032796388,0.0008564358,0.00065169804,0.0018181996,0.00007666518,0.00058968004,0.0006042408],"genre_scores_gemma":[0.9883478,0.000017769606,0.0012359361,0.008391405,0.0014307229,0.00011853886,0.0001388942,0.000054266708,0.0002646273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99635154,0.0002261642,0.0010109703,0.0009047939,0.0007884759,0.0007180674],"domain_scores_gemma":[0.9967091,0.000118207274,0.00045942122,0.0008372938,0.00033890936,0.001537063],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00125972,0.00034001173,0.0005624457,0.0002541666,0.0005092213,0.00010309009,0.0001338514,0.00015905278,0.0001345667],"category_scores_gemma":[0.00032169538,0.00031317852,0.00008650833,0.00046602712,0.00013214177,0.00016367655,0.000056629033,0.00043240414,0.00012624646],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025683655,0.0012680432,0.9842259,0.00082290993,0.00003984101,0.00020515875,0.0005825725,0.0000023410792,0.000009544667,0.000034284665,0.0048662173,0.005374812],"study_design_scores_gemma":[0.0058841733,0.0034456453,0.9790853,0.0001811178,0.000060121667,0.00019877224,0.000037979455,0.00044969132,0.00000848987,0.000040961484,0.010376449,0.00023127953],"about_ca_topic_score_codex":0.0009878995,"about_ca_topic_score_gemma":0.00051154476,"teacher_disagreement_score":0.00753497,"about_ca_system_score_codex":0.00038421914,"about_ca_system_score_gemma":0.003037509,"threshold_uncertainty_score":0.99993205},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8251307,0.00079103705,0.0015710081,0.16902371,0.0004447093,0.0015365145,0.0014323633,0.000029357652,0.00004062759],"genre_scores_gemma":[0.97524023,0.000060851038,0.0011283106,0.021315409,0.001120205,0.000017885823,0.0010418466,0.000044691555,0.000030572628],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9938288,0.0015676958,0.0014211627,0.0007989154,0.0005769526,0.0018064787],"domain_scores_gemma":[0.9890034,0.00048437802,0.00095690164,0.003127421,0.00061868737,0.0058091944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020569248,0.00029044913,0.0012475314,0.0007066197,0.000445379,0.00035287137,0.0032993976,0.000062668965,0.00009851595],"category_scores_gemma":[0.0046776216,0.00020959273,0.000057750876,0.0012384537,0.0004726572,0.0007072418,0.00038033104,0.0007229656,0.000018773939],"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":[0.00005609238,0.00044308713,0.8684957,0.000035208224,0.00011476842,0.000034817607,0.0027261039,0.0000041903504,3.7213496e-7,0.000018230958,0.09602343,0.032047976],"study_design_scores_gemma":[0.0012796822,0.0022696124,0.69110334,0.000035486373,0.000007643164,0.000070824724,0.0072813793,0.0010300091,2.7860349e-8,0.0000030684182,0.29678395,0.00013499084],"about_ca_topic_score_codex":0.06035086,"about_ca_topic_score_gemma":0.7865762,"teacher_disagreement_score":0.7262253,"about_ca_system_score_codex":0.0010713517,"about_ca_system_score_gemma":0.01847701,"threshold_uncertainty_score":0.9870873},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056443986,0.012197892,0.29718354,0.62171704,0.004291003,0.0018334757,0.00084780966,0.00027767045,0.0052076],"genre_scores_gemma":[0.5675421,0.012639516,0.18031937,0.22740969,0.011154808,0.000010075248,0.00065392867,0.00009411826,0.00017639826],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931956,0.00048498707,0.003675114,0.00012587651,0.0012246945,0.0012937072],"domain_scores_gemma":[0.9920723,0.00018216524,0.0034272645,0.00077211374,0.0016225121,0.0019236341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012730996,0.00026929367,0.0008929359,0.00018859234,0.0004465635,0.00022449979,0.0007953511,0.00009135787,0.00013054356],"category_scores_gemma":[0.0034746698,0.00017509786,0.00021900421,0.0015254505,0.00032642751,0.0011321338,0.0002275957,0.00074057176,0.00007685586],"study_design_candidate":"not_applicable","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":[0.000060625065,0.00053657073,0.031375624,0.0010860803,0.00026005795,0.000015095656,0.0032251568,0.000053011496,1.3004642e-7,0.0063465675,0.18854062,0.76850045],"study_design_scores_gemma":[0.0006367937,0.0014373132,0.022002332,0.0006627433,0.000016711174,0.0010661512,0.0025886593,0.0056066173,2.3451678e-7,0.00042236078,0.96539843,0.00016163672],"about_ca_topic_score_codex":0.000034931254,"about_ca_topic_score_gemma":0.00013593172,"teacher_disagreement_score":0.7768578,"about_ca_system_score_codex":0.0010788363,"about_ca_system_score_gemma":0.008436804,"threshold_uncertainty_score":0.99718446},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984324,0.0005731386,0.000076307,0.00022301529,0.00021621653,0.00011273674,0.000025295789,0.000010211237,0.00033068075],"genre_scores_gemma":[0.99603146,0.0002503673,0.0018013457,0.0001302643,0.0003738951,0.0000026682756,0.0000042478464,0.000011044153,0.0013947028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993037,0.000031061958,0.0002338164,0.00011264341,0.00018821715,0.00013055043],"domain_scores_gemma":[0.99927396,0.000013554662,0.00020509832,0.000120074,0.0003007718,0.00008653009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017629388,0.00008809251,0.00025286368,0.00008870231,0.000061655584,0.000015900048,0.000057479305,0.000045334447,0.00013964268],"category_scores_gemma":[0.000033898414,0.000072630224,0.000060865408,0.00016299402,0.00015182792,0.00019114625,0.000026274305,0.00013685387,0.000025029807],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027681694,0.0009812883,0.7041528,0.000091213704,0.00005599708,0.0007205165,0.0051971893,5.3622404e-7,0.07469327,0.000022456592,0.050953727,0.16036278],"study_design_scores_gemma":[0.0012468349,0.0018243009,0.98317313,0.000161682,0.00008355945,0.0004029856,0.00015455688,0.000030216353,0.0011116202,0.000050895826,0.011672168,0.00008804406],"about_ca_topic_score_codex":0.000012778039,"about_ca_topic_score_gemma":0.000017894574,"teacher_disagreement_score":0.27902028,"about_ca_system_score_codex":0.00013556989,"about_ca_system_score_gemma":0.00018499052,"threshold_uncertainty_score":0.29617745},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959136,0.00013976922,0.00022424132,0.00066485355,0.00021335077,0.0011113564,0.001398264,0.000039440994,0.0002951105],"genre_scores_gemma":[0.9966002,0.000014348193,0.0010125632,0.0003978654,0.000057663405,0.000012508329,0.0018587188,0.000012152098,0.00003399476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980577,0.00022663709,0.00084307464,0.00023837015,0.00034045993,0.00029375474],"domain_scores_gemma":[0.99848753,0.00003270831,0.00048588405,0.0006729454,0.00005672225,0.00026419596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008574905,0.00012655529,0.00036279942,0.00031999612,0.0002195704,0.000015339636,0.00014054487,0.00004069436,0.000138992],"category_scores_gemma":[0.00003569463,0.000114632334,0.000025285526,0.00059552205,0.000049474424,0.0005347849,0.000193085,0.00017047851,0.000016287504],"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":[0.0004740351,0.0024379753,0.87851524,0.00038961924,0.000081797465,0.000114283735,0.018447615,0.000017344651,0.00005146899,0.000014522241,0.0076663457,0.09178977],"study_design_scores_gemma":[0.0070056017,0.0012767132,0.94028974,0.00051216024,0.00001950174,0.0009912456,0.004898625,0.042768545,0.000005409535,0.0000066643347,0.0020736416,0.0001521734],"about_ca_topic_score_codex":0.00703161,"about_ca_topic_score_gemma":0.0025221275,"teacher_disagreement_score":0.09163759,"about_ca_system_score_codex":0.0003992379,"about_ca_system_score_gemma":0.00065997674,"threshold_uncertainty_score":0.9995807},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012220026,0.99036646,8.246347e-7,0.0029091656,0.0002450988,0.0048604603,0.00057574833,0.000058194808,0.00086184713],"genre_scores_gemma":[0.00007205129,0.9961015,0.00020240866,0.0013023659,0.00042857789,0.0007233369,0.00067918486,0.00009363043,0.0003969721],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99191713,0.002455014,0.0024914807,0.0010641409,0.0008276598,0.001244569],"domain_scores_gemma":[0.99520636,0.0003799024,0.0012456034,0.0010689184,0.000356278,0.001742921],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0085254535,0.00054856617,0.004758359,0.000781557,0.00017300526,0.00001444652,0.00025710065,0.00034697572,0.00007206093],"category_scores_gemma":[0.00034608497,0.00045159,0.00020567789,0.00052988366,0.00060065306,0.00010298116,0.0002543847,0.0012353112,0.000024826411],"study_design_candidate":"not_applicable","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":[0.000056808298,0.00014331614,0.000008100491,0.4276464,0.000023679951,0.00003470397,0.00014447414,2.802356e-9,5.9909033e-9,0.000039634822,0.013450445,0.5584524],"study_design_scores_gemma":[0.0006420682,0.0013654807,0.00065736606,0.143721,0.00016453335,0.00016835681,0.00018579185,0.0000022023944,1.2589461e-8,0.000032792603,0.85283774,0.00022264563],"about_ca_topic_score_codex":0.0005478859,"about_ca_topic_score_gemma":0.00057035196,"teacher_disagreement_score":0.8393873,"about_ca_system_score_codex":0.0003900673,"about_ca_system_score_gemma":0.0065614465,"threshold_uncertainty_score":0.9997936},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004831992,0.97380215,0.009317288,0.004377187,0.0009638757,0.008974652,0.0013770274,0.00068890414,0.00049408927],"genre_scores_gemma":[0.000022348868,0.94349694,0.047215715,0.0017206947,0.0013229278,0.0027501443,0.0029675893,0.0002831254,0.00022048963],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9846957,0.004701427,0.004828539,0.0021829805,0.0009732898,0.002618072],"domain_scores_gemma":[0.9860277,0.0023409498,0.0048295865,0.002356479,0.0017340669,0.0027112158],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.026454914,0.0013133154,0.00901704,0.00089869206,0.0006569292,0.00022204663,0.0009598386,0.00053714955,0.000038694925],"category_scores_gemma":[0.009858405,0.0011588671,0.0010866136,0.0024224173,0.0004892782,0.00042052346,0.00050980283,0.00097441406,0.0000115284865],"study_design_candidate":"not_applicable","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":[0.000013874786,0.00011036163,0.001787859,0.36156297,0.00017160834,0.0000027862152,0.00007745091,8.5498647e-10,1.7133813e-7,0.00010040444,0.0066862754,0.62948626],"study_design_scores_gemma":[0.00053003244,0.0005370741,0.00058785564,0.07784729,0.000017528835,0.00014150575,0.00006731874,0.000007054165,4.074963e-7,0.000021160755,0.91943616,0.0008066203],"about_ca_topic_score_codex":0.000085506705,"about_ca_topic_score_gemma":0.00002981219,"teacher_disagreement_score":0.9127499,"about_ca_system_score_codex":0.0009859247,"about_ca_system_score_gemma":0.009360447,"threshold_uncertainty_score":0.99996185},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99426615,0.0006733252,0.0030958725,0.000102733095,0.00016919307,0.0009932923,0.00004638496,0.00011804133,0.0005349972],"genre_scores_gemma":[0.996162,0.000023829485,0.0017658385,0.0007712037,0.00028686033,0.00014187096,0.0008095144,0.000025880967,0.000013015353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719864,0.000060911127,0.0009683137,0.00017269424,0.0011025964,0.00049684616],"domain_scores_gemma":[0.99864787,0.00016836544,0.00028761724,0.00026546977,0.00024022975,0.00039046162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007750068,0.00019096899,0.00045876912,0.00031113185,0.000051381452,0.000019923084,0.00020370823,0.00021435908,0.000063953135],"category_scores_gemma":[0.0016599955,0.00016595804,0.00008921241,0.00058361987,0.00022108058,0.0003146508,0.000105911706,0.0005995719,0.0000062343547],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024944238,0.00020630857,0.024757884,0.0029076026,0.000025258705,0.000049107697,0.0071410025,0.0000015572857,0.00002407842,0.0000063227653,0.00009870693,0.96453273],"study_design_scores_gemma":[0.023513874,0.0020030176,0.11305132,0.013472606,0.00019740948,0.00026023402,0.010012738,0.83190095,0.0015016826,0.00016703604,0.0027863735,0.0011327744],"about_ca_topic_score_codex":0.000033782617,"about_ca_topic_score_gemma":0.0001930356,"teacher_disagreement_score":0.96339995,"about_ca_system_score_codex":0.0005004956,"about_ca_system_score_gemma":0.0020355317,"threshold_uncertainty_score":0.6767573},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21387868,0.0024596837,0.6292027,0.12044637,0.008266649,0.0027786174,0.022265997,0.00013450952,0.00056680525],"genre_scores_gemma":[0.9588841,0.00020736059,0.025481448,0.010716106,0.0029188609,0.0000105259915,0.0015225958,0.00004310553,0.00021586832],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976589,0.000087421795,0.00086626987,0.00024121317,0.0008645884,0.00028161987],"domain_scores_gemma":[0.9966269,0.00019754776,0.00060682575,0.000424412,0.0017282312,0.0004160576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015852883,0.00012779402,0.00036605506,0.0004506659,0.00007608315,0.00003822831,0.00073790306,0.00004022463,0.000549775],"category_scores_gemma":[0.0011079205,0.000113787246,0.00011022464,0.0001755084,0.00010288067,0.00013621416,0.00017996825,0.00017347441,0.00012417584],"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":[0.005152355,0.0011278142,0.075930916,0.00023805215,0.0013673336,0.00021222807,0.00030874464,0.000040862607,0.00017880574,0.00054683187,0.38615763,0.52873844],"study_design_scores_gemma":[0.0039470564,0.003499353,0.24341321,0.00074433844,0.000106923195,0.0005068517,0.000036231977,0.005973219,0.00002120031,0.0010738374,0.74045295,0.00022481903],"about_ca_topic_score_codex":0.00023854017,"about_ca_topic_score_gemma":0.00024500024,"teacher_disagreement_score":0.7450055,"about_ca_system_score_codex":0.00014092762,"about_ca_system_score_gemma":0.00094817195,"threshold_uncertainty_score":0.6019651},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931891,0.00019086945,0.000045933528,0.0032684503,0.000118705946,0.0022140604,0.00074978615,0.000016328482,0.00020676234],"genre_scores_gemma":[0.9985236,0.000023657243,0.000013389993,0.00038934711,0.00012505041,0.000100121884,0.00079088146,0.000009581919,0.000024379342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996558,0.001446157,0.0005396841,0.00030810345,0.00089570007,0.00025234674],"domain_scores_gemma":[0.9972745,0.00075971487,0.00043181572,0.0011476068,0.0002834138,0.00010292972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008098718,0.00011482441,0.00028384436,0.000073252806,0.00024704225,0.00006980145,0.00037040055,0.00004356211,0.0001201436],"category_scores_gemma":[0.002576105,0.00006462373,0.00004221934,0.0007201332,0.0001712603,0.00019966182,0.00013164873,0.00016889742,0.0000029164048],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000711819,0.0003659866,0.9735111,0.00008463514,0.000019106628,2.1013818e-7,0.0010338061,0.000021816344,8.8924895e-7,0.000017230452,0.004386337,0.02048772],"study_design_scores_gemma":[0.00064219546,0.00036965215,0.9710362,0.00005110342,0.00000279382,0.0000042288284,0.00031868348,0.027043214,8.14848e-8,0.0000193013,0.00046159982,0.000050970328],"about_ca_topic_score_codex":0.0016338077,"about_ca_topic_score_gemma":0.0038336718,"teacher_disagreement_score":0.027021397,"about_ca_system_score_codex":0.00014239525,"about_ca_system_score_gemma":0.0005775346,"threshold_uncertainty_score":0.30840245},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99468124,0.0027073661,0.00012327656,0.0010051543,0.00010331099,0.00091336376,0.00016740116,0.00008685324,0.0002120197],"genre_scores_gemma":[0.99579334,0.0005528964,0.0009119033,0.0015266492,0.000150008,0.00010556941,0.0009145691,0.000029874156,0.000015175007],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973115,0.00016008543,0.00065512874,0.00066695013,0.0003783686,0.0008279891],"domain_scores_gemma":[0.99840295,0.00019113268,0.00023858818,0.00025409262,0.000042553496,0.00087067997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001693502,0.00028693362,0.00054924353,0.00035131388,0.00032866254,0.00014752112,0.0000919348,0.000112457004,0.000033492644],"category_scores_gemma":[0.00015203745,0.00028210078,0.000023792878,0.00036829917,0.0002541033,0.00045916557,0.00017224751,0.00026680782,0.0000031683892],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012444993,0.000056827415,0.88570684,0.00019323247,0.000023109873,0.000020510985,0.00269043,7.529542e-8,0.000022759894,0.000042684354,0.00037339173,0.1107457],"study_design_scores_gemma":[0.0021522709,0.00027548088,0.96250236,0.00004409941,6.2255623e-7,0.000071950955,0.0017783429,0.00026277444,0.0000050278104,0.000015965039,0.032632038,0.00025903934],"about_ca_topic_score_codex":0.00017176695,"about_ca_topic_score_gemma":0.0016059084,"teacher_disagreement_score":0.110486664,"about_ca_system_score_codex":0.00016100974,"about_ca_system_score_gemma":0.00048643214,"threshold_uncertainty_score":0.9999631},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946697,0.0021831854,0.00026557746,0.002368657,0.00002259578,0.000319502,0.00010768642,0.000009784224,0.000053363652],"genre_scores_gemma":[0.9935842,0.0004378074,0.005235232,0.00058562553,0.000050348655,0.000010521682,0.0000400158,0.0000058148635,0.000050437462],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99933237,0.000013056502,0.00029543188,0.00012942772,0.00008500265,0.0001446944],"domain_scores_gemma":[0.9994351,0.000055049808,0.0001367333,0.000099251236,0.00016396014,0.00010988767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041588975,0.00006601674,0.00024902017,0.000053072225,0.00014049489,0.000010744841,0.000021477636,0.000035063236,0.000007825124],"category_scores_gemma":[0.000091520735,0.00005688647,0.000013563607,0.000094437746,0.00015712436,0.000041293708,0.000034309443,0.000035612327,1.9526287e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052830105,0.00007708482,0.75143164,0.0006668857,0.00007479414,5.086816e-7,0.0026468702,1.2496226e-7,0.00012795992,0.0008939853,0.030059598,0.21349224],"study_design_scores_gemma":[0.0006027557,0.00042338454,0.97306716,0.00033884603,0.000007245622,0.0000019450881,0.00021488802,0.0000061630503,0.0009586963,0.00008787039,0.02423421,0.000056802663],"about_ca_topic_score_codex":0.000026380916,"about_ca_topic_score_gemma":0.00008136639,"teacher_disagreement_score":0.22163555,"about_ca_system_score_codex":0.0000041199473,"about_ca_system_score_gemma":0.00018752765,"threshold_uncertainty_score":0.23197629},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2651868,0.20528321,0.000088796085,0.37845802,0.0004899082,0.012262553,0.00022859016,0.00051968306,0.13748246],"genre_scores_gemma":[0.87309057,0.03649532,0.0001186622,0.08922804,0.0005255816,0.00017683518,0.00020138094,0.000040750918,0.00012287202],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98911154,0.007598162,0.0007755447,0.00056308834,0.00092940737,0.0010222803],"domain_scores_gemma":[0.9963029,0.0012624139,0.00016571855,0.00096843945,0.0005930186,0.0007075219],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.033282917,0.00018008334,0.000550484,0.00019520342,0.00039650226,0.00010354019,0.00036231344,0.000083586434,0.00008320562],"category_scores_gemma":[0.0056655873,0.00012919906,0.000044137112,0.0012379275,0.0006649435,0.00013914457,0.00016211851,0.0012207011,0.00018813755],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013490414,0.00032369816,0.72687143,0.02789043,0.000027596416,0.0001601608,0.0037275075,9.553289e-8,0.000007707121,0.0012031164,0.15306138,0.08659195],"study_design_scores_gemma":[0.00096035213,0.00038449565,0.5543908,0.007256586,6.025175e-7,0.00009375692,0.00034702485,0.000018877467,1.7758673e-7,0.000026843498,0.43634483,0.0001756578],"about_ca_topic_score_codex":0.00014318174,"about_ca_topic_score_gemma":0.0010690831,"teacher_disagreement_score":0.6079038,"about_ca_system_score_codex":0.00008221214,"about_ca_system_score_gemma":0.002512634,"threshold_uncertainty_score":0.9954387},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9832172,0.003725362,0.0003070355,0.005668972,0.0005693303,0.0018591885,0.0023708055,0.0012278166,0.0010543335],"genre_scores_gemma":[0.98698163,0.006290664,0.00041593998,0.0013239913,0.0003735507,0.00012322728,0.0042872108,0.000050605,0.00015317273],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99566764,0.0012205848,0.0010295355,0.0009289217,0.00028350498,0.0008698356],"domain_scores_gemma":[0.99400896,0.0018333734,0.0005298152,0.0015514666,0.00039665008,0.0016797142],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029870893,0.0002972673,0.0011199892,0.00028490386,0.0002635605,0.00002521034,0.000269546,0.00014101119,0.00016090226],"category_scores_gemma":[0.006180872,0.00027582227,0.00007668369,0.0008567288,0.00052208215,0.00023880615,0.00022347104,0.00020022222,0.00013927321],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032306684,0.00032876612,0.88495076,0.00051030534,0.000062386294,0.000038669612,0.000093921844,7.9050875e-7,0.00010815586,0.00017362247,0.105539165,0.007870411],"study_design_scores_gemma":[0.0014347723,0.00063509983,0.9178234,0.0000657485,0.0000033557967,0.00011070299,0.000016476817,0.0062549664,4.3935978e-7,0.000050677103,0.073390685,0.00021372303],"about_ca_topic_score_codex":0.00018890071,"about_ca_topic_score_gemma":0.00010213138,"teacher_disagreement_score":0.03287261,"about_ca_system_score_codex":0.00019862858,"about_ca_system_score_gemma":0.0015991598,"threshold_uncertainty_score":0.9999694},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99144226,0.0027256706,0.00078260695,0.00040091993,0.0011696691,0.00011712778,0.000058602105,0.00003095776,0.0032721586],"genre_scores_gemma":[0.99817806,0.00025074204,0.000057817564,0.00021413805,0.0012487709,0.000003413771,0.000018048115,0.000011676194,0.00001731727],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974763,0.00009923059,0.0013876663,0.00015312577,0.00068129966,0.00020241133],"domain_scores_gemma":[0.99562395,0.00027890588,0.00212333,0.00020908797,0.0016155614,0.0001491727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072589825,0.00012736932,0.0003398964,0.00011623622,0.00006259591,0.000044079967,0.00028087804,0.00004421603,0.000042731917],"category_scores_gemma":[0.0032097562,0.00009388114,0.00024150655,0.00023535576,0.0002183921,0.00016187206,0.00008631941,0.00015768138,0.0000052527616],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069498963,0.00022023045,0.9780412,0.000007958269,0.0002565651,0.00015901614,0.000033115197,0.00029730698,0.00021422698,0.00030679177,0.0008196742,0.018948948],"study_design_scores_gemma":[0.0011594382,0.00020566124,0.98918045,0.00031290096,0.000034917364,0.000897304,0.00012526482,0.0008736669,0.00016851445,0.0014644775,0.0054823603,0.0000950564],"about_ca_topic_score_codex":0.00012861729,"about_ca_topic_score_gemma":0.00064923236,"teacher_disagreement_score":0.018853892,"about_ca_system_score_codex":0.00028757015,"about_ca_system_score_gemma":0.0003892973,"threshold_uncertainty_score":0.38426098},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98962873,0.0015906616,0.000388457,0.0051429328,0.00027218927,0.0018856291,0.000103942155,0.00037353576,0.00061390037],"genre_scores_gemma":[0.99740326,0.00006453868,0.0010363222,0.00048521464,0.0004348172,0.00020403728,0.00017110299,0.00006695986,0.00013371829],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952801,0.00062697777,0.00094192434,0.0011177593,0.0008236301,0.0012096267],"domain_scores_gemma":[0.99705195,0.00027681582,0.00038100348,0.00080543564,0.000477388,0.0010074218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027664094,0.0004455955,0.0012318654,0.00042803292,0.00035478326,0.00017333607,0.00026967333,0.00011283097,0.000034324676],"category_scores_gemma":[0.00036093028,0.00038805226,0.000059227576,0.0013328099,0.00038325082,0.0003842579,0.00008966432,0.0003724275,0.00002755534],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000534723,0.0005647555,0.98979914,0.00050157134,0.000059906673,0.00013973046,0.0011158771,0.000002298858,0.000011848697,0.00049295713,0.0010889971,0.0056881877],"study_design_scores_gemma":[0.0033318202,0.0016756441,0.9860627,0.0004466577,0.0000017003161,0.0003838418,0.0018292989,0.0015053294,4.0453372e-7,0.000009089303,0.0043592835,0.00039421133],"about_ca_topic_score_codex":0.0004071219,"about_ca_topic_score_gemma":0.0010436858,"teacher_disagreement_score":0.007774537,"about_ca_system_score_codex":0.0006351806,"about_ca_system_score_gemma":0.001489077,"threshold_uncertainty_score":0.9998571},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086556874,0.38839707,0.008355195,0.41989517,0.005971549,0.007161979,0.010828694,0.0011384173,0.07169507],"genre_scores_gemma":[0.98551196,0.0010801943,0.00038708563,0.008510346,0.00027895722,0.00003321616,0.0038596138,0.00002620315,0.00031241882],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99696463,0.00049131457,0.0006537961,0.00048603982,0.0008042019,0.00060001126],"domain_scores_gemma":[0.98994493,0.00020001318,0.0003284502,0.0067092488,0.00036154798,0.0024558075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029182527,0.00022082207,0.00056836085,0.00001919054,0.00030096676,0.00009483464,0.0012560689,0.000032540178,0.000020483598],"category_scores_gemma":[0.0038458395,0.00016306243,0.000047276113,0.0005867694,0.00050368224,0.00016012207,0.0019626298,0.00027376896,0.000004617476],"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":[0.00060342596,0.00017565073,0.43208718,0.00015093287,0.0002966021,0.0032745216,0.000048946094,0.00009849847,3.7631466e-8,0.0023639938,0.52224183,0.03865841],"study_design_scores_gemma":[0.0010154722,0.000023132172,0.0644082,0.00011061387,0.00009276873,0.00060712383,0.00033655346,0.0051939003,5.6684566e-8,0.00096110214,0.9270468,0.0002043077],"about_ca_topic_score_codex":0.3516053,"about_ca_topic_score_gemma":0.5770854,"teacher_disagreement_score":0.8989551,"about_ca_system_score_codex":0.00075959205,"about_ca_system_score_gemma":0.023883633,"threshold_uncertainty_score":0.98165005},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96373725,0.0017979197,0.00023803172,0.02893458,0.00023838365,0.0011434631,0.0031059678,0.0002662704,0.000538159],"genre_scores_gemma":[0.9823207,0.001052956,0.00014233144,0.013591373,0.0003233018,0.000115990304,0.002025305,0.000037186885,0.00039082163],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961045,0.00048548778,0.0008509584,0.0008777447,0.00045028247,0.0012310534],"domain_scores_gemma":[0.99607456,0.00012391702,0.000540821,0.0007497524,0.0004959586,0.0020149655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019616652,0.00034040777,0.0014606094,0.00025919953,0.00085434754,0.0002087153,0.0001550288,0.00011726907,0.00014118546],"category_scores_gemma":[0.0003635865,0.00032076862,0.00014118303,0.0010925692,0.0006177711,0.0003246147,0.00016339948,0.00036939955,0.000039676524],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097482036,0.00015019195,0.9816885,0.00024503795,0.00027710287,0.0000110068195,0.0014843849,3.923615e-8,0.00003519016,0.00018040117,0.00650204,0.009328631],"study_design_scores_gemma":[0.0013770673,0.00071773207,0.9246792,0.000038662274,0.000006070414,0.00004034387,0.0006952265,0.00041954475,7.224739e-7,0.000013527349,0.07176872,0.00024317084],"about_ca_topic_score_codex":0.0009096556,"about_ca_topic_score_gemma":0.004196352,"teacher_disagreement_score":0.06526668,"about_ca_system_score_codex":0.0003677364,"about_ca_system_score_gemma":0.0007175134,"threshold_uncertainty_score":0.9999244},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8162141,0.052298043,0.0003055541,0.02894898,0.007987187,0.0032713136,0.018834062,0.0015209048,0.070619844],"genre_scores_gemma":[0.9845471,0.00024149162,0.00003007279,0.005121391,0.00041994313,0.00006577947,0.008090774,0.0000654737,0.001417975],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99718505,0.0001942433,0.0005078281,0.00054778956,0.0009788587,0.0005862215],"domain_scores_gemma":[0.98863983,0.0003129772,0.00025877255,0.00870673,0.0004502447,0.0016314423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063366053,0.00028892257,0.00047747252,0.00009436337,0.0003721523,0.00007966318,0.0012463462,0.000041107658,0.00007523981],"category_scores_gemma":[0.004319392,0.00022568938,0.00009731311,0.0006970956,0.00021402039,0.00019372247,0.0017671587,0.00030524083,0.00002588362],"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":[0.00015321386,0.00027952308,0.34774166,0.00014883671,0.00034222705,0.0081771,0.0000772644,0.00009134018,0.0000069215557,0.0000325225,0.6350123,0.007937024],"study_design_scores_gemma":[0.0010964226,0.000019743831,0.06470281,0.00017185995,0.0004167787,0.00027241476,0.0005751404,0.002317156,0.0000080907585,0.00004846737,0.9300067,0.00036444134],"about_ca_topic_score_codex":0.0868056,"about_ca_topic_score_gemma":0.13418023,"teacher_disagreement_score":0.29499432,"about_ca_system_score_codex":0.00050593494,"about_ca_system_score_gemma":0.010035196,"threshold_uncertainty_score":0.995577},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95499736,0.006058025,0.00007168782,0.0010305535,0.0005479857,0.0014389079,0.0016350562,0.00020889637,0.034011524],"genre_scores_gemma":[0.99704504,0.00000836494,0.00015886026,0.00023655499,0.00004708571,0.00011235811,0.0022381893,0.00003203989,0.000121530284],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99463946,0.00059060036,0.0006971778,0.00031648073,0.003493968,0.0002622933],"domain_scores_gemma":[0.991995,0.0001369367,0.0004367865,0.0016156981,0.00494404,0.00087158394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041537797,0.00019268063,0.0004994831,0.00009480771,0.00007554524,0.000014011941,0.00032407182,0.000029367655,0.00009190033],"category_scores_gemma":[0.0032710782,0.00019749683,0.00010136532,0.0004018584,0.000099480974,0.000094027964,0.000111788424,0.00014830829,0.000007390304],"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":[0.001571414,0.000924883,0.7917064,0.00156362,0.0009860479,0.005645916,0.000042753913,0.018777315,0.0002716449,0.0002727579,0.1762641,0.0019731587],"study_design_scores_gemma":[0.013598755,0.00007282917,0.48203444,0.0010371426,0.0012198704,0.0012051513,0.0004878932,0.41043752,0.0003527066,0.00009736779,0.08861525,0.0008410317],"about_ca_topic_score_codex":0.13332242,"about_ca_topic_score_gemma":0.090347774,"teacher_disagreement_score":0.3916602,"about_ca_system_score_codex":0.0026124802,"about_ca_system_score_gemma":0.048714675,"threshold_uncertainty_score":0.9566782},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99415296,0.00021250703,0.00095326477,0.0015927255,0.0001773863,0.0016437231,0.00006030998,0.00032300444,0.0008841308],"genre_scores_gemma":[0.9880412,0.0000075446533,0.0002799203,0.010777859,0.00027072322,0.00040541912,0.0001370414,0.00003219656,0.00004807699],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971992,0.000351216,0.00065756484,0.00046210547,0.00059477944,0.0007351698],"domain_scores_gemma":[0.9974337,0.00012160456,0.00032235816,0.00066609797,0.00048154348,0.00097465224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012717942,0.0003141202,0.00058872736,0.00037044048,0.00028828377,0.00016229363,0.00013163475,0.000096452764,0.000105702784],"category_scores_gemma":[0.00027324457,0.00025619118,0.000048465954,0.0008313038,0.00019527659,0.00062421954,0.000065391425,0.00024330024,0.000098032426],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010792064,0.00022725531,0.93381333,0.0002754598,0.000051210973,0.000016055918,0.0021765605,6.152516e-7,0.00000656065,4.5015776e-7,0.0013269443,0.061026365],"study_design_scores_gemma":[0.0032481772,0.0023879628,0.8631873,0.000043161574,0.000002550123,0.000040298866,0.00061732833,0.00043003407,0.0000046469877,9.448059e-7,0.12979048,0.00024710436],"about_ca_topic_score_codex":0.00008673969,"about_ca_topic_score_gemma":0.0009939793,"teacher_disagreement_score":0.12846354,"about_ca_system_score_codex":0.00016793165,"about_ca_system_score_gemma":0.0009109086,"threshold_uncertainty_score":0.99998903},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954821,0.00016219381,0.0001807723,0.00073502125,0.000059609105,0.00046171003,0.0002223626,0.0000636218,0.0026326105],"genre_scores_gemma":[0.99667394,0.0000083355035,0.002515977,0.00024272453,0.000059823902,0.000028047802,0.00046145802,0.0000074119926,0.0000022675724],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985344,0.00030532328,0.00019867824,0.00016868579,0.0006899198,0.000103021855],"domain_scores_gemma":[0.9978722,0.000044651424,0.00010158581,0.0006639099,0.001204337,0.000113333816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041798785,0.00007874248,0.00016140066,0.000023029157,0.000074412426,0.000013996312,0.00008717166,0.00006921581,0.0003650784],"category_scores_gemma":[0.00048937876,0.00006703818,0.000022396283,0.00014089054,0.000248327,0.00010756154,0.000029566208,0.00014310869,0.00007406405],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047526302,0.0023313796,0.9613756,0.00012876921,0.00029825082,0.0000027808721,0.00058751577,0.000020949785,0.0017661748,0.0043238956,0.0077709714,0.020918438],"study_design_scores_gemma":[0.0041027744,0.00069208577,0.9510907,0.00048478864,0.0006925267,0.00001935305,0.000091280584,0.037488215,0.0020094982,0.002699043,0.0004454073,0.0001843541],"about_ca_topic_score_codex":0.000015265856,"about_ca_topic_score_gemma":0.00022188289,"teacher_disagreement_score":0.037467264,"about_ca_system_score_codex":0.00008850386,"about_ca_system_score_gemma":0.00033762027,"threshold_uncertainty_score":0.39973524},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109026775,0.25034782,0.0011040312,0.6274016,0.0059336256,0.0053861924,0.00041866678,0.000108837856,0.00027245018],"genre_scores_gemma":[0.014426681,0.052234206,0.004064366,0.90639013,0.0210622,0.00010835763,0.00049035024,0.0001863798,0.0010373439],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9572519,0.026911821,0.013265458,0.0010028636,0.00065657636,0.00091139105],"domain_scores_gemma":[0.88770586,0.08919263,0.019970052,0.0015575117,0.000803505,0.0007704362],"candidate_categories":["metaresearch","metaepi_narrow","metaepi_broad","research_integrity"],"consensus_categories":["metaresearch","research_integrity"],"category_scores_codex":[0.038921144,0.00083046884,0.022356395,0.0006619338,0.00006239322,0.000026787426,0.00073188334,0.0023276717,0.00015769919],"category_scores_gemma":[0.3166293,0.00053017307,0.0023100297,0.0004417824,0.00075786316,0.00017646974,0.00021392564,0.0059371567,0.00019321655],"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":[0.0004405601,0.00012427199,0.31856513,0.06867583,0.00062170153,0.005228426,0.00001642166,0.0000016950244,0.000002663669,0.0000021215085,0.6061805,0.0001406844],"study_design_scores_gemma":[0.0070052897,0.0021325054,0.6405303,0.19862154,0.0037755526,0.0029168855,0.000053875476,0.00027628592,0.0000022256095,0.00068483123,0.14318113,0.0008196157],"about_ca_topic_score_codex":0.000024980705,"about_ca_topic_score_gemma":0.000058524056,"teacher_disagreement_score":0.46299937,"about_ca_system_score_codex":0.00016058344,"about_ca_system_score_gemma":0.00031839192,"threshold_uncertainty_score":0.999715},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.797289,0.00095068035,0.0759392,0.057758294,0.0298393,0.0019333309,0.0300852,0.00047915181,0.0057258015],"genre_scores_gemma":[0.9594103,0.00020772048,0.023397537,0.0017703952,0.0027500326,0.0000034276338,0.011933594,0.000021233636,0.0005057312],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969807,0.000026578497,0.0004920554,0.00066910556,0.0015328899,0.00029864936],"domain_scores_gemma":[0.996495,0.00008585377,0.00031993157,0.0016209387,0.0011919746,0.00028627974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020184866,0.00012252187,0.0001592059,0.00034321542,0.0005443804,0.00035773084,0.004006613,0.00004429374,0.0004008623],"category_scores_gemma":[0.00183136,0.00010513383,0.000048767462,0.00038986656,0.00041491023,0.0029934247,0.0022141407,0.00016357841,0.00008875634],"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":[0.00068247004,0.00019872609,0.81280005,0.000035238405,0.0001790903,0.00007155259,0.00007683283,0.000024835674,0.0037894296,0.0023221574,0.1321077,0.047711916],"study_design_scores_gemma":[0.0011944735,0.000107068525,0.38337347,0.00031788013,0.00005418199,0.0006133033,0.00002590575,0.055255923,0.000056507033,0.00086231495,0.55794036,0.00019858989],"about_ca_topic_score_codex":0.000071471775,"about_ca_topic_score_gemma":0.00044757372,"teacher_disagreement_score":0.42942655,"about_ca_system_score_codex":0.00018204357,"about_ca_system_score_gemma":0.00028713213,"threshold_uncertainty_score":0.7445351},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98158306,0.00077969505,0.0011379127,0.013829372,0.0005886385,0.00032027,0.0000060100365,0.00007980802,0.0016752146],"genre_scores_gemma":[0.98106885,0.00023037785,0.00018923983,0.00062902365,0.002625315,0.0000042480883,0.000017905639,0.000028813327,0.015206235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977485,0.00005059022,0.00021164671,0.00024858973,0.00030556857,0.001435132],"domain_scores_gemma":[0.9992545,0.000013984135,0.000072173774,0.000347613,0.00011404673,0.00019765069],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010571838,0.000118761694,0.00021880843,0.00014794053,0.000053661603,0.00001982173,0.00013036025,0.00004218091,0.0005401784],"category_scores_gemma":[0.00014139485,0.000103349885,0.000095729505,0.0002484873,0.000013828257,0.000054412485,0.000033328517,0.0010324016,0.002033891],"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":[0.005776512,0.0013196003,0.21051437,0.00019388749,0.0023639405,0.00013332743,0.0006862586,0.0016057555,0.105605885,0.17077075,0.1427212,0.3583085],"study_design_scores_gemma":[0.008473682,0.0055529657,0.06621047,0.00017094855,0.00030618513,0.009778055,0.0025384715,0.0006220688,0.0023158884,0.040998686,0.8619763,0.001056264],"about_ca_topic_score_codex":0.000012655827,"about_ca_topic_score_gemma":0.000077536744,"teacher_disagreement_score":0.7192551,"about_ca_system_score_codex":0.0008537188,"about_ca_system_score_gemma":0.0012248656,"threshold_uncertainty_score":0.9987431},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96742034,0.013776048,0.0005849928,0.014718127,0.00052302907,0.001103033,0.0013490589,0.00016604875,0.0003593109],"genre_scores_gemma":[0.9913783,0.0033398715,0.00084348366,0.001853353,0.00017424625,0.0005571619,0.0017582334,0.000050227038,0.000045106426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99699056,0.0001882758,0.000996437,0.00080047356,0.00025567797,0.00076859794],"domain_scores_gemma":[0.99727,0.0002670787,0.00057274767,0.0008387082,0.00057666545,0.00047480591],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005506649,0.0003742715,0.0013448165,0.00073057733,0.00026977016,0.000021346286,0.00043476222,0.00019985494,0.00020933498],"category_scores_gemma":[0.00016907485,0.0003476041,0.00009831746,0.00047725273,0.0025006216,0.00023709082,0.0001926397,0.00040872148,0.000004204569],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013321055,0.0016805554,0.7587412,0.00042435343,0.0007964222,0.000006731328,0.002668222,2.6054747e-7,0.07231365,0.00057322544,0.008429586,0.15303367],"study_design_scores_gemma":[0.010854645,0.0025460948,0.76951677,0.0002796546,0.00023311538,0.000969778,0.0011088357,0.0023195585,0.021034403,0.0002906353,0.19034916,0.0004973754],"about_ca_topic_score_codex":0.00057673577,"about_ca_topic_score_gemma":0.0005434128,"teacher_disagreement_score":0.18191957,"about_ca_system_score_codex":0.000118897726,"about_ca_system_score_gemma":0.00044889652,"threshold_uncertainty_score":0.9998976},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68725413,0.00024299754,0.27499285,0.01709267,0.0061684,0.0013134863,0.012670736,0.00011453337,0.00015018169],"genre_scores_gemma":[0.8674854,0.000057938447,0.12745957,0.0017359918,0.0010507369,0.0000015400465,0.0021127323,0.000014059513,0.000082038714],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997757,0.000029572562,0.0005144633,0.0005713501,0.0008778695,0.00024974282],"domain_scores_gemma":[0.99713004,0.00006284633,0.00037951794,0.0010454339,0.00093922456,0.00044295617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001668257,0.00010741553,0.00019504642,0.00029646454,0.00043154162,0.0002600647,0.0016383328,0.00002400974,0.00001892824],"category_scores_gemma":[0.0028185798,0.00009861868,0.000020230578,0.00039594254,0.00027697912,0.0019960916,0.0009472956,0.000127931,0.000006209751],"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":[0.0037800085,0.00076387683,0.60513157,0.00020128075,0.00071688945,0.00017173449,0.0025096452,0.0033886184,0.0230042,0.026867913,0.07833446,0.2551298],"study_design_scores_gemma":[0.0026637802,0.0004143366,0.2473095,0.0006890232,0.00007635304,0.000816208,0.00032048658,0.66984344,0.000070067974,0.001508705,0.07593457,0.00035352327],"about_ca_topic_score_codex":0.00014646433,"about_ca_topic_score_gemma":0.00028732623,"teacher_disagreement_score":0.6664548,"about_ca_system_score_codex":0.0002583172,"about_ca_system_score_gemma":0.0007953776,"threshold_uncertainty_score":0.4021553},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81677794,0.00026818135,0.1668607,0.015002546,0.000442646,0.00050916406,0.000050084192,0.00007829155,0.000010424785],"genre_scores_gemma":[0.92805123,0.00014479077,0.07020221,0.00064269174,0.00088581815,0.0000107016185,0.000015810694,0.000027752565,0.000019010635],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963976,0.00018462283,0.0019768986,0.0001281701,0.0005702004,0.00074248575],"domain_scores_gemma":[0.994341,0.0001521611,0.0030649563,0.00026299825,0.001228355,0.000950533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065292693,0.00020808994,0.0007156995,0.0008441132,0.0004150326,0.00012577453,0.00018777058,0.00013768776,0.0000068720137],"category_scores_gemma":[0.002623213,0.00017786279,0.0000962437,0.0006528489,0.00013142088,0.0012984315,0.00010261257,0.00077418453,0.0000022893557],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016257314,0.0004338988,0.08794935,0.0015318032,0.00016893998,0.0000042040742,0.0057926937,0.000022439996,0.00040359946,0.0000791656,0.0003897611,0.90306157],"study_design_scores_gemma":[0.021535909,0.036579162,0.5073863,0.004902922,0.00014764261,0.0037086217,0.029175831,0.12029565,0.0021449467,0.00034565487,0.2724002,0.0013771057],"about_ca_topic_score_codex":0.00003030781,"about_ca_topic_score_gemma":0.000044652672,"teacher_disagreement_score":0.90168446,"about_ca_system_score_codex":0.00064725324,"about_ca_system_score_gemma":0.0031972274,"threshold_uncertainty_score":0.7253034},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958367,0.0006211326,0.00069926115,0.0007483402,0.0002342998,0.0013510968,0.000067117704,0.00020732949,0.0002347667],"genre_scores_gemma":[0.9927374,0.000075501994,0.0061271777,0.00014695498,0.00034476162,0.000037934147,0.00044732634,0.000045560486,0.00003738374],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99623114,0.0011660929,0.00067892065,0.00076494605,0.00048308144,0.0006758326],"domain_scores_gemma":[0.9975916,0.00025769763,0.00036205622,0.0008985784,0.00026754185,0.0006224978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039009934,0.00026566474,0.0007933707,0.00028748633,0.00026561163,0.00011489374,0.00027899304,0.00007576158,0.000012720641],"category_scores_gemma":[0.0008533439,0.00023321166,0.000020609721,0.0008304077,0.00015321285,0.00038330568,0.00030082636,0.00043071134,0.000012013824],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024457727,0.00069301395,0.93836457,0.00012271946,0.000040947107,0.000014142658,0.0013915456,0.000002837903,0.000042263062,0.0000039189104,0.00008493022,0.05899456],"study_design_scores_gemma":[0.0037753775,0.00217742,0.95628625,0.00007370891,0.000002805792,0.000013089808,0.0009872469,0.023732735,0.0000017766013,0.0000025787776,0.012695246,0.0002517655],"about_ca_topic_score_codex":0.0017374422,"about_ca_topic_score_gemma":0.0005027851,"teacher_disagreement_score":0.058742795,"about_ca_system_score_codex":0.0002184206,"about_ca_system_score_gemma":0.000711223,"threshold_uncertainty_score":0.9510096},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8065699,0.009252303,0.000017001868,0.0033957355,0.000490016,0.0016381601,0.0024603754,0.00058030075,0.17559624],"genre_scores_gemma":[0.9907367,0.00019783173,0.00026368044,0.004123935,0.00010670921,0.000114842944,0.002560188,0.000059197737,0.0018369152],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99739015,0.00020428085,0.000726765,0.00044572528,0.0005472486,0.0006858034],"domain_scores_gemma":[0.9879132,0.00012372817,0.0002460333,0.008192012,0.0009651743,0.0025598384],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044436834,0.00029006443,0.00049084955,0.00013508857,0.0006402254,0.000061839695,0.0012683372,0.00008682334,0.0006025627],"category_scores_gemma":[0.0010226577,0.00030515392,0.0001065198,0.0005792631,0.0004989484,0.00018016034,0.00048239456,0.00038406992,0.000056180073],"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":[0.00020362462,0.00019362476,0.83386284,0.000116902476,0.00032220889,0.0034115845,0.00010616842,0.000024278621,0.00005280273,0.0003753378,0.15857376,0.002756884],"study_design_scores_gemma":[0.0005677467,0.00003652458,0.23789376,0.00015802607,0.00017980863,0.0003581926,0.00031129684,0.00070760545,0.0000070664223,0.00010431271,0.7593374,0.00033822912],"about_ca_topic_score_codex":0.9794934,"about_ca_topic_score_gemma":0.99848545,"teacher_disagreement_score":0.6007637,"about_ca_system_score_codex":0.00217748,"about_ca_system_score_gemma":0.041659597,"threshold_uncertainty_score":0.99994004},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43441075,0.0007004266,0.21790259,0.33995226,0.0011311868,0.0031311456,0.0022210581,0.00021493736,0.0003356591],"genre_scores_gemma":[0.39190194,0.0009753697,0.5502897,0.05051528,0.00154374,0.00005221217,0.004584482,0.00006810977,0.00006919605],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959244,0.00017471064,0.0026311204,0.00014656286,0.00038763697,0.0007355457],"domain_scores_gemma":[0.9939409,0.00019013396,0.002353416,0.00077601377,0.002131127,0.0006084077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008419043,0.00016755893,0.000706644,0.0015617275,0.00019190955,0.00007430361,0.00060588855,0.0001431489,0.000035789963],"category_scores_gemma":[0.005637853,0.00014582348,0.000039494025,0.0026477918,0.00010751798,0.0015786802,0.00016202003,0.00064337207,0.0000047269364],"study_design_candidate":"not_applicable","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":[0.00013850252,0.0012987518,0.024515446,0.00093718065,0.00010466635,0.0000042023753,0.004877355,0.0000060751495,0.000008218713,0.00026552507,0.06650056,0.9013435],"study_design_scores_gemma":[0.0045040334,0.010761752,0.008978688,0.00046330164,0.000008116582,0.00042276707,0.010261481,0.05459465,0.000011480664,0.00011650501,0.9096752,0.00020206513],"about_ca_topic_score_codex":0.000034820325,"about_ca_topic_score_gemma":0.00032904555,"teacher_disagreement_score":0.90114146,"about_ca_system_score_codex":0.00061537826,"about_ca_system_score_gemma":0.00983177,"threshold_uncertainty_score":0.99578154},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97860146,0.00003986964,0.008356747,0.00084544776,0.0017940915,0.0007080091,0.009609832,0.00002256786,0.000021981245],"genre_scores_gemma":[0.95527875,0.000005492739,0.040320106,0.0002568855,0.0013979223,0.000005118544,0.002677404,0.0000110462115,0.000047261583],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99590987,0.00028463898,0.00068614405,0.0009369647,0.0017899763,0.00039242796],"domain_scores_gemma":[0.9948595,0.0006660337,0.00035229026,0.0014679712,0.002248083,0.00040611372],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0057716216,0.00014461439,0.00026452352,0.00024069767,0.0008279444,0.00028614872,0.0029008812,0.00004289058,0.000086448024],"category_scores_gemma":[0.014531241,0.00011534393,0.000026677655,0.00040622996,0.0005455995,0.001982545,0.0016270325,0.00038903326,0.000007691299],"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":[0.001139145,0.0010814746,0.95870084,0.000010112983,0.00031464014,0.0089535285,0.00030101705,0.000112136695,0.0028713867,0.00013402964,0.013922963,0.012458743],"study_design_scores_gemma":[0.00198196,0.00035496175,0.61797786,0.00010241585,0.000059393045,0.003155086,0.0018804272,0.36847547,0.000034764682,0.00013848835,0.0056292876,0.00020990378],"about_ca_topic_score_codex":0.3733717,"about_ca_topic_score_gemma":0.54236513,"teacher_disagreement_score":0.36836332,"about_ca_system_score_codex":0.00070406933,"about_ca_system_score_gemma":0.0040589073,"threshold_uncertainty_score":0.99376976},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93884254,0.0039324877,0.0001801012,0.05323518,0.0002247017,0.0030426837,0.00040840884,0.00009515448,0.000038755363],"genre_scores_gemma":[0.9948223,0.0031540187,0.00009332131,0.0013596943,0.00011724651,0.00020238567,0.00012656591,0.000056946803,0.000067494824],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9850741,0.008239823,0.0029597094,0.0010551075,0.0008064815,0.0018647846],"domain_scores_gemma":[0.994376,0.00070777937,0.0016435316,0.0012737104,0.0004771286,0.0015218206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.02331807,0.00042197751,0.0015360742,0.00071432977,0.00068279065,0.000056339733,0.00035171412,0.00010263386,0.00007494599],"category_scores_gemma":[0.0030252885,0.00033986956,0.000092184244,0.0013826885,0.0002955979,0.00032053818,0.00041559825,0.0005934678,0.0000027306494],"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":[0.00028473823,0.0009694321,0.9425562,0.0018678723,0.00007666075,0.00008200844,0.011448352,4.002536e-7,0.000007703448,0.000055455665,0.00065598555,0.041995205],"study_design_scores_gemma":[0.0030020655,0.0020864187,0.9756703,0.00014447965,0.0000014649426,0.00031308245,0.015237278,0.00077089673,1.9282788e-7,0.000016347449,0.0025150254,0.00024247824],"about_ca_topic_score_codex":0.0073094023,"about_ca_topic_score_gemma":0.026389822,"teacher_disagreement_score":0.0559798,"about_ca_system_score_codex":0.0006391175,"about_ca_system_score_gemma":0.003849562,"threshold_uncertainty_score":0.99990535},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088382155,0.0006315098,0.0041888813,0.00009555815,0.89977235,0.002393583,0.0037285355,0.00052216754,0.0002852482],"genre_scores_gemma":[0.019354139,0.0004254359,0.00015369242,0.00023904622,0.97335225,0.0003255978,0.005321693,0.0001291252,0.0006990109],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844253,0.00003397534,0.00030974278,0.0006613761,0.00022194996,0.0003304196],"domain_scores_gemma":[0.9984208,0.00019668606,0.00019691371,0.00036949845,0.000556074,0.00026006557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001437278,0.0003313208,0.0005177753,0.00026204585,0.00020827772,0.00030029358,0.00009840342,0.00038192899,0.000065935885],"category_scores_gemma":[0.0015800174,0.0003148694,0.00017175647,0.00019163606,0.00007879023,0.00027343712,0.00022902977,0.00023042562,0.0000397389],"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":[0.00027066114,0.00014850279,0.009417755,0.0012386317,0.000103284445,0.0000039818046,0.000014652351,0.0000014549445,0.003334645,0.0000013338713,0.97452086,0.010944261],"study_design_scores_gemma":[0.001535443,0.0007386768,0.01466161,0.0003632402,0.00047458764,0.000010563057,0.0000014308749,0.000036514717,0.00066917174,0.000028577251,0.98111844,0.0003617645],"about_ca_topic_score_codex":0.00088377343,"about_ca_topic_score_gemma":0.00065555715,"teacher_disagreement_score":0.07357989,"about_ca_system_score_codex":0.00016446484,"about_ca_system_score_gemma":0.0002754194,"threshold_uncertainty_score":0.9999303},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8398412,0.0025785556,0.07701817,0.0072271745,0.019076468,0.0013070825,0.05098098,0.00021024392,0.0017601486],"genre_scores_gemma":[0.96951044,0.00026153534,0.0054872287,0.0003809892,0.0038614809,0.0000038041446,0.020068051,0.00001713843,0.0004093086],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99756676,0.000043499334,0.00044435466,0.000795683,0.000867225,0.00028248696],"domain_scores_gemma":[0.99685776,0.00007521862,0.0001292817,0.0016954928,0.00094847794,0.0002937675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022191233,0.00014570753,0.0001903742,0.0003039681,0.0003830365,0.0004234707,0.0035923976,0.00004424071,0.000109318076],"category_scores_gemma":[0.001637344,0.00011863153,0.000025593805,0.00033941932,0.00046770586,0.004207355,0.0016955831,0.00017510896,0.000050602048],"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":[0.00070769346,0.00052858103,0.15066779,0.0001634367,0.00031647872,0.00007095573,0.0017206501,0.0000033261558,0.006253277,0.001485212,0.1470491,0.6910335],"study_design_scores_gemma":[0.001763224,0.00011622203,0.33740646,0.00025234808,0.00006838004,0.00061714166,0.00019274384,0.07034337,0.00006807436,0.0002822388,0.5886291,0.0002606831],"about_ca_topic_score_codex":0.000106807005,"about_ca_topic_score_gemma":0.0003277585,"teacher_disagreement_score":0.69077283,"about_ca_system_score_codex":0.00009809204,"about_ca_system_score_gemma":0.00032533114,"threshold_uncertainty_score":0.66756284},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946591,0.00020382262,0.0015278789,0.0009417175,0.00009216207,0.00088102993,0.0006815193,0.00013440625,0.0008783122],"genre_scores_gemma":[0.99881744,0.000030512047,0.00018482917,0.00021759502,0.00017131236,0.000036233134,0.00036566,0.000015878202,0.00016056473],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99663275,0.00062285515,0.00036278565,0.0009846835,0.0008005718,0.00059636554],"domain_scores_gemma":[0.99460536,0.0004653958,0.000073957686,0.0013417103,0.0029933213,0.00052026036],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.002469177,0.00019627716,0.0005303059,0.01861754,0.00020761583,0.00009468884,0.00023612702,0.000112826965,0.00024706102],"category_scores_gemma":[0.00875792,0.00018185344,0.000110936606,0.15178499,0.00043429455,0.00012414246,0.0002931399,0.00014215619,0.00012746116],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007242039,0.00024781603,0.9843161,0.00009400279,0.00020806602,0.000044713695,0.00006773925,0.000012509542,0.000016984795,0.00037137285,0.0054002833,0.008496171],"study_design_scores_gemma":[0.0005755117,0.0005681488,0.9923052,0.000015353919,0.000088262845,0.0000027459416,0.00015772684,0.00053679995,0.000041755025,0.0028123213,0.0027310436,0.00016510139],"about_ca_topic_score_codex":0.0052058655,"about_ca_topic_score_gemma":0.0017792771,"teacher_disagreement_score":0.13316745,"about_ca_system_score_codex":0.0015121885,"about_ca_system_score_gemma":0.00047750937,"threshold_uncertainty_score":0.9995917},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9870445,0.0018286234,0.000028676373,0.005361443,0.00026928596,0.00416278,0.00014339435,0.000106430096,0.0010548374],"genre_scores_gemma":[0.9921744,0.0008760249,0.0007261989,0.004931075,0.0004643418,0.000359203,0.00009406463,0.000057506833,0.000317203],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9910528,0.0041900226,0.0012346742,0.00096204854,0.0009776611,0.0015828029],"domain_scores_gemma":[0.9959486,0.0005272334,0.00038828165,0.00086394604,0.0004573386,0.0018145755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.011782396,0.00030139138,0.0009863088,0.00078308163,0.0005778296,0.000048365757,0.00019732473,0.00011508643,0.00012802686],"category_scores_gemma":[0.00063837523,0.000262787,0.000054704375,0.0015455203,0.00039184795,0.00018282411,0.00012939284,0.00097453385,0.00016538386],"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":[0.010641681,0.01894526,0.22126946,0.013128376,0.00016868974,0.00027583324,0.33612418,0.0000044705434,0.000070545364,0.0011539725,0.120844215,0.2773733],"study_design_scores_gemma":[0.017238157,0.041586876,0.67771333,0.0018289405,0.00004690062,0.00007587277,0.10332667,0.0015971015,0.000013525567,0.0007321999,0.15490587,0.00093452196],"about_ca_topic_score_codex":0.0054659336,"about_ca_topic_score_gemma":0.011233152,"teacher_disagreement_score":0.4564439,"about_ca_system_score_codex":0.0011300105,"about_ca_system_score_gemma":0.0032195847,"threshold_uncertainty_score":0.9999824},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898099,0.0022848295,0.00012879,0.0039498736,0.00018859626,0.00094417913,0.0009990837,0.00003784489,0.0016569399],"genre_scores_gemma":[0.9988708,0.00015190741,0.00017385503,0.00036267884,0.000095346346,0.00005402223,0.00015807024,0.000012621062,0.000120710516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99835706,0.000115814866,0.00048293726,0.00026447195,0.00048406632,0.00029563007],"domain_scores_gemma":[0.99856704,0.000027202568,0.00013342783,0.00041068706,0.00029145865,0.00057019445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073119835,0.000119338234,0.00042025532,0.00017037743,0.00003256733,0.000011653324,0.000109282795,0.00003941352,0.00015371997],"category_scores_gemma":[0.00037287237,0.00011230343,0.000032998774,0.00031963672,0.0001258881,0.00007302496,0.000052780262,0.00007716625,0.00003099331],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040682204,0.0003005047,0.9876585,0.00013002088,0.000018424033,0.0000053574345,0.0005436666,0.0000027440421,0.000039225764,0.000012997727,0.0089416085,0.0019400999],"study_design_scores_gemma":[0.00080922147,0.00025976368,0.94940937,0.000092669936,0.0000010259182,7.985604e-7,0.0001504735,0.00025713042,0.000003863216,0.000007920697,0.048921727,0.00008603242],"about_ca_topic_score_codex":0.00032569037,"about_ca_topic_score_gemma":0.003116088,"teacher_disagreement_score":0.039980117,"about_ca_system_score_codex":0.00020397146,"about_ca_system_score_gemma":0.0011411649,"threshold_uncertainty_score":0.4579601},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98424023,0.010098528,0.00015280694,0.00021755222,0.000090914444,0.0008128099,0.0039184983,0.000040021816,0.00042864928],"genre_scores_gemma":[0.99468684,0.0018574855,0.0020216773,0.00024270553,0.00005551293,0.00002487745,0.0010334684,0.000034823126,0.00004262184],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965326,0.0010188546,0.0009680343,0.00046689855,0.00046240477,0.0005511979],"domain_scores_gemma":[0.9965805,0.00048439132,0.00048276893,0.0014058672,0.00043023052,0.0006162555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028943496,0.00023128412,0.0013265258,0.00022638227,0.000054334338,0.00002163747,0.00039668978,0.00009373469,0.00010176232],"category_scores_gemma":[0.0013455696,0.00020726092,0.0000701533,0.00071022386,0.00037831275,0.00020648642,0.00033874766,0.00016742571,0.0000107087935],"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":[0.0002483313,0.000792602,0.98833543,0.0013515818,0.000029377736,0.0000026887492,0.00018756598,4.2660517e-7,0.000010708326,0.000009136211,0.0024733979,0.006558736],"study_design_scores_gemma":[0.0025232153,0.00029038286,0.9938236,0.00025163573,0.000009827042,0.0000037862353,0.000100954734,0.0015911297,0.000009087772,0.000016168624,0.001223604,0.00015661641],"about_ca_topic_score_codex":0.018815348,"about_ca_topic_score_gemma":0.020557765,"teacher_disagreement_score":0.010446608,"about_ca_system_score_codex":0.00011376504,"about_ca_system_score_gemma":0.0018879852,"threshold_uncertainty_score":0.9973145},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99874544,0.000019071993,0.00005618929,0.00017682315,0.000028107315,0.00041133733,0.0001259275,0.0000652767,0.00037179832],"genre_scores_gemma":[0.9978534,0.000014811351,0.0012285619,0.00046544868,0.00023228399,0.000017848952,0.000082077364,0.000018574161,0.00008696824],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99920005,0.000029155255,0.00019773637,0.00019105675,0.00022152966,0.00016044815],"domain_scores_gemma":[0.9992794,0.000036341116,0.000051895706,0.00039041074,0.00013984535,0.00010211759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007948272,0.000078960235,0.0002469229,0.00017481923,0.000029964938,0.000011238535,0.0000659051,0.000032204527,0.00006880887],"category_scores_gemma":[0.00021617516,0.000077912766,0.000039353192,0.00045185242,0.00006365616,0.00012095449,0.000057307054,0.00008998488,0.00029834785],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046010442,0.0019930303,0.91233695,0.000042434076,0.00018726624,0.0000021424985,0.00012151847,0.0000055236337,0.083879754,0.000029764333,0.0004865305,0.0008691003],"study_design_scores_gemma":[0.00042148633,0.0001927636,0.972559,0.0004094803,0.00008898922,0.0000010775501,0.000007865689,0.000111012094,0.025930882,0.000049267954,0.00014278882,0.00008539792],"about_ca_topic_score_codex":0.00024931543,"about_ca_topic_score_gemma":0.00020457048,"teacher_disagreement_score":0.060222067,"about_ca_system_score_codex":0.000038519327,"about_ca_system_score_gemma":0.00003637092,"threshold_uncertainty_score":0.38347575},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98938096,0.00001196563,0.007639476,0.001090908,0.000117689306,0.0016011358,0.00010326886,0.00001108199,0.00004350374],"genre_scores_gemma":[0.99744505,0.0000019419695,0.0016372397,0.0006695641,0.00013220505,0.000069681155,0.00002401941,0.000016420565,0.000003871074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99840206,0.00042127364,0.00040616485,0.00025233132,0.00025032624,0.0002678261],"domain_scores_gemma":[0.9987821,0.00009802192,0.00020388096,0.00062457303,0.00011122082,0.00018020319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007805915,0.00011229395,0.00028779122,0.000073276744,0.00006277221,0.0000048005686,0.00018197278,0.000039626022,0.0000092240225],"category_scores_gemma":[0.0002479071,0.00007468923,0.000038017373,0.000656901,0.00002990955,0.000051274354,0.00007974919,0.00014189605,0.0000036338813],"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":[0.00033880147,0.00033818439,0.81543845,0.00006319969,0.000026051604,0.000001244268,0.006688849,0.00039372087,0.00052029337,0.0000128766915,0.0006104631,0.17556787],"study_design_scores_gemma":[0.00058461324,0.0006381642,0.99645615,0.00011630755,0.0000041139288,0.000004879875,0.0005732212,0.0011200436,0.00027523408,0.000024797126,0.00013555014,0.00006693136],"about_ca_topic_score_codex":0.8574567,"about_ca_topic_score_gemma":0.9738508,"teacher_disagreement_score":0.18101768,"about_ca_system_score_codex":0.00052939454,"about_ca_system_score_gemma":0.00092752674,"threshold_uncertainty_score":0.30457386},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63284343,0.0066786073,0.31451753,0.024439694,0.0003221687,0.007898562,0.009338282,0.00017675142,0.003784978],"genre_scores_gemma":[0.9690831,0.0002821652,0.028513378,0.0007741239,0.0002810188,0.000016631113,0.000983203,0.000017874612,0.000048553142],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967075,0.00081265415,0.0004184417,0.0006944958,0.00082325935,0.00054364523],"domain_scores_gemma":[0.99559647,0.001784744,0.00020128101,0.0011005056,0.0007583899,0.0005586334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057517467,0.00012538763,0.0004924384,0.00026778027,0.00029148412,0.00006272962,0.00021940909,0.00010041017,0.00009152356],"category_scores_gemma":[0.0011463863,0.00010951038,0.000035517878,0.00076458853,0.00039037116,0.0003759127,0.0005556449,0.00029741018,0.000004039054],"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":[0.014201781,0.0009921468,0.94180864,0.018640457,0.001441093,0.00004183621,0.003860006,0.000001939387,0.00016023035,0.0023091435,0.008516527,0.008026173],"study_design_scores_gemma":[0.0024299624,0.0009372171,0.677791,0.00019560712,0.00027181653,0.000043763044,0.008195453,0.28261116,0.000026188913,0.00022925607,0.027109724,0.0001588193],"about_ca_topic_score_codex":0.008401088,"about_ca_topic_score_gemma":0.018076919,"teacher_disagreement_score":0.33623964,"about_ca_system_score_codex":0.000105700565,"about_ca_system_score_gemma":0.0011209503,"threshold_uncertainty_score":0.9998406},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9935438,0.0011145803,0.0010901045,0.00020482768,0.00039150185,0.0018677274,0.0015213873,0.00022437406,0.000041691466],"genre_scores_gemma":[0.9976309,0.0002030835,0.00035125145,0.0007865568,0.0003287465,0.00040589008,0.00019198244,0.00008350222,0.000018120847],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99563557,0.00062002224,0.0012289234,0.0009980641,0.00042429846,0.0010930964],"domain_scores_gemma":[0.995957,0.0006757791,0.0005889821,0.0008326131,0.0008296494,0.0011159544],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030636513,0.00047566052,0.0011282832,0.00069020566,0.00047848705,0.00011313376,0.00020775817,0.0002581341,0.000008620705],"category_scores_gemma":[0.002130287,0.0004839176,0.00009694616,0.0012121017,0.0004370976,0.0005285028,0.00011708375,0.00036749477,0.0000039154265],"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":[0.0015836939,0.00069380144,0.9764366,0.0009929375,0.000048454065,0.00000272149,0.0006383035,0.000014471624,0.008860678,0.000011854335,0.000027922193,0.010688575],"study_design_scores_gemma":[0.0069485647,0.0009735685,0.9799196,0.00013930339,0.0000023056311,0.000028028864,0.00016638824,0.0017714213,0.00059704354,0.000006593387,0.008983853,0.0004633335],"about_ca_topic_score_codex":0.0048756073,"about_ca_topic_score_gemma":0.03761979,"teacher_disagreement_score":0.03274418,"about_ca_system_score_codex":0.00021403853,"about_ca_system_score_gemma":0.0011507042,"threshold_uncertainty_score":0.9997612},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99432737,0.00015862143,0.00062035304,0.0015432457,0.000021435362,0.0009891593,0.0022684617,0.000040564522,0.000030808664],"genre_scores_gemma":[0.98248,0.000039143724,0.00022402467,0.00047088444,0.000019414763,0.000025166923,0.016710684,0.0000181712,0.000012512623],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774724,0.00037844756,0.0005657899,0.00046930127,0.00041528267,0.00042392744],"domain_scores_gemma":[0.9978343,0.00049429265,0.00026978794,0.0008863648,0.0002562392,0.00025896236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018741591,0.00018374225,0.00047584454,0.00030097298,0.00017209585,0.00017589204,0.00028322765,0.000057219324,0.000020314847],"category_scores_gemma":[0.0006163997,0.00011838767,0.00003831122,0.001240641,0.00013448483,0.0007005939,0.00013711756,0.000286488,0.0000013797168],"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":[0.00022184636,0.00012302028,0.9787079,0.00018976623,0.00009049079,0.0000091559705,0.002404607,0.004164685,0.00004233747,0.000022304877,0.000022581082,0.014001324],"study_design_scores_gemma":[0.00070434203,0.00015328331,0.2748267,0.000022734022,0.000008445765,0.000016186134,0.0017365733,0.72234845,2.908252e-7,0.000008583893,0.000084245694,0.00009017795],"about_ca_topic_score_codex":0.001602266,"about_ca_topic_score_gemma":0.0011939184,"teacher_disagreement_score":0.71818376,"about_ca_system_score_codex":0.000047762172,"about_ca_system_score_gemma":0.00038940477,"threshold_uncertainty_score":0.4827709},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95400906,0.011081737,0.00037884657,0.0006288534,0.0003192935,0.0017736229,0.031596523,0.00011391176,0.00009814384],"genre_scores_gemma":[0.9520131,0.001526943,0.007637079,0.0012708125,0.00015245099,0.000016020236,0.037348136,0.00002980421,0.000005657341],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99402016,0.0007225609,0.001271046,0.0013269773,0.001873377,0.000785876],"domain_scores_gemma":[0.99496007,0.00015001152,0.0010928666,0.002826419,0.00030548446,0.0006651549],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0052190213,0.00041853587,0.0010219307,0.00013054341,0.00038940928,0.000041167943,0.0008116333,0.000114901544,0.00006873382],"category_scores_gemma":[0.00037172984,0.0004024774,0.000039628805,0.00057264033,0.00056089635,0.0006095,0.0012760463,0.00022367312,0.00004556032],"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":[0.00041861756,0.00034770355,0.7837098,0.0005757324,0.0002900352,0.000010834113,0.00083786587,0.000009125338,0.000023098259,0.000028140721,0.012528838,0.2012202],"study_design_scores_gemma":[0.0024371499,0.00046527784,0.9768465,0.00023055715,0.000040398878,0.00008568216,0.00048372895,0.01024787,0.000008771316,0.000054551943,0.008784632,0.0003148583],"about_ca_topic_score_codex":0.0018639539,"about_ca_topic_score_gemma":0.019443251,"teacher_disagreement_score":0.20090535,"about_ca_system_score_codex":0.00057054195,"about_ca_system_score_gemma":0.003577808,"threshold_uncertainty_score":0.9998427},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919492,0.000024645338,0.0037760315,0.00013039561,0.00008263243,0.00021577699,0.0030391132,0.0000241154,0.0007580999],"genre_scores_gemma":[0.99756527,0.000008617185,0.0014354577,0.0000533419,0.000017849867,0.0000031161355,0.00083904614,0.0000135772825,0.00006369693],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979193,0.000042068412,0.0007220451,0.000493062,0.0005534049,0.00027007068],"domain_scores_gemma":[0.9977794,0.0004944388,0.0005725111,0.00089518004,0.00013106645,0.000127362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000873364,0.00018073106,0.0008104211,0.0002360916,0.00005156208,0.00000862189,0.00031253987,0.00004626296,0.00007475201],"category_scores_gemma":[0.0005721815,0.00012311322,0.00010141938,0.0006590653,0.0017191622,0.00015529597,0.00026702954,0.00011765244,0.0000054483417],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028658218,0.0019638804,0.73306304,0.0005359194,0.000150972,0.000020636751,0.00037420532,0.0051740576,0.22536626,0.0019576875,0.0002644669,0.028263034],"study_design_scores_gemma":[0.0015337976,0.00020825342,0.5994411,0.00017740557,0.00014702769,0.000017337772,0.00012553412,0.39156413,0.005969117,0.0005083674,0.00004965101,0.0002582401],"about_ca_topic_score_codex":0.00009827143,"about_ca_topic_score_gemma":0.000024007766,"teacher_disagreement_score":0.3863901,"about_ca_system_score_codex":0.000024121633,"about_ca_system_score_gemma":0.000339127,"threshold_uncertainty_score":0.6334325},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96101516,0.000077690805,0.019490097,0.008198354,0.00093201135,0.009124647,0.0007845842,0.00016436836,0.00021308527],"genre_scores_gemma":[0.9753203,0.00007064744,0.01771751,0.0015614894,0.0007808502,0.00048274346,0.0038616883,0.000058883274,0.00014589597],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994899,0.00079881237,0.0011207437,0.0008309624,0.001349535,0.0010009924],"domain_scores_gemma":[0.9941059,0.0028954844,0.00025283173,0.0015871783,0.00063711056,0.0005215363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010590089,0.00021659784,0.0008475034,0.00044601233,0.0003247985,0.00026472643,0.0012035014,0.00018714197,0.000109904344],"category_scores_gemma":[0.0058398293,0.00019195749,0.00012843029,0.0011640813,0.00008572787,0.0011082818,0.0014321931,0.0009092752,0.0004085009],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.06088724,0.0005087589,0.725481,0.0021015892,0.00027773713,0.000009449539,0.002170806,0.000014439943,0.0000111419595,0.005708251,0.13665031,0.06617931],"study_design_scores_gemma":[0.022875646,0.0025282104,0.9078893,0.001327649,0.0000066143753,0.000005392612,0.00049497624,0.002342384,0.000015258613,0.001882941,0.060263347,0.0003682976],"about_ca_topic_score_codex":0.00010964014,"about_ca_topic_score_gemma":0.000078548364,"teacher_disagreement_score":0.18240832,"about_ca_system_score_codex":0.0004900811,"about_ca_system_score_gemma":0.0011201557,"threshold_uncertainty_score":0.7827799},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97712016,0.0032899177,0.0000010983039,0.015763376,0.00022251163,0.001709159,0.00026262432,0.00014709402,0.0014840378],"genre_scores_gemma":[0.99599355,0.00078400143,0.000016747812,0.0013886545,0.00056162535,0.00021387263,0.0002205557,0.000026242402,0.0007947217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964921,0.00074595783,0.00067872606,0.00074161164,0.00046438284,0.0008772192],"domain_scores_gemma":[0.9975989,0.0004730589,0.00020537074,0.0007524768,0.00008297019,0.0008872607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016702928,0.00032599957,0.00084826595,0.00021486191,0.00018355351,0.000081835955,0.00021328073,0.0001569253,0.00008120057],"category_scores_gemma":[0.00028029893,0.00021377634,0.00006248656,0.0005368095,0.00014246842,0.0002040643,0.0002571399,0.0005339776,0.000013332539],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040169654,0.00034264143,0.97916573,0.00032010116,0.00003678922,0.000105752944,0.00092015864,0.000003415803,0.0000028037305,0.00004844107,0.0038787466,0.014773707],"study_design_scores_gemma":[0.003009441,0.0006662815,0.8847946,0.000021694186,0.0000012699495,0.00020322052,0.0016753663,0.00024715537,1.3574716e-7,0.0000037520006,0.10919906,0.00017800044],"about_ca_topic_score_codex":0.00046303865,"about_ca_topic_score_gemma":0.0016737878,"teacher_disagreement_score":0.10532032,"about_ca_system_score_codex":0.0001349256,"about_ca_system_score_gemma":0.00036996193,"threshold_uncertainty_score":0.8717546},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98479986,0.00065371516,0.0025406391,0.00711716,0.00014484677,0.0019574515,0.0026020755,0.00010509363,0.00007913247],"genre_scores_gemma":[0.9929498,0.00005049055,0.0009255987,0.001953264,0.00007714363,0.00017767343,0.0037351584,0.00003576485,0.00009513367],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966856,0.00030389655,0.0010329826,0.0006673604,0.00063592277,0.000674197],"domain_scores_gemma":[0.9957957,0.0009025444,0.00047518057,0.0006272571,0.0004519655,0.0017473268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001835215,0.0002992374,0.000806483,0.000475684,0.000115560426,0.00005751665,0.00016783204,0.00008582809,0.000031781256],"category_scores_gemma":[0.0024408593,0.00029671934,0.00015312558,0.00081717485,0.00015961738,0.00026305305,0.000058701324,0.00018343118,0.0000062547683],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006069464,0.00015405701,0.9913655,0.0031521511,0.00003121209,0.000004971463,0.00020709044,0.0008396938,0.000038570026,0.00036451116,0.0005138898,0.002721424],"study_design_scores_gemma":[0.0034134006,0.00014468694,0.8040957,0.00019300512,0.000006071671,6.467641e-7,0.00025004864,0.18846107,0.0000015021823,0.0001515104,0.003038042,0.0002443532],"about_ca_topic_score_codex":0.00007136444,"about_ca_topic_score_gemma":0.00019900505,"teacher_disagreement_score":0.18762137,"about_ca_system_score_codex":0.0002580628,"about_ca_system_score_gemma":0.00443485,"threshold_uncertainty_score":0.9999485},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98240495,0.0008491298,0.00042214498,0.011981599,0.00080251234,0.000632625,0.00072003185,0.0000052179403,0.0021817787],"genre_scores_gemma":[0.9987573,0.000022398193,0.000017627071,0.0001903292,0.0009535978,0.000007937977,0.000012425069,0.0000083384575,0.000030065612],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99885434,0.00010749558,0.0005116554,0.00011884587,0.00024104406,0.00016660635],"domain_scores_gemma":[0.99882257,0.0002412548,0.00048064056,0.00023297108,0.00018367048,0.000038894454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037463154,0.000092025824,0.00025235384,0.000036063277,0.00023220706,0.00006410404,0.0003407435,0.000028582475,0.000011744071],"category_scores_gemma":[0.0003624439,0.000045168243,0.000100278725,0.000040549112,0.00024361082,0.00016770029,0.00008711947,0.00011770588,9.4871456e-7],"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":[0.01927695,0.001551997,0.12638223,0.00019977306,0.005178308,0.000036705384,0.05174065,0.000114140836,0.00057432195,0.0033592742,0.31293255,0.4786531],"study_design_scores_gemma":[0.0154777495,0.005047826,0.4730896,0.00045066045,0.0010063307,0.00009209689,0.021045497,0.0052843546,0.0002237862,0.0058746445,0.47192693,0.00048052147],"about_ca_topic_score_codex":0.0000083996565,"about_ca_topic_score_gemma":0.00016470974,"teacher_disagreement_score":0.47817257,"about_ca_system_score_codex":0.00015032398,"about_ca_system_score_gemma":0.00022941724,"threshold_uncertainty_score":0.18419075},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93043727,0.020263115,0.0000015372497,0.042938866,0.0036871089,0.00027444618,0.000050485556,0.000020118625,0.0023270748],"genre_scores_gemma":[0.94279706,0.023383604,0.00005491274,0.0047825472,0.0015444668,0.00001847257,0.0000050283506,0.000039311468,0.027374564],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861014,0.00013969478,0.0006555052,0.0001829672,0.00014322519,0.0002684915],"domain_scores_gemma":[0.99804664,0.00062992086,0.0006083049,0.0004986073,0.00012159455,0.00009490734],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001047675,0.00020123144,0.00065814744,0.00009407734,0.00013958776,0.000118455966,0.00032020983,0.000032555956,0.00097287906],"category_scores_gemma":[0.00014370294,0.00006802451,0.0002546779,0.00009877646,0.00020304075,0.00034799427,0.00015200178,0.00023537336,0.00025852842],"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":[0.004563678,0.0004013156,0.24065621,0.00024572163,0.01204357,0.00024536208,0.008962737,0.0013084565,0.0002537546,0.0011301807,0.60175556,0.12843347],"study_design_scores_gemma":[0.0043065683,0.00076505146,0.22504787,0.00085543987,0.0006146813,0.00017366806,0.023300314,0.0001378235,0.00032006705,0.0007422809,0.74340177,0.0003344513],"about_ca_topic_score_codex":0.00001117,"about_ca_topic_score_gemma":0.00026898246,"teacher_disagreement_score":0.14164622,"about_ca_system_score_codex":0.00026941832,"about_ca_system_score_gemma":0.00016348693,"threshold_uncertainty_score":0.9999404},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9818263,0.00020114228,0.0049393727,0.0002305758,0.006968254,0.0015588894,0.0012922313,0.00021421177,0.002769008],"genre_scores_gemma":[0.9781359,0.0000014980116,0.0065104063,0.00008051426,0.00013813011,0.00002606598,0.010727299,0.000027028213,0.004353165],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968463,0.00004667775,0.0005372021,0.0013491354,0.000767645,0.00045305036],"domain_scores_gemma":[0.99594384,0.00019692972,0.00021266083,0.0031249723,0.00028063357,0.00024095202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024637,0.00016450373,0.00045555973,0.000077589895,0.00013369757,0.00019269071,0.00029010096,0.000046783698,0.0006082824],"category_scores_gemma":[0.0013465951,0.00014839688,0.00007391149,0.00027064272,0.00021707911,0.00023272682,0.00033788962,0.000115435156,0.00025245576],"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":[0.00013912155,0.0001309095,0.79683495,0.00024272343,0.000049177335,0.00031619292,0.000022422802,0.000017719067,0.0019270545,0.0002080521,0.19737203,0.0027396448],"study_design_scores_gemma":[0.0010379178,0.00006462659,0.48633516,0.00010991767,0.00004722345,0.00044556273,0.000027616905,0.019566312,0.00020789276,0.0008494299,0.49098042,0.0003279228],"about_ca_topic_score_codex":0.000013510464,"about_ca_topic_score_gemma":0.000040788644,"teacher_disagreement_score":0.31049982,"about_ca_system_score_codex":0.00006193745,"about_ca_system_score_gemma":0.0004886733,"threshold_uncertainty_score":0.6660266},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98954076,0.00030869665,0.000004324403,0.0043156096,0.00036864544,0.0035073813,0.001607208,0.00008607628,0.0002612896],"genre_scores_gemma":[0.9917547,0.00010629862,0.000043911063,0.006141594,0.00006639397,0.000101406134,0.0011902669,0.0000322306,0.00056319084],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968607,0.0007819205,0.0006976124,0.00051849487,0.0006018723,0.000539425],"domain_scores_gemma":[0.9984221,0.000084699335,0.0003412492,0.00056239445,0.000046794266,0.00054276007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012559171,0.0002358709,0.0008080256,0.00007506593,0.000061766186,0.0000062683976,0.00018601226,0.00005513082,0.00039353815],"category_scores_gemma":[0.000022658349,0.00020889626,0.00010907244,0.00013351941,0.00007356753,0.000100884674,0.00020385934,0.00033678612,0.000294976],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001282811,0.0023567933,0.961152,0.0010250765,0.00008202701,0.0000057169314,0.0051528867,4.475372e-7,0.00004394563,0.000058911297,0.023700224,0.0051391297],"study_design_scores_gemma":[0.003930663,0.0064217965,0.9764999,0.00053248624,0.000003395271,0.000010493993,0.0057409317,0.000059208593,0.00005661117,0.0000063840375,0.0065934835,0.00014461852],"about_ca_topic_score_codex":0.0010346898,"about_ca_topic_score_gemma":0.0004716194,"teacher_disagreement_score":0.01710674,"about_ca_system_score_codex":0.0008385288,"about_ca_system_score_gemma":0.00056492764,"threshold_uncertainty_score":0.85185426},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8695422,0.00023257904,0.00004333571,0.10272315,0.0001493136,0.012123751,0.0010855604,0.000077141725,0.014023013],"genre_scores_gemma":[0.99465287,0.00001522585,0.00043814792,0.0025799836,0.0001781261,0.00023871603,0.00065623765,0.00002235981,0.0012183599],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9944187,0.003206729,0.0003433214,0.000762477,0.00084144424,0.00042737625],"domain_scores_gemma":[0.9970689,0.0010106185,0.00015041544,0.0012112155,0.00031210127,0.0002467378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008192557,0.00017347728,0.00058816787,0.00008712457,0.00035538082,0.00035862948,0.0011745058,0.000047387744,0.00023661145],"category_scores_gemma":[0.0010783578,0.000113492926,0.000037990332,0.00039173942,0.0006156345,0.00037655295,0.0012180248,0.00049310335,0.00018128924],"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":[0.02414838,0.008100616,0.2660752,0.00079612003,0.001279717,0.00017980987,0.4227375,0.000004336967,0.00008680843,0.04145957,0.22258312,0.012548814],"study_design_scores_gemma":[0.022750333,0.0044749165,0.8150049,0.0002388123,0.00004983395,0.0000041873277,0.1381029,0.0015387146,0.000008130315,0.00032209372,0.017108485,0.00039672104],"about_ca_topic_score_codex":0.0020274813,"about_ca_topic_score_gemma":0.0012603961,"teacher_disagreement_score":0.5489297,"about_ca_system_score_codex":0.00013183885,"about_ca_system_score_gemma":0.0017332386,"threshold_uncertainty_score":0.46281072},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96892637,0.0010046916,0.008112693,0.012831802,0.000551381,0.0068145255,0.0005222894,0.0004704066,0.0007658347],"genre_scores_gemma":[0.9950084,0.00011731462,0.0006635047,0.0010291743,0.00049241685,0.00059867126,0.0017277898,0.00006446567,0.00029823417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.995987,0.0004229067,0.00085513643,0.0009955195,0.0008126314,0.000926802],"domain_scores_gemma":[0.99726844,0.0004907824,0.00033843363,0.00047109026,0.0006005017,0.00083076215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024000714,0.00038738662,0.0010017904,0.00022168469,0.00038040613,0.00009619762,0.00019651274,0.00016947978,0.000066881614],"category_scores_gemma":[0.00029498443,0.00035097863,0.00016653184,0.00060798106,0.00009404624,0.00046135805,0.00006496492,0.00030920922,0.000021065312],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010425316,0.000836697,0.922617,0.00083039905,0.00008627848,0.000005046072,0.0016535177,0.0000071439713,0.000026185116,0.00036389395,0.0053664506,0.06716483],"study_design_scores_gemma":[0.0050705606,0.000789872,0.9294183,0.00002454724,0.0000026762737,0.000027373504,0.0004995346,0.012377027,4.3051537e-7,0.00021849108,0.0512049,0.00036624528],"about_ca_topic_score_codex":0.00009149763,"about_ca_topic_score_gemma":0.00035897936,"teacher_disagreement_score":0.06679858,"about_ca_system_score_codex":0.00023886425,"about_ca_system_score_gemma":0.0013463387,"threshold_uncertainty_score":0.9998942},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09765066,0.010063324,0.86427087,0.0022811342,0.006443531,0.0015216237,0.0063535874,0.00060026156,0.010815019],"genre_scores_gemma":[0.9932355,0.0003337808,0.0048449775,0.00040482898,0.00033465886,0.00000749981,0.00039521116,0.0000493554,0.00039417308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961797,0.000047738926,0.0006659758,0.0010940613,0.001507231,0.0005053185],"domain_scores_gemma":[0.99722725,0.000266383,0.00040351163,0.0012810393,0.00049445877,0.00032737394],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028085551,0.0004921056,0.0012339513,0.0011594242,0.00013270631,0.000035849636,0.00072557235,0.00020509769,0.00013285094],"category_scores_gemma":[0.00022162548,0.00043294713,0.0002550255,0.001014961,0.0009683701,0.00013241368,0.00035238342,0.00057879207,0.0000060033844],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003553719,0.0001975226,0.023183705,0.0003818237,0.0014611975,0.0017830363,0.0016071234,0.016706,0.0011564783,0.00007350023,0.0018152844,0.951279],"study_design_scores_gemma":[0.006856892,0.0016801475,0.29855874,0.0047785887,0.005087669,0.00046160136,0.00002137756,0.5317773,0.010689313,0.0005537835,0.13219517,0.0073393825],"about_ca_topic_score_codex":0.028121699,"about_ca_topic_score_gemma":0.5246525,"teacher_disagreement_score":0.94393957,"about_ca_system_score_codex":0.00045634268,"about_ca_system_score_gemma":0.0040403884,"threshold_uncertainty_score":0.99981225},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9176411,0.0021684498,0.017476799,0.02735256,0.0074443617,0.0044832667,0.00032662065,0.0011430205,0.021963822],"genre_scores_gemma":[0.9898723,0.00013121458,0.0035045415,0.0046014916,0.0001441726,0.00004552336,0.0015290597,0.00003550329,0.0001362271],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962576,0.0006702406,0.001075342,0.00048027575,0.0006375986,0.00087892206],"domain_scores_gemma":[0.9972997,0.00003543105,0.0004942386,0.0009990555,0.00017564428,0.0009959367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025463006,0.00026081505,0.0009140504,0.0007717931,0.00016630437,0.00010210404,0.00032596223,0.00011794608,0.000042087948],"category_scores_gemma":[0.00015614358,0.00024780183,0.00008456393,0.0008404951,0.00006170283,0.0016488472,0.00020884574,0.0004175907,0.0002803206],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040222757,0.0003750779,0.7399746,0.004738403,0.00010534987,0.000028845665,0.012579383,0.000055980938,0.0000025242682,0.003173935,0.10064846,0.1379152],"study_design_scores_gemma":[0.004049458,0.0014875252,0.7982204,0.0009266254,0.000004004555,0.00009848987,0.016552806,0.018277828,0.0000014136913,0.00025363415,0.1596543,0.00047351074],"about_ca_topic_score_codex":0.0003632145,"about_ca_topic_score_gemma":0.000092803166,"teacher_disagreement_score":0.1374417,"about_ca_system_score_codex":0.0021372412,"about_ca_system_score_gemma":0.0028496457,"threshold_uncertainty_score":0.99999744},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920348,0.000736283,0.004472583,0.0009116865,0.000084706975,0.0006572789,0.0006838223,0.00007754101,0.00034130373],"genre_scores_gemma":[0.99652827,0.00038691258,0.0010045094,0.0003626817,0.000051117648,0.00003632222,0.0014739565,0.000014083654,0.00014213394],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974905,0.0004554668,0.0005917883,0.00058290845,0.00045059825,0.00042871354],"domain_scores_gemma":[0.99817437,0.00009531733,0.0003946261,0.00071288575,0.00013714809,0.0004856397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016736711,0.00017315798,0.0008067794,0.0014805174,0.000070222886,0.000052536783,0.00010034702,0.00009138262,0.0005579874],"category_scores_gemma":[0.00016682892,0.00016272557,0.00027446702,0.0054308823,0.000053088614,0.00025935753,0.000052412113,0.00024367252,0.000012053508],"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":[0.00004490532,0.0001143025,0.994908,0.0001942795,0.0017790195,0.000002095443,0.00032253086,0.00044619042,5.924152e-7,0.00007528857,0.00027611994,0.0018366955],"study_design_scores_gemma":[0.0010012562,0.000043715354,0.906293,0.000020422793,0.0010203884,2.933822e-7,0.00013265535,0.0907724,2.3147895e-7,0.00001196786,0.0005752476,0.0001284742],"about_ca_topic_score_codex":0.03955191,"about_ca_topic_score_gemma":0.063284695,"teacher_disagreement_score":0.090326205,"about_ca_system_score_codex":0.00029417677,"about_ca_system_score_gemma":0.0007972935,"threshold_uncertainty_score":0.9668438},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9855816,0.00059060875,0.0000063124735,0.01113891,0.0003067835,0.0005127723,0.0014660336,0.000079263504,0.0003177146],"genre_scores_gemma":[0.9893551,0.0002679043,0.000069810325,0.00094943447,0.0013478412,0.000024293877,0.007912632,0.000031058593,0.000041966898],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977928,0.00045669373,0.0003518057,0.00036668827,0.0006183594,0.0004136674],"domain_scores_gemma":[0.9887423,0.010348843,0.00015227591,0.0005439018,0.000110378045,0.00010231504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018933269,0.00019232633,0.0003524474,0.000034317123,0.00057163305,0.00008910214,0.0005820977,0.00019822796,0.000035275396],"category_scores_gemma":[0.0030640303,0.00012514168,0.00011674487,0.00064833206,0.00060984766,0.0001660458,0.00027651395,0.00065925915,0.000062120576],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006571886,0.000059420712,0.71685785,0.00008688221,0.00014310288,0.0000048033585,0.15028769,1.644711e-7,0.000015870235,0.0019638557,0.12839739,0.0021172226],"study_design_scores_gemma":[0.0012787813,0.000013342197,0.9165021,0.000024392511,0.00011132821,8.7585806e-7,0.059334,0.000094473704,0.0000027006668,0.0040157083,0.018475074,0.00014720614],"about_ca_topic_score_codex":0.000043321124,"about_ca_topic_score_gemma":0.0005372048,"teacher_disagreement_score":0.19964424,"about_ca_system_score_codex":0.00008712112,"about_ca_system_score_gemma":0.0002099126,"threshold_uncertainty_score":0.510313},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99591124,0.00028634624,0.0007436668,0.00023071944,0.0002576517,0.00033967127,0.00016743269,0.00014086593,0.0019223961],"genre_scores_gemma":[0.9971864,0.000014850893,0.0021084605,0.00013345266,0.00007798377,0.00001544031,0.00038510942,0.000020649717,0.000057659297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987999,0.00007042045,0.00033449792,0.00029808327,0.00025219165,0.00024490152],"domain_scores_gemma":[0.99934417,0.0001155988,0.000111128975,0.000288733,0.00005158523,0.000088811285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002840759,0.00013486427,0.00029325805,0.00009584754,0.000041007486,0.00003088169,0.00007786161,0.00009123303,0.00008968665],"category_scores_gemma":[0.00024168972,0.00013581138,0.00005279575,0.00021932334,0.00007096726,0.00014323654,0.000051772146,0.00020852173,0.000040465253],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012860869,0.000037466536,0.98200256,0.00008585889,0.000016458956,0.000012280166,0.00013284577,0.00026791956,0.0055551846,0.00013519217,0.00013036792,0.0114952875],"study_design_scores_gemma":[0.0017202623,0.00007945637,0.979716,0.00032026766,0.000009481533,0.000011922723,0.0000532644,0.015963921,0.00035126225,0.000052170722,0.0015638,0.00015816775],"about_ca_topic_score_codex":0.00067875744,"about_ca_topic_score_gemma":0.00041856573,"teacher_disagreement_score":0.015696002,"about_ca_system_score_codex":0.00005080669,"about_ca_system_score_gemma":0.0000734863,"threshold_uncertainty_score":0.55382276},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000055307287,0.99377245,0.0028006868,0.00026703035,0.0005887165,0.0023156372,0.00017447383,0.000017962899,0.000057512418],"genre_scores_gemma":[0.00003080338,0.997779,0.0010547643,0.00048574046,0.00013691343,0.000024906003,0.00043536705,0.00003112291,0.000021359121],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9890596,0.00071152626,0.008010342,0.000121967445,0.0017170567,0.00037949978],"domain_scores_gemma":[0.9882683,0.0005099761,0.0094533665,0.0006477079,0.00060372293,0.00051689806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007820323,0.00037543196,0.0069763632,0.001113443,0.000023092854,0.000031598305,0.0004302601,0.00031966573,0.000040723375],"category_scores_gemma":[0.0041474756,0.00024673794,0.0008777911,0.0015493317,0.00011885507,0.00036840976,0.00007778465,0.0008973944,0.00005420013],"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":[0.000005023636,0.00014532192,0.0000053797044,0.8216198,0.00030173032,0.000012157417,0.00009808207,6.2316026e-8,9.215501e-8,0.000003046458,0.00086991146,0.17693938],"study_design_scores_gemma":[0.0007076104,0.00049235777,0.00001800069,0.79666555,0.00054505625,0.0008336499,0.0000741519,0.00026411083,1.1487917e-7,0.0000022565393,0.20022996,0.00016717426],"about_ca_topic_score_codex":0.0000054204193,"about_ca_topic_score_gemma":0.0000047047306,"teacher_disagreement_score":0.19936004,"about_ca_system_score_codex":0.0007594187,"about_ca_system_score_gemma":0.003256058,"threshold_uncertainty_score":0.9999985},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9510787,0.0035323189,0.00022283627,0.035856854,0.00079149194,0.0019493172,0.00005102966,0.0002225855,0.006294868],"genre_scores_gemma":[0.98323154,0.0008118695,0.00019118164,0.011858419,0.00040661293,0.00009448172,0.00015349322,0.00003937393,0.0032130298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99679303,0.00056052656,0.00062755874,0.00066760974,0.0005539337,0.0007973612],"domain_scores_gemma":[0.9973987,0.0005194765,0.0003636564,0.00080364186,0.00016838763,0.00074618123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014129365,0.00029569186,0.0006926283,0.0001427458,0.00031046086,0.00013860219,0.00024090019,0.00009960125,0.000121987556],"category_scores_gemma":[0.00045936703,0.00020777617,0.00010349285,0.00033552296,0.00013472994,0.00023304153,0.00018082037,0.00051714625,0.0002805904],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026771287,0.0001662247,0.9163113,0.0007549099,0.00017653401,0.00004014716,0.0058264183,0.0000114569375,0.00012929828,0.001451477,0.04063709,0.034227427],"study_design_scores_gemma":[0.0021966316,0.00032906857,0.53882587,0.00025705353,0.0000024185247,0.00003528891,0.0009133356,0.0011121345,0.000012415046,0.00017267848,0.4558144,0.00032871088],"about_ca_topic_score_codex":0.00017180292,"about_ca_topic_score_gemma":0.000066566805,"teacher_disagreement_score":0.41517732,"about_ca_system_score_codex":0.00025777728,"about_ca_system_score_gemma":0.0007458257,"threshold_uncertainty_score":0.8472867},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042892606,0.0006015202,0.9911569,0.0006832998,0.0005730537,0.0005229332,0.000008881632,0.00021348594,0.001950661],"genre_scores_gemma":[0.92415863,0.00016979706,0.07172492,0.0013936209,0.00056405395,0.000041626492,0.00023577345,0.00007876599,0.0016328027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972759,0.00011312097,0.0006153618,0.0010859844,0.00040281197,0.00050681893],"domain_scores_gemma":[0.99793,0.0008576202,0.00023969811,0.0007132036,0.000114941904,0.00014457379],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022834449,0.0002665096,0.0006265369,0.0007319707,0.000079813646,0.000042742795,0.00050976884,0.00023241395,0.000024317356],"category_scores_gemma":[0.0019219122,0.00024567475,0.00009226897,0.00056870683,0.0005655623,0.00012097829,0.00034398073,0.00081585866,0.00014360495],"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":[0.000059962138,0.00007169213,0.028345095,0.00023161252,0.000023876315,0.00019624784,0.00078111276,0.090239584,0.00042834334,0.003310161,0.000031846856,0.8762805],"study_design_scores_gemma":[0.00064963085,0.00014112322,0.10347057,0.0005915372,0.000027776885,0.00009130691,0.0000019342076,0.8294191,0.00017592283,0.06393059,0.00096258946,0.0005379495],"about_ca_topic_score_codex":0.000034108052,"about_ca_topic_score_gemma":0.00013422489,"teacher_disagreement_score":0.91986936,"about_ca_system_score_codex":0.00023340063,"about_ca_system_score_gemma":0.0001830503,"threshold_uncertainty_score":0.9999995},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9982981,0.00003360533,0.000002091283,0.0003100197,0.000044384655,0.00048519636,0.00013311536,0.000035595174,0.00065786904],"genre_scores_gemma":[0.99951565,0.000002083361,0.000100989346,0.000090027235,0.00015040164,0.000017789083,0.000027810058,0.000015906251,0.0000793407],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99902225,0.0000933136,0.00022617534,0.00021925238,0.00025889257,0.00018012611],"domain_scores_gemma":[0.99921024,0.00011888517,0.00006641732,0.00031305072,0.000047297188,0.00024409372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018559046,0.00011990583,0.0003181571,0.000113443384,0.000053002466,0.000007940131,0.00005602583,0.000028296585,0.0002859896],"category_scores_gemma":[0.000738533,0.00010194102,0.000030023517,0.00018700192,0.00014123658,0.00004420751,0.000076573306,0.0000852518,0.00011051026],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023698008,0.014077688,0.8025853,0.0012188484,0.00057190267,0.054251708,0.045096695,0.000005964697,0.00058707746,0.00042616166,0.05877453,0.02003431],"study_design_scores_gemma":[0.0030154693,0.00018769795,0.9946008,0.00009383803,0.00011384289,0.00010874178,0.00093388156,0.000537061,0.000015780715,0.0001679144,0.00010844484,0.000116570925],"about_ca_topic_score_codex":0.00012930814,"about_ca_topic_score_gemma":0.0004295944,"teacher_disagreement_score":0.19201544,"about_ca_system_score_codex":0.000025066838,"about_ca_system_score_gemma":0.0000733299,"threshold_uncertainty_score":0.41570342},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99222654,0.00050390896,0.00013297045,0.000093115224,0.00015182978,0.0017207324,0.0038199376,0.00012440064,0.0012265845],"genre_scores_gemma":[0.99759287,0.00022286104,0.000050290568,0.0000904742,0.000034729193,0.00034861846,0.0015503095,0.00004362566,0.000066196815],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99824315,0.00014289371,0.0004497538,0.00035607052,0.00048255984,0.0003255581],"domain_scores_gemma":[0.996994,0.00015913203,0.0005457877,0.0012152314,0.00050769636,0.00057815766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004257399,0.00021942769,0.00046505957,0.00008028543,0.00018284708,0.000044057408,0.00010887583,0.000051504943,0.000019189512],"category_scores_gemma":[0.00036871873,0.00020016919,0.00008116335,0.0002227302,0.00009669856,0.00031681277,0.00010528762,0.00020378869,4.2827926e-7],"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":[0.00080567884,0.0002071503,0.99689543,0.00044601064,0.0002756242,0.0002165131,0.00002938593,0.00007641276,0.0000616911,0.000049134025,0.00011802682,0.00081893813],"study_design_scores_gemma":[0.002011708,0.00027375898,0.99100584,0.0003728461,0.00068817264,0.000020906575,0.00018306478,0.004150105,0.000008383445,0.00009221269,0.0010151801,0.00017782162],"about_ca_topic_score_codex":0.083283685,"about_ca_topic_score_gemma":0.15054308,"teacher_disagreement_score":0.06725939,"about_ca_system_score_codex":0.0003918847,"about_ca_system_score_gemma":0.0020447534,"threshold_uncertainty_score":0.9228208},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97747004,0.00018301609,0.017164143,0.000120925135,0.0030280782,0.0019084602,0.000031568023,0.000029031038,0.000064729546],"genre_scores_gemma":[0.99265707,0.000011003215,0.0068726833,0.000021441869,0.0003301213,0.00003619953,0.000025593275,0.000029231342,0.000016680431],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99733317,0.00047023132,0.00079474587,0.00021137347,0.0010084605,0.00018199759],"domain_scores_gemma":[0.9958114,0.00015156111,0.0009525235,0.0002777605,0.0027123198,0.00009443502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057149986,0.00016946829,0.0005454684,0.00028898127,0.000042144726,0.000029948087,0.00008460914,0.00008572341,0.000091307375],"category_scores_gemma":[0.0014466313,0.00014536288,0.00039349782,0.00025312704,0.00002992252,0.0003466605,0.000026150186,0.00020155792,0.00001052441],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021039401,0.011338235,0.78593904,0.0004775372,0.002035399,0.000028049042,0.0006925132,0.0103067085,0.03611938,0.000020313866,0.001646198,0.14929268],"study_design_scores_gemma":[0.024217805,0.025946958,0.8866364,0.00034618453,0.003139993,0.000029816465,0.0005008497,0.050385743,0.007657044,0.00027037624,0.0005334681,0.00033541286],"about_ca_topic_score_codex":0.000023490817,"about_ca_topic_score_gemma":0.0000050844747,"teacher_disagreement_score":0.14895725,"about_ca_system_score_codex":0.00024474683,"about_ca_system_score_gemma":0.00047581323,"threshold_uncertainty_score":0.5927726},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9799759,0.001088074,0.009778348,0.0039082314,0.0003608286,0.0045042373,0.00021762394,0.00015301256,0.000013782869],"genre_scores_gemma":[0.9971858,0.00016447618,0.00076963735,0.0006437918,0.00022915719,0.00025110255,0.00056720735,0.000036067864,0.00015274166],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975252,0.000300804,0.0006036075,0.0007725055,0.00040297888,0.0003948712],"domain_scores_gemma":[0.99780566,0.0001366317,0.00043410825,0.00047719103,0.00024811883,0.0008982793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011069837,0.00023694003,0.00051055546,0.000158306,0.00021142216,0.00006721745,0.000039454822,0.000114761,0.0000054959373],"category_scores_gemma":[0.00035054004,0.00018702063,0.000044219785,0.00019609918,0.00006337421,0.0003254166,0.000033977278,0.00021438774,5.2369074e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018082057,0.00035762778,0.9905373,0.00060704665,0.00005391824,0.000009344495,0.00032272332,0.0000023443192,0.00005152273,0.000008738189,0.0009216969,0.005319517],"study_design_scores_gemma":[0.0036207796,0.0015850343,0.9645374,0.00023712098,0.000015382073,0.00006518753,0.0003317782,0.020789048,1.1464031e-7,0.000026804708,0.00864071,0.00015063977],"about_ca_topic_score_codex":0.000043286153,"about_ca_topic_score_gemma":0.000014942114,"teacher_disagreement_score":0.025999913,"about_ca_system_score_codex":0.00037678733,"about_ca_system_score_gemma":0.00042359938,"threshold_uncertainty_score":0.762648},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9624197,0.0062253373,0.00035309725,0.02344263,0.0003858444,0.00078697654,0.00011198644,0.00020527854,0.0060691885],"genre_scores_gemma":[0.9778735,0.0018128658,0.004425433,0.014615807,0.00015137254,0.00009927727,0.00028668655,0.000036064073,0.00069898344],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99626034,0.00093528733,0.0007626973,0.000757584,0.00024277973,0.0010412831],"domain_scores_gemma":[0.99681544,0.0011175502,0.0002582304,0.00069342554,0.00010135122,0.0010139998],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028085036,0.00030370904,0.001283951,0.000501445,0.00007358544,0.000037665945,0.00015567138,0.00033096113,0.00028818235],"category_scores_gemma":[0.0015625064,0.00027385558,0.000066614346,0.00084533513,0.00022311452,0.00017787797,0.00016745988,0.00062908384,0.000051609622],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021227647,0.0000640835,0.90269244,0.00030793314,0.000021812437,0.00001357498,0.0004570232,0.0000010656105,0.0000093523895,0.0027538477,0.003196008,0.09027058],"study_design_scores_gemma":[0.0015663401,0.00038355548,0.7921371,0.00003177814,4.5331387e-7,0.00009450564,0.00015525507,0.00028361042,1.6184833e-7,0.00034390652,0.20479846,0.00020485515],"about_ca_topic_score_codex":0.0001332706,"about_ca_topic_score_gemma":0.00023046523,"teacher_disagreement_score":0.20160246,"about_ca_system_score_codex":0.00012247221,"about_ca_system_score_gemma":0.0005237127,"threshold_uncertainty_score":0.9999714},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97368324,0.00054650055,0.016265055,0.0013212244,0.000018125093,0.0029884293,0.0050457343,0.00003698875,0.00009469263],"genre_scores_gemma":[0.98787147,0.0002224496,0.0110951485,0.00010031969,0.00002473905,0.000049403552,0.0005340671,0.000010037041,0.00009237691],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99842936,0.0002516176,0.00024440416,0.000508026,0.00028379008,0.0002828097],"domain_scores_gemma":[0.9978817,0.0004041067,0.00017260751,0.00047041292,0.00076700817,0.00030418232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009369253,0.000048947168,0.00045336824,0.000093069,0.00022379872,0.00000848801,0.00019048108,0.000032085365,0.000020086549],"category_scores_gemma":[0.0006919555,0.00011280187,0.000056610574,0.00056192366,0.00081195217,0.00011920491,0.00011036064,0.000090704205,0.0000064585674],"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":[0.0010450168,0.0014746941,0.64411193,0.0009076879,0.00015385197,0.000023438053,0.00039646984,0.0000042258926,0.00033033206,0.000028357983,0.015169286,0.33635473],"study_design_scores_gemma":[0.0016641147,0.0007084561,0.9920644,0.00078839925,0.000030875173,0.0000019804913,0.0034122257,0.0002276983,0.0000025832546,0.00008541688,0.0009236148,0.0000902174],"about_ca_topic_score_codex":0.013456131,"about_ca_topic_score_gemma":0.1811288,"teacher_disagreement_score":0.34795249,"about_ca_system_score_codex":0.00020257045,"about_ca_system_score_gemma":0.0004103652,"threshold_uncertainty_score":0.99311334},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96661806,0.0050373985,0.0005525731,0.026342431,0.0004882152,0.00048086038,0.000032653476,0.000034608962,0.0004131707],"genre_scores_gemma":[0.99530315,0.00029155868,0.00008081812,0.0027232573,0.0007864975,0.0000024538035,0.00004805212,0.000039858205,0.00072432373],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972533,0.00024451726,0.00066457916,0.00019524748,0.0011429613,0.0004993683],"domain_scores_gemma":[0.9974961,0.00023456001,0.00065980054,0.0005561118,0.00046028694,0.0005931327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00176997,0.00024001749,0.00083983864,0.00016525463,0.0001150002,0.000014420337,0.00037684,0.000046604888,0.00042389054],"category_scores_gemma":[0.00024148612,0.000118095755,0.000043671575,0.0003393204,0.0004040254,0.00018469676,0.00007425682,0.00050473306,0.0000588495],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014064735,0.0028373047,0.75705487,0.003050036,0.0046362695,0.0013157071,0.026249193,0.0006914846,0.019034548,0.00023121093,0.043009803,0.12782487],"study_design_scores_gemma":[0.060106825,0.15238526,0.5362295,0.0047927652,0.0023555986,0.026319182,0.098501146,0.0022627315,0.00008479703,0.00007446214,0.11554961,0.001338136],"about_ca_topic_score_codex":0.00021191467,"about_ca_topic_score_gemma":0.00008081437,"teacher_disagreement_score":0.22082536,"about_ca_system_score_codex":0.00018515524,"about_ca_system_score_gemma":0.0010218669,"threshold_uncertainty_score":0.48158053},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98584014,0.000076921584,0.00026935403,0.0098166345,0.00006168622,0.0011207432,0.0023250973,0.000088472145,0.00040093393],"genre_scores_gemma":[0.84242296,0.0042939847,0.0042961095,0.00049865566,0.0001479293,0.0000109586645,0.0034492703,0.00018134159,0.14469878],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987493,0.00014733424,0.000266247,0.00035239852,0.00019369053,0.00029101453],"domain_scores_gemma":[0.99856085,0.00025548995,0.0002904941,0.00062816445,0.00015478909,0.00011023428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003511451,0.00022351807,0.0006947294,0.00066911365,0.00004709173,0.0000037517814,0.00059571123,0.0003508958,0.00037078603],"category_scores_gemma":[0.00014269922,0.00013329987,0.00006466627,0.0003978446,0.0010180727,0.000019075755,0.00029717796,0.00047932463,0.0000011632305],"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":[0.0021530855,0.00026204946,0.8612878,0.00039156928,0.0008751051,0.000096691,0.0002007727,0.00015351047,0.0000633077,0.0041834,0.12403715,0.0062955758],"study_design_scores_gemma":[0.008525094,0.00023109833,0.2813444,0.00095996994,0.00021807363,0.0000084123185,0.0006159826,0.001121316,0.000028224067,0.0005968258,0.70608926,0.00026134384],"about_ca_topic_score_codex":0.8295126,"about_ca_topic_score_gemma":0.80531424,"teacher_disagreement_score":0.5820521,"about_ca_system_score_codex":0.0001700841,"about_ca_system_score_gemma":0.00064200436,"threshold_uncertainty_score":0.54358107},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912216,0.000982063,0.00005085301,0.003179632,0.00017579785,0.001762182,0.0013734227,0.00022221163,0.0010322462],"genre_scores_gemma":[0.990844,0.0009979005,0.00094208424,0.0037880836,0.00019050414,0.000043861888,0.002484755,0.00008616181,0.00062264083],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946898,0.0007872795,0.0010678605,0.0012979666,0.0006990944,0.0014579596],"domain_scores_gemma":[0.9954973,0.0005065025,0.00042730788,0.0020685657,0.00030600073,0.0011943224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003634736,0.00043896964,0.0014216622,0.00040807138,0.00010576927,0.0001874648,0.0006282573,0.00013736427,0.00028886972],"category_scores_gemma":[0.00062165165,0.00040903763,0.00006544089,0.0011431279,0.0003906508,0.0005076353,0.0006320187,0.0005313705,0.00014937614],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004979181,0.00013542753,0.98772603,0.00065116753,0.000048619655,0.000022157217,0.00041050027,0.0000027001122,0.0002048263,0.000050704664,0.002509668,0.0077402703],"study_design_scores_gemma":[0.0026567203,0.00038687864,0.86493915,0.000086937194,9.675618e-7,0.000028437942,0.00026209664,0.029058896,0.0000019992967,0.000003340378,0.1021535,0.00042109555],"about_ca_topic_score_codex":0.0050142054,"about_ca_topic_score_gemma":0.0015973008,"teacher_disagreement_score":0.12278692,"about_ca_system_score_codex":0.0004021433,"about_ca_system_score_gemma":0.0024742254,"threshold_uncertainty_score":0.99983615},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767354,0.00093475066,0.0043631606,0.012526019,0.00023260033,0.0026108497,0.002065861,0.00014211563,0.0003892521],"genre_scores_gemma":[0.99411935,0.0002585462,0.002346597,0.0011762785,0.00025387187,0.00006996319,0.0016774541,0.00003008844,0.00006782952],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99716127,0.00029544396,0.0007668722,0.00088316115,0.00033471116,0.00055854913],"domain_scores_gemma":[0.99641526,0.0003407886,0.00041476922,0.0013779487,0.00079757144,0.00065369287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026659993,0.00023909302,0.00076812634,0.00015554656,0.00019578704,0.00015161533,0.00049091,0.00011841304,0.000043957174],"category_scores_gemma":[0.003340105,0.00020416707,0.000041878393,0.00046987378,0.00040272705,0.0008223957,0.00064976787,0.00019430343,0.0000011720132],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047214123,0.00008870915,0.95021445,0.0015219165,0.00005300672,6.973519e-7,0.0002805552,5.151072e-7,0.000036511767,0.00022078812,0.007915588,0.03919514],"study_design_scores_gemma":[0.0015325787,0.00055557064,0.91673636,0.000062127125,0.0000018849544,0.000014080159,0.000078468285,0.013541469,0.0000028665854,0.00015104726,0.06715399,0.00016953288],"about_ca_topic_score_codex":0.00012769316,"about_ca_topic_score_gemma":0.00056232786,"teacher_disagreement_score":0.059238397,"about_ca_system_score_codex":0.00007136845,"about_ca_system_score_gemma":0.0009854056,"threshold_uncertainty_score":0.8325691},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920582,0.00021186635,0.0001148342,0.00025414614,0.0000447846,0.00011399183,0.00020384653,0.000051174437,0.0069471197],"genre_scores_gemma":[0.97880524,0.000026783033,0.01994057,0.00006152494,0.000079452715,0.000003930195,0.00009418952,0.00002931002,0.0009590004],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989643,0.000025102503,0.00028315562,0.00021406288,0.00028285387,0.00023052157],"domain_scores_gemma":[0.9984757,0.00006429592,0.00033580462,0.0008823982,0.0001162674,0.00012554406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023429294,0.000112542555,0.0004632946,0.000089164496,0.000120522054,0.00001576199,0.00020077582,0.00005515014,0.000116744406],"category_scores_gemma":[0.0012024184,0.00011055173,0.000079390345,0.00007299501,0.0001166575,0.00013398728,0.0001297234,0.00012377778,0.000017248245],"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":[0.00038748205,0.0024912024,0.787842,0.0014444388,0.0010792773,0.00015521936,0.00019802839,0.00006413169,0.15783882,0.00010341427,0.0005304766,0.047865562],"study_design_scores_gemma":[0.0030504055,0.0002798991,0.41291654,0.0029318426,0.00114258,0.000032905744,0.0000516899,0.56567544,0.01288105,0.00028795184,0.0003169942,0.00043269436],"about_ca_topic_score_codex":0.00008296019,"about_ca_topic_score_gemma":0.000048130893,"teacher_disagreement_score":0.5656113,"about_ca_system_score_codex":0.000069555594,"about_ca_system_score_gemma":0.00005681453,"threshold_uncertainty_score":0.45081687},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913471,0.00031546585,0.0061560054,0.000544705,0.00006038489,0.00093207555,0.0003996529,0.00003336334,0.0002112968],"genre_scores_gemma":[0.9987117,0.000049108643,0.00031931887,0.00014833364,0.00011112117,0.000049571285,0.00024839814,0.000012681732,0.00034973628],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999049,0.000052993542,0.00021051361,0.00017528233,0.00036855636,0.00014363497],"domain_scores_gemma":[0.9988081,0.00021483554,0.00013516958,0.0006670586,0.00014157114,0.000033299715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003662881,0.000073067065,0.0002907233,0.0000770603,0.000053326945,0.000014256379,0.00016796669,0.000028416958,0.00022774207],"category_scores_gemma":[0.00019445171,0.000041661075,0.00016286914,0.00025355976,0.00003641244,0.0000412498,0.00006198318,0.00008079568,0.000042160173],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013290064,0.0019735002,0.8661708,0.0014464258,0.020846874,0.0000041396406,0.0012015785,0.0002407606,0.09733521,0.0003609236,0.00061758753,0.008473199],"study_design_scores_gemma":[0.0007094609,0.00006359503,0.9433727,0.0001259318,0.005914679,2.2742856e-7,0.00012593651,0.044047616,0.005399767,0.000044604574,0.00012717808,0.000068253416],"about_ca_topic_score_codex":0.00032627035,"about_ca_topic_score_gemma":0.00013024284,"teacher_disagreement_score":0.09193544,"about_ca_system_score_codex":0.000025216928,"about_ca_system_score_gemma":0.000026431248,"threshold_uncertainty_score":0.2493616},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7561221,0.0006527746,0.24082774,0.0004896113,0.00033232203,0.0010028508,0.00014638623,0.000083812745,0.0003423696],"genre_scores_gemma":[0.95192647,0.0002495487,0.04297127,0.0042120256,0.00010278444,0.000036703957,0.0004469173,0.00002499649,0.000029289842],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996293,0.000116540585,0.0012702795,0.0003122345,0.0015126589,0.0004952558],"domain_scores_gemma":[0.99376065,0.004044144,0.00044770777,0.00044251527,0.00015119211,0.0011537843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031920448,0.0002388893,0.0007222792,0.0001537362,0.00015931718,0.000083503066,0.00017686935,0.00015913055,0.00045007071],"category_scores_gemma":[0.0045055524,0.00018930306,0.000112137735,0.00020476553,0.00009126851,0.00016140878,0.0001192069,0.00033590454,0.000017144172],"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":[0.001760031,0.000106705455,0.6203653,0.0012933715,0.000033912005,0.000010640005,0.00009755035,0.000028397168,1.4057105e-7,0.0016096021,0.002270222,0.37242413],"study_design_scores_gemma":[0.00457743,0.0006011919,0.21857132,0.0020106246,0.000018055363,0.000037194666,0.00021037634,0.76732075,3.0082148e-7,0.0018995411,0.0044941716,0.00025902622],"about_ca_topic_score_codex":0.000018107621,"about_ca_topic_score_gemma":0.0002854635,"teacher_disagreement_score":0.7672924,"about_ca_system_score_codex":0.00010594611,"about_ca_system_score_gemma":0.0025675725,"threshold_uncertainty_score":0.7719555},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8815249,0.0018469077,0.07939717,0.000297543,0.00027422648,0.004771522,0.029363416,0.00009539247,0.0024289056],"genre_scores_gemma":[0.9895979,0.0000923705,0.0043841363,0.000097990815,0.000017891909,0.00016869389,0.005615747,0.000023541683,0.0000016992983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99757266,0.0005929713,0.0009006354,0.00030182063,0.00043121297,0.00020069188],"domain_scores_gemma":[0.9944069,0.0017250311,0.00046837833,0.0025309166,0.0007932662,0.000075555356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014326135,0.00013909153,0.0005178011,0.00011947476,0.000049028356,0.000006517795,0.000425343,0.000059609334,0.00015646183],"category_scores_gemma":[0.00089401694,0.00014306522,0.00007725568,0.00033254424,0.00029228636,0.00028501876,0.00029898252,0.000105213425,0.000003188983],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036031072,0.00083431054,0.9319931,0.00057415746,0.00025697588,1.9811709e-7,0.00008524796,0.0056414357,0.0006055924,0.03344416,0.00068112125,0.025523385],"study_design_scores_gemma":[0.0047538094,0.00047180048,0.78599143,0.0002201403,0.0005622169,0.000003134992,0.000200117,0.16284591,0.00019052319,0.044058476,0.00046860412,0.00023380539],"about_ca_topic_score_codex":0.0009866355,"about_ca_topic_score_gemma":0.0034312007,"teacher_disagreement_score":0.15720448,"about_ca_system_score_codex":0.00011835635,"about_ca_system_score_gemma":0.00023485119,"threshold_uncertainty_score":0.58340305},"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":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08681255,0.0000029865175,0.00030713694,0.00021260035,0.0000069056955,0.91159874,0.0006724899,0.00017880234,0.0002077613],"genre_scores_gemma":[0.35314265,9.974887e-7,0.000058807997,0.00002984331,0.000046200494,0.64651066,0.00006825741,0.000046312656,0.00009626494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99330145,0.00391117,0.00073321117,0.00057979865,0.0007598927,0.00071449793],"domain_scores_gemma":[0.99457616,0.0015603001,0.00033389882,0.001470223,0.0015236301,0.00053580414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050911848,0.00036446573,0.00082839956,0.00027848574,0.00042351225,0.000164898,0.000481312,0.00015195235,0.00002484987],"category_scores_gemma":[0.001190728,0.00024366603,0.000116040894,0.0005235108,0.00060448,0.0002963487,0.00024688174,0.000881701,0.00001557626],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.2105121,0.008131286,0.3850723,0.0827049,0.00092384074,0.000055586774,0.006081957,0.000021570171,0.19835208,0.0004042256,0.011081011,0.09665914],"study_design_scores_gemma":[0.09837795,0.0393986,0.659983,0.027521376,0.00010077561,0.00009003017,0.00736229,0.026331972,0.028230209,0.00031919844,0.11028259,0.0020020267],"about_ca_topic_score_codex":0.0007107672,"about_ca_topic_score_gemma":0.00044431543,"teacher_disagreement_score":0.2749107,"about_ca_system_score_codex":0.00022859036,"about_ca_system_score_gemma":0.00073357,"threshold_uncertainty_score":0.99364126},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920358,0.0016822431,0.000020430383,0.0011274873,0.00017241808,0.0039783847,0.000082882034,0.00021516433,0.00068516313],"genre_scores_gemma":[0.9981057,0.00006819766,0.000014179168,0.00064164517,0.00011572426,0.00038023115,0.0005239831,0.00004096952,0.00010934946],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99538875,0.0012889076,0.0006304889,0.00080682896,0.000900154,0.0009848493],"domain_scores_gemma":[0.99718994,0.000092596114,0.0002641328,0.0008817472,0.00026608375,0.0013054973],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004411075,0.0002638174,0.00064684264,0.00022464129,0.000116106195,0.00011277906,0.00018199967,0.00006554967,0.00007836147],"category_scores_gemma":[0.0002576089,0.00024851118,0.000048879087,0.0006797608,0.00003426867,0.0004421134,0.00005476879,0.0003376366,0.00008492599],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037233933,0.0005087285,0.98610973,0.0004413672,0.000028575765,0.000019073303,0.0006042127,0.00001745645,0.000009868735,0.00015025765,0.00031253556,0.011425836],"study_design_scores_gemma":[0.0037906975,0.0011447886,0.9746097,0.000058208476,0.0000025707366,0.0000057445595,0.001588809,0.011397968,6.9951874e-8,0.000006272369,0.007170465,0.00022475726],"about_ca_topic_score_codex":0.00037604282,"about_ca_topic_score_gemma":0.00079090457,"teacher_disagreement_score":0.0115001,"about_ca_system_score_codex":0.0009239631,"about_ca_system_score_gemma":0.002824604,"threshold_uncertainty_score":0.9999967},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99270254,0.0014958753,0.000023469505,0.0025642188,0.00012564019,0.002449913,0.00050151907,0.000036011836,0.00010078812],"genre_scores_gemma":[0.9968794,0.000497919,0.0000057175607,0.0014917845,0.0000393219,0.00025490357,0.00079998287,0.000013439117,0.00001752402],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99467534,0.0024147257,0.00077550166,0.00053201313,0.000913328,0.00068909914],"domain_scores_gemma":[0.99727386,0.00095313415,0.00048373383,0.00061356067,0.00038875002,0.00028693557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005506265,0.00025588647,0.0009654626,0.00026396153,0.00007309154,0.000071622366,0.00025587186,0.0001033151,0.000021424492],"category_scores_gemma":[0.00029670144,0.00018708782,0.00005275299,0.0010690097,0.00013943561,0.00022173175,0.000064146414,0.0003805964,0.000008139784],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034059136,0.0002867352,0.9909164,0.0007043078,0.00002272523,0.0000035218986,0.0051700003,0.00007227194,0.000007989479,0.00007903594,0.00030491722,0.0020915351],"study_design_scores_gemma":[0.0028265484,0.00026994507,0.9681617,0.00014031156,4.3884648e-7,0.000011037331,0.0047069867,0.01519894,3.281605e-7,0.000008598294,0.008509211,0.00016595944],"about_ca_topic_score_codex":0.004395916,"about_ca_topic_score_gemma":0.0047689783,"teacher_disagreement_score":0.022754675,"about_ca_system_score_codex":0.0002210563,"about_ca_system_score_gemma":0.0007495625,"threshold_uncertainty_score":0.762922},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99771994,0.00013972551,0.00076433003,0.0004572126,0.00015694225,0.000533561,0.00009635268,0.000015593689,0.00011635419],"genre_scores_gemma":[0.99588835,0.000009999923,0.0008545448,0.0005764282,0.000026080377,0.000005822275,0.0025263946,0.0000069861735,0.00010540929],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981681,0.00012083987,0.00061160285,0.00024183736,0.0006842389,0.00017336856],"domain_scores_gemma":[0.99880266,0.00009209573,0.00043077787,0.00037404065,0.0001603169,0.00014010635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060707075,0.00008968756,0.0004957192,0.0006339463,0.000020222087,0.00000403119,0.000056074987,0.000064843436,0.00023663945],"category_scores_gemma":[0.00029800885,0.00008976631,0.0000734693,0.002355913,0.0000057169796,0.00006895903,0.00001533262,0.00016706536,0.0000021756714],"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":[0.00006692963,0.000075224685,0.99686736,0.00016632448,0.00009810845,0.0000017529204,0.0000932875,0.0016670077,0.000002267046,0.00013790118,0.00027721352,0.00054660934],"study_design_scores_gemma":[0.0005398897,0.00003787341,0.9902193,0.000028928896,0.000102466554,6.7886134e-7,0.0000086689115,0.008285396,8.70605e-7,0.000043087224,0.0006695383,0.000063308624],"about_ca_topic_score_codex":0.99672943,"about_ca_topic_score_gemma":0.99787575,"teacher_disagreement_score":0.00664808,"about_ca_system_score_codex":0.0015204346,"about_ca_system_score_gemma":0.0022507613,"threshold_uncertainty_score":0.39927536},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99514127,0.00013305184,0.000003369438,0.00097262184,0.00013028685,0.00040741268,0.00036841468,0.000033472268,0.0028100861],"genre_scores_gemma":[0.99795306,0.00005925734,0.0008812915,0.00023001635,0.00019332905,0.000015784217,0.000008810562,0.000026701684,0.00063174043],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983863,0.000014622546,0.00047863825,0.0003468867,0.00043544304,0.00033810287],"domain_scores_gemma":[0.9739405,0.000070827904,0.00045901717,0.00029001734,0.025036983,0.00020266081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041195593,0.00023884905,0.0005597278,0.00017006691,0.00008677474,0.00042935807,0.0006593955,0.000121154946,0.0000334324],"category_scores_gemma":[0.007114558,0.00017571522,0.00010332754,0.00038561196,0.0005105368,0.0024483625,0.0004705114,0.00022069775,0.000005819474],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015053093,0.00016075498,0.68803114,0.0016304951,0.0001508562,0.0000024404933,0.027063023,0.0000012228476,0.26367998,0.00028257893,0.007793267,0.009698919],"study_design_scores_gemma":[0.00089084584,0.0012126962,0.77077955,0.0019086279,0.00014442601,0.000058060396,0.0011165553,0.0006210456,0.19481666,0.000100935475,0.028060887,0.0002897017],"about_ca_topic_score_codex":0.000041099916,"about_ca_topic_score_gemma":0.000004846434,"teacher_disagreement_score":0.082748406,"about_ca_system_score_codex":0.000032894393,"about_ca_system_score_gemma":0.000121041405,"threshold_uncertainty_score":0.85173047},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98793393,0.00051577523,0.0000077365285,0.008882556,0.00010894771,0.0017253574,0.00008170111,0.00005450486,0.000689494],"genre_scores_gemma":[0.9971851,0.00018262553,0.000024957893,0.0005677883,0.0001100141,0.00011918274,0.00023135892,0.000014463124,0.0015644986],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976574,0.00054032926,0.00041356974,0.00041676528,0.0005195419,0.0004523833],"domain_scores_gemma":[0.99854505,0.00046605605,0.00020109034,0.00025345563,0.00021035655,0.00032400744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032904267,0.00014123466,0.00040965137,0.00009103365,0.00010963954,0.00012518166,0.00009169566,0.00007303514,0.000012792797],"category_scores_gemma":[0.0010038578,0.0001079852,0.000028005987,0.00031196908,0.000052532967,0.00021884739,0.00009588671,0.0003219593,0.000014685559],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094534604,0.00010527173,0.989968,0.000080945974,0.000051839867,9.814535e-7,0.0008031291,1.06667805e-7,0.0000040482996,0.00014203595,0.0023114309,0.006437668],"study_design_scores_gemma":[0.0016062604,0.00039736394,0.98050284,0.0000163341,8.6982936e-7,0.000001569117,0.00057339645,0.00020898346,1.4841342e-7,0.000056453697,0.016542457,0.000093337534],"about_ca_topic_score_codex":0.00032458483,"about_ca_topic_score_gemma":0.0009718345,"teacher_disagreement_score":0.014231025,"about_ca_system_score_codex":0.00049489236,"about_ca_system_score_gemma":0.000382936,"threshold_uncertainty_score":0.4403509},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99888176,0.00022262381,0.0004081549,0.00015137083,0.00009499677,0.00013000541,0.000023897106,0.0000130026,0.00007417438],"genre_scores_gemma":[0.99785656,0.000022218434,0.001723818,0.00029026027,0.00005278751,8.2191815e-7,0.000016828631,0.0000066999733,0.000030014235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99802434,0.00010212771,0.0005653689,0.000054424796,0.0011013114,0.00015243815],"domain_scores_gemma":[0.99713725,0.000051505063,0.0022580968,0.00012125509,0.00019104917,0.00024085707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007984001,0.00010376736,0.00037740235,0.000050665556,0.00008171998,0.000022303415,0.00012733752,0.00003108197,0.000023384559],"category_scores_gemma":[0.0014912287,0.0000689091,0.00008436564,0.00024448903,0.00008442702,0.0002553403,0.00013628764,0.00037361903,0.0000018598354],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022463837,0.0001849843,0.9487114,0.00027862552,0.00015843188,0.000009283007,0.0013631684,0.000015194677,0.001877646,0.000003482216,0.00012567692,0.047047496],"study_design_scores_gemma":[0.00283244,0.00022027502,0.9203701,0.00093063165,0.00023297755,0.00009587555,0.0010651861,0.07266411,0.00027093213,0.000005371745,0.0011144715,0.00019762859],"about_ca_topic_score_codex":0.000057757974,"about_ca_topic_score_gemma":0.000030205618,"teacher_disagreement_score":0.07264891,"about_ca_system_score_codex":0.0006133889,"about_ca_system_score_gemma":0.000629389,"threshold_uncertainty_score":0.28100315},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9893234,0.0019809818,0.000004291993,0.00036398903,0.00017250559,0.0077545713,0.000208879,0.0000928899,0.00009850354],"genre_scores_gemma":[0.99651766,0.00017065562,0.000039190687,0.0017607303,0.00012225527,0.0012064261,0.00006983165,0.000030268315,0.000082961546],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99715984,0.00038009186,0.00070440175,0.00062158564,0.00033206385,0.0008020271],"domain_scores_gemma":[0.99782956,0.0004532693,0.00020705839,0.0008792448,0.000107054424,0.0005238446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002636588,0.00022254858,0.00059308065,0.00006790573,0.00023553516,0.000028208226,0.00016493215,0.00008416137,0.00002360924],"category_scores_gemma":[0.00017942413,0.00016077522,0.00006381658,0.0002729699,0.00013266777,0.000091000475,0.00007540274,0.0003161204,0.000039916795],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086637086,0.0006984878,0.9889837,0.00060063135,0.000022398573,0.0000011121398,0.00048439822,0.000009268177,0.0000025374138,0.000049653892,0.0010226449,0.0072588096],"study_design_scores_gemma":[0.0043996964,0.00096209865,0.98417455,0.000026004956,0.000018814568,0.0000051697302,0.00054915686,0.00027248383,0.0000012497028,0.000039149858,0.009407512,0.00014413198],"about_ca_topic_score_codex":0.00034536916,"about_ca_topic_score_gemma":0.00066140224,"teacher_disagreement_score":0.008384868,"about_ca_system_score_codex":0.00018887047,"about_ca_system_score_gemma":0.000616563,"threshold_uncertainty_score":0.65562236},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30364695,0.009038819,0.0018463986,0.3882209,0.020029508,0.0022410422,0.008187796,0.0015288723,0.2652597],"genre_scores_gemma":[0.001910223,0.000014387375,0.0012114993,0.9725623,0.018239485,0.000002656488,0.00033367542,0.00033724357,0.0053885244],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99428254,0.0015848308,0.0010500867,0.00087393395,0.0012603189,0.0009482847],"domain_scores_gemma":[0.9965459,0.00005735281,0.0007181952,0.0014167833,0.00041266144,0.0008491113],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012100356,0.00072242157,0.0010675017,0.0005424739,0.00026234397,0.00032032287,0.0009047791,0.0002961682,0.0008009263],"category_scores_gemma":[0.00040256872,0.0006553513,0.00047916287,0.0007134608,0.00016959306,0.00020856378,0.0004101631,0.0028001394,0.0030783042],"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":[0.000113081325,0.00003487353,0.02186314,0.00016847784,0.00021344674,0.020150939,0.000026493834,0.0001020243,0.00037788373,0.0000016518371,0.9520897,0.0048582535],"study_design_scores_gemma":[0.0014910912,0.00028398485,0.034252398,0.0009952052,0.00024175554,0.0016045842,0.0000074315435,0.000020549856,0.000021133576,0.000005451797,0.96044064,0.00063575245],"about_ca_topic_score_codex":0.00000974005,"about_ca_topic_score_gemma":0.0000032534863,"teacher_disagreement_score":0.5843414,"about_ca_system_score_codex":0.00076942396,"about_ca_system_score_gemma":0.0013424275,"threshold_uncertainty_score":0.9995898},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9911023,0.0025456669,0.00001667923,0.0016933103,0.00012256968,0.0020685063,0.0024229714,0.000013838165,0.000014188424],"genre_scores_gemma":[0.9885586,0.000533256,0.00026580095,0.001273452,0.00002982103,0.000059310933,0.009228815,0.000021028862,0.000029926283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972013,0.0005196825,0.0007092697,0.000738667,0.0003137511,0.00051733025],"domain_scores_gemma":[0.99837077,0.00025587247,0.00019236105,0.0003858227,0.00011700628,0.0006781591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024709315,0.00018567423,0.00064404163,0.00022148858,0.00007667466,0.00006257333,0.00015250027,0.000044278986,0.0000066907146],"category_scores_gemma":[0.0003116445,0.00018294112,0.000010145164,0.0004575977,0.000019998437,0.0003876659,0.00033347297,0.00014458534,1.657888e-7],"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":[0.00024161384,0.00009439468,0.94061005,0.000806568,0.000014957832,0.0000021410387,0.00041805973,0.0000023427817,2.513993e-7,0.0000022859977,0.0012423401,0.056565013],"study_design_scores_gemma":[0.0020477308,0.00034768862,0.9928558,0.000041134434,7.244569e-7,0.0000033431686,0.0003600929,0.0025415018,1.012092e-8,0.0000029267405,0.0016536125,0.00014546158],"about_ca_topic_score_codex":0.47705033,"about_ca_topic_score_gemma":0.95971906,"teacher_disagreement_score":0.48266873,"about_ca_system_score_codex":0.0006041904,"about_ca_system_score_gemma":0.0027407117,"threshold_uncertainty_score":0.7460123},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59472626,0.0022546565,0.39571717,0.0010716032,0.0011082389,0.0033063467,0.00042368463,0.00071567297,0.00067639555],"genre_scores_gemma":[0.58708507,0.000017552338,0.40660036,0.0049533583,0.0007008417,0.00012321318,0.0001958203,0.0001300566,0.00019371252],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99539965,0.001193666,0.0010286759,0.000764364,0.00042471342,0.0011889479],"domain_scores_gemma":[0.9952727,0.001307302,0.0006236336,0.0010195465,0.00048723968,0.0012895543],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009935678,0.00031366944,0.0010439341,0.00023919978,0.00020981499,0.0000934613,0.00028003816,0.00015821417,0.00006210461],"category_scores_gemma":[0.006117101,0.0002999621,0.00026088537,0.0006036276,0.000047995327,0.00030181865,0.00014008608,0.00037807418,0.00011199989],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021926124,0.00008932349,0.95665586,0.0046139234,0.000115200215,0.0000033388415,0.00009233962,0.00010907677,0.00039141494,0.0009273996,0.001186311,0.035596576],"study_design_scores_gemma":[0.0076030153,0.00043736616,0.40135473,0.0012378405,0.00004118328,0.00033354454,0.0010943467,0.36582825,0.000047900176,0.000081998274,0.22108552,0.00085431756],"about_ca_topic_score_codex":0.00038068797,"about_ca_topic_score_gemma":0.0000653091,"teacher_disagreement_score":0.55530113,"about_ca_system_score_codex":0.0015242214,"about_ca_system_score_gemma":0.004458749,"threshold_uncertainty_score":0.9999452},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29614344,0.6940736,0.00001941837,0.0036515635,0.0008575267,0.00076963904,0.00022701213,0.000077494944,0.004180318],"genre_scores_gemma":[0.38211438,0.6083117,0.0009254344,0.0064457403,0.0006767004,0.000013982675,0.0001485605,0.00006716959,0.0012962976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99754095,0.00026051744,0.0008146158,0.000312765,0.0006801641,0.00039098813],"domain_scores_gemma":[0.9973026,0.00017310919,0.0005354513,0.0007807533,0.0009002308,0.00030786483],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016313571,0.00023726746,0.0010584187,0.000116126736,0.000058403697,0.000015579853,0.00026800702,0.00007409246,0.0012505994],"category_scores_gemma":[0.0012895892,0.000189426,0.00032721492,0.0003808467,0.00012412183,0.00016522607,0.00012138339,0.00057257945,0.00047340823],"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":[0.00071631843,0.00079744036,0.1628699,0.043521203,0.001761118,0.00025220314,0.0015725538,0.000003518529,0.0014634825,0.00019431364,0.77482724,0.012020742],"study_design_scores_gemma":[0.00303262,0.00033667538,0.023330478,0.027768698,0.000186466,0.0014081708,0.00030713255,0.000015573374,0.0001382308,0.0009633952,0.9420734,0.00043915943],"about_ca_topic_score_codex":0.000010996824,"about_ca_topic_score_gemma":0.000028824421,"teacher_disagreement_score":0.1672462,"about_ca_system_score_codex":0.00010214916,"about_ca_system_score_gemma":0.00026878715,"threshold_uncertainty_score":0.9996624},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004362221,0.9849808,0.0000013642435,0.00021213836,0.000031027004,0.01368682,0.00058786705,0.00001048457,0.00044587665],"genre_scores_gemma":[0.00008326771,0.9978982,0.000053163607,0.000609173,0.000083103136,0.0007924575,0.00034765585,0.00005729703,0.00007564881],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99038583,0.0040798387,0.0033488288,0.0007384311,0.0007421316,0.0007049385],"domain_scores_gemma":[0.99536633,0.00090863556,0.0015591942,0.0010445592,0.0006932983,0.00042798772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.012719122,0.000417344,0.0072876,0.0006261755,0.000042248525,0.000013685254,0.00039313824,0.00019389215,0.000037212973],"category_scores_gemma":[0.004760772,0.0002987418,0.0005011152,0.0018394296,0.00031501718,0.00023538829,0.00016068527,0.0010923271,0.000119741984],"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":[0.000057137364,0.00013191477,0.0003687701,0.56580925,0.000027002134,0.0000029856487,0.00003087742,1.7216978e-9,4.169626e-7,0.000056854686,0.0020899023,0.43142492],"study_design_scores_gemma":[0.0004219438,0.00049791794,0.0006761106,0.45080402,0.00011374792,0.000010643756,0.0000050499316,0.0000016514015,1.1632559e-7,0.0000044086923,0.5473686,0.00009576964],"about_ca_topic_score_codex":0.000089178146,"about_ca_topic_score_gemma":0.00006694838,"teacher_disagreement_score":0.5452787,"about_ca_system_score_codex":0.00033767783,"about_ca_system_score_gemma":0.005029541,"threshold_uncertainty_score":0.9999465},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9972938,0.00039397393,0.00019562559,0.000702875,0.00027687816,0.0008169503,0.00018890767,0.000008676256,0.00012237136],"genre_scores_gemma":[0.9992172,0.000028791625,0.0003303431,0.0003873181,0.000019008647,0.0000010853914,0.000013699599,0.0000019749966,5.838173e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9949788,0.0002338429,0.0027784607,0.00018321899,0.0014651748,0.00036052117],"domain_scores_gemma":[0.99289167,0.000045835877,0.0055655884,0.0008487156,0.00040758605,0.00024059642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016681679,0.000092000766,0.00052511645,0.00015640668,0.00013819365,0.0000522484,0.0007042536,0.000011546276,0.0000018792822],"category_scores_gemma":[0.0010358789,0.000057232,0.00003262309,0.0013598955,0.000093291,0.001071654,0.00017894778,0.0001882712,0.0000044920425],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114865885,0.00027916246,0.9605354,0.00083396566,0.000012904932,0.00091811235,0.0020921063,0.00023090914,0.00000256416,0.000027652542,0.0007630855,0.034189284],"study_design_scores_gemma":[0.0014736888,0.0011604165,0.93149287,0.00063517515,0.000011315656,0.022719247,0.040584546,0.0016234502,6.630993e-7,0.0000044337444,0.0002374307,0.000056780358],"about_ca_topic_score_codex":0.0049448274,"about_ca_topic_score_gemma":0.00038008686,"teacher_disagreement_score":0.038492437,"about_ca_system_score_codex":0.0006117255,"about_ca_system_score_gemma":0.0037974548,"threshold_uncertainty_score":0.74751323},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9348572,0.0132225845,0.00007493477,0.04521947,0.0004998004,0.0017544854,0.00037826961,0.00015878616,0.0038344495],"genre_scores_gemma":[0.99183285,0.00067905884,0.00084287365,0.0017435936,0.00016078929,0.000018660456,0.00038335557,0.00003697789,0.004301822],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99749154,0.00038380254,0.00066000526,0.00041373866,0.0004605071,0.00059042824],"domain_scores_gemma":[0.99758697,0.0006588511,0.0005028364,0.0007362413,0.00008531042,0.0004298125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025859568,0.00017506901,0.00059564674,0.00009420202,0.0002471212,0.000031815835,0.00015596303,0.0000790964,0.000124675],"category_scores_gemma":[0.0005009946,0.000120854245,0.000054377982,0.00030209392,0.00012776734,0.00015162272,0.00009005079,0.0002515278,0.00009950514],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048304992,0.00007558467,0.6724453,0.00136528,0.00008322129,0.0000012092837,0.0011912052,0.000014073518,0.00005250821,0.0001879882,0.2957545,0.028346121],"study_design_scores_gemma":[0.001624388,0.0003731436,0.6088247,0.00036463907,0.000014301544,0.000012483158,0.000953006,0.00011141558,0.0000019885886,0.00001906911,0.38758254,0.00011835756],"about_ca_topic_score_codex":0.0022264149,"about_ca_topic_score_gemma":0.0007437509,"teacher_disagreement_score":0.09182805,"about_ca_system_score_codex":0.0005842544,"about_ca_system_score_gemma":0.0016786968,"threshold_uncertainty_score":0.4928293},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.977413,0.001035863,0.0000055118003,0.016917331,0.00023928662,0.0017242967,0.000770432,0.000077527795,0.0018167448],"genre_scores_gemma":[0.99804556,0.00014117241,0.000010718647,0.00024051232,0.00011345876,0.0002797095,0.0005907204,0.0000062489116,0.000571915],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978961,0.0006081171,0.00033383502,0.0001813068,0.000726504,0.00025418462],"domain_scores_gemma":[0.9985631,0.00017848455,0.00019654668,0.00057529385,0.00041369122,0.00007286848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010499835,0.000053549767,0.00014794,0.00014912972,0.0001282903,0.00006207284,0.00042353375,0.00006486902,0.0000032617918],"category_scores_gemma":[0.0057645105,0.000039628467,0.000025208517,0.00028781034,0.0001252199,0.0001422906,0.000066171895,0.0003585068,0.0000091566135],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016105427,0.0001759633,0.8882798,0.00019573487,0.000026360467,0.000009088694,0.0008688562,0.0000027731535,0.00006282935,0.0010053255,0.08030406,0.028908143],"study_design_scores_gemma":[0.0003775806,0.000007772236,0.99709874,0.000035767338,0.0000010570802,0.0000033297335,0.000016027438,0.00018192112,0.00005005811,0.00028471116,0.0019129572,0.000030047033],"about_ca_topic_score_codex":0.00018467467,"about_ca_topic_score_gemma":0.00033598545,"teacher_disagreement_score":0.108818956,"about_ca_system_score_codex":0.00010636322,"about_ca_system_score_gemma":0.0001973271,"threshold_uncertainty_score":0.6901074},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9840653,0.00036621542,0.000502183,0.00034757055,0.00032616968,0.0016270939,0.011836399,0.000121931844,0.000807122],"genre_scores_gemma":[0.9827837,0.00041658618,0.000634333,0.00012670108,0.000047582893,0.00002050906,0.015896715,0.000018837078,0.000055035765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974627,0.000060393177,0.0009923624,0.00071084,0.00045230423,0.00032140885],"domain_scores_gemma":[0.9974865,0.000094461415,0.0004893958,0.0015715094,0.0001906167,0.0001675162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013881625,0.00023895837,0.0007116213,0.00019884025,0.000056976387,0.0001339336,0.00067457295,0.00007134363,0.000047408812],"category_scores_gemma":[0.00045131738,0.00022094093,0.000026662545,0.00069439306,0.000036488524,0.0024312162,0.0010331385,0.00019436907,0.000048252732],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011316681,0.00014389721,0.9201993,0.003300313,0.00012672695,0.0000050517883,0.0005797816,0.0000034786585,0.00015878098,0.000016042744,0.038781453,0.035553522],"study_design_scores_gemma":[0.0028658337,0.00019974013,0.9775398,0.0005424854,0.000018911009,0.000009808094,0.0013969255,0.0034982373,0.00003273668,0.000004872127,0.013691833,0.00019884981],"about_ca_topic_score_codex":0.0016742753,"about_ca_topic_score_gemma":0.00023804566,"teacher_disagreement_score":0.05734048,"about_ca_system_score_codex":0.0004245031,"about_ca_system_score_gemma":0.00013634638,"threshold_uncertainty_score":0.90097094},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977394,0.00023627935,0.000007785805,0.00016252257,0.00010436234,0.00082745985,0.00016947715,0.000044696622,0.00070807146],"genre_scores_gemma":[0.9991323,0.000036705907,0.00025037958,0.0001242658,0.000023264864,0.00006705676,0.000073756506,0.00002841804,0.000263856],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99730545,0.000017109482,0.0014124253,0.00048307003,0.00042284318,0.00035909403],"domain_scores_gemma":[0.99810016,0.00013501004,0.0009068539,0.00038896583,0.00024806365,0.00022095768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007713582,0.00019736096,0.0009125435,0.00025573658,0.0000106208645,0.000009587894,0.00018916768,0.000049893883,0.00007782187],"category_scores_gemma":[0.0027479588,0.00018268259,0.000098538956,0.0008134087,0.00005449409,0.00016106885,0.00012352566,0.00013808032,0.000026178213],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038144365,0.00022761039,0.98247904,0.00075796427,0.000017948209,0.000038658018,0.00081175927,0.00006426753,0.013884868,0.000015439164,0.00031903654,0.0010019604],"study_design_scores_gemma":[0.00068040245,0.00012592027,0.9915189,0.00043122572,0.000006405655,0.0000014587387,0.00050701696,0.000102352424,0.0059796968,0.00006239702,0.00041290576,0.00017129509],"about_ca_topic_score_codex":0.00019458377,"about_ca_topic_score_gemma":0.00010081973,"teacher_disagreement_score":0.009039878,"about_ca_system_score_codex":0.00009779753,"about_ca_system_score_gemma":0.0001293458,"threshold_uncertainty_score":0.744958},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002660503,0.0006489768,0.0000046276646,0.0008707491,0.00026843537,0.0012870792,0.00038934473,0.0002116183,0.9960531],"genre_scores_gemma":[0.015718576,0.00040382933,0.00012307067,0.00083130697,0.00049518916,0.000023216378,0.00077895256,0.0001481402,0.98147774],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99722564,0.00004645571,0.00030923574,0.00070873497,0.0012634706,0.00044648754],"domain_scores_gemma":[0.99452347,0.0029799808,0.00007751173,0.0016920579,0.0003860463,0.0003409189],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004915052,0.00033159196,0.0006305483,0.00041779137,0.0000494809,0.000029271143,0.0002792342,0.00033486658,0.009635855],"category_scores_gemma":[0.0016723056,0.00026694348,0.00017878626,0.000060061204,0.00017031144,0.000034996054,0.00019812553,0.0010123859,0.04529735],"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":[0.00037796143,0.00018446789,0.0023745026,0.0005212651,0.00026618608,0.0013181887,0.000016291111,0.0000014327154,0.000025877747,0.14891747,0.811528,0.034468308],"study_design_scores_gemma":[0.0012558539,0.0007978619,0.015578111,0.0015173444,0.00007308247,0.000014091732,0.000003405368,0.000008518634,0.000028860952,0.0029031434,0.9774768,0.00034293006],"about_ca_topic_score_codex":0.000030079462,"about_ca_topic_score_gemma":0.00006586037,"teacher_disagreement_score":0.16594875,"about_ca_system_score_codex":0.0002125022,"about_ca_system_score_gemma":0.00055878196,"threshold_uncertainty_score":0.9999783},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938254,0.0020735597,0.000016141832,0.00082251086,0.00042446877,0.0017721667,0.00020004383,0.00012525367,0.000740461],"genre_scores_gemma":[0.9983424,0.000105066814,0.00014422354,0.00053374516,0.0003033219,0.00012697424,0.0002979848,0.00004424671,0.00010202736],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976293,0.00010232276,0.000622695,0.00053720304,0.00044398406,0.0006645374],"domain_scores_gemma":[0.9970792,0.00034561608,0.00042590624,0.0007397434,0.00040145038,0.0010080801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007538772,0.00024631506,0.00077598414,0.00016876541,0.00007737197,0.00003706164,0.000104562816,0.00011122971,0.00003465797],"category_scores_gemma":[0.0014294746,0.0002059671,0.00019914898,0.00026457937,0.000065316366,0.00013811364,0.000055412012,0.00019115406,0.000016867725],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030244776,0.00011804214,0.9713441,0.0008285119,0.000055423367,0.000012764595,0.0018058753,0.0000057986067,0.000025805339,0.000028376611,0.0014734495,0.023999417],"study_design_scores_gemma":[0.0017454395,0.0021447975,0.92211306,0.000116419855,0.000001265433,0.000033052816,0.00078074634,0.000037403628,0.000007764511,0.000011389227,0.07280308,0.00020559675],"about_ca_topic_score_codex":0.0002230514,"about_ca_topic_score_gemma":0.000011226425,"teacher_disagreement_score":0.07132963,"about_ca_system_score_codex":0.00023760644,"about_ca_system_score_gemma":0.0015237187,"threshold_uncertainty_score":0.83990943},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002021748,0.98265135,0.000014622988,0.00022280743,0.0016122644,0.009657634,0.002870156,0.00018023879,0.0027889144],"genre_scores_gemma":[0.0000019469207,0.98891723,0.000052370942,0.0020702342,0.0020467604,0.0020993997,0.0039222585,0.00015843156,0.0007313661],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99679476,0.00019416997,0.00097104016,0.00088143605,0.00061506825,0.00054351264],"domain_scores_gemma":[0.99678904,0.0005199803,0.00075192424,0.0012941837,0.00032272795,0.0003221612],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006251593,0.0006174405,0.004353152,0.00016672535,0.00005689871,0.000086843844,0.0010424686,0.00038163806,0.00046495773],"category_scores_gemma":[0.0014403657,0.00048159724,0.0012919682,0.0005473253,0.00007726401,0.00023333888,0.00020845645,0.00050542527,0.0013484971],"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":[0.000022178141,0.00007740195,0.00001046883,0.35000673,0.00029189265,0.0000509015,0.000013515628,3.5044836e-9,2.4222482e-8,0.000036449623,0.11451108,0.53497934],"study_design_scores_gemma":[0.0005457096,0.00005479317,0.0000055014184,0.13783637,0.0041454206,0.00006505225,0.0000010426695,0.0000013915469,2.8101962e-7,0.0001531061,0.856784,0.00040736605],"about_ca_topic_score_codex":0.00002896709,"about_ca_topic_score_gemma":0.000017737386,"teacher_disagreement_score":0.74227285,"about_ca_system_score_codex":0.0001911456,"about_ca_system_score_gemma":0.0020145858,"threshold_uncertainty_score":0.99976355},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9719957,0.0012492064,0.00078893465,0.014000974,0.00016686342,0.00055131095,0.000039736293,0.000078158715,0.011129076],"genre_scores_gemma":[0.9789545,0.00029682697,0.009015452,0.008006355,0.0012621776,0.000004309878,0.00003255474,0.000049270282,0.0023785634],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99725276,0.0008876259,0.0007143505,0.00025216278,0.00049407186,0.0003990206],"domain_scores_gemma":[0.99771523,0.00049022515,0.0004082032,0.00065246056,0.00012839839,0.0006054799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029445882,0.00025049283,0.000786923,0.000135726,0.000053792166,0.000039724408,0.0009064904,0.00021344885,0.000598206],"category_scores_gemma":[0.00072539365,0.00012244434,0.000112108704,0.00033414448,0.00015986872,0.000857424,0.0001200237,0.0012950388,0.00014609647],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.024533866,0.0004982014,0.7119076,0.00040738675,0.00068344886,0.00022310088,0.0047545116,0.00056301587,0.016216932,0.005083098,0.2323665,0.0027622913],"study_design_scores_gemma":[0.0041664336,0.0044378373,0.91469675,0.0009089616,0.00049449643,0.0028487497,0.0025049997,0.001091167,0.0012306529,0.0037266568,0.06330959,0.0005836871],"about_ca_topic_score_codex":0.000011145398,"about_ca_topic_score_gemma":2.8014486e-7,"teacher_disagreement_score":0.20278913,"about_ca_system_score_codex":0.000047336365,"about_ca_system_score_gemma":0.00019899102,"threshold_uncertainty_score":0.65499365},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79907465,0.00038725536,0.19514053,0.0013053268,0.00045573185,0.00078269234,0.0028225395,0.000022420707,0.000008841274],"genre_scores_gemma":[0.95634264,0.00022284154,0.040856507,0.0016959332,0.00022298479,0.0000057779466,0.00062737666,0.000023581508,0.00000233558],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969028,0.00063162943,0.001289599,0.00024104444,0.00066248997,0.00027243773],"domain_scores_gemma":[0.99578446,0.0006623074,0.0011860818,0.0006749255,0.001340386,0.00035185862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054055867,0.0001779832,0.0005215148,0.0006783833,0.00013305213,0.00007830404,0.00058015506,0.000074251606,0.0000036509825],"category_scores_gemma":[0.00055957324,0.00013379999,0.00015316655,0.0004531724,0.00008826949,0.0007309129,0.00019858271,0.00029385128,0.0000013354121],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017852616,0.00026388792,0.93904847,0.00082792045,0.00092700665,0.00018610002,0.0005354862,0.0013903014,0.0000404423,0.0022208723,0.0010532966,0.051720973],"study_design_scores_gemma":[0.008406359,0.003214543,0.8526116,0.0013267536,0.00043503192,0.020599017,0.003286651,0.053306248,0.000028210003,0.00023890445,0.05600659,0.00054008455],"about_ca_topic_score_codex":0.0030413212,"about_ca_topic_score_gemma":0.00075360487,"teacher_disagreement_score":0.15726799,"about_ca_system_score_codex":0.00006192364,"about_ca_system_score_gemma":0.00043800988,"threshold_uncertainty_score":0.5456205},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846054,0.0037872994,0.000026605767,0.008315436,0.000116768424,0.00217605,0.000060349343,0.00010174616,0.00081037794],"genre_scores_gemma":[0.9966626,0.0005290015,0.00007879361,0.0015267644,0.000063385974,0.00008331216,0.00024258532,0.000025685502,0.0007878336],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99740547,0.0005397746,0.00047875498,0.00056563586,0.00035758718,0.00065276626],"domain_scores_gemma":[0.9981814,0.00033141742,0.00014141375,0.00044909344,0.00013543479,0.00076121424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002427301,0.00020817669,0.00059903436,0.00024147915,0.000088493885,0.00007656615,0.00009231509,0.00008187432,0.000096316275],"category_scores_gemma":[0.00048197818,0.00018550738,0.000030169951,0.00048421312,0.00008770581,0.00024661672,0.000089747744,0.0003003353,0.00007422312],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016631608,0.00028719057,0.97789174,0.00028784547,0.000016959333,0.000014775163,0.0005884738,0.0000029641515,0.000009839116,0.000053107065,0.0023776437,0.018303122],"study_design_scores_gemma":[0.0026822411,0.0005888949,0.97015643,0.000038215112,5.373498e-7,0.000017197453,0.0009799876,0.0017384253,2.4918768e-7,0.000018937477,0.02361391,0.00016498507],"about_ca_topic_score_codex":0.0007353974,"about_ca_topic_score_gemma":0.0017486135,"teacher_disagreement_score":0.021236265,"about_ca_system_score_codex":0.00012354944,"about_ca_system_score_gemma":0.00063627114,"threshold_uncertainty_score":0.7564772},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99659646,0.00047050198,0.00006509636,0.000983925,0.00037198543,0.0011654734,0.000027494088,0.000025089053,0.0002939657],"genre_scores_gemma":[0.9984,0.00007021436,0.000091776295,0.000071841714,0.000562829,0.0000073008405,0.000034687633,0.000035477988,0.0007259179],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9889358,0.0018009533,0.0009966887,0.00045027546,0.0072152657,0.0006009633],"domain_scores_gemma":[0.9952386,0.00058199547,0.0002094393,0.00081895984,0.0014222877,0.0017287353],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.014461364,0.00018286123,0.00066818047,0.00048891344,0.000058496862,0.00009763142,0.00081433525,0.00014961306,0.0034382022],"category_scores_gemma":[0.00482202,0.00013940287,0.00023751748,0.00057908904,0.00022264602,0.0002481578,0.0005110182,0.0019343796,0.00018905163],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018107889,0.0027368423,0.98767835,0.00017765234,0.00010049136,0.0019303662,0.0001430882,0.000012520171,0.00019054688,0.000020655645,0.0025399514,0.0026587427],"study_design_scores_gemma":[0.003061177,0.0011987878,0.98782593,0.0004684218,0.00004278729,0.00020668004,0.00045211133,0.006101747,0.000022569271,0.000020144995,0.0004803907,0.00011926551],"about_ca_topic_score_codex":0.00082952593,"about_ca_topic_score_gemma":0.00010018104,"teacher_disagreement_score":0.009639343,"about_ca_system_score_codex":0.00061464735,"about_ca_system_score_gemma":0.0011918637,"threshold_uncertainty_score":0.99747276},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99056506,0.0023302292,0.0039627706,0.00028543224,0.0012243246,0.0005845636,0.00034751388,0.00026379348,0.00043629494],"genre_scores_gemma":[0.9913613,0.001452348,0.00023173771,0.000438126,0.000099039324,0.000022807211,0.006136026,0.000027769478,0.00023084976],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99733865,0.00054329337,0.0008282333,0.00077178207,0.000116161194,0.0004018632],"domain_scores_gemma":[0.9982202,0.00032714102,0.00039121692,0.00079598767,0.000082224404,0.00018320214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002265132,0.00024062824,0.0006573039,0.0000932435,0.0001634411,0.0000059292497,0.0001184406,0.00025326814,0.00037851866],"category_scores_gemma":[0.0007806455,0.00022400655,0.00006813381,0.00019384854,0.00006387973,0.00039132158,0.00017539853,0.00034153395,0.00019247594],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015644582,0.000067759145,0.9863908,0.00011378209,0.00014995762,0.0000035160015,0.000030050267,0.0033873226,0.00007015366,0.000057216195,0.008119863,0.0014531254],"study_design_scores_gemma":[0.0011702491,0.00022829665,0.7388968,0.00007952352,0.000069366935,0.000080233585,0.000007603657,0.22641857,0.000013637959,0.00033158573,0.03243074,0.00027339856],"about_ca_topic_score_codex":0.00068697653,"about_ca_topic_score_gemma":0.00016876211,"teacher_disagreement_score":0.24749401,"about_ca_system_score_codex":0.000068451765,"about_ca_system_score_gemma":0.000055342916,"threshold_uncertainty_score":0.9134722},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895786,0.000047466874,0.00034332086,0.000063471816,0.0070285127,0.0023420684,0.00001803355,0.000056643417,0.00052186265],"genre_scores_gemma":[0.99694043,3.9852546e-7,0.00022525535,0.00004428816,0.000089279856,0.00009899873,0.0000330413,0.000018405884,0.0025498932],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997667,0.0000705899,0.00067752955,0.00063375797,0.0006781674,0.0002729513],"domain_scores_gemma":[0.997197,0.00006270789,0.0004374097,0.0016916915,0.00046920814,0.0001420299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022804036,0.00013493156,0.0002855208,0.00010923187,0.00032675258,0.00014388472,0.00011001834,0.000037976635,0.00007364823],"category_scores_gemma":[0.00016440902,0.000093035866,0.00016598305,0.00030363724,0.00016606683,0.000109813904,0.00010816576,0.00010327837,0.00001504459],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021443411,0.0012769579,0.932374,0.00027793806,0.0003956105,0.012937658,0.000903811,0.00015811791,0.02731711,0.000016298383,0.008051776,0.016076282],"study_design_scores_gemma":[0.006546045,0.0021941254,0.72246593,0.00019652316,0.0008709392,0.030905338,0.014737244,0.004373306,0.008867902,0.0011447225,0.20688783,0.00081011106],"about_ca_topic_score_codex":0.000103465114,"about_ca_topic_score_gemma":0.00018080467,"teacher_disagreement_score":0.20990808,"about_ca_system_score_codex":0.00007282636,"about_ca_system_score_gemma":0.00030278286,"threshold_uncertainty_score":0.37938926},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9065346,0.000019121804,0.08976836,0.00024893027,0.00020418355,0.0026215129,0.0002420573,0.00019851966,0.00016271642],"genre_scores_gemma":[0.9674598,0.00000974344,0.031279147,0.0002918158,0.000054082957,0.00026055987,0.00005673628,0.00006289562,0.0005252218],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99840176,0.00008364423,0.00035927718,0.00049341813,0.00023672509,0.0004251532],"domain_scores_gemma":[0.9987099,0.00015840282,0.000065087166,0.0007692262,0.000115857576,0.00018148478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041822882,0.00020273775,0.00034156998,0.00043151592,0.0001369596,0.00005018487,0.00015439434,0.000108324224,0.00077076623],"category_scores_gemma":[0.00016448258,0.00021280702,0.00016301619,0.0006205135,0.0000355829,0.00043052447,0.000010637627,0.00032855,0.000071911345],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031824524,0.0024077797,0.7114207,0.0018004773,0.00066672056,0.0001047756,0.0021978177,0.0143977115,0.10380018,0.00032773966,0.00007756385,0.1596161],"study_design_scores_gemma":[0.01322525,0.0017507927,0.83850443,0.001941748,0.0007214938,0.00021113217,0.0010983215,0.08135419,0.00985371,0.00086507155,0.04814608,0.002327765],"about_ca_topic_score_codex":0.000602921,"about_ca_topic_score_gemma":0.00019616043,"teacher_disagreement_score":0.15728833,"about_ca_system_score_codex":0.000120603596,"about_ca_system_score_gemma":0.00011457944,"threshold_uncertainty_score":0.86780185},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99481016,0.0012610451,0.00018278288,0.0023269353,0.00024355618,0.00009797735,0.00003326401,0.00001890786,0.0010253486],"genre_scores_gemma":[0.9974034,0.00024632708,0.0005425625,0.00049216463,0.000868651,3.7132835e-7,0.00008385829,0.000020897029,0.0003417954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980561,0.000092115,0.00061133347,0.00020510278,0.0008289955,0.0002063319],"domain_scores_gemma":[0.99819046,0.00006982581,0.0006421894,0.0004837479,0.00025240937,0.00036134437],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014798122,0.00018575828,0.00076126267,0.0005048899,0.000072555915,0.0000137498255,0.00017370438,0.000048274112,0.0014435791],"category_scores_gemma":[0.00008847136,0.00010307975,0.00015196102,0.00073979475,0.00019184231,0.0001912608,0.00003934912,0.00027559834,0.0000022245113],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015362136,0.00019105349,0.9139525,0.000056949,0.00145509,0.00041119978,0.0015355875,0.0002845481,0.002794166,0.00026205595,0.0012303157,0.076290295],"study_design_scores_gemma":[0.0017134898,0.0005518536,0.98917013,0.00031593014,0.002758898,0.0007117124,0.00054352824,0.0029234937,0.00003044892,0.00013991684,0.0010286992,0.000111873065],"about_ca_topic_score_codex":0.000038856266,"about_ca_topic_score_gemma":0.0000041090775,"teacher_disagreement_score":0.07617842,"about_ca_system_score_codex":0.000060088194,"about_ca_system_score_gemma":0.000114093506,"threshold_uncertainty_score":0.9994692},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013419922,0.86201465,0.09222757,0.0010920049,0.0015870865,0.002915152,0.04011872,0.000018721923,0.000012665168],"genre_scores_gemma":[0.000110587964,0.96296257,0.028374242,0.00397329,0.00090233306,0.000004922038,0.0036110575,0.00005186429,0.000009118577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99372977,0.0005563032,0.003073298,0.0007756841,0.0012196518,0.00064530544],"domain_scores_gemma":[0.9888904,0.0009454842,0.00500534,0.0038986376,0.0006186621,0.0006414411],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00454491,0.0004387695,0.0034015004,0.00011684538,0.0001513818,0.00010176522,0.002562269,0.00014832753,0.0000108320055],"category_scores_gemma":[0.0035072507,0.0002844811,0.00037163816,0.0009303536,0.000049085706,0.00067479635,0.00146675,0.00037080838,0.000013140125],"study_design_candidate":"not_applicable","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":[0.00027894587,0.00013021719,0.00016562353,0.013680393,0.00025906024,0.000009944431,0.000011068474,0.000012789774,9.608304e-9,0.00016842192,0.13635617,0.8489274],"study_design_scores_gemma":[0.0007509304,0.00073365745,0.0006466931,0.044915963,0.0005515721,0.00042928438,0.00002599803,0.0008006205,3.4718526e-9,0.000082662445,0.95088995,0.00017269116],"about_ca_topic_score_codex":0.0002248073,"about_ca_topic_score_gemma":0.0002309463,"teacher_disagreement_score":0.84875464,"about_ca_system_score_codex":0.0033042976,"about_ca_system_score_gemma":0.01801048,"threshold_uncertainty_score":0.9999607},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991374,0.0006539072,0.0011787118,0.00044128887,0.00074252195,0.004203718,0.0012261376,0.00017125322,0.000008420201],"genre_scores_gemma":[0.9957524,0.00012457723,0.001158442,0.00054275733,0.00055315613,0.0017905206,0.000009891344,0.00006183048,0.000006402023],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99645436,0.0007163238,0.00058961945,0.0011232648,0.00058853277,0.00052791776],"domain_scores_gemma":[0.997208,0.00036372026,0.00033954746,0.0013133968,0.00055329874,0.00022202305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029590714,0.0004497129,0.00077462767,0.00018909835,0.000059391015,0.00013894196,0.00075743726,0.00039050763,0.00006967003],"category_scores_gemma":[0.0013162044,0.00034316842,0.00014771953,0.00039251804,0.000104017425,0.00016006514,0.00039117193,0.0010801429,0.00003366595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007275125,0.0016686409,0.98706126,0.0007722808,0.00029780224,0.0000571949,0.00022105493,0.0002300596,0.006378718,0.0003209928,0.0022374222,0.000027069798],"study_design_scores_gemma":[0.0013521523,0.00039335145,0.98916626,0.00034602662,0.00014829639,2.0084935e-8,0.000024002948,0.0059953923,0.00023291979,0.000004588019,0.0019502897,0.00038670102],"about_ca_topic_score_codex":0.0003179391,"about_ca_topic_score_gemma":0.00005673493,"teacher_disagreement_score":0.0061457986,"about_ca_system_score_codex":0.00042358023,"about_ca_system_score_gemma":0.0012268192,"threshold_uncertainty_score":0.999902},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010937911,0.02188194,0.00072816573,0.9472389,0.005085393,0.0016397184,0.008149629,0.00016554493,0.0041727447],"genre_scores_gemma":[0.003129802,0.046709426,0.0037158763,0.91343004,0.019812372,0.00056629616,0.0056455284,0.000093328854,0.00689733],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986649,0.00023096953,0.000246311,0.00038572078,0.00026335905,0.00020871383],"domain_scores_gemma":[0.99843836,0.0006327654,0.00024170172,0.00048201426,0.00016577287,0.000039360766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025291397,0.00020561765,0.0005168497,0.00008411423,0.000059427024,0.000014747098,0.00015580886,0.00038757757,0.00014903072],"category_scores_gemma":[0.0001718519,0.00013085903,0.00012665008,0.00023604272,0.0001848941,0.000053765783,0.00007438581,0.0011003257,0.000017724567],"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":[0.000039292183,0.0000050672834,0.001393861,0.00036730358,0.00030979782,0.00003110017,0.00016329969,0.0000024623412,0.00033738543,0.00000539093,0.9623705,0.03497451],"study_design_scores_gemma":[0.00029008073,0.000036601556,0.04457003,0.00019521119,0.00031872737,0.0000271451,0.000053040367,0.00002772647,0.00004356654,0.000024928631,0.954275,0.00013792921],"about_ca_topic_score_codex":0.000029183759,"about_ca_topic_score_gemma":0.000006200088,"teacher_disagreement_score":0.043176167,"about_ca_system_score_codex":0.00017714269,"about_ca_system_score_gemma":0.00032368494,"threshold_uncertainty_score":0.5336276},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98767143,0.0028609724,0.0015450506,0.0041832183,0.00016219234,0.0018322812,0.00056847744,0.00036128206,0.00081510213],"genre_scores_gemma":[0.9895508,0.0000072954026,0.0012724447,0.00080720603,0.0001509598,0.0001101072,0.008050664,0.00004288421,0.000007629051],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99406195,0.00012521389,0.0040053427,0.000592364,0.00053898024,0.0006761621],"domain_scores_gemma":[0.9929569,0.00015273657,0.0054778378,0.0007542804,0.0004214193,0.00023686203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007740162,0.00022349891,0.0009242771,0.00017869032,0.00012259929,0.000022288803,0.0001556329,0.000060509938,0.00004221374],"category_scores_gemma":[0.003875635,0.00023907996,0.00012835301,0.0015973388,0.000046633933,0.0001369296,0.000072371055,0.0004592873,0.000009625175],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017717881,0.00041676886,0.9762701,0.0033610906,0.00018983659,0.00019673147,0.0007718363,0.0022012405,0.0035301037,0.0024047126,0.002287879,0.006597956],"study_design_scores_gemma":[0.01050724,0.000988885,0.9201414,0.0035659135,0.0003367,0.000010184403,0.0065194503,0.037448652,0.001953897,0.000046353383,0.017616497,0.0008648071],"about_ca_topic_score_codex":0.001022924,"about_ca_topic_score_gemma":0.0006502895,"teacher_disagreement_score":0.056128636,"about_ca_system_score_codex":0.0017849136,"about_ca_system_score_gemma":0.005991183,"threshold_uncertainty_score":0.9996439},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99612486,0.00035197198,0.000010604419,0.0027756297,0.000020045669,0.00032339155,0.00029666294,0.0000052009827,0.00009160503],"genre_scores_gemma":[0.99914044,0.0003430242,0.00014367588,0.00016337719,0.000063448504,0.000016072538,0.000114673865,0.000008056305,0.0000072413222],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998848,0.00017948828,0.00021893317,0.0001720401,0.0004308,0.000150745],"domain_scores_gemma":[0.99950105,0.0000940602,0.0000402419,0.00024309491,0.000028199998,0.00009336705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040781815,0.000063930114,0.0001792989,0.000046792757,0.000019533876,0.000012325887,0.00009847296,0.000038403563,0.00012923796],"category_scores_gemma":[0.000107663815,0.000044765304,0.000033591827,0.000118294185,0.00017332459,0.00014984985,0.00006238353,0.00016349128,0.00000450102],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066550483,0.00017449512,0.995347,0.000118020915,0.000020266709,0.000007539798,0.0007787252,1.0865713e-7,0.001874498,0.000043239044,0.00032418992,0.0006463973],"study_design_scores_gemma":[0.0006629412,0.00032599404,0.9977819,0.000043849548,0.000012636473,0.000003474686,0.000613116,0.00010268316,0.00002397068,0.000048435915,0.00034356726,0.00003747497],"about_ca_topic_score_codex":0.00026040344,"about_ca_topic_score_gemma":0.000350144,"teacher_disagreement_score":0.0030155424,"about_ca_system_score_codex":0.000013432728,"about_ca_system_score_gemma":0.00003536213,"threshold_uncertainty_score":0.18254761},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96636987,0.0033571972,0.00056818256,0.02604113,0.00018616435,0.0028098554,0.00029187268,0.00018636655,0.00018936285],"genre_scores_gemma":[0.989176,0.00018805545,0.0000698347,0.010029822,0.00022780626,0.0001507224,0.000050503826,0.0000625548,0.00004472403],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99404144,0.0018531773,0.0012064013,0.00079889933,0.00087720586,0.0012228554],"domain_scores_gemma":[0.9961563,0.00064256013,0.0007739288,0.0010862894,0.00024753623,0.0010934328],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0043401816,0.00042987577,0.0012144104,0.00013977446,0.00081346143,0.0001079367,0.00040393864,0.00008097631,0.000037575854],"category_scores_gemma":[0.0010948975,0.00027205303,0.00017191604,0.0008960894,0.0003721906,0.00018249321,0.00015480674,0.0005371603,0.000009955597],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055571034,0.00037686992,0.9801964,0.0007864903,0.00011197468,0.000018855262,0.008327883,0.0002275946,0.000040543542,0.00002097705,0.0023681307,0.0069685476],"study_design_scores_gemma":[0.002994174,0.0011081888,0.9676152,0.00013252927,0.000003634166,0.000013943787,0.0069629787,0.009834057,0.000004198008,0.0000031365232,0.011043707,0.00028427597],"about_ca_topic_score_codex":0.000586416,"about_ca_topic_score_gemma":0.00034354432,"teacher_disagreement_score":0.022806108,"about_ca_system_score_codex":0.0003802177,"about_ca_system_score_gemma":0.0031004485,"threshold_uncertainty_score":0.9999732},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95700777,0.0029450816,0.0017431963,0.01208102,0.0004049869,0.0010442253,0.0077872984,0.00065712404,0.016329298],"genre_scores_gemma":[0.99141836,0.00009160877,0.00045999963,0.0020227314,0.001953696,0.000005523676,0.0037865108,0.000084505045,0.0001770461],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996693,0.00006859487,0.0005883495,0.0009785495,0.0010737444,0.0005977194],"domain_scores_gemma":[0.99609476,0.00025013604,0.00017097825,0.0014997975,0.00017854445,0.0018057802],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003596085,0.000350223,0.0006717752,0.00013175288,0.00013258484,0.00007299543,0.000638276,0.000066412766,0.00044451052],"category_scores_gemma":[0.005847251,0.00027577157,0.00009314691,0.0006864513,0.00037150335,0.00073406973,0.00078835106,0.0003721873,0.00024668605],"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":[0.003709904,0.00064343424,0.72209597,0.0018676695,0.0007649344,0.0063701603,0.0010995923,0.00017146378,0.0037041989,0.00018116471,0.07723943,0.1821521],"study_design_scores_gemma":[0.015512795,0.0015254762,0.0928149,0.0030617125,0.0016793968,0.00033810813,0.0013572313,0.7016114,0.00018178139,0.0002372304,0.18022655,0.0014534154],"about_ca_topic_score_codex":0.000043908207,"about_ca_topic_score_gemma":0.000003804777,"teacher_disagreement_score":0.7014399,"about_ca_system_score_codex":0.00008549392,"about_ca_system_score_gemma":0.00047852474,"threshold_uncertainty_score":0.9999694},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000032853518,0.96456414,0.010803716,0.0016381328,0.0005008071,0.021272946,0.0007900523,0.00039226396,0.000034628974],"genre_scores_gemma":[0.00019980215,0.9851062,0.0014625248,0.0036376007,0.00020384454,0.007643088,0.0013769205,0.00019774234,0.00017227222],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99170625,0.0017514585,0.002995425,0.0016388372,0.0006668627,0.0012411803],"domain_scores_gemma":[0.9930144,0.0014516203,0.0018816686,0.0011902504,0.00038920154,0.0020728377],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004563243,0.0011142045,0.009221047,0.00032430742,0.00038483218,0.00025968594,0.0003896817,0.00058418064,0.000011998191],"category_scores_gemma":[0.003705474,0.0008903435,0.00041931676,0.00086286204,0.000107576096,0.00016695756,0.0002458552,0.000800039,0.00006391195],"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":[0.00002361555,0.00004879237,0.00004011335,0.899363,0.00040131636,0.000014890762,0.000057331243,2.2929546e-8,2.6535089e-8,0.00017247282,0.0013492067,0.098529235],"study_design_scores_gemma":[0.000566291,0.00027471108,0.000062777384,0.19867148,0.00012397084,0.00078202673,0.0000342859,0.00007600097,9.480758e-9,0.000013526229,0.7986741,0.0007207984],"about_ca_topic_score_codex":0.000010911561,"about_ca_topic_score_gemma":0.000034592824,"teacher_disagreement_score":0.7973249,"about_ca_system_score_codex":0.0007004263,"about_ca_system_score_gemma":0.003037342,"threshold_uncertainty_score":0.9993547},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.882317,0.00034056455,0.0007035953,0.11230832,0.00025064553,0.00049711263,0.0035175206,0.000011920207,0.000053298354],"genre_scores_gemma":[0.93606406,0.000009857688,0.0016096011,0.061482802,0.0001077986,0.000012085734,0.00064492587,0.000030708517,0.00003814088],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975522,0.0002979539,0.0006169646,0.0003743515,0.00032702112,0.0008314804],"domain_scores_gemma":[0.9887768,0.00017953369,0.00027609547,0.00028591848,0.00044949964,0.010032144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013747732,0.0002220862,0.000561239,0.00033082903,0.00009807436,0.00008392671,0.00024776845,0.000040121417,0.00020521382],"category_scores_gemma":[0.008142418,0.00021844228,0.00003383626,0.0008610142,0.000040184423,0.00015133315,0.000025654766,0.0005125753,0.000009782566],"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":[0.00009143158,0.000017551802,0.8798847,0.00009756174,0.000029530423,0.00063358113,0.0012841285,0.00008265306,0.000005944257,0.0000020741893,0.10764595,0.0102248965],"study_design_scores_gemma":[0.0007390828,0.00018848604,0.6276305,0.00021071224,0.0000041223598,0.000022341685,0.00028687445,0.00032255915,0.0000025206707,8.3358503e-7,0.3704265,0.00016549107],"about_ca_topic_score_codex":0.9869172,"about_ca_topic_score_gemma":0.9999487,"teacher_disagreement_score":0.26278055,"about_ca_system_score_codex":0.0035971254,"about_ca_system_score_gemma":0.085460484,"threshold_uncertainty_score":0.9747823},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060188364,0.00021291441,0.00007276915,0.90426993,0.0007013954,0.0037546144,0.01600838,0.005413257,0.009378392],"genre_scores_gemma":[0.11443638,0.00007350077,0.000036151767,0.87030745,0.006653714,0.00046496716,0.0075333547,0.00016915043,0.0003253552],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959035,0.0009320416,0.00054957543,0.0010940257,0.00070409494,0.00081675587],"domain_scores_gemma":[0.99578035,0.0009844826,0.00029398035,0.0018727101,0.00013750518,0.0009309788],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00023620678,0.0006787688,0.001584979,0.00036801185,0.00014363721,0.0001445312,0.00077664974,0.00029405218,0.001118247],"category_scores_gemma":[0.0016168298,0.000535295,0.00035760735,0.0007195656,0.00023751355,0.0001736549,0.0001524275,0.001669832,0.0016262939],"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":[0.0017814591,0.0003776654,0.048883654,0.0006376984,0.00020988271,0.0032243396,0.00017702449,0.00032290522,0.00013897968,0.0000041468948,0.94367725,0.0005649751],"study_design_scores_gemma":[0.005211885,0.0007932546,0.038884126,0.00036335137,0.00055783,0.000047088433,0.000026368562,0.0011970156,0.000014942384,0.00026813286,0.9518514,0.0007845538],"about_ca_topic_score_codex":0.0005736049,"about_ca_topic_score_gemma":0.00027839097,"teacher_disagreement_score":0.054248016,"about_ca_system_score_codex":0.0009740527,"about_ca_system_score_gemma":0.0017273759,"threshold_uncertainty_score":0.99979484},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8756601,0.00030204715,0.00017023888,0.122404315,0.00010903067,0.000997871,0.0002575242,0.00006263556,0.00003619331],"genre_scores_gemma":[0.9951701,0.000071568,0.000059372374,0.0035650465,0.00017460703,0.000029566816,0.0009061988,0.000018613871,0.000004914957],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99680114,0.00084724004,0.00072461634,0.00039803784,0.000775262,0.00045371777],"domain_scores_gemma":[0.9986874,0.00023318434,0.00033263955,0.00019301177,0.000094018666,0.0004597527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014307747,0.00019041386,0.00062822894,0.00014995768,0.0002440961,0.0000542099,0.00018500746,0.000047996833,0.00001991328],"category_scores_gemma":[0.0005525893,0.0001194491,0.00006634257,0.00061729376,0.000091743575,0.00013296504,0.00008278824,0.00033413805,0.0000030145789],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017639263,0.0005136542,0.93086535,0.0005214929,0.00002857268,0.000010161336,0.040143233,0.000024239585,8.1138637e-7,0.00035630655,0.0036873238,0.023672488],"study_design_scores_gemma":[0.0015301856,0.0003674357,0.97558594,0.000031423104,7.5668487e-7,0.000001744464,0.018037431,0.0017805933,5.8847377e-8,0.000046764104,0.0025107765,0.000106866326],"about_ca_topic_score_codex":0.00015276266,"about_ca_topic_score_gemma":0.00091224705,"teacher_disagreement_score":0.119509965,"about_ca_system_score_codex":0.00012954137,"about_ca_system_score_gemma":0.0006201618,"threshold_uncertainty_score":0.4870993},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99073505,0.00021884666,0.00005287759,0.0003491549,0.0006038583,0.00052325224,0.003524453,0.00008859199,0.0039038972],"genre_scores_gemma":[0.99675053,0.00001831643,0.0004013784,0.00011515563,0.0009913296,0.000119429234,0.001521779,0.000019634354,0.00006242412],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99858123,0.000055365097,0.00028569103,0.0003022273,0.00044815542,0.00032735968],"domain_scores_gemma":[0.99864876,0.00032694015,0.00015889601,0.00040760398,0.00024030007,0.00021747846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016901345,0.00013223152,0.00026784878,0.000090049296,0.000060133207,0.000025419446,0.00017042983,0.000062525265,0.0006277293],"category_scores_gemma":[0.001037804,0.0001300445,0.000094390285,0.00014385137,0.00021412177,0.00020206583,0.00013076319,0.00009941755,0.000056197478],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.066585325,0.0020906276,0.49389985,0.0005272583,0.0036669855,0.0008178805,0.0007128265,0.000012918476,0.036142766,0.0023733347,0.20949815,0.18367209],"study_design_scores_gemma":[0.0032932116,0.00010684152,0.9868559,0.000046375724,0.00013288418,0.000004940635,0.00006163215,0.000028638193,0.006869296,0.00011224376,0.0023566657,0.00013136676],"about_ca_topic_score_codex":0.0004684305,"about_ca_topic_score_gemma":0.00057614874,"teacher_disagreement_score":0.49295607,"about_ca_system_score_codex":0.000064978056,"about_ca_system_score_gemma":0.00010568376,"threshold_uncertainty_score":0.6873195},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46127108,0.00023522634,0.53799975,0.00004284103,0.00008082276,0.00024772834,0.00005856089,0.000017814806,0.000046169942],"genre_scores_gemma":[0.51792806,0.0000941622,0.4800607,0.0017552968,0.000064014006,0.000024403147,0.00006238738,0.000010771455,2.1344044e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755144,0.00015612635,0.0010884248,0.00020154689,0.00080395525,0.00019850525],"domain_scores_gemma":[0.99581826,0.0032242038,0.0002690159,0.0002920719,0.0000989247,0.00029754118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024382097,0.00014666808,0.00045553886,0.00010700794,0.00007317454,0.00005175674,0.00015609326,0.00006867076,0.00001428102],"category_scores_gemma":[0.00396252,0.000102693295,0.00005123345,0.00026533782,0.000052875235,0.00030460686,0.00057277724,0.0002865846,0.0000013313497],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020000851,0.000013429256,0.027863622,0.0014942968,0.000012380916,0.000006773945,0.006603192,0.000115639305,0.00007709127,0.000010705491,0.000031500924,0.96357137],"study_design_scores_gemma":[0.004219594,0.00020518996,0.18238847,0.0042592823,0.000052944742,0.00002363984,0.022843385,0.7846413,0.00013447263,0.00031235523,0.00057109626,0.0003482328],"about_ca_topic_score_codex":0.000075594806,"about_ca_topic_score_gemma":0.00008550132,"teacher_disagreement_score":0.9632231,"about_ca_system_score_codex":0.000050065595,"about_ca_system_score_gemma":0.0001509979,"threshold_uncertainty_score":0.47437927},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043950066,0.0016465092,0.000039645827,0.9454931,0.0021794713,0.0010966868,0.0050307033,0.00014832162,0.0004155147],"genre_scores_gemma":[0.077144645,0.006487919,0.000041389023,0.90820414,0.00084815716,0.000053913267,0.0067687766,0.000045907425,0.00040515288],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972167,0.0007606249,0.00092939107,0.00072405953,0.00007001888,0.00029918106],"domain_scores_gemma":[0.98957765,0.0089997,0.00066252856,0.00053090515,0.00012673029,0.000102456106],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00045662432,0.00031363353,0.0008727002,0.000049669314,0.00009937911,0.000015539317,0.00018106466,0.00086003594,0.0003308873],"category_scores_gemma":[0.025181001,0.00022977535,0.00030840404,0.00019589196,0.0010200544,0.00006961646,0.00021209507,0.0021125171,0.00008892109],"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":[0.00004255672,0.00011326829,0.07777697,0.00019309598,0.00012719039,0.00004286587,0.000012311686,5.955745e-8,0.00704754,0.0000029732892,0.91301453,0.0016266193],"study_design_scores_gemma":[0.0009320017,0.0007302277,0.03226423,0.0005011252,0.00019558666,0.0001488831,0.00000345928,0.0000035282794,0.00062546827,0.000049717913,0.96432215,0.00022365025],"about_ca_topic_score_codex":0.00009338733,"about_ca_topic_score_gemma":0.000021310849,"teacher_disagreement_score":0.05130757,"about_ca_system_score_codex":0.00003217505,"about_ca_system_score_gemma":0.00016573841,"threshold_uncertainty_score":0.9830303},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9158236,0.0007373989,0.002913381,0.012789168,0.00027958816,0.00088044937,0.00023457964,0.00037407418,0.065967776],"genre_scores_gemma":[0.9871426,0.000058075948,0.0047767544,0.006632075,0.00033833328,0.000020232983,0.0007052126,0.000117492855,0.00020920542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9958066,0.001456218,0.00089800684,0.00089106866,0.00030772164,0.0006404188],"domain_scores_gemma":[0.9975717,0.00044028828,0.00028281208,0.0010149782,0.00006516601,0.0006250396],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043763628,0.00039930083,0.0008925264,0.0004987817,0.000055133325,0.000024604133,0.00081922894,0.00006302249,0.0006773404],"category_scores_gemma":[0.0015317276,0.00038584194,0.00030578056,0.00042543595,0.00076033943,0.00016095037,0.0006466155,0.0006135091,0.00082502246],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005436568,0.0018223304,0.7631843,0.0018532542,0.00056518044,0.0073011983,0.014827284,0.00010532533,0.13573915,0.001101855,0.037362754,0.030700771],"study_design_scores_gemma":[0.0066354587,0.0014277953,0.7997571,0.00028437405,0.000075437616,0.000066988076,0.00029012887,0.0023154663,0.0014718839,0.00013292304,0.18696631,0.00057609694],"about_ca_topic_score_codex":0.000023652026,"about_ca_topic_score_gemma":0.00007670966,"teacher_disagreement_score":0.14960358,"about_ca_system_score_codex":0.00003953764,"about_ca_system_score_gemma":0.00026945732,"threshold_uncertainty_score":0.999953},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98176605,0.003996849,0.0005900937,0.010841545,0.000042998472,0.0007195578,0.0015844869,0.00017292268,0.0002855238],"genre_scores_gemma":[0.98877704,0.00079838565,0.00087163126,0.0036379537,0.00008907712,0.00002872533,0.0057501914,0.000027000657,0.000019996227],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9954002,0.0009231888,0.0010251038,0.0013912307,0.0006184213,0.00064186123],"domain_scores_gemma":[0.995564,0.00079753675,0.00050216785,0.0016858289,0.00007952536,0.0013709409],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0034617642,0.00026952178,0.0012666343,0.0003879662,0.00020230867,0.00007679676,0.0007467919,0.000054685763,0.00006763113],"category_scores_gemma":[0.007799245,0.0002609793,0.000029853001,0.0031338776,0.00016908546,0.0007273755,0.0013567967,0.00036374785,0.0000011346917],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038981932,0.00032515978,0.95297325,0.0005382827,0.00026600368,0.000049507118,0.0056045107,0.00002225294,0.000004366988,0.0000048171946,0.0006469922,0.03917501],"study_design_scores_gemma":[0.00236524,0.0013202066,0.89787996,0.000024345843,0.00002931826,0.000010148779,0.0050170394,0.07736084,3.5852768e-8,9.207454e-7,0.015796443,0.00019551575],"about_ca_topic_score_codex":0.0019829052,"about_ca_topic_score_gemma":0.013554246,"teacher_disagreement_score":0.077338584,"about_ca_system_score_codex":0.0000875252,"about_ca_system_score_gemma":0.00072512147,"threshold_uncertainty_score":0.99998426},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034244025,0.0000019127674,0.25811848,0.013922726,0.0000030246697,0.7271544,0.00053088233,0.00018979306,0.000044556],"genre_scores_gemma":[0.0001363963,1.0846212e-7,0.2982254,0.0007771525,0.0005397557,0.699988,0.00023165732,0.00006913482,0.000032348496],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99179876,0.0022508975,0.00081737974,0.0017491804,0.0016915973,0.0016922058],"domain_scores_gemma":[0.9915064,0.0031505872,0.00021573804,0.0021932386,0.0016156281,0.0013184181],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016928546,0.0002709905,0.0007420027,0.00026818502,0.0006375321,0.0005505377,0.0025782047,0.0002180153,0.00007127824],"category_scores_gemma":[0.02278466,0.0002225353,0.000105425795,0.002018905,0.00033535357,0.0004886582,0.002954468,0.0012239257,0.000053198335],"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":[0.053748194,0.0034339705,0.004092099,0.036332324,0.00016984083,0.000021978172,0.0013296846,0.00004747513,0.012958455,0.0023226696,0.3771145,0.5084288],"study_design_scores_gemma":[0.002933951,0.0057461704,0.00047907158,0.0033263064,0.0000028855284,0.0000035248302,0.00019772124,0.007181849,0.0020286483,0.0016581982,0.97622186,0.00021983284],"about_ca_topic_score_codex":0.0000382546,"about_ca_topic_score_gemma":0.000017765678,"teacher_disagreement_score":0.5991073,"about_ca_system_score_codex":0.00037812465,"about_ca_system_score_gemma":0.0015578435,"threshold_uncertainty_score":0.9854468},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7715046,0.0006387394,0.008966329,0.21105815,0.00013645802,0.0051100715,0.0002145797,0.00026065315,0.0021104359],"genre_scores_gemma":[0.9344557,0.00022676874,0.00010670102,0.0645429,0.000052463874,0.00014389609,0.00043141432,0.00000825929,0.000031888685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962817,0.0014561418,0.0010410398,0.00030967977,0.00049446715,0.00041699797],"domain_scores_gemma":[0.99729395,0.00039346286,0.00065368455,0.00038567244,0.00012580249,0.0011474051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004254287,0.00018006055,0.00066372607,0.00008835181,0.00014554456,0.00004669463,0.00018282632,0.00005986335,0.00011088024],"category_scores_gemma":[0.002501063,0.00011879269,0.000072864816,0.0003638266,0.00010136413,0.00019632158,0.00010716549,0.00032327592,0.000015203127],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016880267,0.00040094848,0.9338034,0.0007712549,0.000046488214,0.000002069822,0.0019799974,0.00000929761,5.667443e-7,0.0027042038,0.026485346,0.033627648],"study_design_scores_gemma":[0.0013902688,0.0027412665,0.823054,0.000020569594,6.225246e-7,0.0000039005286,0.0010383768,0.0014809376,4.89355e-8,0.000021693038,0.17016348,0.0000848374],"about_ca_topic_score_codex":0.00012912214,"about_ca_topic_score_gemma":0.00006951647,"teacher_disagreement_score":0.16295113,"about_ca_system_score_codex":0.0002295984,"about_ca_system_score_gemma":0.00286748,"threshold_uncertainty_score":0.5086786},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8254994,0.0003889062,0.0017737014,0.16797782,0.00028889434,0.0009854789,0.0029937287,0.000024237,0.000067837915],"genre_scores_gemma":[0.9882386,0.00040044723,0.0010243901,0.007747154,0.00044975808,0.000029636067,0.0020977994,0.0000064643505,0.000005756118],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967778,0.00018338319,0.0005919678,0.0004182157,0.0018417552,0.00018688918],"domain_scores_gemma":[0.9981365,0.00035697818,0.00038353688,0.00051190297,0.0002592396,0.0003518124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041710474,0.00010848395,0.00021246592,0.000075373646,0.00029478667,0.0001774505,0.001366929,0.000019425906,0.0001477499],"category_scores_gemma":[0.0025658065,0.00006189691,0.000037798116,0.00026634542,0.0002816847,0.00094333885,0.00028030938,0.00023243873,0.0000037435962],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013904626,0.00008313984,0.98596424,0.000009937228,0.00013670305,0.00004189921,0.0009417067,0.000004782743,0.000002167315,0.0004895901,0.0046071107,0.007579674],"study_design_scores_gemma":[0.0015834898,0.00012336018,0.965888,0.00006724768,0.000068292306,0.00012442234,0.00037765087,0.011517796,0.000007725926,0.00007336964,0.02009431,0.00007433937],"about_ca_topic_score_codex":0.0054724594,"about_ca_topic_score_gemma":0.0056415587,"teacher_disagreement_score":0.1627392,"about_ca_system_score_codex":0.0001418291,"about_ca_system_score_gemma":0.0013601921,"threshold_uncertainty_score":0.82727575},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852295,0.00008017879,0.0074066804,0.002935226,0.000028194047,0.0028951024,0.0011949587,0.00021599428,0.000014149093],"genre_scores_gemma":[0.98096853,0.0000222203,0.0013082105,0.004893472,0.000085825544,0.00030407228,0.012390639,0.000024633873,0.0000024095275],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99720436,0.0008217445,0.0006382246,0.00044128863,0.00044702718,0.00044734398],"domain_scores_gemma":[0.9983681,0.00021301533,0.00029538362,0.00035096146,0.00023376867,0.00053877116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020893302,0.00019304348,0.00050777424,0.00017742196,0.00011100101,0.000073318326,0.00020730554,0.000060542006,0.000010696001],"category_scores_gemma":[0.0003517716,0.00015071698,0.000044053446,0.00084807724,0.00004457947,0.00020501742,0.000036697977,0.00018807594,8.897152e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022185217,0.00068192877,0.98413944,0.0006154776,0.000030719686,0.000005173523,0.005608827,0.0001007071,0.000001431401,0.000015838277,0.000969574,0.0076090023],"study_design_scores_gemma":[0.002859205,0.001079659,0.69294506,0.000014728875,0.000001668789,0.0000038826665,0.004658016,0.2920569,5.124908e-7,0.000002773155,0.0062502585,0.0001273329],"about_ca_topic_score_codex":0.00043597745,"about_ca_topic_score_gemma":0.000098169236,"teacher_disagreement_score":0.2919562,"about_ca_system_score_codex":0.000063866006,"about_ca_system_score_gemma":0.0004990404,"threshold_uncertainty_score":0.614606},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5795898,0.010039334,0.0070077074,0.38013825,0.00027636887,0.0037550617,0.0005036898,0.0012602257,0.017429577],"genre_scores_gemma":[0.88998306,0.0008472366,0.00065316824,0.10757384,0.00019778851,0.00009234966,0.0005470637,0.000032989163,0.000072485236],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967968,0.0007318321,0.00068355154,0.00069592416,0.00027113257,0.00082074327],"domain_scores_gemma":[0.99599797,0.00027864013,0.0001932258,0.00038592,0.00013734133,0.0030069312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017175969,0.00023359491,0.00080755696,0.00015054477,0.00010953863,0.00004481311,0.00019485578,0.00011795239,0.00005915023],"category_scores_gemma":[0.0065702363,0.00021450492,0.00004863544,0.0011708387,0.0001586373,0.00016627026,0.000117107345,0.00046264645,0.000034837507],"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":[0.00021203009,0.00011765053,0.9581037,0.0010212782,0.000013617101,0.000059496877,0.0011725485,0.000010608497,0.000006707896,0.00056066096,0.021841452,0.016880237],"study_design_scores_gemma":[0.0016981566,0.00019547307,0.45216796,0.00000724193,3.228117e-7,0.00005754299,0.00018933961,0.0020060912,5.903815e-8,0.0000092626715,0.5435094,0.0001591451],"about_ca_topic_score_codex":0.00009958737,"about_ca_topic_score_gemma":0.000073952084,"teacher_disagreement_score":0.52166796,"about_ca_system_score_codex":0.00016093919,"about_ca_system_score_gemma":0.0026973698,"threshold_uncertainty_score":0.87472564},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022249327,0.00014352494,0.000028387676,0.9890518,0.0011039503,0.00074888044,0.006089247,0.000014992476,0.0005942535],"genre_scores_gemma":[0.01460031,0.00013859716,0.000069707654,0.97485685,0.007272789,0.000033464043,0.0028125688,0.00003768091,0.00017803695],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994461,0.0016872083,0.0008392612,0.0007077611,0.0015477538,0.0007569926],"domain_scores_gemma":[0.99109244,0.0019072025,0.00060089765,0.00075612485,0.00026153034,0.0053817853],"candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031571873,0.00030426434,0.000922101,0.0002133271,0.0002559343,0.000567791,0.004464301,0.0004965779,0.0018765933],"category_scores_gemma":[0.043420713,0.00022493557,0.00005084965,0.00039759267,0.00006526739,0.0005894074,0.0012769722,0.0051190197,0.00003954067],"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":true,"study_design_scores_codex":[0.000037167247,0.000006379277,0.13836932,0.000027994382,0.00015003796,0.009027964,0.00010023741,0.0000031041102,8.4477584e-8,0.0000011177108,0.8465766,0.0057000453],"study_design_scores_gemma":[0.0007698603,0.000041746214,0.06796597,0.000131239,0.000065018416,0.00032519302,0.00033188667,0.0004394417,7.002345e-8,0.00007479568,0.9296297,0.00022507251],"about_ca_topic_score_codex":0.96567893,"about_ca_topic_score_gemma":0.99653137,"teacher_disagreement_score":0.083053164,"about_ca_system_score_codex":0.0097661065,"about_ca_system_score_gemma":0.050865933,"threshold_uncertainty_score":0.99903584},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6182096,0.0538331,0.038854197,0.15726852,0.031525973,0.03665027,0.01858261,0.012049668,0.033026062],"genre_scores_gemma":[0.9851424,0.00029849392,0.004899843,0.0032320276,0.001584001,0.0009777447,0.0036817843,0.00012578368,0.000057931],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99310035,0.00058299256,0.0015749393,0.0018908607,0.0013232909,0.0015275871],"domain_scores_gemma":[0.99371326,0.00015643863,0.0015052605,0.0020672758,0.0003742059,0.0021835305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032383678,0.0006836031,0.0018425063,0.00019837971,0.00014261436,0.00024254204,0.0005146347,0.00038654916,0.000058253365],"category_scores_gemma":[0.0065891044,0.0007144675,0.00041668708,0.0008589983,0.00019046554,0.0001629901,0.0013713196,0.0009297212,0.000018105009],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030717478,0.000369065,0.7799167,0.00257262,0.0009778963,0.003824014,0.0003543961,0.00022171738,0.000012843371,0.00012784984,0.060706086,0.15060966],"study_design_scores_gemma":[0.00078582385,0.00002170127,0.9713323,0.00021674922,0.00007048945,0.00040075602,0.000056202618,0.0006339028,0.0000052881287,0.00084623386,0.024986543,0.0006439757],"about_ca_topic_score_codex":0.00063005247,"about_ca_topic_score_gemma":0.00021562344,"teacher_disagreement_score":0.3669328,"about_ca_system_score_codex":0.0019547844,"about_ca_system_score_gemma":0.0027787893,"threshold_uncertainty_score":0.9995306},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9480828,0.0029518702,0.00028454998,0.04357502,0.00014333785,0.0028475064,0.0018495901,0.00016340642,0.000101893835],"genre_scores_gemma":[0.96853775,0.00024670918,0.00007093487,0.03034534,0.00012509504,0.0004319165,0.00017802004,0.000034523902,0.000029713312],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996637,0.0007204558,0.0008311099,0.0006428508,0.00036767437,0.00080089725],"domain_scores_gemma":[0.9949253,0.0006775136,0.00036885354,0.000652322,0.0001492825,0.0032266814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020038106,0.0002624575,0.0007946992,0.00010632867,0.00017427976,0.000030313728,0.00023622753,0.000094143,0.00003445177],"category_scores_gemma":[0.0061406964,0.00020536564,0.000149185,0.00079786417,0.00029368637,0.000114543654,0.00014130662,0.00028432047,0.0000039813576],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006492724,0.00014365235,0.98568815,0.0021744838,0.00002393843,0.00018886353,0.0009125934,0.0000061339047,0.000009565195,0.00020150394,0.0067355703,0.0032662742],"study_design_scores_gemma":[0.0034861674,0.00020772462,0.85291207,0.000022986744,0.0000018692936,0.00019543548,0.0002437335,0.0012997045,3.3196412e-7,0.000042461306,0.14136977,0.0002177308],"about_ca_topic_score_codex":0.000111880894,"about_ca_topic_score_gemma":0.00048160824,"teacher_disagreement_score":0.13463421,"about_ca_system_score_codex":0.00020273149,"about_ca_system_score_gemma":0.0037733824,"threshold_uncertainty_score":0.83745676},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37146023,0.010369165,0.00988261,0.6052618,0.00028315274,0.001184916,0.0011303468,0.00002999548,0.00039777928],"genre_scores_gemma":[0.9051888,0.0024899372,0.013871972,0.07783775,0.00017406401,0.0000034371185,0.00041978862,0.000012892443,0.0000013359911],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9860081,0.009869499,0.0024977229,0.0003288932,0.000429985,0.0008658064],"domain_scores_gemma":[0.98350483,0.010563963,0.002316474,0.0024273149,0.00045860605,0.0007288222],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.082104236,0.00015926073,0.0016318514,0.00022188234,0.00037095256,0.000009608208,0.0028435464,0.00006602253,0.000009555913],"category_scores_gemma":[0.05039039,0.00010907782,0.00013690862,0.0009303388,0.0009835956,0.0004237309,0.0005123335,0.0015937092,0.0000018270824],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066657667,0.0009153518,0.9018989,0.0013873476,0.00008749976,0.000020950567,0.0027390914,0.000030078836,0.00008632296,0.0018267238,0.06431144,0.026029686],"study_design_scores_gemma":[0.0016090828,0.002287373,0.91004384,0.00020651489,0.00000775832,0.00012300616,0.0011691845,0.00067560276,8.813705e-7,0.00058197696,0.083215974,0.00007880521],"about_ca_topic_score_codex":0.0018390142,"about_ca_topic_score_gemma":0.0024747432,"teacher_disagreement_score":0.5337286,"about_ca_system_score_codex":0.00029507533,"about_ca_system_score_gemma":0.005387729,"threshold_uncertainty_score":0.9576086},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9888272,0.0013110442,0.00005590436,0.0063696126,0.00017257393,0.001148172,0.0018607776,0.00016582216,0.0000888526],"genre_scores_gemma":[0.99374473,0.0002060126,0.00011431623,0.002838422,0.00027014772,0.00004931209,0.0027398942,0.000026885764,0.000010277412],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966269,0.0007181569,0.00094119844,0.00082134636,0.00038542663,0.0005069441],"domain_scores_gemma":[0.9975929,0.00038786154,0.00050148397,0.0005107979,0.0002648213,0.0007421278],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016686863,0.00027818826,0.0011888344,0.0001287151,0.00023192535,0.00005462079,0.00023645688,0.00013310357,0.0000380662],"category_scores_gemma":[0.001078448,0.00025754402,0.000052775616,0.0006039192,0.00025647116,0.00027585134,0.0003456956,0.00034675107,0.000009672622],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027081874,0.00038313,0.9751087,0.00041133663,0.0001272437,0.000009620556,0.0011844229,3.7866414e-7,0.00018869544,0.000046690064,0.016546074,0.0057229316],"study_design_scores_gemma":[0.0021714936,0.0007061345,0.9412168,0.000009468227,0.0000043427567,0.00001351753,0.00037427704,0.00027064394,0.0000010093216,0.00000386873,0.055031016,0.00019737822],"about_ca_topic_score_codex":0.00004691225,"about_ca_topic_score_gemma":0.000326444,"teacher_disagreement_score":0.038484942,"about_ca_system_score_codex":0.00006912365,"about_ca_system_score_gemma":0.0008615537,"threshold_uncertainty_score":0.99998766},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21393506,0.00070149085,0.0001601679,0.0038709738,0.001734302,0.0026656121,0.0014480673,0.0000487853,0.7754355],"genre_scores_gemma":[0.79973745,0.00028273833,0.0006630616,0.00024676576,0.00079364044,0.00012872457,0.00022632872,0.00001567901,0.19790561],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.999144,0.00007013857,0.00023463329,0.00025420237,0.00019002848,0.00010695474],"domain_scores_gemma":[0.9983726,0.000077356926,0.00028284188,0.00097040215,0.00021854207,0.000078221114],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00060172315,0.0000928945,0.00028388388,0.00008593063,0.0018224793,0.0005118433,0.00022625763,0.000055775705,0.0003512569],"category_scores_gemma":[0.0005867522,0.000065050495,0.000057606936,0.0004916285,0.0002037872,0.00012586932,0.0001686704,0.000115437244,0.0000037831037],"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":[0.0007765274,0.00031119192,0.035272297,0.00007160893,0.00023863485,0.000081684426,0.00008788038,0.000003953383,0.0004649404,0.00051461195,0.9507123,0.011464372],"study_design_scores_gemma":[0.0067090355,0.00026425725,0.5033423,0.000034889777,0.00010900285,0.00037790497,0.0001237045,0.0007850801,0.00058050023,0.00027682842,0.48719904,0.00019749966],"about_ca_topic_score_codex":0.0014450804,"about_ca_topic_score_gemma":0.0060053025,"teacher_disagreement_score":0.5858024,"about_ca_system_score_codex":0.000041241896,"about_ca_system_score_gemma":0.0001668465,"threshold_uncertainty_score":0.999477},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898474,0.0058526373,0.0007150569,0.0005225953,0.00013430456,0.0016196292,0.0009181416,0.000038298018,0.00035190722],"genre_scores_gemma":[0.99542415,0.0037078215,0.0001432022,0.00021608036,0.000087337765,0.00009620078,0.000303961,0.000018406228,0.0000028360269],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974194,0.00019606283,0.0012646563,0.00020805812,0.00057202467,0.00033978865],"domain_scores_gemma":[0.99387354,0.00016920778,0.0038575425,0.00060092006,0.0010952449,0.0004035706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017339744,0.00022909004,0.000942162,0.0013308034,0.0003944895,0.0006444164,0.0010212698,0.00007947202,0.00003934081],"category_scores_gemma":[0.0018947363,0.0001980669,0.00010938155,0.00042894817,0.00037475416,0.00896785,0.00034241934,0.00016368789,4.735508e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002878728,0.00011094922,0.98108065,0.004397696,0.00019881931,9.664878e-7,0.0014296005,0.00016743736,0.0003928567,0.00069109077,0.00031828927,0.010923747],"study_design_scores_gemma":[0.0010127522,0.000082725135,0.9923151,0.001652428,0.000056699944,0.0000069796306,0.0012722482,0.0015238125,0.00019218476,0.0013188788,0.00038827705,0.0001778943],"about_ca_topic_score_codex":0.0010108761,"about_ca_topic_score_gemma":0.00003598643,"teacher_disagreement_score":0.0112344455,"about_ca_system_score_codex":0.000085070555,"about_ca_system_score_gemma":0.0006915541,"threshold_uncertainty_score":0.80769336},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9280911,0.010698964,0.00023719137,0.05770176,0.00025447132,0.0018789464,0.0009226973,0.000114010814,0.00010087862],"genre_scores_gemma":[0.9872258,0.005173785,0.001036039,0.005511995,0.00023704152,0.00004772132,0.000201784,0.00006639102,0.0004994712],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973981,0.000345539,0.0006262747,0.00060264143,0.00051105255,0.00051639584],"domain_scores_gemma":[0.99672365,0.0005785144,0.0004946113,0.0005247772,0.00015256836,0.0015259002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067047885,0.000320339,0.0005987894,0.00015738903,0.0004078365,0.00023350857,0.00018980174,0.00008620238,0.00005702397],"category_scores_gemma":[0.0023590473,0.0002517149,0.000076745,0.00031685975,0.00035336422,0.00044172947,0.00012038377,0.00038751372,0.00001676862],"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":[0.006874747,0.00060416723,0.12184569,0.0036168203,0.0034922422,0.0007122391,0.041263282,0.00013107542,0.012710039,0.002138282,0.5819017,0.22470966],"study_design_scores_gemma":[0.004185008,0.0015064657,0.07487873,0.001024607,0.00034272863,0.00023445573,0.0039734067,0.002341893,0.0005704741,0.00035456358,0.90983486,0.000752824],"about_ca_topic_score_codex":0.005943346,"about_ca_topic_score_gemma":0.004858199,"teacher_disagreement_score":0.32793307,"about_ca_system_score_codex":0.00035276596,"about_ca_system_score_gemma":0.0007005638,"threshold_uncertainty_score":0.9999935},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93043524,0.0016794308,0.0010625675,0.06366504,0.000056448924,0.0012217086,0.001486249,0.00023748862,0.0001558115],"genre_scores_gemma":[0.9841586,0.00028340405,0.00037644344,0.013237787,0.00013855995,0.000098309014,0.001517489,0.000031119176,0.00015830596],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99679095,0.0006369842,0.00073453714,0.00063719536,0.00056886545,0.0006314822],"domain_scores_gemma":[0.9941949,0.0004469318,0.00022478082,0.0005793786,0.00028196044,0.004272029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010701766,0.00026779188,0.00087352714,0.0002359791,0.0001628502,0.000021418406,0.00021139668,0.00007432368,0.00020664994],"category_scores_gemma":[0.0022998874,0.0002176007,0.00006560772,0.0011637877,0.00028586472,0.00016617587,0.00014212963,0.00041491177,0.000013642719],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011740199,0.00069138815,0.97619575,0.0005083392,0.000061774386,0.00007886083,0.0013090958,0.00011560375,0.0000020216587,0.00009580439,0.0023722393,0.017395105],"study_design_scores_gemma":[0.0032751667,0.0018263331,0.9455038,0.000016112654,0.000004570273,0.00002120913,0.0006379951,0.0043878425,4.0562707e-8,0.000004291648,0.044138834,0.00018384383],"about_ca_topic_score_codex":0.00031991358,"about_ca_topic_score_gemma":0.00074725307,"teacher_disagreement_score":0.053723324,"about_ca_system_score_codex":0.00014889723,"about_ca_system_score_gemma":0.0042255484,"threshold_uncertainty_score":0.8873499},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6205469,0.00047344534,0.007426029,0.36106735,0.00036992997,0.0004926052,0.00024260172,0.00007727263,0.009303854],"genre_scores_gemma":[0.9712954,0.0001170336,0.0002615648,0.026314171,0.0015799834,0.000003673025,0.000010282803,0.00001536163,0.0004024865],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9910622,0.0001747158,0.00067062356,0.00037933927,0.0069731674,0.0007399295],"domain_scores_gemma":[0.9897607,0.0004183696,0.00013544699,0.00033927202,0.0011186791,0.00822753],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0048735337,0.00014242265,0.00046204915,0.00043158964,0.00007313938,0.00025640076,0.0018317325,0.00013072931,0.0008696242],"category_scores_gemma":[0.07289511,0.0001059529,0.00016124791,0.00236763,0.0011575895,0.00028417597,0.0012703099,0.0011256197,0.00022615465],"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":[0.0024282897,0.00035857057,0.07666173,0.00021817783,0.00014116675,0.005521802,0.0010269972,0.0000113736405,0.00032539218,0.001208094,0.87345976,0.03863866],"study_design_scores_gemma":[0.00439062,0.0032217146,0.016761104,0.0005979182,0.000025455676,0.0027467215,0.00089141144,0.0030123002,0.00011670567,0.00091836206,0.9670752,0.0002424608],"about_ca_topic_score_codex":0.00020538546,"about_ca_topic_score_gemma":0.000033349614,"teacher_disagreement_score":0.35074854,"about_ca_system_score_codex":0.0011236923,"about_ca_system_score_gemma":0.010838871,"threshold_uncertainty_score":0.99476874},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53188306,0.00062681956,0.04472093,0.42019984,0.00038534863,0.0013889363,0.00040966526,0.00009012093,0.00029525708],"genre_scores_gemma":[0.98231214,0.0003973801,0.0038639924,0.012994504,0.0002684329,0.0000011404683,0.00010939006,0.000022741893,0.000030256655],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.9946913,0.0003871123,0.0032205677,0.000096934215,0.001135173,0.00046887834],"domain_scores_gemma":[0.9952287,0.00010797833,0.0027330024,0.00028147936,0.00032790023,0.0013209528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035106998,0.00019658639,0.0011438676,0.00047235744,0.00012535226,0.000043222077,0.00024911124,0.0000433033,0.00006335398],"category_scores_gemma":[0.0014798439,0.00013336577,0.00016915506,0.00077499973,0.000064708925,0.0004068344,0.00008820762,0.0005610901,0.000009652523],"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":[0.0013769421,0.022647832,0.31496584,0.013173846,0.002679075,0.00018691477,0.17432092,0.0013585347,0.000016533537,0.0015157594,0.3348392,0.1329186],"study_design_scores_gemma":[0.021348061,0.064783394,0.23416185,0.004436439,0.00028385237,0.00032577536,0.44714573,0.072974354,0.0000064678034,0.00018924926,0.1535214,0.0008234275],"about_ca_topic_score_codex":0.000018780505,"about_ca_topic_score_gemma":0.000015038366,"teacher_disagreement_score":0.45042908,"about_ca_system_score_codex":0.00056647323,"about_ca_system_score_gemma":0.002730336,"threshold_uncertainty_score":0.5438498},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98375547,0.0003587904,0.0014290195,0.013801743,0.00016366463,0.00019044099,0.000027895738,0.000028681025,0.0002442923],"genre_scores_gemma":[0.994918,0.000029535773,0.0015962352,0.0028932171,0.0005075413,0.00000451295,0.00002608654,0.000012630191,0.00001226213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99845016,0.00031444355,0.000576233,0.00015007562,0.0003288373,0.000180232],"domain_scores_gemma":[0.9986052,0.00017364073,0.00029066764,0.00018189255,0.00034641416,0.0004022022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007152448,0.00010571266,0.0004265437,0.00015618696,0.000040112838,0.00003073507,0.00021955899,0.000054926342,0.00012915088],"category_scores_gemma":[0.0027866527,0.000091331145,0.00007059111,0.00041423168,0.00009474808,0.0005886342,0.00007536923,0.00039447768,0.000039089773],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033001337,0.0007305928,0.9882545,0.00003385775,0.00013190255,0.00045879686,0.0032118005,0.000035568282,0.00019274758,0.00032587908,0.0042480472,0.002046281],"study_design_scores_gemma":[0.00247265,0.0014815916,0.9807716,0.00003518112,0.000071379975,0.00012635115,0.0014929109,0.0007897385,0.000004310993,0.00024331237,0.012417428,0.00009352621],"about_ca_topic_score_codex":0.00002394525,"about_ca_topic_score_gemma":0.00014558661,"teacher_disagreement_score":0.011162507,"about_ca_system_score_codex":0.000080659236,"about_ca_system_score_gemma":0.00046496146,"threshold_uncertainty_score":0.3724376},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91227615,0.01065144,0.0005423941,0.065119624,0.0009811217,0.0004971957,0.000011015289,0.00005718386,0.009863899],"genre_scores_gemma":[0.92886233,0.038010668,0.006152428,0.025987564,0.00080289494,0.0000066476236,0.00002018572,0.000047161913,0.00011011373],"study_design_codex":"randomized_trial","study_design_gemma":"not_applicable","domain_scores_codex":[0.99345255,0.003158757,0.0022782672,0.00039793763,0.00030667183,0.00040582992],"domain_scores_gemma":[0.9922282,0.005152304,0.0012673402,0.0005789547,0.00041753208,0.00035566912],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005088644,0.00024772563,0.0010879115,0.0001122814,0.00010866564,0.000026615424,0.00074329734,0.00040338593,0.00009452563],"category_scores_gemma":[0.008186949,0.00017333134,0.0002461084,0.00035879607,0.00056842744,0.0004000321,0.00034053432,0.0024868757,0.000074560194],"study_design_candidate":"randomized_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":[0.6050737,0.02833543,0.047560815,0.00022902124,0.13297343,0.005156445,0.014602986,0.00035656313,0.014723199,0.008183857,0.017110651,0.12569393],"study_design_scores_gemma":[0.026831636,0.010649363,0.45631626,0.00035126007,0.0022073772,0.0034297993,0.014015011,0.0006520262,0.000111120135,0.00040808643,0.48442358,0.00060445844],"about_ca_topic_score_codex":0.00005693776,"about_ca_topic_score_gemma":0.000051898864,"teacher_disagreement_score":0.57824206,"about_ca_system_score_codex":0.00003420476,"about_ca_system_score_gemma":0.0003078849,"threshold_uncertainty_score":0.99981445},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988398,0.00088359776,0.004033267,0.00022042957,0.0015236302,0.0012799739,0.0031097236,0.00025500433,0.0002963789],"genre_scores_gemma":[0.99557126,0.00006677355,0.0019307241,0.000087763685,0.0013893018,0.000100282254,0.00070794835,0.000080122634,0.0000658517],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977587,0.00006681731,0.0006066764,0.00086814485,0.00042956226,0.0002701172],"domain_scores_gemma":[0.9965217,0.00014083,0.0004098474,0.002435471,0.00025838183,0.00023375683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043288307,0.00029880594,0.0007537641,0.00011949491,0.00003832189,0.000028927212,0.00074001774,0.00023309674,0.00001505806],"category_scores_gemma":[0.0014980083,0.00029978773,0.00018489738,0.00014594046,0.00007567088,0.00010986429,0.0014386298,0.0005444909,0.000046451492],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014408944,0.00018342101,0.8965947,0.0044659274,0.0008085365,0.00006048483,0.00043805988,0.000052558073,0.058043264,0.000010504128,0.00073753856,0.037164103],"study_design_scores_gemma":[0.0016234181,0.00022506624,0.94148195,0.0009653811,0.00053755107,0.0000052354967,0.00005509338,0.0014302229,0.03365591,0.00025650804,0.01937786,0.0003857992],"about_ca_topic_score_codex":0.00017935855,"about_ca_topic_score_gemma":0.000015569702,"teacher_disagreement_score":0.044887245,"about_ca_system_score_codex":0.00007055633,"about_ca_system_score_gemma":0.00020749687,"threshold_uncertainty_score":0.9999454},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98476666,0.0019348493,0.00017831846,0.0040570293,0.00016794934,0.0062797526,0.0019368132,0.0004613562,0.0002172986],"genre_scores_gemma":[0.98764724,0.000042345244,0.0007245086,0.006663707,0.00043017956,0.00085175654,0.003549751,0.00007338259,0.000017124534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99559647,0.00058641273,0.0010805617,0.0011679835,0.0006408405,0.0009277513],"domain_scores_gemma":[0.99644804,0.00030708025,0.00037610665,0.0006478288,0.0006348642,0.0015860606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013298113,0.000428895,0.0012411865,0.0001837825,0.000222137,0.00011304601,0.00030291174,0.00009093864,0.00000800016],"category_scores_gemma":[0.0005883879,0.00040278255,0.00007582567,0.00097278925,0.00009992017,0.000370765,0.00009747345,0.0003971641,0.00000663575],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026363048,0.0010697709,0.9845466,0.0027908848,0.000059976668,0.000028966548,0.001951769,0.000019331113,0.000018943812,0.000054419223,0.0006581592,0.0061648735],"study_design_scores_gemma":[0.007540357,0.005395535,0.97049445,0.0000855241,0.0000018029456,0.000010638235,0.00704563,0.0006791787,2.3646024e-7,0.0000032694104,0.008384158,0.00035920547],"about_ca_topic_score_codex":0.0003464264,"about_ca_topic_score_gemma":0.0023897896,"teacher_disagreement_score":0.014052136,"about_ca_system_score_codex":0.00068614184,"about_ca_system_score_gemma":0.0025551666,"threshold_uncertainty_score":0.9998424},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008370504,0.028434087,0.00003073104,0.95613086,0.0000854847,0.004494846,0.0012284975,0.000057043115,0.001167922],"genre_scores_gemma":[0.51213914,0.0032866213,0.000008516267,0.47818533,0.0043025548,0.0006375894,0.00084511674,0.00007142585,0.0005237243],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99050456,0.005898273,0.0008779557,0.00045981826,0.0013368946,0.00092248444],"domain_scores_gemma":[0.9877368,0.010609063,0.0002459301,0.00081012904,0.00017673199,0.00042129768],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.015908137,0.0002207846,0.0007301822,0.0006308279,0.00038693403,0.00007037382,0.00046894499,0.00032148865,0.000008413042],"category_scores_gemma":[0.0065647834,0.00012191579,0.00009645295,0.0011181382,0.00040833734,0.00006023016,0.0000836921,0.0029492772,0.000027761416],"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":[0.0026669668,0.00006567873,0.01145597,0.016773423,0.00008169615,0.00019685707,0.031770848,0.0000062321296,0.00008592554,0.00076563196,0.92827886,0.007851912],"study_design_scores_gemma":[0.0008724103,0.00029418565,0.0027894112,0.0005867328,0.000007340548,0.0001636268,0.0021291699,0.00053700927,4.886844e-7,0.00050171354,0.99202347,0.000094442345],"about_ca_topic_score_codex":0.008847284,"about_ca_topic_score_gemma":0.00063154614,"teacher_disagreement_score":0.5037686,"about_ca_system_score_codex":0.000637115,"about_ca_system_score_gemma":0.0060139513,"threshold_uncertainty_score":0.99962103},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68122345,0.00014491311,0.29942593,0.0038849886,0.00097211625,0.0028183062,0.007694039,0.0005249099,0.0033113658],"genre_scores_gemma":[0.9980052,0.00000881574,0.00043649014,0.00044125746,0.00033632253,0.000043701064,0.00041806308,0.000027357913,0.0002827656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984175,0.00008299369,0.00037026973,0.00044224295,0.00047878848,0.00020821916],"domain_scores_gemma":[0.99919254,0.00017310062,0.000070977265,0.00017289868,0.00011001175,0.00028045467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021503326,0.00015526623,0.00040987815,0.00016039656,0.00018932973,0.000057279507,0.00014770663,0.00007809626,0.000047784077],"category_scores_gemma":[0.000021799475,0.0001656076,0.000050518185,0.0008211236,0.00005092499,0.00012819663,0.000006768945,0.00015460627,0.00014998908],"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":[0.015361296,0.005682877,0.02231576,0.0034585872,0.0038524172,0.0008550599,0.01692661,0.56136554,0.0069387346,0.0014719247,0.2578803,0.10389088],"study_design_scores_gemma":[0.009729611,0.001067302,0.10189125,0.0004216338,0.00032319938,0.00006349304,0.0008781666,0.8599147,0.00007643671,0.00012832215,0.024461282,0.0010445998],"about_ca_topic_score_codex":0.00085954537,"about_ca_topic_score_gemma":0.00020678999,"teacher_disagreement_score":0.3167818,"about_ca_system_score_codex":0.00018393385,"about_ca_system_score_gemma":0.0002663319,"threshold_uncertainty_score":0.6753282},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94564116,0.0064146887,0.023633096,0.016720567,0.00027383293,0.005305834,0.001463845,0.00038775124,0.00015921151],"genre_scores_gemma":[0.9919623,0.00015608592,0.001924152,0.0044266754,0.0009368719,0.000097188975,0.00041597214,0.000041881078,0.00003888484],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997497,0.000055338023,0.0010325805,0.00063715293,0.00018589984,0.0005920128],"domain_scores_gemma":[0.99591726,0.0004556233,0.0009744189,0.00020787716,0.00007431189,0.0023704793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008145169,0.0002311556,0.0008075676,0.000042546493,0.0003093537,0.00009082221,0.000045786608,0.00010101618,0.0000037939342],"category_scores_gemma":[0.00134264,0.00021180612,0.00006626957,0.000145201,0.000041833322,0.00017144515,0.00004244133,0.00015347237,5.711037e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00466724,0.000041460644,0.8889908,0.03663728,0.00006631616,0.000063629246,0.0011636963,0.000014195359,0.000014309749,0.0008103501,0.03142392,0.03610681],"study_design_scores_gemma":[0.013309947,0.002453244,0.89999056,0.0007307818,0.00012900725,0.00035876586,0.00077221094,0.014653834,0.000003546233,0.00023688503,0.06679963,0.0005616079],"about_ca_topic_score_codex":0.00025837915,"about_ca_topic_score_gemma":0.00013716781,"teacher_disagreement_score":0.046321113,"about_ca_system_score_codex":0.00012850284,"about_ca_system_score_gemma":0.0012036497,"threshold_uncertainty_score":0.8637203},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04577285,0.0048150513,0.41910276,0.47158366,0.0005062745,0.017498586,0.03974802,0.0005164235,0.00045637248],"genre_scores_gemma":[0.94700575,0.000044478846,0.022607267,0.0056242514,0.00056832813,0.0006925449,0.023373391,0.00007573445,0.000008275462],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959218,0.00056248123,0.0009529723,0.0008001536,0.00093298114,0.0008296187],"domain_scores_gemma":[0.9910398,0.0022304447,0.00026256937,0.0033805901,0.0009595944,0.0021269687],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003742371,0.00019709083,0.00061440305,0.000105581035,0.00065981864,0.00009463502,0.0014293287,0.00017671431,0.000033227087],"category_scores_gemma":[0.02025411,0.00021345205,0.00007812382,0.0007746729,0.00031652357,0.00017400445,0.0007590087,0.0011879266,0.0000010255524],"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":[0.007625693,0.006307273,0.055863827,0.0091141965,0.0010307325,0.0025052428,0.010874165,0.00055458734,0.00026893907,0.13144378,0.6255142,0.14889738],"study_design_scores_gemma":[0.0007428411,0.00018240829,0.010270641,0.0003350395,0.00015540102,0.000031331238,0.0052293325,0.13418567,0.00001123154,0.022833075,0.82528615,0.00073686143],"about_ca_topic_score_codex":0.058519997,"about_ca_topic_score_gemma":0.42253506,"teacher_disagreement_score":0.9012329,"about_ca_system_score_codex":0.00077095313,"about_ca_system_score_gemma":0.024402773,"threshold_uncertainty_score":0.9879987},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9163728,0.00053790776,0.00053768075,0.079419784,0.000019770234,0.00053629134,0.0023734386,0.000040164072,0.0001621563],"genre_scores_gemma":[0.97961104,0.0007172146,0.000058864145,0.005270402,0.000027298009,0.000028798358,0.014265092,0.000012504856,0.000008800364],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99621856,0.0013760282,0.0008633873,0.00057865,0.00048138254,0.0004819707],"domain_scores_gemma":[0.9971757,0.00054090895,0.00031126483,0.00096430216,0.00024370031,0.00076414086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045018075,0.00016395118,0.0008368977,0.0013188012,0.000048010806,0.000047397545,0.0005271349,0.00007042209,0.00011730518],"category_scores_gemma":[0.0038715191,0.00012876163,0.00006180615,0.008872133,0.00021368556,0.0002755952,0.00020616147,0.00033179412,0.0000015236907],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018042757,0.0002548232,0.98124295,0.00042361423,0.00013782895,0.000011221267,0.004303313,0.000032810432,0.000008320958,0.00037045364,0.0028430468,0.010191165],"study_design_scores_gemma":[0.0015563531,0.00029767337,0.9380888,0.000017235936,0.0000054719185,0.0000020643427,0.0029186287,0.03899841,2.7966803e-7,0.00001296061,0.018005665,0.00009646934],"about_ca_topic_score_codex":0.0019022577,"about_ca_topic_score_gemma":0.012004496,"teacher_disagreement_score":0.074149385,"about_ca_system_score_codex":0.00024888577,"about_ca_system_score_gemma":0.0013620553,"threshold_uncertainty_score":0.669879},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15815838,0.00036073502,0.20611651,0.54595083,0.008754192,0.0052442937,0.0018796532,0.00058075663,0.07295466],"genre_scores_gemma":[0.8042752,0.00004426622,0.00047014255,0.0045558475,0.19031039,0.000009634106,0.00015010529,0.000033282893,0.00015113107],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985752,0.00008059196,0.00031265253,0.00038001573,0.0004396487,0.00021186608],"domain_scores_gemma":[0.9990957,0.00007849483,0.00008761754,0.0002490088,0.000059149846,0.00043007074],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023605135,0.00014973617,0.00032605356,0.0000637294,0.00014280884,0.000031973817,0.00012848679,0.00006121639,0.007322436],"category_scores_gemma":[0.0014436477,0.00012132355,0.00006505517,0.00029314958,0.00011529063,0.00008358437,0.00012659874,0.0001748491,0.00012129964],"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":[0.0048678447,0.00011486357,0.0027748395,0.0002856478,0.00014935169,0.000055746175,0.011105618,0.003730515,0.003136215,0.0057577286,0.9532257,0.014795974],"study_design_scores_gemma":[0.0028288364,0.00070471887,0.0008938944,0.000045156172,0.00020511368,0.000019043344,0.0026452972,0.015005293,0.0005628202,0.0013438411,0.9753566,0.00038943364],"about_ca_topic_score_codex":0.0002260103,"about_ca_topic_score_gemma":0.0002928228,"teacher_disagreement_score":0.6461168,"about_ca_system_score_codex":0.0002054862,"about_ca_system_score_gemma":0.0001994329,"threshold_uncertainty_score":0.993585},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899596,0.00012140642,0.0022708678,0.0016288405,0.00032290292,0.00052561256,0.0026803303,0.0001961944,0.002294257],"genre_scores_gemma":[0.99845004,0.00002748545,0.00006515675,0.000031849675,0.00017372234,0.00003574594,0.0011116104,0.000010464262,0.00009394848],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989101,0.00005922417,0.0004000796,0.0002958316,0.00019728094,0.00013743129],"domain_scores_gemma":[0.9994086,0.0000103265465,0.00019492333,0.00019126417,0.00007230697,0.00012254942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014216844,0.0001213413,0.00026952403,0.000051103125,0.000044590863,0.000011494232,0.000038054306,0.000087504064,0.000093637],"category_scores_gemma":[0.00021476323,0.000119971315,0.000045588313,0.00019792581,0.000050778523,0.00014186712,0.000031956246,0.00018212144,0.000004539957],"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":[0.04175239,0.0013159624,0.113462664,0.0009894085,0.00035478585,0.00011924338,0.0057847383,0.000011936244,0.023507321,0.0002632116,0.008997323,0.803441],"study_design_scores_gemma":[0.0024614963,0.0021460215,0.97898906,0.00007987844,0.000033321732,0.000017902588,0.00007638373,0.0005703479,0.0012155459,0.00000795371,0.014290342,0.00011174214],"about_ca_topic_score_codex":0.034245726,"about_ca_topic_score_gemma":0.15283063,"teacher_disagreement_score":0.8655264,"about_ca_system_score_codex":0.00043298176,"about_ca_system_score_gemma":0.00007421375,"threshold_uncertainty_score":0.9721853},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014369831,0.0060555423,0.0018016838,0.20072278,0.7647647,0.003477608,0.00095326995,0.000116303956,0.0077382755],"genre_scores_gemma":[0.026818475,0.008383036,0.0009749998,0.002712877,0.9476955,0.00006233804,0.00043628365,0.00019153534,0.012724975],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9809699,0.0010798072,0.0015159335,0.0004913715,0.015201549,0.00074141304],"domain_scores_gemma":[0.99030113,0.002465585,0.0007684905,0.0011162065,0.0031289314,0.0022196372],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.010655458,0.000369305,0.0013612327,0.0008020373,0.000049249087,0.00017855238,0.0039829207,0.0009191168,0.0009114499],"category_scores_gemma":[0.033005986,0.00022531943,0.0004362218,0.0008004977,0.00052335474,0.00020550148,0.0016869437,0.007339899,0.0001045544],"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":[0.0018030801,0.00018257211,0.0002614871,0.0009739976,0.00080875464,0.0028180291,0.00040025264,0.0000016318468,0.000037494403,0.00006253845,0.9766382,0.016011992],"study_design_scores_gemma":[0.0018500381,0.002128474,0.00034486357,0.0033778201,0.00016367793,0.0001274523,0.00035456882,0.0002082387,0.00005714351,0.00009120309,0.9911191,0.00017744047],"about_ca_topic_score_codex":0.00009924959,"about_ca_topic_score_gemma":0.00007715288,"teacher_disagreement_score":0.1980099,"about_ca_system_score_codex":0.000516164,"about_ca_system_score_gemma":0.0023397026,"threshold_uncertainty_score":0.99797374},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96827406,0.00075266015,0.01629109,0.01022417,0.00005651051,0.003927175,0.00016714144,0.000113517446,0.00019365957],"genre_scores_gemma":[0.98906934,0.00006479095,0.0013451409,0.00924327,0.00006286649,0.000120109355,0.000029076193,0.000037832662,0.000027567079],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962191,0.0009072192,0.00089782604,0.000800298,0.0004674219,0.00070814363],"domain_scores_gemma":[0.995941,0.00023835622,0.00029435553,0.00050052296,0.00017133143,0.0028544355],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023173185,0.00027420194,0.0010517511,0.00024895062,0.0001310844,0.000106487765,0.0004774945,0.00007041179,0.00006674493],"category_scores_gemma":[0.0032496566,0.00025992075,0.000056330275,0.0016022552,0.00011041856,0.00025696505,0.0004706282,0.00030754556,0.000013187112],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010282448,0.00037469028,0.95175785,0.00056670606,0.000045752236,0.000068295594,0.015478071,0.0001258018,0.00014016865,0.00003879595,0.0007649202,0.029610725],"study_design_scores_gemma":[0.002587025,0.0040122834,0.9561128,0.00009195464,0.0000027657622,0.0000262043,0.0070882183,0.0023746893,0.000017979926,0.000055414337,0.02714935,0.00048132706],"about_ca_topic_score_codex":0.0034280147,"about_ca_topic_score_gemma":0.0010040012,"teacher_disagreement_score":0.029129399,"about_ca_system_score_codex":0.00033828264,"about_ca_system_score_gemma":0.0025823612,"threshold_uncertainty_score":0.9999853},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062234204,0.0037742644,0.012899983,0.9156109,0.0003540678,0.0033477123,0.0003034318,0.00030396358,0.0011714752],"genre_scores_gemma":[0.72981465,0.0004589811,0.003922023,0.26432198,0.00033097694,0.00020399608,0.0001382924,0.000033640517,0.0007754422],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987717,0.000089067,0.0002691261,0.00027057898,0.00020765331,0.00039185502],"domain_scores_gemma":[0.99842525,0.00053830835,0.000095393705,0.0002525112,0.0000308775,0.00065764866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009612315,0.00008241623,0.00020819764,0.000033788034,0.00020873816,0.000018698966,0.00016935049,0.000024922923,0.000020040834],"category_scores_gemma":[0.0025713025,0.00006175557,0.000036000216,0.00038950483,0.00007286255,0.000048327744,0.0000503564,0.00013361423,0.000044921057],"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":[0.0024631878,0.00014532098,0.077556044,0.0035576664,0.000026699403,0.000100666475,0.008828376,0.0005307817,0.0005347048,0.0026663868,0.88015515,0.023435006],"study_design_scores_gemma":[0.0017233946,0.000163417,0.019008992,0.00005810712,0.0000024570875,0.000008566471,0.0010366059,0.009577644,0.000007513399,0.000062652754,0.9682717,0.000078956335],"about_ca_topic_score_codex":0.00018547112,"about_ca_topic_score_gemma":0.00026620785,"teacher_disagreement_score":0.6675805,"about_ca_system_score_codex":0.00021979159,"about_ca_system_score_gemma":0.0015219006,"threshold_uncertainty_score":0.3078275},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.595271,0.00866079,0.21639055,0.1709676,0.003636888,0.0036696785,0.0008357134,0.00035821827,0.00020957629],"genre_scores_gemma":[0.9678233,0.00027392645,0.016111583,0.013923847,0.0016428952,0.000028112,0.00014579485,0.00004686601,0.000003672577],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99653035,0.0002046725,0.0013093122,0.00031352864,0.0011368834,0.0005052416],"domain_scores_gemma":[0.9959797,0.00033543867,0.0014245185,0.0002360153,0.0014351858,0.0005890921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018830937,0.00023902861,0.000621162,0.00046113378,0.00017999951,0.00006691204,0.00058566144,0.000099470024,0.000043204578],"category_scores_gemma":[0.0013612043,0.00022136139,0.0005864371,0.0005634892,0.00009733165,0.00018014923,0.000084558,0.0007250509,0.000003705828],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024770284,0.00036012637,0.9555009,0.0005208572,0.000823609,0.00022579596,0.000185745,0.0030323279,0.0000480537,0.0005449463,0.0043057837,0.031974833],"study_design_scores_gemma":[0.018020295,0.005167441,0.7284224,0.0027555814,0.00030913274,0.00039487024,0.00043662006,0.023007361,0.00009082154,0.0028797134,0.21774636,0.0007693901],"about_ca_topic_score_codex":0.00006144884,"about_ca_topic_score_gemma":0.000031636813,"teacher_disagreement_score":0.3725523,"about_ca_system_score_codex":0.00009004087,"about_ca_system_score_gemma":0.0009794341,"threshold_uncertainty_score":0.9026856},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04429532,0.00023525348,0.9017079,0.027859697,0.00013192468,0.009228852,0.011986523,0.0014352994,0.003119268],"genre_scores_gemma":[0.97286963,0.00001085069,0.007734608,0.0014356619,0.0002352731,0.0000034337768,0.01690658,0.00078345084,0.000020532922],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99758905,0.00018458549,0.00048430043,0.0009445767,0.00032424866,0.00047325075],"domain_scores_gemma":[0.99814856,0.00014536895,0.0001532158,0.0008104394,0.00031651682,0.00042588613],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0014796566,0.00019492258,0.0003288337,0.00027630423,0.00037045704,0.00034963194,0.00094434695,0.000073775554,0.00034901366],"category_scores_gemma":[0.012975487,0.00021073787,0.000048734655,0.0007607086,0.00006470052,0.0003109683,0.0009805422,0.00020038972,0.00020300136],"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":[0.022475746,0.0032791041,0.03567574,0.015337463,0.00036050868,0.00017293156,0.014762251,0.0045449734,0.037834074,0.001260722,0.4335417,0.43075478],"study_design_scores_gemma":[0.00391002,0.00034276082,0.03722914,0.00009234329,0.00002808746,0.000025723999,0.00050534325,0.31390262,0.00009246794,0.0000510519,0.6434099,0.00041056794],"about_ca_topic_score_codex":0.00012612775,"about_ca_topic_score_gemma":0.000028238903,"teacher_disagreement_score":0.92857426,"about_ca_system_score_codex":0.00028442589,"about_ca_system_score_gemma":0.000044240845,"threshold_uncertainty_score":0.9953386},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9161034,0.00011169791,0.00013745819,0.08198984,0.000011730619,0.0014031066,0.00015553234,0.000049101942,0.000038085942],"genre_scores_gemma":[0.97961015,0.00017978843,0.00006022409,0.01932505,0.000036607802,0.00024159819,0.00052447076,0.000014250285,0.000007891029],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9958278,0.0021475288,0.0006855344,0.00040034484,0.00034456613,0.0005942405],"domain_scores_gemma":[0.9964649,0.002029775,0.00028132432,0.0002969958,0.00018643617,0.00074054854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038811865,0.0001754511,0.0006270027,0.00028712006,0.000054403212,0.000028948598,0.00020787198,0.000051143248,0.000025123156],"category_scores_gemma":[0.005342854,0.00012450536,0.000028667342,0.0014554434,0.000114626564,0.00009619136,0.00007804162,0.0004989007,0.0000034641748],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052126346,0.00050910184,0.9885984,0.00046752876,0.000022751909,0.00007031265,0.005439921,0.00006155529,4.797914e-7,0.0000418057,0.0035939503,0.0006729462],"study_design_scores_gemma":[0.0031651622,0.002321036,0.9794007,0.000062768464,9.69456e-7,0.000024702878,0.006029801,0.0014109052,3.891202e-8,0.0000058783216,0.0074799014,0.00009815286],"about_ca_topic_score_codex":0.0024561202,"about_ca_topic_score_gemma":0.009400179,"teacher_disagreement_score":0.06350667,"about_ca_system_score_codex":0.00035516892,"about_ca_system_score_gemma":0.001347757,"threshold_uncertainty_score":0.6396281},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99019325,0.00030647335,0.00012753568,0.0054357923,0.00014369629,0.00092473504,0.0008662878,0.00014773705,0.0018544771],"genre_scores_gemma":[0.99720365,0.00001979331,0.00054712704,0.0016761462,0.0002094902,0.00012305917,0.00017244343,0.000020711883,0.000027608592],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99879646,0.000043150474,0.00026284845,0.00026352718,0.00032930143,0.00030473198],"domain_scores_gemma":[0.9989732,0.00008597407,0.00009319867,0.00028680658,0.000088766246,0.00047203898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015992881,0.00010972923,0.00028416724,0.00009084367,0.000028261755,0.000020030155,0.0001542823,0.00003260356,0.000043374057],"category_scores_gemma":[0.00083408307,0.00010428362,0.00011947273,0.0003193331,0.000047328012,0.00008932774,0.00013837645,0.0001010953,0.000027667995],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0063827317,0.0004988037,0.5857236,0.0012587241,0.0008294632,0.0006906118,0.0015069245,0.000083732106,0.010871221,0.0005406523,0.077896595,0.31371698],"study_design_scores_gemma":[0.0007991732,0.0000402998,0.9867915,0.0000217798,0.00005809398,0.0000065587496,0.00007623971,0.000008548714,0.0018776954,0.000058002435,0.0101546785,0.000107458836],"about_ca_topic_score_codex":0.00013834971,"about_ca_topic_score_gemma":0.000016699678,"teacher_disagreement_score":0.4010679,"about_ca_system_score_codex":0.000015935895,"about_ca_system_score_gemma":0.000031589785,"threshold_uncertainty_score":0.42525625},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8849418,0.0003352209,0.0025720873,0.10762926,0.00012242516,0.00054685015,0.0008158668,0.00025168742,0.0027847774],"genre_scores_gemma":[0.9834032,0.00012734806,0.0004682572,0.013569066,0.000108361695,0.000038868424,0.001935403,0.000015324486,0.00033415432],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974598,0.00021232084,0.00088252395,0.00037172824,0.00048178778,0.00059181714],"domain_scores_gemma":[0.99703753,0.000058449816,0.00032697964,0.0004891255,0.00030971452,0.0017781896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000699901,0.00020974,0.0008289467,0.00053066993,0.00011404863,0.00009873327,0.0001993758,0.00010139605,0.00024054783],"category_scores_gemma":[0.00056335476,0.00019817305,0.00015947314,0.002413932,0.00004054222,0.00043257646,0.00014085602,0.00021518006,0.000023252982],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004258274,0.0000685777,0.9193665,0.00030884636,0.00020486384,0.000004678167,0.0011405672,0.000008835736,0.000005283604,0.00052873173,0.004712986,0.07360757],"study_design_scores_gemma":[0.0007356503,0.00019714833,0.72583675,0.000007809805,0.0000049376645,0.0000042653282,0.00015946443,0.003032159,6.042279e-7,0.0000032880127,0.26987216,0.00014579453],"about_ca_topic_score_codex":0.00015081109,"about_ca_topic_score_gemma":0.00020479344,"teacher_disagreement_score":0.26515916,"about_ca_system_score_codex":0.00009429008,"about_ca_system_score_gemma":0.00053227635,"threshold_uncertainty_score":0.8081262},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9029282,0.0011145273,0.000008498064,0.09226008,0.00017778865,0.00090381625,0.0008892779,0.000038911905,0.0016789006],"genre_scores_gemma":[0.98689234,0.00016652384,0.000020145622,0.012245709,0.00023240614,0.000052544536,0.00028209834,0.000030048388,0.00007819885],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979517,0.00038743918,0.00047775166,0.00039541136,0.0004015088,0.00038619622],"domain_scores_gemma":[0.99778664,0.0011504794,0.00015314239,0.0006919483,0.00005179248,0.00016598082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044099314,0.0002489647,0.0005718048,0.000027890363,0.00008898529,0.00005047868,0.00045887576,0.00006543809,0.00007883579],"category_scores_gemma":[0.001209544,0.0001220894,0.00007218986,0.00037652315,0.0003130453,0.000077541044,0.00023068923,0.00055049226,0.00001981878],"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":[0.0016204498,0.000045693152,0.942661,0.000042290532,0.00026102675,0.00041708097,0.008908247,0.00008306226,0.0002973245,0.00072027545,0.011733838,0.033209752],"study_design_scores_gemma":[0.004989628,0.00003042455,0.9686784,0.00010255153,0.000092241884,0.000008862518,0.0031017908,0.0022586982,0.00008140777,0.00017984328,0.020284595,0.00019153264],"about_ca_topic_score_codex":0.8608977,"about_ca_topic_score_gemma":0.9493056,"teacher_disagreement_score":0.08840786,"about_ca_system_score_codex":0.00018261456,"about_ca_system_score_gemma":0.0006618713,"threshold_uncertainty_score":0.49786612},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64917076,0.04128221,0.121482246,0.048474945,0.005836402,0.016151542,0.110447496,0.0020128565,0.0051415595],"genre_scores_gemma":[0.9815017,0.0012639196,0.0040885624,0.0016027954,0.0005512872,0.00009955538,0.010594606,0.000063917454,0.0002336665],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979262,0.00010559677,0.0004905597,0.0008790088,0.0003251931,0.00027346314],"domain_scores_gemma":[0.99571824,0.0013417226,0.00033879335,0.002080992,0.00013867932,0.00038155707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010606383,0.00027060244,0.00069918233,0.00003777943,0.00010104028,0.000047084992,0.00085367484,0.0001616454,0.000018040255],"category_scores_gemma":[0.0067071314,0.00018899459,0.000100660654,0.00008121608,0.00024209103,0.000048210877,0.0024938504,0.00037520865,0.0000036702806],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008693606,0.00007753693,0.85158956,0.0030888186,0.00064321875,0.00004210296,0.00032973374,0.00013814907,0.00007275119,0.0006626542,0.1395496,0.0029364866],"study_design_scores_gemma":[0.006330437,0.000672323,0.52354443,0.0006809602,0.00049440254,0.000044845016,0.0011358726,0.047221337,0.000032933174,0.005586381,0.41307926,0.0011768189],"about_ca_topic_score_codex":0.00016309046,"about_ca_topic_score_gemma":0.000053655134,"teacher_disagreement_score":0.33233094,"about_ca_system_score_codex":0.00006683635,"about_ca_system_score_gemma":0.00081340095,"threshold_uncertainty_score":0.80295473},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9809966,0.00009428072,0.012280801,0.0036400782,0.000065467866,0.00022233705,0.0026653234,0.0000048707498,0.000030205854],"genre_scores_gemma":[0.9855861,0.00008212695,0.013351799,0.0008364929,0.000054630123,0.000002639886,0.00007757476,0.000005883367,0.0000027232616],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99918693,0.000030549363,0.00035258543,0.00013883272,0.000208726,0.00008235903],"domain_scores_gemma":[0.9984902,0.0006993356,0.00020482573,0.000037799804,0.0002867656,0.00028104355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000318546,0.00007745352,0.00023089134,0.000055297773,0.00002265712,0.000031378644,0.0000542506,0.000017159482,0.000028129076],"category_scores_gemma":[0.0017601552,0.00006844752,0.000018230467,0.000039925682,0.00005604741,0.0000877085,0.000036156398,0.000109799636,1.05382995e-7],"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":[0.0076922663,0.00014377196,0.9503661,0.0007262067,0.00038938524,0.00055721094,0.0009113441,0.00030661808,0.0015350722,0.0014828305,0.003577546,0.03231166],"study_design_scores_gemma":[0.013543298,0.001048552,0.6426579,0.00040592244,0.00017910133,0.00022759195,0.0003880182,0.32131448,0.00014966523,0.00906508,0.010663504,0.00035688112],"about_ca_topic_score_codex":0.027994616,"about_ca_topic_score_gemma":0.067059405,"teacher_disagreement_score":0.32100788,"about_ca_system_score_codex":0.0003850077,"about_ca_system_score_gemma":0.0015388601,"threshold_uncertainty_score":0.9784781},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49614894,0.0077055823,0.47182235,0.019561619,0.0003648611,0.0023361377,0.0005303463,0.0012286347,0.00030151548],"genre_scores_gemma":[0.9976793,0.00019190199,0.0016362936,0.0002639647,0.0001274274,0.000036254016,0.00003764595,0.000020810241,0.000006392393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998703,0.00005407638,0.00035624552,0.00025778785,0.00042825643,0.00020061455],"domain_scores_gemma":[0.99844307,0.00033605006,0.00008418897,0.0002409944,0.000030835738,0.00086487824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046365213,0.00014584413,0.00042150327,0.00022396752,0.00003835446,0.0000099041,0.00014960393,0.00008520953,0.000043929333],"category_scores_gemma":[0.001414162,0.00009112385,0.00011303782,0.0012074875,0.00019284725,0.000028216831,0.00008517668,0.00021300263,0.000009526325],"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":[0.0107482895,0.0015583114,0.027947797,0.090809666,0.0037534905,0.0020053128,0.013709304,0.0072920513,0.21231037,0.007338083,0.019845003,0.6026823],"study_design_scores_gemma":[0.015128671,0.00053607684,0.045912966,0.000801,0.00037698552,0.00015177725,0.0003875392,0.65714353,0.0013891053,0.000012140021,0.27768725,0.00047294918],"about_ca_topic_score_codex":0.000030415229,"about_ca_topic_score_gemma":8.968812e-8,"teacher_disagreement_score":0.6498515,"about_ca_system_score_codex":0.000059033868,"about_ca_system_score_gemma":0.000095945965,"threshold_uncertainty_score":0.37159228},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44095078,0.019113947,0.17180023,0.09244568,0.0010861057,0.023575783,0.027660128,0.0051838625,0.21818349],"genre_scores_gemma":[0.9884689,0.000031093656,0.0042827455,0.0032513302,0.00009717465,0.00006072711,0.0021229454,0.000026348405,0.0016587406],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918777,0.000035574983,0.00017515048,0.00024466196,0.00016509253,0.00019177386],"domain_scores_gemma":[0.998597,0.000069524205,0.000044490153,0.00052134786,0.00022190527,0.0005457219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000046351943,0.00011240625,0.00027370936,0.00002478463,0.00006739372,0.000011569107,0.00014437393,0.00002375581,0.00040155824],"category_scores_gemma":[0.00043480765,0.000097554635,0.00015748524,0.00017951358,0.00005053602,0.00006561769,0.00012351703,0.00007896107,0.00006612213],"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":[0.011122436,0.001490419,0.5085893,0.005489844,0.001531699,0.00030110535,0.004484275,0.00033183646,0.00095764914,0.0101127485,0.4415479,0.014040768],"study_design_scores_gemma":[0.006973638,0.000614067,0.07682271,0.00019758038,0.0007771623,0.0000014152189,0.0033454774,0.021563612,0.0007172897,0.00014107175,0.8884133,0.00043265536],"about_ca_topic_score_codex":0.000015210928,"about_ca_topic_score_gemma":0.00002007291,"teacher_disagreement_score":0.54751813,"about_ca_system_score_codex":0.000045001314,"about_ca_system_score_gemma":0.00014399069,"threshold_uncertainty_score":0.4396781},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0940069,0.0026944487,0.00013824066,0.89001393,0.000045286048,0.00075915677,0.00059523334,0.00036018787,0.0113866],"genre_scores_gemma":[0.88503265,0.0004083959,0.0003559708,0.11364017,0.00020325479,0.000016875738,0.00029944026,0.000023311406,0.000019931344],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812865,0.00021846124,0.0004599702,0.00025454807,0.00027281468,0.0006655628],"domain_scores_gemma":[0.99815625,0.00016652653,0.0001918864,0.00088010123,0.000037111186,0.0005681492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008456118,0.00013414476,0.0006480608,0.00004141357,0.00011617563,0.00008762223,0.0004564933,0.00002396858,0.000022824273],"category_scores_gemma":[0.00048021827,0.00009797933,0.00006150532,0.00052703236,0.00007203877,0.00020925913,0.00023369455,0.00041358796,0.00014049154],"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":[0.0004849864,0.0006201982,0.3151717,0.0018396293,0.000086728505,0.00004570941,0.012316736,0.0000107925525,0.000004757722,0.004111886,0.36444554,0.30086136],"study_design_scores_gemma":[0.004713546,0.0009786098,0.3582709,0.00047237249,0.000005474572,0.00005719307,0.004069138,0.0018766142,0.0000028699646,0.00089225674,0.6283028,0.00035823564],"about_ca_topic_score_codex":0.00028291423,"about_ca_topic_score_gemma":0.00013717511,"teacher_disagreement_score":0.79102576,"about_ca_system_score_codex":0.00015449498,"about_ca_system_score_gemma":0.00069445884,"threshold_uncertainty_score":0.3995481},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9544996,0.0142914215,0.026833398,0.0028112077,0.00045873557,0.00060127594,0.000029130806,0.00027641119,0.00019879805],"genre_scores_gemma":[0.99553984,0.0007580426,0.0014209605,0.0015900556,0.00033355787,0.000034722805,0.00027839505,0.00003206964,0.000012344274],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99744415,0.0002626284,0.00089368515,0.00081231067,0.00016461032,0.0004226258],"domain_scores_gemma":[0.9982788,0.0001828613,0.0003789284,0.0004592533,0.000109854176,0.00059030706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010058596,0.00025483317,0.000968814,0.000056231922,0.00006271138,0.000006009203,0.0000634133,0.000101731355,0.00006300275],"category_scores_gemma":[0.0018523084,0.00019129552,0.00012326254,0.0001201277,0.00016920321,0.000090537345,0.00009311067,0.00033485287,0.000017921482],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003401414,0.00015515293,0.9803912,0.00044410088,0.00023229154,0.0014777313,0.0001810354,0.0028390116,0.00047422192,0.00020298178,0.0055727605,0.0076893684],"study_design_scores_gemma":[0.004678516,0.001610699,0.7611411,0.0013924701,0.0015592101,0.0076593775,0.00017666048,0.18714057,0.00012724452,0.00322046,0.02949764,0.0017960399],"about_ca_topic_score_codex":0.000018538702,"about_ca_topic_score_gemma":0.0000029478863,"teacher_disagreement_score":0.2192501,"about_ca_system_score_codex":0.000032012045,"about_ca_system_score_gemma":0.00012382554,"threshold_uncertainty_score":0.7800805},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011148786,0.99200875,0.0009945584,0.002768256,0.00009472088,0.0023979351,0.0001304296,0.000077097095,0.0015271475],"genre_scores_gemma":[2.8322208e-7,0.9777838,0.0064949277,0.014936685,0.00010940709,0.000055463617,0.00057504396,0.000031178377,0.000013238233],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99269295,0.00040744545,0.0047534597,0.00018738996,0.0013858352,0.0005729094],"domain_scores_gemma":[0.9932064,0.0015381005,0.0022638848,0.0006638071,0.0001355136,0.0021923091],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006297173,0.00043038127,0.005340092,0.00022439865,0.00005320077,0.000028742796,0.000536896,0.00046418604,0.0001664632],"category_scores_gemma":[0.018121019,0.00031128578,0.00026163596,0.00035768672,0.0005310838,0.00023068016,0.002377521,0.00094675756,0.00008178986],"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":[0.0000021336157,0.00001612133,0.000016104797,0.3969824,0.00008113922,0.0000040850555,0.00015917128,4.6247806e-9,2.5320643e-10,0.00019757143,0.008221738,0.5943195],"study_design_scores_gemma":[0.00030380653,0.00015697641,0.0000022924785,0.39074343,0.00017371422,0.00024782532,0.000056845078,0.0002274636,3.0955023e-9,0.000030794414,0.6079137,0.00014317578],"about_ca_topic_score_codex":0.000006997095,"about_ca_topic_score_gemma":0.0000033630424,"teacher_disagreement_score":0.5996919,"about_ca_system_score_codex":0.0000994295,"about_ca_system_score_gemma":0.0054657436,"threshold_uncertainty_score":0.9999339},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65923244,0.000286798,0.31593296,0.014653219,0.0050331983,0.0013273383,0.0028808,0.00012240658,0.00053084193],"genre_scores_gemma":[0.9933343,0.00021118436,0.00040055436,0.0025110154,0.00028900662,0.000011341447,0.0032297275,0.000010203994,0.0000026441958],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9962477,0.00009726675,0.001681397,0.00011067897,0.0015811783,0.00028177822],"domain_scores_gemma":[0.99646366,0.00011703158,0.0013635883,0.00026059366,0.0015640915,0.0002310142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006784147,0.0002263052,0.00035511053,0.00060961925,0.00012959333,0.0004236976,0.00069912884,0.000098244585,0.000098854056],"category_scores_gemma":[0.001009717,0.00018474624,0.00019220862,0.00028755114,0.00008888269,0.0069358316,0.0002370569,0.000504862,0.00017407817],"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":[0.018704584,0.0004379086,0.26924944,0.0006194466,0.001785952,0.0003903772,0.0072501265,0.026447786,0.00088495476,0.00067384343,0.026800688,0.64675486],"study_design_scores_gemma":[0.008657206,0.00032107843,0.7653365,0.0005365788,0.000098771874,0.00040156083,0.0015749693,0.09615292,0.00053080585,0.00013529108,0.1258898,0.00036450633],"about_ca_topic_score_codex":0.0005304158,"about_ca_topic_score_gemma":0.00006434186,"teacher_disagreement_score":0.6463904,"about_ca_system_score_codex":0.00022879882,"about_ca_system_score_gemma":0.00025764047,"threshold_uncertainty_score":0.7533733},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923141,0.0006260086,0.000019361649,0.004345599,0.00019498204,0.0010114183,0.00013353175,0.0004905131,0.0008644649],"genre_scores_gemma":[0.99267644,0.00009109182,0.00004042083,0.0053702476,0.00022004977,0.00033806797,0.00023244592,0.00004552645,0.0009857244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99755996,0.00049972703,0.0004907733,0.00065240543,0.00041204735,0.00038506615],"domain_scores_gemma":[0.9972784,0.00021842554,0.0001426408,0.0009896378,0.000059922506,0.0013109607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025563288,0.00030430354,0.00082851056,0.00012400818,0.0002901917,0.000019440924,0.0004092172,0.00014128005,0.00033000045],"category_scores_gemma":[0.00076721,0.00020777385,0.00029975356,0.0006029366,0.0001354485,0.00012055728,0.00043349722,0.0005131661,0.00015449018],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011391499,0.00024075736,0.9546894,0.00012914771,0.0013280144,0.0043961587,0.0075283493,0.00009812581,0.0038127813,0.000011485158,0.025601134,0.0010254843],"study_design_scores_gemma":[0.002869778,0.000595855,0.8965531,0.000020368168,0.00030319166,0.0012246165,0.00083589094,0.000057744663,0.00001156086,0.000017652852,0.09726514,0.00024508862],"about_ca_topic_score_codex":0.000050749026,"about_ca_topic_score_gemma":0.000026698779,"teacher_disagreement_score":0.071664006,"about_ca_system_score_codex":0.00030200635,"about_ca_system_score_gemma":0.0004315837,"threshold_uncertainty_score":0.84727716},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899336,0.00084590056,0.0019083227,0.0052582496,0.00006533893,0.0013616235,0.000107220294,0.00016128662,0.00035844918],"genre_scores_gemma":[0.99807626,0.000070325834,0.0002440231,0.0011173902,0.00013698096,0.00012430588,0.00019564595,0.000028192808,0.0000068547615],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971624,0.00064004574,0.00059821253,0.00063484814,0.00046569915,0.00049877184],"domain_scores_gemma":[0.9984045,0.00013201268,0.000142646,0.00022434685,0.000221721,0.00087477465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017702586,0.00021781055,0.000552939,0.00026428117,0.00020749694,0.00006885215,0.00006418991,0.00007507436,0.000026307855],"category_scores_gemma":[0.0007248789,0.00021566176,0.000034074903,0.0010513098,0.000054306864,0.00027537363,0.00005542614,0.0003456339,0.0000013537682],"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":[0.00018761033,0.0004109402,0.92273396,0.00022342241,0.000045402903,0.000009360891,0.0052954634,0.00079719705,0.00002856559,0.0000059630997,0.00006592491,0.07019622],"study_design_scores_gemma":[0.0027701722,0.00066639937,0.40791327,0.000024852887,0.0000038960347,0.000018930052,0.0012863358,0.58674693,5.5099623e-8,0.0000052593477,0.0004365821,0.00012728428],"about_ca_topic_score_codex":0.00026667508,"about_ca_topic_score_gemma":0.00068499905,"teacher_disagreement_score":0.5859498,"about_ca_system_score_codex":0.00015919267,"about_ca_system_score_gemma":0.0009600872,"threshold_uncertainty_score":0.8794431},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90683573,0.00040794743,0.000021931643,0.090540394,0.00009132861,0.001259422,0.00012602952,0.0001437816,0.00057345594],"genre_scores_gemma":[0.97255546,0.00026153773,0.000024382978,0.026621459,0.000094488336,0.00020931408,0.00016486643,0.000015780372,0.00005272132],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99737847,0.0005981503,0.0005621651,0.00057075074,0.0002866938,0.00060377654],"domain_scores_gemma":[0.998024,0.00019354708,0.00016224761,0.00035638516,0.00009944425,0.0011644054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008009139,0.00021639073,0.00074307626,0.00023236049,0.000105391635,0.000035004505,0.00015123971,0.00007883824,0.000083278675],"category_scores_gemma":[0.0009075557,0.0001858325,0.00005160104,0.0006824105,0.00014112647,0.00009299557,0.000070211645,0.0003558405,0.00002866083],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015889361,0.00029940187,0.9849694,0.00014021133,0.000026808037,0.000025610818,0.0019883546,5.474219e-7,8.0947973e-7,0.00008436816,0.008748623,0.003557016],"study_design_scores_gemma":[0.0020182338,0.0013134383,0.96223146,0.000012412914,6.776322e-7,0.0000050747235,0.0013553309,0.00032362857,2.8830303e-8,0.000010692579,0.03258129,0.0001477538],"about_ca_topic_score_codex":0.00058336474,"about_ca_topic_score_gemma":0.0010412624,"teacher_disagreement_score":0.06571974,"about_ca_system_score_codex":0.000056671623,"about_ca_system_score_gemma":0.0006595534,"threshold_uncertainty_score":0.7578029},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99287796,0.00023335441,0.00032070754,0.003262852,0.00017994658,0.002628289,0.00039361863,0.000059205824,0.000044086515],"genre_scores_gemma":[0.99651355,0.000029750227,0.0001101305,0.002816223,0.0003760851,0.00009480188,0.000025388226,0.000026755928,0.000007342637],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981901,0.00022264346,0.0004830953,0.00040500573,0.000336228,0.0003629281],"domain_scores_gemma":[0.9986427,0.000138366,0.0003086788,0.00021768083,0.00010181784,0.0005907543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005996611,0.00017225574,0.0005553958,0.00009450247,0.00021741117,0.000010041443,0.000099780445,0.000091667185,0.000009368853],"category_scores_gemma":[0.0008694274,0.00012645623,0.00007938998,0.00058597256,0.00008805365,0.00007598859,0.00007029593,0.00028757032,0.0000017476291],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029141111,0.0004031782,0.97538686,0.0015295254,0.000030180385,0.0000063555217,0.01675242,0.000004161211,0.00020113308,0.00004553238,0.0007236451,0.0020028735],"study_design_scores_gemma":[0.0046555763,0.00054809486,0.99259245,0.000022756833,0.00006067488,0.0000064368137,0.0009237009,0.000604763,0.000005880107,0.00019198548,0.00029153578,0.000096153504],"about_ca_topic_score_codex":0.0010602886,"about_ca_topic_score_gemma":0.0033615388,"teacher_disagreement_score":0.017205557,"about_ca_system_score_codex":0.00034507952,"about_ca_system_score_gemma":0.0009004966,"threshold_uncertainty_score":0.5156735},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9196223,0.0015852203,0.07093112,0.0040439777,0.00015487234,0.00090415124,0.0026056012,0.00012382651,0.000028921684],"genre_scores_gemma":[0.9956029,0.00036744133,0.0011929033,0.0010286788,0.00030187442,0.000017338198,0.0014224941,0.000046842368,0.000019520952],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643904,0.00041366825,0.000978074,0.0009677245,0.0005379462,0.00066354935],"domain_scores_gemma":[0.9969298,0.000104067614,0.000829003,0.0007856056,0.00034330605,0.0010081686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013953545,0.0003104475,0.00083331723,0.00013934972,0.00020971423,0.00014910898,0.0003312626,0.00011371282,0.000014479934],"category_scores_gemma":[0.00039069328,0.0002974487,0.00006074326,0.00031778464,0.00018258137,0.00059380964,0.00048180512,0.00028095508,0.0000037285317],"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":[0.00053078064,0.00015518178,0.98549956,0.0016110874,0.00015181385,0.000038870654,0.0038353375,0.0005170821,0.0003100003,0.000043441494,0.00029199276,0.0070148213],"study_design_scores_gemma":[0.0021346912,0.00018334427,0.36345991,0.00008745082,0.0000046570635,0.000038833427,0.00037601247,0.62930715,0.00000779542,0.000013196921,0.0040727407,0.00031421686],"about_ca_topic_score_codex":0.0004998617,"about_ca_topic_score_gemma":0.00016852596,"teacher_disagreement_score":0.6287901,"about_ca_system_score_codex":0.00007890048,"about_ca_system_score_gemma":0.00059750356,"threshold_uncertainty_score":0.9999478},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967759,0.0015668266,0.00019745319,0.0006194761,0.00008642648,0.00050526176,0.00017998314,0.000015217525,0.000053403535],"genre_scores_gemma":[0.998881,0.00044131107,0.00013250111,0.00013698434,0.000039614275,0.0000032674998,0.00033462967,0.000018823366,0.00001187479],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998121,0.00041892543,0.00075626746,0.0002726228,0.00032481493,0.0001063188],"domain_scores_gemma":[0.9968721,0.00037929227,0.0010225392,0.0015171961,0.00017095087,0.00003786518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091512426,0.00013543377,0.0004121269,0.00003314536,0.00006687655,0.000010484926,0.0003377439,0.000052626867,0.00000665183],"category_scores_gemma":[0.0010952585,0.00007474214,0.00005270104,0.00047782573,0.00009123717,0.00015369858,0.00053447625,0.00013029578,4.736815e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005828617,0.00013511912,0.8808637,0.0006111176,0.000097014985,0.0000028587071,0.00043397106,0.00006276072,0.11397761,0.0004030854,0.00017041132,0.0026594412],"study_design_scores_gemma":[0.0009870126,0.000113158254,0.99365175,0.00032370194,0.000051191553,0.000010084109,0.0006137065,0.0018548655,0.0021242613,0.000025789213,0.00016696639,0.000077486126],"about_ca_topic_score_codex":0.00050925626,"about_ca_topic_score_gemma":0.00014326074,"teacher_disagreement_score":0.11278803,"about_ca_system_score_codex":0.000013308845,"about_ca_system_score_gemma":0.000062445484,"threshold_uncertainty_score":0.30478963},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9007803,0.0003042582,0.00017467471,0.09515578,0.00014785543,0.0023815674,0.0005630343,0.00023609768,0.0002563923],"genre_scores_gemma":[0.9672029,0.00010292707,0.000020429989,0.031973753,0.00023960003,0.00013630462,0.00019514412,0.000040659863,0.000088287765],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953659,0.0011610319,0.0009136841,0.0007319854,0.0010422186,0.00078517926],"domain_scores_gemma":[0.9958381,0.00047236896,0.00060081796,0.0011825946,0.00027603464,0.0016300862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019137312,0.0003497528,0.00076301384,0.00014499357,0.0004771087,0.00011092529,0.0005833126,0.00011437896,0.000062517436],"category_scores_gemma":[0.0033249538,0.00021922178,0.00016345589,0.0013210485,0.00029538796,0.00018763829,0.00025104298,0.00064817443,0.000010412944],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040939252,0.0005888714,0.99286443,0.0006122261,0.000052284293,0.000007820614,0.00074137613,0.000024571838,0.000026514504,0.000044200588,0.0014850305,0.003143266],"study_design_scores_gemma":[0.003987124,0.00063172146,0.92734116,0.00002320194,0.0000020783073,0.000013706411,0.00063658465,0.0017987927,2.476794e-7,0.00000474018,0.06537188,0.00018874387],"about_ca_topic_score_codex":0.00020632753,"about_ca_topic_score_gemma":0.00067491527,"teacher_disagreement_score":0.06642255,"about_ca_system_score_codex":0.00024916575,"about_ca_system_score_gemma":0.0020135727,"threshold_uncertainty_score":0.8939605},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9650405,0.00038860628,0.0005103333,0.029061519,0.000061428844,0.0012593152,0.0033583106,0.00009803879,0.00022194129],"genre_scores_gemma":[0.9817836,0.000067280605,0.00055234996,0.017016845,0.00017196918,0.000024910163,0.0003289042,0.000022079017,0.000032056898],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99872845,0.000086590866,0.00041879373,0.00024278491,0.000178405,0.00034497777],"domain_scores_gemma":[0.9982971,0.0007220674,0.00019654843,0.0003828247,0.00005731603,0.00034413894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053398375,0.00011808204,0.00054982543,0.000034857298,0.000040273,0.00003551576,0.00017165788,0.000025623922,0.000013862875],"category_scores_gemma":[0.005195576,0.000083958024,0.000055343655,0.00024855623,0.00008302327,0.00019373224,0.000097228854,0.00013807631,0.000016732718],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0081136925,0.00081594,0.5741313,0.0064621745,0.00013659123,0.00004344284,0.003135093,0.0001840383,0.000391815,0.0021519645,0.05846768,0.34596628],"study_design_scores_gemma":[0.046352796,0.009844425,0.69681805,0.0028042379,0.000121032,0.00008367892,0.0011038289,0.085748196,0.00023193187,0.005927629,0.14988442,0.0010797681],"about_ca_topic_score_codex":0.00007374159,"about_ca_topic_score_gemma":0.0001395003,"teacher_disagreement_score":0.3448865,"about_ca_system_score_codex":0.000108751025,"about_ca_system_score_gemma":0.00058406295,"threshold_uncertainty_score":0.6219966},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9889461,0.0013687911,0.0008718803,0.0020227078,0.00013068925,0.0007524578,0.0008295855,0.00025339657,0.004824419],"genre_scores_gemma":[0.9922906,0.00018724047,0.0034565548,0.0017069838,0.00018087053,0.000013740363,0.0014955441,0.00012313116,0.0005453225],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9960382,0.0011115328,0.00096573675,0.000920693,0.0003643148,0.0005994959],"domain_scores_gemma":[0.99765027,0.0006040048,0.0003884813,0.000739077,0.0001113793,0.0005067733],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000285677,0.00048001026,0.0010384157,0.0004168821,0.00007486797,0.000025400223,0.0007416227,0.00006839065,0.00020322598],"category_scores_gemma":[0.0011562271,0.00042074092,0.00015705725,0.0006307122,0.0017246339,0.00034946355,0.00067279587,0.0007032868,0.00022149466],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.016472293,0.00163267,0.82762855,0.0010657393,0.00090501836,0.015001121,0.031952962,0.00043776218,0.07151315,0.0012842953,0.0040441174,0.02806231],"study_design_scores_gemma":[0.006202505,0.0040460713,0.9701929,0.0006821417,0.00015315943,0.0004414175,0.0011203442,0.0016045796,0.0018745648,0.00015751414,0.0128719015,0.00065286603],"about_ca_topic_score_codex":0.00007313567,"about_ca_topic_score_gemma":0.000105619976,"teacher_disagreement_score":0.14256437,"about_ca_system_score_codex":0.000029905785,"about_ca_system_score_gemma":0.0002424024,"threshold_uncertainty_score":0.99982446},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92409545,0.0030220302,0.0015067402,0.05513329,0.000116169445,0.0066707185,0.005510862,0.00013011631,0.0038146242],"genre_scores_gemma":[0.9928792,0.0009913145,0.00059420126,0.00077815104,0.0004899316,0.0005470237,0.0028799102,0.000051528128,0.00078872434],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9941025,0.0016848403,0.0006996585,0.0012506795,0.0014272934,0.0008350502],"domain_scores_gemma":[0.99460876,0.003281958,0.0002035902,0.0011896822,0.00029898164,0.0004170055],"candidate_categories":["scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007413396,0.00036606006,0.0010007905,0.00076710066,0.0007228587,0.0014325597,0.0022773428,0.00021805166,0.00042131214],"category_scores_gemma":[0.0017090258,0.00024329525,0.0003107361,0.0018867468,0.0006639869,0.00019141662,0.0032774583,0.0024424742,0.000007326959],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027701326,0.00050923997,0.95202017,0.0051360773,0.0031758223,0.00055280194,0.009987545,0.009298333,0.0010387878,0.0024980155,0.007404898,0.00560819],"study_design_scores_gemma":[0.0051041604,0.00024824045,0.92152554,0.0008488801,0.00057439663,0.00006603295,0.016809545,0.022431068,0.000029887919,0.0037505792,0.027952392,0.0006592938],"about_ca_topic_score_codex":0.0039002532,"about_ca_topic_score_gemma":0.006252583,"teacher_disagreement_score":0.06878377,"about_ca_system_score_codex":0.0005431751,"about_ca_system_score_gemma":0.0017248705,"threshold_uncertainty_score":0.9998589},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.817689,0.05431417,0.011221189,0.079846635,0.00066614157,0.009243079,0.008323814,0.0006163774,0.018079598],"genre_scores_gemma":[0.9923407,0.0023544978,0.0007486189,0.0006290095,0.00061066804,0.000045579203,0.0032036207,0.000028313374,0.00003899253],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9896839,0.004871441,0.0009387243,0.00081428216,0.0031235137,0.0005681182],"domain_scores_gemma":[0.98324645,0.008412317,0.00017620856,0.0066177547,0.00010545005,0.001441797],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01490593,0.00013325304,0.0006714963,0.00011723671,0.00008926665,0.000037826798,0.0036034426,0.00004818719,0.00023206507],"category_scores_gemma":[0.058351737,0.00008313571,0.000039590523,0.0006549477,0.0009964667,0.00022137094,0.0035528545,0.0009678959,0.000058474554],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005101012,0.0007606845,0.052894346,0.010328058,0.00016971388,0.00042714237,0.0037699726,0.0000024615642,0.0013322908,0.0012780966,0.018261407,0.91026574],"study_design_scores_gemma":[0.0032324109,0.0005860057,0.35275117,0.005511129,0.000053110565,0.000029798255,0.004325051,0.31648874,0.000036018595,0.00046201027,0.3161866,0.0003379309],"about_ca_topic_score_codex":0.0024843188,"about_ca_topic_score_gemma":0.0005035154,"teacher_disagreement_score":0.9099278,"about_ca_system_score_codex":0.000019650592,"about_ca_system_score_gemma":0.0011648178,"threshold_uncertainty_score":0.9495802},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99647593,0.00021977452,0.0009267016,0.00087420846,0.00013145807,0.00025240783,0.0008738169,0.000050357066,0.00019533507],"genre_scores_gemma":[0.9942992,0.00008558982,0.0024663096,0.0025271894,0.00015853951,0.00001634266,0.00039477047,0.00002102256,0.000031064534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99782014,0.0002258222,0.0007063345,0.0006204264,0.00026734895,0.0003599473],"domain_scores_gemma":[0.9987681,0.00019165983,0.00015293904,0.00029393367,0.00009493586,0.00049846567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002768804,0.00020569954,0.0010975478,0.00013068058,0.000042924443,0.000010087977,0.0001323259,0.000100222016,0.00041467848],"category_scores_gemma":[0.00030604214,0.00018590613,0.0002298472,0.00025846664,0.0005730935,0.000035641944,0.00019111115,0.0002758747,0.000032515265],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015822617,0.0013517862,0.9514421,0.000053453903,0.002281511,0.00018304259,0.00039954195,0.00009244033,0.024625376,0.0004052207,0.0028965403,0.0004463544],"study_design_scores_gemma":[0.006284189,0.021610131,0.951792,0.000043897966,0.00086519675,0.000011006889,0.00014715006,0.014594939,0.0035485812,0.000015393442,0.0009266407,0.00016088472],"about_ca_topic_score_codex":0.000005679223,"about_ca_topic_score_gemma":0.000014958644,"teacher_disagreement_score":0.021076795,"about_ca_system_score_codex":0.0000452073,"about_ca_system_score_gemma":0.000033389977,"threshold_uncertainty_score":0.7581032},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98347396,0.0041377274,0.0014433408,0.00824899,0.00006202782,0.001728194,0.00010858197,0.00019370732,0.00060350326],"genre_scores_gemma":[0.9816434,0.00030875322,0.0012143848,0.014275645,0.000116677365,0.00025197183,0.0021493407,0.00003064231,0.000009208328],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99622434,0.00059189065,0.0011336998,0.0008201494,0.00036739066,0.00086255383],"domain_scores_gemma":[0.9968517,0.00019951619,0.0003856675,0.00048229966,0.00015468133,0.001926171],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017120547,0.00027334408,0.0010393845,0.00018576893,0.00009421758,0.000018974575,0.00023425138,0.00016047245,0.000018543715],"category_scores_gemma":[0.00061515975,0.00028011165,0.000047611913,0.0009948775,0.00012726727,0.00014713769,0.00013153962,0.0003428661,0.0000065424356],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004550734,0.00018145959,0.9370573,0.002386995,0.000012776609,0.000020066642,0.0027733454,0.000040269722,0.000035965782,0.000037401438,0.00044237103,0.05655698],"study_design_scores_gemma":[0.002978856,0.00014673249,0.91496927,0.000029768797,3.6998713e-7,0.000020896387,0.00038954403,0.0015828022,3.8253063e-7,0.000010169899,0.07964948,0.00022173565],"about_ca_topic_score_codex":0.00021964443,"about_ca_topic_score_gemma":0.0060953116,"teacher_disagreement_score":0.07920711,"about_ca_system_score_codex":0.0006255593,"about_ca_system_score_gemma":0.006145499,"threshold_uncertainty_score":0.99996513},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.939678,0.025369024,0.003581935,0.0042230086,0.0004904057,0.0006957036,0.0038281018,0.00027202014,0.021861762],"genre_scores_gemma":[0.98580354,0.0074715675,0.0012170468,0.00042398387,0.0005504471,0.000051021303,0.0021165558,0.000046397054,0.0023194512],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99576575,0.0016977409,0.00055402133,0.0005583746,0.00049375056,0.00093036494],"domain_scores_gemma":[0.99748135,0.00094348425,0.00029297208,0.00044616047,0.0003515734,0.00048443515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009939085,0.00054087647,0.00059808657,0.00012825146,0.00046798485,0.00020228374,0.00027961086,0.00031550037,0.00030349146],"category_scores_gemma":[0.0006260811,0.00058523205,0.0001603245,0.00038963452,0.0019827937,0.00050954823,0.00006413046,0.0004724739,0.000003610698],"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":[0.00073323085,0.0012218939,0.6511187,0.0068141683,0.00083358324,0.0031489558,0.0063564936,0.0011039535,0.0019979528,0.07682343,0.09622461,0.15362301],"study_design_scores_gemma":[0.00087373133,0.00035738244,0.89042884,0.002797583,0.00027199194,0.0003647537,0.0013758793,0.0009360524,0.005331211,0.0035249046,0.093214355,0.00052332226],"about_ca_topic_score_codex":0.3545642,"about_ca_topic_score_gemma":0.9280903,"teacher_disagreement_score":0.5735261,"about_ca_system_score_codex":0.0032649182,"about_ca_system_score_gemma":0.0062100394,"threshold_uncertainty_score":0.9996599},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24891287,0.0018643119,0.71379846,0.021013407,0.00067242316,0.0071060327,0.004697555,0.0016710449,0.00026385929],"genre_scores_gemma":[0.97983295,0.000020942018,0.01424286,0.004424257,0.00016958652,0.0004927874,0.0007672813,0.000040876144,0.0000084390995],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981303,0.00006968806,0.00039615945,0.0006645791,0.00027840264,0.00046084987],"domain_scores_gemma":[0.9971002,0.0012037662,0.00014405178,0.00063000154,0.00017261253,0.000749405],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00069796975,0.0001788467,0.0004077077,0.000115985255,0.0001335825,0.00025120148,0.00037671524,0.00007119349,0.000008966301],"category_scores_gemma":[0.029501716,0.00015585736,0.000044404125,0.00046912584,0.000053868753,0.0005735731,0.00036364276,0.00013180486,0.000005863406],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010178193,0.000048377253,0.12690882,0.0021518487,0.0003016789,0.00023730294,0.00025369343,0.0000070596698,0.000026259251,0.0013931426,0.018671641,0.84898233],"study_design_scores_gemma":[0.0046886685,0.00005183905,0.38730937,0.00085987424,0.00027888844,0.00023493504,0.001798145,0.01792586,0.000037173013,0.0001392159,0.5859599,0.0007161308],"about_ca_topic_score_codex":0.00002206478,"about_ca_topic_score_gemma":0.00006722492,"teacher_disagreement_score":0.84826624,"about_ca_system_score_codex":0.00028880042,"about_ca_system_score_gemma":0.00045770276,"threshold_uncertainty_score":0.9786732},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.839879,0.13396281,0.00033681045,0.0021641564,0.0016586138,0.00078355597,0.0026103344,0.00057911564,0.018025627],"genre_scores_gemma":[0.9093444,0.042504158,0.007281842,0.0008717066,0.002884364,0.00019005791,0.00065462827,0.00022260446,0.036046255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9930938,0.0009863937,0.001150186,0.0011117138,0.0008983572,0.002759546],"domain_scores_gemma":[0.9957269,0.0008547099,0.00041381808,0.0012409734,0.0005839824,0.0011796177],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.0007871366,0.0011043862,0.0014474648,0.0002697235,0.0017414016,0.0001340226,0.00078259804,0.00037892768,0.0043237587],"category_scores_gemma":[0.0012028292,0.0011655798,0.00035121394,0.0006394455,0.0040726457,0.0007455362,0.0004048548,0.0006631676,0.000058149315],"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":true,"study_design_scores_codex":[0.0001464487,0.00018865864,0.8663361,0.0038493779,0.00049736287,0.0013781148,0.0033093032,0.0005986843,0.00015427078,0.0011654817,0.11703751,0.005338671],"study_design_scores_gemma":[0.00071236625,0.00029514916,0.7489309,0.0026513159,0.00016905327,0.0007283097,0.0010641144,0.00085573195,0.002134484,0.00043213114,0.24103524,0.0009912304],"about_ca_topic_score_codex":0.8279264,"about_ca_topic_score_gemma":0.9946684,"teacher_disagreement_score":0.16674207,"about_ca_system_score_codex":0.009665933,"about_ca_system_score_gemma":0.009246276,"threshold_uncertainty_score":0.9995582},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959725,0.00023246967,0.0000068475506,0.0019246037,0.000037914135,0.0007637397,0.00085838,0.00009302012,0.00011052306],"genre_scores_gemma":[0.9955892,0.00001385639,0.00010888456,0.003916686,0.0000778655,0.00005860171,0.00017651088,0.000025400472,0.000032970136],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981508,0.000118599,0.0003470953,0.0004928319,0.0006270974,0.00026353053],"domain_scores_gemma":[0.99875295,0.00020239125,0.0000796044,0.00029298154,0.0000807867,0.0005913047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026803717,0.0001635487,0.00048037648,0.00006492961,0.000057322894,0.000026771757,0.00012161036,0.000038061982,0.00035709902],"category_scores_gemma":[0.0011383378,0.00017036007,0.000027944818,0.00032115457,0.00006612973,0.000051696912,0.00006677913,0.0002191687,0.000040438303],"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":[0.00050355477,0.0011969317,0.99484587,0.00035046297,0.00014059231,0.00057802524,0.000053551335,0.00006796284,0.0015249237,0.0000013763967,0.0007253036,0.000011442195],"study_design_scores_gemma":[0.0054989676,0.00021843846,0.9899143,0.000030441362,0.00002590783,0.000007742534,0.000051743133,0.0038095561,0.00009721127,0.0000020698817,0.0001796315,0.0001639935],"about_ca_topic_score_codex":0.26130897,"about_ca_topic_score_gemma":0.69822747,"teacher_disagreement_score":0.43691853,"about_ca_system_score_codex":0.0006730983,"about_ca_system_score_gemma":0.003961768,"threshold_uncertainty_score":0.74361},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9192041,0.0023047854,0.011269348,0.065459244,0.000028282158,0.0005068251,0.0010438341,0.000010512071,0.00017306166],"genre_scores_gemma":[0.9978964,0.00024508228,0.000069740796,0.00034675212,0.00014004721,0.000001997227,0.0008197926,0.000012822942,0.00046738365],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956028,0.0008690397,0.00053404545,0.0003651391,0.0024350574,0.0001939478],"domain_scores_gemma":[0.99721414,0.0007788228,0.00021901181,0.0008126495,0.00041198058,0.0005633805],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005357036,0.0000955872,0.00033196565,0.00023643789,0.000078428355,0.00009615609,0.001168824,0.00008669584,0.00025177663],"category_scores_gemma":[0.012744266,0.00005348345,0.00004468096,0.000876988,0.00056474376,0.00023555337,0.0039107157,0.0004491057,0.000001690826],"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":[0.0011649072,0.00019657012,0.23767908,0.0006457187,0.0010240778,0.00011737839,0.004809942,0.0000034895356,0.0003369645,0.00013028255,0.56018716,0.19370446],"study_design_scores_gemma":[0.00225918,0.0019338221,0.3257355,0.0017079714,0.0012120793,0.00040109883,0.015178062,0.06250828,0.00074376876,0.000019191686,0.58809197,0.00020911044],"about_ca_topic_score_codex":0.000050803174,"about_ca_topic_score_gemma":0.00070044294,"teacher_disagreement_score":0.19349535,"about_ca_system_score_codex":0.000062481944,"about_ca_system_score_gemma":0.00024938697,"threshold_uncertainty_score":0.9955718},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9831398,0.0078116837,7.9706064e-7,0.007888084,0.00015826097,0.0006285915,0.0001534389,0.00004745086,0.00017188168],"genre_scores_gemma":[0.9966578,0.0019896906,0.000026828133,0.0007737259,0.00021919045,0.00009470261,0.000020937292,0.000022773913,0.00019438867],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998499,0.00007799833,0.00023457433,0.0004951553,0.0003965338,0.00029676466],"domain_scores_gemma":[0.9991708,0.00010659927,0.000077837925,0.00024386139,0.000107812055,0.00029313462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000155194,0.00022282699,0.0006741862,0.000045611636,0.00004327466,0.000022149321,0.000078305646,0.000030987616,0.00005765583],"category_scores_gemma":[0.0004397515,0.00018979501,0.00004771326,0.00027833995,0.000085286316,0.00010859839,0.00017321872,0.0002281443,0.00001581049],"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":[0.00092967384,0.0001534362,0.968781,0.000108575165,0.00088245457,0.0016062624,0.00059456646,0.0000022977558,0.000008248801,0.0000017468018,0.02616436,0.00076735724],"study_design_scores_gemma":[0.0014261205,0.0003216977,0.9842574,0.00017015783,0.00027257224,0.0000072122616,0.008688134,0.000013518758,0.0000033454464,0.000005232821,0.004634904,0.000199716],"about_ca_topic_score_codex":0.054452557,"about_ca_topic_score_gemma":0.25028232,"teacher_disagreement_score":0.19582978,"about_ca_system_score_codex":0.0001404603,"about_ca_system_score_gemma":0.0005050619,"threshold_uncertainty_score":0.9518439},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9169565,0.00021746288,0.06928296,0.00074716465,0.008318566,0.0019847483,0.0006952202,0.0003424671,0.0014549366],"genre_scores_gemma":[0.97085655,0.0000021937137,0.027331436,0.00056628266,0.00020658171,0.000040281164,0.00069081195,0.000024689003,0.0002811679],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978341,0.000015312828,0.0006762028,0.0006377963,0.00062455155,0.00021203916],"domain_scores_gemma":[0.9980655,0.00007790621,0.00033644063,0.0008434433,0.00035504988,0.00032171444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080144947,0.00010835843,0.0002943956,0.00009794869,0.00008908164,0.000045472403,0.000090908434,0.00008218436,0.0003238442],"category_scores_gemma":[0.003031921,0.00009637047,0.00016114888,0.0005094379,0.00015673535,0.00013263519,0.000065567656,0.00009702566,0.0000356757],"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":[0.0010751972,0.000416422,0.58994675,0.0008012076,0.00013806764,0.00082142325,0.0014541836,0.00008730302,0.03427166,0.00047049863,0.33376482,0.036752455],"study_design_scores_gemma":[0.0013414477,0.00066218246,0.17370084,0.0005195939,0.00021061552,0.00025215492,0.00015930392,0.0013004562,0.02107954,0.024145868,0.77636415,0.00026386476],"about_ca_topic_score_codex":0.0000022938273,"about_ca_topic_score_gemma":0.0000014058407,"teacher_disagreement_score":0.44259933,"about_ca_system_score_codex":0.000030098165,"about_ca_system_score_gemma":0.00029125597,"threshold_uncertainty_score":0.39298737},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11700769,0.00042613258,0.88102573,0.0004091716,0.00020812803,0.00027462537,0.00062676595,0.0000099749,0.000011772931],"genre_scores_gemma":[0.7581279,0.00012798277,0.24101159,0.0003649242,0.0001271412,0.000002003016,0.0002054664,0.0000149440975,0.000017999724],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973524,0.0016379914,0.00056850514,0.00014199916,0.00016639699,0.00013269908],"domain_scores_gemma":[0.99451214,0.004194802,0.0005120019,0.00006726004,0.0005013901,0.00021241033],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00437858,0.00010057451,0.0006789394,0.00007252662,0.000056712288,0.000014816246,0.000060802584,0.000040236067,0.000024896364],"category_scores_gemma":[0.01571069,0.00008350674,0.000040501054,0.00009597405,0.00004419174,0.000035811223,0.00003291967,0.00026296894,0.0000010943021],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033888328,0.00017226358,0.9656792,0.00010741817,0.00020934049,0.000049064194,0.0006244579,0.000086549364,0.0004411006,0.000119918135,0.0054607056,0.023661146],"study_design_scores_gemma":[0.003222549,0.0043178964,0.9726246,0.000013934061,0.00022055025,0.00004138786,0.0002994243,0.010798906,0.000040625757,0.00015339974,0.008177914,0.0000888016],"about_ca_topic_score_codex":0.0000794246,"about_ca_topic_score_gemma":0.000096625176,"teacher_disagreement_score":0.64112025,"about_ca_system_score_codex":0.000016276404,"about_ca_system_score_gemma":0.00006769545,"threshold_uncertainty_score":0.9925804},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5039505,0.00044082015,0.0008530473,0.41582444,0.00027734778,0.001384484,0.07663494,0.00025246292,0.00038197584],"genre_scores_gemma":[0.9124523,0.00031186774,0.0002138229,0.029637508,0.00019827751,0.000012617287,0.057103135,0.000040491796,0.000030007277],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970481,0.00019052766,0.0006147316,0.0008174805,0.0005865529,0.00074264023],"domain_scores_gemma":[0.9931313,0.000063378044,0.00033758767,0.0016535674,0.00012610042,0.0046880883],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000798649,0.00026540091,0.00062029815,0.0002589092,0.00019328533,0.000036053305,0.0007045092,0.000106920794,0.00053011155],"category_scores_gemma":[0.0023307102,0.00026956626,0.00010092693,0.0009692431,0.00010910782,0.00025583777,0.00028079894,0.00029559198,0.000052804095],"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":[0.0006785511,0.0011684208,0.3021712,0.0016402736,0.0007010994,0.0012135946,0.0011348696,0.000006133299,0.0068588606,0.0014045275,0.6214443,0.06157817],"study_design_scores_gemma":[0.00057196437,0.00026925246,0.38958478,0.0000794765,0.00004544998,0.000021876644,0.00005161599,0.00020565455,0.000010118216,0.00000717697,0.6089458,0.00020683656],"about_ca_topic_score_codex":0.060405385,"about_ca_topic_score_gemma":0.019341974,"teacher_disagreement_score":0.4085018,"about_ca_system_score_codex":0.0005451625,"about_ca_system_score_gemma":0.0058334614,"threshold_uncertainty_score":0.9999757},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6934477,0.08477983,0.007113002,0.052756354,0.0045324834,0.0055127568,0.015104702,0.00041875395,0.13633442],"genre_scores_gemma":[0.9891852,0.0015684245,0.000013723949,0.0001775638,0.00013591792,0.000005509124,0.00003839178,0.000034182358,0.008841056],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982812,0.00009830777,0.00067262596,0.00039397852,0.00040405287,0.00014983976],"domain_scores_gemma":[0.99741274,0.0011210232,0.00078922324,0.00049890124,0.000078735975,0.00009939715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025521367,0.00024084911,0.00052385015,0.00004916346,0.00015339765,0.00007763642,0.00022827579,0.0001651924,0.00000794563],"category_scores_gemma":[0.001470974,0.00013632502,0.00009865318,0.000096598975,0.0004264548,0.00013887469,0.0005273021,0.0005163656,0.000001008978],"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":[0.003348506,0.00033823875,0.7365688,0.016915472,0.0013222665,0.00027155204,0.007287665,0.016126677,0.00013881148,0.12603514,0.0006270991,0.09101978],"study_design_scores_gemma":[0.0012656851,0.00015507409,0.011441302,0.0099258665,0.00021652579,0.00017338614,0.003787677,0.0043719546,0.00017606586,0.0020690253,0.9658351,0.0005823517],"about_ca_topic_score_codex":0.00011819155,"about_ca_topic_score_gemma":0.000090772046,"teacher_disagreement_score":0.965208,"about_ca_system_score_codex":0.000087548826,"about_ca_system_score_gemma":0.0001754605,"threshold_uncertainty_score":0.55591726},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15315387,0.022830624,0.7893535,0.023044338,0.0013149076,0.004436931,0.0017425951,0.00040429377,0.0037189208],"genre_scores_gemma":[0.869115,0.00073818915,0.12705708,0.0029702373,0.00007098072,0.000012982186,0.00001556394,0.000012638842,0.0000073416722],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9970626,0.0002456568,0.00044071497,0.0005923325,0.0011190278,0.0005396158],"domain_scores_gemma":[0.99516225,0.002098536,0.0002167525,0.0013163539,0.0006140066,0.0005920977],"candidate_categories":["metaresearch","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.01293996,0.0001672098,0.00040958522,0.00046378284,0.0016322356,0.00016154193,0.0018229034,0.000042657703,0.000036693928],"category_scores_gemma":[0.0168317,0.00011782897,0.00007863587,0.013510322,0.0046530063,0.00094988686,0.00074596965,0.00038284363,0.0000055632354],"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":[0.0005449447,0.0002786349,0.032056097,0.00093492714,0.00004577116,0.0000038798626,0.006744713,0.00013778941,0.07332747,0.050177388,0.00015231178,0.8355961],"study_design_scores_gemma":[0.00082862936,0.00040707996,0.9402328,0.00035120375,0.00006763453,0.000016647107,0.0025534239,0.024348663,0.0031305966,0.0021274996,0.025657652,0.00027816388],"about_ca_topic_score_codex":0.000041673236,"about_ca_topic_score_gemma":0.0000038770595,"teacher_disagreement_score":0.9081767,"about_ca_system_score_codex":0.000116560426,"about_ca_system_score_gemma":0.0010176821,"threshold_uncertainty_score":0.9996675},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91693515,0.0039102267,0.003110668,0.060301784,0.00006478087,0.008245331,0.0010070899,0.00028159525,0.006143348],"genre_scores_gemma":[0.9959245,0.00008751081,0.00022097964,0.00276605,0.00009709661,0.00022970323,0.00056487456,0.000025676713,0.00008365647],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818796,0.00027286992,0.00030912645,0.00026720046,0.00048580556,0.0004770297],"domain_scores_gemma":[0.9990177,0.0002036169,0.000058430855,0.0001843489,0.00022130077,0.00031457143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008349941,0.000102337035,0.00025820185,0.00017760732,0.00014333648,0.000042045387,0.00008880974,0.00004415282,0.00006326859],"category_scores_gemma":[0.00029484017,0.00009084959,0.00003492521,0.00044756685,0.000102886916,0.00028928972,0.0001269698,0.0002857999,0.000031465097],"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":[0.0066921683,0.0006538572,0.06943978,0.020737078,0.00018128433,0.00008381388,0.33360028,0.00008189449,0.0017045122,0.004951694,0.37446293,0.18741068],"study_design_scores_gemma":[0.005379061,0.0019975451,0.12342474,0.0005118575,0.0000049733844,0.0000058824057,0.016393976,0.010716018,0.00019252872,0.00014424279,0.8409954,0.00023381754],"about_ca_topic_score_codex":0.000048597645,"about_ca_topic_score_gemma":0.000033688062,"teacher_disagreement_score":0.4665324,"about_ca_system_score_codex":0.00019385904,"about_ca_system_score_gemma":0.00020712664,"threshold_uncertainty_score":0.37047386},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013694239,0.00071910315,0.20353584,0.7633027,0.0001458197,0.012371568,0.0037287157,0.000776226,0.0017258249],"genre_scores_gemma":[0.7118151,0.00093029015,0.0073475977,0.26292434,0.00018576175,0.002464831,0.014240444,0.000036883077,0.000054699736],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978092,0.00012030494,0.00072391354,0.0005340435,0.00041646732,0.00039609958],"domain_scores_gemma":[0.99698937,0.000058853857,0.0003521766,0.0005519501,0.00009725279,0.0019503689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005679547,0.00024536165,0.0006378113,0.0004281848,0.00010509019,0.00021782971,0.0003454829,0.00010693029,0.00014717515],"category_scores_gemma":[0.00046101704,0.00024068469,0.00005863326,0.00051210175,0.00005053251,0.00039694385,0.0011832822,0.0005095412,0.00009807832],"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":[0.0005420702,0.00060903584,0.34159926,0.02998559,0.00022336529,0.000014845503,0.008435068,0.00032340712,0.000006400871,0.012539402,0.35631552,0.24940605],"study_design_scores_gemma":[0.0014823457,0.00014000112,0.27362463,0.00019951681,0.000009077014,0.000013702521,0.00053820043,0.0026264023,0.0000010114604,0.000724306,0.72030604,0.00033479364],"about_ca_topic_score_codex":0.0020121918,"about_ca_topic_score_gemma":0.0012049536,"teacher_disagreement_score":0.6981209,"about_ca_system_score_codex":0.00097340514,"about_ca_system_score_gemma":0.005028728,"threshold_uncertainty_score":0.9814837},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954509,0.00004803108,0.000018426033,0.0022184462,0.00045722676,0.00018272888,0.0010799003,0.000075394964,0.00046890957],"genre_scores_gemma":[0.9975519,0.000012737639,0.00009180132,0.0005742254,0.00012296652,0.00002173419,0.00092128984,0.000017010307,0.00068632787],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9984387,0.000074310345,0.00025168533,0.00031067358,0.0006367217,0.00028786753],"domain_scores_gemma":[0.998931,0.000048073332,0.000183653,0.000351413,0.00014743283,0.000338448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000704326,0.00013217809,0.0002523322,0.000087013905,0.00013349309,0.00003487476,0.00026952804,0.000015613641,0.0006059395],"category_scores_gemma":[0.000104902574,0.00010217415,0.000078346726,0.0020742756,0.000672904,0.00007059298,0.00009520652,0.00013962202,0.000012699546],"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":[0.002570655,0.0006956833,0.31138098,0.000048649053,0.0002312134,0.0003261008,0.0009446313,0.00016764253,0.0066869273,0.00008166333,0.6525027,0.024363127],"study_design_scores_gemma":[0.0026748907,0.002102625,0.65843606,0.000029875304,0.0005441932,0.000021874625,0.001696036,0.0007627247,0.0011781497,0.000002702466,0.33225527,0.00029557844],"about_ca_topic_score_codex":0.77628845,"about_ca_topic_score_gemma":0.36496297,"teacher_disagreement_score":0.41132548,"about_ca_system_score_codex":0.00013953676,"about_ca_system_score_gemma":0.0006225149,"threshold_uncertainty_score":0.66346127},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012763525,0.8792528,0.027090888,0.078358054,0.000054254062,0.00065477565,0.0011269086,0.0001598372,0.00053898717],"genre_scores_gemma":[0.42923442,0.55351955,0.00017939258,0.015942434,0.00020923444,0.000004310152,0.0007426473,0.000025123454,0.00014289908],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974538,0.00020398169,0.0004078398,0.00054260774,0.00031865758,0.0010731223],"domain_scores_gemma":[0.9980162,0.0000827715,0.00019817735,0.00063242676,0.00008189816,0.0009885584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022343695,0.00019671861,0.00071646605,0.00007890509,0.00012714173,0.00004295878,0.0003345779,0.000046432277,0.000120796205],"category_scores_gemma":[0.0012285745,0.0001649217,0.00008699892,0.0005741922,0.00009439905,0.00026821144,0.00023967263,0.0009559205,0.000010109917],"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":[0.0018540254,0.00047893575,0.6657057,0.008015475,0.02220853,0.00057373557,0.00069741026,0.00032466635,0.0001408914,0.020803453,0.17981006,0.09938715],"study_design_scores_gemma":[0.008878956,0.001374516,0.024082316,0.0005497618,0.01362718,0.004924166,0.00085795403,0.051372766,0.0000029830824,0.026358088,0.8663354,0.0016359278],"about_ca_topic_score_codex":0.000031124062,"about_ca_topic_score_gemma":0.00050911854,"teacher_disagreement_score":0.68652534,"about_ca_system_score_codex":0.00022451523,"about_ca_system_score_gemma":0.0028045021,"threshold_uncertainty_score":0.6725311},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866828,0.00011176806,0.000046738318,0.006752216,0.000117351025,0.0011472855,0.00022888713,0.00007966632,0.0048333206],"genre_scores_gemma":[0.9914488,0.000024895513,0.000065925655,0.0077643106,0.00054574915,0.00001836491,0.000095897885,0.000009152319,0.000026859167],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970701,0.00016768053,0.00035662815,0.00090144464,0.0011329898,0.0003712004],"domain_scores_gemma":[0.9982136,0.00010291937,0.00013138473,0.00059375906,0.000100311925,0.0008580097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024816315,0.00017576353,0.0005388686,0.000020090432,0.00045419132,0.000059033275,0.0011645559,0.00004702658,0.00029345],"category_scores_gemma":[0.0017723241,0.000120926554,0.000016289123,0.0005781473,0.0018116543,0.0005995817,0.0015133235,0.00023485259,0.000010552751],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010904379,0.001090383,0.66569054,0.000064009844,0.000087000226,0.008982949,0.039885256,0.000004033291,0.00249398,0.0007987328,0.014834189,0.2649785],"study_design_scores_gemma":[0.016396612,0.0108550545,0.62997174,0.00011861273,0.0006359277,0.0013229416,0.19377664,0.0073672305,0.000020329933,0.00031879597,0.13823244,0.0009836497],"about_ca_topic_score_codex":0.0020224992,"about_ca_topic_score_gemma":0.00034020896,"teacher_disagreement_score":0.26399484,"about_ca_system_score_codex":0.0001685324,"about_ca_system_score_gemma":0.00031617572,"threshold_uncertainty_score":0.6675116},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9968925,0.0021654428,6.890936e-7,0.00039835,0.000034387005,0.00029236227,0.00009342426,0.00001777219,0.00010503424],"genre_scores_gemma":[0.99892443,0.00059716083,0.000008634017,0.00019022723,0.00015789045,0.000044630906,0.000031162162,0.0000108568465,0.00003497608],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990297,0.00016120246,0.00025551455,0.00020798622,0.00016953613,0.00017607925],"domain_scores_gemma":[0.99933714,0.00025610253,0.000118685784,0.00017872576,0.000021891216,0.00008742827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003238957,0.00010751942,0.000245304,0.00008980263,0.000055012348,0.000019852923,0.000083056286,0.000037896032,0.000024080662],"category_scores_gemma":[0.00012078346,0.00006459628,0.000034770877,0.00036956734,0.00008546849,0.00006016602,0.00016164548,0.00016303218,0.0000022424208],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038906914,0.000099516874,0.98965406,0.00005322859,0.0000348931,0.000024463623,0.0042859055,0.000002460169,0.000013583417,0.000009824613,0.0014474813,0.004335675],"study_design_scores_gemma":[0.0010465821,0.00015821536,0.9954354,0.000011575982,0.0000763583,0.0000016127416,0.0015092604,0.00021776065,5.2482557e-7,0.000007066058,0.0014772054,0.00005844743],"about_ca_topic_score_codex":0.00004787613,"about_ca_topic_score_gemma":0.00010005126,"teacher_disagreement_score":0.0057813297,"about_ca_system_score_codex":0.000011019503,"about_ca_system_score_gemma":0.000046317364,"threshold_uncertainty_score":0.263416},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00029722828,0.99317104,0.00009996884,0.0011396151,0.00033789873,0.00081879454,0.0039341887,0.000034568482,0.00016672973],"genre_scores_gemma":[0.0070703523,0.99159765,0.000281284,0.00048525422,0.00040012063,0.0000034181305,0.00010641691,0.000046091136,0.00000941032],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952439,0.00059864944,0.0024526808,0.0003739516,0.00085656374,0.00047423685],"domain_scores_gemma":[0.99349123,0.00022364213,0.004171051,0.00037721053,0.0004409726,0.0012958703],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019524577,0.00042133476,0.0050524552,0.00016483267,0.00007061118,0.00003994469,0.00029329787,0.00013977152,0.000007247707],"category_scores_gemma":[0.0007655384,0.0003226413,0.0006262264,0.0006047581,0.00007441081,0.00018041002,0.00012653561,0.0007412115,0.000010533512],"study_design_candidate":"design_other","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":[0.00043793375,0.00008864437,0.0019333594,0.049224474,0.0003401079,0.00029362697,0.00006735209,2.695347e-7,5.7824046e-8,0.000040413852,0.0039468263,0.94362694],"study_design_scores_gemma":[0.0012195959,0.0014169526,0.0008641564,0.08101016,0.00027629235,0.007824004,0.00013382646,0.000004769957,2.1296575e-8,0.000030257526,0.9070369,0.00018307948],"about_ca_topic_score_codex":0.000048863858,"about_ca_topic_score_gemma":0.000056811055,"teacher_disagreement_score":0.94344383,"about_ca_system_score_codex":0.0023109019,"about_ca_system_score_gemma":0.018626843,"threshold_uncertainty_score":0.9999226},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9643682,0.0013701427,0.029864619,0.0021688102,0.000049055754,0.0018550747,0.00018047894,0.00013714125,0.00000645868],"genre_scores_gemma":[0.9970079,0.00009114737,0.0014482598,0.0012013499,0.00006412386,0.00009111023,0.0000643919,0.00002730009,0.0000043807263],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99688536,0.00078143243,0.00071444723,0.00072226074,0.0003996026,0.0004968771],"domain_scores_gemma":[0.99704593,0.00021045523,0.00037841473,0.000499448,0.00024822127,0.0016175073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014081466,0.00025539522,0.0007950086,0.00028647383,0.00021949393,0.00005150621,0.00009217387,0.000056642617,0.000039969265],"category_scores_gemma":[0.0010493336,0.00024287582,0.00010952028,0.001603837,0.00009974999,0.00018964536,0.000078875724,0.00017755857,0.0000010180602],"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":[0.0013871833,0.00038588708,0.95135224,0.0011602776,0.00025371194,0.00041111154,0.00052058,0.00020309754,0.00072185893,0.0000013374546,0.00015122243,0.043451518],"study_design_scores_gemma":[0.0033358005,0.0010473549,0.38632703,0.000020268191,0.000067042085,0.000030879466,0.0007290041,0.6073429,0.0000102572685,0.0000011395273,0.0007773216,0.00031102385],"about_ca_topic_score_codex":0.00033902455,"about_ca_topic_score_gemma":0.00027807595,"teacher_disagreement_score":0.60713977,"about_ca_system_score_codex":0.000070899456,"about_ca_system_score_gemma":0.0011480252,"threshold_uncertainty_score":0.99041885},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64255095,0.0012606554,0.33687353,0.007848467,0.0002552308,0.0035324623,0.0043060845,0.00039208593,0.002980526],"genre_scores_gemma":[0.9754621,0.00013493065,0.023594059,0.0004872875,0.00005281236,0.000042705702,0.00016418763,0.000036669553,0.000025277184],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703985,0.0003047469,0.0003086429,0.0005055786,0.0012583324,0.00058286294],"domain_scores_gemma":[0.9981847,0.00039906637,0.000033726752,0.00021442906,0.000386371,0.0007817509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012768927,0.00016180842,0.00027006696,0.00018735213,0.00011357171,0.00006559745,0.00012987983,0.00007576263,0.000055896053],"category_scores_gemma":[0.00173452,0.00014457195,0.000018494717,0.00054949307,0.0004929029,0.00008577362,0.00015269665,0.0007808309,0.000020707952],"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":[0.02352904,0.0010485093,0.7706008,0.0065650605,0.0003980067,0.0021384368,0.0010873385,0.01177291,0.12554124,0.0025770245,0.011156234,0.04358541],"study_design_scores_gemma":[0.003830861,0.00033941463,0.028748719,0.0001993802,0.000034863682,0.0000036109138,0.00010190951,0.96444476,0.001563871,0.00014909146,0.00044854652,0.00013497996],"about_ca_topic_score_codex":0.00001902959,"about_ca_topic_score_gemma":0.00006190881,"teacher_disagreement_score":0.9526718,"about_ca_system_score_codex":0.00022037902,"about_ca_system_score_gemma":0.00041532362,"threshold_uncertainty_score":0.5895473},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5258486,0.0013601125,0.013537067,0.44191915,0.0056170234,0.0019838363,0.008424508,0.00015237463,0.0011573039],"genre_scores_gemma":[0.99096286,0.00012852158,0.0029709022,0.0015253659,0.0009426298,0.000004781747,0.003408694,0.0000103309785,0.000045884626],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967064,0.00010617948,0.00042637475,0.000530074,0.0019215802,0.0003093922],"domain_scores_gemma":[0.9971624,0.00029035716,0.00015728065,0.0011865348,0.00093284197,0.00027056364],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.006339717,0.000075836935,0.00011859414,0.00033199697,0.00035326745,0.0006341553,0.0057768403,0.000026009822,0.000084326624],"category_scores_gemma":[0.016648145,0.000054779663,0.000023997773,0.0009495046,0.00026517795,0.0037168206,0.0023927325,0.00045351434,0.0000371732],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057273626,0.00033140308,0.73522764,0.0000624632,0.00011460986,0.00027662874,0.0006089824,0.00022774863,0.0049349293,0.02024233,0.15679117,0.080609344],"study_design_scores_gemma":[0.0012358314,0.00005878247,0.37551785,0.00013376818,0.000011003287,0.00016589284,0.00032470957,0.35205704,0.000027768425,0.0012517172,0.26908147,0.0001341519],"about_ca_topic_score_codex":0.00007959957,"about_ca_topic_score_gemma":0.00022519704,"teacher_disagreement_score":0.46511427,"about_ca_system_score_codex":0.00017227561,"about_ca_system_score_gemma":0.00064196554,"threshold_uncertainty_score":0.9996024},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99029523,0.0007138721,0.0005788108,0.0018379946,0.00010326467,0.006168313,0.00016702231,0.00008724013,0.0000482798],"genre_scores_gemma":[0.99827856,0.00012717053,0.00018086015,0.00037748905,0.000050679817,0.000867374,0.00010874819,0.000008237175,8.888348e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975003,0.0005625095,0.00084502605,0.00034450486,0.00029624984,0.00045139674],"domain_scores_gemma":[0.998395,0.00021976397,0.00032411274,0.0002738303,0.0001001009,0.00068716484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021104435,0.00018084161,0.00059469265,0.00006112965,0.0002722685,0.000058675487,0.00009978948,0.000034436165,3.0956102e-7],"category_scores_gemma":[0.0005227956,0.00014029646,0.00002950636,0.00037786612,0.00005606221,0.00029901427,0.00008068987,0.00011008567,5.767751e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030280373,0.00019150152,0.72222036,0.0028099865,0.000037743146,0.0000011448361,0.0019183757,0.0000053275908,7.618042e-8,0.00011154848,0.000037833906,0.27236333],"study_design_scores_gemma":[0.0031768586,0.0010634623,0.98875153,0.000055545148,0.0000017340566,0.0000045875026,0.003980388,0.0010909208,1.540462e-7,0.0000104578485,0.0017597482,0.000104583654],"about_ca_topic_score_codex":0.00022597438,"about_ca_topic_score_gemma":0.00028290096,"teacher_disagreement_score":0.27225873,"about_ca_system_score_codex":0.0005352373,"about_ca_system_score_gemma":0.0004608477,"threshold_uncertainty_score":0.5721123},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019122186,0.9570839,0.000098269666,0.016383028,0.00014337675,0.0232713,0.0025635,0.00023068624,0.00020681218],"genre_scores_gemma":[0.00034778836,0.9482664,0.000063531086,0.036510177,0.000119707554,0.010944003,0.0035499027,0.00010785503,0.00009064703],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9886818,0.0068063573,0.0020904378,0.0007651866,0.0006551718,0.0010010342],"domain_scores_gemma":[0.9916338,0.003087175,0.0009388867,0.0020426037,0.00020072566,0.0020968635],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009068499,0.00073929026,0.005722015,0.0008175481,0.00038578734,0.00018344558,0.00081197114,0.00017969348,0.000009918647],"category_scores_gemma":[0.008751911,0.00049265166,0.0006068096,0.0030742793,0.00008961433,0.00010358079,0.00019075071,0.0011629873,0.00005471679],"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":[0.00007996722,0.00016713624,0.00057423254,0.8997648,0.00013546528,0.00006517332,0.00013765672,4.5627704e-8,8.239895e-9,0.00010372731,0.01295395,0.086017884],"study_design_scores_gemma":[0.00083272957,0.000280777,0.0002074342,0.19708106,0.00011835977,0.000062925305,0.00004395055,0.000037884627,3.5844056e-10,0.000004922521,0.8009873,0.0003426472],"about_ca_topic_score_codex":0.000023960172,"about_ca_topic_score_gemma":0.00011623073,"teacher_disagreement_score":0.78803337,"about_ca_system_score_codex":0.0004945186,"about_ca_system_score_gemma":0.004807328,"threshold_uncertainty_score":0.9997525},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98974216,0.000030742776,0.0006424416,0.0016253033,0.00007314294,0.0008921839,0.0061030495,0.00005300434,0.0008379754],"genre_scores_gemma":[0.9953784,0.0000045425722,0.0000112582275,0.0011693375,0.00003728101,0.000011271415,0.00337099,0.0000043965692,0.000012552193],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890435,0.000071609385,0.0004435656,0.00008127455,0.00037963546,0.00011956658],"domain_scores_gemma":[0.99880034,0.00007538458,0.0002730454,0.0006647918,0.0000952433,0.0000912042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033393293,0.00009032181,0.00019089565,0.000080105456,0.00014423115,0.000033708024,0.00021413868,0.000027429542,0.000039076043],"category_scores_gemma":[0.0002752311,0.00004921844,0.00008177859,0.00079753896,0.000068408364,0.00028037236,0.0001383928,0.00010452396,0.00006207603],"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":[0.0019251192,0.000522274,0.3798814,0.00036379122,0.005417657,0.00042065143,0.032797508,0.008411614,0.000045143737,0.00018515936,0.41790518,0.15212446],"study_design_scores_gemma":[0.0030430842,0.00048591685,0.27829456,0.000036985173,0.0034036639,0.00014013435,0.025917564,0.07856116,0.00017786921,0.0000042508586,0.6096579,0.00027693406],"about_ca_topic_score_codex":0.0013867266,"about_ca_topic_score_gemma":0.0008293457,"teacher_disagreement_score":0.19175269,"about_ca_system_score_codex":0.000038318118,"about_ca_system_score_gemma":0.00008673605,"threshold_uncertainty_score":0.2096325},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971539,0.00011382368,0.00058711227,0.0010795546,0.000045562916,0.0000625581,0.00058282353,0.000013141252,0.00036153107],"genre_scores_gemma":[0.9985367,0.00014301346,0.00011999728,0.00026321178,0.00008272434,3.934562e-8,0.000073911244,0.000007372594,0.00077303516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99845874,0.00017442279,0.0002735355,0.00023735367,0.00064152904,0.00021442278],"domain_scores_gemma":[0.99856275,0.00012315586,0.0001867417,0.00010632379,0.000066064946,0.00095494464],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021412259,0.00013019048,0.0005399995,0.00023715102,0.000057513822,0.000011104969,0.00020017273,0.000114204784,0.014912233],"category_scores_gemma":[0.00030487927,0.00010650695,0.0001908504,0.00033549487,0.00008601689,0.00011847716,0.00014113211,0.0005718927,0.000018269666],"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":[0.00061604835,0.000099078185,0.9846655,0.0000093456865,0.0011740477,0.0005584314,0.00832606,0.000022982502,0.000055935034,9.433702e-7,0.00047813507,0.003993506],"study_design_scores_gemma":[0.0029985595,0.000105290186,0.9775842,0.0000946788,0.0016151349,0.0000063454627,0.0039793635,0.0064489776,0.000015192998,0.0000028149063,0.007022558,0.0001269098],"about_ca_topic_score_codex":0.017539809,"about_ca_topic_score_gemma":0.051246956,"teacher_disagreement_score":0.033707146,"about_ca_system_score_codex":0.0001455976,"about_ca_system_score_gemma":0.00038242817,"threshold_uncertainty_score":0.98900247},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9447533,0.0047472436,0.002543397,0.003729667,0.00032907687,0.011318603,0.024797242,0.00038839746,0.0073930887],"genre_scores_gemma":[0.9633761,0.00054661336,0.004829128,0.00012209902,0.0013055077,0.0004482182,0.028969105,0.0003018448,0.00010135572],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9806991,0.005035441,0.0012971597,0.0031488854,0.00653922,0.00328019],"domain_scores_gemma":[0.9786626,0.0031242,0.0003591066,0.005830029,0.008707861,0.003316213],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.011492042,0.00080824166,0.0017931104,0.0026373514,0.0011627871,0.00065565744,0.002261759,0.00062727055,0.00042898435],"category_scores_gemma":[0.008563834,0.0008232826,0.00028434722,0.0054644435,0.0006802474,0.00049252814,0.0075821904,0.004639879,0.00025285486],"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":[0.002437672,0.0008479343,0.8538365,0.044626955,0.001030201,0.017262165,0.0070514902,0.005416792,0.0009770676,0.00019994556,0.06260619,0.0037070895],"study_design_scores_gemma":[0.0025329483,0.0012296435,0.93527997,0.024757255,0.0002486018,0.0009776005,0.010702088,0.017390713,0.00006058859,0.00028616984,0.0049216244,0.0016127955],"about_ca_topic_score_codex":0.21347466,"about_ca_topic_score_gemma":0.34179366,"teacher_disagreement_score":0.128319,"about_ca_system_score_codex":0.0037895867,"about_ca_system_score_gemma":0.016737795,"threshold_uncertainty_score":0.99978745},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9855717,0.0035234347,0.00081905694,0.006831503,0.00010736502,0.0018600597,0.0010935585,0.00009539523,0.000097931654],"genre_scores_gemma":[0.9826521,0.00024273814,0.0013967728,0.003852207,0.00037147576,0.000098173026,0.011276536,0.000035291752,0.00007469825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99558294,0.001210795,0.00086454145,0.0010029073,0.0006331035,0.00070568663],"domain_scores_gemma":[0.99559885,0.00096981693,0.00037180338,0.0014722609,0.0001955887,0.001391674],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008346495,0.00021292467,0.00058266456,0.00014741025,0.00020730283,0.00020520859,0.0003060014,0.00008995706,0.00008971715],"category_scores_gemma":[0.009617244,0.00019012584,0.000044611457,0.0008012729,0.000053230906,0.0005455636,0.0001149667,0.00026192956,0.000002364642],"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":[0.00025364835,0.00016382564,0.97526294,0.00022985793,0.00001364628,0.000084347266,0.0011720972,0.000033646214,0.0000674249,0.000012680627,0.0021128978,0.020592982],"study_design_scores_gemma":[0.002836554,0.00006291372,0.9151627,0.000103528924,0.0000053395875,0.00003432371,0.0006815125,0.063492365,1.5336506e-7,0.00022333229,0.017192157,0.00020511549],"about_ca_topic_score_codex":0.0066681486,"about_ca_topic_score_gemma":0.013754464,"teacher_disagreement_score":0.06345872,"about_ca_system_score_codex":0.00038665184,"about_ca_system_score_gemma":0.0130200675,"threshold_uncertainty_score":0.99994653},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9424258,0.004549401,0.00070967467,0.01342178,0.00048575355,0.00091206585,0.037322592,0.0001284879,0.00004444899],"genre_scores_gemma":[0.99032813,0.0005273898,0.00027958042,0.0014097785,0.0004287136,0.000071599585,0.005077489,0.000068097055,0.0018092149],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967233,0.0003578353,0.00029411292,0.0016694372,0.00049145817,0.0004638752],"domain_scores_gemma":[0.99377364,0.00029314865,0.00013387512,0.0051777703,0.00014431989,0.00047725855],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000794728,0.00032682746,0.00057409884,0.00009398882,0.00007636563,0.000669141,0.0007449986,0.00012386126,0.00005587338],"category_scores_gemma":[0.0008537794,0.00028964045,0.000031777465,0.00014449883,0.00007997033,0.00067645014,0.0032737632,0.0002098406,0.00001138022],"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":[0.0015078557,0.00016306712,0.82301754,0.0017195407,0.0004764453,0.0009806614,0.0032274602,4.2059438e-7,0.021409258,0.0000035932267,0.11978556,0.027708625],"study_design_scores_gemma":[0.004849804,0.0003551496,0.22172941,0.0021512832,0.00067034987,0.0010225944,0.007894562,0.0023558051,0.00053579593,0.00001576001,0.7572576,0.0011619153],"about_ca_topic_score_codex":0.00005751876,"about_ca_topic_score_gemma":0.0002452886,"teacher_disagreement_score":0.637472,"about_ca_system_score_codex":0.00007902298,"about_ca_system_score_gemma":0.00021095094,"threshold_uncertainty_score":0.9999556},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98266244,0.00030984724,0.00018087651,0.0074602184,0.00069109263,0.001021365,0.007390068,0.000108976026,0.0001751076],"genre_scores_gemma":[0.97960293,0.00002147617,0.00053239946,0.004611537,0.00018146975,0.00007470014,0.014737349,0.000073625,0.00016451882],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952491,0.0009568166,0.00068960374,0.0015379887,0.00087894767,0.0006875693],"domain_scores_gemma":[0.9925467,0.00091696857,0.00045190737,0.005209064,0.00018476175,0.00069061934],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017253618,0.0005486994,0.0010815094,0.0001604642,0.00018589427,0.0000903602,0.0011356929,0.00023227934,0.00054049527],"category_scores_gemma":[0.0046135676,0.00045224416,0.0001301307,0.0004465172,0.00015348001,0.00007630635,0.0020851407,0.0023544645,0.0000020400232],"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":[0.00019686898,0.00042631038,0.94790655,0.00092346856,0.00021748048,0.044149555,0.00039399083,0.0011949083,0.00020852652,0.0000012368426,0.0031566285,0.0012244737],"study_design_scores_gemma":[0.0022128024,0.000051606545,0.95674455,0.0009472088,0.0004151014,0.00067461806,0.00032808827,0.020836027,0.00019806123,0.0000067918745,0.016821325,0.00076383963],"about_ca_topic_score_codex":0.9968609,"about_ca_topic_score_gemma":0.9999464,"teacher_disagreement_score":0.04347494,"about_ca_system_score_codex":0.0031538876,"about_ca_system_score_gemma":0.042282283,"threshold_uncertainty_score":0.99994713},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9636514,0.033637676,0.00015351536,0.00023651418,0.00030151493,0.00042645747,0.0013812623,0.000020081567,0.00019158916],"genre_scores_gemma":[0.9824844,0.015542933,0.0010811249,0.000008344847,0.00004718463,0.000017730476,0.0006687792,0.000016104972,0.00013338264],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946912,0.0005631875,0.0009260078,0.0013411774,0.0020228273,0.0004555884],"domain_scores_gemma":[0.9935903,0.0005719842,0.0004155714,0.0030389875,0.0021780136,0.00020516914],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.008930435,0.0002306928,0.0013371848,0.10881316,0.00008527557,0.000044816232,0.0012706032,0.0001192746,0.00006855635],"category_scores_gemma":[0.016596474,0.00021231174,0.000121747245,0.22667614,0.0014608502,0.00025529275,0.0052354126,0.0005282138,0.0000034734912],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016788719,0.0002591514,0.9874648,0.0009396534,0.0008538004,0.00006767342,0.00033024926,0.00016486786,0.006032962,0.000018521421,0.00076261105,0.00293785],"study_design_scores_gemma":[0.00044625168,0.000049798324,0.99242824,0.0006092199,0.0005403941,0.0000022387796,0.00085652474,0.0044370685,0.00036139344,0.000038905597,0.00007783807,0.00015215193],"about_ca_topic_score_codex":0.0010227237,"about_ca_topic_score_gemma":0.0010307311,"teacher_disagreement_score":0.11786298,"about_ca_system_score_codex":0.00021796071,"about_ca_system_score_gemma":0.002313071,"threshold_uncertainty_score":0.9916872},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903699,0.0019440644,0.0033801293,0.000010706076,0.0006604005,0.00055932184,0.0022022026,0.00031960712,0.0005536297],"genre_scores_gemma":[0.9981192,0.000047875372,0.00034181974,0.00007805322,0.00069380493,0.00008239464,0.00047965316,0.00004578503,0.00011145542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984973,0.00007459489,0.00037340593,0.0004989558,0.00028411546,0.00027162101],"domain_scores_gemma":[0.99757123,0.0002655345,0.00017180314,0.0013237955,0.00043536763,0.00023224746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001221199,0.00018744162,0.00039389532,0.000102236576,0.00009801823,0.00003894902,0.00016269498,0.00007317163,0.00004099449],"category_scores_gemma":[0.0020142884,0.00019407523,0.00018179938,0.00028271158,0.00007404663,0.0002820114,0.00021075053,0.00009628335,0.00003274782],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004073428,0.00044608256,0.98108286,0.00066104025,0.00017690049,0.000020862939,0.00003402568,0.000040217506,0.005380789,0.000019263198,0.00028962686,0.011440989],"study_design_scores_gemma":[0.0028307547,0.00021138023,0.9795363,0.00021027568,0.0005120028,0.000027317525,0.00007915594,0.0002778332,0.011397177,0.00015825247,0.0045451648,0.00021440804],"about_ca_topic_score_codex":0.00011955196,"about_ca_topic_score_gemma":0.00011406436,"teacher_disagreement_score":0.0112265805,"about_ca_system_score_codex":0.00008287365,"about_ca_system_score_gemma":0.00037922018,"threshold_uncertainty_score":0.7914158},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936336,0.0023960508,0.000790178,0.00042096514,0.00012676352,0.0022177666,0.00015320138,0.00014071881,0.00012073516],"genre_scores_gemma":[0.9975435,0.00021956337,0.0002535349,0.00058566435,0.00007197617,0.0009175551,0.00017994667,0.000026100428,0.00020216226],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99802643,0.00021408306,0.00052200997,0.00052207295,0.00028406142,0.00043133882],"domain_scores_gemma":[0.997917,0.00013380505,0.00018647683,0.00039358094,0.00033883203,0.0010303156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008181398,0.00018973977,0.0005587144,0.00012639596,0.00017445367,0.000052549658,0.000054332162,0.00005175695,0.0000090502845],"category_scores_gemma":[0.00054971647,0.00018032687,0.0000538707,0.00033331034,0.000072609415,0.00012009463,0.00008869231,0.00011683699,0.0000026778343],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030707565,0.00066133164,0.88625443,0.000986268,0.00007230329,0.000014776932,0.001356048,4.204918e-7,0.00005665041,0.0000098353585,0.00023081308,0.110050045],"study_design_scores_gemma":[0.0030138171,0.0008684405,0.9177771,0.000028099174,0.0000033384163,0.000017195116,0.00053965795,0.0001039512,0.000011028625,0.0000114801205,0.07746424,0.00016163524],"about_ca_topic_score_codex":0.000043015458,"about_ca_topic_score_gemma":0.00035450322,"teacher_disagreement_score":0.109888405,"about_ca_system_score_codex":0.000092922564,"about_ca_system_score_gemma":0.0012314126,"threshold_uncertainty_score":0.7353516},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9214447,0.0069166888,0.0071912603,0.056390695,0.00007840466,0.004857256,0.002531662,0.0004865253,0.000102819264],"genre_scores_gemma":[0.9756634,0.0003304592,0.000082408355,0.020699004,0.00017196544,0.0014242398,0.0015694251,0.000053927364,0.000005145704],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9929948,0.002661575,0.0010577177,0.0013082788,0.0009796591,0.0009979747],"domain_scores_gemma":[0.99317807,0.0017681146,0.0006747371,0.0008562351,0.0002563644,0.003266503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0044773305,0.0005036492,0.0011124867,0.00017774907,0.0004001637,0.00016240339,0.00037560373,0.00013778296,0.000025256088],"category_scores_gemma":[0.0024584993,0.00039068155,0.00009331375,0.0010097183,0.0003263027,0.00019734412,0.000060790113,0.00067291665,0.0000167756],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016580842,0.00038930457,0.99063396,0.0018648687,0.000033738583,0.000041990257,0.00054629537,0.0012877057,0.000009741699,0.00025831614,0.0017447431,0.0015312266],"study_design_scores_gemma":[0.004062244,0.000400075,0.8178633,0.000039542585,0.0000033519236,0.00002844669,0.00027610944,0.13726181,1.9575222e-7,0.000010577084,0.039681125,0.0003732667],"about_ca_topic_score_codex":0.00033604575,"about_ca_topic_score_gemma":0.0013492163,"teacher_disagreement_score":0.17277072,"about_ca_system_score_codex":0.0003154323,"about_ca_system_score_gemma":0.0033265518,"threshold_uncertainty_score":0.9998545},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9827204,0.0007095479,0.000048481557,0.012144495,0.00027790805,0.00081791356,0.0011077039,0.00016616903,0.0020074204],"genre_scores_gemma":[0.9584636,0.0016182567,0.007496037,0.005581693,0.00046966644,0.00038348432,0.024842052,0.00007775194,0.0010674254],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982861,0.000059216578,0.00039202103,0.0005412052,0.0001889286,0.0005324773],"domain_scores_gemma":[0.99855053,0.00014291145,0.000118742886,0.000928722,0.000036078585,0.00022302827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034457823,0.00021480076,0.00037077512,0.00019862592,0.00017879957,0.000047812744,0.0003628724,0.00006822479,0.00020065944],"category_scores_gemma":[0.00015128244,0.00019807833,0.00003959947,0.0002637218,0.00011969182,0.00089863397,0.0005065538,0.00019098406,0.00001569585],"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":[0.00023195954,0.00039576422,0.46275714,0.00014454909,0.0001854478,0.000033321747,0.00025673513,0.0000055564747,0.0022063218,0.000024882873,0.4033377,0.13042063],"study_design_scores_gemma":[0.010416393,0.00070114277,0.47618425,0.0014329253,0.00031536375,0.000064649874,0.000797296,0.018863622,0.00031827742,0.00022832461,0.48994005,0.00073771656],"about_ca_topic_score_codex":0.000788807,"about_ca_topic_score_gemma":0.0005135057,"teacher_disagreement_score":0.12968291,"about_ca_system_score_codex":0.00018276066,"about_ca_system_score_gemma":0.00009463013,"threshold_uncertainty_score":0.80774},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99409246,0.0010148021,0.0025761782,0.00044402236,0.00010468809,0.00022778942,0.0000031061388,0.000016582697,0.001520386],"genre_scores_gemma":[0.9988713,0.00033339992,0.0005099378,0.000054522745,0.00010164001,0.000008192534,0.00001257533,0.000009765343,0.00009866239],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985201,0.00008168689,0.00041894085,0.0001110184,0.0002025066,0.00066576013],"domain_scores_gemma":[0.99924964,0.000045704932,0.00033928826,0.00020057743,0.00011118552,0.00005363295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013670684,0.000075895405,0.0001355747,0.000032767224,0.000081605765,0.000010951494,0.00012263126,0.00007520995,0.00002569571],"category_scores_gemma":[0.00035540023,0.000055532302,0.00004136313,0.00016373607,0.00006895601,0.000146064,0.00001090678,0.0012650953,0.000004851452],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002445762,0.00011016881,0.927738,0.0000144782,0.00007118837,0.0000069312127,0.00028521105,0.000018671362,0.0020682565,0.04785085,0.00009979686,0.021491842],"study_design_scores_gemma":[0.0022872188,0.0003957895,0.9292584,0.00015227443,0.00006578206,0.00036188102,0.0033427065,0.010390302,0.0011214269,0.052200984,0.00024520128,0.00017803868],"about_ca_topic_score_codex":0.000018284642,"about_ca_topic_score_gemma":0.00043736637,"teacher_disagreement_score":0.021313803,"about_ca_system_score_codex":0.00040271328,"about_ca_system_score_gemma":0.0017947651,"threshold_uncertainty_score":0.549628},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97871155,0.00009795323,0.01091113,0.000005619927,0.00012505618,0.00039497795,0.008882609,0.000019390416,0.00085173536],"genre_scores_gemma":[0.89621484,0.00005041887,0.0022321625,0.000018930856,0.000040665505,0.000011905256,0.10081929,0.00002299091,0.0005887977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99801034,0.000052211024,0.00085134804,0.00031336947,0.0006227471,0.00015001542],"domain_scores_gemma":[0.9982957,0.000074319534,0.0007291526,0.00030886184,0.00050515163,0.0000867746],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001359852,0.00021566554,0.0008869828,0.0006272401,0.000022406977,0.000010012961,0.00007908474,0.00011480762,0.0016932596],"category_scores_gemma":[0.0000826957,0.00021124749,0.000144465,0.00034176582,0.000055122822,0.000049323095,0.000024550875,0.000095870746,0.0000024651265],"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":[0.001299743,0.00044840825,0.8332845,0.003275858,0.005339591,0.00008546009,0.0012655267,0.000022467422,0.0031958886,0.00022022502,0.004752151,0.14681019],"study_design_scores_gemma":[0.0010274147,0.00023938631,0.9836034,0.00006819237,0.010491263,0.0000016499511,0.0013088563,0.00068743323,0.0023361198,0.000026788586,0.00005509765,0.00015440446],"about_ca_topic_score_codex":0.08608947,"about_ca_topic_score_gemma":0.18564478,"teacher_disagreement_score":0.15031889,"about_ca_system_score_codex":0.00005241463,"about_ca_system_score_gemma":0.0002352841,"threshold_uncertainty_score":0.9992193},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90419716,0.0144476555,0.0045567886,0.040486068,0.0153711485,0.0048534772,0.0028834105,0.00014693361,0.013057338],"genre_scores_gemma":[0.9985203,0.0001373655,0.000116247815,0.000053797972,0.00054666895,0.000007722352,0.00002411703,0.0000049770547,0.0005888031],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857765,0.00007929706,0.0004960931,0.000028906616,0.00071695354,0.00010107158],"domain_scores_gemma":[0.99628764,0.00020516128,0.0018337589,0.00012593855,0.0014811063,0.00006638987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002406591,0.00006113923,0.00020267566,0.000075420256,0.00014909779,0.00017269664,0.00012019977,0.000059928214,4.266096e-7],"category_scores_gemma":[0.0009446441,0.000032821008,0.000100849196,0.00014298152,0.000030426048,0.00083841407,0.000028973622,0.00010472191,0.0000010760506],"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":[0.00081713946,0.000053203417,0.61697996,0.0010159671,0.00085383176,4.5423403e-7,0.007253691,0.0006758722,0.000070396934,0.007964526,0.35813454,0.0061804233],"study_design_scores_gemma":[0.002376856,0.00026540697,0.13862135,0.00029833135,0.00013624551,0.000054201508,0.024583187,0.0031520554,0.0001296101,0.00015682231,0.8301531,0.00007282909],"about_ca_topic_score_codex":0.0000074187797,"about_ca_topic_score_gemma":0.000010234779,"teacher_disagreement_score":0.4783586,"about_ca_system_score_codex":0.00021202293,"about_ca_system_score_gemma":0.00020649709,"threshold_uncertainty_score":0.16653177},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76147145,0.007046312,0.0019124756,0.22263737,0.0016630059,0.00008732342,0.004884638,0.0000141148785,0.00028328333],"genre_scores_gemma":[0.9656306,0.00016397359,0.0005560529,0.031058218,0.00247054,0.0000010073546,0.000094241776,0.000014151711,0.000011249332],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.993444,0.00074020744,0.0016097061,0.0003103235,0.003327227,0.00056853396],"domain_scores_gemma":[0.99321884,0.00021389633,0.001846071,0.00039704505,0.0030630073,0.0012611167],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010324774,0.00032280057,0.0010416725,0.0001766301,0.0003135638,0.00031594426,0.0010135275,0.0000669718,0.00024018154],"category_scores_gemma":[0.001437256,0.00024574765,0.00071276654,0.0016081479,0.00006308631,0.00026326772,0.0001922849,0.0007364532,0.0000018743484],"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":true,"study_design_scores_codex":[0.0011522927,0.0002326564,0.66759515,0.00012907601,0.04829081,0.041458517,0.00020466109,0.0053851376,0.00008831749,0.0017238637,0.21250252,0.021236997],"study_design_scores_gemma":[0.0027947621,0.00013719586,0.7786304,0.0008946543,0.0035160019,0.01886494,0.0012526426,0.0011822304,0.000038112437,0.0011760545,0.19099519,0.0005178179],"about_ca_topic_score_codex":0.99474156,"about_ca_topic_score_gemma":0.9971465,"teacher_disagreement_score":0.2041591,"about_ca_system_score_codex":0.018993624,"about_ca_system_score_gemma":0.046178903,"threshold_uncertainty_score":0.99999946},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9221305,0.004486722,0.0001986812,0.055769894,0.0010094134,0.00039813216,0.015684046,0.000044493296,0.00027809013],"genre_scores_gemma":[0.9643077,0.000045369594,0.0018993054,0.030418022,0.00088088965,0.0000045070306,0.00200911,0.00004453804,0.00039053347],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99775225,0.00050147914,0.0003741163,0.00043381762,0.0006530179,0.00028532656],"domain_scores_gemma":[0.99397147,0.0016485165,0.00022111065,0.003887915,0.000089713045,0.00018129664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057177444,0.00017696494,0.0003490859,0.000007492013,0.00031503823,0.00003865016,0.0014463919,0.000054905107,0.00059529085],"category_scores_gemma":[0.002970481,0.00009088256,0.00009820342,0.00024660103,0.00017250047,0.00008487596,0.0009629796,0.00036876544,0.000016579395],"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":[0.00020963106,0.0001247175,0.25155222,0.00006678737,0.00071060663,0.00021624618,0.00093051867,0.00023088689,0.011216028,0.000059903283,0.7330785,0.0016039509],"study_design_scores_gemma":[0.0018908749,0.000011391567,0.5132221,0.00047867702,0.00076102855,0.000055168915,0.0019091576,0.008610014,0.0021602039,0.0010672417,0.46950242,0.00033174528],"about_ca_topic_score_codex":0.6451478,"about_ca_topic_score_gemma":0.4970812,"teacher_disagreement_score":0.2635761,"about_ca_system_score_codex":0.000206055,"about_ca_system_score_gemma":0.0032907699,"threshold_uncertainty_score":0.6518017},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9593766,0.0045398455,0.031125413,0.0025417933,0.0007837624,0.0011863176,0.00022910461,0.00018132007,0.000035840214],"genre_scores_gemma":[0.9948784,0.001070391,0.0017135153,0.000838748,0.00016161051,0.00019848779,0.0010634577,0.000019069927,0.000056318004],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9980839,0.00027434406,0.0007616822,0.00034858225,0.00025768898,0.00027378876],"domain_scores_gemma":[0.9980258,0.00047123505,0.0003691614,0.0002666951,0.00033003525,0.0005370877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012483967,0.00014730966,0.00037989244,0.000077430275,0.00016843349,0.00001782362,0.00004400958,0.000078452285,0.000021026563],"category_scores_gemma":[0.0014577989,0.00012368827,0.00007057685,0.00016320389,0.00006799838,0.00018333182,0.000038921407,0.00014388829,0.0000021072535],"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":[0.00018527778,0.00026617208,0.2941236,0.0004192739,0.000092472044,0.0000039054426,0.00049220107,0.0000013381604,0.00014912093,0.000015055823,0.0005516681,0.70369995],"study_design_scores_gemma":[0.0014349129,0.00010862757,0.87231237,0.000036060173,0.0000053358617,0.000011179434,0.0001451963,0.007302588,0.000046942187,0.000017463206,0.11846114,0.00011816134],"about_ca_topic_score_codex":0.00008938198,"about_ca_topic_score_gemma":0.00017753035,"teacher_disagreement_score":0.70358175,"about_ca_system_score_codex":0.00024336144,"about_ca_system_score_gemma":0.0016475258,"threshold_uncertainty_score":0.5043861},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9499831,0.005382313,0.00018062638,0.03961475,0.00012198431,0.0019233271,0.002620478,0.00010291203,0.000070514834],"genre_scores_gemma":[0.9689634,0.0008140906,0.0010688517,0.022883607,0.000079254445,0.00011459896,0.00590313,0.000028730085,0.00014432751],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9941141,0.00097384176,0.002133185,0.0012886196,0.00075603387,0.0007342427],"domain_scores_gemma":[0.9934245,0.00039660916,0.001245696,0.0016501242,0.00041313545,0.0028699713],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0074404404,0.0002614008,0.0011018207,0.000110275476,0.00029294656,0.000070672715,0.00023374175,0.00007093682,0.000089410125],"category_scores_gemma":[0.011059609,0.0002536064,0.000051594303,0.0010472462,0.00013489938,0.00024188409,0.00051540555,0.000234424,0.0000034109262],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011616977,0.00072182674,0.92492306,0.0010015441,0.000073400275,0.00004049112,0.0002920528,5.8084646e-7,0.000012012668,0.000012960528,0.066309005,0.0064969244],"study_design_scores_gemma":[0.001413289,0.00033300192,0.81108457,0.000017894046,0.0000019095876,0.00006322936,0.00033496387,0.00010705262,3.1629585e-7,0.000011528074,0.18648353,0.00014868987],"about_ca_topic_score_codex":0.00086379406,"about_ca_topic_score_gemma":0.0031907526,"teacher_disagreement_score":0.12017453,"about_ca_system_score_codex":0.00018932631,"about_ca_system_score_gemma":0.006056788,"threshold_uncertainty_score":0.9999916},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86964047,0.00045095276,0.039133146,0.054480117,0.003302881,0.0008190087,0.032085333,0.00003753781,0.000050567618],"genre_scores_gemma":[0.9794142,0.000083748266,0.0047966903,0.0056971638,0.00045324405,0.000006791367,0.009520341,0.000008853982,0.000018930801],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983619,0.000050649865,0.00034642257,0.00040420794,0.0006842448,0.00015259559],"domain_scores_gemma":[0.9974702,0.000745553,0.00028826206,0.00085017754,0.00048073527,0.00016504986],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0022674708,0.00008515966,0.00015230772,0.00010100076,0.0002596094,0.00012493327,0.0014045318,0.00002065333,0.0000213798],"category_scores_gemma":[0.009318244,0.00006337694,0.000032290198,0.00016206095,0.000186311,0.00077805057,0.0006688732,0.00011034921,6.3554825e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041303932,0.00014310669,0.9091144,0.000040861796,0.00011771915,0.000025392214,0.00010346906,0.00047516433,0.00052061287,0.0023594466,0.082481705,0.004205076],"study_design_scores_gemma":[0.003396281,0.00026693742,0.7137569,0.00029165536,0.00006317397,0.000364419,0.00047249632,0.13046363,0.000090353424,0.0028765958,0.14771007,0.000247474],"about_ca_topic_score_codex":0.000066032306,"about_ca_topic_score_gemma":0.000037791113,"teacher_disagreement_score":0.1953575,"about_ca_system_score_codex":0.00012399476,"about_ca_system_score_gemma":0.00037823545,"threshold_uncertainty_score":0.9990267},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95594263,0.02166255,0.008501051,0.006751579,0.005017255,0.0010342356,0.0006998968,0.000086144675,0.00030463378],"genre_scores_gemma":[0.96897423,0.026452715,0.00004909037,0.0026972513,0.00072314136,0.000060954495,0.00076112757,0.0000274567,0.00025400717],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99677366,0.00029284394,0.001241293,0.0002213688,0.0010774577,0.00039339735],"domain_scores_gemma":[0.99538684,0.000307808,0.00085074117,0.00035454056,0.0027045202,0.00039554975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001272452,0.0003036483,0.0003287332,0.0006670303,0.0007668034,0.00048503277,0.0004102551,0.00017491807,0.00022780748],"category_scores_gemma":[0.0013041397,0.00021573664,0.0005544388,0.0010289437,0.00040478818,0.001341527,0.0001189344,0.0005602839,0.000012723486],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022658992,0.00036352477,0.88514,0.0013096182,0.002194141,0.000049795734,0.003060499,0.003224831,0.0009899983,0.028314017,0.0024702016,0.070617445],"study_design_scores_gemma":[0.0059214346,0.00024482433,0.49816814,0.0019370101,0.00089590444,0.0012763372,0.0025616004,0.006950634,0.0004276909,0.007050553,0.47411042,0.00045544896],"about_ca_topic_score_codex":0.00009374702,"about_ca_topic_score_gemma":0.000032742104,"teacher_disagreement_score":0.4716402,"about_ca_system_score_codex":0.00009741095,"about_ca_system_score_gemma":0.0006034025,"threshold_uncertainty_score":0.87974846},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068324865,0.7007277,0.0018605852,0.098180756,0.001504493,0.007987582,0.0012032378,0.001519656,0.1801835],"genre_scores_gemma":[0.9553893,0.016878322,0.00009750975,0.017739335,0.00070372166,0.00018165617,0.004669248,0.00011982762,0.0042210775],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9957569,0.00016207495,0.0014302827,0.00071599596,0.0011913979,0.0007433074],"domain_scores_gemma":[0.996125,0.0004612721,0.00045037124,0.0013076194,0.000604862,0.0010509097],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00097411877,0.0003938715,0.0022196393,0.00014813186,0.000093357245,0.000180057,0.00024432916,0.00006976683,0.00047180557],"category_scores_gemma":[0.01219849,0.00030014245,0.00022456149,0.0010864002,0.00023526445,0.000688118,0.0001839196,0.00026228736,0.00045945842],"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":[0.000049292048,0.0006503633,0.027105313,0.8697345,0.00045789458,0.0014857339,0.00019214308,4.095516e-7,0.000033388427,0.0003520502,0.0548902,0.04504871],"study_design_scores_gemma":[0.003221168,0.0006248074,0.0071903504,0.96690017,0.00010387818,0.0019634482,0.00041880482,0.000034448876,0.0000064074607,0.000057273683,0.018919623,0.0005596128],"about_ca_topic_score_codex":0.0000038570665,"about_ca_topic_score_gemma":0.000012571026,"teacher_disagreement_score":0.94855684,"about_ca_system_score_codex":0.00023766588,"about_ca_system_score_gemma":0.0014290297,"threshold_uncertainty_score":0.99994504},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58448297,0.068456955,0.20195664,0.1087769,0.020319903,0.0038993433,0.009896834,0.000146995,0.0020634618],"genre_scores_gemma":[0.88491017,0.099659406,0.00030147875,0.010136302,0.0004787725,0.000052731386,0.0028043028,0.00004076542,0.0016160733],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969495,0.00034012852,0.0013125944,0.00017334263,0.00095342717,0.00027104185],"domain_scores_gemma":[0.99447334,0.00038077132,0.0013751938,0.00029973546,0.003169158,0.0003018025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017446761,0.00023701879,0.00037104049,0.00062456983,0.0004220831,0.00024595272,0.00032735398,0.00012916148,0.00013486164],"category_scores_gemma":[0.0017957073,0.00017621633,0.00032676916,0.0006969036,0.00093837344,0.0009814431,0.00020156076,0.00023481436,0.0000013422917],"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":[0.0019064498,0.00075337035,0.21720026,0.0055498825,0.004491545,0.000039880782,0.01392756,0.0053073782,0.001009526,0.48926032,0.045717645,0.2148362],"study_design_scores_gemma":[0.004010511,0.00028528503,0.054636832,0.0011386565,0.0007022616,0.0010413484,0.0046951077,0.01811828,0.00088421116,0.014163687,0.8999629,0.0003608988],"about_ca_topic_score_codex":0.00006302944,"about_ca_topic_score_gemma":0.00008979228,"teacher_disagreement_score":0.85424525,"about_ca_system_score_codex":0.00005549673,"about_ca_system_score_gemma":0.00087182585,"threshold_uncertainty_score":0.71858937},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99253917,0.0026120257,0.00004396385,0.0013297267,0.0002967714,0.0009910223,0.0012741032,0.00010428167,0.0008089426],"genre_scores_gemma":[0.99580574,0.00012427733,0.000004371071,0.0032729707,0.00015252098,0.00020893283,0.0002315026,0.000030059875,0.00016960803],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99784184,0.00059073756,0.0003390813,0.00036810435,0.00043502776,0.00042520385],"domain_scores_gemma":[0.995813,0.0028349985,0.000146239,0.0007986234,0.00009524287,0.00031186186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068161066,0.00023717232,0.0006551193,0.000039847222,0.0002541915,0.000027528384,0.00023774062,0.0000535458,0.00014601226],"category_scores_gemma":[0.001728758,0.00014326337,0.00019161917,0.0002553041,0.00014754718,0.000037043195,0.00004536214,0.0002672673,0.0000044680137],"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":[0.0040514865,0.00014796968,0.957954,0.0004845579,0.0001615811,0.00019614407,0.000039897546,0.0006866779,0.00042715066,0.00012701815,0.031882964,0.0038405736],"study_design_scores_gemma":[0.005443098,0.00054429704,0.81740826,0.00015884743,0.00010250484,0.000034912493,0.0000142562885,0.000109134366,0.00083267025,0.00020325917,0.1749605,0.00018823039],"about_ca_topic_score_codex":0.33826047,"about_ca_topic_score_gemma":0.94866025,"teacher_disagreement_score":0.6103998,"about_ca_system_score_codex":0.0009951531,"about_ca_system_score_gemma":0.004487344,"threshold_uncertainty_score":0.7960356},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899595,0.0013058606,0.0027566084,0.0044247145,0.000065087785,0.00037165437,0.0005895789,0.000115293646,0.00041175314],"genre_scores_gemma":[0.9922932,0.00023592896,0.0035325252,0.0009562813,0.000048923834,0.000020803953,0.0021461502,0.000024510542,0.00074167695],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972831,0.00055006467,0.0006496549,0.0005002439,0.00047532635,0.0005416177],"domain_scores_gemma":[0.99790525,0.00018908326,0.000228406,0.0006070246,0.00044566943,0.0006245579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011894996,0.00020514244,0.0009060286,0.00031875892,0.00010000618,0.00008241162,0.00011116146,0.000092844726,0.00032250775],"category_scores_gemma":[0.00070024957,0.00020022271,0.00013814661,0.0016024417,0.00019527701,0.0002881972,0.00013031653,0.00018258744,0.000016802687],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023768039,0.00023617728,0.970249,0.0011573251,0.00051669835,0.000053953667,0.0009797313,0.00013449832,0.000107080516,0.00020583089,0.0028276485,0.023294358],"study_design_scores_gemma":[0.00122266,0.0003469597,0.8983498,0.00009977271,0.000014024787,0.0001851249,0.0005765246,0.0666042,0.000060977487,0.00001760236,0.032194387,0.00032795576],"about_ca_topic_score_codex":0.0002544577,"about_ca_topic_score_gemma":0.00029488702,"teacher_disagreement_score":0.071899205,"about_ca_system_score_codex":0.00014611598,"about_ca_system_score_gemma":0.0019543061,"threshold_uncertainty_score":0.8164846},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9933086,0.0019962417,0.000017064885,0.002093215,0.000116979456,0.00076118635,0.000141551,0.00008825095,0.001476914],"genre_scores_gemma":[0.99350697,0.00016745743,0.00007937742,0.0053878273,0.000071371695,0.00003226643,0.0005578948,0.000026284759,0.00017056192],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99791294,0.0007963659,0.00042877207,0.00029887864,0.00019446609,0.0003685999],"domain_scores_gemma":[0.9984783,0.00017677098,0.00010355758,0.0007463402,0.00014880905,0.00034624684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013182356,0.00018461896,0.0006310775,0.00013424548,0.00032610018,0.000039432936,0.00008267516,0.0000562304,0.00011435493],"category_scores_gemma":[0.00010907355,0.00018819024,0.000048616217,0.00029546642,0.000084127525,0.000050707353,0.00008318166,0.00054554484,0.000014704221],"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":[0.00013249683,0.0012677072,0.9802468,0.00055255764,0.000046698708,0.0077631497,0.0039849994,0.0000028590093,0.000039054477,0.0001021036,0.0016154699,0.004246106],"study_design_scores_gemma":[0.007958913,0.0008557964,0.94416714,0.00014970005,0.000021057105,0.0006733006,0.008473169,0.00013594143,0.000015453874,0.0000092095315,0.03731713,0.00022321005],"about_ca_topic_score_codex":0.0146715455,"about_ca_topic_score_gemma":0.086295165,"teacher_disagreement_score":0.071623616,"about_ca_system_score_codex":0.00013810475,"about_ca_system_score_gemma":0.00086703914,"threshold_uncertainty_score":0.99188983},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97704715,0.0024099697,0.000316728,0.019208338,0.00008463767,0.0003975234,0.0002496736,0.000006214765,0.00027978912],"genre_scores_gemma":[0.98363245,0.0036955404,0.011892127,0.00042468388,0.00011030308,0.000013728835,0.0001689739,0.000012266706,0.000049906008],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9957729,0.0005191094,0.00075150176,0.00030322655,0.002166385,0.00048686189],"domain_scores_gemma":[0.99805903,0.00044261655,0.00014490803,0.00032814656,0.00034441243,0.0006809035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008073417,0.00007702768,0.00026462745,0.0008536747,0.00006480983,0.00006705996,0.0006216232,0.00004629235,0.00009490354],"category_scores_gemma":[0.0011106493,0.00007773932,0.000023250695,0.00045288567,0.00020119821,0.00024062108,0.0008246983,0.00066121534,0.000008219003],"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":[0.00029269294,0.0011427748,0.11809891,0.00015701873,0.0000965133,0.0008779993,0.0007065156,0.0000013668201,0.0019475715,0.00053490844,0.00073961465,0.8754041],"study_design_scores_gemma":[0.0013685165,0.0021771502,0.8193937,0.0022234076,0.0000030424942,0.00097225065,0.014110837,0.00015614343,0.0031804037,0.0015964453,0.15456654,0.00025156248],"about_ca_topic_score_codex":0.00020327387,"about_ca_topic_score_gemma":0.00031597217,"teacher_disagreement_score":0.8751525,"about_ca_system_score_codex":0.0008052934,"about_ca_system_score_gemma":0.0027751424,"threshold_uncertainty_score":0.49229836},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93021876,0.0016248748,0.000043534557,0.06683587,0.000040134815,0.00033735932,0.0006623538,0.000035234076,0.0002018822],"genre_scores_gemma":[0.98706377,0.0032604816,0.000015814754,0.007843041,0.000018998337,0.000075104384,0.0016416358,0.000012253646,0.00006891031],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9949898,0.0025622367,0.0008189017,0.0005571123,0.00039264586,0.0006792956],"domain_scores_gemma":[0.9944828,0.0032588437,0.00027966755,0.000921335,0.00023116883,0.0008261579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044457563,0.00022741585,0.0007107306,0.0006951961,0.00019803227,0.0001603669,0.00025626307,0.00008259768,0.00003896141],"category_scores_gemma":[0.007205159,0.00014759164,0.00007082459,0.0046544983,0.00037235054,0.00014143587,0.000121553174,0.00039511887,0.0000012050824],"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":[0.00018819887,0.00028859187,0.97825575,0.00026632435,0.000079943755,0.00016812464,0.0012144232,0.000013344527,0.0000014298312,0.000841207,0.0019840363,0.016698625],"study_design_scores_gemma":[0.0013488568,0.00021903422,0.91775256,0.000028565171,0.0000033956533,0.000030317182,0.0034177538,0.0011149183,2.3646965e-7,0.00004980573,0.07590558,0.0001289843],"about_ca_topic_score_codex":0.0031683866,"about_ca_topic_score_gemma":0.09807207,"teacher_disagreement_score":0.09490368,"about_ca_system_score_codex":0.00019423984,"about_ca_system_score_gemma":0.0016426669,"threshold_uncertainty_score":0.91838574},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7018592,0.000053224452,0.2947833,0.0002571131,0.00021646879,0.0013711356,0.00075724523,0.000327734,0.00037459825],"genre_scores_gemma":[0.83459413,0.000027812355,0.15203436,0.0059041944,0.0008945276,0.00032870454,0.0060224,0.000078538935,0.000115360184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961749,0.0003181065,0.0011914935,0.0004453626,0.0014312954,0.00043888195],"domain_scores_gemma":[0.9966605,0.00059069187,0.00036954187,0.0011837297,0.00029719446,0.0008983445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012637363,0.0002887989,0.0006535595,0.00014764922,0.00012647484,0.00008363624,0.00037102404,0.0002260288,0.00019562339],"category_scores_gemma":[0.0046951747,0.00025939368,0.000089524925,0.0005123754,0.00005529757,0.00089166185,0.0006537087,0.0010458485,0.000111500565],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005038719,0.0015531683,0.02262369,0.00020721159,0.0004605131,0.00013911015,0.03317444,0.0036148576,0.0010553703,0.000007841502,0.0011025553,0.93555737],"study_design_scores_gemma":[0.003879358,0.0004931926,0.049158555,0.00026851904,0.0002751806,0.000045287856,0.042111557,0.89810276,0.00015330722,0.0000541303,0.0052119787,0.00024616427],"about_ca_topic_score_codex":0.00009739168,"about_ca_topic_score_gemma":0.00027497404,"teacher_disagreement_score":0.9353112,"about_ca_system_score_codex":0.00017813659,"about_ca_system_score_gemma":0.00048725406,"threshold_uncertainty_score":0.9999858},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5577646,0.078415416,0.11004698,0.23373878,0.00051352795,0.013524407,0.004993155,0.00076046464,0.00024268628],"genre_scores_gemma":[0.9856335,0.003540408,0.00051026855,0.005605074,0.00011345436,0.001310637,0.0032042474,0.00004674511,0.000035658344],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99473965,0.0020198291,0.0011422614,0.00053858693,0.00046491376,0.0010947341],"domain_scores_gemma":[0.9936557,0.0021808401,0.00082998164,0.0014723059,0.0008976911,0.0009635205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0064628096,0.00030619802,0.0010509513,0.00015313216,0.00099119,0.00011854268,0.00034994542,0.00012353422,0.000005822892],"category_scores_gemma":[0.00078420073,0.00026186317,0.00012759602,0.000976966,0.00033613222,0.00016313362,0.00011743528,0.00056521466,0.000003660838],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024185743,0.0010277249,0.8515601,0.014080591,0.00023857054,0.0000018934746,0.00085152825,0.00001024122,0.00029825224,0.030614175,0.0044263336,0.096648686],"study_design_scores_gemma":[0.0019097622,0.0003001878,0.6177827,0.000089030465,0.000003115687,0.000042246622,0.0009059688,0.0024798063,0.0000046700325,0.000075200485,0.37616843,0.00023883636],"about_ca_topic_score_codex":0.0005550594,"about_ca_topic_score_gemma":0.00090966537,"teacher_disagreement_score":0.42786893,"about_ca_system_score_codex":0.0003808382,"about_ca_system_score_gemma":0.0037619027,"threshold_uncertainty_score":0.9999834},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99424374,0.0006153645,0.0001304029,0.0035907496,0.0003268555,0.00037980272,0.00068268256,0.000007900488,0.000022507895],"genre_scores_gemma":[0.99884546,0.00006879259,0.00044101733,0.0005362215,0.000047003236,0.0000042210354,0.00003900959,0.000008471415,0.000009834793],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984075,0.0004882846,0.00032122096,0.00020724321,0.00035491431,0.0002208472],"domain_scores_gemma":[0.99914825,0.00008151883,0.000119316035,0.0002135788,0.00009985362,0.0003374754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007689449,0.00007408158,0.0003401964,0.00009393859,0.000120292825,0.000009353266,0.000051047915,0.000035387537,0.000007946585],"category_scores_gemma":[0.0008780583,0.000062421954,0.000016316211,0.00034649053,0.00014431075,0.00006353398,0.00009864647,0.00014485989,8.49078e-9],"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":[0.00003244024,0.00021986809,0.97526515,0.0004758594,0.00006413443,0.00029675663,0.009715839,0.0000070084902,0.000011460684,0.000010897205,0.010098931,0.0038016858],"study_design_scores_gemma":[0.003179003,0.00012083486,0.91534096,0.0000482206,0.000032130596,0.0004605537,0.074177906,0.002438187,0.0000066738803,0.000026895566,0.0040959967,0.00007262836],"about_ca_topic_score_codex":0.42377844,"about_ca_topic_score_gemma":0.5935912,"teacher_disagreement_score":0.16981278,"about_ca_system_score_codex":0.0004747461,"about_ca_system_score_gemma":0.008521578,"threshold_uncertainty_score":0.99709916},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97755396,0.007392481,0.00007648167,0.014160095,0.00043518655,0.00030264645,0.00002693909,0.000010244425,0.000041954638],"genre_scores_gemma":[0.98894,0.0010853108,0.0009949494,0.008554499,0.00030632038,0.0000058937057,0.000016877755,0.000016313279,0.000079806676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.996092,0.0015249492,0.0013435849,0.00033463683,0.000169763,0.0005350365],"domain_scores_gemma":[0.99720395,0.00094434957,0.00069500715,0.00030661654,0.0002556236,0.000594455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041665216,0.00020366482,0.001457102,0.00018005335,0.00008589193,0.000010028845,0.00014665283,0.00008417941,0.00009454931],"category_scores_gemma":[0.006916351,0.00012668657,0.00015530612,0.00043795243,0.0001842144,0.0001412785,0.00014137878,0.00097226223,0.000007591739],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010376383,0.00041228827,0.97674793,0.00005711037,0.00010437411,0.0008693001,0.0001572121,0.000007235886,0.000013771283,0.00024943895,0.011317865,0.009025818],"study_design_scores_gemma":[0.0030577194,0.0019637393,0.9863358,0.0001801368,0.00004619814,0.00070012634,0.0004320009,0.00007203119,0.0000035122985,0.0008102873,0.0063030766,0.0000953666],"about_ca_topic_score_codex":0.00041421637,"about_ca_topic_score_gemma":0.009421685,"teacher_disagreement_score":0.011386057,"about_ca_system_score_codex":0.00028567947,"about_ca_system_score_gemma":0.0006073902,"threshold_uncertainty_score":0.8280018},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014476052,0.0202248,0.000020648917,0.96825856,0.0007993909,0.00030667847,0.008233859,0.00015165951,0.0018596313],"genre_scores_gemma":[0.00012625413,0.0023981035,0.004032833,0.86188644,0.005613645,0.000015505004,0.117684335,0.00006810096,0.008174799],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99799,0.000059990525,0.0003508413,0.00092570035,0.00040057435,0.00027289413],"domain_scores_gemma":[0.9970306,0.00014754977,0.00015186144,0.00217449,0.00036118107,0.00013428135],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00032165356,0.0002906739,0.0005111336,0.000117373624,0.000063468586,0.0000901301,0.0005648283,0.0024032958,0.0009734037],"category_scores_gemma":[0.0024969135,0.00025047,0.00007811791,0.00021377133,0.00005295184,0.00014461766,0.0008489152,0.0071311425,0.000045058856],"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":[0.000012456019,0.000015391292,0.0012426643,0.00037049482,0.00015897925,0.00075478246,0.000019503796,1.4243786e-7,0.000019687443,0.000008546638,0.9931938,0.004203555],"study_design_scores_gemma":[0.00043201708,0.000009919954,0.0009598602,0.0007777912,0.00019148244,0.00014263127,0.000020300513,0.00013128144,0.0000066213497,0.0000564648,0.9970391,0.00023255018],"about_ca_topic_score_codex":0.000025458954,"about_ca_topic_score_gemma":0.000047602018,"teacher_disagreement_score":0.109450474,"about_ca_system_score_codex":0.00006538534,"about_ca_system_score_gemma":0.000294286,"threshold_uncertainty_score":0.99999475},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.956771,0.0005143593,0.000088412984,0.016806604,0.00032238584,0.002508783,0.022848869,0.00008739411,0.000052187912],"genre_scores_gemma":[0.94228625,0.00025046695,0.00042310246,0.0014344135,0.0005315352,0.000610378,0.05423448,0.000086448585,0.00014292182],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955205,0.0008371596,0.0003077759,0.0013862781,0.0011046622,0.0008436726],"domain_scores_gemma":[0.99616927,0.00057880685,0.00018298363,0.0019513128,0.00029585397,0.00082176866],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022314098,0.00038263528,0.0010200114,0.0003306797,0.00021202638,0.00019142701,0.00043611665,0.00017936117,0.00009885891],"category_scores_gemma":[0.0007407357,0.00035909005,0.00018001399,0.0002841335,0.0002414121,0.00010738292,0.0019392879,0.0015479326,0.00003574684],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021507747,0.0119029535,0.47592226,0.0075985296,0.0014024259,0.0012994711,0.0018997843,0.000008347054,0.0018641701,0.00007864554,0.431027,0.06484565],"study_design_scores_gemma":[0.006837095,0.0017291532,0.9188879,0.0014003348,0.00032303823,0.000074462994,0.00038835313,0.0026554218,0.0011142688,0.00018521669,0.065463215,0.0009415008],"about_ca_topic_score_codex":0.00040504997,"about_ca_topic_score_gemma":0.00023114214,"teacher_disagreement_score":0.4429657,"about_ca_system_score_codex":0.0002905792,"about_ca_system_score_gemma":0.0008926573,"threshold_uncertainty_score":0.9998861},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000068287327,0.9724615,0.015047087,0.0043992787,0.00036003528,0.001646498,0.0059520905,0.000030815412,0.00009585679],"genre_scores_gemma":[0.00064506335,0.98255146,0.000105733256,0.0013648452,0.00067176303,0.000089662986,0.014306853,0.0000342678,0.0002303796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99771976,0.00067204837,0.0005869271,0.0004946149,0.00036582138,0.00016081732],"domain_scores_gemma":[0.99659616,0.00043481166,0.00036620905,0.0023235125,0.00008680615,0.00019249387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009494876,0.00023923867,0.0012383102,0.00006411828,0.00009375045,0.000032018514,0.0006188311,0.000088629975,0.00032666564],"category_scores_gemma":[0.0014760488,0.00012506956,0.00015100914,0.00027823274,0.00011909802,0.00004899489,0.0007802384,0.0002178507,0.00004064289],"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":[0.00007388723,0.00009582274,0.026039084,0.042001102,0.00064278155,0.00009536881,0.0002434924,0.000003156133,8.800957e-8,0.00023285921,0.097387806,0.83318454],"study_design_scores_gemma":[0.00050526,0.000019219853,0.0016392303,0.004473921,0.00078848354,0.00007348469,0.00002401198,0.00008951233,5.1088026e-8,0.000008837243,0.9922535,0.0001244781],"about_ca_topic_score_codex":0.00097089115,"about_ca_topic_score_gemma":0.0012439222,"teacher_disagreement_score":0.8948657,"about_ca_system_score_codex":0.00007566365,"about_ca_system_score_gemma":0.00061912934,"threshold_uncertainty_score":0.5100189},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.376804,0.52856463,0.000016655848,0.07146501,0.0008073334,0.0017210133,0.00023372388,0.00018615134,0.020201514],"genre_scores_gemma":[0.97029805,0.012099313,0.00037735677,0.015724037,0.000052226373,0.00018502866,0.0006083119,0.000036982932,0.00061869493],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983753,0.0003535267,0.000535475,0.00029234786,0.0001641349,0.0002792154],"domain_scores_gemma":[0.99872947,0.00009123518,0.00016548621,0.0007911569,0.00014623559,0.000076417324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000071652896,0.00015044857,0.00075931725,0.00013224673,0.000029344883,0.000006415526,0.00018766796,0.00015195302,0.00040269402],"category_scores_gemma":[0.0002636936,0.00011181523,0.00017911897,0.0011070712,0.00013190226,0.00006624496,0.00016572181,0.00029518685,0.00003140521],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014908556,0.00085111836,0.6821698,0.13414122,0.00019423527,0.01117471,0.0007852383,0.0000012972556,0.002200833,0.001982178,0.15337867,0.0129716145],"study_design_scores_gemma":[0.005097183,0.00004327826,0.47383967,0.3922764,0.00020530385,0.048057944,0.00011091438,0.00007426013,0.015682198,0.0002718476,0.0637349,0.00060611666],"about_ca_topic_score_codex":0.0000021859787,"about_ca_topic_score_gemma":0.00006829831,"teacher_disagreement_score":0.59349406,"about_ca_system_score_codex":0.000027249118,"about_ca_system_score_gemma":0.00026319793,"threshold_uncertainty_score":0.45596927},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949511,0.0010498819,0.0009151594,0.0005943828,0.00045481193,0.0008450329,0.0006951245,0.000096197335,0.00039830583],"genre_scores_gemma":[0.9854562,0.00034339353,0.0020056516,0.00023883677,0.00016751848,0.000046026384,0.01167887,0.00002222243,0.00004123827],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970535,0.00068189285,0.00043912887,0.0010284996,0.0006629999,0.00013395265],"domain_scores_gemma":[0.99561816,0.000114272036,0.00027126106,0.0031361028,0.00054458465,0.00031559862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037272705,0.00017791626,0.00049723446,0.00010104418,0.000041513053,0.00002845412,0.00025443948,0.0001915137,0.00028020042],"category_scores_gemma":[0.008021256,0.00017078703,0.00005496575,0.00016889756,0.00013559806,0.00017810272,0.0008183856,0.00034677208,0.0000033686101],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047332112,0.0006020759,0.97556424,0.004333647,0.0001553861,0.00004575604,0.0005868457,0.00045224716,0.006829758,0.000014373884,0.0018494037,0.0090929195],"study_design_scores_gemma":[0.001186424,0.00013953245,0.97392356,0.00023425896,0.0005789426,0.0000251073,0.00016111821,0.015818255,0.0005167251,0.0006550435,0.006533258,0.00022774312],"about_ca_topic_score_codex":0.00040847322,"about_ca_topic_score_gemma":0.0003934632,"teacher_disagreement_score":0.015366008,"about_ca_system_score_codex":0.0002084886,"about_ca_system_score_gemma":0.0015279838,"threshold_uncertainty_score":0.9602772},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9518315,0.0016599622,0.00023242139,0.04513581,0.0002209532,0.0005031239,0.00011842336,0.0000779435,0.00021982237],"genre_scores_gemma":[0.9942541,0.00083122926,0.0006676575,0.0030013763,0.00013909952,0.000018944625,0.00034012966,0.000031953092,0.000715497],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99736434,0.00039945976,0.00056222547,0.0005747176,0.00046566737,0.00063358387],"domain_scores_gemma":[0.9977362,0.000106750995,0.00022197422,0.00047048228,0.00024587722,0.0012187256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010763034,0.00021525896,0.00064454373,0.00020153173,0.00020640953,0.00006704436,0.000096378084,0.00008407463,0.000055473112],"category_scores_gemma":[0.0013380192,0.00016113231,0.00006326614,0.00053636724,0.000068590554,0.00015680463,0.00020737541,0.00030329853,0.000006407802],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110348024,0.0002113195,0.94758433,0.0007073112,0.000033597742,0.000038901428,0.0004496528,0.0000024173864,0.0005397406,0.00011387076,0.00069070427,0.04951779],"study_design_scores_gemma":[0.001443107,0.00040476868,0.8471512,0.00020452845,0.000001901371,0.00009064916,0.00032113324,0.0014045566,0.00001930268,0.000049386403,0.14868647,0.00022295938],"about_ca_topic_score_codex":0.00006966476,"about_ca_topic_score_gemma":0.00013293311,"teacher_disagreement_score":0.14799577,"about_ca_system_score_codex":0.00011479084,"about_ca_system_score_gemma":0.0006328344,"threshold_uncertainty_score":0.65707844},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952567,0.00096089527,0.00036150022,0.0020733087,0.000068346504,0.00077315955,0.00029657132,0.00006040168,0.0001490937],"genre_scores_gemma":[0.9978201,0.0005711641,0.00037578662,0.00039351496,0.000075652184,0.00006627831,0.0005846786,0.0000109555,0.00010187509],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977698,0.00032594774,0.0004496566,0.00046528236,0.00051067746,0.00047868065],"domain_scores_gemma":[0.9982905,0.00020498081,0.00011543181,0.00061317737,0.00027608607,0.00049986265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015912866,0.00015898087,0.00037275706,0.00012393497,0.00009854043,0.00016712326,0.00017999094,0.000042100848,0.000022220871],"category_scores_gemma":[0.0004362334,0.00014225236,0.000011818522,0.00045533513,0.000053574335,0.0009618221,0.0003605695,0.00028219668,0.0000039662345],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000710799,0.00023187498,0.9841054,0.00095871824,0.000023743749,0.00003974053,0.0020657603,0.0000010328182,0.0000012391336,0.000018509825,0.00019560652,0.012287296],"study_design_scores_gemma":[0.0013892353,0.00018452732,0.9893319,0.0001551726,9.033332e-7,0.000035934336,0.0016707599,0.0010138191,4.026962e-7,0.0000016625601,0.006087521,0.00012815572],"about_ca_topic_score_codex":0.0002508677,"about_ca_topic_score_gemma":0.0005730614,"teacher_disagreement_score":0.012159141,"about_ca_system_score_codex":0.00010336483,"about_ca_system_score_gemma":0.0010535934,"threshold_uncertainty_score":0.58008826},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.2976515e-7,0.89413327,0.00006476497,0.08321956,0.00064760115,0.0038557937,0.017484901,0.000070915004,0.00052275596],"genre_scores_gemma":[2.999488e-7,0.94169885,0.0062745474,0.003360826,0.0010123767,0.00031536006,0.047236294,0.00003592905,0.000065491586],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99545646,0.00089882326,0.001597101,0.0012868302,0.00027751716,0.0004832709],"domain_scores_gemma":[0.99304426,0.001045136,0.0028996712,0.0020280716,0.00014536636,0.00083750737],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004028765,0.00039061837,0.0026349316,0.00010438735,0.00012456003,0.00003472963,0.00032283485,0.00033202168,0.000023542785],"category_scores_gemma":[0.012197912,0.00035028387,0.00015465917,0.00048522904,0.000052814696,0.00021124739,0.00035584503,0.00045124887,0.0000107275855],"study_design_candidate":"not_applicable","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":[0.000029860335,0.00019367832,0.0001688636,0.05141319,0.00016141296,0.00015062261,0.000022927932,7.3802646e-8,3.1211514e-10,0.00075658393,0.1402483,0.8068545],"study_design_scores_gemma":[0.00062892435,0.00013415505,0.00012145705,0.012797099,0.00049330713,0.0014901152,0.00054766185,0.000018475062,4.5849677e-10,0.00006094398,0.9834693,0.00023857507],"about_ca_topic_score_codex":0.000722195,"about_ca_topic_score_gemma":0.008737407,"teacher_disagreement_score":0.843221,"about_ca_system_score_codex":0.00117289,"about_ca_system_score_gemma":0.011834509,"threshold_uncertainty_score":0.9998949},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99188584,0.002181768,0.000035215056,0.0016729229,0.0007011617,0.002230671,0.00015847028,0.00010537635,0.0010285891],"genre_scores_gemma":[0.9978332,0.00016380234,0.000259474,0.0009584664,0.000104951265,0.00034769188,0.00015307189,0.00004040088,0.00013897229],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99088496,0.0051248223,0.0012589992,0.00085221906,0.0011032509,0.000775757],"domain_scores_gemma":[0.99614066,0.0003675082,0.00058885076,0.0013986474,0.0009974027,0.0005069347],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010797742,0.00033555896,0.0011119607,0.00019815378,0.0002520302,0.000055164637,0.00030679107,0.00012747035,0.000035833928],"category_scores_gemma":[0.0012239694,0.00026674254,0.00012227647,0.0017211456,0.00014766345,0.00021699713,0.00029101086,0.00039610165,0.00000611928],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017728301,0.00040232996,0.9525099,0.0012242846,0.000102325066,0.000021071151,0.0006405045,0.000012703051,0.00013230958,0.000030890395,0.0011364772,0.043609962],"study_design_scores_gemma":[0.003688069,0.00019618234,0.97806215,0.00012534684,0.0000062589033,0.000073837764,0.0024361257,0.004780027,0.000020424635,0.000001961058,0.01034484,0.00026480228],"about_ca_topic_score_codex":0.00005680963,"about_ca_topic_score_gemma":0.0004392172,"teacher_disagreement_score":0.04334516,"about_ca_system_score_codex":0.00040599966,"about_ca_system_score_gemma":0.003173485,"threshold_uncertainty_score":0.9999785},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99408925,0.0015446427,0.00036836587,0.002965001,0.00011083967,0.0006529867,0.00007594535,0.000029644129,0.00016335843],"genre_scores_gemma":[0.99839324,0.00030220865,0.00021579905,0.00063823396,0.00007499979,0.00001588091,0.00025601144,0.000028353372,0.00007525308],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978936,0.00026139047,0.00067630084,0.00058694236,0.00017337108,0.00040844755],"domain_scores_gemma":[0.99871385,0.00012092914,0.00021915467,0.00028710745,0.000119844626,0.00053913554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067498616,0.0002507032,0.0006033456,0.0002858504,0.00014595756,0.000086254244,0.00003787193,0.0001439456,0.00001762771],"category_scores_gemma":[0.00010636453,0.00024294434,0.00003710757,0.00026777203,0.00011602378,0.00026138267,0.00012513112,0.00031004546,8.4007814e-7],"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":[0.0009071185,0.00047669126,0.92244214,0.0006098845,0.00007671639,0.0005774535,0.006332047,0.00004020741,0.0016628498,0.000040492276,0.00028561318,0.06654878],"study_design_scores_gemma":[0.005749893,0.0008225372,0.9805432,0.00022904704,0.00002866222,0.0010052511,0.002110468,0.0023249052,0.00019104643,0.00009970988,0.006539156,0.00035611677],"about_ca_topic_score_codex":0.23720384,"about_ca_topic_score_gemma":0.7274576,"teacher_disagreement_score":0.49025375,"about_ca_system_score_codex":0.00019546741,"about_ca_system_score_gemma":0.0005162595,"threshold_uncertainty_score":0.9906983},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997571,0.0006800083,0.000018951603,0.00009431794,0.00006785556,0.00043965076,0.0010597806,0.00002148753,0.00004694417],"genre_scores_gemma":[0.9993175,0.00006347099,0.000086874665,0.00025888652,0.00002041268,0.00003132887,0.00018609027,0.00001676587,0.000018629178],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99868715,0.00011488723,0.00030811355,0.00055508973,0.000091626964,0.00024311527],"domain_scores_gemma":[0.9987924,0.00009106688,0.00008002385,0.0003239885,0.000063202606,0.00064928003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017227209,0.00021634607,0.0004562693,0.00007867422,0.000116028794,0.00003013436,0.0000441345,0.000049942693,0.00007266851],"category_scores_gemma":[0.00015736462,0.00018821472,0.00004980427,0.00012634574,0.00009726054,0.000096111624,0.00014609711,0.00017014166,1.4408532e-7],"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":[0.00011596371,0.0009007123,0.99594975,0.000082568105,0.000078342884,0.0001438674,0.00077841454,0.000035619396,0.0015799865,0.000008079036,0.000028363746,0.00029834692],"study_design_scores_gemma":[0.0013161185,0.000291196,0.99534696,0.000037168924,0.00012085098,0.00000886122,0.002225639,0.00028495217,0.000044028875,0.000044888497,0.00009049428,0.00018882567],"about_ca_topic_score_codex":0.15192747,"about_ca_topic_score_gemma":0.9649508,"teacher_disagreement_score":0.8130233,"about_ca_system_score_codex":0.000114396775,"about_ca_system_score_gemma":0.0011303102,"threshold_uncertainty_score":0.8537199},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15815838,0.00036073502,0.20611651,0.54595083,0.008754192,0.0052442937,0.0018796532,0.00058075663,0.07295466],"genre_scores_gemma":[0.8042752,0.00004426622,0.00047014255,0.0045558475,0.19031039,0.000009634106,0.00015010529,0.000033282893,0.00015113107],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985752,0.00008059196,0.00031265253,0.00038001573,0.0004396487,0.00021186608],"domain_scores_gemma":[0.9990957,0.00007849483,0.00008761754,0.0002490088,0.000059149846,0.00043007074],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023605135,0.00014973617,0.00032605356,0.0000637294,0.00014280884,0.000031973817,0.00012848679,0.00006121639,0.007322436],"category_scores_gemma":[0.0014436477,0.00012132355,0.00006505517,0.00029314958,0.00011529063,0.00008358437,0.00012659874,0.0001748491,0.00012129964],"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":[0.0048678447,0.00011486357,0.0027748395,0.0002856478,0.00014935169,0.000055746175,0.011105618,0.003730515,0.003136215,0.0057577286,0.9532257,0.014795974],"study_design_scores_gemma":[0.0028288364,0.00070471887,0.0008938944,0.000045156172,0.00020511368,0.000019043344,0.0026452972,0.015005293,0.0005628202,0.0013438411,0.9753566,0.00038943364],"about_ca_topic_score_codex":0.0002260103,"about_ca_topic_score_gemma":0.0002928228,"teacher_disagreement_score":0.6461168,"about_ca_system_score_codex":0.0002054862,"about_ca_system_score_gemma":0.0001994329,"threshold_uncertainty_score":0.993585},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93686956,0.0013735612,0.054777924,0.0016571061,0.0009900996,0.0022807266,0.0005501686,0.00043586237,0.0010649966],"genre_scores_gemma":[0.97939193,0.00027934607,0.011737865,0.0015270092,0.00074397615,0.00043574176,0.0029965658,0.00010164547,0.0027859213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964146,0.00045716306,0.0009942906,0.0010878318,0.0004388662,0.0006072564],"domain_scores_gemma":[0.99424934,0.001745408,0.00043079682,0.0026263706,0.0006137387,0.00033433366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00305853,0.00043001477,0.00100264,0.00006346199,0.00023473361,0.00015279002,0.0005807221,0.00046310414,0.0001599248],"category_scores_gemma":[0.0077605797,0.00031208605,0.0004965125,0.00013370493,0.00015894849,0.00016214793,0.00089067244,0.0013026067,0.00001781198],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053632935,0.00013139358,0.94742167,0.00067058223,0.00040602448,0.000025379066,0.00022399552,0.00018929137,0.00012821479,0.00028078404,0.007579087,0.04240725],"study_design_scores_gemma":[0.0011487242,0.000121737,0.85396504,0.00044865225,0.0002710041,0.000012003222,0.00023273444,0.11857458,0.00008482116,0.0026742814,0.021880608,0.0005857949],"about_ca_topic_score_codex":0.00043934444,"about_ca_topic_score_gemma":0.00035062784,"teacher_disagreement_score":0.11838529,"about_ca_system_score_codex":0.000235041,"about_ca_system_score_gemma":0.0008617248,"threshold_uncertainty_score":0.9999331},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99699974,0.000050721996,0.00071651535,0.0010474807,0.000045040666,0.0005673759,0.0001628832,0.000024746507,0.0003855018],"genre_scores_gemma":[0.99649215,0.0000025173072,0.0006130711,0.00036456858,0.000010070581,0.00020714774,0.0022055649,0.000005694625,0.00009919573],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988668,0.00011614117,0.0005017045,0.00015553106,0.00019951508,0.0001603095],"domain_scores_gemma":[0.99899596,0.00025607468,0.00011901892,0.00027515992,0.00029511756,0.000058668105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014887759,0.00008871314,0.0005417766,0.00039064407,0.000012678815,0.000009922967,0.00009601813,0.00006427006,0.00020250856],"category_scores_gemma":[0.0002765873,0.000087153865,0.00012183261,0.0009136302,0.000049219503,0.00016374719,0.000075920456,0.00008434203,0.000014811796],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018262072,0.00038837426,0.9944106,0.0001365512,0.0007506358,0.00007829523,0.00094814744,0.000025648062,0.00004383585,0.00032540978,0.0020608068,0.00064910855],"study_design_scores_gemma":[0.0020589347,0.00007037854,0.98518807,0.000015638017,0.00020556606,0.00003381361,0.0007542668,0.0070554507,0.00026402966,0.000032077194,0.0042523574,0.00006942246],"about_ca_topic_score_codex":0.000031149153,"about_ca_topic_score_gemma":0.0004779907,"teacher_disagreement_score":0.009222502,"about_ca_system_score_codex":0.000041341133,"about_ca_system_score_gemma":0.00022418781,"threshold_uncertainty_score":0.35540313},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9633925,0.0007106465,0.02865127,0.0019531223,0.00036426596,0.0039451327,0.00044647214,0.00040744018,0.00012913832],"genre_scores_gemma":[0.9859836,0.00008231958,0.0013359315,0.0022423374,0.00025377225,0.0009768571,0.009033121,0.000049147624,0.000042930682],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99604976,0.00085333484,0.0008700577,0.0008706484,0.00069725583,0.0006589703],"domain_scores_gemma":[0.9961145,0.00023567751,0.0005772303,0.0003389719,0.0019484041,0.00078524236],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00241477,0.00028489094,0.0006878802,0.00023108108,0.0005780002,0.00012697586,0.00009303645,0.00010874185,0.000022273765],"category_scores_gemma":[0.0012949036,0.0003065804,0.00006779324,0.0007545163,0.00004131734,0.0003565109,0.00007800218,0.00020048195,0.0000045393585],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002620054,0.0014429553,0.8987188,0.0005801686,0.00012828902,0.000002529487,0.004575784,0.00000973594,0.000042245985,0.00026059037,0.0013645103,0.09261243],"study_design_scores_gemma":[0.0030983135,0.00079834193,0.91574377,0.000031770618,0.0000010464851,0.00001272012,0.0011357984,0.001856032,0.000007878969,0.000021838414,0.07701641,0.00027610618],"about_ca_topic_score_codex":0.00021297969,"about_ca_topic_score_gemma":0.009135333,"teacher_disagreement_score":0.09233633,"about_ca_system_score_codex":0.0013850627,"about_ca_system_score_gemma":0.0060128737,"threshold_uncertainty_score":0.9999386},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98403484,0.0038679629,0.000062801686,0.00944137,0.0005957646,0.00037524154,0.000010270113,0.000008573797,0.0016032052],"genre_scores_gemma":[0.9968882,0.00080949377,0.000042252355,0.00045596613,0.00040878527,0.000023032202,0.000007041461,0.000025192146,0.001339998],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935578,0.0011981147,0.001296953,0.0003047578,0.0031387992,0.00050357834],"domain_scores_gemma":[0.996573,0.0010941379,0.00033577968,0.0005301229,0.00095657504,0.0005104227],"candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008674052,0.00017391403,0.0008586638,0.00036662698,0.000024869194,0.000059541526,0.0011393711,0.00016690482,0.0019515721],"category_scores_gemma":[0.007327528,0.000110081935,0.00027755628,0.00063755794,0.00026604804,0.00013493616,0.00083200465,0.002429298,0.000025207257],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021467106,0.0032666756,0.90325534,0.00033519467,0.0011628716,0.018959112,0.004794636,0.0000057231227,0.0012175367,0.0007656195,0.032874078,0.03121652],"study_design_scores_gemma":[0.020562448,0.007859369,0.8008156,0.015732294,0.00025450086,0.0019120018,0.017717868,0.0046921317,0.009824579,0.00049427437,0.11962082,0.0005141356],"about_ca_topic_score_codex":0.0015608205,"about_ca_topic_score_gemma":0.0037935483,"teacher_disagreement_score":0.102439746,"about_ca_system_score_codex":0.00045785104,"about_ca_system_score_gemma":0.001790322,"threshold_uncertainty_score":0.99987215},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81338334,0.0023098944,0.00015421044,0.022072187,0.0009814216,0.0002481162,0.00021546755,0.0006293311,0.16000605],"genre_scores_gemma":[0.9891986,0.000051667113,0.00083382044,0.0028866841,0.00014362176,0.00006632983,0.00057868025,0.000026932566,0.0062136375],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989309,0.0000910738,0.00023024176,0.00030837135,0.00014612032,0.00029333774],"domain_scores_gemma":[0.99903166,0.000055961587,0.00005307575,0.00058087293,0.000100510544,0.0001779339],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000030941483,0.00012499522,0.00038990285,0.000106955624,0.000040567764,0.000010806465,0.00008865169,0.00011476294,0.002412536],"category_scores_gemma":[0.000091798625,0.00012113763,0.00012172257,0.00035909892,0.00007521649,0.00005466249,0.00008313765,0.00013932603,0.0008631143],"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":[0.00022843752,0.0007168917,0.25753748,0.00028279895,0.00029279134,0.012051855,0.00023601914,0.0000011866483,0.0036517961,0.0032300504,0.65475553,0.067015134],"study_design_scores_gemma":[0.0018162975,0.000028919325,0.24082497,0.000025815652,0.00005604542,0.005270959,0.00006520098,0.00007984997,0.004092714,0.00013773338,0.74744135,0.00016016424],"about_ca_topic_score_codex":0.000004317371,"about_ca_topic_score_gemma":0.000025583757,"teacher_disagreement_score":0.1758153,"about_ca_system_score_codex":0.000030342302,"about_ca_system_score_gemma":0.00013201192,"threshold_uncertainty_score":0.9999148},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90816873,0.0004802867,0.0011357398,0.08340828,0.00064212625,0.0008511678,0.0015345847,0.000045440476,0.0037336654],"genre_scores_gemma":[0.9953322,0.000065323875,0.0013135261,0.00041047536,0.00037659617,0.00017806662,0.0015595432,0.000011082969,0.0007532172],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997793,0.00036523404,0.00024507384,0.0003978593,0.0008212863,0.00037750573],"domain_scores_gemma":[0.9980494,0.0005760675,0.000041354262,0.00023624954,0.0009180694,0.00017888169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021999197,0.00010357776,0.00015042118,0.00023320713,0.00006559717,0.00026714886,0.00034456656,0.00007182605,0.00029363556],"category_scores_gemma":[0.0033911017,0.000079488156,0.000071013856,0.0006212573,0.00024685622,0.0001872017,0.0003186205,0.00025915034,0.000034431712],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022907385,0.0026957074,0.51139146,0.0005240198,0.00088063325,0.0006907361,0.0021422487,9.538699e-7,0.013055396,0.09302488,0.23038645,0.14291675],"study_design_scores_gemma":[0.0027333032,0.00020406213,0.85884494,0.00013419021,0.000011276264,0.00017616423,0.0009204893,0.004648815,0.00035693354,0.0038226729,0.12801893,0.00012819681],"about_ca_topic_score_codex":0.000086633256,"about_ca_topic_score_gemma":0.000506878,"teacher_disagreement_score":0.34745347,"about_ca_system_score_codex":0.00036343644,"about_ca_system_score_gemma":0.00047174087,"threshold_uncertainty_score":0.40597105},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00048933295,0.99062705,0.0010248949,0.00034916977,0.00050746213,0.0058875335,0.00086605555,0.0001811845,0.00006731776],"genre_scores_gemma":[0.00039513462,0.99456996,0.00021793516,0.0009962048,0.00055442285,0.0006726468,0.0021246318,0.0001249405,0.00034415064],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.995432,0.00025893265,0.0018127732,0.0012721352,0.0006582374,0.00056591455],"domain_scores_gemma":[0.9924488,0.004821296,0.00048827322,0.0011142822,0.00044428837,0.0006830547],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013442963,0.0007016451,0.004127028,0.00036552083,0.00010313637,0.00019039732,0.00030029766,0.000195296,0.00011033554],"category_scores_gemma":[0.04150988,0.00055394566,0.0004069979,0.00086502417,0.0002398483,0.0003726825,0.0003495096,0.00036028362,0.000048485836],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002479815,0.00005573273,0.000027322154,0.15213959,0.0001732454,0.0006452629,0.00020089268,1.1131323e-7,0.0000031837724,0.000018645369,0.0036511705,0.84283686],"study_design_scores_gemma":[0.00014221473,0.00025485147,0.000014079247,0.5810097,0.0011225324,0.0011418819,0.00010934785,0.0000017153118,7.7642545e-7,0.000013097131,0.41584897,0.0003408209],"about_ca_topic_score_codex":0.000015972575,"about_ca_topic_score_gemma":0.00001680748,"teacher_disagreement_score":0.84249604,"about_ca_system_score_codex":0.00034964242,"about_ca_system_score_gemma":0.0015371346,"threshold_uncertainty_score":0.9996912},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98063296,0.0011201504,0.00010695517,0.0017097755,0.00032189986,0.003123124,0.012636698,0.00025565035,0.00009277343],"genre_scores_gemma":[0.9857116,0.0007027245,0.00013153347,0.0003250494,0.00022225844,0.00024158621,0.012383593,0.000072463066,0.00020922325],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9938022,0.0003801543,0.0006529706,0.0013868868,0.002444104,0.0013336828],"domain_scores_gemma":[0.9934351,0.00046509248,0.00022749869,0.0021787402,0.0025258886,0.0011676984],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018753516,0.00044990287,0.0008655004,0.00042886793,0.00034200962,0.00088988966,0.0008860876,0.00036673216,0.00007855215],"category_scores_gemma":[0.0011742284,0.00043493687,0.00014095704,0.0020471471,0.00013718798,0.00032529896,0.0051430226,0.0016232461,0.000033660755],"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":[0.00017921344,0.00016289912,0.979627,0.008784102,0.00053958443,0.0004700611,0.00019704943,0.00009116906,0.00004107644,0.00000336245,0.0091926,0.0007118683],"study_design_scores_gemma":[0.00072667084,0.000058471825,0.9550917,0.0021837954,0.000058781687,0.000027132648,0.00058406394,0.0005097623,0.000055938708,0.000010805906,0.040305298,0.00038756384],"about_ca_topic_score_codex":0.46828854,"about_ca_topic_score_gemma":0.18707193,"teacher_disagreement_score":0.28121662,"about_ca_system_score_codex":0.0009075904,"about_ca_system_score_gemma":0.0023858421,"threshold_uncertainty_score":0.9998102},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95382494,0.0033930405,0.0377183,0.0015722549,0.0014643348,0.00090787787,0.0008877158,0.00012019768,0.00011134302],"genre_scores_gemma":[0.99614143,0.000053974156,0.0029651609,0.00048678892,0.00014735431,0.00004176093,0.000067754736,0.0000063503794,0.00008944689],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988598,0.000032175965,0.00021296374,0.00029412087,0.00034549678,0.00025544033],"domain_scores_gemma":[0.9987699,0.0002740788,0.000101002726,0.00040354938,0.00035527578,0.000096243144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023429257,0.00007624998,0.00014017354,0.00004111131,0.00026464838,0.000027048774,0.00016310217,0.000021880223,0.000016959302],"category_scores_gemma":[0.0019053229,0.00005341525,0.00006792331,0.0005266282,0.0007431878,0.00057344645,0.00012937779,0.00006879511,0.0000021334038],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008827136,0.00021640828,0.7036797,0.00013491709,0.000036731966,0.000018216595,0.0005165194,0.18002899,0.034479033,0.00016810857,0.0030596207,0.07677904],"study_design_scores_gemma":[0.0012344206,0.00019521345,0.48169807,0.000082211285,0.000056957633,0.000032235643,0.00021685449,0.37510574,0.009147148,0.00007471565,0.1320282,0.00012823807],"about_ca_topic_score_codex":0.00003319566,"about_ca_topic_score_gemma":0.0003483888,"teacher_disagreement_score":0.22198164,"about_ca_system_score_codex":0.0000633532,"about_ca_system_score_gemma":0.00017059493,"threshold_uncertainty_score":0.27383062},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9183176,0.0001403989,0.000008262198,0.0024147097,0.0002917693,0.00026780213,0.00047112588,0.00009088432,0.07799741],"genre_scores_gemma":[0.99146706,0.00002010865,0.0003737687,0.007344936,0.0004588652,0.0000053537224,0.00014542871,0.000045595938,0.00013888296],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988533,0.000023491113,0.00025246618,0.00039080586,0.000064104825,0.0004158095],"domain_scores_gemma":[0.9986171,0.000035502908,0.00007801773,0.00024223085,0.000028749886,0.0009983776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089679124,0.00017386998,0.0003264344,0.000077870805,0.000091714166,0.000031484098,0.00013990939,0.00006710172,0.0007999467],"category_scores_gemma":[0.00014885004,0.00019407006,0.00012521184,0.000078701705,0.00006814111,0.0001154245,0.000030991894,0.00014122705,0.0002693446],"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":[0.00042678992,0.00004327953,0.8808794,0.0006051559,0.00083795097,0.0016324349,0.0027377526,0.0025836176,0.022972152,0.0008072807,0.06421833,0.022255853],"study_design_scores_gemma":[0.007273207,0.00039294228,0.10788875,0.00036045897,0.0005654379,0.00051436265,0.0021724235,0.1689419,0.0027737208,0.0000612505,0.706902,0.0021534911],"about_ca_topic_score_codex":0.020919027,"about_ca_topic_score_gemma":0.01664097,"teacher_disagreement_score":0.77299064,"about_ca_system_score_codex":0.0006989754,"about_ca_system_score_gemma":0.0007526895,"threshold_uncertainty_score":0.98560077},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025094522,0.10609675,0.0018164716,0.01073417,0.00096398784,0.0041256584,0.024609365,0.0012268741,0.84791726],"genre_scores_gemma":[0.5403762,0.22815189,0.016339252,0.10935569,0.0052177818,0.000649317,0.04761538,0.0015388952,0.050755616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99563557,0.00018227419,0.0009753258,0.0014276536,0.0011310489,0.00064813154],"domain_scores_gemma":[0.99551225,0.00022705403,0.0007519527,0.0013132709,0.0006554425,0.0015400469],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004689787,0.0006509111,0.0015798319,0.0008493236,0.00025602762,0.000032854663,0.00028919865,0.00043508713,0.0011707154],"category_scores_gemma":[0.000585331,0.00068441767,0.0008621126,0.0007618608,0.00083011365,0.00006014571,0.00016773609,0.00063116377,0.000043111955],"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":[0.0010844036,0.0013921503,0.07686431,0.0030932953,0.002304918,0.00040445488,0.00086266984,0.000049607843,0.0000027484703,0.8762515,0.031676017,0.006013916],"study_design_scores_gemma":[0.006293896,0.0053519197,0.22344299,0.004291762,0.00092144805,0.00029894448,0.0074853683,0.000037954396,0.0000018193192,0.02004357,0.7297509,0.0020794412],"about_ca_topic_score_codex":0.00044865833,"about_ca_topic_score_gemma":0.0001502284,"teacher_disagreement_score":0.85620797,"about_ca_system_score_codex":0.0006561991,"about_ca_system_score_gemma":0.002241016,"threshold_uncertainty_score":0.9997423},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39383045,0.37965328,0.00075687154,0.008488705,0.0057160556,0.003372838,0.0059414976,0.0012246472,0.20101567],"genre_scores_gemma":[0.799799,0.004266478,0.00076187536,0.008869186,0.0022610868,0.00007294797,0.0031966392,0.00023568931,0.18053709],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951279,0.00056249276,0.0011598896,0.0012409532,0.0009409395,0.0009678343],"domain_scores_gemma":[0.99501526,0.00032153437,0.0003843823,0.0028410705,0.0005437307,0.0008940086],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008964818,0.0006093633,0.0013054546,0.00012274853,0.00017893816,0.00044873642,0.00051602744,0.00028458072,0.013779487],"category_scores_gemma":[0.0021003045,0.0006079013,0.00050621486,0.00094835914,0.00015043822,0.00028469073,0.00070827734,0.00065005,0.0077467524],"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":[0.00037163685,0.0010950374,0.16649635,0.0036294993,0.0011177064,0.0036985546,0.0013874623,0.000010885543,0.0015202778,0.008429676,0.7845769,0.027666012],"study_design_scores_gemma":[0.0021380403,0.00012339244,0.077343695,0.0022282389,0.0005340654,0.00009989433,0.00067785813,0.0001644462,0.0002528219,0.0000956805,0.915648,0.000693898],"about_ca_topic_score_codex":0.000041129515,"about_ca_topic_score_gemma":0.000053670276,"teacher_disagreement_score":0.40596855,"about_ca_system_score_codex":0.00035517654,"about_ca_system_score_gemma":0.0008061686,"threshold_uncertainty_score":0.99963725},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9858707,0.0013281975,0.0032031124,0.0070571853,0.0002231851,0.0013775257,0.00032955813,0.00054027926,0.00007022751],"genre_scores_gemma":[0.9635901,0.00009546168,0.033700716,0.00078345207,0.000041842035,0.00005374683,0.0016421713,0.000060112816,0.000032415937],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99357927,0.0016962157,0.0017422801,0.0011354815,0.0007042445,0.0011425394],"domain_scores_gemma":[0.99622184,0.00018711262,0.0007396301,0.0009109514,0.00044012495,0.0015003346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0061765066,0.00038168163,0.0011558491,0.0004493853,0.00022105422,0.00009041335,0.00019917099,0.000116375675,0.000026681882],"category_scores_gemma":[0.001291899,0.00040883696,0.000058414473,0.001501419,0.000059093454,0.00025787865,0.00015939986,0.00021219844,0.000009696057],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041646132,0.0012949513,0.85628617,0.014608089,0.00016068059,0.00019628278,0.0026434145,0.00021212388,0.00088538614,0.00016604687,0.0016219164,0.1215085],"study_design_scores_gemma":[0.0020744135,0.0003768406,0.95674425,0.0006956593,0.0000034048194,0.00013285886,0.00060512725,0.029651027,0.00025367856,0.0000014193223,0.009024311,0.0004370033],"about_ca_topic_score_codex":0.00047525467,"about_ca_topic_score_gemma":0.0046177213,"teacher_disagreement_score":0.1210715,"about_ca_system_score_codex":0.0010735875,"about_ca_system_score_gemma":0.0063193557,"threshold_uncertainty_score":0.9998363},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908121,0.00062952394,0.0015907261,0.005009643,0.0002331507,0.0011476575,0.00042628724,0.000065193366,0.00008567013],"genre_scores_gemma":[0.98161036,0.000016786029,0.015760511,0.001339435,0.000017278908,0.00017387987,0.0009495163,0.000021194273,0.00011103162],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975223,0.00025016902,0.00077812764,0.00049608265,0.00043874796,0.0005146038],"domain_scores_gemma":[0.997404,0.0003912677,0.00029109584,0.0003937465,0.00019237277,0.001327543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017498437,0.0001877628,0.0007203713,0.00012064131,0.00010174753,0.00002346405,0.00011475182,0.0000715208,0.00008114136],"category_scores_gemma":[0.0011959756,0.00017526782,0.00004700452,0.00026602167,0.00003414508,0.0000668746,0.00005474985,0.00019327468,4.5317103e-7],"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":true,"study_design_scores_codex":[0.00005190645,0.00023919881,0.9924648,0.0028825032,0.00003808694,0.000060002934,0.00022468728,0.000014991355,0.000011968048,0.00013473108,0.0026625865,0.0012145753],"study_design_scores_gemma":[0.003014554,0.00011068054,0.90856385,0.0002542924,0.0000054200545,0.000035120636,0.0007238739,0.00060529425,0.000009780145,0.0000014575307,0.08651917,0.000156526],"about_ca_topic_score_codex":0.5229846,"about_ca_topic_score_gemma":0.9891045,"teacher_disagreement_score":0.46611992,"about_ca_system_score_codex":0.0068075014,"about_ca_system_score_gemma":0.21924888,"threshold_uncertainty_score":0.99700516},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99757725,0.000108276,0.0007321479,0.00038295175,0.00007136476,0.000865414,0.00016684395,0.000035422374,0.000060317954],"genre_scores_gemma":[0.99616337,0.000018941319,0.0012647727,0.001945551,0.00013269942,0.00011704696,0.00033233417,0.000012156958,0.0000131066845],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9977581,0.00018403734,0.000860288,0.00013559051,0.000826416,0.00023560293],"domain_scores_gemma":[0.9975917,0.0012613481,0.00032900143,0.00042856173,0.00014241331,0.00024700345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016511633,0.0001473859,0.00043798852,0.00008472889,0.000065298496,0.000016646052,0.00025446995,0.00011592372,0.000019507865],"category_scores_gemma":[0.0048897443,0.000096095784,0.000038042745,0.0003412816,0.00024514075,0.0004117841,0.0002686656,0.00036767224,0.000002303342],"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":[0.0003793463,0.0001685169,0.36482102,0.0008794989,0.00010235506,0.00001961791,0.6275545,0.0000138529085,0.000008993971,0.000030340529,0.00074090605,0.005281107],"study_design_scores_gemma":[0.008447749,0.00015837602,0.24324302,0.00030829752,0.0001069086,0.000050465267,0.741927,0.003377032,0.000016244023,0.0000539108,0.0021240616,0.00018690077],"about_ca_topic_score_codex":0.00003532042,"about_ca_topic_score_gemma":0.00027442127,"teacher_disagreement_score":0.121577986,"about_ca_system_score_codex":0.00008150005,"about_ca_system_score_gemma":0.0009156444,"threshold_uncertainty_score":0.5853834},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98402756,0.0004340022,0.00005295641,0.00046303144,0.0004506722,0.0010495607,0.000395172,0.00022447498,0.0129025895],"genre_scores_gemma":[0.9925202,0.00022137386,0.0020231586,0.00063464965,0.0002501241,0.00011495009,0.0026181852,0.00008655522,0.0015307901],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99669623,0.00055596494,0.0007560442,0.0010665297,0.0006315464,0.00029367447],"domain_scores_gemma":[0.9948169,0.00013641559,0.0006481805,0.0032428033,0.00017726577,0.0009784164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010171816,0.00033799067,0.0011552991,0.00028011375,0.00007710368,0.00009497363,0.0013993887,0.00033709337,0.0008009348],"category_scores_gemma":[0.00074092275,0.00033118675,0.00019389272,0.00033938864,0.000246665,0.000116676056,0.0020217774,0.0005205488,0.00004849491],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045560062,0.00097002694,0.93737674,0.0027108982,0.00057435007,0.0024081925,0.0011867477,0.000072238574,0.04512205,0.00004037932,0.008448528,0.0006342624],"study_design_scores_gemma":[0.0019201254,0.00030738022,0.96728426,0.0010758167,0.0004118167,0.000115826886,0.00027081158,0.00020377024,0.010327739,0.00010103272,0.017284786,0.00069664436],"about_ca_topic_score_codex":0.0011943548,"about_ca_topic_score_gemma":0.000032689233,"teacher_disagreement_score":0.034794312,"about_ca_system_score_codex":0.00023898663,"about_ca_system_score_gemma":0.0031223707,"threshold_uncertainty_score":0.999914},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9363454,0.003300412,0.005479291,0.0056643737,0.00054886803,0.0008215302,0.0006406886,0.00022080266,0.046978623],"genre_scores_gemma":[0.98801565,0.00013140695,0.0023240233,0.00032809458,0.00033959103,0.000013708244,0.0020859123,0.00003463756,0.006726989],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99755335,0.0002059779,0.00053843315,0.0005067133,0.0009001988,0.00029534491],"domain_scores_gemma":[0.9979735,0.00033249788,0.00017356686,0.0006857848,0.00053189736,0.00030277946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063612795,0.00020509992,0.0005542659,0.00013660407,0.00003141616,0.0000052927403,0.00014386482,0.000086042666,0.0006580271],"category_scores_gemma":[0.0007505296,0.00015166998,0.00007510422,0.0006356048,0.00025959508,0.00010210265,0.00010562255,0.00026953,0.00005927217],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03487277,0.00267633,0.8423684,0.0028980528,0.0010259089,0.008368236,0.0026239543,0.00037139922,0.047816742,0.0019544587,0.028809598,0.026214097],"study_design_scores_gemma":[0.0047497675,0.00070524664,0.9776715,0.0013542008,0.00023468611,0.0008272692,0.00027733835,0.008348264,0.0007145837,0.000038588874,0.00490936,0.00016919586],"about_ca_topic_score_codex":0.00022555815,"about_ca_topic_score_gemma":0.000056882473,"teacher_disagreement_score":0.13530305,"about_ca_system_score_codex":0.00008319703,"about_ca_system_score_gemma":0.0005174953,"threshold_uncertainty_score":0.7204935},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6085061,0.003552056,0.005324378,0.036161073,0.0020390507,0.0031079222,0.34002325,0.000189711,0.0010964749],"genre_scores_gemma":[0.6801311,0.0006561057,0.0038679834,0.007118463,0.00024937803,0.00013276155,0.3055641,0.000027096545,0.0022530335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998178,0.00017533623,0.00027680228,0.00073641975,0.0004030891,0.00023034244],"domain_scores_gemma":[0.9963354,0.0019700548,0.00008078423,0.0012208734,0.00017427467,0.00021862512],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0041055353,0.00009117717,0.0002006614,0.000054336506,0.0001767106,0.000055567143,0.00040192434,0.0000277118,0.0003239176],"category_scores_gemma":[0.017341813,0.000064642,0.000018669693,0.00037148735,0.000097294076,0.00019051634,0.0005132772,0.00009839968,0.000014070043],"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":[0.00008963676,0.00005582127,0.1091578,0.00009579112,0.000025489957,0.000064491345,0.000019152469,0.00003755119,0.0026005183,0.00031716205,0.8841216,0.0034149634],"study_design_scores_gemma":[0.002568923,0.0000150512615,0.4455865,0.00004094175,0.00006773534,0.00016169465,0.00016513786,0.07966857,0.00006850252,0.00092255126,0.47052565,0.0002087516],"about_ca_topic_score_codex":0.06945659,"about_ca_topic_score_gemma":0.6832219,"teacher_disagreement_score":0.6137653,"about_ca_system_score_codex":0.0005422721,"about_ca_system_score_gemma":0.006607014,"threshold_uncertainty_score":0.9990246},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.970332,0.0023739045,0.00009843225,0.026179885,0.00031100673,0.00014111194,0.0000712524,0.000010320673,0.00048203018],"genre_scores_gemma":[0.9976921,0.0002691884,0.00019744225,0.0007957875,0.00020335458,0.000007130348,0.000055368488,0.000013670048,0.0007659405],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9946184,0.0011979286,0.0009544859,0.0003947654,0.0022889401,0.0005454588],"domain_scores_gemma":[0.996642,0.00063477486,0.00013168015,0.00041900002,0.00073205837,0.0014405152],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005014677,0.00015315766,0.00072881917,0.0006708756,0.000020205422,0.000082449325,0.00064715993,0.00021009028,0.0036405225],"category_scores_gemma":[0.0109602595,0.00011848569,0.00010672688,0.0010812712,0.00024358813,0.00014461404,0.0009548149,0.0013513776,0.0000624887],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084255444,0.0012830369,0.78642803,0.000023038541,0.00024025133,0.007504743,0.0006479056,0.0000015105733,0.000056049706,0.00003187386,0.104896456,0.09804452],"study_design_scores_gemma":[0.004101008,0.0017737622,0.9793684,0.0005259199,0.000026749556,0.00068618986,0.0017035097,0.0007105474,0.000009495656,0.00018749242,0.010801439,0.00010547441],"about_ca_topic_score_codex":0.0013078813,"about_ca_topic_score_gemma":0.014753046,"teacher_disagreement_score":0.19294035,"about_ca_system_score_codex":0.0005361658,"about_ca_system_score_gemma":0.0008998925,"threshold_uncertainty_score":0.99737084},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98888934,0.0014422628,0.000021729777,0.008276287,0.00014178622,0.0005553106,0.000088244364,0.00003226424,0.000552757],"genre_scores_gemma":[0.99735624,0.00005851265,0.00019263316,0.0018720316,0.000024997056,0.000035761794,0.00040808533,0.000007975449,0.000043742886],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983277,0.0001584003,0.0007160489,0.00018586623,0.00031672258,0.0002952865],"domain_scores_gemma":[0.99876004,0.00009773064,0.00016664878,0.00054740236,0.00018342756,0.00024475803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003630982,0.00012337559,0.00031311758,0.00014345799,0.00005105087,0.000023170893,0.00010994581,0.000094735246,0.00010538961],"category_scores_gemma":[0.00023718958,0.000099370394,0.000053078835,0.00040787336,0.00002358922,0.0002132103,0.000020443322,0.00037804613,0.000016137674],"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":[0.0009133366,0.00008270858,0.7962794,0.0004025147,0.000016532676,0.00009689375,0.015555737,0.00004401389,0.00010208612,0.0010571822,0.0012403817,0.18420927],"study_design_scores_gemma":[0.0016888605,0.00030025365,0.87834615,0.00015517163,0.0000048220136,0.00003531851,0.00685218,0.00010980904,0.0000222523,0.0010785103,0.11133453,0.00007211571],"about_ca_topic_score_codex":0.05675125,"about_ca_topic_score_gemma":0.7665094,"teacher_disagreement_score":0.70975816,"about_ca_system_score_codex":0.00074759877,"about_ca_system_score_gemma":0.0032917315,"threshold_uncertainty_score":0.94952995},"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":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07072083,0.000004575217,0.0012943349,0.0001253884,0.000005835054,0.9276852,0.0000932754,0.00005022926,0.00002033466],"genre_scores_gemma":[0.25956863,1.4342696e-7,0.000092697795,0.000016926118,0.000036324072,0.7401588,0.000017198861,0.000023827288,0.00008544388],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.995788,0.001761316,0.0006460774,0.0004404045,0.00095616956,0.0004080763],"domain_scores_gemma":[0.9968433,0.0007390593,0.00039048077,0.00067103695,0.0011226799,0.00023348875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004677123,0.00015559446,0.0004503828,0.00015266427,0.00022998909,0.000042255644,0.00017373225,0.000098842225,0.00004086394],"category_scores_gemma":[0.0032296232,0.000121276185,0.00011593474,0.0007384589,0.00033341083,0.00012730875,0.00021690126,0.00027765546,9.934544e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017057635,0.008538867,0.8867913,0.014103858,0.00078130746,0.00005602064,0.0021040128,0.00018034824,0.049937528,0.000036347155,0.0015617921,0.018851],"study_design_scores_gemma":[0.055096656,0.015400213,0.82413757,0.004953397,0.00008832661,0.000027484577,0.0034364592,0.02133222,0.031469718,0.00055735954,0.04288724,0.0006133833],"about_ca_topic_score_codex":0.00012222548,"about_ca_topic_score_gemma":0.00049193413,"teacher_disagreement_score":0.18884782,"about_ca_system_score_codex":0.0002519496,"about_ca_system_score_gemma":0.0012906796,"threshold_uncertainty_score":0.49454993},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80656874,0.0041796076,0.00088171236,0.18772236,0.0002023074,0.00037100393,0.000032426728,0.000022536738,0.000019331439],"genre_scores_gemma":[0.98706746,0.006180569,0.00036140368,0.004774428,0.00102902,0.000013327626,0.00006872543,0.00003240415,0.00047264382],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9877673,0.00519122,0.00165276,0.00046731,0.0040178923,0.0009035317],"domain_scores_gemma":[0.98946553,0.003697832,0.0005297199,0.00040141534,0.002835041,0.003070482],"candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03545618,0.00020428693,0.0011850913,0.0005518134,0.00012986553,0.00030501044,0.00059494085,0.00035532148,0.0011735033],"category_scores_gemma":[0.07817466,0.00014694186,0.00019702055,0.0007189155,0.0002844558,0.00040745296,0.0008673155,0.0027048532,0.00001799649],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015256797,0.0006503831,0.9306482,0.00017313578,0.00047072006,0.0007658674,0.0029402648,0.0000013172237,0.000033928187,0.0000065301206,0.037202403,0.026954658],"study_design_scores_gemma":[0.0073424373,0.0029158446,0.8251595,0.00091261556,0.00014415383,0.0013748037,0.030260667,0.0038973466,0.000027407403,0.000094306044,0.12760887,0.0002620775],"about_ca_topic_score_codex":0.0005685232,"about_ca_topic_score_gemma":0.0010885359,"teacher_disagreement_score":0.18294793,"about_ca_system_score_codex":0.0017474199,"about_ca_system_score_gemma":0.014680986,"threshold_uncertainty_score":0.9997396},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9574015,0.0069240197,0.0000071336253,0.03510454,0.00003765703,0.00009130304,0.00030317646,0.0000016039627,0.00012905162],"genre_scores_gemma":[0.97896594,0.020698354,0.00004156021,0.00015518615,0.000081868326,0.00000193527,0.000026913602,0.0000040198383,0.000024252307],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981566,0.00059133535,0.0003625355,0.00015287177,0.00053090154,0.000205722],"domain_scores_gemma":[0.9989542,0.00045468722,0.00014057312,0.00009282176,0.00009315386,0.0002646001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002658157,0.0000670471,0.00017265604,0.00014495106,0.00008370733,0.00006882615,0.00014264068,0.000027083533,0.000060277795],"category_scores_gemma":[0.0010102991,0.000044155808,0.000024094259,0.00020650691,0.00019639725,0.00019033553,0.00012971993,0.00030015456,3.5654617e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006652285,0.00063855975,0.96043897,0.000046721365,0.000090854424,0.00019829335,0.001473879,0.0000060699995,0.001103967,0.0008542659,0.00034767282,0.03413555],"study_design_scores_gemma":[0.001337142,0.000308024,0.99053204,0.000069735805,0.0000014459632,0.0006423802,0.004387269,0.0005301682,0.000006853693,0.0003120404,0.0018432694,0.000029657596],"about_ca_topic_score_codex":0.00026418906,"about_ca_topic_score_gemma":0.0007414739,"teacher_disagreement_score":0.03494935,"about_ca_system_score_codex":0.0005640488,"about_ca_system_score_gemma":0.00049239816,"threshold_uncertainty_score":0.18006216},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9719295,0.00011557117,0.02466514,0.0001633619,0.00015729062,0.00095693104,0.000064956366,0.00008951065,0.0018577769],"genre_scores_gemma":[0.971729,0.000045720153,0.017716955,0.000009502695,0.000019692887,0.0000011870379,0.0001611735,0.000013876722,0.010302919],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992337,0.000022516291,0.00014849617,0.0002888722,0.00016955349,0.00013681738],"domain_scores_gemma":[0.9992234,0.00006719174,0.00015106339,0.00027180603,0.00018775857,0.000098764365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121682715,0.00010063534,0.00021559923,0.00021139716,0.00007602987,0.000010810778,0.00008654159,0.00007596534,0.000016092048],"category_scores_gemma":[0.000045434663,0.00011253531,0.00004411294,0.00030693118,0.000049157603,0.00016370365,0.00004931479,0.000056575504,0.00001928619],"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":[0.019296262,0.0025344973,0.010198937,0.004702812,0.0014935202,0.00014482839,0.015628012,0.050818034,0.7357369,0.050025165,0.013610693,0.095810324],"study_design_scores_gemma":[0.029449511,0.0016592104,0.047846153,0.0024529262,0.0016562458,0.00014111833,0.039658997,0.24357842,0.13323864,0.0011371328,0.496557,0.0026246572],"about_ca_topic_score_codex":0.000013228016,"about_ca_topic_score_gemma":0.000011994535,"teacher_disagreement_score":0.6024983,"about_ca_system_score_codex":0.00013118428,"about_ca_system_score_gemma":0.00014776704,"threshold_uncertainty_score":0.45890567},"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":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006233735,0.000008907343,0.0011538452,0.0010852636,0.000008885751,0.995348,0.00072514,0.00010181419,0.00094479165],"genre_scores_gemma":[0.0022470485,9.4381215e-7,0.0017462857,0.0001723149,0.00013230875,0.99298805,0.0005076025,0.00006667266,0.002138784],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941242,0.001646822,0.0009014811,0.0010869376,0.0012974659,0.00094313605],"domain_scores_gemma":[0.9945811,0.0009760953,0.00023895004,0.0024600874,0.0010693979,0.00067437184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005497893,0.00025075322,0.00070868863,0.00035625495,0.00017320135,0.00006208696,0.00073452474,0.00019290044,0.0006745027],"category_scores_gemma":[0.0041700476,0.00024261093,0.00010985901,0.0017897644,0.00036184065,0.00024915082,0.0007176478,0.0006028241,0.000065431144],"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":[0.021652231,0.011341767,0.2863613,0.03212225,0.00010388535,0.000662733,0.0005646822,0.000035494275,0.07728628,0.00007725872,0.56401247,0.0057796217],"study_design_scores_gemma":[0.020544056,0.0014897664,0.035136003,0.0014142605,0.000004154233,0.000021580094,0.00018759926,0.002711494,0.022999989,0.0002645229,0.9148985,0.00032807642],"about_ca_topic_score_codex":0.00020666923,"about_ca_topic_score_gemma":0.0010406477,"teacher_disagreement_score":0.35088602,"about_ca_system_score_codex":0.0005675272,"about_ca_system_score_gemma":0.0056541506,"threshold_uncertainty_score":0.9999829},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8509091,0.005712776,0.0011599861,0.14078991,0.0005293859,0.00022351019,0.00024915268,0.000010283816,0.0004159027],"genre_scores_gemma":[0.9894094,0.00013504157,0.00021432742,0.009860009,0.0001507962,0.000001725494,0.000027212272,0.000016484795,0.00018501261],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980323,0.0004152486,0.0007075352,0.00015747892,0.00022439632,0.00046307128],"domain_scores_gemma":[0.9941424,0.00013471987,0.00036325137,0.0002746388,0.0003684425,0.004716536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017037621,0.000100876925,0.00043167637,0.0004858715,0.00007708553,0.000025822821,0.000100087054,0.000064500884,0.00063350855],"category_scores_gemma":[0.004724095,0.00010177031,0.00010336636,0.00068086135,0.00009015979,0.000164224,0.000012524063,0.00034440352,0.0000073933797],"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":[0.00013628004,0.00024084558,0.62088346,0.0015910247,0.00025445654,0.108210914,0.005363791,0.00014416907,0.00020743046,0.00013979434,0.11402591,0.14880192],"study_design_scores_gemma":[0.0033868607,0.0003778581,0.11007245,0.00018772898,0.000027628736,0.03901079,0.004978916,0.00018108972,0.000035814584,0.000023730492,0.8415415,0.00017558358],"about_ca_topic_score_codex":0.3601626,"about_ca_topic_score_gemma":0.96805423,"teacher_disagreement_score":0.72751564,"about_ca_system_score_codex":0.0026621758,"about_ca_system_score_gemma":0.07435793,"threshold_uncertainty_score":0.9308896},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80132055,0.12854163,0.00081002194,0.0027780677,0.0034122525,0.0006767975,0.0005814526,0.0005112247,0.061367996],"genre_scores_gemma":[0.9264716,0.011092033,0.004197197,0.0008691905,0.0031799914,0.0001525594,0.0016443781,0.00019940255,0.052193664],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9940526,0.0007857517,0.001423052,0.0014271497,0.00082990306,0.001481525],"domain_scores_gemma":[0.9959424,0.0002389321,0.00056025723,0.0019807422,0.00053565716,0.0007419759],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019572051,0.00063045125,0.001194767,0.00044902758,0.00031064978,0.00019301118,0.00025550227,0.00033695324,0.0035079936],"category_scores_gemma":[0.0019725605,0.00065993494,0.0007166172,0.0017884753,0.00080218026,0.00040121196,0.00027013742,0.0005250343,0.00015558237],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016545245,0.0010440777,0.6538017,0.0015208847,0.00076992967,0.10762937,0.0013184994,0.000030222916,0.004497358,0.00004974558,0.07558291,0.15358986],"study_design_scores_gemma":[0.00130154,0.00019936732,0.5909191,0.0013297433,0.0011597563,0.018719777,0.0010927613,0.00025439452,0.005007901,0.00016258282,0.37920895,0.00064416893],"about_ca_topic_score_codex":0.002563366,"about_ca_topic_score_gemma":0.0027282166,"teacher_disagreement_score":0.30362603,"about_ca_system_score_codex":0.00074600254,"about_ca_system_score_gemma":0.0030069482,"threshold_uncertainty_score":0.9995852},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930464,0.0034237846,0.0015484662,0.00069876027,0.0003560454,0.0001734416,0.00004167064,0.000014405806,0.0006970151],"genre_scores_gemma":[0.99523675,0.00021605846,0.0036890311,0.00028661685,0.00003801253,0.0000061386495,0.0004599624,0.000003842296,0.000063591826],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99891216,0.0002832909,0.0002573353,0.00026811255,0.000080505,0.00019857449],"domain_scores_gemma":[0.99939024,0.0001305769,0.000066745066,0.0003567502,0.000027806662,0.000027884935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008048264,0.00007203459,0.00028983317,0.000075819524,0.000044068318,0.0000054208435,0.00014113182,0.00013369416,0.000019746087],"category_scores_gemma":[0.0009260814,0.00005797822,0.00002029855,0.00017772865,0.00020197064,0.000084333566,0.00012644072,0.00022377308,0.0000015878163],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008455634,0.0001439888,0.99052113,0.000044097917,0.00001774681,0.00020438504,0.00011971757,0.000010727587,0.000278831,0.00006648814,0.005746387,0.0027619663],"study_design_scores_gemma":[0.0009781728,0.00004563824,0.9916763,0.00004134087,0.0000309536,0.00006224554,0.00074265973,0.0029947269,0.0000053014805,0.00090662844,0.0024615056,0.000054528966],"about_ca_topic_score_codex":0.000026282994,"about_ca_topic_score_gemma":0.00071391754,"teacher_disagreement_score":0.0032848816,"about_ca_system_score_codex":0.00008909211,"about_ca_system_score_gemma":0.00013710838,"threshold_uncertainty_score":0.23642832},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93894494,0.0015630333,0.0050642686,0.0035109422,0.04269381,0.0017171239,0.00048278784,0.00053039833,0.0054926774],"genre_scores_gemma":[0.9672681,0.0000022658853,0.002905563,0.00021092189,0.00031154774,0.00006954732,0.0018334104,0.000025636624,0.02737298],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968908,0.00014066741,0.0007479611,0.0011822985,0.00056204526,0.0004762367],"domain_scores_gemma":[0.99763584,0.00012541239,0.00027435482,0.0012133052,0.00048885716,0.0002622436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033603332,0.00018673834,0.0004187003,0.00010333173,0.00018308613,0.00015721621,0.0000852577,0.00008265648,0.00022905557],"category_scores_gemma":[0.003673407,0.00018864968,0.00017486078,0.0012969285,0.00013597752,0.00014238678,0.000100078054,0.00013442813,0.000028147262],"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":[0.000103263985,0.0003303856,0.5742956,0.00019915157,0.000039810886,0.0063248468,0.00013093287,0.00009132344,0.0071061566,0.000044558416,0.4033226,0.008011379],"study_design_scores_gemma":[0.0012008178,0.00006325969,0.3290033,0.00032219937,0.000045052628,0.003182426,0.00025008482,0.0064221825,0.0032853403,0.00082810427,0.65495515,0.00044207886],"about_ca_topic_score_codex":0.000021434,"about_ca_topic_score_gemma":0.0004853349,"teacher_disagreement_score":0.25163257,"about_ca_system_score_codex":0.0002399368,"about_ca_system_score_gemma":0.000790153,"threshold_uncertainty_score":0.76929104},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886126,0.0021344165,0.0028555088,0.0025764941,0.00024069096,0.0016471505,0.0017692826,0.000053117805,0.00011073955],"genre_scores_gemma":[0.9810769,0.00012885859,0.0129353665,0.004054212,0.00011947295,0.00014979618,0.0014851211,0.000038107108,0.00001217439],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928156,0.0029067078,0.0015270228,0.001120325,0.00086526154,0.0007650784],"domain_scores_gemma":[0.9945644,0.0019725314,0.000513994,0.0021096712,0.00039069096,0.00044869678],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00850478,0.00035233915,0.0011966261,0.00024498196,0.00017641569,0.00011235559,0.00056089036,0.00012395739,0.000048184407],"category_scores_gemma":[0.0013094394,0.00029784834,0.00006498756,0.0017513519,0.00015483981,0.00043168842,0.0005423044,0.0005637478,0.0000013073773],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009233131,0.0015826703,0.98706144,0.00031300518,0.000075573684,0.0008575994,0.0017367383,0.0000253627,0.00018904169,0.000063254745,0.00030916266,0.0076938095],"study_design_scores_gemma":[0.003835964,0.00009999275,0.96379876,0.00006317479,0.0000030801523,0.0005451739,0.006622885,0.012771402,5.298184e-7,0.0000147539195,0.011962683,0.00028158855],"about_ca_topic_score_codex":0.005610856,"about_ca_topic_score_gemma":0.015273466,"teacher_disagreement_score":0.023262678,"about_ca_system_score_codex":0.00025679244,"about_ca_system_score_gemma":0.0037802851,"threshold_uncertainty_score":0.99994737},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99525195,0.00008884534,0.0010475673,0.002342705,0.00003400829,0.00035044117,0.000059676015,0.000029899462,0.0007949121],"genre_scores_gemma":[0.9962904,0.00016372604,0.000097606535,0.0016564627,0.000010298349,0.000006669085,0.0017667457,0.0000038405155,0.0000042467286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991451,0.00009713626,0.00036055676,0.00006309563,0.0002451374,0.000088958324],"domain_scores_gemma":[0.9988869,0.000110015775,0.00035860555,0.00017672729,0.0004238822,0.00004391285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003898745,0.00007618244,0.00013590069,0.00013919544,0.000040548046,0.00006979844,0.000051885152,0.000028886954,0.000032521355],"category_scores_gemma":[0.0019110638,0.000056561043,0.0000089658215,0.0008835707,0.000034405955,0.0019112291,0.00002270435,0.000100511075,0.000011002285],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002458249,0.000057053338,0.9736618,0.00034580738,0.000011311676,0.000009099934,0.008873336,0.00046803185,0.000053649695,0.001238674,0.00021993546,0.014815474],"study_design_scores_gemma":[0.0018014949,0.000033346,0.97491646,0.00012501208,0.000012509397,0.000022470826,0.01136809,0.0025774331,0.0002277343,0.00001798241,0.008808893,0.00008858013],"about_ca_topic_score_codex":0.00010151243,"about_ca_topic_score_gemma":0.0002322047,"teacher_disagreement_score":0.014726894,"about_ca_system_score_codex":0.00004451412,"about_ca_system_score_gemma":0.00024394217,"threshold_uncertainty_score":0.23064925},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8139423,0.102829896,0.00037084223,0.07752703,0.0011325735,0.0018648157,0.0011888965,0.00019060017,0.0009530563],"genre_scores_gemma":[0.9868292,0.003937952,0.00006527775,0.0042483094,0.00023326815,0.00013433894,0.0041299094,0.0000372635,0.00038448066],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9903759,0.0032981553,0.0017209866,0.0013163238,0.00174023,0.0015484507],"domain_scores_gemma":[0.99326414,0.00081185176,0.0008922923,0.0020520408,0.0013880156,0.001591649],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.010416446,0.00043133297,0.001950493,0.025861064,0.00037022636,0.00091430027,0.00059185456,0.0001921916,0.00003433642],"category_scores_gemma":[0.0023326995,0.0003289846,0.0003033736,0.18841624,0.00039816095,0.00045930166,0.00017166272,0.00061479287,0.000008956325],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056509958,0.0004893298,0.93644834,0.0018012688,0.0002831768,0.00016667022,0.0021859058,0.0000047628832,0.000004114774,0.00032709833,0.01249436,0.045738444],"study_design_scores_gemma":[0.0011634921,0.00019203985,0.8104667,0.00013057371,0.0000049323107,0.0001936095,0.0008056444,0.0032582788,1.0192358e-7,0.0000066326575,0.18353996,0.00023804264],"about_ca_topic_score_codex":0.00030508664,"about_ca_topic_score_gemma":0.0015160274,"teacher_disagreement_score":0.17288691,"about_ca_system_score_codex":0.00026055548,"about_ca_system_score_gemma":0.003657938,"threshold_uncertainty_score":0.9999162},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98488927,0.0011589305,0.0002016551,0.011340447,0.0011664046,0.00066130725,0.00005734135,0.000021291826,0.00050338183],"genre_scores_gemma":[0.9931901,0.00007750753,0.00004890722,0.006122219,0.00032129907,0.0000053965205,0.000016588121,0.0000323541,0.00018561674],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972837,0.000591023,0.00059916277,0.0003278959,0.00085554074,0.0003426705],"domain_scores_gemma":[0.9978065,0.0006857933,0.0003376407,0.00076197303,0.00025677073,0.00015134783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083788636,0.00021738591,0.0009264446,0.00007825518,0.00015040758,0.00000545775,0.00023216034,0.00005950654,0.00008844498],"category_scores_gemma":[0.001390659,0.00012301978,0.00015640087,0.00072827644,0.00031484236,0.000023173625,0.00014481008,0.00021166008,0.000004930455],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029247238,0.001844156,0.554842,0.00934047,0.0014754416,0.00072006194,0.07816623,0.00003624876,0.12017023,0.0004933412,0.16255094,0.06743618],"study_design_scores_gemma":[0.004357476,0.0007558967,0.9582543,0.0019438358,0.0002942792,0.00003632328,0.008315859,0.000048748767,0.016335765,0.00002918954,0.009415192,0.00021318549],"about_ca_topic_score_codex":0.0008804321,"about_ca_topic_score_gemma":0.000172758,"teacher_disagreement_score":0.40341228,"about_ca_system_score_codex":0.00012339992,"about_ca_system_score_gemma":0.00039681562,"threshold_uncertainty_score":0.5016601},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920968,0.0001929897,0.00017603539,0.00007654152,0.00009440942,0.0015472651,0.0057934923,0.000017860808,0.000004602725],"genre_scores_gemma":[0.99481076,0.00019199694,0.00006042952,0.000043511307,0.000041829484,0.00006212565,0.004663205,0.00006529756,0.000060850347],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.995612,0.0008889696,0.00042355174,0.00089101266,0.0014823904,0.00070205756],"domain_scores_gemma":[0.99798584,0.00021325803,0.00018549472,0.00064593914,0.00045318026,0.000516286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007759836,0.00035646674,0.0008481515,0.00024564014,0.00010317641,0.0003304959,0.00014133584,0.00014429174,0.000025500472],"category_scores_gemma":[0.000985565,0.00030897825,0.00004994386,0.00030996525,0.00015470095,0.00020279214,0.0011261009,0.0011963543,1.0435423e-7],"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":[0.000088409935,0.00011265383,0.9955558,0.001619765,0.00027182247,0.0012991269,0.00020257519,0.000028264783,0.000039728042,1.7712159e-7,0.00012373415,0.00065789995],"study_design_scores_gemma":[0.0007633704,0.000041043575,0.9866048,0.007625562,0.00010125112,0.0000036181661,0.0010122303,0.0035057089,0.000034238932,9.427185e-7,0.000018709046,0.00028853954],"about_ca_topic_score_codex":0.89542055,"about_ca_topic_score_gemma":0.941074,"teacher_disagreement_score":0.045653455,"about_ca_system_score_codex":0.0037800546,"about_ca_system_score_gemma":0.0041604345,"threshold_uncertainty_score":0.9999362},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7293594,0.0030418118,0.0013595787,0.2623901,0.00031625788,0.0017051307,0.0009015766,0.00007840299,0.000847723],"genre_scores_gemma":[0.984749,0.00010835352,0.0007212416,0.013941468,0.0001047288,0.00004294521,0.00024550638,0.00003472187,0.000052010335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99171245,0.004836425,0.0013209998,0.0005632748,0.0006832464,0.0008836122],"domain_scores_gemma":[0.9933806,0.0026156036,0.00083580706,0.0021746366,0.0004369852,0.0005563812],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012347165,0.00024664463,0.00081112416,0.0002522573,0.00033566917,0.000028035487,0.00043477258,0.00008494238,0.00015379369],"category_scores_gemma":[0.00949351,0.0001308457,0.00027018896,0.0021999574,0.00033762644,0.00012529809,0.0002091003,0.00068725605,0.0000066774896],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024085499,0.003901866,0.88556933,0.0027829772,0.00026898034,0.00039250945,0.004371774,0.00080406567,0.0002767484,0.021508235,0.031731382,0.048151296],"study_design_scores_gemma":[0.0037935087,0.0003803296,0.9466344,0.0007709956,0.000030884185,0.00059700676,0.0045310874,0.023973824,0.00007131553,0.00019243521,0.018759735,0.00026445804],"about_ca_topic_score_codex":0.004148412,"about_ca_topic_score_gemma":0.013504768,"teacher_disagreement_score":0.25538963,"about_ca_system_score_codex":0.0007350864,"about_ca_system_score_gemma":0.014260232,"threshold_uncertainty_score":0.9988499},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9131941,0.0006974983,0.00001703205,0.08090204,0.000079392295,0.0010273113,0.0039941855,0.000008474595,0.000079925136],"genre_scores_gemma":[0.96539366,0.0004325749,0.0001203306,0.018752456,0.00001846264,0.000075811404,0.0017952009,0.000010665975,0.013400836],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99605423,0.001975083,0.0010030689,0.00010133612,0.0007000669,0.00016621289],"domain_scores_gemma":[0.9968239,0.0007491279,0.00062310224,0.0012365934,0.00042567734,0.00014156895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004807869,0.000114004404,0.00068556337,0.00013691079,0.00007124487,0.000004045421,0.0003732825,0.000045083845,0.00073296856],"category_scores_gemma":[0.005193173,0.000058755304,0.0002740042,0.0010394946,0.00023940598,0.000037212198,0.0003911531,0.00020622002,0.00000456817],"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":[0.002447007,0.00043045738,0.78685266,0.0010248087,0.00037632635,0.000004708232,0.02639728,0.00010786403,0.0008420543,0.00004995682,0.18054055,0.0009263248],"study_design_scores_gemma":[0.0009090627,0.00006383291,0.6105943,0.00015267012,0.00011425883,0.00001716917,0.0034660683,0.00004812301,0.00011649461,0.0000058401793,0.3844638,0.000048450842],"about_ca_topic_score_codex":0.0007898195,"about_ca_topic_score_gemma":0.018559923,"teacher_disagreement_score":0.20392323,"about_ca_system_score_codex":0.00023894629,"about_ca_system_score_gemma":0.0011152239,"threshold_uncertainty_score":0.9993488},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98169506,0.0015608864,0.00018560454,0.014742359,0.00019527343,0.0011722087,0.000091777234,0.000060226554,0.0002966343],"genre_scores_gemma":[0.9947514,0.0002666897,0.0014450928,0.0031071259,0.00004843497,0.00018829688,0.00008484735,0.000014731353,0.000093374016],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99727637,0.00071310916,0.000524791,0.00061155885,0.0004845364,0.0003896425],"domain_scores_gemma":[0.99757415,0.00039544993,0.00019705413,0.00052602997,0.0002488317,0.0010584734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017593103,0.0001999095,0.0005619391,0.00005960107,0.00031180956,0.00005776639,0.00009177125,0.000061314415,0.00003640192],"category_scores_gemma":[0.0013411569,0.00014919728,0.000037081143,0.00033529932,0.0002412515,0.00012230365,0.00026790198,0.00021801998,5.9417295e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000905812,0.0005834719,0.9695663,0.00045396498,0.000071747745,0.000012272969,0.0051135845,1.9739322e-7,0.000040334424,0.00015065228,0.0019491146,0.02196781],"study_design_scores_gemma":[0.0016197668,0.00012426022,0.82886785,0.000013284664,0.0000012146913,0.000044974095,0.007863886,0.00004800681,0.0000034150933,0.000009271955,0.16129017,0.00011390098],"about_ca_topic_score_codex":0.00016055637,"about_ca_topic_score_gemma":0.0008033064,"teacher_disagreement_score":0.15934105,"about_ca_system_score_codex":0.0002982167,"about_ca_system_score_gemma":0.005418378,"threshold_uncertainty_score":0.961197},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9279679,0.023732958,0.000027609245,0.034007926,0.00080825726,0.0021167288,0.001421471,0.00077320676,0.0091439085],"genre_scores_gemma":[0.98545337,0.0003056618,0.00016869043,0.0029074207,0.00015280354,0.0003580881,0.0041768122,0.00009707544,0.0063801096],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99581385,0.00072415784,0.00064670015,0.0007537421,0.0012760253,0.0007855068],"domain_scores_gemma":[0.9900463,0.00036938483,0.0003248858,0.0061112046,0.0009806136,0.002167614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007857526,0.00042304065,0.00069810625,0.000044303095,0.0006547961,0.0001395688,0.0008581953,0.000061769264,0.00041727896],"category_scores_gemma":[0.0034323812,0.00035061175,0.0002948688,0.0008471636,0.00039962542,0.00014571594,0.00060118,0.000655231,0.000014469181],"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":[0.00064690626,0.0007367577,0.7704372,0.0005050785,0.0005010733,0.051524885,0.000038921295,0.00026675302,0.00006684434,0.00008514313,0.17033844,0.0048520225],"study_design_scores_gemma":[0.00062657194,0.0000118146945,0.5026985,0.00012009907,0.00017358962,0.0001638029,0.00015343771,0.0004633337,0.00002230448,0.00001796919,0.49524838,0.00030021352],"about_ca_topic_score_codex":0.057727028,"about_ca_topic_score_gemma":0.2676257,"teacher_disagreement_score":0.32490996,"about_ca_system_score_codex":0.0006682827,"about_ca_system_score_gemma":0.016861869,"threshold_uncertainty_score":0.99989456},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9527866,0.0035085652,0.0000058419455,0.038069047,0.00030479123,0.002154679,0.0023749953,0.00008740916,0.0007080505],"genre_scores_gemma":[0.9949659,0.000030545478,0.000084692045,0.0013044663,0.00004932695,0.00043893317,0.0014482158,0.000058220965,0.0016196606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959737,0.0008334257,0.0005612545,0.00059838453,0.0011968298,0.0008363858],"domain_scores_gemma":[0.99037886,0.0002259441,0.00038985643,0.0052077984,0.0011005247,0.0026970308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006831105,0.0003336976,0.000648567,0.000051341754,0.00060587516,0.00010886617,0.00075974123,0.00003964163,0.0001236562],"category_scores_gemma":[0.0015911101,0.00021657615,0.00015025616,0.0009277747,0.0004055698,0.00012981376,0.00038374952,0.0003080555,0.000004393458],"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":[0.00031185738,0.0002003837,0.8935487,0.00013950223,0.0002554114,0.0024190992,0.000077229015,0.0009528392,0.000012889385,0.00003014824,0.10165611,0.00039578686],"study_design_scores_gemma":[0.00053096446,0.000018218869,0.88763565,0.0002947159,0.00024596218,0.000031916534,0.00046209808,0.0002062953,0.000037637492,0.000012995401,0.11025738,0.00026616786],"about_ca_topic_score_codex":0.8324216,"about_ca_topic_score_gemma":0.9905671,"teacher_disagreement_score":0.15814547,"about_ca_system_score_codex":0.00081735326,"about_ca_system_score_gemma":0.021094728,"threshold_uncertainty_score":0.98445475},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31511942,0.028439568,0.023023853,0.61918586,0.00036774034,0.00648086,0.003859935,0.00040862733,0.0031141546],"genre_scores_gemma":[0.9699313,0.0048224726,0.0008767989,0.021172065,0.00005852254,0.00025645646,0.0025983274,0.000025822199,0.00025825642],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99713355,0.0007447196,0.00065888994,0.0005505221,0.0003529144,0.00055941235],"domain_scores_gemma":[0.9973465,0.000833352,0.00028495595,0.00050159555,0.0001600964,0.00087350386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001977944,0.00020368716,0.000658708,0.00018638565,0.0004912653,0.0001292759,0.00010396075,0.0000701229,0.000058030786],"category_scores_gemma":[0.0011317017,0.00020251844,0.000042834054,0.00059728074,0.00013800176,0.0002989251,0.00010509471,0.00020430631,0.0000013337632],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025240803,0.00056294224,0.9634633,0.00083708676,0.000038861897,0.00001374789,0.003984392,0.000004446405,6.039531e-7,0.0050475136,0.01597866,0.009816041],"study_design_scores_gemma":[0.007411105,0.0004411381,0.6011281,0.0000418096,0.0000011990221,0.000036299054,0.0040559266,0.000673502,1.03743865e-7,0.00049246446,0.3855159,0.00020242801],"about_ca_topic_score_codex":0.00056910806,"about_ca_topic_score_gemma":0.011574734,"teacher_disagreement_score":0.65481186,"about_ca_system_score_codex":0.00090764073,"about_ca_system_score_gemma":0.0033942796,"threshold_uncertainty_score":0.8258462},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96191514,0.011809989,0.00046435228,0.0073523824,0.0006574862,0.0057991156,0.008489708,0.00017537008,0.0033364608],"genre_scores_gemma":[0.9874453,0.00004930188,0.00048751585,0.0045009186,0.00008937059,0.00092544686,0.0055594654,0.00009059003,0.0008520683],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960171,0.00038514717,0.0008870132,0.000714041,0.0013036391,0.0006930845],"domain_scores_gemma":[0.9888421,0.0002642525,0.00045792392,0.0042580897,0.0040621706,0.002115423],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018855948,0.00035073786,0.00077454216,0.00016600895,0.0003173417,0.000044186043,0.0005396411,0.00012252732,0.00015990112],"category_scores_gemma":[0.00440094,0.00038962197,0.00019326126,0.00091672764,0.00011153052,0.00016598027,0.00022209322,0.0002120232,0.000014863417],"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":[0.001032805,0.00041819518,0.47017,0.00086058635,0.0008520587,0.00070033735,0.00023434064,0.007356166,0.0024056772,0.00011383292,0.5051311,0.010724884],"study_design_scores_gemma":[0.0019250024,0.000026043113,0.29382345,0.00025264654,0.00038370502,0.000057522455,0.0002885997,0.0026459023,0.0002594807,0.0000475341,0.6998171,0.0004729942],"about_ca_topic_score_codex":0.7767555,"about_ca_topic_score_gemma":0.9607479,"teacher_disagreement_score":0.194686,"about_ca_system_score_codex":0.0020239856,"about_ca_system_score_gemma":0.044887062,"threshold_uncertainty_score":0.9998556},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921496,0.00038069295,0.0025126496,0.0029068186,0.000015472626,0.0011078656,0.00045762066,0.00002594504,0.0004433364],"genre_scores_gemma":[0.99881595,0.000008124189,0.00021583143,0.00026341973,0.000039707036,0.00004366457,0.00039865094,0.000014224504,0.0002004179],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984158,0.00022457918,0.00044839614,0.00030636947,0.0003431432,0.00026170295],"domain_scores_gemma":[0.99877286,0.00013419792,0.00027438987,0.0003119044,0.00019430759,0.00031231364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075199205,0.00015464523,0.00046649144,0.0000650013,0.00014952238,0.0000492041,0.000046701258,0.000047031317,0.000015388468],"category_scores_gemma":[0.00033746494,0.00011021936,0.000015706557,0.00041998265,0.000104740735,0.00015600924,0.00006675694,0.00009054373,1.0458929e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020462585,0.00016886731,0.8158378,0.0002588546,0.00004245662,0.0000021198096,0.0021879098,0.000042159103,0.000009803976,0.000031158714,0.00061661546,0.18059765],"study_design_scores_gemma":[0.0016806555,0.0007927882,0.96502686,0.00004329253,0.0000053089807,0.000021942458,0.0017909026,0.028049296,0.00001446573,0.000006699054,0.00244988,0.00011792492],"about_ca_topic_score_codex":0.001359332,"about_ca_topic_score_gemma":0.0012491842,"teacher_disagreement_score":0.18047974,"about_ca_system_score_codex":0.00006991456,"about_ca_system_score_gemma":0.0003171715,"threshold_uncertainty_score":0.4494615},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7829429,0.012268549,0.000012003076,0.19259886,0.00012419232,0.0014403432,0.010016472,0.00001616879,0.00058050966],"genre_scores_gemma":[0.9297156,0.0077564046,0.0000502089,0.046261825,0.000023080056,0.00005553727,0.0070104264,0.000011304226,0.0091156615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99751943,0.0006188723,0.00078771415,0.000073758165,0.0008708762,0.000129327],"domain_scores_gemma":[0.99725425,0.00038499743,0.00061256066,0.0009800486,0.0006890513,0.00007907525],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002039836,0.000092169714,0.0005860682,0.00006330233,0.000052198502,0.0000023574526,0.00031148846,0.000031404725,0.0009855855],"category_scores_gemma":[0.002143498,0.000046424506,0.0002543256,0.00072345516,0.00027772068,0.000035590157,0.0002988584,0.00017991781,0.0000020331304],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012875194,0.0003219152,0.81174606,0.007424505,0.00046742332,0.00000264566,0.00484345,0.000024877863,0.00005143902,0.0000622565,0.17421886,0.0007078201],"study_design_scores_gemma":[0.0012917335,0.000043172713,0.7190337,0.0013538983,0.00028870392,0.000033980315,0.0032330593,0.00007888024,0.000070640664,0.000011246228,0.27449915,0.00006181645],"about_ca_topic_score_codex":0.0008313188,"about_ca_topic_score_gemma":0.0125932,"teacher_disagreement_score":0.14677265,"about_ca_system_score_codex":0.00016164783,"about_ca_system_score_gemma":0.0010561832,"threshold_uncertainty_score":0.99992764},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62339675,0.2797771,0.000919228,0.012174008,0.0068642483,0.0054776105,0.0056769704,0.0006867346,0.06502735],"genre_scores_gemma":[0.9281042,0.054910623,0.00047543555,0.009796592,0.0034872966,0.00011764321,0.00051235734,0.000459486,0.0021363192],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9867352,0.0015900363,0.0037898212,0.0023432828,0.0034855048,0.002056131],"domain_scores_gemma":[0.9875741,0.001226255,0.0032522061,0.0026716874,0.0014611146,0.003814635],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0032288148,0.0016854928,0.0043442864,0.0014082702,0.000646552,0.0028916132,0.009012582,0.0005956691,0.10193582],"category_scores_gemma":[0.0050636134,0.0016534054,0.0012650891,0.0043436717,0.0008500811,0.004804482,0.004904316,0.0024443767,0.0025054363],"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":[0.0021768599,0.0011604292,0.4443153,0.0015608513,0.0017512739,0.0016414926,0.000635449,0.00014298745,0.039322212,0.0001245232,0.47405165,0.033116966],"study_design_scores_gemma":[0.0046409066,0.00013976466,0.62534845,0.0033924785,0.0012871971,0.000078428006,0.0003853407,0.00069935626,0.009291093,0.0009344304,0.35189128,0.0019112992],"about_ca_topic_score_codex":0.0013156798,"about_ca_topic_score_gemma":0.00011585767,"teacher_disagreement_score":0.3047075,"about_ca_system_score_codex":0.0005361337,"about_ca_system_score_gemma":0.001728747,"threshold_uncertainty_score":0.999857},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8818456,0.0037089218,0.000121993166,0.10244039,0.0002383442,0.0029832285,0.008243155,0.00024933123,0.00016905309],"genre_scores_gemma":[0.9810826,0.0018550851,0.00023551335,0.0052921567,0.00051987596,0.0004985491,0.010167112,0.000110217086,0.00023885],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99174356,0.001431482,0.00076276326,0.0022512863,0.0013680097,0.0024428887],"domain_scores_gemma":[0.99125594,0.0017472173,0.00036291385,0.0021711856,0.0009326766,0.0035300555],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0052179666,0.0005946378,0.0015210238,0.00042693733,0.0008824341,0.001249441,0.00054675,0.00021176129,0.000028766568],"category_scores_gemma":[0.00316217,0.0005683808,0.00009897385,0.0011257855,0.0006604761,0.0013002792,0.001201489,0.0009890011,0.000015276995],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004203331,0.002036201,0.7149979,0.0018358002,0.00015628885,0.000093881405,0.00055919425,1.007747e-7,0.000052642925,0.00029107186,0.031089498,0.24846712],"study_design_scores_gemma":[0.005731525,0.0012169067,0.69179195,0.000074077514,0.0000033883352,0.0002580771,0.00067473226,0.0026703307,0.000002200145,0.00003772856,0.29695463,0.0005844699],"about_ca_topic_score_codex":0.00020465757,"about_ca_topic_score_gemma":0.0011131545,"teacher_disagreement_score":0.26586515,"about_ca_system_score_codex":0.00039150935,"about_ca_system_score_gemma":0.003821952,"threshold_uncertainty_score":0.99978733},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99393684,0.0009281393,0.00000528298,0.004313982,0.000057171867,0.00050305715,0.00014828659,0.000013387148,0.000093879935],"genre_scores_gemma":[0.9967635,0.0006545449,0.00008641618,0.0010447016,0.000030457062,0.000608755,0.0007853411,0.000011222871,0.000015060431],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975651,0.0011005705,0.0006232174,0.0002921037,0.00015004829,0.00026894594],"domain_scores_gemma":[0.997924,0.0012965093,0.00028456075,0.0002348356,0.0001798769,0.00008027363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001683544,0.00015435716,0.0005303734,0.00041511605,0.000036785394,0.000008567763,0.00017016804,0.00017282247,0.000025788004],"category_scores_gemma":[0.0010760882,0.00010478678,0.00006161843,0.0005437585,0.00038667436,0.00011784912,0.00033237285,0.00042882943,4.323511e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022446581,0.000059967893,0.99119645,0.00007949582,0.000057688925,0.000031936128,0.004743118,0.000023064144,0.00044613393,0.0012812318,0.00008231784,0.0017741331],"study_design_scores_gemma":[0.0012912023,0.00016300693,0.9930524,0.0001566684,0.00004485813,0.00005611003,0.0006404454,0.0038539611,0.00016083961,0.00022565927,0.00027030325,0.00008453383],"about_ca_topic_score_codex":0.002572479,"about_ca_topic_score_gemma":0.010831983,"teacher_disagreement_score":0.008259504,"about_ca_system_score_codex":0.0001507745,"about_ca_system_score_gemma":0.00015183326,"threshold_uncertainty_score":0.60445005},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99556965,0.00029894017,0.000044418175,0.00023202083,0.00011430446,0.0027262205,0.00091274985,0.000056740973,0.000044932418],"genre_scores_gemma":[0.99675065,0.000065150474,0.00044363135,0.00037957972,0.00014607284,0.0013112983,0.0007306674,0.000035155386,0.00013781173],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971357,0.0005325208,0.0006880071,0.00065734313,0.0004758383,0.00051060924],"domain_scores_gemma":[0.99804896,0.00031619804,0.00011871505,0.0010152316,0.00026036554,0.00024054678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010561473,0.00027087654,0.00072309066,0.00012367999,0.0000999588,0.000035800942,0.0003240531,0.00014139885,0.0003339257],"category_scores_gemma":[0.00042256963,0.00019547295,0.00015723909,0.00029852282,0.00013410475,0.00014565604,0.00017026714,0.00036807705,0.0000072288017],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020968767,0.004971687,0.9730868,0.000068384295,0.000023024797,0.0007243086,0.0013046403,0.0000018332697,0.0021286653,0.00004107745,0.00017438816,0.01726553],"study_design_scores_gemma":[0.0027727627,0.0014633671,0.98909515,0.00005441898,0.00028518922,0.00013634619,0.005128559,0.000024020428,0.00029779255,0.000009526216,0.0005349258,0.00019791274],"about_ca_topic_score_codex":0.0002974379,"about_ca_topic_score_gemma":0.0005861409,"teacher_disagreement_score":0.017067617,"about_ca_system_score_codex":0.000051303996,"about_ca_system_score_gemma":0.00017160804,"threshold_uncertainty_score":0.79711556},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973342,0.0004302956,0.00001193716,0.000712973,0.00004498346,0.00017443634,0.0010951932,0.000026491223,0.00016953147],"genre_scores_gemma":[0.9984359,0.00009172631,0.000025377767,0.00019457583,0.00007822933,0.000026977787,0.00072365004,0.000012643851,0.00041092196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99851114,0.00037119543,0.0004207301,0.000373251,0.000104075596,0.00021961484],"domain_scores_gemma":[0.99848765,0.00039460318,0.00022268368,0.00051066116,0.0000816739,0.00030271403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025663286,0.00018464233,0.00058330566,0.00022646075,0.00006660003,0.000015635476,0.000080515114,0.000094143936,0.000109153225],"category_scores_gemma":[0.00037038914,0.00013030705,0.00015512752,0.0003962287,0.00026306574,0.00008536037,0.00023971262,0.00017777891,0.0000016402328],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038921766,0.00005248659,0.9939727,0.00007635783,0.0004063384,0.000034795692,0.00007572811,0.000058483853,0.000024320198,0.00012256036,0.0015327396,0.003254246],"study_design_scores_gemma":[0.0007845917,0.000083952276,0.99743927,0.00010283309,0.00042613051,0.000005995354,0.00001404497,0.00052311044,0.0000068961867,0.00018857635,0.00030369195,0.00012092291],"about_ca_topic_score_codex":0.00024798146,"about_ca_topic_score_gemma":0.0010738163,"teacher_disagreement_score":0.0034665284,"about_ca_system_score_codex":0.0000702669,"about_ca_system_score_gemma":0.00013203493,"threshold_uncertainty_score":0.5313767},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9692992,0.0089372825,0.011386751,0.000977804,0.00047485137,0.0004011965,0.00010160773,0.000018572277,0.0084027145],"genre_scores_gemma":[0.9500449,0.04183388,0.004136909,0.0006406232,0.0003732643,0.000006718338,0.000015028094,0.000020809837,0.0029278705],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99896216,0.00010053957,0.0003297033,0.00015628767,0.0002557551,0.00019554615],"domain_scores_gemma":[0.9994099,0.00004223592,0.000112761554,0.0001751667,0.00013185537,0.0001280432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006784781,0.00009751467,0.000323725,0.0001863692,0.00004011672,0.000025984094,0.00007881351,0.000035940328,0.00009184074],"category_scores_gemma":[0.00014048288,0.0000753906,0.000070778704,0.00033484233,0.00002943313,0.00014262075,0.00013532065,0.00022242559,0.00001030713],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024194752,0.0005824963,0.031698566,0.0005460587,0.00007565353,0.013780656,0.0006049717,0.00037311643,0.000054001037,0.0008724669,0.010794779,0.93819773],"study_design_scores_gemma":[0.005770865,0.00038397114,0.7330286,0.0005302982,0.00017634558,0.00015452944,0.0005446789,0.00103818,0.000074739066,0.0013611325,0.2567461,0.00019054869],"about_ca_topic_score_codex":0.000020829639,"about_ca_topic_score_gemma":0.0001320588,"teacher_disagreement_score":0.93800724,"about_ca_system_score_codex":0.00005839295,"about_ca_system_score_gemma":0.00011607543,"threshold_uncertainty_score":0.30743396},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022364146,0.00014948574,0.00043304102,0.0017853851,0.0001328346,0.0014718765,0.9942339,0.0005223531,0.0010474599],"genre_scores_gemma":[0.0002549262,0.00030009027,0.00036602904,0.0041744667,0.00057540095,8.887836e-7,0.99162287,0.0018275839,0.0008777423],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99531126,0.0007445307,0.0006623667,0.0013950962,0.0009809483,0.00090578786],"domain_scores_gemma":[0.99416363,0.00014831129,0.00049443863,0.0027520526,0.0010722494,0.0013693041],"candidate_categories":["metaresearch","metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000829415,0.00053135445,0.0008980699,0.0006240533,0.0015596084,0.00073470373,0.0017879693,0.00014171746,0.04034191],"category_scores_gemma":[0.00838243,0.0005517488,0.00023203874,0.0013456852,0.00062764913,0.00018301987,0.002336297,0.0007118191,0.012032404],"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":[0.00067090424,0.00043554694,0.000014752852,0.0009862668,0.00041616152,0.00035154604,0.00004315042,0.0000035992934,0.000024846388,0.000016819242,0.9939556,0.003080779],"study_design_scores_gemma":[0.0017432269,0.0005989801,0.0006632243,0.00007121369,0.00032445398,0.00027779167,0.0002719241,0.000025867386,0.000007782717,0.000006626574,0.9954839,0.0005250493],"about_ca_topic_score_codex":0.00046377364,"about_ca_topic_score_gemma":0.000055568304,"teacher_disagreement_score":0.02830951,"about_ca_system_score_codex":0.0012379199,"about_ca_system_score_gemma":0.00011721433,"threshold_uncertainty_score":0.9999704},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94737685,0.03183506,0.0017019709,0.016044222,0.00005626112,0.0018781867,0.0009851021,0.00006826987,0.000054086227],"genre_scores_gemma":[0.9828699,0.0056237592,0.0015213576,0.0081795,0.000086482025,0.00019332852,0.001449487,0.000027231346,0.00004892619],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99617296,0.0011974922,0.0006337171,0.0008222779,0.00026748568,0.0009060883],"domain_scores_gemma":[0.9968284,0.0010537544,0.00023683926,0.00043256738,0.00012132678,0.001327156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038725059,0.00027548027,0.0008170141,0.00014251552,0.0006351123,0.00007803196,0.00011584938,0.0001247378,0.0000107543265],"category_scores_gemma":[0.0018928688,0.0002013949,0.00005422538,0.0004274995,0.00032454618,0.00015119511,0.00029654006,0.0004765779,3.868654e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022156523,0.000088285065,0.9840454,0.0033352608,0.000026562506,0.000011887635,0.0013976913,0.000008889883,0.000016661783,0.00011718325,0.00041818092,0.010312473],"study_design_scores_gemma":[0.003779927,0.00013614575,0.9685609,0.00009374469,0.0000021669948,0.00022496772,0.004876123,0.002824555,2.6291798e-7,0.000049103917,0.01919344,0.00025870538],"about_ca_topic_score_codex":0.00044826086,"about_ca_topic_score_gemma":0.0016865477,"teacher_disagreement_score":0.035493087,"about_ca_system_score_codex":0.00033092144,"about_ca_system_score_gemma":0.0027033605,"threshold_uncertainty_score":0.82126456},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.109056e-8,0.60517377,0.0000061200203,0.0014550213,0.00001728387,0.3918092,0.0004659292,0.0000541382,0.0010184583],"genre_scores_gemma":[1.7453486e-7,0.57539195,0.000036017973,0.0025471642,0.000049261034,0.4209924,0.0007340867,0.000034635435,0.00021434361],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996256,0.00081853877,0.001148599,0.0009485598,0.00043402897,0.00039428187],"domain_scores_gemma":[0.9962033,0.00056973274,0.000600184,0.0020399261,0.0000861117,0.00050074764],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025003524,0.00048254666,0.003229809,0.0002236483,0.00008092442,0.0002650148,0.0012030253,0.00019001341,0.00015911326],"category_scores_gemma":[0.012559716,0.0003060055,0.00026479733,0.0014582291,0.0002145826,0.00019037693,0.001579941,0.0004649128,0.000056542598],"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":[0.000059986334,0.00010010583,0.011688098,0.4849531,0.00006506205,0.0012776724,0.000006270782,3.137652e-8,4.7231095e-9,0.00006452972,0.011452818,0.49033234],"study_design_scores_gemma":[0.0006400074,0.00002777982,0.000062302955,0.33305308,0.00011875637,0.00064408284,0.000006558484,3.5684712e-7,1.0403401e-9,0.000026808253,0.6651724,0.00024788675],"about_ca_topic_score_codex":0.0000108638915,"about_ca_topic_score_gemma":0.000105060775,"teacher_disagreement_score":0.65371954,"about_ca_system_score_codex":0.00018917656,"about_ca_system_score_gemma":0.006899934,"threshold_uncertainty_score":0.9999392},"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":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039464657,0.22002059,0.012299497,0.6573233,0.0039926567,0.018159311,0.005448264,0.0016193392,0.080742374],"genre_scores_gemma":[0.00969161,0.4818565,0.034875855,0.2548711,0.0006451523,0.004112561,0.0017265812,0.00027106036,0.21194959],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980357,0.000017807384,0.0010651591,0.00021575435,0.0001959523,0.00046958504],"domain_scores_gemma":[0.997431,0.0006772848,0.0005675249,0.0007517334,0.00031406793,0.00025842985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009688274,0.00031382803,0.0009743402,0.00068427395,0.00039009386,0.000018561563,0.00024754682,0.0003615378,0.000026112924],"category_scores_gemma":[0.0034842691,0.00023136179,0.00007558198,0.00012756625,0.0009932576,0.00008497511,0.0005685718,0.00058076886,0.0000088826355],"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":[0.00015907236,0.00003551007,0.0004618873,0.015504758,0.00088915764,0.000064621774,0.0031515416,0.0000034622417,2.8914428e-7,0.69059,0.15245087,0.13668883],"study_design_scores_gemma":[0.0007366932,0.00053707394,0.00002245773,0.0005265693,0.00005789521,0.00006586133,0.01206075,0.000082838225,0.000001110309,0.0074918936,0.9782145,0.0002023402],"about_ca_topic_score_codex":0.000004618168,"about_ca_topic_score_gemma":0.00013519717,"teacher_disagreement_score":0.82576364,"about_ca_system_score_codex":0.00042848595,"about_ca_system_score_gemma":0.0016210749,"threshold_uncertainty_score":0.943466},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.981211,0.0004518412,0.008852057,0.0060212645,0.00022938784,0.0029234057,0.00016377411,0.00005627596,0.00009099613],"genre_scores_gemma":[0.9969487,0.00003798326,0.00094589597,0.001399454,0.00013850472,0.000049131435,0.0004151611,0.000016240514,0.00004890646],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99785274,0.00028275847,0.0005896552,0.00039799375,0.00058319984,0.00029364353],"domain_scores_gemma":[0.9978968,0.00029989745,0.0002090242,0.0009498284,0.0002327582,0.00041172665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017676217,0.00013712971,0.00035932433,0.000091267386,0.0003369387,0.000031575433,0.00018659998,0.000034564877,0.000023571349],"category_scores_gemma":[0.00048351366,0.00010438008,0.000015700374,0.0008549292,0.000048249796,0.0001779227,0.00019550764,0.0001760082,0.0000043962136],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000815178,0.0019076372,0.9734547,0.0010704559,0.00012208687,0.00003690577,0.008155223,0.00006764123,0.000022059305,0.00009702708,0.007698632,0.0065524327],"study_design_scores_gemma":[0.0017300616,0.0009029419,0.98252976,0.000099404984,0.00011875203,0.000014257432,0.012750494,0.0012270107,0.0000032303437,0.00004835648,0.0004964026,0.000079332676],"about_ca_topic_score_codex":0.000118456504,"about_ca_topic_score_gemma":0.00030758476,"teacher_disagreement_score":0.015737725,"about_ca_system_score_codex":0.00022237748,"about_ca_system_score_gemma":0.0019395718,"threshold_uncertainty_score":0.4256496},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9924669,0.0011030356,0.00012830488,0.0049923565,0.00007739998,0.00042591186,0.00017201983,0.00007844662,0.00055558403],"genre_scores_gemma":[0.9970499,0.00020979352,0.0009578643,0.00069685973,0.00008185672,0.000030749565,0.00021548946,0.00002965191,0.00072781654],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974913,0.0004988496,0.00058698584,0.00041376613,0.0004232275,0.0005858694],"domain_scores_gemma":[0.9983818,0.00022862104,0.00018148999,0.0004898793,0.00028362148,0.00043458457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014339298,0.00018820408,0.0005832321,0.00008259849,0.00011111834,0.0000587028,0.00014175048,0.00008913346,0.0001251602],"category_scores_gemma":[0.00080778816,0.00014086689,0.00006159606,0.000530638,0.00022841654,0.00024979733,0.00019578074,0.00031146314,0.000006873276],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001598203,0.0001037176,0.992928,0.00061554764,0.00002437281,0.00001601573,0.0019157344,0.000037830778,0.00030521557,0.00023743545,0.0009712197,0.002685062],"study_design_scores_gemma":[0.001187057,0.000115291245,0.85957545,0.0003786327,0.0000016170598,0.00018649497,0.0015042573,0.13238291,0.000057487727,0.00003717076,0.004353939,0.00021969876],"about_ca_topic_score_codex":0.0004738009,"about_ca_topic_score_gemma":0.00034005102,"teacher_disagreement_score":0.1333526,"about_ca_system_score_codex":0.00014918052,"about_ca_system_score_gemma":0.002099781,"threshold_uncertainty_score":0.5744385},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.405224,0.0018788987,0.00063495565,0.0011941863,0.0010445816,0.00096186454,0.00019495997,0.00090273906,0.5879638],"genre_scores_gemma":[0.98294985,0.00008597702,0.0020060395,0.0020165066,0.00023485073,0.000048632835,0.00012348483,0.00007160747,0.01246304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99792,0.00010542581,0.00038874525,0.00052845874,0.00047145144,0.000585957],"domain_scores_gemma":[0.998095,0.00008260388,0.000103196224,0.0010933618,0.00014547736,0.00048036515],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00036502868,0.0002868859,0.000473468,0.00014318177,0.00009959607,0.00003719173,0.00016894432,0.00012796988,0.0040738084],"category_scores_gemma":[0.0006694068,0.00025771096,0.00020823216,0.0004133418,0.00012420607,0.00016042957,0.000058354453,0.00026944853,0.0033724979],"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":[0.00071703875,0.0016571077,0.3818903,0.0005498259,0.0006603761,0.0017374094,0.00048724958,0.000032862896,0.011762962,0.028012633,0.50048625,0.072006],"study_design_scores_gemma":[0.0024013189,0.000199345,0.1699135,0.00008213182,0.00010652891,0.00019908321,0.00008758795,0.000047209498,0.0040566055,0.00086384744,0.8216418,0.00040101833],"about_ca_topic_score_codex":0.000021152387,"about_ca_topic_score_gemma":0.0000134814045,"teacher_disagreement_score":0.5777259,"about_ca_system_score_codex":0.00010315393,"about_ca_system_score_gemma":0.00022684612,"threshold_uncertainty_score":0.9999875},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9029542,0.03146073,0.015355819,0.00002422197,0.0016175304,0.002842356,0.00018161649,0.00088358496,0.044679895],"genre_scores_gemma":[0.9785346,0.012440658,0.0028004632,0.000044251847,0.0006180511,0.0009002161,0.0024741176,0.000109321096,0.0020783187],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963326,0.0007600588,0.0006759758,0.0008804207,0.0008119615,0.00053898885],"domain_scores_gemma":[0.99662143,0.00075621303,0.0006571129,0.00059380126,0.0011620187,0.00020945202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017321145,0.00061938085,0.0009372644,0.00037133662,0.00035423465,0.000067569206,0.000268152,0.00045665333,0.00017527571],"category_scores_gemma":[0.0045363926,0.00055756123,0.0002465588,0.00043656654,0.0004694745,0.00053068297,0.00004403228,0.00047312744,0.00013897284],"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":[0.0013238335,0.00042621332,0.084961854,0.009183256,0.00062073895,0.000039494786,0.0021974267,0.0021693448,0.0021755034,0.0009025703,0.00036287055,0.8956369],"study_design_scores_gemma":[0.0015435815,0.001754654,0.86569315,0.009189696,0.002568165,0.00015589788,0.0050171353,0.0020416968,0.022388466,0.06655779,0.020910108,0.0021796315],"about_ca_topic_score_codex":0.00042967717,"about_ca_topic_score_gemma":0.007958725,"teacher_disagreement_score":0.89345723,"about_ca_system_score_codex":0.00075368653,"about_ca_system_score_gemma":0.0008480966,"threshold_uncertainty_score":0.9996876},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99105537,0.000010516953,0.003658358,0.0025374545,0.000026915734,0.000904023,0.0017304455,0.000011447742,0.00006544335],"genre_scores_gemma":[0.9738815,0.000013789303,0.025207926,0.00004925117,0.000006190343,0.0000016189518,0.0008236188,0.0000029008265,0.000013234142],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984833,0.00035121193,0.0002290646,0.00024348563,0.0004561475,0.00023677592],"domain_scores_gemma":[0.99821085,0.00027195588,0.00009883717,0.000427717,0.00060125854,0.0003894123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004168877,0.00007184174,0.00029699944,0.00034410963,0.0000992553,0.00001597256,0.0002397573,0.000079433434,0.0000062311246],"category_scores_gemma":[0.0015563183,0.000078118646,0.000010407997,0.0006872012,0.00014065641,0.00044623885,0.00064716715,0.00030357443,0.0000031501056],"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":[0.0016873656,0.0005879154,0.84297353,0.0053531285,0.00026993226,0.000043873435,0.0153554045,0.000007463443,0.03756703,0.013090893,0.0031606634,0.07990281],"study_design_scores_gemma":[0.000887639,0.00029303483,0.957799,0.00012588981,0.00000691848,0.000006094593,0.0029495552,0.00057678355,0.00011268711,0.0000072080206,0.037165567,0.00006960424],"about_ca_topic_score_codex":0.0021264232,"about_ca_topic_score_gemma":0.021160092,"teacher_disagreement_score":0.1148255,"about_ca_system_score_codex":0.00007026937,"about_ca_system_score_gemma":0.000587963,"threshold_uncertainty_score":0.9967012},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7451126,0.0045923754,0.00476649,0.009045977,0.0020351254,0.0025700612,0.000846096,0.0028460065,0.22818528],"genre_scores_gemma":[0.98880994,0.00012228014,0.0026052406,0.003243048,0.0007906371,0.000079684265,0.0003702127,0.00012370922,0.0038552685],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966962,0.000076941775,0.0006164832,0.00087072904,0.00084452383,0.00089513586],"domain_scores_gemma":[0.9971655,0.000079763144,0.00019155488,0.0015500955,0.00025169188,0.00076138636],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00040514077,0.00048507983,0.0007548728,0.00024742965,0.00017368323,0.000065565815,0.00043317076,0.00022256508,0.0010449594],"category_scores_gemma":[0.00043021466,0.00044801497,0.00035362903,0.0006334848,0.00023809703,0.0002729249,0.0001998553,0.0005050122,0.0042669345],"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":[0.0065594693,0.009644795,0.21460801,0.0031466833,0.0032416356,0.009067207,0.0037593285,0.0038214687,0.1115301,0.049295828,0.44470024,0.14062522],"study_design_scores_gemma":[0.019106045,0.0012579439,0.2657198,0.00089502917,0.00059714814,0.00096198067,0.00037378407,0.00037513443,0.021167548,0.008454766,0.67903906,0.002051761],"about_ca_topic_score_codex":0.00019441101,"about_ca_topic_score_gemma":0.00009293462,"teacher_disagreement_score":0.24369733,"about_ca_system_score_codex":0.0003689295,"about_ca_system_score_gemma":0.00058681413,"threshold_uncertainty_score":0.9998682},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9803293,0.01696112,0.000343536,0.00025021084,0.000084132036,0.0007810649,0.000133581,0.00009412219,0.0010229745],"genre_scores_gemma":[0.9967174,0.0016692667,0.00018920636,0.0006260783,0.00008302298,0.00008958211,0.00057045196,0.000017603066,0.000037419846],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980778,0.0002964788,0.00032770864,0.0007328496,0.00022172097,0.00034345474],"domain_scores_gemma":[0.99885017,0.00024099345,0.000080878184,0.0002043088,0.00015722562,0.00046642098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002954338,0.0002446495,0.00041640905,0.00007664784,0.00018955964,0.00006140808,0.000057236826,0.00007866285,0.0005371189],"category_scores_gemma":[0.00049626565,0.00021093954,0.00008318322,0.00025609863,0.00016638466,0.0001518188,0.00018212805,0.00018182785,0.000014038333],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042157987,0.0017969679,0.9422749,0.00003196339,0.00047693955,0.00031288702,0.00018465228,8.218985e-7,0.00005311492,0.0001339447,0.00009599654,0.05421619],"study_design_scores_gemma":[0.0032396903,0.000501112,0.9899884,0.000015103569,0.0017324075,0.000015691681,0.0020588543,0.00018576796,0.0000050719223,0.00045507317,0.0015367065,0.00026610834],"about_ca_topic_score_codex":0.00001223779,"about_ca_topic_score_gemma":0.000058383837,"teacher_disagreement_score":0.053950083,"about_ca_system_score_codex":0.000007772298,"about_ca_system_score_gemma":0.00009799279,"threshold_uncertainty_score":0.86018646},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.973882,0.0013603688,0.0149883805,0.009060406,0.00023273136,0.00027244812,0.00010119303,0.00006731524,0.000035108682],"genre_scores_gemma":[0.9949119,0.00011552506,0.004515438,0.00018639505,0.0000531388,0.0000053202593,0.00019666283,0.0000150190735,6.2055665e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99793434,0.00012315919,0.0010448224,0.000108228516,0.0005070418,0.00028239755],"domain_scores_gemma":[0.9980033,0.00006297985,0.00060495903,0.0002282546,0.000825113,0.00027539203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000686122,0.0001329156,0.00051563024,0.00042277898,0.000022992574,0.000023750426,0.00008629567,0.000077056815,0.000008787945],"category_scores_gemma":[0.00032728378,0.00010834863,0.00009346438,0.0009433458,0.00002193063,0.00046129295,0.000012226377,0.00044094888,9.4214147e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079726905,0.00060217886,0.95287395,0.012348452,0.00031932228,0.00068250025,0.0012934541,0.007913269,0.0010827074,0.0060382853,0.00047320424,0.015575424],"study_design_scores_gemma":[0.0043199016,0.00071135326,0.94110155,0.0047544497,0.000030635263,0.0007397432,0.0037648727,0.03548164,0.0002447977,0.000001796904,0.008664234,0.00018500285],"about_ca_topic_score_codex":0.0001425322,"about_ca_topic_score_gemma":0.00015653948,"teacher_disagreement_score":0.02756837,"about_ca_system_score_codex":0.00046399338,"about_ca_system_score_gemma":0.0023570913,"threshold_uncertainty_score":0.4418329},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990193,0.0010691192,0.0054886187,0.0012939593,0.000057850484,0.00077657594,0.000493181,0.000035039688,0.00059263024],"genre_scores_gemma":[0.9845341,0.0004505165,0.0139164245,0.0001557404,0.00005197301,0.000042891603,0.00068914326,0.00002684035,0.00013239987],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957498,0.00062529056,0.0007901792,0.0009838417,0.0014018175,0.00044910744],"domain_scores_gemma":[0.99638957,0.00035827025,0.00025311206,0.0016121251,0.001028873,0.0003580287],"candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0027323484,0.0002389236,0.0009230482,0.11693319,0.000079822275,0.000092623544,0.00033556763,0.00027143478,0.00022738842],"category_scores_gemma":[0.00063068734,0.00018251789,0.00017157813,0.19115122,0.00013420642,0.00007219063,0.0011382289,0.0011639484,9.711769e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000671719,0.00042612696,0.6004186,0.006081427,0.0011332387,0.00005920044,0.003769428,0.30601457,0.0014917959,0.0002562097,0.0007467252,0.07893092],"study_design_scores_gemma":[0.0003791562,0.000038498656,0.7807989,0.00024218729,0.00028443793,0.0000037157413,0.00057745597,0.21712767,0.00006863123,0.0003436677,0.0000106018515,0.00012508463],"about_ca_topic_score_codex":0.0036570604,"about_ca_topic_score_gemma":0.0026602296,"teacher_disagreement_score":0.18038025,"about_ca_system_score_codex":0.00041101422,"about_ca_system_score_gemma":0.0010921621,"threshold_uncertainty_score":0.89307564},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99543494,0.0005036436,0.00024866898,0.00084521296,0.00036652514,0.00059673557,0.0004515981,0.00006489305,0.0014877814],"genre_scores_gemma":[0.9970552,0.00009546444,0.00021101916,0.00032532171,0.000051719424,0.000048723283,0.0018505201,0.00003700414,0.00032503542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972344,0.0006492803,0.00070834014,0.00046512386,0.00060338905,0.000339448],"domain_scores_gemma":[0.99709666,0.00032734525,0.00027065875,0.0013986743,0.0001895451,0.0007171391],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00072179566,0.0003471743,0.00102942,0.00024532177,0.00013296728,0.000020982257,0.00041187034,0.00025976897,0.0009462461],"category_scores_gemma":[0.0005402546,0.00035914558,0.00035128603,0.0003167047,0.00010691496,0.00004697841,0.00046161993,0.0023290005,0.0000018671644],"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":[0.000076316275,0.00038639744,0.9937908,0.0014157547,0.00025516003,0.00018446529,0.0019026253,0.000525164,0.0001484181,0.000002426658,0.00073513505,0.0005773137],"study_design_scores_gemma":[0.0009883621,0.00011147898,0.9767619,0.00084447087,0.00026473345,0.000012381523,0.00039724863,0.00004714346,0.00011577884,0.000017508259,0.020170348,0.00026868985],"about_ca_topic_score_codex":0.9933204,"about_ca_topic_score_gemma":0.9967284,"teacher_disagreement_score":0.019435212,"about_ca_system_score_codex":0.0024958267,"about_ca_system_score_gemma":0.013512238,"threshold_uncertainty_score":0.99997264},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9829065,0.0020838669,0.0008605259,0.0070062703,0.0007142015,0.0017663342,0.0045423945,0.00007564548,0.000044267726],"genre_scores_gemma":[0.99249095,0.00031972595,0.0003526166,0.0020076479,0.00020124674,0.00016645096,0.0043652575,0.000037850143,0.000058232185],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9941725,0.0017045083,0.0012750168,0.0006988675,0.0013276514,0.0008214635],"domain_scores_gemma":[0.99263376,0.00083448365,0.00087331556,0.00097546965,0.0029362577,0.0017466908],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015440486,0.00025072202,0.00081575714,0.000188829,0.00045431228,0.00009555492,0.00030296252,0.0001432661,0.00007398989],"category_scores_gemma":[0.011155016,0.00020880104,0.00013982801,0.0011053393,0.00028571865,0.00038291494,0.00010565408,0.0002599223,9.69664e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017259744,0.00053679594,0.95804906,0.0011694224,0.000063333195,6.3045326e-7,0.00040716093,0.000058603346,0.00002992116,0.00010815531,0.0006147839,0.03878951],"study_design_scores_gemma":[0.003166882,0.0002844082,0.97881967,0.000033777513,0.0000017484526,0.000024727993,0.00048151982,0.0029124822,0.000008833808,0.000035481018,0.0140601415,0.00017034855],"about_ca_topic_score_codex":0.0013193154,"about_ca_topic_score_gemma":0.0042739427,"teacher_disagreement_score":0.038619164,"about_ca_system_score_codex":0.00074622117,"about_ca_system_score_gemma":0.019754138,"threshold_uncertainty_score":0.99717444},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95375735,0.0023861516,0.00046164947,0.0412447,0.0001240303,0.0011565516,0.0006371644,0.000115134855,0.000117285694],"genre_scores_gemma":[0.96507245,0.0019101345,0.000057426274,0.031795263,0.00006876658,0.00011241376,0.0008541083,0.000023320743,0.00010614336],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99523205,0.001987201,0.0008833603,0.0005842144,0.00053180207,0.00078135665],"domain_scores_gemma":[0.9955722,0.0010070236,0.00027764493,0.0010080424,0.00064834335,0.0014867362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031852361,0.00023681666,0.0007404178,0.0001927272,0.00026475269,0.00003850251,0.0002896048,0.000084885025,0.00024034167],"category_scores_gemma":[0.0036757507,0.00017582606,0.0000711691,0.0012828487,0.0002934212,0.00011084984,0.00027979095,0.00039965232,0.000010953896],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001506999,0.00041128293,0.9863571,0.00073201786,0.00007479479,0.000056892004,0.0029838216,0.000040625633,0.000006877878,0.00020127007,0.006395102,0.0025894972],"study_design_scores_gemma":[0.0015111018,0.0004487304,0.8010342,0.000017947505,0.0000017638299,0.00004740966,0.007481686,0.0007300259,0.0000010881816,0.000021839713,0.18855262,0.0001515866],"about_ca_topic_score_codex":0.001394956,"about_ca_topic_score_gemma":0.0015049473,"teacher_disagreement_score":0.18532291,"about_ca_system_score_codex":0.000091347305,"about_ca_system_score_gemma":0.0034852305,"threshold_uncertainty_score":0.71699786},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51426107,0.3347072,0.0119331265,0.12852955,0.0040483857,0.00068750617,0.0020871086,0.00026599763,0.0034800437],"genre_scores_gemma":[0.98999506,0.0011746333,0.0057317712,0.0012032404,0.00036646207,0.000036091318,0.0013254561,0.000019073343,0.00014819208],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979482,0.0002043831,0.00043865156,0.000613281,0.00043357877,0.00036184923],"domain_scores_gemma":[0.9985111,0.00014738295,0.00008992749,0.000565359,0.00011374522,0.0005724848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035534118,0.00021647339,0.0006239532,0.00023999745,0.00003870768,0.000026839858,0.00018309934,0.000082769184,0.0007388576],"category_scores_gemma":[0.0020709373,0.00019971775,0.000053471256,0.0012211464,0.0002732261,0.00027862305,0.00014897238,0.00023239203,0.000008128425],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023768692,0.0001599771,0.87533253,0.000025306937,0.00015204729,0.0008238579,0.00011733766,0.0000023210944,0.000020912465,0.00022200974,0.07019553,0.052710462],"study_design_scores_gemma":[0.0025304467,0.000058921574,0.976956,0.0010056195,0.00028310803,0.000016590384,0.00027689277,0.0036625233,0.000003082026,0.0009529209,0.014087925,0.0001659305],"about_ca_topic_score_codex":0.00024005746,"about_ca_topic_score_gemma":0.0013327927,"teacher_disagreement_score":0.475734,"about_ca_system_score_codex":0.00022077218,"about_ca_system_score_gemma":0.00038973574,"threshold_uncertainty_score":0.81442535},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7111814,0.010851298,0.04450671,0.17787342,0.0015545858,0.0401193,0.012093874,0.00054430345,0.0012751061],"genre_scores_gemma":[0.968429,0.00077547913,0.0042004273,0.015787672,0.00034190158,0.0039739674,0.0053342953,0.00010788311,0.0010493402],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9958654,0.00029140493,0.00103258,0.0014602424,0.0005399774,0.000810409],"domain_scores_gemma":[0.9865474,0.010320995,0.00038672527,0.0021073269,0.00015567253,0.00048189762],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017246146,0.00062072277,0.0013450382,0.00025105537,0.00018050721,0.000778056,0.00093373214,0.00044540176,0.00008140047],"category_scores_gemma":[0.010773337,0.00041437242,0.00039873697,0.0006163518,0.00012563494,0.0002403035,0.00084430206,0.0012807281,0.0000065691747],"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":[0.0039618555,0.0006818952,0.8890356,0.008996839,0.000555008,0.0003097368,0.005780032,0.0021501929,0.000009229858,0.0045475136,0.051249154,0.032722943],"study_design_scores_gemma":[0.011888753,0.0003324538,0.7971029,0.004407505,0.0001496548,0.00015804151,0.010929257,0.0079951165,0.000021879345,0.011205277,0.15398036,0.0018287761],"about_ca_topic_score_codex":0.0015318115,"about_ca_topic_score_gemma":0.12245307,"teacher_disagreement_score":0.25724763,"about_ca_system_score_codex":0.0005650661,"about_ca_system_score_gemma":0.0031294646,"threshold_uncertainty_score":0.9998308},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32730186,0.00033152325,0.08976592,0.10859708,0.0022816537,0.009288247,0.026558526,0.0059894007,0.4298858],"genre_scores_gemma":[0.94246936,0.00004922171,0.0041143536,0.050236303,0.0007472795,0.00011473485,0.0008843921,0.000052257503,0.001332113],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99500597,0.00004513504,0.0006360177,0.002036446,0.0014553169,0.0008210926],"domain_scores_gemma":[0.9933035,0.00009264993,0.00020042759,0.0022034375,0.00042988453,0.003770094],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005319137,0.0005354588,0.00088878616,0.0003085643,0.0001431508,0.00046619188,0.0015373466,0.00027330645,0.0002689276],"category_scores_gemma":[0.009260701,0.00050810596,0.00030962774,0.0016474405,0.00058643357,0.00019581463,0.0075183217,0.000582526,0.0010162883],"study_design_candidate":"not_applicable","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":[0.0018560113,0.00073859113,0.5419328,0.0025405316,0.0005746329,0.0013614995,0.0013429737,0.0027438367,0.00073411805,0.012748263,0.39641663,0.037010107],"study_design_scores_gemma":[0.0052737035,0.0006089398,0.24869686,0.00077628414,0.00027971787,0.00054742483,0.00070187816,0.0039192503,0.00017745432,0.034648858,0.70135355,0.0030160914],"about_ca_topic_score_codex":0.001016882,"about_ca_topic_score_gemma":0.00012067854,"teacher_disagreement_score":0.6151675,"about_ca_system_score_codex":0.0024041685,"about_ca_system_score_gemma":0.013771688,"threshold_uncertainty_score":0.9997615},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9965909,0.0007339679,0.00016578409,0.0012328578,0.0002132698,0.00063686125,0.00014562835,0.00009832228,0.00018237047],"genre_scores_gemma":[0.99603707,0.0002961459,0.00018246753,0.0024360332,0.00009485139,0.000112934824,0.0006942505,0.00002057147,0.00012565269],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99653906,0.0007681171,0.0013301966,0.0006682335,0.00014215815,0.000552245],"domain_scores_gemma":[0.99716896,0.0008016496,0.0003649713,0.0013547657,0.000077032964,0.00023263204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002151285,0.00029152556,0.0009088072,0.00044878866,0.000097394135,0.000027270009,0.00044867865,0.0002457144,0.00008201943],"category_scores_gemma":[0.0034116704,0.00023676784,0.000059606853,0.0006902615,0.00027690377,0.0004757262,0.0012497128,0.0007341226,0.000012709512],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052947557,0.0001818562,0.9943433,0.0001529575,0.000058196536,0.0002349317,0.001475781,0.0006398644,0.0007528717,0.000083405066,0.00030997675,0.0012374088],"study_design_scores_gemma":[0.0044661537,0.00007831248,0.98437625,0.000068201414,0.000037389138,0.00059839943,0.001209373,0.0058939196,0.000015384845,0.00012520776,0.0029212378,0.00021017942],"about_ca_topic_score_codex":0.00078572385,"about_ca_topic_score_gemma":0.012492148,"teacher_disagreement_score":0.011706425,"about_ca_system_score_codex":0.0004306131,"about_ca_system_score_gemma":0.0003140032,"threshold_uncertainty_score":0.96551126},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03859971,0.18567103,0.00060719834,0.13706675,0.6342481,0.0012042407,0.0024174429,0.000095979594,0.00008959347],"genre_scores_gemma":[0.013595094,0.32074818,0.07347722,0.010729291,0.5743108,0.000040733717,0.006516538,0.00030107683,0.00028111564],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99240464,0.001538135,0.002370979,0.00053203857,0.0025633401,0.000590883],"domain_scores_gemma":[0.99342006,0.0016693942,0.0027925987,0.00040188807,0.0007875225,0.0009285095],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.012177872,0.00038744276,0.0016042243,0.0020014415,0.0001676366,0.00021281329,0.0008775051,0.00031798397,0.00010837517],"category_scores_gemma":[0.0042915936,0.00038583582,0.0002152304,0.0004881433,0.00015100763,0.0003652809,0.00074838445,0.003054192,7.798857e-7],"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":[0.0003426005,0.00032121225,0.12397124,0.00053532585,0.0008254907,0.00019742367,0.0016426978,0.0000032907674,8.053746e-7,0.00014019567,0.56791896,0.30410075],"study_design_scores_gemma":[0.0031923847,0.0008775217,0.07162935,0.0008452914,0.000044545148,0.00028989324,0.00027580198,0.000024761186,3.557127e-7,0.0010657331,0.92153245,0.00022191854],"about_ca_topic_score_codex":0.002169793,"about_ca_topic_score_gemma":0.00044710858,"teacher_disagreement_score":0.3536135,"about_ca_system_score_codex":0.00060953404,"about_ca_system_score_gemma":0.0073594353,"threshold_uncertainty_score":0.99985933},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55162424,0.001348679,0.046067715,0.14576706,0.0026611655,0.02151602,0.0029932908,0.0027429478,0.22527887],"genre_scores_gemma":[0.9636715,0.00064598006,0.014755298,0.013137447,0.00037233124,0.0003728091,0.000906067,0.00010682545,0.006031761],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967751,0.00015342073,0.00070646615,0.0010046241,0.00094712304,0.00041325676],"domain_scores_gemma":[0.99542654,0.00009372687,0.00036730734,0.0032138973,0.00034877745,0.0005497458],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005799709,0.00049047684,0.0009213087,0.00019937633,0.00004833751,0.00008841967,0.0013155818,0.0002685823,0.00042119995],"category_scores_gemma":[0.0005089543,0.00035110736,0.00032132334,0.00033250957,0.00010268185,0.00010874256,0.0036459542,0.0008881741,0.00023551406],"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":[0.0026061311,0.0007704932,0.09718379,0.011279074,0.0047185845,0.00086253963,0.0041796397,0.0057114004,0.0042048204,0.01056857,0.7309741,0.12694088],"study_design_scores_gemma":[0.0025196278,0.00047649507,0.1951935,0.0015833187,0.00092024927,0.00005056612,0.0012123797,0.0038950553,0.0019158619,0.0039065606,0.78696537,0.0013610261],"about_ca_topic_score_codex":0.000113103095,"about_ca_topic_score_gemma":0.000078787394,"teacher_disagreement_score":0.4120472,"about_ca_system_score_codex":0.00018307634,"about_ca_system_score_gemma":0.0002466734,"threshold_uncertainty_score":0.9998941},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97634685,0.00022453934,0.005719187,0.0123273395,0.0001303793,0.0033973118,0.0010329629,0.0004328682,0.0003885896],"genre_scores_gemma":[0.98995006,0.00007750555,0.0004251366,0.002789658,0.00010837691,0.0001951797,0.006306519,0.00006929848,0.00007824126],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928721,0.0025753598,0.001152134,0.0018017223,0.0010552745,0.0005433937],"domain_scores_gemma":[0.99241203,0.0032794478,0.00048474208,0.0027453175,0.00032947797,0.0007489982],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0036064188,0.0006864635,0.0011853506,0.0003134074,0.00028300178,0.00018733882,0.0011527293,0.00065166573,0.00010509124],"category_scores_gemma":[0.010426545,0.0003779421,0.00012059667,0.0010083673,0.00037016912,0.00015465001,0.0020621384,0.00245227,0.00006037381],"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":[0.007645224,0.00022765365,0.9328764,0.0003845365,0.0006900401,0.00018716541,0.0025324763,0.04611189,0.0059911725,0.00012371951,0.0012068955,0.0020228103],"study_design_scores_gemma":[0.003585872,0.00013265271,0.8992761,0.00026406848,0.00013474419,0.000013129308,0.00016499689,0.094836354,0.00015368137,0.00024014033,0.00074086554,0.00045743072],"about_ca_topic_score_codex":0.008728741,"about_ca_topic_score_gemma":0.01359894,"teacher_disagreement_score":0.048724465,"about_ca_system_score_codex":0.00042515254,"about_ca_system_score_gemma":0.0021344447,"threshold_uncertainty_score":0.99986726},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99382263,0.0041669942,0.00029222408,0.0006706637,0.00038369925,0.0003500117,0.000092988936,0.000052527907,0.00016826767],"genre_scores_gemma":[0.99859464,0.00010041023,0.0008404959,0.00016438057,0.000039986895,0.000016877442,0.00010867472,0.000042169973,0.000092342074],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99632573,0.00031894742,0.0010830181,0.00024663575,0.0016427616,0.00038291843],"domain_scores_gemma":[0.99715257,0.00032059086,0.0011053612,0.0006041581,0.0006895386,0.00012780672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018117392,0.00023503826,0.0008298422,0.0005350822,0.00038365333,0.00003309288,0.00054867973,0.00004358868,0.00017372963],"category_scores_gemma":[0.00031197743,0.00015822715,0.000432093,0.0019049592,0.00037753876,0.000252835,0.00021744147,0.00025617567,0.000005692498],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010189676,0.00078253716,0.9843529,0.00015932138,0.0006907078,0.00069655845,0.0006178809,0.0011596258,0.006941386,0.00031464436,0.0015033791,0.0017620933],"study_design_scores_gemma":[0.001861682,0.0015236908,0.9876406,0.00020369542,0.00042602894,0.00395017,0.0019072172,0.0002663798,0.00057559717,0.00008898461,0.0013598974,0.00019601457],"about_ca_topic_score_codex":0.00005153272,"about_ca_topic_score_gemma":0.0000020534105,"teacher_disagreement_score":0.006365789,"about_ca_system_score_codex":0.000098606135,"about_ca_system_score_gemma":0.00038649086,"threshold_uncertainty_score":0.6452316},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9648355,0.000080286954,2.3467747e-7,0.00364432,0.00001349046,0.0002240813,0.00008794018,0.000016432572,0.031097671],"genre_scores_gemma":[0.99689174,0.000029561996,0.001915651,0.0006434499,0.00004902359,0.0000069496205,0.0000023025057,0.000004291369,0.00045703573],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99796313,0.000017182378,0.0003786673,0.00029125894,0.0012394602,0.00011029979],"domain_scores_gemma":[0.99876505,0.00011993456,0.0005787316,0.000024453877,0.0003708167,0.0001410341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015552867,0.000072416144,0.00024440946,0.00015788572,0.0001370367,0.000022048958,0.0010899693,0.00006196111,0.00006521573],"category_scores_gemma":[0.0015859717,0.00005146039,0.000057029196,0.0010832754,0.0009803819,0.00044831043,0.00031952755,0.00009887533,0.0000015018932],"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":[0.00007191086,0.00020624511,0.12912138,0.00021128372,0.00003639064,1.507107e-7,0.0002725505,0.000017177208,0.8556034,0.00930403,0.004995958,0.00015952434],"study_design_scores_gemma":[0.00049923,0.00011848826,0.51094973,0.00015847679,0.000032172906,0.000041618438,0.00034330433,0.00023034606,0.47956052,0.00659568,0.0013816254,0.00008881227],"about_ca_topic_score_codex":0.000025563615,"about_ca_topic_score_gemma":6.5466914e-8,"teacher_disagreement_score":0.38182834,"about_ca_system_score_codex":0.000057039684,"about_ca_system_score_gemma":0.00050989084,"threshold_uncertainty_score":0.36122578},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98866385,0.00011433113,0.00044627266,0.00549958,0.00014352631,0.0025536886,0.0024588257,0.00008851726,0.000031387175],"genre_scores_gemma":[0.9812437,0.00020792574,0.0005858145,0.002283172,0.00008365749,0.0002426658,0.015167342,0.000035456396,0.00015023368],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947954,0.0014511915,0.0011669729,0.0011960132,0.0006965766,0.00069388596],"domain_scores_gemma":[0.99470913,0.002804096,0.00029074206,0.0013476106,0.00042771766,0.00042071997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020810708,0.00056772813,0.0013669282,0.000593994,0.00008900185,0.00010343796,0.0012385097,0.00035002004,0.0003142855],"category_scores_gemma":[0.0030383212,0.00034185374,0.00021632478,0.001310477,0.00057948846,0.00011272392,0.0019705067,0.0020361247,0.00000643276],"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":[0.033981036,0.0006855401,0.7823832,0.0007906787,0.002961217,0.0006552813,0.004759625,0.16647686,0.00006617263,0.0025622142,0.0018974701,0.0027806945],"study_design_scores_gemma":[0.0057221167,0.00025921658,0.7082981,0.00019583025,0.0004742425,0.000005383607,0.0013931798,0.28102386,0.000020547863,0.00063409325,0.0015746424,0.00039880307],"about_ca_topic_score_codex":0.93064034,"about_ca_topic_score_gemma":0.9657893,"teacher_disagreement_score":0.11454701,"about_ca_system_score_codex":0.00082424085,"about_ca_system_score_gemma":0.0010228934,"threshold_uncertainty_score":0.9999033},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73780125,0.0027357396,0.21269709,0.0074352557,0.00056749943,0.0137289725,0.023641676,0.00067792117,0.0007145809],"genre_scores_gemma":[0.9846783,0.0013565164,0.0038132232,0.0005638488,0.00014858581,0.00052781263,0.0087975385,0.00005083105,0.000063330954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965886,0.000342578,0.00096178456,0.0012776462,0.00049526687,0.00033412146],"domain_scores_gemma":[0.994413,0.0015408855,0.00066884427,0.0029322915,0.00026907653,0.0001759061],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016857388,0.00045237315,0.0013489156,0.00032504342,0.00014743583,0.00014844048,0.00087620795,0.00026034212,0.000090895424],"category_scores_gemma":[0.0012194226,0.000278117,0.00032495326,0.0006177309,0.0006489271,0.00022990993,0.0015637842,0.00072822085,0.0000059932136],"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":[0.007648406,0.0013832517,0.952652,0.0021132317,0.006587144,0.000020397167,0.0022137873,0.013926155,0.00078535505,0.0031883663,0.0028555295,0.0066263718],"study_design_scores_gemma":[0.00878754,0.00012089892,0.56921834,0.00027640982,0.00314725,0.000010057688,0.00046056823,0.41178352,0.000059400943,0.00039126488,0.005362702,0.00038205012],"about_ca_topic_score_codex":0.0066361935,"about_ca_topic_score_gemma":0.033069115,"teacher_disagreement_score":0.39785734,"about_ca_system_score_codex":0.00013778661,"about_ca_system_score_gemma":0.0004194934,"threshold_uncertainty_score":0.9999787},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4686302,0.005682844,0.29974478,0.11254198,0.0015394472,0.016834384,0.06678631,0.0024232809,0.02581677],"genre_scores_gemma":[0.94014317,0.00025167456,0.01314689,0.01111304,0.00037720133,0.00096249866,0.032346264,0.000080010024,0.0015792423],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9923742,0.0011567192,0.0010437269,0.0021148054,0.0019034793,0.0014070881],"domain_scores_gemma":[0.99098337,0.0007845639,0.0003811506,0.004660167,0.0020297538,0.0011609796],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00558696,0.0005643995,0.0021039855,0.0011803098,0.00025723156,0.0007280807,0.0010320062,0.0003854582,0.0012275897],"category_scores_gemma":[0.0024802845,0.00050680304,0.0012750917,0.0029091868,0.0003084072,0.00017464207,0.0017177825,0.001001567,0.000079051744],"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":[0.0026919753,0.005968075,0.69998425,0.007441403,0.029870583,0.00026309345,0.00047361574,0.01717339,0.000037345,0.029527286,0.15190223,0.05466674],"study_design_scores_gemma":[0.0037543867,0.0008822959,0.7616626,0.0006627592,0.0015987142,0.000008326765,0.0017515116,0.008493264,0.00016323877,0.0007154916,0.21920523,0.0011022007],"about_ca_topic_score_codex":0.011636976,"about_ca_topic_score_gemma":0.012660313,"teacher_disagreement_score":0.471513,"about_ca_system_score_codex":0.002066702,"about_ca_system_score_gemma":0.008435291,"threshold_uncertainty_score":0.99973834},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96305895,0.0012906453,0.0014613689,0.025263878,0.00026095248,0.0040932996,0.00045028506,0.0005546328,0.0035659715],"genre_scores_gemma":[0.9782838,0.0008664587,0.00012526431,0.017605562,0.00033407038,0.0007393908,0.0018784275,0.000029940731,0.00013704723],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9937049,0.002382672,0.0009756895,0.0011042791,0.001299044,0.0005334253],"domain_scores_gemma":[0.9954862,0.0016890827,0.00044664965,0.0017680017,0.00021720654,0.0003928531],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0068670437,0.00047854803,0.00093663903,0.00019129783,0.00021151148,0.000087615386,0.0008710511,0.00050937885,0.0019286578],"category_scores_gemma":[0.008364944,0.00025582436,0.00020956443,0.0006036464,0.00026878194,0.000066232125,0.0008910657,0.0019789936,0.00008486813],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001008339,0.00016371276,0.9773025,0.0011234689,0.0001808826,0.00006143622,0.002043561,0.0049645645,0.0000735707,0.0003049903,0.009844359,0.0029286267],"study_design_scores_gemma":[0.0026546738,0.00008764364,0.96789974,0.000111580375,0.00018693537,0.00006750069,0.00035189072,0.0063652196,0.000003728221,0.0008724591,0.02110382,0.0002948291],"about_ca_topic_score_codex":0.00092597783,"about_ca_topic_score_gemma":0.0013039621,"teacher_disagreement_score":0.015224872,"about_ca_system_score_codex":0.0011407216,"about_ca_system_score_gemma":0.0018143171,"threshold_uncertainty_score":0.9999894},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97662693,0.008589469,0.00015827968,0.0013169827,0.00045716143,0.0001812542,0.00007559118,0.00009768991,0.012496614],"genre_scores_gemma":[0.9972542,0.00091948546,0.000015480398,0.00091744994,0.00045921013,0.0000013245684,0.0000040949844,0.000016306747,0.00041248236],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999029,0.000116260795,0.00030036134,0.000065923006,0.00033001084,0.00015847253],"domain_scores_gemma":[0.99879944,0.00015199432,0.00024473097,0.00034242374,0.00021406013,0.000247331],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019081586,0.000106872736,0.00025386133,0.00009389388,0.0000882155,0.000019690067,0.00016112244,0.000023332814,0.0013368181],"category_scores_gemma":[0.0005328211,0.00006250974,0.0001770931,0.00019560446,0.00010576217,0.00014171474,0.000036576286,0.00018051243,0.00018621497],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006246709,0.001680226,0.53666204,0.00012639756,0.0006762353,0.000501254,0.0003640915,0.00016982837,0.00086545054,0.00011368456,0.43724626,0.020969838],"study_design_scores_gemma":[0.0064532957,0.0017337193,0.8995244,0.00037711262,0.002090614,0.00566451,0.0002732708,0.0005694929,0.0002733208,0.0011963584,0.081478804,0.00036513258],"about_ca_topic_score_codex":0.000007517434,"about_ca_topic_score_gemma":0.0000042594634,"teacher_disagreement_score":0.36286232,"about_ca_system_score_codex":0.000056209352,"about_ca_system_score_gemma":0.00005527844,"threshold_uncertainty_score":0.9995761},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98508054,0.0013006036,0.00006710035,0.0007095406,0.000288901,0.0022672077,0.0003824049,0.00023634077,0.009667391],"genre_scores_gemma":[0.99800235,0.000016482722,0.00044304438,0.0002756634,0.00015598921,0.000758565,0.0001614042,0.000035612396,0.0001508689],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99676037,0.00011623549,0.0004068137,0.00042721268,0.00196165,0.00032774653],"domain_scores_gemma":[0.99794745,0.0000638928,0.00027017557,0.00030395726,0.0012667784,0.00014772949],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0033359195,0.00017332213,0.000429372,0.00014451025,0.00013953635,0.000019573963,0.00021494552,0.000040723997,0.0014752069],"category_scores_gemma":[0.0010200632,0.00017715643,0.00012452822,0.0005828527,0.00008194423,0.00011264112,0.00011997665,0.00022287911,0.000027708256],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013751531,0.0022808374,0.59732527,0.000580024,0.00086974015,0.000051485236,0.0016674853,0.00029027235,0.031152662,0.00065867224,0.049707044,0.31404135],"study_design_scores_gemma":[0.024417784,0.0021843659,0.7746117,0.00017478653,0.0013100447,0.00018726542,0.0027140393,0.056019913,0.0023553907,0.0011344153,0.13393983,0.00095047837],"about_ca_topic_score_codex":0.000060486087,"about_ca_topic_score_gemma":0.000009642395,"teacher_disagreement_score":0.31309086,"about_ca_system_score_codex":0.0002795331,"about_ca_system_score_gemma":0.00040357572,"threshold_uncertainty_score":0.9994376},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890964,0.0035686768,0.000088756715,0.00036108302,0.00054072315,0.001523049,0.00043154188,0.0007620824,0.0036276844],"genre_scores_gemma":[0.99694735,0.000019855484,0.00004630251,0.00013516484,0.0003369622,0.00079366355,0.0009991399,0.00010113101,0.00062044524],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99558395,0.00021300795,0.00066612643,0.00094234216,0.0017377808,0.00085680303],"domain_scores_gemma":[0.99739987,0.00007170758,0.00039085586,0.00056948856,0.0010457172,0.00052239373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021561559,0.0003975684,0.00064351753,0.0002927821,0.0006397111,0.000116282405,0.0004232771,0.00006302281,0.0007762598],"category_scores_gemma":[0.00054364314,0.00040077037,0.00020145033,0.0011965996,0.0000750609,0.00027903786,0.00024380865,0.0006109748,0.00025711232],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002839334,0.00045660976,0.91895294,0.004136471,0.0010842191,0.0005959051,0.003347458,0.00066625926,0.004787335,0.012727428,0.04413114,0.006274887],"study_design_scores_gemma":[0.013269141,0.0018796469,0.68323857,0.0022259038,0.0016966654,0.0006825434,0.041921716,0.16922419,0.00035060124,0.00021063424,0.08242318,0.0028771937],"about_ca_topic_score_codex":0.00028960477,"about_ca_topic_score_gemma":0.000017048767,"teacher_disagreement_score":0.23571438,"about_ca_system_score_codex":0.002982133,"about_ca_system_score_gemma":0.0011582725,"threshold_uncertainty_score":0.99984443},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997116,0.000039774608,0.0000222061,0.00014900177,0.000012021621,0.0016966868,0.0008524414,0.000023962706,0.00008785265],"genre_scores_gemma":[0.9908383,0.000004767831,0.00036841497,0.000098389995,0.00003497627,0.00030502817,0.008309343,0.000017219649,0.000023593053],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963848,0.00095979654,0.0006019504,0.0003720882,0.0013237188,0.0003576587],"domain_scores_gemma":[0.9974953,0.0005741327,0.00020231155,0.0008509093,0.0007621704,0.00011519446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033928992,0.00012158015,0.00039282336,0.0003984426,0.000060016522,0.000028739714,0.00029671975,0.00008036435,0.00006342831],"category_scores_gemma":[0.0010670343,0.00010991797,0.000025284771,0.0016192744,0.000048729657,0.0008263672,0.00015626106,0.0005272052,0.000013541579],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025977867,0.0019860668,0.9937866,0.00007073515,0.00006536349,0.000006248986,0.0012188012,0.0012594409,0.000056383604,0.00003344288,0.00033447996,0.0009227085],"study_design_scores_gemma":[0.0033857455,0.00047117195,0.98561275,0.00011703461,0.0000083328205,0.0000013226334,0.0015249136,0.008606773,0.000048454018,0.000046367823,0.000078173725,0.00009893931],"about_ca_topic_score_codex":0.0009341716,"about_ca_topic_score_gemma":0.0029045125,"teacher_disagreement_score":0.008173779,"about_ca_system_score_codex":0.0006147369,"about_ca_system_score_gemma":0.0007952893,"threshold_uncertainty_score":0.44823247},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9926718,0.00020554403,0.00014982503,0.0014167058,0.000055855624,0.0013301083,0.001946653,0.000078241304,0.002145268],"genre_scores_gemma":[0.99569046,0.00009482726,0.000116193325,0.000049723032,0.00012925858,0.0022412802,0.0011468375,0.00003145762,0.00049992977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99662435,0.0008221811,0.00037815873,0.00039460787,0.0012491479,0.0005315804],"domain_scores_gemma":[0.99731576,0.00149225,0.00012740455,0.00052028336,0.00032900998,0.0002153066],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0033538628,0.0001668628,0.00043448264,0.0006481531,0.0013840673,0.00008828972,0.00027615664,0.000033187134,0.00023790279],"category_scores_gemma":[0.00039966995,0.00013797317,0.00020458044,0.0018103688,0.0003047429,0.0006393643,0.00047340855,0.0010915303,0.00003658674],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046085435,0.00016151412,0.9303459,0.00017357401,0.0019883914,0.00002089827,0.033401854,0.000033023472,0.000013427697,0.0018096641,0.030627912,0.0009629773],"study_design_scores_gemma":[0.0011555157,0.00022630992,0.9652929,0.000035004276,0.000154572,0.0000042461297,0.014919861,0.0007521299,0.000018461145,0.0013022755,0.015986077,0.00015267926],"about_ca_topic_score_codex":0.000104167695,"about_ca_topic_score_gemma":0.00015140118,"teacher_disagreement_score":0.034946956,"about_ca_system_score_codex":0.00057040865,"about_ca_system_score_gemma":0.0001213148,"threshold_uncertainty_score":0.999916},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036869675,0.0046386295,0.7796289,0.035874847,0.0042978465,0.0043073553,0.12371651,0.0009646279,0.009701652],"genre_scores_gemma":[0.974975,0.000112872964,0.012532107,0.0012244801,0.004271272,0.00012123347,0.0052246377,0.00006829592,0.001470094],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99877673,0.0000920679,0.00018967557,0.00028763062,0.00042126467,0.0002326276],"domain_scores_gemma":[0.9990667,0.000057649504,0.000075576965,0.00033179967,0.000053732703,0.00041454492],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009035021,0.00013500285,0.00020223926,0.00005868574,0.00019802459,0.00002288212,0.000090386035,0.00001809352,0.0015667749],"category_scores_gemma":[0.000042860232,0.00013767603,0.000031357078,0.0001276441,0.0000739329,0.00003020459,0.00020660703,0.00018387519,0.000015498645],"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":[0.0010131259,0.00046864757,0.26159328,0.00046496102,0.00015722585,0.0020518939,0.0004365593,0.00006270615,0.00005942415,0.0037020643,0.48016143,0.2498287],"study_design_scores_gemma":[0.0008192987,0.00011271554,0.39939412,0.000007248968,0.000065132685,0.0000145030135,0.00010817205,0.005735953,0.0000012912684,0.0009161214,0.592684,0.00014143159],"about_ca_topic_score_codex":0.00013391716,"about_ca_topic_score_gemma":0.000014801297,"teacher_disagreement_score":0.93810534,"about_ca_system_score_codex":0.00005905129,"about_ca_system_score_gemma":0.00010805983,"threshold_uncertainty_score":0.9993459},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98992157,0.000605457,0.0000027920664,0.0003269134,0.00033011773,0.00068626367,0.00012406091,0.00006400342,0.0079388395],"genre_scores_gemma":[0.99946666,0.000005192985,0.0000271431,0.00010884045,0.00006347738,0.0001190238,0.000028490413,0.000019159588,0.0001619968],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976465,0.00012574656,0.0003207211,0.0002648091,0.0014473241,0.00019487608],"domain_scores_gemma":[0.9989632,0.000043061187,0.00019471643,0.00029865376,0.00045536,0.00004503066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031564797,0.00010533155,0.0002601109,0.00010466593,0.000096242926,0.00000821935,0.00025176743,0.000029593615,0.00015922647],"category_scores_gemma":[0.0006298387,0.000085640226,0.000079498655,0.00066240586,0.000047778263,0.000069497,0.0001938369,0.00020531958,0.00001414826],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017965044,0.00024410652,0.96000457,0.0005086678,0.00008613842,0.000012045988,0.0014656092,0.00034477966,0.011021256,0.001480977,0.015348237,0.009303935],"study_design_scores_gemma":[0.00241402,0.00006558797,0.98078793,0.00015819186,0.000092505405,0.00007802194,0.0019521296,0.008190562,0.00097225147,0.00011744984,0.0050251023,0.00014622278],"about_ca_topic_score_codex":0.00014922328,"about_ca_topic_score_gemma":0.00004096979,"teacher_disagreement_score":0.020783357,"about_ca_system_score_codex":0.0005048364,"about_ca_system_score_gemma":0.0002861273,"threshold_uncertainty_score":0.3492307},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002535131,0.0015202004,0.0004690629,0.00022207542,0.0010239413,0.00032597987,0.0009214975,0.00012442753,0.9951393],"genre_scores_gemma":[0.008540968,0.000119696386,0.00023077449,0.0012693651,0.0010811463,0.000021206111,0.010829354,0.00005745702,0.97785],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876946,0.000023666202,0.00025370126,0.0003734852,0.0003240066,0.00025567238],"domain_scores_gemma":[0.99893093,0.000026141444,0.00008759231,0.0007024693,0.00009502699,0.00015785526],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000056178164,0.00024164049,0.0005293449,0.00012704384,0.00002105683,0.000019063873,0.0001352496,0.00025866518,0.04665733],"category_scores_gemma":[0.000026097921,0.00019727094,0.00015213984,0.00013995646,0.00005442468,0.00002724309,0.000074292846,0.00030192427,0.09008061],"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":[0.00003971554,0.0000730016,0.0015353874,0.00019395642,0.00006540438,0.00006045695,0.0000012069778,0.0000052034293,0.000012064883,0.000018069299,0.99615884,0.0018366704],"study_design_scores_gemma":[0.000600143,0.000031405685,0.013681763,0.00011556426,0.000045466528,0.000016527518,0.000002177184,0.000030842304,0.00003194242,0.000008417634,0.9852332,0.00020253335],"about_ca_topic_score_codex":0.00031277654,"about_ca_topic_score_gemma":0.00005177027,"teacher_disagreement_score":0.043423276,"about_ca_system_score_codex":0.00007713009,"about_ca_system_score_gemma":0.0002924135,"threshold_uncertainty_score":0.95421416},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845301,0.00027131126,0.0007173559,0.0036730585,0.00073065853,0.0071056588,0.0008057587,0.000961886,0.0012042156],"genre_scores_gemma":[0.97575545,0.00014656885,0.0040878295,0.006605049,0.00017807748,0.0008731199,0.0070572016,0.00012119924,0.0051755104],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9937389,0.0009117784,0.0015157366,0.0022267879,0.00086952385,0.0007372539],"domain_scores_gemma":[0.99278194,0.0004116474,0.00019636992,0.0039450065,0.0008792577,0.0017858112],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017503016,0.0006832561,0.0018919575,0.0005452976,0.00009747362,0.00006916799,0.00088023656,0.00073707366,0.008909302],"category_scores_gemma":[0.0051630586,0.00062841555,0.0003948337,0.00066244794,0.0000796187,0.00006497515,0.0054959217,0.0023176244,0.00072097976],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046996473,0.0059619,0.97487694,0.00056930166,0.00063908286,0.002705473,0.001915383,0.003506628,0.00020677924,0.000027195132,0.006649559,0.002471783],"study_design_scores_gemma":[0.0035197346,0.0006193711,0.9850954,0.00019219128,0.0005507071,0.00017548268,0.0049946485,0.0004876064,0.000026879608,0.0000322131,0.0036260907,0.00067971967],"about_ca_topic_score_codex":0.00426272,"about_ca_topic_score_gemma":0.003002721,"teacher_disagreement_score":0.010218405,"about_ca_system_score_codex":0.0008158949,"about_ca_system_score_gemma":0.0032789346,"threshold_uncertainty_score":0.9999841},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06156812,0.0047903555,0.583631,0.29339537,0.00067539205,0.021535657,0.011282956,0.0012374856,0.02188371],"genre_scores_gemma":[0.9778193,0.0015575802,0.0063068816,0.007934502,0.00006894169,0.00042105548,0.0055533266,0.0000444307,0.00029394316],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99729085,0.0003426817,0.0007626641,0.00084733526,0.0004768283,0.000279625],"domain_scores_gemma":[0.99743956,0.0007226151,0.00042228057,0.00095987855,0.00010900709,0.00034668288],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009510332,0.0003227278,0.00083796156,0.000213614,0.0002120519,0.00010555762,0.00029422247,0.00015571846,0.0002974306],"category_scores_gemma":[0.00085820275,0.00031823662,0.00010307822,0.00022691872,0.00013581432,0.00015201882,0.00089609245,0.0005276311,0.0000066574703],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031786615,0.002488698,0.84298635,0.0052125514,0.0007754436,0.00005911161,0.02399045,0.002320027,0.000007565475,0.044055656,0.07021862,0.0047068456],"study_design_scores_gemma":[0.037261646,0.0015121145,0.6870375,0.0011483004,0.00032151453,0.00002967174,0.018694093,0.024041919,0.000014015772,0.058568522,0.16972093,0.0016497675],"about_ca_topic_score_codex":0.0027416258,"about_ca_topic_score_gemma":0.007827136,"teacher_disagreement_score":0.91625124,"about_ca_system_score_codex":0.0018848573,"about_ca_system_score_gemma":0.0014340375,"threshold_uncertainty_score":0.999927},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99638736,0.00004763571,0.0000025914715,0.0026491983,0.00007769909,0.0005684643,0.00006135468,0.00016368243,0.000041992567],"genre_scores_gemma":[0.9917027,0.000025459361,0.00005348212,0.008055357,0.00005675081,0.00005463379,0.000031948988,0.0000063836887,0.000013324779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99644625,0.0013137377,0.0003660048,0.00095686497,0.00038480907,0.000532327],"domain_scores_gemma":[0.9987582,0.0003983447,0.000059352875,0.00028337163,0.000020556783,0.00048017982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016980564,0.00017212924,0.00041076465,0.00019136252,0.0004333036,0.000026575068,0.0005431894,0.00007153938,0.0003915344],"category_scores_gemma":[0.0016836168,0.0001173065,0.00004116534,0.0006320264,0.0009155823,0.00011367395,0.0011046315,0.0008436268,0.000004568593],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006605628,0.0012570918,0.99327475,0.000013070892,0.000003481696,0.0010782393,0.00019680116,0.0010162604,0.00044795778,0.0000630361,0.00001024628,0.0019785026],"study_design_scores_gemma":[0.0012236037,0.012239807,0.9822376,0.0000012163297,0.0000064130563,0.0005112979,0.00042753413,0.0025816832,0.0000017662823,0.00030130762,0.00036427745,0.000103518214],"about_ca_topic_score_codex":0.000093881186,"about_ca_topic_score_gemma":0.0015542002,"teacher_disagreement_score":0.011037175,"about_ca_system_score_codex":0.000049790935,"about_ca_system_score_gemma":0.00012722482,"threshold_uncertainty_score":0.47836202},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9900955,0.0015904142,0.000017931488,0.0033883287,0.00019668142,0.0014179414,0.0013686327,0.000113144415,0.001811428],"genre_scores_gemma":[0.9978913,0.00004448086,0.0000022941335,0.001160178,0.00012909668,0.0003583601,0.00019012319,0.000026220678,0.0001979188],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956157,0.00032460658,0.0004587745,0.00044132385,0.0027964388,0.00036310704],"domain_scores_gemma":[0.99831396,0.0002117319,0.00038597552,0.00030335918,0.0005982479,0.00018675065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046085753,0.00018877107,0.0002895849,0.000075054944,0.00030644765,0.000024870391,0.00049941975,0.000036756475,0.0001386558],"category_scores_gemma":[0.0034519848,0.00013221134,0.00013283922,0.0012484482,0.00013100391,0.00011642811,0.00025122476,0.00041317687,0.000009874483],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006083671,0.00020180781,0.9661787,0.00039279615,0.000101143385,0.0000071066333,0.00041236792,0.0022015928,0.0014239372,0.0020364216,0.025985204,0.00045051696],"study_design_scores_gemma":[0.0030843725,0.000083920044,0.9497416,0.000056912926,0.00018622192,0.000117029434,0.0009999169,0.025074024,0.000023950264,0.0006477953,0.019751903,0.00023236088],"about_ca_topic_score_codex":0.0002204072,"about_ca_topic_score_gemma":0.00017528742,"teacher_disagreement_score":0.022872431,"about_ca_system_score_codex":0.004257018,"about_ca_system_score_gemma":0.0027108232,"threshold_uncertainty_score":0.9995655},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9834312,0.00048245658,0.00018289256,0.0012963029,0.0007788759,0.0020232166,0.0008892348,0.00026070612,0.010655092],"genre_scores_gemma":[0.99469525,0.000033816632,0.0002798977,0.0018735486,0.00053538143,0.0009504698,0.0005241652,0.000053091157,0.0010544078],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977699,0.000054240438,0.00036206396,0.0005602572,0.0008982314,0.0003553073],"domain_scores_gemma":[0.9983692,0.000071035014,0.00021684253,0.00031015804,0.0008656136,0.00016716076],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017648654,0.0001998371,0.00033761372,0.00018516523,0.00044000457,0.000043702443,0.0001642487,0.00005925667,0.002152118],"category_scores_gemma":[0.0006854901,0.0002198974,0.00014942484,0.0006254635,0.000052617746,0.00022790885,0.00019963247,0.00024055115,0.000044992878],"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":[0.0007855765,0.00025764699,0.5006125,0.00021300149,0.0003472563,0.000013965661,0.00034588642,0.000062829706,0.0069304975,0.00030198068,0.47188896,0.018239917],"study_design_scores_gemma":[0.0049900496,0.00077331,0.36129472,0.00004293828,0.0003009302,0.00008580547,0.00021724672,0.0016661196,0.0007209317,0.0009999111,0.62842816,0.00047989842],"about_ca_topic_score_codex":0.000044810356,"about_ca_topic_score_gemma":0.000030931402,"teacher_disagreement_score":0.15653917,"about_ca_system_score_codex":0.00048460354,"about_ca_system_score_gemma":0.00035730828,"threshold_uncertainty_score":0.99876004},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84635663,0.0015194715,0.12220282,0.017902287,0.00065207476,0.00045646128,0.010747566,0.000010184226,0.00015247968],"genre_scores_gemma":[0.98610306,0.0005417982,0.012477065,0.00033520014,0.000105021965,0.000004368134,0.00039954862,0.000012470852,0.000021495676],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9937603,0.0010693219,0.0009030322,0.00025233102,0.0037758863,0.00023913576],"domain_scores_gemma":[0.991065,0.0070624263,0.0002981001,0.00025797702,0.00086801546,0.00044845697],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01272141,0.000083175415,0.00035572026,0.00066152983,0.00006564289,0.000023207027,0.00071523467,0.000052718413,0.0005737655],"category_scores_gemma":[0.07557517,0.000080183294,0.000023332605,0.00040920445,0.0003619436,0.00011895704,0.0008679351,0.0012826977,0.0000011459433],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041711926,0.0012359101,0.57155734,0.00040641648,0.00019892085,0.12416393,0.0017280752,0.0009964939,0.00046438328,0.0010017371,0.020264257,0.27381134],"study_design_scores_gemma":[0.01535605,0.0007415249,0.6174093,0.00059860543,0.000020643787,0.020623812,0.0030344473,0.31718138,0.000048619855,0.009308111,0.015403841,0.00027366434],"about_ca_topic_score_codex":0.0015135347,"about_ca_topic_score_gemma":0.001986188,"teacher_disagreement_score":0.3161849,"about_ca_system_score_codex":0.00058833655,"about_ca_system_score_gemma":0.002302239,"threshold_uncertainty_score":0.93221164},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7825155,0.090852804,0.058046997,0.055382255,0.0008031341,0.0071384967,0.0043297824,0.0007180434,0.0002129539],"genre_scores_gemma":[0.970731,0.00017779437,0.003652123,0.017555295,0.00010341952,0.00083690195,0.006757552,0.000054377953,0.00013152463],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953047,0.0005754876,0.0014765548,0.00077502435,0.0007247821,0.0011434599],"domain_scores_gemma":[0.9962203,0.00028684048,0.0009793523,0.00048966176,0.00023755783,0.0017862945],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004830491,0.0003411162,0.0011363832,0.00032497282,0.000915712,0.00013055735,0.00031905487,0.000059198486,0.00010541596],"category_scores_gemma":[0.0010366681,0.00031272045,0.00013771087,0.0006030162,0.000073795716,0.0003875396,0.0003285238,0.0004028005,0.0000028896523],"study_design_candidate":"design_other","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":[0.0004665688,0.00074233726,0.15288466,0.009234075,0.00017844739,0.000026570198,0.019995207,0.0000014430901,0.00008173519,0.000094877636,0.007347787,0.8089463],"study_design_scores_gemma":[0.010776739,0.000758863,0.17169589,0.0003339524,0.0000063286225,0.0001329912,0.019908326,0.004042628,0.000009174603,0.000056006098,0.79144734,0.0008317267],"about_ca_topic_score_codex":0.000318007,"about_ca_topic_score_gemma":0.0016144485,"teacher_disagreement_score":0.8081146,"about_ca_system_score_codex":0.0011869983,"about_ca_system_score_gemma":0.007558582,"threshold_uncertainty_score":0.99993247},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069491,0.0017740282,0.00010434847,0.0017748006,0.9086179,0.002912834,0.00539858,0.0007805471,0.009145958],"genre_scores_gemma":[0.055577606,0.0014424676,0.0011900105,0.012675184,0.8767827,0.004134613,0.04505364,0.00047316603,0.0026706234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994228,0.000437585,0.0018745214,0.0014427373,0.00061268144,0.0014044645],"domain_scores_gemma":[0.99111825,0.005115641,0.00091863435,0.0015333235,0.00087669113,0.00043744047],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0011912708,0.0008284949,0.003017174,0.00081034796,0.000089198424,0.000026643136,0.00078964554,0.0019715393,0.00079855736],"category_scores_gemma":[0.03897872,0.0008257128,0.0004671793,0.00053111685,0.00048619029,0.0002857699,0.0017630957,0.0057581696,0.00028512912],"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":[0.0013425563,0.00012627628,0.11357132,0.0002776186,0.00013526488,0.0007841757,0.0002771981,0.00019343363,0.000009322635,0.000104534316,0.88192374,0.0012545353],"study_design_scores_gemma":[0.0027552824,0.0007484477,0.018116824,0.00022580916,0.00006501129,0.00011631201,0.00028189388,0.00026375524,4.5715353e-7,0.0010414381,0.9757085,0.00067630544],"about_ca_topic_score_codex":0.00018627408,"about_ca_topic_score_gemma":0.00014225865,"teacher_disagreement_score":0.0954545,"about_ca_system_score_codex":0.001288382,"about_ca_system_score_gemma":0.002137104,"threshold_uncertainty_score":0.9994194},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99116933,0.0005835979,0.0000018096166,0.00035339687,0.0002263798,0.0012788092,0.00013773413,0.00005654341,0.0061924127],"genre_scores_gemma":[0.99929565,0.0000073546516,0.000032199863,0.00012462933,0.00005901397,0.00025286997,0.00003504155,0.000025928075,0.00016733697],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99706835,0.00020012431,0.00053916825,0.00038984834,0.0015284291,0.0002740771],"domain_scores_gemma":[0.9989177,0.00006264249,0.00025415423,0.00032599183,0.0003762972,0.00006323735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003959095,0.00015028396,0.0003659891,0.00013986666,0.00007438255,0.000012945149,0.00029582245,0.00004412385,0.00013643091],"category_scores_gemma":[0.0005585172,0.00012496316,0.000077036864,0.0010733387,0.000062645515,0.00010306718,0.00022940965,0.00031833246,0.000008830496],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012064844,0.00018570111,0.9894195,0.00042370908,0.00002542741,0.00002531036,0.00060588686,0.00022871944,0.0029400678,0.0005286199,0.0030752085,0.0024212024],"study_design_scores_gemma":[0.0026466423,0.000046016943,0.98211884,0.00024173749,0.000045644632,0.00004371006,0.0021570998,0.0071062767,0.0003702669,0.00008797154,0.0049771736,0.00015860106],"about_ca_topic_score_codex":0.0003672008,"about_ca_topic_score_gemma":0.0006683796,"teacher_disagreement_score":0.008126309,"about_ca_system_score_codex":0.0009819869,"about_ca_system_score_gemma":0.0005892283,"threshold_uncertainty_score":0.50958496},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6498489,0.006516788,0.000017299566,0.34028342,0.00047389936,0.0014606691,0.0003838574,0.000034429228,0.0009807716],"genre_scores_gemma":[0.9073997,0.0025836572,0.000013662899,0.08835603,0.0004294224,0.000044638426,0.00005078132,0.00009689839,0.0010251696],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99700785,0.0007294054,0.0005695916,0.00031434407,0.001018533,0.00036030472],"domain_scores_gemma":[0.99656844,0.0015803265,0.0010001533,0.0005637523,0.00014144773,0.00014587409],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0010761202,0.00030130075,0.000697771,0.00024480573,0.00038188414,0.000024578188,0.0006015546,0.00021555283,0.0002561781],"category_scores_gemma":[0.002590722,0.00017359635,0.00044448537,0.0004432338,0.00076496875,0.000115715164,0.00031863534,0.0028556646,0.0000017931062],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008063287,0.000104996376,0.7083521,0.00079046853,0.00088364305,0.00025208906,0.0013376622,0.0006744684,0.00015189215,0.000003268503,0.28562474,0.0010183434],"study_design_scores_gemma":[0.0035530804,0.00057891256,0.7259827,0.0004369613,0.00047588968,0.00070981,0.0012446751,0.000042037227,0.000028273107,0.00039419948,0.2662786,0.00027480442],"about_ca_topic_score_codex":0.00057482003,"about_ca_topic_score_gemma":0.0011240247,"teacher_disagreement_score":0.2575509,"about_ca_system_score_codex":0.0005544511,"about_ca_system_score_gemma":0.0006751015,"threshold_uncertainty_score":0.9994448},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8480797,0.10415994,0.00023443917,0.0077110077,0.001557054,0.00074532215,0.025468828,0.00024025184,0.011803485],"genre_scores_gemma":[0.93533754,0.056863803,0.00027638432,0.0002774405,0.0007731687,0.0002150366,0.00068393315,0.0001488972,0.0054237894],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9905299,0.003342077,0.0010409026,0.001188121,0.0015501993,0.0023488002],"domain_scores_gemma":[0.99296093,0.0041707237,0.00053657865,0.0007048797,0.0004474275,0.001179432],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001279182,0.0011386981,0.0014342713,0.00024347642,0.0036857757,0.00018753705,0.0009469565,0.00033353144,0.0053521423],"category_scores_gemma":[0.0012869155,0.0012114851,0.0004104513,0.0008097871,0.0019723056,0.00044833776,0.0002627087,0.0015436944,0.0000030520052],"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":true,"study_design_scores_codex":[0.00047472655,0.0006269052,0.91753733,0.006068287,0.0020591505,0.0016707707,0.0056571844,0.0051454953,0.00042650328,0.004229561,0.027864847,0.02823924],"study_design_scores_gemma":[0.0008954785,0.00035298124,0.63306314,0.0018118824,0.00034593226,0.00020314046,0.021536136,0.0003825578,0.0007639713,0.00017800911,0.33952537,0.00094137795],"about_ca_topic_score_codex":0.9981608,"about_ca_topic_score_gemma":0.9999386,"teacher_disagreement_score":0.31166053,"about_ca_system_score_codex":0.008441801,"about_ca_system_score_gemma":0.07644159,"threshold_uncertainty_score":0.9990335},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98458683,0.0018432104,0.0006145147,0.00020063398,0.00045279262,0.0015979008,0.010228215,0.00013537746,0.00034054907],"genre_scores_gemma":[0.99737257,0.0003717342,0.0005260947,0.000062740284,0.00010953498,0.00017320582,0.0012747016,0.000054689688,0.000054740452],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99539447,0.00024810995,0.0025684051,0.0010345336,0.0004439402,0.00031057026],"domain_scores_gemma":[0.9925938,0.00053285225,0.003992775,0.0019737843,0.00074101955,0.00016575388],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033707495,0.0003897422,0.0014865192,0.00015446283,0.000071017545,0.000012197849,0.00032912422,0.0001295729,0.000026631655],"category_scores_gemma":[0.014576478,0.00034370983,0.0003744935,0.00026017652,0.00028440607,0.00005003712,0.0010233134,0.0004373606,8.048237e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002392821,0.00031943558,0.97540736,0.010524625,0.00020868132,0.0000491411,0.00017921528,0.00009103254,0.007414675,0.000009300761,0.0004906407,0.0029130897],"study_design_scores_gemma":[0.0016109583,0.00012935483,0.98246795,0.00080952974,0.0001364737,0.000014665746,0.00003578205,0.005886827,0.006360512,0.0004273078,0.0017983501,0.00032226075],"about_ca_topic_score_codex":0.00016212791,"about_ca_topic_score_gemma":0.00007861921,"teacher_disagreement_score":0.012785752,"about_ca_system_score_codex":0.000059972004,"about_ca_system_score_gemma":0.0005424375,"threshold_uncertainty_score":0.9999015},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97775465,0.0008044804,0.0020945037,0.0014059258,0.00013039107,0.009794949,0.0076091895,0.0003936439,0.000012250774],"genre_scores_gemma":[0.9821097,0.00017747085,0.010449791,0.00001973911,0.000121270634,0.001770415,0.0051184213,0.00016015465,0.000073004776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9910243,0.0014056405,0.0014199503,0.0021021103,0.0023407084,0.0017072603],"domain_scores_gemma":[0.9923462,0.0012774416,0.00046359745,0.0015341094,0.0030695929,0.0013090294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0052533103,0.000624985,0.001198631,0.00044985933,0.0007473344,0.00019801143,0.0006309917,0.00036748624,0.000024850176],"category_scores_gemma":[0.0036657522,0.0006400291,0.00022652691,0.0008578518,0.00028492403,0.00016698736,0.0012928935,0.00094716187,0.0000033583178],"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":[0.027430546,0.0012000223,0.6854842,0.19177362,0.0030032583,0.0006707645,0.001970323,0.0032044253,0.062306058,0.00010314394,0.010773419,0.012080234],"study_design_scores_gemma":[0.007236038,0.0013691226,0.82833695,0.009339139,0.00015180273,0.00014239064,0.0010321036,0.14134493,0.004899921,0.0001532411,0.004758903,0.0012354249],"about_ca_topic_score_codex":0.011718348,"about_ca_topic_score_gemma":0.03202172,"teacher_disagreement_score":0.18243448,"about_ca_system_score_codex":0.005850523,"about_ca_system_score_gemma":0.0042106174,"threshold_uncertainty_score":0.9996051},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9939206,0.0010844027,0.000004969466,0.0017107371,0.000115093426,0.0012869535,0.00036072507,0.00008445331,0.0014320685],"genre_scores_gemma":[0.99783033,0.00061682693,0.00004931318,0.000518541,0.00007988436,0.00030502075,0.00014830704,0.000033631477,0.00041814198],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972696,0.00076219946,0.00058603735,0.0006856575,0.0004132834,0.00028325844],"domain_scores_gemma":[0.9977624,0.00042737284,0.00022993506,0.0013776656,0.00005590606,0.00014670921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008447266,0.0003263771,0.0008374348,0.00020264443,0.000036289068,0.000025648289,0.00042886336,0.0001814156,0.00038809964],"category_scores_gemma":[0.00027821012,0.00018172927,0.00014219663,0.00026837803,0.0003873208,0.00005567882,0.0017864541,0.00080271333,0.000031574877],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022827431,0.00048775884,0.98127264,0.00011119394,0.00068736007,0.00011808516,0.0060190614,0.000038797818,0.000051216342,0.0000946444,0.004687238,0.006203718],"study_design_scores_gemma":[0.0017787104,0.00022282058,0.99339706,0.000113241724,0.00018301708,0.0000046624177,0.002148183,0.00020461233,0.000008128468,0.00018919424,0.0016064714,0.0001438733],"about_ca_topic_score_codex":0.0007414795,"about_ca_topic_score_gemma":0.0015174123,"teacher_disagreement_score":0.012124433,"about_ca_system_score_codex":0.00010816961,"about_ca_system_score_gemma":0.0003007787,"threshold_uncertainty_score":0.74107045},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875855,0.0017793187,0.0005917528,0.0010698407,0.000060529073,0.0013999739,0.0066486844,0.0000698942,0.0007944951],"genre_scores_gemma":[0.9922975,0.000015421427,0.0011896293,0.00010634151,0.000021139398,0.000040325573,0.006294807,0.000004549041,0.000030299057],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989515,0.000033370816,0.00043552773,0.00013003686,0.00034267837,0.00010688066],"domain_scores_gemma":[0.9990427,0.000042171006,0.00041043127,0.00025760534,0.0001864648,0.00006060857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035946167,0.00010012321,0.00024625685,0.00009288497,0.000077117395,0.000018658475,0.00018271476,0.000031445164,0.000016338945],"category_scores_gemma":[0.00037002182,0.00007165923,0.000027674436,0.00031558363,0.00004827459,0.00062039134,0.00019927374,0.00011156634,3.5077053e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010499578,0.0019151261,0.57551575,0.03935232,0.0001489522,0.000020690028,0.012718157,0.0017884056,0.012659867,0.0023671098,0.10307266,0.2399414],"study_design_scores_gemma":[0.001794173,0.00017205012,0.9822679,0.000340294,0.000035696812,0.000022089744,0.0023570687,0.0064830575,0.00018769903,0.0000805881,0.006100436,0.00015895031],"about_ca_topic_score_codex":0.000018923936,"about_ca_topic_score_gemma":0.000016265973,"teacher_disagreement_score":0.40675217,"about_ca_system_score_codex":0.000029115168,"about_ca_system_score_gemma":0.00014343878,"threshold_uncertainty_score":0.29221785},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99194235,0.00030708444,0.000009581141,0.0027984658,0.00035460538,0.0012613597,0.003210629,0.000074598975,0.000041311454],"genre_scores_gemma":[0.9412267,0.000021353471,0.0000695211,0.0072771176,0.00009161192,0.00030076594,0.05095377,0.00001989139,0.000039260754],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971434,0.0011887507,0.00045732042,0.00047124492,0.00043421992,0.0003050709],"domain_scores_gemma":[0.9984865,0.00011370916,0.00014908926,0.0010658861,0.000012330904,0.0001724917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011373119,0.00013823295,0.0003033151,0.0003051806,0.0002539754,0.000010485117,0.00041852388,0.000023185063,0.00016170854],"category_scores_gemma":[0.000032607226,0.000110219975,0.000021500427,0.0007231214,0.000016913149,0.000105767205,0.00068422646,0.00029952504,0.00002558965],"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":[0.00077546225,0.001141148,0.907903,0.00003555352,0.00001771404,0.000030672065,0.011120379,0.00009439341,0.000019287107,0.0000027938227,0.058587637,0.020271948],"study_design_scores_gemma":[0.0017899796,0.000600492,0.9049534,0.000050851562,0.0000075422186,0.000003600811,0.03439302,0.0016206545,6.5483493e-7,0.000012903,0.05647046,0.000096398595],"about_ca_topic_score_codex":0.008232378,"about_ca_topic_score_gemma":0.0037571653,"teacher_disagreement_score":0.050715655,"about_ca_system_score_codex":0.00045942623,"about_ca_system_score_gemma":0.00009742509,"threshold_uncertainty_score":0.9983719},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.975555,0.0019983938,0.000059276947,0.00015473974,0.00010345933,0.0007925951,0.020870337,0.000079185316,0.00038701453],"genre_scores_gemma":[0.96979463,0.00048284815,0.0018296918,0.000017362094,0.00009687125,0.00017189837,0.026822409,0.00006600271,0.00071827444],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9914058,0.0027800868,0.001332711,0.0010554456,0.0025379614,0.00088801014],"domain_scores_gemma":[0.9939953,0.0005371325,0.00036959775,0.0029929997,0.0016784022,0.0004265765],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004289043,0.00034648253,0.002156848,0.004495887,0.00014211489,0.00002105311,0.001098806,0.00039739173,0.007011777],"category_scores_gemma":[0.0010410949,0.00035100855,0.00078766176,0.004207094,0.0008290736,0.00010196608,0.0031343629,0.0023443527,0.0000044109634],"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":[0.000866694,0.003664192,0.94514436,0.0024371285,0.0026337476,0.0003777417,0.0011508666,0.019299962,0.007348594,0.00018112412,0.00015191232,0.016743701],"study_design_scores_gemma":[0.00071660767,0.0007929485,0.95758283,0.00037282746,0.0014426465,0.000009017675,0.0017969128,0.036342844,0.00013143093,0.000018207878,0.0005499457,0.00024376268],"about_ca_topic_score_codex":0.012721944,"about_ca_topic_score_gemma":0.000751948,"teacher_disagreement_score":0.01704288,"about_ca_system_score_codex":0.00072693854,"about_ca_system_score_gemma":0.0021495102,"threshold_uncertainty_score":0.99995726},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9412092,0.0014312227,0.00019816715,0.027687581,0.00011118825,0.0020188852,0.004618231,0.00025085467,0.022474682],"genre_scores_gemma":[0.99209386,0.000015335712,0.000083051375,0.0023793175,0.00024954873,0.0001295399,0.0019645337,0.000014104912,0.0030707247],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99796075,0.00073061686,0.00014710378,0.00035306968,0.0005635817,0.00024488903],"domain_scores_gemma":[0.9876681,0.010545215,0.000030501473,0.0014759363,0.000033644756,0.00024660042],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032511856,0.00006905146,0.00013073573,0.00006790507,0.00080631336,0.000027443075,0.00037406394,0.000020872096,0.001986472],"category_scores_gemma":[0.0034843832,0.00004732862,0.000016633305,0.00053388294,0.00015917174,0.00005216968,0.0011141393,0.00047699612,0.00008284166],"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":[0.000073282536,0.000026639005,0.54084927,0.000016053566,0.000015630432,0.00006334972,0.00003915591,0.00012841252,4.069932e-7,0.0032482613,0.45521805,0.00032149366],"study_design_scores_gemma":[0.00042916212,0.0002726653,0.43605798,0.000011113674,0.000024836594,0.00003610608,0.0013405159,0.00042980976,7.29106e-7,0.0012630034,0.56006175,0.00007232925],"about_ca_topic_score_codex":0.00024587102,"about_ca_topic_score_gemma":0.00009369839,"teacher_disagreement_score":0.104843706,"about_ca_system_score_codex":0.00010210515,"about_ca_system_score_gemma":0.0002951988,"threshold_uncertainty_score":0.99892586},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09508777,0.061852764,0.00024936796,0.62399685,0.004364829,0.002125636,0.17912993,0.00092026347,0.032272615],"genre_scores_gemma":[0.9333508,0.000078225494,0.00033767236,0.047470525,0.00048754184,0.000027490862,0.017090777,0.000022799435,0.0011341862],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998717,0.00017479112,0.000105332,0.0003426522,0.0005162828,0.00014390446],"domain_scores_gemma":[0.9977129,0.0002730096,0.000051267314,0.0018354594,0.00003474355,0.00009258508],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004689539,0.00008866642,0.00019687587,0.000018975588,0.00018633148,0.000016730277,0.00084212783,0.00015139431,0.0022125877],"category_scores_gemma":[0.0008796316,0.000058572867,0.0000541978,0.00021369009,0.00008586547,0.000054687,0.0010040672,0.0021857065,0.000059810714],"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":[0.00060678925,0.00006359859,0.008074618,0.000008187244,0.000103370316,0.000114685725,0.00010134694,0.0000066857947,0.00006642166,0.0012539933,0.98613805,0.0034622755],"study_design_scores_gemma":[0.002480812,0.000013693091,0.025092844,0.0000069845732,0.00008502483,0.000022755128,0.000119409,0.00039607714,0.000007110914,0.00041170805,0.9712933,0.000070314476],"about_ca_topic_score_codex":0.00013765696,"about_ca_topic_score_gemma":0.000081706734,"teacher_disagreement_score":0.83826303,"about_ca_system_score_codex":0.00005671526,"about_ca_system_score_gemma":0.00022914815,"threshold_uncertainty_score":0.99869955},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014458853,0.0040646233,0.000011619706,0.0008295374,0.0005125147,0.000408397,0.004640031,0.00015751235,0.9879299],"genre_scores_gemma":[0.013125971,0.03560217,0.0021036228,0.0012349648,0.0013765721,0.0005027194,0.010952256,0.0006738852,0.93442786],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998828,0.000022789545,0.00026955234,0.00035051387,0.00039400626,0.00013516207],"domain_scores_gemma":[0.9991177,0.00004920737,0.0002554137,0.0003497974,0.00010877238,0.00011910736],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007320729,0.00018132584,0.0003035865,0.0007211615,0.000025276944,0.000007460685,0.00027197614,0.00008869497,0.018326223],"category_scores_gemma":[0.00010150353,0.00018504287,0.00016694405,0.00019831867,0.0000801315,0.00003843176,0.00020215186,0.00017917348,0.000046556157],"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":[0.00005321702,0.00028478922,0.3800801,0.00017924412,0.00081685116,0.000024164901,0.000022198952,6.720244e-7,0.00003309801,0.003376613,0.6067791,0.008349954],"study_design_scores_gemma":[0.00037082672,0.00003440494,0.236181,0.00005436912,0.00007166596,0.00001740465,0.000007998156,0.0000035967585,0.0000014084105,0.00012092688,0.7630207,0.000115685514],"about_ca_topic_score_codex":0.00014453148,"about_ca_topic_score_gemma":0.000028078797,"teacher_disagreement_score":0.15624161,"about_ca_system_score_codex":0.00002278209,"about_ca_system_score_gemma":0.000073368414,"threshold_uncertainty_score":0.9825712},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9478186,0.010851186,0.00201676,0.01585801,0.00027889694,0.01949075,0.0031945847,0.000045716995,0.0004455109],"genre_scores_gemma":[0.99630165,0.00012616582,0.00016414943,0.00044288146,0.000010553207,0.0024301482,0.00045775704,0.000008385461,0.00005828891],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984814,0.0002665921,0.0003675254,0.0002691218,0.0004064543,0.00020891035],"domain_scores_gemma":[0.99898744,0.00003345134,0.00022269659,0.00016567673,0.0000736955,0.00051702035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012165933,0.000102370184,0.00018546268,0.00009710154,0.0005743758,0.00001410832,0.00004962355,0.00001470167,0.000023341192],"category_scores_gemma":[0.000029339699,0.0000953575,0.000016552864,0.00014729469,0.000054449178,0.0000570194,0.00007800696,0.00006625244,6.699406e-8],"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":true,"study_design_scores_codex":[0.0005624809,0.00058530906,0.2996949,0.06538694,0.00045099872,0.0000132962905,0.0038997473,0.0010315867,0.0000072639095,0.021015825,0.0055593997,0.6017923],"study_design_scores_gemma":[0.006549752,0.00015352618,0.9165291,0.00035874214,0.00013384758,0.000022499877,0.0049401787,0.021835811,0.000003498957,0.00096636656,0.048273046,0.00023361725],"about_ca_topic_score_codex":0.22258411,"about_ca_topic_score_gemma":0.97392035,"teacher_disagreement_score":0.7513362,"about_ca_system_score_codex":0.0044640605,"about_ca_system_score_gemma":0.011785608,"threshold_uncertainty_score":0.99935764},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98901814,0.0008582696,0.000592495,0.0013533594,0.000022957154,0.0013401497,0.004730652,0.000036421003,0.0020475627],"genre_scores_gemma":[0.9971896,0.00006586329,0.0004396267,0.000057482062,0.000017025635,0.00030871568,0.0015712584,0.000012869927,0.00033756316],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99678034,0.0006258479,0.00053821324,0.00024311863,0.0011696583,0.00064283016],"domain_scores_gemma":[0.9984524,0.0004892368,0.00013717268,0.0003320091,0.0002807614,0.0003083796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046512526,0.00013099398,0.0005457191,0.003408386,0.00033596324,0.0000497755,0.00016933365,0.000038842758,0.00013568648],"category_scores_gemma":[0.00020296309,0.000121719815,0.00009347582,0.0034179762,0.00023498679,0.0006001885,0.00047548048,0.0006173229,0.0000055496457],"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":[0.004668379,0.0010109906,0.19145505,0.0031847768,0.0013698267,0.000014614947,0.44100508,0.0017091532,0.00022318646,0.003429968,0.025471106,0.32645786],"study_design_scores_gemma":[0.007062007,0.0036909448,0.7906252,0.0001298877,0.00010364265,0.0000095937085,0.051587425,0.118855834,0.000055871657,0.0002643834,0.027281133,0.0003340717],"about_ca_topic_score_codex":0.00016296939,"about_ca_topic_score_gemma":0.00003670127,"teacher_disagreement_score":0.59917015,"about_ca_system_score_codex":0.00032330738,"about_ca_system_score_gemma":0.00038746625,"threshold_uncertainty_score":0.496359},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9533398,0.00016603156,0.034899656,0.001021345,0.0007635423,0.0016370208,0.0076964623,0.00018792169,0.00028821407],"genre_scores_gemma":[0.9523488,0.00000911951,0.02763233,0.0033888945,0.00038739466,0.00004066552,0.016143354,0.00003588265,0.000013550531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99401665,0.00020728193,0.002335265,0.00033854038,0.0025894456,0.00051284226],"domain_scores_gemma":[0.9957213,0.00020311463,0.00084353785,0.0023389717,0.000118557684,0.00077455654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027231134,0.00023920403,0.0008573453,0.00017928526,0.0002768067,0.000031379386,0.0016345484,0.00009215876,0.00048502386],"category_scores_gemma":[0.0016407936,0.0002176469,0.000052382024,0.0004758232,0.00017153718,0.00033015967,0.005228559,0.0006610632,0.000023931443],"study_design_candidate":"design_other","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":[0.00023299715,0.00089401554,0.018889233,0.0048712688,0.0005755968,0.000087499204,0.011311509,0.00006348917,0.000020072046,0.00015568422,0.035221245,0.9276774],"study_design_scores_gemma":[0.0025663765,0.00018043192,0.0035924888,0.00067508663,0.00006543944,0.00016929697,0.0040604062,0.8869055,0.000009015323,0.0000060523153,0.10150441,0.00026548174],"about_ca_topic_score_codex":0.00025094976,"about_ca_topic_score_gemma":0.00060547754,"teacher_disagreement_score":0.9274119,"about_ca_system_score_codex":0.0003204479,"about_ca_system_score_gemma":0.007031704,"threshold_uncertainty_score":0.9985975},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046426704,0.98235273,0.010575518,0.0001943091,0.00011714874,0.004985825,0.0012885677,0.000016091813,0.0000055681194],"genre_scores_gemma":[0.00041834882,0.9939544,0.003912522,0.00093900756,0.000031314605,0.0005635151,0.00014235455,0.0000316355,0.000006867675],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942381,0.00025686854,0.0035558846,0.00029132026,0.0010977202,0.0005601487],"domain_scores_gemma":[0.98561823,0.009291592,0.003799756,0.0005512437,0.00024011682,0.0004990718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069745146,0.00034953383,0.0033450054,0.00036306054,0.00022131912,0.000044618046,0.00027768384,0.00014055918,0.00006261598],"category_scores_gemma":[0.008242073,0.00023359827,0.00023139632,0.000922989,0.00021477134,0.00016455521,0.0003226586,0.00036471747,1.687667e-7],"study_design_candidate":"design_other","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":[0.000062896375,0.000030428851,0.0050703227,0.17607434,0.000104530896,1.6126879e-7,0.00004922309,0.0000018893585,3.7219636e-9,0.000053677544,0.00065089174,0.8179016],"study_design_scores_gemma":[0.0015330124,0.0008194544,0.0021179754,0.28837484,0.00018377246,0.00003081181,0.00004455375,0.0017935587,3.120357e-8,0.000023093693,0.70480764,0.00027127052],"about_ca_topic_score_codex":0.00009926874,"about_ca_topic_score_gemma":0.0023817653,"teacher_disagreement_score":0.81763035,"about_ca_system_score_codex":0.00024662784,"about_ca_system_score_gemma":0.0075749666,"threshold_uncertainty_score":0.99805117},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990846,0.0006572686,0.0009336196,0.0020949643,0.0008381158,0.0014209289,0.0007949314,0.00042920982,0.001985012],"genre_scores_gemma":[0.98548234,0.000048176084,0.0031815097,0.0014806066,0.00031774593,0.001440107,0.006928003,0.000070438815,0.0010510597],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9940174,0.0015040847,0.0016201437,0.0013292625,0.0005203025,0.0010087809],"domain_scores_gemma":[0.9957989,0.0004537011,0.00044246664,0.002582941,0.00030196368,0.00042006865],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022752662,0.00050206576,0.0012499279,0.0005244414,0.00042329996,0.000011783435,0.0013825619,0.00019346291,0.0022792586],"category_scores_gemma":[0.002542224,0.00046994415,0.00019036267,0.00092480803,0.0001961566,0.00023934462,0.0042414176,0.0014609968,0.00010486677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062956737,0.0009725009,0.8886231,0.00007456821,0.00026050286,0.00019636357,0.0016952185,0.001626273,0.00007552424,0.000258199,0.088263944,0.017324194],"study_design_scores_gemma":[0.0041936906,0.0046777157,0.6881178,0.000036398003,0.00021266557,0.0003533869,0.0049479716,0.20689365,0.000004646734,0.00037253514,0.08933054,0.0008590205],"about_ca_topic_score_codex":0.0008314826,"about_ca_topic_score_gemma":0.0007218531,"teacher_disagreement_score":0.20526737,"about_ca_system_score_codex":0.0004600803,"about_ca_system_score_gemma":0.00036231184,"threshold_uncertainty_score":0.99977523},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8926841,0.0005689785,0.0070540397,0.0782062,0.00037177987,0.007086976,0.009297101,0.00031910426,0.004411694],"genre_scores_gemma":[0.9616648,0.000017435252,0.0012675461,0.0027696139,0.000073606236,0.0037335146,0.0015038266,0.00002308537,0.02894657],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99653417,0.0011037735,0.00081635074,0.00059223926,0.00062928436,0.0003241864],"domain_scores_gemma":[0.99728465,0.0009815823,0.00031939917,0.0007108957,0.00033424204,0.00036921157],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0052028806,0.00019816389,0.0010824777,0.0010424078,0.00012188407,0.0000089018495,0.00026626515,0.000091954156,0.0024492168],"category_scores_gemma":[0.039575048,0.0001746844,0.00042748195,0.0033664305,0.00004479946,0.000082764636,0.0002220245,0.00032155518,0.00001526809],"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":[0.042682108,0.0015648031,0.40034634,0.0001671796,0.0010851941,0.00012442347,0.0058341594,0.002018742,0.0023423345,0.0017107793,0.53986657,0.0022573676],"study_design_scores_gemma":[0.0018958102,0.0013600712,0.60160863,0.000011212813,0.00034285488,0.000005753064,0.00029166174,0.0006886027,0.00001768792,0.0003019947,0.39333028,0.00014542055],"about_ca_topic_score_codex":0.00006303273,"about_ca_topic_score_gemma":0.000115849594,"teacher_disagreement_score":0.20126231,"about_ca_system_score_codex":0.0011467409,"about_ca_system_score_gemma":0.0004294222,"threshold_uncertainty_score":0.9984627},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35683623,0.028374892,0.011859622,0.4290916,0.0012611904,0.021772848,0.053274646,0.0014969606,0.09603199],"genre_scores_gemma":[0.96191806,0.00037403498,0.0005134862,0.006883144,0.000092270064,0.0029824502,0.0066308137,0.00002391023,0.020581821],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99755985,0.00030673816,0.00061904656,0.000446234,0.0008190302,0.00024912972],"domain_scores_gemma":[0.9979014,0.00048250583,0.0003038279,0.0005326142,0.000583633,0.00019602539],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021151658,0.00015702023,0.00091012346,0.00048603746,0.00008760685,0.0000048300594,0.00021063337,0.00006250834,0.0035435497],"category_scores_gemma":[0.017938932,0.00013821314,0.00039965834,0.00237259,0.000049070077,0.00008007574,0.00015715366,0.00028034212,0.000005938317],"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":[0.001952696,0.0011733632,0.4170243,0.001471205,0.0014026674,0.00007729685,0.00080690923,0.0004052996,0.00010659612,0.0025635308,0.5713976,0.001618524],"study_design_scores_gemma":[0.002744456,0.0009616935,0.72197676,0.00011615986,0.0009616385,0.000011690573,0.00023201104,0.0012824147,0.000008921341,0.00072356005,0.2707918,0.00018889034],"about_ca_topic_score_codex":0.000057955458,"about_ca_topic_score_gemma":0.000056907415,"teacher_disagreement_score":0.60508186,"about_ca_system_score_codex":0.0007898576,"about_ca_system_score_gemma":0.00039996204,"threshold_uncertainty_score":0.9973673},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9239313,0.0003579321,0.007730721,0.031337593,0.00042823816,0.0038611938,0.0110780485,0.00082023436,0.020454735],"genre_scores_gemma":[0.9841409,0.000008308818,0.00036262203,0.0022784616,0.00009844562,0.0024379988,0.0031036504,0.000018463958,0.0075511504],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979813,0.0002503199,0.00043070465,0.0005631229,0.00043545943,0.00033906876],"domain_scores_gemma":[0.9984001,0.00052483927,0.0001738849,0.00049587776,0.00013526772,0.00027002016],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00086285215,0.00017434638,0.0006443175,0.0007259369,0.00018790495,0.00001329825,0.00020664676,0.000082326114,0.003108547],"category_scores_gemma":[0.0051180557,0.00016200653,0.00033645585,0.00252238,0.00004834287,0.000101658305,0.00017866082,0.00032737528,0.000013041814],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00312352,0.0009868467,0.87713677,0.000044454184,0.0012520155,0.00014391987,0.0012050844,0.0024436088,0.00015322467,0.002541561,0.109669246,0.0012997524],"study_design_scores_gemma":[0.003419018,0.00091117644,0.90269274,0.0000035503465,0.00052141486,0.000011754436,0.0004734389,0.0068112444,0.000007824108,0.0012792066,0.083675586,0.00019304248],"about_ca_topic_score_codex":0.00011236505,"about_ca_topic_score_gemma":0.00020780762,"teacher_disagreement_score":0.060209595,"about_ca_system_score_codex":0.0014038995,"about_ca_system_score_gemma":0.0003583371,"threshold_uncertainty_score":0.99780273},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950162,0.0011206781,0.0004174664,0.00015172052,0.0019048227,0.0002609863,0.00039964853,0.00010409633,0.00062436325],"genre_scores_gemma":[0.9977963,0.000013784441,0.00054200675,0.000057346137,0.00070812734,0.000025463973,0.00043056958,0.000026561476,0.00039982487],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998392,0.000095697455,0.00029790384,0.00029808911,0.00069962774,0.00021667265],"domain_scores_gemma":[0.9994059,0.000029762581,0.00014317074,0.00023374958,0.00007322481,0.0001141463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014327651,0.00013969757,0.00030720737,0.00009612991,0.0001355733,0.0000041323806,0.00018230287,0.00006482948,0.00067249546],"category_scores_gemma":[0.00014732679,0.0001376015,0.00017987344,0.0003634572,0.000019219722,0.00009514827,0.00023612249,0.0004357047,0.000017458284],"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":[0.0055669798,0.00081623776,0.036005653,0.00028828127,0.00082183146,0.00007385851,0.0065844674,0.000066105225,0.09316872,0.000036814912,0.02180751,0.8347635],"study_design_scores_gemma":[0.0034698804,0.00021946446,0.98452556,0.000021218777,0.00018211312,0.000016719168,0.0005415964,0.003993671,0.0008089344,0.00026244385,0.00563536,0.0003230358],"about_ca_topic_score_codex":0.00038300402,"about_ca_topic_score_gemma":0.00009782967,"teacher_disagreement_score":0.9485199,"about_ca_system_score_codex":0.00016252676,"about_ca_system_score_gemma":0.00012187254,"threshold_uncertainty_score":0.7363354},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9840443,0.0005521009,0.000012851939,0.0005629457,0.0006038939,0.00050787546,0.00052285625,0.0001664948,0.013026648],"genre_scores_gemma":[0.99368536,0.0000063974253,0.0001415398,0.0010178814,0.00035940934,0.0014338223,0.0003364748,0.000046435558,0.002972676],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974104,0.00012803859,0.000466984,0.0004917281,0.0008626299,0.00064021937],"domain_scores_gemma":[0.99865204,0.00008050723,0.00013235782,0.0005946386,0.00006057039,0.00047988733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089911243,0.00022131893,0.00039828153,0.0014100453,0.00016430713,0.000033085777,0.00028321432,0.000056801884,0.0007467514],"category_scores_gemma":[0.00029892262,0.0002238854,0.00013644194,0.0033881175,0.000026396861,0.00022497169,0.00042942236,0.00031530313,0.000024629644],"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":[0.0005684465,0.00028950608,0.27263653,0.00003297251,0.000019743646,0.00011671079,0.0002099651,0.0003702056,0.00001322504,0.000015418726,0.019105295,0.706622],"study_design_scores_gemma":[0.0015682395,0.00007772252,0.81989634,0.000005074389,0.000051631883,0.000015586611,0.00007797494,0.00021300255,0.000015308591,0.0000067830993,0.17786023,0.00021210412],"about_ca_topic_score_codex":0.00005221556,"about_ca_topic_score_gemma":0.00008556485,"teacher_disagreement_score":0.7064099,"about_ca_system_score_codex":0.0007009049,"about_ca_system_score_gemma":0.00014839593,"threshold_uncertainty_score":0.9129782},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35683623,0.028374892,0.011859622,0.4290916,0.0012611904,0.021772848,0.053274646,0.0014969606,0.09603199],"genre_scores_gemma":[0.96191806,0.00037403498,0.0005134862,0.006883144,0.000092270064,0.0029824502,0.0066308137,0.00002391023,0.020581821],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99755985,0.00030673816,0.00061904656,0.000446234,0.0008190302,0.00024912972],"domain_scores_gemma":[0.9979014,0.00048250583,0.0003038279,0.0005326142,0.000583633,0.00019602539],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021151658,0.00015702023,0.00091012346,0.00048603746,0.00008760685,0.0000048300594,0.00021063337,0.00006250834,0.0035435497],"category_scores_gemma":[0.017938932,0.00013821314,0.00039965834,0.00237259,0.000049070077,0.00008007574,0.00015715366,0.00028034212,0.000005938317],"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":[0.001952696,0.0011733632,0.4170243,0.001471205,0.0014026674,0.00007729685,0.00080690923,0.0004052996,0.00010659612,0.0025635308,0.5713976,0.001618524],"study_design_scores_gemma":[0.002744456,0.0009616935,0.72197676,0.00011615986,0.0009616385,0.000011690573,0.00023201104,0.0012824147,0.000008921341,0.00072356005,0.2707918,0.00018889034],"about_ca_topic_score_codex":0.000057955458,"about_ca_topic_score_gemma":0.000056907415,"teacher_disagreement_score":0.60508186,"about_ca_system_score_codex":0.0007898576,"about_ca_system_score_gemma":0.00039996204,"threshold_uncertainty_score":0.9973673},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92405134,0.0010471831,0.0074126273,0.002222223,0.0014101353,0.00044207234,0.00002273291,0.0010235246,0.062368147],"genre_scores_gemma":[0.9947219,0.00030411102,0.0027763082,0.0001412204,0.000044155993,0.00001003857,0.00097016594,0.000016345395,0.0010157906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99716336,0.00027148364,0.0013819647,0.00024152244,0.0007067396,0.00023493802],"domain_scores_gemma":[0.99687105,0.000038117625,0.0013287142,0.00040621217,0.0012093335,0.00014657044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014262012,0.00016057634,0.00052522664,0.0021019448,0.00013922139,0.000116980664,0.00028079783,0.00007268692,0.00059618586],"category_scores_gemma":[0.0006613972,0.00015545932,0.00022842726,0.0016573301,0.000057061716,0.0004997256,0.00026198,0.00040696855,0.00007510776],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020769905,0.0021415758,0.5802719,0.00065560907,0.026584048,0.00357653,0.0053713447,0.01700951,0.00022089177,0.010313548,0.0068610767,0.3467863],"study_design_scores_gemma":[0.002307148,0.0003210046,0.826955,0.0012581863,0.0018715078,0.002165311,0.007899225,0.12103979,0.000115915216,0.00072028564,0.034987807,0.00035882054],"about_ca_topic_score_codex":0.000009656981,"about_ca_topic_score_gemma":0.000017025839,"teacher_disagreement_score":0.34642744,"about_ca_system_score_codex":0.00040898606,"about_ca_system_score_gemma":0.0002444043,"threshold_uncertainty_score":0.65278167},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35683623,0.028374892,0.011859622,0.4290916,0.0012611904,0.021772848,0.053274646,0.0014969606,0.09603199],"genre_scores_gemma":[0.96191806,0.00037403498,0.0005134862,0.006883144,0.000092270064,0.0029824502,0.0066308137,0.00002391023,0.020581821],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99755985,0.00030673816,0.00061904656,0.000446234,0.0008190302,0.00024912972],"domain_scores_gemma":[0.9979014,0.00048250583,0.0003038279,0.0005326142,0.000583633,0.00019602539],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021151658,0.00015702023,0.00091012346,0.00048603746,0.00008760685,0.0000048300594,0.00021063337,0.00006250834,0.0035435497],"category_scores_gemma":[0.017938932,0.00013821314,0.00039965834,0.00237259,0.000049070077,0.00008007574,0.00015715366,0.00028034212,0.000005938317],"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":[0.001952696,0.0011733632,0.4170243,0.001471205,0.0014026674,0.00007729685,0.00080690923,0.0004052996,0.00010659612,0.0025635308,0.5713976,0.001618524],"study_design_scores_gemma":[0.002744456,0.0009616935,0.72197676,0.00011615986,0.0009616385,0.000011690573,0.00023201104,0.0012824147,0.000008921341,0.00072356005,0.2707918,0.00018889034],"about_ca_topic_score_codex":0.000057955458,"about_ca_topic_score_gemma":0.000056907415,"teacher_disagreement_score":0.60508186,"about_ca_system_score_codex":0.0007898576,"about_ca_system_score_gemma":0.00039996204,"threshold_uncertainty_score":0.9973673},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96647376,0.0033602756,0.0011981337,0.0086616045,0.000063299434,0.0004644588,0.019649928,0.00011900698,0.000009558348],"genre_scores_gemma":[0.9402763,0.0003721432,0.0015566834,0.0022217897,0.00010452192,0.000030073163,0.055369362,0.000033452132,0.000035633875],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99540216,0.0012391706,0.0010188565,0.0010775664,0.00061877246,0.0006434905],"domain_scores_gemma":[0.9912836,0.005726172,0.00055395655,0.0010698372,0.00010266852,0.0012637831],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038676627,0.00025533795,0.0012415958,0.0003333075,0.00042113865,0.00007140251,0.00051923556,0.000076716446,0.00016449516],"category_scores_gemma":[0.009903823,0.00025753968,0.000051317867,0.00094285124,0.00025344576,0.00032183295,0.0013782217,0.00042288325,7.5873635e-7],"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":[0.00062967185,0.00029430582,0.9468571,0.00043079082,0.00030769678,0.00002031491,0.003742435,0.000012493126,0.00039010277,0.000007160083,0.026037995,0.021269957],"study_design_scores_gemma":[0.005468315,0.00034909198,0.28432998,0.00004427098,0.000079132864,0.000019487865,0.005791187,0.54403603,0.0000040064565,0.000011295083,0.1593185,0.0005487107],"about_ca_topic_score_codex":0.016198084,"about_ca_topic_score_gemma":0.015036782,"teacher_disagreement_score":0.6625271,"about_ca_system_score_codex":0.00044464288,"about_ca_system_score_gemma":0.0014633156,"threshold_uncertainty_score":0.99998766},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84538805,0.007636266,0.0028281626,0.13038383,0.009018976,0.0006105823,0.000246276,0.0001695581,0.003718317],"genre_scores_gemma":[0.9960958,0.0009870455,0.00091072626,0.0008949986,0.00071515556,0.0000046988594,0.000046944646,0.00002218061,0.00032248173],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982254,0.00010375965,0.0005926862,0.00016953144,0.0005518242,0.0003568217],"domain_scores_gemma":[0.9983861,0.0001562608,0.00048435346,0.0003989335,0.00025097537,0.00032336838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016949336,0.00012987548,0.00028092862,0.00012719924,0.00033628254,0.00003938095,0.00029708582,0.000044505225,0.0004079051],"category_scores_gemma":[0.00031826808,0.00008637559,0.00014488831,0.00018719459,0.00010300629,0.000071322065,0.000037177964,0.0006016019,0.00002262878],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000699414,0.00027436728,0.83490354,0.00011793134,0.0001932733,0.00006182095,0.00016114226,0.00020751863,0.00126029,0.0004995276,0.032713436,0.12890777],"study_design_scores_gemma":[0.0035734468,0.0003853844,0.81933284,0.00028722384,0.000021878066,0.0005237015,0.00011126254,0.0046419627,0.00034087597,0.00045268066,0.1701314,0.00019735534],"about_ca_topic_score_codex":0.000024149138,"about_ca_topic_score_gemma":0.00015726611,"teacher_disagreement_score":0.15070774,"about_ca_system_score_codex":0.000051531468,"about_ca_system_score_gemma":0.00073219463,"threshold_uncertainty_score":0.4466275},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98952615,0.0005260141,0.000096186326,0.0030622373,0.0015457035,0.0017533624,0.0021639469,0.000054402375,0.0012719784],"genre_scores_gemma":[0.9892185,0.00020116226,0.001967543,0.0012030195,0.00043173536,0.00040189386,0.0019909278,0.00006930697,0.0045159482],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978932,0.0001431159,0.0006697181,0.00046197008,0.0005632215,0.0002687756],"domain_scores_gemma":[0.9958995,0.00039144425,0.00046150878,0.002689354,0.0004927355,0.00006548704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058044266,0.00030510675,0.0007122951,0.00004321121,0.00020747056,0.00010679564,0.0010452335,0.00010105672,0.00043255434],"category_scores_gemma":[0.00014386629,0.0001385019,0.00047997473,0.00015944491,0.0004946098,0.00009098841,0.0019625819,0.00061418716,0.000003102255],"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":[0.008115384,0.0053102416,0.65064454,0.06152,0.015107847,0.000051591596,0.12715404,0.001056849,0.010619525,0.019816607,0.05954572,0.041057672],"study_design_scores_gemma":[0.0010494131,0.00012587184,0.20159313,0.0018697022,0.00078398245,0.0000060881853,0.0060574124,0.00036409032,0.0036894407,0.0007419102,0.7834003,0.00031864332],"about_ca_topic_score_codex":0.11972303,"about_ca_topic_score_gemma":0.4394906,"teacher_disagreement_score":0.7238546,"about_ca_system_score_codex":0.00015563307,"about_ca_system_score_gemma":0.00249166,"threshold_uncertainty_score":0.8861388},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98464805,0.0002652199,0.0005047387,0.00009962439,0.00070971117,0.010613037,0.0011859656,0.0006373204,0.0013363257],"genre_scores_gemma":[0.9943935,0.00003293897,0.0008000981,0.00008817515,0.0003243586,0.00019229311,0.003075816,0.00012559956,0.0009671633],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935352,0.0008728575,0.0010310961,0.0022584214,0.0016442537,0.000658158],"domain_scores_gemma":[0.9931192,0.00014475685,0.00033679817,0.0048420355,0.0006842708,0.00087298575],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019767976,0.00068119186,0.0013209726,0.00027932637,0.0001299733,0.00015253006,0.00057671964,0.00032306937,0.00091459893],"category_scores_gemma":[0.00068934343,0.0006567995,0.0004689872,0.0003013304,0.00010220152,0.0001605435,0.0024221155,0.0012563674,0.00035421684],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007522955,0.003298302,0.9893091,0.00091961614,0.00015260783,0.00020837621,0.000093063194,0.004036827,0.00006969308,0.000009690442,0.00027508632,0.00087536627],"study_design_scores_gemma":[0.0017780215,0.00012725424,0.98410314,0.00024215117,0.00030509877,0.000008939276,0.00016900079,0.012576985,0.0000100879015,0.00003823431,0.000038790055,0.00060227595],"about_ca_topic_score_codex":0.0040859133,"about_ca_topic_score_gemma":0.0003628645,"teacher_disagreement_score":0.010420743,"about_ca_system_score_codex":0.0009749382,"about_ca_system_score_gemma":0.0010782854,"threshold_uncertainty_score":0.9999987},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98952615,0.0005260141,0.000096186326,0.0030622373,0.0015457035,0.0017533624,0.0021639469,0.000054402375,0.0012719784],"genre_scores_gemma":[0.9892185,0.00020116226,0.001967543,0.0012030195,0.00043173536,0.00040189386,0.0019909278,0.00006930697,0.0045159482],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978932,0.0001431159,0.0006697181,0.00046197008,0.0005632215,0.0002687756],"domain_scores_gemma":[0.9958995,0.00039144425,0.00046150878,0.002689354,0.0004927355,0.00006548704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058044266,0.00030510675,0.0007122951,0.00004321121,0.00020747056,0.00010679564,0.0010452335,0.00010105672,0.00043255434],"category_scores_gemma":[0.00014386629,0.0001385019,0.00047997473,0.00015944491,0.0004946098,0.00009098841,0.0019625819,0.00061418716,0.000003102255],"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":[0.008115384,0.0053102416,0.65064454,0.06152,0.015107847,0.000051591596,0.12715404,0.001056849,0.010619525,0.019816607,0.05954572,0.041057672],"study_design_scores_gemma":[0.0010494131,0.00012587184,0.20159313,0.0018697022,0.00078398245,0.0000060881853,0.0060574124,0.00036409032,0.0036894407,0.0007419102,0.7834003,0.00031864332],"about_ca_topic_score_codex":0.11972303,"about_ca_topic_score_gemma":0.4394906,"teacher_disagreement_score":0.7238546,"about_ca_system_score_codex":0.00015563307,"about_ca_system_score_gemma":0.00249166,"threshold_uncertainty_score":0.8861388},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006808369,0.0026781054,0.0005597442,0.00011542651,0.000670507,0.0027729995,0.0271503,0.00030881056,0.9650633],"genre_scores_gemma":[0.079666875,0.00037496202,0.009742872,0.00427121,0.0023073545,0.0004935623,0.0051016356,0.0003899737,0.89765155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974077,0.000018253495,0.0004729983,0.001076862,0.00051690644,0.00050727825],"domain_scores_gemma":[0.9973858,0.00006355612,0.0002359007,0.0013994551,0.00021509052,0.0007001747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020727783,0.0005638994,0.00075734494,0.00008118056,0.000120771925,0.000059217462,0.00023055804,0.00012031322,0.0002000651],"category_scores_gemma":[0.000068738824,0.00054016203,0.0002369517,0.00001980376,0.00030315248,0.00004817848,0.00041277747,0.00012742054,0.00025042295],"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":[0.0022368242,0.00009156714,0.004576614,0.0024157537,0.000758026,0.00061693764,0.000010584258,0.0000012294389,0.000003449278,0.75544065,0.22505099,0.00879739],"study_design_scores_gemma":[0.0021839608,0.00019270644,0.007068728,0.0008994128,0.000496137,0.000027223618,0.000007870309,0.00006258628,0.0000042924025,0.030990021,0.9573582,0.00070885033],"about_ca_topic_score_codex":0.000025674428,"about_ca_topic_score_gemma":0.000114279435,"teacher_disagreement_score":0.7323072,"about_ca_system_score_codex":0.00021805539,"about_ca_system_score_gemma":0.00018373266,"threshold_uncertainty_score":0.999705},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08034226,0.000090951486,0.9018159,0.007933073,0.0012244558,0.0005750311,0.007913348,0.000098142205,0.000006874471],"genre_scores_gemma":[0.8566136,0.000023751372,0.13655467,0.0019404275,0.0003709994,0.000006985251,0.0044705574,0.000015604171,0.0000034158118],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99745715,0.00003484287,0.000602713,0.0007064553,0.0009342884,0.00026455958],"domain_scores_gemma":[0.9965021,0.00082139386,0.00040955623,0.0006476948,0.0011002024,0.00051902933],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018379906,0.0001353083,0.00022067639,0.00032121167,0.00047841598,0.0012238871,0.0022018831,0.00003788879,0.000016110254],"category_scores_gemma":[0.04699555,0.00011654502,0.000036431655,0.00046900118,0.00011718816,0.005604439,0.00096158823,0.0001412971,0.0000029507446],"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":[0.0030272966,0.00014467642,0.5066554,0.0005496721,0.00043293048,0.00023391018,0.00047563622,0.00080985966,0.0016512707,0.028455764,0.026795786,0.43076783],"study_design_scores_gemma":[0.0024567712,0.0000881619,0.073590815,0.00108463,0.00008428036,0.0011051169,0.00073482725,0.7597853,0.000018454011,0.00073747593,0.15996245,0.0003517113],"about_ca_topic_score_codex":0.00005206611,"about_ca_topic_score_gemma":0.00008857566,"teacher_disagreement_score":0.77627134,"about_ca_system_score_codex":0.0004885831,"about_ca_system_score_gemma":0.0013130077,"threshold_uncertainty_score":0.99981296},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945819,0.00068559934,0.0000069655043,0.00033775397,0.00020201835,0.0012052961,0.0009483162,0.000033563694,0.0019985817],"genre_scores_gemma":[0.99480474,0.0002844188,0.0006927298,0.0001526858,0.0000523059,0.00005597806,0.00026009223,0.00003721343,0.0036598174],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970975,0.00013450091,0.0007009788,0.0010675619,0.00045503737,0.0005444051],"domain_scores_gemma":[0.9986044,0.00009142822,0.00013855414,0.00074531074,0.000056533732,0.00036378522],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007066996,0.00034669536,0.000816261,0.00016584912,0.000028924293,0.00003556892,0.00018897175,0.00015472365,0.0014223175],"category_scores_gemma":[0.00013205074,0.00031461162,0.00006448683,0.00020452583,0.00004966909,0.00008081074,0.0011167073,0.0004812326,0.000034626024],"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":[0.00026946922,0.000129063,0.97735935,0.00050364976,0.00007326094,0.00012005907,0.00037155254,0.00046346683,0.00016401587,0.0000011071255,0.002073253,0.018471774],"study_design_scores_gemma":[0.0010220412,0.0000653448,0.990594,0.0003322315,0.000021686432,0.00000790906,0.00017587101,0.005829578,0.000094303396,0.000022387338,0.0014941781,0.0003404444],"about_ca_topic_score_codex":0.7176515,"about_ca_topic_score_gemma":0.9531827,"teacher_disagreement_score":0.23553121,"about_ca_system_score_codex":0.00095187535,"about_ca_system_score_gemma":0.001858946,"threshold_uncertainty_score":0.9999306},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854681,0.0013595398,0.008358416,0.000313581,0.002407487,0.0013394429,0.00022774622,0.00027270213,0.00025301788],"genre_scores_gemma":[0.9965274,0.00008346798,0.0019339602,0.0003714084,0.00034603116,0.00012954067,0.0001943535,0.00005531701,0.000358562],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927267,0.0028335454,0.0014557681,0.0012631868,0.00082739926,0.000893442],"domain_scores_gemma":[0.99485856,0.0008839602,0.00062123843,0.0011083399,0.0014566897,0.0010712014],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005880611,0.00042485565,0.001256431,0.0002481894,0.0004574923,0.00017620242,0.00019216629,0.00024073858,0.00011770246],"category_scores_gemma":[0.0035428558,0.00042928432,0.00023374082,0.0014743555,0.0003287199,0.0003679712,0.0002154147,0.0006019704,0.000009468457],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025608516,0.0008641573,0.9626036,0.00040815322,0.00013364188,0.000023859233,0.00034468094,0.000013486712,0.00074874173,0.000008077824,0.00023346675,0.03436202],"study_design_scores_gemma":[0.0023964306,0.00034378242,0.9857369,0.000022647997,0.0000033523602,0.0002789545,0.00046146006,0.0020564056,0.00008734122,0.000015644016,0.008263423,0.00033367923],"about_ca_topic_score_codex":0.0009114147,"about_ca_topic_score_gemma":0.0016332049,"teacher_disagreement_score":0.034028344,"about_ca_system_score_codex":0.00046686723,"about_ca_system_score_gemma":0.0021202478,"threshold_uncertainty_score":0.9998159},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877877,0.00035043733,0.004035965,0.00033169647,0.00016063036,0.00045346143,0.00079492264,0.00002462332,0.0060605654],"genre_scores_gemma":[0.97275585,0.000017854914,0.02660098,0.00020962457,0.0002844399,0.0000052479636,0.00008606815,0.000010543243,0.000029376588],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885315,0.000036551686,0.0004417777,0.000112021335,0.0003464861,0.00021004018],"domain_scores_gemma":[0.9987895,0.00014101417,0.0002926337,0.00018747842,0.00020968496,0.0003797223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081250834,0.00010993728,0.0003701658,0.000042359374,0.000042409065,0.000015660591,0.0000637505,0.000045759276,0.00019151063],"category_scores_gemma":[0.0002253562,0.00008502423,0.00005239825,0.00006729654,0.00022097792,0.00012654775,0.00004081387,0.00021802359,0.000006602753],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027818282,0.0012551062,0.88027436,0.0006220648,0.00036756264,0.000035820114,0.00046684162,0.000015432146,0.0025150236,0.057971288,0.032075107,0.021619592],"study_design_scores_gemma":[0.0020564643,0.00019699062,0.95207024,0.000039554674,0.00016836746,0.00021987026,0.00009402529,0.000029055878,0.00038903594,0.02693439,0.017652072,0.00014994915],"about_ca_topic_score_codex":0.0000018038868,"about_ca_topic_score_gemma":0.0000023462394,"teacher_disagreement_score":0.07179589,"about_ca_system_score_codex":0.00008480235,"about_ca_system_score_gemma":0.00012055934,"threshold_uncertainty_score":0.34671873},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00878573,0.004874703,0.94055796,0.03516829,0.0019666771,0.004200272,0.0032851882,0.00061250624,0.0005486678],"genre_scores_gemma":[0.049959753,0.0010615698,0.9003639,0.015173722,0.0008550287,0.00038325085,0.028286202,0.00017923418,0.003737302],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99680096,0.00018601945,0.00075551396,0.0010491286,0.0005751476,0.0006332095],"domain_scores_gemma":[0.995757,0.0007586161,0.00029104904,0.00173551,0.0006027159,0.00085512764],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011620189,0.0003914373,0.0011019014,0.0002276215,0.000079931124,0.00013153815,0.0003785303,0.00026671207,0.0012221878],"category_scores_gemma":[0.0021064244,0.0003448021,0.00041949993,0.00019242302,0.000063670755,0.00007413082,0.00069819356,0.0005078013,0.00007253179],"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":[0.0005822156,0.0022485198,0.002132424,0.002904246,0.00083577825,0.0002075998,0.00018858159,0.00041211583,0.000025488658,0.0002602595,0.7146376,0.2755652],"study_design_scores_gemma":[0.009113933,0.0010234737,0.040925715,0.004246783,0.00031821575,0.00010705399,0.0015683647,0.021908367,0.00007757745,0.0034587665,0.91546065,0.0017911071],"about_ca_topic_score_codex":0.00010245568,"about_ca_topic_score_gemma":0.00024038993,"teacher_disagreement_score":0.27377412,"about_ca_system_score_codex":0.0004223554,"about_ca_system_score_gemma":0.0048687067,"threshold_uncertainty_score":0.9999004},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050397713,0.00015526252,0.0014799533,0.00010206539,0.00036789008,0.00037530268,0.0019734758,0.00021018276,0.99528545],"genre_scores_gemma":[0.004307277,0.000063378626,0.0016938434,0.00078448013,0.0010897943,0.0000056364715,0.0026572272,0.00009409542,0.98930424],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987013,0.000008077028,0.00027845762,0.00041045985,0.0003977845,0.00020394239],"domain_scores_gemma":[0.99866635,0.00004915877,0.0001187327,0.00072320335,0.00017458109,0.00026796694],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007661331,0.00027439868,0.00048897415,0.00010052666,0.000028990591,0.000015541138,0.00011862721,0.00016199831,0.11940733],"category_scores_gemma":[0.000077515164,0.00023516096,0.00011243643,0.000011784896,0.00019492542,0.000023042721,0.00008803927,0.00019001887,0.0096981665],"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":[0.00026327017,0.0000406513,0.00042793175,0.00030210003,0.00035590233,0.0005470862,0.000027781922,4.9942045e-8,0.000014763708,0.031800516,0.90081716,0.06540281],"study_design_scores_gemma":[0.00066801644,0.00024358918,0.0029020836,0.00028273946,0.00021625223,0.000033447603,0.0000022294998,0.00005612069,0.000010339734,0.0052229976,0.9900525,0.00030968475],"about_ca_topic_score_codex":0.00012929589,"about_ca_topic_score_gemma":0.0004883796,"teacher_disagreement_score":0.109709166,"about_ca_system_score_codex":0.000097161974,"about_ca_system_score_gemma":0.00026920135,"threshold_uncertainty_score":0.9910729},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9617136,0.0014100719,0.011713262,0.00066280866,0.00017217247,0.003266511,0.020682564,0.00006753069,0.00031147042],"genre_scores_gemma":[0.9118425,0.0004947769,0.058462534,0.0004727326,0.00017819162,0.00024055422,0.028228303,0.00005969159,0.000020696518],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950485,0.0006397933,0.0012250285,0.0017671406,0.0007067386,0.00061278464],"domain_scores_gemma":[0.9933708,0.002958034,0.0007371333,0.0026817166,0.000031274125,0.00022104086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0044097127,0.0005958242,0.0013926245,0.00022386643,0.0001496376,0.000081162325,0.0013859903,0.0005262474,0.00026417038],"category_scores_gemma":[0.00086214097,0.000491509,0.00016033818,0.00009564458,0.0011095592,0.00016589978,0.003129607,0.0008220872,0.000007182904],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0039732405,0.0013141275,0.77007496,0.0010531548,0.0019032009,0.00015678618,0.012200555,0.000051495874,0.0024830932,0.00047912984,0.0026506947,0.20365953],"study_design_scores_gemma":[0.008082926,0.00030335298,0.92771333,0.0006556092,0.00036919388,0.00006878972,0.011233248,0.0038158915,0.0022827112,0.0015763851,0.042639457,0.0012591248],"about_ca_topic_score_codex":0.00082053326,"about_ca_topic_score_gemma":0.0029554905,"teacher_disagreement_score":0.20240042,"about_ca_system_score_codex":0.00012936957,"about_ca_system_score_gemma":0.00039830452,"threshold_uncertainty_score":0.99975365},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45900762,0.0065122633,0.15408549,0.0626286,0.0034228244,0.003213365,0.113100715,0.0017459447,0.19628318],"genre_scores_gemma":[0.96923643,0.00007034286,0.010141983,0.0019627009,0.00010157789,0.0000013277668,0.01751872,0.000012262411,0.00095467875],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803823,0.00004795847,0.00027596325,0.0007872574,0.0006316545,0.00021893103],"domain_scores_gemma":[0.9929046,0.00009221944,0.00008552906,0.0062727565,0.00019385426,0.00045102832],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010039255,0.00009288089,0.0002658899,0.00007209377,0.000060406495,0.00002615178,0.0015560128,0.000025607234,0.002362757],"category_scores_gemma":[0.005138209,0.00007960671,0.000019005367,0.00061237405,0.00045508775,0.0005488622,0.0034092895,0.00007691054,0.000093841736],"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":[0.00021957654,0.0008504694,0.57624596,0.0007431272,0.00012072333,0.0010468297,0.000092551556,0.0000059575123,0.016682725,0.0015060486,0.3882128,0.014273239],"study_design_scores_gemma":[0.0038529986,0.00015761182,0.17712805,0.000135742,0.00020627113,0.00023485257,0.00053013396,0.025564566,0.009276973,0.00022376984,0.7822147,0.00047431485],"about_ca_topic_score_codex":0.0017736399,"about_ca_topic_score_gemma":0.001507206,"teacher_disagreement_score":0.5102288,"about_ca_system_score_codex":0.000043380354,"about_ca_system_score_gemma":0.0043314006,"threshold_uncertainty_score":0.9985492},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98603934,0.005933752,0.00009728473,0.000904262,0.0056884354,0.00021878895,0.0006951683,0.000027221578,0.00039577534],"genre_scores_gemma":[0.978133,0.015284528,0.00003410218,0.00011173892,0.005913885,0.0000025153436,0.0001655563,0.00003192083,0.00032271267],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983704,0.0001157867,0.00042975985,0.00034483423,0.00042733387,0.00031185636],"domain_scores_gemma":[0.9970899,0.00004003969,0.00060705905,0.0016222588,0.00020381242,0.0004369036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058832276,0.00021074661,0.00043236619,0.00012607248,0.00034157615,0.00041847114,0.00048519345,0.00008084807,0.00008896276],"category_scores_gemma":[0.000065770924,0.00017005239,0.0000991835,0.000058169197,0.00013177644,0.00076578243,0.00036209048,0.0005458403,0.0000140192715],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080890175,0.000089491274,0.8505577,0.00025327547,0.00010923441,0.00009397701,0.00015036698,3.8090067e-7,0.000060333037,0.000006196991,0.003733977,0.14486417],"study_design_scores_gemma":[0.0014403836,0.00008805222,0.86972654,0.00084324647,0.00014360803,0.00045412694,0.00019755437,0.000027813763,0.000071842456,0.00001796341,0.12683824,0.00015064169],"about_ca_topic_score_codex":0.00008682018,"about_ca_topic_score_gemma":0.0005163463,"teacher_disagreement_score":0.14471354,"about_ca_system_score_codex":0.000046751025,"about_ca_system_score_gemma":0.00019571438,"threshold_uncertainty_score":0.6934535},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006808369,0.0026781054,0.0005597442,0.00011542651,0.000670507,0.0027729995,0.0271503,0.00030881056,0.9650633],"genre_scores_gemma":[0.079666875,0.00037496202,0.009742872,0.00427121,0.0023073545,0.0004935623,0.0051016356,0.0003899737,0.89765155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974077,0.000018253495,0.0004729983,0.001076862,0.00051690644,0.00050727825],"domain_scores_gemma":[0.9973858,0.00006355612,0.0002359007,0.0013994551,0.00021509052,0.0007001747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020727783,0.0005638994,0.00075734494,0.00008118056,0.000120771925,0.000059217462,0.00023055804,0.00012031322,0.0002000651],"category_scores_gemma":[0.000068738824,0.00054016203,0.0002369517,0.00001980376,0.00030315248,0.00004817848,0.00041277747,0.00012742054,0.00025042295],"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":[0.0022368242,0.00009156714,0.004576614,0.0024157537,0.000758026,0.00061693764,0.000010584258,0.0000012294389,0.000003449278,0.75544065,0.22505099,0.00879739],"study_design_scores_gemma":[0.0021839608,0.00019270644,0.007068728,0.0008994128,0.000496137,0.000027223618,0.000007870309,0.00006258628,0.0000042924025,0.030990021,0.9573582,0.00070885033],"about_ca_topic_score_codex":0.000025674428,"about_ca_topic_score_gemma":0.000114279435,"teacher_disagreement_score":0.7323072,"about_ca_system_score_codex":0.00021805539,"about_ca_system_score_gemma":0.00018373266,"threshold_uncertainty_score":0.999705},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015059519,0.00007323565,0.000023888493,0.010235735,0.004171232,0.0006980212,0.0028275016,0.000036612568,0.9804278],"genre_scores_gemma":[0.31206024,0.00014835443,0.000038348855,0.0016669075,0.0002398631,0.000014033178,0.001701904,0.000028885954,0.68410146],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833757,0.00000915304,0.0003615446,0.0003688964,0.00068200886,0.00024082021],"domain_scores_gemma":[0.9985282,0.000027934017,0.00041647587,0.00033217567,0.0005323621,0.00016289865],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014430728,0.00021225626,0.0004848961,0.00012090053,0.00002426903,0.000108280736,0.00053665624,0.000107461055,0.0066964505],"category_scores_gemma":[0.00016509726,0.00013005815,0.00014768953,0.00014901119,0.00011140909,0.00015261913,0.00022200482,0.00033308935,0.0047711553],"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":[0.000053830365,0.00017818566,0.0028917247,0.00034258698,0.0001241713,4.0845774e-7,0.00004125247,2.7780487e-7,0.000019420304,0.0056305844,0.98804516,0.0026724301],"study_design_scores_gemma":[0.0006825655,0.00019587192,0.004131455,0.00065656856,0.000007423771,0.000013607938,0.00012229633,0.00007904817,0.000030731484,0.000025794023,0.99390656,0.00014807573],"about_ca_topic_score_codex":0.000019702371,"about_ca_topic_score_gemma":0.0000032311536,"teacher_disagreement_score":0.3105543,"about_ca_system_score_codex":0.00017035463,"about_ca_system_score_gemma":0.0012664198,"threshold_uncertainty_score":0.99600375},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.982539,0.0005439574,0.00039823411,0.008065424,0.00050347624,0.0015915794,0.0026757545,0.00035424047,0.0033283157],"genre_scores_gemma":[0.99297035,0.00006089615,0.0014546056,0.0020527465,0.000089645764,0.00018452083,0.0027763646,0.000041088933,0.00036979656],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99591386,0.00035915477,0.0009820953,0.0018298402,0.00039395932,0.0005210844],"domain_scores_gemma":[0.996989,0.00013041883,0.00022790891,0.0016687491,0.00014329245,0.0008406445],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00048343555,0.00056599855,0.0015188943,0.0004228611,0.000024822557,0.000087398774,0.00063614966,0.00041858983,0.0013257997],"category_scores_gemma":[0.0010430362,0.0005309757,0.00016588003,0.0005879151,0.00009965163,0.00009451998,0.0044320347,0.0008093311,0.00028000935],"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":[0.00048858474,0.00055471767,0.9460516,0.00006743004,0.00031737672,0.00021174976,0.0006477189,0.00010372652,0.000018588291,0.000027958913,0.017023955,0.034486566],"study_design_scores_gemma":[0.0023368024,0.00041292064,0.99280006,0.00015425071,0.00010761025,0.000010159844,0.0005804915,0.0011785777,0.0000010766508,0.0006249802,0.0013714165,0.00042163712],"about_ca_topic_score_codex":0.008840156,"about_ca_topic_score_gemma":0.044958003,"teacher_disagreement_score":0.04674845,"about_ca_system_score_codex":0.0006552277,"about_ca_system_score_gemma":0.00034513976,"threshold_uncertainty_score":0.9997142},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7829429,0.012268549,0.000012003076,0.19259886,0.00012419232,0.0014403432,0.010016472,0.00001616879,0.00058050966],"genre_scores_gemma":[0.9297156,0.0077564046,0.0000502089,0.046261825,0.000023080056,0.00005553727,0.0070104264,0.000011304226,0.0091156615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99751943,0.0006188723,0.00078771415,0.000073758165,0.0008708762,0.000129327],"domain_scores_gemma":[0.99725425,0.00038499743,0.00061256066,0.0009800486,0.0006890513,0.00007907525],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002039836,0.000092169714,0.0005860682,0.00006330233,0.000052198502,0.0000023574526,0.00031148846,0.000031404725,0.0009855855],"category_scores_gemma":[0.002143498,0.000046424506,0.0002543256,0.00072345516,0.00027772068,0.000035590157,0.0002988584,0.00017991781,0.0000020331304],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012875194,0.0003219152,0.81174606,0.007424505,0.00046742332,0.00000264566,0.00484345,0.000024877863,0.00005143902,0.0000622565,0.17421886,0.0007078201],"study_design_scores_gemma":[0.0012917335,0.000043172713,0.7190337,0.0013538983,0.00028870392,0.000033980315,0.0032330593,0.00007888024,0.000070640664,0.000011246228,0.27449915,0.00006181645],"about_ca_topic_score_codex":0.0008313188,"about_ca_topic_score_gemma":0.0125932,"teacher_disagreement_score":0.14677265,"about_ca_system_score_codex":0.00016164783,"about_ca_system_score_gemma":0.0010561832,"threshold_uncertainty_score":0.99992764},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00878573,0.004874703,0.94055796,0.03516829,0.0019666771,0.004200272,0.0032851882,0.00061250624,0.0005486678],"genre_scores_gemma":[0.049959753,0.0010615698,0.9003639,0.015173722,0.0008550287,0.00038325085,0.028286202,0.00017923418,0.003737302],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99680096,0.00018601945,0.00075551396,0.0010491286,0.0005751476,0.0006332095],"domain_scores_gemma":[0.995757,0.0007586161,0.00029104904,0.00173551,0.0006027159,0.00085512764],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011620189,0.0003914373,0.0011019014,0.0002276215,0.000079931124,0.00013153815,0.0003785303,0.00026671207,0.0012221878],"category_scores_gemma":[0.0021064244,0.0003448021,0.00041949993,0.00019242302,0.000063670755,0.00007413082,0.00069819356,0.0005078013,0.00007253179],"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":[0.0005822156,0.0022485198,0.002132424,0.002904246,0.00083577825,0.0002075998,0.00018858159,0.00041211583,0.000025488658,0.0002602595,0.7146376,0.2755652],"study_design_scores_gemma":[0.009113933,0.0010234737,0.040925715,0.004246783,0.00031821575,0.00010705399,0.0015683647,0.021908367,0.00007757745,0.0034587665,0.91546065,0.0017911071],"about_ca_topic_score_codex":0.00010245568,"about_ca_topic_score_gemma":0.00024038993,"teacher_disagreement_score":0.27377412,"about_ca_system_score_codex":0.0004223554,"about_ca_system_score_gemma":0.0048687067,"threshold_uncertainty_score":0.9999004},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9852144,0.0017831094,0.00013381259,0.0007255519,0.0010135823,0.002905413,0.00009101132,0.000119622724,0.008013471],"genre_scores_gemma":[0.9969087,0.00031695163,0.00020430617,0.00054713147,0.00013627895,0.00054607325,0.00019248309,0.00005903453,0.0010890117],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965064,0.00036352407,0.0011355849,0.0010272204,0.0005547062,0.00041259133],"domain_scores_gemma":[0.9967893,0.000221349,0.00047762803,0.0020807371,0.00026486485,0.00016616362],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011010156,0.00048033832,0.001468685,0.0001966275,0.000025966412,0.00007470141,0.000844144,0.00026448828,0.00082908064],"category_scores_gemma":[0.0005521889,0.00035512896,0.00043041058,0.0002695535,0.000086232096,0.000083326224,0.0030783152,0.0013348297,0.00002413094],"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":[0.00039360533,0.0012294722,0.9847086,0.00065972126,0.0010112763,0.000621023,0.0041613514,0.00026609242,0.0003341505,0.00018307076,0.00073224615,0.0056993924],"study_design_scores_gemma":[0.0032572534,0.00036132697,0.980678,0.0037403207,0.00027353867,0.000015507612,0.005623555,0.0015066873,0.0014320526,0.00009018209,0.0024935857,0.00052794616],"about_ca_topic_score_codex":0.012953376,"about_ca_topic_score_gemma":0.02255991,"teacher_disagreement_score":0.011694304,"about_ca_system_score_codex":0.00053414586,"about_ca_system_score_gemma":0.0008762329,"threshold_uncertainty_score":0.9998901},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3412401,0.004429438,0.6382742,0.013121896,0.0010038064,0.0010443424,0.0006307552,0.00017959926,0.000075892414],"genre_scores_gemma":[0.97933793,0.007733855,0.004608864,0.0049624853,0.0003179454,0.000013180806,0.002980923,0.000029922441,0.000014864361],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99724746,0.00014123031,0.0009553099,0.00031115563,0.00093810057,0.00040671733],"domain_scores_gemma":[0.99721545,0.0002057705,0.00074896525,0.0004449714,0.0008794955,0.0005053716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015790026,0.00026750122,0.00038377903,0.0009766198,0.0009420571,0.00061744975,0.0004853985,0.0001343442,0.000025185494],"category_scores_gemma":[0.0009928271,0.00021302624,0.00019016396,0.0008723141,0.00030643432,0.0018043924,0.000227821,0.00033129004,0.0000012413839],"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":[0.0015697769,0.0007494001,0.608252,0.00083137397,0.0059395093,0.000050577255,0.023432905,0.00089442346,0.00056137267,0.04621889,0.049411055,0.26208875],"study_design_scores_gemma":[0.0070408913,0.0007209612,0.047261387,0.0013756533,0.0008272337,0.0063564437,0.0062254067,0.34233248,0.000050788156,0.0063278247,0.5802544,0.0012265495],"about_ca_topic_score_codex":0.0002696829,"about_ca_topic_score_gemma":0.000017709406,"teacher_disagreement_score":0.6380979,"about_ca_system_score_codex":0.00007140966,"about_ca_system_score_gemma":0.00045107835,"threshold_uncertainty_score":0.8686958},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81763303,0.003582836,0.003578608,0.009106772,0.0013327216,0.014289987,0.0024562879,0.0020114863,0.14600827],"genre_scores_gemma":[0.8486974,0.0011676867,0.1357161,0.00078822824,0.0011001988,0.000808201,0.004638524,0.00033466963,0.0067489548],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99240774,0.0018794003,0.00083677005,0.0019863362,0.0017015209,0.0011882621],"domain_scores_gemma":[0.9937441,0.00076306565,0.00031234862,0.0025626477,0.0018055578,0.0008122865],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006253469,0.0005362972,0.0012283665,0.0005595465,0.00044377177,0.001083433,0.0016235069,0.0007278355,0.0007137367],"category_scores_gemma":[0.0015600685,0.00051462505,0.00016078351,0.0006920325,0.0007753069,0.00031880056,0.0068300753,0.0018890122,0.00045760305],"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":[0.008223244,0.0038693447,0.05786675,0.010955359,0.0071282843,0.0019815369,0.0043417634,0.00039993506,0.2705829,0.0060718767,0.487621,0.14095797],"study_design_scores_gemma":[0.0053581954,0.0006334988,0.21645825,0.0044806944,0.00049770565,0.00016246359,0.0006972824,0.6378664,0.006665463,0.0059392666,0.11943942,0.0018013632],"about_ca_topic_score_codex":0.00076029974,"about_ca_topic_score_gemma":0.00009331857,"teacher_disagreement_score":0.63746643,"about_ca_system_score_codex":0.0006872377,"about_ca_system_score_gemma":0.002302435,"threshold_uncertainty_score":0.9999535},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99057686,0.00015617722,0.0004320452,0.00123443,0.0006034769,0.003016434,0.0018250603,0.00023053875,0.0019249541],"genre_scores_gemma":[0.9951685,0.00012631022,0.00029073554,0.0017870363,0.0002222131,0.00019891973,0.0014845422,0.00005081089,0.0006709451],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952977,0.00117833,0.0010594941,0.0011746611,0.00081859884,0.00047126893],"domain_scores_gemma":[0.9966219,0.00020624859,0.0005572735,0.0017917941,0.00034557286,0.00047719252],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025493104,0.00046057106,0.0014432659,0.00011695476,0.000036221092,0.000016152964,0.0005407179,0.0002580729,0.0015299272],"category_scores_gemma":[0.00048211496,0.0003963027,0.00027499578,0.00008393732,0.00023783465,0.00005110019,0.0025987115,0.001027146,0.00029653186],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010068074,0.0021876423,0.95566034,0.0013065494,0.000582401,0.000023499435,0.0022810728,0.0000072857715,0.000011737629,0.000054740434,0.034450106,0.0024278064],"study_design_scores_gemma":[0.0018627433,0.0021108398,0.9899584,0.0010701815,0.000040916755,0.000006557984,0.0017345718,0.0002021193,0.00022592011,0.00012552642,0.0023429883,0.00031922522],"about_ca_topic_score_codex":0.0017693127,"about_ca_topic_score_gemma":0.000799186,"teacher_disagreement_score":0.03429806,"about_ca_system_score_codex":0.00048103806,"about_ca_system_score_gemma":0.0008901816,"threshold_uncertainty_score":0.9998489},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014135941,0.00014414391,0.008515081,0.00015469278,0.00029096552,0.0003467571,0.0011138385,0.00021030723,0.98921007],"genre_scores_gemma":[0.014128662,0.000057914807,0.0012596007,0.0010739481,0.00070212537,0.000006259547,0.0028056065,0.000096365766,0.97986954],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998716,0.000011027626,0.0002883348,0.00039206626,0.0003957404,0.00019685483],"domain_scores_gemma":[0.9986827,0.000077020704,0.000125944,0.00072172034,0.000115210256,0.0002774474],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00009181303,0.00027813518,0.0005654026,0.00009884475,0.000025209027,0.000014540639,0.00011452974,0.00015881089,0.029193554],"category_scores_gemma":[0.000090593305,0.00023917577,0.00012323089,0.000009415128,0.00010798707,0.000012856846,0.00007220824,0.00023167941,0.0047018807],"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":[0.00022614666,0.0000314039,0.0007025896,0.00052703987,0.0003515365,0.0004460718,0.000012161055,4.8299347e-7,0.00001610054,0.23322818,0.49300286,0.27145544],"study_design_scores_gemma":[0.0006985177,0.00015167252,0.0033817098,0.00024970775,0.00020176644,0.000026759211,7.5157806e-7,0.00013177455,0.000004915283,0.0029762057,0.9918788,0.00029742377],"about_ca_topic_score_codex":0.00015445659,"about_ca_topic_score_gemma":0.00036682477,"teacher_disagreement_score":0.49887595,"about_ca_system_score_codex":0.00008214,"about_ca_system_score_gemma":0.00019200673,"threshold_uncertainty_score":0.99607307},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17884073,0.001915854,0.73602283,0.019804955,0.002341403,0.01994308,0.024134029,0.001467848,0.015529252],"genre_scores_gemma":[0.94342136,0.000058197875,0.029172473,0.0064734016,0.0005766837,0.002207171,0.017471408,0.00011706203,0.00050225895],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971041,0.000073041156,0.00064344995,0.0013014078,0.00039556948,0.00048243397],"domain_scores_gemma":[0.9970061,0.0003830372,0.00031873473,0.0010470772,0.00082862854,0.0004163758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021917044,0.00051706727,0.0011380645,0.00019000843,0.00017472023,0.00015376495,0.00026301062,0.00036146102,0.00006550849],"category_scores_gemma":[0.00069375435,0.000503967,0.00039063848,0.00018378641,0.00011707758,0.00007644994,0.00019012828,0.00038087514,0.0000091028505],"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":[0.019805979,0.005792143,0.07188759,0.028433941,0.0048365984,0.000043125252,0.005080593,0.0037095284,0.008963388,0.014564419,0.60944164,0.22744104],"study_design_scores_gemma":[0.013580135,0.0016188287,0.27463996,0.005059506,0.0022083274,0.000007692101,0.008623836,0.49951157,0.010304261,0.0007956419,0.18086314,0.002787082],"about_ca_topic_score_codex":0.00008917196,"about_ca_topic_score_gemma":0.0001527397,"teacher_disagreement_score":0.7645806,"about_ca_system_score_codex":0.00029957644,"about_ca_system_score_gemma":0.0004982357,"threshold_uncertainty_score":0.9997412},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9069106,0.029664844,0.03061312,0.0044978773,0.002467157,0.0040254234,0.020021826,0.0004248733,0.0013742673],"genre_scores_gemma":[0.9769093,0.0020936977,0.004601587,0.00082053797,0.00038442807,0.00009972243,0.014597038,0.00005610209,0.00043760813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978718,0.00013302724,0.00049209676,0.00087684294,0.0003330155,0.00029321352],"domain_scores_gemma":[0.99504393,0.0014510042,0.00031739654,0.002668879,0.00022987691,0.00028890645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014187637,0.00026246725,0.00070212106,0.000044379023,0.000110551795,0.0000741727,0.00071149686,0.0001824043,0.0000307732],"category_scores_gemma":[0.0062482017,0.00018702308,0.00011101405,0.000084585365,0.00023563088,0.000060872077,0.0025065332,0.00034706635,0.0000012387123],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037916278,0.000110792855,0.95093477,0.0025025362,0.0006292579,0.000058323072,0.00030948626,0.00018506096,0.00006457497,0.00022318879,0.04138049,0.00322234],"study_design_scores_gemma":[0.006138689,0.00041066413,0.7756695,0.0012150661,0.0005178168,0.0000986185,0.0031879244,0.026626747,0.000056422592,0.0012910021,0.18354568,0.0012418504],"about_ca_topic_score_codex":0.00024810774,"about_ca_topic_score_gemma":0.00016108148,"teacher_disagreement_score":0.17526527,"about_ca_system_score_codex":0.00007959351,"about_ca_system_score_gemma":0.001123236,"threshold_uncertainty_score":0.762658},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007828931,0.012521143,0.0017723136,0.000031553594,0.0010505057,0.0029986976,0.007733863,0.0004554517,0.9656075],"genre_scores_gemma":[0.9937156,0.00002208254,0.00029481048,0.000041285275,0.00042315648,0.00005929368,0.0001492057,0.000050414947,0.0052441508],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988029,0.000014519756,0.00037136753,0.00030988222,0.00026812332,0.00023320425],"domain_scores_gemma":[0.9988519,0.000040455852,0.00032027037,0.00036685914,0.00018151157,0.00023899868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010571216,0.00026657438,0.0005568375,0.00007225675,0.00006606992,0.000014630956,0.00006567779,0.00017999686,0.000016653254],"category_scores_gemma":[0.000040738283,0.00026049593,0.0001973887,0.000013678797,0.000088109045,0.000055524575,0.00005144423,0.000077576966,0.000011801119],"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":[0.0022470742,0.00007847576,0.0024692775,0.014150928,0.0012500318,0.000043293658,0.00009740352,0.0000036089211,0.002718242,0.6751624,0.0021900758,0.29958922],"study_design_scores_gemma":[0.032503642,0.0048994143,0.040887088,0.034111746,0.02379198,0.0014150079,0.0010768091,0.0022784208,0.02354177,0.15187876,0.676369,0.007246325],"about_ca_topic_score_codex":0.000023103952,"about_ca_topic_score_gemma":0.000022394748,"teacher_disagreement_score":0.9858867,"about_ca_system_score_codex":0.00020982628,"about_ca_system_score_gemma":0.00010223832,"threshold_uncertainty_score":0.99998474},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9633563,0.0006320969,0.00040369545,0.01544516,0.00029802002,0.0047490564,0.014541262,0.00029219658,0.0002821694],"genre_scores_gemma":[0.9678673,0.00049146335,0.00067847787,0.0007910109,0.00045226768,0.00058232085,0.028756924,0.00012569045,0.00025453235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927659,0.0006739429,0.0005665108,0.0030669929,0.0016183627,0.0013082726],"domain_scores_gemma":[0.9919628,0.0012380272,0.00029745811,0.0043292292,0.0009074382,0.0012650491],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0030620669,0.0006688644,0.00151765,0.000410327,0.00030265647,0.0016841108,0.0010254289,0.00034833397,0.00009689763],"category_scores_gemma":[0.0021073997,0.0006314215,0.00015838775,0.0003765836,0.0005361327,0.00083430344,0.009196598,0.0019699782,0.000022080354],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013367484,0.008257962,0.66809165,0.0059421696,0.0013695216,0.00038853666,0.0008810887,0.0000043617083,0.00054193666,0.00018151305,0.035361305,0.2776432],"study_design_scores_gemma":[0.007984976,0.0010356347,0.9491152,0.00065733684,0.000126701,0.00018705748,0.0011361279,0.0164496,0.00010371152,0.00012751938,0.02166351,0.0014126227],"about_ca_topic_score_codex":0.00072959193,"about_ca_topic_score_gemma":0.0010439325,"teacher_disagreement_score":0.28102356,"about_ca_system_score_codex":0.0004478424,"about_ca_system_score_gemma":0.0017728334,"threshold_uncertainty_score":0.9996137},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24078633,0.004286991,0.61406475,0.029568268,0.0006231139,0.01475312,0.0013576694,0.0015957669,0.09296398],"genre_scores_gemma":[0.71739185,0.0003857974,0.27543506,0.0027429129,0.00014357515,0.0003142745,0.002551539,0.00006108825,0.0009739198],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99781144,0.000040087183,0.0008133421,0.0007501268,0.00039244615,0.00019259006],"domain_scores_gemma":[0.99821717,0.00003391521,0.00039580787,0.00083728676,0.00015739401,0.0003584222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044693332,0.00023887465,0.0007344734,0.000081826,0.000028932458,0.000011119117,0.00019448802,0.00014096609,0.00007806792],"category_scores_gemma":[0.0001166899,0.0002261077,0.000072618874,0.00009324038,0.00006784952,0.000017494114,0.00084519846,0.00029451406,0.00007363048],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052229845,0.0008759392,0.09225549,0.019878997,0.0010043246,0.000033159322,0.0044481326,0.00005714547,0.0053695417,0.0058072032,0.06689299,0.8028548],"study_design_scores_gemma":[0.0017056958,0.0001110132,0.69893026,0.0008044244,0.00008461846,0.00002218614,0.0005899374,0.0030824975,0.0066532562,0.00061070157,0.2868232,0.00058220234],"about_ca_topic_score_codex":0.00033432196,"about_ca_topic_score_gemma":0.000059093698,"teacher_disagreement_score":0.80227256,"about_ca_system_score_codex":0.00014431724,"about_ca_system_score_gemma":0.0012183373,"threshold_uncertainty_score":0.92204046},"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":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066366266,0.00012314387,0.0002661475,0.00015045954,0.00018325864,0.9890545,0.0033185114,0.000056128214,0.00021120289],"genre_scores_gemma":[0.0005312906,0.0000052331693,0.0034979016,0.000021512145,0.00019211948,0.9934891,0.00038672748,0.00013447646,0.0017416442],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9884584,0.005036736,0.0020947766,0.0019063349,0.0012991489,0.0012045873],"domain_scores_gemma":[0.9938576,0.0016850512,0.00091772963,0.00190016,0.00072407397,0.000915372],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.016509328,0.0007359555,0.0027565132,0.0011022983,0.00019034177,0.00016290642,0.0006124257,0.0005378415,0.000088699686],"category_scores_gemma":[0.0028492392,0.00064536196,0.00021984628,0.0010673407,0.001083076,0.00055941805,0.0008271159,0.0018051689,0.000003076618],"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":[0.27673894,0.002742941,0.015390033,0.030207546,0.0004213561,0.000008827512,0.0005173436,0.0000030372673,0.007170263,6.608925e-7,0.3365765,0.33022255],"study_design_scores_gemma":[0.20580538,0.04609886,0.045062777,0.012301194,0.00008478865,0.000011884045,0.00044106622,0.0017905917,0.003015167,0.000026446622,0.68422157,0.0011402558],"about_ca_topic_score_codex":0.0002684285,"about_ca_topic_score_gemma":0.00039870193,"teacher_disagreement_score":0.3476451,"about_ca_system_score_codex":0.00042842055,"about_ca_system_score_gemma":0.0019214529,"threshold_uncertainty_score":0.99959975},"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":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000075778924,0.0002982704,0.10038697,0.000057319154,0.0004769365,0.0046575223,0.013034894,0.000779561,0.88023275],"genre_scores_gemma":[0.03271455,0.0008045882,0.52228886,0.0024062607,0.004955467,0.0006885223,0.056667186,0.0008190049,0.37865555],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979069,0.000021074618,0.00049461616,0.0008844617,0.00041970765,0.0002732266],"domain_scores_gemma":[0.99749464,0.00014351842,0.0003178147,0.0015503895,0.00023754244,0.00025612095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016391547,0.0003954943,0.0005235729,0.00010302683,0.000087007036,0.00008060505,0.0003053158,0.00044002238,0.0001275498],"category_scores_gemma":[0.0001644877,0.00040639867,0.00007898623,0.00002785281,0.00009106411,0.0001594853,0.00038841556,0.00013450622,0.00021971489],"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":[0.00040990193,0.000059706803,0.0003232164,0.0010198522,0.00096185156,0.000044091747,0.0000446353,0.000062446714,0.000012445716,0.90648896,0.041369203,0.04920368],"study_design_scores_gemma":[0.0049460744,0.0004477337,0.005216534,0.00304697,0.0023943202,0.00003390502,0.000014728129,0.3386641,0.0000037278985,0.3060858,0.3378752,0.0012709218],"about_ca_topic_score_codex":0.000018607418,"about_ca_topic_score_gemma":0.000004666605,"teacher_disagreement_score":0.6004032,"about_ca_system_score_codex":0.00016405452,"about_ca_system_score_gemma":0.000085926345,"threshold_uncertainty_score":0.99983877},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98010457,0.00018012316,0.00024761006,0.00028306976,0.00037452535,0.00073452166,0.01713731,0.00010383164,0.00083444006],"genre_scores_gemma":[0.9811162,0.00012994619,0.0009813345,0.00009450136,0.00026330253,0.000040762796,0.017052995,0.00008518633,0.0002357779],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943735,0.00055283535,0.0021363813,0.0015956898,0.00077168626,0.000569901],"domain_scores_gemma":[0.99366945,0.0009156437,0.0016492197,0.0031062942,0.00032339798,0.00033602025],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013923154,0.0007381177,0.002116501,0.0005890933,0.00006917842,0.000023412007,0.0009094127,0.00038731453,0.0010383718],"category_scores_gemma":[0.0030509755,0.00059495016,0.0004431102,0.0005281959,0.001153639,0.00019507886,0.003457491,0.00090047,0.000087778906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017470168,0.0021952826,0.98959345,0.0007625281,0.0006234332,0.00017931941,0.00031566064,0.0010033301,0.002039061,0.00013882073,0.00031968614,0.0010824135],"study_design_scores_gemma":[0.0027092972,0.00017346822,0.97497815,0.0012498474,0.0005383507,0.0000871541,0.00017132555,0.017631536,0.001415861,0.00031111264,0.00024642903,0.00048747958],"about_ca_topic_score_codex":0.00039587924,"about_ca_topic_score_gemma":0.0018847848,"teacher_disagreement_score":0.016628208,"about_ca_system_score_codex":0.00017014475,"about_ca_system_score_gemma":0.0004716587,"threshold_uncertainty_score":0.99987483},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17884073,0.001915854,0.73602283,0.019804955,0.002341403,0.01994308,0.024134029,0.001467848,0.015529252],"genre_scores_gemma":[0.94342136,0.000058197875,0.029172473,0.0064734016,0.0005766837,0.002207171,0.017471408,0.00011706203,0.00050225895],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971041,0.000073041156,0.00064344995,0.0013014078,0.00039556948,0.00048243397],"domain_scores_gemma":[0.9970061,0.0003830372,0.00031873473,0.0010470772,0.00082862854,0.0004163758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021917044,0.00051706727,0.0011380645,0.00019000843,0.00017472023,0.00015376495,0.00026301062,0.00036146102,0.00006550849],"category_scores_gemma":[0.00069375435,0.000503967,0.00039063848,0.00018378641,0.00011707758,0.00007644994,0.00019012828,0.00038087514,0.0000091028505],"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":[0.019805979,0.005792143,0.07188759,0.028433941,0.0048365984,0.000043125252,0.005080593,0.0037095284,0.008963388,0.014564419,0.60944164,0.22744104],"study_design_scores_gemma":[0.013580135,0.0016188287,0.27463996,0.005059506,0.0022083274,0.000007692101,0.008623836,0.49951157,0.010304261,0.0007956419,0.18086314,0.002787082],"about_ca_topic_score_codex":0.00008917196,"about_ca_topic_score_gemma":0.0001527397,"teacher_disagreement_score":0.7645806,"about_ca_system_score_codex":0.00029957644,"about_ca_system_score_gemma":0.0004982357,"threshold_uncertainty_score":0.9997412},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9877401,0.00029426202,0.00009831707,0.004078163,0.00006415514,0.0025212513,0.0042647035,0.000105898966,0.0008331328],"genre_scores_gemma":[0.99716806,0.00005543268,0.000022533315,0.0005454617,0.0001297275,0.00079139625,0.0006968322,0.000021013144,0.00056955474],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99438506,0.0020198848,0.0005928717,0.00047193162,0.0019542552,0.0005760141],"domain_scores_gemma":[0.98903406,0.008954857,0.00018667063,0.00045501592,0.00043480739,0.00093461754],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.007207943,0.00018161972,0.00035763302,0.00025194863,0.0042279568,0.00033763942,0.00032315272,0.00007407757,0.0002674255],"category_scores_gemma":[0.009293197,0.00014289885,0.000074234056,0.0006933253,0.0007549754,0.000304979,0.0008760285,0.0010471637,0.000021091844],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006271805,0.00044848977,0.9799515,0.00009141065,0.00025163728,0.0001333217,0.005928834,0.000019902158,0.000020334473,0.0004872133,0.011121072,0.00091911975],"study_design_scores_gemma":[0.002842449,0.0012320073,0.96636915,0.000004041982,0.000021206451,0.000048233796,0.0048876535,0.000073073694,0.0000028775278,0.00080786063,0.023572627,0.00013881602],"about_ca_topic_score_codex":0.00059546373,"about_ca_topic_score_gemma":0.0007756379,"teacher_disagreement_score":0.013582332,"about_ca_system_score_codex":0.0010905017,"about_ca_system_score_gemma":0.0022947183,"threshold_uncertainty_score":0.9990519},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4094048,0.0014783788,0.013135059,0.5537678,0.00036469987,0.01713731,0.0041119526,0.00052345695,0.00007656354],"genre_scores_gemma":[0.7943264,0.0000781177,0.002174393,0.19494027,0.00005677854,0.005294646,0.0029752527,0.00004797234,0.00010616881],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99109995,0.0032118554,0.0017855704,0.0008247803,0.0012009101,0.0018769176],"domain_scores_gemma":[0.99287856,0.0006568516,0.0006451753,0.00075617945,0.000293241,0.0047699725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.023994163,0.000360713,0.0011713655,0.001316333,0.0011498064,0.00015894114,0.00031828426,0.000054945973,0.00013637262],"category_scores_gemma":[0.0043104924,0.00037810532,0.000073957,0.0018744898,0.000057902744,0.00036463662,0.0002723799,0.00040939683,0.000009600632],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.012074149,0.0019876566,0.5444825,0.0037725677,0.0000656036,0.000023465775,0.028493226,0.000060489845,9.618811e-7,0.000117222764,0.14537068,0.26355144],"study_design_scores_gemma":[0.006144418,0.0031558352,0.32551125,0.000016502341,2.0654213e-7,0.000010408443,0.002803787,0.00022332514,3.9199488e-8,0.0000045321453,0.66192114,0.00020855456],"about_ca_topic_score_codex":0.0005446747,"about_ca_topic_score_gemma":0.0031041738,"teacher_disagreement_score":0.5165504,"about_ca_system_score_codex":0.005508785,"about_ca_system_score_gemma":0.043417837,"threshold_uncertainty_score":0.9998671},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99266064,0.00014321106,0.003998438,0.0010939783,0.0002007535,0.0014571961,0.00030386675,0.00007820082,0.0000637192],"genre_scores_gemma":[0.9895495,0.00022813863,0.0038344976,0.00075710187,0.00012507263,0.0022105635,0.003252852,0.000023390505,0.000018861076],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966639,0.0012172988,0.00096891564,0.00042063676,0.00024524928,0.00048398072],"domain_scores_gemma":[0.9958858,0.0028682624,0.0004552257,0.00056607055,0.0001354388,0.00008918352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003391768,0.00019152116,0.0006135832,0.00034890446,0.00018727289,0.000004924659,0.00036913308,0.00011053224,0.00005793766],"category_scores_gemma":[0.003341781,0.0001595465,0.00012963009,0.0005267189,0.0002633044,0.00013277911,0.0002861023,0.0005562295,0.000004244592],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001404424,0.00036171102,0.95574564,0.0004600065,0.000094061295,0.000007996801,0.0069549493,0.03106782,0.0020606383,0.00027307853,0.0008481462,0.00072155456],"study_design_scores_gemma":[0.0032292742,0.00037731783,0.82574826,0.000055823024,0.00006341232,0.00004576761,0.008465288,0.1501349,0.00009487889,0.000726658,0.0108006615,0.0002577233],"about_ca_topic_score_codex":0.0003815601,"about_ca_topic_score_gemma":0.000051664552,"teacher_disagreement_score":0.12999733,"about_ca_system_score_codex":0.00021975371,"about_ca_system_score_gemma":0.00017612327,"threshold_uncertainty_score":0.65061176},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69464004,0.06459089,0.020543084,0.20923668,0.0021242653,0.006193921,0.00096884975,0.0011744578,0.0005278368],"genre_scores_gemma":[0.9686375,0.0037019257,0.017611407,0.009331832,0.000037928134,0.00048960303,0.00011421459,0.000018108416,0.000057427238],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99772,0.00012490532,0.0010288567,0.00020852625,0.00023730099,0.0006804212],"domain_scores_gemma":[0.9983422,0.00043315187,0.0005377026,0.00044950139,0.00011547792,0.000121976824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002120031,0.0001569378,0.00068545324,0.00044750675,0.0006914806,0.00000563176,0.00017834442,0.00008801909,0.000013124342],"category_scores_gemma":[0.0022307006,0.00015352944,0.000042291296,0.00091352564,0.0004938564,0.000108664775,0.0006199435,0.0006221476,0.0000023472628],"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":[0.00019477856,0.00030570515,0.5804062,0.0038162444,0.00035249075,0.0000073817737,0.0152853485,0.000099744415,3.2272536e-7,0.33333963,0.01382938,0.052362777],"study_design_scores_gemma":[0.006984311,0.0033694762,0.10231345,0.000889942,0.000015590218,0.00026239856,0.34854853,0.00292368,0.0000030327076,0.08485799,0.44913802,0.0006935656],"about_ca_topic_score_codex":0.000012412243,"about_ca_topic_score_gemma":0.000025680127,"teacher_disagreement_score":0.47809276,"about_ca_system_score_codex":0.00039972717,"about_ca_system_score_gemma":0.0004573534,"threshold_uncertainty_score":0.6260749},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015281711,0.9905706,0.000030035064,0.0001542757,0.0006329786,0.0012377698,0.004781546,0.00015173407,0.000912885],"genre_scores_gemma":[0.0003215619,0.98939115,0.00014690399,0.0015881668,0.00052950886,0.0005393519,0.005704603,0.00009863511,0.0016801361],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99785197,0.00013815534,0.00058404333,0.00074431393,0.00041995497,0.00026155967],"domain_scores_gemma":[0.9982519,0.00011761157,0.00026973832,0.0009316927,0.000048282975,0.00038074632],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001749,0.0003727269,0.0011653568,0.0005561754,0.000049182436,0.000018725348,0.0002447314,0.0000752094,0.0041110707],"category_scores_gemma":[0.00028355038,0.00036115033,0.00017238813,0.00049880065,0.00002872807,0.00004152937,0.00050267554,0.0003930784,0.0002542911],"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":[0.000032987802,0.00014343999,0.010515703,0.022272628,0.00034043583,0.0006516919,0.00013494425,1.0683601e-7,0.0000013806941,0.000020877793,0.02163751,0.9442483],"study_design_scores_gemma":[0.00030689352,0.000020255238,0.008707864,0.006611933,0.00041268664,0.000013794787,0.000010955275,0.0000046780747,2.3020407e-8,0.000003761427,0.98358035,0.00032677772],"about_ca_topic_score_codex":0.000035853463,"about_ca_topic_score_gemma":0.0000075410403,"teacher_disagreement_score":0.96194285,"about_ca_system_score_codex":0.00074242515,"about_ca_system_score_gemma":0.00069397246,"threshold_uncertainty_score":0.99988407},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9964783,0.00045350366,0.00024092993,0.0016232494,0.000030281186,0.0003245339,0.00054107147,0.00008839557,0.00021970393],"genre_scores_gemma":[0.99668854,0.000071487244,0.00036088814,0.0015143486,0.00004407715,0.000037550748,0.0005866731,0.000018285991,0.0006781246],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987922,0.000088307526,0.00022104537,0.00027342417,0.0004591503,0.00016589096],"domain_scores_gemma":[0.99842525,0.000071483715,0.000070842456,0.0013040489,0.00002987813,0.00009848951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033528008,0.00008161052,0.0002285956,0.0000514898,0.00012672941,0.000005857333,0.0004678229,0.000017222043,0.0006183318],"category_scores_gemma":[0.00029691693,0.00006585265,0.00003288034,0.0001410112,0.000039855746,0.00005357877,0.00075294304,0.00022953525,0.0000146956845],"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":[0.0007769101,0.0023416397,0.91645354,0.0023412905,0.0012212142,0.00015927192,0.0015095491,0.0076684356,0.06555023,0.000031029118,0.0015861613,0.00036073534],"study_design_scores_gemma":[0.0044721677,0.00017196371,0.054841712,0.00018354163,0.0007898357,0.00010350875,0.0009857201,0.93437344,0.00019001617,0.00033927985,0.0031553046,0.0003935077],"about_ca_topic_score_codex":0.000088725435,"about_ca_topic_score_gemma":0.000023980321,"teacher_disagreement_score":0.926705,"about_ca_system_score_codex":0.00015419125,"about_ca_system_score_gemma":0.00013392906,"threshold_uncertainty_score":0.67702997},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96469486,0.00090562546,0.028921153,0.004417953,0.000220256,0.00040052153,0.00035012458,0.000018629336,0.00007084885],"genre_scores_gemma":[0.9983879,0.00011132367,0.00090491195,0.00029564946,0.00013471456,0.000004086596,0.00008294631,0.000042342996,0.00003615951],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976705,0.0009273075,0.00069220806,0.00016733189,0.00034001668,0.00020264974],"domain_scores_gemma":[0.99833876,0.00018357502,0.00085303903,0.00031011488,0.00015941754,0.00015507969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028609666,0.00013359667,0.00030052057,0.00027816583,0.00028061017,0.00003321997,0.00027198237,0.000013810331,0.00002740866],"category_scores_gemma":[0.00051352597,0.00009128025,0.00024658203,0.00041501832,0.00029037142,0.00011088321,0.00017557613,0.00029713166,2.968068e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004764491,0.00030583618,0.95159334,0.0012570225,0.00049789797,0.00060192187,0.0025744145,0.011169622,0.0013773754,0.0074294712,0.0010415838,0.017387033],"study_design_scores_gemma":[0.0018068627,0.00066675484,0.9786692,0.00033913067,0.00037296876,0.0010206755,0.0020512955,0.008588734,0.000010709169,0.0049366625,0.0013914363,0.00014553488],"about_ca_topic_score_codex":0.000010034255,"about_ca_topic_score_gemma":0.000018689878,"teacher_disagreement_score":0.03369298,"about_ca_system_score_codex":0.00011488366,"about_ca_system_score_gemma":0.000058986992,"threshold_uncertainty_score":0.37223005},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57211065,0.0006302431,0.029450292,0.01683508,0.00037912646,0.36616823,0.009936517,0.001227213,0.003262661],"genre_scores_gemma":[0.7480585,0.000020224246,0.010384569,0.0035859009,0.00011581706,0.23415029,0.0026107805,0.00004411514,0.0010298013],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977681,0.00014781154,0.00067558093,0.00050139264,0.0004785678,0.00042856188],"domain_scores_gemma":[0.9986189,0.000026885184,0.00028734864,0.0004018213,0.000075490745,0.0005895339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071262196,0.00019348408,0.00045930877,0.00009305042,0.00029004237,0.000060559454,0.00017224613,0.00004084284,0.0006489911],"category_scores_gemma":[0.00022842115,0.00018132184,0.0001446552,0.00030579817,0.000048591486,0.00015362441,0.00072369055,0.00044784974,0.000011034605],"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":[0.0019791368,0.002427966,0.79681975,0.0013995094,0.00065884105,0.0011063295,0.0014368326,0.0050900592,0.00046362515,0.00034098345,0.017553573,0.17072338],"study_design_scores_gemma":[0.008927307,0.0067837634,0.30158406,0.00072030525,0.00012588072,0.00035172602,0.0007602807,0.34294027,0.000012437063,0.0032456806,0.33372042,0.0008278653],"about_ca_topic_score_codex":0.00045287848,"about_ca_topic_score_gemma":0.00035411317,"teacher_disagreement_score":0.4952357,"about_ca_system_score_codex":0.0005959323,"about_ca_system_score_gemma":0.00050559506,"threshold_uncertainty_score":0.73940897},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9450926,0.016832441,0.000061023078,0.0002535523,0.0011126158,0.001432581,0.034861453,0.00024923103,0.00010451471],"genre_scores_gemma":[0.9734449,0.0010691944,0.002300331,0.00014330605,0.00015935591,0.00041313257,0.019434765,0.0002409133,0.0027941514],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98584586,0.0031605968,0.0045647537,0.0021379297,0.0021338686,0.002156964],"domain_scores_gemma":[0.9917074,0.0005418069,0.0020489036,0.004058332,0.0007778356,0.00086575694],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0029533796,0.0012935289,0.005321905,0.00351484,0.0007680321,0.00006192333,0.001771533,0.00065975357,0.012629468],"category_scores_gemma":[0.00038504376,0.0014775301,0.001397275,0.0059070354,0.0020703601,0.0005904241,0.0022365607,0.0021604907,0.000015079677],"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":[0.0010722915,0.009516771,0.8216719,0.00025082557,0.0021426326,0.001176717,0.0024234287,0.0024054989,0.06853954,0.00019117737,0.00011279604,0.09049644],"study_design_scores_gemma":[0.004186297,0.002417953,0.9439363,0.000228738,0.020345014,0.0003642234,0.0027909034,0.021218618,0.00026549792,0.0000033138783,0.0031430447,0.0011000872],"about_ca_topic_score_codex":0.020180112,"about_ca_topic_score_gemma":0.0006998248,"teacher_disagreement_score":0.12226443,"about_ca_system_score_codex":0.0010560686,"about_ca_system_score_gemma":0.0016690409,"threshold_uncertainty_score":0.99998164},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98774827,0.00087040087,0.00000587606,0.00042147047,0.00068596244,0.0019380468,0.0018244741,0.00008752318,0.0064180032],"genre_scores_gemma":[0.9935504,0.0001912162,0.00001978375,0.0012542345,0.00019787534,0.0028081732,0.00008721083,0.000047086494,0.0018440259],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981173,0.00015523714,0.00031193552,0.0004619272,0.00065010577,0.0003034694],"domain_scores_gemma":[0.9990479,0.00005318865,0.00019848575,0.00043205367,0.00010837566,0.00015999457],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010799477,0.00020691712,0.00049350585,0.00011286276,0.00011529273,0.0000109948405,0.00020358538,0.000033094642,0.005595953],"category_scores_gemma":[0.000038361264,0.00019379168,0.00012837605,0.00044896148,0.00006550305,0.000077674595,0.00018934303,0.00030608472,0.000029937713],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0059811617,0.0029463477,0.8530338,0.00011499018,0.0005652725,0.00023772131,0.0053118807,0.0017068211,0.003747568,0.000041790692,0.08470332,0.041609373],"study_design_scores_gemma":[0.0071109664,0.0031753024,0.82395506,0.000103055216,0.00018915349,0.0000077734885,0.006354414,0.00026231344,0.00077976735,0.00002675566,0.15763423,0.00040120172],"about_ca_topic_score_codex":0.0054639103,"about_ca_topic_score_gemma":0.0045746807,"teacher_disagreement_score":0.07293091,"about_ca_system_score_codex":0.0009720214,"about_ca_system_score_gemma":0.0005387448,"threshold_uncertainty_score":0.99531305},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931414,0.00008963962,0.000001687139,0.0022534847,0.0009671686,0.0016426466,0.00015039626,0.00006150652,0.0016920713],"genre_scores_gemma":[0.9922941,0.0000011394063,0.0000024100827,0.001938107,0.000044504177,0.00016688007,0.00009221946,0.0000114792,0.005449215],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99515647,0.0014761084,0.0006806048,0.00073088467,0.0016209215,0.00033499137],"domain_scores_gemma":[0.99473935,0.0016348356,0.0006789944,0.0026333763,0.00009695573,0.00021651099],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011136935,0.00020413933,0.00036683626,0.00012352112,0.0013731022,0.000055805373,0.0006147638,0.000030092531,0.0019053768],"category_scores_gemma":[0.006927539,0.00007777519,0.00032711684,0.0012714886,0.00090660393,0.000041517447,0.00076850335,0.0005145558,0.000021901318],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026968188,0.00040959482,0.82576245,0.0000055930554,0.00006684112,0.00017737193,0.0004979481,0.0010360673,0.0012003897,0.000023178702,0.17045468,0.000096174466],"study_design_scores_gemma":[0.000384718,0.0004084873,0.97317237,0.000019290708,0.000055420176,0.00035676832,0.00091215124,0.00007991308,0.00014247534,0.0008794963,0.023472546,0.0001163389],"about_ca_topic_score_codex":0.001150461,"about_ca_topic_score_gemma":0.0011865861,"teacher_disagreement_score":0.14740992,"about_ca_system_score_codex":0.0007128925,"about_ca_system_score_gemma":0.0011246969,"threshold_uncertainty_score":0.999927},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98207724,0.006696734,0.0053569754,0.00009300352,0.0004543381,0.0017297774,0.0002401951,0.00012550199,0.0032262509],"genre_scores_gemma":[0.97204953,0.00008098762,0.0059952955,0.000036985515,0.00009254349,0.000054272125,0.021575691,0.000047054025,0.000067609515],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736583,0.00012629657,0.0011861918,0.00049515313,0.0005326114,0.00029393],"domain_scores_gemma":[0.99732757,0.00004492089,0.0012087829,0.00063086883,0.00056711043,0.00022074103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032032043,0.00038873256,0.0010537168,0.00070477347,0.00012996675,0.00006267346,0.00013687224,0.00039550997,0.000049256236],"category_scores_gemma":[0.000104201994,0.00039861808,0.00016412183,0.00043032164,0.00026191483,0.0004734837,0.00003701865,0.00024484345,0.0000018308112],"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":[0.002428904,0.0017337312,0.3782314,0.041733872,0.0017569842,0.00007669003,0.021741616,0.000082522034,0.36667812,0.003998882,0.000535066,0.1810022],"study_design_scores_gemma":[0.0076296376,0.0016585763,0.25699416,0.008422075,0.0011185247,0.00033493643,0.31466895,0.07819606,0.32570013,0.00012510836,0.0027337766,0.002418058],"about_ca_topic_score_codex":0.0019047674,"about_ca_topic_score_gemma":0.00012645063,"teacher_disagreement_score":0.29292732,"about_ca_system_score_codex":0.00009058182,"about_ca_system_score_gemma":0.0003884317,"threshold_uncertainty_score":0.9998466},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951382,0.00013827653,0.00008715274,0.0005387522,0.00042756973,0.0010428978,0.00025052245,0.00028345056,0.002093144],"genre_scores_gemma":[0.9956023,0.00003897187,0.0019236697,0.0009035577,0.00017212963,0.00046301502,0.0006198546,0.00006252005,0.00021396682],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956601,0.0015319253,0.00087282824,0.0007891201,0.00042763093,0.00071838323],"domain_scores_gemma":[0.99717563,0.0008568886,0.00063475955,0.0010181419,0.00013050331,0.00018405037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002525913,0.00040411286,0.0012133374,0.00036209787,0.00021097042,0.000006659546,0.00040988854,0.00016278795,0.00023289361],"category_scores_gemma":[0.002263551,0.0003697262,0.00018933225,0.00043110174,0.0003735701,0.00027094487,0.0008091861,0.0009045015,0.000010545544],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022965933,0.00023527232,0.98364824,0.000434416,0.00021515462,0.000034461198,0.0012549676,0.0041284175,0.003375663,0.00011906522,0.003803089,0.00045466342],"study_design_scores_gemma":[0.0015608142,0.0010026611,0.97710204,0.0001440281,0.00013449282,0.00029543377,0.0013124789,0.005710284,0.0010824816,0.000079236794,0.010987514,0.0005885228],"about_ca_topic_score_codex":0.000039108785,"about_ca_topic_score_gemma":5.318889e-7,"teacher_disagreement_score":0.007184425,"about_ca_system_score_codex":0.00027255347,"about_ca_system_score_gemma":0.00023763698,"threshold_uncertainty_score":0.9998755},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99355036,0.001278481,0.000017864051,0.0029751102,0.00014056683,0.0011417601,0.00038436014,0.000049752158,0.00046176696],"genre_scores_gemma":[0.99677026,0.0010968604,0.00037610586,0.0015787433,0.00003231555,0.00008352389,0.000043814493,0.000010438522,0.000007938973],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974329,0.00012713378,0.00079062657,0.00017782256,0.001207011,0.00026451377],"domain_scores_gemma":[0.998283,0.00021096492,0.00036008673,0.00068756274,0.00007622483,0.00038215992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016637436,0.00014203925,0.0004933259,0.00004204745,0.00012726513,0.000009751916,0.00024571648,0.00007247704,0.00018952302],"category_scores_gemma":[0.00061052956,0.00010281825,0.000076391516,0.00017001088,0.0004383791,0.0001775007,0.0005670342,0.000490592,0.0000020329771],"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":[0.0008053973,0.0010517028,0.34072822,0.006460345,0.00013244402,0.000011242933,0.020393578,0.0000025089068,0.000011008668,0.0007113542,0.0038952595,0.6257969],"study_design_scores_gemma":[0.0014147216,0.00031275876,0.9677533,0.00013220262,0.000011651849,0.000026079602,0.002077367,0.0008621544,0.0000071398963,0.00012363138,0.027175006,0.000103998325],"about_ca_topic_score_codex":0.000029671455,"about_ca_topic_score_gemma":0.00003674777,"teacher_disagreement_score":0.62702507,"about_ca_system_score_codex":0.00010492298,"about_ca_system_score_gemma":0.0006050496,"threshold_uncertainty_score":0.41928068},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001842241,0.97257656,0.000010236287,0.010536008,0.00068822113,0.013631505,0.00210151,0.00020649799,0.0002310207],"genre_scores_gemma":[0.0007183544,0.98926955,0.0000053092876,0.002931412,0.00028576565,0.0048387595,0.0013667071,0.0001447345,0.0004394238],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9800369,0.009961482,0.0047127134,0.0012298923,0.0018228398,0.0022361877],"domain_scores_gemma":[0.9860851,0.0038991778,0.004627882,0.0035881065,0.00025746928,0.0015422495],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.024160866,0.00092342496,0.008895615,0.00015174394,0.0013644269,0.00022636373,0.0012979008,0.00020075805,0.00007388633],"category_scores_gemma":[0.0032761847,0.0005188944,0.0007810795,0.0018539097,0.00048606907,0.00011163579,0.00053697557,0.001343064,0.000043244774],"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":[0.000024251303,0.00016040841,0.0014603316,0.9174426,0.0004727924,0.000011873444,0.00020445645,1.779152e-7,2.3856512e-9,0.0003181674,0.01789083,0.062014073],"study_design_scores_gemma":[0.0004283692,0.00038739844,0.0010190044,0.09296653,0.000041054795,0.0002479577,0.00032807852,0.00002916749,7.931314e-10,0.0000025538923,0.9041513,0.0003985812],"about_ca_topic_score_codex":0.0002370172,"about_ca_topic_score_gemma":0.00042523598,"teacher_disagreement_score":0.88626045,"about_ca_system_score_codex":0.0006438083,"about_ca_system_score_gemma":0.00675012,"threshold_uncertainty_score":0.9999357},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011445492,0.03992326,0.0009829358,0.8468423,0.038954593,0.0013007271,0.038039565,0.000063898326,0.02244721],"genre_scores_gemma":[0.15225148,0.0016663453,0.00049662136,0.686022,0.12430564,0.000058258127,0.030485693,0.0003358055,0.0043781893],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99761313,0.00043856996,0.0006197655,0.00043567098,0.0003544265,0.00053841533],"domain_scores_gemma":[0.9973873,0.00015849413,0.0004389401,0.0010656058,0.00025230562,0.0006973528],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004600056,0.0002792981,0.0011208999,0.000988549,0.00014919802,0.000038568767,0.00067586015,0.0003250584,0.00017069853],"category_scores_gemma":[0.0005957087,0.00027292903,0.00020016028,0.00021862988,0.0002700672,0.000057701607,0.00014209282,0.0017310993,0.00002362947],"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":[0.00003462076,0.0000012537904,0.010925914,0.0000744079,0.000813441,0.010198322,0.000045952427,0.0000058308106,0.0000031575403,0.0000016923174,0.9488903,0.02900506],"study_design_scores_gemma":[0.00091619766,0.00020942837,0.010593581,0.000062636806,0.00047459747,0.0027091547,0.00018675298,0.0000017511423,6.674523e-7,0.000034745466,0.9845956,0.00021491821],"about_ca_topic_score_codex":0.00088892796,"about_ca_topic_score_gemma":0.00198702,"teacher_disagreement_score":0.16082036,"about_ca_system_score_codex":0.00035511996,"about_ca_system_score_gemma":0.0042859754,"threshold_uncertainty_score":0.9999723},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9635515,0.0021354062,0.02022874,0.0011376948,0.0008822529,0.007804986,0.003965406,0.00028917682,0.000004787193],"genre_scores_gemma":[0.9693997,0.000055008186,0.002585685,0.00032785066,0.0014837974,0.010308382,0.015672525,0.00006881654,0.00009824505],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99692214,0.00039447215,0.000616541,0.00080151844,0.0006212648,0.00064409146],"domain_scores_gemma":[0.9979634,0.0003774748,0.00023278604,0.00049238,0.00035259864,0.000581343],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0015377557,0.00030214174,0.00045737918,0.0001253024,0.0015298215,0.00014204501,0.0001239182,0.00003611644,0.000015149898],"category_scores_gemma":[0.00025445895,0.00025010537,0.00007890041,0.00030739704,0.0000505564,0.00015595631,0.00015633735,0.00023755578,6.0392307e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022418855,0.0010009501,0.7365931,0.00027245042,0.00021561346,0.00027878006,0.0010150435,0.000020093035,0.000010466637,0.0000011654828,0.0019308785,0.25843725],"study_design_scores_gemma":[0.0045605227,0.00091522903,0.9533614,0.00006724343,0.00002863235,0.0002611039,0.0022262316,0.017922863,0.0000026786875,0.0000020234595,0.02020041,0.00045166435],"about_ca_topic_score_codex":0.00028024323,"about_ca_topic_score_gemma":0.0012565156,"teacher_disagreement_score":0.25798556,"about_ca_system_score_codex":0.0003468146,"about_ca_system_score_gemma":0.0010745445,"threshold_uncertainty_score":0.9999951},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951563,0.000040530067,0.001871501,0.00015145192,0.000113215625,0.0007886041,0.000018386922,0.000072750954,0.0017872687],"genre_scores_gemma":[0.9983697,0.0000032931873,0.0007841977,0.00038061783,0.00006767113,0.00008857069,0.0002232816,0.000014422103,0.0000682624],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971959,0.00019040504,0.0008340542,0.000120526536,0.0014204422,0.00023869745],"domain_scores_gemma":[0.9987348,0.00012982861,0.00043218205,0.00034327756,0.00011195997,0.0002479884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006865872,0.00014619404,0.00045818352,0.00015677883,0.00018928156,0.000014657132,0.00022265699,0.000048073056,0.0008353168],"category_scores_gemma":[0.00015462517,0.00012737983,0.00008046134,0.0003899271,0.00017253413,0.0001905724,0.00028405956,0.0006388982,0.000026410156],"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":[0.0007911134,0.0050026164,0.6797223,0.0010799597,0.0008250483,0.00013523776,0.27878,0.0028621273,0.00067097915,0.0002828553,0.0047646924,0.025083086],"study_design_scores_gemma":[0.003907793,0.0009323188,0.21455067,0.00020325251,0.0001871168,0.000079244826,0.2825486,0.49058893,0.00003180655,0.00001598275,0.0066410424,0.0003132205],"about_ca_topic_score_codex":0.000015000329,"about_ca_topic_score_gemma":0.0000055573882,"teacher_disagreement_score":0.4877268,"about_ca_system_score_codex":0.00014646459,"about_ca_system_score_gemma":0.00021450924,"threshold_uncertainty_score":0.9146133},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9950863,0.00019818808,0.002308806,0.00007557011,0.00037942288,0.0017723714,0.0001284623,0.000026846461,0.000024039056],"genre_scores_gemma":[0.9987013,0.0000036332747,0.0006479224,0.00009700649,0.00017559931,0.00030047007,0.000016924096,0.000038152048,0.000019020277],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964413,0.0011685638,0.0009039621,0.00045039225,0.0005789361,0.00045683247],"domain_scores_gemma":[0.9988897,0.00012010925,0.0002490245,0.00042698876,0.00007251072,0.00024165706],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020636215,0.00024047898,0.00079856534,0.0005377077,0.00014177164,0.00003011931,0.00026387445,0.000025316243,0.0006934037],"category_scores_gemma":[0.00012131595,0.00024525737,0.00008450301,0.00049027463,0.0000144210135,0.00009088374,0.0006673077,0.0006796137,0.0000011398199],"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":[0.001016368,0.0020523346,0.9488734,0.00006571545,0.0001733088,0.013178621,0.015892228,0.010943555,0.00014521892,1.4233173e-7,0.000026250287,0.0076328563],"study_design_scores_gemma":[0.007395447,0.0007743229,0.94799733,0.00013567234,0.00011619706,0.0007241626,0.022982363,0.019598208,0.00003347599,0.0000015427814,0.000027370952,0.00021391868],"about_ca_topic_score_codex":0.05560477,"about_ca_topic_score_gemma":0.11815559,"teacher_disagreement_score":0.06255082,"about_ca_system_score_codex":0.00045773954,"about_ca_system_score_gemma":0.00015052935,"threshold_uncertainty_score":0.99999994},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85645366,0.0015430236,0.122926004,0.005559992,0.0016519323,0.0060277493,0.003844262,0.00061152375,0.0013818757],"genre_scores_gemma":[0.8838014,0.000046182744,0.07414497,0.009338386,0.0001385547,0.00028761625,0.031513527,0.0001053561,0.00062401284],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971734,0.00039921905,0.00083075603,0.00061306456,0.00046475025,0.0005188058],"domain_scores_gemma":[0.9963263,0.0007493467,0.00027944808,0.0020059242,0.00024711638,0.00039181733],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018811014,0.00036812213,0.0011299908,0.00029336585,0.00013201813,0.00007213899,0.00042715922,0.00029365032,0.00044065184],"category_scores_gemma":[0.00079250726,0.0003513982,0.00027098611,0.00017015309,0.000047343314,0.00006036437,0.0007980301,0.0014907643,0.00003055953],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034542943,0.0032742696,0.63841987,0.011802385,0.00030445194,0.00067894225,0.0017185337,0.011433427,0.00013477525,0.000046786306,0.30421108,0.024521159],"study_design_scores_gemma":[0.017070517,0.0012248829,0.768579,0.016598232,0.0001835973,0.000037284022,0.002625306,0.029111953,0.00026986064,0.000409378,0.16232471,0.0015652562],"about_ca_topic_score_codex":0.0027318664,"about_ca_topic_score_gemma":0.0044509545,"teacher_disagreement_score":0.14188638,"about_ca_system_score_codex":0.0005205254,"about_ca_system_score_gemma":0.002965678,"threshold_uncertainty_score":0.9998938},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99651575,0.0005937021,0.000051847608,0.0021845102,0.00013621105,0.000106784515,0.00028117478,0.0000068298486,0.00012317918],"genre_scores_gemma":[0.9967536,0.0009591969,0.00044772602,0.0004334679,0.0013574297,0.000001551288,0.00003277238,0.000013739699,5.172759e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988455,0.000011716274,0.00074541464,0.00013000253,0.0001497636,0.00011758442],"domain_scores_gemma":[0.9974595,0.00095722236,0.0011311229,0.0001327111,0.00020152229,0.000117930365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001989448,0.00012351884,0.0006179997,0.000035709843,0.00004878429,0.000026425654,0.00022224324,0.00007134172,0.000053245967],"category_scores_gemma":[0.00068306463,0.000089355264,0.00008508567,0.00011103486,0.00014570773,0.00021847086,0.000110940935,0.00018197406,0.0000019672461],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041827387,0.00012592523,0.9448011,0.0004725191,0.0009084558,0.000011467574,0.014365509,0.0010172097,0.007396214,0.00007809093,0.002344447,0.024296341],"study_design_scores_gemma":[0.0018087425,0.000038631093,0.99214494,0.00026957673,0.0002563501,0.000002701224,0.0017555782,0.00059776235,0.0010987989,0.00023255797,0.0016976864,0.00009666143],"about_ca_topic_score_codex":0.00017167129,"about_ca_topic_score_gemma":0.00013464678,"teacher_disagreement_score":0.047343872,"about_ca_system_score_codex":0.000030400253,"about_ca_system_score_gemma":0.00025542275,"threshold_uncertainty_score":0.3643802},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99029326,0.00006613102,0.0016763024,0.0063130353,0.000075629054,0.00096674054,0.00040894476,0.000041870404,0.00015809391],"genre_scores_gemma":[0.9951012,0.0000054387388,0.0026576747,0.001014563,0.000031956988,0.0005406951,0.00055941386,0.000016205126,0.00007282757],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99531436,0.0027487737,0.0006133054,0.00063812046,0.0003325761,0.00035289256],"domain_scores_gemma":[0.99619716,0.0025519636,0.00019457759,0.0007625894,0.000037827504,0.00025590582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004641481,0.00023174405,0.00057316024,0.00014580776,0.00009031219,0.0000108688655,0.00039723236,0.000081642655,0.000050304698],"category_scores_gemma":[0.0017745245,0.00017047432,0.0000660793,0.000308464,0.00018605051,0.000103543985,0.0010191266,0.0006738469,0.0000045874817],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030983489,0.00007165758,0.9565033,0.000234302,0.00008983966,0.000025916457,0.0005526041,0.00054996763,0.0006907418,0.00060008484,0.0017614244,0.035821863],"study_design_scores_gemma":[0.0012517497,0.00046747978,0.9785813,0.00008577832,0.000049861,0.000017075532,0.00005896147,0.014793387,0.00034436025,0.00063279,0.003529318,0.0001879328],"about_ca_topic_score_codex":0.0000638406,"about_ca_topic_score_gemma":0.00009727882,"teacher_disagreement_score":0.03563393,"about_ca_system_score_codex":0.00028260626,"about_ca_system_score_gemma":0.00023578519,"threshold_uncertainty_score":0.6951741},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9744968,0.00015040312,0.017736524,0.0044472716,0.00014764897,0.0018379694,0.0005886163,0.000081657665,0.0005130979],"genre_scores_gemma":[0.98042077,0.000037753663,0.016615609,0.0003214044,0.00006119961,0.00028669505,0.001954084,0.000041112344,0.00026139177],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9935874,0.0024189278,0.0009677194,0.001692323,0.00089894276,0.0004347379],"domain_scores_gemma":[0.99472857,0.0021197104,0.00029564239,0.002158842,0.00015807459,0.000539162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0058196746,0.0005468476,0.0010714568,0.00020105501,0.00005781282,0.00011522021,0.0006700798,0.0002581412,0.00011442878],"category_scores_gemma":[0.0031785346,0.00039240308,0.00017178505,0.00032927332,0.00019075868,0.00016807437,0.003108647,0.0013275534,0.000008319434],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038069233,0.00027755866,0.77641857,0.004249611,0.0008145319,0.00015189714,0.0011791518,0.0008114213,0.0017048242,0.00029245167,0.0011922693,0.20910081],"study_design_scores_gemma":[0.0014955929,0.00017462578,0.96465516,0.0024276038,0.00027943184,0.0000120517925,0.00012538637,0.020186504,0.0090515055,0.00044025562,0.0006061095,0.00054576166],"about_ca_topic_score_codex":0.00031470298,"about_ca_topic_score_gemma":0.00083012955,"teacher_disagreement_score":0.20855506,"about_ca_system_score_codex":0.00045496755,"about_ca_system_score_gemma":0.00082585064,"threshold_uncertainty_score":0.9998528},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987617,0.00036842684,0.001716239,0.0006757217,0.0010318534,0.0008129737,0.004256615,0.00024321915,0.0032779456],"genre_scores_gemma":[0.99266624,0.00002255382,0.0040010335,0.0014348911,0.00014579872,0.00008312721,0.0015746806,0.000019016708,0.000052678693],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983156,0.000070384755,0.0005809953,0.00017470495,0.0005687865,0.00028956644],"domain_scores_gemma":[0.99883,0.000068410074,0.00016849097,0.0005784907,0.000030907973,0.000323672],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017698543,0.00018042306,0.00029999219,0.00012691901,0.00008228568,0.000035991587,0.00018492302,0.000058122685,0.0012216639],"category_scores_gemma":[0.00028414073,0.00017275815,0.000102259415,0.0004900733,0.000047053156,0.0002312055,0.0002565265,0.0002496143,0.0004495045],"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":[0.0009799814,0.0007356653,0.31366113,0.0005435342,0.00017296738,0.0004528847,0.0028148007,0.00035400433,0.00010684432,0.00006469405,0.0182579,0.6618556],"study_design_scores_gemma":[0.0036475766,0.00014631033,0.72971183,0.00011861542,0.0001604719,0.000033459797,0.0026804665,0.23426932,0.00003390545,0.00036321185,0.028412959,0.00042189387],"about_ca_topic_score_codex":0.00021633394,"about_ca_topic_score_gemma":0.0002962276,"teacher_disagreement_score":0.6614337,"about_ca_system_score_codex":0.00039569568,"about_ca_system_score_gemma":0.00018920412,"threshold_uncertainty_score":0.99969137},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9792449,0.0022311218,0.00090815936,0.0025588647,0.0003446119,0.0006055195,0.0131866075,0.00023812111,0.0006821264],"genre_scores_gemma":[0.9740859,0.000286206,0.00010758941,0.00015602531,0.000109805595,0.00007322178,0.023329007,0.000053115084,0.0017991286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99681044,0.00028056453,0.00086979446,0.0010275273,0.00060560677,0.00040606214],"domain_scores_gemma":[0.9955927,0.00022995092,0.0007305881,0.0030928832,0.00013519493,0.00021866392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00104027,0.0003890251,0.0014174213,0.00070031144,0.00017698566,0.00012908065,0.0010246482,0.00011778602,0.0005687088],"category_scores_gemma":[0.0006321546,0.00030232986,0.00028757498,0.0012575105,0.00041831896,0.00014373747,0.004151079,0.0007108854,0.000003986603],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007108012,0.00028436838,0.9258754,0.0004824556,0.003936583,0.000066581495,0.0018432939,0.0000574918,0.0007072882,0.0000058844694,0.0022096473,0.063820206],"study_design_scores_gemma":[0.0012196905,0.00030341523,0.97438204,0.00029608433,0.0035739685,0.00001850618,0.0014205598,0.0041395547,0.000044076914,0.000032046293,0.014077482,0.00049257383],"about_ca_topic_score_codex":0.00032239134,"about_ca_topic_score_gemma":0.0005667709,"teacher_disagreement_score":0.06332763,"about_ca_system_score_codex":0.00017982688,"about_ca_system_score_gemma":0.0005242782,"threshold_uncertainty_score":0.9999429},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017187996,0.9973889,0.0000129636055,0.00016509337,0.0003444734,0.0013742998,0.00032737842,0.000012398091,0.00020263389],"genre_scores_gemma":[0.007940191,0.99150884,0.000112650705,0.000081484286,0.00006839793,0.0000603662,0.00021596042,0.000011928125,1.5068652e-7],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99461174,0.0010984298,0.0022907855,0.00016844891,0.0015625836,0.00026800667],"domain_scores_gemma":[0.9957765,0.00038905404,0.0022970287,0.00023028164,0.0011389075,0.00016823728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005722059,0.00019141997,0.0012614459,0.0009688363,0.0002502404,0.00009040803,0.00039477926,0.000082254934,0.0000037882428],"category_scores_gemma":[0.0003580269,0.00014334467,0.00028708018,0.0007896287,0.0001479174,0.00023549917,0.00025937392,0.00072649535,6.273408e-7],"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":[0.000051850817,0.000018531622,0.014229883,0.23658335,0.00047418042,0.000034267683,0.00009221997,9.736531e-7,7.180008e-9,0.0014361381,0.000029182453,0.7470494],"study_design_scores_gemma":[0.00055878324,0.0002395901,0.00232472,0.68417203,0.0003461319,0.0017591574,0.0019247652,0.000021305383,1.4718807e-8,0.000017797784,0.30853152,0.00010415938],"about_ca_topic_score_codex":0.00022952356,"about_ca_topic_score_gemma":0.00005805975,"teacher_disagreement_score":0.74694526,"about_ca_system_score_codex":0.00055928814,"about_ca_system_score_gemma":0.0014981069,"threshold_uncertainty_score":0.58454263},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9919761,0.004112425,0.00023583355,0.0008011648,0.00083912123,0.0006233223,0.0007427019,0.00007051556,0.0005988165],"genre_scores_gemma":[0.9847081,0.00015230471,0.011053842,0.00015281672,0.000042599433,0.0001297436,0.0028976996,0.000054247892,0.00080869254],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99765193,0.00029662094,0.0005502607,0.0006850266,0.0005026364,0.00031352192],"domain_scores_gemma":[0.9979021,0.0005867281,0.00048280502,0.0007803038,0.00010214565,0.00014591953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009960872,0.0003517811,0.00096838933,0.00016256326,0.00006820963,0.00003198267,0.0003423746,0.00020675485,0.00038076472],"category_scores_gemma":[0.0018461238,0.00032979026,0.0000848997,0.00011145057,0.00010502487,0.00006721994,0.0008889338,0.0011153258,5.122587e-7],"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":[0.00021461926,0.000055569304,0.9708105,0.0011761497,0.00024214789,0.00017540787,0.0018598124,0.00081123254,0.0076651284,0.000009245092,0.012574224,0.004406013],"study_design_scores_gemma":[0.0014599491,0.00009261553,0.9837528,0.0006314161,0.00039366592,0.000026458512,0.0015228279,0.0025965683,0.0031246478,0.00029276105,0.0054343552,0.00067192875],"about_ca_topic_score_codex":0.9837762,"about_ca_topic_score_gemma":0.8999626,"teacher_disagreement_score":0.08381359,"about_ca_system_score_codex":0.00061418465,"about_ca_system_score_gemma":0.0017965751,"threshold_uncertainty_score":0.9999154},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99178755,0.001663783,0.0000040503314,0.0027821977,0.00020879695,0.0027931938,0.00028751238,0.00021309312,0.00025979537],"genre_scores_gemma":[0.99622035,0.000042527263,0.00009810742,0.0026232246,0.000046714886,0.0006592745,0.00021010906,0.000045188415,0.00005450607],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99489945,0.001425822,0.00092977204,0.0011667841,0.00073370594,0.00084447366],"domain_scores_gemma":[0.99756,0.00033627567,0.00025985038,0.00074510387,0.00009335728,0.0010053951],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004570932,0.0004285313,0.0010761806,0.00035262245,0.00058350025,0.0001145919,0.00018431901,0.00006205779,0.000023335795],"category_scores_gemma":[0.00041318077,0.00039905673,0.00003871464,0.00071995414,0.00014562882,0.00012921919,0.00027405578,0.00071206724,0.0000037227232],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072538044,0.0009830444,0.99127316,0.0011330469,0.000029948307,0.000101662416,0.0009140357,0.000018704064,0.000008899957,0.000059998543,0.000551173,0.004200948],"study_design_scores_gemma":[0.0051386463,0.0015841796,0.9752725,0.000065166714,8.4128874e-7,0.000105655716,0.001301669,0.0011440634,2.6342207e-7,0.0000020788693,0.015049581,0.0003353953],"about_ca_topic_score_codex":0.0003795329,"about_ca_topic_score_gemma":0.0008825661,"teacher_disagreement_score":0.0160007,"about_ca_system_score_codex":0.0006724218,"about_ca_system_score_gemma":0.0013916023,"threshold_uncertainty_score":0.99984616},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993021,0.0002036534,0.0000030926913,0.00151179,0.00004079518,0.00021701178,0.00034255043,0.00002927585,0.0046308017],"genre_scores_gemma":[0.9985375,0.0000055498217,0.000120539364,0.00015134084,0.00023828376,0.000058175872,0.000377634,0.000024397594,0.00048659428],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966766,0.000525527,0.00023660867,0.00033970183,0.0017672586,0.00045429083],"domain_scores_gemma":[0.9994189,0.00015508803,0.000068120964,0.00010682634,0.00009185085,0.00015920373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002800225,0.000094298935,0.000197812,0.0010916595,0.00066780386,0.000027053538,0.00014382316,0.000051847113,0.00056607137],"category_scores_gemma":[0.00028812114,0.000103980965,0.00005541765,0.0018795644,0.0002814918,0.0000602954,0.00038398342,0.0006458162,0.000005233938],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011600561,0.0012708686,0.8690136,0.0001599293,0.00020866047,0.0003910645,0.017734766,0.00012070251,0.0005828515,0.0045215683,0.024342638,0.08049328],"study_design_scores_gemma":[0.009300934,0.0008520493,0.8524205,0.00017992446,0.00006628083,0.000121860816,0.034122318,0.02105037,0.00010427031,0.0029072445,0.07796332,0.0009108977],"about_ca_topic_score_codex":0.0002803598,"about_ca_topic_score_gemma":0.0000463817,"teacher_disagreement_score":0.079582386,"about_ca_system_score_codex":0.0009497016,"about_ca_system_score_gemma":0.00059779594,"threshold_uncertainty_score":0.61980844},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6137575,0.002050386,0.020894578,0.0059536607,0.00061314966,0.0027774526,0.35347724,0.00013172576,0.00034429587],"genre_scores_gemma":[0.625557,0.00034357008,0.012071078,0.00052978005,0.00016813997,0.00044623716,0.36039492,0.00009813599,0.00039112096],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99083567,0.0028551086,0.0015574365,0.001960367,0.0019687496,0.0008226854],"domain_scores_gemma":[0.9872313,0.0027703333,0.0008232611,0.0070780814,0.0013945812,0.0007024077],"candidate_categories":["metaresearch","metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008887329,0.00041169612,0.00161874,0.0006351389,0.0006608569,0.00014136679,0.0016353716,0.00024261643,0.0032199386],"category_scores_gemma":[0.014690687,0.0004172613,0.00035381355,0.0027776181,0.00032419752,0.00016451337,0.009009435,0.0017938038,0.000008036088],"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":[0.00203556,0.0014162788,0.11047363,0.0043932972,0.031134123,0.21318616,0.0049421038,0.003374434,0.0030940278,0.000110270674,0.58368784,0.042152304],"study_design_scores_gemma":[0.003990887,0.00023936165,0.053467896,0.00038661415,0.0046407036,0.00259359,0.02967163,0.05146882,0.0014812071,0.00032619011,0.8502983,0.0014348363],"about_ca_topic_score_codex":0.8539906,"about_ca_topic_score_gemma":0.90025705,"teacher_disagreement_score":0.26661044,"about_ca_system_score_codex":0.00330092,"about_ca_system_score_gemma":0.014346123,"threshold_uncertainty_score":0.9998279},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8602484,0.0001163202,0.009277781,0.074948534,0.0028615273,0.002439024,0.049933054,0.000090839436,0.00008455665],"genre_scores_gemma":[0.9564396,0.000039615003,0.00072163093,0.030617123,0.0003019151,0.00010714011,0.011040202,0.00001832579,0.00071445765],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99546105,0.00017799155,0.00076102186,0.0006558045,0.0025156974,0.0004284121],"domain_scores_gemma":[0.9958496,0.0009181672,0.0004200253,0.0018322205,0.00035665242,0.00062334153],"candidate_categories":["metaresearch","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015559522,0.00011601669,0.00021017191,0.00029630293,0.002203899,0.0012105881,0.0067089563,0.000032797605,0.0009317682],"category_scores_gemma":[0.030033112,0.000112154754,0.000058141628,0.0012176703,0.00024250803,0.0021391776,0.004196397,0.0005065301,0.000017577673],"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":[0.0026103638,0.00023331333,0.67691976,0.000018618894,0.0000927244,0.00030913742,0.0006361339,0.0035180927,0.000060032777,0.0005054362,0.31111217,0.0039842096],"study_design_scores_gemma":[0.0014373165,0.00007815621,0.42247814,0.000016291411,0.000019045072,0.001149996,0.0005983836,0.020071693,1.5871184e-7,0.00046476416,0.55357975,0.000106326],"about_ca_topic_score_codex":0.011026622,"about_ca_topic_score_gemma":0.06443271,"teacher_disagreement_score":0.25444162,"about_ca_system_score_codex":0.0016319976,"about_ca_system_score_gemma":0.003225027,"threshold_uncertainty_score":0.9999815},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9520327,0.0004896227,0.0000042056554,0.04456184,0.00013883946,0.0012405475,0.00095621106,0.00008413557,0.0004919165],"genre_scores_gemma":[0.97143245,0.00015472533,0.000025283982,0.022896534,0.00014315221,0.0007341886,0.004047163,0.00003041166,0.00053611625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9943713,0.0027796,0.000681281,0.0006936178,0.0006114373,0.00086277945],"domain_scores_gemma":[0.9971903,0.0008497861,0.00017354729,0.0009398107,0.00009058542,0.00075596897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038920569,0.00027445675,0.0007576553,0.0006936019,0.0004743756,0.000057267294,0.0004302791,0.000055056284,0.0006674668],"category_scores_gemma":[0.0004986291,0.00020828504,0.00005662224,0.0019163331,0.000057443318,0.0000725851,0.00029453821,0.0007645892,0.000030773426],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005195969,0.0010256356,0.90453035,0.000030295849,0.00003982092,0.00007670536,0.0041401936,0.000037131154,0.000007898279,0.00007693941,0.07298485,0.016530601],"study_design_scores_gemma":[0.0020591228,0.002071662,0.7237188,0.000005720709,8.370662e-7,0.0000066795365,0.0034747669,0.00015784155,8.2285446e-8,0.000029391911,0.26833355,0.0001415138],"about_ca_topic_score_codex":0.006385492,"about_ca_topic_score_gemma":0.004794172,"teacher_disagreement_score":0.1953487,"about_ca_system_score_codex":0.000243944,"about_ca_system_score_gemma":0.00036077225,"threshold_uncertainty_score":0.96529967},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4253713,0.002308882,0.553635,0.002684992,0.00036857286,0.0027026795,0.007275235,0.00043360755,0.005219716],"genre_scores_gemma":[0.9221663,0.000034057382,0.072134964,0.00017805755,0.000010723284,0.00003356143,0.0053806524,0.000017187936,0.00004450969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985559,0.00034077154,0.00048452526,0.00027098184,0.00021973699,0.00012809351],"domain_scores_gemma":[0.99729234,0.0013047885,0.00015366396,0.0010663387,0.00012625463,0.00005660978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005901946,0.00010130359,0.00019063486,0.00025867947,0.00019010819,0.000038093513,0.00037950155,0.000025866437,0.00005378406],"category_scores_gemma":[0.00091457635,0.00012577412,0.000008850294,0.00059785764,0.00008806726,0.00014327688,0.0010316569,0.00031273175,0.000011427052],"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":[0.000109429275,0.00053361384,0.16125654,0.00006402295,0.00002093905,0.0000119474635,0.00090386625,0.71795523,0.00005709201,0.0017063399,0.001145159,0.1162358],"study_design_scores_gemma":[0.0010285218,0.00004511452,0.15442732,0.000036003723,0.000015252483,0.0000049933724,0.00024704728,0.8393565,2.7030373e-7,0.0010458955,0.0036970438,0.000096051546],"about_ca_topic_score_codex":0.00006252402,"about_ca_topic_score_gemma":0.00014063092,"teacher_disagreement_score":0.496795,"about_ca_system_score_codex":0.00014666824,"about_ca_system_score_gemma":0.00014571376,"threshold_uncertainty_score":0.51289195},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7200743,0.007925061,0.07269033,0.13886145,0.0014115697,0.011985848,0.043826457,0.0017792953,0.0014456717],"genre_scores_gemma":[0.9903584,0.00004617813,0.00036574292,0.003290102,0.00011508185,0.0015337395,0.0038737725,0.000030685205,0.0003863021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961621,0.0007926769,0.0007664831,0.0008279702,0.00058190385,0.0008688336],"domain_scores_gemma":[0.99714303,0.00028042233,0.00031617229,0.0007475967,0.00024302737,0.0012697786],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021053557,0.00030151196,0.00088150817,0.0004321702,0.0005692077,0.00008820529,0.00021373451,0.000060812603,0.00025996467],"category_scores_gemma":[0.0006743003,0.00032320546,0.00026178124,0.0031065717,0.00010190346,0.00020169363,0.00013037054,0.00020509234,0.000009418113],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011032036,0.0004265527,0.98453736,0.00028493922,0.0001630535,0.000022210957,0.00006120124,0.0006138148,0.0000031294292,0.0002150103,0.002465739,0.010103786],"study_design_scores_gemma":[0.0022023665,0.00033170142,0.8449528,0.0000050842596,0.000008322493,0.000011457367,0.00007470936,0.033548526,1.07770184e-7,0.00006808116,0.11852458,0.00027225885],"about_ca_topic_score_codex":0.00018804832,"about_ca_topic_score_gemma":0.0023239066,"teacher_disagreement_score":0.2702841,"about_ca_system_score_codex":0.0007194512,"about_ca_system_score_gemma":0.0022954694,"threshold_uncertainty_score":0.999922},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017004922,0.9890875,0.0000060455764,5.7943475e-7,0.00013615265,0.0021274718,0.0067180926,0.00006759853,0.0001560444],"genre_scores_gemma":[0.0000036713093,0.9800207,0.00006715414,0.019255094,0.00032321754,0.00016873794,0.000032451273,0.00012738205,0.0000015641983],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957675,0.0008354808,0.0015407151,0.00092027086,0.00062725105,0.0003088308],"domain_scores_gemma":[0.9963984,0.00073282165,0.0013628819,0.0011877766,0.00008577312,0.00023233725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013554976,0.00056687155,0.0029329804,0.0002940813,0.00015154642,0.00006672786,0.0004748023,0.00014790162,0.00021077022],"category_scores_gemma":[0.00052492,0.00047770937,0.00019612383,0.00038470025,0.00039428647,0.00033512487,0.00037452028,0.00039063735,0.000007732735],"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":[0.00019442306,0.00024914418,0.051510412,0.78392196,0.0018724251,0.00021853899,0.00013166483,0.000017726004,0.00000485873,0.00020507032,0.0007666325,0.16090713],"study_design_scores_gemma":[0.00048157678,0.00008881283,0.000055695127,0.20070942,0.00056016643,0.00010619602,0.000013505989,0.00000782351,3.580069e-7,0.000002844636,0.79763603,0.00033755237],"about_ca_topic_score_codex":0.00016539071,"about_ca_topic_score_gemma":0.000020473255,"teacher_disagreement_score":0.7968694,"about_ca_system_score_codex":0.00009399628,"about_ca_system_score_gemma":0.00092301145,"threshold_uncertainty_score":0.9997675},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99195105,0.0012982569,0.00047896864,0.00077971804,0.00010250897,0.00027577407,0.00011233674,0.00011729624,0.0048841173],"genre_scores_gemma":[0.9968732,0.000021826661,0.00014922123,0.00014872494,0.000022181444,0.00024322736,0.00027479144,0.000015408932,0.0022514279],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986045,0.00018497091,0.000386682,0.00033246016,0.00019258502,0.0002987972],"domain_scores_gemma":[0.99908346,0.00007475457,0.0001904482,0.0004649133,0.000077447236,0.00010895405],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001377515,0.00014111944,0.00060192414,0.0010554936,0.00017999568,0.0000082053475,0.00010839469,0.00005789064,0.0016034013],"category_scores_gemma":[0.000037319216,0.00014967618,0.00016880032,0.0019439781,0.000050379276,0.00012457221,0.00022438704,0.00020140558,0.000013894434],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034580042,0.00063769147,0.9725662,0.00014573876,0.0007419245,0.00015530782,0.0001713158,0.00044580124,0.00047606212,0.0013414744,0.0204768,0.0024958742],"study_design_scores_gemma":[0.0027238012,0.00024125875,0.9296201,0.0000084094445,0.0007882709,0.0007503816,0.0003892189,0.00488073,0.00031972214,0.00032757001,0.059742462,0.00020809086],"about_ca_topic_score_codex":0.00046772958,"about_ca_topic_score_gemma":0.00026948177,"teacher_disagreement_score":0.04294613,"about_ca_system_score_codex":0.00018853149,"about_ca_system_score_gemma":0.00010615038,"threshold_uncertainty_score":0.99930924},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9853495,0.000100039055,0.011163645,0.00004808682,0.0001931332,0.0014880823,0.0015466579,0.00006103819,0.00004983596],"genre_scores_gemma":[0.98420477,0.000006510381,0.014702432,0.000020729572,0.000053129435,0.00008853579,0.0008709171,0.000039402195,0.000013568668],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99637544,0.0008308897,0.0010626372,0.0006775861,0.00081748975,0.00023597124],"domain_scores_gemma":[0.99654007,0.0017281473,0.00041248347,0.0009468327,0.0002539337,0.000118552256],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016835715,0.00026119075,0.0012073319,0.00026594833,0.000029823688,0.00000959891,0.00026847865,0.0001250274,0.00037864243],"category_scores_gemma":[0.004461212,0.00025668356,0.000111095294,0.000411482,0.00006370703,0.000030459822,0.0007522756,0.00075304054,0.0000020514212],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043507523,0.0021595752,0.93339247,0.0014598796,0.00016248037,0.00007296618,0.0035459418,0.056199122,0.0015334839,0.000027175436,0.000009905812,0.0010019402],"study_design_scores_gemma":[0.0020957945,0.00031836831,0.96315,0.00025617867,0.0002038947,0.0000011348632,0.0016020309,0.031771354,0.00009957138,0.00010724934,0.00019684648,0.00019756253],"about_ca_topic_score_codex":0.0004304138,"about_ca_topic_score_gemma":0.00014253632,"teacher_disagreement_score":0.029757557,"about_ca_system_score_codex":0.00014980919,"about_ca_system_score_gemma":0.00025618816,"threshold_uncertainty_score":0.99998856},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9775757,0.00025771247,0.00015435687,0.020121878,0.0009961036,0.000297005,0.00037791612,0.000020035648,0.00019926109],"genre_scores_gemma":[0.97985226,0.000005099852,0.00007353632,0.019031523,0.00086999603,0.00006368037,0.000040608877,0.000026530408,0.000036776877],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99725896,0.00029755128,0.00048779938,0.00021884785,0.0011722049,0.00056461553],"domain_scores_gemma":[0.9981785,0.0001390526,0.0002471191,0.00011803057,0.00015856014,0.0011587742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019311396,0.00012874031,0.00029737377,0.00031974746,0.00044591006,0.0000326064,0.00011725395,0.00009338226,0.0005722526],"category_scores_gemma":[0.0007698917,0.000140473,0.00007291103,0.00031642412,0.000014231025,0.00012837033,0.00003568957,0.00084342394,0.0000019357144],"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":true,"study_design_scores_codex":[0.00010324917,0.000017740456,0.8557429,0.0001131419,0.00017210162,0.0025550018,0.00044504608,0.000085744505,0.00033730446,0.000006838278,0.054324057,0.08609689],"study_design_scores_gemma":[0.0041937204,0.00012395624,0.32778487,0.00018127532,0.00008825502,0.00062788016,0.0054915086,0.0063440423,0.000040023817,0.000010822568,0.6547779,0.00033575477],"about_ca_topic_score_codex":0.4006968,"about_ca_topic_score_gemma":0.9677886,"teacher_disagreement_score":0.60045385,"about_ca_system_score_codex":0.007894121,"about_ca_system_score_gemma":0.0063830195,"threshold_uncertainty_score":0.9992499},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6794126,0.026804112,0.002349585,0.286501,0.00064721185,0.0009778225,0.0010756854,0.00013507031,0.0020968942],"genre_scores_gemma":[0.99477124,0.0012116185,0.00048792857,0.003358493,0.00004282615,0.0000028637903,0.00007433511,0.000011098956,0.000039611627],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99796414,0.00015409983,0.000785202,0.000107277345,0.00028783822,0.00070146425],"domain_scores_gemma":[0.9977082,0.000031804502,0.00047072908,0.00027862945,0.00005568979,0.0014549327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016050853,0.00011941287,0.00042184023,0.00084103784,0.00009470426,0.000047348934,0.00020723636,0.000068826965,0.00019731675],"category_scores_gemma":[0.0005666567,0.00011389435,0.000060269005,0.0003509151,0.000115337454,0.00060993736,0.000014079728,0.00039268908,0.000052263415],"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":[0.0000057034235,0.00005250497,0.4283673,0.00009181961,0.000019265082,0.000065228116,0.0014599087,0.000017045397,0.0000017136852,0.000111796995,0.025585035,0.5442227],"study_design_scores_gemma":[0.0016032796,0.00045660904,0.5927003,0.00012838557,0.0000014586456,0.00046973533,0.002094368,0.0013968323,0.0000011854795,0.000104349114,0.4008905,0.00015303069],"about_ca_topic_score_codex":0.0029857005,"about_ca_topic_score_gemma":0.027222328,"teacher_disagreement_score":0.54406965,"about_ca_system_score_codex":0.0020270136,"about_ca_system_score_gemma":0.0035271773,"threshold_uncertainty_score":0.99052835},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019950768,0.0035934362,0.8884661,0.018479742,0.0012825759,0.003664782,0.03311781,0.00084264023,0.03060213],"genre_scores_gemma":[0.21631418,0.0011313083,0.60636,0.0006514233,0.00040142285,0.0003372326,0.1147067,0.0004015738,0.059696153],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99104744,0.004934303,0.0008868964,0.0016306326,0.00083873747,0.00066200417],"domain_scores_gemma":[0.98809403,0.0020525423,0.00072751334,0.0061761225,0.0024296162,0.00052015804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00640519,0.00054448715,0.0010363419,0.00019540614,0.00032814674,0.0002754338,0.002056153,0.00028801349,0.0005364352],"category_scores_gemma":[0.003672607,0.00057916826,0.00031531724,0.00038701275,0.00018880301,0.00015805587,0.0021742191,0.00073193933,0.000038290393],"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":[0.0015471627,0.0047053476,0.036407057,0.010022753,0.00936868,0.00063364784,0.013068267,0.0016644828,0.028031308,0.023643108,0.59564245,0.27526575],"study_design_scores_gemma":[0.0035952711,0.0000016054429,0.008887596,0.0035228466,0.0010224843,0.000044543176,0.00009595262,0.53365844,0.0073672165,0.0006101779,0.43958154,0.0016123126],"about_ca_topic_score_codex":0.19340864,"about_ca_topic_score_gemma":0.34164894,"teacher_disagreement_score":0.5319939,"about_ca_system_score_codex":0.00045090946,"about_ca_system_score_gemma":0.0016490554,"threshold_uncertainty_score":0.999666},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61856484,0.048348777,0.04506654,0.2820335,0.0025818686,0.00094765594,0.00019106829,0.00009919011,0.0021665632],"genre_scores_gemma":[0.99504876,0.00039309953,0.00043438072,0.003095198,0.0009101149,0.0000029330588,0.000015345497,0.00001875914,0.00008139561],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99679047,0.0005089363,0.00086138863,0.00020428463,0.0003575155,0.001277403],"domain_scores_gemma":[0.9943701,0.00012165918,0.00067461724,0.0002913275,0.0004217403,0.004120551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052373894,0.0001577677,0.00041263137,0.0001933851,0.0007794003,0.00031944847,0.00024147375,0.00006490531,0.000023385179],"category_scores_gemma":[0.0010037665,0.00012278053,0.00008935468,0.0007452382,0.00021214798,0.00032015724,0.000022428592,0.0004576238,0.000008788152],"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":[0.000011538241,0.000018098945,0.46641934,0.000045862707,0.000043842323,0.000051657644,0.00022326622,0.000023498676,2.867363e-7,0.002549246,0.010095531,0.5205178],"study_design_scores_gemma":[0.00024439022,0.00034308925,0.6122575,0.000071770875,0.0000044002823,0.0005210808,0.00029349737,0.0001156004,2.1630076e-7,0.00050605193,0.3855658,0.00007663365],"about_ca_topic_score_codex":0.054316103,"about_ca_topic_score_gemma":0.326589,"teacher_disagreement_score":0.52044123,"about_ca_system_score_codex":0.0019788207,"about_ca_system_score_gemma":0.015340121,"threshold_uncertainty_score":0.990242},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45145762,0.017341712,0.004327781,0.02813817,0.0030342252,0.021646554,0.027028063,0.006226711,0.44079915],"genre_scores_gemma":[0.9509762,0.0032741781,0.00053339667,0.0065685012,0.00096339034,0.000010517505,0.010750232,0.0037253406,0.023198243],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9934505,0.0014434786,0.0009419545,0.0015195982,0.0014986437,0.0011457899],"domain_scores_gemma":[0.9962565,0.00018818383,0.0005056204,0.001449923,0.00062519667,0.0009746074],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001867406,0.0006697513,0.0010440949,0.0005396842,0.0033746858,0.000657495,0.00132442,0.00013058627,0.053017125],"category_scores_gemma":[0.0018700253,0.00068392564,0.00021495315,0.0013563689,0.0004325249,0.00044562874,0.0036804734,0.001173868,0.01115113],"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":[0.0010982312,0.00049692625,0.0006885779,0.000638286,0.0004155612,0.0003621008,0.00094228925,0.000039061975,0.0019553548,0.0011838084,0.92441773,0.06776208],"study_design_scores_gemma":[0.0022804923,0.00064124947,0.008277499,0.00014491663,0.00017263576,0.00033693135,0.00058256084,0.0006037359,0.00035188667,0.000045098634,0.9858984,0.00066463265],"about_ca_topic_score_codex":0.00085798465,"about_ca_topic_score_gemma":0.00015589189,"teacher_disagreement_score":0.49951857,"about_ca_system_score_codex":0.00082112226,"about_ca_system_score_gemma":0.00007409618,"threshold_uncertainty_score":0.9995612},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9886805,0.0038727208,0.0000886072,0.0026097095,0.00031179932,0.0013049378,0.00040675103,0.0004046368,0.002320356],"genre_scores_gemma":[0.9963841,0.00019424105,0.00016394584,0.0020597535,0.00008594838,0.00054912287,0.00029810527,0.00004949843,0.00021528476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9961934,0.00064226723,0.0008584555,0.0008037608,0.00054543564,0.00095666584],"domain_scores_gemma":[0.9978883,0.00023339795,0.00028948576,0.00046142962,0.00011604242,0.0010113163],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025458918,0.00032204099,0.0008044464,0.00047344336,0.00074190483,0.0001297599,0.00016054472,0.0000747297,0.000027948223],"category_scores_gemma":[0.0002511725,0.00033880144,0.000057012043,0.0010557516,0.000105876,0.00029502608,0.00037321044,0.0004977938,0.000007913279],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093505216,0.00015058489,0.9783313,0.00079052633,0.00006254641,0.0001103324,0.00059406774,0.000006222895,0.000014416993,0.00039636064,0.002365016,0.017085094],"study_design_scores_gemma":[0.001626161,0.00022794423,0.68408895,0.000052163945,7.888691e-7,0.00034439855,0.0007876477,0.00023233017,3.901136e-7,0.00000490333,0.31233114,0.0003031895],"about_ca_topic_score_codex":0.000081476,"about_ca_topic_score_gemma":0.00040251968,"teacher_disagreement_score":0.30996612,"about_ca_system_score_codex":0.0009168184,"about_ca_system_score_gemma":0.0017743532,"threshold_uncertainty_score":0.9999064},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98029685,0.0047707697,0.012272055,0.00049194397,0.0005420733,0.00089953793,0.00049982074,0.000021702903,0.00020527256],"genre_scores_gemma":[0.9945341,0.000458879,0.003890346,0.0006547159,0.000051158753,0.00022176524,0.000071981995,0.00002335407,0.00009375265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981087,0.0004798742,0.00045658788,0.000390307,0.00023373193,0.00033080459],"domain_scores_gemma":[0.99914044,0.00012575094,0.00020509107,0.00032784513,0.000052672745,0.0001482146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006701048,0.00010908541,0.00064134557,0.00013341187,0.00010998671,0.000024654513,0.0002336705,0.00006570201,0.00014219785],"category_scores_gemma":[0.00024651588,0.00014619871,0.00006678663,0.0003760326,0.0004159065,0.00007143082,0.0003664211,0.00036235712,1.2611254e-7],"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":[0.0007768807,0.0001806207,0.969204,0.0001139893,0.000047897265,0.00092681457,0.0006205224,0.0031593167,0.00003950627,0.000008326664,0.0022332673,0.022688873],"study_design_scores_gemma":[0.006881141,0.00011083105,0.7486693,0.00025860275,0.000094253584,0.000059379912,0.0016248361,0.23770481,0.0000018494914,0.0023811616,0.0020029768,0.00021082145],"about_ca_topic_score_codex":0.09304387,"about_ca_topic_score_gemma":0.25683203,"teacher_disagreement_score":0.2345455,"about_ca_system_score_codex":0.0007172478,"about_ca_system_score_gemma":0.0007748633,"threshold_uncertainty_score":0.91299564},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869921,0.00002254391,0.00021401346,0.00003903598,0.0011654259,0.0010175719,0.00032039965,0.0026907763,0.0075380974],"genre_scores_gemma":[0.9914787,0.0000029817118,0.0058102123,0.000042205167,0.0001905455,0.00026990444,0.0019994986,0.00005693299,0.0001490385],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9969411,0.00025237538,0.00078147394,0.000755863,0.001020849,0.00024833396],"domain_scores_gemma":[0.99825144,0.00007863826,0.00042938726,0.00084813416,0.00020211036,0.00019028959],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089783204,0.00031659147,0.00074905064,0.00026821674,0.00014148917,0.000019571205,0.00032946438,0.00020172355,0.0008668626],"category_scores_gemma":[0.00014621878,0.00030725912,0.00017062067,0.00018178474,0.000046045265,0.000036467056,0.00087308337,0.0006347316,0.0000993345],"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":[0.01097188,0.00834335,0.0068692383,0.039715786,0.006230769,0.0013117652,0.06490421,0.0048940065,0.050818525,0.0018915052,0.022281613,0.78176737],"study_design_scores_gemma":[0.005762027,0.00019853558,0.83086854,0.0019387779,0.00056102616,0.00006138287,0.016403263,0.032788157,0.09150489,0.000069602596,0.017899746,0.0019440259],"about_ca_topic_score_codex":0.0000901042,"about_ca_topic_score_gemma":0.00031846607,"teacher_disagreement_score":0.82399935,"about_ca_system_score_codex":0.0010374899,"about_ca_system_score_gemma":0.0008130433,"threshold_uncertainty_score":0.99993795},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28855252,0.0017704241,0.48256707,0.020839943,0.007059268,0.0048533664,0.19309892,0.0005172692,0.000741236],"genre_scores_gemma":[0.5582592,0.0000065725394,0.19808261,0.00083208893,0.00039085752,0.00012344844,0.23792899,0.00002942144,0.004346841],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983611,0.00011654335,0.00035195184,0.00060321373,0.00037791388,0.00018927226],"domain_scores_gemma":[0.9975324,0.000332198,0.00020449841,0.0016962027,0.000113734066,0.00012096348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00231921,0.0000839874,0.00024436094,0.00010455226,0.00019992715,0.000030239318,0.00056266546,0.00003396393,0.0005227705],"category_scores_gemma":[0.0076557575,0.00007449171,0.000050940333,0.00076696085,0.00012242337,0.00017439079,0.00046252547,0.00012494683,0.000022418684],"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":[0.00039078965,0.00020086228,0.0040983493,0.000063978936,0.000033643035,0.000004654266,0.00014887193,0.00008806879,0.0026287641,0.006555359,0.9599775,0.02580914],"study_design_scores_gemma":[0.0014824708,0.00033475313,0.030647475,0.00005440825,0.00010262461,0.000007843216,0.00027631668,0.019587317,0.0003199586,0.02547435,0.9215236,0.00018889378],"about_ca_topic_score_codex":0.000014662625,"about_ca_topic_score_gemma":0.000015211734,"teacher_disagreement_score":0.28448445,"about_ca_system_score_codex":0.000037559603,"about_ca_system_score_gemma":0.00037256416,"threshold_uncertainty_score":0.916521},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99481744,0.0006227734,0.0022589536,0.0002642982,0.00021287602,0.0004666302,0.0013389593,0.000010660408,0.000007398973],"genre_scores_gemma":[0.995959,0.000029998995,0.0034514507,0.00018544494,0.00006155186,0.000004826102,0.00029668142,0.000007140519,0.0000038982776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982416,0.00024247434,0.00075798604,0.00011675491,0.00050674344,0.00013446092],"domain_scores_gemma":[0.9983209,0.0001613212,0.0006781856,0.00021721734,0.00045166217,0.00017070869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000847442,0.00010262792,0.00040477177,0.0001457607,0.000079273625,0.000022430504,0.00016350384,0.000043499742,0.000025240688],"category_scores_gemma":[0.0015002517,0.00009374233,0.00006142062,0.00039045344,0.000043388398,0.0010128971,0.000029647053,0.00022853422,1.7861332e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043347497,0.002352696,0.95176333,0.00003449142,0.00006140797,0.000005432792,0.0016952675,0.0022728166,0.000431632,0.000056387205,0.0005938338,0.036397945],"study_design_scores_gemma":[0.003119153,0.0034718504,0.98757917,0.00013556535,0.00011894957,0.00002690403,0.003321835,0.0004914087,0.000019621162,0.0006905032,0.0009445031,0.00008051546],"about_ca_topic_score_codex":0.000028858309,"about_ca_topic_score_gemma":0.00008351009,"teacher_disagreement_score":0.03631743,"about_ca_system_score_codex":0.0002043291,"about_ca_system_score_gemma":0.00012352983,"threshold_uncertainty_score":0.38227013},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98749256,0.004211611,0.0038501618,0.0013262428,0.00009851368,0.00096382725,0.0019826104,0.00004920423,0.000025248683],"genre_scores_gemma":[0.9974108,0.000725804,0.000011922417,0.000117038995,0.000036383855,0.000057696063,0.0016166362,0.000020146035,0.0000036129875],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99533767,0.001769093,0.00089509407,0.00066696445,0.0008255233,0.0005056788],"domain_scores_gemma":[0.9972317,0.0003972363,0.0004928388,0.0010830249,0.00022108962,0.0005740969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035486554,0.00026991015,0.0011822407,0.0004511336,0.00029650689,0.00010330538,0.00029623433,0.000053490352,0.000009879792],"category_scores_gemma":[0.00078480464,0.00019814364,0.00013910723,0.00124425,0.0001603948,0.00023391498,0.00032330354,0.00042478085,1.2425348e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007858031,0.00043352004,0.9201224,0.0006292706,0.000373867,0.000011748258,0.00048478253,0.07035877,0.00002364431,0.00008617633,0.00006660021,0.006623451],"study_design_scores_gemma":[0.00066273974,0.00016584073,0.5704122,0.000024140145,0.000012998287,0.0000059290296,0.00034683937,0.4282217,3.0830268e-8,0.000023400802,0.000027301248,0.00009684675],"about_ca_topic_score_codex":0.0054337177,"about_ca_topic_score_gemma":0.0026691514,"teacher_disagreement_score":0.35786292,"about_ca_system_score_codex":0.00029552195,"about_ca_system_score_gemma":0.0014693455,"threshold_uncertainty_score":0.82141924},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005521319,0.11508675,0.090930946,0.02168474,0.60944295,0.03266632,0.027505971,0.0039520566,0.09320896],"genre_scores_gemma":[0.0031502536,0.008198397,0.13341233,0.026641812,0.010484341,0.0025242255,0.066149816,0.0006115014,0.74882734],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99561703,0.0011610178,0.00089707435,0.0010870283,0.00044937377,0.00078845693],"domain_scores_gemma":[0.9968252,0.001136679,0.0006505656,0.0008349012,0.000006422724,0.0005462074],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026853713,0.00048491426,0.001432717,0.00012161063,0.0005329302,0.000012677313,0.00025441864,0.00035968816,0.0046498436],"category_scores_gemma":[0.0010666053,0.00046405965,0.00049742917,0.00012957749,0.00014362452,0.000049264956,0.00029124942,0.0017743752,0.00008890754],"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":[0.00028685448,0.00022810306,0.0012659712,0.00039795143,0.00009099679,0.000022049115,0.00006888077,0.000009012438,0.00003230305,0.0000026520722,0.8901181,0.107477106],"study_design_scores_gemma":[0.00093083846,0.0013076913,0.02124912,0.0001436862,0.00008171545,0.00012975956,0.00028076972,0.0011457365,0.00001562347,0.000044225155,0.9743216,0.00034924492],"about_ca_topic_score_codex":0.000075492615,"about_ca_topic_score_gemma":0.000016268737,"teacher_disagreement_score":0.65561837,"about_ca_system_score_codex":0.0023727417,"about_ca_system_score_gemma":0.0004279875,"threshold_uncertainty_score":0.99978113},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92624503,0.0058568404,0.00009843105,0.016097903,0.00015410697,0.0022935225,0.04914194,0.00007197658,0.00004026004],"genre_scores_gemma":[0.93781435,0.0006543115,0.0006832114,0.0020173753,0.00003668446,0.000034930898,0.058656756,0.000041934458,0.00006045297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9950446,0.0006877647,0.0007878291,0.0010830427,0.0014986322,0.00089814037],"domain_scores_gemma":[0.996571,0.00010150835,0.0001442808,0.002453128,0.000040393556,0.00068966276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022984964,0.00025228783,0.00094357674,0.0012168349,0.0004507574,0.000031306063,0.0011507416,0.000031926207,0.00022691653],"category_scores_gemma":[0.00001872255,0.00027608735,0.000091114794,0.0031919822,0.00012220307,0.00026563945,0.0023955835,0.00054870173,0.0000039946503],"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":[0.0019383759,0.0019842503,0.8988102,0.0027084476,0.0013175481,0.000067535875,0.029510887,0.0010862087,0.00014980095,0.00008820281,0.04189662,0.020441888],"study_design_scores_gemma":[0.0040362105,0.0011827963,0.89986587,0.0003098981,0.000120080425,0.000033752694,0.05745241,0.026121018,0.000017966951,0.000002318044,0.010459257,0.00039843842],"about_ca_topic_score_codex":0.013111232,"about_ca_topic_score_gemma":0.008302694,"teacher_disagreement_score":0.031437363,"about_ca_system_score_codex":0.0020196256,"about_ca_system_score_gemma":0.0014035121,"threshold_uncertainty_score":0.9999691},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24532019,0.00062496326,0.0024359995,0.012738572,0.00072609296,0.003045171,0.012995717,0.0069438326,0.7151695],"genre_scores_gemma":[0.9785839,0.00007925017,0.0006223568,0.0015102609,0.0003368415,2.684196e-7,0.010677972,0.0023278997,0.0058612884],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977315,0.00039959545,0.00025773482,0.00049030466,0.00071561016,0.0004052206],"domain_scores_gemma":[0.99808973,0.00002775116,0.00010027924,0.0010708327,0.000400973,0.0003104295],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006602427,0.00015221517,0.0002203571,0.00023875223,0.0023135608,0.0002380087,0.0011695256,0.000032996755,0.08760731],"category_scores_gemma":[0.000826936,0.0001674343,0.000092334485,0.000650245,0.00016771139,0.00017421792,0.0032310754,0.0004529604,0.01078894],"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":[0.000504752,0.0004920062,0.00015711883,0.00009520856,0.00011071997,0.00020709686,0.0012329328,0.000054966647,0.002378876,0.003145441,0.96696514,0.024655748],"study_design_scores_gemma":[0.0012746162,0.000549464,0.006045,0.00001709606,0.000031732834,0.00033659014,0.00054657995,0.00017810392,0.00007972638,0.00015586993,0.9906133,0.0001719542],"about_ca_topic_score_codex":0.000030614137,"about_ca_topic_score_gemma":5.823339e-7,"teacher_disagreement_score":0.7332637,"about_ca_system_score_codex":0.0003078736,"about_ca_system_score_gemma":0.000012514161,"threshold_uncertainty_score":0.9989853},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25426927,0.0008121229,0.0017167241,0.012048699,0.00093321624,0.0036466008,0.012084365,0.006926264,0.70756274],"genre_scores_gemma":[0.9761042,0.00010026962,0.00057200016,0.001537099,0.00035062546,3.5035586e-7,0.010765127,0.0026069423,0.007963375],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99734706,0.0005028843,0.00030254395,0.0005550998,0.00081148173,0.0004809043],"domain_scores_gemma":[0.9979359,0.000033676846,0.00012481255,0.0011762449,0.00037460926,0.00035478352],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00074521505,0.00017921077,0.00026260497,0.0002660393,0.002305049,0.0002313498,0.0012246951,0.00004074831,0.08294657],"category_scores_gemma":[0.0011171703,0.0002009424,0.00011122173,0.00057291973,0.00017581272,0.00015628854,0.0033520518,0.0005049198,0.011635204],"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":[0.0007471295,0.00066171546,0.00013075634,0.000109790875,0.00014180885,0.00029395692,0.0013571408,0.00008543719,0.002613686,0.0016236958,0.9613679,0.030866984],"study_design_scores_gemma":[0.0016239877,0.00080891995,0.006466547,0.000022496515,0.000040063256,0.00037867896,0.00066442176,0.00024464529,0.00010251215,0.00014307018,0.9892981,0.00020652454],"about_ca_topic_score_codex":0.000032877608,"about_ca_topic_score_gemma":7.5419985e-7,"teacher_disagreement_score":0.72183496,"about_ca_system_score_codex":0.0003493601,"about_ca_system_score_gemma":0.000015821057,"threshold_uncertainty_score":0.9989938},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48832646,0.0010937777,0.0013079637,0.0038592375,0.00067467237,0.0030004436,0.012935399,0.004533345,0.4842687],"genre_scores_gemma":[0.9768186,0.00018750121,0.00011854467,0.001001389,0.00022492185,3.6297718e-7,0.012014384,0.0016908147,0.007943475],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99726033,0.0005544709,0.00030330135,0.00060639664,0.0008031122,0.00047236728],"domain_scores_gemma":[0.9982135,0.000040412375,0.00013796227,0.00085781614,0.0003983506,0.00035191528],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00066564616,0.00019299584,0.00026707264,0.00023213358,0.0023287788,0.000272257,0.0008640998,0.000040129722,0.19254453],"category_scores_gemma":[0.00072598766,0.00021771365,0.000103784296,0.0006527431,0.00015466788,0.00020746342,0.0025284018,0.00054219057,0.018132595],"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":[0.0007639934,0.00082906213,0.00039689595,0.00017250025,0.00021549949,0.0005298693,0.0018093343,0.00020408146,0.0047790105,0.0019638743,0.9227424,0.06559348],"study_design_scores_gemma":[0.0013741602,0.00059504446,0.005335804,0.000020659334,0.000035157987,0.0003296244,0.00075693754,0.0004097797,0.00011052018,0.00010610273,0.9907079,0.00021832777],"about_ca_topic_score_codex":0.000053660184,"about_ca_topic_score_gemma":4.6475688e-7,"teacher_disagreement_score":0.48849213,"about_ca_system_score_codex":0.00041948093,"about_ca_system_score_gemma":0.000019845502,"threshold_uncertainty_score":0.99897003},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7742,0.0003098499,0.011314477,0.0010823319,0.000013748193,0.002041625,0.211002,0.000022811333,0.000013130424],"genre_scores_gemma":[0.9759456,0.000026192336,0.001134835,0.00007593472,0.000019676361,0.000319533,0.022331893,0.000020384086,0.00012598798],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971257,0.0007267358,0.00039741286,0.0005863852,0.0006818622,0.00048193347],"domain_scores_gemma":[0.9959995,0.002114478,0.00015702745,0.00068960956,0.00032559255,0.0007137856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029428957,0.00015146311,0.00047772506,0.0008630287,0.00044207138,0.00006900367,0.00023120831,0.000032491233,0.000054858065],"category_scores_gemma":[0.0019047519,0.00014062345,0.00006332234,0.0007354514,0.0004715194,0.00077320373,0.00091332634,0.00018415047,5.359613e-7],"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":[0.17579879,0.011378357,0.5045335,0.0320068,0.041106835,0.0014655194,0.060444146,0.0009992148,0.008181001,0.029882714,0.07067206,0.06353107],"study_design_scores_gemma":[0.0072201462,0.0014784702,0.09063763,0.00006141024,0.0010036959,0.00004994246,0.0045150295,0.89306843,0.000010589885,0.0005735519,0.0012145119,0.00016659425],"about_ca_topic_score_codex":0.00035543836,"about_ca_topic_score_gemma":0.00012295108,"teacher_disagreement_score":0.8920692,"about_ca_system_score_codex":0.00027681736,"about_ca_system_score_gemma":0.0006618233,"threshold_uncertainty_score":0.5734458},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97867304,0.013619093,0.000007992183,0.0003953336,0.0001017018,0.0005635464,0.0061981804,0.000034321292,0.00040680784],"genre_scores_gemma":[0.9924419,0.000020284255,0.00026496407,0.0040501766,0.00004970958,0.000007767706,0.0031253535,0.000021099419,0.000018748433],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998113,0.000106403495,0.000530197,0.0004348685,0.00053535774,0.0002801897],"domain_scores_gemma":[0.9985984,0.000111208945,0.00019662097,0.00050356635,0.000029616946,0.0005605711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003411547,0.000113763104,0.00042753966,0.00008369164,0.0000956733,0.00002651977,0.0002792095,0.00002228623,0.000031573956],"category_scores_gemma":[0.00053662655,0.00011535223,0.000020915402,0.00027832613,0.000053729396,0.00029908097,0.00074255327,0.00023176125,4.5050137e-7],"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":[0.00035104097,0.00030853576,0.8704051,0.0038506056,0.000035366582,0.00022603293,0.0022781303,0.000007918168,0.00002017667,0.000002894465,0.0022868847,0.12022735],"study_design_scores_gemma":[0.0020734607,0.00031077795,0.96481895,0.00042497963,0.000031933163,0.00005827389,0.021302642,0.00082937384,0.000005298227,0.0000026017792,0.009851104,0.00029061557],"about_ca_topic_score_codex":0.21585824,"about_ca_topic_score_gemma":0.37331516,"teacher_disagreement_score":0.15745693,"about_ca_system_score_codex":0.0028648651,"about_ca_system_score_gemma":0.008783129,"threshold_uncertainty_score":0.9968361},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9821558,0.0019292608,0.000024314379,0.006831141,0.00018802079,0.004103226,0.0042452062,0.000107945794,0.00041505528],"genre_scores_gemma":[0.9951246,0.00031074588,0.00019496215,0.0007681507,0.00011931733,0.0010831289,0.0021764736,0.000041862124,0.00018076132],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99549145,0.0011182223,0.0010044008,0.0008405274,0.0007393439,0.00080607063],"domain_scores_gemma":[0.9962623,0.0008457975,0.0005635376,0.0015816106,0.0003211461,0.00042556782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006868002,0.00024059348,0.00082777935,0.00019488191,0.0005734211,0.000055139986,0.0006569241,0.00003074091,0.000024094823],"category_scores_gemma":[0.0012813719,0.00020651873,0.000069126494,0.0011261258,0.00015378866,0.00016355666,0.00060476776,0.00037676346,0.0000012605504],"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":[0.0005820027,0.0006420791,0.9812133,0.00009978065,0.00006159355,0.0000074824484,0.00045379158,0.000003726531,0.000009071444,0.0006402363,0.0033702075,0.012916752],"study_design_scores_gemma":[0.001612324,0.0006787905,0.848678,0.000005774288,9.0078333e-7,0.000009922587,0.0011913084,0.001283028,3.6485199e-7,0.00014314384,0.14624415,0.0001522963],"about_ca_topic_score_codex":0.0012011064,"about_ca_topic_score_gemma":0.024201622,"teacher_disagreement_score":0.14287394,"about_ca_system_score_codex":0.00038689608,"about_ca_system_score_gemma":0.0015427745,"threshold_uncertainty_score":0.9936042},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96213543,0.013848421,0.00082242326,0.021380475,0.00034937507,0.00034432183,0.00048343252,0.000015068668,0.0006210658],"genre_scores_gemma":[0.9904843,0.007948252,0.00009863577,0.0005692371,0.0005700036,0.000021099211,0.00010994133,0.000021400989,0.00017715753],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9841964,0.007393199,0.000885161,0.00062422804,0.006171896,0.00072908454],"domain_scores_gemma":[0.9604492,0.03162788,0.00044699633,0.0014311353,0.0026704331,0.003374365],"candidate_categories":["metaresearch","bibliometrics","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.05942258,0.00014696253,0.0008420181,0.073808916,0.000444821,0.00007839372,0.0011367287,0.00008934459,0.0018666801],"category_scores_gemma":[0.17684849,0.000114039416,0.00015424978,0.0735261,0.001129818,0.0003111811,0.0045472328,0.004883637,0.0000133500735],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026207333,0.00072211924,0.5942931,0.00011645874,0.0017593595,0.0042761634,0.00018117054,0.000003837166,0.00008474886,0.000046272085,0.057316832,0.33857918],"study_design_scores_gemma":[0.0018421947,0.0012083178,0.8478453,0.00015228502,0.00030707024,0.0007168249,0.00084554416,0.0041119135,0.000009173326,0.00088488084,0.14196761,0.000108895816],"about_ca_topic_score_codex":0.0004755083,"about_ca_topic_score_gemma":0.00006779132,"teacher_disagreement_score":0.33847028,"about_ca_system_score_codex":0.0003822568,"about_ca_system_score_gemma":0.0017884861,"threshold_uncertainty_score":0.9990457},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030159295,0.00069788203,0.000073534604,0.00039748306,0.00035693365,0.00036363117,0.00972229,0.00018308623,0.98820215],"genre_scores_gemma":[0.00024132362,0.0005695729,0.00053271174,0.0015724769,0.00044850013,0.000008041494,0.029626584,0.00008354836,0.9669172],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998389,0.000012050525,0.00023733731,0.000662665,0.0005451219,0.00015383564],"domain_scores_gemma":[0.9970832,0.000059630263,0.00012701222,0.0025087656,0.000059076072,0.00016235295],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014705003,0.00023811893,0.0004429453,0.00010169543,0.000039410494,0.000011236882,0.0006410018,0.00007260691,0.10531963],"category_scores_gemma":[0.00006643417,0.00021348332,0.00008897517,0.000017453835,0.00007012988,0.00009179583,0.0014661916,0.00041233006,0.0016344289],"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":[0.00016375181,0.000057166388,0.0001255171,0.000121135854,0.0007610589,0.0014800937,0.000017274551,0.0000011609485,0.0000052791224,0.25488952,0.7220564,0.02032167],"study_design_scores_gemma":[0.00043858177,0.00006502463,0.0000954538,0.000059242724,0.00015039566,0.000040559986,0.0000034724908,0.0001006713,0.0000020755417,0.0025746953,0.99626046,0.00020933931],"about_ca_topic_score_codex":0.00008077752,"about_ca_topic_score_gemma":0.00003484527,"teacher_disagreement_score":0.2742041,"about_ca_system_score_codex":0.000106881715,"about_ca_system_score_gemma":0.0002029363,"threshold_uncertainty_score":0.9991429},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9890395,0.00016311753,0.00015904654,0.0009795582,0.00008867257,0.0029709244,0.0030974054,0.00010555254,0.003396263],"genre_scores_gemma":[0.99489236,0.0000091503525,0.00095421245,0.00007581638,0.000011617741,0.00046389792,0.0033116743,0.000032269952,0.0002490282],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9941318,0.0015151665,0.00077351567,0.0005740239,0.0023995093,0.000605994],"domain_scores_gemma":[0.997033,0.00044718696,0.00023263191,0.0014307152,0.0003847526,0.0004717178],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0059762225,0.00019663258,0.0005372188,0.0007391213,0.0007455542,0.000029891416,0.0011009651,0.000041944742,0.0014883837],"category_scores_gemma":[0.0009531469,0.00017691845,0.000057252517,0.0019355298,0.0003291003,0.0003927759,0.0031293088,0.0009017043,0.0002200245],"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":[0.0039039152,0.013145979,0.13411726,0.002042688,0.0027259272,0.0010761785,0.7073174,0.00008144574,0.0021451698,0.00212574,0.11733288,0.013985376],"study_design_scores_gemma":[0.00851614,0.0032657462,0.46676823,0.000111258574,0.00004896619,0.00006020532,0.36985773,0.0026618945,0.00022101411,0.00021564438,0.14781377,0.00045939803],"about_ca_topic_score_codex":0.00009622128,"about_ca_topic_score_gemma":0.00007919303,"teacher_disagreement_score":0.3374597,"about_ca_system_score_codex":0.0006416939,"about_ca_system_score_gemma":0.0026188155,"threshold_uncertainty_score":0.9994244},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923966,0.00040842648,0.000017913037,0.00040362857,0.00034310628,0.004958738,0.000529007,0.000025819694,0.00091679126],"genre_scores_gemma":[0.99748826,0.000022414635,0.000002906093,0.000043843804,0.00004806931,0.0019368285,0.00032689178,0.000012603036,0.000118195654],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98822397,0.005470182,0.00087012607,0.00035080715,0.0045617335,0.0005231748],"domain_scores_gemma":[0.9958911,0.00035096606,0.00032114485,0.00057842495,0.0027416751,0.00011668055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021628505,0.00014082361,0.00043721803,0.00041195934,0.0008872041,0.000104178296,0.00038977154,0.00003364564,0.00028268667],"category_scores_gemma":[0.00032846496,0.000108107924,0.00005431303,0.0019239418,0.00032467037,0.0005114961,0.0003720208,0.0007571646,0.00003793586],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003816191,0.0010770684,0.9788269,0.0001821653,0.00006219041,0.0000010832629,0.010783149,0.0062123192,0.000022544184,0.00022594487,0.0018577243,0.00036728175],"study_design_scores_gemma":[0.0027569896,0.0010536949,0.93598545,0.00003766351,0.0000018703498,0.000013435984,0.0059629413,0.0510416,0.000013263961,0.0000050140466,0.0030442446,0.000083841056],"about_ca_topic_score_codex":0.00025303336,"about_ca_topic_score_gemma":0.00029579684,"teacher_disagreement_score":0.04482928,"about_ca_system_score_codex":0.0023254605,"about_ca_system_score_gemma":0.0026947944,"threshold_uncertainty_score":0.7496051},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9779814,0.00024484657,0.0008424254,0.001429982,0.0002918422,0.00834497,0.007979325,0.00013225507,0.002752946],"genre_scores_gemma":[0.99499935,0.0000036103963,0.00019333487,0.00006921591,0.00008401946,0.00391407,0.00040552308,0.000031800602,0.0002990729],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98802644,0.008011698,0.0007676512,0.00054357527,0.0019114368,0.0007392271],"domain_scores_gemma":[0.99351597,0.004197022,0.00035664882,0.0010573087,0.0006295294,0.00024350327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00872312,0.0002514412,0.00089223165,0.00024038505,0.00094335625,0.000027112888,0.00076534366,0.000046772886,0.00014520262],"category_scores_gemma":[0.001305691,0.0001547404,0.00034817183,0.0011209155,0.00049598207,0.00010649149,0.0010734025,0.0010127994,0.00000793733],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005823153,0.014508312,0.760474,0.006573564,0.0019636091,0.00008868637,0.114382066,0.003714473,0.0008282085,0.01689367,0.06925563,0.0054946155],"study_design_scores_gemma":[0.0033015015,0.003700002,0.8712525,0.00015652973,0.00003577839,0.000018548682,0.11324393,0.002145038,0.00005909778,0.00069586403,0.0051444005,0.00024684766],"about_ca_topic_score_codex":0.000067691704,"about_ca_topic_score_gemma":0.000020223959,"teacher_disagreement_score":0.11077844,"about_ca_system_score_codex":0.0006627215,"about_ca_system_score_gemma":0.0005138614,"threshold_uncertainty_score":0.7255629},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9466366,0.002197511,0.002608909,0.0005375486,0.0007102622,0.0053270883,0.041089036,0.0008570339,0.000036005553],"genre_scores_gemma":[0.9593182,0.00048392205,0.036182687,0.0011228228,0.00031476567,0.001628839,0.0003207585,0.00044032844,0.00018768155],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939781,0.00036043997,0.0011459742,0.002580681,0.0008761195,0.0010586795],"domain_scores_gemma":[0.99273336,0.00043784475,0.0007554123,0.004906901,0.0005307627,0.0006357186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020896723,0.0009438939,0.0018649397,0.00037274198,0.00023110938,0.0005998384,0.0020721315,0.0002823506,0.00039034375],"category_scores_gemma":[0.0024259733,0.0010600727,0.0003019318,0.0011376884,0.00022145297,0.0010202833,0.005846398,0.0012126698,0.00006958796],"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":[0.0023734632,0.0017788352,0.11506764,0.0049144784,0.0011500006,0.0013869471,0.00002921146,0.00069375744,0.8591885,0.0014073853,0.011999551,0.000010257289],"study_design_scores_gemma":[0.012601474,0.00029073228,0.118433155,0.0035125013,0.0011227245,4.3062101e-7,0.0000331957,0.0034906617,0.13436724,0.0000060879584,0.72233474,0.003807047],"about_ca_topic_score_codex":0.0005915045,"about_ca_topic_score_gemma":0.000051862877,"teacher_disagreement_score":0.7248212,"about_ca_system_score_codex":0.00084903697,"about_ca_system_score_gemma":0.002632087,"threshold_uncertainty_score":0.99918497},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97846615,0.00066513714,0.000102154896,0.018850615,0.00043611522,0.0003054992,0.0006978206,0.000033710792,0.0004427935],"genre_scores_gemma":[0.9969696,0.00044705675,0.0000037822003,0.00018413735,0.00022093678,0.0000075193457,0.0002522609,0.00001916715,0.0018955324],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99264365,0.0013713306,0.0010935498,0.00027269626,0.0041786684,0.00044009602],"domain_scores_gemma":[0.9930058,0.00423524,0.00055749837,0.0012194277,0.0002824572,0.0006995638],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0146295475,0.00014636107,0.0008301262,0.0009811246,0.0000918323,0.00006478979,0.0023237416,0.000119462085,0.0005036803],"category_scores_gemma":[0.020354135,0.00007020413,0.00039146256,0.002629567,0.0009895595,0.00012117498,0.0013522758,0.0013308356,0.000025025205],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034216575,0.00027476944,0.5369822,0.00025676124,0.0023027814,0.0003267706,0.00045048338,0.000014217399,0.0003133632,0.00001564224,0.19605702,0.25958434],"study_design_scores_gemma":[0.008734854,0.013773818,0.63812333,0.0035125117,0.0018904537,0.0008084662,0.0012736582,0.023254199,0.0006205509,0.000054266213,0.3075963,0.00035756323],"about_ca_topic_score_codex":0.0003008811,"about_ca_topic_score_gemma":0.00048147817,"teacher_disagreement_score":0.25922677,"about_ca_system_score_codex":0.00027345505,"about_ca_system_score_gemma":0.0008807036,"threshold_uncertainty_score":0.9878978},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9128323,0.0002936355,0.07573038,0.009597418,0.00045236447,0.0005478789,0.00039415568,0.00008161718,0.000070252914],"genre_scores_gemma":[0.83252144,0.0008313441,0.15589684,0.00945015,0.00060735596,0.000009899007,0.00033048968,0.00003957309,0.0003129009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776417,0.000023969293,0.0013238178,0.00005455537,0.0005973334,0.00023613403],"domain_scores_gemma":[0.99820393,0.00003744264,0.00063078426,0.00012880587,0.000313561,0.0006854669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011466105,0.00010859987,0.00045050523,0.00030514578,0.00005771393,0.0000140792845,0.00009978743,0.00006905643,0.0000152028715],"category_scores_gemma":[0.00019067217,0.00008469697,0.00005273716,0.00041022716,0.00010525123,0.0002876718,0.000057374666,0.00018079703,0.000036648013],"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":[0.005909135,0.0015737395,0.117418766,0.03395201,0.0008100225,0.00015917455,0.040101416,0.04173065,0.001174963,0.00013915676,0.3246309,0.43240008],"study_design_scores_gemma":[0.002861658,0.0013358422,0.055489417,0.0015934152,0.000038821356,0.00019437517,0.0004990103,0.92095506,0.00006574274,0.000060080343,0.01675109,0.00015549157],"about_ca_topic_score_codex":0.000015208613,"about_ca_topic_score_gemma":0.000010244418,"teacher_disagreement_score":0.8792244,"about_ca_system_score_codex":0.0000728139,"about_ca_system_score_gemma":0.00068013236,"threshold_uncertainty_score":0.34538424},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7249628,0.0009936042,0.20539343,0.021072812,0.0051337373,0.018057019,0.01878961,0.003706628,0.0018903961],"genre_scores_gemma":[0.8650021,0.00007352593,0.06786324,0.011384452,0.00032193222,0.00083640206,0.05056576,0.00030773948,0.0036448282],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.997174,0.00030553705,0.00085673184,0.00060256093,0.00048501094,0.0005761372],"domain_scores_gemma":[0.99613583,0.0011062796,0.00028698661,0.0019204773,0.00016753872,0.00038286365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002545144,0.00036947784,0.0010449025,0.00045508414,0.00014256427,0.000046526486,0.0005076361,0.00029204032,0.0001777985],"category_scores_gemma":[0.0009352603,0.00034461645,0.00024326982,0.00020682487,0.00005117078,0.000046004763,0.0008396428,0.0014408351,0.00018907845],"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":[0.0022805259,0.0010075335,0.39138922,0.0061831735,0.00014772077,0.00023526723,0.00062368775,0.009602193,0.000027263344,0.000044140055,0.58017695,0.008282342],"study_design_scores_gemma":[0.01103881,0.0010166375,0.80092573,0.009651415,0.00010783695,0.000010179744,0.0008106484,0.030680979,0.00005776955,0.0015559593,0.14309672,0.0010473263],"about_ca_topic_score_codex":0.003854204,"about_ca_topic_score_gemma":0.0049654655,"teacher_disagreement_score":0.4370802,"about_ca_system_score_codex":0.0005073111,"about_ca_system_score_gemma":0.0019169794,"threshold_uncertainty_score":0.9999006},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9615176,0.0014091646,0.018375399,0.0070026903,0.001319243,0.0045186905,0.0031206296,0.0014659634,0.0012706544],"genre_scores_gemma":[0.9787446,0.000012992326,0.010334579,0.0037665272,0.00022515666,0.000058273523,0.0065785875,0.00010177298,0.00017749499],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9955723,0.00022131768,0.0012593868,0.0011269256,0.001265048,0.0005549797],"domain_scores_gemma":[0.9967146,0.00020625687,0.0010217885,0.00087099784,0.00048721168,0.0006991727],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007813918,0.0004969541,0.0014922107,0.00048566845,0.00019777758,0.00006790425,0.0003024484,0.0003027912,0.00018637087],"category_scores_gemma":[0.0021856758,0.00044572257,0.00034677694,0.00048511324,0.00019187824,0.00009952618,0.0009850806,0.0007532393,0.000018706702],"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":[0.005871474,0.00542374,0.3461189,0.083897814,0.009195054,0.0034520377,0.3604878,0.008513427,0.005289015,0.0012638377,0.069982916,0.100504],"study_design_scores_gemma":[0.012040601,0.00046874466,0.7618894,0.003442982,0.0011267046,0.0003801676,0.20167273,0.011865097,0.0006301818,0.0017662481,0.0009970097,0.0037201492],"about_ca_topic_score_codex":0.001532098,"about_ca_topic_score_gemma":0.0005691768,"teacher_disagreement_score":0.41577047,"about_ca_system_score_codex":0.0011984853,"about_ca_system_score_gemma":0.003808464,"threshold_uncertainty_score":0.99979943},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99665666,0.000012479763,0.0000023088194,0.0003904953,0.000055779048,0.0014796393,0.0009820675,0.0003361211,0.00008443427],"genre_scores_gemma":[0.99194276,0.0000073029573,0.0000018685878,0.0013588147,0.000022340566,0.00010845061,0.0063850936,0.000034558157,0.00013883357],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975411,0.0007888183,0.0003269727,0.00041498154,0.000567407,0.00036068703],"domain_scores_gemma":[0.99806416,0.0008265002,0.000085735264,0.0006472186,0.0000675574,0.00030881685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015634686,0.00023346877,0.0003515629,0.0005976735,0.00016458596,0.00006800896,0.0002650037,0.00006302865,0.00016991807],"category_scores_gemma":[0.00062626286,0.00016293107,0.00005679049,0.0015478712,0.00017121926,0.00006799146,0.00006164823,0.00027647955,0.00005003735],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013697309,0.00044531442,0.9702025,0.0000847614,0.00004409461,0.00019290575,0.005238529,0.00008427019,0.00003721656,0.0000053108192,0.023484373,0.00004374888],"study_design_scores_gemma":[0.0037990003,0.00034686996,0.97489065,0.000019744066,0.000024315015,8.460155e-7,0.007694175,0.0003257838,0.0000038094179,0.00001418958,0.0127089815,0.00017166183],"about_ca_topic_score_codex":0.0024291761,"about_ca_topic_score_gemma":0.0066015595,"teacher_disagreement_score":0.010775391,"about_ca_system_score_codex":0.00013225875,"about_ca_system_score_gemma":0.00021615447,"threshold_uncertainty_score":0.66441363},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9887474,0.0012875502,0.00053616473,0.006911837,0.00003547767,0.0017906729,0.0005253901,0.00014409528,0.000021406466],"genre_scores_gemma":[0.99223125,0.0012147744,0.0041945926,0.000980465,0.000099257,0.00007241532,0.0011361027,0.000041733645,0.000029432984],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966016,0.0006769524,0.00068893255,0.00072537374,0.00039662037,0.0009105362],"domain_scores_gemma":[0.9975556,0.0003631578,0.00043866033,0.0008484915,0.00013132996,0.0006627756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028032942,0.00024281011,0.0008729765,0.00016645125,0.00032605583,0.0001702392,0.00018832664,0.000040522962,0.000002262352],"category_scores_gemma":[0.0005103073,0.00017218187,0.000021832959,0.0007173047,0.00021917018,0.00037901234,0.00035410322,0.00038903937,6.0554527e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017638676,0.00027860215,0.92390156,0.00075671467,0.00006435817,0.000008943346,0.00063244195,0.0000025219792,0.0000017831578,0.000004939445,0.0020199649,0.07215179],"study_design_scores_gemma":[0.002825781,0.0020994465,0.72309256,0.00014372027,0.0000043670384,0.00016393843,0.0029272877,0.20894244,3.6090658e-8,0.0000011379183,0.05963336,0.00016595407],"about_ca_topic_score_codex":0.0005036152,"about_ca_topic_score_gemma":0.0021258374,"teacher_disagreement_score":0.20893992,"about_ca_system_score_codex":0.000062972606,"about_ca_system_score_gemma":0.0013154664,"threshold_uncertainty_score":0.70213723},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99579215,0.000031740445,0.00013492955,0.00027180157,0.00011297428,0.0018536447,0.00085086265,0.0002779072,0.00067401794],"genre_scores_gemma":[0.99643415,0.000038385806,0.00036156422,0.000029914188,0.00021736302,0.000097741184,0.0025397171,0.000037293416,0.00024384072],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953599,0.00074859016,0.00046518788,0.00059388875,0.0018828767,0.00094955775],"domain_scores_gemma":[0.9972678,0.00053317694,0.000057346184,0.0012106253,0.00059404375,0.00033702477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051724426,0.0001911392,0.00042335773,0.00065845205,0.00048265274,0.00012144646,0.0006299719,0.00010252047,0.00026276082],"category_scores_gemma":[0.0008045679,0.00015623734,0.00007039722,0.002526876,0.00016273714,0.0008729005,0.0014250217,0.0009484765,0.0007366248],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056167913,0.0028944383,0.91834044,0.00050773256,0.00032380057,0.0004619675,0.006294943,0.00045553083,0.0009614112,0.0001546069,0.017137684,0.051905736],"study_design_scores_gemma":[0.0014081426,0.00040080945,0.94545496,0.00007000772,0.000013209447,0.000021497925,0.0035439944,0.047434766,0.00004704474,0.000047539565,0.0014072886,0.00015077234],"about_ca_topic_score_codex":0.00017559106,"about_ca_topic_score_gemma":0.00006722968,"teacher_disagreement_score":0.051754966,"about_ca_system_score_codex":0.00034868476,"about_ca_system_score_gemma":0.0002559813,"threshold_uncertainty_score":0.9468067},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71212906,0.011216094,0.00006173436,0.2754318,0.0002664687,0.0002567137,0.00014296826,0.00012600263,0.0003691378],"genre_scores_gemma":[0.9433307,0.01854426,0.00069677643,0.036084898,0.00049406156,0.000049572845,0.0006561706,0.00002896605,0.00011458709],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9952572,0.0024006157,0.0010464634,0.0004801836,0.000081539496,0.00073399063],"domain_scores_gemma":[0.99185234,0.0072874785,0.0002594105,0.00030643307,0.000113469985,0.00018087562],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011078505,0.00016214083,0.0015024589,0.00025340298,0.000035766905,7.0180425e-7,0.00012459139,0.00041098293,0.000083944724],"category_scores_gemma":[0.037232045,0.0001401261,0.00015514079,0.00029112457,0.000645178,0.000032021868,0.00017138057,0.0006756128,0.00012099205],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022464886,0.00006442849,0.71269906,0.00022269014,0.000047591417,0.00002557645,0.00013104608,0.000008990602,0.000052583557,0.02271089,0.23767091,0.026141606],"study_design_scores_gemma":[0.0004098063,0.0001342469,0.79292935,0.000066139684,0.000010980945,0.000008771819,0.000025572208,0.00042831883,0.000014111702,0.02961976,0.17626089,0.0000920621],"about_ca_topic_score_codex":0.00018808318,"about_ca_topic_score_gemma":0.000048580798,"teacher_disagreement_score":0.23934692,"about_ca_system_score_codex":0.000010333002,"about_ca_system_score_gemma":0.00006585173,"threshold_uncertainty_score":0.97087777},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20829819,0.00012145073,0.00084662804,0.038342636,0.005534581,0.02884344,0.52713585,0.0019339906,0.18894324],"genre_scores_gemma":[0.5770834,0.000016459606,0.0009983344,0.0144476555,0.001212133,0.001797249,0.37022963,0.00011439335,0.034100775],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997435,0.00013725502,0.00041754704,0.000800685,0.0008406702,0.00036889344],"domain_scores_gemma":[0.9971187,0.00027392321,0.00011198272,0.0013960125,0.0003218259,0.0007775706],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004599174,0.00015468225,0.0002950784,0.00019499917,0.0002639606,0.000308188,0.0016732864,0.00008291591,0.0024305542],"category_scores_gemma":[0.009209922,0.00015673832,0.00005460995,0.0007947971,0.00011100134,0.0004116629,0.0020594576,0.0001647783,0.0015634344],"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":[0.00018460708,0.00003953563,0.65697974,0.00005764955,0.00008008991,0.00008212659,0.000025716365,0.00010584163,0.0000030949257,0.0005077972,0.34184965,0.000084170235],"study_design_scores_gemma":[0.0012486839,0.00002602506,0.7997227,0.000019517007,0.000005605116,0.000052740375,0.000016350612,0.0016156127,9.2139084e-7,0.00025463148,0.19688056,0.00015667704],"about_ca_topic_score_codex":0.054068703,"about_ca_topic_score_gemma":0.050114058,"teacher_disagreement_score":0.3687852,"about_ca_system_score_codex":0.00042411723,"about_ca_system_score_gemma":0.007696021,"threshold_uncertainty_score":0.999214},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.997542,0.000029158657,0.0000012884826,0.00023338522,0.00005106905,0.0016250536,0.00019431741,0.000058799906,0.0002649146],"genre_scores_gemma":[0.99955934,0.0000067003048,0.000010240454,0.000016311878,0.00003566157,0.0002351125,0.000096938275,0.000014075267,0.000025634796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971286,0.0007610698,0.0006004736,0.00019884556,0.00083753146,0.00047344426],"domain_scores_gemma":[0.9989596,0.00021785959,0.00015284547,0.0002357414,0.00031646024,0.000117492644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026957763,0.000089934445,0.0003554158,0.000433352,0.00023591424,0.000013208853,0.000097202996,0.00003703809,0.000021792874],"category_scores_gemma":[0.00018646198,0.00007965387,0.000044797656,0.0015550996,0.00012385429,0.00019055213,0.00023987045,0.00037263753,0.00006475938],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019858057,0.001115985,0.75710416,0.0007277806,0.000032016465,0.0036642952,0.11356723,0.000003523647,0.000021958043,0.0000073142855,0.0028808482,0.12067632],"study_design_scores_gemma":[0.0019400832,0.0018871664,0.89483166,0.00034818117,0.00000263675,0.00021057676,0.09771548,0.0026860067,0.00007709995,0.000039037634,0.00018296826,0.00007910887],"about_ca_topic_score_codex":0.00065831153,"about_ca_topic_score_gemma":0.00027834083,"teacher_disagreement_score":0.1377275,"about_ca_system_score_codex":0.00023825296,"about_ca_system_score_gemma":0.00032988348,"threshold_uncertainty_score":0.32481906},"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":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001005794,0.0047399327,0.25780523,0.0005530587,0.00067788636,0.004086573,0.58055264,0.001270586,0.1502135],"genre_scores_gemma":[0.00149339,0.00878811,0.300054,0.0021829004,0.0038708402,0.00123747,0.20061286,0.0028942393,0.47886616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99750143,0.0001348921,0.00047708247,0.0010231681,0.0005599722,0.00030343584],"domain_scores_gemma":[0.9942639,0.002932013,0.00047901538,0.001828708,0.00023805407,0.00025832606],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015614033,0.00048428282,0.0006763206,0.0001525122,0.00014064703,0.0001288337,0.0007690689,0.00015612668,0.00034803027],"category_scores_gemma":[0.002894686,0.0003437597,0.00002468555,0.00034751114,0.0004694591,0.00013029958,0.00060725224,0.00042876025,0.00021799815],"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":[0.000011319684,0.000083938176,0.00038527764,0.00019519955,0.0006303593,0.00014784645,0.00058406766,0.0000018007936,0.00001327972,0.013789793,0.96627766,0.017879473],"study_design_scores_gemma":[0.00091390824,0.000071637376,0.004458129,0.0005381467,0.00050480367,0.0000036828587,0.0038414316,0.005000903,0.000018929875,0.010950015,0.9734255,0.0002729079],"about_ca_topic_score_codex":0.022238266,"about_ca_topic_score_gemma":0.07272205,"teacher_disagreement_score":0.3799398,"about_ca_system_score_codex":0.000097515906,"about_ca_system_score_gemma":0.0006030113,"threshold_uncertainty_score":0.9999014},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9409469,0.00008313913,0.05310751,0.00022062773,0.00059145154,0.0019788807,0.00068135385,0.0011363447,0.0012538082],"genre_scores_gemma":[0.9939162,0.00014234996,0.00030174686,0.00009214173,0.00023064739,0.000016994338,0.00052026915,0.00012662695,0.004652981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751157,0.000082596685,0.000319925,0.0013491494,0.00015936214,0.00057741295],"domain_scores_gemma":[0.997487,0.0002816261,0.0003204073,0.0011496687,0.00043329093,0.0003280064],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000340576,0.00046155302,0.00070658175,0.00036332817,0.00016861342,0.000057067875,0.0005541166,0.00038022568,0.000028272765],"category_scores_gemma":[0.00038993522,0.00051895273,0.00041793604,0.00044071634,0.0001271577,0.00025187424,0.00031356933,0.00061297347,0.00009584326],"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":[0.006610908,0.0013938103,0.15288778,0.014758546,0.0024541898,0.0016433187,0.002517647,0.80461806,0.0007600398,0.0043549812,0.0068246713,0.0011760546],"study_design_scores_gemma":[0.009439541,0.0003402997,0.023072433,0.0021443258,0.0016148884,0.000029956782,0.0014070772,0.8991669,0.0017827782,0.058602095,0.0005216689,0.0018780784],"about_ca_topic_score_codex":0.0001181737,"about_ca_topic_score_gemma":0.00015099051,"teacher_disagreement_score":0.12981534,"about_ca_system_score_codex":0.00028088214,"about_ca_system_score_gemma":0.00032261302,"threshold_uncertainty_score":0.9997262},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8785089,0.00028501631,0.00020828018,0.07507074,0.0042090253,0.0006344136,0.00032647047,0.00010109339,0.040656067],"genre_scores_gemma":[0.99047434,0.0014481362,0.00007482043,0.0009867253,0.0042128037,0.00004575584,0.0003712555,0.000038532715,0.0023476386],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9899714,0.00010604117,0.00075810374,0.0003430905,0.008313921,0.0005074373],"domain_scores_gemma":[0.99580896,0.00096274796,0.0002840167,0.00032902323,0.0021216339,0.0004936423],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0049117594,0.00018076767,0.0003283165,0.001033695,0.0001958569,0.0001521128,0.0018684082,0.0001396031,0.006589839],"category_scores_gemma":[0.0014036149,0.000124831,0.0002865341,0.00074839644,0.00046724788,0.00013685088,0.0011971941,0.0011634539,0.0005316508],"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":[0.009961226,0.00068106526,0.072125316,0.00003889233,0.002474841,0.0037112483,0.0005905774,0.00044565322,0.020266568,0.0015006902,0.802564,0.08563989],"study_design_scores_gemma":[0.010874802,0.0007327096,0.15863429,0.000567744,0.00011872698,0.0016647902,0.00079819106,0.005443695,0.009101043,0.0048768595,0.80667627,0.0005109003],"about_ca_topic_score_codex":0.00010096289,"about_ca_topic_score_gemma":0.00014761645,"teacher_disagreement_score":0.11196543,"about_ca_system_score_codex":0.0003954866,"about_ca_system_score_gemma":0.0008368688,"threshold_uncertainty_score":0.99431825},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9714999,0.0000708684,0.000008252636,0.025800154,0.000004953448,0.0007241825,0.0017894922,0.000049693583,0.00005246677],"genre_scores_gemma":[0.9986594,0.000035373057,0.00006791051,0.00056245236,0.000031800766,0.000048551225,0.00047317392,0.000016294574,0.00010504278],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988465,0.00018674231,0.00021033175,0.00022564457,0.00026900103,0.00026179495],"domain_scores_gemma":[0.9991385,0.0002489315,0.00010870007,0.00026173773,0.00006955119,0.00017260852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003059394,0.00012217759,0.00039441037,0.000048765858,0.000055685545,0.000033332264,0.00009400947,0.000021971427,0.000008377161],"category_scores_gemma":[0.0009267189,0.000078900666,0.000025903551,0.0004986966,0.0003909336,0.00006266529,0.00011331038,0.00013853922,0.000029153842],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084463053,0.00046074233,0.911455,0.00013827934,0.00029630822,0.000026025748,0.00067697733,0.0000029059358,0.077283904,0.00006300594,0.007946032,0.00080622185],"study_design_scores_gemma":[0.0031810596,0.00038263074,0.9877124,0.00015123714,0.00009523323,0.0000065436548,0.00009731491,0.0074311914,0.00018094128,0.0000062227878,0.0006415908,0.000113657516],"about_ca_topic_score_codex":0.0003872075,"about_ca_topic_score_gemma":0.00019105739,"teacher_disagreement_score":0.07710296,"about_ca_system_score_codex":0.000039107323,"about_ca_system_score_gemma":0.00003627222,"threshold_uncertainty_score":0.3217476},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99286115,0.000154076,0.00012603306,0.0021621345,0.000078909805,0.002323416,0.0014355482,0.00019081245,0.0006679002],"genre_scores_gemma":[0.9965228,0.00029383402,0.00008041141,0.00026368847,0.00006804726,0.0004778007,0.0020232468,0.000020669393,0.000249511],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971354,0.00027998933,0.0006360898,0.00024307785,0.0009438139,0.0007616471],"domain_scores_gemma":[0.998385,0.00053007936,0.00013999219,0.00034028495,0.0003635894,0.0002410215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038250918,0.00016138102,0.00042895233,0.0015233953,0.00023694693,0.00006146968,0.00016932483,0.00008109987,0.000036747362],"category_scores_gemma":[0.00035597844,0.00014342746,0.000047367903,0.0017777453,0.00013355534,0.0009204856,0.00022423822,0.0005858851,0.00012215778],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033769906,0.0024572713,0.40958646,0.004805933,0.00029970138,0.000056999514,0.18414108,0.000061786166,0.00016504468,0.00069970015,0.14793712,0.24641189],"study_design_scores_gemma":[0.0066227983,0.0021046232,0.89754766,0.00034585525,0.0000067878213,0.000013184611,0.027796771,0.019983098,0.000009762612,0.00014568162,0.045230113,0.0001936812],"about_ca_topic_score_codex":0.0002256692,"about_ca_topic_score_gemma":0.0004422521,"teacher_disagreement_score":0.48796117,"about_ca_system_score_codex":0.0005186819,"about_ca_system_score_gemma":0.0004464695,"threshold_uncertainty_score":0.58488023},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9379583,0.00014521496,0.059509546,0.000083876956,0.0003520849,0.0005419693,0.0011446127,0.0002458923,0.000018493762],"genre_scores_gemma":[0.93091714,0.00004823465,0.048718724,0.0004930684,0.0007439428,0.0000051662355,0.018843565,0.00007356717,0.0001565812],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976423,0.00035606718,0.0005716565,0.00085323706,0.00014735223,0.00042938994],"domain_scores_gemma":[0.9977248,0.000164496,0.00025711098,0.0015763593,0.00008012114,0.00019717023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001622979,0.0002308877,0.00045638124,0.00013935592,0.00021685073,0.000067933935,0.00053268805,0.00018229072,0.000023651504],"category_scores_gemma":[0.0009773534,0.00019706531,0.00008183045,0.00028492318,0.00018278476,0.00021040121,0.00070222653,0.00021995917,0.00014964495],"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":[0.0005441062,0.00033061995,0.036452964,0.00030800764,0.00008773258,0.000054027652,0.00031294045,0.027147643,0.9250579,0.000011707026,0.005601586,0.004090779],"study_design_scores_gemma":[0.0010760176,0.000026231688,0.016164374,0.00009007405,0.00015460566,0.00009377812,0.00022476824,0.9738246,0.0007912315,0.000043654407,0.007201219,0.00030944392],"about_ca_topic_score_codex":0.00012206466,"about_ca_topic_score_gemma":0.000016817961,"teacher_disagreement_score":0.94667697,"about_ca_system_score_codex":0.00021968462,"about_ca_system_score_gemma":0.00014484131,"threshold_uncertainty_score":0.803609},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2789284,0.008340378,0.56855696,0.09609525,0.0031157474,0.022819936,0.010456253,0.005711622,0.0059754434],"genre_scores_gemma":[0.2867516,0.003765894,0.6645546,0.01624855,0.0010211354,0.0031449778,0.0017433914,0.00069143396,0.022078427],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99180573,0.0007507342,0.001798877,0.0030027695,0.0008291367,0.0018127578],"domain_scores_gemma":[0.98815876,0.0055230954,0.0007033499,0.0033255485,0.0006119184,0.0016773177],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008850112,0.0006896751,0.0024410207,0.0011284417,0.0005018561,0.00053923164,0.0006732886,0.00038097828,0.00030228836],"category_scores_gemma":[0.03246664,0.00065933046,0.0003743961,0.0006895464,0.00014925546,0.0003231651,0.004078984,0.000815217,0.00013406045],"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":[0.011914859,0.00091807084,0.033136066,0.015865846,0.0012756874,0.00020036307,0.0018425692,0.0010231279,0.00026380655,0.0009282538,0.31949148,0.61313987],"study_design_scores_gemma":[0.010304168,0.0046973424,0.19392394,0.018366247,0.00022566841,0.00084454374,0.0059420443,0.003836584,0.00070019445,0.025814675,0.7311623,0.0041822954],"about_ca_topic_score_codex":0.00056638755,"about_ca_topic_score_gemma":0.0002127353,"teacher_disagreement_score":0.6089576,"about_ca_system_score_codex":0.001013528,"about_ca_system_score_gemma":0.004168885,"threshold_uncertainty_score":0.9995858},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99116796,0.0009410173,0.00056731614,0.0013089932,0.00016789223,0.0022463526,0.0034085151,0.00010420364,0.00008774677],"genre_scores_gemma":[0.981612,0.006249448,0.000047097703,0.00040840913,0.0003152061,0.009836767,0.0012950288,0.000037062273,0.00019894351],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979034,0.00013887785,0.0003701325,0.000879303,0.00036765455,0.0003405971],"domain_scores_gemma":[0.9976255,0.0003209515,0.0002980335,0.0015161764,0.00011132106,0.00012799514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006241768,0.00023443789,0.0005392846,0.00022160284,0.00016127522,0.00010229773,0.0008469009,0.000058490274,0.00008431715],"category_scores_gemma":[0.00016201193,0.00016910602,0.000092144495,0.0019170155,0.00009231524,0.0008784052,0.00056340377,0.0002715486,0.0000044751814],"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":[0.00023613784,0.000057742032,0.9684955,0.000109045424,0.0005982435,0.000007376343,0.001524373,0.00050605985,0.000052823427,0.000013694304,0.0072388784,0.021160139],"study_design_scores_gemma":[0.00072615553,0.000018850937,0.91399145,0.00013115976,0.00062675314,6.8164695e-7,0.0009732141,0.008914143,0.000049651302,0.00003424143,0.0743429,0.00019081376],"about_ca_topic_score_codex":0.05828079,"about_ca_topic_score_gemma":0.08562047,"teacher_disagreement_score":0.06710402,"about_ca_system_score_codex":0.0002830393,"about_ca_system_score_gemma":0.00023712602,"threshold_uncertainty_score":0.9479902},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98970807,0.00046714937,0.00029755547,0.004345816,0.00052157656,0.0030172032,0.00076249236,0.0007140754,0.00016608479],"genre_scores_gemma":[0.9933412,0.00015643766,0.000032203207,0.003196979,0.00028714063,0.0015966492,0.0009438358,0.000057542118,0.00038801442],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99532205,0.00070327276,0.0010595763,0.0010520596,0.00060387165,0.0012591456],"domain_scores_gemma":[0.99615735,0.00087807886,0.0002832713,0.0006422128,0.00018077293,0.0018582914],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006063901,0.00033088904,0.00079209585,0.0005902177,0.0004394014,0.00015266205,0.0001861049,0.0001606321,0.000058796384],"category_scores_gemma":[0.0031279027,0.00032902838,0.000126298,0.0016108588,0.0001945001,0.00024736804,0.0001045468,0.0003191922,0.00004350633],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007083342,0.00042650523,0.98292845,0.0006094284,0.000042004052,0.000036288617,0.0005575823,0.000023815232,0.000018351197,0.000035695735,0.004257794,0.010355773],"study_design_scores_gemma":[0.0050135907,0.00068352866,0.8858227,0.000006322947,6.1254394e-7,0.00002593436,0.0003243174,0.0016794913,6.117077e-7,0.00004427542,0.10614086,0.0002577311],"about_ca_topic_score_codex":0.00032938702,"about_ca_topic_score_gemma":0.010395199,"teacher_disagreement_score":0.10188307,"about_ca_system_score_codex":0.000517996,"about_ca_system_score_gemma":0.0016397331,"threshold_uncertainty_score":0.9999162},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95172983,0.012918468,0.0008190742,0.013195861,0.0011613059,0.0067274687,0.006441358,0.0026918994,0.004314757],"genre_scores_gemma":[0.979607,0.000262167,0.0004274553,0.0011223177,0.00013138863,0.0009164031,0.01296369,0.00019039017,0.0043792157],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99509424,0.0004152351,0.001122012,0.0008752768,0.0012241412,0.0012691218],"domain_scores_gemma":[0.98939574,0.0010271712,0.0005283765,0.0058789323,0.00081299804,0.0023567851],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015689911,0.00056965154,0.0010976794,0.00022570115,0.0009806519,0.00013026489,0.001101587,0.00009887635,0.0001229241],"category_scores_gemma":[0.0035327293,0.0005877364,0.00043480628,0.001215441,0.00023847051,0.00025002827,0.0006547863,0.0005276729,0.000030903815],"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":[0.0023161266,0.0004693533,0.8751461,0.001660769,0.00042945213,0.009931596,0.000042233078,0.0048482735,0.00015978346,0.00016652994,0.102700494,0.0021292742],"study_design_scores_gemma":[0.0042453366,0.00011863339,0.659569,0.0010917311,0.00047135874,0.000051277173,0.00022003775,0.044191953,0.00007502277,0.00027832313,0.28854647,0.0011408211],"about_ca_topic_score_codex":0.06656756,"about_ca_topic_score_gemma":0.0882264,"teacher_disagreement_score":0.21557708,"about_ca_system_score_codex":0.00088982476,"about_ca_system_score_gemma":0.012438209,"threshold_uncertainty_score":0.9996574},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028424278,0.00013850551,0.9674243,0.00029681984,0.000025701771,0.00033010443,0.0024520191,0.00025836958,0.0006499304],"genre_scores_gemma":[0.5928281,0.00015088997,0.40310332,0.00064299087,0.000055648456,0.000012568192,0.0028937394,0.00006224102,0.00025050444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820197,0.000051682207,0.00042548662,0.0004939705,0.0004996296,0.00032724312],"domain_scores_gemma":[0.99702466,0.00062128203,0.00011498175,0.0015314996,0.00006651401,0.00064104336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070714584,0.00018476216,0.00061192666,0.0006225936,0.00009729027,0.00006167049,0.00028266053,0.00007514347,0.00012657668],"category_scores_gemma":[0.00194372,0.00016679618,0.00007101838,0.0023902776,0.000113923525,0.000214829,0.00045478987,0.00012240873,0.000045323843],"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":[0.0003689015,0.0034740379,0.01977446,0.014044688,0.009932079,0.0036342803,0.008477518,0.7356979,0.0002205812,0.13361381,0.06149921,0.009262491],"study_design_scores_gemma":[0.0004291369,0.000023991834,0.00025562212,0.00003013123,0.0013748342,0.000033580913,0.00019665377,0.94864583,0.0000013083813,0.048743024,0.00010699201,0.00015888244],"about_ca_topic_score_codex":0.00007673353,"about_ca_topic_score_gemma":0.000025032143,"teacher_disagreement_score":0.56440383,"about_ca_system_score_codex":0.00008720453,"about_ca_system_score_gemma":0.00023976156,"threshold_uncertainty_score":0.68017507},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9842155,0.0028527582,0.000024255965,0.0053075585,0.0012211793,0.00073623704,0.00076094805,0.00040286247,0.0044787107],"genre_scores_gemma":[0.9893018,0.0006819034,0.00009115052,0.000733205,0.00022143265,0.0003476184,0.0005299987,0.000071399634,0.008021519],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971865,0.0009540647,0.00039320716,0.0005154178,0.0005947101,0.00035611755],"domain_scores_gemma":[0.99604154,0.0010964251,0.00019806836,0.002370165,0.00007974081,0.00021407728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024446738,0.00025757588,0.0004496988,0.000090166206,0.00030138623,0.00004669042,0.0007495228,0.00012578198,0.00086914905],"category_scores_gemma":[0.0017429085,0.00018495633,0.00026014046,0.00017601228,0.00020310956,0.000017393177,0.0018466536,0.0014156263,0.00020110417],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036567305,0.00012462307,0.9450761,0.00013215997,0.00026439843,0.00039942196,0.00019557509,0.000042838605,0.00061893964,0.00010555805,0.045134105,0.004249517],"study_design_scores_gemma":[0.00033684075,0.000043627002,0.5206635,0.00005193821,0.00006718127,0.00002303634,0.000052399155,0.00006798029,0.000014203791,0.00022521822,0.47832438,0.00012967447],"about_ca_topic_score_codex":0.000037568854,"about_ca_topic_score_gemma":0.00009272081,"teacher_disagreement_score":0.4331903,"about_ca_system_score_codex":0.00021797755,"about_ca_system_score_gemma":0.00060014025,"threshold_uncertainty_score":0.9516573},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006007953,0.8860989,0.040575374,0.00008428755,0.00017971464,0.0015533955,0.07144178,0.00003941633,0.000021112946],"genre_scores_gemma":[0.002609073,0.8686932,0.000038955437,0.000063310166,0.0004410523,0.00001739416,0.12810224,0.000032208085,0.0000025707789],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99804115,0.00022036438,0.00067836174,0.0006154613,0.0002757359,0.00016894955],"domain_scores_gemma":[0.99647135,0.0018212167,0.0006715069,0.00068215065,0.00024282026,0.00011096236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012934832,0.00022169776,0.0010062192,0.00013167014,0.00017540561,0.00003314514,0.00034737535,0.000081791826,0.0000064223364],"category_scores_gemma":[0.005081721,0.00016185708,0.000089946094,0.00038294995,0.00024269464,0.00016515021,0.0012038221,0.00014195815,0.0000010974228],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000283829,0.000028818109,0.00008775732,0.13934785,0.00025997483,0.0000010748672,0.0000605099,1.9847335e-9,8.599031e-8,0.000052079235,0.0020164144,0.85811704],"study_design_scores_gemma":[0.0017951542,0.00009405703,0.008942741,0.4995219,0.020301545,0.000024893438,0.002217948,0.010515399,3.567779e-8,0.00009547124,0.45606112,0.00042971765],"about_ca_topic_score_codex":0.0000034506602,"about_ca_topic_score_gemma":0.000009669783,"teacher_disagreement_score":0.85768735,"about_ca_system_score_codex":0.00003426641,"about_ca_system_score_gemma":0.0003175423,"threshold_uncertainty_score":0.66003406},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9929364,0.0000542041,0.00422721,0.00006312065,0.000023180375,0.002225828,0.00043402115,0.000027996213,0.000008089944],"genre_scores_gemma":[0.9988003,0.000047275287,0.000077207435,0.000033630644,0.000014865412,0.0001658266,0.00083299284,0.000017485601,0.000010443226],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976003,0.0009056511,0.00036430906,0.00029781318,0.00054406637,0.00028785315],"domain_scores_gemma":[0.99716914,0.0013446396,0.0001169907,0.00018189156,0.0010029359,0.0001844135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042681764,0.00013011011,0.0002866006,0.0007048296,0.0001657843,0.000103138475,0.00006139517,0.000036035064,0.000012042563],"category_scores_gemma":[0.00036774416,0.000118878386,0.000018396258,0.001640825,0.00017051004,0.0004242947,0.00021245907,0.00019747685,0.0000026702467],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032227798,0.0002358636,0.981045,0.00031138284,0.00009600018,0.0000031999434,0.005712542,0.00008067571,0.00047292205,0.0000053603057,0.0001965849,0.011518208],"study_design_scores_gemma":[0.002070093,0.00040701483,0.9664462,0.00025369113,0.000022409633,0.0000021046826,0.0131353745,0.017267171,0.00028055743,0.000005368876,0.000010672,0.00009930699],"about_ca_topic_score_codex":0.0012667181,"about_ca_topic_score_gemma":0.0007906724,"teacher_disagreement_score":0.017186495,"about_ca_system_score_codex":0.00004771947,"about_ca_system_score_gemma":0.000105474515,"threshold_uncertainty_score":0.484772},"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":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.5588386e-10,0.17353952,0.00012894026,0.00028986632,0.0000052502037,0.8226173,0.00019538622,0.00035363407,0.00287013],"genre_scores_gemma":[3.281601e-10,0.3905037,0.0014249807,0.00009310385,0.00012778367,0.6059425,0.00061454065,0.00019928098,0.0010941264],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.98572665,0.0037914687,0.0031116433,0.0024387017,0.0026162446,0.0023152682],"domain_scores_gemma":[0.99301565,0.00090238836,0.000960085,0.0035055426,0.00064870203,0.0009676418],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.022842476,0.00081635616,0.004648537,0.0019231165,0.00025644762,0.00029621023,0.0017686724,0.00039194402,0.00020049947],"category_scores_gemma":[0.002227358,0.0006427237,0.00090340205,0.0043288544,0.00020423718,0.00028749357,0.0021593934,0.0019638322,0.00087385485],"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":[0.00011361351,0.0007505251,0.000011378035,0.51185775,0.00006435564,0.000037605812,0.0000126708965,6.6234094e-8,8.0794536e-9,0.00010647806,0.024941618,0.46210396],"study_design_scores_gemma":[0.0017948394,0.00036167735,0.000017055536,0.49234197,0.000008362002,0.000013044528,0.0000064000637,0.000007869672,2.9116404e-8,0.000037970807,0.50517887,0.00023189656],"about_ca_topic_score_codex":0.000011174541,"about_ca_topic_score_gemma":0.000015748852,"teacher_disagreement_score":0.48023725,"about_ca_system_score_codex":0.0015448529,"about_ca_system_score_gemma":0.005585684,"threshold_uncertainty_score":0.9999041},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9786473,0.00032812686,0.0196713,0.0002672941,0.000043990665,0.0006358053,0.0001432501,0.00013285394,0.00013010531],"genre_scores_gemma":[0.87880623,0.000009315931,0.11793771,0.0001270552,0.000043701777,0.00013267534,0.0028474291,0.000016959799,0.00007890317],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975931,0.0007600383,0.00042746813,0.00046844292,0.00044878488,0.00030216243],"domain_scores_gemma":[0.99840593,0.00050970796,0.00018763378,0.00046255026,0.00024090707,0.00019327908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049570994,0.0001520953,0.00042220263,0.00016641288,0.00006587789,0.000010607248,0.00021215194,0.000054496537,0.000069103575],"category_scores_gemma":[0.0015088993,0.00011787407,0.00002215418,0.0002954013,0.000112956994,0.00015625947,0.0007319484,0.00027497314,0.000022308344],"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":[0.00039253256,0.000047603306,0.96100867,0.00031664263,0.00008591274,0.000032668344,0.0015017701,0.009287914,0.000059368962,0.000051332525,0.0003460713,0.026869489],"study_design_scores_gemma":[0.0011695732,0.00006798775,0.38313064,0.00016714809,0.00006311741,0.000015089755,0.00021014544,0.61452246,0.00001806206,0.000043574968,0.0005114044,0.00008080127],"about_ca_topic_score_codex":0.00010916898,"about_ca_topic_score_gemma":0.00024733477,"teacher_disagreement_score":0.60523456,"about_ca_system_score_codex":0.00005701617,"about_ca_system_score_gemma":0.00040112803,"threshold_uncertainty_score":0.48067653},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921025,0.0002673574,3.0184583e-7,0.0018564744,0.00008954023,0.0026752506,0.0027142959,0.00017323288,0.00012102153],"genre_scores_gemma":[0.9991059,0.000092219394,0.0000011368332,0.00034825705,0.000087350214,0.000142705,0.000100544254,0.000040924617,0.0000810125],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972479,0.00014138664,0.0006317937,0.00038555847,0.001014715,0.0005786795],"domain_scores_gemma":[0.9979286,0.00046600975,0.00027183842,0.00075291755,0.00026148016,0.00031915822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005360037,0.00020120513,0.0005782896,0.00021150544,0.0001687812,0.00007602779,0.0003857622,0.000057634647,0.00000795372],"category_scores_gemma":[0.0010165797,0.00013110695,0.00008359586,0.0009780385,0.00022397997,0.00030341157,0.00037993083,0.00035532934,0.000008815197],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003025958,0.0007109243,0.9708034,0.0014392155,0.00004306984,0.000040696974,0.025084091,0.000013738761,0.000011708113,0.000016436687,0.000042108084,0.001492007],"study_design_scores_gemma":[0.0017551378,0.00073225,0.9845677,0.00034069023,0.00001650199,0.000010690209,0.011940526,0.000023492013,0.000029492168,0.000009563537,0.00046271042,0.0001112264],"about_ca_topic_score_codex":0.0019812582,"about_ca_topic_score_gemma":0.007840471,"teacher_disagreement_score":0.013764312,"about_ca_system_score_codex":0.0004143564,"about_ca_system_score_gemma":0.00081630505,"threshold_uncertainty_score":0.5346386},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9813433,0.00044601146,0.0010519093,0.013932087,0.00003413193,0.0014146114,0.0013513184,0.00017131127,0.0002552701],"genre_scores_gemma":[0.99789345,0.00018405914,0.00079986366,0.00032399723,0.000035860165,0.00006286814,0.00067174685,0.000019020183,0.00000911804],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99768806,0.00021117179,0.00058966764,0.00037520408,0.00055579114,0.0005800986],"domain_scores_gemma":[0.99860847,0.00005073925,0.00028975977,0.0004889263,0.00017211908,0.00038996973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054567493,0.00018484109,0.0006277591,0.00014698406,0.00004106596,0.000011423711,0.00016778747,0.00009899623,0.0000216738],"category_scores_gemma":[0.00029467637,0.00016161596,0.00006983585,0.00150722,0.00014280592,0.00018721633,0.00019736655,0.0001793901,0.00002639347],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026459713,0.00087723846,0.98688555,0.0010156935,0.00006347106,0.000010324853,0.0004413002,0.000001994549,0.00018341036,0.0030177191,0.0023070509,0.0049316436],"study_design_scores_gemma":[0.0024901654,0.00041073508,0.98506665,0.00036754084,0.000024996769,0.000016086075,0.000235699,0.006547342,0.000057104,0.00067848613,0.0039486745,0.00015650495],"about_ca_topic_score_codex":0.00064650853,"about_ca_topic_score_gemma":0.00047211937,"teacher_disagreement_score":0.016550118,"about_ca_system_score_codex":0.00018842642,"about_ca_system_score_gemma":0.00041858258,"threshold_uncertainty_score":0.65905076},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65896887,0.000626083,0.3263122,0.002888308,0.0008386909,0.0060921204,0.00081313844,0.0028323613,0.0006282338],"genre_scores_gemma":[0.9886955,0.000011483076,0.005697781,0.00093567465,0.00036817905,0.00058695226,0.0034509487,0.00010251393,0.000150988],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99210244,0.0029956885,0.0014655658,0.0009925348,0.0014638057,0.0009799802],"domain_scores_gemma":[0.9946836,0.0010414837,0.0008879431,0.0011447916,0.001093795,0.0011483566],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.02881491,0.00033516632,0.00095546525,0.0010579623,0.0003711215,0.00020717799,0.00027975213,0.0001870569,0.000039139224],"category_scores_gemma":[0.006912053,0.00032703456,0.00021442346,0.0026489536,0.00005476471,0.00093962916,0.00006107219,0.00028924932,0.00032425756],"study_design_candidate":"simulation_or_modeling","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":[0.0027282014,0.002487204,0.501834,0.025087196,0.00084061286,0.000036908656,0.0009331848,0.10162816,0.004838064,0.0022392443,0.03538999,0.32195726],"study_design_scores_gemma":[0.002798911,0.00021112706,0.15028208,0.00022074707,0.000028246302,0.000012142115,0.00027383788,0.8452469,0.000026253003,0.00009484519,0.00053976156,0.00026510895],"about_ca_topic_score_codex":0.0008286266,"about_ca_topic_score_gemma":0.00049778144,"teacher_disagreement_score":0.7436188,"about_ca_system_score_codex":0.0023398765,"about_ca_system_score_gemma":0.006388531,"threshold_uncertainty_score":0.99991816},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991221,0.00045425483,0.000004774024,0.004796962,0.000057995767,0.00019676094,0.0026614473,0.00014960021,0.0004571748],"genre_scores_gemma":[0.9978785,0.00031242473,0.000005746743,0.00025537095,0.000055444394,0.0001693675,0.00048707158,0.000030373796,0.00080571393],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998516,0.00008568439,0.0003631091,0.00043808325,0.00023412809,0.00036295727],"domain_scores_gemma":[0.998805,0.00009032636,0.000175161,0.00043364274,0.000082186976,0.00041369602],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022450379,0.0001984064,0.0005632854,0.0013634515,0.000037307324,0.000023340031,0.00012841992,0.000095266805,0.0030653095],"category_scores_gemma":[0.000086552114,0.00016395558,0.00024195974,0.0043062493,0.00011973118,0.00013647285,0.00008746418,0.00020822608,0.000013781779],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050462637,0.000103640195,0.95393044,0.00008626286,0.0014515818,0.000072494644,0.0002867921,0.0009052696,0.00024666134,0.00002528582,0.009863221,0.032523714],"study_design_scores_gemma":[0.0020751925,0.00009766015,0.99150074,0.00017730585,0.00022643588,0.00000781445,0.000072075934,0.0038984495,0.0000055947353,0.000008157724,0.0017751958,0.00015537221],"about_ca_topic_score_codex":0.0043880856,"about_ca_topic_score_gemma":0.005006597,"teacher_disagreement_score":0.037570294,"about_ca_system_score_codex":0.0005791659,"about_ca_system_score_gemma":0.00038412143,"threshold_uncertainty_score":0.997846},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86568236,0.00079150614,0.020840432,0.09359392,0.0011337065,0.006253093,0.0091247605,0.0014044633,0.0011757236],"genre_scores_gemma":[0.97586524,0.0002006995,0.0015587657,0.0014584765,0.00085324916,0.00038580372,0.019012049,0.00003397534,0.0006317313],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9969995,0.00038985925,0.00064183207,0.0007908619,0.0005539419,0.0006239868],"domain_scores_gemma":[0.99599874,0.0011163822,0.00023298658,0.0016256396,0.00025773857,0.0007685189],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027288357,0.00032568522,0.00078353146,0.00031836663,0.00014487686,0.000025987745,0.0005696227,0.00008663925,0.00016245105],"category_scores_gemma":[0.015466368,0.00024911505,0.00013273985,0.0007083366,0.00021208786,0.00057351834,0.0010473093,0.00020415105,0.00019778052],"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":[0.013138853,0.00028686156,0.12535031,0.0017811331,0.00039205112,0.000701997,0.00036985867,0.000038415863,0.00745323,0.0003808234,0.8115448,0.038561705],"study_design_scores_gemma":[0.0022238959,0.0022637898,0.7095025,0.00041894277,0.00008783378,0.00004422248,0.00042262624,0.0066051963,0.000008764945,0.00011587089,0.27805007,0.00025626755],"about_ca_topic_score_codex":0.000016184475,"about_ca_topic_score_gemma":0.0000027178348,"teacher_disagreement_score":0.5841522,"about_ca_system_score_codex":0.000102497615,"about_ca_system_score_gemma":0.00012791516,"threshold_uncertainty_score":0.9999961},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.614015,0.0010728448,0.2409831,0.005766625,0.0017602143,0.011638807,0.11320889,0.005179385,0.0063751573],"genre_scores_gemma":[0.94947994,0.000046040383,0.0025272893,0.004501576,0.00011964995,0.00035215184,0.042825896,0.000025843206,0.00012162136],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987736,0.00003707393,0.0004355652,0.00018010425,0.00026414418,0.00030952095],"domain_scores_gemma":[0.99880457,0.0002716482,0.00020915212,0.00033699512,0.00012417241,0.00025344576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005761078,0.00018303067,0.0002838209,0.0003951558,0.00026755765,0.00018636583,0.00011835448,0.00006068979,0.000056592915],"category_scores_gemma":[0.0011072721,0.0001663149,0.00004685451,0.0004143211,0.0001771211,0.0012099623,0.00008742377,0.00007650488,0.00006507343],"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":[0.006047562,0.00011752628,0.13088484,0.051127665,0.00057255576,0.0000865506,0.00971702,0.017332358,0.0005293436,0.0018779212,0.6632563,0.11845035],"study_design_scores_gemma":[0.0034688723,0.00024190465,0.039411403,0.00039804302,0.00011804807,0.000039021088,0.0007607792,0.13256419,0.00040030686,0.0005347636,0.8216271,0.00043561068],"about_ca_topic_score_codex":0.00012864452,"about_ca_topic_score_gemma":0.00036162365,"teacher_disagreement_score":0.33546495,"about_ca_system_score_codex":0.00028855004,"about_ca_system_score_gemma":0.00036327366,"threshold_uncertainty_score":0.67821246},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98651934,0.0038817655,0.0013993979,0.0069578723,0.00008275453,0.00065469265,0.00002666219,0.00036518704,0.00011232282],"genre_scores_gemma":[0.97723264,0.01931489,0.0016098828,0.0016984418,0.000005969366,0.00006718102,0.000058717356,0.0000051178326,0.0000071605787],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988005,0.000021981237,0.00069999596,0.000059082722,0.000108449014,0.00030998306],"domain_scores_gemma":[0.99939317,0.00013091178,0.00017572852,0.00015192451,0.000053511132,0.00009475601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007174015,0.00011720861,0.00036721423,0.0016041896,0.00013480538,0.000010307235,0.00006222685,0.0001283955,0.0000011278869],"category_scores_gemma":[0.00085906277,0.00010509184,0.00001086126,0.0016175768,0.00036011692,0.00041048313,0.0001804625,0.00026706627,0.00000979367],"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":[0.00032411588,0.00011091695,0.66648096,0.017027102,0.00018319214,0.00004350781,0.20490688,0.017360762,9.189541e-7,0.011554347,0.0063715093,0.07563577],"study_design_scores_gemma":[0.007446848,0.0013138745,0.021556761,0.0019397492,0.00002642611,0.00009399034,0.4498251,0.46577886,0.00001123853,0.012221289,0.039242618,0.00054324116],"about_ca_topic_score_codex":0.000045882436,"about_ca_topic_score_gemma":0.000021024824,"teacher_disagreement_score":0.6449242,"about_ca_system_score_codex":0.00012910095,"about_ca_system_score_gemma":0.00015104054,"threshold_uncertainty_score":0.4285521},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9902269,0.0018392056,0.0012603679,0.0018126004,0.0002911571,0.00133347,0.003033572,0.000196139,0.000006554842],"genre_scores_gemma":[0.99823743,0.00023841909,0.00022548575,0.00038215297,0.000052853884,0.00014876666,0.00069645734,0.000015798625,0.000002619157],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984545,0.000042253985,0.00069650717,0.0002182709,0.00013494455,0.00045349073],"domain_scores_gemma":[0.99818814,0.0009683764,0.0002928576,0.00032359108,0.000076492935,0.0001505681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082950335,0.00015932009,0.00048504694,0.00031550173,0.00038279308,0.000026019108,0.00019590482,0.000053316428,1.916285e-7],"category_scores_gemma":[0.004822014,0.00012928557,0.000017335886,0.0005231065,0.0002818063,0.00031162135,0.0004972851,0.00018673112,1.2847025e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016776651,0.000012761957,0.9663394,0.008395519,0.00006561038,0.000019152085,0.0047589773,0.00015527115,5.2427776e-8,0.00053464295,0.00084180775,0.018709032],"study_design_scores_gemma":[0.0041716495,0.0002189731,0.14459434,0.0010530999,0.00005782162,0.000046897254,0.14793704,0.69407684,1.7047842e-7,0.00075179647,0.006702792,0.00038860654],"about_ca_topic_score_codex":0.006752694,"about_ca_topic_score_gemma":0.14558902,"teacher_disagreement_score":0.8217451,"about_ca_system_score_codex":0.0005988582,"about_ca_system_score_gemma":0.0010326843,"threshold_uncertainty_score":0.9998614},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9545935,0.0074063125,0.000060596965,0.025885131,0.00020004192,0.0016635467,0.009351733,0.00043059757,0.00040854345],"genre_scores_gemma":[0.9832734,0.003786022,0.00032483067,0.0036469256,0.00007439883,0.00019146783,0.008479938,0.00003148917,0.00019156504],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967472,0.00041094693,0.0006023946,0.0010755399,0.00039241038,0.00077148626],"domain_scores_gemma":[0.99669844,0.00042778652,0.00015127313,0.0015053286,0.000085787346,0.0011313737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025245952,0.00025791966,0.00094989134,0.00017446784,0.00010963094,0.00006418534,0.0003292723,0.00010358288,0.00018177532],"category_scores_gemma":[0.0009058581,0.00022500847,0.00004274257,0.00092872145,0.00019732611,0.00033285635,0.00052550004,0.00028217104,0.000065230786],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001059329,0.00014505115,0.9407608,0.00050114177,0.00014155418,0.00005145569,0.00026306964,5.884955e-7,0.0000088561565,0.00006842748,0.038089097,0.019864034],"study_design_scores_gemma":[0.0010645909,0.000063347914,0.8216575,0.000009155512,0.00000592961,0.000009688104,0.00014984616,0.0031826952,2.9528184e-7,0.00010406308,0.17357317,0.00017976829],"about_ca_topic_score_codex":0.001314771,"about_ca_topic_score_gemma":0.0022452117,"teacher_disagreement_score":0.13548407,"about_ca_system_score_codex":0.00006146293,"about_ca_system_score_gemma":0.00072765886,"threshold_uncertainty_score":0.9175579},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9800231,0.0018071337,0.00015636801,0.012077752,0.00023193313,0.0014709485,0.0015852959,0.0007237372,0.0019237186],"genre_scores_gemma":[0.9920377,0.00019326196,0.00025943358,0.00061538967,0.000036079764,0.00047738187,0.0054391026,0.000044919827,0.0008966876],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997848,0.00012252273,0.0006233166,0.00052263465,0.00035517043,0.00052837754],"domain_scores_gemma":[0.99579144,0.0005068819,0.00024637402,0.0020765217,0.0008452074,0.0005335953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082064676,0.00023605794,0.0005450681,0.00031345477,0.0003828838,0.00002760814,0.000272076,0.00009303611,0.00010606967],"category_scores_gemma":[0.004015767,0.0002420845,0.000068454094,0.0024339682,0.00038739614,0.00015022475,0.00032704207,0.00033036852,0.0000027373696],"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":[0.00052967086,0.00019927952,0.86746943,0.00050827576,0.0004901685,0.0017728636,0.00004485607,0.00008336322,0.00051261537,0.004133438,0.12054576,0.0037103035],"study_design_scores_gemma":[0.0020323617,0.000067260706,0.64077836,0.00024222104,0.00025913958,0.00020088768,0.002209772,0.0013647295,0.0000694618,0.004190372,0.34808174,0.0005036792],"about_ca_topic_score_codex":0.0083104,"about_ca_topic_score_gemma":0.09541769,"teacher_disagreement_score":0.22753598,"about_ca_system_score_codex":0.00038904912,"about_ca_system_score_gemma":0.0047925417,"threshold_uncertainty_score":0.99829334},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99139565,0.00009038778,0.000023319808,0.0012695236,0.000079603094,0.0036217999,0.0024980674,0.00029149893,0.0007301642],"genre_scores_gemma":[0.9972216,0.00016468804,0.000012697244,0.000113816735,0.00003720429,0.00057395396,0.00038439146,0.00004559703,0.001446068],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99476385,0.0013633941,0.0006077326,0.00073951634,0.0016400993,0.00088538724],"domain_scores_gemma":[0.99498314,0.001829499,0.00020949414,0.0009490047,0.0005903452,0.001438529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045689144,0.00028101492,0.0009171886,0.00090505043,0.00044342384,0.000030889863,0.00030152078,0.00012662508,0.00036516433],"category_scores_gemma":[0.006708597,0.00024134567,0.00014086126,0.0029985742,0.00052164996,0.00040946633,0.0010891946,0.0007303185,0.00043632864],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032817426,0.000731754,0.91383004,0.000532092,0.0002728507,0.00017182372,0.010169097,0.000015160862,0.0028504105,0.000020744226,0.06695953,0.0011647415],"study_design_scores_gemma":[0.003269446,0.0016461834,0.98654515,0.00011457533,0.000034155943,0.000016528267,0.00357124,0.0005912516,0.00019708478,0.00040146292,0.003387625,0.00022529865],"about_ca_topic_score_codex":0.00040318182,"about_ca_topic_score_gemma":0.0002620479,"teacher_disagreement_score":0.072715096,"about_ca_system_score_codex":0.001714277,"about_ca_system_score_gemma":0.000772211,"threshold_uncertainty_score":0.9841791},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95496774,0.003925205,0.01122667,0.026647074,0.0002192776,0.001673135,0.000880199,0.00015483086,0.00030586592],"genre_scores_gemma":[0.99555624,0.0012383969,0.00004569305,0.0011540324,0.00028192607,0.00006919667,0.0016319182,0.000008924846,0.0000136905155],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989865,0.00009243187,0.0002648222,0.00027586854,0.00024491036,0.00013548622],"domain_scores_gemma":[0.99860793,0.0006502042,0.00007791952,0.00033898267,0.000049593433,0.00027539264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002918952,0.00010435604,0.0002524521,0.00009607286,0.00006568976,0.000008736018,0.0000894386,0.000030613082,0.00004057526],"category_scores_gemma":[0.0036440785,0.000067062225,0.000022760143,0.00027284733,0.00026135411,0.00016177713,0.000048685375,0.00009508856,0.0000012485139],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005849795,0.00011805175,0.9696308,0.0008748081,0.000017102255,0.000042321946,0.00036283603,0.0000022261918,0.000054487806,0.0011739944,0.0065066614,0.02063176],"study_design_scores_gemma":[0.0030508055,0.00054336863,0.9632486,0.00017204467,0.00014017474,0.000007902916,0.00041535322,0.0046322043,0.000002671603,0.0025148052,0.025181442,0.00009060177],"about_ca_topic_score_codex":0.00010646954,"about_ca_topic_score_gemma":0.00015030701,"teacher_disagreement_score":0.04058848,"about_ca_system_score_codex":0.000020688656,"about_ca_system_score_gemma":0.00009763117,"threshold_uncertainty_score":0.43625656},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9748528,0.0150820725,0.0030128926,0.0008585145,0.00035468274,0.0019294573,0.0037983675,0.000043501477,0.00006773775],"genre_scores_gemma":[0.99689734,0.00197146,0.000026816493,0.0000030113715,0.00029246995,0.000029876694,0.00056994864,0.00003161455,0.00017746522],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968255,0.00031156835,0.0008520656,0.00047426173,0.00064838235,0.0008882247],"domain_scores_gemma":[0.996487,0.00011611896,0.00085957453,0.0014463816,0.0009310703,0.00015983688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030662066,0.00026279586,0.0007592783,0.00012654084,0.00010776222,0.000033099983,0.000870378,0.00013411634,0.000006856063],"category_scores_gemma":[0.0012740272,0.00017592513,0.00018723067,0.0004870269,0.00018003512,0.00012358974,0.0013734486,0.00094220095,0.0000011818195],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00935156,0.0019647337,0.85013455,0.017903775,0.0033620808,0.000031532945,0.004938058,0.010224114,0.00084414164,0.010497248,0.0042654653,0.08648275],"study_design_scores_gemma":[0.0077234423,0.0062548122,0.8324788,0.013529546,0.0075842417,0.00050452846,0.041729376,0.054804612,0.000494433,0.03097653,0.0026147878,0.00130485],"about_ca_topic_score_codex":0.00066724635,"about_ca_topic_score_gemma":0.000605632,"teacher_disagreement_score":0.0851779,"about_ca_system_score_codex":0.0012169123,"about_ca_system_score_gemma":0.0073448573,"threshold_uncertainty_score":0.99828255},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986812,0.00009223104,0.0027635247,0.004249593,0.0012003097,0.0013417639,0.0011604216,0.0010981918,0.0012819681],"genre_scores_gemma":[0.99611413,0.0003195733,0.0004948766,0.001005758,0.00039459727,0.00005743239,0.0007874615,0.000055028995,0.00077111286],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99605983,0.000808923,0.001075526,0.0008516504,0.00041521597,0.0007888567],"domain_scores_gemma":[0.99707496,0.0015625029,0.0002707384,0.0006508332,0.0001406896,0.0003002769],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037034862,0.00038871667,0.0010370897,0.00021143148,0.000163512,0.000024549896,0.0002616869,0.00020066118,0.0003572569],"category_scores_gemma":[0.0017692101,0.0002940089,0.00022341456,0.0006472488,0.00007450232,0.00018884751,0.00022882143,0.00054238876,0.00028764998],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.029792655,0.00071420457,0.53991836,0.0006205464,0.00040821312,0.001101118,0.0007952176,0.0039850534,0.3305061,0.0010037775,0.0038485243,0.08730624],"study_design_scores_gemma":[0.0054334113,0.00022238184,0.9719752,0.000115834744,0.000026843796,0.000018652396,0.00010836975,0.010161097,0.005263962,0.0005181533,0.005703943,0.00045212093],"about_ca_topic_score_codex":0.00032710243,"about_ca_topic_score_gemma":0.00094887865,"teacher_disagreement_score":0.43205684,"about_ca_system_score_codex":0.00032419353,"about_ca_system_score_gemma":0.00015932665,"threshold_uncertainty_score":0.9999512},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026134145,0.09928022,0.8311158,0.00052221084,0.002988785,0.008289815,0.028494837,0.001207667,0.0019665507],"genre_scores_gemma":[0.72747713,0.009506155,0.0015560606,0.00024871074,0.0006253268,0.000019711662,0.25892946,0.00022159237,0.0014158413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973602,0.00015562166,0.001151001,0.00073750055,0.0003432035,0.00025246508],"domain_scores_gemma":[0.9973282,0.0003302371,0.0010020753,0.0010784362,0.0001430691,0.00011796336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009681751,0.0003250798,0.0014422132,0.0005373879,0.00005747503,0.000030706746,0.00017616124,0.00062972965,0.000029783128],"category_scores_gemma":[0.0002764399,0.00030238822,0.00012565582,0.0005127716,0.00008887912,0.000081122496,0.00015771376,0.0006169631,0.0000015738344],"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":[0.00120015,0.0004756811,0.09449261,0.006472262,0.015015929,0.00029453772,0.0005440821,0.18762116,0.0002524279,0.0003940728,0.0033146027,0.6899225],"study_design_scores_gemma":[0.00042439613,0.00009111402,0.0020599212,0.0034738546,0.0015391315,0.00001702358,0.0000044969324,0.9901681,0.0000017360438,0.00031223788,0.0015962783,0.00031172784],"about_ca_topic_score_codex":0.0007429225,"about_ca_topic_score_gemma":0.0014544035,"teacher_disagreement_score":0.8295597,"about_ca_system_score_codex":0.00015829208,"about_ca_system_score_gemma":0.0000743169,"threshold_uncertainty_score":0.99994284},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18178454,0.03300323,0.0029718257,0.01526448,0.009249732,0.07583391,0.22306846,0.012425503,0.44639832],"genre_scores_gemma":[0.12928832,0.00025543311,0.00014139607,0.00087266107,0.0009198001,0.0017116144,0.0053340895,0.0006892907,0.8607874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99577737,0.00012680142,0.0009881741,0.001469497,0.0006221178,0.0010160296],"domain_scores_gemma":[0.99434984,0.001025707,0.0006851581,0.0012931565,0.00047189026,0.002174259],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014241586,0.0008333252,0.0013883649,0.00049548276,0.00042196966,0.00031967575,0.00027965938,0.000195574,0.00001695423],"category_scores_gemma":[0.0029422366,0.0008457439,0.0006361778,0.00006322298,0.00028931713,0.00023436341,0.00031674828,0.00036711083,0.000044784116],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008223683,0.000083687984,0.007375428,0.003712988,0.0006175008,0.00018907386,0.0011892972,0.0000030616454,0.0000028278,0.0007866489,0.0016359339,0.9761799],"study_design_scores_gemma":[0.0034266969,0.00050132483,0.026245246,0.0016511997,0.00024190343,0.000014681238,0.000052860356,0.00020410976,1.4185275e-7,0.002912798,0.96373886,0.001010197],"about_ca_topic_score_codex":0.0000020924895,"about_ca_topic_score_gemma":0.00003335268,"teacher_disagreement_score":0.97516966,"about_ca_system_score_codex":0.0004939006,"about_ca_system_score_gemma":0.0022325704,"threshold_uncertainty_score":0.9993993},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9098272,0.00053598336,0.07666029,0.0005349781,0.00031035306,0.0013245058,0.01069084,0.00007125358,0.000044611334],"genre_scores_gemma":[0.99587923,0.0000056584063,0.0015041162,0.00067471666,0.0000377942,0.000020156776,0.0018485491,0.000024958308,0.000004846725],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982968,0.000039748116,0.00059567526,0.00035734227,0.0003201279,0.0003902951],"domain_scores_gemma":[0.9990426,0.00019999524,0.00015382555,0.0002741272,0.00004082956,0.00028863768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004585576,0.00014386082,0.00060625054,0.00027061603,0.000066669345,0.000015911588,0.00014929363,0.0000769466,0.000001226594],"category_scores_gemma":[0.00044501852,0.00015675691,0.000031606378,0.00056165794,0.00010545758,0.0003142505,0.000029805537,0.00014408241,1.0257239e-7],"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":[0.004452177,0.00043148486,0.6650428,0.0068512917,0.00009479277,0.000029185741,0.009984617,0.010731221,0.00005399108,0.000014181566,0.034584746,0.2677295],"study_design_scores_gemma":[0.007570046,0.00021867223,0.061209507,0.00024360453,0.000026276883,0.000002319637,0.017431306,0.90853125,0.00006784862,0.0008388254,0.0035636867,0.00029667627],"about_ca_topic_score_codex":0.057862855,"about_ca_topic_score_gemma":0.7306877,"teacher_disagreement_score":0.8978,"about_ca_system_score_codex":0.0008471276,"about_ca_system_score_gemma":0.0051156254,"threshold_uncertainty_score":0.9484109},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97867966,0.000009695414,4.70751e-8,0.00495071,0.0024249533,0.0005416551,0.00006962184,0.00008753557,0.013236108],"genre_scores_gemma":[0.9986625,0.00003431252,0.00000970699,0.00009279518,0.00047722508,0.000027928814,0.00004071213,0.000030354387,0.00062446523],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99672693,0.000017614797,0.0004440333,0.00038540812,0.0020550804,0.00037093117],"domain_scores_gemma":[0.99868554,0.0002231923,0.00011233686,0.00018774356,0.00065100874,0.00014015097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009668554,0.0001738691,0.00028677963,0.00026811473,0.000079817655,0.000086305394,0.0009546535,0.00008157674,0.00011802916],"category_scores_gemma":[0.0005185754,0.00013376752,0.00014594449,0.00071198103,0.00013301201,0.00013305857,0.0005706464,0.00049637177,0.00013842875],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018900682,0.00057670154,0.87441754,0.0003200388,0.00020925874,0.0000438452,0.0007730086,0.0002586111,0.069352224,0.04481,0.0066313897,0.00071733503],"study_design_scores_gemma":[0.0029184562,0.00024077184,0.96708286,0.00035261424,0.000016597101,0.000013533109,0.00022745568,0.0012428521,0.024438744,0.0019731105,0.0013204111,0.00017258649],"about_ca_topic_score_codex":0.0002375279,"about_ca_topic_score_gemma":0.0000054156712,"teacher_disagreement_score":0.09266535,"about_ca_system_score_codex":0.00039579734,"about_ca_system_score_gemma":0.0001258313,"threshold_uncertainty_score":0.5454881},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003585101,0.00021718538,0.0001113728,0.0001815428,0.00081485824,0.0023149052,0.0035613638,0.00082703703,0.9919359],"genre_scores_gemma":[0.0011047951,0.00018927308,0.0008278577,0.0011937675,0.00060156785,0.000078017125,0.0043385075,0.0003906483,0.99127555],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964849,0.000041398333,0.00084221957,0.0014725763,0.0006467019,0.0005122287],"domain_scores_gemma":[0.99453753,0.0002346702,0.00046253577,0.004164091,0.0002170499,0.0003841475],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008390307,0.000654692,0.0013814057,0.000286778,0.00013507393,0.00035313453,0.0035586883,0.0005159957,0.0020105536],"category_scores_gemma":[0.0003694365,0.0005325996,0.00017323239,0.0000455896,0.00036016252,0.00026836642,0.0071833027,0.0011818254,0.004785334],"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":[0.0022185785,0.000070350136,0.00027994774,0.0007658521,0.0016145628,0.005216917,0.00016693551,4.9065096e-7,0.0004342076,0.010137882,0.41842464,0.56066966],"study_design_scores_gemma":[0.0015846569,0.00013037077,0.00007795683,0.0047794846,0.00046053718,0.00025105636,0.000043082964,0.000036704136,0.000051256637,0.0055074785,0.9863824,0.0006950164],"about_ca_topic_score_codex":0.00022859023,"about_ca_topic_score_gemma":0.0009074301,"teacher_disagreement_score":0.56795776,"about_ca_system_score_codex":0.0002324085,"about_ca_system_score_gemma":0.0013979487,"threshold_uncertainty_score":0.9997125},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.616031,0.07875783,0.0061258064,0.25538135,0.0038082208,0.020121505,0.014117142,0.0040040696,0.0016530876],"genre_scores_gemma":[0.95252025,0.010038234,0.0003316025,0.031854097,0.0005456562,0.0013632893,0.0030205783,0.00007758244,0.0002487365],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99259305,0.0008831834,0.0014186208,0.0014860523,0.0009935709,0.0026255297],"domain_scores_gemma":[0.9927806,0.00045557437,0.000593711,0.0011689705,0.00039171,0.004609426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0040084682,0.00062791345,0.0014886856,0.0001895218,0.0006180366,0.00015799644,0.00033767062,0.00013116168,0.000061221835],"category_scores_gemma":[0.002180021,0.00058725395,0.0002790669,0.0021180897,0.0001740339,0.00022718962,0.00019412623,0.00027487616,0.0002251722],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014818325,0.00095612975,0.6138965,0.0058908435,0.00016068452,0.00007520738,0.00021928347,0.000021331069,0.000003474013,0.0034546093,0.057186615,0.31665352],"study_design_scores_gemma":[0.0036740024,0.000710711,0.55563664,0.00021429935,0.0000031834932,0.00001138095,0.000112663336,0.0030706069,4.357304e-7,0.00018842758,0.43592396,0.00045370054],"about_ca_topic_score_codex":0.000089601825,"about_ca_topic_score_gemma":0.0002834887,"teacher_disagreement_score":0.37873733,"about_ca_system_score_codex":0.0013371692,"about_ca_system_score_gemma":0.0041445764,"threshold_uncertainty_score":0.99965787},"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":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26080623,5.256576e-7,0.00001659726,0.00005917085,0.000010119921,0.73664916,0.0022562258,0.00016904017,0.000032911325],"genre_scores_gemma":[0.33596623,2.5736266e-8,0.000043073742,0.000002961812,0.000087008724,0.6636546,0.00014639746,0.000040960436,0.000058753856],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99551517,0.00074946095,0.0008800552,0.0009896413,0.0010391212,0.0008265462],"domain_scores_gemma":[0.9961346,0.00041466003,0.00029314772,0.0010063812,0.0019317078,0.00021953441],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0033976163,0.00027152555,0.00067596504,0.0005747229,0.00030345406,0.00005447338,0.00029475364,0.00013704204,0.000038090548],"category_scores_gemma":[0.00066613435,0.0002483602,0.00022266615,0.0015733214,0.00016406363,0.00023144198,0.0003936363,0.00038114615,0.000017880684],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004212211,0.0016911722,0.9771623,0.0063368334,0.00014984848,0.000056145975,0.0009789033,0.0000029807757,0.0006648084,0.000020878668,0.008306321,0.00041761575],"study_design_scores_gemma":[0.02436219,0.010421025,0.9557757,0.0011734099,0.000011013896,0.0000041859803,0.0040995586,0.000028750577,0.00087021827,0.000029896226,0.0030129568,0.00021112074],"about_ca_topic_score_codex":0.00013544703,"about_ca_topic_score_gemma":0.0018868465,"teacher_disagreement_score":0.07515999,"about_ca_system_score_codex":0.0025058612,"about_ca_system_score_gemma":0.0009902879,"threshold_uncertainty_score":0.99999684},"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":"protocol","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061715883,0.39006618,0.00085901056,0.0010977245,0.001992764,0.571858,0.019486316,0.002815771,0.005652653],"genre_scores_gemma":[0.058494847,0.4080547,0.020313524,0.018364485,0.0034012767,0.2566012,0.23037969,0.003990225,0.0004000219],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9934009,0.0032514366,0.0015819495,0.0004877543,0.0007908767,0.00048710447],"domain_scores_gemma":[0.99510044,0.00019862363,0.00065864646,0.0029324128,0.00018581115,0.00092405046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0030939651,0.0005641414,0.0026626312,0.001041378,0.00021545585,0.00033571073,0.0015339087,0.00016101972,0.00003661958],"category_scores_gemma":[0.0004126491,0.00049780303,0.0003217442,0.0024721778,0.00007864908,0.0005609434,0.001100601,0.0008480202,0.00041354555],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013112843,0.0025457067,0.040984012,0.08189753,0.0013802145,0.0011393694,0.001879743,0.00014782001,5.4908092e-8,0.00004112826,0.0022504479,0.8664227],"study_design_scores_gemma":[0.0057414076,0.0012874017,0.12195611,0.18642545,0.008454887,0.000053709173,0.009620891,0.0012800979,4.1272333e-8,0.000004000077,0.6627834,0.0023926562],"about_ca_topic_score_codex":0.0027930273,"about_ca_topic_score_gemma":0.0009907951,"teacher_disagreement_score":0.86403006,"about_ca_system_score_codex":0.00036418333,"about_ca_system_score_gemma":0.0018144917,"threshold_uncertainty_score":0.99974734},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89962894,0.0001784932,0.0014058127,0.09734864,0.000466346,0.00065305544,0.0002277842,0.000041474905,0.00004945831],"genre_scores_gemma":[0.9948671,0.000030323588,0.0015963364,0.0028922406,0.00016995237,0.00002637917,0.00026202123,0.000034517638,0.000121148645],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99470407,0.0014232697,0.0016247625,0.0002965631,0.0009018107,0.0010495498],"domain_scores_gemma":[0.989903,0.00012664429,0.000896986,0.00041147156,0.0021135688,0.00654837],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.021195775,0.00017288119,0.00052969885,0.0029079828,0.00062355696,0.00005842032,0.00028167892,0.00006481847,0.00057985546],"category_scores_gemma":[0.016620563,0.00017990023,0.0000713226,0.004080468,0.000058676735,0.0006405779,0.00002560112,0.0003626684,0.000026765465],"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":true,"study_design_scores_codex":[0.0005714447,0.00040802674,0.3344061,0.0013660237,0.00042851397,0.0002725301,0.087479256,0.0004918368,0.001784602,0.0005068142,0.04507131,0.5272136],"study_design_scores_gemma":[0.0010724565,0.00040863157,0.8752895,0.00010361609,0.000005402268,0.00010488981,0.003506639,0.00008158316,0.000022435985,0.000015589618,0.11925578,0.00013344857],"about_ca_topic_score_codex":0.0022672617,"about_ca_topic_score_gemma":0.073563725,"teacher_disagreement_score":0.5408834,"about_ca_system_score_codex":0.00735288,"about_ca_system_score_gemma":0.17837326,"threshold_uncertainty_score":0.9964577},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99202716,0.0039163665,0.0015577319,0.0012431534,0.00037340328,0.00065765297,0.00017984054,0.000029932771,0.000014761911],"genre_scores_gemma":[0.9712028,0.027363652,0.00028464093,0.00009355385,0.0003692412,0.00017494634,0.0004688468,0.000017286273,0.000025055731],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99708045,0.00020889116,0.0003020927,0.00037818137,0.0016550558,0.00037533217],"domain_scores_gemma":[0.9975752,0.00053993816,0.000051615236,0.00021905903,0.00028390563,0.0013302795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014110993,0.00011239706,0.0002528483,0.00023994545,0.000109981156,0.00004062948,0.00012365273,0.00004688831,0.0001589194],"category_scores_gemma":[0.0040714536,0.00009748101,0.000036672092,0.0005773075,0.0003083368,0.00010211655,0.0003685903,0.00025124892,0.000037570037],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016599309,0.0015693943,0.5203164,0.0058147586,0.00014436191,0.00019952864,0.000749964,0.0000075414364,0.02821744,0.00035774105,0.032921247,0.40804166],"study_design_scores_gemma":[0.0037442276,0.0010972795,0.92795014,0.010260966,0.000060814353,0.000006408317,0.00029388972,0.0064779073,0.024700113,0.0012718238,0.023764031,0.0003724118],"about_ca_topic_score_codex":0.000048642763,"about_ca_topic_score_gemma":0.0000041587537,"teacher_disagreement_score":0.40766925,"about_ca_system_score_codex":0.00005665786,"about_ca_system_score_gemma":0.00042943002,"threshold_uncertainty_score":0.48742044},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98616815,0.0002507222,0.00003532539,0.008303197,0.0002266632,0.00018128278,0.000030671086,0.000328076,0.0044758944],"genre_scores_gemma":[0.9982881,0.00003238272,0.00010260491,0.0006604347,0.00005280078,0.0000651617,0.0004312905,0.0000283698,0.0003388792],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986541,0.00013034299,0.0003201691,0.0003561547,0.00010875519,0.0004304632],"domain_scores_gemma":[0.99921465,0.000108791464,0.0000743248,0.00036887085,0.000044664623,0.00018868575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013200294,0.00015616935,0.000424747,0.0005807147,0.00004497851,0.000056111567,0.00012167644,0.00014875535,0.00017234046],"category_scores_gemma":[0.00012762424,0.000147004,0.000048117574,0.0007455513,0.00014498299,0.00030069784,0.00017692558,0.00025052443,0.00034679766],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007441917,0.00023717756,0.8705696,0.00030966397,0.000069142545,0.0032419502,0.00031862312,5.4088525e-7,0.0017386355,0.0009568654,0.053781785,0.068701595],"study_design_scores_gemma":[0.0017053795,0.00003460943,0.9176838,0.00008096087,0.00001597563,0.0011039997,0.00027745054,0.0014204632,0.0001075108,0.00022829749,0.07714907,0.00019251615],"about_ca_topic_score_codex":0.00001774609,"about_ca_topic_score_gemma":0.000328348,"teacher_disagreement_score":0.06850908,"about_ca_system_score_codex":0.00004230572,"about_ca_system_score_gemma":0.00007300766,"threshold_uncertainty_score":0.5994649},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88489705,0.0032916728,0.091093704,0.018727707,0.00022424162,0.00048020735,0.0009880437,0.00014041972,0.00015692814],"genre_scores_gemma":[0.9735721,0.00038076835,0.022405833,0.00091595436,0.00037222978,0.00002627292,0.002307192,0.000015706946,0.000003959716],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99543965,0.0021610851,0.0012395595,0.00048655944,0.00014517251,0.0005279725],"domain_scores_gemma":[0.9845016,0.012404085,0.0010984294,0.00081925583,0.00076984015,0.0004067697],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012604497,0.00014441399,0.0012113878,0.00021700704,0.0002046643,0.000010698059,0.0005103726,0.00007746036,0.000005922112],"category_scores_gemma":[0.021129193,0.00011812455,0.00007933189,0.0010836835,0.0007174208,0.00014732196,0.0002791551,0.0003266228,0.00003549397],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039537728,0.000039068047,0.9677427,0.00008407659,0.00026221646,0.000012572109,0.00002390395,0.0002633743,0.0001913253,0.0016487634,0.018615754,0.010720871],"study_design_scores_gemma":[0.00073457666,0.00045311803,0.97373945,0.00035425503,0.00013884669,0.00026406342,0.0007106517,0.018830094,0.0000019630902,0.002493608,0.0021847696,0.00009462492],"about_ca_topic_score_codex":0.00091497425,"about_ca_topic_score_gemma":0.000109668246,"teacher_disagreement_score":0.08867501,"about_ca_system_score_codex":0.00028011625,"about_ca_system_score_gemma":0.00030056835,"threshold_uncertainty_score":0.9871163},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986928,0.000016941363,0.000013574009,0.000040994346,0.000014495768,0.0008104104,0.00021836422,0.00006907708,0.00012332611],"genre_scores_gemma":[0.9994726,0.000004126455,0.00008090688,0.00003352319,0.000010347409,0.00005876352,0.0003107431,0.000009656654,0.000019321502],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986947,0.00008238644,0.0005270838,0.000110351604,0.00045497337,0.00013051035],"domain_scores_gemma":[0.9989622,0.00016857455,0.0002166377,0.0003503098,0.00024138676,0.00006084848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043687256,0.00008722649,0.00034187702,0.00042977434,0.000015182734,0.0000048334214,0.00006391976,0.00003791093,0.000032952732],"category_scores_gemma":[0.0008573852,0.00008734014,0.00003199031,0.0009379592,0.000026962378,0.00017563273,0.000034249544,0.00007743096,0.000029102663],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027849188,0.003602329,0.9887063,0.0008594815,0.00009638736,0.00021235591,0.0015612971,0.0002223835,0.0030289227,0.000003764233,0.00011427036,0.0013140246],"study_design_scores_gemma":[0.0026582112,0.00050431927,0.9826098,0.0005525177,0.000058478592,0.0000057042735,0.0023676346,0.00850482,0.002625244,0.0000038310413,0.000013412777,0.00009603488],"about_ca_topic_score_codex":0.00032146333,"about_ca_topic_score_gemma":0.000541672,"teacher_disagreement_score":0.008282437,"about_ca_system_score_codex":0.000044514054,"about_ca_system_score_gemma":0.00006927456,"threshold_uncertainty_score":0.35616276},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.973412,0.003240664,0.0017899361,0.016028866,0.0002787823,0.0016045299,0.0021073404,0.00031032236,0.0012275605],"genre_scores_gemma":[0.99430954,0.00059050834,0.0000869031,0.0027367957,0.000113774986,0.000098956836,0.0018987244,0.000020006562,0.00014479538],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99557704,0.0012894341,0.0011630246,0.0007106605,0.00051722635,0.0007426425],"domain_scores_gemma":[0.99511963,0.0013393356,0.0006852474,0.0019823166,0.00021244412,0.00066101964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005525315,0.00022578452,0.0012838481,0.00031393036,0.00020336566,0.000016747586,0.0005527274,0.000099858444,0.00009152632],"category_scores_gemma":[0.0026762665,0.00016065167,0.00010252321,0.0043086386,0.00027706253,0.00019165015,0.0005210679,0.00027210923,0.0000048546713],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003119822,0.00031036962,0.9502097,0.0002683782,0.0008067967,0.000010210095,0.005136077,0.00023342157,0.0000022955248,0.000104064165,0.040896326,0.0017103911],"study_design_scores_gemma":[0.001563358,0.0009653559,0.9288627,0.000014255612,0.000027895247,0.0000064418496,0.005271585,0.0021635452,1.5220243e-7,0.000036356378,0.060959857,0.00012853232],"about_ca_topic_score_codex":0.00078613823,"about_ca_topic_score_gemma":0.0032091842,"teacher_disagreement_score":0.02134702,"about_ca_system_score_codex":0.00008047536,"about_ca_system_score_gemma":0.001412384,"threshold_uncertainty_score":0.6551185},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945466,0.000038260143,0.0011225492,0.0016690629,0.000010132073,0.00025680134,0.00006091206,0.00010579276,0.0021898847],"genre_scores_gemma":[0.99874955,0.000018716764,0.00013613261,0.00021875634,0.000011130443,0.000040851864,0.0002823348,0.000005533836,0.00053698936],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935603,0.000028953022,0.0001496518,0.00017419065,0.00020134951,0.00008981262],"domain_scores_gemma":[0.99928933,0.00014144376,0.000045038007,0.00037833554,0.000048713104,0.000097128286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029540423,0.00006091304,0.00015205171,0.00010697644,0.00003604921,0.000004771661,0.000057871013,0.000011435177,0.000058180878],"category_scores_gemma":[0.00011890867,0.00003730152,0.000042881104,0.00083277334,0.000043782875,0.000012834317,0.000039431456,0.000028347773,0.00009138352],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000120247896,0.00005119977,0.98278165,0.000049952287,0.0002750146,0.0000026867385,0.00006209859,0.000064800726,0.00013275073,0.0011381011,0.0014466142,0.0138749],"study_design_scores_gemma":[0.00014326493,0.000009428875,0.9906725,0.000005816428,0.000058486858,7.0944665e-8,0.000039299797,0.003757956,0.00019128025,0.00003276692,0.0050457595,0.000043334272],"about_ca_topic_score_codex":0.000011173648,"about_ca_topic_score_gemma":0.000060225615,"teacher_disagreement_score":0.013831565,"about_ca_system_score_codex":0.000013421906,"about_ca_system_score_gemma":0.000043009022,"threshold_uncertainty_score":0.15211119},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9924376,0.00006201419,0.00003319729,0.0018272744,0.0014437803,0.000109393,0.00018565111,0.000038217622,0.0038628955],"genre_scores_gemma":[0.99890053,0.000070607086,0.00001730822,0.00020689514,0.0001520597,0.0000024040116,0.0003058423,0.000009504579,0.00033485348],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99853176,0.00003291847,0.0006570451,0.0000769251,0.00055241626,0.00014892135],"domain_scores_gemma":[0.99898773,0.00010839892,0.00031309502,0.00013590205,0.00036869044,0.00008620689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040756448,0.00009950448,0.00018650373,0.00044977025,0.000020738435,0.00012626404,0.00040651407,0.000041928757,0.00010167251],"category_scores_gemma":[0.00055172236,0.000069815826,0.00013944322,0.0004083216,0.000070861235,0.00097456627,0.00014062373,0.00024861333,0.00044791432],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097604695,0.00023699747,0.8831923,0.000023916493,0.00039510892,0.0012242773,0.00060233875,0.00047972612,0.00072495715,0.00074758363,0.0078131305,0.103583634],"study_design_scores_gemma":[0.0027552538,0.00011480863,0.97642833,0.00018591032,0.000009869447,0.0012527159,0.0008652718,0.0010243949,0.00019477379,0.00031309124,0.016755318,0.000100281184],"about_ca_topic_score_codex":0.000010249194,"about_ca_topic_score_gemma":0.00003565948,"teacher_disagreement_score":0.10348336,"about_ca_system_score_codex":0.0001857946,"about_ca_system_score_gemma":0.00009261206,"threshold_uncertainty_score":0.5757182},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37146202,0.0008353161,0.44492438,0.14859991,0.00088874303,0.0066146976,0.024551323,0.0019500243,0.0001735871],"genre_scores_gemma":[0.7931163,0.000045408924,0.060013186,0.005954657,0.0007435342,0.0003372238,0.13963582,0.00006260755,0.00009130312],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800885,0.00012071479,0.0004776725,0.00064947805,0.00018285176,0.0005604319],"domain_scores_gemma":[0.9968061,0.0017626489,0.00018942617,0.000554199,0.00039991704,0.0002877495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008892526,0.00016703205,0.00046111163,0.0003037686,0.00020098497,0.00002770051,0.00041089015,0.00010528917,0.000029527173],"category_scores_gemma":[0.0019009067,0.00015632705,0.000118060125,0.00043256377,0.00012991228,0.00009443319,0.0002539931,0.00012451381,0.00007521587],"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":[0.0025796436,0.0023621412,0.36426926,0.0033715921,0.0037104276,0.000038114853,0.0007315197,0.0024930516,0.0019618561,0.040889464,0.41198462,0.1656083],"study_design_scores_gemma":[0.0064801914,0.0012845562,0.27892298,0.000114104696,0.000096446536,0.00003187289,0.000098830475,0.4039063,0.000004243795,0.021637455,0.2869956,0.00042744522],"about_ca_topic_score_codex":0.0000175063,"about_ca_topic_score_gemma":0.000017885244,"teacher_disagreement_score":0.42165425,"about_ca_system_score_codex":0.000050269173,"about_ca_system_score_gemma":0.0008273924,"threshold_uncertainty_score":0.63748324},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1767691,0.007871661,0.03235512,0.041151907,0.031889115,0.035823718,0.09599395,0.025840858,0.55230457],"genre_scores_gemma":[0.25834045,0.004956718,0.05937001,0.007889413,0.004757523,0.0006530023,0.17830293,0.00092466373,0.4848053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99723315,0.00009522074,0.00050996034,0.0013487848,0.00042889855,0.00038400243],"domain_scores_gemma":[0.9946414,0.0001745279,0.00021287476,0.0045630154,0.000112072536,0.00029606328],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009059423,0.00033449978,0.00088740815,0.00012459503,0.0000677453,0.00029541604,0.0027034378,0.00025567226,0.0018331826],"category_scores_gemma":[0.0007662128,0.00024528295,0.00008223756,0.00021114213,0.00010038597,0.00024665447,0.02170257,0.00072702096,0.0015752473],"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":[0.00065644464,0.0002358274,0.09208788,0.0016028085,0.0006685923,0.0010521022,0.00007262094,0.000044569104,0.000102120044,0.00010075097,0.8648217,0.03855459],"study_design_scores_gemma":[0.006279246,0.00013777033,0.23847584,0.007614565,0.0013615215,0.0002062463,0.0005726144,0.012848282,0.00007638852,0.002699357,0.72777075,0.0019574414],"about_ca_topic_score_codex":0.0011109052,"about_ca_topic_score_gemma":0.0011934792,"teacher_disagreement_score":0.14638796,"about_ca_system_score_codex":0.00012604035,"about_ca_system_score_gemma":0.0016680512,"threshold_uncertainty_score":0.99999994},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1767691,0.007871661,0.03235512,0.041151907,0.031889115,0.035823718,0.09599395,0.025840858,0.55230457],"genre_scores_gemma":[0.25834045,0.004956718,0.05937001,0.007889413,0.004757523,0.0006530023,0.17830293,0.00092466373,0.4848053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99723315,0.00009522074,0.00050996034,0.0013487848,0.00042889855,0.00038400243],"domain_scores_gemma":[0.9946414,0.0001745279,0.00021287476,0.0045630154,0.000112072536,0.00029606328],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009059423,0.00033449978,0.00088740815,0.00012459503,0.0000677453,0.00029541604,0.0027034378,0.00025567226,0.0018331826],"category_scores_gemma":[0.0007662128,0.00024528295,0.00008223756,0.00021114213,0.00010038597,0.00024665447,0.02170257,0.00072702096,0.0015752473],"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":[0.00065644464,0.0002358274,0.09208788,0.0016028085,0.0006685923,0.0010521022,0.00007262094,0.000044569104,0.000102120044,0.00010075097,0.8648217,0.03855459],"study_design_scores_gemma":[0.006279246,0.00013777033,0.23847584,0.007614565,0.0013615215,0.0002062463,0.0005726144,0.012848282,0.00007638852,0.002699357,0.72777075,0.0019574414],"about_ca_topic_score_codex":0.0011109052,"about_ca_topic_score_gemma":0.0011934792,"teacher_disagreement_score":0.14638796,"about_ca_system_score_codex":0.00012604035,"about_ca_system_score_gemma":0.0016680512,"threshold_uncertainty_score":0.99999994},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99632293,0.00009121001,0.0006652343,0.00046661342,0.00039650514,0.000271521,0.00018390849,0.00025628763,0.0013457765],"genre_scores_gemma":[0.99571913,0.0006300368,0.0001609116,0.000272124,0.0006427035,0.000022082768,0.002024976,0.000013735052,0.00051432045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989127,0.00006244881,0.00025094807,0.00025794283,0.0003775629,0.00013842071],"domain_scores_gemma":[0.9992422,0.00015102534,0.00007422047,0.00021933868,0.0001911435,0.00012207449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004450879,0.00010184428,0.00016142048,0.00022646686,0.00005567892,0.00003273924,0.00015410209,0.000053626765,0.00032546322],"category_scores_gemma":[0.0012059796,0.000092392984,0.0000305501,0.00035668907,0.00003294239,0.00017203072,0.00018685548,0.00014940255,0.00011252055],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009546586,0.0003303446,0.9913625,0.000013688705,0.000040299616,0.00008406849,0.00018575318,0.00010284476,0.000054362125,0.00049094285,0.0021900663,0.005049633],"study_design_scores_gemma":[0.0012458358,0.0002081718,0.9886541,0.0000028414509,0.00003701124,0.000017565942,0.00011166261,0.0040276824,0.0000023186894,0.00034441228,0.0052527003,0.00009572021],"about_ca_topic_score_codex":0.000017958062,"about_ca_topic_score_gemma":0.0000103054535,"teacher_disagreement_score":0.0049539125,"about_ca_system_score_codex":0.000053082753,"about_ca_system_score_gemma":0.000046756755,"threshold_uncertainty_score":0.37676764},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.101118915,0.009014573,0.6589314,0.024992164,0.0011364953,0.046947043,0.15478066,0.0024243013,0.0006544837],"genre_scores_gemma":[0.96364516,0.0007629415,0.006812228,0.00015223752,0.00031666114,0.0017615334,0.025091067,0.0002816441,0.001176503],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928069,0.00034576067,0.00062636635,0.0018556826,0.0033786052,0.0009866854],"domain_scores_gemma":[0.99221224,0.00024475402,0.00022154914,0.0035228368,0.0027227232,0.0010759097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0057172873,0.00046980687,0.00087606785,0.0005391119,0.00028075944,0.0002237594,0.0007510305,0.00022967598,0.000038988896],"category_scores_gemma":[0.0033021725,0.00039323568,0.0001227813,0.0004048787,0.0002938951,0.00013230664,0.003664336,0.0013406937,0.000061295104],"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":[0.0673324,0.0063797715,0.13729295,0.20466168,0.013541813,0.009275269,0.007028477,0.13431504,0.0027309537,0.0015095039,0.3897538,0.026178343],"study_design_scores_gemma":[0.0030220652,0.00022842683,0.052649107,0.007710426,0.00026588535,0.000005908373,0.00015555065,0.9317967,0.0000045654156,0.00097611087,0.0027534768,0.00043173195],"about_ca_topic_score_codex":0.00049306353,"about_ca_topic_score_gemma":0.0006871093,"teacher_disagreement_score":0.8625263,"about_ca_system_score_codex":0.0007004059,"about_ca_system_score_gemma":0.0053214533,"threshold_uncertainty_score":0.99985194},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2615798,0.020779314,0.03192096,0.63552916,0.0063577984,0.00081313855,0.0014445867,0.0009051514,0.040670075],"genre_scores_gemma":[0.9157627,0.006815412,0.02264059,0.046289083,0.0038983955,0.0000010496503,0.0023992928,0.00015869187,0.0020347815],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99209994,0.005137646,0.0015391984,0.00033367134,0.00019266477,0.000696894],"domain_scores_gemma":[0.9958295,0.0011307371,0.000958485,0.0011313779,0.00016566234,0.000784222],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.030525565,0.00013030511,0.00082084234,0.00028152525,0.0000607318,0.000007139341,0.0007704471,0.000022811828,0.0001852855],"category_scores_gemma":[0.027455542,0.00009880312,0.00013205467,0.00038003107,0.00014595546,0.00017159668,0.00041136376,0.00043277748,0.0011723357],"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":[0.00007666564,0.00006076927,0.071567416,0.000046068883,0.00014036572,0.0012518255,0.00005378248,0.000019178642,0.00004184619,0.0003120055,0.8507214,0.07570867],"study_design_scores_gemma":[0.0006965677,0.00035823428,0.36422396,0.000062014726,0.000013697456,0.0005984077,0.0000347755,0.00033859615,3.243085e-7,0.00008628981,0.63353133,0.00005581754],"about_ca_topic_score_codex":0.0000042301963,"about_ca_topic_score_gemma":0.0000022570657,"teacher_disagreement_score":0.6541829,"about_ca_system_score_codex":0.000056884022,"about_ca_system_score_gemma":0.00040129898,"threshold_uncertainty_score":0.99960536},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85051197,0.00031490502,0.1486164,0.00007407599,0.0000502657,0.00033562823,0.000028192764,0.000059559872,0.000008995433],"genre_scores_gemma":[0.9810117,0.00021394252,0.017475381,0.0008689699,0.00007766987,0.000021737857,0.0003101796,0.000016200765,0.000004256667],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981184,0.000093274764,0.00060067547,0.00017508538,0.00070590613,0.0003066686],"domain_scores_gemma":[0.9954158,0.0039390405,0.00016630806,0.00018695323,0.00005964154,0.0002322402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019357471,0.00016269204,0.00026982703,0.0003243449,0.0002680963,0.0001631399,0.00013272335,0.00006150949,0.000014489548],"category_scores_gemma":[0.004474575,0.00010771615,0.00004920158,0.00046589202,0.00012240322,0.00020911261,0.00027045203,0.00025375822,0.0000021436003],"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":[0.0011256505,0.00007236557,0.847772,0.0018007568,0.000048566664,0.00048299905,0.005811339,0.049134295,0.000004905892,0.00007289751,0.00043613775,0.09323808],"study_design_scores_gemma":[0.0011515203,0.00003958595,0.041762102,0.001156483,0.000046053075,0.00015439691,0.00578373,0.94910705,7.741969e-7,0.0004834882,0.0001983328,0.0001164995],"about_ca_topic_score_codex":0.00003882914,"about_ca_topic_score_gemma":0.000018037634,"teacher_disagreement_score":0.89997274,"about_ca_system_score_codex":0.00002249749,"about_ca_system_score_gemma":0.00012844622,"threshold_uncertainty_score":0.5356808},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9034789,0.003686176,0.006156595,0.076232396,0.0005262015,0.006704981,0.002107835,0.00071643235,0.00039045548],"genre_scores_gemma":[0.9833775,0.00023041472,0.0004019484,0.0027945524,0.00010835244,0.000813513,0.011997337,0.000041550647,0.00023483331],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.991166,0.0044450606,0.0015863135,0.00062014785,0.0012456086,0.0009368858],"domain_scores_gemma":[0.9928919,0.00092651235,0.000985444,0.0012369441,0.0016399687,0.0023192337],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.0468459,0.00026216405,0.00092678476,0.00051820755,0.00064593274,0.00007472763,0.0002025839,0.00012992935,0.00009275181],"category_scores_gemma":[0.020939363,0.00024753914,0.00019717087,0.0013188436,0.00016520287,0.00026604245,0.0001766548,0.0003654703,0.000009499806],"study_design_candidate":"observational","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":[0.0004939708,0.0018276226,0.3131115,0.0075115035,0.0002828436,0.00059458654,0.0050848406,0.000014254971,0.000012965995,0.00024187319,0.12086726,0.5499568],"study_design_scores_gemma":[0.013249401,0.002476218,0.44705862,0.0001731581,0.0000133878075,0.0063676857,0.005601353,0.016461018,0.0000011685091,0.0005500878,0.5075297,0.0005182559],"about_ca_topic_score_codex":0.0015679392,"about_ca_topic_score_gemma":0.0031695582,"teacher_disagreement_score":0.54943854,"about_ca_system_score_codex":0.00082927494,"about_ca_system_score_gemma":0.0075009502,"threshold_uncertainty_score":0.9999977},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938857,0.00028729034,4.845809e-7,0.0042931377,0.0001075326,0.00048002246,0.00012754356,0.00013098067,0.0006872962],"genre_scores_gemma":[0.9980363,0.00013730735,0.000010884822,0.00017399041,0.000023421513,0.00004305643,0.00015016522,0.000022347918,0.0014024844],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987925,0.00017574789,0.000273766,0.00025663638,0.0001882673,0.00031307395],"domain_scores_gemma":[0.9988488,0.00035104237,0.000052715215,0.0004672932,0.00010266307,0.00017750324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021090591,0.00014883679,0.00045284943,0.00018017543,0.000058079517,0.000019813757,0.00016796081,0.0001190876,0.00013687264],"category_scores_gemma":[0.00026426677,0.00008969641,0.00013547676,0.0004905933,0.00016271273,0.000036011268,0.00018002688,0.00024897666,0.00024187274],"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":[0.0011137028,0.00017616547,0.42277846,0.00019843676,0.0016198143,0.00057163596,0.0008265184,0.000032647218,0.00307547,0.00086262793,0.56557035,0.0031741972],"study_design_scores_gemma":[0.0011828328,0.00046808893,0.98585844,0.000059278987,0.00008930767,0.00045105538,0.00009951583,0.0006098353,0.0029003946,0.00023989621,0.007902419,0.00013894888],"about_ca_topic_score_codex":0.00012613571,"about_ca_topic_score_gemma":0.000053580938,"teacher_disagreement_score":0.56307995,"about_ca_system_score_codex":0.000032591077,"about_ca_system_score_gemma":0.00007171795,"threshold_uncertainty_score":0.36577135},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005660377,0.987166,0.000045532113,0.0003265446,0.0054771435,0.001729863,0.0046461676,0.000034675435,0.000008040283],"genre_scores_gemma":[0.0009811345,0.996919,0.000020977126,0.00056749466,0.0012094392,0.000059128746,0.00013672544,0.000065816115,0.000040332867],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99444836,0.0007283588,0.0030985568,0.0002893774,0.00085267477,0.00058266445],"domain_scores_gemma":[0.9900923,0.0006908194,0.0069957073,0.0007769133,0.00090514414,0.00053913763],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003603849,0.00042115635,0.0045396215,0.00010630333,0.00011850621,0.00008116635,0.00054043415,0.0002197051,0.000004730976],"category_scores_gemma":[0.0009922433,0.00025817574,0.0009163434,0.0006084292,0.0001400664,0.00016932888,0.0000984326,0.0004921741,0.000028428512],"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":[0.0005512892,0.00033196853,0.0134223085,0.24778542,0.0016925395,0.00021530977,0.000094422285,0.000007761582,3.0257052e-8,0.0002676743,0.05649524,0.67913604],"study_design_scores_gemma":[0.0006262334,0.000378832,0.014129517,0.063422635,0.00050053134,0.00033774122,0.00029243706,0.000014882974,8.9824255e-9,0.000015959366,0.9200925,0.0001887234],"about_ca_topic_score_codex":0.000050653525,"about_ca_topic_score_gemma":0.000052911506,"teacher_disagreement_score":0.8635973,"about_ca_system_score_codex":0.0012913445,"about_ca_system_score_gemma":0.0048345323,"threshold_uncertainty_score":0.99998707},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10892588,0.002601878,0.16417699,0.062365606,0.0016110705,0.0051666545,0.011453692,0.0022544647,0.6414438],"genre_scores_gemma":[0.89868104,0.007202889,0.05202905,0.012421642,0.00024352667,0.00016660838,0.008523337,0.00006476168,0.02066714],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986546,0.000024031466,0.00056438503,0.00030100901,0.00028987954,0.00016607154],"domain_scores_gemma":[0.99728966,0.0001996351,0.00019175594,0.0017556478,0.00015680226,0.00040649675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040979384,0.00017932407,0.000383922,0.002299825,0.0002930691,0.00023613758,0.0007983308,0.00007666685,0.00006096405],"category_scores_gemma":[0.00017592293,0.00017799757,0.000066053275,0.0011238015,0.0005342235,0.0012339887,0.0017656619,0.00025754346,0.00006695499],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028952627,0.00016962894,0.06133127,0.0005551369,0.0011119754,0.00004276569,0.038251933,0.0022231298,0.00006922226,0.065384835,0.013219702,0.81735086],"study_design_scores_gemma":[0.0015913082,0.00014075954,0.48496798,0.0006121335,0.00040744545,0.000028091164,0.0004953843,0.11207709,0.000006651389,0.0015259451,0.3972915,0.0008556806],"about_ca_topic_score_codex":0.00021395732,"about_ca_topic_score_gemma":0.00067995576,"teacher_disagreement_score":0.8164952,"about_ca_system_score_codex":0.00018446699,"about_ca_system_score_gemma":0.00020143006,"threshold_uncertainty_score":0.725853},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971557,0.00014441935,0.00018704672,0.0008461143,0.00013426402,0.00006463476,0.0014115836,0.000019549201,0.000036654234],"genre_scores_gemma":[0.996205,0.0001777083,0.0010989512,0.00041428278,0.00023675762,0.000001844072,0.0017834043,0.000018872477,0.00006322983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99674,0.0004336596,0.0009937669,0.00028284924,0.001142855,0.00040688546],"domain_scores_gemma":[0.9979607,0.0002525308,0.0002155255,0.00042733495,0.00017545417,0.0009684608],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020024048,0.00014559712,0.0009891827,0.0020325517,0.000035311474,0.00000603879,0.00037446202,0.0001874747,0.005153057],"category_scores_gemma":[0.0010149651,0.00012411481,0.00022689943,0.0021968503,0.00016380873,0.00009855067,0.00017184569,0.0008003823,0.000022098564],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029903065,0.00053163664,0.9765457,0.000016077758,0.0004292712,0.0010135167,0.00051555433,0.00046626423,0.0018168687,0.0000042579295,0.0010677184,0.017294101],"study_design_scores_gemma":[0.0010860922,0.0002511851,0.98109525,0.00011563701,0.00042409758,0.000056951358,0.00048719536,0.016153619,0.00007593476,0.000010870691,0.00014742342,0.00009573519],"about_ca_topic_score_codex":0.002785766,"about_ca_topic_score_gemma":0.0015626346,"teacher_disagreement_score":0.017198365,"about_ca_system_score_codex":0.00012089443,"about_ca_system_score_gemma":0.0006631516,"threshold_uncertainty_score":0.9957564},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.616421,0.03408577,0.00012692178,0.33740535,0.0002034576,0.0068230685,0.004556857,0.00009691316,0.000280652],"genre_scores_gemma":[0.993086,0.0040049297,0.000007124194,0.001744654,0.0005646965,0.0002974789,0.00008984861,0.000025356057,0.00017990443],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.98746157,0.0074126963,0.0009099529,0.0005715907,0.0022990957,0.001345091],"domain_scores_gemma":[0.959858,0.037513923,0.00023902854,0.0009994822,0.00039373382,0.0009958122],"candidate_categories":["metaresearch","sts"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.030760642,0.00016132835,0.00070572214,0.00060743187,0.0014506237,0.00016345404,0.000450335,0.00009478313,0.0000035064222],"category_scores_gemma":[0.017305097,0.00009870993,0.00003232793,0.0023140016,0.0005683113,0.0000704735,0.0002736211,0.0011327032,0.0000072542307],"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":[0.00025210637,0.0000623877,0.51937217,0.005477887,0.00008929637,0.00012466978,0.0072194645,0.00016450774,0.000006397668,0.0070250756,0.45019197,0.010014066],"study_design_scores_gemma":[0.001554508,0.00050014036,0.38241416,0.0040110564,0.0000075948956,0.00012755711,0.046828266,0.0028490655,1.73461e-7,0.00067988527,0.5608565,0.00017106926],"about_ca_topic_score_codex":0.9930196,"about_ca_topic_score_gemma":0.9775723,"teacher_disagreement_score":0.376665,"about_ca_system_score_codex":0.0022133708,"about_ca_system_score_gemma":0.04108115,"threshold_uncertainty_score":0.9998494},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90692484,0.0012421706,0.000119460645,0.08639344,0.0003088229,0.0012150665,0.0026650776,0.00047961483,0.00065152685],"genre_scores_gemma":[0.99553114,0.0017629663,0.00003686578,0.0008716159,0.00003846068,0.00014958397,0.0006488919,0.00003323136,0.00092723954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997135,0.0004295874,0.00082089216,0.00036465825,0.0005616209,0.00068828644],"domain_scores_gemma":[0.996988,0.0008278336,0.00033678534,0.00093115144,0.000296364,0.0006198551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051487517,0.00015403879,0.000495811,0.00023212939,0.000103771024,0.000038522732,0.00022818732,0.000028101502,0.000052295887],"category_scores_gemma":[0.007941319,0.00010869521,0.0000772537,0.0015618833,0.00017450623,0.00010525114,0.00008463647,0.00018134592,0.00007928349],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045710776,0.00023448125,0.87470776,0.0008754081,0.00011102932,0.00087503844,0.00082983426,0.000026711488,0.0000062979116,0.00023461894,0.039302424,0.08233928],"study_design_scores_gemma":[0.00034529608,0.00003106774,0.7434146,0.000120551005,0.000003124802,0.000014713831,0.0002205947,0.00097471225,0.0000031761908,0.000108950175,0.2546747,0.000088517576],"about_ca_topic_score_codex":0.00113198,"about_ca_topic_score_gemma":0.0012704901,"teacher_disagreement_score":0.2153723,"about_ca_system_score_codex":0.00032878626,"about_ca_system_score_gemma":0.0044764983,"threshold_uncertainty_score":0.9507074},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017821237,0.014728684,0.8672335,0.015156818,0.00012525114,0.0029847524,0.0015539065,0.003448175,0.07694768],"genre_scores_gemma":[0.4770891,0.22711414,0.27577233,0.0028187546,0.00057696423,0.0034799627,0.0050152037,0.0002962964,0.00783725],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943955,0.0019747794,0.00090197986,0.0010759596,0.00047761863,0.0011741669],"domain_scores_gemma":[0.9951286,0.0019902056,0.00046431203,0.0010491395,0.0004408332,0.0009269318],"candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003866799,0.0006790081,0.00085393444,0.00037599032,0.00076394423,0.00013755166,0.00062067236,0.0015091551,0.00017436178],"category_scores_gemma":[0.011824431,0.0006339539,0.00030685082,0.00095151714,0.0032407027,0.00025617398,0.0004214014,0.0014194575,0.000111894915],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0006261155,0.003810368,0.40353602,0.014413109,0.0013837004,0.0022316675,0.005586229,0.0015156673,0.14909902,0.111762665,0.102169506,0.20386595],"study_design_scores_gemma":[0.0017940974,0.0005443764,0.25806043,0.002521833,0.0014108459,0.0033619385,0.0059539373,0.0025272763,0.02041572,0.074078694,0.62757325,0.0017575845],"about_ca_topic_score_codex":0.0017389432,"about_ca_topic_score_gemma":0.0023032438,"teacher_disagreement_score":0.5914612,"about_ca_system_score_codex":0.0041958913,"about_ca_system_score_gemma":0.013921396,"threshold_uncertainty_score":0.9997871},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96744347,0.0040045637,0.00009975842,0.022256037,0.0001295509,0.0038926464,0.0012600815,0.00017311874,0.00074076775],"genre_scores_gemma":[0.9867102,0.00029012113,0.00017625713,0.009910051,0.00012481454,0.0004354593,0.0006542789,0.000063858504,0.0016349662],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927564,0.002467912,0.0009958826,0.0016286181,0.0008767002,0.001274468],"domain_scores_gemma":[0.99484783,0.0013046074,0.0003352038,0.00095732353,0.000105866035,0.0024491572],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0058918432,0.00051900494,0.001360678,0.00043840686,0.0004511931,0.00015705756,0.0003491321,0.00014745786,0.0029273408],"category_scores_gemma":[0.001542521,0.0004880654,0.00009153061,0.0011382636,0.00029766196,0.00013666599,0.0007084453,0.000903336,0.000069707836],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069710234,0.0011465537,0.9547231,0.0003304439,0.00005528843,0.00021472824,0.00042168747,0.000019603698,0.0000300318,0.00001806756,0.022663802,0.01967957],"study_design_scores_gemma":[0.005846715,0.001020509,0.8054146,0.000010805141,0.0000020724624,0.000102549675,0.00043977317,0.0016240563,1.6966733e-7,0.000026811344,0.18509898,0.00041292614],"about_ca_topic_score_codex":0.0013863138,"about_ca_topic_score_gemma":0.0040183975,"teacher_disagreement_score":0.16243517,"about_ca_system_score_codex":0.00059782714,"about_ca_system_score_gemma":0.0029923255,"threshold_uncertainty_score":0.9997571},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98718035,0.0011654263,0.000027316255,0.0014016181,0.00023921109,0.007609194,0.0005711592,0.0016663099,0.00013941206],"genre_scores_gemma":[0.99202996,0.0001538518,0.000013818924,0.0042721243,0.00018725204,0.0027601328,0.0003401863,0.000105121784,0.00013751848],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99136907,0.0025208923,0.0014630242,0.0015615317,0.0012420513,0.0018434413],"domain_scores_gemma":[0.99362105,0.0014155166,0.00058914494,0.0013820799,0.00052496535,0.0024672174],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0074213124,0.0006837493,0.0015724613,0.0006005588,0.00093847024,0.00026024095,0.0005360334,0.00022670403,0.00003900921],"category_scores_gemma":[0.0046931906,0.00053798384,0.00014209695,0.003941516,0.0003754035,0.0002952649,0.00035640693,0.0008193235,0.000060208677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013610518,0.0002904252,0.99261296,0.0014442861,0.00018578839,0.0002128817,0.0022079544,0.00004787561,0.000028481869,0.000025127723,0.00080545637,0.0007777299],"study_design_scores_gemma":[0.008188444,0.00026094934,0.9673704,0.00006850577,0.000003871369,0.00021001561,0.003655322,0.0012469472,6.1888943e-7,0.000006329897,0.018434098,0.00055451115],"about_ca_topic_score_codex":0.0014779401,"about_ca_topic_score_gemma":0.005652089,"teacher_disagreement_score":0.025242554,"about_ca_system_score_codex":0.0015296815,"about_ca_system_score_gemma":0.003371245,"threshold_uncertainty_score":0.99970716},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013045906,0.0012501823,0.000021156577,0.9894069,0.0019395148,0.0011960294,0.0032670123,0.0012439649,0.00037067145],"genre_scores_gemma":[0.0011201503,0.00018179268,0.0013511499,0.93893534,0.0035168435,0.00010607135,0.008691563,0.00022233145,0.04587474],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996519,0.00012335311,0.00036292608,0.0009983489,0.00095062755,0.001045714],"domain_scores_gemma":[0.99704945,0.00013117758,0.00023524655,0.0018277718,0.00036351048,0.00039283355],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00032322653,0.00046273766,0.0009234336,0.0010974421,0.0000866133,0.00005011095,0.0006349634,0.0067083538,0.00018908575],"category_scores_gemma":[0.002282347,0.00042437552,0.00019254995,0.0017925574,0.000060824343,0.00011276956,0.00042238875,0.013875097,0.002487224],"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":[0.000023096734,0.000044432727,0.0050534443,0.0004171669,0.00015288137,0.0012029499,0.00002280354,2.3295016e-7,0.000017473905,0.00004825791,0.99015564,0.0028616088],"study_design_scores_gemma":[0.000421754,0.00013439516,0.0101100495,0.00041661347,0.00003328062,0.000062897474,0.000015368912,0.0000021775377,0.000011743414,0.00008111277,0.9883902,0.000320408],"about_ca_topic_score_codex":0.000045080204,"about_ca_topic_score_gemma":0.00008484192,"teacher_disagreement_score":0.050471522,"about_ca_system_score_codex":0.000471216,"about_ca_system_score_gemma":0.00081579667,"threshold_uncertainty_score":0.9998208},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9909668,0.000063697,0.000001693387,0.003834532,0.000033188,0.0017875003,0.00041062955,0.00011641525,0.0027855749],"genre_scores_gemma":[0.99746144,0.00006657137,0.000016408005,0.00029451514,0.000046788828,0.0010514455,0.00062622427,0.000028624207,0.00040798646],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99602085,0.0012726017,0.0004165521,0.00050897896,0.001024986,0.0007560153],"domain_scores_gemma":[0.9972795,0.0015486524,0.000056440924,0.0005052534,0.00027778157,0.00033232427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003057032,0.00022443062,0.0005279902,0.0018577738,0.00018504055,0.000049644732,0.0001701484,0.00012680783,0.00010365496],"category_scores_gemma":[0.00048238077,0.00018329312,0.000056634224,0.0017024705,0.00038490377,0.00017202336,0.00018110036,0.0008596986,0.0001902595],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03882189,0.012110881,0.8433273,0.002700647,0.00018362571,0.0009697018,0.011825579,0.00016299557,0.0021953024,0.0002399188,0.04864898,0.038813192],"study_design_scores_gemma":[0.013063696,0.011267527,0.934864,0.00050160615,0.0000070597134,0.0000034018112,0.0052670133,0.0330366,0.00009274928,0.000018315228,0.0016980662,0.00018000857],"about_ca_topic_score_codex":0.0001590065,"about_ca_topic_score_gemma":0.0001476094,"teacher_disagreement_score":0.09153667,"about_ca_system_score_codex":0.00026175065,"about_ca_system_score_gemma":0.00015994391,"threshold_uncertainty_score":0.74744767},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99498665,0.00041763214,0.0012329449,0.00040114307,0.000036515194,0.0025468979,0.00019568708,0.000052245927,0.0001302799],"genre_scores_gemma":[0.9988891,0.00012562494,0.00003872291,0.00001561436,0.0000644829,0.0007143479,0.00008995457,0.000019220497,0.000042914762],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.996452,0.0005542825,0.0006504426,0.0002493673,0.0015623663,0.0005315843],"domain_scores_gemma":[0.9972954,0.0012315462,0.00020741983,0.00046826224,0.0006007104,0.00019668862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004898813,0.000119329496,0.00049403426,0.0004304421,0.00029035402,0.000007067755,0.00035900806,0.00010137869,0.000016771502],"category_scores_gemma":[0.001196474,0.00007977266,0.000103802326,0.0011806844,0.0003039516,0.00009492166,0.0003530212,0.0004642763,0.0000041477715],"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":[0.008625762,0.005108114,0.3868185,0.019415427,0.0017878887,0.000020923377,0.52396405,0.0006281661,0.0060397102,0.00018697376,0.016485238,0.030919243],"study_design_scores_gemma":[0.003100403,0.0026832847,0.8940971,0.00014418871,0.00001460661,0.000008618933,0.094227955,0.004076018,0.0005674604,0.0000060014845,0.0009889479,0.00008542047],"about_ca_topic_score_codex":0.0005743291,"about_ca_topic_score_gemma":0.00020418514,"teacher_disagreement_score":0.50727856,"about_ca_system_score_codex":0.00012311927,"about_ca_system_score_gemma":0.00032734117,"threshold_uncertainty_score":0.32530347},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8410411,0.00065901986,0.0014421301,0.1105798,0.0009081023,0.011899033,0.0009188803,0.0028919175,0.029660052],"genre_scores_gemma":[0.9906578,0.00008769586,0.000103256476,0.004788797,0.00017283046,0.0005713509,0.00035198432,0.00005835329,0.0032079301],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974917,0.00023851109,0.00051543367,0.0008817849,0.000517288,0.00035524325],"domain_scores_gemma":[0.9979624,0.0004728395,0.0002889011,0.00084038713,0.00020672464,0.00022870315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015616318,0.00030568495,0.0005444536,0.00029613744,0.00008973527,0.00004250806,0.00030188292,0.00018624144,0.0004109513],"category_scores_gemma":[0.0058950614,0.00020291547,0.00017746478,0.00034193855,0.00014039311,0.00003128757,0.00061178993,0.0011792433,0.0003098943],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012034453,0.0010929668,0.867125,0.004386226,0.0013865742,0.0025803477,0.043667722,0.0009472713,0.00058942905,0.03375828,0.035436805,0.00782594],"study_design_scores_gemma":[0.00644268,0.0003220239,0.81222343,0.007126174,0.00017308308,0.00013215154,0.07322741,0.01483445,0.000120794284,0.06282786,0.02087419,0.00169575],"about_ca_topic_score_codex":0.0021653725,"about_ca_topic_score_gemma":0.0021715085,"teacher_disagreement_score":0.14961673,"about_ca_system_score_codex":0.001834568,"about_ca_system_score_gemma":0.001642605,"threshold_uncertainty_score":0.82746524},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8410411,0.00065901986,0.0014421301,0.1105798,0.0009081023,0.011899033,0.0009188803,0.0028919175,0.029660052],"genre_scores_gemma":[0.9906578,0.00008769586,0.000103256476,0.004788797,0.00017283046,0.0005713509,0.00035198432,0.00005835329,0.0032079301],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974917,0.00023851109,0.00051543367,0.0008817849,0.000517288,0.00035524325],"domain_scores_gemma":[0.9979624,0.0004728395,0.0002889011,0.00084038713,0.00020672464,0.00022870315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015616318,0.00030568495,0.0005444536,0.00029613744,0.00008973527,0.00004250806,0.00030188292,0.00018624144,0.0004109513],"category_scores_gemma":[0.0058950614,0.00020291547,0.00017746478,0.00034193855,0.00014039311,0.00003128757,0.00061178993,0.0011792433,0.0003098943],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012034453,0.0010929668,0.867125,0.004386226,0.0013865742,0.0025803477,0.043667722,0.0009472713,0.00058942905,0.03375828,0.035436805,0.00782594],"study_design_scores_gemma":[0.00644268,0.0003220239,0.81222343,0.007126174,0.00017308308,0.00013215154,0.07322741,0.01483445,0.000120794284,0.06282786,0.02087419,0.00169575],"about_ca_topic_score_codex":0.0021653725,"about_ca_topic_score_gemma":0.0021715085,"teacher_disagreement_score":0.14961673,"about_ca_system_score_codex":0.001834568,"about_ca_system_score_gemma":0.001642605,"threshold_uncertainty_score":0.82746524},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882903,0.00009512375,0.0047954256,0.0000826336,0.00091165403,0.0029781559,0.0006356617,0.00048442424,0.0017266044],"genre_scores_gemma":[0.9972696,0.00007566543,0.0012229091,0.00014461545,0.000094811134,0.00019101473,0.0003380125,0.000065683475,0.0005976897],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968065,0.00012616307,0.0010703792,0.0009387109,0.0006623406,0.0003959261],"domain_scores_gemma":[0.99700564,0.00026511855,0.00047741417,0.0015745119,0.00028981382,0.000387484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009200564,0.00040932323,0.0010992431,0.0006813046,0.0000660254,0.00004411789,0.0003711818,0.0003255763,0.0003840744],"category_scores_gemma":[0.0023796188,0.00042061493,0.00024140094,0.00070752983,0.00016107954,0.00012184978,0.0019251591,0.0009953975,0.00010853718],"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":[0.00022733252,0.0011996174,0.98192716,0.0012924336,0.00041924673,0.00055984245,0.0016798276,0.011293567,0.0002272802,0.000086656975,0.0008356724,0.00025135244],"study_design_scores_gemma":[0.004219281,0.00027617294,0.97954637,0.0007909754,0.00023047903,0.00012803162,0.0077190045,0.0042410507,0.00013847729,0.0013325008,0.00074819993,0.0006294652],"about_ca_topic_score_codex":0.0129573485,"about_ca_topic_score_gemma":0.010168282,"teacher_disagreement_score":0.008979281,"about_ca_system_score_codex":0.001319571,"about_ca_system_score_gemma":0.0022124446,"threshold_uncertainty_score":0.9998246},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9016479,0.0035273721,0.0020531572,0.056936253,0.0012268443,0.003175693,0.030164618,0.0008383007,0.0004298597],"genre_scores_gemma":[0.9610037,0.0007021217,0.0019225992,0.008119543,0.0020050367,0.00029168886,0.025583046,0.00007603292,0.00029618718],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99361765,0.0014368813,0.00094275613,0.0014975411,0.0010191076,0.0014860434],"domain_scores_gemma":[0.9930552,0.0028654279,0.00039219655,0.0015397297,0.00038436733,0.0017630442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009282221,0.0003510034,0.001259298,0.00081599766,0.00079760153,0.00031031182,0.00040055448,0.00016804521,0.000085207634],"category_scores_gemma":[0.0055108913,0.00032563196,0.00010273944,0.0018106762,0.00025911114,0.00020667083,0.00031218122,0.0003791257,0.000013272935],"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":[0.0012103156,0.0002825567,0.396272,0.00073098793,0.0005008782,0.000023452947,0.0004528596,0.000033033903,0.00011208368,0.0001387327,0.49072164,0.10952146],"study_design_scores_gemma":[0.0059956834,0.0010007563,0.15526946,0.00009808266,0.000008737066,0.0000021442572,0.00030499836,0.24934511,5.8595424e-7,0.000009598165,0.5876498,0.0003149977],"about_ca_topic_score_codex":0.002329845,"about_ca_topic_score_gemma":0.011304184,"teacher_disagreement_score":0.24931207,"about_ca_system_score_codex":0.00021264327,"about_ca_system_score_gemma":0.0053863516,"threshold_uncertainty_score":0.9999196},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97262216,0.0010453493,0.001114384,0.0004082275,0.0025105772,0.001686894,0.020161971,0.00029529125,0.00015513155],"genre_scores_gemma":[0.97332585,0.0017758398,0.003651797,0.000046817648,0.00033186685,0.00012761062,0.020323204,0.00020905233,0.00020795969],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99259704,0.0008509477,0.002486252,0.0020178137,0.0010131923,0.0010347308],"domain_scores_gemma":[0.9939545,0.00039182484,0.001515676,0.0030845513,0.00058906374,0.00046438698],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00355761,0.0009004334,0.0017268807,0.000843647,0.00018518968,0.00007326343,0.0010062698,0.00025579025,0.0001658063],"category_scores_gemma":[0.003276987,0.0009953474,0.00022036779,0.0016495475,0.00016249483,0.00038136917,0.0025792914,0.0010806053,0.00005129316],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013164537,0.00048321296,0.97929925,0.0017376742,0.00022575007,0.00041907077,0.000031760068,0.0045963293,0.0030755377,0.0000017502267,0.009924777,0.00007325991],"study_design_scores_gemma":[0.001920469,0.00012010547,0.95176506,0.001590263,0.00015195119,0.000062870655,0.000053481337,0.04254694,0.000101553676,0.000032205342,0.0008021618,0.0008529518],"about_ca_topic_score_codex":0.00084704766,"about_ca_topic_score_gemma":0.005086677,"teacher_disagreement_score":0.03795061,"about_ca_system_score_codex":0.0003360493,"about_ca_system_score_gemma":0.0010112213,"threshold_uncertainty_score":0.9992497},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98234415,0.00010118413,0.011114223,0.00005095354,0.00008540002,0.001419366,0.00007250766,0.00037003413,0.0044421535],"genre_scores_gemma":[0.9684747,0.0000019376268,0.029924054,0.00009883841,0.000043618526,0.00009266835,0.00015543238,0.000024207815,0.0011845705],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880254,0.0001926134,0.00036750056,0.0002539465,0.00016395403,0.00021944211],"domain_scores_gemma":[0.99846715,0.00084372045,0.00014215121,0.0002530903,0.0001960037,0.000097910706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009451228,0.0001296387,0.0005416141,0.00015509759,0.00007315768,0.000008366623,0.00009201594,0.000028227163,0.00008220202],"category_scores_gemma":[0.0009825848,0.000104206185,0.00008210437,0.00046128564,0.000033078584,0.000060418737,0.00012254923,0.0001261534,0.000026501539],"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":[0.0017014972,0.0012063049,0.87990683,0.000586933,0.0011086026,0.00003559996,0.011360945,0.0033302084,0.006546764,0.00023533263,0.00385295,0.09012802],"study_design_scores_gemma":[0.013375729,0.0040767873,0.32042712,0.00016967126,0.00044772355,0.000015693766,0.020798527,0.59799033,0.003991343,0.0002766788,0.037954006,0.00047636052],"about_ca_topic_score_codex":0.000119251585,"about_ca_topic_score_gemma":0.000082518105,"teacher_disagreement_score":0.59466016,"about_ca_system_score_codex":0.00002201775,"about_ca_system_score_gemma":0.000038951122,"threshold_uncertainty_score":0.4249405},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9974931,0.00070031214,0.000031916446,0.0008915582,0.00006144715,0.0002781948,0.00018687666,0.00008430647,0.00027227632],"genre_scores_gemma":[0.9881581,0.00007566123,0.0098936325,0.0000995421,0.000021830203,0.000030163894,0.00084674254,0.000022676964,0.00085167325],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988923,0.00006978879,0.00024484962,0.0002569161,0.00031460696,0.00022152794],"domain_scores_gemma":[0.99899626,0.00043292422,0.0001242362,0.00027536586,0.00007965607,0.00009157689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003544546,0.0001320054,0.00044702803,0.00009552059,0.000037011403,0.000014573543,0.00009575987,0.00007231941,0.000059645365],"category_scores_gemma":[0.0015290435,0.000120626144,0.000021955577,0.00016343434,0.000054437354,0.00009131527,0.000092732225,0.00020481835,0.0000029985308],"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":[0.0002557869,0.00024516357,0.67481536,0.0011202104,0.00048728188,0.000114859926,0.0022308608,0.00006910461,0.29387024,0.000013147143,0.021902125,0.004875861],"study_design_scores_gemma":[0.0021677432,0.00015257868,0.92202115,0.0010495838,0.00045432412,0.000006372347,0.0015954131,0.0071344757,0.0645769,0.00018763154,0.00024888467,0.0004049693],"about_ca_topic_score_codex":0.9442036,"about_ca_topic_score_gemma":0.8336551,"teacher_disagreement_score":0.24720578,"about_ca_system_score_codex":0.0001624834,"about_ca_system_score_gemma":0.00038643077,"threshold_uncertainty_score":0.49189913},"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":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005704974,0.055667702,0.0070844,0.91882527,0.00072498235,0.009232419,0.00082680205,0.0015750278,0.00035841358],"genre_scores_gemma":[0.032593485,0.104507774,0.003957219,0.8509484,0.001023814,0.0037258873,0.0019866433,0.0002011118,0.0010556804],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98846775,0.0045699305,0.0014582174,0.0016243028,0.0010842134,0.0027955538],"domain_scores_gemma":[0.9871801,0.0018949684,0.0005503769,0.0019533823,0.0004516071,0.0079695685],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.027896438,0.00059699704,0.0021566092,0.0006153839,0.00088697125,0.0002363542,0.0007565989,0.00014763571,0.00015802465],"category_scores_gemma":[0.01573794,0.00045828422,0.00017096645,0.004911666,0.00017460609,0.0001936784,0.00060499844,0.0005599944,0.00034420192],"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":[0.000086656386,0.00010097731,0.21141867,0.018531386,0.00007007175,0.000043338943,0.0013308158,0.000003712355,0.0000074756185,0.0001706385,0.39965367,0.36858258],"study_design_scores_gemma":[0.00077758485,0.00042726085,0.11807599,0.0014782139,0.0000010654765,0.00022596202,0.00022078765,0.00007817008,1.3607455e-7,0.000012918891,0.87827814,0.0004237929],"about_ca_topic_score_codex":0.00048162113,"about_ca_topic_score_gemma":0.010003394,"teacher_disagreement_score":0.47862443,"about_ca_system_score_codex":0.00073867344,"about_ca_system_score_gemma":0.007398444,"threshold_uncertainty_score":0.9997869},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93908316,0.0021266483,0.02700539,0.010426543,0.006504487,0.006651716,0.0029304065,0.002548275,0.002723383],"genre_scores_gemma":[0.98350364,0.0000147854935,0.0041324995,0.0034235816,0.0003765342,0.00007392007,0.0076745898,0.00010071746,0.0006997526],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99566346,0.00023026692,0.0012162536,0.0011517945,0.0012199929,0.00051822845],"domain_scores_gemma":[0.99664557,0.00023213062,0.001062915,0.00081432826,0.0005962983,0.0006487605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006653957,0.00051670946,0.0014290974,0.0004778683,0.00026502545,0.00007618782,0.0002650134,0.0003622398,0.00038234744],"category_scores_gemma":[0.0022567688,0.00047206937,0.000363395,0.0005983983,0.00017440335,0.00011225844,0.0006960242,0.00092269026,0.000025365713],"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":[0.005774954,0.0042949864,0.26665667,0.048717927,0.007000621,0.0023806563,0.26204655,0.01511398,0.002090954,0.0003108203,0.2705561,0.115055814],"study_design_scores_gemma":[0.010920546,0.0006356011,0.70429265,0.004384803,0.0012200122,0.0006546916,0.24497499,0.02530247,0.0006356343,0.00068666996,0.0024544138,0.0038375063],"about_ca_topic_score_codex":0.0014843682,"about_ca_topic_score_gemma":0.00062937965,"teacher_disagreement_score":0.43763602,"about_ca_system_score_codex":0.0013843876,"about_ca_system_score_gemma":0.0036572136,"threshold_uncertainty_score":0.9997731},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97268486,0.00023352932,0.00018290312,0.024883226,0.00012245683,0.00034898738,0.00014814205,0.00002085077,0.0013750376],"genre_scores_gemma":[0.998693,0.00007425178,0.00036317395,0.0002903367,0.0001287145,0.000024910394,0.00013585827,0.0000138354035,0.0002759095],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986505,0.0002483018,0.00038323243,0.00016106291,0.00024588135,0.00031103258],"domain_scores_gemma":[0.9991651,0.00021476777,0.00013350569,0.00027678348,0.00009315676,0.000116664996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007492801,0.00011074628,0.00047449514,0.0009920913,0.000040703653,0.000013208894,0.00017393476,0.000052192037,0.00010836676],"category_scores_gemma":[0.00011291169,0.00006835434,0.00032808585,0.0014481426,0.00006814951,0.000045146226,0.0000127402,0.00016832858,0.000028131586],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046379133,0.0015667899,0.78518426,0.000063678184,0.005005272,0.0046776813,0.009679276,0.0012291176,0.0002690959,0.000705458,0.014392184,0.17258926],"study_design_scores_gemma":[0.0063208286,0.0064341403,0.94660753,0.000011510896,0.0020166035,0.0005047935,0.0024048204,0.0013038225,0.000024290299,0.00026346598,0.034000296,0.00010790725],"about_ca_topic_score_codex":0.0000062898757,"about_ca_topic_score_gemma":0.000089423425,"teacher_disagreement_score":0.17248134,"about_ca_system_score_codex":0.00006156977,"about_ca_system_score_gemma":0.00011116944,"threshold_uncertainty_score":0.2787409},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012218198,0.000069108355,0.9810732,0.0003343249,0.00033045045,0.00045269268,0.0050608274,0.00007296866,0.0003882256],"genre_scores_gemma":[0.95482045,0.00005561767,0.044457532,0.00015880712,0.00020010419,0.000014162633,0.00010976336,0.000053518586,0.0001300355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9967988,0.000099442936,0.0008239741,0.0002396981,0.0016383015,0.0003997753],"domain_scores_gemma":[0.9975099,0.00037352336,0.00066678727,0.00034530903,0.0007673875,0.00033710594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058645295,0.0002687215,0.000751407,0.000043284166,0.00020302198,0.000041559346,0.00024103018,0.0000755303,0.00017674909],"category_scores_gemma":[0.00045573432,0.00017342872,0.00020884695,0.00033552095,0.00049206696,0.000057537483,0.0001279457,0.0004122087,0.000016246357],"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":[0.019711038,0.0030673277,0.055166036,0.0052602873,0.008705623,0.00089294335,0.0033371174,0.52199507,0.0016222503,0.108320594,0.24846448,0.02345721],"study_design_scores_gemma":[0.018350115,0.00459613,0.51840705,0.0013000397,0.0064090453,0.00020949166,0.002174068,0.37922388,0.00063928124,0.05688839,0.010205212,0.0015973101],"about_ca_topic_score_codex":0.000087911976,"about_ca_topic_score_gemma":0.00003674047,"teacher_disagreement_score":0.9426023,"about_ca_system_score_codex":0.00023781275,"about_ca_system_score_gemma":0.0005616988,"threshold_uncertainty_score":0.70722175},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96298736,0.0018101112,0.0000057192683,0.027924702,0.00051990227,0.0016654101,0.0026819722,0.00019815545,0.002206665],"genre_scores_gemma":[0.984354,0.0013002888,0.000029774244,0.0078037935,0.000078395766,0.00037534104,0.0030300848,0.00007691013,0.0029514027],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99514824,0.0004657881,0.0009950858,0.0007682883,0.0014687236,0.0011538832],"domain_scores_gemma":[0.99237597,0.0008934044,0.00023231206,0.004218347,0.00025107048,0.0020289149],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017771919,0.00040510055,0.00082570285,0.00012950231,0.00043242658,0.000042483072,0.0013389873,0.000060387312,0.00034648928],"category_scores_gemma":[0.0029109474,0.00035383296,0.00010455094,0.0022438564,0.00016525977,0.000103318496,0.0010263657,0.0007072155,0.000060666673],"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":true,"study_design_scores_codex":[0.00028354555,0.000029024412,0.7429412,0.00029979885,0.00007318382,0.013452373,0.000018205257,0.0021248665,0.000013195101,0.0000049944865,0.2404097,0.00034990392],"study_design_scores_gemma":[0.00054553605,0.00001408291,0.6312763,0.00007943887,0.000024635949,0.0004918362,0.00021591998,0.0015893354,0.0000012544499,0.00000828321,0.3654714,0.000281953],"about_ca_topic_score_codex":0.9954872,"about_ca_topic_score_gemma":0.9995792,"teacher_disagreement_score":0.12506169,"about_ca_system_score_codex":0.005863943,"about_ca_system_score_gemma":0.06306929,"threshold_uncertainty_score":0.99989134},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065692544,0.0000460763,0.87731916,0.000055032175,0.0000696484,0.0003467051,0.056333635,0.000064540254,0.00007267745],"genre_scores_gemma":[0.030640397,0.000009483723,0.96021545,0.00001851381,0.000025098072,0.000013022689,0.009051994,0.00002115398,0.000004868419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972407,0.00073093723,0.0007449434,0.00029833073,0.000785209,0.00019985139],"domain_scores_gemma":[0.9799578,0.01813942,0.00038167895,0.00084914686,0.00055064616,0.000121306046],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0067684664,0.00014036008,0.0007023966,0.00007138305,0.000049151236,0.00001785853,0.00020547715,0.0000711036,0.0002468952],"category_scores_gemma":[0.13266502,0.0001257252,0.000029698718,0.00030634226,0.0002642483,0.00009524186,0.0003833574,0.00010820599,0.0000059094064],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005332855,0.0015973772,0.100655854,0.009234239,0.0018097878,0.000022815624,0.0029373015,0.00036406858,0.004386616,0.0630037,0.0064609065,0.80899405],"study_design_scores_gemma":[0.00057157985,0.00003624844,0.08499465,0.00019746994,0.0005051069,6.1677656e-7,0.00011727634,0.53257364,0.00026391554,0.3806261,0.00003137076,0.000082036204],"about_ca_topic_score_codex":0.00039239152,"about_ca_topic_score_gemma":0.000037884805,"teacher_disagreement_score":0.80891204,"about_ca_system_score_codex":0.00003381117,"about_ca_system_score_gemma":0.00014712264,"threshold_uncertainty_score":0.8746409},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016793994,0.0038916804,0.022214482,0.003628031,0.95354414,0.0029019,0.013432907,0.00026352092,0.0001065318],"genre_scores_gemma":[0.0021564667,0.019818045,0.017626764,0.00015183377,0.9327816,0.00096249307,0.026324825,0.000094712086,0.00008328165],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944577,0.00046117615,0.0016491851,0.00056786207,0.00195844,0.00090559066],"domain_scores_gemma":[0.9969908,0.00044334828,0.00063481444,0.00076569204,0.000846244,0.00031911134],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005544442,0.00029464808,0.0011561593,0.0025808727,0.00010334771,0.00012860108,0.00046490703,0.0008236975,0.000006270645],"category_scores_gemma":[0.0015467132,0.00029946506,0.000041205047,0.0015899568,0.00033655856,0.0003609584,0.00038169653,0.0023523585,0.000012411106],"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":[0.00007787311,0.00010111322,0.001977495,0.009938621,0.000030729174,0.000002021066,0.0008462976,9.581862e-7,4.0467704e-8,0.00012929105,0.793375,0.19352055],"study_design_scores_gemma":[0.0010353043,0.00035518737,0.0011498765,0.0011312785,0.000006334103,6.49588e-7,0.0027949768,0.00079076726,2.1238534e-7,0.001467678,0.9911048,0.00016292991],"about_ca_topic_score_codex":0.018233364,"about_ca_topic_score_gemma":0.0059034,"teacher_disagreement_score":0.19772978,"about_ca_system_score_codex":0.0016264434,"about_ca_system_score_gemma":0.010675667,"threshold_uncertainty_score":0.9999493},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031328786,0.00014262226,0.011752331,0.00047301681,0.00038847086,0.00088573707,0.005511379,0.00040383948,0.9804395],"genre_scores_gemma":[0.00067417236,0.0004248975,0.0071640452,0.0024079627,0.0005126405,0.000043181863,0.0059570526,0.00023587624,0.9825802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977008,0.000043092547,0.0004446124,0.000653237,0.0007687721,0.00038948597],"domain_scores_gemma":[0.9972772,0.0009403752,0.00021594159,0.0012849886,0.00012210065,0.00015939484],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00033860625,0.00048139194,0.00066168734,0.00011779809,0.00012575647,0.000047972248,0.00031969647,0.0002328736,0.0020316779],"category_scores_gemma":[0.0003792248,0.00031986812,0.00018736451,0.00007609986,0.00022008373,0.00003585795,0.00021355726,0.0006956517,0.0017251886],"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":[0.00016318548,0.00004279776,0.000020358995,0.00031646743,0.00036434244,0.00021871402,0.000119083175,0.00005542362,0.0000011751943,0.6182055,0.37197164,0.008521293],"study_design_scores_gemma":[0.001796037,0.000386491,0.0006961233,0.0011873756,0.0006207321,0.00004482311,0.00018448646,0.0066759903,0.00000492375,0.071687266,0.9155437,0.0011720775],"about_ca_topic_score_codex":0.000053714448,"about_ca_topic_score_gemma":0.000046623318,"teacher_disagreement_score":0.54651827,"about_ca_system_score_codex":0.00013714364,"about_ca_system_score_gemma":0.0001869001,"threshold_uncertainty_score":0.9999253},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96943295,0.021507513,0.004842251,0.0014790873,0.00016766578,0.0012248673,0.0011866415,0.00013485449,0.000024145618],"genre_scores_gemma":[0.9963799,0.0006894684,0.0007721479,0.00027673968,0.00014035782,0.000048060716,0.0016409556,0.00004625952,0.0000061014703],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969818,0.00031359593,0.0008840701,0.0008613212,0.00030860936,0.00065060577],"domain_scores_gemma":[0.998262,0.00020551964,0.00032465465,0.00047931832,0.00008572216,0.00064281427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025300514,0.0002691577,0.00087119144,0.0002551177,0.00015736897,0.00036045953,0.00015114501,0.00009161168,6.2444184e-7],"category_scores_gemma":[0.0003924725,0.00025270382,0.000042437503,0.00033090604,0.00015145344,0.00053901074,0.00022862198,0.00020768236,2.6711038e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017989405,0.000086247885,0.89432174,0.008969121,0.00009111006,0.000024248715,0.0011036905,0.000027844537,0.00063603016,0.0003297382,0.000092423885,0.09413789],"study_design_scores_gemma":[0.003495819,0.0007069524,0.07328612,0.0011310038,0.0000063581774,0.0004463378,0.0024703227,0.9150527,0.000009583051,0.000033317418,0.0027878934,0.00057360536],"about_ca_topic_score_codex":0.00031997758,"about_ca_topic_score_gemma":0.00015657877,"teacher_disagreement_score":0.9150248,"about_ca_system_score_codex":0.00012072334,"about_ca_system_score_gemma":0.0010242796,"threshold_uncertainty_score":0.99999255},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99271315,0.0004662379,0.00009067753,0.002315757,0.000044817592,0.0033563145,0.00039599548,0.00018979835,0.00042723957],"genre_scores_gemma":[0.9973894,0.000039343315,0.00011311735,0.00014371127,0.00009365864,0.0015946693,0.000107555716,0.000028306298,0.0004902337],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9963322,0.0011021249,0.00049171224,0.00051149,0.0007302411,0.00083219964],"domain_scores_gemma":[0.99257845,0.0062425667,0.000052934025,0.00044424678,0.000339233,0.000342553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058710584,0.0002051423,0.00041699997,0.00064505223,0.0003703596,0.00012679661,0.00035523277,0.00010188183,0.00007813236],"category_scores_gemma":[0.004227434,0.00013645459,0.00005735098,0.0011847996,0.00055132544,0.00029357505,0.00037472163,0.0010941685,0.000110074914],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005041816,0.00020345411,0.98344266,0.0017827554,0.00005436957,0.00004593251,0.0065832343,0.00008528514,0.0003094429,0.00003541589,0.0037105484,0.0032427176],"study_design_scores_gemma":[0.0033163887,0.0009669563,0.9275592,0.0003034537,0.000012651191,0.000046563087,0.0053755906,0.057500143,0.000013331449,0.00020614573,0.0045397906,0.0001598063],"about_ca_topic_score_codex":0.0001250004,"about_ca_topic_score_gemma":0.0003133419,"teacher_disagreement_score":0.057414856,"about_ca_system_score_codex":0.0005097162,"about_ca_system_score_gemma":0.0013374466,"threshold_uncertainty_score":0.55644566},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7561137,0.19326927,0.0013659525,0.022887604,0.012924538,0.00035759778,0.0047965953,0.000020223053,0.008264528],"genre_scores_gemma":[0.9721181,0.00506165,0.00827279,0.0014008142,0.0063534984,0.0000025879417,0.00017076617,0.0002354462,0.006384353],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9893156,0.0012449158,0.0039726305,0.0007442868,0.0015613358,0.0031612294],"domain_scores_gemma":[0.9705271,0.00097574026,0.0030521378,0.0012055954,0.0066968035,0.017542599],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034168852,0.0012007214,0.0029566463,0.010400247,0.0010539166,0.0010276154,0.0020373852,0.0010450307,0.01988187],"category_scores_gemma":[0.0059454725,0.0013086634,0.0019823567,0.0066424804,0.0013818484,0.0019345905,0.000078503464,0.0041605667,0.00033128122],"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":true,"study_design_scores_codex":[0.00010255109,0.00018481919,0.8235597,0.0006059096,0.002410037,0.041548677,0.00043601065,0.0004429136,0.00015271407,0.00005525381,0.11811323,0.012388188],"study_design_scores_gemma":[0.0034516526,0.00060003035,0.7962052,0.0061887307,0.0013891263,0.03565131,0.0034069463,0.00013311786,0.0007747641,0.000034736102,0.15107968,0.0010846542],"about_ca_topic_score_codex":0.01432479,"about_ca_topic_score_gemma":0.03475838,"teacher_disagreement_score":0.2160044,"about_ca_system_score_codex":0.0049429904,"about_ca_system_score_gemma":0.08485066,"threshold_uncertainty_score":0.9989363},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001113923,0.010279827,0.00022168824,0.00011428642,0.0016441314,0.0021588225,0.008606012,0.00033121745,0.97653264],"genre_scores_gemma":[0.0026800241,0.0005157054,0.00045930085,0.00029999984,0.001890204,0.0000989829,0.0029871955,0.00017884285,0.9908897],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975901,0.000012539185,0.00061466114,0.0011519863,0.0003230858,0.00030761844],"domain_scores_gemma":[0.99747187,0.00015994777,0.00016347831,0.0018269176,0.00012589051,0.00025191685],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044213628,0.00043715085,0.000753877,0.00016207666,0.00006434723,0.000075130476,0.00045295205,0.00028867077,0.00008812033],"category_scores_gemma":[0.00013947894,0.0003902964,0.00015079236,0.000017294935,0.0002773385,0.000041091887,0.00070548046,0.00040350223,0.00022030671],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001855298,0.000010962018,0.000013750081,0.001077217,0.000268732,0.00008173956,0.0001499252,7.802169e-8,0.000026854892,0.004543376,0.0011204878,0.99252135],"study_design_scores_gemma":[0.000116097915,0.00009331142,0.000007603854,0.001389542,0.00070378755,0.00007598767,0.000024179064,0.00022655759,0.000014700807,0.046434652,0.95053905,0.00037454732],"about_ca_topic_score_codex":6.3704994e-7,"about_ca_topic_score_gemma":0.000093330746,"teacher_disagreement_score":0.9921468,"about_ca_system_score_codex":0.00008740403,"about_ca_system_score_gemma":0.000386999,"threshold_uncertainty_score":0.9998549},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94142413,0.02851886,0.0065012407,0.012642876,0.000524045,0.0024098484,0.007509987,0.00034812558,0.00012089871],"genre_scores_gemma":[0.98710215,0.004720536,0.004118695,0.0019829401,0.00013424017,0.000033044947,0.0016450996,0.000039990482,0.00022329653],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99762326,0.0003019327,0.0006340295,0.00053966185,0.0003169655,0.00058413035],"domain_scores_gemma":[0.9976893,0.00035232102,0.00016741124,0.00040090873,0.00024130495,0.0011487186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010108473,0.0002115623,0.0005992249,0.00024047984,0.00015999592,0.00014022477,0.00006861336,0.00007534065,0.00003618636],"category_scores_gemma":[0.0006394893,0.0001955735,0.000035873283,0.0005829435,0.00013873533,0.00026803548,0.000059497408,0.00018111222,0.000004142316],"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":[0.00092225464,0.0004778212,0.43927255,0.01137118,0.00022607017,0.00037751495,0.001547221,0.000027244478,0.00069778296,0.0026729768,0.08246567,0.4599417],"study_design_scores_gemma":[0.0021182348,0.0020665908,0.7767285,0.00071932015,0.000010010077,0.00067169114,0.0008140381,0.01357216,0.000011538204,0.00011107646,0.20255199,0.0006248198],"about_ca_topic_score_codex":0.0008344758,"about_ca_topic_score_gemma":0.00022165127,"teacher_disagreement_score":0.45931688,"about_ca_system_score_codex":0.00016707732,"about_ca_system_score_gemma":0.0021697863,"threshold_uncertainty_score":0.7975256},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9143562,0.008778115,0.061143067,0.004389798,0.0015821969,0.0076378286,0.00061320595,0.0011299971,0.00036963462],"genre_scores_gemma":[0.99320394,0.000041568688,0.004835987,0.00045224107,0.00058287743,0.00051476696,0.00015954926,0.00006737318,0.0001417228],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99719065,0.00033069917,0.0006744025,0.0007517457,0.00032666803,0.0007258197],"domain_scores_gemma":[0.9972507,0.00035602774,0.00019589336,0.0005588132,0.00023834573,0.0014002372],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023543804,0.00028983672,0.00068282016,0.0002646281,0.00048179665,0.00034271632,0.00009689382,0.000101790974,0.000004100381],"category_scores_gemma":[0.0009534691,0.0002716317,0.00008732914,0.00051837193,0.00007478566,0.0003403249,0.00013558051,0.00027066583,0.0000029189537],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005023072,0.00016890047,0.82715166,0.018035797,0.00037259833,0.00023120269,0.0028841489,0.0001342831,0.0010754192,0.0004958003,0.005244283,0.13918287],"study_design_scores_gemma":[0.008718878,0.002514597,0.36534297,0.0028968567,0.000056432582,0.00077387155,0.0017452931,0.3415031,0.000072916846,0.00018095694,0.27440536,0.001788761],"about_ca_topic_score_codex":0.0005729051,"about_ca_topic_score_gemma":0.0006383501,"teacher_disagreement_score":0.46180865,"about_ca_system_score_codex":0.0005486132,"about_ca_system_score_gemma":0.0012951168,"threshold_uncertainty_score":0.9999736},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08713015,0.019730046,0.0006486754,0.88884604,0.00020727782,0.0016876084,0.001116092,0.00037308532,0.00026104468],"genre_scores_gemma":[0.7790698,0.0060483073,0.0041644303,0.20734894,0.0022031842,0.0001928346,0.00069706567,0.00009801208,0.00017744918],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99448484,0.0012710026,0.0010386575,0.0009096842,0.0009239129,0.0013719185],"domain_scores_gemma":[0.9905094,0.0056645684,0.0007623778,0.0012432121,0.00023760831,0.001582857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005397608,0.00042053405,0.0013098774,0.000198757,0.00072703016,0.00012105872,0.000492832,0.000060292867,0.00002022739],"category_scores_gemma":[0.0022247946,0.0002258983,0.00009841304,0.0020122873,0.00069754594,0.0001562097,0.00020085684,0.000778063,0.000026186037],"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":[0.0012038433,0.00027517026,0.13982846,0.004552056,0.00028265672,0.00012220914,0.014309057,0.00004342803,0.000008116465,0.0026873301,0.379162,0.45752567],"study_design_scores_gemma":[0.0017308241,0.0018657896,0.36662418,0.0008616626,0.000011654479,0.00076318404,0.0015908984,0.0004706343,3.772512e-7,0.0002152295,0.62556577,0.00029983034],"about_ca_topic_score_codex":0.002427198,"about_ca_topic_score_gemma":0.0025678498,"teacher_disagreement_score":0.6919396,"about_ca_system_score_codex":0.00097114,"about_ca_system_score_gemma":0.0049535674,"threshold_uncertainty_score":0.92118657},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92576736,0.0014488746,0.00039042954,0.0008097442,0.00047254117,0.0021957227,0.00035032356,0.00013608702,0.06842892],"genre_scores_gemma":[0.9985827,0.000053940123,0.00031881983,0.0001306311,0.00013744058,0.000090665795,0.00006576766,0.000033466884,0.00058652484],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99807507,0.00014888683,0.0005676871,0.00052862655,0.00047388146,0.00020582513],"domain_scores_gemma":[0.99847883,0.00008671756,0.00025028782,0.00095265324,0.00019838121,0.00003311418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071924354,0.00026949574,0.0007026496,0.000100685626,0.000014816246,0.000016412367,0.00024905874,0.00020120184,0.00016427007],"category_scores_gemma":[0.0002077763,0.00018664698,0.00026030897,0.00014742125,0.00013358404,0.000057150934,0.0007287857,0.00053494924,0.000032212538],"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":[0.008055397,0.004093022,0.21459988,0.024355276,0.004926205,0.00032739653,0.04092708,0.00023164824,0.62851214,0.002679554,0.021271246,0.05002116],"study_design_scores_gemma":[0.0039643063,0.0006590671,0.63806087,0.0040869494,0.0012269631,0.00006058755,0.0030509536,0.010418124,0.3269529,0.007180469,0.003254072,0.0010847405],"about_ca_topic_score_codex":0.016483096,"about_ca_topic_score_gemma":0.017477047,"teacher_disagreement_score":0.423461,"about_ca_system_score_codex":0.0003685041,"about_ca_system_score_gemma":0.00016957613,"threshold_uncertainty_score":0.99006623},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92576736,0.0014488746,0.00039042954,0.0008097442,0.00047254117,0.0021957227,0.00035032356,0.00013608702,0.06842892],"genre_scores_gemma":[0.9985827,0.000053940123,0.00031881983,0.0001306311,0.00013744058,0.000090665795,0.00006576766,0.000033466884,0.00058652484],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99807507,0.00014888683,0.0005676871,0.00052862655,0.00047388146,0.00020582513],"domain_scores_gemma":[0.99847883,0.00008671756,0.00025028782,0.00095265324,0.00019838121,0.00003311418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071924354,0.00026949574,0.0007026496,0.000100685626,0.000014816246,0.000016412367,0.00024905874,0.00020120184,0.00016427007],"category_scores_gemma":[0.0002077763,0.00018664698,0.00026030897,0.00014742125,0.00013358404,0.000057150934,0.0007287857,0.00053494924,0.000032212538],"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":[0.008055397,0.004093022,0.21459988,0.024355276,0.004926205,0.00032739653,0.04092708,0.00023164824,0.62851214,0.002679554,0.021271246,0.05002116],"study_design_scores_gemma":[0.0039643063,0.0006590671,0.63806087,0.0040869494,0.0012269631,0.00006058755,0.0030509536,0.010418124,0.3269529,0.007180469,0.003254072,0.0010847405],"about_ca_topic_score_codex":0.016483096,"about_ca_topic_score_gemma":0.017477047,"teacher_disagreement_score":0.423461,"about_ca_system_score_codex":0.0003685041,"about_ca_system_score_gemma":0.00016957613,"threshold_uncertainty_score":0.99006623},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95294625,0.0038947754,0.02793814,0.01060654,0.00004848518,0.0028291317,0.001674054,0.000043836633,0.000018787654],"genre_scores_gemma":[0.9941466,0.0003522897,0.00012026427,0.0045208978,0.000030193645,0.00029246628,0.00050394144,0.000019874476,0.000013460423],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975061,0.00038570544,0.0007550082,0.00042219908,0.0004333363,0.00049766625],"domain_scores_gemma":[0.9977777,0.0009352273,0.00017914613,0.00034062366,0.00016629252,0.0006009954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041933763,0.00021210258,0.0006012158,0.0010224602,0.00018271197,0.00014135588,0.00014887417,0.00005307429,0.0000026211867],"category_scores_gemma":[0.0007406656,0.00014600287,0.00008698566,0.0030781948,0.0000994918,0.00019046738,0.00007555443,0.00018124262,2.3550908e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020223623,0.000056491466,0.98284936,0.0019184645,0.00029174646,0.0000029799003,0.0032196892,0.0008265859,0.000035801037,0.0010229141,0.0014278345,0.006325766],"study_design_scores_gemma":[0.004355897,0.000677539,0.4537846,0.0006229793,0.00005097546,0.000008316577,0.0062793293,0.43885434,9.621285e-7,0.0004023242,0.094525605,0.00043712367],"about_ca_topic_score_codex":0.00017522114,"about_ca_topic_score_gemma":0.00046197727,"teacher_disagreement_score":0.5290648,"about_ca_system_score_codex":0.00014176703,"about_ca_system_score_gemma":0.00036162604,"threshold_uncertainty_score":0.5953824},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98261136,0.010010376,0.00021849695,0.00023164452,0.0000588172,0.002942004,0.0036690242,0.00003812463,0.00022017818],"genre_scores_gemma":[0.9974291,0.0006347715,0.00083864987,0.00007236826,0.000034880224,0.00022451425,0.00067904085,0.00003354121,0.000053157248],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9975656,0.00022854781,0.00056012534,0.00040105308,0.00069285725,0.00055179885],"domain_scores_gemma":[0.99797827,0.00086971116,0.00008807505,0.0003447343,0.00034991896,0.00036927735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001690063,0.00012599451,0.00035369548,0.00042376117,0.000097071665,0.00004616353,0.0001412463,0.00005303134,0.00006008603],"category_scores_gemma":[0.00011612734,0.00011571163,0.000052281917,0.0005558037,0.00008191046,0.00023610477,0.00022687964,0.00019354775,0.0000042907845],"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":[0.0031946355,0.0001983685,0.5294612,0.18588158,0.0002583939,0.0000611739,0.2465079,0.000734988,0.0002476702,0.0012728836,0.0011982265,0.030983003],"study_design_scores_gemma":[0.013184388,0.0045264605,0.30235508,0.0068416367,0.000117473406,0.000004944308,0.5844518,0.0653306,0.0011186387,0.002163406,0.019280735,0.00062485354],"about_ca_topic_score_codex":0.00017549144,"about_ca_topic_score_gemma":0.0007921374,"teacher_disagreement_score":0.33794388,"about_ca_system_score_codex":0.00035761978,"about_ca_system_score_gemma":0.0011427859,"threshold_uncertainty_score":0.47185832},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8975042,0.06992835,0.019117897,0.0025302977,0.0053003207,0.001238974,0.0018268611,0.0003866425,0.0021664503],"genre_scores_gemma":[0.99621,0.0020960462,0.00017752092,0.00014108332,0.00047935103,0.00025076538,0.0002723994,0.000024662155,0.0003482205],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99808013,0.0001795099,0.00044085953,0.0006195802,0.00028563713,0.000394301],"domain_scores_gemma":[0.999016,0.00020184892,0.00007866187,0.00040933487,0.00009703838,0.00019711528],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035163673,0.00020571076,0.00062893477,0.00054367614,0.000028072864,0.000019138177,0.0001915177,0.00012453076,0.0014038541],"category_scores_gemma":[0.00016302665,0.00018631345,0.00020753883,0.0012241758,0.00012221285,0.00010176985,0.00014018593,0.00047025894,0.0001298307],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019455538,0.00045681174,0.7345639,0.0012714637,0.0006137189,0.0003608031,0.0004088397,0.000107345906,0.00021970258,0.00005453915,0.008004691,0.25374362],"study_design_scores_gemma":[0.0011287304,0.00013113717,0.7964209,0.00043490832,0.001740649,0.000017884922,0.00001506089,0.088025555,0.00003279737,0.00004245941,0.11178282,0.00022714051],"about_ca_topic_score_codex":0.00030606764,"about_ca_topic_score_gemma":0.0025322435,"teacher_disagreement_score":0.25351647,"about_ca_system_score_codex":0.0007988372,"about_ca_system_score_gemma":0.0007290183,"threshold_uncertainty_score":0.999509},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057487497,0.96107984,0.022724817,0.000058616795,0.0021137611,0.003893043,0.0013622364,0.0022325856,0.0007863267],"genre_scores_gemma":[0.081484586,0.90579545,0.00020044758,0.00011262699,0.0018253205,0.0015122199,0.008042734,0.000647591,0.00037900909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98974526,0.0008213313,0.0032274828,0.0034903765,0.0013627493,0.0013528212],"domain_scores_gemma":[0.99093986,0.0023909202,0.0012434247,0.0037671477,0.00030316107,0.0013555109],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00070106686,0.0021006113,0.0042247875,0.0010536566,0.00065344624,0.00053824385,0.00085765374,0.00094674167,0.000065821616],"category_scores_gemma":[0.000850334,0.0015799976,0.0023797085,0.0025372365,0.00035237076,0.0005245645,0.0010811456,0.0033042473,0.00034968182],"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":[0.0005782887,0.0017483708,0.010486147,0.022937687,0.0032536844,0.019349018,0.002418778,0.82813925,2.5098998e-7,0.000247834,0.0019972913,0.10884341],"study_design_scores_gemma":[0.0019608883,0.000064675034,0.00009094983,0.11906531,0.01894597,0.00072050776,0.00033922272,0.63075334,3.523892e-8,0.010467886,0.21456295,0.003028247],"about_ca_topic_score_codex":0.0006693667,"about_ca_topic_score_gemma":0.0004254557,"teacher_disagreement_score":0.21256566,"about_ca_system_score_codex":0.0021679022,"about_ca_system_score_gemma":0.0034222794,"threshold_uncertainty_score":0.9991735},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01675037,0.0009966784,0.93660474,0.0018280143,0.00008803126,0.0005074354,0.043063577,0.00004811343,0.00011301993],"genre_scores_gemma":[0.7059674,0.00027342438,0.28662145,0.00029479095,0.00019678359,0.000020979945,0.0065359864,0.000019523643,0.00006965266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969112,0.00006540007,0.0005586228,0.00091518206,0.0010132886,0.0005362567],"domain_scores_gemma":[0.99537283,0.0018043579,0.00025431797,0.0015563814,0.000568408,0.00044372454],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0032803377,0.00023783238,0.00048422956,0.0002732124,0.00072092394,0.0016125968,0.0017137503,0.00002891526,0.00008944123],"category_scores_gemma":[0.0038377258,0.00012282527,0.0000401392,0.0017805308,0.0014889144,0.004390135,0.0014710703,0.00030985137,0.0000054583347],"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":[0.0210323,0.0017244259,0.15347819,0.009048446,0.026946789,0.0057237386,0.015345705,0.0043520667,0.00013391284,0.010517534,0.26163873,0.49005815],"study_design_scores_gemma":[0.0009457997,0.00031895787,0.029718323,0.00044321478,0.0033328093,0.00089748285,0.0013579315,0.96021897,0.0000039113006,0.0005750611,0.0019397953,0.00024776504],"about_ca_topic_score_codex":0.000028282306,"about_ca_topic_score_gemma":0.00018774875,"teacher_disagreement_score":0.9558669,"about_ca_system_score_codex":0.00005052706,"about_ca_system_score_gemma":0.0014408325,"threshold_uncertainty_score":0.9994238},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13367078,0.008736152,0.00019689907,0.8401015,0.0005997381,0.0023745296,0.013426908,0.00051104487,0.00038245862],"genre_scores_gemma":[0.6379744,0.0033555287,0.0016588018,0.3451342,0.002731749,0.00045692688,0.003537502,0.00023488981,0.0049160016],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99301344,0.001292358,0.0010120509,0.0015430001,0.0014525129,0.0016866288],"domain_scores_gemma":[0.99262524,0.00064480776,0.00021250501,0.002602165,0.0001683679,0.0037469475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006577941,0.0005061009,0.0009079736,0.00028776555,0.00044103648,0.0004477207,0.0011323895,0.00018991376,0.00082013826],"category_scores_gemma":[0.0016592573,0.0003676492,0.00010443955,0.0016609547,0.00011594245,0.000502189,0.00089574134,0.0009094057,0.0006724077],"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":[0.0004984254,0.000051315426,0.017847244,0.00041615928,0.0000871236,0.000016985985,0.001887318,6.814696e-7,0.000016976355,0.000067501715,0.85845643,0.12065385],"study_design_scores_gemma":[0.0004332804,0.00040653985,0.1783217,0.00006641028,0.000001819355,0.000045440014,0.00080849556,0.00064529374,4.108437e-7,0.000010837301,0.81898576,0.00027400083],"about_ca_topic_score_codex":0.00010629214,"about_ca_topic_score_gemma":0.00024707863,"teacher_disagreement_score":0.50430363,"about_ca_system_score_codex":0.0006658711,"about_ca_system_score_gemma":0.0039869375,"threshold_uncertainty_score":0.9998776},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035084557,0.008280384,0.00012777558,0.00256638,0.9154017,0.0016273446,0.0005106421,0.0008549277,0.035546303],"genre_scores_gemma":[0.10616862,0.000051098436,0.00011393991,0.00010440398,0.0023350948,0.000112547714,0.027579732,0.00011500096,0.86341953],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954296,0.000095478455,0.00087406643,0.0018691246,0.0012965911,0.00043514234],"domain_scores_gemma":[0.99690974,0.00006137569,0.000460839,0.0018272728,0.00023134118,0.00050940644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011173437,0.00039293163,0.0006102937,0.00089967373,0.00013993465,0.00048488547,0.0001624904,0.00028394783,0.0005761181],"category_scores_gemma":[0.0013690366,0.00037943022,0.00024310804,0.0010707064,0.00020289537,0.00025386235,0.00021397139,0.0009199029,0.0003623727],"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":[0.000069414426,0.00008174949,0.018731045,0.0003730046,0.00006242887,0.010063283,0.00022448,0.00006162186,0.00004310973,0.0000044462486,0.96924096,0.0010444405],"study_design_scores_gemma":[0.00017054238,0.000015266447,0.041620214,0.0026208586,0.0002374814,0.000100217796,0.00011944153,0.0031428249,0.000032752003,0.00077341177,0.9507807,0.00038630277],"about_ca_topic_score_codex":0.00038843765,"about_ca_topic_score_gemma":0.0013152808,"teacher_disagreement_score":0.91306657,"about_ca_system_score_codex":0.00054666057,"about_ca_system_score_gemma":0.0018386521,"threshold_uncertainty_score":0.9998658},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99317765,0.0017905856,0.000118766824,0.001293605,0.00028910654,0.002727929,0.0003816679,0.00007881682,0.00014184914],"genre_scores_gemma":[0.98526406,0.00032752275,0.010862494,0.00021789545,0.00008414493,0.0013153648,0.0010250547,0.000038892693,0.00086458825],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99799544,0.000055268643,0.00066259946,0.00061768235,0.0003815052,0.0002874899],"domain_scores_gemma":[0.9988075,0.00012860556,0.00008424099,0.00039773158,0.00042292327,0.00015901482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010385157,0.0002379877,0.00070147135,0.0007356441,0.000019483408,0.000030208435,0.00012619332,0.00012275387,0.000042361335],"category_scores_gemma":[0.00031127335,0.00019908434,0.000084165855,0.0002846434,0.00008039545,0.000033155244,0.0011834773,0.00027901435,0.000012582354],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048084008,0.0032203053,0.50414574,0.16793986,0.018747224,0.0006269724,0.039699454,0.00046446256,0.049985018,0.0016611525,0.057069443,0.15163195],"study_design_scores_gemma":[0.0026652084,0.00009880514,0.94946706,0.0032857135,0.00041250107,0.000012128637,0.0010603444,0.00044258556,0.03321677,0.0014604654,0.0072509074,0.0006274788],"about_ca_topic_score_codex":0.000055326494,"about_ca_topic_score_gemma":0.00020543781,"teacher_disagreement_score":0.44532132,"about_ca_system_score_codex":0.00010879395,"about_ca_system_score_gemma":0.00026502262,"threshold_uncertainty_score":0.81184244},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9856538,0.00978713,0.000030345758,0.0010768855,0.00035241048,0.00089310936,0.0019161875,0.000084882646,0.00020524974],"genre_scores_gemma":[0.9970909,0.0009087102,0.00003441984,0.00026198223,0.00013199639,0.0001641291,0.0012568167,0.000037976177,0.000113122514],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959316,0.0008971318,0.0011588234,0.0006718973,0.00067672116,0.0006637868],"domain_scores_gemma":[0.99651194,0.0013105243,0.00031343207,0.0006887074,0.000358785,0.00081660604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031326204,0.0002623736,0.00066279405,0.00018673918,0.00014474224,0.00014815603,0.00022470122,0.00011840093,0.00012343502],"category_scores_gemma":[0.0025104303,0.00020366766,0.00012368434,0.000817252,0.00041144047,0.00021307175,0.00011436413,0.00028942287,0.000020794858],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009907562,0.00020007865,0.98733795,0.00042408542,0.000068778354,0.000011545163,0.00038651086,0.0000066040025,0.000093283976,0.00051970873,0.0002749182,0.0096857995],"study_design_scores_gemma":[0.0009495392,0.00014010414,0.9621201,0.000053463915,7.985347e-7,0.000002986088,0.000071318624,0.0018124211,0.0000052403034,0.0004393549,0.03423799,0.00016666793],"about_ca_topic_score_codex":0.0011845508,"about_ca_topic_score_gemma":0.0020176999,"teacher_disagreement_score":0.03396307,"about_ca_system_score_codex":0.00016107598,"about_ca_system_score_gemma":0.002017205,"threshold_uncertainty_score":0.8305326},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9874477,0.0014355925,0.008960144,0.00009569653,0.00021745409,0.0012077204,0.00050814234,0.000060647886,0.00006690301],"genre_scores_gemma":[0.99112314,0.000016013682,0.008463057,0.000016723088,0.00007823311,0.00004547914,0.00019504565,0.000031360596,0.00003093685],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99739546,0.00040909837,0.00086023443,0.0007143946,0.00039016496,0.00023066685],"domain_scores_gemma":[0.99851465,0.00038293021,0.0002535145,0.0005551768,0.000113701426,0.00018004858],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057981274,0.00034006964,0.0011204801,0.00032804022,0.00003837569,0.000048374375,0.00012925187,0.0001382539,0.000030921135],"category_scores_gemma":[0.00040196237,0.00029115158,0.000067551715,0.0002456289,0.000039228777,0.000047095626,0.00075951515,0.00060986914,3.902761e-7],"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":[0.000058896076,0.0001546964,0.8927191,0.0017915132,0.0006100346,0.0028460599,0.0016192538,0.00023767943,0.000027503564,0.0000020951047,0.000050298775,0.09988286],"study_design_scores_gemma":[0.0021146832,0.00062320416,0.97401595,0.0015725497,0.000990649,0.00016191957,0.0038017528,0.01571234,0.0001974958,0.00033700353,0.000006528277,0.00046593824],"about_ca_topic_score_codex":0.7989788,"about_ca_topic_score_gemma":0.9179061,"teacher_disagreement_score":0.118927285,"about_ca_system_score_codex":0.00022782007,"about_ca_system_score_gemma":0.0008421947,"threshold_uncertainty_score":0.99995404},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95823497,0.0005899282,0.036744077,0.0016931702,0.000907312,0.00036144932,0.0004264074,0.00003887137,0.0010038393],"genre_scores_gemma":[0.96035564,0.000031266147,0.039175134,0.000023079947,0.00012643397,1.1725467e-8,0.000011501536,0.000005342851,0.0002715628],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906254,0.000012106872,0.00016424188,0.00020116166,0.00041441972,0.0001455229],"domain_scores_gemma":[0.99900097,0.00007824905,0.00013748213,0.0002838805,0.00025983772,0.000239574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063683314,0.00006232751,0.0001841892,0.00028909591,0.00011374319,0.000030218094,0.0006094314,0.00002156959,0.000028356553],"category_scores_gemma":[0.00038720056,0.000053346193,0.000077368335,0.00049896404,0.00047723818,0.0010106666,0.00024300248,0.00010351106,0.000004004953],"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":[0.0070364834,0.00081433705,0.03418287,0.0019994616,0.00046060953,0.0025824814,0.0035981953,0.00052743,0.61512876,0.005222417,0.15554385,0.17290308],"study_design_scores_gemma":[0.018607594,0.0036186653,0.11678238,0.008882786,0.0021074503,0.0027907463,0.010109158,0.35560814,0.042038117,0.0004981183,0.43788537,0.001071476],"about_ca_topic_score_codex":0.000026569176,"about_ca_topic_score_gemma":0.000002580974,"teacher_disagreement_score":0.5730907,"about_ca_system_score_codex":0.00015571345,"about_ca_system_score_gemma":0.0019134223,"threshold_uncertainty_score":0.3394329},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001018087,0.000030164938,0.000848518,0.0010460296,0.00021406246,0.003044969,0.9933335,0.00033052047,0.00013410051],"genre_scores_gemma":[0.013142931,0.00021160426,0.00007198527,0.00023458281,0.00029552972,0.0000011464039,0.98456097,0.0012490207,0.00023224266],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99498266,0.0015040258,0.0011446403,0.000907715,0.0009608031,0.00050013297],"domain_scores_gemma":[0.99207,0.0005783025,0.0008790302,0.0051057567,0.0011107699,0.00025611397],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0025825007,0.00037893627,0.00058182376,0.00038223088,0.0017560212,0.0003941453,0.0035173683,0.00017635817,0.002296729],"category_scores_gemma":[0.005123059,0.00034502777,0.0001301143,0.00084072916,0.00036330908,0.00024240061,0.004545732,0.00091070746,0.003588248],"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":[0.0016766269,0.0004925138,0.000010136349,0.00037101854,0.0005581204,0.000020062289,0.0003184849,0.000036070905,0.00051133736,0.00000597509,0.99116135,0.004838307],"study_design_scores_gemma":[0.003341667,0.00020878267,0.0042910967,0.000254519,0.0003671466,0.000017134793,0.000547579,0.0020155308,0.0001293577,0.00005994623,0.9884685,0.0002986879],"about_ca_topic_score_codex":0.0012589147,"about_ca_topic_score_gemma":0.000094573625,"teacher_disagreement_score":0.012124844,"about_ca_system_score_codex":0.00024362153,"about_ca_system_score_gemma":0.000037976482,"threshold_uncertainty_score":0.99990016},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022364146,0.00014948574,0.00043304102,0.0017853851,0.0001328346,0.0014718765,0.9942339,0.0005223531,0.0010474599],"genre_scores_gemma":[0.0002549262,0.00030009027,0.00036602904,0.0041744667,0.00057540095,8.887836e-7,0.99162287,0.0018275839,0.0008777423],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99531126,0.0007445307,0.0006623667,0.0013950962,0.0009809483,0.00090578786],"domain_scores_gemma":[0.99416363,0.00014831129,0.00049443863,0.0027520526,0.0010722494,0.0013693041],"candidate_categories":["metaresearch","metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000829415,0.00053135445,0.0008980699,0.0006240533,0.0015596084,0.00073470373,0.0017879693,0.00014171746,0.04034191],"category_scores_gemma":[0.00838243,0.0005517488,0.00023203874,0.0013456852,0.00062764913,0.00018301987,0.002336297,0.0007118191,0.012032404],"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":[0.00067090424,0.00043554694,0.000014752852,0.0009862668,0.00041616152,0.00035154604,0.00004315042,0.0000035992934,0.000024846388,0.000016819242,0.9939556,0.003080779],"study_design_scores_gemma":[0.0017432269,0.0005989801,0.0006632243,0.00007121369,0.00032445398,0.00027779167,0.0002719241,0.000025867386,0.000007782717,0.000006626574,0.9954839,0.0005250493],"about_ca_topic_score_codex":0.00046377364,"about_ca_topic_score_gemma":0.000055568304,"teacher_disagreement_score":0.02830951,"about_ca_system_score_codex":0.0012379199,"about_ca_system_score_gemma":0.00011721433,"threshold_uncertainty_score":0.9999704},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016500951,0.95753884,0.000030688047,0.007764198,0.00042786828,0.029676596,0.0041581546,0.00016002326,0.0002271136],"genre_scores_gemma":[0.000022150474,0.9896213,0.0015922005,0.0042028716,0.00021807328,0.0018174992,0.0022314063,0.00015316713,0.00014137098],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9869423,0.00607884,0.0037077887,0.0011760992,0.0011610229,0.000933945],"domain_scores_gemma":[0.9890043,0.0027095694,0.0030661514,0.004070066,0.00041212598,0.00073775876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.014556776,0.0009478183,0.009396361,0.00021720867,0.00034619184,0.00039817422,0.0021198324,0.00024507486,0.00021625518],"category_scores_gemma":[0.004256294,0.0006847563,0.0005616099,0.0014550987,0.00045630822,0.0007871426,0.0033999304,0.0014640597,0.00019760274],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004929569,0.000080085956,0.0037139982,0.9254499,0.00046870843,0.000042990687,0.0002426677,1.842221e-8,3.078465e-9,0.000025649419,0.005212566,0.06471411],"study_design_scores_gemma":[0.0003983603,0.00020765104,0.0018049115,0.7873167,0.00025223487,0.00007983809,0.000046405185,0.0000012874201,1.6873628e-8,0.000028931345,0.2094176,0.00044609114],"about_ca_topic_score_codex":0.0120306825,"about_ca_topic_score_gemma":0.0055474923,"teacher_disagreement_score":0.20420504,"about_ca_system_score_codex":0.00094542914,"about_ca_system_score_gemma":0.013441363,"threshold_uncertainty_score":0.99956036},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038018553,0.0003769654,9.761648e-7,0.000066445755,0.000053068463,0.0010037848,0.9945432,0.0001419768,0.000011718969],"genre_scores_gemma":[0.0017602908,0.0000106304215,0.0023499564,0.00009037857,0.000044104105,0.00044787247,0.995232,0.000045313274,0.000019453793],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99616927,0.0005065849,0.0012436485,0.00092497095,0.000663074,0.00049244775],"domain_scores_gemma":[0.9963676,0.000720336,0.0011295545,0.0008287842,0.00049970055,0.00045403859],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041673004,0.00044162266,0.0011783474,0.00038888602,0.00008431032,0.000034142537,0.00025390155,0.00021878826,0.33085647],"category_scores_gemma":[0.004908051,0.000490714,0.00007666442,0.0007324314,0.00002227466,0.000106611624,0.00022956435,0.0002448043,0.000091708316],"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":[0.0000755251,0.00014407803,0.000062948246,0.007908828,0.00006909562,0.00010664397,0.000021198612,0.000020839492,0.000005576116,2.1068422e-8,0.98990595,0.0016792669],"study_design_scores_gemma":[0.00057493255,0.00020799981,0.05191297,0.019119902,0.0000071816876,0.00005951921,0.00003856213,0.0020565453,0.00006126379,8.0746446e-8,0.9255136,0.0004474194],"about_ca_topic_score_codex":0.00007085914,"about_ca_topic_score_gemma":0.00089943537,"teacher_disagreement_score":0.33076477,"about_ca_system_score_codex":0.0003397324,"about_ca_system_score_gemma":0.0019317339,"threshold_uncertainty_score":0.9997544},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009961811,0.00069605256,6.407456e-7,0.000839205,0.00008240819,0.0012158459,0.9970607,0.00004560871,0.000049548966],"genre_scores_gemma":[0.000009407551,0.000041848612,0.00022166347,0.013033018,0.00005906252,0.0003707629,0.9859236,0.00003831457,0.00030234936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974191,0.00009832363,0.00061866886,0.0009847119,0.00052523153,0.00035391998],"domain_scores_gemma":[0.993767,0.0014207897,0.00044700754,0.003728979,0.00026713166,0.00036909452],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027973417,0.0003586915,0.00090637244,0.00021793728,0.00003181934,0.000033780732,0.0013071771,0.00026675162,0.15461825],"category_scores_gemma":[0.04340999,0.00037034153,0.00012429658,0.00032902777,0.000020013036,0.00018201375,0.0014369775,0.0006768734,0.0004090453],"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":[0.000093028044,0.00023636628,0.0006957547,0.0036972805,0.000060168466,0.0007199147,0.000019843872,0.000004079684,0.0000034628865,1.1812809e-7,0.9944023,0.00006767958],"study_design_scores_gemma":[0.001373535,0.0001026086,0.01305023,0.0072098617,0.00008858215,0.000039055372,0.000018199338,0.00004146035,0.00000451927,0.0000026438033,0.9777568,0.00031250145],"about_ca_topic_score_codex":0.02101656,"about_ca_topic_score_gemma":0.646327,"teacher_disagreement_score":0.6253104,"about_ca_system_score_codex":0.0005014274,"about_ca_system_score_gemma":0.006974789,"threshold_uncertainty_score":0.99987483},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016027481,0.0012179115,0.000012125998,0.00105687,0.000075500335,0.0018859971,0.9957002,0.000026625767,0.000008738751],"genre_scores_gemma":[0.000012724496,0.0007052411,0.00022485723,0.004212874,0.00026456238,0.0020405205,0.992439,0.000022225317,0.00007800664],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977767,0.00023914273,0.0005295911,0.0007296664,0.0003853561,0.00033955497],"domain_scores_gemma":[0.98870283,0.009555842,0.0003401644,0.0007954232,0.0003104128,0.00029532917],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000823977,0.00030723357,0.00072903646,0.000095584,0.00010973738,0.00004162586,0.00038938067,0.00018975233,0.025274584],"category_scores_gemma":[0.07597043,0.00022146065,0.000108646185,0.00018667082,0.000009291913,0.0001516167,0.0003100596,0.000326868,0.00003277157],"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":[0.00018972957,0.000028931709,0.00046181996,0.0014329763,0.00009885586,0.000056177552,0.0000010980181,0.000007765102,0.0000036060676,0.0000017263872,0.99711025,0.0006070391],"study_design_scores_gemma":[0.0012363186,0.00003486493,0.0074179615,0.00049870263,0.000121351746,0.000051135965,0.000009767331,0.0008333529,0.0000010144113,0.000030008077,0.98953503,0.00023048552],"about_ca_topic_score_codex":0.11957717,"about_ca_topic_score_gemma":0.80136776,"teacher_disagreement_score":0.6817906,"about_ca_system_score_codex":0.0011568922,"about_ca_system_score_gemma":0.0066910274,"threshold_uncertainty_score":0.9989401},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009961811,0.00069605256,6.407456e-7,0.000839205,0.00008240819,0.0012158459,0.9970607,0.00004560871,0.000049548966],"genre_scores_gemma":[0.000009407551,0.000041848612,0.00022166347,0.013033018,0.00005906252,0.0003707629,0.9859236,0.00003831457,0.00030234936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974191,0.00009832363,0.00061866886,0.0009847119,0.00052523153,0.00035391998],"domain_scores_gemma":[0.993767,0.0014207897,0.00044700754,0.003728979,0.00026713166,0.00036909452],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027973417,0.0003586915,0.00090637244,0.00021793728,0.00003181934,0.000033780732,0.0013071771,0.00026675162,0.15461825],"category_scores_gemma":[0.04340999,0.00037034153,0.00012429658,0.00032902777,0.000020013036,0.00018201375,0.0014369775,0.0006768734,0.0004090453],"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":[0.000093028044,0.00023636628,0.0006957547,0.0036972805,0.000060168466,0.0007199147,0.000019843872,0.000004079684,0.0000034628865,1.1812809e-7,0.9944023,0.00006767958],"study_design_scores_gemma":[0.001373535,0.0001026086,0.01305023,0.0072098617,0.00008858215,0.000039055372,0.000018199338,0.00004146035,0.00000451927,0.0000026438033,0.9777568,0.00031250145],"about_ca_topic_score_codex":0.02101656,"about_ca_topic_score_gemma":0.646327,"teacher_disagreement_score":0.6253104,"about_ca_system_score_codex":0.0005014274,"about_ca_system_score_gemma":0.006974789,"threshold_uncertainty_score":0.99987483},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6071631e-7,0.001454821,2.64966e-8,0.00023797438,0.00034479253,0.0007935463,0.9968502,0.00014766523,0.00017083103],"genre_scores_gemma":[2.10721e-7,0.00011085995,0.000011497291,0.001984913,0.00042554844,0.0005561884,0.9956255,0.000070290356,0.0012149969],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972149,0.000116041454,0.000441773,0.0008557135,0.0008689206,0.0005026549],"domain_scores_gemma":[0.996604,0.00015670438,0.00024375173,0.0025008111,0.00008182364,0.00041285777],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008808705,0.0004978382,0.0007938496,0.0002208612,0.000126139,0.000053302247,0.0009127099,0.00026746097,0.98781973],"category_scores_gemma":[0.0010048809,0.0004968722,0.00024789234,0.0003508037,0.000016754002,0.00011744968,0.0005178,0.0010413288,0.03059048],"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":[0.00010407465,0.00019272977,9.76956e-7,0.0016498897,0.00015818267,0.0015449424,0.000002761887,0.0000015314788,0.000001037254,7.172316e-8,0.99602854,0.00031526884],"study_design_scores_gemma":[0.00087664113,0.000100097124,0.00014900179,0.001180205,0.0001961511,0.00011840839,0.000004498815,0.000002394041,0.000003947761,0.0000010033547,0.9968878,0.00047987007],"about_ca_topic_score_codex":0.00008386653,"about_ca_topic_score_gemma":0.00013410118,"teacher_disagreement_score":0.95722926,"about_ca_system_score_codex":0.00014237314,"about_ca_system_score_gemma":0.00053054345,"threshold_uncertainty_score":0.9997483},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009961811,0.00069605256,6.407456e-7,0.000839205,0.00008240819,0.0012158459,0.9970607,0.00004560871,0.000049548966],"genre_scores_gemma":[0.000009407551,0.000041848612,0.00022166347,0.013033018,0.00005906252,0.0003707629,0.9859236,0.00003831457,0.00030234936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974191,0.00009832363,0.00061866886,0.0009847119,0.00052523153,0.00035391998],"domain_scores_gemma":[0.993767,0.0014207897,0.00044700754,0.003728979,0.00026713166,0.00036909452],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027973417,0.0003586915,0.00090637244,0.00021793728,0.00003181934,0.000033780732,0.0013071771,0.00026675162,0.15461825],"category_scores_gemma":[0.04340999,0.00037034153,0.00012429658,0.00032902777,0.000020013036,0.00018201375,0.0014369775,0.0006768734,0.0004090453],"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":[0.000093028044,0.00023636628,0.0006957547,0.0036972805,0.000060168466,0.0007199147,0.000019843872,0.000004079684,0.0000034628865,1.1812809e-7,0.9944023,0.00006767958],"study_design_scores_gemma":[0.001373535,0.0001026086,0.01305023,0.0072098617,0.00008858215,0.000039055372,0.000018199338,0.00004146035,0.00000451927,0.0000026438033,0.9777568,0.00031250145],"about_ca_topic_score_codex":0.02101656,"about_ca_topic_score_gemma":0.646327,"teacher_disagreement_score":0.6253104,"about_ca_system_score_codex":0.0005014274,"about_ca_system_score_gemma":0.006974789,"threshold_uncertainty_score":0.99987483},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003145099,0.0018040559,0.0000013468822,0.00016808887,0.000062499945,0.00048740994,0.9970687,0.000060529717,0.000032885422],"genre_scores_gemma":[0.000073592695,0.000103017526,0.0002096527,0.0017801379,0.0002699789,0.00018906045,0.9969068,0.000044756434,0.00042296876],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99576014,0.00016697426,0.0007849812,0.0018429199,0.00087369187,0.00057131826],"domain_scores_gemma":[0.99390316,0.00035107115,0.00040961496,0.0039922427,0.00031530205,0.0010285929],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00010777052,0.0007375231,0.0024927086,0.0010873047,0.00010948238,0.00027123388,0.00078930723,0.00042433242,0.60640496],"category_scores_gemma":[0.0009831712,0.0007342189,0.0007140718,0.0029034964,0.000019214945,0.00014540105,0.0013129027,0.00071850955,0.006590552],"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":[0.00008820028,0.00014918845,0.0040881387,0.00021534841,0.0175374,0.0009861555,0.000021963297,0.00015856847,0.0000010966148,8.71364e-9,0.9766205,0.00013341983],"study_design_scores_gemma":[0.00040467858,0.000028063523,0.14209746,0.0006448451,0.019222358,0.0000061899386,0.00001601807,0.00012183542,0.0000025955326,4.991145e-7,0.836866,0.0005894352],"about_ca_topic_score_codex":0.08807316,"about_ca_topic_score_gemma":0.72278136,"teacher_disagreement_score":0.6347082,"about_ca_system_score_codex":0.00032089927,"about_ca_system_score_gemma":0.00060638663,"threshold_uncertainty_score":0.9995109},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009961811,0.00069605256,6.407456e-7,0.000839205,0.00008240819,0.0012158459,0.9970607,0.00004560871,0.000049548966],"genre_scores_gemma":[0.000009407551,0.000041848612,0.00022166347,0.013033018,0.00005906252,0.0003707629,0.9859236,0.00003831457,0.00030234936],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974191,0.00009832363,0.00061866886,0.0009847119,0.00052523153,0.00035391998],"domain_scores_gemma":[0.993767,0.0014207897,0.00044700754,0.003728979,0.00026713166,0.00036909452],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027973417,0.0003586915,0.00090637244,0.00021793728,0.00003181934,0.000033780732,0.0013071771,0.00026675162,0.15461825],"category_scores_gemma":[0.04340999,0.00037034153,0.00012429658,0.00032902777,0.000020013036,0.00018201375,0.0014369775,0.0006768734,0.0004090453],"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":[0.000093028044,0.00023636628,0.0006957547,0.0036972805,0.000060168466,0.0007199147,0.000019843872,0.000004079684,0.0000034628865,1.1812809e-7,0.9944023,0.00006767958],"study_design_scores_gemma":[0.001373535,0.0001026086,0.01305023,0.0072098617,0.00008858215,0.000039055372,0.000018199338,0.00004146035,0.00000451927,0.0000026438033,0.9777568,0.00031250145],"about_ca_topic_score_codex":0.02101656,"about_ca_topic_score_gemma":0.646327,"teacher_disagreement_score":0.6253104,"about_ca_system_score_codex":0.0005014274,"about_ca_system_score_gemma":0.006974789,"threshold_uncertainty_score":0.99987483},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7975477,0.017249484,0.010683258,0.11581982,0.005663451,0.004078476,0.017740073,0.0028185942,0.02839916],"genre_scores_gemma":[0.99779063,0.000058328336,0.0002717393,0.0005375438,0.00024695537,0.000024454908,0.0009244256,0.00001986306,0.00012603629],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99579453,0.00077001716,0.0018642955,0.00047593072,0.0006183616,0.00047684423],"domain_scores_gemma":[0.9968154,0.00056106964,0.00090508984,0.00090176746,0.0004835132,0.00033317466],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013340563,0.00016575548,0.0005509108,0.000056008637,0.00010756961,0.000107573636,0.00019755198,0.00006507654,0.000013776045],"category_scores_gemma":[0.01016922,0.00010707239,0.000110821005,0.0011220152,0.00010717943,0.00013645279,0.00007409075,0.00024271088,0.000042434942],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042932577,0.00026331015,0.7570643,0.0044981563,0.00031998055,0.00022594273,0.0010227198,0.000010732143,0.000025364287,0.008846843,0.10343712,0.12385624],"study_design_scores_gemma":[0.0005510223,0.000085516476,0.9549479,0.000517698,0.0000068418194,0.00018560169,0.0004085563,0.006074472,0.0000031942648,0.000007883765,0.037111375,0.00009995466],"about_ca_topic_score_codex":0.005099728,"about_ca_topic_score_gemma":0.0034531653,"teacher_disagreement_score":0.20024297,"about_ca_system_score_codex":0.0006908398,"about_ca_system_score_gemma":0.0028202513,"threshold_uncertainty_score":0.9981685},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70732695,0.00015136534,0.29054224,0.00025657847,0.00021049482,0.0004317272,0.0010446341,0.00001240584,0.000023614437],"genre_scores_gemma":[0.95375156,0.00000391604,0.046095118,0.000028722065,0.00007822242,0.0000023555015,0.000014476621,0.0000179998,0.0000076004812],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99729186,0.0005046884,0.0010638508,0.00018725505,0.0007642857,0.00018808307],"domain_scores_gemma":[0.9946986,0.0043367664,0.00023749957,0.0002533812,0.00033261153,0.0001411474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012165871,0.00013695724,0.0006558317,0.00014459206,0.000024794981,0.000021262358,0.00017692543,0.000049445593,0.00011224546],"category_scores_gemma":[0.006821583,0.00008775646,0.00009604089,0.0004161583,0.0001209235,0.00007390342,0.000083060964,0.00045038396,0.0000025178995],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013489728,0.01813175,0.61450464,0.010681925,0.0038887134,0.0046695145,0.031020885,0.08218168,0.068641335,0.029855637,0.0023708309,0.12056334],"study_design_scores_gemma":[0.0043458757,0.0017705007,0.8618503,0.001695163,0.0008104429,0.00011123467,0.0023169809,0.12114457,0.00037232408,0.00507137,0.00030521062,0.00020599212],"about_ca_topic_score_codex":0.000057070192,"about_ca_topic_score_gemma":0.00003733855,"teacher_disagreement_score":0.24734569,"about_ca_system_score_codex":0.00009893208,"about_ca_system_score_gemma":0.00032735246,"threshold_uncertainty_score":0.8166565},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13308156,0.85197043,0.000023865634,0.00388553,0.0013409985,0.003940125,0.005273802,0.00027201494,0.00021164517],"genre_scores_gemma":[0.82994413,0.14332063,0.0005999599,0.01869251,0.0008427118,0.0008809152,0.0040489426,0.00032100608,0.0013491729],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99640757,0.00010169857,0.0016559572,0.0008652316,0.000684056,0.00028547522],"domain_scores_gemma":[0.99694204,0.00019123325,0.0011685821,0.0008367488,0.00049460836,0.00036678073],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021475524,0.0005602652,0.0014494853,0.00011719392,0.00010483371,0.00010669193,0.00011143304,0.00018654147,0.00011457056],"category_scores_gemma":[0.0015132661,0.00035943495,0.00032378768,0.00042074436,0.000093233844,0.00009780175,0.00045894532,0.00053765083,0.0000077794275],"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":[0.00007095514,0.00055164105,0.01769558,0.07456678,0.0011849815,0.0005045628,0.0005268298,0.000006040342,0.0001537374,0.0000013777785,0.3197201,0.58501744],"study_design_scores_gemma":[0.00035841737,0.0008536847,0.008680329,0.09853595,0.008725577,0.00041173873,0.00015759638,0.000022772696,0.00008151236,0.000018623345,0.8814042,0.000749605],"about_ca_topic_score_codex":0.00024058463,"about_ca_topic_score_gemma":0.000091342896,"teacher_disagreement_score":0.7086498,"about_ca_system_score_codex":0.00012978751,"about_ca_system_score_gemma":0.00026733486,"threshold_uncertainty_score":0.99988574},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99451184,0.00026984245,0.0001743791,0.000010661832,0.003326323,0.00076821627,0.00014966827,0.00014976875,0.00063931046],"genre_scores_gemma":[0.997283,0.000002669293,0.0012732175,9.1248387e-7,0.00012634108,0.000061528386,0.00010136829,0.00003380481,0.0011172045],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9974277,0.00006535759,0.0008063702,0.0007542925,0.00069603155,0.0002502554],"domain_scores_gemma":[0.99785995,0.00008892663,0.00033169004,0.001114596,0.00034182434,0.00026299778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015957278,0.00017348737,0.00046128908,0.00050253916,0.00012505696,0.000100199766,0.000065939465,0.0000328869,0.00005831212],"category_scores_gemma":[0.00025896003,0.00015174472,0.00024037909,0.0008429239,0.00016536415,0.00017803015,0.000092165676,0.0000713791,0.0000014738407],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058048943,0.0014328145,0.90375197,0.0011563804,0.00036204583,0.027349804,0.0007840885,0.00036616036,0.056822684,0.000012641393,0.001181282,0.0067220693],"study_design_scores_gemma":[0.0041180663,0.0014604224,0.8933118,0.0043294025,0.0059008715,0.013519908,0.006725632,0.050268333,0.012792452,0.001108863,0.0050550997,0.0014091698],"about_ca_topic_score_codex":0.00036668856,"about_ca_topic_score_gemma":0.000022197299,"teacher_disagreement_score":0.049902175,"about_ca_system_score_codex":0.00015860259,"about_ca_system_score_gemma":0.0006086029,"threshold_uncertainty_score":0.61879706},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005361522,0.00004775052,0.0000012089928,0.00024511942,0.000618681,0.0006087804,0.99745756,0.00020022143,0.00028455077],"genre_scores_gemma":[0.000012717742,0.000105177205,0.000001572338,0.0017818654,0.0000021523442,0.00001107997,0.998085,2.5341162e-8,3.5825755e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977952,0.00009630939,0.00047970805,0.00032676847,0.0009526411,0.0003493466],"domain_scores_gemma":[0.99801004,0.000022196591,0.00035186624,0.0010085929,0.0002711619,0.0003361697],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031799803,0.00033077973,0.00048577657,0.00012895433,0.00031175357,0.00007047471,0.0004965081,0.0003302629,0.10260775],"category_scores_gemma":[0.00033321668,0.00035261028,0.00026213532,0.00046551882,0.00018646347,0.0006492413,0.0007342106,0.00070750806,0.13231534],"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":[0.00030922022,0.00010877443,0.006582341,0.00043424248,0.00008515213,0.00002587093,0.000018513074,0.000004623436,3.3052395e-8,4.1989757e-8,0.99095833,0.001472862],"study_design_scores_gemma":[0.00087185344,0.00008992385,0.0013870451,0.0000035716973,0.0001546104,0.000026870051,0.00009666801,1.12001096e-7,5.940776e-7,2.4803256e-7,0.9970687,0.00029977958],"about_ca_topic_score_codex":0.00041524944,"about_ca_topic_score_gemma":0.000009762243,"teacher_disagreement_score":0.029707583,"about_ca_system_score_codex":0.0007744375,"about_ca_system_score_gemma":0.00045216523,"threshold_uncertainty_score":0.9998926},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8084825,0.014496323,0.025852278,0.13643456,0.0038080479,0.00086602016,0.00005844357,0.00016537361,0.009836433],"genre_scores_gemma":[0.9924771,0.00019004967,0.0012172404,0.0011116804,0.004792216,0.0000031083714,0.000007460298,0.000039100083,0.00016204613],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9976977,0.00014825215,0.0007798841,0.000268583,0.0007099325,0.00039563572],"domain_scores_gemma":[0.9986591,0.00021566074,0.00018607454,0.00023852206,0.00016081086,0.00053984526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004552749,0.00024353676,0.0006983665,0.00024471327,0.00013629471,0.000064912725,0.00020172089,0.00010552395,0.00011009151],"category_scores_gemma":[0.00046361276,0.0001352494,0.00016418488,0.000446075,0.0003628644,0.00014088367,0.00006731408,0.00061106856,0.000007548612],"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":[0.0026901665,0.0002333132,0.2067136,0.0006230702,0.0018567567,0.004153054,0.012498259,0.00005332265,0.0045692003,0.0009700643,0.25113246,0.51450676],"study_design_scores_gemma":[0.0078055044,0.0053639812,0.57953894,0.007692476,0.0027236175,0.0091405315,0.031806514,0.014002764,0.000030335983,0.00057659205,0.3406042,0.00071458216],"about_ca_topic_score_codex":0.000008874016,"about_ca_topic_score_gemma":0.000010322325,"teacher_disagreement_score":0.51379216,"about_ca_system_score_codex":0.00011113902,"about_ca_system_score_gemma":0.00020515829,"threshold_uncertainty_score":0.551531},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80727196,0.14418828,0.004033057,0.015381502,0.00044513898,0.0021712098,0.025381872,0.00024243933,0.000884519],"genre_scores_gemma":[0.9764811,0.0024509295,0.012019099,0.0005355799,0.000014605018,0.000015132604,0.008363992,0.000034693105,0.00008490193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973188,0.00039471354,0.001092844,0.0004562828,0.00044744433,0.00028993195],"domain_scores_gemma":[0.9938852,0.00047782843,0.00032019234,0.0043500606,0.00039814776,0.00056856364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027855274,0.00016679187,0.00074952206,0.00026450172,0.000049771916,0.000039086208,0.00046827027,0.000034715253,0.000041762207],"category_scores_gemma":[0.0026419053,0.00016353272,0.000040108334,0.0017685135,0.0001724561,0.0005078997,0.0005948011,0.00023056047,1.0726984e-7],"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":[0.0001232711,0.0012152578,0.92828715,0.0064249975,0.00062646926,0.0015056859,0.0001069113,0.00009651235,0.00026552234,0.0038675452,0.01670081,0.0407799],"study_design_scores_gemma":[0.00053686195,0.000036141446,0.7651306,0.00074174267,0.00007194593,0.00012022246,0.00011398222,0.036914676,0.0000035500334,0.00004100333,0.19609809,0.00019118898],"about_ca_topic_score_codex":0.2836763,"about_ca_topic_score_gemma":0.4187199,"teacher_disagreement_score":0.17939727,"about_ca_system_score_codex":0.0002649351,"about_ca_system_score_gemma":0.013964664,"threshold_uncertainty_score":0.99162525},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48145932,0.0013622367,0.30695364,0.014946896,0.020469679,0.0028737143,0.17080459,0.0005290077,0.00060089404],"genre_scores_gemma":[0.98498905,0.000053303513,0.009006267,0.00038886123,0.0008130123,0.000044796023,0.0046037827,0.000016999875,0.00008392504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983463,0.000014879602,0.00039254752,0.00041227273,0.00060279795,0.00023117171],"domain_scores_gemma":[0.9986135,0.0003087364,0.00011845045,0.0003643297,0.00037613144,0.00021887907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009615441,0.00010673412,0.00014334264,0.0004069756,0.00037333972,0.0005025579,0.0005716625,0.000029073994,0.000075953685],"category_scores_gemma":[0.0016680898,0.000092867944,0.000108811975,0.00025780493,0.00011744943,0.0018218145,0.00010071966,0.00010803755,0.000016454722],"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":[0.003167189,0.0013019532,0.091863096,0.00042992004,0.0015247684,0.003463995,0.0006252294,0.0020710095,0.024271466,0.08274215,0.2070931,0.5814461],"study_design_scores_gemma":[0.006167144,0.00024750002,0.08584666,0.0007166113,0.00030979933,0.0115459785,0.00012782853,0.68934,0.00020780442,0.0038607027,0.20121326,0.000416715],"about_ca_topic_score_codex":0.000053727952,"about_ca_topic_score_gemma":0.0001332723,"teacher_disagreement_score":0.687269,"about_ca_system_score_codex":0.00017554652,"about_ca_system_score_gemma":0.00021478854,"threshold_uncertainty_score":0.48461774},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.3679197e-7,0.9924628,0.00045289472,0.00004906396,0.00050891493,0.0036897918,0.00020085298,0.000113359325,0.0025219913],"genre_scores_gemma":[0.000040572373,0.99618757,0.000072930576,0.00002670525,0.00050208514,0.0003314693,0.0003901549,0.00017723125,0.0022712613],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99121284,0.0014384158,0.0033026678,0.00066847383,0.0011943494,0.0021832252],"domain_scores_gemma":[0.9952357,0.0003480019,0.0026938224,0.0011620447,0.00030710496,0.0002533402],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.011437331,0.00080833124,0.007760361,0.00040056533,0.00008720967,0.00009623329,0.0006948323,0.0002430258,0.000015054355],"category_scores_gemma":[0.0010447336,0.0005271717,0.0018065962,0.0008973055,0.000080078375,0.00009184535,0.00015732653,0.004323062,0.000115575014],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004165071,0.0000759029,0.0000045998854,0.96288556,0.0038785674,0.00007069026,0.000005338039,2.9319773e-7,4.3518458e-8,0.010782556,0.00037538796,0.02191688],"study_design_scores_gemma":[0.00018981032,0.00017287326,6.4729744e-7,0.9035183,0.011354243,0.008106758,0.000115662006,0.00004058711,7.892591e-9,0.00022115895,0.07589554,0.00038436573],"about_ca_topic_score_codex":0.000014281832,"about_ca_topic_score_gemma":0.000015995764,"teacher_disagreement_score":0.07552016,"about_ca_system_score_codex":0.0035250566,"about_ca_system_score_gemma":0.0145366,"threshold_uncertainty_score":0.999718},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98403466,0.0038269358,0.00064249476,0.0001665781,0.00018712865,0.004983682,0.0032678784,0.00096920395,0.001921452],"genre_scores_gemma":[0.9950144,0.00020507629,0.0014517785,0.00008288704,0.00072010397,0.00037009257,0.0010038329,0.00024521217,0.00090659456],"study_design_codex":"case_report","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9921623,0.0014374075,0.0008012422,0.0017772788,0.0021521032,0.001669698],"domain_scores_gemma":[0.9944062,0.0011392887,0.00018566092,0.002153439,0.0006792942,0.0014360981],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0076531284,0.0005706525,0.0011188037,0.0011794101,0.00048474787,0.00025551172,0.000481233,0.0003741412,0.0004359992],"category_scores_gemma":[0.0045781285,0.0005347105,0.0003548343,0.0010399492,0.00014646993,0.00014780801,0.0053725443,0.003481386,0.0001916082],"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":[0.0015388065,0.0028868304,0.07271005,0.045112688,0.0017694671,0.7789885,0.04964963,0.023745978,0.00295479,0.00008498457,0.011157182,0.009401141],"study_design_scores_gemma":[0.0036834264,0.00064889283,0.00027630225,0.0062681125,0.00051894825,0.008173896,0.072857715,0.90393287,0.000017710381,0.0017443957,0.00088055665,0.0009971531],"about_ca_topic_score_codex":0.012916154,"about_ca_topic_score_gemma":0.0028184017,"teacher_disagreement_score":0.8801869,"about_ca_system_score_codex":0.0027331065,"about_ca_system_score_gemma":0.0045075803,"threshold_uncertainty_score":0.99971044},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.988891,0.00343431,0.0053319777,0.0014990777,0.0000711557,0.000075794844,0.00010411669,0.000006587603,0.0005859721],"genre_scores_gemma":[0.9968134,0.0023653628,0.00026475172,0.00048718747,0.000014746256,2.8042507e-7,0.000047411984,0.0000012667697,0.000005588534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99877506,0.000018595607,0.0005541296,0.000080664795,0.0004321405,0.00013937762],"domain_scores_gemma":[0.9990646,0.000121892735,0.0002408158,0.00024821682,0.00016007885,0.00016439943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036602083,0.00005153635,0.0002458652,0.0012444794,0.000029383224,0.00005916569,0.0002657665,0.000018380011,0.000025497244],"category_scores_gemma":[0.0020039633,0.00003485394,0.000042973,0.002820005,0.00011265876,0.0033077018,0.00007606441,0.00015318145,0.000001661266],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054113625,0.000117430776,0.92174304,0.0008437304,0.0006385038,0.000055836485,0.0028092684,0.0064064744,0.00452167,0.010743723,0.0033940484,0.048185132],"study_design_scores_gemma":[0.0007419513,0.00020789538,0.4857345,0.00008262186,0.0002567921,0.00016589928,0.00014509312,0.44725302,0.000030343797,0.00043972884,0.06485433,0.0000878166],"about_ca_topic_score_codex":0.00001298795,"about_ca_topic_score_gemma":0.000022396087,"teacher_disagreement_score":0.44084653,"about_ca_system_score_codex":0.00013669419,"about_ca_system_score_gemma":0.0016712743,"threshold_uncertainty_score":0.29647687},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.961577,0.0014276695,0.028457034,0.0020738328,0.0010560319,0.0031376525,0.00020006114,0.0007639274,0.0013067706],"genre_scores_gemma":[0.99102104,0.000017128395,0.0032434587,0.003426912,0.00012335175,0.0017623199,0.000109816974,0.0000503273,0.0002456431],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99354255,0.0033609862,0.0010499764,0.0010218502,0.0002670308,0.00075760146],"domain_scores_gemma":[0.99389434,0.0043276814,0.00015569007,0.000913338,0.00014205907,0.0005668771],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004794686,0.00039997842,0.0011602222,0.0005712897,0.00008877473,0.000031306354,0.000363648,0.00021348106,0.00043179558],"category_scores_gemma":[0.0059557846,0.00035814766,0.00016655291,0.0013967251,0.00037522707,0.00010793321,0.00026514055,0.00073440786,0.00019383164],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021049564,0.001613025,0.97049445,0.00047398385,0.00028328132,0.0008942077,0.0035759404,0.0017923639,0.00032122832,0.00049026974,0.01076586,0.0071904324],"study_design_scores_gemma":[0.0029371064,0.0035949096,0.9308717,0.00012819647,0.00010157246,0.000037339185,0.002223933,0.039238136,0.00021857228,0.002430096,0.017520526,0.0006978722],"about_ca_topic_score_codex":0.00036242607,"about_ca_topic_score_gemma":0.0014288068,"teacher_disagreement_score":0.039622713,"about_ca_system_score_codex":0.0012519703,"about_ca_system_score_gemma":0.0031017964,"threshold_uncertainty_score":0.99988705},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069184724,0.04841269,0.00013916477,0.0038203085,0.13792208,0.0032616,0.015111574,0.0015279104,0.7891128],"genre_scores_gemma":[0.058920246,0.02849802,0.000725167,0.0020819514,0.073563255,0.000054595945,0.02753366,0.0013784998,0.8072446],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99321467,0.00005255095,0.0012587088,0.0028434584,0.001568213,0.0010623919],"domain_scores_gemma":[0.9917514,0.0003148152,0.0004126588,0.0064525832,0.00021321943,0.0008553437],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.00091328187,0.0013366174,0.0017710343,0.0004682647,0.00016116204,0.0010596734,0.0020196468,0.0010058519,0.020075109],"category_scores_gemma":[0.00041101658,0.001297871,0.0006029471,0.00012491966,0.0005047815,0.0012999262,0.0035988411,0.0021020498,0.059296057],"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":[0.007139128,0.0007399274,0.00031320486,0.008071533,0.01615476,0.018085295,0.0015114031,0.000012503333,0.0013666464,0.31254482,0.40020475,0.23385602],"study_design_scores_gemma":[0.0019319996,0.00019195376,0.00028034236,0.004636549,0.0026258556,0.00006772411,0.000070271046,0.00026839654,0.000044501005,0.0035712444,0.98511505,0.0011960848],"about_ca_topic_score_codex":0.00011612256,"about_ca_topic_score_gemma":0.00013537388,"teacher_disagreement_score":0.58491033,"about_ca_system_score_codex":0.00039087102,"about_ca_system_score_gemma":0.0009121138,"threshold_uncertainty_score":0.99997735},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91323483,0.005314409,0.06665309,0.0019896713,0.00030361424,0.0011487156,0.01110123,0.00021154214,0.00004291808],"genre_scores_gemma":[0.98857707,0.0009540076,0.006428936,0.0000896909,0.00014609967,0.0000034176162,0.0037468176,0.000028309061,0.000025651481],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673194,0.00041509335,0.0009539009,0.0013721113,0.000096772805,0.00043019644],"domain_scores_gemma":[0.99284893,0.0011742924,0.0039111665,0.0011727252,0.00036741132,0.0005254822],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022884547,0.00030573612,0.0011083563,0.00016955388,0.00026994792,0.00006715901,0.00025357347,0.0001934029,0.000004117626],"category_scores_gemma":[0.01804118,0.00030275172,0.00012501943,0.00035324416,0.00032464028,0.00022592388,0.0022818202,0.0005558771,8.9725665e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011604207,0.00022377755,0.9793799,0.008152244,0.0003912637,0.0002881143,0.000057554356,0.0023072634,0.000008469716,0.003593747,0.00029235543,0.005189296],"study_design_scores_gemma":[0.0010033841,0.0003288572,0.08673753,0.002048254,0.00066201604,0.00027394414,0.0003641051,0.8989585,8.827463e-7,0.008216579,0.0008780086,0.00052793184],"about_ca_topic_score_codex":0.0022618237,"about_ca_topic_score_gemma":0.0005518584,"teacher_disagreement_score":0.89665127,"about_ca_system_score_codex":0.0002667142,"about_ca_system_score_gemma":0.0011983095,"threshold_uncertainty_score":0.9999425},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9854689,0.0029540162,0.00003803455,0.001095365,0.00024623735,0.0010358593,0.0087986,0.00005098843,0.00031199146],"genre_scores_gemma":[0.98473734,0.0010649982,0.00041102906,0.000035172452,0.00006239369,0.000069097136,0.012484951,0.000027675369,0.0011073317],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9938259,0.00084606046,0.0014343912,0.0022976766,0.0010914241,0.0005045633],"domain_scores_gemma":[0.99106836,0.0015544082,0.00039729744,0.0052607963,0.0012353522,0.00048380427],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","open_science"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.00989314,0.00032780346,0.002056612,0.22914046,0.000057315283,0.00018211888,0.0015127956,0.00034761228,0.00044545086],"category_scores_gemma":[0.011625415,0.0002674489,0.00008947983,0.20510998,0.00056500843,0.00019560501,0.013373905,0.0013656382,0.000008618955],"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":[0.00024124696,0.0008198003,0.9501857,0.00040782298,0.0017333986,0.00010099214,0.0013382891,0.000018450039,0.0003792151,0.000015315254,0.021553807,0.023205975],"study_design_scores_gemma":[0.0014099726,0.0004118301,0.99078006,0.0004690906,0.0004995495,0.000002162451,0.0011366283,0.003796324,0.000010506269,0.000119483215,0.0011906446,0.00017377376],"about_ca_topic_score_codex":0.005692861,"about_ca_topic_score_gemma":0.026785534,"teacher_disagreement_score":0.040594347,"about_ca_system_score_codex":0.00008241131,"about_ca_system_score_gemma":0.00029675174,"threshold_uncertainty_score":0.99997777},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7653073,0.0037587543,0.089193486,0.045452617,0.005659132,0.018329808,0.004589886,0.0038120165,0.06389697],"genre_scores_gemma":[0.9890193,0.00023219464,0.0049627083,0.0021448454,0.00067269406,0.00010754178,0.00177798,0.000086860055,0.0009959306],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99503577,0.00030460348,0.0010876203,0.0023050879,0.0006125691,0.0006543543],"domain_scores_gemma":[0.9960789,0.00031924865,0.00044671024,0.0021251596,0.00034196817,0.0006879786],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027265558,0.0005910409,0.0015307119,0.00017652965,0.00015944976,0.00017728616,0.00038241502,0.00042102247,0.00033747262],"category_scores_gemma":[0.0006490244,0.00053405174,0.00043917264,0.00014420674,0.00025785138,0.00005406946,0.0038395578,0.0012813334,0.00007178984],"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":[0.004781182,0.0025902893,0.16270195,0.091265105,0.00314364,0.00027569974,0.002925716,0.00016324606,0.00020824517,0.006467582,0.19828875,0.5271886],"study_design_scores_gemma":[0.0038856503,0.0005098697,0.83540696,0.0031904432,0.00071255944,0.0000604763,0.0001778059,0.04425967,0.00063550397,0.08341524,0.026376648,0.0013691698],"about_ca_topic_score_codex":0.0044485196,"about_ca_topic_score_gemma":0.0014284265,"teacher_disagreement_score":0.672705,"about_ca_system_score_codex":0.00043931164,"about_ca_system_score_gemma":0.001353138,"threshold_uncertainty_score":0.9997111},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922393,0.0028779556,0.000090756585,0.0006372193,0.0007961214,0.001047702,0.0019563716,0.00019886643,0.00015570193],"genre_scores_gemma":[0.99512947,0.0020133613,0.000023685854,0.00021874701,0.00021224942,0.00012049458,0.001972801,0.000056500234,0.00025269217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955559,0.0009084031,0.001225238,0.0009135609,0.00061035046,0.0007865852],"domain_scores_gemma":[0.9973384,0.0006129371,0.0002989483,0.00051319203,0.00031983174,0.0009166751],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035225756,0.000388877,0.0010899798,0.00039311036,0.00013335908,0.00031403083,0.00015317278,0.00021856409,0.000040466806],"category_scores_gemma":[0.0007938938,0.00034481467,0.00009210292,0.00063534145,0.0003996845,0.0003209747,0.00012411838,0.0004584711,0.000010427508],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022166286,0.00014123687,0.9909803,0.0025500013,0.00005155662,0.000024177123,0.00035373648,0.0000017305946,0.00005347355,0.00012272333,0.00053537084,0.004963986],"study_design_scores_gemma":[0.00094347214,0.00020876122,0.98812103,0.00015466167,8.1602536e-7,0.00006140292,0.000098843084,0.0039103515,1.6441186e-7,0.0000064691812,0.006212572,0.00028143084],"about_ca_topic_score_codex":0.001256616,"about_ca_topic_score_gemma":0.00150764,"teacher_disagreement_score":0.0056772013,"about_ca_system_score_codex":0.00031796488,"about_ca_system_score_gemma":0.0007193596,"threshold_uncertainty_score":0.9999004},"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":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16282153,0.0022355395,0.000037423823,0.82378125,0.00023219168,0.0010594144,0.009509517,0.00018581496,0.00013733345],"genre_scores_gemma":[0.09303835,0.0020311095,0.00032573668,0.6372426,0.00081433106,0.0020968479,0.26332116,0.00012614795,0.0010037094],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965945,0.00072614907,0.0007866103,0.00075075775,0.00031886506,0.0008231501],"domain_scores_gemma":[0.9973677,0.0013613973,0.00017217739,0.0009176682,0.00011260915,0.00006846255],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035485634,0.00046698682,0.0014690356,0.0048772497,0.00004798126,0.000056548895,0.0005753533,0.00092364923,0.000082213206],"category_scores_gemma":[0.00017908054,0.0003192199,0.00055687805,0.004680024,0.00043872246,0.000046795918,0.00014109933,0.0019508624,0.000021624679],"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":[0.00014958784,0.00025314052,0.22546616,0.0013187068,0.0011807843,0.0032751623,0.0003109845,0.00003872965,6.285068e-7,0.00006323358,0.7673447,0.0005981693],"study_design_scores_gemma":[0.0010908911,0.00010613517,0.1403767,0.00015360535,0.00095688784,0.00016150347,0.0003751743,0.0036176073,0.0000011516225,0.00068172516,0.85213065,0.0003479488],"about_ca_topic_score_codex":0.0009508854,"about_ca_topic_score_gemma":0.0036090412,"teacher_disagreement_score":0.25381166,"about_ca_system_score_codex":0.0000944407,"about_ca_system_score_gemma":0.00011355977,"threshold_uncertainty_score":0.999926},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94609433,0.01647341,0.02952989,0.0032963601,0.0004738204,0.0022719782,0.001086985,0.000554845,0.00021835265],"genre_scores_gemma":[0.99253666,0.00022349964,0.005748548,0.00093646103,0.00010378401,0.00018197206,0.00019336636,0.000039487375,0.000036188507],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99764997,0.00014006587,0.00068898726,0.0006351004,0.0003873375,0.00049854646],"domain_scores_gemma":[0.99788535,0.00023856324,0.00014136943,0.00030030447,0.00016550565,0.0012689196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010730753,0.0002653889,0.0006581988,0.0003309407,0.00018705963,0.00015831643,0.000083617204,0.000084483414,0.0000057194015],"category_scores_gemma":[0.00029398347,0.00023396609,0.000038457387,0.0005638281,0.000092121496,0.00025211024,0.00014660935,0.00014380968,0.0000039400406],"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":[0.00015085723,0.0000878957,0.1762267,0.011300413,0.00013224325,0.0000372859,0.0027159383,0.0000020633395,0.000098926095,0.0002300228,0.0010449507,0.80797267],"study_design_scores_gemma":[0.002446532,0.00044260564,0.71388316,0.0012847093,0.000012245839,0.00025978984,0.0015434972,0.0056549064,0.000043488406,0.000021589982,0.27375194,0.00065554905],"about_ca_topic_score_codex":0.000082558814,"about_ca_topic_score_gemma":0.000066914414,"teacher_disagreement_score":0.80731714,"about_ca_system_score_codex":0.0001905732,"about_ca_system_score_gemma":0.0011740247,"threshold_uncertainty_score":0.954086},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992142,0.0014949964,0.00008897334,0.0032321566,0.00007809118,0.0010317034,0.0018202396,0.000072994844,0.000038828883],"genre_scores_gemma":[0.9981658,0.00015718257,0.000087529035,0.0003103166,0.000066571476,0.000048461836,0.001002642,0.000019145316,0.00014236307],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767673,0.0003324605,0.0006032859,0.000603087,0.0003729645,0.00041149132],"domain_scores_gemma":[0.9985086,0.00030821015,0.00023506988,0.00029388926,0.000113194365,0.00054107193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001431056,0.00020938272,0.00090552145,0.0005663856,0.000057381254,0.00008836003,0.00009987055,0.00008733526,0.0000780525],"category_scores_gemma":[0.00033122065,0.00017752619,0.000056210196,0.00093542034,0.000026843829,0.00028047516,0.00007045671,0.00022518812,0.000011646947],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012115799,0.000159929,0.9911082,0.00011942354,0.00036981195,0.000009910233,0.0032998715,0.000016008318,0.000007328614,0.0000149220095,0.0012266086,0.003546849],"study_design_scores_gemma":[0.0008563408,0.0005633362,0.9924905,0.00003931724,0.000017626895,6.6158486e-7,0.0005202853,0.0042514894,8.810539e-7,0.000005250352,0.0010947414,0.00015959745],"about_ca_topic_score_codex":0.0036038037,"about_ca_topic_score_gemma":0.0102687515,"teacher_disagreement_score":0.0066649476,"about_ca_system_score_codex":0.00034750268,"about_ca_system_score_gemma":0.00081887393,"threshold_uncertainty_score":0.7239308},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10002678,0.0019559483,0.8942547,0.0007342059,0.00067937997,0.0004738598,0.0017952928,0.00006563059,0.000014195552],"genre_scores_gemma":[0.44100326,0.0018217168,0.55506104,0.00095335394,0.0005031015,0.00006925302,0.00050884596,0.00003505029,0.00004435623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972868,0.000104910476,0.0015964964,0.00012498366,0.0005170748,0.00036974985],"domain_scores_gemma":[0.99777514,0.00061921537,0.00039683422,0.00030537954,0.00023981371,0.000663609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054835627,0.00017280759,0.0008020651,0.00060718245,0.0001441832,0.00012247058,0.00015127329,0.000064865475,0.0004636908],"category_scores_gemma":[0.000683554,0.00012922275,0.0005778559,0.00054888934,0.00004356169,0.0002595534,0.000061946426,0.0002506063,0.000015012524],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018578287,0.0012819769,0.22516464,0.010206319,0.004062875,0.00006901781,0.003154612,0.0005103471,0.000048305268,0.00033502025,0.11585228,0.6374568],"study_design_scores_gemma":[0.0028043143,0.00060689286,0.4006942,0.00097507087,0.0016885072,0.00013528173,0.00024567125,0.56004316,0.00004991424,0.00021236825,0.032347724,0.00019691826],"about_ca_topic_score_codex":0.000011904039,"about_ca_topic_score_gemma":0.000014205322,"teacher_disagreement_score":0.63725984,"about_ca_system_score_codex":0.00016481502,"about_ca_system_score_gemma":0.0004988545,"threshold_uncertainty_score":0.5269551},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9899025,0.0004995818,0.0030776055,0.000055922395,0.0002831469,0.00041665218,0.0053155953,0.00009400901,0.0003550061],"genre_scores_gemma":[0.99916303,0.00020526513,0.00013865065,0.00010686749,0.00011723972,0.000009729943,0.00008693158,0.000030970714,0.00014133405],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985303,0.00012659913,0.00046217113,0.00030426736,0.0003792482,0.00019737649],"domain_scores_gemma":[0.9986112,0.00033376258,0.00019745661,0.0005423412,0.00007205186,0.00024315124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037712793,0.00018022732,0.0005477501,0.00021739642,0.000020434578,0.000007629459,0.00015658293,0.000089641435,0.00015193476],"category_scores_gemma":[0.00061438483,0.00014809742,0.00021861134,0.00024032533,0.00008925329,0.000064914944,0.00008808008,0.00020640399,0.000022903498],"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":[0.0020370537,0.00014939258,0.95896024,0.0012765296,0.00033810255,0.000053367563,0.00053069403,0.010143385,0.014977879,0.000008065395,0.0028698477,0.008655469],"study_design_scores_gemma":[0.001516612,0.0007960437,0.99034655,0.00062510034,0.00010408978,0.000009818731,0.000026376776,0.0020185038,0.0017177882,0.000046688758,0.0026462227,0.00014622736],"about_ca_topic_score_codex":0.01204385,"about_ca_topic_score_gemma":0.00081341946,"teacher_disagreement_score":0.031386312,"about_ca_system_score_codex":0.00027899758,"about_ca_system_score_gemma":0.0005399036,"threshold_uncertainty_score":0.994535},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99031013,0.00021232903,0.0024020826,0.0015436526,0.00014779843,0.0048518763,0.00039185214,0.00007057768,0.00006971117],"genre_scores_gemma":[0.99875575,0.00008021747,0.00025686697,0.00012552309,0.000039250743,0.00064394914,0.0000516301,0.000017801118,0.000029009927],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953727,0.0018920652,0.0005526406,0.0005084262,0.0010130918,0.0006610742],"domain_scores_gemma":[0.99617577,0.0017026801,0.000083899424,0.0006030233,0.0007412336,0.0006933943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013547629,0.00013149607,0.00033278082,0.0005369401,0.00043457522,0.00015961405,0.0003693171,0.00002468687,0.000009154526],"category_scores_gemma":[0.0013602425,0.00008819295,0.000055823744,0.0012671822,0.00075373583,0.00037709324,0.0003680665,0.0002927401,0.0000119465085],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033399062,0.0028799435,0.7374721,0.00099651,0.00021609224,0.00003200876,0.008411828,0.000006613206,0.0000071082763,0.0010923533,0.0047337613,0.24081177],"study_design_scores_gemma":[0.0012916897,0.005402798,0.9876102,0.000105969084,0.000008304595,0.0000014235917,0.0019239059,0.0016613234,0.000002555975,0.00011615501,0.0017951401,0.000080524536],"about_ca_topic_score_codex":0.00003694434,"about_ca_topic_score_gemma":0.000043350145,"teacher_disagreement_score":0.2501381,"about_ca_system_score_codex":0.00022088882,"about_ca_system_score_gemma":0.0010112358,"threshold_uncertainty_score":0.46953648},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9929084,0.000038638547,0.00007796411,0.00030411666,0.0006622146,0.003762167,0.0016259118,0.000033318032,0.00058723753],"genre_scores_gemma":[0.9973165,0.000009181815,0.00015603041,0.00022457897,0.00003953694,0.00013775477,0.0020490533,0.00001686013,0.000050520914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984684,0.00022160064,0.0004571094,0.00043594447,0.00022440523,0.00019252807],"domain_scores_gemma":[0.9987316,0.00022852108,0.00012109762,0.00064630655,0.000105754865,0.00016671914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001067398,0.00011969904,0.00044628908,0.00030564325,0.000041456806,0.000039031194,0.00021696434,0.000033417375,0.000031571177],"category_scores_gemma":[0.0012489511,0.000117775155,0.000029026154,0.00064020185,0.000026500858,0.00027282358,0.000111407346,0.0001256772,0.00001414864],"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":[0.000118986194,0.0003514137,0.9842731,0.0001832928,0.00004136731,0.000026760044,0.00020053695,0.00005626562,0.0000622981,0.000009758678,0.011541198,0.003135073],"study_design_scores_gemma":[0.0005456001,0.0006063778,0.99258244,0.0005160263,0.00003426897,0.0000019943034,0.00007601221,0.0022577865,0.00059028366,0.000003741472,0.002693048,0.00009242771],"about_ca_topic_score_codex":0.96313953,"about_ca_topic_score_gemma":0.9876177,"teacher_disagreement_score":0.02447814,"about_ca_system_score_codex":0.00055650476,"about_ca_system_score_gemma":0.0028792985,"threshold_uncertainty_score":0.5107752},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93030053,0.049020883,0.01725509,0.0016688195,0.000128423,0.0010394972,0.0005254387,0.000038211565,0.000023114055],"genre_scores_gemma":[0.99686575,0.0020258625,0.00022069752,0.00012191931,0.00054263644,0.00004969548,0.000051624997,0.00006921604,0.000052621403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973584,0.00018675621,0.0006168205,0.0006238028,0.00034667843,0.0008675656],"domain_scores_gemma":[0.99817693,0.00046065814,0.00039272438,0.0006229623,0.00013070875,0.00021604243],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0030827548,0.0003319838,0.00063497614,0.00013697962,0.00033497353,0.00036339354,0.00033689284,0.0001778419,7.975592e-7],"category_scores_gemma":[0.0003804671,0.00021264247,0.00007264666,0.00009547125,0.00018850536,0.00018773922,0.0014881985,0.0025061083,5.9057896e-7],"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":[0.0046736132,0.00044803348,0.2968298,0.016533129,0.0139571065,0.00010445678,0.046365235,0.0039309743,0.002431872,0.03242204,0.0011876372,0.5811161],"study_design_scores_gemma":[0.0055446187,0.0013371506,0.04382857,0.0140855275,0.0054637194,0.0048945285,0.021425227,0.59326965,0.000038829316,0.3054561,0.0031980227,0.0014580889],"about_ca_topic_score_codex":0.00073886197,"about_ca_topic_score_gemma":0.0011247102,"teacher_disagreement_score":0.58933866,"about_ca_system_score_codex":0.00021769838,"about_ca_system_score_gemma":0.00069729285,"threshold_uncertainty_score":0.99979514},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97422576,0.0035496776,0.00040981328,0.0120662,0.00008920157,0.009260895,0.00017064232,0.00009709889,0.00013071294],"genre_scores_gemma":[0.995927,0.00012846947,0.00024090234,0.00010172552,0.00008268615,0.003321191,0.00002455177,0.00003069086,0.0001427776],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9925374,0.001803862,0.0007481018,0.00060932763,0.0031621472,0.001139185],"domain_scores_gemma":[0.99693626,0.001487404,0.000054506792,0.0005035277,0.00058161095,0.00043669876],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.025298404,0.0002176574,0.00047200342,0.0003246555,0.001794178,0.00037505347,0.0002592545,0.000049562637,0.00003643859],"category_scores_gemma":[0.0006056953,0.00014666612,0.000059564478,0.0004525669,0.0007811266,0.00017748066,0.00057072757,0.0006209469,0.00013230689],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001241601,0.0015652189,0.0033140401,0.0020418423,0.000511442,0.000017458602,0.13123327,0.0000011909259,0.0003515832,0.0019749105,0.06997084,0.7877766],"study_design_scores_gemma":[0.0019608592,0.0031883877,0.3611725,0.00041638393,0.000016434939,0.0000062138415,0.040433705,0.00094812596,0.00023456442,0.00042278774,0.59096193,0.00023812262],"about_ca_topic_score_codex":0.000037661204,"about_ca_topic_score_gemma":0.0002858271,"teacher_disagreement_score":0.78753847,"about_ca_system_score_codex":0.0013592229,"about_ca_system_score_gemma":0.0009897301,"threshold_uncertainty_score":0.99950534},"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":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006181569,0.003926174,0.0004989837,0.97950536,0.0009505909,0.0018763108,0.0038413012,0.0010965032,0.007686593],"genre_scores_gemma":[0.10245548,0.000875649,0.000035459212,0.8836264,0.0095615415,0.0006022554,0.0019942124,0.0001707982,0.0006781755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99628305,0.00030762397,0.0007202809,0.0007954207,0.0006032723,0.0012903459],"domain_scores_gemma":[0.9977482,0.00031167053,0.00013643138,0.0012797274,0.00010131074,0.0004226466],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006038127,0.000513318,0.0012690346,0.00054466096,0.00013048292,0.00021930152,0.0005533126,0.00024402735,0.0003597051],"category_scores_gemma":[0.00044554874,0.00037174032,0.00034166424,0.0013150985,0.00018140627,0.00010356831,0.00014418579,0.0018217431,0.0015538757],"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":[0.000095178075,0.00022108055,0.001061319,0.0010798238,0.00016748905,0.0005005224,0.0003809137,0.000015886078,9.4237447e-7,0.0004581498,0.9209484,0.0750703],"study_design_scores_gemma":[0.00012006738,0.0002723953,0.002033183,0.00056288636,0.00013227797,0.000054079814,0.000040068433,0.000062072126,0.0000033564106,0.012774939,0.9835574,0.00038727326],"about_ca_topic_score_codex":0.00019352905,"about_ca_topic_score_gemma":0.00047020978,"teacher_disagreement_score":0.10183732,"about_ca_system_score_codex":0.00064271025,"about_ca_system_score_gemma":0.001042539,"threshold_uncertainty_score":0.99987346},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8811832,0.006088012,0.065076,0.032338362,0.0012563857,0.009175933,0.0026485224,0.001699703,0.0005338686],"genre_scores_gemma":[0.99232954,0.0002004464,0.00025450875,0.003852875,0.0003855576,0.0009021935,0.0013796439,0.00006717171,0.0006280687],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9953575,0.00082000723,0.00094583764,0.0012798959,0.0006154848,0.0009812379],"domain_scores_gemma":[0.9957668,0.00068067317,0.00024401353,0.00079134,0.00037329344,0.0021438338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00423469,0.00043205856,0.001195444,0.00086032675,0.00040674358,0.00033773735,0.00018572951,0.00016387968,0.000081963706],"category_scores_gemma":[0.0016169208,0.00038187168,0.00032608124,0.002257592,0.0001544568,0.00033225885,0.00011008738,0.00034755492,0.00002698542],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008319046,0.0004761927,0.928501,0.0016537261,0.0011383528,0.00004929651,0.00306846,0.000016190506,0.0000096731255,0.0000662164,0.013476977,0.050712023],"study_design_scores_gemma":[0.0034111424,0.001444709,0.647422,0.000019997591,0.000036707595,0.00004737477,0.0025183985,0.015604043,8.039742e-7,0.000052311152,0.32894522,0.0004972965],"about_ca_topic_score_codex":0.000839797,"about_ca_topic_score_gemma":0.007601031,"teacher_disagreement_score":0.31546825,"about_ca_system_score_codex":0.0007335842,"about_ca_system_score_gemma":0.0023011137,"threshold_uncertainty_score":0.9998633},"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":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042190578,0.3936419,0.0011332389,0.45415932,0.0057994355,0.009522682,0.02433639,0.0013780902,0.06783837],"genre_scores_gemma":[0.09385196,0.482812,0.0011845084,0.08482334,0.00066786585,0.0003559718,0.19533628,0.00036155657,0.14060651],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99653834,0.0004583745,0.00092967064,0.0008960372,0.000665663,0.0005119387],"domain_scores_gemma":[0.9973246,0.0007328306,0.00025786235,0.0011741932,0.00029856403,0.0002119352],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017467922,0.0005878483,0.0013035034,0.0004891612,0.00004761048,0.00010320461,0.00063611456,0.00026056592,0.0011605466],"category_scores_gemma":[0.00067296444,0.00037838283,0.00019468079,0.0011628856,0.00020589022,0.00008467914,0.0004019274,0.0016227785,0.00021225087],"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":[0.000037431586,0.0001035364,0.008423183,0.009047694,0.00025409515,0.00028442385,0.000067844645,0.0000014378119,0.0000016063003,0.0010675716,0.97401905,0.006692098],"study_design_scores_gemma":[0.0005404343,0.00018425066,0.011768799,0.014948547,0.0007494264,0.00010625833,0.0003366834,0.049540326,5.1427025e-7,0.00030773817,0.92097014,0.0005468976],"about_ca_topic_score_codex":0.0013667782,"about_ca_topic_score_gemma":0.0038621644,"teacher_disagreement_score":0.36933598,"about_ca_system_score_codex":0.00032942946,"about_ca_system_score_gemma":0.0006332184,"threshold_uncertainty_score":0.9998668},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949241,0.00025150587,0.00056745607,0.000019158222,0.00011874522,0.0038773557,0.00007264137,0.00015192706,0.000017065027],"genre_scores_gemma":[0.9968929,0.00002863769,0.0012833531,0.000009135068,0.0001107067,0.0012063688,0.00029265744,0.000037004043,0.00013921078],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99716413,0.00013406604,0.00043215876,0.0007356948,0.0012618095,0.00027212984],"domain_scores_gemma":[0.99883133,0.00008142147,0.00015827925,0.00041618184,0.0002520948,0.00026068077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093739945,0.0002909886,0.00046805496,0.0003894688,0.00027400756,0.00035330126,0.00013791185,0.000038099322,0.00008028134],"category_scores_gemma":[0.000047052366,0.00022477364,0.00006921974,0.00042362313,0.000048237194,0.0003355922,0.000103552324,0.00018185876,0.0000028399204],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015946948,0.00041383025,0.98592967,0.00013770492,0.00274352,0.000012807508,0.0052903914,0.000025960182,0.00017581285,0.0000017375107,0.00009695814,0.0050121606],"study_design_scores_gemma":[0.0027235874,0.0004691428,0.98955446,0.00017187466,0.0022789158,4.0531634e-7,0.0035401392,0.00058351445,0.000118715136,0.000007814244,0.00030410793,0.0002472907],"about_ca_topic_score_codex":0.00008641148,"about_ca_topic_score_gemma":0.0006747472,"teacher_disagreement_score":0.00476487,"about_ca_system_score_codex":0.0005895788,"about_ca_system_score_gemma":0.00019596332,"threshold_uncertainty_score":0.9166003},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3799065,0.057431266,0.5280877,0.00038922313,0.00076452567,0.004334202,0.027685514,0.0010019196,0.00039916416],"genre_scores_gemma":[0.9484047,0.0059180977,0.041699957,0.000065197106,0.00013847844,0.00020687626,0.0032420515,0.00020660409,0.0001180073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99285173,0.00017057039,0.0023000825,0.0018129778,0.0017015433,0.0011630998],"domain_scores_gemma":[0.995847,0.00066560536,0.00039044901,0.001583698,0.00064476626,0.00086850504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014011782,0.0010038749,0.0018296833,0.0017200696,0.0002972608,0.00024406759,0.00055055146,0.0006676639,0.0004969672],"category_scores_gemma":[0.0014026972,0.00097212294,0.00024241832,0.0023647519,0.000974251,0.0005417518,0.00073843804,0.0013469207,0.000027951113],"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":[0.011002108,0.022538373,0.2502888,0.03327528,0.0041847974,0.0094069,0.0034769033,0.008498658,0.0492882,0.09023344,0.090206034,0.4276005],"study_design_scores_gemma":[0.0065372004,0.0013884263,0.020981396,0.0011168489,0.0037549154,0.00067367265,0.0011290248,0.9544283,0.00039624763,0.0019292965,0.0052364566,0.0024281736],"about_ca_topic_score_codex":0.00096687116,"about_ca_topic_score_gemma":0.00027066772,"teacher_disagreement_score":0.9459297,"about_ca_system_score_codex":0.00022871657,"about_ca_system_score_gemma":0.00033474335,"threshold_uncertainty_score":0.99927294},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9931311,0.004105611,0.000093858675,0.00017310645,0.0010313524,0.0007575437,0.00022603844,0.000059784397,0.00042159582],"genre_scores_gemma":[0.9934074,0.0012674907,0.0010814182,0.00059473334,0.00025692597,0.00005488897,0.00016321392,0.00003304983,0.003140847],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845475,0.000041395484,0.0006491088,0.00042516342,0.00027541787,0.00015416539],"domain_scores_gemma":[0.99894494,0.00004803427,0.00024130296,0.0003940236,0.00018391888,0.00018778353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028562106,0.00012932238,0.00039723268,0.00007386382,0.000020043468,0.00002795832,0.000038087503,0.00003850304,0.00011381688],"category_scores_gemma":[0.0010953075,0.000105461186,0.00009841543,0.0001578565,0.000046459176,0.00012106565,0.000095047646,0.00009517383,0.000021786329],"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":[0.00021190994,0.0002760357,0.6782379,0.001410624,0.00022810607,0.0032107525,0.00067567866,0.000022418099,0.001409519,0.000021417633,0.11924962,0.19504602],"study_design_scores_gemma":[0.00036593617,0.00020466716,0.34029174,0.0021650838,0.0002264235,0.00011806275,0.0000096735985,0.00026061013,0.00052792096,0.0000828814,0.6555325,0.00021448925],"about_ca_topic_score_codex":0.00059531984,"about_ca_topic_score_gemma":0.000038441663,"teacher_disagreement_score":0.5362829,"about_ca_system_score_codex":0.000045882905,"about_ca_system_score_gemma":0.00009743813,"threshold_uncertainty_score":0.43005824},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9525173,0.0071873968,0.0000047539165,0.0043012872,0.00016563191,0.00026664708,0.035412192,0.00006735158,0.00007743937],"genre_scores_gemma":[0.9966314,0.0019096208,0.000005337372,0.00012315171,0.0002639062,0.00041457266,0.0001984058,0.000046438225,0.00040716314],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976437,0.000085385764,0.00065654295,0.00064430543,0.0005337918,0.00043629398],"domain_scores_gemma":[0.998385,0.00012858701,0.00030737833,0.00059442804,0.00032692825,0.00025764763],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022520001,0.0003361821,0.0010864668,0.0010729932,0.00003790063,0.0000461913,0.00026380486,0.00013943472,0.0021084577],"category_scores_gemma":[0.00001813142,0.0002830583,0.00016811557,0.0026019176,0.0003358258,0.0004911746,0.00014501826,0.00029305267,0.0000059538206],"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":[0.00044990832,0.0000973589,0.9724848,0.001022481,0.0012977427,0.000029067129,0.00015613063,0.0008644816,0.00034591963,0.00015592987,0.0012951277,0.02180104],"study_design_scores_gemma":[0.0010128499,0.00005931001,0.99241126,0.0017913833,0.00066867826,0.0000050411186,0.000030607036,0.00032026626,0.00019491333,0.000010579296,0.0032333748,0.00026174815],"about_ca_topic_score_codex":0.018963674,"about_ca_topic_score_gemma":0.041731782,"teacher_disagreement_score":0.0441141,"about_ca_system_score_codex":0.0005509794,"about_ca_system_score_gemma":0.000707186,"threshold_uncertainty_score":0.99996215},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01944145,0.00006240009,0.9740677,0.0005224546,0.002975631,0.0006383134,0.0018294387,0.00041419017,0.000048448528],"genre_scores_gemma":[0.96951157,0.0000091557085,0.02967574,0.00029676125,0.00016380618,0.00008906248,0.00008794772,0.00003460215,0.00013137024],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801075,0.00011075185,0.00029816403,0.0008506631,0.0003714962,0.00035818372],"domain_scores_gemma":[0.99861205,0.0005055536,0.000063943065,0.0005463048,0.0001291944,0.0001429416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007075033,0.00019970091,0.0002936225,0.00043698226,0.0005333109,0.00008393438,0.00027960818,0.00009857835,0.00005520176],"category_scores_gemma":[0.00011626965,0.00019099213,0.00016224288,0.0010798731,0.00020268245,0.000359507,0.000003703513,0.00022258992,0.000025225545],"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":[0.001526236,0.0007338873,0.000025837555,0.0005356101,0.00017236258,0.00007660649,0.00041590183,0.0014539616,0.68464506,0.000035967256,0.0022997758,0.3080788],"study_design_scores_gemma":[0.0014632501,0.00029516115,0.0004181083,0.0001688743,0.00044425394,0.0001946117,0.000080671125,0.7304913,0.2579698,0.0000883115,0.0080518,0.00033386803],"about_ca_topic_score_codex":0.0000926265,"about_ca_topic_score_gemma":0.0004345602,"teacher_disagreement_score":0.9500701,"about_ca_system_score_codex":0.00034305113,"about_ca_system_score_gemma":0.000725582,"threshold_uncertainty_score":0.7788433},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97144014,0.013234489,0.012584774,0.0005997217,0.0005663782,0.0013905304,0.00007077997,0.000058334495,0.00005485961],"genre_scores_gemma":[0.99347496,0.005043365,0.00065220887,0.000034409393,0.00049319194,0.00011253834,0.000113453505,0.000043830394,0.00003202062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99573445,0.0008006126,0.001022615,0.00054516667,0.00095413666,0.00094300596],"domain_scores_gemma":[0.99763685,0.00047367628,0.000879845,0.000579079,0.00035751396,0.00007302149],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0036747546,0.00037005165,0.00056929275,0.00023339949,0.00029522597,0.00023455957,0.00043963114,0.00014321889,0.000029732719],"category_scores_gemma":[0.0006231349,0.00024235967,0.00015697257,0.00074639305,0.00021551347,0.0002680642,0.00045109098,0.0035025466,0.000009390445],"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":[0.0015432072,0.0029451721,0.33397335,0.0007230736,0.0036906796,0.00023243838,0.052003514,0.034771904,0.001208195,0.0007339185,0.0007297836,0.5674448],"study_design_scores_gemma":[0.0055286163,0.002418995,0.28091392,0.010868133,0.003278624,0.0012744395,0.116088204,0.45809832,0.00087722146,0.11778145,0.0011652107,0.0017068845],"about_ca_topic_score_codex":0.0017800936,"about_ca_topic_score_gemma":0.0049015796,"teacher_disagreement_score":0.5657379,"about_ca_system_score_codex":0.0024162442,"about_ca_system_score_gemma":0.005481367,"threshold_uncertainty_score":0.9987964},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011588719,0.9356644,0.041100584,0.004627241,0.0012060804,0.0049341437,0.00006859889,0.0004955655,0.00031464547],"genre_scores_gemma":[0.54137164,0.36171505,0.011101945,0.08340883,0.0012522949,0.0004475024,0.00021061745,0.00022424386,0.0002678926],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99559206,0.0003622415,0.002808546,0.00012549966,0.0006076411,0.0005040237],"domain_scores_gemma":[0.99741775,0.00045250126,0.00088553113,0.00033961987,0.00019383535,0.0007107896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063526635,0.0002273504,0.0012028314,0.00030803925,0.00022618884,0.00024820789,0.0001254455,0.0000707152,0.000026816397],"category_scores_gemma":[0.0011494824,0.00016771092,0.00013017163,0.00033337876,0.000046139496,0.0004987035,0.000040049723,0.0008730029,0.00014260174],"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":[0.0000143877705,0.000009656341,0.0037574898,0.98764133,0.00007644799,0.00005735324,0.0006414314,0.000068142566,8.81817e-7,0.00005111087,0.005888443,0.001793331],"study_design_scores_gemma":[0.0005457378,0.00018098055,0.0003976198,0.9348266,0.00015887657,0.003960657,0.00017555627,0.058428515,2.7546434e-7,0.000013144482,0.0011595103,0.00015250569],"about_ca_topic_score_codex":0.000012942087,"about_ca_topic_score_gemma":0.000002363589,"teacher_disagreement_score":0.5739494,"about_ca_system_score_codex":0.00037489278,"about_ca_system_score_gemma":0.0020524974,"threshold_uncertainty_score":0.6839053},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941274,0.0017400242,0.000017193102,0.00058770715,0.00005208629,0.0017500996,0.0015705718,0.00003464795,0.00012027124],"genre_scores_gemma":[0.99841255,0.000113295515,0.00018116074,0.000009974669,0.000090983114,0.0001452792,0.00084818184,0.00005333299,0.00014524265],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99622566,0.0006713993,0.00064561894,0.0008253353,0.0011430297,0.00048897695],"domain_scores_gemma":[0.9985818,0.00024860245,0.00012211874,0.00068819145,0.0001955554,0.00016370861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025195295,0.00024205625,0.00064489956,0.0012696347,0.000017382354,0.000062138264,0.00028134638,0.00036326033,0.0003091711],"category_scores_gemma":[0.0010656429,0.00024511732,0.000080769976,0.00083876273,0.00020598558,0.00005369307,0.0012904509,0.0018107456,0.000048036218],"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":[0.0012653419,0.0002900349,0.9913714,0.0015249178,0.00006966605,0.00040941394,0.0025487472,0.00014371515,0.0009437963,0.0001882524,0.000101180034,0.0011435492],"study_design_scores_gemma":[0.0013787496,0.00015795894,0.98894197,0.003728728,0.000015070945,0.000004388325,0.00043293292,0.0020879898,0.000023497687,0.0030301798,0.000012792937,0.00018576974],"about_ca_topic_score_codex":0.041985698,"about_ca_topic_score_gemma":0.03154433,"teacher_disagreement_score":0.0104413675,"about_ca_system_score_codex":0.00061526714,"about_ca_system_score_gemma":0.001284086,"threshold_uncertainty_score":0.99955946},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98961776,0.005396617,0.00015058852,0.00075691607,0.00029318235,0.00043470503,0.0014325352,0.0000975127,0.0018201923],"genre_scores_gemma":[0.99768686,0.00033763706,0.00024165904,0.00024648925,0.00010713029,0.000040881983,0.0010505436,0.000028723141,0.0002600701],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99805754,0.00012982536,0.00054358115,0.0006816688,0.00038662553,0.00020075211],"domain_scores_gemma":[0.9984707,0.00009827669,0.00017349192,0.0008533466,0.000060927938,0.00034321324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006289506,0.0002515471,0.0010793271,0.00033949077,0.000012247446,0.000020168269,0.00018480269,0.00019301772,0.00089492364],"category_scores_gemma":[0.0005129882,0.00022803218,0.00026533107,0.00040506897,0.00012171638,0.000025042758,0.0007459939,0.00035492962,0.000011706962],"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":[0.00021277492,0.00010063235,0.99372745,0.0016113766,0.0012381027,0.00038097517,0.00047268055,0.00004994248,0.00015369033,0.000058744372,0.00071932096,0.0012743126],"study_design_scores_gemma":[0.0005899677,0.000047937614,0.9880826,0.00020547195,0.0019755417,0.0000036843385,0.000119443015,0.0043363245,0.0000347711,0.0002841404,0.0041054427,0.00021469574],"about_ca_topic_score_codex":0.037290074,"about_ca_topic_score_gemma":0.077309825,"teacher_disagreement_score":0.04001975,"about_ca_system_score_codex":0.0004746162,"about_ca_system_score_gemma":0.0006191855,"threshold_uncertainty_score":0.97987866},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000029442692,0.011241104,0.00029449063,0.00310043,0.0006428952,0.00058491493,0.0009667687,0.00063874276,0.98252773],"genre_scores_gemma":[0.000518046,0.0013954648,0.00031840714,0.0038848377,0.0006021786,0.000012287742,0.002207598,0.00007644234,0.99098474],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982958,0.000027759466,0.00040461062,0.00056657026,0.00041194956,0.00029333966],"domain_scores_gemma":[0.99858,0.000056417426,0.00009337162,0.0007968675,0.00008224309,0.00039109404],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025142633,0.00028315658,0.0007018774,0.00014481765,0.000025520958,0.000026726573,0.00017539864,0.00017202788,0.0029138303],"category_scores_gemma":[0.00005981218,0.00023469125,0.00021049731,0.000097361706,0.00009315859,0.00002983429,0.0001759902,0.0006502778,0.009063201],"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":[0.00002203125,0.0000347484,0.00002392509,0.0020480065,0.00010793623,0.00020155615,0.00006815027,1.2209304e-7,1.7444705e-7,0.016447349,0.9595238,0.02152218],"study_design_scores_gemma":[0.000063205945,0.00011730835,0.00005494919,0.0011494675,0.00005838521,0.000056383487,0.000010340379,0.000067632864,0.0000046924542,0.018386267,0.9798355,0.0001958428],"about_ca_topic_score_codex":0.000054128275,"about_ca_topic_score_gemma":0.00014479946,"teacher_disagreement_score":0.021326337,"about_ca_system_score_codex":0.00047645986,"about_ca_system_score_gemma":0.0025347571,"threshold_uncertainty_score":0.99799764},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000048075967,0.9867029,0.0021765954,0.00031354334,0.00015305357,0.009360983,0.0008313513,0.00014781463,0.00030890273],"genre_scores_gemma":[0.000057364923,0.98696315,0.00016143972,0.0042638206,0.00012612292,0.0023138358,0.006002954,0.0000564543,0.00005486206],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9925208,0.00029814767,0.004386964,0.0003434967,0.0019256222,0.0005249897],"domain_scores_gemma":[0.9950762,0.0002091404,0.0021053667,0.0016074231,0.00018482767,0.0008170598],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014739714,0.0005655366,0.006577242,0.000718647,0.00003369802,0.000023245195,0.00050925877,0.00029902562,0.00020413127],"category_scores_gemma":[0.00082297524,0.0003696165,0.0011501362,0.0021728668,0.000055258264,0.00006322935,0.00018272168,0.0008873183,0.0012382638],"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":[0.000008826419,0.0001069771,7.164633e-7,0.64345294,0.0011549722,0.000003200682,0.00013793688,8.6596486e-8,3.2901848e-9,0.00006490647,0.0023739655,0.35269544],"study_design_scores_gemma":[0.000102506645,0.00046731232,0.0000054410257,0.5543355,0.009431027,0.000021213813,0.000020168889,0.000490805,4.1586453e-8,0.0000031439215,0.43489775,0.00022512783],"about_ca_topic_score_codex":0.000041522522,"about_ca_topic_score_gemma":0.000028073453,"teacher_disagreement_score":0.4325238,"about_ca_system_score_codex":0.00075006916,"about_ca_system_score_gemma":0.0020279398,"threshold_uncertainty_score":0.9998756},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020205039,0.06786754,0.00044020044,0.016652755,0.0014360992,0.002796975,0.0012286131,0.0011416534,0.90641564],"genre_scores_gemma":[0.0030112148,0.0014278705,0.0030579555,0.004882857,0.0002641443,0.00003640617,0.0023432309,0.00011183598,0.9848645],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983638,0.000006753981,0.00040215324,0.00058080786,0.000383985,0.00026251457],"domain_scores_gemma":[0.9981848,0.0000216343,0.00010590418,0.00037729094,0.000057835263,0.001252558],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017283717,0.0003173585,0.00054611405,0.00018507788,0.000083024315,0.00003563087,0.00008051881,0.00008918578,0.0003298169],"category_scores_gemma":[0.000058077738,0.00030136245,0.00007957624,0.00003160309,0.00007905123,0.000027684726,0.00025377382,0.00030179086,0.00063383224],"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":[0.00041636426,0.0001663327,0.013391785,0.01998533,0.0018957831,0.003282841,0.002400254,4.1609516e-7,0.0000054572897,0.06580233,0.42732945,0.46532366],"study_design_scores_gemma":[0.00025865313,0.000017797756,0.018662486,0.0027513225,0.00003825324,0.000019582569,0.000009940056,0.0000051506427,7.561347e-7,0.0032195193,0.974732,0.00028458162],"about_ca_topic_score_codex":0.000002982575,"about_ca_topic_score_gemma":0.000009063606,"teacher_disagreement_score":0.5474025,"about_ca_system_score_codex":0.00038922208,"about_ca_system_score_gemma":0.0023123538,"threshold_uncertainty_score":0.99994385},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936813,0.00010860861,0.000021346274,0.00054415397,0.00010515632,0.001775894,0.0011966595,0.00010764919,0.002459196],"genre_scores_gemma":[0.99911845,0.00008110745,0.0000074830423,0.000022904713,0.00015423873,0.00013240796,0.000055772492,0.00004092492,0.00038671342],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99572545,0.0007288194,0.0002825032,0.00056305714,0.0020924578,0.00060772774],"domain_scores_gemma":[0.9982407,0.0002466249,0.000045578607,0.0005570512,0.0005512889,0.00035873736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018435894,0.00022857844,0.00048030555,0.0006346962,0.00015957748,0.000096170435,0.00022491637,0.000087609274,0.0001552715],"category_scores_gemma":[0.00017584191,0.00018193836,0.00010704298,0.0008953094,0.00032098248,0.00040333503,0.00045641878,0.0014310103,0.0001783248],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020637424,0.0034025484,0.95503485,0.0020858468,0.00035472465,0.00031818706,0.007231486,7.5571563e-7,0.001800567,0.00040407275,0.005365392,0.021937804],"study_design_scores_gemma":[0.0018762057,0.0033196,0.987737,0.0012381175,0.000018285047,0.000015076732,0.0025175111,0.0017544291,0.00092079624,0.00004907539,0.00040770418,0.00014621108],"about_ca_topic_score_codex":0.00010590723,"about_ca_topic_score_gemma":0.00006166554,"teacher_disagreement_score":0.032702114,"about_ca_system_score_codex":0.00047460906,"about_ca_system_score_gemma":0.00041235847,"threshold_uncertainty_score":0.7419231},"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":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042190578,0.3936419,0.0011332389,0.45415932,0.0057994355,0.009522682,0.02433639,0.0013780902,0.06783837],"genre_scores_gemma":[0.09385196,0.482812,0.0011845084,0.08482334,0.00066786585,0.0003559718,0.19533628,0.00036155657,0.14060651],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99653834,0.0004583745,0.00092967064,0.0008960372,0.000665663,0.0005119387],"domain_scores_gemma":[0.9973246,0.0007328306,0.00025786235,0.0011741932,0.00029856403,0.0002119352],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017467922,0.0005878483,0.0013035034,0.0004891612,0.00004761048,0.00010320461,0.00063611456,0.00026056592,0.0011605466],"category_scores_gemma":[0.00067296444,0.00037838283,0.00019468079,0.0011628856,0.00020589022,0.00008467914,0.0004019274,0.0016227785,0.00021225087],"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":[0.000037431586,0.0001035364,0.008423183,0.009047694,0.00025409515,0.00028442385,0.000067844645,0.0000014378119,0.0000016063003,0.0010675716,0.97401905,0.006692098],"study_design_scores_gemma":[0.0005404343,0.00018425066,0.011768799,0.014948547,0.0007494264,0.00010625833,0.0003366834,0.049540326,5.1427025e-7,0.00030773817,0.92097014,0.0005468976],"about_ca_topic_score_codex":0.0013667782,"about_ca_topic_score_gemma":0.0038621644,"teacher_disagreement_score":0.36933598,"about_ca_system_score_codex":0.00032942946,"about_ca_system_score_gemma":0.0006332184,"threshold_uncertainty_score":0.9998668},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952757,0.00023422932,0.00002854444,0.0003917851,0.00012557986,0.0005372213,0.0019582273,0.00010493683,0.0013437718],"genre_scores_gemma":[0.98943084,0.00008635075,0.009331685,0.00006536756,0.000062743005,0.000003978787,0.0006695479,0.000022893122,0.00032661034],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980344,0.00031463185,0.00052209606,0.0005630104,0.0003244606,0.00024141184],"domain_scores_gemma":[0.998518,0.0004762504,0.00012259785,0.00064545445,0.000110332556,0.00012739618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061352237,0.0001887818,0.0006640993,0.00026167659,0.00004312544,0.000032291955,0.000106998596,0.000051501982,0.000052792086],"category_scores_gemma":[0.00017680174,0.00017230828,0.000041756757,0.0007344268,0.0001682922,0.000119479606,0.00017491696,0.00021572359,0.0000051459906],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012935149,0.00085971877,0.84346396,0.003178925,0.00093648006,0.0011122794,0.0054860064,0.0010853068,0.13142017,0.0003573431,0.008689042,0.0021172557],"study_design_scores_gemma":[0.0012200086,0.00004235806,0.7687067,0.00050352263,0.00006855003,0.000023118408,0.0007317311,0.22596169,0.0009443717,0.000010107452,0.0016018598,0.00018599244],"about_ca_topic_score_codex":0.0024318877,"about_ca_topic_score_gemma":0.0021441078,"teacher_disagreement_score":0.22487637,"about_ca_system_score_codex":0.0001323623,"about_ca_system_score_gemma":0.00028358633,"threshold_uncertainty_score":0.70265275},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7534588,0.029633373,0.06460249,0.12554058,0.0035590688,0.0019285439,0.005096134,0.0013283299,0.014852689],"genre_scores_gemma":[0.987711,0.0011969727,0.0070853033,0.0020634332,0.0002643058,0.000020602733,0.0007524506,0.000033042703,0.00087290636],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975337,0.00007638042,0.0013160564,0.00014169078,0.00039927277,0.00053289754],"domain_scores_gemma":[0.9980166,0.00017822086,0.00041319247,0.00026871945,0.00024738695,0.00087588135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017637999,0.00020836215,0.0005953148,0.0011192381,0.00028565014,0.00041598084,0.00013939592,0.000096615055,0.00028739672],"category_scores_gemma":[0.0003997754,0.00016375666,0.00026456852,0.0012461956,0.00025212305,0.00072804076,0.00007889117,0.00061726634,0.000030514268],"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":[0.00062645035,0.00029115332,0.066354856,0.0063877003,0.0061126607,0.00011496187,0.014503779,0.00006239147,0.000010669068,0.011296722,0.46301946,0.43121922],"study_design_scores_gemma":[0.003104722,0.0008924183,0.026968513,0.00083484576,0.000858066,0.0020040302,0.0021420966,0.30046004,0.0000020805614,0.00063565286,0.661745,0.00035249654],"about_ca_topic_score_codex":0.000002680347,"about_ca_topic_score_gemma":0.000058631984,"teacher_disagreement_score":0.43086672,"about_ca_system_score_codex":0.00025724425,"about_ca_system_score_gemma":0.0012537976,"threshold_uncertainty_score":0.6677803},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025130096,0.24321198,0.0052024266,0.014857873,0.0068023736,0.014725385,0.104976535,0.0026830388,0.6050274],"genre_scores_gemma":[0.9018181,0.0149162505,0.00041261213,0.007477192,0.0055424734,0.002647116,0.005746536,0.0003586927,0.061081015],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99369544,0.00013081165,0.0011038246,0.0025140024,0.0016025977,0.0009533222],"domain_scores_gemma":[0.9958257,0.000121502606,0.0006030272,0.0015251596,0.0007811444,0.001143468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007938495,0.0009682431,0.0022765473,0.0010637545,0.00026251515,0.00012971375,0.00034520586,0.00035110506,0.00072055735],"category_scores_gemma":[0.00008839697,0.0009365797,0.0013692809,0.0015149825,0.00023231897,0.00006600683,0.00018552507,0.0008430458,0.0005835992],"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":[0.011500696,0.0018301588,0.41458073,0.012412257,0.06375385,0.0008373423,0.0012786861,0.008432839,0.000008361749,0.24687198,0.13437694,0.10411616],"study_design_scores_gemma":[0.0010640087,0.0013494453,0.38124135,0.0031073752,0.0023487222,0.000031368985,0.0008036773,0.00044091116,8.58376e-7,0.0019829953,0.6055954,0.002033899],"about_ca_topic_score_codex":0.013118042,"about_ca_topic_score_gemma":0.004164071,"teacher_disagreement_score":0.8993051,"about_ca_system_score_codex":0.0023740232,"about_ca_system_score_gemma":0.0007521508,"threshold_uncertainty_score":0.99930847},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018897456,0.80362445,0.0002472588,0.017698552,0.00065677386,0.0028149565,0.027679203,0.00028152362,0.1468083],"genre_scores_gemma":[0.020766526,0.8480148,0.09706656,0.023611989,0.0016761161,0.000076110315,0.003387151,0.00038411535,0.0050166803],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99570507,0.00009437235,0.0006996324,0.001872274,0.001037875,0.0005907645],"domain_scores_gemma":[0.99637777,0.00010279213,0.00048952946,0.0016944521,0.00041331808,0.00092216587],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029344622,0.00048877083,0.0013565397,0.00059670926,0.00021902565,0.000059334176,0.00048691637,0.00014970431,0.000060937597],"category_scores_gemma":[0.00017718929,0.000443204,0.000148946,0.00053035014,0.0018165713,0.00017892706,0.00088803074,0.00051373633,0.000013481774],"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":[0.000446326,0.00024320073,0.0027372073,0.008854445,0.0006660779,0.000051486702,0.00079652085,2.9413872e-7,0.0000018354013,0.3990676,0.016939383,0.5701956],"study_design_scores_gemma":[0.0025159088,0.005019429,0.20171818,0.010741794,0.00051614153,0.00026693032,0.0016164988,0.0003083101,0.000001198396,0.058248565,0.7177864,0.0012606509],"about_ca_topic_score_codex":0.0014508583,"about_ca_topic_score_gemma":0.00022110669,"teacher_disagreement_score":0.700847,"about_ca_system_score_codex":0.00047373833,"about_ca_system_score_gemma":0.0020914031,"threshold_uncertainty_score":0.999802},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9846776,0.003407219,0.00014166931,0.0097460095,0.00018448124,0.0011774522,0.00016499161,0.00010094587,0.0003996235],"genre_scores_gemma":[0.997972,0.000026347358,0.00021984708,0.0006541136,0.000027782557,0.00064320856,0.00032029347,0.00003294958,0.00010346796],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980226,0.00024506333,0.0007336793,0.00048715383,0.000060273476,0.00045118737],"domain_scores_gemma":[0.99867123,0.0004342856,0.00015844642,0.00047763134,0.00006561245,0.00019279234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025974732,0.00019990042,0.0008744781,0.0005488934,0.00005297015,0.00001636811,0.00016672192,0.00012523595,0.000012945437],"category_scores_gemma":[0.00014282767,0.00018729677,0.00011746721,0.00038532127,0.0000818632,0.00011089612,0.00012365988,0.00021669193,0.0000056412414],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009513734,0.0005314936,0.9840951,0.00048867083,0.00008296065,0.00007883319,0.0009489663,3.1352056e-7,0.00023496895,0.0010607346,0.007897249,0.0044855806],"study_design_scores_gemma":[0.0037410727,0.00053540635,0.9789739,0.000056609282,0.00006270025,0.00024080549,0.0004944885,0.00041617392,0.000027626877,0.00015708787,0.015153708,0.00014041584],"about_ca_topic_score_codex":0.00022492815,"about_ca_topic_score_gemma":0.0013552336,"teacher_disagreement_score":0.013294382,"about_ca_system_score_codex":0.000092454735,"about_ca_system_score_gemma":0.00033489722,"threshold_uncertainty_score":0.7637741},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029171372,0.9615355,0.0009276514,0.00017172811,0.00011615304,0.002353287,0.005612158,0.00010265297,0.000009487651],"genre_scores_gemma":[0.092092946,0.9018514,0.0020473653,0.00025135058,0.00087463146,0.0004146956,0.0021256737,0.00029843178,0.000043499287],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99694365,0.0002390092,0.0009844538,0.0011256345,0.00029532897,0.00041192462],"domain_scores_gemma":[0.997604,0.00030460922,0.0004006995,0.0008125767,0.00014909859,0.0007289977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008466543,0.0004780185,0.001993049,0.00031553826,0.00018186052,0.00016713295,0.0001689649,0.00035492933,9.0414386e-7],"category_scores_gemma":[0.00025446844,0.0004094931,0.00007427251,0.00018609721,0.00011741845,0.0003493581,0.0005496794,0.00054888387,0.0000013706862],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008640603,0.000032808057,0.0042961794,0.17352925,0.0002406362,0.000028822888,0.002043931,0.0000020822415,0.0000020627283,0.00013856594,0.00008285352,0.8195164],"study_design_scores_gemma":[0.0038450286,0.0021356985,0.00561686,0.42411226,0.008763719,0.0014672733,0.0051292237,0.110429496,0.0000016112284,0.0014391645,0.43442756,0.0026321085],"about_ca_topic_score_codex":0.0016637521,"about_ca_topic_score_gemma":0.00065271027,"teacher_disagreement_score":0.8168843,"about_ca_system_score_codex":0.00010481826,"about_ca_system_score_gemma":0.00024847558,"threshold_uncertainty_score":0.99983567},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000023544399,0.0009805289,0.008954044,0.0005998771,0.00045718128,0.00080250617,0.005566324,0.000288036,0.98234916],"genre_scores_gemma":[0.0013469222,0.00040116603,0.0074782576,0.002542517,0.00062348787,0.000035579313,0.003971604,0.00020142006,0.98339903],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99781257,0.000026424068,0.0004288468,0.0007004606,0.0006891006,0.00034262738],"domain_scores_gemma":[0.997878,0.000553515,0.0001434813,0.0011751336,0.00009809739,0.00015177892],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000261756,0.0005061598,0.00062143826,0.00011452495,0.00008948617,0.000075984484,0.00028132452,0.00022631286,0.003336373],"category_scores_gemma":[0.00015862615,0.00031896945,0.00020812119,0.000060589395,0.00022086402,0.000035459943,0.00021143352,0.00085536286,0.0017173741],"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":[0.00010469508,0.000032330707,0.0000051023753,0.0004712496,0.0003561089,0.00022424957,0.00010460455,0.000023348286,0.0000010212724,0.7242097,0.26931277,0.005154818],"study_design_scores_gemma":[0.00051420636,0.0001736438,0.00004775343,0.00087093597,0.00048670862,0.000043868466,0.00006866362,0.0038890874,0.0000027777528,0.07223725,0.92115116,0.0005139623],"about_ca_topic_score_codex":0.000036862235,"about_ca_topic_score_gemma":0.000026571515,"teacher_disagreement_score":0.6519725,"about_ca_system_score_codex":0.00017375742,"about_ca_system_score_gemma":0.00018385914,"threshold_uncertainty_score":0.9999262},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05976839,0.13322146,0.5731159,0.22966094,0.00074517727,0.00083383406,0.00082488207,0.00015243661,0.0016769741],"genre_scores_gemma":[0.9922675,0.0030973013,0.002104592,0.0018060021,0.00046532392,0.0000011921751,0.0001602743,0.00003724111,0.000060573235],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9944549,0.0029563513,0.0010084356,0.0004090035,0.00057875435,0.00059255795],"domain_scores_gemma":[0.99677885,0.0004205029,0.0007381295,0.0005587854,0.0006203766,0.0008833767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02573748,0.00017153632,0.0007259882,0.00051097624,0.00024495902,0.0002460675,0.00048815826,0.000015982438,0.000048325106],"category_scores_gemma":[0.0026624824,0.00011760751,0.00023220498,0.0016061687,0.00013937984,0.00049215864,0.00022995406,0.00048476292,0.0000044123835],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048788817,0.00021363699,0.030948808,0.0007842314,0.005619739,0.00017087792,0.009007871,0.0001099037,0.0000060171683,0.0019215542,0.009506753,0.94166183],"study_design_scores_gemma":[0.0014417598,0.00092801155,0.63383585,0.00028147025,0.00054875354,0.0004002941,0.012684399,0.030086482,1.6108118e-7,0.000034097935,0.3195977,0.00016098692],"about_ca_topic_score_codex":0.000119899516,"about_ca_topic_score_gemma":0.00007203732,"teacher_disagreement_score":0.94150084,"about_ca_system_score_codex":0.00066868507,"about_ca_system_score_gemma":0.0024986595,"threshold_uncertainty_score":0.8920148},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5084254,0.025745623,0.39961076,0.049782146,0.00898875,0.00097455963,0.005335519,0.00045629055,0.0006809372],"genre_scores_gemma":[0.98286873,0.0011117137,0.008644254,0.0005161047,0.005811582,0.0000010379941,0.0007918072,0.00008040907,0.00017433274],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970901,0.00092795456,0.0009236709,0.00030659654,0.00040676096,0.00034491718],"domain_scores_gemma":[0.99783176,0.0001800703,0.0003825202,0.00062474696,0.00025351672,0.000727387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053487374,0.00014255334,0.00035425214,0.00023621733,0.00007226685,0.00013167088,0.00040018884,0.000020679572,0.000051131152],"category_scores_gemma":[0.0012714744,0.000116692325,0.00012209635,0.00025843282,0.00002252455,0.0005361641,0.00012215703,0.00044542455,0.000041397292],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005796196,0.00021773852,0.002761537,0.0002107813,0.0001761172,0.0002886021,0.00018806796,0.0000035271707,0.0007862541,0.000002489614,0.009572743,0.98573416],"study_design_scores_gemma":[0.0012737861,0.0005985287,0.26162183,0.0007516194,0.000056192246,0.00020215347,0.00030696712,0.001580494,0.000034476165,0.000011099492,0.7334543,0.000108558794],"about_ca_topic_score_codex":0.000070155635,"about_ca_topic_score_gemma":0.00004668338,"teacher_disagreement_score":0.9856256,"about_ca_system_score_codex":0.00024861895,"about_ca_system_score_gemma":0.00095906993,"threshold_uncertainty_score":0.4758575},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9844323,0.0014464987,0.00031659805,0.01004796,0.0004370725,0.0002676173,0.0009582026,0.0004405714,0.0016531924],"genre_scores_gemma":[0.99233043,0.000025966829,0.00010216441,0.002440458,0.00014590743,0.00006284953,0.0016797954,0.00004700346,0.0031654483],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982371,0.00013848385,0.0003022095,0.00046034958,0.00037426775,0.00048759612],"domain_scores_gemma":[0.99877894,0.00038737874,0.00003423374,0.0005096317,0.000083458086,0.000206339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000484681,0.00023291651,0.00031192033,0.00023183411,0.000041391846,0.00016976641,0.00028184164,0.000058889706,0.00043940588],"category_scores_gemma":[0.00019301452,0.00016422484,0.00012010361,0.0014647852,0.00012671346,0.000079342535,0.00010204242,0.00029377808,0.00031366627],"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":[0.0036783137,0.0009122283,0.25735408,0.00087295426,0.0007221356,0.0033515864,0.013693512,0.022776758,0.00081829826,0.0012682608,0.4897949,0.20475696],"study_design_scores_gemma":[0.0010981382,0.00016951392,0.447963,0.0002586711,0.000016043105,0.000021461967,0.00042943275,0.0647978,0.0001968137,0.00006527552,0.48471606,0.00026781583],"about_ca_topic_score_codex":0.0006265572,"about_ca_topic_score_gemma":0.001761129,"teacher_disagreement_score":0.20448916,"about_ca_system_score_codex":0.00013341504,"about_ca_system_score_gemma":0.000100666824,"threshold_uncertainty_score":0.6696895},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91278905,0.0052771578,0.0046060816,0.061998714,0.0013408677,0.0070607113,0.0013316475,0.0011078261,0.0044879387],"genre_scores_gemma":[0.9890837,0.0008595185,0.0006558675,0.0064475248,0.00022302067,0.00045762412,0.0021947757,0.00002856277,0.000049377188],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964214,0.00054678635,0.0015166918,0.00041555666,0.00030496976,0.0007945763],"domain_scores_gemma":[0.99184823,0.00613357,0.00037880873,0.00057397457,0.0007086186,0.00035679864],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002157777,0.00033247066,0.0008113736,0.00048329146,0.00058056955,0.00012335429,0.00023864846,0.00037670272,0.000017380582],"category_scores_gemma":[0.0015275705,0.0002480646,0.000097577795,0.0004354227,0.0008183641,0.00061808276,0.00029043548,0.0010743131,0.00015145162],"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":[0.0035845563,0.00009466657,0.19392607,0.0034850917,0.0012753508,0.00001356791,0.008465878,0.0022385693,0.000082237195,0.06866973,0.4079288,0.31023547],"study_design_scores_gemma":[0.0014618344,0.0009042249,0.42469728,0.00014467926,0.000026835707,0.00010855795,0.0003876121,0.06779236,0.0000031401228,0.0018935661,0.5023656,0.00021435315],"about_ca_topic_score_codex":0.00011443447,"about_ca_topic_score_gemma":0.000043233566,"teacher_disagreement_score":0.31002113,"about_ca_system_score_codex":0.00028111046,"about_ca_system_score_gemma":0.00019600498,"threshold_uncertainty_score":0.99999714},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996423,0.00036449797,0.0016080602,0.00017554265,0.00017836083,0.00087791187,0.00013578932,0.00016427902,0.000072575174],"genre_scores_gemma":[0.99821985,0.0000120932245,0.0003633854,0.00021614206,0.00010863467,0.000105799605,0.00094244763,0.00002150331,0.000010129636],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998343,0.00022314362,0.0005163817,0.0005008828,0.0001775066,0.0002391059],"domain_scores_gemma":[0.9991505,0.00044553363,0.000050775678,0.00014715621,0.00012191011,0.00008410433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002045214,0.00018539141,0.00039191698,0.0006654547,0.000043152308,0.000016998007,0.00005851533,0.000089583606,0.00002473198],"category_scores_gemma":[0.00010951794,0.0001722045,0.00005083083,0.0008468964,0.00005855266,0.00010741372,0.000039590795,0.00030375022,0.00003126173],"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":[0.00011679562,0.0010286592,0.97994816,0.00032052724,0.00019301473,0.008081917,0.0044607082,0.0031426603,0.00045484427,0.00020147223,0.000035268633,0.0020159679],"study_design_scores_gemma":[0.002179055,0.0005767659,0.9330356,0.00018598455,0.000053981978,0.00092289166,0.0004031647,0.060702737,0.000021187952,0.0011611563,0.0005164225,0.00024105534],"about_ca_topic_score_codex":0.0012776806,"about_ca_topic_score_gemma":0.02044389,"teacher_disagreement_score":0.05756008,"about_ca_system_score_codex":0.00021036065,"about_ca_system_score_gemma":0.00033204438,"threshold_uncertainty_score":0.99743044},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85012627,0.0018888551,0.00035635775,0.14272058,0.00019206075,0.0021411101,0.00095411245,0.00079271087,0.0008279221],"genre_scores_gemma":[0.99564266,0.0001115928,0.00024751268,0.00085952674,0.00011208954,0.00008881265,0.0011469375,0.000042642696,0.0017482351],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970437,0.0005229044,0.00074678764,0.0008410262,0.0003329337,0.00051263557],"domain_scores_gemma":[0.9984071,0.00018285522,0.00008898397,0.0004272728,0.0001138201,0.00077993463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013196683,0.00024424717,0.0006415422,0.0011099814,0.0001048367,0.00004156568,0.00012292323,0.0000962546,0.0005911743],"category_scores_gemma":[0.0013888973,0.00021407388,0.00009663544,0.0017985483,0.00006454938,0.00027089706,0.00011887523,0.0005499289,0.00013193593],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015297708,0.0072197216,0.87637436,0.0007470873,0.00024441228,0.00070592546,0.055344116,0.00011350183,0.000018737905,0.001142117,0.012358919,0.04420131],"study_design_scores_gemma":[0.0030750658,0.0049418523,0.9563518,0.00024343393,0.0000188654,0.00011454499,0.018843675,0.0009580305,4.5966004e-7,0.00040155568,0.014823669,0.00022706213],"about_ca_topic_score_codex":0.00045831365,"about_ca_topic_score_gemma":0.0011403844,"teacher_disagreement_score":0.14551637,"about_ca_system_score_codex":0.0017196066,"about_ca_system_score_gemma":0.0010823718,"threshold_uncertainty_score":0.8729679},"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":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052697547,0.9049919,0.0056769866,0.0027935614,0.002482759,0.023151483,0.007957259,0.00023572134,0.000012753954],"genre_scores_gemma":[0.18404926,0.7942642,0.0070797126,0.003596326,0.0004893906,0.004132234,0.004471075,0.00033689837,0.0015809028],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9971181,0.00013936499,0.0012647661,0.00080630765,0.0003781864,0.00029328655],"domain_scores_gemma":[0.99599063,0.0022029646,0.0006564374,0.0005862183,0.0003787511,0.00018498697],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011208904,0.00045329455,0.001836237,0.00033980963,0.000027523214,0.00002227367,0.00013532156,0.00025841568,0.000025341902],"category_scores_gemma":[0.0059483577,0.00040451004,0.00042139637,0.0003576346,0.000082189676,0.00011767611,0.00016010542,0.00023144338,0.0000015453434],"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":[0.0004893562,0.0003336321,0.0015688982,0.6254388,0.0016501234,0.00002378305,0.000045402205,0.000001767957,0.0012760188,0.0000011213828,0.12508918,0.24408193],"study_design_scores_gemma":[0.012747461,0.003504507,0.1535216,0.42318842,0.023728976,0.00016911009,0.000028411881,0.0004355415,0.0041225096,0.000054226413,0.37709013,0.0014091252],"about_ca_topic_score_codex":0.0003341266,"about_ca_topic_score_gemma":0.00091838685,"teacher_disagreement_score":0.25200093,"about_ca_system_score_codex":0.00012216385,"about_ca_system_score_gemma":0.00019089162,"threshold_uncertainty_score":0.9998407},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95543224,0.007582585,0.0006834683,0.03219294,0.0024439332,0.00016526722,0.00015657247,0.00000719328,0.0013358237],"genre_scores_gemma":[0.99793303,0.0005659104,0.0001794628,0.00061156513,0.0002758896,6.079328e-7,0.0000040994,0.000022346774,0.00040707257],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986604,0.00004864867,0.00055596523,0.00015912038,0.00025550768,0.000320392],"domain_scores_gemma":[0.99808943,0.000040235387,0.00030768602,0.00024175433,0.00040225365,0.00091863156],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002698095,0.00013712386,0.00035613685,0.0003230476,0.00008281551,0.000016118138,0.00013390923,0.00007555274,0.0018622268],"category_scores_gemma":[0.0003413215,0.000104657665,0.00011190836,0.00015649281,0.00015444697,0.00033228964,0.000007399413,0.00014805085,0.000004626282],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023489424,0.00008417919,0.49930573,0.00016370251,0.00024709883,0.00060897355,0.0033835499,0.0000048973334,0.0037027788,0.00017127146,0.2659434,0.2261495],"study_design_scores_gemma":[0.0049984925,0.0011584585,0.85819846,0.015278171,0.0006365647,0.000921749,0.00635259,0.00016270952,0.003060971,0.0043305345,0.10413673,0.0007645381],"about_ca_topic_score_codex":0.0017418811,"about_ca_topic_score_gemma":0.005282775,"teacher_disagreement_score":0.35889277,"about_ca_system_score_codex":0.00019917148,"about_ca_system_score_gemma":0.000648214,"threshold_uncertainty_score":0.9990502},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054421455,0.11898067,0.021629503,0.7110105,0.0046141343,0.022302855,0.055955235,0.0052996525,0.0057859994],"genre_scores_gemma":[0.8610374,0.011467882,0.00075366267,0.04495084,0.00074717007,0.00041597625,0.080037534,0.00011975472,0.00046980823],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920531,0.0021379024,0.0016277152,0.0011255657,0.0015676443,0.0014880911],"domain_scores_gemma":[0.9943449,0.00051190745,0.00053966924,0.0014531936,0.0012124762,0.0019378614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.013539877,0.00045475777,0.001052281,0.000616642,0.0006760717,0.0007108066,0.00058518,0.00012649763,0.00031471965],"category_scores_gemma":[0.0038985002,0.00042408655,0.00005242247,0.0035233651,0.00014103675,0.0030721752,0.0005607325,0.0005275391,0.00021606966],"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":[0.00005594753,0.0008201625,0.35355613,0.003931276,0.0001601295,0.000009360876,0.007740259,5.99393e-7,1.1892596e-7,0.00077403785,0.49969846,0.13325353],"study_design_scores_gemma":[0.0010945769,0.0005193381,0.41424805,0.000060201917,7.2339293e-7,0.00004231901,0.0007179262,0.0010885665,9.230008e-9,0.000023603432,0.581974,0.00023067216],"about_ca_topic_score_codex":0.0023082285,"about_ca_topic_score_gemma":0.0058651366,"teacher_disagreement_score":0.8066159,"about_ca_system_score_codex":0.0009874089,"about_ca_system_score_gemma":0.014877097,"threshold_uncertainty_score":0.99982107},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13299511,0.011295682,0.6222266,0.07470521,0.009562557,0.018385535,0.11540155,0.0019135186,0.01351426],"genre_scores_gemma":[0.98735064,0.00007079935,0.0026941246,0.007494255,0.00019330237,0.000370755,0.0007492561,0.00005623756,0.0010206073],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948889,0.000030850213,0.0010787827,0.0011755398,0.00094363745,0.001882263],"domain_scores_gemma":[0.9969363,0.00005011226,0.00010436688,0.0006149949,0.00022874732,0.0020654579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010058008,0.00041656432,0.0009337949,0.00027639256,0.0002927529,0.00070787646,0.0005304366,0.00010736616,0.0000021833378],"category_scores_gemma":[0.00026471182,0.00040771507,0.00019953895,0.0023409335,0.00037170784,0.0013847699,0.00019282158,0.0002261611,0.00009480293],"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":true,"study_design_scores_codex":[0.0015252978,0.00073825347,0.024918536,0.007991052,0.00019293599,0.00014384187,0.0015820127,0.004443701,0.000004923299,0.0039719045,0.54388124,0.41060632],"study_design_scores_gemma":[0.0070639425,0.0077196946,0.09717804,0.011515248,0.00007340348,0.0003080553,0.004668374,0.06728359,0.000008706527,0.006320322,0.79544747,0.0024131483],"about_ca_topic_score_codex":0.00053077965,"about_ca_topic_score_gemma":0.00037380587,"teacher_disagreement_score":0.8543556,"about_ca_system_score_codex":0.006854528,"about_ca_system_score_gemma":0.006608179,"threshold_uncertainty_score":0.99983746},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014119212,0.97090656,5.417132e-7,0.007130243,0.0002840613,0.0070590097,0.014405779,0.000057092115,0.00014258038],"genre_scores_gemma":[0.00003020884,0.97959584,0.00028394719,0.0019234734,0.00011152472,0.0002068485,0.01742259,0.00008337581,0.00034221684],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99509406,0.00074283755,0.0017502775,0.0010842716,0.00068582443,0.0006426999],"domain_scores_gemma":[0.99757826,0.00012970013,0.00028120104,0.0012646639,0.000032776847,0.00071338087],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019555276,0.0004764059,0.0034421445,0.00027146595,0.00004307132,0.000025101337,0.0005146347,0.00011845978,0.00019425045],"category_scores_gemma":[0.000121323064,0.00037060416,0.00019179165,0.00046319648,0.000245892,0.00013585646,0.0011952033,0.00080234394,0.00013715218],"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":[0.000030086167,0.0002873221,0.00004213137,0.46509007,0.00005573736,0.000027284023,0.00012282927,1.6429336e-10,4.8154558e-8,0.000058966252,0.12942466,0.40486085],"study_design_scores_gemma":[0.00036132327,0.00038779763,0.0000839106,0.32285175,0.00008191716,0.00021307242,0.000038119208,0.000007457557,6.172362e-8,0.0000025670229,0.6758172,0.00015481384],"about_ca_topic_score_codex":0.0004973159,"about_ca_topic_score_gemma":0.00019058152,"teacher_disagreement_score":0.54639256,"about_ca_system_score_codex":0.00085841183,"about_ca_system_score_gemma":0.0026117992,"threshold_uncertainty_score":0.9998746},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015441268,0.82224506,0.0000013078946,0.103294425,0.00049272215,0.00085843226,0.005009513,0.00004056012,0.06804253],"genre_scores_gemma":[0.00033717832,0.87598246,0.00016660556,0.030690724,0.00020474644,0.0002559172,0.008109579,0.00005559987,0.08419721],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969349,0.00045048943,0.0010658762,0.000494234,0.00081572233,0.00023881438],"domain_scores_gemma":[0.997104,0.00059162005,0.00037279192,0.0015665131,0.00027879886,0.00008628546],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020346227,0.00036037512,0.0023451447,0.0003275894,0.000008469218,0.0000057438974,0.00036669665,0.00013203337,0.0048424234],"category_scores_gemma":[0.0013627042,0.00021067183,0.0007886969,0.0015123007,0.00011916068,0.000034096578,0.0001771183,0.0005037088,0.00014179593],"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":[0.000005728334,0.00014801978,0.00009244644,0.18573196,0.00014214509,0.0000946862,0.000009480483,1.5023083e-8,0.0000012054303,0.000037352384,0.73719245,0.07654449],"study_design_scores_gemma":[0.000142923,0.00007168594,0.000980985,0.22975348,0.0014503107,0.000024968005,0.000004522019,0.0000014737363,0.000018699497,0.000011553501,0.7674015,0.0001378947],"about_ca_topic_score_codex":0.00045368017,"about_ca_topic_score_gemma":0.00049385504,"teacher_disagreement_score":0.0764066,"about_ca_system_score_codex":0.000056474084,"about_ca_system_score_gemma":0.0002260001,"threshold_uncertainty_score":0.9960673},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90065855,0.0051114005,0.0005393586,0.08819123,0.0024232087,0.00038958405,0.0012787291,0.0000197718,0.0013881926],"genre_scores_gemma":[0.9925403,0.000104721614,0.0003342637,0.0061741965,0.00030719925,0.0000071604427,0.00014986236,0.00002301324,0.00035929997],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978609,0.00015835263,0.00087043643,0.00018560288,0.00036456663,0.0005601118],"domain_scores_gemma":[0.99733585,0.00008654641,0.00020463392,0.00020078127,0.0009187949,0.0012533863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001638646,0.0001329298,0.00038383974,0.0007922411,0.00008861837,0.00023739986,0.0001507985,0.000037835423,0.00017575773],"category_scores_gemma":[0.0010267169,0.0001338037,0.00003144521,0.0015216302,0.000052292886,0.0006526671,0.000012760763,0.00026247802,0.0000062705144],"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":[0.000054881533,0.000038238042,0.52004874,0.0012387377,0.00033219514,0.0067210244,0.007723829,0.000044686476,0.0001335303,0.016660076,0.32083082,0.12617323],"study_design_scores_gemma":[0.0006475037,0.00023730964,0.32270393,0.00036240145,0.000007523309,0.0003393756,0.012118539,0.000119495286,0.000017882041,0.00011568911,0.6631021,0.00022823187],"about_ca_topic_score_codex":0.8674483,"about_ca_topic_score_gemma":0.9977694,"teacher_disagreement_score":0.34227127,"about_ca_system_score_codex":0.005006155,"about_ca_system_score_gemma":0.15762194,"threshold_uncertainty_score":0.99881345},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99255866,0.0023404595,0.000112045345,0.0035151767,0.00008535286,0.00016290773,0.00017594117,0.00012713237,0.0009223422],"genre_scores_gemma":[0.99872905,0.00010148392,0.00011366802,0.00019867464,0.00003800034,0.000055798646,0.00034657549,0.000020919857,0.00039583343],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986324,0.00010736592,0.00043511792,0.00040528702,0.000120656114,0.00029920114],"domain_scores_gemma":[0.99924606,0.000108437955,0.00007136132,0.0003656552,0.000049791157,0.00015871467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001027596,0.00016816742,0.0006207633,0.0012849178,0.000015220103,0.000029925426,0.00012897559,0.00012911022,0.00029001335],"category_scores_gemma":[0.0001105693,0.00014658581,0.00014263137,0.0012242538,0.000095763826,0.00012925848,0.00011188726,0.00018986719,0.000027804484],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007207304,0.00027605263,0.9692561,0.0013865225,0.0007876251,0.0031476913,0.00040171843,6.474299e-7,0.00050971133,0.0015013585,0.0036413057,0.019019173],"study_design_scores_gemma":[0.0011030529,0.0000983735,0.94654953,0.00028175226,0.0003818065,0.0030645828,0.00020751894,0.002037186,0.00030250577,0.00009237155,0.045660842,0.00022045511],"about_ca_topic_score_codex":0.00004445076,"about_ca_topic_score_gemma":0.0028033506,"teacher_disagreement_score":0.04201954,"about_ca_system_score_codex":0.00003731761,"about_ca_system_score_gemma":0.00012664661,"threshold_uncertainty_score":0.5977596},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24478121,0.0131032355,0.7348064,0.00043740953,0.00042560053,0.001085044,0.0042775315,0.0010144905,0.000069031805],"genre_scores_gemma":[0.98859286,0.003193655,0.0025877587,0.0001846107,0.0002442115,0.00017214037,0.004449302,0.00016623334,0.0004092126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99576443,0.00017005896,0.0006983967,0.001972366,0.0006156237,0.00077915017],"domain_scores_gemma":[0.9956519,0.00054875016,0.00020163562,0.0019594217,0.00034370055,0.0012945621],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076775823,0.000637616,0.00092476455,0.00072546967,0.00044324173,0.00042401574,0.00029753882,0.000119045624,0.000032139375],"category_scores_gemma":[0.0002673718,0.00059917365,0.00043598315,0.0014584379,0.00018253643,0.00080459984,0.00027161933,0.00041170768,0.000033767643],"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":[0.0009781875,0.00026957016,0.04416185,0.0017482816,0.0013546379,0.00035671945,0.00013218934,0.94768924,0.000033309134,0.00036967857,0.0004174369,0.0024889265],"study_design_scores_gemma":[0.001172325,0.00006593439,0.004020302,0.0005687023,0.00242943,0.000013439792,0.000008340384,0.98203003,0.000003416788,0.0052463515,0.003802442,0.0006392955],"about_ca_topic_score_codex":0.0002513185,"about_ca_topic_score_gemma":0.00017898751,"teacher_disagreement_score":0.74381167,"about_ca_system_score_codex":0.00030823651,"about_ca_system_score_gemma":0.00054670585,"threshold_uncertainty_score":0.99964595},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6757251,0.0024054179,0.31446344,0.0030612997,0.0008708461,0.0027477797,0.00032525012,0.0003167558,0.00008410804],"genre_scores_gemma":[0.92221165,0.00007826153,0.07585441,0.0013633365,0.000096222415,0.00014042741,0.00003934294,0.00002909093,0.0001872667],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711525,0.00014759156,0.00054367766,0.0009978713,0.00044369602,0.00075190444],"domain_scores_gemma":[0.99653035,0.0023377137,0.00015517986,0.00056852796,0.0002231547,0.00018506164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019831061,0.0003776011,0.0006676023,0.00026689962,0.00025583178,0.0002696932,0.00062984746,0.000065830165,0.0000024755564],"category_scores_gemma":[0.0003212657,0.00024523854,0.0002013119,0.00088390155,0.00083539094,0.00039965767,0.00013390582,0.00023636164,0.000010300793],"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":[0.010365038,0.0025280837,0.034673348,0.030952323,0.006442903,0.06795289,0.10854832,0.27910283,0.012860409,0.028551998,0.22346331,0.19455856],"study_design_scores_gemma":[0.0012028966,0.0015347124,0.0004194761,0.0026709698,0.0002457425,0.0033213543,0.0027148407,0.9828739,0.00036206076,0.0030214163,0.0010949863,0.0005376286],"about_ca_topic_score_codex":0.000044928573,"about_ca_topic_score_gemma":0.00006171273,"teacher_disagreement_score":0.7037711,"about_ca_system_score_codex":0.000114344395,"about_ca_system_score_gemma":0.00030548347,"threshold_uncertainty_score":1},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008408791,0.9511983,0.040527616,0.0007550485,0.000344727,0.0024191488,0.004589455,0.00015270669,0.000004590974],"genre_scores_gemma":[0.00011209322,0.9732437,0.024959667,0.0002748698,0.00014677974,0.000093613606,0.0010424713,0.000092544185,0.00003425933],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970699,0.0004091278,0.0010339397,0.0009896335,0.00020440812,0.0002930003],"domain_scores_gemma":[0.996008,0.0012890683,0.00043407714,0.0016834923,0.0001771786,0.00040820125],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014319603,0.00041805778,0.0019400861,0.00028159423,0.000200163,0.000029818824,0.0003303744,0.00016041117,0.0000049994164],"category_scores_gemma":[0.0010763843,0.00034104593,0.0003578411,0.00043113332,0.00010048958,0.000064448264,0.000092972106,0.00054593716,0.0000022161232],"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":[0.000027015434,0.00002343209,0.0000050013837,0.39649054,0.00014362477,0.0000032115702,0.000012674792,2.6281245e-7,6.438519e-8,0.0000027771919,0.00004565213,0.60324574],"study_design_scores_gemma":[0.00027088606,0.000206537,0.000048478614,0.32958806,0.0048450707,0.0001504413,0.000021058626,0.0011924006,5.370569e-7,0.00006281377,0.6633408,0.00027292024],"about_ca_topic_score_codex":0.000054772707,"about_ca_topic_score_gemma":0.00003697749,"teacher_disagreement_score":0.66329515,"about_ca_system_score_codex":0.00008706036,"about_ca_system_score_gemma":0.00046795316,"threshold_uncertainty_score":0.99990416},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861948,0.0071324673,0.00018508195,0.0027088064,0.00019721632,0.0010621486,0.0014798425,0.00012592976,0.000913736],"genre_scores_gemma":[0.99525183,0.0024531796,0.00022641901,0.00042505062,0.00006346074,0.00007647452,0.001312086,0.00002316414,0.00016834885],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970306,0.00077811285,0.000782307,0.00050652906,0.000384512,0.0005179285],"domain_scores_gemma":[0.9982302,0.00042609803,0.00025980736,0.00049674965,0.00008155719,0.00050558173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014806135,0.00025373587,0.0013723102,0.0005077136,0.000078519115,0.000047695085,0.00007759063,0.00006095366,0.00007087894],"category_scores_gemma":[0.00011215475,0.00019237805,0.00021545311,0.0014649086,0.000105311185,0.00015611347,0.000030924904,0.00010511404,0.000008348353],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020317423,0.00035685074,0.6222253,0.0019307886,0.0003983724,0.000010525658,0.00030978175,0.0000010285355,0.000010318426,0.00021657122,0.0005685486,0.37376872],"study_design_scores_gemma":[0.003270395,0.019629296,0.87205285,0.0003071241,0.000027094537,0.000022217322,0.00003719323,0.005106224,0.000044560897,0.000013848563,0.09921794,0.0002712552],"about_ca_topic_score_codex":0.0011522956,"about_ca_topic_score_gemma":0.00009000102,"teacher_disagreement_score":0.3734975,"about_ca_system_score_codex":0.0002986089,"about_ca_system_score_gemma":0.0010374874,"threshold_uncertainty_score":0.78449494},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007874815,0.9935285,0.00055540033,0.002206715,0.0016684923,0.0017878059,0.00009607185,0.00002691702,0.0001222402],"genre_scores_gemma":[0.000001254376,0.97823197,0.013496792,0.006012145,0.0018968445,0.00005950384,0.000113586175,0.00007700115,0.00011088661],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.8886112,0.027370116,0.08182331,0.001119927,0.00030161766,0.00077382603],"domain_scores_gemma":[0.58420694,0.16200982,0.24768999,0.0028051294,0.001369185,0.0019189419],"candidate_categories":["metaresearch","metaepi_narrow","metaepi_broad","research_integrity"],"consensus_categories":["metaresearch","metaepi_broad"],"category_scores_codex":[0.24342027,0.0008414085,0.082744494,0.000420118,0.000037539463,0.00000505785,0.00049054704,0.0010620985,0.00018623628],"category_scores_gemma":[0.7407177,0.00047038135,0.012495778,0.00048007304,0.0006901933,0.000090888025,0.00043404417,0.0037169994,0.00006175353],"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":[0.000024845533,0.000057316545,0.002103488,0.4296501,0.005149013,0.00034751513,0.0000026865632,3.1339287e-8,3.1110186e-8,0.00008069262,0.009418879,0.55316544],"study_design_scores_gemma":[0.00013677665,0.00024390407,0.00053820526,0.45870078,0.036250997,0.0027051433,8.369e-7,0.0000037241566,3.1504421e-9,0.00022869212,0.5010246,0.00016636388],"about_ca_topic_score_codex":0.000005261656,"about_ca_topic_score_gemma":0.0000013339447,"teacher_disagreement_score":0.5529991,"about_ca_system_score_codex":0.00007384746,"about_ca_system_score_gemma":0.0018099813,"threshold_uncertainty_score":0.9997748},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9691071,0.015918255,0.000031798987,0.004811557,0.00030173684,0.003029103,0.0058865203,0.00039180336,0.0005221403],"genre_scores_gemma":[0.99308974,0.00087445276,0.000032335534,0.0021588132,0.00018578455,0.00013124257,0.0034088085,0.00007323066,0.000045584537],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99316514,0.0019516341,0.0014607817,0.0015165512,0.00079599355,0.00110992],"domain_scores_gemma":[0.9939939,0.00074553525,0.0003729309,0.0024588227,0.0002471556,0.0021816932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004586168,0.00049538986,0.0015998759,0.00057007157,0.00018679303,0.0001407703,0.00036201673,0.00009839304,0.000037038313],"category_scores_gemma":[0.0011509178,0.00042179893,0.00016850398,0.0014525687,0.00018047809,0.00040563283,0.0003541289,0.0006252402,0.00001492342],"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":[0.00054918474,0.001292796,0.9079497,0.0039433287,0.00011581178,0.0015802607,0.0017908079,0.0000067091582,9.387427e-7,0.000043232794,0.008150076,0.074577145],"study_design_scores_gemma":[0.0026303537,0.0018523695,0.97963405,0.00013393932,0.0000018282828,0.00029204067,0.00083975104,0.0002932964,1.8155664e-8,0.000005403368,0.013985056,0.0003318883],"about_ca_topic_score_codex":0.0076085026,"about_ca_topic_score_gemma":0.011656248,"teacher_disagreement_score":0.07424526,"about_ca_system_score_codex":0.0011952264,"about_ca_system_score_gemma":0.009273897,"threshold_uncertainty_score":0.9998234},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94595414,0.016623387,0.000023324583,0.002224007,0.0011812613,0.0013077798,0.031830806,0.00009626282,0.00075904257],"genre_scores_gemma":[0.9913828,0.0005812555,0.00025714477,0.00031880295,0.00011007709,0.00006486346,0.006370136,0.00006708439,0.0008478494],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9965458,0.00012173818,0.0007171299,0.0012755435,0.0009038425,0.00043593813],"domain_scores_gemma":[0.9954255,0.00012048815,0.00024844002,0.0035848343,0.00010981054,0.0005108979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049920904,0.00036656158,0.00077743595,0.00014406511,0.000017681827,0.000028372067,0.0013550897,0.000100445446,0.00016999373],"category_scores_gemma":[0.0009704727,0.00034643887,0.000075238044,0.00031385192,0.00010612595,0.00008430215,0.0042912983,0.0009037142,0.000044069202],"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":[0.00033948585,0.00025764413,0.8227112,0.008722124,0.00014535617,0.0030239413,0.000039276285,0.0001586258,0.00051039224,0.000032574448,0.16116722,0.0028921438],"study_design_scores_gemma":[0.0015419811,0.00006301894,0.7506003,0.011630144,0.00074432936,0.00001861465,0.00015458457,0.02194983,0.000586406,0.0005564114,0.21077411,0.0013803066],"about_ca_topic_score_codex":0.73219764,"about_ca_topic_score_gemma":0.7469177,"teacher_disagreement_score":0.07211096,"about_ca_system_score_codex":0.00075726194,"about_ca_system_score_gemma":0.017606284,"threshold_uncertainty_score":0.9998988},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82025534,0.0026944715,0.14448307,0.0053487294,0.00062510744,0.005225268,0.0016451308,0.00041916958,0.019303728],"genre_scores_gemma":[0.99836195,0.00049728766,0.00018726487,0.00007078778,0.000059763704,0.000014584807,0.00065051124,0.000022650991,0.00013519422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987965,0.00012435552,0.00022239806,0.0006048167,0.000096124415,0.00015581226],"domain_scores_gemma":[0.99863446,0.00017395447,0.00017647266,0.0008386322,0.00012273037,0.000053766984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003391302,0.00018067392,0.0003367428,0.00022694828,0.000022453489,0.000013580234,0.00031416462,0.00018991122,0.000006530826],"category_scores_gemma":[0.000054927394,0.00015924676,0.00019787699,0.00026193482,0.000079833575,0.000068855814,0.00012052679,0.00029289827,0.000004367054],"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":[0.0034944061,0.0014565698,0.012444872,0.009615009,0.00051238865,0.00029479232,0.0015101569,0.50995356,0.0013847328,0.44685456,0.002100912,0.010378046],"study_design_scores_gemma":[0.0010734729,0.000079207595,0.012399947,0.0004017963,0.00021030765,6.251334e-7,0.0003586691,0.94521654,0.000118955555,0.039828844,0.00015782214,0.00015383327],"about_ca_topic_score_codex":0.000033764296,"about_ca_topic_score_gemma":0.00022736717,"teacher_disagreement_score":0.43526298,"about_ca_system_score_codex":0.00013451073,"about_ca_system_score_gemma":0.00029528516,"threshold_uncertainty_score":0.64938945},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000018692836,0.9667912,0.000013941934,0.0054073413,0.0011597446,0.009150191,0.017206956,0.00020493196,0.00006382533],"genre_scores_gemma":[0.0000024358058,0.89158934,0.00026850146,0.006165784,0.0012823283,0.0020544755,0.09685915,0.00015936786,0.0016186466],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942042,0.0005682764,0.0015418165,0.0015183293,0.0013804144,0.0007869196],"domain_scores_gemma":[0.9966285,0.00014967263,0.00035198347,0.0018495928,0.00008972015,0.0009304934],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022840633,0.00068404735,0.0031512543,0.00023861455,0.00011267627,0.00018337442,0.0011649549,0.0003869898,0.00076708995],"category_scores_gemma":[0.00035901603,0.00048787295,0.00070608646,0.0006500123,0.00011145842,0.00022513064,0.0013636734,0.0014515896,0.00076881296],"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":[0.000025599233,0.00013102262,0.0000021677963,0.2905102,0.00013915072,0.00026300838,0.000028106082,3.937033e-10,5.859384e-9,0.000014830231,0.29916415,0.40972176],"study_design_scores_gemma":[0.00034456825,0.00016586835,0.000004320086,0.4321834,0.00050294347,0.00084909436,0.000006343946,0.00002783273,2.2575428e-8,0.000002358851,0.56571084,0.00020239639],"about_ca_topic_score_codex":0.000039426446,"about_ca_topic_score_gemma":0.00001620196,"teacher_disagreement_score":0.40951937,"about_ca_system_score_codex":0.00069480063,"about_ca_system_score_gemma":0.004357371,"threshold_uncertainty_score":0.9997573},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98131967,0.005097092,0.0000855309,0.0018673012,0.00032570362,0.0016652447,0.009413632,0.00011259259,0.000113229704],"genre_scores_gemma":[0.9920583,0.000878723,0.0008565085,0.0004139382,0.00062059995,0.00041334247,0.0027836482,0.0000920507,0.0018828909],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99742925,0.0001578086,0.00045124575,0.0010008739,0.00031057055,0.00065022486],"domain_scores_gemma":[0.99851936,0.00013172826,0.000042972442,0.0008486339,0.00011010603,0.00034718224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008799651,0.0002662623,0.00059141393,0.00057259185,0.000083743595,0.0000613571,0.0003019958,0.00010228845,0.00047940822],"category_scores_gemma":[0.00012567398,0.00023063808,0.00005753019,0.0008309638,0.00016333367,0.00032795768,0.00047189163,0.0004886366,0.000008706178],"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":[0.0011244486,0.0004120934,0.8894578,0.0012910324,0.0007470616,0.00029444613,0.0023642643,0.00080650277,0.0012533056,0.000014004124,0.03954389,0.062691115],"study_design_scores_gemma":[0.004016586,0.00048522808,0.79235023,0.0005823281,0.00025181577,0.00003126742,0.00087799213,0.17380048,0.000019097974,0.000011456234,0.027151957,0.00042157873],"about_ca_topic_score_codex":0.007825861,"about_ca_topic_score_gemma":0.0069501433,"teacher_disagreement_score":0.17299397,"about_ca_system_score_codex":0.0005978265,"about_ca_system_score_gemma":0.0016865764,"threshold_uncertainty_score":0.9987811},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91903776,0.0650372,0.00009874999,0.004952844,0.0016335566,0.0009021318,0.0043376917,0.0007261517,0.0032739341],"genre_scores_gemma":[0.9465533,0.005675023,0.030143807,0.00038569016,0.0016489004,0.00012789354,0.014927936,0.00023217058,0.0003052598],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958521,0.00036479463,0.00084793573,0.0019527884,0.00047652484,0.0005058303],"domain_scores_gemma":[0.9950346,0.0006642842,0.0003234585,0.0036448,0.00007415463,0.00025874012],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018365734,0.0005357135,0.0012554721,0.00024503373,0.000041538977,0.00007510092,0.0011413171,0.00034239131,0.00029334685],"category_scores_gemma":[0.0029542118,0.00051015214,0.00014189014,0.00020062612,0.00010695602,0.00013414565,0.006033193,0.0017995249,0.0012722363],"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":[0.00076675526,0.00088389684,0.4667108,0.02016339,0.0027277688,0.01080912,0.0028125516,0.0025139092,0.005419948,0.00006630108,0.022408899,0.46471664],"study_design_scores_gemma":[0.0012481832,0.000034878027,0.20332436,0.018813191,0.0005677682,0.00005581332,0.00006424407,0.7563402,0.00039534873,0.012801433,0.00520804,0.0011465489],"about_ca_topic_score_codex":0.0005399252,"about_ca_topic_score_gemma":0.00019846333,"teacher_disagreement_score":0.75382626,"about_ca_system_score_codex":0.00016409453,"about_ca_system_score_gemma":0.00039203258,"threshold_uncertainty_score":0.999735},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903661,0.0028438035,0.0008734271,0.0043316525,0.0002380005,0.0011210025,0.000068514964,0.000045948866,0.00011154535],"genre_scores_gemma":[0.99560994,0.0029998869,0.000049836435,0.00034096546,0.00018348737,0.000049755945,0.0003906779,0.000023821767,0.00035160346],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.99617404,0.0005888371,0.0007937781,0.00050204503,0.0005557605,0.0013855615],"domain_scores_gemma":[0.9988359,0.00015843954,0.00023222752,0.00039631905,0.00024216062,0.00013493365],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0034292946,0.00032964157,0.00066954765,0.0006292638,0.0001934145,0.00011174575,0.00039799392,0.00015072023,0.000017644614],"category_scores_gemma":[0.0003276989,0.00025039603,0.00016100226,0.00056823523,0.000036306083,0.000039886272,0.00040528443,0.0042475024,0.000004076865],"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":[0.009077186,0.0101108495,0.45744777,0.001530964,0.030037079,0.0028396938,0.22088365,0.108789444,0.00036727567,0.024193741,0.0053658974,0.12935646],"study_design_scores_gemma":[0.033552922,0.0030450863,0.17665084,0.0048382054,0.0066015753,0.0010131507,0.35493562,0.20584372,0.000017409811,0.20754018,0.0027519611,0.0032093506],"about_ca_topic_score_codex":0.00075312774,"about_ca_topic_score_gemma":0.0030213848,"teacher_disagreement_score":0.28079692,"about_ca_system_score_codex":0.0014329054,"about_ca_system_score_gemma":0.0017895267,"threshold_uncertainty_score":0.9999948},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.489977,0.080298476,0.029123222,0.1985801,0.0037794507,0.008477934,0.16410431,0.00075328205,0.024906209],"genre_scores_gemma":[0.9920927,0.0005225092,0.0002500515,0.003948496,0.00010356789,0.000015329853,0.0022896146,0.000016387497,0.00076133804],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984343,0.000198047,0.00042623788,0.00047004104,0.0002493636,0.0002219974],"domain_scores_gemma":[0.9914699,0.0009186566,0.00018886077,0.007175904,0.00009097448,0.00015570626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012564232,0.00013592326,0.00032590982,0.00003734554,0.00018842577,0.000020244468,0.0021955562,0.000040111587,0.000087389155],"category_scores_gemma":[0.005866126,0.00007831193,0.000052165247,0.0003966939,0.00069995504,0.00014214766,0.0019421313,0.0001650573,0.000015474427],"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":[0.0013759629,0.00044571207,0.047277085,0.0018066161,0.0006315869,0.0009053709,0.00036447978,0.000048241094,0.00060895825,0.0061780713,0.91331273,0.027045198],"study_design_scores_gemma":[0.0018719852,0.00006540556,0.008220935,0.00014959255,0.0002757807,0.00013210339,0.0006500835,0.004073291,0.00013185275,0.00047544652,0.9838258,0.00012774531],"about_ca_topic_score_codex":0.00090316153,"about_ca_topic_score_gemma":0.0011493706,"teacher_disagreement_score":0.50211567,"about_ca_system_score_codex":0.00004388292,"about_ca_system_score_gemma":0.0010832389,"threshold_uncertainty_score":0.7022725},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97996104,0.009031083,0.0017299373,0.0066172853,0.00075526105,0.00031551978,0.0010190523,0.000033824897,0.00053696975],"genre_scores_gemma":[0.9924304,0.00013731551,0.001640964,0.005241286,0.000069485824,0.000049067847,0.00030471332,0.000007504141,0.00011926531],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989027,0.000093804454,0.00022520308,0.00042595822,0.00010091661,0.00025143067],"domain_scores_gemma":[0.9995015,0.0000580061,0.000028337014,0.00019116043,0.00005481589,0.00016614785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011118471,0.00013301645,0.00036325376,0.00033996743,0.00002625464,0.000023880537,0.000111859175,0.00010077669,0.000050599756],"category_scores_gemma":[0.00040892392,0.00013720816,0.000023274493,0.00050238305,0.00017917083,0.0000950646,0.00018612503,0.000103567516,0.0000049097725],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039799346,0.00009331162,0.88026124,0.000035536526,0.00006624912,0.00007890821,0.000023807184,0.0000096952945,0.000015490701,0.00062643876,0.11215077,0.006240557],"study_design_scores_gemma":[0.0017463621,0.00004225815,0.9333166,0.000061172024,0.000025455354,0.0000048536476,0.000086273525,0.00064861024,0.0000014548277,0.008878071,0.055093057,0.0000958382],"about_ca_topic_score_codex":0.00028677366,"about_ca_topic_score_gemma":0.0020337433,"teacher_disagreement_score":0.057057712,"about_ca_system_score_codex":0.00021339388,"about_ca_system_score_gemma":0.0002840267,"threshold_uncertainty_score":0.55951864},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7913435,0.15163206,0.00021379492,0.04551869,0.0010938402,0.005220695,0.0021307715,0.00027097636,0.0025756666],"genre_scores_gemma":[0.98422194,0.0075791874,0.00017040326,0.006947978,0.00013422937,0.00025839877,0.0005801724,0.000038976214,0.000068743146],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99328965,0.0025767786,0.0012795441,0.0008415795,0.0008125049,0.0011999303],"domain_scores_gemma":[0.99577755,0.0007548381,0.00067741185,0.001854181,0.00044929486,0.00048672422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027546291,0.0004421819,0.0014601463,0.00022398346,0.00043446908,0.00010487186,0.00079925027,0.00013408643,0.0000064801634],"category_scores_gemma":[0.00037518592,0.00029515303,0.00018663563,0.0019188238,0.00031058604,0.00029755168,0.000371869,0.00066539337,0.000004118148],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011133242,0.00021595604,0.96002436,0.01480321,0.00008249064,0.000020665988,0.0027315442,0.0000024690162,0.000015390007,0.0009889536,0.0045812726,0.016422348],"study_design_scores_gemma":[0.0021673397,0.00014544897,0.8932822,0.0013166305,0.0000019020863,0.0000763091,0.006035768,0.00007558268,7.91089e-7,0.000007201602,0.09667958,0.00021126877],"about_ca_topic_score_codex":0.0014318153,"about_ca_topic_score_gemma":0.00072204944,"teacher_disagreement_score":0.19287841,"about_ca_system_score_codex":0.0008628448,"about_ca_system_score_gemma":0.004701081,"threshold_uncertainty_score":0.99995005},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9773625,0.0039264066,0.014067897,0.00029114273,0.00060270535,0.002280328,0.0009974702,0.00031771496,0.00015382],"genre_scores_gemma":[0.9926999,0.000045288438,0.0050296905,0.00039810815,0.00023230449,0.00041279965,0.0010798415,0.00006352603,0.000038545244],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9955627,0.00049252727,0.0009662916,0.0015227484,0.00063923624,0.0008165348],"domain_scores_gemma":[0.9973715,0.0003031555,0.00028504603,0.0010493089,0.0006334275,0.000357591],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011538836,0.0005179383,0.00079113286,0.0037451438,0.00042870638,0.001067927,0.00078032,0.00020906114,0.00012330724],"category_scores_gemma":[0.00006631227,0.0005506267,0.00024110527,0.0052003227,0.00061650167,0.0014648546,0.0011582451,0.0010136487,5.7532725e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027369434,0.00087982876,0.98377407,0.00035629357,0.0013174057,0.00006458417,0.00019668207,0.0002011143,0.00017164015,0.00010045708,0.0017766352,0.010887591],"study_design_scores_gemma":[0.004014036,0.000111391,0.92125344,0.0012230648,0.00016402345,0.00001232882,0.00039461645,0.07069832,0.000021777469,0.000007511934,0.001680142,0.00041931123],"about_ca_topic_score_codex":0.0038778416,"about_ca_topic_score_gemma":0.0067498274,"teacher_disagreement_score":0.07049721,"about_ca_system_score_codex":0.00020973726,"about_ca_system_score_gemma":0.00039384968,"threshold_uncertainty_score":0.99996907},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97896504,0.0023294506,0.015252061,0.0007361976,0.0002779845,0.0007775083,0.000089647394,0.00008269404,0.0014894089],"genre_scores_gemma":[0.9965299,0.00031093202,0.0023394583,0.00014238468,0.00017814543,0.000025351204,0.00025384134,0.000024517023,0.00019546927],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977346,0.00015654146,0.0008803227,0.0006194136,0.00028242735,0.0003267072],"domain_scores_gemma":[0.9979126,0.0010134425,0.00024567757,0.00044584356,0.00025120092,0.00013126137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024783816,0.00020109686,0.00064244674,0.0003785879,0.000114373936,0.000016929875,0.0001945197,0.00008863195,0.00006993598],"category_scores_gemma":[0.004414708,0.00018696956,0.0000394682,0.0004680697,0.0004431947,0.00013095546,0.00024597498,0.0002467096,0.0000030275228],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007185728,0.00010344919,0.6861001,0.0006051571,0.00010685111,0.000056894172,0.0009340498,0.00006844837,0.013608209,0.0015272716,0.00037682228,0.29579416],"study_design_scores_gemma":[0.0025156182,0.0019266391,0.6820081,0.0059327637,0.00041968317,0.00006335446,0.0061381524,0.26273608,0.0025480103,0.014199259,0.020592876,0.0009194999],"about_ca_topic_score_codex":0.00029493772,"about_ca_topic_score_gemma":0.00018014805,"teacher_disagreement_score":0.29487467,"about_ca_system_score_codex":0.000043403354,"about_ca_system_score_gemma":0.00012144259,"threshold_uncertainty_score":0.76243967},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031970756,0.0033286891,0.0000019328183,0.000035949823,0.00032778943,0.0004453937,0.000354456,0.000112686736,0.9950734],"genre_scores_gemma":[0.010398273,0.00075484475,0.00013475517,0.000015029767,0.00015427385,5.164175e-7,0.00008567096,0.00005489067,0.9884018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986059,0.000013011789,0.00024057737,0.0005495323,0.00034598244,0.00024501776],"domain_scores_gemma":[0.9987713,0.000071604125,0.00015874395,0.00068692473,0.00011689249,0.00019450023],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007906796,0.00033751543,0.00065778184,0.00006601191,0.000038079852,0.000011289493,0.00032697417,0.00028535226,0.0006440793],"category_scores_gemma":[0.000012763613,0.00039142265,0.0002754069,0.0000014166861,0.00018813642,0.000088908026,0.0003804226,0.00032749205,0.00005682841],"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":[0.0070612566,0.00033874772,0.0020166305,0.007977325,0.005481577,0.009258004,0.02748453,0.0002610601,0.0005380993,0.81571984,0.023573996,0.100288905],"study_design_scores_gemma":[0.0010052895,0.00009967892,0.000617632,0.0021438356,0.00035439836,0.000008269061,0.00006705398,0.00016002475,0.000027469532,0.00042386272,0.9947258,0.00036666592],"about_ca_topic_score_codex":0.1903824,"about_ca_topic_score_gemma":0.05741196,"teacher_disagreement_score":0.9711518,"about_ca_system_score_codex":0.00050508784,"about_ca_system_score_gemma":0.00009382559,"threshold_uncertainty_score":0.9998538},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90527445,0.0016862357,0.08301325,0.0007184055,0.0016651917,0.0043877997,0.002340847,0.00086073944,0.000053077074],"genre_scores_gemma":[0.9777178,0.00043024783,0.019969404,0.0003620361,0.0005194209,0.0008457121,0.000017240094,0.0001245847,0.000013510086],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99589837,0.00018839467,0.0011216339,0.0014724558,0.0005126551,0.00080648076],"domain_scores_gemma":[0.99673367,0.00025156277,0.00052793766,0.0013016375,0.0009265108,0.00025867115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014221529,0.0006898767,0.0011463175,0.00059857365,0.0001460625,0.00017045293,0.0005285023,0.0005192735,0.000018179331],"category_scores_gemma":[0.0010969283,0.00079894054,0.00025321989,0.0006875058,0.00010606916,0.00028326057,0.0006503638,0.00078149774,0.000020678632],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033813491,0.0009080776,0.89136344,0.014438893,0.0011933954,0.00025956132,0.00012293071,0.0024562324,0.07530413,0.009741849,0.00069277454,0.00013737776],"study_design_scores_gemma":[0.0040368177,0.00020675593,0.8756878,0.0057553146,0.0002445369,6.662005e-8,0.000015121697,0.07866027,0.02756881,0.00003807499,0.005938077,0.0018483817],"about_ca_topic_score_codex":0.000135789,"about_ca_topic_score_gemma":0.000053536445,"teacher_disagreement_score":0.07620404,"about_ca_system_score_codex":0.001362718,"about_ca_system_score_gemma":0.0022572293,"threshold_uncertainty_score":0.99944615},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9582884,0.005210045,0.021355808,0.00061258266,0.00050983863,0.0009161716,0.0024601063,0.0023477578,0.008299347],"genre_scores_gemma":[0.9879196,0.0004885934,0.00033789204,0.00244368,0.00015635508,0.000013934317,0.006621853,0.00006347982,0.0019546007],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973539,0.00032745025,0.0003303342,0.0008527689,0.0007425268,0.0003930053],"domain_scores_gemma":[0.9970427,0.0009899727,0.00006966501,0.0011877841,0.0000501975,0.0006596815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024532722,0.00023003397,0.00039161684,0.000094401854,0.00012741041,0.00019426303,0.0006410291,0.000060836865,0.00050720957],"category_scores_gemma":[0.0012810306,0.00017607324,0.000030817155,0.00044760836,0.00010851546,0.00023277997,0.00054433825,0.00040954832,0.0001452406],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018673162,0.00004225714,0.93358785,0.00016262957,0.0002772435,0.00022934163,0.00022372807,0.000011928144,0.00013290074,0.000017385102,0.062359326,0.002768657],"study_design_scores_gemma":[0.0036313455,0.00013198279,0.7546975,0.0009880812,0.00029569515,0.0001152515,0.0011007579,0.008416176,0.0000129806185,0.000006283109,0.22993894,0.0006650136],"about_ca_topic_score_codex":0.002843123,"about_ca_topic_score_gemma":0.0015480518,"teacher_disagreement_score":0.17889038,"about_ca_system_score_codex":0.00023254353,"about_ca_system_score_gemma":0.00078295177,"threshold_uncertainty_score":0.71800584},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017313475,0.8691146,0.00002109555,0.07001244,0.0034620245,0.014159888,0.009087383,0.00011343871,0.016715657],"genre_scores_gemma":[0.19487344,0.6024376,0.00011814847,0.1596391,0.000760678,0.001732292,0.008716129,0.00024377496,0.031478822],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947436,0.0010628324,0.002059503,0.0007514957,0.0009598426,0.0004227204],"domain_scores_gemma":[0.99389714,0.0016736899,0.001071928,0.0025942537,0.0005982584,0.00016470498],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018334454,0.00059874175,0.0028005287,0.00016223507,0.00007649759,0.000019489462,0.0009595997,0.00018712241,0.00019751315],"category_scores_gemma":[0.013680793,0.00033485144,0.0016789985,0.0009259422,0.00044949987,0.00010347086,0.0006742875,0.0008855726,0.000013334807],"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":[0.0004265364,0.0010657064,0.0031505337,0.1539589,0.0011976425,0.00034158485,0.00004180827,5.789582e-7,0.0008220969,0.00018693328,0.8337222,0.0050854944],"study_design_scores_gemma":[0.0038788132,0.00014946627,0.0015890404,0.3337822,0.0067751543,0.000038048955,0.000033989956,0.000009902813,0.00053905585,0.00014298818,0.65258783,0.00047348265],"about_ca_topic_score_codex":0.00054857647,"about_ca_topic_score_gemma":0.001698081,"teacher_disagreement_score":0.26667696,"about_ca_system_score_codex":0.00023516244,"about_ca_system_score_gemma":0.004172597,"threshold_uncertainty_score":0.99991035},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017318316,0.8696355,0.000021127773,0.06937879,0.003463823,0.014171453,0.009092653,0.000113454014,0.016804833],"genre_scores_gemma":[0.19683912,0.6008609,0.000117901945,0.15928355,0.0007582572,0.0017290686,0.008720108,0.00024329078,0.031447805],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947434,0.0010628513,0.0020596199,0.0007515391,0.00095985364,0.00042272333],"domain_scores_gemma":[0.99389565,0.0016736864,0.0010720348,0.0025955753,0.00059831975,0.00016471164],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001833042,0.00059875875,0.0028008078,0.00016222747,0.00007651062,0.000019488512,0.00096002524,0.00018712184,0.00019740686],"category_scores_gemma":[0.013686241,0.0003348562,0.0016790172,0.0009256761,0.00044954655,0.00010347169,0.0006745679,0.00088558346,0.000013340711],"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":[0.00042664268,0.0010700044,0.0032303284,0.15484425,0.0012024562,0.00034114087,0.000041977655,5.812893e-7,0.00082170655,0.00019248419,0.83278435,0.0050440854],"study_design_scores_gemma":[0.0038753045,0.00014969769,0.0015755071,0.33519867,0.006777744,0.000038517697,0.000034030752,0.000009824305,0.00054438174,0.00014187615,0.65118086,0.00047356499],"about_ca_topic_score_codex":0.00054616353,"about_ca_topic_score_gemma":0.0017196467,"teacher_disagreement_score":0.26877466,"about_ca_system_score_codex":0.00023361259,"about_ca_system_score_gemma":0.004172306,"threshold_uncertainty_score":0.99991035},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9952974,0.00041716,0.0016611797,0.00025353098,0.00010203238,0.0011800893,0.00063314434,0.00039396065,0.00006151886],"genre_scores_gemma":[0.99636775,0.000018362154,0.0021348994,0.00025439664,0.000051152052,0.000104233106,0.00025500692,0.000040083505,0.0007741008],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982088,0.00012212472,0.00048108355,0.00049500004,0.00038702495,0.00030595993],"domain_scores_gemma":[0.99802005,0.00034495667,0.00014338086,0.00064678385,0.0003679078,0.00047689982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084889085,0.00022598215,0.00048565835,0.0002875308,0.00012128249,0.00003061412,0.000095784235,0.00007978279,0.00008113859],"category_scores_gemma":[0.007505921,0.00023652989,0.00012835652,0.00066509127,0.000076926706,0.00019495455,0.00009575945,0.00014522743,0.0000481898],"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":[0.0048105787,0.010599842,0.19011408,0.0023101445,0.0012616875,0.003630374,0.00038515174,0.0036576127,0.776458,0.00003088794,0.00036156867,0.006380034],"study_design_scores_gemma":[0.0087942155,0.0016986575,0.35635778,0.016564153,0.0053847907,0.00010211919,0.00027374635,0.15059511,0.45712638,0.00036610122,0.0010593031,0.0016776404],"about_ca_topic_score_codex":0.0001420899,"about_ca_topic_score_gemma":0.00022744041,"teacher_disagreement_score":0.31933165,"about_ca_system_score_codex":0.00023056765,"about_ca_system_score_gemma":0.00014946674,"threshold_uncertainty_score":0.9645409},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9970991,0.00037519276,0.0000243021,0.0011043705,0.000074344665,0.0009521175,0.00011589462,0.00006474078,0.00018994125],"genre_scores_gemma":[0.9969784,0.000036643065,0.000026975315,0.00012628036,0.00009116658,0.00069529563,0.000041291885,0.00001575693,0.0019882133],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998836,0.0001211128,0.00020378987,0.00035762656,0.00024143406,0.00024005977],"domain_scores_gemma":[0.999474,0.000041608368,0.000053568696,0.00024118247,0.00009070532,0.00009895159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019129824,0.00014160392,0.00029923036,0.00015508938,0.00004615569,0.000038702092,0.00007010641,0.00005326677,0.00037645377],"category_scores_gemma":[0.000025203864,0.00013223992,0.0000299285,0.00043460386,0.000054208544,0.0002869133,0.00011408588,0.00018162373,0.0000070340498],"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":[0.0012441623,0.00021414449,0.9418899,0.00012471303,0.00008470842,0.000023643297,0.0010537154,0.00009059146,0.0001749628,0.000003981138,0.00045755616,0.05463797],"study_design_scores_gemma":[0.0014634868,0.000054215343,0.99531287,0.00037719557,0.000048636397,0.0000037564978,0.00022274107,0.002144844,0.00010076148,0.0000028292077,0.0001552129,0.00011344331],"about_ca_topic_score_codex":0.009159794,"about_ca_topic_score_gemma":0.013647681,"teacher_disagreement_score":0.054524526,"about_ca_system_score_codex":0.00022417823,"about_ca_system_score_gemma":0.00010797522,"threshold_uncertainty_score":0.9974383},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5793195,0.0016968703,0.3361656,0.0006458083,0.0010796401,0.0071685924,0.070851564,0.0008744437,0.0021980142],"genre_scores_gemma":[0.90547806,0.00012258595,0.07933328,0.000720512,0.00010294841,0.00029174404,0.011199038,0.000028278273,0.0027235511],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99598265,0.00006153365,0.00069793663,0.001869888,0.0008093083,0.00057868974],"domain_scores_gemma":[0.9959762,0.00009610877,0.00021088151,0.0029076815,0.0005540732,0.0002550426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010745211,0.00024841056,0.0010772226,0.0003776698,0.00015677317,0.00041217802,0.0017552237,0.00017889649,0.00012470274],"category_scores_gemma":[0.0009772729,0.00039905554,0.00015758215,0.0010958485,0.0001847804,0.00021867068,0.0033086187,0.00050754304,4.0110874e-7],"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":[0.000035119643,0.0002169752,0.90280944,0.0008640582,0.00067833764,0.0002692315,0.000058317666,0.00046007105,0.000041747746,0.0000013860315,0.017013462,0.07755183],"study_design_scores_gemma":[0.0011168007,0.000024019497,0.60684115,0.0008213111,0.00095143734,0.00000865925,0.0001062551,0.3857715,0.000064644766,0.00005851927,0.0036357092,0.0005999546],"about_ca_topic_score_codex":0.98877865,"about_ca_topic_score_gemma":0.99965495,"teacher_disagreement_score":0.38531145,"about_ca_system_score_codex":0.00088924635,"about_ca_system_score_gemma":0.010787035,"threshold_uncertainty_score":0.99984616},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99359304,0.00042564978,0.00025173786,0.0004091024,0.000031141128,0.00029539294,0.0046338406,0.0000271999,0.00033290256],"genre_scores_gemma":[0.9965966,0.0001601251,0.000050298597,0.00019950309,0.0000106432835,0.000019223848,0.0028951943,0.0000070901383,0.000061363644],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99850506,0.00034221666,0.00045000113,0.0002251998,0.00030717207,0.00017033692],"domain_scores_gemma":[0.9983845,0.0002634177,0.00019652754,0.00088490825,0.00017822428,0.00009243934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005173446,0.00012317467,0.0005335025,0.00042863213,0.000027624932,0.000011925807,0.00023287618,0.000033561675,0.000049417078],"category_scores_gemma":[0.00050390186,0.00008924503,0.00016296578,0.0014872968,0.00021191714,0.000061548904,0.00005478119,0.000113358714,0.0000011924792],"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":[0.00040038658,0.00046902377,0.9945651,0.00012369495,0.0007993897,0.000033495937,0.00028729605,0.0007634987,0.000021011747,0.0008561962,0.00052964507,0.0011512544],"study_design_scores_gemma":[0.00072799006,0.000066448316,0.990722,0.000062691644,0.0023018592,1.0159312e-7,0.00034866814,0.003998569,0.000103852035,0.0010678159,0.00052496634,0.00007500933],"about_ca_topic_score_codex":0.029261414,"about_ca_topic_score_gemma":0.0113407355,"teacher_disagreement_score":0.017920678,"about_ca_system_score_codex":0.000024440391,"about_ca_system_score_gemma":0.000113499314,"threshold_uncertainty_score":0.97720283},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8792916,0.0025013506,0.03977104,0.0027223465,0.0014907889,0.0012859086,0.0007121929,0.0013863654,0.07083842],"genre_scores_gemma":[0.9949218,0.000014902606,0.0011011247,0.00038889793,0.00035418154,0.000015530022,0.0007600408,0.000020963169,0.0024225528],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985822,0.00009153973,0.0003091787,0.00043367044,0.0002939149,0.00028953046],"domain_scores_gemma":[0.998515,0.00037018396,0.00008656507,0.00081368233,0.000100699704,0.00011387854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005887781,0.00014203238,0.00041252837,0.00010004734,0.000096348995,0.000035464785,0.00031343204,0.00006466733,0.00009303225],"category_scores_gemma":[0.0008086504,0.00010688531,0.00003023563,0.00030981295,0.00006714363,0.00018606342,0.00023901455,0.00020557301,0.000120967496],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013387694,0.00031450577,0.69312143,0.0024491514,0.0015593689,0.0005617639,0.0024694125,0.000034792592,0.009794789,0.0073713325,0.25471744,0.026267255],"study_design_scores_gemma":[0.006857383,0.00016662749,0.87733346,0.0029883203,0.0008873579,0.00006660461,0.0037186956,0.0076279608,0.0010272661,0.00011252659,0.09831508,0.0008986954],"about_ca_topic_score_codex":0.00027391344,"about_ca_topic_score_gemma":0.000348128,"teacher_disagreement_score":0.18421204,"about_ca_system_score_codex":0.00006977142,"about_ca_system_score_gemma":0.00028785437,"threshold_uncertainty_score":0.43586564},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767246,0.0013138327,0.0050474126,0.00293152,0.00053085905,0.0038936127,0.0030981316,0.00023100234,0.006229014],"genre_scores_gemma":[0.99062026,0.0006562599,0.0028343338,0.000233399,0.00003941866,0.0004062594,0.004297017,0.000037182628,0.000875855],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967033,0.00048530125,0.0008502597,0.000904913,0.0005355909,0.0005206374],"domain_scores_gemma":[0.99604243,0.00049446814,0.00032595606,0.002349714,0.0005209237,0.00026648803],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015004583,0.00036117315,0.00078673195,0.00067437126,0.00013253515,0.00013219999,0.0009788506,0.0001918776,0.00023910038],"category_scores_gemma":[0.0017302865,0.000366658,0.00019362627,0.000415857,0.00005402356,0.0001141093,0.002047304,0.0013021218,0.00002078889],"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":[0.0005954038,0.0005484893,0.94875026,0.0024415844,0.0012034304,0.000029809602,0.0003948054,0.0028044397,0.00021016365,0.0010752914,0.013100041,0.02884631],"study_design_scores_gemma":[0.003908047,0.00007157034,0.940192,0.004885266,0.0002610714,0.0000022159336,0.0016328839,0.03834604,0.0021804746,0.0012917481,0.0063613825,0.0008673028],"about_ca_topic_score_codex":0.77342033,"about_ca_topic_score_gemma":0.9480822,"teacher_disagreement_score":0.17466183,"about_ca_system_score_codex":0.0035138566,"about_ca_system_score_gemma":0.004107068,"threshold_uncertainty_score":0.9998785},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9961813,0.00056217785,0.0010520662,0.00018038289,0.00006941427,0.0014360036,0.0002060277,0.00008692355,0.00022568519],"genre_scores_gemma":[0.9985263,0.00008005969,0.00028887508,0.0003306294,0.000034048415,0.00035975376,0.00034376938,0.000014618023,0.000021992351],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964787,0.00092997,0.00085344084,0.0008402134,0.00041323446,0.00048448757],"domain_scores_gemma":[0.99805087,0.00035213708,0.0002175257,0.001067395,0.00008372923,0.00022834694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016032864,0.00029101956,0.00070027175,0.00044653288,0.00008220225,0.000010563245,0.00021461108,0.00011380382,0.000063872736],"category_scores_gemma":[0.0008408091,0.00027189814,0.00027574805,0.00044309188,0.0006560601,0.00033514478,0.00025973126,0.0002542926,0.0000019274505],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007410462,0.00115387,0.97396225,0.00072904443,0.000052365715,0.00004079439,0.00043959872,0.0011336843,0.00003525731,0.00012134645,0.0000148343,0.021575913],"study_design_scores_gemma":[0.0014890135,0.00032107273,0.9928269,0.00006291469,0.000097019154,2.5372802e-7,0.0031458505,0.00019600839,0.000030592637,0.0010629485,0.0005678961,0.00019952821],"about_ca_topic_score_codex":0.00013173587,"about_ca_topic_score_gemma":0.000011824372,"teacher_disagreement_score":0.021376384,"about_ca_system_score_codex":0.00075667043,"about_ca_system_score_gemma":0.00016614354,"threshold_uncertainty_score":0.9999733},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99659,0.00001536475,0.001821625,0.0003408761,0.00023895087,0.0006530498,0.00003616888,0.000016395723,0.00028756337],"genre_scores_gemma":[0.8785232,5.5373147e-7,0.12113723,0.00008913932,0.000015338848,0.000058731195,0.00007768368,0.0000036783788,0.00009445298],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986165,0.000009951934,0.00034931197,0.00036425306,0.00045151994,0.00020843647],"domain_scores_gemma":[0.99897027,0.000095123025,0.000069953865,0.00016514302,0.00063104945,0.000068454574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036996586,0.00008793828,0.00017706885,0.0003276709,0.000020174451,0.000026839994,0.00030064204,0.000023400073,0.000010954636],"category_scores_gemma":[0.0013092773,0.00008155821,0.00002473743,0.00068848435,0.00027452424,0.0002698708,0.000093595634,0.000081876235,3.364462e-7],"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":[0.0018058465,0.0019041038,0.46146297,0.00018757282,0.00011669996,0.00007149666,0.005266055,0.00041288728,0.05608179,0.044364616,0.00031551556,0.42801043],"study_design_scores_gemma":[0.0017540122,0.00010628885,0.5021937,0.0007389849,0.000015714397,0.0000055740634,0.0070738024,0.08812159,0.39684904,0.0013300529,0.0014809958,0.00033027196],"about_ca_topic_score_codex":0.11934551,"about_ca_topic_score_gemma":0.89026433,"teacher_disagreement_score":0.77091885,"about_ca_system_score_codex":0.0014355965,"about_ca_system_score_gemma":0.0037313702,"threshold_uncertainty_score":0.88651884},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97020495,0.003798676,0.0005782947,0.023071561,0.00011898933,0.0006720346,0.00056429306,0.000058297963,0.00093291805],"genre_scores_gemma":[0.99553597,0.0013872279,0.00008083239,0.0012679729,0.000024273271,0.00008578216,0.0014283421,0.00001238272,0.0001772297],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975182,0.0007246534,0.00064056984,0.0003330188,0.00031475554,0.00046879158],"domain_scores_gemma":[0.99741143,0.000497441,0.0004109063,0.0007502178,0.00032641756,0.0006035879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015025117,0.00017593801,0.0005898642,0.00036820787,0.0005366217,0.00020729247,0.00025673193,0.000076316144,0.000025802416],"category_scores_gemma":[0.0015571469,0.00013644955,0.00010567364,0.0011236696,0.00015665706,0.00024762598,0.00008282405,0.00024140789,0.0000015882256],"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":[0.000027828964,0.00006802483,0.9497531,0.00017880162,0.00018251048,0.000001378971,0.00079769146,0.0000012401797,0.00004163408,0.000043939057,0.0036792287,0.045224633],"study_design_scores_gemma":[0.0005349737,0.000018280087,0.97954047,0.00006520335,0.000011409924,0.000002705542,0.0023420905,0.00063385355,0.0000038597536,0.0000062921163,0.016728537,0.00011233789],"about_ca_topic_score_codex":0.040996682,"about_ca_topic_score_gemma":0.122336715,"teacher_disagreement_score":0.08134004,"about_ca_system_score_codex":0.00030122008,"about_ca_system_score_gemma":0.00091273827,"threshold_uncertainty_score":0.96538943},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9382113,0.0023310867,0.0007820792,0.0518001,0.00095272786,0.005081934,0.00022594597,0.00018330225,0.00043152022],"genre_scores_gemma":[0.9863617,0.0006395317,0.00025622218,0.009319244,0.00030269232,0.0004774756,0.0019139469,0.000030508989,0.0006986966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99424994,0.0012471649,0.0014555966,0.00091014185,0.0007164653,0.0014207055],"domain_scores_gemma":[0.9973655,0.0005917189,0.00036029943,0.0004958651,0.00051674794,0.00066986994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015782317,0.00021784274,0.00070178,0.0008637238,0.000681615,0.00020380865,0.0002553938,0.00009647005,0.00002886712],"category_scores_gemma":[0.0016714968,0.00023088015,0.00008292139,0.0009317939,0.00008815897,0.0002467711,0.000120391036,0.0007718327,0.0000049699674],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004337202,0.00064112584,0.95334595,0.00043245585,0.000045509907,0.0000019034031,0.00054774573,0.000005125388,1.579592e-7,0.0017098644,0.0059644086,0.036872],"study_design_scores_gemma":[0.0031366074,0.0009925197,0.8580656,0.000051242183,2.0198769e-7,0.0000057470747,0.0004911442,0.000967637,1.0921374e-8,0.0005584242,0.13561687,0.00011398774],"about_ca_topic_score_codex":0.0033255185,"about_ca_topic_score_gemma":0.0153792845,"teacher_disagreement_score":0.12965246,"about_ca_system_score_codex":0.002126655,"about_ca_system_score_gemma":0.006946806,"threshold_uncertainty_score":0.99868286},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9615098,0.001744755,0.020615539,0.014924758,0.00030373337,0.0003589452,0.0001140271,0.000016613623,0.0004117986],"genre_scores_gemma":[0.9956385,0.00088189554,0.002131928,0.0010415921,0.00018689907,0.000007730769,0.00007555628,0.000007277606,0.000028638196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981284,0.0001794242,0.0007810218,0.00020000408,0.0005399864,0.00017113457],"domain_scores_gemma":[0.99878144,0.00041530945,0.00028193934,0.00025509275,0.00019206556,0.00007412865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034902163,0.000120245786,0.0004931139,0.00031230584,0.000043097614,0.0000030500862,0.00021944533,0.000037209364,0.00013907884],"category_scores_gemma":[0.00090239325,0.00007506307,0.0000814942,0.00032786466,0.00022454798,0.0000414481,0.000046225457,0.00018288165,0.0000013007185],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017644889,0.0013292157,0.93915004,0.0024050414,0.00042113682,0.00012851322,0.0016397174,0.013442192,0.00038281592,0.00788522,0.0018304815,0.029621115],"study_design_scores_gemma":[0.0031353803,0.00046631286,0.6890739,0.0034567579,0.00015857955,0.000077473385,0.00092503574,0.30024642,0.000059723236,0.001494007,0.0007990655,0.0001073211],"about_ca_topic_score_codex":0.0024178452,"about_ca_topic_score_gemma":0.00008294867,"teacher_disagreement_score":0.2868042,"about_ca_system_score_codex":0.00011283124,"about_ca_system_score_gemma":0.0002763869,"threshold_uncertainty_score":0.36550748},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83849066,0.022665955,0.019071316,0.0012503548,0.04743465,0.03930827,0.009597139,0.00085404,0.02132759],"genre_scores_gemma":[0.9950457,0.000090587135,0.000024342244,0.000018959025,0.0001648219,0.0004480571,0.0030623963,0.000033909255,0.0011111952],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98841417,0.0070779864,0.0013244923,0.0009251882,0.0016418479,0.00061631005],"domain_scores_gemma":[0.99443614,0.0024844157,0.0005171193,0.0014888145,0.0007856306,0.00028786573],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.016606143,0.00036518768,0.0014866131,0.002620358,0.00020864462,0.0002594503,0.00066614256,0.0005095126,0.00002646502],"category_scores_gemma":[0.0077258563,0.00034161526,0.00006025358,0.0025088047,0.0003284566,0.0006743032,0.0010270382,0.005557008,0.0000072132766],"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":[0.0013464852,0.0044121477,0.45519656,0.021488056,0.00022135004,0.0001688315,0.004992171,0.00008428534,0.00010095255,0.0000021005153,0.44223094,0.06975613],"study_design_scores_gemma":[0.0061475113,0.0012565735,0.55793566,0.0077919536,0.000047054964,0.000009936172,0.032789607,0.37543124,0.00002365217,0.0000044847093,0.018176135,0.000386191],"about_ca_topic_score_codex":0.015588103,"about_ca_topic_score_gemma":0.032794345,"teacher_disagreement_score":0.4240548,"about_ca_system_score_codex":0.0019982827,"about_ca_system_score_gemma":0.0051317452,"threshold_uncertainty_score":0.99990356},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9226417,0.019747177,0.023280183,0.030308904,0.002427976,0.0007451212,0.00024700098,0.00006778345,0.0005341492],"genre_scores_gemma":[0.98534906,0.004688246,0.0062451717,0.0032666938,0.00028201094,0.0000105438,0.0000846231,0.000018190323,0.000055458502],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980212,0.00007646682,0.00092426623,0.00020453324,0.00049295294,0.00028062006],"domain_scores_gemma":[0.99759525,0.00038309745,0.0005925356,0.00017617125,0.0010537228,0.00019924688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018219822,0.00015197284,0.0004625318,0.001247854,0.00014034766,0.00010974898,0.00025250568,0.000075717064,0.000016780972],"category_scores_gemma":[0.0007046518,0.00013663959,0.0002892518,0.00036805775,0.000107176995,0.00033159932,0.00007452231,0.0003400049,7.5981933e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075693795,0.00017495181,0.7255323,0.0004056133,0.000969387,0.000055361248,0.00010696997,0.000025451533,0.00010120982,0.0034347998,0.0014401956,0.2669968],"study_design_scores_gemma":[0.0044994513,0.00057294185,0.86377376,0.0017692163,0.00022516346,0.0003735978,0.00036257354,0.0010542388,0.000054078686,0.0036859615,0.12342528,0.00020374771],"about_ca_topic_score_codex":0.000038728176,"about_ca_topic_score_gemma":0.000020294496,"teacher_disagreement_score":0.26679304,"about_ca_system_score_codex":0.00008772848,"about_ca_system_score_gemma":0.00048922247,"threshold_uncertainty_score":0.5572001},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1421426,0.017860938,0.83004147,0.0030387011,0.0006324444,0.0053240056,0.000068869806,0.0005399076,0.0003510538],"genre_scores_gemma":[0.8114737,0.00151828,0.18512,0.00039320398,0.00004491981,0.0012202773,0.000080673744,0.000016687782,0.00013223266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982253,0.00008419193,0.0009878309,0.00018359344,0.000110553374,0.00040854188],"domain_scores_gemma":[0.9982561,0.0008315434,0.0004246773,0.0001969245,0.0002430796,0.000047696383],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002773455,0.00015755932,0.0006428739,0.00054585625,0.0004330581,0.000011337581,0.00006634066,0.00018847093,2.2380598e-7],"category_scores_gemma":[0.008486094,0.0001393885,0.00004056152,0.0005146824,0.00021203926,0.00015395286,0.0001263924,0.0005296565,6.7215245e-7],"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":[0.0016351404,0.0002869339,0.36498952,0.062476326,0.0012005633,0.000004741962,0.006450626,0.0031247719,0.00004829909,0.015004987,0.0016100389,0.54316807],"study_design_scores_gemma":[0.0027215995,0.0015483847,0.0027824214,0.0008064067,0.0000792686,0.000052205567,0.012730914,0.97068846,0.00002888426,0.00057789317,0.007829517,0.00015401283],"about_ca_topic_score_codex":0.000027078058,"about_ca_topic_score_gemma":0.000009570962,"teacher_disagreement_score":0.96756375,"about_ca_system_score_codex":0.00021640798,"about_ca_system_score_gemma":0.00016793626,"threshold_uncertainty_score":0.99986583},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99258465,0.0032175174,0.000118621734,0.0023387184,0.00008080168,0.0002749901,0.000010382569,0.00005224372,0.0013220977],"genre_scores_gemma":[0.9959549,0.0019143011,0.0016147671,0.0004717685,0.0000018108304,0.00001619917,0.000008593185,0.0000013014311,0.00001641184],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994288,0.000011194942,0.0003968059,0.00003392257,0.00004220515,0.00008707841],"domain_scores_gemma":[0.9997317,0.000040487426,0.00008716272,0.00009666045,0.000034369263,0.000009586604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001673209,0.000039105795,0.00019008435,0.00036097487,0.000021229747,9.5332047e-7,0.000024544463,0.000041457242,0.0000026322655],"category_scores_gemma":[0.0002557366,0.000034318407,0.0000060761026,0.00048019612,0.00014594958,0.000050893115,0.000034668243,0.0000788869,0.0000034666323],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045208595,0.00010323902,0.5587743,0.006771702,0.000047172998,0.0000033645601,0.0031274718,0.000010443223,0.0000066630655,0.023089169,0.0031613566,0.40485993],"study_design_scores_gemma":[0.0029547832,0.00043280245,0.90787935,0.004577699,0.000027589078,0.000013681079,0.026250103,0.026082147,0.00016767206,0.024703262,0.006755292,0.0001556102],"about_ca_topic_score_codex":0.000007342784,"about_ca_topic_score_gemma":0.000038825812,"teacher_disagreement_score":0.40470433,"about_ca_system_score_codex":0.00005472053,"about_ca_system_score_gemma":0.000052690837,"threshold_uncertainty_score":0.13994642},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017269525,0.9955667,0.00006979606,0.00021271197,0.00046859623,0.0020575142,0.0011022075,0.00014660296,0.00020315929],"genre_scores_gemma":[0.0009438021,0.9957298,0.00016634472,0.00041495083,0.00019303366,0.00028109408,0.0011395412,0.00007636225,0.0010550956],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957533,0.00074355904,0.0011223727,0.0011403507,0.0006360506,0.0006043938],"domain_scores_gemma":[0.9957229,0.0016855679,0.00041231315,0.0015856964,0.0001916755,0.00040183598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001412909,0.00070770824,0.003148872,0.00052899745,0.00006116064,0.00007239261,0.00045847837,0.0003861072,0.00020695021],"category_scores_gemma":[0.0015479947,0.0006075679,0.00031990086,0.0007406257,0.00023142311,0.00017783238,0.00018311472,0.0006123928,0.00017547801],"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":[0.0004400063,0.00035664867,0.027818052,0.5229113,0.0003170229,0.0019387302,0.000094263894,0.000006495225,5.916301e-7,0.00010923162,0.002407599,0.44360006],"study_design_scores_gemma":[0.0011156718,0.00015183425,0.0036226548,0.2275829,0.00033671694,0.000032340165,0.000014045578,0.000147921,0.0000010174377,0.000013294261,0.7663612,0.00062041904],"about_ca_topic_score_codex":0.00009119423,"about_ca_topic_score_gemma":0.0005433161,"teacher_disagreement_score":0.76395357,"about_ca_system_score_codex":0.00045567448,"about_ca_system_score_gemma":0.0022369477,"threshold_uncertainty_score":0.99963754},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9913706,0.0011628146,0.000048965,0.0058062067,0.000052821168,0.00050231285,0.000100183475,0.0000792298,0.00087687327],"genre_scores_gemma":[0.99819565,0.00031120967,0.00004435186,0.0009893114,0.00002258499,0.00014156764,0.00022019651,0.000011002956,0.000064110776],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99808687,0.00035619244,0.00052468287,0.00049084995,0.00008502001,0.00045636194],"domain_scores_gemma":[0.9988041,0.0004266236,0.00006781477,0.00047756633,0.000054632237,0.00016926852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038892785,0.00018707529,0.00072174077,0.0014301765,0.00003500274,0.000024043698,0.00019919968,0.0001594111,0.000085723725],"category_scores_gemma":[0.0006709429,0.0001765028,0.00012181341,0.0016079788,0.00026340058,0.00015865406,0.00028335882,0.00040636267,0.000009972319],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001742333,0.00019583541,0.9896284,0.00015518708,0.00014776071,0.000038570714,0.00012655032,0.00007316831,0.00009915728,0.0011996205,0.00023290247,0.007928637],"study_design_scores_gemma":[0.001348207,0.000042392243,0.9887422,0.00008617912,0.000081406884,0.0000029109551,0.00014536551,0.0069318525,0.0000017046507,0.000658269,0.0018282167,0.0001313044],"about_ca_topic_score_codex":0.000119993165,"about_ca_topic_score_gemma":0.005586369,"teacher_disagreement_score":0.0077973325,"about_ca_system_score_codex":0.00013666348,"about_ca_system_score_gemma":0.00020419575,"threshold_uncertainty_score":0.7197575},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22508454,0.03651821,0.39134395,0.020190945,0.0045388374,0.09493243,0.15490927,0.0025686405,0.069913164],"genre_scores_gemma":[0.96081716,0.0014888362,0.017587423,0.00018726035,0.0005248631,0.0013747434,0.015808806,0.00011355193,0.0020973326],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9933267,0.0010153728,0.0008659515,0.0013656212,0.0020686043,0.0013577283],"domain_scores_gemma":[0.99269086,0.0010892997,0.00035650705,0.001891153,0.0031973333,0.00077486207],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0049763373,0.0004214724,0.0013281279,0.0015075738,0.00023203724,0.00008961443,0.00089105975,0.00046995716,0.00010093964],"category_scores_gemma":[0.005999175,0.00039814244,0.00039316554,0.0015137721,0.00034938814,0.0001342103,0.0020834033,0.0016714442,0.0000141642195],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034747147,0.0056714355,0.41345963,0.13539124,0.004202762,0.000033942437,0.006410552,0.0011533899,0.00043877086,0.0018096078,0.19671707,0.23123689],"study_design_scores_gemma":[0.012430692,0.0075131403,0.63285106,0.028290981,0.00023216565,0.00000626286,0.007128868,0.2218213,0.00030106734,0.0037709274,0.08389012,0.0017634288],"about_ca_topic_score_codex":0.000077947065,"about_ca_topic_score_gemma":0.000008885238,"teacher_disagreement_score":0.7357326,"about_ca_system_score_codex":0.0010319427,"about_ca_system_score_gemma":0.0072519863,"threshold_uncertainty_score":0.99984705},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01937762,0.0064767837,0.00023953315,0.96973026,0.00009590592,0.0013853176,0.00073016976,0.0007664334,0.0011979694],"genre_scores_gemma":[0.97235644,0.004186391,0.0010600771,0.019363515,0.00012986835,0.000118178585,0.0011271769,0.000023549781,0.0016347963],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967932,0.0003268147,0.0005023217,0.00070852094,0.00031361065,0.0013555018],"domain_scores_gemma":[0.99626464,0.00030584194,0.00018738449,0.0023095591,0.00032202658,0.0006105273],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0026347858,0.00018612746,0.00066140015,0.0018256288,0.0006133312,0.00008346774,0.00053434505,0.00026499652,0.000013821285],"category_scores_gemma":[0.009718386,0.0001673245,0.000027261623,0.0033439433,0.0013711096,0.00020945424,0.0008919717,0.0008370635,0.000018501469],"study_design_candidate":"not_applicable","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":[0.00013408264,0.0005051661,0.017627832,0.002913669,0.00012942862,0.000026532836,0.00020589227,5.3770588e-8,0.000025008492,0.12994507,0.2657988,0.58268845],"study_design_scores_gemma":[0.0014347474,0.00043060916,0.015571948,0.00021897916,0.000004090972,0.000047081856,0.00038678694,0.00016740979,0.000024664405,0.0050452584,0.97657204,0.00009637428],"about_ca_topic_score_codex":0.002746398,"about_ca_topic_score_gemma":0.002733693,"teacher_disagreement_score":0.95297885,"about_ca_system_score_codex":0.00067915156,"about_ca_system_score_gemma":0.01264384,"threshold_uncertainty_score":0.9986232},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896454,0.0021900344,0.0007483184,0.0018081677,0.00009477174,0.00092567067,0.00030507293,0.000034916742,0.00424762],"genre_scores_gemma":[0.9984487,0.000009654012,0.00018543992,0.00035085896,0.00006214235,0.00049557775,0.00004846359,0.000010084658,0.0003890656],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991036,0.000029314051,0.00028791648,0.00025780118,0.00017512206,0.00014626425],"domain_scores_gemma":[0.99938345,0.00009873071,0.00007006303,0.00025398325,0.00006957388,0.00012419395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000096888805,0.000107050226,0.0002970631,0.00017409488,0.000072619936,0.000019433719,0.00007569359,0.000036586538,0.000009508158],"category_scores_gemma":[0.00003895312,0.000095105526,0.000012385126,0.00035595824,0.000061282524,0.00004352491,0.000048546623,0.000063297644,0.0000018116536],"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":[0.0001544086,0.00014361313,0.94299674,0.00056633167,0.00016820383,0.00001826777,0.007949163,0.00001675208,0.0050909123,0.00245944,0.022147287,0.018288871],"study_design_scores_gemma":[0.0008451826,0.00001005816,0.9395419,0.00020685761,0.000019672434,0.000008328809,0.0020183714,0.000054149215,0.00010230248,0.000010096713,0.057071347,0.000111749345],"about_ca_topic_score_codex":0.1827205,"about_ca_topic_score_gemma":0.31397262,"teacher_disagreement_score":0.13125212,"about_ca_system_score_codex":0.00010620093,"about_ca_system_score_gemma":0.00024305345,"threshold_uncertainty_score":0.82272184},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.3618287e-8,0.9161016,0.000043952223,0.02368932,0.0003930697,0.023949219,0.033947606,0.00018110617,0.0016940829],"genre_scores_gemma":[0.000037204845,0.85370713,0.00072642654,0.0038773988,0.00025154953,0.012211274,0.12587807,0.00014167234,0.0031692781],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9928397,0.0009645732,0.0021360202,0.0012533691,0.001336198,0.0014701496],"domain_scores_gemma":[0.98675144,0.0019904594,0.0015175914,0.0046452996,0.0028377152,0.002257474],"candidate_categories":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0052524917,0.0009974543,0.0039729006,0.0003002545,0.0013113891,0.0003248587,0.0017577724,0.00028218873,0.000031310297],"category_scores_gemma":[0.011858381,0.0007260844,0.0012563544,0.0020273088,0.0002633727,0.00011960168,0.0002952985,0.0009517246,0.0000020362668],"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":true,"study_design_scores_codex":[0.000035159097,0.00017276128,0.00033940285,0.24157238,0.00085310923,0.00024994285,0.000004261312,0.0000015917516,5.7201816e-10,0.00043811902,0.43729585,0.3190374],"study_design_scores_gemma":[0.0005434535,0.000039785573,0.000449834,0.055411797,0.0005012624,0.00027655734,0.0000081383305,0.00018438953,2.6580371e-9,0.00001563271,0.9419681,0.00060103636],"about_ca_topic_score_codex":0.16511309,"about_ca_topic_score_gemma":0.9481473,"teacher_disagreement_score":0.78303415,"about_ca_system_score_codex":0.0038557998,"about_ca_system_score_gemma":0.23932771,"threshold_uncertainty_score":0.9999888},"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":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007178363,0.005669258,0.07365351,0.0056292964,0.9088093,0.0013800223,0.003417072,0.00043624468,0.0009981267],"genre_scores_gemma":[0.00032374516,0.006830128,0.0038710334,0.001528377,0.9818675,0.0001754533,0.0032155688,0.00016226052,0.0020259419],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.991575,0.0007278188,0.0019167069,0.0021151067,0.0022910503,0.0013743078],"domain_scores_gemma":[0.99414456,0.0013875557,0.00090853457,0.0015543844,0.0010958058,0.0009091601],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0019854663,0.0012186791,0.0025857904,0.0007969884,0.00030329215,0.00043206755,0.0016405893,0.0029144054,0.00032438466],"category_scores_gemma":[0.018254463,0.0012097261,0.0006344127,0.0015350794,0.0007144323,0.0006298484,0.00039087163,0.0045063533,0.00018045731],"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":[0.0016062461,0.00031945386,0.0003335086,0.0011148927,0.00031613893,0.0003039545,0.00049763155,0.0004449109,0.000014948466,0.0005796733,0.974301,0.020167656],"study_design_scores_gemma":[0.0003310146,0.00022805922,0.0000132867635,0.000879544,0.000313074,0.0000046166415,0.0029418436,0.00091182615,0.000088519664,0.02197386,0.9713582,0.00095619267],"about_ca_topic_score_codex":0.000760376,"about_ca_topic_score_gemma":0.00094179116,"teacher_disagreement_score":0.073058195,"about_ca_system_score_codex":0.0019836628,"about_ca_system_score_gemma":0.0075954017,"threshold_uncertainty_score":0.99903524},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9750621,0.0034402104,0.0026370487,0.01757078,0.00033035263,0.00046743886,0.000046417354,0.000012985309,0.00043267207],"genre_scores_gemma":[0.9973565,0.00077056995,0.00065041095,0.00019251627,0.00011198431,0.000022660723,0.00006711768,0.000018821609,0.0008094239],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.994501,0.0012941693,0.0011613824,0.0002626914,0.0022170101,0.0005637421],"domain_scores_gemma":[0.99555725,0.0014487208,0.00028114492,0.00051699823,0.0015548134,0.00064105244],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01566535,0.00013739467,0.00053368515,0.0011580919,0.00009963868,0.00014707756,0.0013773853,0.00015559953,0.00035614084],"category_scores_gemma":[0.018701833,0.00011181358,0.00013117278,0.0010008665,0.000278515,0.00023316802,0.0006411587,0.002402881,0.0000066001203],"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":[0.0027864731,0.001410436,0.6617235,0.0012948604,0.0017257411,0.0009664341,0.0014903364,0.00003870533,0.0013234824,0.0034575425,0.27000442,0.053778097],"study_design_scores_gemma":[0.02824252,0.0018583896,0.68633336,0.026635041,0.00034172725,0.00035747341,0.015002154,0.11269251,0.010871043,0.0051413854,0.11165408,0.0008703227],"about_ca_topic_score_codex":0.098800234,"about_ca_topic_score_gemma":0.43366522,"teacher_disagreement_score":0.33486497,"about_ca_system_score_codex":0.0014016641,"about_ca_system_score_gemma":0.0046231346,"threshold_uncertainty_score":0.9998986},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97931105,0.000042540232,0.018622182,0.000023009536,0.00007760099,0.00068174757,0.00025290204,0.00020039474,0.000788592],"genre_scores_gemma":[0.9956159,0.000008390769,0.001437665,0.00009473629,0.00005578168,0.000043170327,0.0027197294,0.000008379088,0.000016236341],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988037,0.00010787357,0.00042483432,0.00017857697,0.00025464638,0.00023037921],"domain_scores_gemma":[0.9993099,0.00004923648,0.00013749163,0.00023649441,0.00010576214,0.00016114216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012400851,0.00014197516,0.00022035121,0.00036309002,0.0001427526,0.000066192435,0.00008504368,0.00006950804,0.000013262772],"category_scores_gemma":[0.0007828573,0.00013301727,0.000048096455,0.00044515217,0.000019455892,0.0015775324,0.00006481133,0.00022629472,0.0000088954475],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006119115,0.00013933277,0.96658856,0.00029961436,0.000018208375,0.000002052491,0.0021663639,0.025215013,0.000030260757,0.00004084912,0.000018204686,0.0048696278],"study_design_scores_gemma":[0.00091912685,0.0001087583,0.60459596,0.00022776095,0.000035660207,0.0000018325177,0.0013409511,0.39237857,0.000051868006,0.0000063140023,0.00025453002,0.000078626785],"about_ca_topic_score_codex":0.0005781182,"about_ca_topic_score_gemma":0.00003682763,"teacher_disagreement_score":0.36716357,"about_ca_system_score_codex":0.00023028911,"about_ca_system_score_gemma":0.00008513233,"threshold_uncertainty_score":0.5424287},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9819971,0.000290504,0.00064205826,0.00018718212,0.00049032556,0.0029984724,0.013299816,0.000066632885,0.000027927494],"genre_scores_gemma":[0.99283737,0.00021163934,0.0029509747,0.00011752872,0.0009568473,0.000105034196,0.0023127387,0.000047031175,0.00046083797],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98223287,0.0075347917,0.006418347,0.0012138974,0.0018997309,0.00070035947],"domain_scores_gemma":[0.97278553,0.021142099,0.0011058431,0.0009970801,0.0033792802,0.00059013924],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03567179,0.00036373758,0.0050167344,0.000819916,0.00022710665,0.00009690878,0.00036800094,0.00046211504,0.0001800085],"category_scores_gemma":[0.05499569,0.00029752415,0.0020273393,0.0018523312,0.00042782846,0.000121888435,0.0003224773,0.0008163469,0.0000041198873],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0221533,0.007867768,0.93608546,0.00090562314,0.02177976,0.0000077372715,0.0000921739,0.0010271327,0.0057384116,0.0000085791435,0.00012953358,0.0042044953],"study_design_scores_gemma":[0.015590023,0.00043925713,0.8414934,0.0010100221,0.0073218457,2.707735e-7,0.000044540695,0.12993093,0.0039206184,0.0000060282964,0.00002557629,0.00021742788],"about_ca_topic_score_codex":0.0007106407,"about_ca_topic_score_gemma":0.00013412979,"teacher_disagreement_score":0.1289038,"about_ca_system_score_codex":0.0004039471,"about_ca_system_score_gemma":0.0017491209,"threshold_uncertainty_score":0.99994767},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9782971,0.000021388674,0.017229188,0.00036260387,0.00006676431,0.0027642325,0.00003345589,0.00015419623,0.0010710594],"genre_scores_gemma":[0.9679802,8.8604577e-7,0.027276644,0.0033015683,0.00002989803,0.0011040926,0.000165291,0.000016491113,0.0001249348],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735814,0.00004637774,0.0010719158,0.00018323831,0.0009324072,0.00040790797],"domain_scores_gemma":[0.99879605,0.00016936286,0.00013904671,0.0004016885,0.00015502598,0.00033885255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011971984,0.00021344941,0.00048096493,0.00021921878,0.00008417281,0.000031724485,0.00027446172,0.00015166473,0.000042906548],"category_scores_gemma":[0.0010743045,0.00017830607,0.00008142097,0.00042003155,0.000097830576,0.00018497344,0.00015507567,0.0004389267,0.000023525745],"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":[0.0011997045,0.0033624507,0.9187926,0.003775244,0.00020105451,0.000028945376,0.015416417,0.0025537864,0.0000037828047,0.0005007955,0.0038058374,0.05035939],"study_design_scores_gemma":[0.009095812,0.0002552908,0.47401103,0.00084433553,0.000040552502,0.0000016299339,0.0006165206,0.5065868,0.000023727078,0.00028735318,0.007990264,0.0002466657],"about_ca_topic_score_codex":0.0000066160787,"about_ca_topic_score_gemma":0.000035047517,"teacher_disagreement_score":0.504033,"about_ca_system_score_codex":0.00026108613,"about_ca_system_score_gemma":0.002465734,"threshold_uncertainty_score":0.72711104},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9690856,0.0017630646,0.011402291,0.009757879,0.00026228707,0.0051437435,0.002196521,0.00019628792,0.0001923625],"genre_scores_gemma":[0.99277097,0.00017597218,0.00009058262,0.0048979754,0.00010327402,0.0011499451,0.00074961135,0.000024249794,0.000037397844],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947969,0.002684575,0.00086901477,0.0007295493,0.0003246793,0.00059530576],"domain_scores_gemma":[0.9924647,0.005343332,0.00040818416,0.00094858545,0.00026049552,0.0005746706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003963095,0.00031420257,0.0010379516,0.00044739668,0.00025625734,0.000052106327,0.0002767807,0.00013892402,0.000020195026],"category_scores_gemma":[0.006824554,0.00022220053,0.00016107214,0.0011516538,0.00016265936,0.00009054058,0.0000561606,0.0003228107,0.000001934274],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015479184,0.0010285011,0.9654263,0.0002947359,0.00014119207,0.0000040756067,0.0012384458,0.000044615535,0.000032107204,0.00008627593,0.0020560056,0.028099818],"study_design_scores_gemma":[0.0047547,0.0012592322,0.9763725,0.000024247149,0.0000070237606,0.000008488288,0.0012066277,0.00086791744,0.0000014336473,0.00036020984,0.014965503,0.00017210998],"about_ca_topic_score_codex":0.0024052307,"about_ca_topic_score_gemma":0.012343851,"teacher_disagreement_score":0.027927708,"about_ca_system_score_codex":0.00037089747,"about_ca_system_score_gemma":0.0019878931,"threshold_uncertainty_score":0.9061075},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95546764,0.0006816143,0.036075503,0.0012700785,0.00034120723,0.005106855,0.000056002766,0.00018504656,0.0008160269],"genre_scores_gemma":[0.99587446,0.000064317755,0.0020733713,0.0009872826,0.000020394544,0.0003962629,0.0005621539,0.000012622815,0.000009115478],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9968844,0.0010214999,0.00091648503,0.00036727305,0.0003701297,0.00044019628],"domain_scores_gemma":[0.998112,0.00025419472,0.0002893822,0.00041143535,0.0005227671,0.0004101722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030396974,0.00019101075,0.0006763683,0.0007281593,0.00021408746,0.0001713981,0.00010026452,0.0000771238,0.000001798555],"category_scores_gemma":[0.0020097278,0.00019113414,0.000034077922,0.0017665281,0.000036655576,0.0004352823,0.00010589824,0.00024523877,0.0000025490788],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013610107,0.00019842347,0.96551955,0.0003841976,0.000042190553,0.0000016758376,0.0017835295,0.00022705323,0.000004163053,0.00011496939,0.00033325702,0.031254884],"study_design_scores_gemma":[0.001471817,0.00022323913,0.9578978,0.00011592405,0.000001743428,0.0000032485068,0.0020667054,0.03237666,4.6960727e-7,0.0000065457252,0.005697681,0.00013815895],"about_ca_topic_score_codex":0.00074083987,"about_ca_topic_score_gemma":0.0005049707,"teacher_disagreement_score":0.040406812,"about_ca_system_score_codex":0.00052341365,"about_ca_system_score_gemma":0.0005492204,"threshold_uncertainty_score":0.7794224},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009380399,0.053758394,0.7745876,0.00037806408,0.0017308118,0.008536145,0.0013900183,0.00026506235,0.14997356],"genre_scores_gemma":[0.99667007,0.00032656855,0.00006533275,0.0002919588,0.00039796534,0.000060420596,0.00034566462,0.000049867867,0.0017921327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981839,0.000110216584,0.00071535725,0.0004731846,0.0002676339,0.00024967996],"domain_scores_gemma":[0.9981453,0.0007581686,0.0002943639,0.0006188952,0.00011393666,0.00006937089],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050223904,0.0003636864,0.0008703723,0.00030830243,0.00006279592,0.00003557368,0.00013174799,0.0006009228,0.000008671939],"category_scores_gemma":[0.00013731842,0.0002615356,0.00016171487,0.00018032736,0.00012356667,0.000054445187,0.000070689995,0.0009279728,8.90637e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001187034,0.000083911735,0.006327215,0.00035217794,0.00017261154,0.00001322839,0.00010960255,0.98860604,0.0000055103783,0.0013349344,0.00020888084,0.0026671635],"study_design_scores_gemma":[0.0008127381,0.00008302708,0.0019402152,0.0013659593,0.00019242038,0.0000111447525,0.0000035148387,0.9901415,3.1117767e-7,0.00043482863,0.0047948095,0.00021951634],"about_ca_topic_score_codex":0.000056471174,"about_ca_topic_score_gemma":0.000119145676,"teacher_disagreement_score":0.98728967,"about_ca_system_score_codex":0.00012324985,"about_ca_system_score_gemma":0.00016179947,"threshold_uncertainty_score":0.99998367},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99354905,0.0003440233,0.000012658992,0.0044128126,0.00009566414,0.0008657134,0.00011178116,0.00006811754,0.00054017856],"genre_scores_gemma":[0.9968517,0.000049791342,0.00009005188,0.0017486482,0.000020682986,0.00052458065,0.0002914186,0.000016236225,0.00040689047],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973549,0.0006863988,0.0006961467,0.00060001167,0.00012003461,0.00054247124],"domain_scores_gemma":[0.9987022,0.00037842512,0.00024328649,0.0004209153,0.000026415644,0.00022875727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008355861,0.00027437377,0.00078463234,0.0006718709,0.000036663077,0.000013880458,0.00017772749,0.0002358584,0.000050531366],"category_scores_gemma":[0.0011303162,0.0002419218,0.000058089703,0.00044945534,0.0001495526,0.00019940393,0.00028933992,0.0005596865,0.000020489373],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009317603,0.00075690274,0.9953697,0.00004328247,0.000109288216,0.00010099489,0.00029327045,0.00007405601,0.000043285774,0.00031075216,0.00023303031,0.0017336947],"study_design_scores_gemma":[0.0043021264,0.00044845327,0.992209,0.00006094906,0.000051345003,0.0000011339278,0.0003593315,0.0013698062,0.0000024786066,0.00019293216,0.0008121582,0.00019027301],"about_ca_topic_score_codex":0.000048456644,"about_ca_topic_score_gemma":0.0010845641,"teacher_disagreement_score":0.003370366,"about_ca_system_score_codex":0.00066003646,"about_ca_system_score_gemma":0.0001466118,"threshold_uncertainty_score":0.9865285},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37233803,0.008056069,0.6035354,0.009819791,0.0040006395,0.0013997027,0.000089773,0.00013484711,0.0006257405],"genre_scores_gemma":[0.995378,0.0007300154,0.002601622,0.00089400774,0.00013584232,0.000039099104,0.000008988916,0.00001567262,0.00019674245],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979309,0.00035787077,0.0007327598,0.00043207605,0.00024342434,0.00030300452],"domain_scores_gemma":[0.9986703,0.00031330786,0.00014926115,0.0006051472,0.00016760093,0.00009441243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001174425,0.00020011984,0.0004504854,0.00039145956,0.00010236,0.00002594663,0.00029634876,0.0001561235,0.000032689215],"category_scores_gemma":[0.0009499493,0.0001589626,0.000106847416,0.0009989416,0.00053261255,0.000113368806,0.00012793505,0.0004496322,0.000016057715],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062410487,0.00017463088,0.047082517,0.00015129411,0.000101412246,0.00003079516,0.0010991766,0.00053301774,0.00550383,0.0036049043,0.0036267773,0.9318506],"study_design_scores_gemma":[0.0005293951,0.001103529,0.18947564,0.0018372175,0.0004850772,0.000076833334,0.03359336,0.21061414,0.30036667,0.18334074,0.07733294,0.0012444708],"about_ca_topic_score_codex":0.00007891571,"about_ca_topic_score_gemma":0.00026132318,"teacher_disagreement_score":0.9306061,"about_ca_system_score_codex":0.00015392511,"about_ca_system_score_gemma":0.0001976386,"threshold_uncertainty_score":0.6482307},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9850231,0.00092306454,0.00021934618,0.0039549237,0.000048315345,0.00091638305,0.0001234091,0.0000372322,0.008754263],"genre_scores_gemma":[0.9973537,0.0016112116,0.00003809328,0.000067146,0.000014679002,0.00018511993,0.0003227945,0.000010339131,0.00039694703],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99767566,0.00039600796,0.0005370609,0.00023132401,0.0005255681,0.0006343693],"domain_scores_gemma":[0.9987047,0.00034760506,0.00005221832,0.0004121193,0.000258733,0.00022464484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018781036,0.00018253335,0.0004349327,0.0059010065,0.00010722859,0.00018467134,0.00021820457,0.00008873956,0.00017021336],"category_scores_gemma":[0.00019642836,0.00016835482,0.000065374494,0.0063575907,0.00029636084,0.00129969,0.00052885956,0.0008418182,0.000037113936],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017144214,0.0015420706,0.55497223,0.002625934,0.0018065588,0.00003473109,0.057316966,0.00011265616,0.0002694011,0.0199897,0.028942611,0.3306727],"study_design_scores_gemma":[0.0028768072,0.00024350645,0.93125314,0.00019707315,0.000012474449,0.0000029525174,0.012033064,0.009799508,0.000054582484,0.00052708096,0.04280702,0.00019278022],"about_ca_topic_score_codex":0.00018744355,"about_ca_topic_score_gemma":0.0037363605,"teacher_disagreement_score":0.37628093,"about_ca_system_score_codex":0.00030759536,"about_ca_system_score_gemma":0.00018397307,"threshold_uncertainty_score":0.686531},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9467929,0.0025998082,0.003628142,0.008078843,0.0018105373,0.006999558,0.003990082,0.0004658948,0.025634255],"genre_scores_gemma":[0.9011102,0.002900015,0.08148213,0.0020866983,0.0011350508,0.0022114054,0.0048385663,0.00024377902,0.003992204],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9933406,0.00038394914,0.0036532243,0.0013101824,0.00061963673,0.0006924151],"domain_scores_gemma":[0.9919256,0.0019578573,0.0033182958,0.0019635523,0.0005618247,0.00027288962],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005591781,0.0005294824,0.0022471799,0.00033726223,0.000036776342,0.000016602173,0.00035907878,0.00045443335,0.0001148577],"category_scores_gemma":[0.02237881,0.00051755965,0.00053905224,0.00039935805,0.00013983849,0.00007640649,0.00063361565,0.0007515002,0.00002329431],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020977075,0.00096439634,0.9271131,0.00838393,0.00056528446,0.00070272275,0.00023653878,0.00037523117,0.00633654,0.00029543188,0.048727132,0.004201957],"study_design_scores_gemma":[0.0053382707,0.00046501952,0.9397858,0.008292286,0.00065852667,0.00019456817,0.00015067447,0.01363581,0.0022718096,0.0011078155,0.0267868,0.0013126557],"about_ca_topic_score_codex":0.00037201884,"about_ca_topic_score_gemma":0.000360883,"teacher_disagreement_score":0.077853985,"about_ca_system_score_codex":0.00036777282,"about_ca_system_score_gemma":0.0017280094,"threshold_uncertainty_score":0.9997276},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9954831,0.00031382643,0.000067791356,0.0006052316,0.00002371407,0.0007564459,0.0015078844,0.00003358768,0.0012084428],"genre_scores_gemma":[0.9972566,0.0003478558,0.00005334392,0.000028491257,0.00003035722,0.00008855805,0.0018830618,0.000009550211,0.00030222698],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99660903,0.00095346745,0.0005151947,0.00038049385,0.0011221798,0.00041960322],"domain_scores_gemma":[0.9971531,0.001124062,0.00012545474,0.0008696255,0.0005892805,0.0001384463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005329099,0.00012036264,0.0005792108,0.0021102747,0.00009906991,0.000048650923,0.0003595543,0.00013313942,0.0000979655],"category_scores_gemma":[0.00096629414,0.00011345267,0.00006465037,0.0030416418,0.00018676717,0.0005338334,0.00069031393,0.00054687157,0.00004508587],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024075278,0.0001530192,0.984319,0.00044696423,0.0018602473,0.0000040061113,0.0019337495,0.0000053663593,0.00086342345,0.000058231693,0.005230503,0.004884773],"study_design_scores_gemma":[0.0019280987,0.00007576183,0.988314,0.00022117306,0.00030311928,1.7401653e-7,0.0014738006,0.005613289,0.00058106985,0.00003300446,0.0013729295,0.00008360897],"about_ca_topic_score_codex":0.000097271804,"about_ca_topic_score_gemma":0.00023301766,"teacher_disagreement_score":0.005607922,"about_ca_system_score_codex":0.00044772404,"about_ca_system_score_gemma":0.00034617144,"threshold_uncertainty_score":0.46264654},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99527043,0.0010272665,0.000023001727,0.0001793604,0.00028915767,0.00054905494,0.000044520635,0.000081940256,0.0025352666],"genre_scores_gemma":[0.9982147,0.0000599675,0.0002577479,0.00041159542,0.00006546483,0.000009239635,0.000116544026,0.00002497971,0.0008397126],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981034,0.00010184614,0.0006191453,0.00059027196,0.00026096104,0.00032442086],"domain_scores_gemma":[0.9986187,0.00020901859,0.000113432696,0.0007141527,0.000229271,0.00011537642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020323091,0.00024532224,0.00060980034,0.0004567007,0.00004405018,0.00009521346,0.00021933825,0.00012038983,0.000054475327],"category_scores_gemma":[0.00056713034,0.00021808357,0.00004965236,0.000618595,0.00022841353,0.00034606305,0.00025062947,0.0004056866,0.000031070795],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009233642,0.0005547866,0.98218346,0.0003412696,0.00008692949,0.00007690266,0.0011004445,0.00004234717,0.0031566147,0.001776709,0.001650672,0.008106488],"study_design_scores_gemma":[0.0019444597,0.00022711208,0.9588555,0.0023684031,0.000033844583,0.0000053765484,0.0001797977,0.0004608905,0.02776826,0.000047772628,0.007897879,0.00021067487],"about_ca_topic_score_codex":0.00084949855,"about_ca_topic_score_gemma":0.0020202133,"teacher_disagreement_score":0.024611644,"about_ca_system_score_codex":0.00017101585,"about_ca_system_score_gemma":0.00018840833,"threshold_uncertainty_score":0.889319},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14101052,0.13974464,0.016321734,0.22276248,0.012097441,0.01224296,0.0067749717,0.0027373093,0.44630796],"genre_scores_gemma":[0.76202637,0.02940024,0.0011439861,0.012165606,0.00089462206,0.00016964838,0.0014952921,0.00015425903,0.19255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970839,0.00043631252,0.0010774898,0.0007042391,0.00017365106,0.0005243919],"domain_scores_gemma":[0.9968287,0.0007297106,0.0008021793,0.0007274498,0.00022139831,0.0006905924],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023996867,0.00037140463,0.0014426216,0.00042646204,0.00023734217,0.000008705385,0.00013965652,0.0003577067,0.00015768438],"category_scores_gemma":[0.00369677,0.00034725812,0.00021393126,0.000092899005,0.00043212512,0.00015866062,0.0001811619,0.0005868937,0.00001790632],"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":[0.002442457,0.00030349943,0.12147642,0.009489942,0.0011056244,0.000050318275,0.00024534948,0.0000036764325,0.000017378316,0.5538888,0.009004702,0.30197182],"study_design_scores_gemma":[0.000682557,0.00097256864,0.28818354,0.0017554804,0.000117610696,0.000015592459,0.000027383852,0.00001252466,0.000008763081,0.022383766,0.6854755,0.00036467347],"about_ca_topic_score_codex":0.00015475247,"about_ca_topic_score_gemma":0.00018300475,"teacher_disagreement_score":0.6764709,"about_ca_system_score_codex":0.00016907357,"about_ca_system_score_gemma":0.0012764788,"threshold_uncertainty_score":0.99989796},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49994594,0.00034121005,0.49736214,0.0002306316,0.00038124662,0.00049559923,0.0007069913,0.000023941011,0.0005122872],"genre_scores_gemma":[0.9786876,0.0009871734,0.019157367,0.00004478367,0.000095533054,0.000015742846,0.00087242783,0.000009907309,0.00012946002],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99810016,0.00012739393,0.0008538596,0.0002368729,0.0005485445,0.00013315646],"domain_scores_gemma":[0.99762475,0.00009268187,0.0012387391,0.00020716831,0.0007547403,0.00008194162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079930935,0.00017408027,0.00036956594,0.00051700533,0.0001765497,0.000029218947,0.00013280592,0.00007122485,0.00003008882],"category_scores_gemma":[0.0003910172,0.00013488892,0.000113048496,0.0003939022,0.00013886635,0.0002483449,0.00006745323,0.00030334666,8.4810154e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045495625,0.0003470341,0.9220426,0.00014262187,0.00019730037,0.00001596982,0.00019050295,0.0088934135,0.000016275151,0.003704161,0.008101149,0.051799394],"study_design_scores_gemma":[0.0026843823,0.0001932892,0.79742193,0.00080856343,0.00031086386,0.000011062141,0.00022189986,0.18907447,0.0000055125947,0.002391661,0.0067861187,0.00009022209],"about_ca_topic_score_codex":0.00013752081,"about_ca_topic_score_gemma":0.00022266009,"teacher_disagreement_score":0.47874168,"about_ca_system_score_codex":0.00014796149,"about_ca_system_score_gemma":0.00021724876,"threshold_uncertainty_score":0.55006105},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9872483,0.0007088876,0.000033489752,0.009568296,0.00008830443,0.0007048635,0.00011300623,0.000046806028,0.0014880712],"genre_scores_gemma":[0.9976374,0.00006130609,0.00012433287,0.0019195927,0.000051154493,0.00005799323,0.00002116449,0.0000064055134,0.00012063625],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978833,0.00039331478,0.0005736137,0.0003539991,0.00044354063,0.00035223516],"domain_scores_gemma":[0.99835473,0.0005821729,0.00015844202,0.0004977968,0.000079518984,0.00032730907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027498,0.00019925764,0.0007105751,0.00005443325,0.00012883978,0.000006302499,0.00042907143,0.00012247324,0.00037259655],"category_scores_gemma":[0.0013719497,0.00008226326,0.000090305184,0.00056268694,0.00096648233,0.000027242993,0.00043090218,0.00023922228,0.0000038392914],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024797558,0.0038464912,0.94491214,0.0003096365,0.0011482065,0.00018173674,0.0031345077,0.00033477668,0.000055380508,0.00078540214,0.0008147813,0.041997213],"study_design_scores_gemma":[0.002286578,0.0023145305,0.9785427,0.00018304064,0.00032593595,0.00004082323,0.00038866742,0.0008588566,0.0000037396799,0.0006933386,0.014237013,0.00012480193],"about_ca_topic_score_codex":0.00013227774,"about_ca_topic_score_gemma":0.0003783514,"teacher_disagreement_score":0.041872412,"about_ca_system_score_codex":0.00004337245,"about_ca_system_score_gemma":0.000169449,"threshold_uncertainty_score":0.4079671},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.968195,0.0004817169,0.00024587434,0.030348388,0.00014823457,0.00035713802,0.000004547329,0.000007330014,0.000211746],"genre_scores_gemma":[0.99859124,0.000249642,0.00003637606,0.00062112533,0.00012309554,0.000009565705,0.000019288831,0.000007609422,0.00034207516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99545366,0.0010218961,0.000782224,0.00020014757,0.0022166702,0.00032542553],"domain_scores_gemma":[0.99788886,0.00096030865,0.00007881724,0.00019134872,0.00057664386,0.00030399804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009676496,0.00010159966,0.0003921843,0.000854703,0.00005597253,0.00007716401,0.00050642615,0.000093096634,0.0002443544],"category_scores_gemma":[0.0046554366,0.00006263332,0.00007127264,0.0009672468,0.00013321123,0.000045344343,0.00033432973,0.0014661116,0.0000075532157],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007736103,0.008218303,0.47907448,0.0006663497,0.0038004066,0.017220845,0.109174326,0.0006017355,0.0005457696,0.0026212942,0.26886368,0.10147673],"study_design_scores_gemma":[0.02477684,0.0036528907,0.46489978,0.006660466,0.0004532925,0.00039528694,0.16332535,0.30695644,0.00012381685,0.0027799609,0.025490988,0.0004848931],"about_ca_topic_score_codex":0.00062521995,"about_ca_topic_score_gemma":0.000321614,"teacher_disagreement_score":0.3063547,"about_ca_system_score_codex":0.00019851238,"about_ca_system_score_gemma":0.00025416934,"threshold_uncertainty_score":0.63696074},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9553511,0.00006859024,0.00002292638,0.0003782401,0.00008970305,0.0010126656,0.00026724057,0.000076934695,0.042732608],"genre_scores_gemma":[0.99853414,0.0000073738092,0.0004384519,0.00056292594,0.000017176468,0.0000624374,0.00007229984,0.000009639852,0.0002955542],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987533,0.00008580943,0.0004704725,0.00026735934,0.0002628826,0.00016014591],"domain_scores_gemma":[0.9990006,0.00014786424,0.00011492382,0.00046214808,0.00022760734,0.000046831865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003606714,0.00010453733,0.00042403137,0.00017855343,0.000024362107,0.0000091703305,0.00009846527,0.000034567212,0.000019825986],"category_scores_gemma":[0.002010265,0.000104370985,0.00003727811,0.00078852044,0.000045721154,0.00006630444,0.000075502285,0.0001370124,0.000017882898],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038186998,0.00035566994,0.989407,0.000087406996,0.000055836543,0.00018100797,0.00025291374,0.000003001343,0.000100544006,0.000028133312,0.008430497,0.0010597954],"study_design_scores_gemma":[0.002679674,0.00010882415,0.9949024,0.00032246838,0.00004265458,0.00000841789,0.0003876927,0.0003891247,0.00045359242,0.000079429854,0.00055196707,0.000073801915],"about_ca_topic_score_codex":0.0032130438,"about_ca_topic_score_gemma":0.001119726,"teacher_disagreement_score":0.043183047,"about_ca_system_score_codex":0.00015490658,"about_ca_system_score_gemma":0.0001245273,"threshold_uncertainty_score":0.48571825},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99291795,0.0005938055,0.002972457,0.0013449948,0.00017744818,0.0017446292,0.00012995549,0.000080542384,0.000038188115],"genre_scores_gemma":[0.9990714,0.00025657928,0.00003284237,0.00023488412,0.000021313066,0.00014894654,0.00016480604,0.000015272857,0.000053931777],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9971238,0.00092642097,0.000655411,0.000436887,0.00042617714,0.00043134356],"domain_scores_gemma":[0.99777627,0.0008877503,0.0004344946,0.0004154536,0.0002760343,0.00021001717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001595467,0.00021353064,0.00061267347,0.00030305053,0.00021196331,0.000058886453,0.00013377507,0.00006358661,0.000011513703],"category_scores_gemma":[0.0025946144,0.00015230627,0.00004519078,0.0009122467,0.00015811838,0.00020753757,0.00013510107,0.0002889559,9.353146e-7],"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":[0.00025219368,0.0004101522,0.9950536,0.00049374864,0.000081943486,0.000020283142,0.001833275,0.00001967834,0.0000037618527,0.000068568246,0.00015829506,0.001604505],"study_design_scores_gemma":[0.002044227,0.000633425,0.99383676,0.00031656935,0.0000051384327,0.000004582141,0.00201835,0.0008460691,9.006086e-7,0.000016496695,0.000154476,0.00012301086],"about_ca_topic_score_codex":0.007897915,"about_ca_topic_score_gemma":0.06552993,"teacher_disagreement_score":0.057632014,"about_ca_system_score_codex":0.00026835254,"about_ca_system_score_gemma":0.000807902,"threshold_uncertainty_score":0.9987086},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96735054,0.00013325292,0.0010105168,0.027362477,0.00039873563,0.0018310053,0.00022620936,0.00023975645,0.0014475117],"genre_scores_gemma":[0.983543,0.00008273309,0.0004951699,0.014873825,0.00007700983,0.00038367818,0.000523077,0.000011196306,0.000010302441],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9928806,0.0028074894,0.002709869,0.00042957117,0.0003918636,0.0007806357],"domain_scores_gemma":[0.9964134,0.00081099954,0.0014380587,0.0007532125,0.0003315963,0.00025270556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0058460683,0.00028943925,0.0018077882,0.00061736157,0.00022215994,0.00001254309,0.00031169385,0.000244637,0.000045884677],"category_scores_gemma":[0.002348757,0.00026429154,0.00018281973,0.00056224916,0.00018547312,0.00025739134,0.00031807515,0.00065872906,0.000020678539],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000348984,0.0009417246,0.95578825,0.00082372,0.00019653808,0.0000019069458,0.0044157645,0.00004822081,0.0000067349406,0.011357776,0.015361032,0.01070933],"study_design_scores_gemma":[0.003382867,0.0005822578,0.98830026,0.000056986784,0.000011736747,0.0000034604986,0.0039651603,0.00011252387,9.488208e-7,0.0004204639,0.0030199331,0.00014339613],"about_ca_topic_score_codex":0.0003481584,"about_ca_topic_score_gemma":0.00018986737,"teacher_disagreement_score":0.032511998,"about_ca_system_score_codex":0.0008004353,"about_ca_system_score_gemma":0.0022642012,"threshold_uncertainty_score":0.9999809},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9764104,0.000055295266,0.00003286199,0.013063602,0.000025274763,0.0020687801,0.008052537,0.0000426186,0.0002486499],"genre_scores_gemma":[0.98591536,0.000021743735,0.000054473374,0.00046550998,0.00006205938,0.0003785186,0.012561515,0.000011842997,0.00052897364],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959729,0.0013717622,0.0004963025,0.00031265113,0.0013022025,0.0005442164],"domain_scores_gemma":[0.9955685,0.0032683292,0.000045947392,0.0003656158,0.0004829717,0.00026863627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030463354,0.0001576543,0.0005014743,0.0008301291,0.000284057,0.00008152298,0.00040759603,0.00012894615,0.00013282376],"category_scores_gemma":[0.0010661088,0.00010378872,0.000052976076,0.001924589,0.00018631083,0.00015910824,0.0004655578,0.0007612008,0.00005294799],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012024104,0.005209131,0.8734184,0.00007691558,0.00048739734,0.000037037586,0.023264252,0.000042636937,0.000009748369,0.00019511847,0.07030549,0.025751485],"study_design_scores_gemma":[0.0025693744,0.0026736571,0.98457766,0.00011096093,0.000023824161,4.5482483e-7,0.0038434537,0.0018764628,0.000009536864,0.0001934548,0.0040271194,0.00009405856],"about_ca_topic_score_codex":0.0007453881,"about_ca_topic_score_gemma":0.001333304,"teacher_disagreement_score":0.11115926,"about_ca_system_score_codex":0.00031849465,"about_ca_system_score_gemma":0.00023551984,"threshold_uncertainty_score":0.42323813},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8806934,0.09966327,0.00004612981,0.014267869,0.00034812983,0.0027100227,0.00041580023,0.000105786545,0.0017496126],"genre_scores_gemma":[0.9477471,0.050248288,0.000083105035,0.0010587805,0.000057244193,0.0003422488,0.00041180814,0.000025969426,0.000025471207],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99372643,0.0023344115,0.0012641874,0.0009122539,0.0008934395,0.00086928444],"domain_scores_gemma":[0.9966923,0.0010687521,0.00047318664,0.00088915013,0.0005586373,0.00031795743],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0064943535,0.0003864122,0.001340253,0.0005349905,0.00016507531,0.00009498447,0.0003111533,0.0001911483,0.00000871887],"category_scores_gemma":[0.0006954825,0.00032908656,0.00007243776,0.0013603832,0.00028526696,0.00025103005,0.00015810145,0.000458246,0.0000031749619],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021824875,0.00015518116,0.9630044,0.0053278026,0.00007936204,0.000046407058,0.0012244273,0.000004649534,0.0000215896,0.0016002136,0.0006331943,0.027684497],"study_design_scores_gemma":[0.003111195,0.00014160179,0.97532165,0.0004767737,0.000003199443,0.000082311395,0.0021762145,0.002086833,8.125398e-7,0.00005845346,0.016304916,0.00023601728],"about_ca_topic_score_codex":0.0010039383,"about_ca_topic_score_gemma":0.009489907,"teacher_disagreement_score":0.067053705,"about_ca_system_score_codex":0.00046922328,"about_ca_system_score_gemma":0.0015286227,"threshold_uncertainty_score":0.99991614},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6569024,0.0010606633,0.021077244,0.11203438,0.0027985158,0.009027811,0.0007715399,0.0010850944,0.19524236],"genre_scores_gemma":[0.95754087,0.00004733078,0.0018042903,0.03326831,0.00014144104,0.00021907162,0.005115352,0.000015718797,0.0018476184],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721956,0.000094878844,0.00089985173,0.00015992971,0.0013919794,0.00023379894],"domain_scores_gemma":[0.9981032,0.00016915661,0.00013634353,0.0010879791,0.0002754307,0.00022789907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025633764,0.0001250697,0.000314771,0.00026672755,0.00003729315,0.000030174355,0.00041152647,0.00015490453,0.00080088567],"category_scores_gemma":[0.004415623,0.000108635555,0.000037181402,0.0005743309,0.00011171916,0.00044354162,0.00030830092,0.00040883434,0.00017448633],"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":[0.00014568171,0.00043078582,0.040805936,0.0008631044,0.00009091334,0.00004156045,0.0016469484,0.000007533534,0.0000051253774,0.002324296,0.60688597,0.34675214],"study_design_scores_gemma":[0.004765363,0.000065001,0.17604405,0.0010077475,0.000091242866,0.000019892213,0.0011152035,0.45511907,0.000023926648,0.0005287031,0.3610448,0.00017498834],"about_ca_topic_score_codex":0.000016525786,"about_ca_topic_score_gemma":0.00005607338,"teacher_disagreement_score":0.45511153,"about_ca_system_score_codex":0.00019760433,"about_ca_system_score_gemma":0.0012878532,"threshold_uncertainty_score":0.87691367},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97545516,0.008668182,0.0026869613,0.007248034,0.00024836624,0.003904218,0.0006556316,0.00032749315,0.0008059552],"genre_scores_gemma":[0.9961008,0.0006455613,0.00012228945,0.0020357054,0.00007326373,0.00028901614,0.00035196313,0.000055908633,0.00032548196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9942112,0.001588444,0.0013147052,0.0011577714,0.0008158388,0.00091205013],"domain_scores_gemma":[0.9953134,0.00092638173,0.0007754973,0.0010134074,0.00068664603,0.0012846588],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035199774,0.0005475989,0.0019723473,0.0003683143,0.000392627,0.00020614333,0.00029067515,0.00017718814,0.000021798809],"category_scores_gemma":[0.0013406194,0.0004609962,0.00007093238,0.0015980132,0.00049488625,0.00031662302,0.0002208851,0.00034233707,0.000004570963],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070117833,0.0009395839,0.9875923,0.0023818412,0.00019114405,0.000019054167,0.0010488301,0.000030390318,0.00003973826,0.00045581377,0.0044870675,0.0021130505],"study_design_scores_gemma":[0.0036923904,0.001312304,0.9583792,0.00014031165,0.0000050519166,0.000036810023,0.0017270551,0.001279159,0.0000043751106,0.000017424094,0.032983348,0.00042257714],"about_ca_topic_score_codex":0.0028041394,"about_ca_topic_score_gemma":0.006379837,"teacher_disagreement_score":0.029213116,"about_ca_system_score_codex":0.00030065654,"about_ca_system_score_gemma":0.0021655336,"threshold_uncertainty_score":0.9997842},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6982084,0.028351046,0.15243725,0.040550765,0.0052366303,0.031466607,0.033253685,0.0012198542,0.009275733],"genre_scores_gemma":[0.97188896,0.000241167,0.001819443,0.00053051824,0.000047354388,0.0005926659,0.023787856,0.000037611182,0.0010544215],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99712384,0.00016542996,0.0013976104,0.00067534123,0.00023609733,0.0004016732],"domain_scores_gemma":[0.995191,0.000388029,0.0010454566,0.00250709,0.00058992114,0.0002785009],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014267967,0.00023421248,0.0006308421,0.0001407047,0.00036767643,0.000056945264,0.00026656679,0.00007991265,0.000009193697],"category_scores_gemma":[0.005864298,0.00026397838,0.00011283208,0.00027676835,0.000069203044,0.00025287553,0.0003021622,0.00020347764,2.1140478e-7],"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":[0.0018483781,0.0003792015,0.8564144,0.0022437973,0.00083383475,0.00060426834,0.00010674785,0.000050825714,0.0004912657,0.00012699474,0.1175228,0.019377531],"study_design_scores_gemma":[0.0065160184,0.00011389849,0.63365096,0.0010848059,0.0009748191,0.000080345446,0.00035409426,0.020739978,0.00003276184,0.0002604405,0.33570844,0.00048342478],"about_ca_topic_score_codex":0.033652056,"about_ca_topic_score_gemma":0.4623183,"teacher_disagreement_score":0.42866623,"about_ca_system_score_codex":0.0006250595,"about_ca_system_score_gemma":0.00362956,"threshold_uncertainty_score":0.9999812},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9750322,0.007860715,0.0039335308,0.0064626466,0.0007576281,0.0038884508,0.00047026822,0.00039120548,0.0012033297],"genre_scores_gemma":[0.99638647,0.0006066211,0.00007845771,0.0015474211,0.00009597272,0.00028509292,0.00031954932,0.000051028837,0.0006294014],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9945855,0.0015101599,0.0012302013,0.0011045272,0.00076017657,0.00080943014],"domain_scores_gemma":[0.99541664,0.00103493,0.0007573212,0.00091039523,0.00073114777,0.0011495549],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003088764,0.00051588274,0.001769428,0.00035509482,0.00043816143,0.00020420805,0.00024531674,0.00017417304,0.000024375777],"category_scores_gemma":[0.0014712629,0.00044181602,0.00006858183,0.001693488,0.00045476056,0.00031596472,0.00018304905,0.00036407993,0.000004507739],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007487546,0.0008562737,0.9796758,0.0016311816,0.0001761492,0.00001563759,0.0009894139,0.000056648176,0.000020740961,0.00020510466,0.0128194485,0.0028047988],"study_design_scores_gemma":[0.003303806,0.0013831859,0.9481452,0.00015667577,0.000005139671,0.00005855348,0.0020498026,0.0030816593,0.000004260802,0.000008968565,0.041400112,0.0004026177],"about_ca_topic_score_codex":0.0032631424,"about_ca_topic_score_gemma":0.007587082,"teacher_disagreement_score":0.03153063,"about_ca_system_score_codex":0.00033555916,"about_ca_system_score_gemma":0.0021304497,"threshold_uncertainty_score":0.99980336},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89302677,0.0007986079,0.007804875,0.007577973,0.0016645171,0.00089535315,0.001489196,0.00030340606,0.08643931],"genre_scores_gemma":[0.9897437,0.00014814759,0.0010977517,0.00054163917,0.00027219745,0.000009974568,0.0074564493,0.0000184632,0.0007116924],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975385,0.00049051264,0.0007366181,0.00041377963,0.00059948437,0.00022111628],"domain_scores_gemma":[0.9985076,0.00032656145,0.0003634435,0.00038077228,0.0002571563,0.00016447212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008181836,0.00017786714,0.0005757086,0.0022175533,0.00019467127,0.00010190371,0.0008817911,0.000037566115,0.0007638347],"category_scores_gemma":[0.00060338323,0.00017012811,0.00013696724,0.0011120336,0.000054462562,0.00019850767,0.00050267513,0.00057293364,0.00006675212],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027074013,0.008713676,0.38940784,0.00062803447,0.036449894,0.00084664277,0.04072751,0.041038968,0.0006135913,0.03419449,0.06308757,0.38158438],"study_design_scores_gemma":[0.0024438177,0.00013107123,0.877652,0.00092100975,0.00072192895,0.0000034992843,0.005526586,0.07449451,0.0000039379706,0.0003640002,0.03754562,0.00019203855],"about_ca_topic_score_codex":0.00008183262,"about_ca_topic_score_gemma":0.0007415878,"teacher_disagreement_score":0.48824415,"about_ca_system_score_codex":0.00035862104,"about_ca_system_score_gemma":0.00012345036,"threshold_uncertainty_score":0.8363454},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9173901,0.007474463,0.0014860295,0.0203066,0.0011905911,0.025182797,0.025602233,0.00037132078,0.0009958401],"genre_scores_gemma":[0.9576697,0.00077739963,0.0005830247,0.012579327,0.0003663144,0.0040867385,0.02339621,0.00007376044,0.00046754218],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99172455,0.0029009169,0.0014432949,0.0018851014,0.0007733628,0.0012727784],"domain_scores_gemma":[0.9923546,0.0020149292,0.000648186,0.003998461,0.00037787895,0.0006059992],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00969808,0.0007245189,0.0014389005,0.00056855404,0.00045723264,0.00040237373,0.0020112707,0.000302339,0.0000116554575],"category_scores_gemma":[0.0025000724,0.00046913084,0.00012605872,0.001292611,0.00029441665,0.00020239218,0.002106659,0.0016395755,0.000004646885],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007463042,0.0013674799,0.9202799,0.0026767522,0.00043810255,0.000039524828,0.020235067,0.00012397776,2.9448157e-7,0.00014639615,0.015694302,0.03825185],"study_design_scores_gemma":[0.0038461653,0.00050304504,0.799446,0.00024162832,0.000021722675,0.000010364371,0.028303202,0.026128957,2.492599e-8,0.000080954065,0.1409362,0.00048174942],"about_ca_topic_score_codex":0.0035728072,"about_ca_topic_score_gemma":0.0046390938,"teacher_disagreement_score":0.12524189,"about_ca_system_score_codex":0.00036557703,"about_ca_system_score_gemma":0.0040164953,"threshold_uncertainty_score":0.99977607},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001088747,0.00019407812,0.0018852815,0.000017796336,0.00009239967,0.0008354507,0.0016023645,0.00008425149,0.99419963],"genre_scores_gemma":[0.005137725,0.0002840238,0.0006182128,0.00007950217,0.000058081816,0.0000019383363,0.0003114975,0.00003207536,0.9934769],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900115,0.000013003854,0.00014787406,0.0004688311,0.00016525315,0.00020390374],"domain_scores_gemma":[0.9992011,0.000091712776,0.000116769654,0.00039864288,0.000025318144,0.00016646582],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000051490755,0.00027033233,0.0003836,0.00011478526,0.0001101218,0.000020694535,0.00018034554,0.00019904767,0.000052368137],"category_scores_gemma":[0.000009272494,0.00030369562,0.00015648472,0.000003007763,0.00022841494,0.00006117998,0.0003748332,0.0002505001,0.000001739663],"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":[0.0048794406,0.00011768994,0.0020336548,0.0022294489,0.0019486134,0.00078736234,0.0013565061,0.0000044602994,0.00026649045,0.41456485,0.0092354575,0.56257606],"study_design_scores_gemma":[0.0006033563,0.00012861444,0.00041301086,0.00033104175,0.00038982587,0.000006727818,0.00005324226,0.00006905118,0.000043394703,0.00015275498,0.9975514,0.00025760324],"about_ca_topic_score_codex":0.0000072488347,"about_ca_topic_score_gemma":0.0000035911394,"teacher_disagreement_score":0.98831594,"about_ca_system_score_codex":0.00011827636,"about_ca_system_score_gemma":0.00005145499,"threshold_uncertainty_score":0.9999415},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9917949,0.0005020391,0.002657552,0.00007766143,0.0031649256,0.00044698347,0.00025004405,0.0000940805,0.0010118379],"genre_scores_gemma":[0.99260217,0.000015087531,0.004296405,0.000009849056,0.00005768475,0.0000071960144,0.0013923116,0.000016737282,0.0016025624],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9972441,0.00009221054,0.00092016417,0.00094638823,0.00044294784,0.0003541659],"domain_scores_gemma":[0.9971118,0.00009058684,0.0005288689,0.0017813065,0.00032835006,0.00015911827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024062651,0.00018099422,0.00039837914,0.00030059527,0.0002893919,0.00012637737,0.00018029596,0.000032867316,0.00015910105],"category_scores_gemma":[0.0019411264,0.00017918815,0.00006803048,0.0009236044,0.00021525542,0.00040657676,0.00057029905,0.00014905733,0.0000071288046],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017202163,0.00017346923,0.94283223,0.0002748784,0.000058420675,0.0003565275,0.000022023552,0.00016532703,0.029773127,0.000018312701,0.025827654,0.00048080014],"study_design_scores_gemma":[0.0009589678,0.00011975306,0.95681965,0.0017856683,0.00033303598,0.006243741,0.00014979701,0.017332748,0.0027537344,0.00082528585,0.012148359,0.00052922784],"about_ca_topic_score_codex":0.00035247626,"about_ca_topic_score_gemma":0.00017733379,"teacher_disagreement_score":0.027019393,"about_ca_system_score_codex":0.0000849177,"about_ca_system_score_gemma":0.000438767,"threshold_uncertainty_score":0.73070806},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000059281145,0.9723911,0.000022825576,0.0013041659,0.0009794233,0.018761953,0.00627274,0.00010266187,0.000105802756],"genre_scores_gemma":[0.000034734716,0.98736304,0.00015079173,0.004725685,0.0000448404,0.0004389492,0.0071912156,0.0000290759,0.000021656677],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99263644,0.0013547156,0.003480883,0.0005945977,0.0010190537,0.0009143356],"domain_scores_gemma":[0.9954913,0.0002130751,0.0024354793,0.0011189305,0.00023157524,0.00050964154],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002269014,0.00067249784,0.0038782435,0.0002993409,0.00040137934,0.00010286795,0.0005337384,0.0001618197,0.0000025210356],"category_scores_gemma":[0.00019160227,0.00052268396,0.0004675168,0.0020983918,0.00009929133,0.0005120496,0.0005367397,0.00052372925,0.000038007453],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005528411,0.00011556198,0.0006390176,0.52092224,0.00014076571,0.000003278714,0.000036274327,9.1135365e-7,5.9547105e-9,0.000088978864,0.010667572,0.46733013],"study_design_scores_gemma":[0.0006659026,0.00014409324,0.00040183906,0.5491133,0.00012564589,0.000065583765,0.000088143795,0.000052981544,8.877784e-9,1.3232737e-7,0.44914427,0.0001981015],"about_ca_topic_score_codex":0.005355908,"about_ca_topic_score_gemma":0.00066701346,"teacher_disagreement_score":0.46713203,"about_ca_system_score_codex":0.0032416924,"about_ca_system_score_gemma":0.0059940577,"threshold_uncertainty_score":0.9997225},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98015624,0.0010649548,0.01077679,0.0043450203,0.000118651005,0.0027287449,0.00027884677,0.00007336336,0.00045740663],"genre_scores_gemma":[0.99657494,0.00016119205,0.00028130534,0.0024899552,0.0000062010577,0.000034146928,0.00034821595,0.0000062040517,0.00009783044],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980892,0.0002900081,0.0007516679,0.00029257932,0.00036480097,0.00021174968],"domain_scores_gemma":[0.9987699,0.000053005362,0.0003410253,0.00028839722,0.00022612806,0.00032158123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078324776,0.0001435542,0.0006099489,0.00042434875,0.00020208438,0.00004551608,0.00005996221,0.00006748881,0.000026370839],"category_scores_gemma":[0.000234318,0.000133493,0.00005319786,0.0015880644,0.000023631866,0.00013293975,0.000070748014,0.00011939355,0.0000015095695],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102353784,0.00049790123,0.9203267,0.000350009,0.0008813555,0.0000018339929,0.06788993,0.00010138546,0.0000047389835,0.0016704254,0.0015801864,0.0065931478],"study_design_scores_gemma":[0.003102338,0.0005632993,0.93114,0.000585255,0.00024916127,0.0000017557901,0.06361215,0.00033269223,0.0000050604704,0.000045418532,0.00025656572,0.00010628293],"about_ca_topic_score_codex":0.005085439,"about_ca_topic_score_gemma":0.0045306454,"teacher_disagreement_score":0.016418727,"about_ca_system_score_codex":0.0003061449,"about_ca_system_score_gemma":0.0007168267,"threshold_uncertainty_score":0.7687697},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99133825,0.0017305127,0.0015354978,0.0034229823,0.00005286014,0.00090920244,0.0007059795,0.000072725954,0.00023196932],"genre_scores_gemma":[0.99515355,0.00018073138,0.000038853203,0.0038608809,0.000018268176,0.000052309584,0.0006054078,0.000012709404,0.00007727124],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99715525,0.00073906645,0.00075917196,0.00048549566,0.00035739987,0.0005036036],"domain_scores_gemma":[0.99654347,0.0018239385,0.00029243616,0.0005320048,0.00017939549,0.00062874216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016667417,0.00022875256,0.00096753176,0.0005084437,0.00013590038,0.000104737,0.0001528656,0.00009822555,0.00002220425],"category_scores_gemma":[0.0015644968,0.00017484662,0.00010396904,0.0018221831,0.0002698713,0.00016469603,0.00008837742,0.0002444333,0.0000011067506],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022832956,0.00011304967,0.98568404,0.0008260202,0.00016958879,0.0000062590275,0.00040441286,0.000026657379,0.0000042104775,0.000027286951,0.0005877313,0.011922402],"study_design_scores_gemma":[0.0022011953,0.00012154341,0.9864925,0.000044542612,0.000008335809,0.0000038367084,0.00018779171,0.00593128,2.034215e-7,0.000017036056,0.0048577823,0.00013395415],"about_ca_topic_score_codex":0.001980465,"about_ca_topic_score_gemma":0.004998153,"teacher_disagreement_score":0.011788448,"about_ca_system_score_codex":0.00015213822,"about_ca_system_score_gemma":0.0015854525,"threshold_uncertainty_score":0.7130038},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36820704,0.0065722913,0.49832648,0.11446268,0.0019898345,0.0013557412,0.007262976,0.00005967582,0.0017632641],"genre_scores_gemma":[0.9892878,0.00032360983,0.007772351,0.0013649145,0.00025166085,0.00002837988,0.0007657265,0.000017399843,0.00018814534],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99418133,0.00050852477,0.0012238915,0.00021622004,0.0034837003,0.00038633135],"domain_scores_gemma":[0.99428314,0.002920421,0.00035027115,0.00012641697,0.0020136072,0.00030611834],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008113445,0.000112124006,0.0004458294,0.0021673946,0.000046077308,0.000031445114,0.00046117278,0.00011294516,0.00031423196],"category_scores_gemma":[0.022353258,0.0001022008,0.000092595525,0.00075580314,0.00030737667,0.00010412424,0.00009618003,0.001407409,0.0000012254153],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004082858,0.001199536,0.7298343,0.00032090876,0.00025771416,0.0006821441,0.00022005643,0.0013889554,0.000036310223,0.0075388686,0.030566532,0.22387178],"study_design_scores_gemma":[0.010367521,0.0009966483,0.7754165,0.0028677257,0.000011065088,0.000067624045,0.00011426795,0.14142238,0.000011425396,0.0027350383,0.06590503,0.00008476827],"about_ca_topic_score_codex":0.00009310341,"about_ca_topic_score_gemma":0.00010170209,"teacher_disagreement_score":0.62108076,"about_ca_system_score_codex":0.00078237377,"about_ca_system_score_gemma":0.003994769,"threshold_uncertainty_score":0.98588187},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30240467,0.019767432,0.0073234704,0.3598879,0.00047133886,0.022533715,0.2868957,0.00035170812,0.00036409587],"genre_scores_gemma":[0.028509365,0.0062879035,0.13326663,0.0057713734,0.00091464916,0.00024333988,0.7963025,0.00025502994,0.028449202],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99233156,0.0013584862,0.0010340235,0.0016788702,0.0029578763,0.00063918275],"domain_scores_gemma":[0.986218,0.0003760864,0.0002884116,0.006904859,0.0053935023,0.0008191926],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026342513,0.00022830698,0.00086613523,0.00016596403,0.0002852323,0.000060808048,0.0010151161,0.000052327756,0.00018878441],"category_scores_gemma":[0.019391103,0.00019066528,0.00004060326,0.0006584098,0.00011999563,0.00049237953,0.0037592389,0.0003900922,0.000016216924],"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":[0.0010077857,0.0011267376,0.003387697,0.0028171963,0.00013187903,0.0004143304,0.00033126472,0.0000021634517,0.0010054256,0.00000544569,0.9853607,0.004409414],"study_design_scores_gemma":[0.002075796,0.001099581,0.002197099,0.0015442123,0.00008521127,0.000097374264,0.0061167204,0.0017715016,0.00012872189,0.0000029426292,0.9846886,0.00019226389],"about_ca_topic_score_codex":0.000593715,"about_ca_topic_score_gemma":0.0058433283,"teacher_disagreement_score":0.5094068,"about_ca_system_score_codex":0.00028984735,"about_ca_system_score_gemma":0.0023456349,"threshold_uncertainty_score":0.988869},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3014394,0.019291509,0.0074061984,0.35820806,0.00046437865,0.022361483,0.2901274,0.00034851194,0.00035307455],"genre_scores_gemma":[0.028947352,0.0061332085,0.13600972,0.005769873,0.0009017845,0.00024780238,0.79338,0.00025531597,0.028354893],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9923305,0.001358736,0.0010334416,0.0016798327,0.0029581694,0.00063931826],"domain_scores_gemma":[0.9861768,0.00038154452,0.00028828793,0.0069236048,0.0054097134,0.00082004286],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.026476152,0.00022837591,0.00086593407,0.00016606723,0.00028521245,0.000060683018,0.0010156522,0.00005232747,0.00019222421],"category_scores_gemma":[0.019611552,0.00019072446,0.000040610717,0.00065889576,0.0001199972,0.00049205957,0.0037597392,0.00039017748,0.000016382914],"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":[0.0010110271,0.0011248708,0.0034857597,0.002787732,0.00013161868,0.00040305415,0.0003303491,0.0000021493936,0.0010375592,0.000005345716,0.98539305,0.004287474],"study_design_scores_gemma":[0.00206677,0.0011017271,0.0022358678,0.0015657956,0.00008568567,0.00009737463,0.0061893407,0.0017865654,0.00012975982,0.0000029267196,0.9845451,0.00019305423],"about_ca_topic_score_codex":0.0005880931,"about_ca_topic_score_gemma":0.0058108717,"teacher_disagreement_score":0.5032526,"about_ca_system_score_codex":0.00028976856,"about_ca_system_score_gemma":0.0023555653,"threshold_uncertainty_score":0.9886467},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003880587,0.691351,0.08848335,0.19229235,0.000712647,0.013522408,0.006643406,0.0007616915,0.00235255],"genre_scores_gemma":[0.394935,0.58150756,0.006482246,0.011358376,0.00012826141,0.000263598,0.0051227887,0.000073364616,0.0001287694],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.994735,0.00089709274,0.0019702218,0.0008751048,0.00070434174,0.00081822684],"domain_scores_gemma":[0.9950615,0.00047995345,0.0009524307,0.0020234645,0.00039647566,0.0010861318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006857629,0.00029628424,0.0016978593,0.00048770255,0.00013172676,0.00004807195,0.00048971316,0.000048154146,0.000025077099],"category_scores_gemma":[0.007460094,0.00024395177,0.000054346383,0.0022098783,0.00024992807,0.001049246,0.00025558154,0.0002834981,0.000004922352],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015862181,0.00069547165,0.07391051,0.86433476,0.00015599295,0.00002598931,0.00014194676,0.00003216847,0.0000014559366,0.0067726285,0.002034454,0.05173598],"study_design_scores_gemma":[0.0025625683,0.002194357,0.09037264,0.7847107,0.00038966446,0.00011854188,0.0026359633,0.06359227,0.00000244411,0.0035298413,0.048494015,0.001397012],"about_ca_topic_score_codex":0.000120489225,"about_ca_topic_score_gemma":0.0005378111,"teacher_disagreement_score":0.39105442,"about_ca_system_score_codex":0.0006430868,"about_ca_system_score_gemma":0.0061274343,"threshold_uncertainty_score":0.9995069},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9352942,0.009669181,0.0042060157,0.039393585,0.00028393467,0.010124584,0.0005358618,0.0003975725,0.00009508157],"genre_scores_gemma":[0.99136513,0.0031268874,0.00045304454,0.002184907,0.00012257829,0.0014986142,0.0006656864,0.00008471329,0.00049843965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9874901,0.0052377074,0.0015577283,0.0018348024,0.001019481,0.0028601338],"domain_scores_gemma":[0.9908203,0.004714968,0.00042031874,0.001228487,0.00088028185,0.0019356429],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.030316897,0.0005824724,0.0016429288,0.0014038632,0.0008220986,0.0004102134,0.00041424035,0.00020505817,0.000016997987],"category_scores_gemma":[0.009891934,0.0005785077,0.00010367493,0.0031552585,0.00034504148,0.0006346232,0.00025494662,0.001274322,0.0000054107677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.021963084,0.001461524,0.90325546,0.0017954443,0.0001195457,0.000029714693,0.00083194365,0.000017208735,0.000037355465,0.00067993224,0.0068308297,0.06297798],"study_design_scores_gemma":[0.006607872,0.0026953197,0.94268215,0.00022951225,0.0000016765524,0.000012829377,0.0019517915,0.0037523904,0.0000010920671,0.0002912472,0.04134276,0.00043136347],"about_ca_topic_score_codex":0.0010853156,"about_ca_topic_score_gemma":0.0066301683,"teacher_disagreement_score":0.06254661,"about_ca_system_score_codex":0.002016276,"about_ca_system_score_gemma":0.011679968,"threshold_uncertainty_score":0.99966663},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9109114,0.005744578,0.0034819595,0.029712308,0.0026880102,0.018209452,0.02611001,0.0002775802,0.00286471],"genre_scores_gemma":[0.99508977,0.00014036955,0.00043195955,0.000981164,0.00021704132,0.00053034426,0.0014752545,0.000050083643,0.0010839872],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.9921162,0.0020792407,0.0026788847,0.0013329072,0.00083026034,0.00096247497],"domain_scores_gemma":[0.99373335,0.001230294,0.000919605,0.0029375972,0.0008300217,0.00034914297],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027964972,0.00051263254,0.0017319963,0.0008270896,0.0005034344,0.00010699061,0.0017457324,0.00020055853,0.000347788],"category_scores_gemma":[0.008129453,0.0004061918,0.00048336637,0.0027718155,0.0003464879,0.00043335682,0.0022540702,0.0005985085,0.000015222655],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062464224,0.09478016,0.1446874,0.021325998,0.013009789,0.0020053294,0.09035549,0.00014143149,0.00015751115,0.023800772,0.3590446,0.24444509],"study_design_scores_gemma":[0.09926907,0.005389748,0.24161638,0.012949769,0.005095595,0.00043261438,0.5252559,0.07540366,0.00004270509,0.0049137156,0.02727682,0.0023539907],"about_ca_topic_score_codex":0.0095945,"about_ca_topic_score_gemma":0.3301891,"teacher_disagreement_score":0.43490043,"about_ca_system_score_codex":0.00069687254,"about_ca_system_score_gemma":0.005258645,"threshold_uncertainty_score":0.999839},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69603735,0.018826295,0.1824131,0.0028507104,0.0017433506,0.0043361844,0.08572805,0.00016642967,0.007898517],"genre_scores_gemma":[0.98319995,0.010538124,0.0041432437,0.00030654704,0.00013832332,0.000045285902,0.0014762172,0.00003950713,0.000112803566],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966587,0.00026869605,0.0014464108,0.00055280403,0.00062992016,0.00044346973],"domain_scores_gemma":[0.9955333,0.0016345453,0.0008118996,0.0011343451,0.00053485134,0.00035106594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000787686,0.00043149976,0.0012344493,0.00029501526,0.0001661972,0.000028253593,0.00047361126,0.00014833943,0.0008083814],"category_scores_gemma":[0.00037232766,0.0003371743,0.0002951306,0.0007485032,0.00066361733,0.00006323919,0.00009944965,0.00034623515,0.000016930786],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.059359215,0.0012397579,0.005592129,0.0030071044,0.0023052578,0.000032978147,0.0011345149,0.00045525303,0.006644725,0.013409253,0.02085309,0.8859667],"study_design_scores_gemma":[0.045427702,0.0012406383,0.37034175,0.009881494,0.010875222,0.0000045716306,0.0031654753,0.04925058,0.035816204,0.44672638,0.025140429,0.0021295494],"about_ca_topic_score_codex":0.00014222735,"about_ca_topic_score_gemma":0.00003084203,"teacher_disagreement_score":0.88383716,"about_ca_system_score_codex":0.000014662693,"about_ca_system_score_gemma":0.0005314408,"threshold_uncertainty_score":0.99990803},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.991428,0.0006687736,8.733211e-7,0.001882873,0.00011776889,0.0002510272,0.00041109094,0.000005929579,0.00523364],"genre_scores_gemma":[0.9988758,0.000133554,0.000008514384,0.0007796016,0.000018584813,0.000012354397,0.00004326229,0.000004027408,0.00012428426],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987538,0.00008881418,0.00043556787,0.00017974518,0.0003962253,0.00014588526],"domain_scores_gemma":[0.9987414,0.00013418052,0.00031195185,0.00070699345,0.00008034869,0.000025157744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047961614,0.00009798721,0.00041234656,0.00012714873,0.000020672718,0.0000071473733,0.00035423427,0.000049143113,0.00016948493],"category_scores_gemma":[0.0003110877,0.00005795469,0.00026881794,0.0007987861,0.00013471693,0.000042059655,0.000080925114,0.00016330105,0.0000013121681],"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":[0.00016022052,0.00027833116,0.99173564,0.00013247662,0.00086200325,0.000012320674,0.00025997026,0.0031564615,0.00039477993,0.000020989797,0.0020535334,0.00093329547],"study_design_scores_gemma":[0.00043207261,0.000018586677,0.9960016,0.00007286668,0.0009245549,3.9808972e-7,0.00021265642,0.00072178233,0.00029143205,0.000030858857,0.001244444,0.000048761958],"about_ca_topic_score_codex":0.043157086,"about_ca_topic_score_gemma":0.048935864,"teacher_disagreement_score":0.007447793,"about_ca_system_score_codex":0.000089121764,"about_ca_system_score_gemma":0.00021437515,"threshold_uncertainty_score":0.9684186},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7377039,0.032622237,0.00031347384,0.22201447,0.000793634,0.0050157923,0.0008190804,0.00024334657,0.00047405504],"genre_scores_gemma":[0.92047715,0.008343413,0.0023526691,0.06512507,0.00037260095,0.000108773645,0.0030185124,0.00006772772,0.00013407804],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99256,0.0022060245,0.0020954758,0.001828545,0.00017874874,0.0011311525],"domain_scores_gemma":[0.9955704,0.0012582752,0.00048266645,0.0021076398,0.00012348204,0.0004575249],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005794276,0.0005323447,0.002827557,0.00043729797,0.00026897338,0.00001565203,0.0007606124,0.0003171619,0.000028859879],"category_scores_gemma":[0.0049278587,0.00050136796,0.00011250859,0.00084856217,0.00048131784,0.0002304387,0.0010336224,0.0007327869,0.000019570736],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002581007,0.00061439135,0.63048893,0.21457091,0.00031848584,0.0007220734,0.0008643981,0.0000026711555,0.04732268,0.0030937032,0.089067124,0.010353622],"study_design_scores_gemma":[0.0039136275,0.00079264655,0.545764,0.42269406,0.00027554654,0.00026903476,0.00008569058,0.000027141152,0.00025602969,0.00012604194,0.024754433,0.0010416795],"about_ca_topic_score_codex":0.0017058611,"about_ca_topic_score_gemma":0.005201338,"teacher_disagreement_score":0.20812316,"about_ca_system_score_codex":0.0002254314,"about_ca_system_score_gemma":0.0005686385,"threshold_uncertainty_score":0.9997438},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9916218,0.00009917215,0.0017081569,0.0033559294,0.00039070303,0.0023055002,0.00010448307,0.000019287607,0.0003949755],"genre_scores_gemma":[0.9973213,0.0000048774305,0.0003477084,0.0019527097,0.00007566888,0.0002703637,0.000010375543,0.000009559712,0.000007434929],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99419475,0.003942327,0.00089433574,0.00034337482,0.00035415607,0.00027103923],"domain_scores_gemma":[0.99107254,0.007722575,0.00032505227,0.0004538217,0.00037115143,0.000054854536],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006134557,0.00022696618,0.0008034127,0.00014260977,0.00017518176,0.000007066649,0.00042939768,0.00013936993,0.000026222706],"category_scores_gemma":[0.008386791,0.00010795335,0.00016245138,0.00042646597,0.00066319003,0.000027790926,0.0005405631,0.00035523262,0.0000038851153],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007073608,0.00058292685,0.8898928,0.00020287669,0.0011413622,0.000004610381,0.0049254177,0.0042089815,0.0188242,0.058539663,0.0012438929,0.013359645],"study_design_scores_gemma":[0.0008770155,0.0008881534,0.9763573,0.00010676457,0.00030556764,0.000008354218,0.0012180969,0.007899028,0.008260356,0.0036906288,0.00024053083,0.00014824067],"about_ca_topic_score_codex":0.0001204907,"about_ca_topic_score_gemma":0.00007767825,"teacher_disagreement_score":0.08646444,"about_ca_system_score_codex":0.00015376176,"about_ca_system_score_gemma":0.00025789344,"threshold_uncertainty_score":0.99996597},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7197869,0.019095473,0.0004913862,0.17789221,0.0008186408,0.004356709,0.06814622,0.00027302062,0.009139438],"genre_scores_gemma":[0.98754686,0.00028686656,0.00031913185,0.00873243,0.000026718122,0.000023178589,0.0028875414,0.00001093981,0.00016632589],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99712056,0.00019386681,0.0006444775,0.000635694,0.0007038708,0.0007015534],"domain_scores_gemma":[0.99694735,0.000084656145,0.00019766454,0.001639728,0.00015859098,0.00097201916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057995203,0.00017827391,0.0005282704,0.000077399956,0.00005078759,0.000024998977,0.00077810994,0.000035614386,0.00006559747],"category_scores_gemma":[0.0019488364,0.00017532776,0.000026737234,0.0009940481,0.000075919306,0.0003332318,0.0005134939,0.00018643317,0.000008752298],"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":[0.000134684,0.0006412841,0.6451705,0.0014501993,0.000047691563,0.00006940299,0.000008716283,0.000004827112,0.000009510938,0.00061625225,0.3404629,0.011384028],"study_design_scores_gemma":[0.0017572658,0.00007625763,0.63753575,0.00094940176,0.000033726217,0.0000032576436,0.00019862656,0.0053825583,0.0000071051695,0.00008078554,0.3537298,0.00024546753],"about_ca_topic_score_codex":0.8299115,"about_ca_topic_score_gemma":0.8137622,"teacher_disagreement_score":0.26775998,"about_ca_system_score_codex":0.0028617622,"about_ca_system_score_gemma":0.062016856,"threshold_uncertainty_score":0.94330066},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96047693,0.00046418747,0.029229546,0.00763585,0.000111478665,0.0016950872,0.00018662469,0.000018327528,0.00018197708],"genre_scores_gemma":[0.9826578,0.000056418736,0.015685404,0.0010472737,0.000041206396,0.000013435167,0.0004831326,0.0000059264303,0.000009407477],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963895,0.00041185084,0.0020719557,0.00008503939,0.0008498028,0.00019181868],"domain_scores_gemma":[0.9976373,0.00012742149,0.0010703197,0.0004930813,0.0005086375,0.0001632415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016072106,0.000098597564,0.0004014745,0.00049244805,0.00007845162,0.00003395384,0.0003894312,0.000030988762,0.000002497908],"category_scores_gemma":[0.001770897,0.00006754049,0.000016943803,0.00062790466,0.00001761076,0.0003295601,0.00018085551,0.00033997092,0.0000017663648],"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":[0.00012061594,0.0010539624,0.21940075,0.0023719452,0.00013280979,0.0000021206101,0.014820075,0.00033736543,0.000005277157,0.00013153937,0.0013997741,0.76022375],"study_design_scores_gemma":[0.002541305,0.00063204096,0.5770957,0.00066834333,0.00007060533,0.00004493023,0.024000768,0.297775,0.0000018260433,0.000006690232,0.0970755,0.00008731334],"about_ca_topic_score_codex":0.000042207164,"about_ca_topic_score_gemma":0.00018441763,"teacher_disagreement_score":0.7601365,"about_ca_system_score_codex":0.00030624363,"about_ca_system_score_gemma":0.0024325852,"threshold_uncertainty_score":0.5570303},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97865033,0.00010591039,0.01889804,0.00066740834,0.00019799381,0.000334924,0.0008407326,0.000008881829,0.0002957934],"genre_scores_gemma":[0.9953709,0.00024864537,0.0036851403,0.000042649655,0.00040814775,0.000007443447,0.00021168604,0.000016445083,0.000008942698],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9967294,0.00011354563,0.0006840602,0.00020391124,0.0019823017,0.0002868333],"domain_scores_gemma":[0.99531287,0.00045739498,0.00031540604,0.00016076275,0.0034787578,0.00027478547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011483387,0.00012795243,0.0003748635,0.00032336035,0.00005976683,0.00006982467,0.0002932089,0.00009043775,0.00037552006],"category_scores_gemma":[0.0008278819,0.00011143403,0.000086715045,0.0002519877,0.00037509546,0.00012607856,0.00019823712,0.0004888379,0.000015444322],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0079062125,0.0016325251,0.51631427,0.00035999552,0.001972451,0.0003234029,0.00038275283,0.0005102855,0.09668298,0.007826064,0.010443359,0.35564572],"study_design_scores_gemma":[0.0031704495,0.0005362432,0.97108066,0.00016785001,0.00004502699,0.00016904113,0.00014027997,0.014623208,0.003601834,0.0045001386,0.0017690564,0.00019623658],"about_ca_topic_score_codex":0.0013518583,"about_ca_topic_score_gemma":0.00010046445,"teacher_disagreement_score":0.45476636,"about_ca_system_score_codex":0.00014654144,"about_ca_system_score_gemma":0.00029531444,"threshold_uncertainty_score":0.45441476},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009630074,0.01330776,0.000006367874,0.0003717644,0.00017281022,0.00079420296,0.91781324,0.0006612176,0.057242572],"genre_scores_gemma":[0.8069628,0.000013651054,0.00020152636,0.0001506881,0.00017332796,0.00011185331,0.19071712,0.000046838522,0.0016222371],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992792,0.000015174136,0.00017144473,0.00018370828,0.00020910474,0.00014134345],"domain_scores_gemma":[0.9994065,0.00013134761,0.000026538191,0.00030726014,0.00004255238,0.000085771884],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000045721317,0.000095699106,0.00020502628,0.00007558568,0.000014187604,0.00001811229,0.00010816852,0.000044658722,0.15627907],"category_scores_gemma":[0.0006528365,0.00007706875,0.00010999464,0.00021930879,0.0000072077623,0.0000814973,0.00007895385,0.00010553283,0.0034005279],"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":[0.00001554791,0.000042649757,0.00018897402,0.003286478,0.000063345375,0.00019156464,0.00006812916,2.493973e-7,0.00031844006,0.00009166161,0.9868036,0.008929363],"study_design_scores_gemma":[0.0011159035,0.00031826703,0.036576547,0.059156623,0.00017516669,0.00012753125,0.00012997462,0.0017045097,0.004790624,0.00057853846,0.8948387,0.0004875963],"about_ca_topic_score_codex":0.0000029571745,"about_ca_topic_score_gemma":0.0000042882243,"teacher_disagreement_score":0.7973327,"about_ca_system_score_codex":0.00002927293,"about_ca_system_score_gemma":0.000064596825,"threshold_uncertainty_score":0.9973754},"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":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.583446,0.00060893357,0.0001594212,0.0043702777,0.010002065,0.0053546177,0.11078287,0.003663648,0.28161213],"genre_scores_gemma":[0.15374039,0.0005447582,0.004921562,0.0007930485,0.0019962355,0.000021235006,0.14877197,0.0005465944,0.6886642],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99541414,0.00023537772,0.00050398585,0.002009562,0.001051445,0.0007854637],"domain_scores_gemma":[0.99496096,0.00043666418,0.00053699047,0.0030277397,0.0002470509,0.00079057284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029886496,0.0006616355,0.0010012791,0.0008472041,0.00032207917,0.0002121104,0.0017806911,0.00049875915,0.00043643848],"category_scores_gemma":[0.0009891656,0.0007080973,0.00021433519,0.0014846954,0.0000602195,0.0018633017,0.0013703213,0.00079084915,0.0005473749],"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":[0.0033798395,0.00012907674,0.0008017352,0.00032845992,0.00048573536,0.0006151671,0.0009592017,0.000022018221,0.0005461105,0.00001477744,0.98679984,0.005918061],"study_design_scores_gemma":[0.008682858,0.00042509928,0.01995511,0.0020869903,0.0011125111,0.0000017994153,0.0100977775,0.0029176637,0.000724953,0.00005207354,0.9523134,0.0016297168],"about_ca_topic_score_codex":0.0021323136,"about_ca_topic_score_gemma":0.3145257,"teacher_disagreement_score":0.42970565,"about_ca_system_score_codex":0.00024023566,"about_ca_system_score_gemma":0.001707023,"threshold_uncertainty_score":0.999537},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4278482,0.0023758993,0.006172255,0.020720584,0.0004756826,0.002326227,0.010582594,0.004841236,0.5246573],"genre_scores_gemma":[0.9810188,0.00029295043,0.00007141574,0.00062782253,0.00006586284,4.153253e-8,0.015776359,0.00053019694,0.0016165619],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99825,0.00023873417,0.00026079733,0.0006430195,0.00028224173,0.00032521592],"domain_scores_gemma":[0.99827707,0.000029849363,0.00007739196,0.001107752,0.00028992197,0.00021803213],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044237636,0.00017355954,0.00025029763,0.0004154403,0.0009503137,0.00038231118,0.00079441606,0.000055382265,0.0028316611],"category_scores_gemma":[0.0010781904,0.000168744,0.00004283882,0.0009779214,0.00016942516,0.0003041367,0.001815652,0.000301109,0.0010534334],"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":[0.00046865075,0.0014397921,0.0031147546,0.00019977163,0.00058199087,0.00004567621,0.00043093463,0.000026048423,0.0014038922,0.0032962218,0.4267841,0.5622082],"study_design_scores_gemma":[0.0023384627,0.0003056429,0.19245118,0.00014600474,0.00012957121,0.000029216435,0.00020948844,0.0017917819,0.00009395291,0.0000631879,0.8022191,0.00022241293],"about_ca_topic_score_codex":0.000038126083,"about_ca_topic_score_gemma":0.000003880557,"teacher_disagreement_score":0.56198573,"about_ca_system_score_codex":0.00009005746,"about_ca_system_score_gemma":0.000008080234,"threshold_uncertainty_score":0.9997244},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993588,0.000016011309,0.0020338567,0.000032602693,0.000243044,0.0016549221,0.00058860745,0.00021705868,0.0016259365],"genre_scores_gemma":[0.99805003,3.3042244e-7,0.00060295244,0.0003958442,0.00006990542,0.00047857402,0.00031669336,0.00003573926,0.00004992741],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99673086,0.00022460133,0.0013330503,0.00053743605,0.0005468489,0.00062717515],"domain_scores_gemma":[0.9956041,0.000033020282,0.00074589165,0.0031819865,0.00014228078,0.00029274513],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016007482,0.00034301588,0.000822397,0.00046217215,0.00020308793,0.00030644197,0.0009661854,0.00012863187,0.000013074286],"category_scores_gemma":[0.000444524,0.00031523407,0.00006128208,0.0005523512,0.00010401724,0.0018714678,0.0006963055,0.00026267456,0.00062270265],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037975804,0.000020550073,0.96506786,0.0008061374,0.000032424163,0.00002461966,0.030598728,0.002094228,0.000001333353,0.0000047505564,0.00014783794,0.0008217431],"study_design_scores_gemma":[0.002318656,0.0000435374,0.97453034,0.00096870575,0.000016952421,0.000026246793,0.008473874,0.011248592,0.000008204476,1.3454923e-7,0.0019727508,0.0003919991],"about_ca_topic_score_codex":0.00017393178,"about_ca_topic_score_gemma":0.000009502451,"teacher_disagreement_score":0.022124855,"about_ca_system_score_codex":0.00028576257,"about_ca_system_score_gemma":0.00011655432,"threshold_uncertainty_score":0.99992996},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949288,0.000013137262,0.00038342967,0.00022067588,0.00022657274,0.0007093739,0.002406037,0.0003578446,0.000754145],"genre_scores_gemma":[0.9990354,0.0000037508478,0.000015118973,0.00005247891,0.000018976561,0.00016425282,0.00030052048,0.0000136236795,0.00039585758],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979008,0.00012821305,0.0010290358,0.00015824666,0.00050084677,0.00028290163],"domain_scores_gemma":[0.9981783,0.00008479816,0.000471241,0.0007472969,0.00036943372,0.00014893127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014154401,0.00016096073,0.00037464802,0.00028145578,0.000073742056,0.00006377067,0.00016479348,0.00003839041,0.000012613849],"category_scores_gemma":[0.0007248299,0.00010608958,0.00008603925,0.0008047432,0.000079826284,0.0002760719,0.0000631811,0.0000862588,0.00053256954],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007352416,0.000003000319,0.98683363,0.0010082916,0.00007644243,0.00022314831,0.009119293,0.00025318106,6.9570297e-7,0.000017463522,0.00082057464,0.00090903975],"study_design_scores_gemma":[0.0007168583,0.00001581767,0.9820447,0.00057951413,0.000021187756,0.0000466008,0.0052793524,0.006195926,0.0000634961,0.0000018515225,0.0048944135,0.00014026873],"about_ca_topic_score_codex":0.000041625084,"about_ca_topic_score_gemma":0.000005025958,"teacher_disagreement_score":0.005942745,"about_ca_system_score_codex":0.00017156616,"about_ca_system_score_gemma":0.0002878177,"threshold_uncertainty_score":0.6845281},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.993588,0.000016011309,0.0020338567,0.000032602693,0.000243044,0.0016549221,0.00058860745,0.00021705868,0.0016259365],"genre_scores_gemma":[0.99805003,3.3042244e-7,0.00060295244,0.0003958442,0.00006990542,0.00047857402,0.00031669336,0.00003573926,0.00004992741],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99673086,0.00022460133,0.0013330503,0.00053743605,0.0005468489,0.00062717515],"domain_scores_gemma":[0.9956041,0.000033020282,0.00074589165,0.0031819865,0.00014228078,0.00029274513],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016007482,0.00034301588,0.000822397,0.00046217215,0.00020308793,0.00030644197,0.0009661854,0.00012863187,0.000013074286],"category_scores_gemma":[0.000444524,0.00031523407,0.00006128208,0.0005523512,0.00010401724,0.0018714678,0.0006963055,0.00026267456,0.00062270265],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037975804,0.000020550073,0.96506786,0.0008061374,0.000032424163,0.00002461966,0.030598728,0.002094228,0.000001333353,0.0000047505564,0.00014783794,0.0008217431],"study_design_scores_gemma":[0.002318656,0.0000435374,0.97453034,0.00096870575,0.000016952421,0.000026246793,0.008473874,0.011248592,0.000008204476,1.3454923e-7,0.0019727508,0.0003919991],"about_ca_topic_score_codex":0.00017393178,"about_ca_topic_score_gemma":0.000009502451,"teacher_disagreement_score":0.022124855,"about_ca_system_score_codex":0.00028576257,"about_ca_system_score_gemma":0.00011655432,"threshold_uncertainty_score":0.99992996},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99790955,0.000032502223,0.000106640044,0.000048013193,0.0001505877,0.000615335,0.00024055818,0.00009560692,0.0008012345],"genre_scores_gemma":[0.99961287,0.0000031448533,0.000062665305,0.00010121857,0.000064895794,0.000035714154,0.00004611633,0.0000062191325,0.000067155306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99866384,0.00008041647,0.00051043014,0.00020295949,0.00029103635,0.00025133113],"domain_scores_gemma":[0.999203,0.0000071736254,0.00016762408,0.00037976936,0.00010887632,0.00013354744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036875237,0.00016782108,0.00031848365,0.00045406332,0.000052936222,0.0000645179,0.000122069374,0.00009338891,0.000047275575],"category_scores_gemma":[0.00003730074,0.00013357706,0.000036650035,0.00032228415,0.00011617282,0.00049583387,0.00007363508,0.00011615126,0.00026843333],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007260377,0.000005432556,0.99042207,0.0004606388,0.000013066549,0.0000037706993,0.008826104,2.1175097e-7,4.8337716e-7,0.000020008418,0.000050862367,0.00012472493],"study_design_scores_gemma":[0.0013111632,0.00006654273,0.99643105,0.00059235643,0.000015266807,0.000042825082,0.00079357484,0.0005612357,0.000019498451,0.0000013639587,0.000025534979,0.00013959897],"about_ca_topic_score_codex":0.00007522381,"about_ca_topic_score_gemma":0.000017163535,"teacher_disagreement_score":0.008032529,"about_ca_system_score_codex":0.00006491751,"about_ca_system_score_gemma":0.00003854455,"threshold_uncertainty_score":0.5447115},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.942928,0.0000016089904,0.048941467,0.002677326,0.0004700048,0.0016285381,0.0015181794,0.0005419631,0.0012929233],"genre_scores_gemma":[0.99704427,9.542948e-8,0.0015480436,0.0008426117,0.00009482555,0.00021526054,0.00009620783,0.000020713585,0.0001379961],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99783665,0.00008305766,0.0007969684,0.0002683342,0.0005839118,0.00043109362],"domain_scores_gemma":[0.9981606,0.00003206674,0.00028532787,0.00085441605,0.00034276317,0.00032485346],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00061644835,0.00023963794,0.0004133545,0.00023237412,0.00007543487,0.00009453253,0.00014028522,0.00008039896,0.00019662149],"category_scores_gemma":[0.0002797508,0.00015772351,0.000117836644,0.00024840015,0.00002701396,0.00054903154,0.000084938154,0.00006826136,0.0067862407],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008045808,0.000022330945,0.980055,0.0008365252,0.0001478751,0.000083714935,0.0033803903,0.000011679838,0.000032367876,0.00031009986,0.01177822,0.0025372272],"study_design_scores_gemma":[0.006208663,0.0004885138,0.9686093,0.00080878375,0.00005320811,0.00016521072,0.0011598825,0.00052052195,0.00072428474,6.4686554e-7,0.020705635,0.00055535726],"about_ca_topic_score_codex":0.000006051273,"about_ca_topic_score_gemma":4.4656542e-7,"teacher_disagreement_score":0.054116257,"about_ca_system_score_codex":0.00040438224,"about_ca_system_score_gemma":0.00023098005,"threshold_uncertainty_score":0.9939871},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.942928,0.0000016089904,0.048941467,0.002677326,0.0004700048,0.0016285381,0.0015181794,0.0005419631,0.0012929233],"genre_scores_gemma":[0.99704427,9.542948e-8,0.0015480436,0.0008426117,0.00009482555,0.00021526054,0.00009620783,0.000020713585,0.0001379961],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99783665,0.00008305766,0.0007969684,0.0002683342,0.0005839118,0.00043109362],"domain_scores_gemma":[0.9981606,0.00003206674,0.00028532787,0.00085441605,0.00034276317,0.00032485346],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00061644835,0.00023963794,0.0004133545,0.00023237412,0.00007543487,0.00009453253,0.00014028522,0.00008039896,0.00019662149],"category_scores_gemma":[0.0002797508,0.00015772351,0.000117836644,0.00024840015,0.00002701396,0.00054903154,0.000084938154,0.00006826136,0.0067862407],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008045808,0.000022330945,0.980055,0.0008365252,0.0001478751,0.000083714935,0.0033803903,0.000011679838,0.000032367876,0.00031009986,0.01177822,0.0025372272],"study_design_scores_gemma":[0.006208663,0.0004885138,0.9686093,0.00080878375,0.00005320811,0.00016521072,0.0011598825,0.00052052195,0.00072428474,6.4686554e-7,0.020705635,0.00055535726],"about_ca_topic_score_codex":0.000006051273,"about_ca_topic_score_gemma":4.4656542e-7,"teacher_disagreement_score":0.054116257,"about_ca_system_score_codex":0.00040438224,"about_ca_system_score_gemma":0.00023098005,"threshold_uncertainty_score":0.9939871},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53645617,0.0000080955915,0.0011053627,0.0000058256173,0.000008246792,0.0009589435,0.46133733,0.0000024486585,0.00011759],"genre_scores_gemma":[0.92233783,4.992549e-7,0.019060412,0.000064681124,0.000043442597,0.0006158437,0.05782221,0.0000142292165,0.00004087383],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99805194,0.0001679011,0.0007263833,0.00048121586,0.00041898698,0.00015357025],"domain_scores_gemma":[0.99716103,0.001456549,0.0004982826,0.0003155646,0.0003213512,0.00024724676],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034305465,0.00014374312,0.00072074303,0.0002723631,0.00006166517,0.000020172542,0.0002228294,0.00005520711,0.15532346],"category_scores_gemma":[0.0030772563,0.00014676727,0.00019396219,0.0011153799,0.00013058473,0.00017396957,0.00021424143,0.00010197447,0.000003918903],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023792,0.0046113967,0.9402483,0.00049442606,0.0017555447,0.00041419646,0.005051167,0.022835659,0.0001438963,5.8900275e-7,0.019829238,0.0022363479],"study_design_scores_gemma":[0.006234542,0.0007664244,0.7919024,0.000056020883,0.0010602378,0.00006741697,0.003485387,0.19173628,0.000029434788,0.000004357642,0.0044123204,0.00024516383],"about_ca_topic_score_codex":0.0017374724,"about_ca_topic_score_gemma":0.001746814,"teacher_disagreement_score":0.40351513,"about_ca_system_score_codex":0.0000916959,"about_ca_system_score_gemma":0.00064341014,"threshold_uncertainty_score":0.8454487},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008757694,0.013474741,2.440047e-7,0.0014537446,0.00016874724,0.0010566644,0.69982916,0.0002392608,0.28376868],"genre_scores_gemma":[0.000530964,0.00024874273,0.000052961943,0.004563963,0.00045667656,0.00019375484,0.7737201,0.0001395186,0.22009335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963262,0.00025617678,0.00074557867,0.0010488697,0.0008236986,0.00079946656],"domain_scores_gemma":[0.99686646,0.0002000406,0.00046759628,0.0014396593,0.00036526824,0.00066097133],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003143178,0.0005375232,0.001312975,0.00038217587,0.00006567929,0.000118636846,0.00039826913,0.00049244903,0.35616717],"category_scores_gemma":[0.0019113614,0.00054663577,0.00028101876,0.00048125235,0.00001968148,0.00023056201,0.00043952971,0.001029762,0.0054599987],"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":[0.000025332103,0.00011976759,0.0015365992,0.0022477687,0.000059023303,0.0006657797,0.00008289418,5.4994086e-7,9.967885e-7,0.000008590575,0.98595124,0.009301454],"study_design_scores_gemma":[0.0008293305,0.0000959976,0.17612675,0.011948218,0.0000043200516,0.000037201768,0.000010631508,0.000010832996,2.7908428e-7,0.000011622677,0.8105556,0.00036924766],"about_ca_topic_score_codex":0.000011051896,"about_ca_topic_score_gemma":0.00039729167,"teacher_disagreement_score":0.35070714,"about_ca_system_score_codex":0.00092489336,"about_ca_system_score_gemma":0.005777086,"threshold_uncertainty_score":0.9998592},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018444608,0.00002241253,0.0000657742,0.00010937065,0.0000029310027,0.0002502959,0.99302703,0.0000870721,0.006250637],"genre_scores_gemma":[0.007985979,0.000001376291,0.0026546642,0.00015472593,0.000090256144,0.00010863385,0.9881706,0.000021680025,0.0008120897],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878275,0.000045226323,0.00024483912,0.00025926402,0.00038827298,0.00027962044],"domain_scores_gemma":[0.9992786,0.00020877636,0.00006197385,0.00020895318,0.00008634357,0.00015532134],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004665993,0.00013104147,0.0003724393,0.00025856373,0.000053089152,0.000010015771,0.00011672681,0.000091116206,0.84347624],"category_scores_gemma":[0.0004003784,0.00012914516,0.00024783975,0.0022346613,0.0000087565395,0.00005622043,0.00006035776,0.000119207674,0.00047945007],"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":[0.00008329817,0.00004559854,0.000009470523,0.00013993005,0.00014996242,0.000010323467,0.00019708746,0.021237802,0.0000027993458,0.0000025158897,0.97631484,0.0018063759],"study_design_scores_gemma":[0.0006167471,0.00003692361,0.05141369,0.0018296839,0.00017721682,0.0000024707606,0.00034973584,0.75292116,0.0000030528288,0.0001160573,0.19227518,0.00025810755],"about_ca_topic_score_codex":0.0000068315785,"about_ca_topic_score_gemma":0.000021385331,"teacher_disagreement_score":0.8429968,"about_ca_system_score_codex":0.00008941043,"about_ca_system_score_gemma":0.00013794236,"threshold_uncertainty_score":0.61625206},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000639068,0.015039739,2.0687196e-8,0.000006957947,0.000005026615,0.00068627694,0.98172927,0.000013659321,0.001879956],"genre_scores_gemma":[0.37209713,0.000010654293,0.00002358567,0.000026224448,0.00000680134,0.0007541988,0.6270782,0.0000025278514,6.971172e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9990774,0.00014390191,0.00023021178,0.00008650343,0.0003709445,0.000091070346],"domain_scores_gemma":[0.99932826,0.00012943058,0.00014669028,0.0001519508,0.00020847013,0.000035205336],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010083557,0.000047687325,0.00015681815,0.00006777515,0.00010366827,0.000009129981,0.00007366793,0.000015770323,0.21447513],"category_scores_gemma":[0.00061937515,0.000041872445,0.00003238261,0.000363716,0.000013590944,0.000100628924,0.0002523122,0.000089927176,0.000009861946],"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":[0.000039316048,0.000017057073,0.006271383,0.11947397,0.000053293177,0.000007102048,0.00018227764,0.000010356237,0.000002680767,0.000057302732,0.87089103,0.002994215],"study_design_scores_gemma":[0.00042885443,0.0000825698,0.11483405,0.83253425,0.000031126223,0.00008725897,0.0034217697,0.000437892,0.00000700026,0.0000021861558,0.048054796,0.000078247846],"about_ca_topic_score_codex":0.00002183305,"about_ca_topic_score_gemma":0.000014672041,"teacher_disagreement_score":0.8228362,"about_ca_system_score_codex":0.00008697398,"about_ca_system_score_gemma":0.00015637433,"threshold_uncertainty_score":0.78624296},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011129464,0.000096745156,0.0000017514881,0.000040306044,0.000011921765,0.0002218136,0.9871919,0.000033487333,0.0012726242],"genre_scores_gemma":[0.47256818,3.435119e-7,0.00019708941,0.000072724666,0.000017072323,0.00009773695,0.52701765,0.0000062231343,0.000022946055],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9980618,0.00046452694,0.00063643063,0.00033222637,0.00033778357,0.00016724877],"domain_scores_gemma":[0.9953415,0.0031696318,0.0005126809,0.0004258293,0.0004106495,0.00013973356],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001294124,0.00014228355,0.0005744812,0.00015019895,0.000025337748,0.000004123239,0.00014638329,0.00010313722,0.81958145],"category_scores_gemma":[0.053240307,0.00013308995,0.00019737758,0.0004529891,0.00003672933,0.000048974867,0.00017039916,0.0002206459,0.000036014164],"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":[0.0014095899,0.0007131441,0.006347582,0.0005650328,0.00007619959,0.00020282927,0.00028691493,0.0005951483,0.00007740437,3.1628528e-7,0.9882531,0.001472736],"study_design_scores_gemma":[0.007734136,0.001776172,0.8712254,0.008111833,0.000114620605,0.000014174886,0.0030503473,0.0024289805,0.0009184235,0.000009383153,0.10427401,0.00034251838],"about_ca_topic_score_codex":0.00007147261,"about_ca_topic_score_gemma":0.00033349346,"teacher_disagreement_score":0.8839791,"about_ca_system_score_codex":0.00010178735,"about_ca_system_score_gemma":0.00061673817,"threshold_uncertainty_score":0.9547346},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07686024,0.000025153277,0.00013599965,0.0000041280564,0.000006483397,0.00087677804,0.9220115,0.000035141402,0.000044559325],"genre_scores_gemma":[0.42777818,1.3412155e-7,0.0010125311,0.00007896596,0.000042441232,0.0011323869,0.5699367,0.000011429888,0.000007221063],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9981786,0.00012485623,0.00063927955,0.00042364886,0.00046937115,0.00016423392],"domain_scores_gemma":[0.9964662,0.0020050681,0.00049398106,0.0002914956,0.0004897462,0.00025351127],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009509756,0.00015472376,0.0006311983,0.0003115095,0.000055327113,0.000009473071,0.0001410826,0.00006746695,0.68540484],"category_scores_gemma":[0.01229948,0.00016182043,0.00027276287,0.0012150852,0.000035047007,0.000118396114,0.0001287891,0.00011997543,0.000008493637],"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":[0.00097374077,0.0028802282,0.30309814,0.0034796235,0.0013786072,0.00045087113,0.002257059,0.02720703,0.000028452328,3.7185194e-7,0.6579902,0.00025564115],"study_design_scores_gemma":[0.0031388518,0.00045346117,0.7858494,0.0002500294,0.0004141604,0.000048232265,0.0009201523,0.20305264,0.000006828081,0.0000024566104,0.005659293,0.00020452427],"about_ca_topic_score_codex":0.00040011824,"about_ca_topic_score_gemma":0.00049694814,"teacher_disagreement_score":0.6853964,"about_ca_system_score_codex":0.00009707739,"about_ca_system_score_gemma":0.00051337946,"threshold_uncertainty_score":0.9960203},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064346724,0.014805037,2.1146283e-8,0.0000069946836,0.000004997385,0.0006875355,0.9820004,0.000013663137,0.0018378473],"genre_scores_gemma":[0.374639,0.000010349654,0.000024055027,0.000026151794,0.0000066829502,0.0007669812,0.6245235,0.0000025245515,6.929162e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99907726,0.00014395098,0.00023010738,0.00008656883,0.00037103772,0.00009110413],"domain_scores_gemma":[0.9993258,0.00013130656,0.00014648677,0.00015224934,0.00020894383,0.00003521135],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010139426,0.000047702637,0.0001567794,0.000067819965,0.00010366061,0.000009110023,0.000073709336,0.00001577023,0.21772777],"category_scores_gemma":[0.0006270091,0.000041886266,0.000032388933,0.00036400155,0.000013591133,0.00010055937,0.00025234802,0.00008994808,0.000009969301],"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":[0.000039116345,0.00001688273,0.0064100656,0.117171414,0.000052730593,0.0000068382133,0.00018020168,0.0000101975365,0.0000027485412,0.00005570876,0.8731727,0.0028813896],"study_design_scores_gemma":[0.00042273122,0.00008193955,0.115780495,0.8336916,0.000031007217,0.0000864141,0.00343154,0.00043757522,0.000006991903,0.0000021525377,0.045949712,0.000077828634],"about_ca_topic_score_codex":0.000021613194,"about_ca_topic_score_gemma":0.000014584909,"teacher_disagreement_score":0.827223,"about_ca_system_score_codex":0.000086948836,"about_ca_system_score_gemma":0.00015707983,"threshold_uncertainty_score":0.78298736},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006454323,0.014941997,2.1413342e-8,0.0000069506127,0.0000050017957,0.00068836514,0.9818539,0.000013668926,0.0018446516],"genre_scores_gemma":[0.38077822,0.000010449297,0.000023677298,0.000026032134,0.0000066594966,0.00074881,0.61840296,0.000002511386,6.7847884e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9990772,0.00014379084,0.00023029612,0.000086539345,0.00037109136,0.00009110767],"domain_scores_gemma":[0.999329,0.00013037545,0.0001465316,0.0001520947,0.00020682032,0.00003520922],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010143054,0.000047699185,0.0001568335,0.0000678291,0.00010370417,0.000009134041,0.000073717434,0.000015770225,0.21055883],"category_scores_gemma":[0.00061790075,0.000041883515,0.000032389384,0.00036381662,0.00001359461,0.00010065318,0.00025226042,0.000089926514,0.000009870348],"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":[0.000041220846,0.000017497618,0.006667663,0.12252505,0.000054604367,0.000007199592,0.00018696174,0.000010421599,0.0000028111683,0.000059502097,0.86739844,0.0030285963],"study_design_scores_gemma":[0.00042735194,0.000083322375,0.11574548,0.8347145,0.00003098758,0.00008657851,0.003428686,0.00044023883,0.000007003012,0.0000021833084,0.044955753,0.00007792564],"about_ca_topic_score_codex":0.000021664984,"about_ca_topic_score_gemma":0.000014747339,"teacher_disagreement_score":0.8224427,"about_ca_system_score_codex":0.000086957356,"about_ca_system_score_gemma":0.00015656723,"threshold_uncertainty_score":0.7901628},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021106493,0.0011794091,1.899804e-7,0.00094937894,0.00010002474,0.00063888915,0.9606195,0.0011682245,0.0142378565],"genre_scores_gemma":[0.08597765,0.000029203771,0.00002947357,0.000723393,0.00025166417,0.0001936444,0.8971682,0.000054661894,0.015572069],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988156,0.00004998521,0.00018877735,0.00031299025,0.00035181746,0.00028083054],"domain_scores_gemma":[0.99892706,0.0001391665,0.00007837854,0.0005612981,0.00008395838,0.00021011585],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008503822,0.00013781723,0.0002450271,0.00009190468,0.00006307169,0.00002449121,0.0001547587,0.000085542175,0.16655746],"category_scores_gemma":[0.0029578006,0.000110619876,0.00008433873,0.0004779072,0.0000068819463,0.00008801036,0.00014740875,0.00014105052,0.02587853],"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":[0.00006205734,0.000024459809,0.0008418634,0.0002151513,0.00001549876,0.00023173532,0.000020748867,4.847398e-7,0.0008695104,0.0000012811751,0.99403113,0.003686088],"study_design_scores_gemma":[0.0011410723,0.00006170267,0.060124505,0.0036014817,0.00001296428,0.000009837621,0.000022599896,0.0000616668,0.0030636007,0.00002040142,0.9317216,0.00015856206],"about_ca_topic_score_codex":0.0000030109704,"about_ca_topic_score_gemma":0.0000022347897,"teacher_disagreement_score":0.14067893,"about_ca_system_score_codex":0.000033306475,"about_ca_system_score_gemma":0.000087590444,"threshold_uncertainty_score":0.9748799},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9912531,0.00088049937,0.00034047247,0.00008152678,0.0001406488,0.00043259395,0.0055672997,0.00006077466,0.0012431231],"genre_scores_gemma":[0.99639773,0.0009185903,0.0005696932,0.00013357273,0.00000805272,0.0000011894223,0.0008035138,0.000014327965,0.0011532969],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99795955,0.00024306524,0.00059004157,0.00038096876,0.0004667048,0.0003596604],"domain_scores_gemma":[0.9977421,0.0001561198,0.00049493497,0.00082066556,0.0006144647,0.00017171688],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006693755,0.00007001793,0.0016045222,0.00027588004,0.000155588,0.00005366836,0.0005550113,0.00013460044,0.0002710677],"category_scores_gemma":[0.00012333937,0.00029810937,0.00038087255,0.002170775,0.00070745713,0.00022048748,0.00056469353,0.00024170736,8.6354385e-7],"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":[0.000053197607,0.0008262436,0.8076876,0.0011797298,0.0007693197,0.00011927202,0.00021248654,0.00008389041,0.000027348487,5.022035e-7,0.015122201,0.17391822],"study_design_scores_gemma":[0.0027720167,0.00012871866,0.98810375,0.0016731278,0.0006097206,0.000007886573,0.0051017758,0.0010299836,8.017648e-7,0.000084973166,0.00031259927,0.00017467437],"about_ca_topic_score_codex":0.9567075,"about_ca_topic_score_gemma":0.9988583,"teacher_disagreement_score":0.18041614,"about_ca_system_score_codex":0.00028203137,"about_ca_system_score_gemma":0.00097301236,"threshold_uncertainty_score":0.99994713},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4278482,0.0023758993,0.006172255,0.020720584,0.0004756826,0.002326227,0.010582594,0.004841236,0.5246573],"genre_scores_gemma":[0.9810188,0.00029295043,0.00007141574,0.00062782253,0.00006586284,4.153253e-8,0.015776359,0.00053019694,0.0016165619],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99825,0.00023873417,0.00026079733,0.0006430195,0.00028224173,0.00032521592],"domain_scores_gemma":[0.99827707,0.000029849363,0.00007739196,0.001107752,0.00028992197,0.00021803213],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044237636,0.00017355954,0.00025029763,0.0004154403,0.0009503137,0.00038231118,0.00079441606,0.000055382265,0.0028316611],"category_scores_gemma":[0.0010781904,0.000168744,0.00004283882,0.0009779214,0.00016942516,0.0003041367,0.001815652,0.000301109,0.0010534334],"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":[0.00046865075,0.0014397921,0.0031147546,0.00019977163,0.00058199087,0.00004567621,0.00043093463,0.000026048423,0.0014038922,0.0032962218,0.4267841,0.5622082],"study_design_scores_gemma":[0.0023384627,0.0003056429,0.19245118,0.00014600474,0.00012957121,0.000029216435,0.00020948844,0.0017917819,0.00009395291,0.0000631879,0.8022191,0.00022241293],"about_ca_topic_score_codex":0.000038126083,"about_ca_topic_score_gemma":0.000003880557,"teacher_disagreement_score":0.56198573,"about_ca_system_score_codex":0.00009005746,"about_ca_system_score_gemma":0.000008080234,"threshold_uncertainty_score":0.9997244},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48758546,0.000048841594,0.490769,0.0008130988,0.00021070143,0.00184492,0.018308716,0.00022167467,0.00019760145],"genre_scores_gemma":[0.982842,0.0000016772271,0.015631221,0.00032751815,0.00014598675,0.00018160499,0.0007360731,0.000013146275,0.00012074541],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99830544,0.000027212138,0.0009466703,0.00024457363,0.00023092523,0.0002451604],"domain_scores_gemma":[0.99790823,0.00013932705,0.00035262026,0.0008890308,0.000602756,0.00010802054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089879165,0.0001650189,0.00049876183,0.00038030377,0.00008330376,0.000097302756,0.000192252,0.00010364783,0.000015605501],"category_scores_gemma":[0.0013325027,0.00010732748,0.00008130407,0.0003035739,0.00003966869,0.0007765449,0.00010650226,0.000034555516,0.000038878843],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044295477,0.0000021910591,0.9905166,0.0036514048,0.00037443914,0.0000015416629,0.0039350167,0.000022504857,4.6761195e-7,0.00025586112,0.0003307403,0.00046624854],"study_design_scores_gemma":[0.011062757,0.00020690824,0.86785585,0.01592327,0.0016837332,0.000034259825,0.009959699,0.08178284,0.00013867466,0.0000286732,0.010491911,0.0008314528],"about_ca_topic_score_codex":0.0000123944465,"about_ca_topic_score_gemma":0.000003901016,"teacher_disagreement_score":0.49525657,"about_ca_system_score_codex":0.000087149136,"about_ca_system_score_gemma":0.00005789372,"threshold_uncertainty_score":0.4376688},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064346724,0.014805037,2.1146283e-8,0.0000069946836,0.000004997385,0.0006875355,0.9820004,0.000013663137,0.0018378473],"genre_scores_gemma":[0.374639,0.000010349654,0.000024055027,0.000026151794,0.0000066829502,0.0007669812,0.6245235,0.0000025245515,6.929162e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99907726,0.00014395098,0.00023010738,0.00008656883,0.00037103772,0.00009110413],"domain_scores_gemma":[0.9993258,0.00013130656,0.00014648677,0.00015224934,0.00020894383,0.00003521135],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010139426,0.000047702637,0.0001567794,0.000067819965,0.00010366061,0.000009110023,0.000073709336,0.00001577023,0.21772777],"category_scores_gemma":[0.0006270091,0.000041886266,0.000032388933,0.00036400155,0.000013591133,0.00010055937,0.00025234802,0.00008994808,0.000009969301],"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":[0.000039116345,0.00001688273,0.0064100656,0.117171414,0.000052730593,0.0000068382133,0.00018020168,0.0000101975365,0.0000027485412,0.00005570876,0.8731727,0.0028813896],"study_design_scores_gemma":[0.00042273122,0.00008193955,0.115780495,0.8336916,0.000031007217,0.0000864141,0.00343154,0.00043757522,0.000006991903,0.0000021525377,0.045949712,0.000077828634],"about_ca_topic_score_codex":0.000021613194,"about_ca_topic_score_gemma":0.000014584909,"teacher_disagreement_score":0.827223,"about_ca_system_score_codex":0.000086948836,"about_ca_system_score_gemma":0.00015707983,"threshold_uncertainty_score":0.78298736},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018125733,0.013320655,0.0000033065246,0.01836182,0.00030691712,0.0014033035,0.9096484,0.0006438356,0.038186014],"genre_scores_gemma":[0.553796,0.00007758397,0.00017531721,0.0076347133,0.00016136831,0.00011761595,0.43701324,0.00004740919,0.0009767596],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99812657,0.00020044748,0.00033756925,0.000476035,0.00036711927,0.0004922602],"domain_scores_gemma":[0.9985038,0.00009799237,0.0001142546,0.0006861788,0.00021392228,0.00038387632],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019863101,0.00016938512,0.00040484796,0.00011703063,0.000049663693,0.00006252764,0.00014800463,0.000087979104,0.20068839],"category_scores_gemma":[0.002144109,0.00016670628,0.0000893452,0.00075691077,0.000008219713,0.00023788004,0.00018675977,0.00026132708,0.0027849996],"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":[0.00002788433,0.00025271837,0.078030534,0.0005633122,0.000022547903,0.00051858736,0.00017084202,0.000001946256,0.000057441837,0.000008444449,0.904134,0.016211754],"study_design_scores_gemma":[0.00064091635,0.000039416453,0.5955609,0.0009690997,7.1150936e-7,0.00002599679,0.000032385637,0.000038371552,0.0000090729045,0.0000036987972,0.4025754,0.00010400117],"about_ca_topic_score_codex":0.000012240127,"about_ca_topic_score_gemma":0.00031807876,"teacher_disagreement_score":0.5356702,"about_ca_system_score_codex":0.00016793457,"about_ca_system_score_gemma":0.00094205333,"threshold_uncertainty_score":0.99799144},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21089835,0.000092570765,0.000015014573,0.0046970174,0.00001608019,0.0009617873,0.7559587,0.0000024431574,0.027358033],"genre_scores_gemma":[0.58197093,0.000002505933,0.004766555,0.00002060996,0.000023901275,0.00001516321,0.41249245,0.0000068450613,0.0007010158],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9971246,0.0018647057,0.00027401195,0.00025592296,0.00032306314,0.00015768666],"domain_scores_gemma":[0.9975243,0.0014854611,0.000100292134,0.00065521616,0.00014422507,0.00009047565],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034936646,0.00005019296,0.00017408385,0.00007204148,0.000031905947,0.00005591403,0.00047913234,0.000020389713,0.07761804],"category_scores_gemma":[0.003308349,0.00003916657,0.000012539304,0.0004824911,0.00016125031,0.00018951032,0.0007562437,0.00021119702,0.00007372923],"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":[0.000011696419,0.00018614258,0.14560178,0.000034657314,0.000016243248,0.000020241265,0.00035031323,4.1521947e-8,0.0000011896337,0.00001702595,0.6486703,0.20509037],"study_design_scores_gemma":[0.00026776028,0.000082734834,0.5604314,0.00023256982,0.0000029845244,0.0000082240385,0.00060280156,0.000016312448,0.000002099381,0.000018267066,0.43831202,0.000022791786],"about_ca_topic_score_codex":0.00018289467,"about_ca_topic_score_gemma":0.0012973346,"teacher_disagreement_score":0.41482967,"about_ca_system_score_codex":0.000020997273,"about_ca_system_score_gemma":0.0005551306,"threshold_uncertainty_score":0.92322516},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000639068,0.015039739,2.0687196e-8,0.000006957947,0.000005026615,0.00068627694,0.98172927,0.000013659321,0.001879956],"genre_scores_gemma":[0.37209713,0.000010654293,0.00002358567,0.000026224448,0.00000680134,0.0007541988,0.6270782,0.0000025278514,6.971172e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9990774,0.00014390191,0.00023021178,0.00008650343,0.0003709445,0.000091070346],"domain_scores_gemma":[0.99932826,0.00012943058,0.00014669028,0.0001519508,0.00020847013,0.000035205336],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010083557,0.000047687325,0.00015681815,0.00006777515,0.00010366827,0.000009129981,0.00007366793,0.000015770323,0.21447513],"category_scores_gemma":[0.00061937515,0.000041872445,0.00003238261,0.000363716,0.000013590944,0.000100628924,0.0002523122,0.000089927176,0.000009861946],"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":[0.000039316048,0.000017057073,0.006271383,0.11947397,0.000053293177,0.000007102048,0.00018227764,0.000010356237,0.000002680767,0.000057302732,0.87089103,0.002994215],"study_design_scores_gemma":[0.00042885443,0.0000825698,0.11483405,0.83253425,0.000031126223,0.00008725897,0.0034217697,0.000437892,0.00000700026,0.0000021861558,0.048054796,0.000078247846],"about_ca_topic_score_codex":0.00002183305,"about_ca_topic_score_gemma":0.000014672041,"teacher_disagreement_score":0.8228362,"about_ca_system_score_codex":0.00008697398,"about_ca_system_score_gemma":0.00015637433,"threshold_uncertainty_score":0.78624296},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018444608,0.00002241253,0.0000657742,0.00010937065,0.0000029310027,0.0002502959,0.99302703,0.0000870721,0.006250637],"genre_scores_gemma":[0.007985979,0.000001376291,0.0026546642,0.00015472593,0.000090256144,0.00010863385,0.9881706,0.000021680025,0.0008120897],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878275,0.000045226323,0.00024483912,0.00025926402,0.00038827298,0.00027962044],"domain_scores_gemma":[0.9992786,0.00020877636,0.00006197385,0.00020895318,0.00008634357,0.00015532134],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004665993,0.00013104147,0.0003724393,0.00025856373,0.000053089152,0.000010015771,0.00011672681,0.000091116206,0.84347624],"category_scores_gemma":[0.0004003784,0.00012914516,0.00024783975,0.0022346613,0.0000087565395,0.00005622043,0.00006035776,0.000119207674,0.00047945007],"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":[0.00008329817,0.00004559854,0.000009470523,0.00013993005,0.00014996242,0.000010323467,0.00019708746,0.021237802,0.0000027993458,0.0000025158897,0.97631484,0.0018063759],"study_design_scores_gemma":[0.0006167471,0.00003692361,0.05141369,0.0018296839,0.00017721682,0.0000024707606,0.00034973584,0.75292116,0.0000030528288,0.0001160573,0.19227518,0.00025810755],"about_ca_topic_score_codex":0.0000068315785,"about_ca_topic_score_gemma":0.000021385331,"teacher_disagreement_score":0.8429968,"about_ca_system_score_codex":0.00008941043,"about_ca_system_score_gemma":0.00013794236,"threshold_uncertainty_score":0.61625206},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07597805,0.0037544875,0.0000067263354,0.05908671,0.000036322333,0.0008655386,0.85892373,0.00048413334,0.0008643092],"genre_scores_gemma":[0.9183069,0.000016481203,0.00001804306,0.0054659755,0.00018556952,0.000058215333,0.07585833,0.000013652621,0.0000768148],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922836,0.00007410491,0.00012285664,0.00019769648,0.00020932477,0.00016766826],"domain_scores_gemma":[0.9977973,0.0014562389,0.000093707735,0.00015059792,0.00009496933,0.00040719812],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000091485155,0.00009415077,0.00016237645,0.00002277383,0.00017450223,0.00011766358,0.000071599025,0.000057168465,0.0041923183],"category_scores_gemma":[0.028458545,0.00007089095,0.000028230197,0.00013669189,0.000031657226,0.000096313546,0.00008820952,0.00010118474,0.0001352744],"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":[0.000031279742,0.0000070519254,0.38511387,0.00029727758,0.00005070116,0.000048378926,0.00008050458,2.8785362e-7,0.000008769154,0.0000128067895,0.6119103,0.002438778],"study_design_scores_gemma":[0.0008178036,0.000065946515,0.22582383,0.00008736679,0.000029854456,0.000003942877,0.000049762166,0.00013032304,0.000024901543,0.000006921277,0.77288145,0.00007789887],"about_ca_topic_score_codex":0.0000048620423,"about_ca_topic_score_gemma":0.000033336753,"teacher_disagreement_score":0.84232885,"about_ca_system_score_codex":0.00007613381,"about_ca_system_score_gemma":0.00033376046,"threshold_uncertainty_score":0.996718},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018740573,0.000022224134,0.000068024696,0.00010989003,0.0000029156536,0.00025105535,0.9931577,0.000087145105,0.0061136503],"genre_scores_gemma":[0.008363138,0.0000013589789,0.0026876477,0.00015488929,0.00008898082,0.000108688604,0.9877717,0.000021710619,0.00080187037],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987822,0.000045230565,0.0002449268,0.00025941592,0.00038846326,0.0002797358],"domain_scores_gemma":[0.9992771,0.00021012209,0.00006189903,0.00020912538,0.000086372376,0.00015535782],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000046981604,0.0001310907,0.00037254853,0.00025877333,0.00005311158,0.000010015704,0.00011676288,0.00009112045,0.8400896],"category_scores_gemma":[0.00040111545,0.00012918445,0.00024793216,0.002236113,0.000008759123,0.000056234894,0.000060347804,0.000119222095,0.00047635395],"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":[0.00008393681,0.00004566537,0.000009818385,0.00014012725,0.00014997828,0.000010206383,0.00019721728,0.021119213,0.0000028938111,0.000002553988,0.9764536,0.0017847859],"study_design_scores_gemma":[0.0006233295,0.000037297665,0.0535841,0.0018713349,0.00017911257,0.0000024874698,0.00035600582,0.762032,0.0000031267434,0.00011696641,0.18093339,0.00026089879],"about_ca_topic_score_codex":0.000006842614,"about_ca_topic_score_gemma":0.000021587935,"teacher_disagreement_score":0.83961326,"about_ca_system_score_codex":0.000089413516,"about_ca_system_score_gemma":0.00013829532,"threshold_uncertainty_score":0.6122725},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018740573,0.000022224134,0.000068024696,0.00010989003,0.0000029156536,0.00025105535,0.9931577,0.000087145105,0.0061136503],"genre_scores_gemma":[0.008363138,0.0000013589789,0.0026876477,0.00015488929,0.00008898082,0.000108688604,0.9877717,0.000021710619,0.00080187037],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987822,0.000045230565,0.0002449268,0.00025941592,0.00038846326,0.0002797358],"domain_scores_gemma":[0.9992771,0.00021012209,0.00006189903,0.00020912538,0.000086372376,0.00015535782],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000046981604,0.0001310907,0.00037254853,0.00025877333,0.00005311158,0.000010015704,0.00011676288,0.00009112045,0.8400896],"category_scores_gemma":[0.00040111545,0.00012918445,0.00024793216,0.002236113,0.000008759123,0.000056234894,0.000060347804,0.000119222095,0.00047635395],"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":[0.00008393681,0.00004566537,0.000009818385,0.00014012725,0.00014997828,0.000010206383,0.00019721728,0.021119213,0.0000028938111,0.000002553988,0.9764536,0.0017847859],"study_design_scores_gemma":[0.0006233295,0.000037297665,0.0535841,0.0018713349,0.00017911257,0.0000024874698,0.00035600582,0.762032,0.0000031267434,0.00011696641,0.18093339,0.00026089879],"about_ca_topic_score_codex":0.000006842614,"about_ca_topic_score_gemma":0.000021587935,"teacher_disagreement_score":0.83961326,"about_ca_system_score_codex":0.000089413516,"about_ca_system_score_gemma":0.00013829532,"threshold_uncertainty_score":0.6122725},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064843986,0.014885786,2.1273877e-8,0.0000069629023,0.0000050473145,0.0006876951,0.981903,0.000013648792,0.0018493691],"genre_scores_gemma":[0.37735644,0.000010507104,0.000023782084,0.000026147114,0.0000067655033,0.0007528943,0.6218202,0.0000025259164,7.0717255e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99907726,0.00014381003,0.00023021201,0.00008653049,0.00037111514,0.000091097114],"domain_scores_gemma":[0.99932534,0.00013012196,0.00014650899,0.00015201545,0.00021081875,0.000035207984],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010134748,0.000047695798,0.00015679571,0.000067796915,0.00010365153,0.000009129083,0.00007366212,0.000015768865,0.21343665],"category_scores_gemma":[0.0006268377,0.000041875395,0.000032386826,0.00036386406,0.000013592118,0.00010063817,0.0002521623,0.00008992936,0.000010080335],"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":[0.00004044977,0.000017343675,0.0065973895,0.12246313,0.000054150834,0.0000071146687,0.0001852214,0.000010484442,0.0000028321567,0.00005689293,0.86755913,0.0030058804],"study_design_scores_gemma":[0.0004290865,0.00008189751,0.11520866,0.83332926,0.000030751402,0.000085670814,0.0034180998,0.00043868614,0.0000071530235,0.0000021609183,0.046890676,0.000077908415],"about_ca_topic_score_codex":0.000021863063,"about_ca_topic_score_gemma":0.000014685848,"teacher_disagreement_score":0.82066846,"about_ca_system_score_codex":0.00008730015,"about_ca_system_score_gemma":0.00015716828,"threshold_uncertainty_score":0.7872824},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018582294,0.000022032964,0.000067347406,0.00010999814,0.0000029128948,0.0002508057,0.9931725,0.000087103974,0.0061014444],"genre_scores_gemma":[0.008128011,0.0000013380209,0.0027192908,0.00015472212,0.00008883219,0.00011094148,0.98796594,0.000021713697,0.00080922496],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878246,0.00004524294,0.00024472017,0.0002594748,0.00038837822,0.00027973214],"domain_scores_gemma":[0.9992748,0.00021202277,0.00006188009,0.00020938931,0.00008655235,0.0001553463],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000046937646,0.00013108665,0.0003723405,0.0002587473,0.000053084936,0.000009992259,0.00011679727,0.00009111563,0.8461795],"category_scores_gemma":[0.0004056817,0.00012919093,0.00024789173,0.0022365418,0.0000087566705,0.0000561787,0.000060366965,0.00011923743,0.0004850548],"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":[0.000083847466,0.000045644552,0.0000098136225,0.00013865907,0.00015005913,0.000010032259,0.00019703637,0.021143662,0.0000029102575,0.0000024703784,0.9764614,0.001754435],"study_design_scores_gemma":[0.00061346963,0.000036993326,0.052414365,0.0018582954,0.00017828347,0.000002469935,0.0003543789,0.7584972,0.0000030799113,0.0001153022,0.18566693,0.0002592317],"about_ca_topic_score_codex":0.000006757725,"about_ca_topic_score_gemma":0.00002124897,"teacher_disagreement_score":0.8456944,"about_ca_system_score_codex":0.00008938268,"about_ca_system_score_gemma":0.00013861085,"threshold_uncertainty_score":0.623456},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99623716,0.00036897633,0.00005834666,0.00023416146,0.0002532848,0.0017552312,0.00010524476,0.00037115044,0.0006164701],"genre_scores_gemma":[0.9984888,0.000004979092,0.00023543711,0.0002194348,0.0005970454,0.0000378033,0.00019131333,0.000052501153,0.0001727056],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970441,0.0004701338,0.0005222782,0.00080916856,0.0007933714,0.00036095612],"domain_scores_gemma":[0.9976267,0.00006415789,0.0001341169,0.0009955949,0.00021527619,0.00096414186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037317624,0.0002481467,0.00046183768,0.000064620064,0.00021992996,0.000060844213,0.00019691326,0.00006556883,0.00004993129],"category_scores_gemma":[0.00035624913,0.00024708238,0.00016315744,0.00035431393,0.00008137224,0.00019155379,0.00017740011,0.00027705322,0.00004864562],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006292009,0.0010163144,0.99313545,0.0001677431,0.00004472659,0.00081913226,0.00018919432,0.000093692965,0.0031827616,0.0000029161513,0.00044714127,0.00027174244],"study_design_scores_gemma":[0.0012916389,0.0002555792,0.99452746,0.000037112706,0.00014399897,0.000025649158,0.00016808492,0.0030677198,0.00011591991,0.0000018033618,0.00013369304,0.00023134724],"about_ca_topic_score_codex":0.00035791876,"about_ca_topic_score_gemma":0.000020263957,"teacher_disagreement_score":0.0030668415,"about_ca_system_score_codex":0.00027824345,"about_ca_system_score_gemma":0.00028685594,"threshold_uncertainty_score":0.99999815},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034883088,0.007395189,0.00014742721,0.00027854228,0.00269149,0.002776092,0.98310333,0.00008064615,0.000038990005],"genre_scores_gemma":[0.0009806546,0.006232324,0.002993503,0.00009959342,0.001628852,0.00011378184,0.98636115,0.0001618558,0.0014283166],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9918127,0.00024383045,0.0012987443,0.0033094955,0.0016252412,0.0017100044],"domain_scores_gemma":[0.99125105,0.00035258415,0.0004592719,0.0067432243,0.000096509044,0.0010973841],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0053695007,0.00086669356,0.0012907723,0.000791745,0.0012697169,0.0019304908,0.0038457524,0.00057232525,0.00032002755],"category_scores_gemma":[0.0016163309,0.00081641256,0.00021618087,0.00066209276,0.0016914993,0.0037208304,0.017192163,0.0013724581,0.00006278086],"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":[0.0007427549,0.00065318204,0.0009938297,0.0040510725,0.0008054222,0.000086581946,0.00003120391,0.000013880957,0.00023631172,0.000027137892,0.9822852,0.010073422],"study_design_scores_gemma":[0.0027747704,0.00017759694,0.0027174202,0.0005472355,0.0010151421,0.00011419658,0.00017043467,0.00273304,0.00003884469,0.000077415796,0.9888991,0.0007348093],"about_ca_topic_score_codex":0.18379459,"about_ca_topic_score_gemma":0.16518527,"teacher_disagreement_score":0.018609319,"about_ca_system_score_codex":0.0002286702,"about_ca_system_score_gemma":0.0003677486,"threshold_uncertainty_score":0.9994287},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022123256,0.00095538766,0.0000013706695,0.0012674159,0.00023105348,0.000966899,0.99409574,0.000023259141,0.00024656422],"genre_scores_gemma":[0.01141166,0.0007048006,0.000032459877,0.0015575711,0.0002496897,0.000031220145,0.9857643,0.000032972457,0.00021528498],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99727976,0.0003109918,0.00037780136,0.0009017666,0.0005686565,0.00056102977],"domain_scores_gemma":[0.99444956,0.000245036,0.00013547928,0.004739131,0.0000661369,0.00036467164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009867228,0.0003745436,0.00059730426,0.0003490818,0.00005679108,0.00010216708,0.001746151,0.0002397984,0.00026714994],"category_scores_gemma":[0.00087348296,0.0002752893,0.000037086164,0.00027517095,0.00011492152,0.00019860087,0.0006930519,0.0008052011,0.0003547385],"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":[0.000074255,0.00007680449,0.013050701,0.0006319201,0.000116726886,0.0006132794,0.000033878026,3.2460125e-7,0.0000015589342,0.0000021344033,0.9846455,0.00075293134],"study_design_scores_gemma":[0.0007538968,0.000077793506,0.06681139,0.0004231376,0.00024313838,0.00012374116,0.00008237409,0.00024066443,2.9354578e-7,0.000008316109,0.9309893,0.0002459306],"about_ca_topic_score_codex":0.1518996,"about_ca_topic_score_gemma":0.51064306,"teacher_disagreement_score":0.35874343,"about_ca_system_score_codex":0.0001613308,"about_ca_system_score_gemma":0.0009774901,"threshold_uncertainty_score":0.9999699},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011775016,0.0011242164,0.00015776513,0.0015444618,0.0018033801,0.0008032333,0.0050147823,0.0016945018,0.9878459],"genre_scores_gemma":[0.00018438711,0.0015113032,0.0013619233,0.00078541395,0.00079586444,0.0000018391922,0.00029437515,0.0006099956,0.9944549],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975024,0.00013590063,0.00021170979,0.001005477,0.0005937223,0.00055081845],"domain_scores_gemma":[0.99727803,0.00006144038,0.0003169983,0.0016278571,0.000032434433,0.00068321335],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002905202,0.0006521355,0.0010286359,0.00039055714,0.00010045037,0.00005644087,0.0010364098,0.00052816485,0.0062485407],"category_scores_gemma":[0.000060481467,0.00065450557,0.00036794718,0.00055991655,0.00022372964,0.00045847183,0.0010252504,0.0006015751,0.0004100044],"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":[0.0004674861,0.000145923,0.000338804,0.00052889425,0.0004923355,0.0018824051,0.00011557138,0.0000020809296,0.000007331536,0.00022572743,0.9955014,0.0002920569],"study_design_scores_gemma":[0.0035895319,0.00014015465,0.00020756968,0.0004728129,0.00038592875,0.0000054842353,0.0001681497,0.00004033117,0.000046777077,0.000028897026,0.9942243,0.0006900448],"about_ca_topic_score_codex":0.0007912514,"about_ca_topic_score_gemma":0.091137245,"teacher_disagreement_score":0.090346,"about_ca_system_score_codex":0.00009004169,"about_ca_system_score_gemma":0.00044610637,"threshold_uncertainty_score":0.99959064},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9659023,0.002453525,0.0036624784,0.0016329915,0.0018667862,0.0065942346,0.003107227,0.00034832474,0.014432133],"genre_scores_gemma":[0.968692,0.0014619903,0.014775984,0.00025085695,0.0023116367,0.00090626586,0.0038492316,0.00041396174,0.0073380796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9907766,0.00035845806,0.0020866236,0.002221551,0.002992728,0.0015640566],"domain_scores_gemma":[0.99127614,0.0013136835,0.00094514986,0.002307942,0.0029730897,0.0011839865],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008125631,0.0008717112,0.0021166485,0.0016844743,0.00028415964,0.00023141889,0.00057673873,0.00073575234,0.00016676854],"category_scores_gemma":[0.018263219,0.0009143332,0.0003219541,0.0010176958,0.00046929644,0.000218854,0.00069415313,0.0016034124,0.000034821398],"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":[0.0040638098,0.0014823622,0.11814638,0.01030158,0.0010381029,0.006310663,0.00048425587,0.00014302348,0.07080511,0.00025538853,0.75942653,0.027542807],"study_design_scores_gemma":[0.0053624604,0.000996425,0.041143954,0.003529913,0.00078717835,0.001763204,0.00013527289,0.00056379574,0.0022914088,0.0048812153,0.9369395,0.0016057093],"about_ca_topic_score_codex":0.000473082,"about_ca_topic_score_gemma":0.00018724689,"teacher_disagreement_score":0.17751294,"about_ca_system_score_codex":0.0013080678,"about_ca_system_score_gemma":0.0032508727,"threshold_uncertainty_score":0.9993307},"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":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07087543,0.0017700383,0.000013452894,0.00034888703,0.0007308403,0.000806512,0.9249313,0.00018714856,0.00033643306],"genre_scores_gemma":[0.005603494,0.0047247247,0.0009130989,0.0010966234,0.0016790851,0.000040084473,0.9854881,0.00012331462,0.00033146242],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.991394,0.0010980873,0.0015645246,0.0033519992,0.0013042187,0.0012872078],"domain_scores_gemma":[0.98450434,0.0012007996,0.0009993148,0.012156175,0.000091073845,0.0010483218],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0031731557,0.0011406674,0.0020835213,0.0002992736,0.00024123519,0.00029819182,0.0070949816,0.00048808142,0.00050159695],"category_scores_gemma":[0.0004096024,0.0009209103,0.00010814318,0.00064590015,0.00041284855,0.0009764748,0.009969443,0.0017401682,0.00030387612],"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":[0.0008220533,0.0003316972,0.12978102,0.0022131726,0.00046369852,0.0005281471,0.0003834439,0.000018403147,0.000015901114,3.586104e-7,0.8633669,0.0020752216],"study_design_scores_gemma":[0.0024642944,0.00006838044,0.18467225,0.0023101957,0.0006527867,0.000032999524,0.00062804547,0.0063282596,9.638819e-7,0.00009280569,0.80166566,0.0010833808],"about_ca_topic_score_codex":0.02261903,"about_ca_topic_score_gemma":0.12119911,"teacher_disagreement_score":0.09858008,"about_ca_system_score_codex":0.00013425533,"about_ca_system_score_gemma":0.0004768964,"threshold_uncertainty_score":0.99932414},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014385366,0.0038076043,0.00041959682,0.0005195426,0.00090549607,0.00081412843,0.0018837196,0.0029266863,0.9885794],"genre_scores_gemma":[0.005633374,0.0014373962,0.00032481327,0.0003103004,0.0014183242,2.1538824e-7,0.01350547,0.01594498,0.9614251],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99694645,0.00021975952,0.0003996024,0.0009925754,0.00082541554,0.00061620004],"domain_scores_gemma":[0.9978683,0.000022312171,0.0002242469,0.0011985435,0.00029036604,0.0003962446],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005588261,0.00041401727,0.0005023661,0.0007944086,0.0005669925,0.0007509444,0.0010486299,0.00023491429,0.15369469],"category_scores_gemma":[0.0007343478,0.00041353205,0.0001549976,0.0010865829,0.00023650404,0.00009282187,0.0020070407,0.0007326956,0.16507891],"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":[0.00007310903,0.0001233369,0.000012964886,0.0007299959,0.00027181208,0.000086144675,0.0002689168,0.0000047308295,0.00005928733,0.00095927675,0.92914724,0.068263166],"study_design_scores_gemma":[0.0005263719,0.000080442325,0.00017262957,0.0011561741,0.00012937849,0.0001255421,0.00014096573,0.000020316484,0.000015072549,0.000062948464,0.99717623,0.00039394866],"about_ca_topic_score_codex":0.00003330518,"about_ca_topic_score_gemma":0.000004086588,"teacher_disagreement_score":0.06802895,"about_ca_system_score_codex":0.00025990527,"about_ca_system_score_gemma":0.000010113783,"threshold_uncertainty_score":0.9998317},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052762784,0.004048573,0.0029813496,0.017855652,0.0014086877,0.0037737894,0.009691506,0.0064450614,0.90103257],"genre_scores_gemma":[0.9581069,0.001479085,0.0008303581,0.0016792788,0.0027454523,1.2502336e-7,0.014506023,0.0056844624,0.014968334],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99459136,0.00092975725,0.0008275702,0.0012059704,0.0012387476,0.0012065861],"domain_scores_gemma":[0.9955006,0.000076160126,0.00032941534,0.0018036966,0.0012760941,0.0010139918],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014135164,0.0005034268,0.00063649914,0.0005218357,0.0024244345,0.0010210328,0.0017553394,0.0002177144,0.12221769],"category_scores_gemma":[0.0023096702,0.0005544523,0.00024689263,0.001218214,0.0005998388,0.0005668632,0.0023146062,0.0008143198,0.15192375],"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":[0.00042861386,0.0006921322,0.00004847894,0.00022386198,0.00035251907,0.00012315698,0.0011995984,0.00009782933,0.003370756,0.0017192343,0.7642295,0.22751433],"study_design_scores_gemma":[0.002310392,0.00046889536,0.0048243613,0.00021751657,0.0001873961,0.00024492227,0.00040721416,0.0015393451,0.0006925407,0.000050973653,0.9885142,0.0005422442],"about_ca_topic_score_codex":0.00004822529,"about_ca_topic_score_gemma":0.0000023968853,"teacher_disagreement_score":0.90534407,"about_ca_system_score_codex":0.0006387488,"about_ca_system_score_gemma":0.000031375806,"threshold_uncertainty_score":0.9996907},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035245463,0.0009522583,0.000041928768,0.00019761767,0.00019601482,0.000988986,0.989197,0.00034241503,0.004559204],"genre_scores_gemma":[0.0017390802,0.0020766077,0.00012437646,0.0002155926,0.00020362942,3.7138483e-7,0.99188864,0.0004390923,0.0033126194],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971981,0.00045097581,0.0004967591,0.0008591174,0.0005974224,0.00039762072],"domain_scores_gemma":[0.99790394,0.000086279164,0.00028592505,0.00093186484,0.0005338042,0.00025818855],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00051742216,0.00033717588,0.0005458616,0.0004205511,0.0010508071,0.00090055907,0.0009822667,0.00012878148,0.018473415],"category_scores_gemma":[0.0010567722,0.00033855284,0.00009533956,0.00045401906,0.00044622642,0.00024349983,0.0022455058,0.00046599814,0.003118743],"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":[0.00012537751,0.00012308826,0.000009004057,0.00033425473,0.00045741638,0.00010453632,0.00020428275,0.0000020200011,0.0015523423,0.000021665435,0.9940538,0.0030122383],"study_design_scores_gemma":[0.0007019338,0.00020076925,0.0015463299,0.00018740348,0.00024582658,0.00002672928,0.00055228395,0.000014505896,0.00019219036,0.000043571374,0.99601674,0.00027169802],"about_ca_topic_score_codex":0.00015092015,"about_ca_topic_score_gemma":0.0000042067945,"teacher_disagreement_score":0.0153546715,"about_ca_system_score_codex":0.00024365423,"about_ca_system_score_gemma":0.000026877773,"threshold_uncertainty_score":0.99990666},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022394244,0.0010966504,0.000025159468,0.000026092759,0.0001768082,0.0007405987,0.9754187,0.000068186695,0.000053577125],"genre_scores_gemma":[0.020360619,0.00030312082,0.00016405823,0.00037021542,0.00023138194,0.00002837116,0.9784793,0.000058431062,0.000004463439],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972297,0.00015193615,0.0007221155,0.00072274107,0.0008391306,0.0003344107],"domain_scores_gemma":[0.9982952,0.00006054765,0.00042270546,0.0008451217,0.00008134688,0.00029511916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022134007,0.0004724089,0.0010437343,0.00014182726,0.000052828243,0.000027618324,0.00022330573,0.00037844485,0.00029889733],"category_scores_gemma":[0.00014395466,0.0004847298,0.00015897595,0.00019954653,0.00015420261,0.00020896383,0.000034262906,0.00028301944,0.0000059566387],"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":[0.001112081,0.0002598221,0.007230875,0.0005925761,0.000344785,0.000120890196,0.00007651112,0.0000032878497,0.0039406233,8.555759e-8,0.9851741,0.001144321],"study_design_scores_gemma":[0.0038698923,0.00018017653,0.35575828,0.0008038848,0.00066867005,0.000003585352,0.00004417418,0.00016155584,0.00039266798,0.00000391025,0.6377101,0.00040310607],"about_ca_topic_score_codex":0.44903907,"about_ca_topic_score_gemma":0.3356727,"teacher_disagreement_score":0.3485274,"about_ca_system_score_codex":0.00052017206,"about_ca_system_score_gemma":0.00035258508,"threshold_uncertainty_score":0.99976045},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014168477,0.000118563315,1.14274634e-7,0.0005785626,0.00023364589,0.00038565721,0.9844168,0.000040648705,0.00005756698],"genre_scores_gemma":[0.0039082267,0.00013255976,0.0000055839278,0.0011963632,0.00017622478,0.00002925442,0.99397975,0.000074086856,0.00049796177],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980249,0.00006940235,0.0005549731,0.0004148663,0.0003580349,0.0005778134],"domain_scores_gemma":[0.9983621,0.00005084994,0.0001576454,0.00097525533,0.00011459669,0.00033951938],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015556527,0.00033484818,0.0007790869,0.0004373044,0.000031480115,0.00002951249,0.000391491,0.00029491223,0.0006270678],"category_scores_gemma":[0.0001717011,0.00026162996,0.00010049358,0.00021911635,0.00009993075,0.00005132971,0.00012497134,0.00043440438,0.0007906402],"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":[0.00005431543,0.00003989608,0.014813258,0.000498915,0.00009483065,0.00038789053,0.00005139901,3.206084e-8,0.00009040149,2.3626082e-7,0.9839112,0.000057605143],"study_design_scores_gemma":[0.00040893658,0.00002598308,0.2475446,0.00021927708,0.000099866134,0.00000970868,0.000012886971,0.0000022760732,0.000058282367,0.0000041514118,0.7513977,0.00021632097],"about_ca_topic_score_codex":0.74057335,"about_ca_topic_score_gemma":0.8897576,"teacher_disagreement_score":0.23273136,"about_ca_system_score_codex":0.00021355158,"about_ca_system_score_gemma":0.00028327145,"threshold_uncertainty_score":0.99998736},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018125733,0.013320655,0.0000033065246,0.01836182,0.00030691712,0.0014033035,0.9096484,0.0006438356,0.038186014],"genre_scores_gemma":[0.553796,0.00007758397,0.00017531721,0.0076347133,0.00016136831,0.00011761595,0.43701324,0.00004740919,0.0009767596],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99812657,0.00020044748,0.00033756925,0.000476035,0.00036711927,0.0004922602],"domain_scores_gemma":[0.9985038,0.00009799237,0.0001142546,0.0006861788,0.00021392228,0.00038387632],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019863101,0.00016938512,0.00040484796,0.00011703063,0.000049663693,0.00006252764,0.00014800463,0.000087979104,0.20068839],"category_scores_gemma":[0.002144109,0.00016670628,0.0000893452,0.00075691077,0.000008219713,0.00023788004,0.00018675977,0.00026132708,0.0027849996],"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":[0.00002788433,0.00025271837,0.078030534,0.0005633122,0.000022547903,0.00051858736,0.00017084202,0.000001946256,0.000057441837,0.000008444449,0.904134,0.016211754],"study_design_scores_gemma":[0.00064091635,0.000039416453,0.5955609,0.0009690997,7.1150936e-7,0.00002599679,0.000032385637,0.000038371552,0.0000090729045,0.0000036987972,0.4025754,0.00010400117],"about_ca_topic_score_codex":0.000012240127,"about_ca_topic_score_gemma":0.00031807876,"teacher_disagreement_score":0.5356702,"about_ca_system_score_codex":0.00016793457,"about_ca_system_score_gemma":0.00094205333,"threshold_uncertainty_score":0.99799144},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937614,0.0000069878774,0.002233699,0.00007769722,0.0005945516,0.0016137537,0.0003766448,0.0009479129,0.00038735365],"genre_scores_gemma":[0.99835193,6.839903e-7,0.000068756366,0.00033183873,0.00014343494,0.00039989056,0.0006156199,0.000022451897,0.000065364744],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982698,0.00010599056,0.0007157763,0.0002286661,0.0003169766,0.00036279374],"domain_scores_gemma":[0.99842215,0.000025638548,0.00037178083,0.0006903597,0.0002990129,0.00019105127],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00072822004,0.00020438361,0.0004940827,0.00036808403,0.0001419205,0.00016612529,0.00017709726,0.00010730308,0.000013192906],"category_scores_gemma":[0.000110312314,0.0001738007,0.000098734425,0.0003980289,0.000017834687,0.0015316933,0.00009775276,0.00009070878,0.0008594495],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002975144,0.00001051804,0.93095475,0.00061711634,0.0001826414,0.0000060494176,0.06623846,0.00022897602,3.366926e-7,0.00004189307,0.00074851105,0.00067323504],"study_design_scores_gemma":[0.0028629075,0.00070781435,0.81784785,0.00023919638,0.000040281422,0.00002947633,0.17106678,0.005615953,0.000006738925,0.0000014363712,0.0013833249,0.00019825752],"about_ca_topic_score_codex":0.0000065152717,"about_ca_topic_score_gemma":0.000013205517,"teacher_disagreement_score":0.11310691,"about_ca_system_score_codex":0.00010774728,"about_ca_system_score_gemma":0.000057842437,"threshold_uncertainty_score":0.9999185},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011954682,0.00008478132,3.6423015e-7,0.0008166193,0.0000030526423,0.00021524404,0.9953654,0.000012069792,0.0023070294],"genre_scores_gemma":[0.2683305,7.940675e-7,0.00024749976,0.00003634544,0.00003276816,0.000042087093,0.7311637,0.0000073880415,0.00013894509],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9977193,0.0013190585,0.00022761333,0.00021400303,0.00035636342,0.00016370071],"domain_scores_gemma":[0.99683833,0.002147172,0.00009749895,0.00059588044,0.00023038275,0.00009070512],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008244888,0.00005294852,0.00014570194,0.00008077439,0.000027811864,0.000023378892,0.00027975443,0.0000246603,0.57524484],"category_scores_gemma":[0.015694663,0.000042382348,0.000017810542,0.0005167522,0.00003596321,0.00012004956,0.00042163284,0.0002476,0.0001736598],"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":[0.0000030849314,0.00008291257,0.023940574,0.00023488949,0.000009182728,0.000016862336,0.000106708045,3.6230222e-8,1.4300207e-7,0.0000074224263,0.95868295,0.016915204],"study_design_scores_gemma":[0.00013400482,0.000048828697,0.53804475,0.0014011186,0.0000011120285,0.0000062198656,0.00014742358,0.000019494357,4.397336e-7,0.0000106394755,0.4601657,0.000020219111],"about_ca_topic_score_codex":0.000034523164,"about_ca_topic_score_gemma":0.00031625608,"teacher_disagreement_score":0.5750712,"about_ca_system_score_codex":0.000021861853,"about_ca_system_score_gemma":0.00042388652,"threshold_uncertainty_score":0.99259657},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064843986,0.014885786,2.1273877e-8,0.0000069629023,0.0000050473145,0.0006876951,0.981903,0.000013648792,0.0018493691],"genre_scores_gemma":[0.37735644,0.000010507104,0.000023782084,0.000026147114,0.0000067655033,0.0007528943,0.6218202,0.0000025259164,7.0717255e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.99907726,0.00014381003,0.00023021201,0.00008653049,0.00037111514,0.000091097114],"domain_scores_gemma":[0.99932534,0.00013012196,0.00014650899,0.00015201545,0.00021081875,0.000035207984],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010134748,0.000047695798,0.00015679571,0.000067796915,0.00010365153,0.000009129083,0.00007366212,0.000015768865,0.21343665],"category_scores_gemma":[0.0006268377,0.000041875395,0.000032386826,0.00036386406,0.000013592118,0.00010063817,0.0002521623,0.00008992936,0.000010080335],"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":[0.00004044977,0.000017343675,0.0065973895,0.12246313,0.000054150834,0.0000071146687,0.0001852214,0.000010484442,0.0000028321567,0.00005689293,0.86755913,0.0030058804],"study_design_scores_gemma":[0.0004290865,0.00008189751,0.11520866,0.83332926,0.000030751402,0.000085670814,0.0034180998,0.00043868614,0.0000071530235,0.0000021609183,0.046890676,0.000077908415],"about_ca_topic_score_codex":0.000021863063,"about_ca_topic_score_gemma":0.000014685848,"teacher_disagreement_score":0.82066846,"about_ca_system_score_codex":0.00008730015,"about_ca_system_score_gemma":0.00015716828,"threshold_uncertainty_score":0.7872824},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07597805,0.0037544875,0.0000067263354,0.05908671,0.000036322333,0.0008655386,0.85892373,0.00048413334,0.0008643092],"genre_scores_gemma":[0.9183069,0.000016481203,0.00001804306,0.0054659755,0.00018556952,0.000058215333,0.07585833,0.000013652621,0.0000768148],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99922836,0.00007410491,0.00012285664,0.00019769648,0.00020932477,0.00016766826],"domain_scores_gemma":[0.9977973,0.0014562389,0.000093707735,0.00015059792,0.00009496933,0.00040719812],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000091485155,0.00009415077,0.00016237645,0.00002277383,0.00017450223,0.00011766358,0.000071599025,0.000057168465,0.0041923183],"category_scores_gemma":[0.028458545,0.00007089095,0.000028230197,0.00013669189,0.000031657226,0.000096313546,0.00008820952,0.00010118474,0.0001352744],"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":[0.000031279742,0.0000070519254,0.38511387,0.00029727758,0.00005070116,0.000048378926,0.00008050458,2.8785362e-7,0.000008769154,0.0000128067895,0.6119103,0.002438778],"study_design_scores_gemma":[0.0008178036,0.000065946515,0.22582383,0.00008736679,0.000029854456,0.000003942877,0.000049762166,0.00013032304,0.000024901543,0.000006921277,0.77288145,0.00007789887],"about_ca_topic_score_codex":0.0000048620423,"about_ca_topic_score_gemma":0.000033336753,"teacher_disagreement_score":0.84232885,"about_ca_system_score_codex":0.00007613381,"about_ca_system_score_gemma":0.00033376046,"threshold_uncertainty_score":0.996718},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9915901,0.00006064794,0.000015752807,0.0057106195,0.000016661428,0.0001786825,0.00025386337,0.000009343345,0.002164375],"genre_scores_gemma":[0.99279505,0.00019269585,0.00012488756,0.000017169283,0.000019436839,5.543109e-7,0.00029017162,0.0000040231203,0.006556016],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998631,0.0002537734,0.00009427287,0.00018056089,0.00062042766,0.00021993778],"domain_scores_gemma":[0.99856776,0.0006944462,0.000053088635,0.0002277573,0.00035305796,0.00010390949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009555511,0.000050870472,0.00014919751,0.00011110386,0.00019792632,0.000022390597,0.00010976089,0.00004360617,0.00020731935],"category_scores_gemma":[0.00058857416,0.000050356717,0.000038697606,0.00021625879,0.00008332332,0.00010125768,0.00015770609,0.0002436002,0.000014064193],"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":[0.00000951307,0.000049908613,0.99278176,0.00002983082,0.00007457303,0.00020562334,0.00040182576,5.623165e-7,0.000112603564,0.00024697636,0.0038759522,0.0022108897],"study_design_scores_gemma":[0.00064382964,0.0000606337,0.91209733,0.000045167155,0.000020530182,0.0000026026298,0.0017139242,0.000012500762,0.00012485948,0.00004184742,0.085197225,0.0000395718],"about_ca_topic_score_codex":0.019681046,"about_ca_topic_score_gemma":0.8183958,"teacher_disagreement_score":0.79871476,"about_ca_system_score_codex":0.0005577521,"about_ca_system_score_gemma":0.000381533,"threshold_uncertainty_score":0.986847},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6515424,0.04730233,0.011911983,0.09813062,0.007065075,0.002182385,0.03560712,0.0014157465,0.14484231],"genre_scores_gemma":[0.9404756,0.0034431266,0.0054987404,0.0034314916,0.0026122306,0.000106279535,0.023436813,0.00011231438,0.020883426],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961271,0.0012843151,0.0006458878,0.00070201216,0.0005418781,0.00069876335],"domain_scores_gemma":[0.996962,0.0009551593,0.00020762536,0.0010103432,0.00031566253,0.0005491765],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007511867,0.00040082683,0.00057862396,0.00022283026,0.00022102743,0.00013322016,0.00028013982,0.00031233113,0.006813334],"category_scores_gemma":[0.0011069699,0.00047620927,0.00028176635,0.0012440265,0.00027510413,0.00065411156,0.000083869534,0.0005820934,0.0012379215],"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":[0.00019832359,0.004532969,0.06584704,0.0027878347,0.0010899301,0.003754137,0.020217903,0.0026088187,0.009459602,0.06310663,0.8112859,0.015110871],"study_design_scores_gemma":[0.0026210838,0.00018045088,0.4473203,0.0012261212,0.00069304154,0.00044261647,0.0029002859,0.0028396442,0.002614999,0.002402469,0.53601986,0.0007391254],"about_ca_topic_score_codex":0.0011411039,"about_ca_topic_score_gemma":0.002567196,"teacher_disagreement_score":0.38147327,"about_ca_system_score_codex":0.0005097586,"about_ca_system_score_gemma":0.00089964084,"threshold_uncertainty_score":0.999769},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003471743,0.014225578,0.00001530116,0.0005895016,0.0018421221,0.000671733,0.000104068924,0.000758278,0.9817587],"genre_scores_gemma":[0.0018468942,0.00096090516,0.00028386727,0.00072590914,0.003242805,0.00007097886,0.00016329189,0.0005245491,0.9921808],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985433,0.00009634639,0.00027925952,0.00044071567,0.00039422396,0.00024611046],"domain_scores_gemma":[0.9983317,0.0000791424,0.00024194812,0.0010590821,0.000036848844,0.00025125343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009044406,0.0002619346,0.0004207035,0.0000915654,0.00011986318,0.000045240307,0.0003105582,0.00022475507,0.00018018587],"category_scores_gemma":[0.00008171923,0.00018075752,0.0002421576,0.00022484963,0.00020337878,0.000013712729,0.0001184232,0.00040566438,0.00043724774],"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":[0.00008846183,0.000045290326,0.00462615,0.000189442,0.00031033516,0.0007442575,0.00001569541,7.975243e-8,0.00014896043,0.00010011984,0.99156475,0.00216644],"study_design_scores_gemma":[0.0003739074,0.00006520202,0.004746274,0.00033142598,0.0001605009,0.000068125315,0.000014662783,0.000015892903,0.000038411443,0.000010215864,0.9939936,0.00018182954],"about_ca_topic_score_codex":0.000095049574,"about_ca_topic_score_gemma":0.00007069806,"teacher_disagreement_score":0.013264673,"about_ca_system_score_codex":0.000037941885,"about_ca_system_score_gemma":0.00021983807,"threshold_uncertainty_score":0.73710775},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9405305,0.0006050786,0.0052659176,0.0012332441,0.00026128924,0.0012823788,0.00094666914,0.0006980194,0.049176928],"genre_scores_gemma":[0.9980059,0.000023062208,0.00004437459,0.0000689652,0.000039399485,6.651126e-8,0.0012342798,0.0002532029,0.00033074062],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99891144,0.00015697218,0.0002512744,0.00024713037,0.00017762359,0.00025554813],"domain_scores_gemma":[0.9990763,0.000014783422,0.000058749993,0.0003663734,0.00028919472,0.00019458993],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021660324,0.000085965046,0.00015629653,0.0005685741,0.0004727192,0.0000874902,0.00024335917,0.00004332835,0.001776045],"category_scores_gemma":[0.00068733894,0.00009896246,0.00004564188,0.0009187137,0.0000801711,0.00013941062,0.00018223193,0.00014544862,0.00047760177],"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":[0.0015037362,0.002270348,0.030508373,0.0036177984,0.0006783259,0.00030758826,0.0029149272,0.0013495001,0.2497545,0.025940144,0.066387765,0.614767],"study_design_scores_gemma":[0.0015935824,0.00028443415,0.7425531,0.00035605655,0.00006479494,0.000016388887,0.0002872511,0.0011439058,0.0025271517,0.00034672677,0.250639,0.0001876142],"about_ca_topic_score_codex":0.005895929,"about_ca_topic_score_gemma":0.0047178534,"teacher_disagreement_score":0.7120447,"about_ca_system_score_codex":0.0006455172,"about_ca_system_score_gemma":0.000046324505,"threshold_uncertainty_score":0.99913645},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937134,0.000023238568,0.0048806956,0.00021393213,0.00006918861,0.00068320957,0.00021560013,0.00006827196,0.00013250775],"genre_scores_gemma":[0.9993537,4.3165414e-7,0.00016974649,0.00018894265,0.000055564793,0.00008214373,0.00013367597,0.000008190402,0.0000075707308],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984355,0.000069922055,0.00097186456,0.00016466592,0.00021541466,0.00014262338],"domain_scores_gemma":[0.99800533,0.00004727074,0.0010516006,0.0004808653,0.0003180443,0.00009687994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088067434,0.00012495946,0.00030018023,0.00010836275,0.000078559046,0.000044937657,0.00007682498,0.000045300483,0.0000028803881],"category_scores_gemma":[0.00047503266,0.00008601993,0.000030095654,0.00021687956,0.0001580655,0.00022000044,0.00005886707,0.000067226254,0.000020193116],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027576092,0.0000034037434,0.9939269,0.001103208,0.000012013599,0.0000012742285,0.002843523,0.000010902918,0.000054728203,0.000020228616,0.00002562059,0.0017224305],"study_design_scores_gemma":[0.0010660767,0.000023765766,0.9756739,0.00019687171,0.000015040774,0.000014211937,0.0008715823,0.021668863,0.00020911297,0.0000012192954,0.00016920608,0.00009012319],"about_ca_topic_score_codex":0.00006496883,"about_ca_topic_score_gemma":0.0000075521975,"teacher_disagreement_score":0.021657959,"about_ca_system_score_codex":0.000035139095,"about_ca_system_score_gemma":0.00010140063,"threshold_uncertainty_score":0.3507791},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48758546,0.000048841594,0.490769,0.0008130988,0.00021070143,0.00184492,0.018308716,0.00022167467,0.00019760145],"genre_scores_gemma":[0.982842,0.0000016772271,0.015631221,0.00032751815,0.00014598675,0.00018160499,0.0007360731,0.000013146275,0.00012074541],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99830544,0.000027212138,0.0009466703,0.00024457363,0.00023092523,0.0002451604],"domain_scores_gemma":[0.99790823,0.00013932705,0.00035262026,0.0008890308,0.000602756,0.00010802054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089879165,0.0001650189,0.00049876183,0.00038030377,0.00008330376,0.000097302756,0.000192252,0.00010364783,0.000015605501],"category_scores_gemma":[0.0013325027,0.00010732748,0.00008130407,0.0003035739,0.00003966869,0.0007765449,0.00010650226,0.000034555516,0.000038878843],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044295477,0.0000021910591,0.9905166,0.0036514048,0.00037443914,0.0000015416629,0.0039350167,0.000022504857,4.6761195e-7,0.00025586112,0.0003307403,0.00046624854],"study_design_scores_gemma":[0.011062757,0.00020690824,0.86785585,0.01592327,0.0016837332,0.000034259825,0.009959699,0.08178284,0.00013867466,0.0000286732,0.010491911,0.0008314528],"about_ca_topic_score_codex":0.0000123944465,"about_ca_topic_score_gemma":0.000003901016,"teacher_disagreement_score":0.49525657,"about_ca_system_score_codex":0.000087149136,"about_ca_system_score_gemma":0.00005789372,"threshold_uncertainty_score":0.4376688},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06821715,0.00041512752,0.016147224,0.00012352207,0.00036238294,0.005033914,0.8991113,0.0000970607,0.010492347],"genre_scores_gemma":[0.7593726,8.833541e-7,0.03478175,0.000056988618,0.00023322683,0.00112713,0.20299636,0.00006465627,0.0013664094],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970914,0.00054467743,0.0007495247,0.0006459464,0.00073577784,0.00023267472],"domain_scores_gemma":[0.9959721,0.002299048,0.00037377005,0.00052279007,0.00062524783,0.00020706511],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003115819,0.00020582454,0.0005426434,0.00031825204,0.00009537421,0.00019229564,0.00024776402,0.0001175008,0.19049619],"category_scores_gemma":[0.003219948,0.00019629404,0.00015574184,0.0006869418,0.000053328575,0.0008084814,0.000065787666,0.00015926578,0.00075045467],"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":[0.0012930118,0.0005195648,0.00032402392,0.0015845732,0.0005652772,0.000032257994,0.00010826996,0.01253946,0.007732919,0.000022601931,0.7368119,0.23846616],"study_design_scores_gemma":[0.0011046305,0.00015931776,0.004857307,0.0016675849,0.000130334,0.00002244084,0.00009825701,0.97400635,0.000902817,0.00007069723,0.01674137,0.00023886068],"about_ca_topic_score_codex":0.00009659942,"about_ca_topic_score_gemma":0.0000977648,"teacher_disagreement_score":0.9614669,"about_ca_system_score_codex":0.00046375493,"about_ca_system_score_gemma":0.0013719497,"threshold_uncertainty_score":0.9645827},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9949288,0.000013137262,0.00038342967,0.00022067588,0.00022657274,0.0007093739,0.002406037,0.0003578446,0.000754145],"genre_scores_gemma":[0.9990354,0.0000037508478,0.000015118973,0.00005247891,0.000018976561,0.00016425282,0.00030052048,0.0000136236795,0.00039585758],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979008,0.00012821305,0.0010290358,0.00015824666,0.00050084677,0.00028290163],"domain_scores_gemma":[0.9981783,0.00008479816,0.000471241,0.0007472969,0.00036943372,0.00014893127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014154401,0.00016096073,0.00037464802,0.00028145578,0.000073742056,0.00006377067,0.00016479348,0.00003839041,0.000012613849],"category_scores_gemma":[0.0007248299,0.00010608958,0.00008603925,0.0008047432,0.000079826284,0.0002760719,0.0000631811,0.0000862588,0.00053256954],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007352416,0.000003000319,0.98683363,0.0010082916,0.00007644243,0.00022314831,0.009119293,0.00025318106,6.9570297e-7,0.000017463522,0.00082057464,0.00090903975],"study_design_scores_gemma":[0.0007168583,0.00001581767,0.9820447,0.00057951413,0.000021187756,0.0000466008,0.0052793524,0.006195926,0.0000634961,0.0000018515225,0.0048944135,0.00014026873],"about_ca_topic_score_codex":0.000041625084,"about_ca_topic_score_gemma":0.000005025958,"teacher_disagreement_score":0.005942745,"about_ca_system_score_codex":0.00017156616,"about_ca_system_score_gemma":0.0002878177,"threshold_uncertainty_score":0.6845281},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037612338,0.000047744063,0.0000018189625,0.000015686048,0.000037450187,0.00018842945,0.96192634,0.000018276909,0.00015193177],"genre_scores_gemma":[0.43857288,0.0000036951833,0.000044382326,0.00002064784,0.00004078413,0.00028313373,0.5609876,0.000008085325,0.00003878934],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9987594,0.0001022418,0.00043517872,0.00023274835,0.00033530704,0.00013514521],"domain_scores_gemma":[0.9988361,0.00030463777,0.00044030376,0.00023299045,0.00011197598,0.00007400434],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006682865,0.00009660243,0.00040604733,0.0003792353,0.000057769816,0.0000050056315,0.000090341564,0.000018915169,0.94470567],"category_scores_gemma":[0.00022600977,0.0001092738,0.00010386685,0.0005490669,0.0000065932786,0.0001563729,0.0001798281,0.00014331973,0.000014909035],"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":[0.00039103316,0.0004266789,0.025169976,0.0033644156,0.00066754996,0.00001753285,0.0014607338,0.0015021517,0.00012892822,1.00446684e-7,0.961217,0.005653939],"study_design_scores_gemma":[0.00081270805,0.0002370692,0.9238631,0.004686769,0.00011397997,0.00000909438,0.0011347944,0.0134761,0.00014519524,8.5266834e-7,0.05538194,0.00013839005],"about_ca_topic_score_codex":0.0024529612,"about_ca_topic_score_gemma":0.009494815,"teacher_disagreement_score":0.9446907,"about_ca_system_score_codex":0.000101995196,"about_ca_system_score_gemma":0.00012069944,"threshold_uncertainty_score":0.52983296},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040808036,0.00012976352,2.468417e-7,0.00008745207,0.000014576949,0.00013499892,0.99480957,0.000009118282,0.00073348626],"genre_scores_gemma":[0.10332569,0.0000035900348,0.000187459,0.00016689424,0.00004733292,0.00008394588,0.89577585,0.000012341377,0.00039690026],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99915004,0.000031172203,0.0002486984,0.00019749196,0.00023008666,0.00014249931],"domain_scores_gemma":[0.9986207,0.00053608016,0.00022282312,0.00022746438,0.00022177717,0.00017119234],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000017710667,0.00010688775,0.00027079147,0.0000785377,0.0000106646785,0.0000027329531,0.00007929156,0.000041227755,0.9920719],"category_scores_gemma":[0.0049876417,0.0000797775,0.00004184263,0.00017764534,0.000023106551,0.00005640108,0.0000548658,0.000060102248,0.00007154897],"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":[0.000043356216,0.00003038187,0.03188992,0.00016924426,0.000076588076,0.000020234742,0.0000057203174,0.0000030837155,0.000027103599,8.4064304e-8,0.966737,0.0009973036],"study_design_scores_gemma":[0.00071355654,0.000031654272,0.5925196,0.0059106466,0.000026124895,0.0000026830264,0.0000096554995,0.000117463416,0.00043019291,0.0000025870447,0.40012133,0.00011449974],"about_ca_topic_score_codex":0.28147036,"about_ca_topic_score_gemma":0.8337911,"teacher_disagreement_score":0.99200034,"about_ca_system_score_codex":0.00007612055,"about_ca_system_score_gemma":0.0016642511,"threshold_uncertainty_score":0.7233144},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11037745,0.00006136096,0.0000021332357,0.000016793298,0.000007771832,0.0009646607,0.8884587,0.000024723475,0.0000863661],"genre_scores_gemma":[0.2107334,0.0000010878056,0.0016525337,0.00019890879,0.000039840295,0.0010509768,0.7862369,0.000026437297,0.00005992141],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9982677,0.000092663766,0.00054055237,0.000271933,0.00062804547,0.00019906623],"domain_scores_gemma":[0.99665874,0.0011427989,0.0005923313,0.0003826047,0.0011295011,0.00009399803],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030499802,0.00014421322,0.0005025561,0.00012385202,0.00001922498,0.000003023037,0.0000957301,0.000056379537,0.82688874],"category_scores_gemma":[0.0052369647,0.00010745668,0.0000661689,0.00021186027,0.000020025771,0.00016334392,0.000033749668,0.000065384214,0.000056910627],"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":[0.0016638152,0.0006060934,0.014968127,0.0009951486,0.000120631456,0.000003835504,0.00019607246,0.00007339678,0.00049152994,0.0000023059506,0.97813094,0.0027481262],"study_design_scores_gemma":[0.0064823306,0.0013308232,0.8768254,0.011242592,0.000021644406,0.000009563924,0.000032021926,0.0011017092,0.00069733633,0.000023298748,0.102035634,0.00019767252],"about_ca_topic_score_codex":0.0013956784,"about_ca_topic_score_gemma":0.04434576,"teacher_disagreement_score":0.8760953,"about_ca_system_score_codex":0.0002777135,"about_ca_system_score_gemma":0.0035210212,"threshold_uncertainty_score":0.97309244},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11037745,0.00006136096,0.0000021332357,0.000016793298,0.000007771832,0.0009646607,0.8884587,0.000024723475,0.0000863661],"genre_scores_gemma":[0.2107334,0.0000010878056,0.0016525337,0.00019890879,0.000039840295,0.0010509768,0.7862369,0.000026437297,0.00005992141],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9982677,0.000092663766,0.00054055237,0.000271933,0.00062804547,0.00019906623],"domain_scores_gemma":[0.99665874,0.0011427989,0.0005923313,0.0003826047,0.0011295011,0.00009399803],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030499802,0.00014421322,0.0005025561,0.00012385202,0.00001922498,0.000003023037,0.0000957301,0.000056379537,0.82688874],"category_scores_gemma":[0.0052369647,0.00010745668,0.0000661689,0.00021186027,0.000020025771,0.00016334392,0.000033749668,0.000065384214,0.000056910627],"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":[0.0016638152,0.0006060934,0.014968127,0.0009951486,0.000120631456,0.000003835504,0.00019607246,0.00007339678,0.00049152994,0.0000023059506,0.97813094,0.0027481262],"study_design_scores_gemma":[0.0064823306,0.0013308232,0.8768254,0.011242592,0.000021644406,0.000009563924,0.000032021926,0.0011017092,0.00069733633,0.000023298748,0.102035634,0.00019767252],"about_ca_topic_score_codex":0.0013956784,"about_ca_topic_score_gemma":0.04434576,"teacher_disagreement_score":0.8760953,"about_ca_system_score_codex":0.0002777135,"about_ca_system_score_gemma":0.0035210212,"threshold_uncertainty_score":0.97309244},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006454323,0.014941997,2.1413342e-8,0.0000069506127,0.0000050017957,0.00068836514,0.9818539,0.000013668926,0.0018446516],"genre_scores_gemma":[0.38077822,0.000010449297,0.000023677298,0.000026032134,0.0000066594966,0.00074881,0.61840296,0.000002511386,6.7847884e-7],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9990772,0.00014379084,0.00023029612,0.000086539345,0.00037109136,0.00009110767],"domain_scores_gemma":[0.999329,0.00013037545,0.0001465316,0.0001520947,0.00020682032,0.00003520922],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010143054,0.000047699185,0.0001568335,0.0000678291,0.00010370417,0.000009134041,0.000073717434,0.000015770225,0.21055883],"category_scores_gemma":[0.00061790075,0.000041883515,0.000032389384,0.00036381662,0.00001359461,0.00010065318,0.00025226042,0.000089926514,0.000009870348],"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":[0.000041220846,0.000017497618,0.006667663,0.12252505,0.000054604367,0.000007199592,0.00018696174,0.000010421599,0.0000028111683,0.000059502097,0.86739844,0.0030285963],"study_design_scores_gemma":[0.00042735194,0.000083322375,0.11574548,0.8347145,0.00003098758,0.00008657851,0.003428686,0.00044023883,0.000007003012,0.0000021833084,0.044955753,0.00007792564],"about_ca_topic_score_codex":0.000021664984,"about_ca_topic_score_gemma":0.000014747339,"teacher_disagreement_score":0.8224427,"about_ca_system_score_codex":0.000086957356,"about_ca_system_score_gemma":0.00015656723,"threshold_uncertainty_score":0.7901628},"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":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94870144,0.0007896867,0.0056275604,0.011351925,0.0006665921,0.0036861459,0.00057878863,0.0002647775,0.028333113],"genre_scores_gemma":[0.45020056,0.00062233093,0.4631771,0.0010512403,0.00034406528,0.00002168009,0.052032273,0.000187077,0.03236365],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982592,0.0003046437,0.0003433159,0.0005552939,0.0001971107,0.0003404787],"domain_scores_gemma":[0.9980934,0.00071731553,0.00043427042,0.0004590774,0.000089455934,0.00020644962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005686025,0.0002451194,0.00077657064,0.0007593279,0.00009493584,0.000025411613,0.00037742197,0.0002345477,0.00017930806],"category_scores_gemma":[0.00084959005,0.0002742998,0.00019821872,0.0006588943,0.000069053116,0.00045025747,0.000091390044,0.00020533196,0.000012660196],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011887447,0.0014950967,0.64326984,0.012982272,0.0018894848,0.000041123138,0.104873866,0.000043838594,0.00030175384,0.023926316,0.08581101,0.11347796],"study_design_scores_gemma":[0.0015263769,0.00060454116,0.9383564,0.00071809924,0.000049175036,0.0000020368714,0.009348134,0.00043620885,0.00005768798,0.00067451096,0.0477856,0.00044125965],"about_ca_topic_score_codex":0.0018147234,"about_ca_topic_score_gemma":0.009614204,"teacher_disagreement_score":0.49850085,"about_ca_system_score_codex":0.000068210684,"about_ca_system_score_gemma":0.0006734002,"threshold_uncertainty_score":0.9999709},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058432156,0.00011333849,0.000009234385,0.00056372787,0.000039080955,0.00065133563,0.991677,0.00045465195,0.0006484097],"genre_scores_gemma":[0.027222645,0.0000052568307,0.0010358318,0.00038633333,0.000302185,0.00018993423,0.96893215,0.000031689502,0.0018940013],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991879,0.000015370064,0.00014605635,0.00022999955,0.00020344384,0.00021724972],"domain_scores_gemma":[0.9991228,0.00015218882,0.000039988117,0.00039591387,0.00016145025,0.00012766522],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007553023,0.000088452674,0.00013802381,0.00009063701,0.000044669032,0.000015868622,0.00010333289,0.000037490998,0.040955614],"category_scores_gemma":[0.0031632434,0.000083485196,0.000066918765,0.00027029202,0.0000043370833,0.00010162576,0.00011082824,0.00006645239,0.004257525],"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":[0.000047235906,0.000039017326,0.00046343834,0.00058165146,0.000019275774,0.000046116937,0.000011111899,0.000015705748,0.0003153297,0.000008629977,0.99102944,0.007423051],"study_design_scores_gemma":[0.0008559029,0.000051505387,0.14954938,0.0011104735,0.000016775604,0.0000065019412,0.000024375773,0.011659577,0.0004311747,0.000033628283,0.8361359,0.00012478455],"about_ca_topic_score_codex":0.0000052826977,"about_ca_topic_score_gemma":0.000022250983,"teacher_disagreement_score":0.15489352,"about_ca_system_score_codex":0.000035914676,"about_ca_system_score_gemma":0.000088312314,"threshold_uncertainty_score":0.9965178},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026753185,0.0014060809,0.00028914286,0.0004292651,0.0003143744,0.00063346035,0.001860883,0.00289769,0.9921424],"genre_scores_gemma":[0.0016247297,0.00022063767,0.00019924997,0.00022325832,0.0007697165,5.2348504e-8,0.011693558,0.015894732,0.96937406],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803,0.000173039,0.0002450271,0.0006847173,0.00047056895,0.00039663617],"domain_scores_gemma":[0.9983272,0.000009272241,0.000112956994,0.0009706376,0.0002272885,0.00035265204],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003145461,0.0002640197,0.00034129436,0.00046751575,0.00035246953,0.0005318685,0.0007063913,0.00017031083,0.42224768],"category_scores_gemma":[0.0005603617,0.00026419156,0.00012087188,0.0005382798,0.00025997972,0.00005858739,0.0008631029,0.0004805578,0.5442028],"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":[0.00003578376,0.00007621279,0.0000024291578,0.00064849533,0.00016792385,0.00020380008,0.0001116004,3.3664782e-7,0.00014978951,0.00071027625,0.9800188,0.017874548],"study_design_scores_gemma":[0.00045405276,0.0001329095,0.000113276576,0.0004753362,0.000088995555,0.00017373681,0.000046879417,0.000019502599,0.000020846534,0.00007880702,0.9981529,0.00024274283],"about_ca_topic_score_codex":0.000013911118,"about_ca_topic_score_gemma":5.45981e-7,"teacher_disagreement_score":0.12195516,"about_ca_system_score_codex":0.00012243033,"about_ca_system_score_gemma":0.000012816352,"threshold_uncertainty_score":0.99998105},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9922178,0.000017057273,0.0063191038,0.00013806827,0.00006988693,0.0007368715,0.00026477847,0.00022176771,0.000014677034],"genre_scores_gemma":[0.99818003,9.3296956e-7,0.000122013116,0.00021626704,0.000067300316,0.0000904512,0.0012876338,0.000014853522,0.000020536065],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987029,0.00014331404,0.000445537,0.00016619352,0.0002767459,0.00026532463],"domain_scores_gemma":[0.99930716,0.000088138295,0.0002019201,0.0002104509,0.00009230526,0.000100014804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078721065,0.00016032936,0.0002111846,0.00041335926,0.00022517605,0.00018149974,0.0000920484,0.000036257017,0.0000026672212],"category_scores_gemma":[0.00026028554,0.00011340974,0.00004752551,0.00047550738,0.00004154094,0.00046002833,0.00007911522,0.00011774532,0.000017073458],"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":[0.00023709102,0.0000020972527,0.9421031,0.0010189456,0.000029948995,0.000056155957,0.037230533,0.019170899,0.0000015512076,0.0000040718705,0.000023564166,0.0001220727],"study_design_scores_gemma":[0.00089802506,0.000021565429,0.3130881,0.0003244073,0.000052337775,0.00008890155,0.031668425,0.65369785,0.000009372591,9.510979e-7,0.000054101205,0.00009598093],"about_ca_topic_score_codex":0.000093015384,"about_ca_topic_score_gemma":5.9248384e-7,"teacher_disagreement_score":0.63452697,"about_ca_system_score_codex":0.00006312432,"about_ca_system_score_gemma":0.000042332227,"threshold_uncertainty_score":0.46247151},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99338645,0.00016297583,0.00273817,0.00007497008,0.00011255457,0.0011998713,0.0006056695,0.0001028236,0.0016165432],"genre_scores_gemma":[0.99935406,0.000009388698,0.000032736927,0.00009388161,0.000032689586,0.00027347694,0.00017056915,0.000004819386,0.000028381008],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99803,0.00023160472,0.00076976756,0.00026066462,0.0004436031,0.00026436654],"domain_scores_gemma":[0.99874663,0.000027750388,0.00036372954,0.0005537614,0.00022410255,0.000084034575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072869286,0.00019490563,0.00051662547,0.00029128205,0.000054049822,0.00018836345,0.00017428785,0.00009019856,0.000007755581],"category_scores_gemma":[0.000098636716,0.00013087453,0.00006294663,0.0012560937,0.000055364853,0.0005847022,0.000037733153,0.00012826815,0.0001189048],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004597264,0.0000048493876,0.99392956,0.00024512433,0.000043897588,0.0000027071146,0.004711914,0.00013004158,8.8414873e-7,0.00009304334,0.000015428983,0.00077655015],"study_design_scores_gemma":[0.0008642538,0.000026353817,0.98015815,0.00014484925,0.00004166554,0.000027914803,0.006322239,0.01221292,0.0000010266241,0.0000028966424,0.000064505424,0.00013321302],"about_ca_topic_score_codex":0.0002076834,"about_ca_topic_score_gemma":0.00008591011,"teacher_disagreement_score":0.013771425,"about_ca_system_score_codex":0.0003181314,"about_ca_system_score_gemma":0.000033533906,"threshold_uncertainty_score":0.5336908},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018125733,0.013320655,0.0000033065246,0.01836182,0.00030691712,0.0014033035,0.9096484,0.0006438356,0.038186014],"genre_scores_gemma":[0.553796,0.00007758397,0.00017531721,0.0076347133,0.00016136831,0.00011761595,0.43701324,0.00004740919,0.0009767596],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99812657,0.00020044748,0.00033756925,0.000476035,0.00036711927,0.0004922602],"domain_scores_gemma":[0.9985038,0.00009799237,0.0001142546,0.0006861788,0.00021392228,0.00038387632],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019863101,0.00016938512,0.00040484796,0.00011703063,0.000049663693,0.00006252764,0.00014800463,0.000087979104,0.20068839],"category_scores_gemma":[0.002144109,0.00016670628,0.0000893452,0.00075691077,0.000008219713,0.00023788004,0.00018675977,0.00026132708,0.0027849996],"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":[0.00002788433,0.00025271837,0.078030534,0.0005633122,0.000022547903,0.00051858736,0.00017084202,0.000001946256,0.000057441837,0.000008444449,0.904134,0.016211754],"study_design_scores_gemma":[0.00064091635,0.000039416453,0.5955609,0.0009690997,7.1150936e-7,0.00002599679,0.000032385637,0.000038371552,0.0000090729045,0.0000036987972,0.4025754,0.00010400117],"about_ca_topic_score_codex":0.000012240127,"about_ca_topic_score_gemma":0.00031807876,"teacher_disagreement_score":0.5356702,"about_ca_system_score_codex":0.00016793457,"about_ca_system_score_gemma":0.00094205333,"threshold_uncertainty_score":0.99799144},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937614,0.0000069878774,0.002233699,0.00007769722,0.0005945516,0.0016137537,0.0003766448,0.0009479129,0.00038735365],"genre_scores_gemma":[0.99835193,6.839903e-7,0.000068756366,0.00033183873,0.00014343494,0.00039989056,0.0006156199,0.000022451897,0.000065364744],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982698,0.00010599056,0.0007157763,0.0002286661,0.0003169766,0.00036279374],"domain_scores_gemma":[0.99842215,0.000025638548,0.00037178083,0.0006903597,0.0002990129,0.00019105127],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00072822004,0.00020438361,0.0004940827,0.00036808403,0.0001419205,0.00016612529,0.00017709726,0.00010730308,0.000013192906],"category_scores_gemma":[0.000110312314,0.0001738007,0.000098734425,0.0003980289,0.000017834687,0.0015316933,0.00009775276,0.00009070878,0.0008594495],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002975144,0.00001051804,0.93095475,0.00061711634,0.0001826414,0.0000060494176,0.06623846,0.00022897602,3.366926e-7,0.00004189307,0.00074851105,0.00067323504],"study_design_scores_gemma":[0.0028629075,0.00070781435,0.81784785,0.00023919638,0.000040281422,0.00002947633,0.17106678,0.005615953,0.000006738925,0.0000014363712,0.0013833249,0.00019825752],"about_ca_topic_score_codex":0.0000065152717,"about_ca_topic_score_gemma":0.000013205517,"teacher_disagreement_score":0.11310691,"about_ca_system_score_codex":0.00010774728,"about_ca_system_score_gemma":0.000057842437,"threshold_uncertainty_score":0.9999185},"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":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018125733,0.013320655,0.0000033065246,0.01836182,0.00030691712,0.0014033035,0.9096484,0.0006438356,0.038186014],"genre_scores_gemma":[0.553796,0.00007758397,0.00017531721,0.0076347133,0.00016136831,0.00011761595,0.43701324,0.00004740919,0.0009767596],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99812657,0.00020044748,0.00033756925,0.000476035,0.00036711927,0.0004922602],"domain_scores_gemma":[0.9985038,0.00009799237,0.0001142546,0.0006861788,0.00021392228,0.00038387632],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019863101,0.00016938512,0.00040484796,0.00011703063,0.000049663693,0.00006252764,0.00014800463,0.000087979104,0.20068839],"category_scores_gemma":[0.002144109,0.00016670628,0.0000893452,0.00075691077,0.000008219713,0.00023788004,0.00018675977,0.00026132708,0.0027849996],"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":[0.00002788433,0.00025271837,0.078030534,0.0005633122,0.000022547903,0.00051858736,0.00017084202,0.000001946256,0.000057441837,0.000008444449,0.904134,0.016211754],"study_design_scores_gemma":[0.00064091635,0.000039416453,0.5955609,0.0009690997,7.1150936e-7,0.00002599679,0.000032385637,0.000038371552,0.0000090729045,0.0000036987972,0.4025754,0.00010400117],"about_ca_topic_score_codex":0.000012240127,"about_ca_topic_score_gemma":0.00031807876,"teacher_disagreement_score":0.5356702,"about_ca_system_score_codex":0.00016793457,"about_ca_system_score_gemma":0.00094205333,"threshold_uncertainty_score":0.99799144},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29184306,0.0014761711,0.0051327506,0.0009744779,0.00014575644,0.0009768985,0.69851965,0.000053284217,0.0008779419],"genre_scores_gemma":[0.75766885,0.000023291243,0.009917768,0.000034013294,0.0001383855,0.000074366704,0.23204818,0.00002492743,0.000070197115],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984305,0.00017283064,0.00024876342,0.0005025017,0.00044217566,0.00020325273],"domain_scores_gemma":[0.99803686,0.0011686812,0.000104994055,0.00022268336,0.00034718908,0.00011958886],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006500747,0.000120631186,0.0002595271,0.00031017148,0.00024700005,0.00008958952,0.00012549071,0.000051140025,0.019310785],"category_scores_gemma":[0.0005950737,0.000108396656,0.000012854658,0.0010519889,0.00023238175,0.00032642583,0.00029682124,0.00030835863,0.000004678389],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011544724,0.00018959044,0.006956517,0.00039035437,0.00029404776,0.000049390786,0.0016085203,0.000041233365,0.00021286866,0.0000174794,0.18672791,0.8023576],"study_design_scores_gemma":[0.0032099306,0.001620804,0.40376085,0.007474024,0.00009405673,0.00007570958,0.0023872333,0.086221755,0.00044250736,0.00010218968,0.4940645,0.0005464167],"about_ca_topic_score_codex":0.00013355668,"about_ca_topic_score_gemma":0.0008256624,"teacher_disagreement_score":0.8018112,"about_ca_system_score_codex":0.00005551026,"about_ca_system_score_gemma":0.0006286432,"threshold_uncertainty_score":0.9815857},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91960144,8.856428e-7,0.06908385,0.000057068046,0.00008186461,0.0015468058,0.009279615,0.00033399265,0.000014502884],"genre_scores_gemma":[0.99234134,9.855271e-8,0.00021314579,0.00007842491,0.00004036211,0.00030908993,0.0069728945,0.000011731153,0.00003293714],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99821794,0.00046082278,0.0005431171,0.00025915675,0.00031054177,0.0002084197],"domain_scores_gemma":[0.998452,0.00012401344,0.000405215,0.00042553086,0.00040777196,0.00018543596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010778763,0.00017926542,0.00050915015,0.0009792075,0.000086387285,0.00003363406,0.00008686435,0.00015271631,0.000017120932],"category_scores_gemma":[0.0005535363,0.00016255258,0.00017173134,0.0015736116,0.00003140666,0.00052678713,0.000020697558,0.00010311055,0.000027339704],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015055166,0.000022096357,0.9795502,0.0010080313,0.00027660592,0.0000041064454,0.008963631,0.0073980396,0.00009299629,0.0000010863371,0.000062677624,0.0011150026],"study_design_scores_gemma":[0.002500706,0.00020170074,0.90745604,0.000055532077,0.00025923213,0.0000041247936,0.0107089095,0.07565791,0.0027314518,6.2963267e-7,0.00027626858,0.0001475084],"about_ca_topic_score_codex":0.0007417165,"about_ca_topic_score_gemma":0.00091169414,"teacher_disagreement_score":0.0727399,"about_ca_system_score_codex":0.0005933214,"about_ca_system_score_gemma":0.00022495502,"threshold_uncertainty_score":0.66287017},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92406046,0.00019950382,0.016522123,0.0032820336,0.0030126246,0.0119527215,0.037851866,0.0010299316,0.0020887179],"genre_scores_gemma":[0.9822882,0.0000021610872,0.0008230519,0.0010733277,0.00012592491,0.0017724636,0.0136975935,0.00004897421,0.00016830492],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9879971,0.00068431353,0.008741649,0.00060933514,0.001254323,0.00071324187],"domain_scores_gemma":[0.9781963,0.00002774155,0.0195188,0.0010360895,0.00086628355,0.00035477066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.028848972,0.00034298486,0.0010395043,0.00054539024,0.0010813734,0.0001742202,0.00021081758,0.00006294032,0.00085501163],"category_scores_gemma":[0.0010864792,0.00037357298,0.00023353384,0.00029934046,0.000043075484,0.0008111564,0.00017130729,0.0002487912,0.00012305146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.003820151,0.00008142713,0.3586144,0.007833299,0.0022033348,0.00003511439,0.12202687,0.16400121,0.00004187463,0.00059474265,0.31628862,0.024458967],"study_design_scores_gemma":[0.018775607,0.0014095184,0.097210035,0.0009896989,0.00045560344,0.0007191662,0.07155469,0.7256011,0.0003518343,0.000016632079,0.08150846,0.0014076816],"about_ca_topic_score_codex":0.00016738336,"about_ca_topic_score_gemma":0.000017552355,"teacher_disagreement_score":0.56159985,"about_ca_system_score_codex":0.011534097,"about_ca_system_score_gemma":0.007356616,"threshold_uncertainty_score":0.9998716},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028169623,0.00001415522,9.8922975e-8,0.00007377578,0.0000058822675,0.0002455111,0.97099406,0.0000090278545,0.00048788477],"genre_scores_gemma":[0.41510198,4.9650896e-7,0.000022439102,0.000088923574,0.000028436547,0.00016188016,0.58451676,0.0000076007723,0.00007151241],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99912107,0.000028610222,0.00020028467,0.00019201751,0.0003248356,0.00013315497],"domain_scores_gemma":[0.9988364,0.0005462065,0.00014835226,0.00019699142,0.00013183607,0.00014023486],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000069169173,0.000095375486,0.00025524458,0.000018638695,0.0000111341105,0.0000027163717,0.00012051902,0.000029931849,0.9810829],"category_scores_gemma":[0.0022362513,0.0000923262,0.000033135148,0.00014478537,0.000010046061,0.000064406464,0.00006925634,0.000102058584,0.00006684042],"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":[0.000056069308,0.000030815103,0.051038623,0.0009423835,0.000010421636,0.00005795164,0.00006346389,0.0000088306915,0.0000012808164,9.31867e-8,0.94744945,0.00034064494],"study_design_scores_gemma":[0.0005535403,0.00015769234,0.90198284,0.005422166,0.0000064888154,0.0000050568765,0.000027148984,0.00017418877,0.00006836866,1.8394205e-7,0.09151252,0.00008981135],"about_ca_topic_score_codex":0.1435817,"about_ca_topic_score_gemma":0.9127897,"teacher_disagreement_score":0.98101604,"about_ca_system_score_codex":0.000032252072,"about_ca_system_score_gemma":0.0006650471,"threshold_uncertainty_score":0.8621213},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91960144,8.856428e-7,0.06908385,0.000057068046,0.00008186461,0.0015468058,0.009279615,0.00033399265,0.000014502884],"genre_scores_gemma":[0.99234134,9.855271e-8,0.00021314579,0.00007842491,0.00004036211,0.00030908993,0.0069728945,0.000011731153,0.00003293714],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99821794,0.00046082278,0.0005431171,0.00025915675,0.00031054177,0.0002084197],"domain_scores_gemma":[0.998452,0.00012401344,0.000405215,0.00042553086,0.00040777196,0.00018543596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010778763,0.00017926542,0.00050915015,0.0009792075,0.000086387285,0.00003363406,0.00008686435,0.00015271631,0.000017120932],"category_scores_gemma":[0.0005535363,0.00016255258,0.00017173134,0.0015736116,0.00003140666,0.00052678713,0.000020697558,0.00010311055,0.000027339704],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015055166,0.000022096357,0.9795502,0.0010080313,0.00027660592,0.0000041064454,0.008963631,0.0073980396,0.00009299629,0.0000010863371,0.000062677624,0.0011150026],"study_design_scores_gemma":[0.002500706,0.00020170074,0.90745604,0.000055532077,0.00025923213,0.0000041247936,0.0107089095,0.07565791,0.0027314518,6.2963267e-7,0.00027626858,0.0001475084],"about_ca_topic_score_codex":0.0007417165,"about_ca_topic_score_gemma":0.00091169414,"teacher_disagreement_score":0.0727399,"about_ca_system_score_codex":0.0005933214,"about_ca_system_score_gemma":0.00022495502,"threshold_uncertainty_score":0.66287017},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011129464,0.000096745156,0.0000017514881,0.000040306044,0.000011921765,0.0002218136,0.9871919,0.000033487333,0.0012726242],"genre_scores_gemma":[0.47256818,3.435119e-7,0.00019708941,0.000072724666,0.000017072323,0.00009773695,0.52701765,0.0000062231343,0.000022946055],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9980618,0.00046452694,0.00063643063,0.00033222637,0.00033778357,0.00016724877],"domain_scores_gemma":[0.9953415,0.0031696318,0.0005126809,0.0004258293,0.0004106495,0.00013973356],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001294124,0.00014228355,0.0005744812,0.00015019895,0.000025337748,0.000004123239,0.00014638329,0.00010313722,0.81958145],"category_scores_gemma":[0.053240307,0.00013308995,0.00019737758,0.0004529891,0.00003672933,0.000048974867,0.00017039916,0.0002206459,0.000036014164],"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":[0.0014095899,0.0007131441,0.006347582,0.0005650328,0.00007619959,0.00020282927,0.00028691493,0.0005951483,0.00007740437,3.1628528e-7,0.9882531,0.001472736],"study_design_scores_gemma":[0.007734136,0.001776172,0.8712254,0.008111833,0.000114620605,0.000014174886,0.0030503473,0.0024289805,0.0009184235,0.000009383153,0.10427401,0.00034251838],"about_ca_topic_score_codex":0.00007147261,"about_ca_topic_score_gemma":0.00033349346,"teacher_disagreement_score":0.8839791,"about_ca_system_score_codex":0.00010178735,"about_ca_system_score_gemma":0.00061673817,"threshold_uncertainty_score":0.9547346},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040808036,0.00012976352,2.468417e-7,0.00008745207,0.000014576949,0.00013499892,0.99480957,0.000009118282,0.00073348626],"genre_scores_gemma":[0.10332569,0.0000035900348,0.000187459,0.00016689424,0.00004733292,0.00008394588,0.89577585,0.000012341377,0.00039690026],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99915004,0.000031172203,0.0002486984,0.00019749196,0.00023008666,0.00014249931],"domain_scores_gemma":[0.9986207,0.00053608016,0.00022282312,0.00022746438,0.00022177717,0.00017119234],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000017710667,0.00010688775,0.00027079147,0.0000785377,0.0000106646785,0.0000027329531,0.00007929156,0.000041227755,0.9920719],"category_scores_gemma":[0.0049876417,0.0000797775,0.00004184263,0.00017764534,0.000023106551,0.00005640108,0.0000548658,0.000060102248,0.00007154897],"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":[0.000043356216,0.00003038187,0.03188992,0.00016924426,0.000076588076,0.000020234742,0.0000057203174,0.0000030837155,0.000027103599,8.4064304e-8,0.966737,0.0009973036],"study_design_scores_gemma":[0.00071355654,0.000031654272,0.5925196,0.0059106466,0.000026124895,0.0000026830264,0.0000096554995,0.000117463416,0.00043019291,0.0000025870447,0.40012133,0.00011449974],"about_ca_topic_score_codex":0.28147036,"about_ca_topic_score_gemma":0.8337911,"teacher_disagreement_score":0.99200034,"about_ca_system_score_codex":0.00007612055,"about_ca_system_score_gemma":0.0016642511,"threshold_uncertainty_score":0.7233144},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018519942,0.00052240794,0.0006899436,0.0016420681,0.00059190043,0.003574198,0.007332959,0.00016226544,0.98529905],"genre_scores_gemma":[0.018599829,0.00022086661,0.008781771,0.013822647,0.00040172617,0.0008347196,0.032268282,0.00064713944,0.92442304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985937,0.000029495892,0.00024338947,0.00044144472,0.00028188952,0.00041007952],"domain_scores_gemma":[0.9987397,0.000016006452,0.00016425518,0.00069478183,0.000044607463,0.00034060568],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020599901,0.00029929276,0.0004890736,0.00049217086,0.00006784725,0.000033919212,0.0002474087,0.00020976619,0.0015175334],"category_scores_gemma":[0.00001933571,0.000281281,0.0001276588,0.00006766755,0.0000656128,0.000045315424,0.000006517536,0.00012906379,0.00007246455],"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":[0.00011692665,0.0001367384,0.012237663,0.00054999546,0.00018499239,0.00020641444,0.000046220794,0.0000023374587,0.0000023835694,0.00036825403,0.98274714,0.0034009442],"study_design_scores_gemma":[0.0022496693,0.00009647384,0.051175613,0.000560889,0.00012454906,2.7496722e-7,0.000031847427,0.000027502143,0.000008127709,0.000033392258,0.9454128,0.0002788457],"about_ca_topic_score_codex":0.112472534,"about_ca_topic_score_gemma":0.9164048,"teacher_disagreement_score":0.80393225,"about_ca_system_score_codex":0.000342483,"about_ca_system_score_gemma":0.0006153485,"threshold_uncertainty_score":0.99996394},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9612184,0.0010817583,0.0000015726997,0.00001220554,0.00050832884,0.0010903832,0.01859322,0.00015213518,0.017341973],"genre_scores_gemma":[0.9876005,0.0003350691,0.0035892765,0.00012499756,0.00006518877,0.000047996924,0.0078368075,0.00017999488,0.00022013865],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9954704,0.00060073996,0.001219056,0.0014053977,0.00068340317,0.0006210007],"domain_scores_gemma":[0.993378,0.0012316769,0.0015685753,0.0026399274,0.00078086776,0.0004009759],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015671874,0.000750904,0.0011369357,0.00026974597,0.00123766,0.00016724049,0.00079093175,0.00047160382,0.00018455024],"category_scores_gemma":[0.0062006013,0.00064842164,0.00021861959,0.00054701476,0.00027866,0.0014039251,0.000511943,0.001429938,0.000026020085],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031980316,0.0024382847,0.0055487473,0.011431952,0.004182197,0.015047628,0.00020191958,0.000011973572,0.15747704,0.03904649,0.00006135008,0.7613544],"study_design_scores_gemma":[0.033643384,0.004122209,0.43837187,0.031267196,0.025815452,0.07275461,0.051030908,0.0054249214,0.19332287,0.034433737,0.08744363,0.022369234],"about_ca_topic_score_codex":0.0016198979,"about_ca_topic_score_gemma":0.0034521136,"teacher_disagreement_score":0.7389852,"about_ca_system_score_codex":0.00022092774,"about_ca_system_score_gemma":0.00042943365,"threshold_uncertainty_score":0.9995967},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99219066,0.00093860924,4.446231e-7,0.0008211444,0.00010479369,0.0013040681,0.0011920915,0.00004882739,0.0033993341],"genre_scores_gemma":[0.9918647,0.00038834513,0.00016515184,0.00014653614,0.000052796167,0.000007722844,0.0015858746,0.00006027695,0.005728631],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964304,0.00049097405,0.00028888497,0.001063568,0.0011134131,0.0006127425],"domain_scores_gemma":[0.99672675,0.00023673906,0.0011257449,0.0009778151,0.0003267794,0.00060619065],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008926525,0.0005201376,0.0009099354,0.0005943258,0.00052861474,0.000081542756,0.0008978663,0.0003162384,0.0005509394],"category_scores_gemma":[0.00031834858,0.00058644067,0.00015725687,0.0010189613,0.0002638679,0.0010055996,0.00025382618,0.0014855376,0.00001121837],"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":[0.0076318774,0.001152128,0.85236365,0.0009870313,0.0010065208,0.0016913936,0.13281335,0.000016671043,0.00028413293,0.00016356382,0.0005125593,0.0013771066],"study_design_scores_gemma":[0.0015687319,0.0006790658,0.7313391,0.0004461058,0.00033597343,0.000065213404,0.25595605,0.000047290436,0.00004513029,0.00003858316,0.008947267,0.00053151464],"about_ca_topic_score_codex":0.23848064,"about_ca_topic_score_gemma":0.9287749,"teacher_disagreement_score":0.69029427,"about_ca_system_score_codex":0.0011179588,"about_ca_system_score_gemma":0.0018344373,"threshold_uncertainty_score":0.9996587},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5024343,0.0018821532,0.000013702617,0.000111638175,0.0009093882,0.0012803747,0.0010348768,0.0005527508,0.4917808],"genre_scores_gemma":[0.91471416,0.000029211309,0.000044624536,0.000011987341,0.0002818578,0.000015106298,0.01252143,0.00019557557,0.072186075],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99610394,0.00030875704,0.0005329651,0.0014825838,0.0006423074,0.0009294519],"domain_scores_gemma":[0.99584377,0.00015501372,0.00041260355,0.0014398287,0.0012085988,0.0009401835],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016069571,0.0010071602,0.0014361108,0.0014999477,0.00047699298,0.00007769018,0.0009103804,0.0008541196,0.0020654392],"category_scores_gemma":[0.0001490153,0.0011737572,0.0008509697,0.0014543799,0.00050918653,0.00050340284,0.00025040924,0.0014715223,0.0005959095],"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":[0.012017301,0.016484134,0.66469854,0.005101109,0.022105342,0.019355603,0.09176379,0.000108618246,0.0015818785,0.0335836,0.12121608,0.011983988],"study_design_scores_gemma":[0.005417648,0.0004277875,0.85503685,0.0017686356,0.003993551,0.000042841402,0.017342886,0.00002555688,0.00012701248,0.00007728029,0.11424437,0.0014955467],"about_ca_topic_score_codex":0.0013382275,"about_ca_topic_score_gemma":0.41511306,"teacher_disagreement_score":0.4195947,"about_ca_system_score_codex":0.0018637567,"about_ca_system_score_gemma":0.0017740906,"threshold_uncertainty_score":0.99907124},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39537942,0.007514265,0.004470823,0.004370165,0.0010549681,0.002020581,0.0023688925,0.00025774844,0.5825631],"genre_scores_gemma":[0.36135507,0.007354434,0.0036769998,0.0004683919,0.00040227943,0.0000024768424,0.00092854386,0.0001775802,0.62563425],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99672854,0.0005678117,0.00042361618,0.0009000367,0.0005130327,0.00086697016],"domain_scores_gemma":[0.99707687,0.00064830756,0.00045039103,0.0011902399,0.00015057728,0.00048362178],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047514687,0.0005794643,0.0013119234,0.000962078,0.00014999852,0.000017072105,0.0009537826,0.00052954006,0.0062533477],"category_scores_gemma":[0.0004231235,0.0007314932,0.00045638467,0.00059069,0.00072579837,0.00018911359,0.00057743426,0.00073275017,0.0007059271],"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":[0.0015129745,0.0011841219,0.055642053,0.0018131498,0.0007707439,0.0030879444,0.021600148,0.00018183012,0.000160394,0.0066445833,0.028489187,0.87891287],"study_design_scores_gemma":[0.0048696725,0.00020198598,0.87430644,0.004658222,0.00072869414,0.000019218909,0.015218497,0.0022462062,0.000011559503,0.0038024285,0.09326716,0.000669937],"about_ca_topic_score_codex":0.9117689,"about_ca_topic_score_gemma":0.9922296,"teacher_disagreement_score":0.8782429,"about_ca_system_score_codex":0.00095152494,"about_ca_system_score_gemma":0.0023565849,"threshold_uncertainty_score":0.9995136},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98171735,0.00024486543,0.000019512128,0.004411459,0.00024770325,0.00078394194,0.000038573817,0.00009479318,0.012441804],"genre_scores_gemma":[0.99802834,0.00001531913,0.00028412405,0.00080556,0.00015658102,0.0000022310667,0.00005999084,0.000027443639,0.00062039745],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9964588,0.0016693677,0.0003579697,0.0004525258,0.0005329977,0.00052833045],"domain_scores_gemma":[0.99684954,0.000344179,0.0003229517,0.0017681302,0.00023582383,0.0004793714],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003285874,0.00024779965,0.00041530546,0.00015955653,0.0013484933,0.00007306702,0.0014336561,0.00017054671,0.000054912773],"category_scores_gemma":[0.00066314946,0.00016306671,0.00017878677,0.00047934096,0.0010079325,0.00040100675,0.00022080455,0.0010261311,0.00007514401],"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":[0.00038558492,0.00026210502,0.9806675,0.00018576579,0.00020903128,0.00023568339,0.0035778016,0.000075097094,0.0018245192,0.005688998,0.0028895887,0.0039983317],"study_design_scores_gemma":[0.0010856409,0.00013867856,0.9579854,0.00035596133,0.00024005983,0.00012282783,0.0023969316,0.00076085486,0.0005096956,0.0007445833,0.03536956,0.00028979173],"about_ca_topic_score_codex":0.043299153,"about_ca_topic_score_gemma":0.06492391,"teacher_disagreement_score":0.03247997,"about_ca_system_score_codex":0.00047556072,"about_ca_system_score_gemma":0.0006416821,"threshold_uncertainty_score":0.9999516},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95810384,0.0012896023,0.00081912184,0.002653919,0.0013780366,0.0038016478,0.0002490546,0.0014966435,0.03020816],"genre_scores_gemma":[0.99418926,0.00007049892,0.0008830983,0.0015393916,0.00043015767,0.00014438093,0.00207568,0.00009306862,0.00057445973],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.995334,0.00038274447,0.0013016051,0.0009000192,0.0010766615,0.0010049535],"domain_scores_gemma":[0.99596584,0.00010693966,0.00065170415,0.0018651368,0.0006301174,0.00078028993],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014398594,0.0005909237,0.0011449273,0.00052296126,0.00033954642,0.00019498738,0.00056306634,0.00019908405,0.00019118362],"category_scores_gemma":[0.0002961171,0.0005331286,0.00022293096,0.0008187288,0.000121119614,0.0020009612,0.0006536275,0.00051887374,0.0027687068],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.011938848,0.0034100772,0.6103369,0.039194856,0.0016727671,0.00076523103,0.04829437,0.0015223478,0.00402558,0.06849776,0.04405164,0.16628961],"study_design_scores_gemma":[0.020582432,0.011078032,0.6908914,0.0060479105,0.00027032866,0.0014571483,0.023956731,0.05154221,0.0007519777,0.0019046322,0.18711808,0.0043991427],"about_ca_topic_score_codex":0.00020266935,"about_ca_topic_score_gemma":0.00008558478,"teacher_disagreement_score":0.16189046,"about_ca_system_score_codex":0.0007750513,"about_ca_system_score_gemma":0.0008747594,"threshold_uncertainty_score":0.99971205},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010418181,0.0011548125,0.000019756653,0.0011848301,0.00054145156,0.0012442647,0.00020851514,0.00018937496,0.99441516],"genre_scores_gemma":[0.074581064,0.00033161568,0.00075902784,0.003979605,0.0017506571,0.00027281605,0.0012668164,0.0005900929,0.9164683],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981494,0.00022631891,0.0003801135,0.0004100936,0.00037556892,0.00045849849],"domain_scores_gemma":[0.9987232,0.0000785965,0.00014133597,0.000900776,0.000021970507,0.00013409638],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004938489,0.00034149797,0.0006235606,0.00040080416,0.000011055628,0.00001945245,0.00038042245,0.00029855644,0.009402746],"category_scores_gemma":[0.00009165228,0.00021522664,0.00009252686,0.00041655425,0.00007688344,0.000072164425,0.000073492236,0.00050699816,0.0011229655],"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":[0.00005464512,0.00019032987,0.26261443,0.00022587649,0.000031475414,0.00008843393,0.00014632464,2.218484e-8,0.0000015355225,0.00033428846,0.7347331,0.0015795283],"study_design_scores_gemma":[0.001393985,0.00007840739,0.22481084,0.0005629741,0.00003536937,0.000020345098,0.00014481421,0.0000073740225,0.0000014557319,0.000110337176,0.7725356,0.00029845687],"about_ca_topic_score_codex":0.0027499949,"about_ca_topic_score_gemma":0.00927243,"teacher_disagreement_score":0.07794687,"about_ca_system_score_codex":0.000087922315,"about_ca_system_score_gemma":0.00011879212,"threshold_uncertainty_score":0.99965477},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87347656,0.025433164,0.05118533,0.009310667,0.0021500336,0.01888391,0.0052491557,0.0039239624,0.010387226],"genre_scores_gemma":[0.8753743,0.00082809397,0.024801321,0.0008070768,0.0024046306,0.0027598434,0.0023423817,0.0015937453,0.0890886],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99456984,0.00028483936,0.001249999,0.0017872,0.0009961379,0.0011120088],"domain_scores_gemma":[0.99593866,0.00013210649,0.0009313415,0.0016914987,0.00046167194,0.0008447477],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009674606,0.0010137131,0.0015463241,0.0010561147,0.00016228341,0.00009299708,0.00057111535,0.00081356446,0.00025750606],"category_scores_gemma":[0.0016851007,0.0010323043,0.0002814885,0.0013892762,0.00021672666,0.00036250276,0.0003371246,0.00076969585,0.00014904446],"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":[0.0071054366,0.010891915,0.105367765,0.019900557,0.004806383,0.0009935427,0.018239086,0.023609309,0.46782452,0.004487843,0.12747552,0.20929812],"study_design_scores_gemma":[0.011090478,0.0044221575,0.39653644,0.028482072,0.0060316417,0.00077367923,0.015353281,0.19530791,0.2725618,0.002781185,0.05721663,0.009442728],"about_ca_topic_score_codex":0.00786885,"about_ca_topic_score_gemma":0.0060421117,"teacher_disagreement_score":0.2911687,"about_ca_system_score_codex":0.0008081798,"about_ca_system_score_gemma":0.00061979383,"threshold_uncertainty_score":0.99921274},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020760393,0.001819581,0.000100826364,0.0014486088,0.0014552099,0.0010835702,0.0014687062,0.0007900434,0.9918127],"genre_scores_gemma":[0.00026779762,0.00029175682,0.0005427787,0.0016408222,0.0020157911,0.000029050565,0.0018038135,0.0004933564,0.99291486],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99806005,0.000033045144,0.00029749802,0.0006694766,0.00051218405,0.00042774496],"domain_scores_gemma":[0.9979143,0.000058236976,0.00018878785,0.0015423241,0.000054095486,0.00024223248],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000089589674,0.00043315746,0.0008003768,0.00019769499,0.000023237533,0.000032004285,0.00030557133,0.00040292402,0.037014775],"category_scores_gemma":[0.000053938762,0.000339535,0.00023852102,0.00018530837,0.000057169465,0.000043337473,0.00011713118,0.00023234893,0.036035374],"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":[0.000045660574,0.00013816511,0.0034373722,0.000346246,0.00020853174,0.000053702435,0.000008157188,3.5373165e-7,0.000012471702,0.00009577114,0.9945429,0.0011106442],"study_design_scores_gemma":[0.0013342561,0.00013050552,0.012949318,0.00045517494,0.00012575941,0.00001574529,0.000014831137,0.000017865148,0.0000060280695,0.00000576798,0.9845503,0.00039445597],"about_ca_topic_score_codex":0.0009712671,"about_ca_topic_score_gemma":0.0010813904,"teacher_disagreement_score":0.009992631,"about_ca_system_score_codex":0.00009375248,"about_ca_system_score_gemma":0.0001954772,"threshold_uncertainty_score":0.99990565},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008141367,0.003611146,0.00037849796,0.00047670677,0.0015289078,0.0020930842,0.07150114,0.00012401739,0.9202784],"genre_scores_gemma":[0.00035267766,0.0001037652,0.0004161354,0.00020835403,0.0015450025,0.0000638567,0.008303113,0.0001364841,0.9888706],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847364,0.000046059282,0.00042083004,0.0005101453,0.00034069087,0.00020862994],"domain_scores_gemma":[0.99618787,0.00025924825,0.00038200404,0.002988135,0.00010120148,0.00008155462],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056233373,0.00021669766,0.00051716936,0.0000720242,0.000025649379,0.000022238806,0.0006597953,0.00013616159,0.0037619898],"category_scores_gemma":[0.00014993602,0.000115505034,0.00007128124,0.00006751231,0.00018430928,0.00003822163,0.00015943497,0.00006048574,0.00035622288],"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":[0.00023823713,0.000060054997,0.000101682046,0.00088483107,0.0004920567,0.000002441577,0.000020395191,5.1866856e-8,0.000010702575,0.000049299837,0.9979505,0.00018973094],"study_design_scores_gemma":[0.001314955,0.000211459,0.0002815166,0.0005198439,0.00016074617,0.000004584904,0.0001625008,0.00025211563,0.0000078339845,0.0000034060383,0.99694604,0.0001350263],"about_ca_topic_score_codex":0.0004430113,"about_ca_topic_score_gemma":0.0015570224,"teacher_disagreement_score":0.06859226,"about_ca_system_score_codex":0.000021933893,"about_ca_system_score_gemma":0.00014020712,"threshold_uncertainty_score":0.9971487},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99122614,0.00033626665,0.00063869194,0.00011856771,0.00030746506,0.00030036957,0.00027653723,0.00006029999,0.0067356797],"genre_scores_gemma":[0.98876745,0.00026722648,0.0009140178,0.00001061995,0.00005455989,2.427466e-7,0.006765106,0.000033200246,0.0031875954],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986951,0.000069602225,0.00018181291,0.00037475233,0.00050276465,0.00017599553],"domain_scores_gemma":[0.998378,0.000022284872,0.0003600766,0.0005531889,0.0005692398,0.00011721575],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011237691,0.00019425606,0.00066247216,0.00021960217,0.000063227584,0.000003223524,0.00022128776,0.00023076081,0.000082094695],"category_scores_gemma":[0.00008959314,0.00026606713,0.00027512416,0.00023739468,0.000035724122,0.00008303559,0.0000791241,0.0002342594,0.000024709158],"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":[0.019056993,0.0041355616,0.83864933,0.024137283,0.007428871,0.0044880225,0.0053641703,0.004318013,0.038498577,0.00032249273,0.024999235,0.028601477],"study_design_scores_gemma":[0.00451588,0.00033256772,0.9109432,0.0039488543,0.0017803501,0.000016300952,0.045318823,0.028927675,0.0015123459,0.0000631855,0.0019340394,0.00070678064],"about_ca_topic_score_codex":0.005780636,"about_ca_topic_score_gemma":0.20832476,"teacher_disagreement_score":0.20254412,"about_ca_system_score_codex":0.00010486193,"about_ca_system_score_gemma":0.00031926666,"threshold_uncertainty_score":0.99997914},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024358977,0.00020737764,0.01184304,0.00016944601,0.00048611776,0.0014318655,0.0025757935,0.00065590773,0.98019457],"genre_scores_gemma":[0.09620797,0.0001580149,0.0041422904,0.00074047066,0.00036627165,0.000102138576,0.0029931793,0.00035903914,0.8949306],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956488,0.00015096739,0.0008127536,0.0013296782,0.0011694984,0.00088831835],"domain_scores_gemma":[0.9973229,0.00027170102,0.0005724553,0.0012415538,0.000114841176,0.00047653393],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00030652012,0.0008737114,0.0010267516,0.00170912,0.00006872172,0.00017905794,0.0012697813,0.00013477576,0.0044095353],"category_scores_gemma":[0.00020832849,0.00069733657,0.0003097878,0.00041143532,0.0010429033,0.00020167194,0.0003813171,0.0008951578,0.0013461936],"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":[0.0016181073,0.00033416963,0.003033466,0.0010218999,0.0005248177,0.00029441062,0.0018137099,0.000015652213,0.00032263994,0.0033458802,0.98380816,0.003867065],"study_design_scores_gemma":[0.0029736517,0.0014762911,0.011538685,0.017720856,0.0003209134,0.00010275538,0.00012293067,0.002429347,0.0015132518,0.0003358651,0.9603854,0.0010800958],"about_ca_topic_score_codex":0.004549425,"about_ca_topic_score_gemma":0.0073349997,"teacher_disagreement_score":0.09377207,"about_ca_system_score_codex":0.00015953509,"about_ca_system_score_gemma":0.0007238376,"threshold_uncertainty_score":0.9995478},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013936623,0.0017076124,0.00006074479,0.001254992,0.00030769187,0.00051853503,0.0017065514,0.00016300743,0.9928872],"genre_scores_gemma":[0.013023262,0.00010325632,0.002942887,0.0005275016,0.0008489958,0.000030906147,0.017884834,0.00020555561,0.9644328],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810845,0.00002959385,0.00046308944,0.0007652618,0.00040263307,0.00023097514],"domain_scores_gemma":[0.9987473,0.000039724655,0.00021024414,0.00070253754,0.00009167813,0.00020855195],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013960444,0.0002869522,0.0004937679,0.00011537412,0.000031077783,0.00019890937,0.000058365527,0.00017037924,0.004508842],"category_scores_gemma":[0.00029326396,0.00023721636,0.00008244316,0.00009082322,0.00008384608,0.000121423414,0.000115528106,0.00015860981,0.00006185822],"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":[0.00006649491,0.000070504444,0.008505595,0.00012983092,0.00019764666,0.0015291256,0.000023235267,4.978914e-7,0.0000199631,0.00004338425,0.9878128,0.0016009044],"study_design_scores_gemma":[0.0008945049,0.00006280459,0.005744043,0.00071863533,0.000060820254,0.00032313523,0.00014783077,0.000041144332,0.000015240661,0.00000657503,0.9916832,0.0003020688],"about_ca_topic_score_codex":0.000045050492,"about_ca_topic_score_gemma":0.00017714669,"teacher_disagreement_score":0.028454402,"about_ca_system_score_codex":0.000028492092,"about_ca_system_score_gemma":0.00037662103,"threshold_uncertainty_score":0.9964012},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015767188,0.0013164496,0.000005701741,0.00021552721,0.0008185512,0.0014874205,0.002846975,0.00074017973,0.9924115],"genre_scores_gemma":[0.0012484933,0.0010184617,0.0034930054,0.0009403427,0.001134865,0.000068320034,0.0046496494,0.000488953,0.9869579],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.995481,0.00023842194,0.0014310942,0.00059782754,0.0011759746,0.0010756684],"domain_scores_gemma":[0.9958699,0.00026119818,0.0012446695,0.0015463845,0.00025443887,0.00082342146],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00050688116,0.0009590541,0.0014438832,0.00034987152,0.00013034955,0.00006145923,0.0007347722,0.00062897004,0.5907948],"category_scores_gemma":[0.00035682015,0.0009284532,0.00058662286,0.00002748938,0.0005130078,0.0000011498028,0.0003752605,0.0009173234,0.09747818],"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":[0.00060609385,0.00029814843,0.0003267161,0.0025457507,0.0004127936,0.0001065381,0.00018957136,0.000014886952,0.0000087685885,0.00003248996,0.9918305,0.0036277326],"study_design_scores_gemma":[0.0020660535,0.00028282395,0.0008280724,0.0011956644,0.00039181794,0.0003127237,0.000060175054,0.00001829647,0.000024737337,0.00001618088,0.9939406,0.00086285174],"about_ca_topic_score_codex":0.0039759846,"about_ca_topic_score_gemma":0.00093695056,"teacher_disagreement_score":0.4933166,"about_ca_system_score_codex":0.00012229073,"about_ca_system_score_gemma":0.0001927012,"threshold_uncertainty_score":0.99931663},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06550067,0.005484187,0.000051155264,0.003592094,0.000986099,0.006870573,0.003619702,0.0015945075,0.912301],"genre_scores_gemma":[0.4263822,0.0013916462,0.0022973444,0.0011396654,0.0009765518,0.00036322096,0.004802622,0.002120305,0.56052643],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920367,0.0013617205,0.0020489697,0.0015829931,0.0014798826,0.001489761],"domain_scores_gemma":[0.9940379,0.00032340427,0.0017314199,0.003225465,0.000060943963,0.0006208448],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005837711,0.0011664825,0.0021368382,0.0011419482,0.000102416554,0.00013496605,0.0019329651,0.0005649555,0.0016417016],"category_scores_gemma":[0.000053799784,0.00092787814,0.0004986868,0.0012032724,0.00044449078,0.0018514085,0.0004289034,0.002031435,0.00004302179],"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":[0.012193475,0.0031976579,0.06746986,0.0102821225,0.004367751,0.0035144507,0.0069150976,0.00015255045,0.011970776,0.010080466,0.8643825,0.0054732617],"study_design_scores_gemma":[0.005764661,0.004500227,0.019498652,0.005817603,0.0008368585,0.00027188513,0.0006547719,0.00022713676,0.027991325,0.0018980011,0.93012494,0.0024139516],"about_ca_topic_score_codex":0.00025600754,"about_ca_topic_score_gemma":0.00010477547,"teacher_disagreement_score":0.36088154,"about_ca_system_score_codex":0.00016314803,"about_ca_system_score_gemma":0.0017247656,"threshold_uncertainty_score":0.99931717},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9471978,0.0032474457,0.000063721585,0.0041955793,0.0005635156,0.0014695745,0.00043486158,0.000038784226,0.042788703],"genre_scores_gemma":[0.9980454,0.00088621455,0.0006068784,0.00022022885,0.000047808193,0.00005724215,0.00004839163,0.000021103751,0.000066767265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99683166,0.00038516682,0.0012075246,0.00039587214,0.0008215561,0.0003582161],"domain_scores_gemma":[0.99760693,0.0002205883,0.00068746624,0.00083595817,0.00044193005,0.00020710452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058772054,0.00025030726,0.0008196051,0.0002568327,0.000054374133,0.000004407738,0.00053136144,0.00013469564,0.00010533523],"category_scores_gemma":[0.0014728301,0.00019683855,0.00018424875,0.000864226,0.000055340755,0.00015276964,0.00023261174,0.00038903343,0.0000012257913],"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":[0.0023312063,0.0036538236,0.4420318,0.011348117,0.00044279255,0.00082311797,0.0037991004,0.00005755327,0.035400808,0.49245512,0.0026681668,0.004988399],"study_design_scores_gemma":[0.0017643104,0.000076504024,0.89577454,0.008567455,0.000098710894,0.0002193249,0.00034376813,0.000054301287,0.009216962,0.000109258835,0.08350925,0.0002656118],"about_ca_topic_score_codex":0.021381602,"about_ca_topic_score_gemma":0.70530117,"teacher_disagreement_score":0.6839196,"about_ca_system_score_codex":0.00088300754,"about_ca_system_score_gemma":0.0047675446,"threshold_uncertainty_score":0.9851351},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011567479,0.0003612776,0.000049834838,0.0012331156,0.00047467498,0.00071811816,0.00014769785,0.00019242041,0.98525536],"genre_scores_gemma":[0.029032532,0.00015468152,0.00008717685,0.0015799112,0.00013780667,0.000001877227,0.0012286252,0.000059434376,0.96771795],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830234,0.00016572764,0.00024081806,0.0005329127,0.00038288478,0.00037532125],"domain_scores_gemma":[0.99873,0.000079253216,0.0002856958,0.00059069,0.000087684806,0.00022669199],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000114193856,0.000348694,0.0004990548,0.00022502834,0.00012336374,0.000041092444,0.00042478458,0.00017693266,0.1547031],"category_scores_gemma":[0.000037984428,0.00029312598,0.00022767858,0.00050481496,0.000051575662,0.00007585438,0.000073028874,0.0005821036,0.0010616784],"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":[0.0010207511,0.0003355034,0.0032018055,0.001040947,0.00063895056,0.00069970905,0.0029377523,0.000006424179,0.000039652048,0.0024626912,0.24739416,0.7402217],"study_design_scores_gemma":[0.00085555366,0.00018717024,0.010652237,0.0011329537,0.000061442915,0.000007515514,0.004606621,0.000118745986,0.00003689967,0.000014069416,0.9820554,0.00027136938],"about_ca_topic_score_codex":0.000159777,"about_ca_topic_score_gemma":0.0005358371,"teacher_disagreement_score":0.7399503,"about_ca_system_score_codex":0.0003843005,"about_ca_system_score_gemma":0.00016533965,"threshold_uncertainty_score":0.9999521},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996195,0.000100854544,0.00002385943,0.00011179428,0.0006181744,0.00086620386,0.0009069585,0.00014930972,0.0010278276],"genre_scores_gemma":[0.9921167,0.00008920938,0.000054315977,0.00011464117,0.000092595525,0.000009414508,0.00032202387,0.000044271965,0.007156811],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99740577,0.00036944106,0.00050321006,0.0008461253,0.00034667403,0.0005287539],"domain_scores_gemma":[0.9985306,0.00015967141,0.00030627978,0.00048785793,0.00011491843,0.00040064924],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004552299,0.000274038,0.00086197583,0.0003536366,0.00035896635,0.00033791387,0.00058317604,0.0001352755,0.00036290986],"category_scores_gemma":[0.000060979397,0.00041859652,0.00016269338,0.0004301972,0.00019907986,0.0005401759,0.0023127769,0.00041917132,0.000055736324],"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":[0.00020222824,0.0003391592,0.994783,0.0002575868,0.00012736439,0.0010334802,0.00080554513,0.0006138289,0.00023076397,0.00008125682,0.0010083017,0.00051750225],"study_design_scores_gemma":[0.003834121,0.00039611192,0.9727154,0.000245911,0.00016004233,0.00019707045,0.006313557,0.00009288652,0.000012279483,0.00003340361,0.015514123,0.00048512273],"about_ca_topic_score_codex":0.024996756,"about_ca_topic_score_gemma":0.045818895,"teacher_disagreement_score":0.022067612,"about_ca_system_score_codex":0.0012855314,"about_ca_system_score_gemma":0.00016359237,"threshold_uncertainty_score":0.9998266},"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":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048819806,0.00018557115,0.0002885127,0.0008781852,0.0025536828,0.0016171375,0.0036761628,0.00096017384,0.9410208],"genre_scores_gemma":[0.063639514,0.0005374425,0.0008336057,0.00045251803,0.00063025457,0.0000013345872,0.0033638293,0.00026967414,0.9302718],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99452776,0.00055955886,0.00045729286,0.0017942504,0.0012059048,0.0014552504],"domain_scores_gemma":[0.99425787,0.00011556461,0.0006951955,0.0017370951,0.0004780423,0.0027162402],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004017094,0.0011561513,0.0013454391,0.0040069907,0.0017701953,0.000082951905,0.0016157234,0.0013646524,0.005445402],"category_scores_gemma":[0.000115241935,0.0014646596,0.00076882594,0.0013664409,0.0017027685,0.0005065388,0.0015392946,0.0018521767,0.00079183216],"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":[0.003889635,0.0016618281,0.02511135,0.0010219836,0.0035954004,0.09031492,0.0005445128,0.00014972726,0.0007840362,0.029397972,0.8400619,0.0034667628],"study_design_scores_gemma":[0.0036372198,0.00034424802,0.0054157265,0.0007845889,0.0006544489,0.0007947759,0.00080918055,0.00007631922,0.000025141808,0.000018328,0.9861233,0.0013167207],"about_ca_topic_score_codex":0.10432187,"about_ca_topic_score_gemma":0.09487242,"teacher_disagreement_score":0.14606142,"about_ca_system_score_codex":0.03607814,"about_ca_system_score_gemma":0.0051705516,"threshold_uncertainty_score":0.9999862},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98289496,0.000085720785,0.00013075522,0.01345662,0.0004533812,0.00089362834,0.000903439,0.00037510664,0.0008063628],"genre_scores_gemma":[0.98189086,0.000022506265,0.00006126544,0.014147981,0.00028703365,0.0000043648156,0.0001909767,0.0000898996,0.0033051213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9949116,0.0006416587,0.00062145875,0.0012757994,0.0016747513,0.0008747303],"domain_scores_gemma":[0.99551684,0.00019512347,0.00036930255,0.0010151269,0.0004479611,0.0024556245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009185594,0.0005713264,0.0009752722,0.0002796157,0.00034139134,0.0001725182,0.00086673885,0.00026339965,0.00023594579],"category_scores_gemma":[0.0007126338,0.0006112733,0.00016579914,0.00088222395,0.000107146196,0.00046894874,0.0007873678,0.0008040721,0.00031936605],"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":[0.00083359453,0.00007907063,0.9421873,0.0006556234,0.00026317997,0.05300034,0.0020302797,0.00003246898,0.00023465039,0.000009688493,0.00027416542,0.000399604],"study_design_scores_gemma":[0.010339191,0.0011138642,0.92798114,0.0031311908,0.0005904165,0.0040236185,0.008730879,0.00016692388,0.0020347727,0.0000014162608,0.03979851,0.0020880573],"about_ca_topic_score_codex":0.0045224032,"about_ca_topic_score_gemma":0.6137407,"teacher_disagreement_score":0.6092183,"about_ca_system_score_codex":0.0009837213,"about_ca_system_score_gemma":0.0015451795,"threshold_uncertainty_score":0.99963385},"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":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00070183084,0.0035844634,0.0000049255214,0.0013608526,0.00051748304,0.00095008174,0.0010380966,0.0002914969,0.99155074],"genre_scores_gemma":[0.0049999873,0.002445613,0.003775577,0.0013104614,0.00068384735,0.000043319415,0.00095639145,0.00022848776,0.9855563],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957055,0.00058990397,0.001311658,0.0005185904,0.0008745506,0.0009998083],"domain_scores_gemma":[0.9964925,0.0005473463,0.0008091348,0.00087009044,0.0003601746,0.00092076993],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007608943,0.0007637661,0.0010990527,0.0001224877,0.00016917616,0.00013477525,0.00070427684,0.0007222538,0.73595446],"category_scores_gemma":[0.0018082332,0.00080019276,0.00045440017,0.00002623904,0.0006821984,9.729168e-7,0.00034056138,0.0008715851,0.059680566],"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":[0.0008531923,0.0002250197,0.00041532656,0.004623111,0.00040483434,0.00056045555,0.0011647602,0.0003291648,0.00001579347,0.000102424885,0.97643495,0.014870995],"study_design_scores_gemma":[0.0019316177,0.00051440654,0.0011425915,0.0023514193,0.0003228451,0.00035915515,0.00044710736,0.00034985432,0.000044388653,0.000026614338,0.99186075,0.00064922724],"about_ca_topic_score_codex":0.005779543,"about_ca_topic_score_gemma":0.0033905138,"teacher_disagreement_score":0.6762739,"about_ca_system_score_codex":0.00044452914,"about_ca_system_score_gemma":0.0003717658,"threshold_uncertainty_score":0.9994449},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027037956,0.0021577212,0.0005536571,0.010762956,0.00031172892,0.0004243939,0.002812015,0.000050045728,0.9558895],"genre_scores_gemma":[0.8733843,0.0009787533,0.004104894,0.007811979,0.0005344802,0.0000331562,0.00041604167,0.00023676774,0.11249964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956744,0.00096851477,0.00047152527,0.00055868935,0.0014073285,0.0009195636],"domain_scores_gemma":[0.99786234,0.000522609,0.00043031806,0.00038875185,0.0000029464634,0.0007930653],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001495397,0.00048771926,0.000621347,0.00006193098,0.00032486784,0.00008039842,0.00044632796,0.00022764235,0.00035145265],"category_scores_gemma":[0.000029354711,0.0005649037,0.00010146524,0.00014589156,0.0002022521,0.0004595735,0.00019291606,0.0005934083,1.0407284e-7],"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":[0.0013462519,0.0006576215,0.25027362,0.00375832,0.0010923492,0.0014301426,0.0045463042,0.0009753324,0.010669624,0.6544035,0.05981812,0.011028823],"study_design_scores_gemma":[0.0014967143,0.00006881745,0.21681857,0.00082198513,0.00014131883,0.000047048008,0.0074827964,0.0015250467,0.00240017,0.0012094509,0.7674574,0.000530675],"about_ca_topic_score_codex":0.0904988,"about_ca_topic_score_gemma":0.25994226,"teacher_disagreement_score":0.8463463,"about_ca_system_score_codex":0.00018387458,"about_ca_system_score_gemma":0.03419195,"threshold_uncertainty_score":0.9996802},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9921111,0.00015530968,0.0018300143,0.000520225,0.00046551353,0.0011444521,0.0028860974,0.00019978054,0.0006875401],"genre_scores_gemma":[0.90612197,0.00036282,0.00071754196,0.00005194549,0.00029527306,0.000009744648,0.090918444,0.000088039196,0.0014342255],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99763507,0.00012618395,0.00022684848,0.0009874825,0.0007013959,0.00032299847],"domain_scores_gemma":[0.99726367,0.00005267117,0.0003196965,0.0018321994,0.00026992898,0.00026186192],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003820613,0.00028886923,0.000848985,0.0010546165,0.00009916184,0.000021095866,0.0010014789,0.0002485257,0.00003435889],"category_scores_gemma":[0.00006666432,0.00039728128,0.00015336093,0.0017334165,0.000041874664,0.0002679011,0.00028512496,0.00030992334,0.00013537279],"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":[0.0014218816,0.00030885605,0.9786145,0.00087401696,0.0014936512,0.00020306633,0.0005819711,0.0005881128,0.00050984166,0.000026013817,0.0062394105,0.009138645],"study_design_scores_gemma":[0.00076387427,0.000053488475,0.9622456,0.00022147365,0.0021568697,3.9693322e-7,0.018667666,0.013500841,0.00001306964,0.0000048293073,0.0020509022,0.0003209785],"about_ca_topic_score_codex":0.15073583,"about_ca_topic_score_gemma":0.9946694,"teacher_disagreement_score":0.8439335,"about_ca_system_score_codex":0.00039428502,"about_ca_system_score_gemma":0.00031427402,"threshold_uncertainty_score":0.9998479},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87477714,0.00030748846,0.0000029869425,0.0522782,0.00030967747,0.0009163253,0.00007348272,0.00012457585,0.07121015],"genre_scores_gemma":[0.90001905,0.000054274227,0.00013205878,0.005052114,0.0005747403,0.00003332031,0.000045603123,0.000046232093,0.09404258],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99697804,0.00031437355,0.00038330548,0.0004646301,0.0011673252,0.00069230783],"domain_scores_gemma":[0.9969691,0.00042851275,0.000096793316,0.0013514119,0.0005755179,0.00057865574],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001356484,0.00021593843,0.0003901308,0.00019653582,0.00017463748,0.0001643148,0.00057007564,0.00021080006,0.005958587],"category_scores_gemma":[0.0014883655,0.00013554696,0.000102649356,0.0007223606,0.00014485297,0.00014388301,0.0003598781,0.0013106251,0.00874552],"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":[0.00014252552,0.00009485205,0.50337297,0.000054807493,0.000044627133,0.00012699,0.00019201268,0.000023068971,0.00046857315,0.0004445711,0.49417016,0.0008648371],"study_design_scores_gemma":[0.00085214735,0.00031921413,0.47364843,0.00027629844,0.000016462589,0.00010084077,0.0021481006,0.000025887062,0.0008475529,0.00014729243,0.52142876,0.0001890073],"about_ca_topic_score_codex":0.0019970534,"about_ca_topic_score_gemma":0.0018082486,"teacher_disagreement_score":0.047226086,"about_ca_system_score_codex":0.00015729267,"about_ca_system_score_gemma":0.00037590196,"threshold_uncertainty_score":0.9949501},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035229668,0.012370573,0.0026438565,0.0023578247,0.00040956071,0.008299675,0.00950092,0.0011662872,0.9280216],"genre_scores_gemma":[0.9221106,0.0032313822,0.0006658096,0.00015458396,0.00039568805,0.0000014170954,0.00555948,0.0032870884,0.06459391],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997371,0.00051414483,0.00057968835,0.00067940244,0.00042982018,0.00042594806],"domain_scores_gemma":[0.9977663,0.000097619326,0.00038398674,0.0008097047,0.00074938365,0.00019298705],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068105635,0.00030839693,0.00065296347,0.0006257067,0.00027973633,0.00010506566,0.0006695628,0.00014836286,0.0028106254],"category_scores_gemma":[0.0023041558,0.00026551876,0.00009595632,0.0008945619,0.00033008252,0.00007031181,0.0011077757,0.0003568804,0.00044518395],"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":[0.0019055627,0.0011158885,0.000983233,0.0036509582,0.0006305022,0.000049684942,0.0026044713,0.000049413058,0.007361094,0.004126049,0.8753854,0.10213776],"study_design_scores_gemma":[0.0017451039,0.0004150799,0.012563329,0.0015018464,0.000101119265,0.000020054584,0.00030246584,0.00010543041,0.0015895531,0.00003907419,0.9813657,0.00025123561],"about_ca_topic_score_codex":0.00020985435,"about_ca_topic_score_gemma":0.00001904346,"teacher_disagreement_score":0.886881,"about_ca_system_score_codex":0.00013356456,"about_ca_system_score_gemma":0.000029914148,"threshold_uncertainty_score":0.9999797},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60688037,0.00003548985,0.000037567974,0.0053676814,0.00003382052,0.0008070194,0.0000066054517,0.00008117431,0.38675025],"genre_scores_gemma":[0.973451,0.0000096333,0.00077648734,0.001445172,0.000068285386,0.00012040206,0.000022358367,0.000012876871,0.024093762],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99894035,0.00007970145,0.00012811317,0.00022450254,0.00032646093,0.00030088215],"domain_scores_gemma":[0.99872786,0.00008764551,0.000013963243,0.000600108,0.00029812814,0.0002723255],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00048695257,0.000054231998,0.00013394027,0.00007035054,0.00006468003,0.0004599766,0.0007248309,0.000025568917,0.02735648],"category_scores_gemma":[0.0002535252,0.000040078492,0.000022542274,0.00039484134,0.000077665,0.0008826567,0.001280815,0.00013712025,0.012649287],"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":[0.000059053848,0.00021692443,0.16386293,0.00002629965,0.000027027894,0.000027512224,0.000026828026,7.697329e-7,0.0039244653,0.001800273,0.80892646,0.02110146],"study_design_scores_gemma":[0.00093313464,0.00006636898,0.92870396,0.000025109464,0.0000052314927,0.000006787752,0.00014385066,0.0004659356,0.0042421953,0.0018804839,0.06344651,0.00008041045],"about_ca_topic_score_codex":0.0020196186,"about_ca_topic_score_gemma":0.0000845296,"teacher_disagreement_score":0.7648411,"about_ca_system_score_codex":0.000037533973,"about_ca_system_score_gemma":0.00011441485,"threshold_uncertainty_score":0.9881195},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46497694,0.000012453924,0.000018976049,0.0010840571,0.000030817955,0.00020909589,0.000009574558,0.00027283124,0.5333853],"genre_scores_gemma":[0.85817796,0.000036825186,0.0063040936,0.001480745,0.00015946715,0.000033477412,0.00016516117,0.000023214263,0.13361904],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9995659,0.000010200533,0.0000836057,0.00011329928,0.0001058706,0.000121152465],"domain_scores_gemma":[0.9994688,0.000019933903,0.000013628456,0.0003033989,0.00005383459,0.00014038834],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003804071,0.000051045383,0.00011020543,0.000022452557,0.000011720637,0.0000092359,0.000044038046,0.000019842439,0.027278287],"category_scores_gemma":[0.0000453326,0.000037612546,0.000032436816,0.00005965575,0.000022135036,0.000064360196,0.000026250873,0.000029758352,0.029963877],"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":[0.000019239473,0.0000926196,0.031728346,0.000025194291,0.000035479166,0.000024833014,0.000017401066,2.4460655e-7,0.048480093,0.00044986425,0.9083126,0.010814039],"study_design_scores_gemma":[0.0008825715,0.00008376464,0.929362,0.000023387618,0.000022133338,0.000020831614,0.000024265788,0.0007673614,0.0015843115,0.0002868014,0.06680272,0.00013985301],"about_ca_topic_score_codex":0.0002545428,"about_ca_topic_score_gemma":0.0000022999266,"teacher_disagreement_score":0.8976337,"about_ca_system_score_codex":0.000014413768,"about_ca_system_score_gemma":0.0000269651,"threshold_uncertainty_score":0.97361094},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24623463,0.00026942717,0.00055516645,0.0019954448,0.00023203423,0.00018136392,0.000034815763,0.00034549186,0.75015163],"genre_scores_gemma":[0.9847783,0.0000040913333,0.0013481717,0.0020876443,0.00010146233,0.000003582924,0.00006180077,0.0000070407987,0.011607944],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996283,0.0000068348636,0.000057211357,0.00007877589,0.00015066462,0.00007823032],"domain_scores_gemma":[0.9995077,0.00000722989,0.000009160989,0.00020978642,0.00007371307,0.00019238555],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009028414,0.00003500831,0.000078377714,0.000014984684,0.000005222031,0.000004457178,0.000031197458,0.000013178875,0.0004998254],"category_scores_gemma":[0.00010054497,0.000025845735,0.000017323138,0.00004966483,0.000017221968,0.000029950157,0.000019106194,0.000026817477,0.0016963696],"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":[0.00010521128,0.000096145384,0.117963396,0.00001579905,0.000024438974,0.000092262264,0.000048496237,0.0000015600289,0.00021212857,0.0018046016,0.87526214,0.0043738415],"study_design_scores_gemma":[0.0011883904,0.00007754737,0.08241384,0.000009674769,0.000014363624,0.00002770117,0.00007756505,0.00022418275,0.0002643571,0.00018895029,0.91544867,0.00006477955],"about_ca_topic_score_codex":0.000029342427,"about_ca_topic_score_gemma":0.000010183112,"teacher_disagreement_score":0.7385437,"about_ca_system_score_codex":0.000029631296,"about_ca_system_score_gemma":0.0000897665,"threshold_uncertainty_score":0.9990809},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8687132,0.003436721,0.06129034,0.039558914,0.00074144715,0.0073521,0.0007901555,0.00019954062,0.01791761],"genre_scores_gemma":[0.94277567,0.00016133882,0.05312335,0.0030730478,0.000027571059,0.000028549362,0.0001083407,0.000012915892,0.0006892094],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9992717,0.000035068733,0.00039124626,0.00007787819,0.00012714109,0.000096950105],"domain_scores_gemma":[0.99875337,0.000099148776,0.0001976685,0.0005539657,0.00033045118,0.000065388296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058506225,0.000055543787,0.00023011843,0.00003157576,0.00002762774,7.0326064e-7,0.00016558434,0.000019356561,0.00006461572],"category_scores_gemma":[0.00027417587,0.000034206958,0.00006216202,0.00008998992,0.00007369051,0.000041789724,0.000057978792,0.000032707645,0.0000033038768],"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":[0.0014352201,0.0005034174,0.4686854,0.0026670066,0.0006048603,6.988119e-7,0.0041362597,0.00008641909,0.005517399,0.001339588,0.47450215,0.040521577],"study_design_scores_gemma":[0.005855076,0.0012504052,0.92906475,0.00096874795,0.00018602384,0.000005736166,0.0021685355,0.024756348,0.021390218,0.0016144031,0.0125118,0.00022797324],"about_ca_topic_score_codex":0.0011568014,"about_ca_topic_score_gemma":0.00015025563,"teacher_disagreement_score":0.46199036,"about_ca_system_score_codex":0.000014685386,"about_ca_system_score_gemma":0.0001525569,"threshold_uncertainty_score":0.17487453},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92385536,0.0005370577,0.006490945,0.061188288,0.0002064072,0.0045235087,0.0005625025,0.00010406454,0.0025318644],"genre_scores_gemma":[0.9906625,0.00025076992,0.0016591003,0.005770476,0.0001676875,0.00014632885,0.00094815606,0.000022999477,0.00037194445],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99803925,0.0002305403,0.0008395117,0.00024768544,0.0003343226,0.00030872016],"domain_scores_gemma":[0.99853075,0.0001467403,0.00055902504,0.00042090396,0.00018046907,0.00016209025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194679,0.00014044491,0.00047665596,0.00017930899,0.0000480426,0.000014554203,0.00020398672,0.00009054203,0.00037521755],"category_scores_gemma":[0.0004328473,0.00009947004,0.00007805161,0.00046229686,0.000117941454,0.0002641957,0.000023955745,0.00025090904,0.0000057446887],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078565464,0.0007790613,0.8452996,0.0013410111,0.00010525097,0.0000023586117,0.007061322,0.000025528368,0.0016634106,0.0012896304,0.101904735,0.039742462],"study_design_scores_gemma":[0.0019251803,0.00047569958,0.9774537,0.00025185893,0.000013107869,0.000008940375,0.00057559245,0.0017141205,0.000335298,0.00012606135,0.017024374,0.00009607638],"about_ca_topic_score_codex":0.0011939022,"about_ca_topic_score_gemma":0.00078568223,"teacher_disagreement_score":0.1321541,"about_ca_system_score_codex":0.000097201584,"about_ca_system_score_gemma":0.0007043016,"threshold_uncertainty_score":0.4108369},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19749142,0.00023337947,0.000027632535,0.001184324,0.00006243727,0.000089570654,0.000016711483,0.00022006455,0.80067444],"genre_scores_gemma":[0.9849604,0.000026126312,0.0004872691,0.0010696426,0.000068620786,0.000002163308,0.000016952072,0.000006445932,0.013362383],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99964005,0.000002710699,0.00007085066,0.00009198361,0.00010049899,0.00009389445],"domain_scores_gemma":[0.99964046,0.0000133079875,0.00001115723,0.00021934722,0.000019243927,0.00009645438],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00002896641,0.000040936557,0.00007700518,0.000019575604,0.00001111922,0.0000048838115,0.000032428772,0.000016083117,0.026034016],"category_scores_gemma":[0.000042247968,0.000031733623,0.000032732736,0.000050349427,0.000016261578,0.000034780907,0.000010609765,0.000040947532,0.005451849],"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":[0.000027704094,0.00033304977,0.069350325,0.000059873353,0.000047127185,0.00026097024,0.00003884206,0.0000010413248,0.0026720318,0.0006026288,0.84688425,0.07972217],"study_design_scores_gemma":[0.000594024,0.00002677109,0.69213367,0.000014654986,0.000009981348,0.000019512476,0.0000120536515,0.00025142301,0.00040506435,0.000030216208,0.30643082,0.00007179862],"about_ca_topic_score_codex":0.000008879617,"about_ca_topic_score_gemma":0.0000036587421,"teacher_disagreement_score":0.78746897,"about_ca_system_score_codex":0.000010161094,"about_ca_system_score_gemma":0.0000044740627,"threshold_uncertainty_score":0.9953225},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25131282,0.0004212384,0.011549737,0.026480597,0.0010621682,0.0005449264,0.00016569102,0.0011607446,0.7073021],"genre_scores_gemma":[0.9843819,0.00005369153,0.0007906396,0.0008301624,0.00014702581,0.0000040633417,0.000017604876,0.0000071334343,0.013767768],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99969196,0.0000068241784,0.00005523791,0.000089132,0.000079344005,0.000077520854],"domain_scores_gemma":[0.9996597,0.000021035668,0.00000935678,0.00020813676,0.000023681998,0.000078067926],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000040242357,0.000032473767,0.0000626295,0.00001681649,0.0000071835457,0.000001810171,0.000028341256,0.000013134515,0.002309251],"category_scores_gemma":[0.000071949326,0.000016294474,0.000023829198,0.000032292206,0.000018754967,0.000036479116,0.000018120867,0.000011318433,0.0018216295],"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":[0.00016515673,0.00012071659,0.24102598,0.000039122257,0.000043085565,0.000026258378,0.000012003358,3.0853332e-8,0.026341276,0.0068592895,0.46883926,0.2565278],"study_design_scores_gemma":[0.0010222144,0.000051651674,0.32911748,0.00005603501,0.0000039113906,0.000011699139,0.0000040982227,0.000004932569,0.0021918875,0.00017185803,0.6673098,0.000054420314],"about_ca_topic_score_codex":0.0000041290646,"about_ca_topic_score_gemma":0.0000015577814,"teacher_disagreement_score":0.7330691,"about_ca_system_score_codex":0.000014192492,"about_ca_system_score_gemma":0.000021915504,"threshold_uncertainty_score":0.99895555},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17042467,0.00014187614,0.14496927,0.0063293288,0.00025060697,0.00085703656,0.00021570173,0.0010141018,0.6757974],"genre_scores_gemma":[0.9748199,0.000006057199,0.009791278,0.00054032536,0.00013332139,0.00001363434,0.00015045181,0.000016498669,0.01452855],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925315,0.000024170482,0.00013897939,0.00022155786,0.0001876118,0.0001745174],"domain_scores_gemma":[0.99928945,0.000024009954,0.0000306852,0.00042479378,0.00004615794,0.00018489106],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010736795,0.00008810391,0.0001616718,0.00004375681,0.000019955176,0.0000069680564,0.00007508227,0.000035273097,0.0020020017],"category_scores_gemma":[0.000079122285,0.000048717862,0.00005625932,0.000087250926,0.000052334264,0.00008291281,0.000035254165,0.000033958804,0.0005920137],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020963178,0.00025533666,0.82928675,0.00006298809,0.00006272078,0.00004465486,0.00002902502,2.5382082e-7,0.0007600481,0.0029555669,0.11881389,0.04751912],"study_design_scores_gemma":[0.004697428,0.00020319724,0.57458705,0.00010353009,0.000059151447,0.00007017649,0.000061796294,0.000702536,0.001685211,0.00058374385,0.41683522,0.00041094553],"about_ca_topic_score_codex":0.00003194759,"about_ca_topic_score_gemma":0.000020765961,"teacher_disagreement_score":0.8043952,"about_ca_system_score_codex":0.000034007768,"about_ca_system_score_gemma":0.00006330553,"threshold_uncertainty_score":0.9989103},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96716857,0.000025321771,0.000019069272,0.0020374795,0.000020707286,0.0005485871,0.00006415456,0.000055490746,0.030060621],"genre_scores_gemma":[0.99809843,0.000016778678,0.00045352938,0.0010526977,0.000019035673,0.00016043634,0.00007382896,0.000015583948,0.000109648965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986608,0.00014197877,0.00023981584,0.00021894471,0.00042880955,0.0003096283],"domain_scores_gemma":[0.99781543,0.0012491995,0.00003693461,0.00055727176,0.0001953254,0.00014582838],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002828065,0.00008596545,0.00022562708,0.00024733576,0.000037650665,0.000016381018,0.00011823677,0.000038031332,0.0016920009],"category_scores_gemma":[0.0007564554,0.000071463066,0.000056182213,0.0006517538,0.00013739502,0.00015087852,0.000044018943,0.00020410295,0.0016688844],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049228594,0.011740042,0.81530786,0.0006720211,0.000110185734,0.0006759244,0.00029476412,0.0005283686,0.026848665,0.016630601,0.09567373,0.031025536],"study_design_scores_gemma":[0.0012260929,0.00033003953,0.9892036,0.0002472636,0.000007265617,0.0000028909099,0.00024314811,0.0025246083,0.0039516417,0.0018574568,0.0003190762,0.00008694494],"about_ca_topic_score_codex":0.0005228824,"about_ca_topic_score_gemma":0.000114229944,"teacher_disagreement_score":0.17389569,"about_ca_system_score_codex":0.0000556543,"about_ca_system_score_gemma":0.00012203962,"threshold_uncertainty_score":0.9992206},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6993074,0.0003727103,0.0002084625,0.02665473,0.00038061166,0.00017101776,0.00013568904,0.00022137261,0.27254802],"genre_scores_gemma":[0.9468862,0.000010570451,0.00037506397,0.0016553106,0.00043063206,0.0000054252855,0.00013823109,0.0000057073166,0.050492864],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995678,0.000006458965,0.00007823958,0.00008719648,0.00016998575,0.000090324946],"domain_scores_gemma":[0.99969536,0.000020525707,0.000015349706,0.000199172,0.000047599708,0.000022006097],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000089476394,0.00003993567,0.00005594606,0.0000072962985,0.000029842544,0.0000236592,0.000112997855,0.000012182542,0.0011906989],"category_scores_gemma":[0.000037497157,0.000022451653,0.000032499454,0.00004067054,0.000043422282,0.00003591061,0.0000514059,0.00003752146,0.0004861562],"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":[0.000052380172,0.00010349047,0.27639207,0.000006554254,0.00003812622,0.000033709817,0.000058539557,0.00000614665,0.0013871053,0.0046907044,0.71175593,0.005475271],"study_design_scores_gemma":[0.00026752296,0.0000069156054,0.40477818,0.000004509673,0.0000034835143,0.000009777366,0.00005951887,0.00012543381,0.0008120159,0.00022909942,0.5936753,0.000028201293],"about_ca_topic_score_codex":0.00033720414,"about_ca_topic_score_gemma":0.00019042872,"teacher_disagreement_score":0.24757881,"about_ca_system_score_codex":0.00001538192,"about_ca_system_score_gemma":0.000012736715,"threshold_uncertainty_score":0.99972236},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3574977,0.00014583532,0.0006296157,0.0025303653,0.000089792586,0.00013570648,0.000012682361,0.00025803663,0.63870025],"genre_scores_gemma":[0.9605926,0.00003295183,0.0007182363,0.0016595252,0.00009813602,0.0000042263828,0.00005916534,0.0000086321415,0.036826488],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960476,0.0000068662143,0.00006855711,0.000106246385,0.00010358694,0.000109993176],"domain_scores_gemma":[0.99960315,0.00001035203,0.0000094444495,0.00024735712,0.000022320453,0.00010737526],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000036250392,0.000045562116,0.000087419496,0.00002137803,0.000014284087,0.000004748714,0.000035380686,0.000014910425,0.0052294293],"category_scores_gemma":[0.0000310555,0.000032685075,0.00003511627,0.000087977496,0.000022154849,0.000038624803,0.00002004656,0.000034883138,0.0016655684],"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":[0.00015969871,0.00022234712,0.7397813,0.000018909732,0.000059631533,0.00033538314,0.000025976684,9.2312683e-7,0.0019924596,0.0020515616,0.21429949,0.0410523],"study_design_scores_gemma":[0.0005271394,0.000029336361,0.42438203,0.000009123254,0.000015654405,0.00005245478,0.00001838956,0.00007641858,0.00019726437,0.0000888056,0.57454413,0.000059236307],"about_ca_topic_score_codex":0.000018896564,"about_ca_topic_score_gemma":0.000013864136,"teacher_disagreement_score":0.60309494,"about_ca_system_score_codex":0.000011160923,"about_ca_system_score_gemma":0.000019383438,"threshold_uncertainty_score":0.9991118},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40270677,0.00220152,0.002818759,0.012635138,0.00069021,0.00101873,0.0004473935,0.0027289353,0.57475257],"genre_scores_gemma":[0.9905991,0.00011752852,0.0012881104,0.001745542,0.00015604797,0.00001578358,0.00036098482,0.000027240296,0.005689659],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987942,0.00013826413,0.00022401017,0.00027478443,0.00027456286,0.00029418824],"domain_scores_gemma":[0.997975,0.00022516333,0.000055448407,0.0013016467,0.000106113956,0.00033665486],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019579643,0.00015601753,0.0003996287,0.000054408472,0.000090061425,0.000026689997,0.00024578982,0.000043822976,0.001031484],"category_scores_gemma":[0.0002933718,0.00012041662,0.00010456294,0.00017219246,0.00011042981,0.000092041635,0.00013398206,0.00009719309,0.0009216158],"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":[0.00036716703,0.0007200034,0.72445244,0.00021675971,0.00015619265,0.000046413246,0.000033025,0.000015535357,0.0008937621,0.02503437,0.23896058,0.00910374],"study_design_scores_gemma":[0.0010955188,0.000087518754,0.48371387,0.000025870972,0.000023151573,0.000010604921,0.000008709376,0.00081466814,0.00018826048,0.00028613216,0.5135779,0.00016778133],"about_ca_topic_score_codex":0.00008384328,"about_ca_topic_score_gemma":0.00008913565,"teacher_disagreement_score":0.58789235,"about_ca_system_score_codex":0.000034586872,"about_ca_system_score_gemma":0.00005110822,"threshold_uncertainty_score":0.9998817},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7503741,0.0013353277,0.00016086228,0.0027938285,0.000145625,0.00037606273,0.00014223004,0.0007183359,0.24395363],"genre_scores_gemma":[0.9721038,0.000020062578,0.0024784268,0.021270059,0.00035187553,0.000012601979,0.00004961049,0.000021010763,0.0036925715],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992859,0.000018686278,0.00016307208,0.00017183687,0.00018364331,0.00017688535],"domain_scores_gemma":[0.9992361,0.000026277976,0.000029132807,0.000392111,0.00010984094,0.00020651874],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001054575,0.00008811673,0.0001616868,0.000045755078,0.000022682401,0.000010089405,0.00006813411,0.000040189203,0.0034897937],"category_scores_gemma":[0.000114957285,0.000073456824,0.00003979591,0.00013361558,0.00005630516,0.00010014413,0.000029190425,0.0000831317,0.0028723176],"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":[0.00036715186,0.0005610918,0.34076723,0.00012497012,0.00019267328,0.00014074924,0.00018703702,0.00001822032,0.011584633,0.0056788926,0.56746197,0.07291539],"study_design_scores_gemma":[0.00096523564,0.000031903135,0.12354245,0.000013683857,0.000017098084,0.0000058046894,0.00002327732,0.0000937938,0.0022584612,0.000009553139,0.87295216,0.000086596614],"about_ca_topic_score_codex":0.000008077326,"about_ca_topic_score_gemma":0.000022985665,"teacher_disagreement_score":0.3054902,"about_ca_system_score_codex":0.00003952388,"about_ca_system_score_gemma":0.00013413226,"threshold_uncertainty_score":0.99790406},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98332214,0.0010498775,0.0042973636,0.0052563995,0.00008923563,0.0006130923,0.00003709245,0.00011267889,0.0052220896],"genre_scores_gemma":[0.9949099,0.0003765636,0.00012914439,0.001175874,0.00003968761,0.000010700212,0.000037188172,0.000007796284,0.0033131896],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99943507,0.00004355177,0.00012866031,0.00019142682,0.00008017766,0.00012110197],"domain_scores_gemma":[0.9995324,0.0000736249,0.000053409683,0.00014687696,0.000029864555,0.00016381797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013225117,0.00007433092,0.00017025224,0.000034981007,0.00003922864,0.0000100347415,0.000015992375,0.000028916393,0.00019054842],"category_scores_gemma":[0.000021829463,0.000045371384,0.000019011302,0.00003434912,0.000031490275,0.0001358537,0.000020672147,0.00002519276,0.000019176654],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003425484,0.00013039038,0.8061493,0.00048290382,0.00009499587,0.0000048913043,0.00008264851,6.363399e-8,0.002992088,0.00046081768,0.0020515737,0.18720776],"study_design_scores_gemma":[0.003959435,0.00037264623,0.98158973,0.00035278843,0.000038141465,0.000049311824,0.000024206196,0.00028845944,0.00009725285,0.00030837988,0.012829545,0.000090104266],"about_ca_topic_score_codex":0.001252276,"about_ca_topic_score_gemma":0.002930781,"teacher_disagreement_score":0.18711765,"about_ca_system_score_codex":0.000024093928,"about_ca_system_score_gemma":0.00003490806,"threshold_uncertainty_score":0.20863716},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9521537,0.00036765594,0.00006281862,0.045563564,0.0000117706,0.0013929369,0.0003548029,0.0000083644945,0.00008440773],"genre_scores_gemma":[0.9989514,0.00018259423,0.00005705389,0.00063199317,0.000008433156,0.00002926179,0.00009524037,0.000011922737,0.000032132615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99621546,0.0019143099,0.00045141362,0.00032059388,0.00058238057,0.0005158197],"domain_scores_gemma":[0.99775416,0.0005393469,0.0002292667,0.0006551988,0.00045343782,0.0003685661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005913856,0.00013048874,0.0003829083,0.00013798958,0.00010524228,0.00006498451,0.0004371212,0.000023401377,0.00017506088],"category_scores_gemma":[0.0018371709,0.000060603645,0.000026410333,0.0006813551,0.00034123837,0.00026727666,0.00020718896,0.0000977062,6.708356e-7],"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":[0.00025120837,0.00028388223,0.98858446,0.000082938146,0.000060985905,9.087401e-7,0.00128072,3.0592125e-7,0.0004774346,0.00010367076,0.002729446,0.006144029],"study_design_scores_gemma":[0.003535647,0.000397322,0.9934088,0.00006767556,0.0000028410464,0.0000025541117,0.00204195,0.00035231354,0.000014300109,0.000009896954,0.00010565523,0.000061055616],"about_ca_topic_score_codex":0.7416851,"about_ca_topic_score_gemma":0.7914063,"teacher_disagreement_score":0.049721144,"about_ca_system_score_codex":0.00075822655,"about_ca_system_score_gemma":0.009755684,"threshold_uncertainty_score":0.9958581},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3060626,0.0007576227,0.30133814,0.007720059,0.0006431572,0.0022526374,0.00016528487,0.0012865042,0.379774],"genre_scores_gemma":[0.96965015,0.000011901441,0.0146461725,0.0029956128,0.00026251408,0.000018329274,0.0001544807,0.000025141477,0.012235728],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992986,0.0000061363526,0.00015938494,0.00017632412,0.00011801283,0.00024158694],"domain_scores_gemma":[0.99925387,0.00014816735,0.00003082568,0.00031736432,0.000077056735,0.0001727101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002690929,0.000074883814,0.00014383055,0.00006332766,0.000049725262,0.000007602735,0.00005909124,0.000037292066,0.0002742257],"category_scores_gemma":[0.00026225366,0.00006211935,0.00008222476,0.00011679492,0.00003495291,0.000051303585,0.000022698903,0.000051424046,0.00016726794],"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":[0.00087360054,0.0004646934,0.12299897,0.00029862256,0.00022618748,0.00018291043,0.00021325595,0.0000022262973,0.010015114,0.10323731,0.6704196,0.09106754],"study_design_scores_gemma":[0.0024864045,0.00016304437,0.17357618,0.0000876409,0.0000807754,0.000060028156,0.00031218288,0.00015196834,0.0044938615,0.0017974247,0.8165382,0.00025227954],"about_ca_topic_score_codex":0.000029073122,"about_ca_topic_score_gemma":0.000116744246,"teacher_disagreement_score":0.6635875,"about_ca_system_score_codex":0.000035639612,"about_ca_system_score_gemma":0.00005097804,"threshold_uncertainty_score":0.30025792},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9703133,0.0025471637,0.0190806,0.0016825533,0.0003384361,0.0012191251,0.0011828553,0.000039485436,0.0035964935],"genre_scores_gemma":[0.9674733,0.0008284743,0.02319395,0.0014849472,0.00024342861,0.00021234888,0.0048312936,0.0001274011,0.0016048476],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9940721,0.0026881292,0.00073961203,0.00091026316,0.00055507594,0.0010348285],"domain_scores_gemma":[0.9963963,0.0014086401,0.0005407554,0.00047270703,0.0004097357,0.00077188795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038014392,0.00069617474,0.00074556255,0.0003054414,0.00062276947,0.00046421276,0.0005177414,0.00068493566,0.00034502815],"category_scores_gemma":[0.0044698026,0.0007610494,0.00019142103,0.00060557784,0.0003472534,0.00045582064,0.00016243929,0.0008804089,0.000033357584],"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":true,"study_design_scores_codex":[0.0024654001,0.0009048,0.63236713,0.0029000444,0.0020009198,0.0030999687,0.08175638,0.0782756,0.1412152,0.0007470716,0.030283902,0.023983562],"study_design_scores_gemma":[0.0030172344,0.00020964541,0.74353814,0.0029353225,0.0022355942,0.000828612,0.045719106,0.08416424,0.10128499,0.004323792,0.010529392,0.0012139464],"about_ca_topic_score_codex":0.8668366,"about_ca_topic_score_gemma":0.9819136,"teacher_disagreement_score":0.115077056,"about_ca_system_score_codex":0.017618118,"about_ca_system_score_gemma":0.054996356,"threshold_uncertainty_score":0.99948406},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9376951,0.009332688,0.00018953181,0.00038819585,0.0047164597,0.0006696314,0.002638051,0.00041258783,0.043957748],"genre_scores_gemma":[0.93948036,0.0018869962,0.00014247114,0.00014689584,0.0010280986,0.00008556917,0.003882096,0.0001169892,0.05323052],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99507034,0.00066755794,0.00069612084,0.0010206478,0.0012307939,0.0013145589],"domain_scores_gemma":[0.99535155,0.0012726586,0.0006550889,0.000749115,0.0008856428,0.0010859146],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0004760381,0.00081324857,0.0009412204,0.00044231344,0.00860619,0.000091664864,0.0005488132,0.0006934964,0.00014629101],"category_scores_gemma":[0.0014724025,0.0009787504,0.0005191133,0.0010342255,0.00082976854,0.0003703737,0.0002630475,0.0009567233,0.0001905894],"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":true,"study_design_scores_codex":[0.00087939575,0.0006067844,0.9059871,0.0011675664,0.002489017,0.018870886,0.022557106,0.0066683874,0.0042089047,0.008545497,0.017698186,0.010321165],"study_design_scores_gemma":[0.0014970533,0.00011353392,0.87382287,0.0010735505,0.0006183808,0.0007621448,0.026960958,0.0012786506,0.00064467295,0.0001427798,0.09225102,0.00083439035],"about_ca_topic_score_codex":0.9634216,"about_ca_topic_score_gemma":0.990634,"teacher_disagreement_score":0.07455283,"about_ca_system_score_codex":0.04392019,"about_ca_system_score_gemma":0.06253894,"threshold_uncertainty_score":0.99926627},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005567942,0.012469922,0.000013326336,0.0002505984,0.0012549257,0.0008062728,0.9635417,0.0001275568,0.020978928],"genre_scores_gemma":[0.3406535,0.00006491403,0.00013702732,0.00023855972,0.0005133615,0.000048261238,0.6533043,0.000025909545,0.0050141625],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973372,0.00014074349,0.0008412971,0.0008302736,0.00047177437,0.00037866653],"domain_scores_gemma":[0.994521,0.00024557405,0.00041736942,0.0039326656,0.0006827228,0.00020063101],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017719912,0.00028782472,0.0006326452,0.00017455583,0.00010040958,0.000077605284,0.001162512,0.0002485549,0.12937263],"category_scores_gemma":[0.011329479,0.00030752315,0.00012413372,0.0008324199,0.00003654568,0.00048017167,0.0015416045,0.00034399697,0.0015840267],"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":[0.00021701695,0.00022345684,0.0036286684,0.004143434,0.00028201586,0.00008903411,0.00006742556,0.0000026933992,0.0007166539,0.000018868352,0.981721,0.008889736],"study_design_scores_gemma":[0.001452678,0.000101859456,0.14182098,0.027531391,0.0003195671,0.000007091664,0.000100711746,0.0021666896,0.0010881941,0.000032214586,0.8251056,0.0002730007],"about_ca_topic_score_codex":0.000046209298,"about_ca_topic_score_gemma":0.000026659878,"teacher_disagreement_score":0.34009674,"about_ca_system_score_codex":0.00013914375,"about_ca_system_score_gemma":0.0011033536,"threshold_uncertainty_score":0.9999377},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006446269,0.009622105,0.0000046657083,0.00049242063,0.0003360849,0.0010024211,0.9299862,0.00022245356,0.057689067],"genre_scores_gemma":[0.17074297,0.00012232408,0.00009707828,0.001664466,0.00051679276,0.00017566222,0.81030935,0.000055744138,0.016315632],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99675256,0.00017132594,0.00054191623,0.00095602893,0.000692498,0.0008856915],"domain_scores_gemma":[0.9964933,0.00037400395,0.0001115889,0.0019708837,0.000501245,0.0005490198],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00019690882,0.00040973822,0.0005835679,0.0003161387,0.00020275227,0.00014813882,0.0007684617,0.0001956189,0.72200215],"category_scores_gemma":[0.0059220884,0.000436341,0.00021613183,0.001191068,0.000035107565,0.0003044336,0.0013673359,0.0007625246,0.042580437],"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":[0.00016663226,0.00025518434,0.0019440097,0.0044712955,0.00022474538,0.0005109841,0.000046747893,0.00000837653,0.00010272784,0.00008592286,0.9714317,0.02075173],"study_design_scores_gemma":[0.0019930957,0.00007854322,0.03269541,0.026932556,0.00011831341,0.0000072468088,0.00012375986,0.0017639418,0.0018148111,0.000008028158,0.93407744,0.00038687576],"about_ca_topic_score_codex":0.00002880492,"about_ca_topic_score_gemma":0.000018475312,"teacher_disagreement_score":0.6794217,"about_ca_system_score_codex":0.000245845,"about_ca_system_score_gemma":0.0016879056,"threshold_uncertainty_score":0.99980885},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021770446,0.0038985172,8.097737e-7,0.0040272754,0.0005380265,0.0011005229,0.8798156,0.00012149309,0.11047598],"genre_scores_gemma":[0.0076327324,0.00015593742,0.000044020417,0.0065395003,0.00038391206,0.00035039638,0.8918581,0.00003531149,0.09300009],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99738824,0.00018928648,0.000488821,0.0009115248,0.00052015774,0.0005019422],"domain_scores_gemma":[0.99481845,0.0006703618,0.0002295933,0.0038219893,0.00028026718,0.0001793687],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016560455,0.00033632753,0.00040678322,0.00014057031,0.00051801594,0.0001970707,0.0016252864,0.00019444988,0.9719855],"category_scores_gemma":[0.0076193404,0.00026898968,0.00013790726,0.001416428,0.000039968385,0.000354298,0.0021220685,0.0005202617,0.036858696],"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":[0.00009414055,0.00012373137,0.00026713547,0.0006980748,0.0002981191,0.00002353274,0.00013085153,3.4975622e-7,0.0000053140425,0.000005359413,0.9722831,0.02607029],"study_design_scores_gemma":[0.00095597224,0.000059151935,0.013566566,0.007681875,0.0001640632,0.000006696783,0.0000914919,0.0061264136,0.00015039026,0.00001673197,0.9709594,0.00022122418],"about_ca_topic_score_codex":0.00009913489,"about_ca_topic_score_gemma":0.00006442319,"teacher_disagreement_score":0.93512684,"about_ca_system_score_codex":0.00022280539,"about_ca_system_score_gemma":0.0013625191,"threshold_uncertainty_score":0.9999762},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9266792,0.0086056255,0.0001490595,0.0016062701,0.00024384844,0.00085888687,0.0005929291,0.00019787211,0.06106634],"genre_scores_gemma":[0.90495753,0.07314915,0.0057594287,0.00021800514,0.00014564683,0.000010771366,0.0009830294,0.00007660528,0.014699813],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99667245,0.001366481,0.00034979603,0.0007914903,0.0004129513,0.00040680438],"domain_scores_gemma":[0.996974,0.0011790723,0.0004211762,0.00060387654,0.00041160846,0.00041025798],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024082114,0.00046229424,0.00085186417,0.0055340696,0.000325845,0.00035554005,0.0008462658,0.0009427076,0.00019929197],"category_scores_gemma":[0.00002514841,0.00048861053,0.000446814,0.0037682713,0.0002809251,0.00076698815,0.00030111894,0.0012239316,0.000008049141],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026187187,0.000561759,0.79391545,0.005209201,0.002113553,0.0036769942,0.0053925854,0.0000030293997,0.023732742,0.0006021484,0.15942062,0.0027532168],"study_design_scores_gemma":[0.001163239,0.00019409596,0.9688077,0.0032676596,0.0010735741,0.00019930549,0.011942325,0.00007953428,0.0012201389,0.00061163236,0.010851102,0.00058969273],"about_ca_topic_score_codex":0.0048207818,"about_ca_topic_score_gemma":0.0011469082,"teacher_disagreement_score":0.17489228,"about_ca_system_score_codex":0.00008096024,"about_ca_system_score_gemma":0.0016157501,"threshold_uncertainty_score":0.9997566},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024385809,0.02052601,0.00005400456,0.0013150448,0.00061430095,0.0010464953,0.8525048,0.00037654943,0.12331893],"genre_scores_gemma":[0.18515958,0.00020151868,0.002656306,0.01097063,0.0011024594,0.0007164034,0.76943153,0.000089705,0.029671865],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966296,0.00071449246,0.00061665935,0.0008900097,0.00041518128,0.00073407724],"domain_scores_gemma":[0.9958073,0.0019284106,0.00018623179,0.0012853625,0.00037250415,0.00042018725],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00037468917,0.00042098167,0.0009164491,0.00019774261,0.00015437904,0.00009574363,0.0006182742,0.00038367405,0.80226564],"category_scores_gemma":[0.03715523,0.00038510122,0.0003999757,0.0008963331,0.000040127583,0.0001400341,0.00074158475,0.0006261191,0.036616985],"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":[0.00018397097,0.00018557684,0.0012906115,0.0017165263,0.00025106035,0.0003629837,0.000025196776,0.00000487481,0.00008214057,0.00011389906,0.9408309,0.054952275],"study_design_scores_gemma":[0.0012052513,0.00010491784,0.10720803,0.009307496,0.00016880488,0.000011196193,0.000048742546,0.00044424116,0.0006802682,0.00021787912,0.88027793,0.00032525993],"about_ca_topic_score_codex":0.000004741241,"about_ca_topic_score_gemma":0.00000732773,"teacher_disagreement_score":0.76564866,"about_ca_system_score_codex":0.0001672411,"about_ca_system_score_gemma":0.00074754906,"threshold_uncertainty_score":0.9998601},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9056159,0.0096671535,0.0053228256,0.007008246,0.001388709,0.001003877,0.002089433,0.0004132063,0.06749059],"genre_scores_gemma":[0.9486368,0.003150329,0.0015019738,0.00034599245,0.0004346072,0.00028306918,0.0025317671,0.000110209156,0.043005288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9899656,0.005739348,0.0013210749,0.00090879644,0.0007553722,0.0013098146],"domain_scores_gemma":[0.99467987,0.0029118303,0.0007238941,0.00066867436,0.00049632916,0.0005194156],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004010502,0.0008000883,0.0009220371,0.00044656193,0.0005041719,0.00026698087,0.00047610587,0.0006161451,0.0023155047],"category_scores_gemma":[0.003941604,0.00090398744,0.00030960364,0.0007511195,0.000820147,0.00074075605,0.00022705135,0.0009733077,0.0000029874393],"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":true,"study_design_scores_codex":[0.00014892238,0.0005126723,0.87946635,0.004114538,0.00089835515,0.0002955211,0.0035913778,0.0024465295,0.00041833482,0.014008377,0.057949524,0.03614952],"study_design_scores_gemma":[0.001146994,0.00023114985,0.88086945,0.0074776392,0.0006152061,0.00008249508,0.0012955051,0.0039131637,0.0037937178,0.000813471,0.09907789,0.0006833042],"about_ca_topic_score_codex":0.99191993,"about_ca_topic_score_gemma":0.99982345,"teacher_disagreement_score":0.04302081,"about_ca_system_score_codex":0.01796642,"about_ca_system_score_gemma":0.033638883,"threshold_uncertainty_score":0.9993411},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98484933,0.0045943167,0.000029802111,0.009138722,0.00013492568,0.00071054464,0.00030712722,0.000025104087,0.00021011835],"genre_scores_gemma":[0.9968123,0.0014683297,0.000006876659,0.00019560588,0.000016545358,0.000083329556,0.00060088263,0.000009734793,0.00080643175],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99805677,0.000383166,0.0005447236,0.00033052292,0.000329921,0.00035491004],"domain_scores_gemma":[0.99908364,0.00012767865,0.00019034042,0.00017252966,0.0000631092,0.00036272043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066975254,0.00013879935,0.00027429275,0.00011045987,0.0001438548,0.000034292036,0.00005706334,0.000038327973,0.000042543837],"category_scores_gemma":[0.00014117353,0.00012976979,0.000022248494,0.00040675225,0.00003609437,0.00010023332,0.00006477458,0.00020171715,2.5479724e-7],"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":true,"study_design_scores_codex":[0.00001537549,0.00007331798,0.97935367,0.0005401018,0.00007005894,0.000009229939,0.000042432224,0.000011348105,3.5499778e-7,0.00020386335,0.0021404135,0.017539851],"study_design_scores_gemma":[0.0013807485,0.000049107453,0.99315476,0.00046568757,0.0000612707,7.607585e-7,0.00012421081,0.0030065957,0.0000021580806,0.0003222174,0.001337285,0.00009519606],"about_ca_topic_score_codex":0.9855494,"about_ca_topic_score_gemma":0.99958634,"teacher_disagreement_score":0.017444655,"about_ca_system_score_codex":0.007508845,"about_ca_system_score_gemma":0.015918883,"threshold_uncertainty_score":0.9963012},"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":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08491784,0.04286399,0.0005063205,0.15613718,0.0092389425,0.038569193,0.003166319,0.0009706202,0.6636296],"genre_scores_gemma":[0.6438283,0.041245274,0.000034409157,0.051046364,0.00058877695,0.0016154321,0.003112216,0.00009311352,0.25843617],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9921468,0.0016713515,0.0016881668,0.0015802666,0.001932957,0.0009804487],"domain_scores_gemma":[0.98259413,0.012368777,0.0007563227,0.003425961,0.00040466897,0.00045013282],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004961653,0.00089467433,0.0010864872,0.0005122186,0.00077030616,0.00026167487,0.002317934,0.00039354197,0.0016724577],"category_scores_gemma":[0.012956904,0.00048266022,0.00043011774,0.0024096016,0.00029596817,0.00021042398,0.00016508311,0.001803809,0.00039828804],"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":[0.012340288,0.0011578319,0.2784815,0.006976943,0.0007941714,0.00041459905,0.0162671,0.0000930639,0.00015277854,0.014989642,0.5440663,0.12426577],"study_design_scores_gemma":[0.003987405,0.00078124774,0.3840785,0.014665074,0.0011456034,0.000012655984,0.021186464,0.0013765104,0.00041051072,0.002874667,0.5679708,0.0015105755],"about_ca_topic_score_codex":0.006482346,"about_ca_topic_score_gemma":0.12187856,"teacher_disagreement_score":0.55891037,"about_ca_system_score_codex":0.0010902361,"about_ca_system_score_gemma":0.006166355,"threshold_uncertainty_score":0.99976254},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977665,0.0003839162,0.000027496935,0.00019536194,0.000027015934,0.00128607,0.000101272555,0.000032211006,0.0001801664],"genre_scores_gemma":[0.9993924,0.000006321322,0.0000416239,0.00023930235,0.000015092512,0.0002329062,0.000042118663,0.000009300522,0.000020973805],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973494,0.0011670548,0.00037401036,0.00024100568,0.0005910692,0.00027745694],"domain_scores_gemma":[0.9977619,0.0010326515,0.00008313904,0.00045158365,0.00060480234,0.000065944725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024745597,0.00012268058,0.00027990103,0.0005040603,0.00010872314,0.00006226894,0.0002705674,0.000053618638,0.000005513703],"category_scores_gemma":[0.0005999788,0.00008367918,0.000029055638,0.0011742987,0.00020921754,0.00029756335,0.00023702095,0.0004917695,0.0000058615037],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004173434,0.0005175824,0.9875824,0.0009463174,0.000031344585,0.000040400664,0.0050570904,0.0000071770387,0.00050054636,0.000045575885,0.00027647623,0.004577772],"study_design_scores_gemma":[0.0015803111,0.0006291494,0.98964494,0.0014429507,0.000010650148,0.0000033199549,0.0036694312,0.0024842424,0.00010387323,0.000067516776,0.00029604154,0.000067596426],"about_ca_topic_score_codex":0.00055061124,"about_ca_topic_score_gemma":0.0005458117,"teacher_disagreement_score":0.0045101754,"about_ca_system_score_codex":0.00010756511,"about_ca_system_score_gemma":0.0004098775,"threshold_uncertainty_score":0.3412338},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94776994,0.010648199,0.000068112,0.033704873,0.0006440345,0.005281966,0.0014432197,0.00015057775,0.0002890755],"genre_scores_gemma":[0.99518347,0.0010209947,0.000021361355,0.002257034,0.00019041938,0.00036410065,0.00046117892,0.000037175687,0.000464246],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.992628,0.0039749914,0.0012588068,0.0007566679,0.0005212303,0.00086034846],"domain_scores_gemma":[0.9963417,0.0014467688,0.00053133623,0.00081156933,0.00014113668,0.00072750193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072955387,0.00034342226,0.0013718442,0.00042421988,0.00024713966,0.00022627354,0.00017879231,0.000078085264,0.000016351727],"category_scores_gemma":[0.0004818993,0.00024243174,0.000065757406,0.00091279694,0.000087298286,0.00019009694,0.00009600976,0.00026205697,0.00005414084],"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":[0.032188646,0.00074554066,0.6321889,0.009137348,0.001091019,0.00017712737,0.025050538,0.0003974922,0.0008569727,0.006027353,0.2622343,0.029904755],"study_design_scores_gemma":[0.0072521046,0.0010579962,0.9605848,0.0013981328,0.000012277315,0.00004197902,0.0069020255,0.0031529788,0.0000022825734,0.000015160954,0.019295752,0.0002844935],"about_ca_topic_score_codex":0.056836423,"about_ca_topic_score_gemma":0.0009307439,"teacher_disagreement_score":0.32839593,"about_ca_system_score_codex":0.0009955475,"about_ca_system_score_gemma":0.0018537289,"threshold_uncertainty_score":0.9886079},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9862153,0.0010085283,0.004361604,0.00014228169,0.000112727306,0.0014076899,0.0001222766,0.0000042615716,0.0066253343],"genre_scores_gemma":[0.9412476,0.000009746336,0.058438003,0.000021808504,0.000025118028,0.000021237554,0.0001151571,0.000015375454,0.00010595514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99500924,0.003114337,0.000605147,0.0003818475,0.0005438735,0.00034554856],"domain_scores_gemma":[0.99851847,0.00026987868,0.00032701317,0.00037422864,0.0003518785,0.00015854488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016992612,0.0001362024,0.00065885985,0.000113932045,0.000051993145,0.00006372191,0.00016449258,0.00006961877,0.00040450704],"category_scores_gemma":[0.003141071,0.000121286386,0.00004620193,0.00039923718,0.000066324545,0.00040604157,0.000089024725,0.00011012021,0.000015871015],"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":[0.0010247123,0.0014784494,0.37141666,0.0008556984,0.00044035705,0.000114918104,0.003306622,0.13996851,0.010805904,0.0008800549,0.0005671013,0.469141],"study_design_scores_gemma":[0.004092565,0.0003231036,0.031491246,0.00052785536,0.000052262487,0.00023554549,0.0011166399,0.9582827,0.0006254595,0.0003248591,0.0025461565,0.0003816063],"about_ca_topic_score_codex":0.00025838258,"about_ca_topic_score_gemma":0.00015558556,"teacher_disagreement_score":0.8183142,"about_ca_system_score_codex":0.0002679344,"about_ca_system_score_gemma":0.0011194401,"threshold_uncertainty_score":0.5889334},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85659325,0.0023234398,0.07042407,0.0009264882,0.0002749825,0.003537401,0.000936703,0.0010007862,0.06398286],"genre_scores_gemma":[0.99460244,0.00004427176,0.0037516898,0.0000409111,0.00004316112,1.12229884e-7,0.0010108586,0.00043603886,0.000070528324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99330544,0.0046438137,0.0005003925,0.0003874879,0.0007501434,0.00041274293],"domain_scores_gemma":[0.9977794,0.00008890482,0.00025802536,0.0004259031,0.0012301496,0.00021756609],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012090237,0.0001343613,0.00039579612,0.0002794787,0.00043092208,0.0001562432,0.00026384782,0.000060402253,0.0014688177],"category_scores_gemma":[0.0072078872,0.0001341256,0.00005090589,0.0007936171,0.00013553746,0.00032779152,0.00023999345,0.00018628534,0.000157927],"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":[0.0021090608,0.004233564,0.009441658,0.005586516,0.001071913,0.00020841892,0.00996066,0.18018006,0.05562503,0.05615529,0.053436693,0.62199116],"study_design_scores_gemma":[0.0054612183,0.001087855,0.01883081,0.00052141456,0.000062200284,0.0012255888,0.0016211402,0.77658874,0.0003532469,0.0009988211,0.19257367,0.00067529327],"about_ca_topic_score_codex":0.000048106725,"about_ca_topic_score_gemma":0.000001256889,"teacher_disagreement_score":0.62131584,"about_ca_system_score_codex":0.00044954979,"about_ca_system_score_gemma":0.000062220635,"threshold_uncertainty_score":0.99944395},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35957375,0.000045600464,0.00025953128,0.00040199107,0.00021204287,0.00023912863,0.000073465344,0.0002550547,0.63893944],"genre_scores_gemma":[0.984559,0.00002208055,0.01088936,0.0012220377,0.0001512926,0.000017630853,0.0000550267,0.000020601721,0.0030629623],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992757,0.00003165472,0.00016225484,0.00019128424,0.00015497542,0.00018413742],"domain_scores_gemma":[0.9992702,0.00002327947,0.000039229497,0.000440444,0.000052912997,0.00017390536],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013991022,0.000084210755,0.00018053436,0.0000392027,0.00002105578,0.000004061736,0.000098654324,0.000045556928,0.0066793747],"category_scores_gemma":[0.00011036443,0.0000725606,0.000073457246,0.00007861127,0.00005885131,0.000048490907,0.000041785348,0.000102299025,0.0011411499],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001416495,0.0010185435,0.53038234,0.00019179742,0.00021827372,0.0010321751,0.00077316974,9.336364e-8,0.021518918,0.012069284,0.33496556,0.09641336],"study_design_scores_gemma":[0.0020247586,0.0003587663,0.7853248,0.00007730537,0.00008541168,0.00006445254,0.000067633875,0.00008062279,0.006137215,0.009222021,0.19630261,0.00025437633],"about_ca_topic_score_codex":0.000008678167,"about_ca_topic_score_gemma":0.000006674015,"teacher_disagreement_score":0.6358765,"about_ca_system_score_codex":0.00002077111,"about_ca_system_score_gemma":0.00006749811,"threshold_uncertainty_score":0.9996366},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801372,0.0066422992,0.0006527117,0.0030750455,0.0007113151,0.0058213673,0.0013191308,0.00014847793,0.00149241],"genre_scores_gemma":[0.90170085,0.0005158094,0.00036248853,0.00026670954,0.00044885048,0.00030492942,0.07158902,0.00006444524,0.024746893],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99705267,0.00027767528,0.0006564096,0.0009189069,0.00061077386,0.00048356346],"domain_scores_gemma":[0.9962251,0.00022186853,0.00056431256,0.002441412,0.00030968746,0.00023762429],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016520899,0.00038192054,0.0009864544,0.00024722348,0.00021048408,0.000113942,0.00054617866,0.00016914021,0.0010566288],"category_scores_gemma":[0.0006285541,0.0003001886,0.00016220665,0.00065083324,0.000031702126,0.0002976063,0.000116580326,0.00038368464,0.00000882596],"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":[0.0024701275,0.005046496,0.6146898,0.0008492809,0.007738356,0.0024546375,0.12972659,0.0000699183,0.000006363968,0.000047936996,0.057377957,0.1795225],"study_design_scores_gemma":[0.004483562,0.00072403636,0.7086289,0.00018644016,0.002976357,0.00010142237,0.110957965,0.0040723756,0.0000036565161,0.000013425487,0.1671697,0.00068218296],"about_ca_topic_score_codex":0.841252,"about_ca_topic_score_gemma":0.99773616,"teacher_disagreement_score":0.17884032,"about_ca_system_score_codex":0.001054523,"about_ca_system_score_gemma":0.0014051798,"threshold_uncertainty_score":0.99994504},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79976904,0.18093406,0.00049475423,0.00043501586,0.0034455345,0.0031115306,0.0011060389,0.00028456235,0.010419483],"genre_scores_gemma":[0.95546365,0.010675566,0.0024483153,0.000247982,0.0011023391,0.00035491123,0.016938232,0.00021589999,0.012553106],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9972649,0.0003965364,0.00058155943,0.0006731526,0.00044775137,0.0006360994],"domain_scores_gemma":[0.9972278,0.00044458907,0.00030157255,0.0011386457,0.0003764011,0.0005109868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006295947,0.00057773344,0.0011426584,0.00020728534,0.00069757696,0.000117953765,0.0003054924,0.00031171538,0.000020548265],"category_scores_gemma":[0.00015619032,0.0006213982,0.00022796032,0.00022907618,0.00008418636,0.00015765868,0.00018205747,0.0015802318,0.000025953466],"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":[0.0018865591,0.0005359189,0.008572926,0.24423298,0.0011252143,0.000059367347,0.41796145,0.00026835344,0.0008231734,0.00057651673,0.004088727,0.3198688],"study_design_scores_gemma":[0.007955752,0.0047763474,0.12566222,0.050206274,0.0025070293,0.000034840818,0.7635906,0.0055455784,0.0010938027,0.0022910903,0.03353536,0.002801109],"about_ca_topic_score_codex":0.0034085535,"about_ca_topic_score_gemma":0.00083101547,"teacher_disagreement_score":0.34562916,"about_ca_system_score_codex":0.0008639678,"about_ca_system_score_gemma":0.0013742963,"threshold_uncertainty_score":0.9996237},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9787202,0.008605643,0.000004570069,0.0030145915,0.0012837185,0.0052586123,0.0006528501,0.000044629433,0.0024151776],"genre_scores_gemma":[0.9899697,0.0010996173,0.0000100115385,0.0007346929,0.0001255966,0.00052665407,0.0016561232,0.00004496462,0.005832672],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99384475,0.0018105303,0.0011201338,0.0009804816,0.0012516254,0.0009925097],"domain_scores_gemma":[0.99553776,0.00062595087,0.0011001526,0.0018732832,0.00046585427,0.000396985],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028019804,0.00061576476,0.0014951412,0.00047703486,0.00025733313,0.00004283068,0.0007677741,0.00018318044,0.00041355763],"category_scores_gemma":[0.0016503992,0.0005073308,0.00011635446,0.0018238167,0.00006558001,0.00007406905,0.000120146055,0.0007614778,0.0000032389275],"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":false,"study_design_scores_codex":[0.00067432044,0.0038923477,0.15785418,0.008988701,0.00097072526,0.015872458,0.74414974,0.000015235997,0.000045052893,0.000039006718,0.0268494,0.04064881],"study_design_scores_gemma":[0.0026023956,0.00083432806,0.094158046,0.0014746588,0.00029178854,0.00033184682,0.89347476,0.00015176572,0.0000065486083,0.000005654613,0.0062125428,0.00045568516],"about_ca_topic_score_codex":0.9046671,"about_ca_topic_score_gemma":0.99843204,"teacher_disagreement_score":0.14932498,"about_ca_system_score_codex":0.0012245179,"about_ca_system_score_gemma":0.028226264,"threshold_uncertainty_score":0.99973786},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9299228,0.029876454,0.0069601536,0.008881247,0.0020505823,0.01485628,0.006550595,0.00021490852,0.0006869534],"genre_scores_gemma":[0.92303246,0.0038574222,0.00031145872,0.00034093406,0.0010619854,0.00054441625,0.06374266,0.000115401155,0.0069932537],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.992059,0.0013780834,0.0017904033,0.0021856118,0.0013595448,0.0012273886],"domain_scores_gemma":[0.9903012,0.0010356768,0.0017243737,0.005435554,0.00087297877,0.00063020457],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0054927375,0.000918474,0.0021739716,0.00061429496,0.0007109287,0.00033896512,0.0015159145,0.00035452662,0.0022953292],"category_scores_gemma":[0.003238945,0.00076439854,0.00038004658,0.0024717436,0.00013269055,0.000854078,0.00039977697,0.00069186155,0.000029182233],"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":[0.0033178092,0.00987469,0.38895068,0.0016189218,0.015406296,0.0030704865,0.16731301,0.00045255068,0.000010841587,0.00005983849,0.01909391,0.39083096],"study_design_scores_gemma":[0.0086405305,0.0017173019,0.3371439,0.00043924127,0.008663726,0.00012903563,0.49800274,0.08640112,0.000005984156,0.000015660118,0.057385813,0.0014549368],"about_ca_topic_score_codex":0.4763561,"about_ca_topic_score_gemma":0.92849463,"teacher_disagreement_score":0.45213854,"about_ca_system_score_codex":0.0013591411,"about_ca_system_score_gemma":0.0019229308,"threshold_uncertainty_score":0.9994807},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95288956,0.0036687632,0.02246925,0.00062243803,0.0021429972,0.0031267237,0.0012348989,0.0020297524,0.011815636],"genre_scores_gemma":[0.95824486,0.00034143106,0.0027042646,0.00026933162,0.0006589985,0.00003747534,0.025235524,0.00045113818,0.012056977],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99505496,0.00026186684,0.00079346175,0.0012836441,0.0014942472,0.0011118203],"domain_scores_gemma":[0.99543715,0.0007069364,0.00076032506,0.0008223103,0.0015375563,0.0007357204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063220743,0.0008803641,0.001288519,0.00048575987,0.0005147154,0.00019906051,0.00039416147,0.00041897502,0.00028758566],"category_scores_gemma":[0.0014764776,0.00082400226,0.000087860244,0.0031629198,0.0003722975,0.00012558226,0.00010724507,0.0013930221,0.00059353426],"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":[0.018812623,0.0027656776,0.20721452,0.08976071,0.005754617,0.018590117,0.18386537,0.0018913177,0.0017514059,0.013194368,0.058474015,0.39792526],"study_design_scores_gemma":[0.007965937,0.0018850005,0.76210463,0.030274004,0.0038531115,0.0001551693,0.117129885,0.06229406,0.00031747954,0.00020892061,0.010044731,0.003767055],"about_ca_topic_score_codex":0.00051475875,"about_ca_topic_score_gemma":0.0007510086,"teacher_disagreement_score":0.55489016,"about_ca_system_score_codex":0.00015900092,"about_ca_system_score_gemma":0.002653116,"threshold_uncertainty_score":0.99942106},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6891893,0.037052155,0.0022707968,0.0072914176,0.0010773957,0.013668574,0.0045086658,0.0058127777,0.23912889],"genre_scores_gemma":[0.9837748,0.00450835,0.00038602727,0.0019093242,0.00031894897,4.076844e-7,0.006348372,0.0013211925,0.0014326016],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99754226,0.0003988715,0.00048120617,0.00050535554,0.00048289404,0.00058943173],"domain_scores_gemma":[0.9984579,0.000027620095,0.00015086023,0.0006271935,0.00032967658,0.0004067161],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0020518776,0.00017933783,0.00040980702,0.00021600454,0.000595091,0.00015657303,0.00047992554,0.00005348123,0.0052321185],"category_scores_gemma":[0.0007087027,0.00018731761,0.00007988118,0.0005796758,0.0001343586,0.00016300929,0.00048334475,0.00033066535,0.003931489],"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":[0.00057296053,0.0007940961,0.0031737974,0.0025023955,0.00008369126,0.00032306762,0.0017932083,0.000008797441,0.00095854996,0.0003538962,0.82365394,0.16578159],"study_design_scores_gemma":[0.0011748844,0.0006586495,0.031774838,0.00056530576,0.000003931693,0.00014833265,0.00036515272,0.000017848637,0.000027037262,0.000014883879,0.9650556,0.000193567],"about_ca_topic_score_codex":0.00007114535,"about_ca_topic_score_gemma":0.000019473464,"teacher_disagreement_score":0.29458544,"about_ca_system_score_codex":0.00040967055,"about_ca_system_score_gemma":0.000023413468,"threshold_uncertainty_score":0.99684405},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6891893,0.037052155,0.0022707968,0.0072914176,0.0010773957,0.013668574,0.0045086658,0.0058127777,0.23912889],"genre_scores_gemma":[0.9837748,0.00450835,0.00038602727,0.0019093242,0.00031894897,4.076844e-7,0.006348372,0.0013211925,0.0014326016],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99754226,0.0003988715,0.00048120617,0.00050535554,0.00048289404,0.00058943173],"domain_scores_gemma":[0.9984579,0.000027620095,0.00015086023,0.0006271935,0.00032967658,0.0004067161],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0020518776,0.00017933783,0.00040980702,0.00021600454,0.000595091,0.00015657303,0.00047992554,0.00005348123,0.0052321185],"category_scores_gemma":[0.0007087027,0.00018731761,0.00007988118,0.0005796758,0.0001343586,0.00016300929,0.00048334475,0.00033066535,0.003931489],"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":[0.00057296053,0.0007940961,0.0031737974,0.0025023955,0.00008369126,0.00032306762,0.0017932083,0.000008797441,0.00095854996,0.0003538962,0.82365394,0.16578159],"study_design_scores_gemma":[0.0011748844,0.0006586495,0.031774838,0.00056530576,0.000003931693,0.00014833265,0.00036515272,0.000017848637,0.000027037262,0.000014883879,0.9650556,0.000193567],"about_ca_topic_score_codex":0.00007114535,"about_ca_topic_score_gemma":0.000019473464,"teacher_disagreement_score":0.29458544,"about_ca_system_score_codex":0.00040967055,"about_ca_system_score_gemma":0.000023413468,"threshold_uncertainty_score":0.99684405},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98565716,0.002341172,0.004609912,0.00023763643,0.00035413812,0.0050060013,0.0011803337,0.0000044319786,0.00060924166],"genre_scores_gemma":[0.990863,0.000010953151,0.00805486,0.000050868606,0.000090325324,0.00008500155,0.00081647723,0.000017476503,0.000011038179],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.98982775,0.0076121935,0.00056511397,0.0005656926,0.000911694,0.00051754684],"domain_scores_gemma":[0.9969944,0.000702019,0.00025806902,0.0011863366,0.00045014115,0.0004090794],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.055011787,0.0001591191,0.0008088941,0.00014091302,0.000048410962,0.000058321017,0.00058601645,0.00008338212,0.00024508956],"category_scores_gemma":[0.0070616216,0.00013492348,0.000032604457,0.0005485403,0.000051660594,0.00049025344,0.0005444785,0.00014786846,0.00004890461],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000433224,0.00055310543,0.52553463,0.00021652102,0.000100292724,0.0000054689954,0.00043239194,0.0005376505,0.005898201,0.000009857126,0.00018483803,0.46609384],"study_design_scores_gemma":[0.0025536143,0.000115961804,0.97631353,0.00042955726,0.000046046236,0.00003471727,0.00038172383,0.0075515085,0.00026023542,0.0000025342729,0.012130456,0.0001800877],"about_ca_topic_score_codex":0.0007705642,"about_ca_topic_score_gemma":0.00038198268,"teacher_disagreement_score":0.46591377,"about_ca_system_score_codex":0.00047515242,"about_ca_system_score_gemma":0.0010209628,"threshold_uncertainty_score":0.97306424},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9022658,0.0035916835,0.072656244,0.0011540052,0.0005629071,0.008135658,0.004652885,0.00076115556,0.0062196907],"genre_scores_gemma":[0.9944785,0.000038804523,0.0007052662,0.00007508009,0.00011472642,3.2540524e-7,0.0041782414,0.00039967525,0.000009330823],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9892131,0.008311379,0.0004338554,0.00047989577,0.0010303104,0.0005314379],"domain_scores_gemma":[0.9967077,0.00018953625,0.0002012492,0.0010547184,0.0013805346,0.00046626414],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.031431552,0.00014467613,0.00044817722,0.00031833883,0.0003689407,0.00013594552,0.00072215166,0.00006532496,0.00087984174],"category_scores_gemma":[0.013452576,0.00013867712,0.000035152436,0.0010004087,0.00010021875,0.0003740226,0.0010836478,0.0002214586,0.00037315092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012712793,0.0022381435,0.022383807,0.0020917375,0.00036887167,0.000019247134,0.0026153699,0.0023806575,0.050824583,0.0010873247,0.021118525,0.89360046],"study_design_scores_gemma":[0.0030112602,0.0003774226,0.5741031,0.00037509727,0.000045410896,0.00019724731,0.00067866105,0.012750487,0.00018389127,0.000012156345,0.40796182,0.00030346873],"about_ca_topic_score_codex":0.000116151125,"about_ca_topic_score_gemma":0.0000026804387,"teacher_disagreement_score":0.89329696,"about_ca_system_score_codex":0.00067553093,"about_ca_system_score_gemma":0.00005770387,"threshold_uncertainty_score":0.99734503},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89120114,0.009068207,0.032675635,0.021967972,0.0007902345,0.025555968,0.013715179,0.00004951427,0.004976148],"genre_scores_gemma":[0.969562,0.000053194322,0.026951179,0.00018438188,0.000064761945,0.00022793775,0.002637782,0.000015802309,0.00030294663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975604,0.00052828377,0.00051984674,0.0006715302,0.000372384,0.00034756868],"domain_scores_gemma":[0.9980949,0.0004935157,0.00027236625,0.0007653321,0.00015164806,0.00022223631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0079352,0.00015172169,0.00050314143,0.00009651667,0.00007640973,0.00016075038,0.0003357934,0.00006979578,0.00055006327],"category_scores_gemma":[0.0039485446,0.00012946287,0.000023054987,0.00024126597,0.000047157828,0.00029096074,0.00026240078,0.000108509186,0.00003704934],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016317217,0.00026025673,0.010682055,0.00017274084,0.000052103365,0.0000059530284,0.0011019708,0.00010320758,0.00013885951,0.000011893219,0.0036866078,0.9836212],"study_design_scores_gemma":[0.006776167,0.0011478434,0.13507508,0.00032215696,0.00008040497,0.00002989385,0.0022896284,0.7761387,0.00004057053,0.00004716852,0.07754192,0.00051045185],"about_ca_topic_score_codex":0.00022216162,"about_ca_topic_score_gemma":0.0002791443,"teacher_disagreement_score":0.9831107,"about_ca_system_score_codex":0.00022922183,"about_ca_system_score_gemma":0.0002566613,"threshold_uncertainty_score":0.60228074},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3748549,0.016686972,0.38847443,0.069981284,0.001620425,0.043773666,0.04877435,0.007129732,0.04870425],"genre_scores_gemma":[0.9852208,0.00017466412,0.0020950304,0.00025252145,0.000101577585,0.0000011042603,0.011583551,0.0003881678,0.00018261402],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99682194,0.0010399645,0.0004677856,0.00067178434,0.0005556664,0.00044283204],"domain_scores_gemma":[0.9979207,0.00017153824,0.0002287963,0.00079759554,0.00058433705,0.00029701836],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006412794,0.00015557768,0.00033893654,0.00023979608,0.0005771155,0.00035842365,0.00049229123,0.00006464683,0.0016861046],"category_scores_gemma":[0.008839534,0.00014959718,0.000029431581,0.0005206293,0.00010519184,0.00026893552,0.0006272159,0.00019098358,0.0002921167],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000265253,0.00050946535,0.00034069235,0.0007835258,0.00010018081,0.000010703357,0.0024597708,0.00027946502,0.0007159885,0.00057147915,0.107427455,0.886536],"study_design_scores_gemma":[0.0029459286,0.0010554474,0.022298994,0.00013483931,0.000032162956,0.00007767085,0.001204855,0.28699425,0.000012781264,0.000060611597,0.6848637,0.00031876113],"about_ca_topic_score_codex":0.00004584987,"about_ca_topic_score_gemma":0.0000028819736,"teacher_disagreement_score":0.88621724,"about_ca_system_score_codex":0.00039572275,"about_ca_system_score_gemma":0.000021580097,"threshold_uncertainty_score":0.99950945},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0673227,0.0004122979,0.000003808432,0.00002702471,0.000034340144,0.0026172698,0.9294491,0.0000318827,0.00010157786],"genre_scores_gemma":[0.21532664,0.0000068834825,0.0011393218,0.00022988803,0.00010546293,0.0019078942,0.781132,0.000060143786,0.0000918093],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9952883,0.00036955316,0.0015912915,0.0006961632,0.0015168133,0.0005378651],"domain_scores_gemma":[0.9899051,0.003751259,0.002097608,0.00089342095,0.0030949693,0.00025760313],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008719223,0.00041009186,0.0013007997,0.00030016934,0.00005638568,0.0000101769265,0.00027518795,0.00016857815,0.9576221],"category_scores_gemma":[0.015439099,0.00033088226,0.00017590592,0.0005456405,0.00006738271,0.00042301576,0.00010653746,0.00018441933,0.00018707063],"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":[0.004307704,0.0016510922,0.012471695,0.0032867468,0.00039023694,0.000008576392,0.0005658784,0.00022839119,0.00040277987,0.0000029903688,0.97102714,0.0056567825],"study_design_scores_gemma":[0.015108667,0.003919163,0.76896346,0.05213942,0.000090967435,0.000014996068,0.0001194765,0.0042362944,0.00077323144,0.000035231038,0.15402704,0.00057206117],"about_ca_topic_score_codex":0.0035357282,"about_ca_topic_score_gemma":0.10114966,"teacher_disagreement_score":0.957435,"about_ca_system_score_codex":0.00086201186,"about_ca_system_score_gemma":0.013053213,"threshold_uncertainty_score":0.99991435},"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":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0673227,0.0004122979,0.000003808432,0.00002702471,0.000034340144,0.0026172698,0.9294491,0.0000318827,0.00010157786],"genre_scores_gemma":[0.21532664,0.0000068834825,0.0011393218,0.00022988803,0.00010546293,0.0019078942,0.781132,0.000060143786,0.0000918093],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9952883,0.00036955316,0.0015912915,0.0006961632,0.0015168133,0.0005378651],"domain_scores_gemma":[0.9899051,0.003751259,0.002097608,0.00089342095,0.0030949693,0.00025760313],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008719223,0.00041009186,0.0013007997,0.00030016934,0.00005638568,0.0000101769265,0.00027518795,0.00016857815,0.9576221],"category_scores_gemma":[0.015439099,0.00033088226,0.00017590592,0.0005456405,0.00006738271,0.00042301576,0.00010653746,0.00018441933,0.00018707063],"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":[0.004307704,0.0016510922,0.012471695,0.0032867468,0.00039023694,0.000008576392,0.0005658784,0.00022839119,0.00040277987,0.0000029903688,0.97102714,0.0056567825],"study_design_scores_gemma":[0.015108667,0.003919163,0.76896346,0.05213942,0.000090967435,0.000014996068,0.0001194765,0.0042362944,0.00077323144,0.000035231038,0.15402704,0.00057206117],"about_ca_topic_score_codex":0.0035357282,"about_ca_topic_score_gemma":0.10114966,"teacher_disagreement_score":0.957435,"about_ca_system_score_codex":0.00086201186,"about_ca_system_score_gemma":0.013053213,"threshold_uncertainty_score":0.99991435},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002003409,0.0010713143,4.3768625e-7,0.00014715227,0.000074824544,0.00038597453,0.9954286,0.0000114473105,0.0008768132],"genre_scores_gemma":[0.120837234,0.000032603784,0.00013431121,0.00021965722,0.00015863216,0.00017699832,0.8776647,0.00003440803,0.0007415014],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974391,0.00014373045,0.000821221,0.0005608017,0.0006054648,0.00042966724],"domain_scores_gemma":[0.995292,0.0020166487,0.0009059403,0.0005824488,0.0006688411,0.0005341176],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000054631884,0.000341888,0.0007764814,0.00020957805,0.00003505158,0.000010556567,0.00025700906,0.0001394366,0.9986929],"category_scores_gemma":[0.016782563,0.00027889555,0.00012372747,0.0005100368,0.00008988408,0.0001603077,0.00019987334,0.00019102394,0.00027138967],"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":[0.00015146105,0.00011333187,0.033565152,0.00080172636,0.0003609347,0.00006508458,0.00002249527,0.000013270075,0.000026293706,1.3499788e-7,0.9620575,0.0028226676],"study_design_scores_gemma":[0.0016320619,0.000091460075,0.4605798,0.030210946,0.000119590964,0.0000039634924,0.000037758393,0.0004698696,0.00043825604,0.0000036144918,0.5060717,0.0003409956],"about_ca_topic_score_codex":0.5504667,"about_ca_topic_score_gemma":0.9362824,"teacher_disagreement_score":0.99842155,"about_ca_system_score_codex":0.0002642787,"about_ca_system_score_gemma":0.0071756276,"threshold_uncertainty_score":0.9999663},"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":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002003409,0.0010713143,4.3768625e-7,0.00014715227,0.000074824544,0.00038597453,0.9954286,0.0000114473105,0.0008768132],"genre_scores_gemma":[0.120837234,0.000032603784,0.00013431121,0.00021965722,0.00015863216,0.00017699832,0.8776647,0.00003440803,0.0007415014],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974391,0.00014373045,0.000821221,0.0005608017,0.0006054648,0.00042966724],"domain_scores_gemma":[0.995292,0.0020166487,0.0009059403,0.0005824488,0.0006688411,0.0005341176],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000054631884,0.000341888,0.0007764814,0.00020957805,0.00003505158,0.000010556567,0.00025700906,0.0001394366,0.9986929],"category_scores_gemma":[0.016782563,0.00027889555,0.00012372747,0.0005100368,0.00008988408,0.0001603077,0.00019987334,0.00019102394,0.00027138967],"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":[0.00015146105,0.00011333187,0.033565152,0.00080172636,0.0003609347,0.00006508458,0.00002249527,0.000013270075,0.000026293706,1.3499788e-7,0.9620575,0.0028226676],"study_design_scores_gemma":[0.0016320619,0.000091460075,0.4605798,0.030210946,0.000119590964,0.0000039634924,0.000037758393,0.0004698696,0.00043825604,0.0000036144918,0.5060717,0.0003409956],"about_ca_topic_score_codex":0.5504667,"about_ca_topic_score_gemma":0.9362824,"teacher_disagreement_score":0.99842155,"about_ca_system_score_codex":0.0002642787,"about_ca_system_score_gemma":0.0071756276,"threshold_uncertainty_score":0.9999663},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98814356,0.00013176525,0.0033243285,0.00030986735,0.00008171298,0.0009232299,0.000097808355,0.0005717111,0.0064159906],"genre_scores_gemma":[0.99753845,0.000040471376,0.00019138819,0.00007416124,0.00012589889,3.0196696e-7,0.0011893107,0.0005711798,0.00026882],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99774665,0.0006605257,0.00033258545,0.00051990914,0.00041683798,0.0003234671],"domain_scores_gemma":[0.9985897,0.00004611914,0.00014508746,0.0005042074,0.0003989596,0.00031593343],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013505706,0.0001633668,0.00023712321,0.00021579949,0.0006101935,0.00021478006,0.00022866995,0.000052869294,0.0007057407],"category_scores_gemma":[0.0026396974,0.0001731551,0.00005307377,0.00040576336,0.00011342322,0.00021980738,0.00033709538,0.0002096069,0.0008500575],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001975745,0.0005808828,0.03838155,0.00078151654,0.000114574075,0.00006651332,0.0010425632,0.0003844211,0.02210105,0.00027312097,0.0031534338,0.93114465],"study_design_scores_gemma":[0.0029412403,0.0008421627,0.86807823,0.0002335345,0.000037613572,0.00009690709,0.00012398353,0.005862161,0.00018921251,0.00046504216,0.12078455,0.00034533994],"about_ca_topic_score_codex":0.00002441753,"about_ca_topic_score_gemma":0.000040276915,"teacher_disagreement_score":0.9307993,"about_ca_system_score_codex":0.0001664438,"about_ca_system_score_gemma":0.000010196338,"threshold_uncertainty_score":0.9999279},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98814356,0.00013176525,0.0033243285,0.00030986735,0.00008171298,0.0009232299,0.000097808355,0.0005717111,0.0064159906],"genre_scores_gemma":[0.99753845,0.000040471376,0.00019138819,0.00007416124,0.00012589889,3.0196696e-7,0.0011893107,0.0005711798,0.00026882],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99774665,0.0006605257,0.00033258545,0.00051990914,0.00041683798,0.0003234671],"domain_scores_gemma":[0.9985897,0.00004611914,0.00014508746,0.0005042074,0.0003989596,0.00031593343],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013505706,0.0001633668,0.00023712321,0.00021579949,0.0006101935,0.00021478006,0.00022866995,0.000052869294,0.0007057407],"category_scores_gemma":[0.0026396974,0.0001731551,0.00005307377,0.00040576336,0.00011342322,0.00021980738,0.00033709538,0.0002096069,0.0008500575],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001975745,0.0005808828,0.03838155,0.00078151654,0.000114574075,0.00006651332,0.0010425632,0.0003844211,0.02210105,0.00027312097,0.0031534338,0.93114465],"study_design_scores_gemma":[0.0029412403,0.0008421627,0.86807823,0.0002335345,0.000037613572,0.00009690709,0.00012398353,0.005862161,0.00018921251,0.00046504216,0.12078455,0.00034533994],"about_ca_topic_score_codex":0.00002441753,"about_ca_topic_score_gemma":0.000040276915,"teacher_disagreement_score":0.9307993,"about_ca_system_score_codex":0.0001664438,"about_ca_system_score_gemma":0.000010196338,"threshold_uncertainty_score":0.9999279},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17129458,0.0039089094,0.0006090788,0.80863076,0.00085576606,0.0014214547,0.006540931,0.0001716821,0.0065668076],"genre_scores_gemma":[0.9781739,0.00017277405,0.00007113502,0.02096038,0.00007428298,0.000022501099,0.00006729585,0.0000101034675,0.00044761295],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980825,0.00019342474,0.00040778058,0.000342726,0.0005586388,0.00041490488],"domain_scores_gemma":[0.99795187,0.00033594048,0.00024596552,0.00068709924,0.000275822,0.0005033228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010720338,0.00014904275,0.00036025263,0.000024990115,0.00017164266,0.0000652998,0.00025209878,0.000111547495,0.000012607913],"category_scores_gemma":[0.007268824,0.00011283786,0.0000892215,0.0006263586,0.0004359217,0.00013488992,0.00027777377,0.00051164866,0.0000057325356],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014557496,0.00014965361,0.96308315,0.0010762099,0.000039961782,0.0000050504714,0.00072760857,0.0000018122847,3.8359755e-7,0.016925149,0.0020338162,0.015811607],"study_design_scores_gemma":[0.00061863597,0.000026307247,0.7039158,0.00012777466,0.0000054307047,0.0000023485754,0.000027510774,0.0000948399,3.9456458e-7,0.00020679383,0.29491502,0.000059157817],"about_ca_topic_score_codex":0.00009983618,"about_ca_topic_score_gemma":0.0000966184,"teacher_disagreement_score":0.80687934,"about_ca_system_score_codex":0.00025036023,"about_ca_system_score_gemma":0.00584263,"threshold_uncertainty_score":0.9997933},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78724885,0.008789241,0.00089770864,0.0019896831,0.0010165871,0.0008479419,0.002398122,0.0020586296,0.19475324],"genre_scores_gemma":[0.99542683,0.0002872856,0.0016229822,0.00026135144,0.0006244975,0.000020268095,0.0011912743,0.00011174962,0.00045375913],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99741364,0.00016173908,0.0004170533,0.00095549616,0.0005174633,0.0005346139],"domain_scores_gemma":[0.9975182,0.00072613696,0.00010745574,0.0012718547,0.00007276128,0.00030358016],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009698529,0.00035908667,0.0005001051,0.00020237928,0.00018410815,0.0001551131,0.00045150204,0.0000996121,0.0007750392],"category_scores_gemma":[0.0021876178,0.0003003214,0.00006481889,0.00039711973,0.00014774922,0.00032851155,0.0006271245,0.0007652767,0.000690661],"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":[0.00076291827,0.0010073949,0.39057288,0.0055754306,0.0015748977,0.008830926,0.005228348,0.00030272646,0.02606897,0.054035246,0.008262937,0.4977773],"study_design_scores_gemma":[0.0087720845,0.0023430504,0.10830006,0.015751513,0.0021837829,0.0023599474,0.0030272787,0.32682145,0.0024689483,0.005028124,0.5189856,0.00395814],"about_ca_topic_score_codex":0.0009713265,"about_ca_topic_score_gemma":0.00012775551,"teacher_disagreement_score":0.5107227,"about_ca_system_score_codex":0.000083632396,"about_ca_system_score_gemma":0.000120706456,"threshold_uncertainty_score":0.99994487},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61145544,0.050420888,0.0010699953,0.0015756034,0.012253615,0.0043871407,0.017635727,0.00289083,0.29831076],"genre_scores_gemma":[0.84587985,0.0024822701,0.0024583712,0.00032356087,0.0018320506,0.000085769316,0.113513455,0.0004885527,0.03293614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9906669,0.00045621954,0.0019744437,0.003938401,0.0015938119,0.0013702136],"domain_scores_gemma":[0.99314404,0.0013254978,0.0009842719,0.0031452838,0.000465224,0.00093571213],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0020374255,0.001673438,0.0019605998,0.000846964,0.00069940276,0.0005520652,0.0013911353,0.00069472723,0.005195814],"category_scores_gemma":[0.007375362,0.0015569065,0.00021471537,0.0010556636,0.00023206753,0.000604399,0.0013445718,0.0037268386,0.0025972496],"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":[0.004132318,0.0024497043,0.26081482,0.05241437,0.00698319,0.012629385,0.008071021,0.0005469662,0.013491312,0.012400688,0.030118197,0.59594804],"study_design_scores_gemma":[0.01253661,0.004350615,0.08557187,0.058862846,0.011603501,0.000795727,0.012240721,0.7544494,0.0008330922,0.001920997,0.047563273,0.009271332],"about_ca_topic_score_codex":0.0022015313,"about_ca_topic_score_gemma":0.001965895,"teacher_disagreement_score":0.75390244,"about_ca_system_score_codex":0.00025132025,"about_ca_system_score_gemma":0.000826458,"threshold_uncertainty_score":0.99960124},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95641077,0.00038075037,0.00045976395,0.0056183045,0.0011573693,0.00044321976,0.03543127,0.00008030944,0.000018260138],"genre_scores_gemma":[0.97614783,0.00014459225,0.0002953333,0.00055561814,0.00029953028,0.0000049908376,0.022506038,0.000017801942,0.000028295506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973931,0.00028288015,0.000465675,0.00037510338,0.0005629129,0.00092033483],"domain_scores_gemma":[0.9978998,0.0000977271,0.00015587173,0.001096307,0.00003543346,0.00071491953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015974714,0.00015511226,0.00042905452,0.00003231207,0.00026514923,0.00016772324,0.0008979042,0.000043382122,0.000035959376],"category_scores_gemma":[0.00025214755,0.00015308744,0.000021055834,0.000164939,0.0002029635,0.00086545566,0.00085244834,0.00048029615,5.8248185e-7],"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":[0.0001228142,0.0002985624,0.8903094,0.00068351964,0.00024346789,0.000016080872,0.003764558,0.0000056559284,0.0000030747485,0.00034650712,0.08834649,0.015859855],"study_design_scores_gemma":[0.00049352634,0.000043161683,0.98409563,0.00011536533,0.000022402695,0.00001136157,0.0025526334,0.0014025206,0.00000318925,0.0000073747556,0.011142621,0.000110210836],"about_ca_topic_score_codex":0.6364928,"about_ca_topic_score_gemma":0.8960248,"teacher_disagreement_score":0.25953206,"about_ca_system_score_codex":0.0015355123,"about_ca_system_score_gemma":0.0021281857,"threshold_uncertainty_score":0.62427247},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97332376,0.0007708561,0.000055383334,0.00007397893,0.0010253981,0.0024358002,0.011790789,0.00021560711,0.010308436],"genre_scores_gemma":[0.8849951,0.00003355374,0.0015951939,0.00041478663,0.00041860624,0.000042305648,0.1079852,0.00012247807,0.0043928],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99665844,0.00010859553,0.0010152912,0.0010458993,0.0007713092,0.0004004585],"domain_scores_gemma":[0.9945673,0.0003690042,0.0005921413,0.0029477512,0.0012266882,0.0002971142],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000953851,0.00045562544,0.00122111,0.00015227793,0.000052040377,0.000042631655,0.00056261016,0.0003178827,0.00027496993],"category_scores_gemma":[0.006231232,0.00041022978,0.00019176518,0.00027431003,0.000097683514,0.00028228384,0.0001984837,0.00028935925,0.00006577711],"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":[0.028713675,0.0020197057,0.27503976,0.015652534,0.0013800126,0.00019835,0.0007545297,0.0000030074366,0.0067027523,0.00031265127,0.5222233,0.14699973],"study_design_scores_gemma":[0.009004777,0.00073079823,0.7926781,0.0016049263,0.00080202834,0.000039350638,0.0010433295,0.0039633727,0.0037054254,0.00054609135,0.18436253,0.0015192189],"about_ca_topic_score_codex":0.00026804704,"about_ca_topic_score_gemma":0.0018463283,"teacher_disagreement_score":0.5176384,"about_ca_system_score_codex":0.000045721004,"about_ca_system_score_gemma":0.0013492422,"threshold_uncertainty_score":0.99983495},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9755337,0.0015491132,0.0003556218,0.00004376959,0.0002890695,0.00081584335,0.0031549605,0.00008347655,0.018174412],"genre_scores_gemma":[0.98376375,0.00016299845,0.0059902193,0.00006545081,0.000088842164,0.000012668995,0.009585081,0.000048692993,0.0002822743],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99829185,0.00013492843,0.00053140044,0.0005669312,0.00025074527,0.00022416655],"domain_scores_gemma":[0.99679154,0.0006498891,0.0006576497,0.0014385041,0.00033503957,0.00012735087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047949006,0.00028490563,0.00053337927,0.000094314426,0.00021406483,0.000101344725,0.00028283728,0.00015044911,0.0002324788],"category_scores_gemma":[0.0017828082,0.00020582974,0.00006776495,0.00018726011,0.00015045908,0.00039769502,0.00016071546,0.00033575523,0.000005385978],"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":[0.0063108895,0.004783699,0.17331608,0.03648763,0.008845732,0.030174255,0.015523274,0.000010782317,0.06879622,0.014127675,0.015303198,0.62632054],"study_design_scores_gemma":[0.0076549104,0.00087041844,0.8274469,0.0064614117,0.006329054,0.021964753,0.09668247,0.013576368,0.00882105,0.0011483597,0.004873531,0.004170779],"about_ca_topic_score_codex":0.0016533701,"about_ca_topic_score_gemma":0.003146991,"teacher_disagreement_score":0.6541308,"about_ca_system_score_codex":0.000028132376,"about_ca_system_score_gemma":0.0007946599,"threshold_uncertainty_score":0.8393493},"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":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11453422,0.0053027533,0.011771668,0.060676713,0.008342579,0.015085675,0.016486652,0.00530409,0.76249564],"genre_scores_gemma":[0.16274424,0.0008445401,0.040693954,0.034643725,0.0005713295,0.00019278588,0.66243476,0.00008772735,0.097786926],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983446,0.00005516407,0.00075362955,0.0002404421,0.0003660837,0.00024008351],"domain_scores_gemma":[0.99836826,0.00010270561,0.00035890387,0.00055667304,0.0004356748,0.00017775399],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016803047,0.00024508045,0.000538892,0.0003737876,0.00014883943,0.00007861683,0.00008966228,0.00016532405,0.00112563],"category_scores_gemma":[0.0008339124,0.00023553125,0.00012031332,0.00016904989,0.000012525326,0.00035735127,0.00003299397,0.00034009662,0.00014704377],"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":[0.00035368733,0.00050525455,0.0042738714,0.0065335757,0.0005435795,0.00002391994,0.004999344,0.000021872498,0.0000824578,0.008639839,0.86854154,0.10548104],"study_design_scores_gemma":[0.0043004476,0.00030800098,0.23127896,0.0045595584,0.00043735703,0.000040513944,0.0041990536,0.0030695996,0.00017218155,0.0036091073,0.7470483,0.000976924],"about_ca_topic_score_codex":0.00038398724,"about_ca_topic_score_gemma":0.0021890758,"teacher_disagreement_score":0.66470873,"about_ca_system_score_codex":0.00020723967,"about_ca_system_score_gemma":0.0026018124,"threshold_uncertainty_score":0.99978745},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97980237,0.0009923192,0.000295515,0.000043887147,0.00017225319,0.0014020136,0.00009744023,0.000053997854,0.01714018],"genre_scores_gemma":[0.9980174,0.0007838109,0.000019765925,0.00001547083,0.000012624569,0.000063647014,0.0010269686,0.000008237179,0.000052081195],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982415,0.00016997373,0.0007593937,0.0001260276,0.0005762472,0.00012689727],"domain_scores_gemma":[0.998687,0.00012509809,0.00037084887,0.00049783674,0.00028228236,0.00003691575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000611401,0.0001542791,0.0003640284,0.0002265244,0.00008073983,0.000008505382,0.00017553757,0.00017644113,0.000018891964],"category_scores_gemma":[0.00010365273,0.0000820324,0.00021750886,0.00048623787,0.000059873084,0.00013072422,0.0000056400195,0.00022678662,0.000003833547],"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":[0.0094981585,0.0006806664,0.096909486,0.025420174,0.00068488915,0.000012557834,0.013768218,0.000012626866,0.0060620396,0.0002848337,0.0009832784,0.8456831],"study_design_scores_gemma":[0.00070912245,0.000102198144,0.98259115,0.0007325092,0.00014840155,0.000015094849,0.006509087,0.0026158819,0.0038483788,0.000009030664,0.002625498,0.00009365057],"about_ca_topic_score_codex":0.00086669915,"about_ca_topic_score_gemma":0.0006575883,"teacher_disagreement_score":0.8856816,"about_ca_system_score_codex":0.000028742386,"about_ca_system_score_gemma":0.000111953814,"threshold_uncertainty_score":0.3345184},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98438686,0.0023979745,0.00031993486,0.00021249772,0.00031642115,0.002507062,0.004919153,0.00023179389,0.004708327],"genre_scores_gemma":[0.71132255,0.00093726395,0.0031511236,0.0002918974,0.00017291403,0.0005878086,0.21990696,0.00011799954,0.0635115],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856985,0.000036007274,0.00027827467,0.0005137266,0.00040246404,0.00019971021],"domain_scores_gemma":[0.99922776,0.00013138454,0.0001457415,0.00028172132,0.00003335193,0.00018001399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009402215,0.00025874152,0.00041449186,0.000111493995,0.00012919634,0.00003449286,0.00008930055,0.00013835539,0.00085836573],"category_scores_gemma":[0.00012982333,0.00020719263,0.000081179045,0.00006972934,0.00003885818,0.00019015555,0.000053792453,0.00023902503,0.00000516532],"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":[0.05784438,0.004810522,0.57238775,0.028727029,0.001501667,0.00062651676,0.005194174,0.0002664166,0.0016273081,0.0014221217,0.16762641,0.15796572],"study_design_scores_gemma":[0.020841295,0.003423973,0.28880805,0.002862319,0.0019273694,0.00018809758,0.003971678,0.030057311,0.0024154242,0.0052332594,0.63651454,0.0037566382],"about_ca_topic_score_codex":0.000045787572,"about_ca_topic_score_gemma":0.00032798183,"teacher_disagreement_score":0.46888816,"about_ca_system_score_codex":0.00006592499,"about_ca_system_score_gemma":0.00022029961,"threshold_uncertainty_score":0.93985033},"labels":[],"label_agreement":null}]}