{"meta":{"query_hash":"a02047a22732","filters":{"topic":"Big Data Technologies and Applications"},"cohort_total":167,"direct_labels_cover":4,"predictions_cover":167,"exported":167,"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/a02047a22732","api":"https://metacan.xera.ac/api/v1/cohort?topic=Big+Data+Technologies+and+Applications"},"results":[{"id":"W1486792363","doi":"10.3968/5186","title":"Is the Digital Revolution Driven by an Ideology","year":2014,"lang":"en","type":"article","venue":"Studies in sociology of science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Ideology; Futurist; Sociology; Enlightenment; Diversity (politics); Aesthetics; Cliché; Face (sociological concept); Philosophy of technology; Epistemology; Social science; Law; Politics; Political science; Linguistics; Philosophy of science; Philosophy; Anthropology","score_opus":0.2583378344107343,"score_gpt":0.45624415763801324,"score_spread":0.1979063232272789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486792363","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04065883,0.01562785,0.0033911816,0.24028115,0.003191886,0.000017946219,0.0001263571,0.00011255627,0.6965923],"genre_scores_gemma":[0.90782833,0.01255279,0.00155916,0.038316038,0.004460425,0.00005708742,0.00009948753,0.00014404759,0.034982618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99537057,0.0017800811,0.00011587998,0.00058685755,0.0011796111,0.0009670135],"domain_scores_gemma":[0.99583554,0.0016890763,0.0005995915,0.00083625864,0.00048187125,0.00055757916],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0047280104,0.00023974148,0.00036662363,0.0019449216,0.0053172694,0.014149846,0.0008007601,0.0045721345,0.009267628],"category_scores_gemma":[0.0064671747,0.0002510867,0.00050870073,0.0022597495,0.030555528,0.016809281,0.0051563373,0.004522636,0.002787965],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018929204,0.000018969176,0.00028678423,0.000031189164,0.000004393633,0.000065169595,0.002713562,0.000041725965,0.000053525055,0.97937495,0.011069337,0.006321501],"study_design_scores_gemma":[0.000034331704,0.000021050993,0.0010666945,0.0002665311,0.0000108876375,0.00014965542,0.005398723,0.00019088063,0.00020735692,0.46753392,0.5250951,0.000024883482],"about_ca_topic_score_codex":0.002173822,"about_ca_topic_score_gemma":0.0013061061,"teacher_disagreement_score":0.9946827,"about_ca_system_score_codex":0.004683205,"about_ca_system_score_gemma":0.003398482,"threshold_uncertainty_score":0.033979237},"labels":[],"label_agreement":null},{"id":"W1496518399","doi":"","title":"Proceedings of the 2001 workshop on Multimedia and security: new challenges","year":2001,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"University of Ottawa","funders":"","keywords":"Multimedia; Computer science; Software; Library science; Telecommunications; World Wide Web","score_opus":0.27735875866211795,"score_gpt":0.3742508454132318,"score_spread":0.09689208675111383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496518399","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009026433,0.22901992,0.08584321,0.35471565,0.085239,0.0002936734,0.0011554223,0.0020457061,0.232661],"genre_scores_gemma":[0.063088864,0.19721438,0.04579435,0.026289081,0.046913728,0.00030400188,0.0034080984,0.0009908687,0.61599654],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99832934,0.00052858866,0.00007973292,0.0002297687,0.0006118698,0.00022068672],"domain_scores_gemma":[0.9948848,0.0017607947,0.00012981745,0.00046844027,0.0013504241,0.0014056726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00442759,0.0011838744,0.0010843467,0.0014205587,0.0022822614,0.009783021,0.0022082543,0.004523499,0.043853786],"category_scores_gemma":[0.0056521613,0.0005574564,0.0005803104,0.0018559236,0.001977521,0.01259703,0.003389732,0.005450539,0.012963392],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106900334,0.00005897914,0.0002813017,0.00026299298,0.00001935559,0.00018433346,0.00053772586,0.00026811153,0.0009279257,0.02377679,0.8214051,0.15217037],"study_design_scores_gemma":[0.00000841552,0.000017152279,0.00023387051,0.00019087207,0.000010381906,0.00018284842,0.00057840743,0.0006603976,0.00031242042,0.007075299,0.9907161,0.000013832412],"about_ca_topic_score_codex":0.00372784,"about_ca_topic_score_gemma":0.01097879,"teacher_disagreement_score":0.043853786,"about_ca_system_score_codex":0.0017894744,"about_ca_system_score_gemma":0.002255071,"threshold_uncertainty_score":0.14670557},"labels":[],"label_agreement":null},{"id":"W1508161314","doi":"10.1177/1536867x0800800406","title":"A Shortcut through Long Loops: An Illustration of Two Alternatives to Looping over Observations","year":2008,"lang":"en","type":"article","venue":"The Stata Journal Promoting communications on statistics and Stata","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Traffic Injury Research Foundation","funders":"","keywords":"Computer science; Search engine indexing; Key (lock); Mathematical optimization; Identifier; Data mining; Operations research; Information retrieval; Mathematics","score_opus":0.5569936812276851,"score_gpt":0.48027112868617966,"score_spread":0.07672255254150545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508161314","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017788533,0.00060907047,0.96832633,0.0035517958,0.00022020921,0.00021174768,0.00028280658,0.0014369568,0.0075725815],"genre_scores_gemma":[0.19855732,0.0004050053,0.793212,0.0012867925,0.00021795701,0.0005561624,0.0003597142,0.0006729312,0.004732121],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9790163,0.012525827,0.0014145878,0.0027771345,0.0032698247,0.000996302],"domain_scores_gemma":[0.8779688,0.08575042,0.008768867,0.021347512,0.0046221334,0.0015423296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022100952,0.0008923686,0.0010900305,0.0021150622,0.0022860602,0.0047338274,0.0038844338,0.0035798622,0.008797207],"category_scores_gemma":[0.1103291,0.0008274324,0.0018984093,0.005570812,0.00583851,0.0107423505,0.0067454847,0.0038553637,0.001222352],"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":[0.0014198879,0.00027137905,0.00754018,0.00052609056,0.000120841905,0.0010731028,0.007808807,0.02010408,0.0038652432,0.7475277,0.008804445,0.20093821],"study_design_scores_gemma":[0.00033741,0.0006372418,0.0020986567,0.00032669774,0.00010050545,0.0008421296,0.001638784,0.08709432,0.008488933,0.79999006,0.09826824,0.00017709259],"about_ca_topic_score_codex":0.0026320983,"about_ca_topic_score_gemma":0.0026162064,"teacher_disagreement_score":0.022100952,"about_ca_system_score_codex":0.001481139,"about_ca_system_score_gemma":0.0038403121,"threshold_uncertainty_score":0.11688244},"labels":[],"label_agreement":null},{"id":"W1622445028","doi":"10.5703/1288284315628","title":"The Punishment for Dreamers: Big Data, Retention, and Academic Libraries","year":2015,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Purdue Pharma (Canada)","funders":"","keywords":"Punishment (psychology); Data retention; Computer science; Big data; Internet privacy; Computer security; Psychology; Operating system; Social psychology","score_opus":0.7045384724497639,"score_gpt":0.44020883294422797,"score_spread":0.2643296395055359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1622445028","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023029244,0.007977207,0.0028985275,0.9286225,0.0010533598,0.000019701829,0.000166722,0.000062344814,0.036170427],"genre_scores_gemma":[0.85451794,0.008369126,0.0019587034,0.11356027,0.0035862217,0.00009460111,0.00011730161,0.0001275791,0.017668238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9837885,0.011297792,0.0004184882,0.0006638472,0.0023213208,0.0015100932],"domain_scores_gemma":[0.8887561,0.06558945,0.013654826,0.00639749,0.010215528,0.0153867],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.024540935,0.0003794654,0.0010047422,0.00229204,0.011982192,0.01785297,0.0021904462,0.007583135,0.019351056],"category_scores_gemma":[0.10536796,0.00042927783,0.00063632434,0.005106704,0.02928271,0.030523006,0.011711269,0.013193947,0.0020501104],"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":[0.0005718693,0.000284529,0.016170086,0.00038231115,0.000118277916,0.00027135038,0.008657636,0.00093125645,0.00007020751,0.5465287,0.34362862,0.082385145],"study_design_scores_gemma":[0.00017602083,0.00013166062,0.00628,0.0010188577,0.00005299533,0.000220195,0.027797876,0.001979482,0.00024428216,0.83927256,0.12267799,0.00014810188],"about_ca_topic_score_codex":0.016284792,"about_ca_topic_score_gemma":0.021690683,"teacher_disagreement_score":0.98214704,"about_ca_system_score_codex":0.0066187405,"about_ca_system_score_gemma":0.014710993,"threshold_uncertainty_score":0.12978637},"labels":[],"label_agreement":null},{"id":"W163543987","doi":"","title":"Advancing Geoinformation Science for a Changing World ed. by Stan Geertman, Wolfgang Reinhardt, Fred Toppen (review)","year":2013,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"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":"Environmental ethics; History; Philosophy","score_opus":0.03422490018015934,"score_gpt":0.36065340669428153,"score_spread":0.3264285065141222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W163543987","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028639774,0.9854417,0.00023490244,0.008585982,0.00477626,0.000006984119,0.000047532692,0.00001750387,0.00086060085],"genre_scores_gemma":[0.00033692186,0.98940057,0.00027667562,0.0032149001,0.003506585,0.000013093938,0.00007722333,0.000012725743,0.0031613668],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988703,0.00025070217,0.00013333207,0.00018439454,0.00049027795,0.000070912145],"domain_scores_gemma":[0.99623966,0.0016552514,0.0003439031,0.00009057137,0.0013190586,0.00035148018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00295002,0.0015185978,0.0019774025,0.0039843717,0.0006420277,0.003512206,0.0013032288,0.003017697,0.01141376],"category_scores_gemma":[0.0056096693,0.00081330724,0.0007695331,0.0058726915,0.0014120418,0.006278935,0.0020100207,0.0049186675,0.011692518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038466915,0.000016363021,0.00012683547,0.0046119243,0.000050072656,0.000061070976,0.00010686303,0.00017886123,0.00030228248,0.0030876463,0.73990256,0.25151706],"study_design_scores_gemma":[0.000011044002,0.000019294946,0.00029323422,0.0022496518,0.000022494296,0.00022894934,0.00006244591,0.000044150063,0.00011014325,0.0015619043,0.99538237,0.000014289738],"about_ca_topic_score_codex":0.0052804197,"about_ca_topic_score_gemma":0.011761388,"teacher_disagreement_score":0.01141376,"about_ca_system_score_codex":0.0015352878,"about_ca_system_score_gemma":0.0037134876,"threshold_uncertainty_score":0.038182795},"labels":[],"label_agreement":null},{"id":"W1724277128","doi":"10.3968/6594","title":"Socio-Economy Plans & Growth Features","year":2015,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Continuance; CLARITY; Sustainability; Civilization; Quality (philosophy); Competition (biology); Business; Economic system; Political economy; Economics; Political science; Ecology; Law","score_opus":0.18178657641399315,"score_gpt":0.4156774503240427,"score_spread":0.23389087391004953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1724277128","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017203087,0.00033985797,0.0021387478,0.0064323316,0.00013803638,0.00006244381,0.00096350344,0.00011032551,0.9726116],"genre_scores_gemma":[0.628646,0.0015005659,0.0045109848,0.00087657303,0.00016024525,0.0002035962,0.00212194,0.00013347557,0.3618466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997212,0.00004748912,0.000009100719,0.0000336792,0.00009245529,0.00009599638],"domain_scores_gemma":[0.9996815,0.000034574692,0.00003014163,0.00003347638,0.00010524567,0.00011506414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048868975,0.0002842899,0.000107740794,0.0012146281,0.0016002702,0.0040074848,0.00039318632,0.00069331366,0.046126127],"category_scores_gemma":[0.0008237981,0.00010799816,0.00020858018,0.0012023454,0.0013511047,0.002751152,0.0017170674,0.000977018,0.0074864603],"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":[0.000017271937,0.00004261702,0.003915043,0.000034510835,0.000004642494,0.00015272181,0.0010128799,0.0006964283,0.00021473342,0.90812856,0.041098848,0.044681598],"study_design_scores_gemma":[0.000006117978,0.000024705021,0.01165255,0.000050555263,0.0000040553455,0.000118613854,0.0034121864,0.00066988997,0.00018625888,0.16609281,0.8177682,0.000014075021],"about_ca_topic_score_codex":0.0057116887,"about_ca_topic_score_gemma":0.011362464,"teacher_disagreement_score":0.046126127,"about_ca_system_score_codex":0.0023881542,"about_ca_system_score_gemma":0.0017993613,"threshold_uncertainty_score":0.15430725},"labels":[],"label_agreement":null},{"id":"W1746751216","doi":"","title":"Four Changes of Modern Universities From the Perspective of “4V” of Big Data","year":2015,"lang":"en","type":"article","venue":"Canadian social science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Variety (cybernetics); Perspective (graphical); Value (mathematics); Discipline; Balance (ability); Independence (probability theory); Volume (thermodynamics); Data science; Sociology; Political science; Public relations; Computer science; Social science; Psychology; Data mining","score_opus":0.5205431722637939,"score_gpt":0.3891481667219264,"score_spread":0.13139500554186745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1746751216","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17715892,0.012423806,0.10235607,0.33339408,0.0033113104,0.00025338458,0.00019307567,0.00036874658,0.37054062],"genre_scores_gemma":[0.96571535,0.0018120331,0.012536347,0.009760501,0.00052083883,0.000117921765,0.000028070055,0.000059774426,0.009449219],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9835332,0.007921469,0.0006208021,0.0014523545,0.0041561676,0.0023159739],"domain_scores_gemma":[0.9901466,0.0023196235,0.0007827468,0.001435159,0.0032798059,0.0020360718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013620766,0.00041282075,0.000513127,0.0025639592,0.00870205,0.016602354,0.0015962609,0.00543871,0.0025559305],"category_scores_gemma":[0.017961752,0.0004954455,0.0008641824,0.0030697593,0.03646705,0.0190315,0.008220404,0.007934855,0.0003771037],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002316397,0.000018016442,0.0008354113,0.00002506895,0.000006421651,0.000028668708,0.0034967014,0.00022102361,0.00010692377,0.98215497,0.0022385663,0.0108450465],"study_design_scores_gemma":[0.000031669682,0.000111758935,0.0029129507,0.00015251152,0.000021685677,0.00012985311,0.013264181,0.0018555474,0.0006328312,0.7999417,0.1808504,0.00009483067],"about_ca_topic_score_codex":0.014223329,"about_ca_topic_score_gemma":0.01257919,"teacher_disagreement_score":0.021967005,"about_ca_system_score_codex":0.021967005,"about_ca_system_score_gemma":0.016979778,"threshold_uncertainty_score":0.15938252},"labels":[],"label_agreement":null},{"id":"W1986663507","doi":"10.1002/bult.2007.1720340104","title":"Folksonomies: Introduction: Folksonomies and image tagging: Seeing the future?","year":2007,"lang":"en","type":"article","venue":"Bulletin of the American Society for Information Science and Technology","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"","keywords":"Folksonomy; Bookmarking; World Wide Web; Computer science; The Internet; Information retrieval; Multimedia","score_opus":0.027997836696210702,"score_gpt":0.30930805775715353,"score_spread":0.28131022106094283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986663507","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034376,0.71012956,0.102876656,0.07351382,0.0418544,0.00022462908,0.002811571,0.0028471,0.062304705],"genre_scores_gemma":[0.032365862,0.6239651,0.10959222,0.031995717,0.08148899,0.00043907363,0.008374571,0.0028857612,0.10889266],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972541,0.00077994616,0.00027671977,0.0005804969,0.00093530456,0.00017337054],"domain_scores_gemma":[0.9900994,0.0051640705,0.00039357744,0.00062438607,0.0029725675,0.00074603624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034408465,0.0019442544,0.0018867295,0.009587033,0.0025024558,0.011204705,0.0022859853,0.005411736,0.018958809],"category_scores_gemma":[0.012489564,0.0014474143,0.001089826,0.01617286,0.0056817783,0.02628181,0.0035963745,0.0075318026,0.018014787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068907415,0.00006896409,0.0014344873,0.0016093098,0.00004900188,0.00013122114,0.0012800904,0.00061800645,0.0007424364,0.0628671,0.6141157,0.31701472],"study_design_scores_gemma":[0.00000895959,0.000025850577,0.0014721,0.0011846968,0.000013771468,0.0005181982,0.0007298496,0.00074973464,0.0002573304,0.03992719,0.9550365,0.00007582569],"about_ca_topic_score_codex":0.0056716935,"about_ca_topic_score_gemma":0.0062643164,"teacher_disagreement_score":0.018958809,"about_ca_system_score_codex":0.003406209,"about_ca_system_score_gemma":0.0015402172,"threshold_uncertainty_score":0.063423514},"labels":[],"label_agreement":null},{"id":"W1991222875","doi":"10.1109/iembs.2010.5625982","title":"Intelligent Clinical Decision Support Systems based on SNOMED CT","year":2010,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Ontario Tech University","funders":"","keywords":"SNOMED CT; Computer science; Decision support system; Clinical decision support system; Systematized Nomenclature of Medicine; Artificial intelligence; Data mining; Intelligent decision support system; Process (computing); Information retrieval; Terminology","score_opus":0.3280054473452616,"score_gpt":0.4708973819526968,"score_spread":0.1428919346074352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991222875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02158601,0.0039455476,0.94922906,0.004681887,0.00027240082,0.0006224848,0.0028197248,0.0050317897,0.011810945],"genre_scores_gemma":[0.1587756,0.0024699988,0.8331844,0.0008971899,0.00021621524,0.00030745598,0.0026732434,0.000104332335,0.0013715065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663985,0.0015617224,0.0005257946,0.0003199861,0.00088198367,0.00007059568],"domain_scores_gemma":[0.9916375,0.0054788543,0.0006703227,0.0006332057,0.0013854299,0.0001947434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038513823,0.0007939311,0.00084744825,0.0052034236,0.0006451307,0.003428524,0.0011791133,0.0009537748,0.0036231347],"category_scores_gemma":[0.015403661,0.00041101876,0.00066759787,0.0034763305,0.0007334114,0.00279862,0.0014967019,0.0010431136,0.001320235],"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":[0.0009197194,0.00025600428,0.005724618,0.0012908841,0.00041192846,0.0019323877,0.000793511,0.10591107,0.0071502784,0.09479643,0.027422825,0.7533903],"study_design_scores_gemma":[0.00027849898,0.00030795686,0.0033194195,0.0010304237,0.0003584752,0.001782515,0.00054507726,0.6174138,0.0112754665,0.28676715,0.076713026,0.00020811557],"about_ca_topic_score_codex":0.0024560227,"about_ca_topic_score_gemma":0.0036820583,"teacher_disagreement_score":0.0052034236,"about_ca_system_score_codex":0.00091811875,"about_ca_system_score_gemma":0.0017799006,"threshold_uncertainty_score":0.020368338},"labels":[],"label_agreement":null},{"id":"W2016395525","doi":"10.3109/0142159x.2014.917761","title":"Better data ≫ Bigger data","year":2014,"lang":"en","type":"letter","venue":"Medical Teacher","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Wilson Centre; University of Toronto","funders":"","keywords":"Computer science; Data science","score_opus":0.6124631028625367,"score_gpt":0.4637355412884244,"score_spread":0.14872756157411227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016395525","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005567953,0.00020600064,0.00009220776,0.99497306,0.0038654138,0.000005166084,0.00001733116,0.000009662566,0.0007755141],"genre_scores_gemma":[0.00053004705,0.00020222901,0.00016115663,0.9871811,0.008707166,0.000015282927,0.000009871295,0.000013509985,0.0031795888],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9743556,0.008006434,0.0022833266,0.003329065,0.009251637,0.0027739243],"domain_scores_gemma":[0.8876341,0.071864605,0.004966011,0.0042649214,0.01180254,0.019467905],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.023179816,0.0012810432,0.0023349784,0.0018295636,0.013312084,0.020904683,0.004681796,0.12552644,0.01696688],"category_scores_gemma":[0.13280699,0.0023222554,0.0030757866,0.0020536399,0.018270457,0.016727464,0.009275029,0.16515923,0.012591593],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001857815,0.00002121066,0.00034403702,0.000027643822,0.000020010619,0.0002965878,0.00018342622,0.00004965511,0.00005317004,0.0068626176,0.9874441,0.0046789567],"study_design_scores_gemma":[0.00013112664,0.000062122395,0.0010383438,0.00051796046,0.000041375886,0.0007893993,0.0013055373,0.0007797938,0.00015804074,0.054582622,0.94046664,0.00012706127],"about_ca_topic_score_codex":0.02379507,"about_ca_topic_score_gemma":0.043159224,"teacher_disagreement_score":0.9768202,"about_ca_system_score_codex":0.012290276,"about_ca_system_score_gemma":0.02405584,"threshold_uncertainty_score":0.12258804},"labels":[],"label_agreement":null},{"id":"W2069440547","doi":"10.1109/mdso.2004.1270710","title":"Guest editors' introduction [Data-intensive computing]","year":2004,"lang":"en","type":"article","venue":"IEEE Distributed Systems Online","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"St. Francis Xavier University","funders":"","keywords":"Computer science","score_opus":0.18926746312373835,"score_gpt":0.38889982800576756,"score_spread":0.1996323648820292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069440547","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000076926604,0.0038752207,0.00046304188,0.01789208,0.9751526,0.000015906513,0.000046718054,0.00009066806,0.002386868],"genre_scores_gemma":[0.0012106561,0.007679222,0.00047517434,0.011672868,0.9614018,0.00002499549,0.00008511862,0.000121410456,0.017328665],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969784,0.0003483066,0.00047819962,0.0004977191,0.0014476117,0.0002497486],"domain_scores_gemma":[0.9641178,0.0064861393,0.0016226231,0.0009795652,0.021461647,0.005332219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044415747,0.0022694073,0.0027438866,0.0032089697,0.002023525,0.006907694,0.0030408462,0.004984341,0.026888875],"category_scores_gemma":[0.019234424,0.00078712904,0.0017952321,0.0019064222,0.0011201794,0.0030776395,0.0011870927,0.011121195,0.01944356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003320073,0.000012193139,0.00006319763,0.00018331103,0.000010555362,0.00009498657,0.000010799391,0.000047286183,0.00014452098,0.000428225,0.98266083,0.016311025],"study_design_scores_gemma":[0.000027987946,0.00007165577,0.0005051125,0.000247646,0.000041411895,0.000435747,0.000042791242,0.00031872638,0.00028571917,0.0012898083,0.9967043,0.000029091563],"about_ca_topic_score_codex":0.0007371057,"about_ca_topic_score_gemma":0.0019112489,"teacher_disagreement_score":0.026888875,"about_ca_system_score_codex":0.000908752,"about_ca_system_score_gemma":0.0018064548,"threshold_uncertainty_score":0.08995223},"labels":[],"label_agreement":null},{"id":"W207420180","doi":"","title":"Using Kerberos to provide secure authentication for DB2","year":2011,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"IBM (Canada)","funders":"","keywords":"Kerberos; Computer security; Computer science; Authentication (law); Authentication protocol; Single sign-on; Password","score_opus":0.7905099832838044,"score_gpt":0.5572253473335264,"score_spread":0.23328463595027804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W207420180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047828328,0.0032973532,0.62686944,0.0028558974,0.0018061602,0.0009540569,0.0011485745,0.098394446,0.2168458],"genre_scores_gemma":[0.6257872,0.002699873,0.16262983,0.0031709142,0.00065879116,0.0006497504,0.003362601,0.010072561,0.19096844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956825,0.0004880682,0.00035243641,0.0005348546,0.0022513282,0.0006909569],"domain_scores_gemma":[0.99739033,0.00023256811,0.00019048862,0.0010342281,0.0008521258,0.00030025738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020630439,0.00093995867,0.00063305965,0.001333111,0.001562156,0.0036705642,0.0012399347,0.00088305096,0.012394473],"category_scores_gemma":[0.004128338,0.0005788576,0.0004836178,0.0011693571,0.0009673425,0.0047585117,0.004830375,0.0017730176,0.01659296],"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":[0.0024051215,0.00063098344,0.004861392,0.0010231533,0.000167208,0.0024600076,0.0035761641,0.0036883452,0.1144715,0.18658024,0.20703271,0.4731032],"study_design_scores_gemma":[0.00012285844,0.00021170134,0.0013918693,0.00011892891,0.000048787944,0.002461393,0.00031983285,0.020483397,0.10606483,0.019514482,0.8490961,0.00016570864],"about_ca_topic_score_codex":0.0014217091,"about_ca_topic_score_gemma":0.00090650056,"teacher_disagreement_score":0.012394473,"about_ca_system_score_codex":0.0010483708,"about_ca_system_score_gemma":0.0014294063,"threshold_uncertainty_score":0.041463614},"labels":[],"label_agreement":null},{"id":"W2145090263","doi":"10.1109/igarss.1989.577763","title":"Specifications For Gis And Image Analysis Data A Government Perspective","year":2005,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Government of British Columbia","funders":"","keywords":"Perspective (graphical); Computer science; Government (linguistics); Image (mathematics); Data science; Information retrieval; Computer vision; Artificial intelligence","score_opus":0.47357637904697797,"score_gpt":0.4478742357616225,"score_spread":0.025702143285355472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145090263","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009987218,0.00036211722,0.9556524,0.007842472,0.00029260924,0.00046324907,0.0035063247,0.003235732,0.018657934],"genre_scores_gemma":[0.20469208,0.00095809996,0.75834656,0.003319498,0.0004925077,0.0014245763,0.013772384,0.0025628023,0.014431509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.952216,0.015244071,0.010021372,0.0024588387,0.017589832,0.0024697941],"domain_scores_gemma":[0.8647133,0.053772457,0.007733835,0.028838223,0.042808052,0.0021342053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040689047,0.0015741452,0.0015109162,0.005244742,0.0030931013,0.01673235,0.0064926054,0.006372299,0.0068734023],"category_scores_gemma":[0.091599256,0.0029453728,0.003330106,0.007945722,0.0042449012,0.015481553,0.0041813403,0.0066978936,0.004174296],"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":[0.0000611703,0.00006705614,0.0021758922,0.000278613,0.00006306644,0.00028034998,0.0009930828,0.014828302,0.0019611034,0.92626137,0.022712618,0.03031751],"study_design_scores_gemma":[0.00006211029,0.0001086089,0.0013266271,0.00055108935,0.00015002271,0.00070100307,0.0016283344,0.11448862,0.015304877,0.61607504,0.24944197,0.0001616269],"about_ca_topic_score_codex":0.023349203,"about_ca_topic_score_gemma":0.016128482,"teacher_disagreement_score":0.040689047,"about_ca_system_score_codex":0.0054953825,"about_ca_system_score_gemma":0.010181746,"threshold_uncertainty_score":0.21518683},"labels":[],"label_agreement":null},{"id":"W2146211415","doi":"10.5430/wjss.v1n1p37","title":"Privacy in the Age of Big Data: Exploring the Role of Modern Identity Management Systems","year":2013,"lang":"en","type":"article","venue":"World Journal of Social Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"York University","keywords":"Identity management; Big data; Identity (music); Digital identity; Government (linguistics); Internet privacy; Information privacy; Identity theft; Data management; Business; Data security; Data Protection Act 1998; Data science; Computer security; Computer science; Authentication (law); Access control","score_opus":0.36061467841899086,"score_gpt":0.39160121053047325,"score_spread":0.03098653211148239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146211415","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12001772,0.063655525,0.19711222,0.46086138,0.0015350614,0.00017516963,0.00034349016,0.00014065218,0.15615882],"genre_scores_gemma":[0.9331064,0.030705355,0.023579555,0.0077839466,0.0014034822,0.00010053448,0.00007565083,0.00003909446,0.0032059904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99184614,0.005803409,0.0001572481,0.0004434181,0.0012101493,0.0005396861],"domain_scores_gemma":[0.9722555,0.022244835,0.0015411889,0.0014440754,0.0014996609,0.0010147707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011509461,0.00039706414,0.0005712062,0.0018271966,0.0043976195,0.012921732,0.001532577,0.0039965543,0.0019381404],"category_scores_gemma":[0.02054141,0.00047952685,0.00059638533,0.0027111794,0.013392535,0.028847922,0.0051387143,0.0055488227,0.00024115435],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047439426,0.000053680196,0.0040044542,0.00017324231,0.000023244644,0.0002467582,0.005962587,0.0019432597,0.00014613598,0.95006406,0.0045414604,0.032793712],"study_design_scores_gemma":[0.000009839437,0.000043160322,0.0015954907,0.00040441434,0.000015734924,0.0003299662,0.01147482,0.012340848,0.00030026995,0.9271217,0.046335172,0.00002851646],"about_ca_topic_score_codex":0.002747144,"about_ca_topic_score_gemma":0.002205073,"teacher_disagreement_score":0.012921732,"about_ca_system_score_codex":0.0029990573,"about_ca_system_score_gemma":0.0033617811,"threshold_uncertainty_score":0.06086856},"labels":[],"label_agreement":null},{"id":"W2189821794","doi":"10.48550/arxiv.1512.02019","title":"Status Report Of The Dphep Collaboration: A Global Effort For Sustainable Data Preservation In High Energy Physics","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Institute of Particle Physics","funders":"","keywords":"Blueprint; National laboratory; Library science; Data sharing; Political science; Medical education; Engineering; Medicine; Computer science; Engineering physics; Alternative medicine","score_opus":0.3405370817150935,"score_gpt":0.3135485827336533,"score_spread":0.02698849898144018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189821794","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048308678,0.038766533,0.11234699,0.2353686,0.042578008,0.00534907,0.14843176,0.008916242,0.3599341],"genre_scores_gemma":[0.19795102,0.032338295,0.18122496,0.023317004,0.020064782,0.009471428,0.32052708,0.009103442,0.20600194],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.97696346,0.00620391,0.0009766266,0.0019400435,0.011529283,0.002386622],"domain_scores_gemma":[0.92874134,0.010022913,0.0045110295,0.011016313,0.022412417,0.023296002],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.048785258,0.0020667107,0.0012392671,0.005015721,0.004185027,0.012465129,0.0025764192,0.003961602,0.0278257],"category_scores_gemma":[0.038858682,0.00086145825,0.0011184296,0.0055699353,0.002317954,0.009412581,0.017139567,0.0044179377,0.017236242],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047883092,0.00028771,0.0055988934,0.0009778142,0.00012982682,0.00037145845,0.0010710038,0.0012878057,0.0015368996,0.03999593,0.8548049,0.093459],"study_design_scores_gemma":[0.00009030839,0.00021462228,0.0049203183,0.00046676755,0.00004059702,0.00015072148,0.00053709454,0.0006247577,0.0024358318,0.012794989,0.97766507,0.000058868674],"about_ca_topic_score_codex":0.0050107245,"about_ca_topic_score_gemma":0.0037495042,"teacher_disagreement_score":0.9974236,"about_ca_system_score_codex":0.003359454,"about_ca_system_score_gemma":0.028960641,"threshold_uncertainty_score":0.2580042},"labels":[],"label_agreement":null},{"id":"W2206422764","doi":"","title":"Big data Analysis: The n ext f rontier","year":2013,"lang":"en","type":"article","venue":"Bank of Canada review","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":8,"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":"Exploit; Big data; Complement (music); Inflation (cosmology); Set (abstract data type); Data science; Economics; Computer science; Data mining; Computer security","score_opus":0.35629134290762593,"score_gpt":0.3743734165200423,"score_spread":0.01808207361241637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206422764","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018867266,0.41563454,0.04766962,0.48550165,0.022302397,0.00016740756,0.0012113742,0.00053999125,0.025086334],"genre_scores_gemma":[0.099977516,0.5529786,0.12396098,0.1134529,0.08795258,0.00060004496,0.0025635622,0.00092391396,0.017589953],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.955418,0.021759866,0.0019263257,0.004096303,0.015911456,0.00088812376],"domain_scores_gemma":[0.8003621,0.1423478,0.00399379,0.016381938,0.032744672,0.0041697784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05800871,0.0015972203,0.0028455146,0.007412522,0.0027335498,0.018554138,0.0041203117,0.006536453,0.0062766233],"category_scores_gemma":[0.1351861,0.0010809762,0.0014379524,0.00944201,0.013653708,0.027025929,0.0072815013,0.012115163,0.0034963193],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018165931,0.000066222085,0.0034702623,0.0025195368,0.0003230848,0.00013502916,0.00085461757,0.0012832792,0.0003757415,0.27958742,0.37716886,0.33403438],"study_design_scores_gemma":[0.000034511966,0.000041267343,0.002066125,0.0035193833,0.00006100529,0.00018508837,0.0012931058,0.0039731255,0.00030599668,0.39189455,0.5965182,0.000107621345],"about_ca_topic_score_codex":0.011463597,"about_ca_topic_score_gemma":0.0091219,"teacher_disagreement_score":0.05800871,"about_ca_system_score_codex":0.0060446626,"about_ca_system_score_gemma":0.008643274,"threshold_uncertainty_score":0.30678308},"labels":[],"label_agreement":null},{"id":"W2216178707","doi":"","title":"Big data curation","year":2014,"lang":"en","type":"article","venue":"International Conference on Management of Data","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Big data; Computer science; Data science; Data curation; Variety (cybernetics); Data management; Analytics; Semantics (computer science); Data modeling; Data mining; Database; Artificial intelligence","score_opus":0.7668386003949124,"score_gpt":0.4910349129574692,"score_spread":0.27580368743744316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216178707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004712134,0.020509398,0.72865474,0.04867149,0.01289907,0.004829557,0.028931215,0.02650213,0.1242903],"genre_scores_gemma":[0.05881045,0.022385132,0.72577757,0.03923267,0.010364168,0.0060063084,0.07732506,0.0128861945,0.047212444],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.8979953,0.03534688,0.010193082,0.014566882,0.039015166,0.0028826378],"domain_scores_gemma":[0.7200003,0.060072046,0.010949081,0.14105375,0.059599686,0.008325146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05907504,0.0027668865,0.0038878452,0.017177707,0.009908003,0.025880005,0.0077426936,0.00504033,0.025214802],"category_scores_gemma":[0.15873231,0.0019441062,0.004574188,0.023071434,0.007317612,0.0238953,0.028317012,0.009575178,0.026334139],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020792772,0.00010407407,0.0033797252,0.005382971,0.00056764693,0.000655498,0.0049894922,0.0013787752,0.00547657,0.18521486,0.52806807,0.26457438],"study_design_scores_gemma":[0.000025076666,0.000019396715,0.0008098411,0.0010229883,0.00006009232,0.00034870184,0.0006783555,0.0011164722,0.0019023899,0.052475143,0.94147223,0.00006929835],"about_ca_topic_score_codex":0.0067785196,"about_ca_topic_score_gemma":0.005811201,"teacher_disagreement_score":0.05907504,"about_ca_system_score_codex":0.004783689,"about_ca_system_score_gemma":0.026547333,"threshold_uncertainty_score":0.3124224},"labels":[],"label_agreement":null},{"id":"W2220295922","doi":"","title":"Predicament and Countermeasures in Exploitation and Utilization of College Archives Compiling Achievements","year":2015,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Exploit; Compiler; Computer science; Staffing; Engineering management; Engineering ethics; Political science; Engineering; Computer security; Programming language","score_opus":0.3092714976103863,"score_gpt":0.43816311471459807,"score_spread":0.12889161710421176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2220295922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9146399,0.0014040748,0.010407858,0.008179227,0.00019009138,0.00012836591,0.00024673497,0.00021208107,0.06459176],"genre_scores_gemma":[0.995269,0.00032568784,0.0017210009,0.0001732591,0.00004849133,0.000034925826,0.00006249355,0.000024991326,0.0023402062],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9864602,0.003802116,0.0012432138,0.0012046631,0.005059097,0.0022307003],"domain_scores_gemma":[0.9572404,0.011030668,0.012488685,0.0056974604,0.0076363278,0.005906476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008266859,0.00038376416,0.0003590329,0.004394737,0.0036805365,0.0060522817,0.0011723165,0.000813336,0.0032422105],"category_scores_gemma":[0.0365501,0.0002516739,0.00036152336,0.0040974612,0.0038830542,0.0044817273,0.006062367,0.0017754548,0.00045417927],"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":[0.00027192998,0.00035016125,0.5680845,0.0004429187,0.00011139239,0.0008927046,0.021375604,0.0017160088,0.003174736,0.072014906,0.0072739944,0.32429126],"study_design_scores_gemma":[0.000028158744,0.00032740267,0.73347884,0.0008864709,0.00016057419,0.0011157959,0.10455315,0.0061249384,0.007492796,0.045609906,0.10001806,0.0002039101],"about_ca_topic_score_codex":0.005496863,"about_ca_topic_score_gemma":0.005393375,"teacher_disagreement_score":0.008266859,"about_ca_system_score_codex":0.002487605,"about_ca_system_score_gemma":0.0070788814,"threshold_uncertainty_score":0.043719828},"labels":[],"label_agreement":null},{"id":"W2261525379","doi":"10.1016/j.ijinfomgt.2014.10.007","title":"Beyond the hype: Big data concepts, methods, and analytics","year":2014,"lang":"en","type":"article","venue":"International Journal of Information Management","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4125,"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":"Big data; Data science; Computer science; Unstructured data; LEAPS; Leverage (statistics); Analytics; Data analysis; Data mining; Artificial intelligence","score_opus":0.23655520585197082,"score_gpt":0.4581771931797269,"score_spread":0.22162198732775606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2261525379","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030594394,0.2684003,0.33803603,0.3332239,0.014421475,0.00022285707,0.0006890855,0.00073938933,0.041207533],"genre_scores_gemma":[0.15688506,0.26232815,0.38733143,0.115657285,0.06093099,0.0017356911,0.0010741099,0.0014436013,0.012613777],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9553612,0.025355501,0.002366366,0.0032957566,0.01276655,0.0008546692],"domain_scores_gemma":[0.867652,0.109303646,0.0032891845,0.0111651,0.006094714,0.002495339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.042523008,0.0023657964,0.0038407024,0.008921994,0.0036457584,0.023205344,0.0035613226,0.0090457825,0.0039723585],"category_scores_gemma":[0.06988299,0.0011773669,0.0018821493,0.012671409,0.041894756,0.048061926,0.0104938885,0.023919381,0.003112856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005102509,0.000038223174,0.00093405775,0.001213929,0.000101892976,0.00011230269,0.0018432586,0.00081196934,0.00024689434,0.8977451,0.039782833,0.05711856],"study_design_scores_gemma":[0.000012773449,0.000028360602,0.00033786436,0.0011511536,0.000017987564,0.0001288967,0.0007116897,0.0018982752,0.00015053838,0.87995136,0.11556301,0.00004809569],"about_ca_topic_score_codex":0.0026588268,"about_ca_topic_score_gemma":0.0014104982,"teacher_disagreement_score":0.042523008,"about_ca_system_score_codex":0.0043437965,"about_ca_system_score_gemma":0.008261864,"threshold_uncertainty_score":0.22488588},"labels":[],"label_agreement":null},{"id":"W2281400598","doi":"10.4033/iee.2015.8.15.e","title":"The problem with data","year":2015,"lang":"en","type":"article","venue":"Ideas in Ecology and Evolution","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Computer science","score_opus":0.23942108589981206,"score_gpt":0.38072477722674514,"score_spread":0.14130369132693307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281400598","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032274185,0.022867588,0.26159865,0.6539957,0.013311211,0.00024505862,0.002532946,0.00071640534,0.041505057],"genre_scores_gemma":[0.21634026,0.023767978,0.30588454,0.35689697,0.048213698,0.0025320866,0.0037490646,0.0016488798,0.04096642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91722906,0.045087036,0.0047980775,0.014468288,0.017117798,0.0012998693],"domain_scores_gemma":[0.59194094,0.2857351,0.008479904,0.0861904,0.023788527,0.0038651552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07886701,0.0012475644,0.003634726,0.005513168,0.0065145167,0.017055107,0.007442005,0.0138099305,0.019603644],"category_scores_gemma":[0.307309,0.0021119728,0.0025656046,0.00819708,0.040182024,0.07457284,0.015365103,0.03444898,0.009100718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006527744,0.00002923667,0.0011296589,0.00044049558,0.00008341892,0.000119227436,0.0008178151,0.000495911,0.00010533641,0.87769735,0.08523666,0.03377962],"study_design_scores_gemma":[0.000037037516,0.000012828064,0.00016813076,0.00034936218,0.000023803252,0.0003596116,0.0005860983,0.0012247094,0.00014915035,0.8703267,0.1267237,0.000038852686],"about_ca_topic_score_codex":0.0041809534,"about_ca_topic_score_gemma":0.00227296,"teacher_disagreement_score":0.07886701,"about_ca_system_score_codex":0.0047723316,"about_ca_system_score_gemma":0.006828076,"threshold_uncertainty_score":0.41709358},"labels":[],"label_agreement":null},{"id":"W2339387061","doi":"10.15168/11572_142585","title":"Big Data: Privacy and Intellectual Property in a Comparative Perspective","year":2016,"lang":"en","type":"article","venue":"Institutional Research Information System (Università degli Studi di Trento)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Intellectual property; Perspective (graphical); Big data; Internet privacy; Information privacy; Property (philosophy); Computer science; Business; Epistemology; Data mining; Artificial intelligence","score_opus":0.6992794951661098,"score_gpt":0.4643512250992411,"score_spread":0.23492827006686867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339387061","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0125649115,0.18540277,0.024719408,0.085014276,0.0017917502,0.00009443229,0.00039878953,0.000052768362,0.689961],"genre_scores_gemma":[0.7169232,0.20261873,0.014737229,0.018898915,0.0068455944,0.00046476,0.00074769673,0.0002145221,0.03854935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9839536,0.009767102,0.0005290111,0.001085573,0.0035230527,0.0011417189],"domain_scores_gemma":[0.97116244,0.022961495,0.0011303696,0.001722152,0.0024022092,0.00062120927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01568759,0.0007305337,0.00096293975,0.011137753,0.007149816,0.024652263,0.001870945,0.0069160108,0.011327887],"category_scores_gemma":[0.020742059,0.0004217805,0.0009962536,0.017484907,0.02988213,0.035138268,0.008094083,0.005994846,0.001171472],"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":[0.000009605042,0.0000067903575,0.00017834888,0.00015068853,0.000008337654,0.00008969442,0.0022704843,0.000113185146,0.000024250714,0.9873043,0.003075185,0.0067691086],"study_design_scores_gemma":[0.0000079667225,0.0000639785,0.0016762576,0.0019259102,0.00002633444,0.00062756514,0.012001451,0.000501399,0.00022589503,0.47773916,0.50518006,0.000024026032],"about_ca_topic_score_codex":0.0044063735,"about_ca_topic_score_gemma":0.0033264314,"teacher_disagreement_score":0.024652263,"about_ca_system_score_codex":0.01248867,"about_ca_system_score_gemma":0.0059558353,"threshold_uncertainty_score":0.090611994},"labels":[],"label_agreement":null},{"id":"W2340450237","doi":"10.5040/9781501306549.0006","title":"Big Data as System of Knowledge: Investigating Canadian Governance","year":2016,"lang":"en","type":"book-chapter","venue":"Bloomsbury Academic eBooks","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Corporate governance; Big data; Data science; Knowledge management; Business; Computer science; Political science; Data mining; Finance","score_opus":0.3485527211434682,"score_gpt":0.36656413680526895,"score_spread":0.018011415661800756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340450237","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58882564,0.0032316407,0.0046005026,0.057936616,0.00017106289,0.00041653856,0.00056309905,0.000055578686,0.34419942],"genre_scores_gemma":[0.9605353,0.0016258913,0.0017763505,0.002172482,0.000018249939,0.00011806426,0.00016763105,0.00003641228,0.033549793],"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9891139,0.0040448117,0.00024816708,0.0006994419,0.0033737351,0.0025199475],"domain_scores_gemma":[0.98310584,0.009919139,0.00078752363,0.0007706421,0.0028575894,0.0025593194],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.013184055,0.0002527536,0.00037857564,0.0031190135,0.0387032,0.016243892,0.0018852332,0.0018659196,0.00522938],"category_scores_gemma":[0.022208288,0.0005321763,0.00025965588,0.011958748,0.020220624,0.00601792,0.006923098,0.0034233576,0.00022466984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.000026520702,0.00002794282,0.009420428,0.00013378049,0.000006691468,0.0007023716,0.8021088,0.00027912966,0.00027822008,0.15255581,0.010157509,0.024302801],"study_design_scores_gemma":[0.000007767904,0.00002306634,0.013488713,0.00032987408,0.0000071104682,0.00011394296,0.7496003,0.0006671324,0.00023528229,0.012412515,0.22307445,0.000039935345],"about_ca_topic_score_codex":0.96997684,"about_ca_topic_score_gemma":0.98758554,"teacher_disagreement_score":0.9612968,"about_ca_system_score_codex":0.19266476,"about_ca_system_score_gemma":0.25799426,"threshold_uncertainty_score":0.93639445},"labels":[],"label_agreement":null},{"id":"W2347110650","doi":"","title":"Big data en gerelateerde begrippen gedefinieerd","year":2015,"lang":"nl","type":"article","venue":"TU/e Research Portal (Eindhoven University of Technology)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Gnowit (Canada)","funders":"","keywords":"Political science; Art; Humanities","score_opus":0.5272846497970922,"score_gpt":0.422525839706591,"score_spread":0.1047588100905012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347110650","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026960347,0.080491565,0.15376543,0.25804943,0.019046376,0.00060969027,0.0140817575,0.0028863188,0.444109],"genre_scores_gemma":[0.2970595,0.11176486,0.13401845,0.051379777,0.01799336,0.0010208747,0.022226725,0.0035876029,0.36094883],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98886675,0.0033579469,0.00048906036,0.0014069091,0.0050326516,0.00084670336],"domain_scores_gemma":[0.9847899,0.007461891,0.00086107926,0.0029699146,0.0026147813,0.0013024447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008325398,0.0012864781,0.0014272804,0.0025773873,0.0033619918,0.015659386,0.0024014877,0.0033720313,0.048242748],"category_scores_gemma":[0.022384688,0.0008316189,0.0011003405,0.0053145667,0.0049569667,0.018669851,0.009387489,0.005309767,0.01814545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000167454,0.00010035035,0.0023838766,0.0018542887,0.0001267991,0.0004483033,0.002749634,0.0018848523,0.0022721083,0.4292307,0.29943112,0.25935057],"study_design_scores_gemma":[0.0000084490775,0.00001729391,0.0007676513,0.0004982451,0.00001299949,0.0001535336,0.0012662889,0.0007370879,0.00056190963,0.09376913,0.90217674,0.000030722218],"about_ca_topic_score_codex":0.004700297,"about_ca_topic_score_gemma":0.005649042,"teacher_disagreement_score":0.048242748,"about_ca_system_score_codex":0.0033744213,"about_ca_system_score_gemma":0.0055802404,"threshold_uncertainty_score":0.16138804},"labels":[],"label_agreement":null},{"id":"W2358695901","doi":"","title":"Research on Hibernated Data Management with Data Blackbox","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Data management; Database","score_opus":0.5800919552134488,"score_gpt":0.5077003854134302,"score_spread":0.0723915698000186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358695901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35775828,0.01630563,0.56038374,0.009322353,0.0017947811,0.00062338583,0.00041504632,0.0038112684,0.049585514],"genre_scores_gemma":[0.8714724,0.004155772,0.10530673,0.001634214,0.00068789034,0.00027022028,0.00038009824,0.00037033067,0.015722334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9942293,0.0022352785,0.00032508242,0.0011229493,0.0011800095,0.0009073168],"domain_scores_gemma":[0.9556142,0.020002063,0.0025974042,0.014718721,0.005699124,0.0013684108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01006583,0.0008003596,0.0015243632,0.0015067662,0.0019792987,0.0070607555,0.0047129844,0.0016317687,0.012668498],"category_scores_gemma":[0.032429267,0.0008149947,0.0008103013,0.0030926114,0.0032026565,0.025090225,0.0029001283,0.0028317673,0.0012655014],"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":[0.001993168,0.0012965938,0.023193259,0.0017478529,0.00044490173,0.0005410361,0.0025408005,0.06375375,0.019487998,0.50433433,0.019717766,0.36094865],"study_design_scores_gemma":[0.00019888885,0.00091212185,0.0045824493,0.0006818043,0.0004130063,0.0007524528,0.0025529156,0.57341653,0.05644904,0.30517745,0.05470858,0.0001547141],"about_ca_topic_score_codex":0.0029460827,"about_ca_topic_score_gemma":0.001467648,"teacher_disagreement_score":0.012668498,"about_ca_system_score_codex":0.0024892266,"about_ca_system_score_gemma":0.0034057314,"threshold_uncertainty_score":0.053233802},"labels":[],"label_agreement":null},{"id":"W2369109021","doi":"","title":"The Exploration of the Consumer Situation: a New Level of Advertising in the Age of Big Data","year":2014,"lang":"en","type":"article","venue":"Journal of Northwest University","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Science North","funders":"","keywords":"Advertising; Point (geometry); Space (punctuation); Business; Big data; Marketing; Computer science; Mathematics; Data mining","score_opus":0.5319355688591457,"score_gpt":0.3614875757777705,"score_spread":0.1704479930813752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2369109021","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10874458,0.10936075,0.22977063,0.3494385,0.0052891998,0.00034820192,0.0030083312,0.0010653494,0.19297443],"genre_scores_gemma":[0.74150527,0.05558445,0.15021478,0.030806877,0.0069981026,0.00039733935,0.0011468944,0.00048215265,0.012864212],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941754,0.003444053,0.00024021682,0.0006066431,0.0012929328,0.00024085843],"domain_scores_gemma":[0.97220683,0.020206984,0.0010882545,0.0027440977,0.002101997,0.0016518303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009674542,0.0008074036,0.0011654039,0.0053594233,0.003157165,0.018173136,0.0016830186,0.0037683647,0.004652552],"category_scores_gemma":[0.016561229,0.0008535986,0.0010479235,0.006268554,0.014286694,0.03898758,0.007926741,0.007889479,0.0008022487],"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":[0.00030700365,0.00018435763,0.032984864,0.0021980188,0.0002666325,0.00086723495,0.048760436,0.001624119,0.0028284136,0.54829293,0.057074312,0.30461162],"study_design_scores_gemma":[0.000024145804,0.00011465921,0.014897389,0.001406986,0.00008487958,0.00094174047,0.0434854,0.005019388,0.0007300889,0.6552279,0.27788016,0.00018726126],"about_ca_topic_score_codex":0.003002113,"about_ca_topic_score_gemma":0.004575699,"teacher_disagreement_score":0.018173136,"about_ca_system_score_codex":0.0032784396,"about_ca_system_score_gemma":0.0025661439,"threshold_uncertainty_score":0.05116445},"labels":[],"label_agreement":null},{"id":"W2374386622","doi":"","title":"Design and Implementation of Security Mechanism of CashCard","year":2004,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Smart card; Computer security; Mechanism (biology); Realization (probability); Contactless smart card; Identification (biology); Security service; Process (computing); MULTOS; Computer security model; Information security; Card security code; Credit card; World Wide Web; Operating system","score_opus":0.09699630332220563,"score_gpt":0.3716422638321056,"score_spread":0.2746459605099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2374386622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10245581,0.0015600863,0.8578337,0.00069554127,0.00048995327,0.0012737358,0.00021368536,0.0040320926,0.031445403],"genre_scores_gemma":[0.8682769,0.0005240571,0.12151436,0.0001728455,0.000086672466,0.0005079354,0.00017578757,0.000056177792,0.008685301],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880743,0.00023819158,0.00012075268,0.00021152331,0.0004173565,0.0002048032],"domain_scores_gemma":[0.99933666,0.00009428883,0.00007757601,0.00011987624,0.00031030938,0.000061215105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012505567,0.00050080754,0.0004155823,0.0011400963,0.00072545017,0.0021741716,0.0016411578,0.0009606063,0.0039678914],"category_scores_gemma":[0.001658066,0.00036106785,0.00036737023,0.0004425801,0.00055405276,0.0014837359,0.0006012491,0.0005599033,0.0007735791],"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":[0.0016420466,0.0003314851,0.005946943,0.0011396519,0.00023090864,0.0013584552,0.0015469546,0.03790923,0.22394016,0.37723467,0.009114886,0.33960462],"study_design_scores_gemma":[0.0010219272,0.0023328364,0.0045938045,0.00027941345,0.00043825922,0.0026636908,0.0004119757,0.27460235,0.51746964,0.039028857,0.15680788,0.00034940089],"about_ca_topic_score_codex":0.0006305511,"about_ca_topic_score_gemma":0.0002443346,"teacher_disagreement_score":0.0039678914,"about_ca_system_score_codex":0.00079422956,"about_ca_system_score_gemma":0.00131816,"threshold_uncertainty_score":0.013273895},"labels":[],"label_agreement":null},{"id":"W2461694268","doi":"10.3968/7672","title":"On the Analysis of Library Information Ethics and the Standard Construction in the Era of Big Data","year":2015,"lang":"en","type":"article","venue":"Studies in literature and language","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Information ethics; Big data; Personally identifiable information; The Internet; Internet privacy; Information security; Freedom of information; Computer science; Sociology; Public relations; Engineering ethics; Political science; World Wide Web; Computer security; Law; Engineering; Data mining","score_opus":0.2588652072134615,"score_gpt":0.4199736750678363,"score_spread":0.16110846785437477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461694268","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14471799,0.012898434,0.2037067,0.1342831,0.0006528805,0.00036157097,0.00016556184,0.00006920132,0.5031446],"genre_scores_gemma":[0.965938,0.0041627334,0.018434383,0.0034474288,0.00036333568,0.00022196566,0.000048024594,0.00004379099,0.007340412],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95652276,0.033046525,0.0015820598,0.0011928845,0.0061213705,0.0015343153],"domain_scores_gemma":[0.8983806,0.08183271,0.0053712036,0.003904719,0.009162401,0.0013484505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027994178,0.00033699715,0.00054451625,0.006670628,0.005755014,0.012200308,0.0010802951,0.002898788,0.0033549613],"category_scores_gemma":[0.051421873,0.0003396829,0.00078475755,0.0060513467,0.035003655,0.016569167,0.0045718714,0.0033566582,0.0003513756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006856049,0.000013052923,0.00041402373,0.000049100297,0.0000038560534,0.000059174945,0.004404018,0.0001469607,0.00003258015,0.9900003,0.00064780295,0.004222297],"study_design_scores_gemma":[0.000007010169,0.000021419657,0.0012299019,0.00036047358,0.000012528743,0.00012059197,0.00945424,0.0014580821,0.00036944065,0.95761997,0.029320583,0.000025794003],"about_ca_topic_score_codex":0.003015206,"about_ca_topic_score_gemma":0.002435603,"teacher_disagreement_score":0.027994178,"about_ca_system_score_codex":0.009928109,"about_ca_system_score_gemma":0.010756275,"threshold_uncertainty_score":0.14804912},"labels":[],"label_agreement":null},{"id":"W2476192065","doi":"10.1108/lhtn-05-2016-0025","title":"Libraries, data and the fourth industrial revolution (Data Deluge Column)","year":2016,"lang":"en","type":"article","venue":"Library Hi Tech News","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"University of Saskatchewan","funders":"","keywords":"Industrial Revolution; Big data; Originality; Information revolution; Humanity; Government (linguistics); Guardian; Value (mathematics); Media studies; Sociology; Political science; Public relations; Computer science; Law; Creativity","score_opus":0.4012844435733259,"score_gpt":0.35252483549302904,"score_spread":0.048759608080296835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2476192065","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019443568,0.026194597,0.0008179176,0.5739925,0.13026272,0.00016396682,0.04919167,0.0029356573,0.21449664],"genre_scores_gemma":[0.025295125,0.03575149,0.0033261634,0.28450328,0.0929013,0.0006454605,0.058307525,0.0045271576,0.49474248],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99217767,0.001245453,0.00060170545,0.0006486283,0.0043977294,0.0009288532],"domain_scores_gemma":[0.9489067,0.017408704,0.0041899174,0.0048978133,0.015640289,0.008956529],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0061124135,0.00086562254,0.0010148865,0.010357173,0.006527541,0.028069232,0.0015895588,0.0029669756,0.108555384],"category_scores_gemma":[0.03615231,0.00073974143,0.00066512043,0.02017074,0.0035728405,0.01823022,0.008742739,0.006292075,0.05578386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004284253,0.000002532982,0.000098329605,0.000055440847,0.0000011398516,0.000008138935,0.00006527503,0.0000040978366,0.000015496491,0.0010912137,0.9950237,0.003630467],"study_design_scores_gemma":[0.0000024256897,0.0000035895553,0.0004293199,0.00017189565,0.000001454539,0.000016388456,0.0002799636,0.000007766908,0.000035905192,0.0004208276,0.9986211,0.000009386853],"about_ca_topic_score_codex":0.017433627,"about_ca_topic_score_gemma":0.0348541,"teacher_disagreement_score":0.97193074,"about_ca_system_score_codex":0.005452157,"about_ca_system_score_gemma":0.010148075,"threshold_uncertainty_score":0.36315393},"labels":[],"label_agreement":null},{"id":"W2492606138","doi":"10.1057/9780230523784_2","title":"Introduction to Applied Probability for Energy Risk Management","year":2005,"lang":"en","type":"book-chapter","venue":"Palgrave Macmillan UK eBooks","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lottery; Actuarial science; Aside; Quarter (Canadian coin); Stock (firearms); Work (physics); Business; Economics; Engineering; Microeconomics; Geography","score_opus":0.08122904166277486,"score_gpt":0.29971952719890643,"score_spread":0.21849048553613157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492606138","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095895486,0.0799207,0.7021568,0.015895706,0.0039817174,0.00017461726,0.0008784415,0.00092316297,0.19510987],"genre_scores_gemma":[0.12485087,0.18236955,0.49013868,0.011704477,0.028304024,0.0018620737,0.0017290978,0.0015056037,0.15753561],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99707925,0.0012665236,0.00021913419,0.00033695606,0.0009923694,0.00010578634],"domain_scores_gemma":[0.9874669,0.010680244,0.00025976685,0.0005340557,0.00088630104,0.00017278727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004142839,0.0017965056,0.0015888988,0.0027256112,0.0009963618,0.004256175,0.0020992104,0.0032270378,0.036702577],"category_scores_gemma":[0.013382867,0.00081087364,0.0017535941,0.0042008753,0.0034820095,0.005721603,0.0020516524,0.007032497,0.011282812],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013938719,0.000043852917,0.00033958224,0.0005372939,0.000070835624,0.00017927011,0.00021083091,0.007875626,0.00016991627,0.7902829,0.08448626,0.11578978],"study_design_scores_gemma":[0.000005550281,0.0000132385785,0.00022382518,0.00028073488,0.000011883973,0.0001781358,0.000040892766,0.008647131,0.000065201915,0.8576355,0.13287829,0.000019685227],"about_ca_topic_score_codex":0.002277477,"about_ca_topic_score_gemma":0.001773465,"teacher_disagreement_score":0.036702577,"about_ca_system_score_codex":0.0025672147,"about_ca_system_score_gemma":0.0017878482,"threshold_uncertainty_score":0.12278235},"labels":[],"label_agreement":null},{"id":"W2497415448","doi":"10.1090/conm/622","title":"Perspectives on Big Data Analysis","year":2014,"lang":"en","type":"book","venue":"Contemporary mathematics - American Mathematical Society","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Mathematics; Big data; Computer science; Data mining","score_opus":0.38667462333794594,"score_gpt":0.400313487758653,"score_spread":0.013638864420707053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2497415448","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015245269,0.34155846,0.14582379,0.23366204,0.023741778,0.00007651337,0.0009764799,0.0006166057,0.2520198],"genre_scores_gemma":[0.06697852,0.4400127,0.11145183,0.09078326,0.09037348,0.00056099467,0.0016621121,0.0011755695,0.19700162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.996617,0.0012732946,0.00014132599,0.00028811398,0.0015667048,0.00011349117],"domain_scores_gemma":[0.98182285,0.014777144,0.00028896698,0.0013269468,0.0013069364,0.0004772086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006392296,0.0012448789,0.0017126015,0.0055476334,0.0016560851,0.00894141,0.00204721,0.00295493,0.01020311],"category_scores_gemma":[0.013419508,0.0008133434,0.0010634926,0.008175609,0.009486192,0.02281066,0.0040838704,0.0123655675,0.0032115988],"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":[0.000007524521,0.000013585678,0.000070005444,0.00020170673,0.000013259605,0.000027456339,0.00017690033,0.0004972964,0.00007155467,0.8587643,0.108919315,0.031237116],"study_design_scores_gemma":[0.0000025936647,0.0000025017264,0.000060488805,0.000118128795,0.000002904767,0.00002917812,0.0000636246,0.0008087668,0.00003138896,0.84123486,0.15763964,0.000005966866],"about_ca_topic_score_codex":0.0012132949,"about_ca_topic_score_gemma":0.0015612979,"teacher_disagreement_score":0.01020311,"about_ca_system_score_codex":0.0034622734,"about_ca_system_score_gemma":0.0027616986,"threshold_uncertainty_score":0.03413284},"labels":[],"label_agreement":null},{"id":"W2500329317","doi":"10.1007/978-3-642-53974-9","title":"Specifying Big Data Benchmarks","year":2013,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Big data; Computer science; Data science; Data mining","score_opus":0.2982924050768856,"score_gpt":0.37123925728618384,"score_spread":0.07294685220929825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500329317","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012743564,0.00082069705,0.89452,0.001968486,0.0005345537,0.00039111657,0.006701245,0.027670225,0.054650147],"genre_scores_gemma":[0.20525624,0.0010866062,0.7124858,0.0011426121,0.0003419437,0.0012018972,0.023823021,0.018174788,0.036487095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9907952,0.0030112697,0.0010044287,0.0009968209,0.0035504766,0.0006418267],"domain_scores_gemma":[0.98376805,0.009865658,0.0004827532,0.0035415036,0.0019220441,0.00042000422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071728295,0.0018562513,0.0011545345,0.0021450815,0.0014484036,0.0076997955,0.0030596182,0.001557241,0.023101496],"category_scores_gemma":[0.034738142,0.0018132663,0.0018881193,0.0028144752,0.0014915506,0.009813894,0.004688934,0.0039743925,0.0050433655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004493183,0.00016120021,0.0027785164,0.00094988605,0.00009914752,0.00051160087,0.00075861707,0.035221573,0.0049438667,0.6172754,0.09287522,0.24397565],"study_design_scores_gemma":[0.000082988314,0.000074128926,0.0005419487,0.00035620382,0.00006839635,0.0003473778,0.000334338,0.12628756,0.01832049,0.67330587,0.18019298,0.0000876365],"about_ca_topic_score_codex":0.0036475842,"about_ca_topic_score_gemma":0.005558914,"teacher_disagreement_score":0.023101496,"about_ca_system_score_codex":0.0018628396,"about_ca_system_score_gemma":0.0024332185,"threshold_uncertainty_score":0.07728225},"labels":[],"label_agreement":null},{"id":"W2508439080","doi":"","title":"RTEMP: Exploring an end-to-end, agnostic platform for multidisciplinary real-time analytics in the space physics community and beyond","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Analytics; Multidisciplinary approach; Data science; Computer science; Space (punctuation); Physics; Sociology; Operating system; Social science","score_opus":0.2438440652087375,"score_gpt":0.3729799717944513,"score_spread":0.1291359065857138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508439080","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03187933,0.00048561025,0.8532771,0.0020633182,0.00086779176,0.00077147974,0.0022689574,0.09283195,0.015554503],"genre_scores_gemma":[0.35748377,0.00056117086,0.6055441,0.0023653533,0.000485513,0.0007348353,0.0075694947,0.005054887,0.020200938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734205,0.0006169926,0.00015202441,0.0006376678,0.00090913114,0.0003421055],"domain_scores_gemma":[0.99524623,0.0011078523,0.00028742533,0.0018152627,0.0008021095,0.0007410814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00379147,0.0012434124,0.0008937194,0.00090158335,0.000838483,0.004666956,0.0046010907,0.0022168115,0.009429927],"category_scores_gemma":[0.010136864,0.00075936044,0.00093515095,0.00083999446,0.0009609131,0.00874321,0.008647824,0.003375775,0.0072957086],"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":[0.010884624,0.0028501367,0.014033374,0.0016741346,0.00084678776,0.0025126692,0.0035022378,0.060126197,0.14019838,0.116297565,0.1942453,0.45282865],"study_design_scores_gemma":[0.00040894933,0.0013062537,0.0031521635,0.00018844286,0.0002068657,0.0008235179,0.0006057968,0.7375048,0.046824962,0.08184638,0.12682489,0.0003070096],"about_ca_topic_score_codex":0.0013446016,"about_ca_topic_score_gemma":0.0013298183,"teacher_disagreement_score":0.009429927,"about_ca_system_score_codex":0.000534048,"about_ca_system_score_gemma":0.0014192476,"threshold_uncertainty_score":0.031546235},"labels":[],"label_agreement":null},{"id":"W2509022704","doi":"10.1109/tsg.2016.2593358","title":"Guest Editorial Big Data Analytics for Grid Modernization","year":2016,"lang":"en","type":"editorial","venue":"IEEE Transactions on Smart Grid","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":51,"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":"Big data; Smart grid; Software deployment; Variety (cybernetics); Data science; Computer science; Analytics; Volume (thermodynamics); Computer security; Grid; Engineering; Data mining","score_opus":0.26379187914905644,"score_gpt":0.3909505788722479,"score_spread":0.12715869972319144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509022704","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000053029133,0.0112191085,0.00040785666,0.0492324,0.9364169,0.000021191303,0.00011944364,0.000086903965,0.0024430908],"genre_scores_gemma":[0.0007558767,0.01095213,0.00018177858,0.013984641,0.9662194,0.000018689154,0.000066122135,0.000050364415,0.0077709975],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997291,0.00037348704,0.00032863696,0.00035546516,0.0014681381,0.00018325675],"domain_scores_gemma":[0.9835772,0.006342359,0.0010607578,0.0004977545,0.0065782345,0.0019436595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003739145,0.0022157286,0.0022505599,0.0024733476,0.001391266,0.007391386,0.002110683,0.0073927827,0.016126031],"category_scores_gemma":[0.017505111,0.00059783546,0.0016382766,0.0011746516,0.0017367764,0.00410625,0.0014142798,0.014340952,0.013304307],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023228758,0.000008454836,0.00002600771,0.00016916044,0.000011568587,0.00009881232,0.000008205704,0.000043546825,0.00004233482,0.0005606723,0.9919464,0.007061711],"study_design_scores_gemma":[0.000033979446,0.000022663467,0.00015282244,0.00043372205,0.000024842524,0.0003141604,0.00002814898,0.00026550927,0.00012473518,0.0023242296,0.99625933,0.000015805046],"about_ca_topic_score_codex":0.00053397013,"about_ca_topic_score_gemma":0.0009964914,"teacher_disagreement_score":0.016126031,"about_ca_system_score_codex":0.0015731893,"about_ca_system_score_gemma":0.0015038155,"threshold_uncertainty_score":0.053946972},"labels":[],"label_agreement":null},{"id":"W2509480191","doi":"","title":"Postglacial Rebound Model ICE-6G_C (VM5a) Constrained by Geodetic and Geologic Observations","year":2014,"lang":"en","type":"article","venue":"2014 AGU Fall Meeting","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Geology; Geodetic datum; Cryosphere; Geodesy; Climatology; Physical geography; Geography; Sea ice","score_opus":0.1536119042783524,"score_gpt":0.33080647046147,"score_spread":0.1771945661831176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509480191","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9015093,0.00035769606,0.005865762,0.00161548,0.0005160782,0.00008571248,0.05879387,0.0032917627,0.027964449],"genre_scores_gemma":[0.9661767,0.00011393204,0.0037217715,0.00030060235,0.00007656041,0.00009737227,0.025612243,0.0006242041,0.00327659],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998093,0.000028026825,0.000008362705,0.00007453569,0.000019669618,0.000060110324],"domain_scores_gemma":[0.99958247,0.00009014623,0.0000374853,0.000076159464,0.000115087096,0.00009854807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005670984,0.0009627027,0.0012683662,0.0007611804,0.0010839619,0.0012640902,0.0029186376,0.0027455194,0.009223896],"category_scores_gemma":[0.0011513777,0.0010448826,0.0016174281,0.0011260101,0.0009643969,0.0010668818,0.00068861234,0.0016457136,0.0019645595],"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":[0.00028907077,0.00008921564,0.0054436917,0.000055872646,0.00016670821,0.00007698882,0.00003710371,0.9831839,0.0011357757,0.0011992374,0.0066844444,0.0016379147],"study_design_scores_gemma":[0.00039108534,0.000042417243,0.0060285456,0.000016714184,0.00008005826,0.00001973473,0.000045369175,0.9892847,0.0005361247,0.00085560954,0.0026435456,0.00005603831],"about_ca_topic_score_codex":0.27837917,"about_ca_topic_score_gemma":0.20236568,"teacher_disagreement_score":0.27837917,"about_ca_system_score_codex":0.0021994903,"about_ca_system_score_gemma":0.0042335186,"threshold_uncertainty_score":0.55351764},"labels":[],"label_agreement":null},{"id":"W2509992382","doi":"","title":"Exploiting Big Earth Data: Computation, Testing, and CyberGIS I Posters","year":2015,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Esri (Canada)","funders":"","keywords":"Big data; Computer science; Earth (classical element); Data mining; Mathematics","score_opus":0.7147836349230444,"score_gpt":0.4329859695462856,"score_spread":0.2817976653767588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509992382","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053666107,0.008850914,0.08988913,0.24303152,0.114624694,0.0012385435,0.005070148,0.0049436307,0.47868526],"genre_scores_gemma":[0.3641724,0.0072781416,0.045815226,0.009153541,0.0408943,0.0003750307,0.0040412764,0.0022333066,0.52603686],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987264,0.00027265574,0.00003588697,0.00023267632,0.0006040185,0.00012846138],"domain_scores_gemma":[0.9940842,0.001758945,0.00018537429,0.00062539935,0.0018209863,0.0015252277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004049583,0.00078689284,0.0003431701,0.001307101,0.0019204306,0.005983582,0.00077796995,0.0013968074,0.06308456],"category_scores_gemma":[0.008519338,0.00038132153,0.00082880503,0.0008133166,0.001231386,0.0033318393,0.0024091525,0.0026554426,0.010207007],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048036734,0.00023492186,0.002802685,0.00019433755,0.00006196911,0.0002910769,0.00041638568,0.0036241498,0.004940887,0.052778035,0.76711196,0.16706322],"study_design_scores_gemma":[0.00016959201,0.00051109964,0.0064524515,0.0004590462,0.00006055363,0.0004225759,0.0014282769,0.018127108,0.014898395,0.15013817,0.80717826,0.00015448497],"about_ca_topic_score_codex":0.0021715858,"about_ca_topic_score_gemma":0.003698728,"teacher_disagreement_score":0.06308456,"about_ca_system_score_codex":0.0013777188,"about_ca_system_score_gemma":0.001528689,"threshold_uncertainty_score":0.21103889},"labels":[],"label_agreement":null},{"id":"W2512799421","doi":"","title":"Exploiting Big Earth Data: Computation, Testing, and CyberGIS III","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Esri (Canada)","funders":"","keywords":"Computer science; Big data; Earth (classical element); Data mining; Mathematics","score_opus":0.47480885034592535,"score_gpt":0.4043836811288045,"score_spread":0.07042516921712083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512799421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6748052,0.0013842471,0.24107522,0.020303618,0.0007867317,0.000416658,0.0023292757,0.0052137286,0.0536853],"genre_scores_gemma":[0.9594343,0.0002905383,0.037062887,0.00035512896,0.00009821611,0.000055313518,0.00081818516,0.0002578621,0.0016276434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99496776,0.0023363596,0.0002205496,0.00046062982,0.0016509701,0.0003638321],"domain_scores_gemma":[0.977099,0.01202911,0.001079527,0.0058371285,0.0030891558,0.0008660628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008096533,0.00079995475,0.00068764045,0.0014753571,0.00081647973,0.0070731994,0.0021142985,0.0011598864,0.005147136],"category_scores_gemma":[0.028725997,0.0004911199,0.0008025588,0.002060752,0.0031716076,0.009262992,0.0026554565,0.0016625863,0.0008454907],"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":[0.0019876335,0.0009728409,0.20980573,0.00045565268,0.0005618517,0.00055443594,0.0009334161,0.14885534,0.014816673,0.15992968,0.028989848,0.43213695],"study_design_scores_gemma":[0.00008815336,0.0003744105,0.028987026,0.000182371,0.00008301065,0.00015576334,0.0015031202,0.78996676,0.022502007,0.14401397,0.012073489,0.000069885915],"about_ca_topic_score_codex":0.011169839,"about_ca_topic_score_gemma":0.006583291,"teacher_disagreement_score":0.011169839,"about_ca_system_score_codex":0.0015410732,"about_ca_system_score_gemma":0.00282706,"threshold_uncertainty_score":0.042819083},"labels":[],"label_agreement":null},{"id":"W2515320058","doi":"10.15353/joci.v12i2.3229","title":"To the Cloud: Big Data in a Turbulent World by Vincent Mosco","year":2016,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Cloud computing; Reading (process); Upload; World Wide Web; Media studies; Computer science; Visual arts; History; Art; Sociology; Law; Political science","score_opus":0.3204031877869045,"score_gpt":0.3920554610042206,"score_spread":0.0716522732173161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515320058","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075151226,0.23253185,0.005333064,0.6817569,0.01369797,0.000036870486,0.0001709762,0.00010901862,0.058848243],"genre_scores_gemma":[0.27886325,0.39448556,0.007872288,0.13476618,0.027852809,0.00010120231,0.00026869102,0.00045931153,0.15533075],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986065,0.0004732499,0.000041417912,0.00016111086,0.00056166435,0.00015610576],"domain_scores_gemma":[0.99683183,0.0017453986,0.0001514006,0.000058302034,0.00054140826,0.0006717205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016805712,0.0004412151,0.00034910714,0.0010068541,0.0022061435,0.005788539,0.0004871011,0.0014119954,0.0023567337],"category_scores_gemma":[0.005579552,0.00028469134,0.00026119556,0.0016913435,0.0029466418,0.005058846,0.0012096484,0.0046804603,0.0009847638],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030073676,0.00002659364,0.00097318034,0.00019784052,0.000014093245,0.00019352257,0.0027226664,0.0008122104,0.00022802161,0.1375395,0.81233954,0.044922642],"study_design_scores_gemma":[0.000003544022,0.0000139496415,0.0006633212,0.00041068468,0.000003707899,0.00015997454,0.0019037258,0.001028289,0.00012713067,0.04410311,0.95155036,0.000032171454],"about_ca_topic_score_codex":0.018806666,"about_ca_topic_score_gemma":0.018263632,"teacher_disagreement_score":0.018806666,"about_ca_system_score_codex":0.0041484255,"about_ca_system_score_gemma":0.004116262,"threshold_uncertainty_score":0.037394404},"labels":[],"label_agreement":null},{"id":"W2518845274","doi":"10.1145/2938503.2939572","title":"Panel","year":2016,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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; McGill University; University of Ottawa; Concordia University; Université du Québec à Montréal","funders":"University of Waterloo","keywords":"Computer science; Data science; Engineering ethics; Internet privacy; Knowledge management; Engineering","score_opus":0.5695457893644045,"score_gpt":0.4220832082759214,"score_spread":0.1474625810884831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518845274","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052352566,0.0034679596,0.004837019,0.3887501,0.10321534,0.002968859,0.007646472,0.00047409954,0.483405],"genre_scores_gemma":[0.017430698,0.0020818806,0.0029803864,0.3304084,0.016308656,0.002321572,0.0031176668,0.0003037458,0.6250469],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99580765,0.00059795874,0.0002096316,0.0010983925,0.0012084532,0.0010779076],"domain_scores_gemma":[0.989144,0.002668401,0.00038280917,0.0008697931,0.0054094545,0.0015255142],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009023285,0.0007260017,0.00063165603,0.0008395639,0.005778255,0.0067235366,0.0026046478,0.01668918,0.15517892],"category_scores_gemma":[0.015826842,0.0007301209,0.0016415532,0.0007830346,0.0012221221,0.004416516,0.003281955,0.011167905,0.060310267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003802244,0.000022112676,0.00021948013,0.000058146074,0.000004478188,0.00008586788,0.000112843874,0.00004726638,0.00039490336,0.0061590746,0.9857261,0.0071317414],"study_design_scores_gemma":[0.000009523678,0.000009883924,0.00050917984,0.00007635159,0.0000040299956,0.000029854951,0.00022880426,0.00003515506,0.0001440556,0.0013141824,0.9976283,0.000010783048],"about_ca_topic_score_codex":0.008482961,"about_ca_topic_score_gemma":0.015449693,"teacher_disagreement_score":0.8448211,"about_ca_system_score_codex":0.0031619123,"about_ca_system_score_gemma":0.008760605,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2531566274","doi":"","title":"Internet-of-things, cyber-physical systems demarcation and contribution to the big data realm","year":2021,"lang":"en","type":"article","venue":"The Journal of Internet Banking and Commerce","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Realm; Cyber-physical system; Internet of Things; Big data; Computer science; The Internet; Data science; World Wide Web; Internet privacy; Political science; Data mining","score_opus":0.21611164448455553,"score_gpt":0.3662666324624272,"score_spread":0.15015498797787166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531566274","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2504866,0.083547294,0.09253385,0.19184841,0.0044743964,0.00014460612,0.0011277488,0.00027261357,0.37556443],"genre_scores_gemma":[0.95003396,0.021902565,0.014799808,0.003927517,0.0011447651,0.00007921457,0.00026369045,0.00008167581,0.0077667385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982869,0.0006851639,0.000082292274,0.00021577248,0.000567513,0.00016228351],"domain_scores_gemma":[0.9942257,0.0029501645,0.0008940753,0.0006688847,0.0007203471,0.0005407411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033098843,0.00036462361,0.00031745672,0.0045872633,0.0026072704,0.007696153,0.0008156671,0.0020834992,0.0034484598],"category_scores_gemma":[0.00602714,0.00021618552,0.0003468999,0.005622545,0.015940106,0.017995464,0.005933063,0.003785713,0.00043351972],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004150113,0.000015421443,0.0029951343,0.00010548277,0.000008227296,0.000114421215,0.0031731625,0.00039102545,0.00023846817,0.96563184,0.002887042,0.024398286],"study_design_scores_gemma":[0.0000042455617,0.000029377326,0.008266878,0.00031159475,0.00001095292,0.0005156664,0.016032198,0.0015638776,0.00039557632,0.8266474,0.14619513,0.000027134758],"about_ca_topic_score_codex":0.002997461,"about_ca_topic_score_gemma":0.0033420145,"teacher_disagreement_score":0.007696153,"about_ca_system_score_codex":0.003185418,"about_ca_system_score_gemma":0.0030636222,"threshold_uncertainty_score":0.02311194},"labels":[],"label_agreement":null},{"id":"W2537383304","doi":"10.1177/2053951716674238","title":"Critical data studies: An introduction","year":2016,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":432,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Arts and Humanities Research Council; University College London","keywords":"Big data; Multitude; Data science; Theme (computing); Sociology; Epistemology; Computer science; Data mining; World Wide Web","score_opus":0.7517157753114562,"score_gpt":0.5101888192272449,"score_spread":0.24152695608421126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537383304","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023665773,0.37327594,0.18943524,0.22138785,0.02259588,0.0026151305,0.0013980742,0.0004495695,0.18647571],"genre_scores_gemma":[0.07272783,0.47535753,0.28920078,0.070346266,0.04418706,0.009582816,0.0013396939,0.0005778585,0.03668027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97865057,0.015115382,0.0016285653,0.0011840964,0.0028168014,0.0006046378],"domain_scores_gemma":[0.91359866,0.07510783,0.0019280207,0.0019939663,0.005609986,0.0017615053],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.023336843,0.0019778372,0.0012776338,0.013985835,0.006764008,0.011408719,0.0035627494,0.008445707,0.014064427],"category_scores_gemma":[0.038111374,0.0013493854,0.0012992376,0.0116051035,0.021857144,0.01739687,0.008615527,0.012807545,0.003773563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017482784,0.00007286341,0.00051757833,0.0017442632,0.000014834786,0.00032734848,0.0056788805,0.000512992,0.00017685062,0.8614625,0.07358447,0.055889893],"study_design_scores_gemma":[0.000007650297,0.000022330927,0.00024725075,0.004040995,0.000004629673,0.0002848763,0.002865813,0.0003321001,0.000073945106,0.37856346,0.6135302,0.000026700736],"about_ca_topic_score_codex":0.0033858358,"about_ca_topic_score_gemma":0.0034216812,"teacher_disagreement_score":0.993236,"about_ca_system_score_codex":0.008895752,"about_ca_system_score_gemma":0.0095627,"threshold_uncertainty_score":0.12341851},"labels":[],"label_agreement":null},{"id":"W2569162955","doi":"10.1609/aaai.v30i1.9910","title":"Big-Data Mechanisms and Energy-Policy Design","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Leverage (statistics); Mechanism design; Incentive; Big data; Stakeholder; Computer science; Task (project management); Mechanism (biology); Risk analysis (engineering); Business; Economics; Microeconomics; Management","score_opus":0.565963653801508,"score_gpt":0.40025083783055404,"score_spread":0.165712815970954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2569162955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007167577,0.0016552402,0.95661986,0.022437155,0.00035280533,0.0010349051,0.0005204139,0.00069986493,0.009512046],"genre_scores_gemma":[0.29334125,0.0016412641,0.69379056,0.0033957579,0.00026344418,0.0036135816,0.0005457927,0.00019771415,0.0032105611],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9433637,0.04416869,0.003125454,0.0034778607,0.0047157,0.0011486581],"domain_scores_gemma":[0.8273917,0.13091154,0.009346293,0.020837905,0.007869408,0.003643265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1005324,0.001275325,0.0016395865,0.0035627428,0.0028670742,0.011939944,0.006037338,0.005622859,0.006247088],"category_scores_gemma":[0.124977835,0.0017342609,0.0021429565,0.0039354614,0.009445966,0.014107795,0.0076469886,0.006638131,0.000945715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013017487,0.00011313867,0.0018864747,0.00056882075,0.00018858253,0.00011858029,0.00044077152,0.04240035,0.0003529104,0.9123189,0.0032226522,0.03825869],"study_design_scores_gemma":[0.00011069599,0.00006192445,0.00020825054,0.00020204537,0.000051634674,0.00005252098,0.00026398795,0.048867088,0.00079854694,0.928673,0.020672599,0.000037759593],"about_ca_topic_score_codex":0.0035057282,"about_ca_topic_score_gemma":0.0034984977,"teacher_disagreement_score":0.1005324,"about_ca_system_score_codex":0.005868904,"about_ca_system_score_gemma":0.014336822,"threshold_uncertainty_score":0.5316725},"labels":[],"label_agreement":null},{"id":"W2581315808","doi":"10.5539/mas.v11n4p1","title":"The Study of Semantic Analysis on Intelligence Research under the Environment of Big Data","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Data science; Semantic technology; Intelligence analysis; Semantic analysis (machine learning); Semantic computing; Visualization; Meaning (existential); Business intelligence; Big data; Information retrieval; Strengths and weaknesses; Semantic Web; Knowledge management; Artificial intelligence; Data mining; Psychology","score_opus":0.6982545098271963,"score_gpt":0.49961235825021594,"score_spread":0.19864215157698034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581315808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043717083,0.047594264,0.7002115,0.07175415,0.0020496515,0.00020984486,0.00041163288,0.00031459954,0.13373737],"genre_scores_gemma":[0.7820853,0.030427286,0.17309202,0.00403624,0.002955739,0.0003401781,0.00029257027,0.00015092232,0.0066197542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99272436,0.004077152,0.00044900907,0.0007502317,0.0017304892,0.00026873068],"domain_scores_gemma":[0.9809008,0.013987155,0.001183398,0.0017080785,0.0017560452,0.00046453442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0105406325,0.00067488156,0.00085624494,0.011272168,0.0029357192,0.011736279,0.0010583098,0.0023799038,0.0022019185],"category_scores_gemma":[0.016347779,0.00047299374,0.0011058142,0.011346106,0.022641469,0.037039343,0.0038973524,0.003047984,0.0003589826],"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":[0.0000064866867,0.000010580034,0.0004117674,0.000101579906,0.000011300075,0.000047331985,0.0009054228,0.00036701513,0.00011870267,0.9872825,0.0007759244,0.00996137],"study_design_scores_gemma":[0.0000035148903,0.000008820023,0.00037413905,0.00013633318,0.000011134552,0.000090287416,0.0014903301,0.0031815988,0.00027056524,0.9774156,0.017003223,0.000014352375],"about_ca_topic_score_codex":0.0018202196,"about_ca_topic_score_gemma":0.0011195159,"teacher_disagreement_score":0.011736279,"about_ca_system_score_codex":0.0041140537,"about_ca_system_score_gemma":0.0042220606,"threshold_uncertainty_score":0.055744886},"labels":[],"label_agreement":null},{"id":"W2592740096","doi":"10.22230/cjc.2017v42n1a3152","title":"Big Data, Little Data, No Data: Scholarship in the Networked World","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Communication","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"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":"Scholarship; Big data; Data science; Computer science; Political science; Data mining","score_opus":0.7184773296688539,"score_gpt":0.4597071714925218,"score_spread":0.2587701581763321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592740096","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076176813,0.10991734,0.0058184853,0.79332435,0.0073384005,0.000017627079,0.00014682,0.000036398425,0.07578285],"genre_scores_gemma":[0.6397316,0.18411297,0.005000117,0.1177343,0.036648378,0.0001216136,0.0002385056,0.00024390956,0.016168615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.983958,0.008372536,0.0008299631,0.0014727667,0.004411856,0.0009547534],"domain_scores_gemma":[0.7764825,0.1841983,0.00448317,0.009778929,0.013115662,0.011941365],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.030481162,0.0005346416,0.0015973245,0.0073211044,0.009738145,0.024637401,0.0028640903,0.009540091,0.007835815],"category_scores_gemma":[0.10195351,0.0004373267,0.00052912504,0.01133994,0.06812295,0.04332898,0.015317915,0.013286097,0.0011411976],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038764,0.000041359406,0.0018134507,0.0005405229,0.00002392763,0.00010925326,0.008466687,0.00024236793,0.000041596428,0.89288044,0.047089577,0.04871211],"study_design_scores_gemma":[0.0000129389655,0.000016411464,0.0006029448,0.0017104244,0.000009731319,0.00007150378,0.009625828,0.00030464915,0.00006043509,0.86660975,0.12095794,0.000017530794],"about_ca_topic_score_codex":0.013083499,"about_ca_topic_score_gemma":0.020514576,"teacher_disagreement_score":0.9753626,"about_ca_system_score_codex":0.01004707,"about_ca_system_score_gemma":0.027609287,"threshold_uncertainty_score":0.16120172},"labels":[],"label_agreement":null},{"id":"W2592881435","doi":"10.24242/jclis.v1i1.22","title":"A Case for Critical Data Studies in Library and Information Studies","year":2017,"lang":"en","type":"article","venue":"Journal of Critical Library and Information Studies","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Argument (complex analysis); Critical theory; Sociology; Politics; Epistemology; Big data; Public relations; Work (physics); Engineering ethics; Social science; Political science; Data science; Computer science; Law; Engineering; Medicine","score_opus":0.5011702460060182,"score_gpt":0.5034852273212985,"score_spread":0.0023149813152802956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592881435","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005206799,0.026548196,0.032133043,0.8795841,0.0063147247,0.0004218501,0.00005939077,0.000108518434,0.049623374],"genre_scores_gemma":[0.5036846,0.021649942,0.08917824,0.3549109,0.013741292,0.005025555,0.00010852973,0.0005495539,0.011151409],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.47556844,0.4646446,0.011331607,0.013504144,0.02815614,0.0067950375],"domain_scores_gemma":[0.2137943,0.7134984,0.011450045,0.02898991,0.022747425,0.009519967],"candidate_categories":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.39250723,0.0019274344,0.0035808112,0.021146748,0.04594018,0.064201765,0.008422331,0.034452073,0.0046289056],"category_scores_gemma":[0.37776148,0.0023631305,0.0026488663,0.015858276,0.291055,0.092529744,0.040739365,0.057571918,0.0013811812],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023042287,0.000028119654,0.00026744857,0.00037322508,0.000013316852,0.00020099958,0.065087,0.00005939655,0.000037328846,0.91342336,0.013993545,0.006493236],"study_design_scores_gemma":[0.000054445973,0.000026066598,0.00013478078,0.0026439608,0.000013887979,0.00024884773,0.058298033,0.00026284214,0.0001384506,0.7666722,0.17144707,0.000059470196],"about_ca_topic_score_codex":0.006894135,"about_ca_topic_score_gemma":0.0052451226,"teacher_disagreement_score":0.95405984,"about_ca_system_score_codex":0.045951616,"about_ca_system_score_gemma":0.051889762,"threshold_uncertainty_score":0.74914676},"labels":[],"label_agreement":null},{"id":"W2606761684","doi":"10.1002/nem.1974","title":"Big data analytics for network and service management","year":2017,"lang":"en","type":"article","venue":"International Journal of Network Management","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Computer science; Big data; Data science; Analytics; Service (business); Data analysis; Data mining","score_opus":0.45428205358572255,"score_gpt":0.43808036888178165,"score_spread":0.0162016847039409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606761684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060627596,0.30272615,0.41240394,0.117884725,0.036619563,0.00093366613,0.011463644,0.010451984,0.10145356],"genre_scores_gemma":[0.18862389,0.3618939,0.29396164,0.020382037,0.04714751,0.0011073613,0.035547927,0.0028208066,0.04851498],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939644,0.0014591982,0.00042098472,0.00073697086,0.0031110714,0.00030735845],"domain_scores_gemma":[0.990584,0.00404539,0.000559456,0.0015609215,0.0027067012,0.0005435598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054789498,0.0015341932,0.00147447,0.0041815946,0.0011310412,0.008695344,0.002323557,0.0022186781,0.0118835475],"category_scores_gemma":[0.014836416,0.0005915153,0.0008738342,0.007865822,0.0014331916,0.014590584,0.0036485773,0.0036942444,0.00674775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012926038,0.00006282861,0.0010436123,0.0021224665,0.00014844898,0.00015750359,0.0003279529,0.0066007716,0.001393356,0.19074576,0.4029884,0.39427957],"study_design_scores_gemma":[0.00001624912,0.000035489513,0.00068467646,0.00080048805,0.000032758075,0.00014969854,0.0002963049,0.024674123,0.0011299808,0.24319176,0.7289397,0.000048757378],"about_ca_topic_score_codex":0.0016694481,"about_ca_topic_score_gemma":0.0011522906,"teacher_disagreement_score":0.0118835475,"about_ca_system_score_codex":0.0020083676,"about_ca_system_score_gemma":0.0027095373,"threshold_uncertainty_score":0.03975445},"labels":[],"label_agreement":null},{"id":"W2626239355","doi":"10.3968/9588","title":"Textual and Quantitative Research on China’s Action Plan for Promoting the Development of Big Data From the Perspective of Policy Tools","year":2017,"lang":"en","type":"article","venue":"Canadian social science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Action plan; China; Perspective (graphical); Government (linguistics); Action (physics); Development plan; Plan (archaeology); Value (mathematics); Computer science; Data science; Political science; Management; Economics; Engineering","score_opus":0.8006922304219257,"score_gpt":0.5643946563422805,"score_spread":0.23629757407964525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626239355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5103019,0.003381843,0.042335615,0.05162084,0.00067626475,0.0016574659,0.011055201,0.0003730787,0.37859777],"genre_scores_gemma":[0.9697571,0.0010583899,0.009420165,0.0011811838,0.000084534826,0.00063329877,0.0019469053,0.00004761242,0.015870828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9910648,0.003395175,0.00056289387,0.00055548875,0.003748881,0.0006728915],"domain_scores_gemma":[0.9725998,0.012101151,0.0020759518,0.0011182164,0.011232581,0.000872372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0147128515,0.0004526812,0.00028854315,0.008093604,0.0024933892,0.0047721798,0.0008393775,0.00070536224,0.008236503],"category_scores_gemma":[0.02091256,0.00022472342,0.00040120786,0.010291982,0.0024625256,0.005189885,0.0013132434,0.0012712016,0.00032870207],"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":[0.00017878131,0.00022397806,0.08199354,0.0018915242,0.000110339635,0.0004460648,0.016414676,0.006051449,0.0033618582,0.7166564,0.047164317,0.12550712],"study_design_scores_gemma":[0.00008979554,0.000295733,0.38392183,0.0024455946,0.00032111435,0.00016016964,0.07726656,0.031745367,0.010308821,0.10503786,0.38813737,0.0002697984],"about_ca_topic_score_codex":0.07141316,"about_ca_topic_score_gemma":0.06351226,"teacher_disagreement_score":0.07141316,"about_ca_system_score_codex":0.024102585,"about_ca_system_score_gemma":0.03616853,"threshold_uncertainty_score":0.17487729},"labels":[],"label_agreement":null},{"id":"W2725980232","doi":"10.4324/9781315270449","title":"The Routledge Handbook of Developments in Digital Journalism Studies","year":2018,"lang":"en","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Journalism; Newspaper; Media studies; Digital media; Audience measurement; Sociology; Political science; Law","score_opus":0.3668295760981434,"score_gpt":0.429821876957204,"score_spread":0.0629923008590606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2725980232","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044132673,0.329446,0.003056735,0.027387345,0.0036351276,0.0000490785,0.00067406433,0.00030838925,0.635002],"genre_scores_gemma":[0.017024117,0.42859012,0.005538919,0.008290993,0.004862159,0.00034869768,0.0011498779,0.00072626676,0.53346884],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972996,0.00087765907,0.00027488804,0.0002526147,0.001111579,0.0001836131],"domain_scores_gemma":[0.9906171,0.006460974,0.0005468322,0.0008513691,0.0009954073,0.0005282741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021343315,0.00088987773,0.0009562917,0.0060755266,0.00278387,0.012762419,0.0015983187,0.003924173,0.069097266],"category_scores_gemma":[0.008913809,0.00075699994,0.00043688086,0.015311898,0.006053237,0.014517928,0.0030091258,0.004325345,0.03709125],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015361114,0.00003536891,0.00015592671,0.0011877578,0.000007851258,0.00014714575,0.0036962517,0.00011945446,0.00011714299,0.30099174,0.51863503,0.17489094],"study_design_scores_gemma":[0.0000013916459,0.0000018681408,0.000078074765,0.00056148303,9.897881e-7,0.00006643513,0.00041763898,0.000011824367,0.000015056058,0.015704254,0.9831381,0.0000029789212],"about_ca_topic_score_codex":0.0050472217,"about_ca_topic_score_gemma":0.008655852,"teacher_disagreement_score":0.069097266,"about_ca_system_score_codex":0.006170673,"about_ca_system_score_gemma":0.008820133,"threshold_uncertainty_score":0.23115337},"labels":[],"label_agreement":null},{"id":"W2737798405","doi":"10.23974/ijol.2017.vol2.1.33","title":"Book Review of the Data Librarian's Handbook","year":2017,"lang":"en","type":"article","venue":"International Journal of Librarianship","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Windsor","funders":"","keywords":"Publishing; Library science; Facet (psychology); Computer science; Art; Psychology; Psychoanalysis; Literature","score_opus":0.43389396930065643,"score_gpt":0.44659151163798116,"score_spread":0.01269754233732473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737798405","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062583125,0.67177737,0.005432296,0.04492713,0.03564758,0.0002416541,0.0034215741,0.001416063,0.23651055],"genre_scores_gemma":[0.0035028604,0.3901552,0.008212171,0.024638826,0.0142823495,0.00027892063,0.005034483,0.0011362171,0.55275905],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99757856,0.00031747328,0.00017977452,0.00022935431,0.0015941144,0.00010070492],"domain_scores_gemma":[0.9897809,0.003172194,0.00040846196,0.00040972934,0.0057120537,0.000516663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017661217,0.0009277071,0.0016213661,0.007976772,0.0011935624,0.005393948,0.001596128,0.0017578104,0.08704198],"category_scores_gemma":[0.012048408,0.00067774014,0.0007837916,0.0124262,0.00089231884,0.00491375,0.0012772602,0.0028145942,0.104058295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005449783,0.000007344936,0.00002729568,0.00029501301,0.000003243271,0.000015034595,0.0000391587,0.000027773638,0.00005812065,0.0010860357,0.9293451,0.06909059],"study_design_scores_gemma":[0.0000012414026,0.0000037925638,0.00007875845,0.00031113854,0.0000027685007,0.00006643641,0.000024165505,0.000016554184,0.000021952577,0.00046073546,0.9990081,0.0000044024896],"about_ca_topic_score_codex":0.0055786655,"about_ca_topic_score_gemma":0.011606511,"teacher_disagreement_score":0.08704198,"about_ca_system_score_codex":0.0019411542,"about_ca_system_score_gemma":0.0059258537,"threshold_uncertainty_score":0.29118443},"labels":[],"label_agreement":null},{"id":"W2738756939","doi":"10.1007/978-981-10-5427-3","title":"Advances in Computing and Data Sciences","year":2017,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"University of Ottawa","funders":"","keywords":"Computer science; Information retrieval; Data science","score_opus":0.3816037324148056,"score_gpt":0.48544327055234704,"score_spread":0.10383953813754143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738756939","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009691895,0.2282888,0.04164873,0.008987462,0.026151877,0.00012375003,0.0008163414,0.0013943684,0.6916196],"genre_scores_gemma":[0.005334263,0.13323256,0.013692422,0.002846543,0.011240573,0.00016034182,0.00092545355,0.00076576043,0.8318021],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987589,0.00011904567,0.000051535495,0.00012626288,0.00088224455,0.00006202569],"domain_scores_gemma":[0.9980956,0.000825851,0.00008648956,0.00021773356,0.0005205252,0.0002538557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024606,0.002260743,0.0018608103,0.0042605926,0.00083903206,0.005119686,0.0012189826,0.0011584697,0.065251596],"category_scores_gemma":[0.0033083668,0.00058843236,0.00062356656,0.0069314,0.0013701452,0.005886446,0.0027598408,0.004578217,0.054337513],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016826298,0.000037689424,0.00008708261,0.00070366805,0.000017834558,0.000036229325,0.000083996645,0.0007840796,0.00064142235,0.058074377,0.5921751,0.34734163],"study_design_scores_gemma":[0.0000036241056,0.000011752577,0.0001510477,0.0002818384,0.0000068464233,0.0000741188,0.000027962044,0.00071211,0.00020477135,0.03272325,0.965794,0.000008549848],"about_ca_topic_score_codex":0.0010804423,"about_ca_topic_score_gemma":0.0024518708,"teacher_disagreement_score":0.065251596,"about_ca_system_score_codex":0.0016155281,"about_ca_system_score_gemma":0.0027681224,"threshold_uncertainty_score":0.2182883},"labels":[],"label_agreement":null},{"id":"W2791337913","doi":"","title":"The Data Librarian's Handbook by Robin Rice and John Southall (review)","year":2017,"lang":"en","type":"article","venue":"Canadian journal of information science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Sociology; History; Data science","score_opus":0.18935163474659197,"score_gpt":0.3617327287424731,"score_spread":0.1723810939958811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791337913","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000031394327,0.95654,0.001304602,0.026587674,0.010505785,0.00007293187,0.0006966595,0.00016136184,0.00409961],"genre_scores_gemma":[0.00033867557,0.95180386,0.0029113682,0.02589859,0.0071034147,0.00016096426,0.0011087782,0.00008846811,0.010585808],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99246913,0.0020357869,0.001244686,0.0005775408,0.003403488,0.00026942493],"domain_scores_gemma":[0.95426416,0.022637906,0.0031150857,0.0016535135,0.016099699,0.0022295734],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01045363,0.0018084818,0.003704797,0.014270138,0.0012909356,0.0068057263,0.0035967815,0.004758254,0.017574627],"category_scores_gemma":[0.041536868,0.0014129094,0.0014865049,0.018806988,0.0023784768,0.009946955,0.0033243801,0.007263056,0.024418272],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016994727,0.000011234298,0.000078405115,0.0028501123,0.00003262006,0.000023213472,0.000053355634,0.000033182892,0.000063498825,0.001036443,0.87233996,0.12346097],"study_design_scores_gemma":[0.00001348015,0.000010733475,0.00026349534,0.0042046686,0.00003413454,0.00015591526,0.000054419223,0.000018522096,0.000039511582,0.00095277827,0.99423486,0.000017506574],"about_ca_topic_score_codex":0.013589857,"about_ca_topic_score_gemma":0.023805268,"teacher_disagreement_score":0.9931943,"about_ca_system_score_codex":0.0035778554,"about_ca_system_score_gemma":0.014047689,"threshold_uncertainty_score":0.05879301},"labels":[],"label_agreement":null},{"id":"W2801061359","doi":"","title":"Dealing with real-life laboratories in energy research : the power of the experimenter","year":2018,"lang":"en","type":"article","venue":"ORBi (University of Liège)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Power (physics); Computer science","score_opus":0.19090141907614092,"score_gpt":0.36956174115216056,"score_spread":0.17866032207601965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801061359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25876617,0.023999294,0.466454,0.16174662,0.004806182,0.0021847833,0.00015078182,0.0006047284,0.08128748],"genre_scores_gemma":[0.9249473,0.0023171282,0.05130083,0.013441227,0.00072735455,0.0036415951,0.00004212378,0.00016873037,0.0034135706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.6058088,0.370832,0.0026907811,0.009511649,0.009043585,0.0021132291],"domain_scores_gemma":[0.6094012,0.33946288,0.012676578,0.026460577,0.007082437,0.0049162763],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.21635315,0.0011720782,0.0016359797,0.0024639375,0.009203081,0.013152954,0.0036763584,0.0068249214,0.0030436548],"category_scores_gemma":[0.24142925,0.0012578556,0.0010654645,0.0014677151,0.08483866,0.019559607,0.017176218,0.010993697,0.00074156414],"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":[0.00060576154,0.000430992,0.012684593,0.0011051103,0.0001987598,0.0012247332,0.41996604,0.0012002427,0.0027004355,0.5005631,0.0054085013,0.05391169],"study_design_scores_gemma":[0.00068910205,0.0013839736,0.004016244,0.0029332272,0.00020391839,0.0015381827,0.16320527,0.005012404,0.0031330814,0.6459687,0.1715568,0.0003591455],"about_ca_topic_score_codex":0.001346352,"about_ca_topic_score_gemma":0.0010601034,"teacher_disagreement_score":0.21635315,"about_ca_system_score_codex":0.0050297836,"about_ca_system_score_gemma":0.007819484,"threshold_uncertainty_score":0.96637607},"labels":[],"label_agreement":null},{"id":"W2911770460","doi":"","title":"Proceedings of the 11th International Symposium on Algorithms and Data Structures","year":2009,"lang":"en","type":"article","venue":"Workshop on Algorithms and Data Structures","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"University of Calgary; Carleton University","funders":"","keywords":"Computer science; Algorithm","score_opus":0.1330494260001287,"score_gpt":0.3836235653197722,"score_spread":0.2505741393196435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911770460","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016999252,0.039809383,0.79451585,0.022108944,0.059110682,0.0003609042,0.002297825,0.0042068777,0.060590282],"genre_scores_gemma":[0.105869964,0.033178695,0.582412,0.004745943,0.026658228,0.00062568556,0.013052228,0.0044595064,0.2289977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99582845,0.0013381479,0.00040363657,0.00072582666,0.0013532076,0.00035073518],"domain_scores_gemma":[0.9903508,0.002864908,0.0002300652,0.0027547518,0.002918709,0.0008808297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065192184,0.0018564697,0.004123023,0.0017392973,0.0017208823,0.008848497,0.0025668093,0.002107591,0.035144717],"category_scores_gemma":[0.009271262,0.0012421464,0.0025110573,0.002353055,0.0020975103,0.0057635987,0.0031632036,0.006997673,0.014009724],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013542239,0.00074551743,0.001909137,0.0006724897,0.00035861222,0.00016997098,0.00051662995,0.006385226,0.0051959557,0.081790335,0.46816957,0.43273228],"study_design_scores_gemma":[0.0002764783,0.0003615447,0.0024422626,0.0005285573,0.00030448454,0.0005211899,0.00030109807,0.08805574,0.008551191,0.16327031,0.73528105,0.00010619272],"about_ca_topic_score_codex":0.0045389705,"about_ca_topic_score_gemma":0.0060805734,"teacher_disagreement_score":0.035144717,"about_ca_system_score_codex":0.0022313283,"about_ca_system_score_gemma":0.0038036196,"threshold_uncertainty_score":0.11757082},"labels":[],"label_agreement":null},{"id":"W2920833540","doi":"10.1017/s1551929519000026","title":"Management, Analysis, and Simulation of Micrographs with Cloud Computing","year":2019,"lang":"en","type":"article","venue":"Microscopy Today","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Automotive Fuel Cell Cooperation (Canada)","funders":"","keywords":"Cloud computing; Micrograph; Computer science; Materials science; Computer graphics (images); Operating system; Composite material","score_opus":0.04500624072573308,"score_gpt":0.35876348383713663,"score_spread":0.31375724311140357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920833540","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54058176,0.00046712492,0.43053254,0.0014139055,0.00027819313,0.00036837213,0.0026573732,0.015401293,0.008299388],"genre_scores_gemma":[0.9026881,0.00019640154,0.09364609,0.000112814145,0.00004589632,0.00013592419,0.0013856866,0.00033325813,0.001455781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956614,0.00009623188,0.000037461636,0.00009243592,0.00013434657,0.00007352281],"domain_scores_gemma":[0.99812824,0.0008652808,0.00014889946,0.00036009093,0.00033125517,0.0001663381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081520126,0.00066216773,0.0004856354,0.0006660082,0.00096484955,0.0016319357,0.0016533305,0.000715723,0.0031534832],"category_scores_gemma":[0.0027963452,0.0003838496,0.00068957015,0.0008904316,0.0005196309,0.0010173043,0.0008201267,0.0006678648,0.00051036227],"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":[0.0007820556,0.0003441939,0.022771725,0.00013919163,0.00011259811,0.0004800449,0.00024249255,0.90440553,0.009356999,0.0065494636,0.005774912,0.049040787],"study_design_scores_gemma":[0.000016987582,0.00001662102,0.00086380413,0.0000027382412,0.000007026845,0.000022153257,0.000034665605,0.99449205,0.0025004873,0.0013982649,0.00063943374,0.0000056281433],"about_ca_topic_score_codex":0.02271916,"about_ca_topic_score_gemma":0.019097127,"teacher_disagreement_score":0.02271916,"about_ca_system_score_codex":0.0013224026,"about_ca_system_score_gemma":0.0022653835,"threshold_uncertainty_score":0.045173824},"labels":[],"label_agreement":null},{"id":"W2945518138","doi":"10.1002/9781119629610","title":"Digital identities in tension : between autonomy and control","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Autonomy; Tension (geology); Control (management); Computer science; Political science; Materials science; Artificial intelligence; Composite material; Law","score_opus":0.06370739542023025,"score_gpt":0.29080931814703315,"score_spread":0.2271019227268029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945518138","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12065386,0.0033412902,0.057722576,0.06881367,0.0010131066,0.00006888552,0.000119924094,0.00009750987,0.7481692],"genre_scores_gemma":[0.99017894,0.00039321606,0.0014797549,0.0006594349,0.00018283857,0.000038427115,0.000030790074,0.00003162146,0.007004996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917945,0.0048642787,0.00021068052,0.00090738863,0.0014341776,0.0007889825],"domain_scores_gemma":[0.9846293,0.009353205,0.0013397967,0.0015829871,0.0011611955,0.0019335898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074114283,0.00043568332,0.0005982341,0.0021794972,0.0063193,0.021407982,0.0016179496,0.00419625,0.013628423],"category_scores_gemma":[0.023162387,0.00043388765,0.00035782313,0.002623016,0.038409345,0.0221103,0.010423495,0.0048636817,0.0013766539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026706093,0.0000138339465,0.00033848436,0.000017994204,0.000002944569,0.000047563288,0.012824202,0.00012132017,0.00006989518,0.979361,0.0012948521,0.0058813454],"study_design_scores_gemma":[0.000025546628,0.000013718822,0.0005277761,0.00006984376,0.0000056455765,0.00007542993,0.015598635,0.00086582545,0.000106521875,0.949264,0.03343015,0.000016976275],"about_ca_topic_score_codex":0.0024011722,"about_ca_topic_score_gemma":0.0015567932,"teacher_disagreement_score":0.021407982,"about_ca_system_score_codex":0.0043987324,"about_ca_system_score_gemma":0.0032582735,"threshold_uncertainty_score":0.045591593},"labels":[],"label_agreement":null},{"id":"W2947081365","doi":"10.5539/jsd.v12n3p146","title":"Harnessing Big Data for Sustainable Development in Nigeria","year":2019,"lang":"en","type":"article","venue":"Journal of Sustainable Development","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Variety (cybernetics); Sustainable development; Diversification (marketing strategy); Sustainable growth rate; Inclusive growth; Corporate governance; Government (linguistics); Business; Poverty; Economic growth; Economics; Marketing; Computer science; Political science; Management","score_opus":0.15868452995225626,"score_gpt":0.35556488311741336,"score_spread":0.1968803531651571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947081365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33574605,0.08067429,0.08682696,0.2520797,0.0035889405,0.0009466778,0.004688666,0.0005302345,0.23491853],"genre_scores_gemma":[0.8943259,0.049752288,0.04533901,0.0027755406,0.00039192344,0.0003094118,0.0009105551,0.000046540546,0.0061488566],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978028,0.0010242902,0.00017069487,0.00016342898,0.000617605,0.00022123121],"domain_scores_gemma":[0.9963942,0.001940057,0.0004527738,0.00020059971,0.0006559033,0.00035649454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030798025,0.00036767698,0.00036798016,0.0028088866,0.00197741,0.006101762,0.00052981294,0.0011521209,0.0013441932],"category_scores_gemma":[0.0049662916,0.000305855,0.00032317018,0.004250107,0.0019453883,0.004835921,0.0036477158,0.001454592,0.00026921774],"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":[0.00024096484,0.00024742392,0.09640548,0.0039446633,0.00016655726,0.0045394874,0.0071819434,0.018663272,0.0036858004,0.36933485,0.037886757,0.45770293],"study_design_scores_gemma":[0.000045812754,0.00014166674,0.05215711,0.0074427784,0.0001100668,0.0013317316,0.044940677,0.0381493,0.006384979,0.3428693,0.50624293,0.00018366185],"about_ca_topic_score_codex":0.007413456,"about_ca_topic_score_gemma":0.014836366,"teacher_disagreement_score":0.007413456,"about_ca_system_score_codex":0.002377859,"about_ca_system_score_gemma":0.009676881,"threshold_uncertainty_score":0.017252624},"labels":[],"label_agreement":null},{"id":"W2947182476","doi":"10.1109/icccbda.2019.8725660","title":"Design and Implementation of Meteorological Big Data Platform Based on Hadoop and Elasticsearch","year":2019,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":6,"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":"Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Computer science; Big data; Operating system; Database","score_opus":0.5454127380640832,"score_gpt":0.4547144353797887,"score_spread":0.09069830268429452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947182476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13945954,0.0010898599,0.7469925,0.00135072,0.0008829616,0.003071149,0.0066033215,0.07796042,0.022589557],"genre_scores_gemma":[0.54203886,0.0006311102,0.4208952,0.0006911907,0.00017250254,0.0022731356,0.019235957,0.0021376465,0.01192435],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990073,0.00010291641,0.00012449265,0.00026354694,0.00032164124,0.00018021029],"domain_scores_gemma":[0.9992836,0.00007370616,0.00003843425,0.00016940478,0.00024839886,0.00018647789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013790171,0.00074428675,0.0006365135,0.000836936,0.0009890143,0.0018884697,0.003083871,0.0005023403,0.0035877237],"category_scores_gemma":[0.0013257577,0.00061070995,0.0008897599,0.0011164784,0.00055360043,0.0020832682,0.0018326665,0.0010829443,0.0011752554],"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":[0.005620564,0.0021627615,0.039895497,0.0030862687,0.0012715152,0.0029485887,0.0026486793,0.15219985,0.13914277,0.061323922,0.19507273,0.39462692],"study_design_scores_gemma":[0.0010809001,0.00075811485,0.01150338,0.00012874795,0.00026279766,0.00073936663,0.0010223265,0.744186,0.076574855,0.023334064,0.13999715,0.00041235474],"about_ca_topic_score_codex":0.0047292374,"about_ca_topic_score_gemma":0.002855506,"teacher_disagreement_score":0.0047292374,"about_ca_system_score_codex":0.0006813945,"about_ca_system_score_gemma":0.0024269947,"threshold_uncertainty_score":0.0120021105},"labels":[],"label_agreement":null},{"id":"W2969189173","doi":"10.3998/ticker.16481003.0003.201","title":"Harnessing the Data Deluge: Introduction","year":2019,"lang":"en","type":"article","venue":"Ticker The Academic Business Librarianship Review","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Data science; Computer science","score_opus":0.38061994551728817,"score_gpt":0.4016453283991342,"score_spread":0.021025382881846022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969189173","genre_codex":"review","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004052568,0.44793043,0.10657096,0.3629052,0.02006987,0.00035790182,0.00097379135,0.00065590907,0.05648343],"genre_scores_gemma":[0.119669616,0.52387995,0.17804164,0.10900042,0.037130564,0.0015045496,0.0013925269,0.0014224399,0.02795817],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.97284544,0.016293902,0.0016555222,0.0018450507,0.006608916,0.000751175],"domain_scores_gemma":[0.9077093,0.07095144,0.0021242818,0.0040698224,0.013399064,0.001746053],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.029804163,0.001379791,0.0013351599,0.013545467,0.0039044023,0.022105243,0.0028249545,0.004722637,0.0069361613],"category_scores_gemma":[0.048528958,0.0011439993,0.0011793234,0.013582361,0.02000383,0.03459784,0.0097002415,0.010765031,0.0025591762],"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":[0.00003989903,0.00004140092,0.0024869211,0.0036527438,0.00009649522,0.0002953424,0.008556075,0.0005765833,0.00044490746,0.5571986,0.18557385,0.24103723],"study_design_scores_gemma":[0.0000053606013,0.000027719414,0.00077578047,0.0035641992,0.0000179617,0.000398172,0.0058168448,0.0005206248,0.00028893273,0.12895149,0.85957485,0.00005806795],"about_ca_topic_score_codex":0.005239416,"about_ca_topic_score_gemma":0.0065326607,"teacher_disagreement_score":0.9778948,"about_ca_system_score_codex":0.00705237,"about_ca_system_score_gemma":0.007439494,"threshold_uncertainty_score":0.15762132},"labels":[],"label_agreement":null},{"id":"W2983654909","doi":"10.23962/10539/27534","title":"A Proposed \"Agricultural Data Commons\" in Support of Food Security","year":2019,"lang":"en","type":"article","venue":"The African Journal of Information and Communication (AJIC)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":12,"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":"Canada First Research Excellence Fund; Social Sciences and Humanities Research Council of Canada; International Development Research Centre","keywords":"Food security; Agriculture; Commons; Business; Computer science; Computer security; Internet privacy; Political science; Biology; Ecology; Law","score_opus":0.1300621430092644,"score_gpt":0.33018160991400264,"score_spread":0.20011946690473822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983654909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0984233,0.00040743998,0.6085043,0.112928316,0.0005671804,0.0018967417,0.0006798639,0.0010876604,0.17550531],"genre_scores_gemma":[0.7871377,0.00020388172,0.18607432,0.006003228,0.00023394401,0.0013164473,0.00036476427,0.00014940208,0.018516283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96707237,0.017098172,0.0018312915,0.0049835616,0.0067862817,0.0022283033],"domain_scores_gemma":[0.92880404,0.029899439,0.0056838463,0.02104242,0.008584611,0.0059856237],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.04160881,0.00036332518,0.00052676623,0.0027322022,0.0042225835,0.014593477,0.004558917,0.0052238433,0.004863781],"category_scores_gemma":[0.055269778,0.000764985,0.0011017249,0.0027659761,0.016187457,0.022994088,0.017447045,0.0050526485,0.0010062471],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031778753,0.000108320186,0.003443116,0.000061172716,0.000014933942,0.00013956646,0.001496989,0.0020285635,0.0006242343,0.9771255,0.003245033,0.011680867],"study_design_scores_gemma":[0.00011391024,0.0002148682,0.0033684073,0.00048548193,0.00004169126,0.0005472963,0.004533727,0.030389225,0.002728665,0.7656856,0.19178076,0.000110370645],"about_ca_topic_score_codex":0.0033351535,"about_ca_topic_score_gemma":0.00348758,"teacher_disagreement_score":0.9954411,"about_ca_system_score_codex":0.0068235504,"about_ca_system_score_gemma":0.01937054,"threshold_uncertainty_score":0.22005105},"labels":[],"label_agreement":null},{"id":"W3000750835","doi":"10.1109/tps.2019.2961571","title":"Special Issue on Machine Learning, Data Science, and Artificial Intelligence in Plasma Research","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Plasma Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Data science; Artificial intelligence; Data mining","score_opus":0.4464670136898698,"score_gpt":0.44894955899565075,"score_spread":0.0024825453057809588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000750835","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044538119,0.037888106,0.007276243,0.033247538,0.8656248,0.00020570969,0.0012815162,0.00093523844,0.05309544],"genre_scores_gemma":[0.0029718832,0.042266365,0.0036164501,0.014206614,0.7585345,0.00022016905,0.0026935753,0.0014935544,0.17399694],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99574226,0.00062929443,0.00043859138,0.0006780868,0.0021176608,0.0003941415],"domain_scores_gemma":[0.985458,0.005209968,0.00079565163,0.001176975,0.005219043,0.0021402556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041063046,0.0020870534,0.0024810573,0.0036636896,0.002009664,0.008832886,0.0028439048,0.004003222,0.15432204],"category_scores_gemma":[0.012944821,0.00073943136,0.0018111496,0.0034778407,0.0012941881,0.0063731205,0.0022612528,0.007079877,0.06866957],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017891094,0.0000262498,0.00007402935,0.00023573145,0.000014152306,0.00003493365,0.000019439723,0.00008549344,0.00017731826,0.0022963535,0.97357655,0.023441916],"study_design_scores_gemma":[0.0000075684857,0.000030359204,0.00021845248,0.00023801585,0.000008508999,0.00009696064,0.000022566275,0.00028471503,0.000107092324,0.002266482,0.9967084,0.000010732937],"about_ca_topic_score_codex":0.0012423149,"about_ca_topic_score_gemma":0.0021252534,"teacher_disagreement_score":0.15432204,"about_ca_system_score_codex":0.0021767563,"about_ca_system_score_gemma":0.0034133662,"threshold_uncertainty_score":0.51625866},"labels":[],"label_agreement":null},{"id":"W3036150657","doi":"10.1177/2053951720935143","title":"Personalization as a promise: Can Big Data change the practice of insurance?","year":2020,"lang":"en","type":"article","venue":"Big Data & Society","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":113,"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 du Québec à Montréal","funders":"","keywords":"Big data; Data science; Pooling; Personalization; Telematics; Computer science; Analytics; Data mining; Artificial intelligence; World Wide Web","score_opus":0.6585193461766956,"score_gpt":0.4307756542434794,"score_spread":0.2277436919332162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036150657","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015351902,0.011314343,0.022937136,0.92315227,0.002322437,0.000040888124,0.00018339104,0.000083939565,0.024613647],"genre_scores_gemma":[0.79782087,0.02049174,0.023665681,0.14332736,0.00948241,0.00025708374,0.00017721235,0.00018517647,0.0045923893],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9651936,0.023897039,0.0009002286,0.0023596375,0.006082327,0.0015671417],"domain_scores_gemma":[0.85644674,0.114874944,0.004865754,0.012547993,0.0063923276,0.004872294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07443371,0.00054885447,0.0011935425,0.0024115131,0.004728645,0.020439634,0.002903891,0.0107374005,0.005881367],"category_scores_gemma":[0.14457072,0.0005837888,0.0010586905,0.0036315082,0.030687826,0.051371474,0.008966375,0.016863964,0.0009709061],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011983953,0.00009177845,0.006127656,0.00021744384,0.00008447169,0.00015737518,0.0048817955,0.0014682915,0.00016654137,0.90159583,0.023702132,0.061386872],"study_design_scores_gemma":[0.000022305114,0.000062938496,0.0018881013,0.0006608469,0.000020027306,0.000084976746,0.006243311,0.0020534096,0.00024499232,0.9150568,0.07360201,0.000060292998],"about_ca_topic_score_codex":0.0039489106,"about_ca_topic_score_gemma":0.003498631,"teacher_disagreement_score":0.07443371,"about_ca_system_score_codex":0.0066124042,"about_ca_system_score_gemma":0.008227389,"threshold_uncertainty_score":0.3936478},"labels":[],"label_agreement":null},{"id":"W3044486411","doi":"10.1111/insr.12395","title":"Interview with Professor Adrian FM Smith","year":2020,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Treasury; Government (linguistics); Majesty; Conversation; Management; State (computer science); Political science; Library science; Law; Media studies; Sociology; Mathematics; Philosophy","score_opus":0.4273704481634134,"score_gpt":0.4698770728995282,"score_spread":0.04250662473611477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044486411","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041113026,0.00517607,0.00004652977,0.9747449,0.013486943,0.000029702162,0.00016024863,0.000029759294,0.0059147174],"genre_scores_gemma":[0.0050165765,0.0040068934,0.00010045091,0.9371981,0.00815688,0.00018031847,0.00009299791,0.00005112532,0.045196537],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965288,0.0011585947,0.0002191371,0.00048705775,0.00087028136,0.00073609233],"domain_scores_gemma":[0.98803574,0.0037933365,0.00060812995,0.00020553307,0.0033651853,0.003992105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066051227,0.0010566924,0.0016667601,0.0016212203,0.009621436,0.0055627115,0.0021260716,0.015193149,0.040585127],"category_scores_gemma":[0.031108823,0.0009765839,0.0008891039,0.002176519,0.0026225196,0.006770187,0.0039926516,0.024009123,0.016665587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060208245,0.000007684912,0.000111563706,0.00003758421,0.0000013435736,0.00010734698,0.0003746081,0.000010518816,0.000016489894,0.0005843096,0.99621564,0.0025269787],"study_design_scores_gemma":[0.00000885165,0.000015549802,0.00050475093,0.00016454507,0.0000023775253,0.00032035596,0.0022547771,0.000021875787,0.000024734625,0.00047251474,0.9961927,0.000016984448],"about_ca_topic_score_codex":0.025705604,"about_ca_topic_score_gemma":0.02715093,"teacher_disagreement_score":0.040585127,"about_ca_system_score_codex":0.010388177,"about_ca_system_score_gemma":0.015216049,"threshold_uncertainty_score":0.1357708},"labels":[],"label_agreement":null},{"id":"W3080961046","doi":"10.1109/dsc50466.2020.00005","title":"Message from the General ChairsDSC 2020","year":2020,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Cyberspace; Pleasure; China; Computer science; World Wide Web; Telecommunications; The Internet; Library science; Political science; Psychology","score_opus":0.2914835952647328,"score_gpt":0.3795147392486793,"score_spread":0.08803114398394651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080961046","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061374705,0.0032187854,0.00086875545,0.4789781,0.46506494,0.00025890404,0.0027343135,0.00063708914,0.04762533],"genre_scores_gemma":[0.004972576,0.00229434,0.0007063961,0.50044084,0.06275524,0.0003472091,0.002406923,0.00043004783,0.42564645],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982734,0.00021380681,0.00009259446,0.0002836148,0.00082240754,0.00031419023],"domain_scores_gemma":[0.9935541,0.00038091702,0.00016476381,0.00015963122,0.0036952891,0.0020453285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033575373,0.0010886416,0.000799851,0.00063165993,0.0023424,0.0045423065,0.0012728331,0.011334152,0.104250684],"category_scores_gemma":[0.008761015,0.00038651103,0.00082714943,0.0006787013,0.0006375065,0.0022259792,0.00201249,0.011757325,0.10564639],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016312291,0.0000052669807,0.000033024156,0.000017824643,8.005664e-7,0.0000125376755,0.0000033093752,0.0000074451855,0.00003404985,0.0002129825,0.9976419,0.0020145117],"study_design_scores_gemma":[0.000013508538,0.000015263187,0.00034604178,0.000043597323,0.0000026421396,0.000024839797,0.00003802978,0.000029382036,0.000104824685,0.00022834216,0.9991442,0.000009344303],"about_ca_topic_score_codex":0.0093516875,"about_ca_topic_score_gemma":0.015158841,"teacher_disagreement_score":0.104250684,"about_ca_system_score_codex":0.003064926,"about_ca_system_score_gemma":0.0077996613,"threshold_uncertainty_score":0.34875327},"labels":[],"label_agreement":null},{"id":"W3081485903","doi":"10.1089/big.2020.29039.cfp","title":"<i>Call for Special Issue Papers:</i> Programming Models and Algorithms for Big Data","year":2020,"lang":"en","type":"article","venue":"Big Data","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Concordia University","funders":"","keywords":"Big data; Computer science; Library science; Algorithm; Data mining","score_opus":0.692660615863904,"score_gpt":0.4300657800872848,"score_spread":0.26259483577661924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081485903","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027763247,0.002996054,0.011664344,0.2880584,0.6348369,0.00027386987,0.002234292,0.0017685267,0.05788999],"genre_scores_gemma":[0.0034136316,0.004451283,0.013374892,0.11929987,0.52373815,0.0006246229,0.0055050263,0.0052285553,0.32436395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99550205,0.0008515007,0.00027386736,0.0006180584,0.002293459,0.00046104976],"domain_scores_gemma":[0.9526037,0.015637392,0.0020636537,0.0045135035,0.01924299,0.005938878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074956813,0.0020736805,0.0026345404,0.0039152475,0.0028311359,0.012505982,0.003966,0.010712212,0.25513485],"category_scores_gemma":[0.03608361,0.0009072825,0.0036079062,0.0038716577,0.0015463282,0.008583862,0.0032639569,0.011648931,0.17464086],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007663986,0.000005843892,0.000016866088,0.00002052768,0.0000033571355,0.0000052530586,0.0000015445644,0.00001974828,0.000028130755,0.0006553718,0.99587363,0.0033620412],"study_design_scores_gemma":[0.000040280775,0.000028746686,0.00032471927,0.00012876725,0.0000187635,0.000051701663,0.000033528122,0.0008680301,0.0002957818,0.0144233685,0.98375446,0.000031784533],"about_ca_topic_score_codex":0.0025824304,"about_ca_topic_score_gemma":0.0067811175,"teacher_disagreement_score":0.25513485,"about_ca_system_score_codex":0.003814088,"about_ca_system_score_gemma":0.004315163,"threshold_uncertainty_score":0.8535111},"labels":[],"label_agreement":null},{"id":"W3094472893","doi":"10.1016/s0262-4079(20)31883-2","title":"Big data's first election victory","year":2020,"lang":"en","type":"article","venue":"The New Scientist","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"World Federation of Science Journalists","funders":"","keywords":"Victory; Big data; Social media; Political science; Computer science; Data science; Law; Politics; Data mining","score_opus":0.40323472136983307,"score_gpt":0.3840087457571395,"score_spread":0.01922597561269357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094472893","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005808426,0.0039559836,0.002115812,0.7733558,0.15089804,0.00009401165,0.0010268396,0.00024314545,0.06250202],"genre_scores_gemma":[0.13644326,0.0026541855,0.0029427118,0.5160028,0.057582688,0.00040313596,0.0019276465,0.00074122386,0.28130233],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9810121,0.0039996337,0.00051039556,0.0016370708,0.008990312,0.0038505048],"domain_scores_gemma":[0.9725758,0.007873792,0.00065199746,0.001852407,0.0065711336,0.010474882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023372943,0.0013619609,0.0016871217,0.0024581149,0.0097396355,0.020589544,0.0022241191,0.013047855,0.020829562],"category_scores_gemma":[0.049467787,0.0008533127,0.0018713777,0.001996343,0.0050271708,0.0072091734,0.0085336855,0.025000706,0.009907714],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015643962,0.00004697967,0.0004965831,0.000047481302,0.000060915587,0.00006683388,0.0001398044,0.0000746009,0.000101767604,0.024967926,0.96241814,0.0114224255],"study_design_scores_gemma":[0.00015508538,0.00009536945,0.0020485194,0.00013762753,0.000035939218,0.00006867029,0.00066784455,0.00048257288,0.00039955872,0.025959918,0.96989644,0.000052456227],"about_ca_topic_score_codex":0.009419577,"about_ca_topic_score_gemma":0.0176074,"teacher_disagreement_score":0.023372943,"about_ca_system_score_codex":0.0070379525,"about_ca_system_score_gemma":0.01320727,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"grok","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"opus","categories":[],"domain":null,"study_design":"not_applicable","genre":"commentary","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W3113601839","doi":"","title":"A History lesson on the future of cities","year":2020,"lang":"en","type":"article","venue":"Journal of Professional Communication","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Niagara Health System","funders":"","keywords":"Hacker; Smart city; Big data; Key (lock); Stakeholder; Public relations; Utopia; Townsend; Data collection; Sociology; Engineering ethics; Political science; Internet of Things; Engineering; Internet privacy; Social science; Computer science; Computer security; Law","score_opus":0.41932294231976236,"score_gpt":0.4171300552817399,"score_spread":0.0021928870380224508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113601839","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021531167,0.20688224,0.00483247,0.5475645,0.03963646,0.000035518566,0.00018364747,0.00024721262,0.19846493],"genre_scores_gemma":[0.09864705,0.35997504,0.0057520736,0.20317754,0.031372942,0.0001633896,0.0004001004,0.00059581164,0.29991612],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988034,0.0004732807,0.000041269057,0.00014093162,0.0003691752,0.00017190802],"domain_scores_gemma":[0.9983367,0.0007975035,0.00009138563,0.00009808315,0.0003861517,0.0002901162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014642641,0.0007286376,0.00053623144,0.0011869492,0.0040747984,0.012865467,0.0011446521,0.0034114784,0.016484395],"category_scores_gemma":[0.0031100325,0.0004188931,0.0005203169,0.0019542351,0.0070245992,0.0140615655,0.004059646,0.008061728,0.0047831605],"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":[0.000014687497,0.000016646607,0.00024596354,0.0003734225,0.000009545113,0.00011711083,0.003116743,0.00034101144,0.000076931225,0.35295463,0.59154624,0.051187098],"study_design_scores_gemma":[0.000002307782,0.000006362864,0.00007374321,0.00035797257,0.0000020097798,0.00004125742,0.0019147675,0.000037512465,0.00003220426,0.027215963,0.9703095,0.0000063078533],"about_ca_topic_score_codex":0.008761375,"about_ca_topic_score_gemma":0.02130835,"teacher_disagreement_score":0.016484395,"about_ca_system_score_codex":0.005528625,"about_ca_system_score_gemma":0.0068001496,"threshold_uncertainty_score":0.05514586},"labels":[],"label_agreement":null},{"id":"W3127641471","doi":"10.21810/jicw.v3i3.2530","title":"Data Analytics and Public Safety","year":2021,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Analytics; Data science; Key (lock); Data analysis; Public security; Big data; Computer security; Business intelligence; Computer science; Business; Knowledge management; Public relations; Political science; Data mining","score_opus":0.49428065406193394,"score_gpt":0.4249144124178849,"score_spread":0.06936624164404903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127641471","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023097824,0.11855647,0.013963236,0.7293516,0.012741471,0.000053792544,0.0006143237,0.00033838223,0.12207097],"genre_scores_gemma":[0.24869081,0.26608804,0.031640075,0.26336807,0.03541073,0.0003643289,0.0020488577,0.00054012524,0.15184897],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98934424,0.004392784,0.00048636654,0.0010348444,0.0038690988,0.000872671],"domain_scores_gemma":[0.98000926,0.011625106,0.0010060561,0.0015731311,0.003985105,0.0018013009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012955841,0.0008223315,0.0007256285,0.004490207,0.0041970294,0.0124945585,0.0008996534,0.0053562596,0.016341385],"category_scores_gemma":[0.027366577,0.000517357,0.00064697454,0.00440756,0.008799701,0.018129865,0.008072214,0.009229484,0.0042850957],"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":[0.000056952787,0.00004515393,0.0011158304,0.00025674808,0.000040039202,0.00006131729,0.00070444477,0.00034974626,0.00024756262,0.47087553,0.39475116,0.1314955],"study_design_scores_gemma":[0.000010908366,0.000031142117,0.0005073345,0.00061276235,0.000008682189,0.00006749844,0.0009132675,0.0005195187,0.00025662594,0.22268043,0.7743668,0.000025081334],"about_ca_topic_score_codex":0.004173333,"about_ca_topic_score_gemma":0.0039457045,"teacher_disagreement_score":0.016341385,"about_ca_system_score_codex":0.0055045867,"about_ca_system_score_gemma":0.006564992,"threshold_uncertainty_score":0.068517864},"labels":[],"label_agreement":null},{"id":"W3136339334","doi":"10.1145/3259177","title":"Session details: Big data","year":2012,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Session (web analytics); Computer science; Big data; World Wide Web; Data mining","score_opus":0.6753629625174868,"score_gpt":0.46186421164325314,"score_spread":0.2134987508742337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136339334","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013157605,0.0041036624,0.003965582,0.050948244,0.06461984,0.002174951,0.12948222,0.00733047,0.7360593],"genre_scores_gemma":[0.0078607015,0.0024676905,0.0012265566,0.0076307566,0.013921692,0.001006737,0.032718882,0.0018907181,0.9312764],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987482,0.00027943743,0.00006512167,0.00025222232,0.000430852,0.00022408526],"domain_scores_gemma":[0.9923678,0.0016315107,0.00025713266,0.0011354166,0.0015397987,0.0030682893],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003342122,0.0012214722,0.0017346779,0.0013509779,0.0024832226,0.0068675596,0.0017157005,0.0035299454,0.89318085],"category_scores_gemma":[0.009890748,0.00044559673,0.001320645,0.0018829502,0.00047952984,0.0032437795,0.0041026208,0.0028872257,0.74700534],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008330202,0.000017479797,0.000044486547,0.00007574478,0.000003867272,0.0000096321,0.0000064142328,0.000015526168,0.000081173755,0.00028561556,0.9908545,0.008522219],"study_design_scores_gemma":[0.000072889736,0.00004240315,0.0006462457,0.00011689577,0.000006213746,0.000022910108,0.000032066822,0.000099395,0.000211102,0.0012133181,0.9975236,0.00001294962],"about_ca_topic_score_codex":0.0015387146,"about_ca_topic_score_gemma":0.0034865888,"teacher_disagreement_score":0.10681915,"about_ca_system_score_codex":0.0011292424,"about_ca_system_score_gemma":0.0024164405,"threshold_uncertainty_score":0.15236449},"labels":[],"label_agreement":null},{"id":"W3149180268","doi":"10.2139/ssrn.3791018","title":"Understanding Big Data: Data Calculus in the Digital Era","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Calculus (dental); Computer science; Data science; Data mining; Medicine","score_opus":0.5917623436835,"score_gpt":0.4080369243857217,"score_spread":0.1837254192977783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149180268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012710924,0.014097191,0.86726093,0.06810581,0.0015067895,0.0001429304,0.0007563261,0.00039940132,0.035019632],"genre_scores_gemma":[0.6087978,0.024291275,0.3395345,0.010939286,0.0054056984,0.0005791937,0.0010504029,0.0004078107,0.008994109],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917726,0.0037866347,0.00073508354,0.0011515876,0.0021572148,0.00039692468],"domain_scores_gemma":[0.95463175,0.034367234,0.0018748557,0.004974058,0.0026846095,0.0014674765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015121012,0.0010184102,0.001784744,0.005813976,0.00289945,0.017437378,0.0030172651,0.0036796397,0.0049426802],"category_scores_gemma":[0.045502096,0.0011175105,0.0019469053,0.0074868714,0.021389948,0.04170647,0.0077230376,0.006839025,0.0009716001],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068003264,0.000012863822,0.0003022875,0.00007127738,0.000010938644,0.000034140343,0.00029021615,0.0010822497,0.00006031427,0.99212205,0.0013218455,0.004685022],"study_design_scores_gemma":[0.000002682677,0.0000024361793,0.00005267578,0.00003731715,0.0000050304266,0.00001741025,0.000119733835,0.0030114006,0.00005719778,0.9914498,0.0052385074,0.000005829262],"about_ca_topic_score_codex":0.0059760516,"about_ca_topic_score_gemma":0.0029092887,"teacher_disagreement_score":0.017437378,"about_ca_system_score_codex":0.005336398,"about_ca_system_score_gemma":0.006492202,"threshold_uncertainty_score":0.07996851},"labels":[],"label_agreement":null},{"id":"W3158455121","doi":"10.1016/j.jsams.2021.04.003","title":"Data analytics in military human performance: Getting in the game","year":2021,"lang":"en","type":"article","venue":"Journal of science and medicine in sport","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"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":"Defence Research and Development Canada; North Atlantic Treaty Organization","keywords":"Analytics; Operationalization; Data science; Relevance (law); Data analysis; Digital transformation; Big data; Business analytics; Computer science; Business; Political science; Marketing; World Wide Web","score_opus":0.31980265141786174,"score_gpt":0.45434160816329383,"score_spread":0.1345389567454321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158455121","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046374433,0.03363951,0.094695516,0.72061765,0.008459569,0.0007587832,0.0014380307,0.0012749737,0.09274143],"genre_scores_gemma":[0.70571905,0.048065145,0.16644381,0.04457272,0.0067718076,0.0008228299,0.0019015908,0.00078431534,0.024918696],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97533494,0.015885321,0.0013773004,0.001490011,0.0046907933,0.001221605],"domain_scores_gemma":[0.93633497,0.034935463,0.0040021935,0.0046664053,0.013477352,0.0065836865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03492369,0.0011018205,0.0007710235,0.0044768983,0.0043531815,0.020453952,0.0026920494,0.0034800775,0.010502304],"category_scores_gemma":[0.0852916,0.00048965827,0.0008776631,0.006838989,0.009912762,0.023033744,0.011275804,0.0070785065,0.003991577],"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":[0.00036839212,0.00039421083,0.029574558,0.0029714087,0.00017787785,0.0004357214,0.027873721,0.0020493562,0.0014308572,0.22769077,0.20014912,0.50688404],"study_design_scores_gemma":[0.00004773328,0.00033015086,0.012659606,0.008435655,0.00008944213,0.00049115525,0.071906894,0.006687092,0.0022700676,0.32976085,0.5670988,0.00022266086],"about_ca_topic_score_codex":0.0062799435,"about_ca_topic_score_gemma":0.0067532267,"teacher_disagreement_score":0.03492369,"about_ca_system_score_codex":0.003544726,"about_ca_system_score_gemma":0.008011657,"threshold_uncertainty_score":0.18469632},"labels":[],"label_agreement":null},{"id":"W3166656511","doi":"10.5430/jms.v12n1p36","title":"Study on Generation and Development of Social Prediction System","year":2021,"lang":"en","type":"article","venue":"Journal of Management and Strategy","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 system; Political system; Politics; Soul; Relation (database); Predictive modelling; Computer science; Political science; Artificial intelligence; Law; Epistemology; Data mining; Machine learning","score_opus":0.32523890941703476,"score_gpt":0.3796087575758754,"score_spread":0.05436984815884066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166656511","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7720643,0.0016449111,0.15680267,0.0075457147,0.00033390088,0.00024344603,0.0010967909,0.00028368234,0.059984554],"genre_scores_gemma":[0.9854197,0.00049506477,0.008218112,0.00013516854,0.000074627365,0.000074583964,0.00035613656,0.000033567907,0.005193088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871826,0.00044310009,0.000055539236,0.0003260728,0.000304297,0.00015282753],"domain_scores_gemma":[0.9932721,0.0035766743,0.0006910598,0.0004929105,0.0015108102,0.00045652417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002623822,0.0003622254,0.0003152355,0.0018157355,0.001301473,0.0021419881,0.0007551656,0.0007291666,0.0066397823],"category_scores_gemma":[0.01498779,0.0002518333,0.00061698863,0.001122485,0.0010378858,0.003247593,0.0013214792,0.0010152705,0.00078972144],"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":[0.00013036183,0.0002854444,0.36941212,0.00022501811,0.000220208,0.0012644294,0.007232593,0.021543419,0.0028407911,0.4145233,0.011886015,0.17043628],"study_design_scores_gemma":[0.00006567081,0.00032049854,0.19290772,0.00020726833,0.00024188947,0.0011613586,0.0050238864,0.44061524,0.0034612888,0.31414834,0.04171535,0.00013153862],"about_ca_topic_score_codex":0.0054008057,"about_ca_topic_score_gemma":0.0031063538,"teacher_disagreement_score":0.0066397823,"about_ca_system_score_codex":0.0015309601,"about_ca_system_score_gemma":0.0014144119,"threshold_uncertainty_score":0.022212267},"labels":[],"label_agreement":null},{"id":"W3186134273","doi":"","title":"A path to Big Data readiness","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Environment and Climate Change Canada","funders":"","keywords":"Big data; Section (typography); Variety (cybernetics); Data science; Context (archaeology); Path (computing); Space (punctuation); Computer science; Checklist; Work (physics); Government (linguistics); Knowledge management; Engineering; Data mining; Psychology; Geography; Artificial intelligence","score_opus":0.28479799167223824,"score_gpt":0.39628025606606204,"score_spread":0.1114822643938238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186134273","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026540412,0.0048961868,0.07256853,0.8487576,0.0021291415,0.000362213,0.00034394205,0.00056237326,0.04383959],"genre_scores_gemma":[0.6014079,0.011207342,0.25766143,0.10769643,0.0018993248,0.0015961654,0.001285283,0.00049947615,0.01674668],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9701624,0.013600791,0.0019455024,0.003115845,0.007431728,0.0037438094],"domain_scores_gemma":[0.9414837,0.026222287,0.0024555125,0.0048314827,0.012728612,0.012278405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040126614,0.0011043645,0.0008934035,0.0033316906,0.008454077,0.01907941,0.0033250595,0.011306987,0.011942752],"category_scores_gemma":[0.05528283,0.0010717814,0.0012534198,0.0041433778,0.02254923,0.041952386,0.024914345,0.023040386,0.0032351958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007976752,0.00029345293,0.004051835,0.00067452824,0.000039929448,0.0003806773,0.0073965644,0.00094073976,0.0008606656,0.8629493,0.059105016,0.0632273],"study_design_scores_gemma":[0.00004434724,0.0002280508,0.0019884184,0.0009875458,0.000019511455,0.00046188,0.024559079,0.0018895406,0.0009174352,0.717312,0.25148946,0.00010277977],"about_ca_topic_score_codex":0.003145659,"about_ca_topic_score_gemma":0.002713069,"teacher_disagreement_score":0.040126614,"about_ca_system_score_codex":0.0058274083,"about_ca_system_score_gemma":0.03697363,"threshold_uncertainty_score":0.21221232},"labels":[],"label_agreement":null},{"id":"W3199027794","doi":"","title":"The Study on Monitoring and Evaluation System of Teaching Quality of China Higher Education Institutions in the Era of Big Data","year":2016,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"China; Christian ministry; Construct (python library); Big data; Quality (philosophy); Monitoring and evaluation; Higher education; Data collection; Political science; Computer science; Business; Engineering management; Data science; Data mining; Engineering; Sociology; Social science","score_opus":0.5055560520441864,"score_gpt":0.5342532820220081,"score_spread":0.028697229977821648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199027794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9786123,0.0006864622,0.008567576,0.0012282315,0.00005136309,0.00011753115,0.0005684999,0.00012130154,0.0100467],"genre_scores_gemma":[0.9973539,0.00015465285,0.0014771349,0.000036098925,0.000016700867,0.000030021536,0.00020343317,0.000005078518,0.00072303443],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99393165,0.0016480088,0.0006965097,0.00078468805,0.0022528146,0.00068650144],"domain_scores_gemma":[0.99005425,0.0020550112,0.0017313043,0.0005230113,0.004741917,0.00089449517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073373434,0.0003390701,0.00046925977,0.0049134004,0.0009394685,0.0035499998,0.0007304828,0.0006130683,0.0011655082],"category_scores_gemma":[0.017015096,0.00019068239,0.00078615313,0.006342857,0.00072214927,0.0030798274,0.0011254782,0.0005641115,0.00016282505],"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":[0.00014462655,0.0001910402,0.88710904,0.0001734822,0.0001715422,0.000111792106,0.0030710262,0.0047473176,0.0012364662,0.0052823173,0.0027047256,0.09505663],"study_design_scores_gemma":[0.000018537794,0.00020595643,0.9412933,0.00007889032,0.00013527708,0.000107431195,0.004266007,0.04667912,0.0019880433,0.001467602,0.0037099854,0.000049723814],"about_ca_topic_score_codex":0.0323866,"about_ca_topic_score_gemma":0.016293112,"teacher_disagreement_score":0.0323866,"about_ca_system_score_codex":0.0060045887,"about_ca_system_score_gemma":0.0040581194,"threshold_uncertainty_score":0.0643962},"labels":[],"label_agreement":null},{"id":"W3210359963","doi":"10.1142/s2424922x21420043","title":"Research on Intelligent Management System of Meteorological Archives Based on Big Data Framework","year":2021,"lang":"en","type":"article","venue":"Advances in Data Science and Adaptive Analysis","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Vanier College","funders":"","keywords":"Big data; Computer science; Geospatial analysis; Data management; Data science; Analytics; Visualization; Data analysis; Data processing; Data mining; Business intelligence; Data visualization; Database; Remote sensing","score_opus":0.5419266624503839,"score_gpt":0.5022757446171617,"score_spread":0.039650917833222166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210359963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090778634,0.0021592514,0.8748683,0.0018030197,0.00039880344,0.00058831985,0.0013049191,0.018087082,0.010011691],"genre_scores_gemma":[0.7036833,0.0021921475,0.28398985,0.00039686752,0.00025169915,0.0003435529,0.0036097122,0.00027910044,0.0052537234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990615,0.00011702454,0.000091220536,0.0002584396,0.00033371447,0.00013802675],"domain_scores_gemma":[0.9993594,0.00006462287,0.000062567764,0.00015490991,0.00022845996,0.0001299876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013191716,0.0005065722,0.00062086445,0.001313687,0.0011889322,0.0027734095,0.0022417137,0.0005410745,0.0012911975],"category_scores_gemma":[0.0015248763,0.00028017917,0.00091585505,0.0014372353,0.0005012746,0.0047619133,0.0016500053,0.00086676935,0.00034412954],"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":[0.0008231434,0.000739135,0.036018174,0.0011853095,0.000558342,0.0010933307,0.0021209104,0.14751033,0.047458004,0.13483882,0.045920253,0.58173424],"study_design_scores_gemma":[0.00011649993,0.00021639463,0.00874869,0.000092606686,0.0002476977,0.00036376022,0.0008132836,0.8564157,0.03125592,0.03228878,0.06929428,0.00014643838],"about_ca_topic_score_codex":0.0134055335,"about_ca_topic_score_gemma":0.008414596,"teacher_disagreement_score":0.0134055335,"about_ca_system_score_codex":0.0012454403,"about_ca_system_score_gemma":0.0030584477,"threshold_uncertainty_score":0.026655018},"labels":[],"label_agreement":null},{"id":"W4200197283","doi":"10.1038/s41561-021-00881-3","title":"Publisher Correction: Machine learning in Earth and environmental science requires education and research policy reforms","year":2021,"lang":"en","type":"article","venue":"Nature Geoscience","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada; University of British Columbia","funders":"","keywords":"Earth system science; Earth (classical element); Political science; Computer science; Earth science; Engineering ethics; Geology; Engineering; Oceanography; Physics","score_opus":0.089396662525709,"score_gpt":0.4143253945636533,"score_spread":0.3249287320379443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200197283","genre_codex":"editorial","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"incentives","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":"incentives","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015687649,0.00086683064,0.00074768317,0.115058266,0.87196684,0.000021777827,0.002160049,0.00056564336,0.008455987],"genre_scores_gemma":[0.022147426,0.0050586383,0.004051784,0.14833744,0.353318,0.00031216646,0.004635449,0.002596938,0.45954219],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9898816,0.0015766444,0.0014697483,0.0014446683,0.004773943,0.00085343164],"domain_scores_gemma":[0.8811868,0.028893284,0.0035512685,0.011325671,0.07238814,0.0026549394],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007781578,0.0021788348,0.0032364875,0.0061396454,0.0053212782,0.010109938,0.0052900137,0.012776924,0.09159629],"category_scores_gemma":[0.121092774,0.0016807057,0.0019088592,0.006546679,0.0038873355,0.005393485,0.003025241,0.01961602,0.055244315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009682962,0.0000021063372,0.00004188239,0.00003929799,0.0000068144136,0.000040216222,0.000014953707,0.000028934215,0.0000086942,0.0013663413,0.9968736,0.0015674251],"study_design_scores_gemma":[0.00006847666,0.000015461937,0.0010590222,0.00035757973,0.000060313847,0.00017088524,0.00013650584,0.000514187,0.00034205773,0.0068506445,0.9903634,0.000061511935],"about_ca_topic_score_codex":0.04549695,"about_ca_topic_score_gemma":0.052587006,"teacher_disagreement_score":0.99221843,"about_ca_system_score_codex":0.0069566662,"about_ca_system_score_gemma":0.01263899,"threshold_uncertainty_score":0.30642015},"labels":[],"label_agreement":null},{"id":"W4206331798","doi":"10.53106/256299802019120101002","title":"Big Data: Ideology vs. Enlightenment","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Auditing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Victoria","funders":"","keywords":"Ideology; Big data; Politics; Epistemology; Philosophy; Political science; Computer science; Law","score_opus":0.24788207223685746,"score_gpt":0.3951213362030417,"score_spread":0.14723926396618423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206331798","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01649265,0.021763751,0.016625797,0.5450064,0.0043646395,0.000087724366,0.0001574242,0.00006622862,0.3954353],"genre_scores_gemma":[0.8400269,0.016221726,0.008119244,0.10066238,0.012143265,0.00046234156,0.00014342363,0.00028039023,0.02194026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96316814,0.022452584,0.0011903142,0.0028415883,0.0078166295,0.0025307713],"domain_scores_gemma":[0.92523694,0.057481155,0.0043598437,0.0046547353,0.004298391,0.003968998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051884815,0.00064917025,0.0007298406,0.004485733,0.009286951,0.024423163,0.0017384165,0.0076587796,0.0064907963],"category_scores_gemma":[0.06946352,0.00060109614,0.00056651654,0.0046148943,0.07194866,0.026572041,0.011884063,0.014111385,0.0014171124],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001702174,0.000009945534,0.00039354057,0.000060782644,0.0000083061595,0.000033908316,0.004198731,0.000041539355,0.000023380557,0.98373055,0.006843959,0.004638268],"study_design_scores_gemma":[0.000037118687,0.000035742745,0.00097369513,0.0007335237,0.000011769343,0.00010676148,0.0076442976,0.00037440704,0.00017495785,0.8483356,0.14154053,0.00003163746],"about_ca_topic_score_codex":0.0029789135,"about_ca_topic_score_gemma":0.0017230511,"teacher_disagreement_score":0.051884815,"about_ca_system_score_codex":0.010287923,"about_ca_system_score_gemma":0.006559751,"threshold_uncertainty_score":0.27439642},"labels":[],"label_agreement":null},{"id":"W4206677790","doi":"10.1007/978-3-030-79891-8","title":"Advances in Data Science","year":2021,"lang":"en","type":"book","venue":"Association for Women in Mathematics series","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science","score_opus":0.1797360089877833,"score_gpt":0.40390702686504715,"score_spread":0.22417101787726385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206677790","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042224574,0.08680719,0.037633117,0.015165373,0.019454584,0.00010191681,0.001842692,0.0019321986,0.8366406],"genre_scores_gemma":[0.002233588,0.028771266,0.008272701,0.0037995554,0.0046353503,0.0000849181,0.001219316,0.000801257,0.950182],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99829644,0.00021099213,0.00008097566,0.00024467954,0.0010898978,0.00007713073],"domain_scores_gemma":[0.9967757,0.0013298548,0.00010851133,0.0006141102,0.00082226377,0.0003496171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002063038,0.0016235505,0.0016682198,0.0043812906,0.001214834,0.007165383,0.001137241,0.00138167,0.1436707],"category_scores_gemma":[0.0059970827,0.00085348473,0.00074198225,0.006363758,0.001571169,0.009075838,0.004000451,0.0052740346,0.12161688],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008620547,0.00001242086,0.00006472771,0.00017692559,0.000008399374,0.000016190092,0.00007600018,0.00015937576,0.00023058575,0.045973253,0.80639964,0.14687385],"study_design_scores_gemma":[0.0000014507549,0.0000036839235,0.00008080985,0.00008154344,0.000002217077,0.000033479213,0.00001832953,0.0001705239,0.000050196893,0.016401846,0.9831526,0.0000033942977],"about_ca_topic_score_codex":0.0016563832,"about_ca_topic_score_gemma":0.0038615363,"teacher_disagreement_score":0.1436707,"about_ca_system_score_codex":0.0020498927,"about_ca_system_score_gemma":0.002488038,"threshold_uncertainty_score":0.48062634},"labels":[],"label_agreement":null},{"id":"W4211174732","doi":"10.1007/978-3-319-32010-6_505","title":"Big Data and Theory","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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; Memorial University of Newfoundland","funders":"","keywords":"Computer science; History","score_opus":0.5387377163173658,"score_gpt":0.39539874161286653,"score_spread":0.14333897470449924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211174732","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00094481517,0.077280916,0.03628348,0.047279883,0.0054064933,0.00006506985,0.00042680622,0.00027698855,0.8320356],"genre_scores_gemma":[0.08233344,0.074189775,0.023535727,0.028529525,0.012879813,0.0004754271,0.0007532762,0.0006667436,0.7766363],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99869424,0.00054579834,0.000034018343,0.00013687603,0.000529128,0.000060019927],"domain_scores_gemma":[0.99699795,0.0021663844,0.000069697904,0.00045636896,0.00022828602,0.00008137871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017280015,0.0010186839,0.0010841164,0.002403403,0.0017356103,0.0061917575,0.00096739276,0.0024874287,0.024500946],"category_scores_gemma":[0.005071551,0.0005896917,0.0003698187,0.0028135842,0.010266926,0.009344129,0.0024686358,0.0047744564,0.008779279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030412266,0.000009928433,0.000037594862,0.00008082986,0.0000045699044,0.000011495556,0.00016494056,0.00026672267,0.000030719988,0.8626735,0.118356064,0.018360415],"study_design_scores_gemma":[0.0000017390656,0.0000019130964,0.000041278083,0.00007524035,0.0000013004881,0.000016809652,0.00006619699,0.0003134311,0.000025183404,0.8235923,0.17586143,0.0000032309667],"about_ca_topic_score_codex":0.0023763732,"about_ca_topic_score_gemma":0.003141175,"teacher_disagreement_score":0.024500946,"about_ca_system_score_codex":0.003433157,"about_ca_system_score_gemma":0.0023723848,"threshold_uncertainty_score":0.08196378},"labels":[],"label_agreement":null},{"id":"W4226151601","doi":"10.1080/2325548x.2022.2036546","title":"Data Lives: How Data Are Made and Shape Our World","year":2022,"lang":"en","type":"article","venue":"The AAG Review of Books","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":57,"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":"Computer science; Internet privacy","score_opus":0.5255716264645494,"score_gpt":0.43481926929440246,"score_spread":0.0907523571701469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226151601","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000896506,0.7153643,0.017950397,0.19639309,0.022556799,0.000051097766,0.0010580355,0.00042223232,0.045307625],"genre_scores_gemma":[0.025331412,0.7629333,0.031392843,0.10559012,0.030853625,0.00017709484,0.0014700908,0.0005814685,0.0416701],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950039,0.0017936364,0.00023447176,0.00036909195,0.002415795,0.00018309995],"domain_scores_gemma":[0.9839113,0.011813314,0.0006313464,0.00077050994,0.0021377911,0.00073582574],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0051912176,0.0008856757,0.0013124332,0.0044477712,0.0016183902,0.01660698,0.001528315,0.003830063,0.0058516436],"category_scores_gemma":[0.017275242,0.0006185106,0.000757959,0.009777032,0.007820035,0.024925098,0.002179093,0.008012631,0.003634814],"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":[0.00002796286,0.000034733435,0.0006359,0.0016967792,0.00007846006,0.000048196944,0.0010396981,0.00041451084,0.0003747796,0.19380331,0.6080412,0.19380438],"study_design_scores_gemma":[0.0000037860377,0.000007930477,0.0005760015,0.001169919,0.000012644002,0.000099124874,0.0006970726,0.00026707954,0.00011689683,0.09439886,0.90262336,0.000027304508],"about_ca_topic_score_codex":0.004017381,"about_ca_topic_score_gemma":0.0072662546,"teacher_disagreement_score":0.9983816,"about_ca_system_score_codex":0.0039341943,"about_ca_system_score_gemma":0.0051166047,"threshold_uncertainty_score":0.028544664},"labels":[],"label_agreement":null},{"id":"W4234131857","doi":"10.1007/978-1-4939-7131-2_100062","title":"Big Data","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science","score_opus":0.7146096992358584,"score_gpt":0.4311886544452919,"score_spread":0.28342104479056646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234131857","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006325556,0.011655912,0.055691723,0.014180516,0.0059288577,0.00017223528,0.003097738,0.0024477479,0.9061927],"genre_scores_gemma":[0.0063203643,0.0120392395,0.019400457,0.006712614,0.0034076597,0.00020701157,0.0044219466,0.0013381991,0.9461526],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992824,0.000112739464,0.000026430585,0.00014401042,0.00038753328,0.00004685391],"domain_scores_gemma":[0.99898535,0.0003425202,0.000035837846,0.00031107865,0.0002256585,0.000099550045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009876738,0.0011286524,0.0006258338,0.0020587407,0.0010301071,0.004957804,0.0012714906,0.0012987822,0.14917025],"category_scores_gemma":[0.002987687,0.0005480183,0.00056654244,0.002852716,0.0011117143,0.007308619,0.0035000776,0.002608795,0.103053756],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011478139,0.000015667047,0.00009061378,0.00023887833,0.000012424114,0.000027233567,0.00014520004,0.0003331426,0.00041180232,0.11506392,0.6960494,0.18760015],"study_design_scores_gemma":[0.0000013904561,0.0000021917751,0.000083065,0.00008472278,0.0000021667424,0.00003635793,0.000037088543,0.00022502943,0.00013241393,0.044225287,0.9551657,0.0000045057736],"about_ca_topic_score_codex":0.0011488107,"about_ca_topic_score_gemma":0.002099796,"teacher_disagreement_score":0.14917025,"about_ca_system_score_codex":0.0010634105,"about_ca_system_score_gemma":0.001534243,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4235267710","doi":"10.17975/sfj-2019-001","title":"2019 National High School Big Data Challenge: Big Data de Terre","year":2019,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Data science; Political science; Computer science; Data mining","score_opus":0.5394255743286756,"score_gpt":0.4134806470450783,"score_spread":0.12594492728359724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235267710","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030474646,0.0049327747,0.04034623,0.8108949,0.03782079,0.00046852752,0.015058926,0.0031180985,0.056885112],"genre_scores_gemma":[0.28205565,0.008227093,0.17585145,0.12844478,0.02308968,0.0014701078,0.06033502,0.0033689782,0.31715733],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944899,0.0014719524,0.00018447483,0.0007981939,0.0023516326,0.0007038762],"domain_scores_gemma":[0.9678068,0.0053957836,0.00066365855,0.0021407108,0.007967066,0.01602596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017457562,0.00079781335,0.0008091886,0.001018958,0.0029915886,0.009223865,0.0017755056,0.0026405088,0.019146895],"category_scores_gemma":[0.021988077,0.00037234667,0.00074586296,0.001342521,0.0017271634,0.004365641,0.0065530865,0.007394525,0.007476755],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008453279,0.00012745446,0.0029465153,0.000063331696,0.000027132404,0.00014612392,0.0003163432,0.00050545065,0.00045683002,0.012582863,0.9401437,0.042599592],"study_design_scores_gemma":[0.00004675976,0.00015291414,0.0040707667,0.00015270109,0.000008893738,0.00011246701,0.0010790776,0.005062213,0.00083599717,0.023895072,0.9645372,0.000045773468],"about_ca_topic_score_codex":0.009937422,"about_ca_topic_score_gemma":0.023195628,"teacher_disagreement_score":0.019146895,"about_ca_system_score_codex":0.0027049955,"about_ca_system_score_gemma":0.0115934815,"threshold_uncertainty_score":0.09232551},"labels":[],"label_agreement":null},{"id":"W4236178279","doi":"10.1007/978-3-030-36365-9","title":"Advances in Data Science, Cyber Security and IT Applications","year":2019,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Network security; Computer security; Information security; Data science","score_opus":0.19542752318846085,"score_gpt":0.42541409552921283,"score_spread":0.22998657234075198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236178279","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011906903,0.38472265,0.019199021,0.007555302,0.014062052,0.00006812114,0.0007235373,0.00065381295,0.5718248],"genre_scores_gemma":[0.0059727593,0.25264552,0.014582356,0.0030641467,0.008703824,0.000082836006,0.00083577033,0.00038045738,0.71373236],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938285,0.000054765045,0.0000315123,0.00008259344,0.00041495913,0.000033203123],"domain_scores_gemma":[0.9988991,0.00056864,0.000056259534,0.00008043474,0.00027125192,0.00012434224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007646883,0.0014200954,0.001108159,0.0033799023,0.0005660135,0.0038953242,0.0008913433,0.0009691557,0.05934818],"category_scores_gemma":[0.0018208519,0.00040940332,0.00048583403,0.006083848,0.0008388366,0.003829943,0.0015249142,0.0027381957,0.030995738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018728142,0.000054137236,0.00011557683,0.0011403256,0.000016718619,0.000049454397,0.00008391787,0.0005994724,0.0011918498,0.046419732,0.4940698,0.45624033],"study_design_scores_gemma":[0.0000030381218,0.0000150977585,0.00023892862,0.00031311114,0.000006785923,0.00008437421,0.000024738994,0.00045639637,0.00017969798,0.014496448,0.9841748,0.0000065608165],"about_ca_topic_score_codex":0.0010025826,"about_ca_topic_score_gemma":0.0024094104,"teacher_disagreement_score":0.05934818,"about_ca_system_score_codex":0.0010484473,"about_ca_system_score_gemma":0.0017921551,"threshold_uncertainty_score":0.19853944},"labels":[],"label_agreement":null},{"id":"W4238254745","doi":"10.1007/978-3-319-95810-1","title":"Applications of Data Management and Analysis","year":2018,"lang":"en","type":"book","venue":"Lecture notes in social networks","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Social network analysis; Computer science; Data science; Segmentation; Network analysis; Data mining; Operations research; Artificial intelligence; World Wide Web; Engineering; Social media","score_opus":0.13290521553753176,"score_gpt":0.38706398945844395,"score_spread":0.2541587739209122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238254745","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012419374,0.022791483,0.86258465,0.008707143,0.00388463,0.00024530786,0.0016633865,0.004254677,0.0946269],"genre_scores_gemma":[0.042769335,0.03920698,0.7535842,0.004227151,0.0073174015,0.0007741762,0.0049378686,0.003148619,0.1440343],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99518377,0.0012887553,0.0004568561,0.0004959999,0.0024654542,0.00010917254],"domain_scores_gemma":[0.987305,0.0076318476,0.00034863953,0.002655134,0.0017522725,0.0003071566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050278413,0.0016519867,0.0014880189,0.005200033,0.00086062844,0.005779433,0.0018440657,0.0013004323,0.022432666],"category_scores_gemma":[0.014361801,0.001003015,0.0012741613,0.007024107,0.0016762281,0.00613758,0.0038398162,0.0031911144,0.014812917],"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":[0.00003786219,0.000051633855,0.00068676745,0.00076435425,0.00009334083,0.00012769147,0.00026474832,0.0022403286,0.0013053529,0.20259638,0.1848321,0.6069995],"study_design_scores_gemma":[0.00000916247,0.000012909438,0.00059422397,0.00026492958,0.00003588521,0.0003600689,0.00011746821,0.018207494,0.001754133,0.42474324,0.55387086,0.000029643876],"about_ca_topic_score_codex":0.0006882702,"about_ca_topic_score_gemma":0.00058648246,"teacher_disagreement_score":0.022432666,"about_ca_system_score_codex":0.0011143464,"about_ca_system_score_gemma":0.0012929703,"threshold_uncertainty_score":0.07504469},"labels":[],"label_agreement":null},{"id":"W4238755784","doi":"10.3138/jsp.38.4.183","title":"The Post-petroleum Future of Academic Libraries","year":2007,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Vision; Abandonment (legal); Petroleum; Economics; Industrial organization; Business; Political science; Natural resource economics; Sociology; Biology","score_opus":0.11420193585016919,"score_gpt":0.34909092409906506,"score_spread":0.23488898824889587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238755784","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05276504,0.04340314,0.002700703,0.44266623,0.0016730374,0.000028058852,0.0008068482,0.0002169334,0.45574003],"genre_scores_gemma":[0.7053123,0.0705875,0.0022924005,0.024225326,0.003198914,0.000068621426,0.00067295355,0.000094828436,0.19354722],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972765,0.0008608877,0.00010244097,0.00015901921,0.0008882008,0.0007130051],"domain_scores_gemma":[0.9925551,0.000795032,0.0005695213,0.00030709014,0.0026785173,0.0030947994],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0031590927,0.00027133332,0.0002570922,0.0023893178,0.005452855,0.01748602,0.0009843657,0.0037472572,0.029215941],"category_scores_gemma":[0.0063807885,0.0001850531,0.00026923956,0.0052297693,0.0041866885,0.014687125,0.004140911,0.0030383782,0.009798652],"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":[0.00018190398,0.0001240042,0.004771403,0.00042020393,0.000020498283,0.00054038677,0.0040143165,0.0004698052,0.00041709677,0.688519,0.13437198,0.16614938],"study_design_scores_gemma":[0.000007529752,0.000040342795,0.004384575,0.0002049861,0.0000065549143,0.0003426235,0.004488274,0.00025893847,0.0004070862,0.07202508,0.9178036,0.00003059562],"about_ca_topic_score_codex":0.013378851,"about_ca_topic_score_gemma":0.02158051,"teacher_disagreement_score":0.98251396,"about_ca_system_score_codex":0.011159526,"about_ca_system_score_gemma":0.016386539,"threshold_uncertainty_score":0.097737014},"labels":[],"label_agreement":null},{"id":"W4241866799","doi":"10.1007/978-3-030-24367-8_2","title":"Big Data","year":2019,"lang":"en","type":"book-chapter","venue":"Advanced information and knowledge processing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Simon Fraser University","funders":"","keywords":"Big data; Data science; Business intelligence; Computer science; Analytics; Business analytics; Data analysis; Variety (cybernetics); Cultural analytics; Unstructured data; Software analytics; Predictive analytics; Cloud computing; Data visualization; Visualization; Knowledge management; Data mining; World Wide Web; Semantic analytics; Artificial intelligence; Business analysis; The Internet; Business model; Management","score_opus":0.2807200847194476,"score_gpt":0.38604348324466553,"score_spread":0.10532339852521794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241866799","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002539955,0.08049295,0.11868765,0.032558344,0.013703495,0.0010745651,0.11268584,0.022147974,0.6161092],"genre_scores_gemma":[0.04066183,0.12914021,0.16094692,0.021003209,0.014094315,0.0020140326,0.2746483,0.007619779,0.3498715],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968924,0.00058202416,0.00021999888,0.00049180206,0.0016670135,0.0001467391],"domain_scores_gemma":[0.9954792,0.0012827601,0.00025701534,0.0012526232,0.0013624602,0.00036587607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022676599,0.0014476259,0.0010905557,0.0038903665,0.0011457049,0.007960331,0.0032877815,0.0020003791,0.106729746],"category_scores_gemma":[0.008904833,0.0005986457,0.0010004081,0.008626417,0.000737019,0.00858221,0.004356432,0.0026027397,0.08615232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052630287,0.00002063923,0.00038711572,0.0010309961,0.000053099455,0.00008278896,0.00011750557,0.0006322037,0.00053924724,0.043726392,0.74631864,0.2070387],"study_design_scores_gemma":[0.000004628528,0.000006896197,0.00020441324,0.00021293617,0.0000070232186,0.00009887306,0.000046805628,0.00046706796,0.00025154243,0.017059214,0.9816297,0.0000109264],"about_ca_topic_score_codex":0.0015574093,"about_ca_topic_score_gemma":0.0016296292,"teacher_disagreement_score":0.106729746,"about_ca_system_score_codex":0.0012995562,"about_ca_system_score_gemma":0.0022193843,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4242595350","doi":"10.4095/223363","title":"Radiometric data","year":2007,"lang":"en","type":"report","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Natural Resources Canada","funders":"","keywords":"Radiometric dating; Computer science; Remote sensing; Environmental science; Geology","score_opus":0.8588006933303317,"score_gpt":0.5665591600270822,"score_spread":0.29224153330324953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242595350","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0104332045,0.0013097526,0.016284076,0.0006811097,0.0010734666,0.0009819577,0.6807592,0.0074314,0.28104588],"genre_scores_gemma":[0.032954924,0.0018861097,0.032778673,0.0012445874,0.00050109415,0.0020095042,0.70157665,0.004417481,0.22263099],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957339,0.00047660305,0.0005848012,0.0007507059,0.0022148306,0.00023913076],"domain_scores_gemma":[0.9921748,0.0007425933,0.0004232061,0.0012367135,0.005242355,0.00018028847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023022657,0.001492595,0.0014320647,0.0082387,0.00146891,0.0022108036,0.0030086294,0.0010082383,0.27509296],"category_scores_gemma":[0.009288044,0.0004897381,0.00063006085,0.016156284,0.0005237685,0.0017701995,0.0018378664,0.001225557,0.29173464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003954485,0.00014150479,0.011665799,0.0010390689,0.00003845631,0.00016810793,0.00045387755,0.0012049145,0.0028171109,0.0053221355,0.7729659,0.20378755],"study_design_scores_gemma":[0.000020283336,0.000045254943,0.008113223,0.000110908404,0.000015916794,0.00019079554,0.00020835389,0.00026036488,0.001459233,0.0011788347,0.98836666,0.000030250316],"about_ca_topic_score_codex":0.008672238,"about_ca_topic_score_gemma":0.006835104,"teacher_disagreement_score":0.27509296,"about_ca_system_score_codex":0.0019000643,"about_ca_system_score_gemma":0.0018458229,"threshold_uncertainty_score":0.9202776},"labels":[],"label_agreement":null},{"id":"W4245289973","doi":"10.3138/jsp.41.1.92","title":"The Current Status of the Publishing Industry in China","year":2009,"lang":"en","type":"article","venue":"Journal of Scholarly Publishing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Publishing; Prosperity; China; Dominance (genetics); Information industry; Business; Economics; Economic geography; Economy; Economic growth; Political science; Law","score_opus":0.1730678444820716,"score_gpt":0.3784927582432827,"score_spread":0.2054249137612111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245289973","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8729402,0.049624886,0.0011456795,0.009363186,0.0003048992,0.00007660437,0.002395772,0.00032067197,0.063828014],"genre_scores_gemma":[0.96513444,0.02366363,0.0007731062,0.00039363824,0.00029774333,0.000026624319,0.0014034113,0.000020180269,0.008287127],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99752074,0.00020000637,0.00038169866,0.00029543665,0.0011439995,0.000458148],"domain_scores_gemma":[0.99344397,0.000726529,0.0016921739,0.00027803343,0.0022389959,0.0016203163],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0020591726,0.00036110243,0.000506023,0.008701619,0.0021383162,0.0053410213,0.0010639643,0.00067994307,0.0050383285],"category_scores_gemma":[0.0033181577,0.00027050212,0.00035550367,0.01566087,0.001363378,0.0029874016,0.0013274306,0.0004013691,0.00076126703],"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":[0.00043764623,0.00013856092,0.4883287,0.002014663,0.00015271326,0.0026395472,0.0074329185,0.0020285153,0.0042514857,0.03517197,0.015672127,0.44173127],"study_design_scores_gemma":[0.000047821733,0.00025259075,0.8114468,0.00032308855,0.00013573786,0.0012976449,0.006201933,0.0034176884,0.002338743,0.0045533227,0.16988215,0.00010239994],"about_ca_topic_score_codex":0.042136054,"about_ca_topic_score_gemma":0.030307543,"teacher_disagreement_score":0.994659,"about_ca_system_score_codex":0.0058904826,"about_ca_system_score_gemma":0.015264274,"threshold_uncertainty_score":0.08378154},"labels":[],"label_agreement":null},{"id":"W4246210298","doi":"10.36227/techrxiv.12094137.v1","title":"Search Is Not Yet a Solved Problem","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Lakehead University","funders":"","keywords":"Reading (process); Perspective (graphical); Quality (philosophy); Index (typography); Transformation (genetics); Computer science; Mathematics education; World Wide Web; Psychology; Artificial intelligence; Linguistics; Epistemology; Philosophy","score_opus":0.5724159156680648,"score_gpt":0.45599132128277525,"score_spread":0.11642459438528957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246210298","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013446165,0.053198952,0.04821113,0.63343763,0.005412191,0.00016506226,0.0044927434,0.0012013513,0.24043481],"genre_scores_gemma":[0.47834376,0.061622974,0.07840773,0.14506395,0.0155323185,0.00071238104,0.012376866,0.0030469815,0.20489304],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9900994,0.0035373408,0.0007443725,0.0028045347,0.0016457931,0.0011685522],"domain_scores_gemma":[0.9490972,0.034046333,0.0017780447,0.0057505895,0.006923571,0.0024043068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0091751395,0.0008434352,0.0029110846,0.0036434724,0.004797705,0.012426916,0.0033400853,0.008940705,0.057160325],"category_scores_gemma":[0.061645217,0.0008966765,0.0015037361,0.0075095044,0.009640884,0.046785492,0.004363244,0.007802308,0.030428123],"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":[0.00016200909,0.000118384174,0.0022847382,0.0012319444,0.00015135692,0.00025960853,0.0012515894,0.0011193762,0.00022797048,0.41937152,0.42590198,0.14791965],"study_design_scores_gemma":[0.000054477743,0.000027583237,0.0006744113,0.0005361264,0.000036539273,0.00057909987,0.0022824986,0.0016817743,0.00017311338,0.6069451,0.38694313,0.00006608084],"about_ca_topic_score_codex":0.013888017,"about_ca_topic_score_gemma":0.008016703,"teacher_disagreement_score":0.057160325,"about_ca_system_score_codex":0.0041242074,"about_ca_system_score_gemma":0.006029337,"threshold_uncertainty_score":0.19122028},"labels":[],"label_agreement":null},{"id":"W4250020071","doi":"10.1108/9781787432956","title":"Becoming Digital","year":2017,"lang":"en","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":168,"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":"Computer science","score_opus":0.41728069732990664,"score_gpt":0.42157374350514704,"score_spread":0.0042930461752404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250020071","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005607218,0.009080163,0.0012792042,0.007786355,0.0033110015,0.000030993888,0.00012409232,0.00014339249,0.97768414],"genre_scores_gemma":[0.0054065753,0.010468138,0.0012460301,0.00526433,0.00084138766,0.000050619517,0.000219725,0.000113194306,0.97639],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99910766,0.00013341336,0.000033939148,0.00014311608,0.00045631608,0.0001255105],"domain_scores_gemma":[0.9994492,0.00010053914,0.0000328663,0.00010227921,0.0001336416,0.0001814725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006143305,0.0006042914,0.00036162694,0.00077198993,0.0023998683,0.010455135,0.00067393965,0.002066703,0.118778974],"category_scores_gemma":[0.0018716182,0.00026735305,0.0003397023,0.0012189948,0.0021506432,0.008946648,0.0050805868,0.0034846263,0.08367202],"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":[0.000007477048,0.000025775713,0.00019837788,0.00012940186,0.000003607336,0.00008457642,0.0016864323,0.00005280069,0.00033313286,0.21685788,0.6057228,0.1748978],"study_design_scores_gemma":[3.8055677e-7,0.000002079049,0.000053603526,0.000054492593,4.5004154e-7,0.000047732105,0.000171618,0.000005599616,0.00002580763,0.004370289,0.9952668,0.0000011648557],"about_ca_topic_score_codex":0.001214118,"about_ca_topic_score_gemma":0.0030668743,"teacher_disagreement_score":0.118778974,"about_ca_system_score_codex":0.0013816562,"about_ca_system_score_gemma":0.0024804908,"threshold_uncertainty_score":0.39735526},"labels":[],"label_agreement":null},{"id":"W4253593273","doi":"10.17975/sfj-2018-006","title":"Abstracts from the High School Big Data Challenge 2017-2018: Think Global, Act Local with Big Data","year":2018,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Data science; Computer science; Data mining","score_opus":0.5067818066705747,"score_gpt":0.3921677662893942,"score_spread":0.1146140403811805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253593273","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001692811,0.006702535,0.0019771308,0.8437807,0.1179442,0.00025173102,0.0061136815,0.0004307183,0.021106493],"genre_scores_gemma":[0.04560214,0.01799054,0.0058214995,0.28644046,0.24968274,0.0012047568,0.029752884,0.0024751932,0.3610297],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9880668,0.0020136288,0.0006273407,0.00089702505,0.0068033445,0.0015918674],"domain_scores_gemma":[0.9053871,0.010532578,0.0036759852,0.0026767245,0.028890561,0.048837095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026591184,0.0016289032,0.0014968179,0.0025320975,0.007437002,0.01644405,0.0021864125,0.00596599,0.08539436],"category_scores_gemma":[0.057590563,0.0006160708,0.0013743234,0.0030025726,0.0026760052,0.010380983,0.00999418,0.013048444,0.048565403],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001637933,0.0000108414815,0.00016073383,0.00004562672,0.0000030833314,0.000008223502,0.00005544291,0.000018852423,0.000025278796,0.00051068346,0.9952003,0.00394454],"study_design_scores_gemma":[0.00004191975,0.000041822834,0.0022624517,0.00035223615,0.000013740992,0.000024357058,0.0012039188,0.0002118691,0.00017526766,0.007866725,0.9877687,0.000036971724],"about_ca_topic_score_codex":0.0114631485,"about_ca_topic_score_gemma":0.0344095,"teacher_disagreement_score":0.08539436,"about_ca_system_score_codex":0.006149963,"about_ca_system_score_gemma":0.03307611,"threshold_uncertainty_score":0.28567255},"labels":[],"label_agreement":null},{"id":"W4288696362","doi":"10.1145/3552490.3552499","title":"Reminiscences on Influential Papers","year":2022,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Column (typography); Computer science; Value (mathematics); Citation; Key (lock); Reading (process); Library science; World Wide Web; Operations research; History; Telecommunications; Law; Computer security; Political science; Mathematics","score_opus":0.22026839757357825,"score_gpt":0.3827781944790289,"score_spread":0.16250979690545064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288696362","genre_codex":"editorial","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016805159,0.038655005,0.00094804395,0.28787377,0.64309734,0.000042158696,0.00033059856,0.00023828277,0.027134283],"genre_scores_gemma":[0.032704327,0.040513817,0.0011923189,0.22208175,0.60377854,0.0001647816,0.0005550676,0.0011592003,0.09785026],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9847339,0.003974069,0.00091485557,0.0018823085,0.006872904,0.0016220526],"domain_scores_gemma":[0.93229485,0.01999638,0.004321875,0.003423074,0.025538165,0.014425734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014233461,0.0022165289,0.0013565808,0.007445313,0.010938416,0.021296136,0.0032121378,0.007203442,0.018969595],"category_scores_gemma":[0.09396681,0.00073070114,0.0022873234,0.007955117,0.0037451305,0.010962461,0.007622335,0.019315636,0.013745218],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002638135,0.000017538541,0.00021380505,0.00018511739,0.00002700071,0.00033842487,0.0015809508,0.000027347895,0.00010838389,0.004167404,0.98129934,0.012008267],"study_design_scores_gemma":[0.0000063356392,0.000011290589,0.00015701338,0.00025820875,0.000018283508,0.000274687,0.0013149527,0.000021955655,0.00009774662,0.0014704289,0.99634796,0.000021234113],"about_ca_topic_score_codex":0.0018345811,"about_ca_topic_score_gemma":0.0038590885,"teacher_disagreement_score":0.021296136,"about_ca_system_score_codex":0.0059338557,"about_ca_system_score_gemma":0.0041517876,"threshold_uncertainty_score":0.07527459},"labels":[],"label_agreement":null},{"id":"W4293070708","doi":"10.5121/ijaia.2022.13305","title":"Data Standardization using Deep Learning for Healthcare Insurance Claims","year":2022,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Laurentian University","funders":"","keywords":"Standardization; Computer science; Metadata; Receipt; Deep learning; Task (project management); Data mining; Artificial intelligence; Data science; Machine learning; Information retrieval; World Wide Web; Engineering","score_opus":0.4530248982000601,"score_gpt":0.49727896117340903,"score_spread":0.04425406297334894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293070708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17866111,0.0024641212,0.7995511,0.0030852791,0.00031591745,0.00027153417,0.0025886546,0.0070466828,0.0060156547],"genre_scores_gemma":[0.8224509,0.00066032406,0.16407274,0.00045111644,0.00013207647,0.00021381446,0.0065129558,0.00015920027,0.005346907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901223,0.00021642116,0.000105426465,0.00026165118,0.0002814922,0.00012271662],"domain_scores_gemma":[0.9982943,0.0005852485,0.0001963336,0.00039993084,0.00044407972,0.0000800947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028322695,0.0006887649,0.00074046676,0.0016212512,0.00042738917,0.0013496707,0.0013020278,0.0011722936,0.0021716065],"category_scores_gemma":[0.006749755,0.0003983361,0.0010188352,0.0019267079,0.0005391388,0.0017948261,0.0017342864,0.002327315,0.0009510639],"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":[0.0003307799,0.0005155403,0.014005889,0.00013531963,0.00018030206,0.0001869772,0.00016995025,0.38999286,0.003250497,0.008723771,0.010266947,0.5722412],"study_design_scores_gemma":[0.000011019311,0.00003984897,0.0012410119,0.000019943258,0.000011883794,0.000022393167,0.000028824586,0.98843926,0.002008361,0.006419089,0.0017488992,0.000009487174],"about_ca_topic_score_codex":0.011325387,"about_ca_topic_score_gemma":0.0111912945,"teacher_disagreement_score":0.011325387,"about_ca_system_score_codex":0.0017032608,"about_ca_system_score_gemma":0.0016639496,"threshold_uncertainty_score":0.022518933},"labels":[],"label_agreement":null},{"id":"W4293791092","doi":"10.3390/app12168248","title":"Big Data Analysis and Visualization: Challenges and Solutions","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"University of Manitoba","funders":"University of Manitoba","keywords":"Big data; Computer science; Visualization; Data science; Core (optical fiber); Data mining; Telecommunications","score_opus":0.6092219036420985,"score_gpt":0.42565579693940025,"score_spread":0.18356610670269824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293791092","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007233707,0.15962914,0.27915582,0.5319611,0.0063556684,0.00027386815,0.0014423095,0.002507628,0.011440803],"genre_scores_gemma":[0.15698121,0.23099254,0.5539903,0.028397616,0.01731937,0.0007866816,0.0031790978,0.001027058,0.0073260632],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98664933,0.0052856524,0.00084984466,0.0012528881,0.0053446717,0.0006177211],"domain_scores_gemma":[0.94165784,0.03135285,0.0026393705,0.0055252924,0.0152807655,0.0035439911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020601155,0.0014918206,0.0024919999,0.0032900276,0.002459373,0.017015774,0.005581148,0.0069660707,0.0041035092],"category_scores_gemma":[0.034868456,0.0014358275,0.0011718192,0.0069854963,0.006320381,0.023848785,0.009028335,0.008816775,0.0029370359],"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":[0.00023786466,0.00024615997,0.0039649894,0.005304146,0.0002832195,0.0002635034,0.0013366617,0.008383767,0.0028332071,0.24925515,0.24938549,0.47850588],"study_design_scores_gemma":[0.000040846946,0.00006364605,0.0013182017,0.0020193888,0.000057758494,0.0003869707,0.0037472434,0.047661766,0.001696385,0.6814461,0.26138586,0.00017580244],"about_ca_topic_score_codex":0.0036987928,"about_ca_topic_score_gemma":0.00368185,"teacher_disagreement_score":0.020601155,"about_ca_system_score_codex":0.002394766,"about_ca_system_score_gemma":0.0068540205,"threshold_uncertainty_score":0.108950615},"labels":[],"label_agreement":null},{"id":"W4294760824","doi":"10.4095/330541","title":"Open disaster risk reduction data platform","year":2022,"lang":"en","type":"report","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Natural Resources Canada","funders":"","keywords":"Reduction (mathematics); Disaster risk reduction; Computer science; Geography; Environmental planning; Mathematics","score_opus":0.7792185618769631,"score_gpt":0.5264706855282165,"score_spread":0.2527478763487466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294760824","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048747472,0.0005539231,0.07159294,0.026637271,0.0034291989,0.0047406373,0.38425356,0.022944978,0.43710014],"genre_scores_gemma":[0.120726615,0.00089165184,0.113213815,0.0032727376,0.00084669684,0.0027467797,0.50774455,0.0028786815,0.24767847],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952329,0.00041009267,0.00015280629,0.00030076277,0.0032745495,0.0006289944],"domain_scores_gemma":[0.98406005,0.002415377,0.0006573899,0.0033451589,0.006251207,0.0032708815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072180796,0.0007968977,0.00069333613,0.0024328285,0.0021667092,0.005309301,0.0022155272,0.0017125388,0.033393074],"category_scores_gemma":[0.010092307,0.000552127,0.00072696264,0.0022582351,0.0012965526,0.0069171246,0.005762872,0.002552596,0.018133463],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010471684,0.0009159218,0.0068087243,0.00025795322,0.000054117696,0.00021471473,0.00032750735,0.006278943,0.005992872,0.05100787,0.8319209,0.09517343],"study_design_scores_gemma":[0.00022215664,0.00021718285,0.014975673,0.00013034543,0.00002796106,0.0001034112,0.00068925583,0.017047796,0.01133269,0.025240492,0.929895,0.00011792766],"about_ca_topic_score_codex":0.043372583,"about_ca_topic_score_gemma":0.03334439,"teacher_disagreement_score":0.043372583,"about_ca_system_score_codex":0.0025880432,"about_ca_system_score_gemma":0.015609183,"threshold_uncertainty_score":0.111711025},"labels":[],"label_agreement":null},{"id":"W4300669487","doi":"10.17615/5mx2-pb29","title":"Mapping Health Data: Improved Privacy Protection With Donut Method Geomasking","year":2020,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"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":"Privacy protection; Internet privacy; Computer science; Data Protection Act 1998; Computer security; Business","score_opus":0.46158372907178014,"score_gpt":0.3930748585083757,"score_spread":0.06850887056340443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300669487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052625503,0.00049167656,0.940328,0.00075485365,0.00018797132,0.00015175405,0.00018224055,0.001789927,0.003487954],"genre_scores_gemma":[0.57022005,0.00025601184,0.4253561,0.00038377594,0.00010056513,0.00018585328,0.00042151476,0.00015435206,0.0029217962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99567235,0.0019670299,0.00020382964,0.00070456526,0.0011877237,0.00026458086],"domain_scores_gemma":[0.9903696,0.0041921944,0.000817726,0.0031510035,0.0012807394,0.00018879022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034903914,0.0006732977,0.0010375744,0.0012053777,0.0010598424,0.0017448656,0.0017531451,0.0012182021,0.0012890225],"category_scores_gemma":[0.016214041,0.0004038787,0.0006787343,0.0016181798,0.0011510163,0.0021388656,0.0026559061,0.0011935709,0.00043446006],"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":[0.0014993976,0.00030600457,0.0132573685,0.00034011324,0.000303234,0.00043703613,0.0017387155,0.16967946,0.025318075,0.049999338,0.0130266445,0.7240947],"study_design_scores_gemma":[0.00011281402,0.00027639556,0.0032895815,0.00004257505,0.00006720608,0.00064921,0.0005096539,0.9159968,0.031522833,0.02663964,0.020796735,0.00009654023],"about_ca_topic_score_codex":0.004382162,"about_ca_topic_score_gemma":0.0034497988,"teacher_disagreement_score":0.004382162,"about_ca_system_score_codex":0.00085827155,"about_ca_system_score_gemma":0.0016705515,"threshold_uncertainty_score":0.018459141},"labels":[],"label_agreement":null},{"id":"W4309978161","doi":"10.18438/b85g85","title":"Call for Proposals – 3rd Qualitative and Quantitative Methods in Libraries International Conference","year":2010,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Library science; World Wide Web; Data science","score_opus":0.28576652700539773,"score_gpt":0.51212428366607,"score_spread":0.22635775666067226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309978161","genre_codex":"commentary","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014447687,0.0031247134,0.0218576,0.73408586,0.21168524,0.0062257866,0.00095894287,0.0007145013,0.019902581],"genre_scores_gemma":[0.012619818,0.0021366747,0.10117937,0.6155969,0.033552475,0.02258903,0.0017062397,0.00075016427,0.20986935],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.874091,0.061937243,0.009056444,0.0055148676,0.043585494,0.0058149067],"domain_scores_gemma":[0.676618,0.14712444,0.0065569235,0.027400399,0.116569005,0.025731232],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.19848762,0.001643663,0.0021232823,0.005855479,0.008814308,0.013566277,0.009734417,0.046606373,0.070369795],"category_scores_gemma":[0.29117778,0.001889171,0.008812066,0.0028986807,0.009196352,0.0099446075,0.010817969,0.026140206,0.017869899],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030922037,0.00029315063,0.00047938363,0.0011496351,0.000110484216,0.00021493879,0.0007305093,0.00045625164,0.0016061162,0.030594505,0.9257832,0.038272537],"study_design_scores_gemma":[0.00021899174,0.00021864387,0.0015968458,0.0014503923,0.00013775183,0.00009841883,0.002081137,0.0007361809,0.0013501762,0.022980034,0.9688996,0.0002318641],"about_ca_topic_score_codex":0.012364875,"about_ca_topic_score_gemma":0.0207631,"teacher_disagreement_score":0.19848762,"about_ca_system_score_codex":0.012678634,"about_ca_system_score_gemma":0.046227984,"threshold_uncertainty_score":0.98840743},"labels":[],"label_agreement":null},{"id":"W4312442817","doi":"10.1007/978-3-031-06947-5","title":"30th Biennial Symposium on Communications 2021","year":2022,"lang":"en","type":"book","venue":"Signals and communication technology","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Manitoba; Université du Québec à Montréal","funders":"","keywords":"Political science; History","score_opus":0.1491956718034218,"score_gpt":0.3650486482847204,"score_spread":0.2158529764812986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312442817","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016864126,0.032342114,0.015909063,0.0101664765,0.1067233,0.00015634974,0.0011249202,0.0012323912,0.83065885],"genre_scores_gemma":[0.0025588654,0.007225318,0.0015463835,0.00061390165,0.0059449603,0.000045477533,0.00059038174,0.00019450388,0.98128027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992078,0.00012098105,0.000026984406,0.00011254875,0.00040917605,0.00012250326],"domain_scores_gemma":[0.99905413,0.000102668804,0.00003668947,0.00008799929,0.00043480608,0.00028364503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010299673,0.0018901655,0.0011567633,0.002446677,0.0012081026,0.0042612785,0.0010506931,0.002244876,0.15516867],"category_scores_gemma":[0.000943088,0.00035704978,0.00055760844,0.0018878623,0.0005454248,0.0019611714,0.0019461031,0.0030978275,0.14710553],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046382313,0.00004177911,0.0000931202,0.00009424663,0.000008905557,0.00003726938,0.000020620122,0.0002513296,0.0006886349,0.004808873,0.88770217,0.10620664],"study_design_scores_gemma":[0.0000039931724,0.000026619207,0.00017550575,0.00007138434,0.000006055372,0.00004216225,0.000019400322,0.00047612458,0.0002495227,0.0013756771,0.9975472,0.000006336904],"about_ca_topic_score_codex":0.0020984684,"about_ca_topic_score_gemma":0.008087925,"teacher_disagreement_score":0.15516867,"about_ca_system_score_codex":0.001263448,"about_ca_system_score_gemma":0.0018000107,"threshold_uncertainty_score":0.5190909},"labels":[],"label_agreement":null},{"id":"W4319165547","doi":"10.48550/arxiv.2302.00695","title":"Versatile Energy-Based Probabilistic Models for High Energy Physics","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut de Valorisation des Données","keywords":"Computer science; Spurious relationship; Probabilistic logic; Generative model; Event (particle physics); Particle physics; Artificial intelligence; Generative grammar; Physics; Machine learning","score_opus":0.42430525959342325,"score_gpt":0.26969133847192295,"score_spread":0.1546139211215003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319165547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063194144,0.00033024105,0.99029255,0.00057041657,0.00003719675,0.000028963914,0.00036069367,0.00022237169,0.001838204],"genre_scores_gemma":[0.63348687,0.001980103,0.34519497,0.0007998319,0.0004145727,0.0006169579,0.0024807814,0.00063199416,0.01439385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988955,0.00043937648,0.000056786845,0.00023140378,0.00027908527,0.000097791344],"domain_scores_gemma":[0.9964862,0.0024343058,0.00028500851,0.00040742775,0.0002446832,0.00014244494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00225171,0.00096323073,0.0011746901,0.0014752642,0.0007309884,0.0022703898,0.0033814928,0.0020796342,0.004611756],"category_scores_gemma":[0.006849708,0.00081174483,0.0016725201,0.001492747,0.001636148,0.0034210323,0.0021654756,0.0032025639,0.0014255986],"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":[0.000040947452,0.000047144254,0.00087485375,0.00006216393,0.000048069232,0.00009016712,0.000092974085,0.5541586,0.0009515962,0.42656556,0.002056739,0.015011111],"study_design_scores_gemma":[0.0000056178355,0.0000063269795,0.00009090962,0.0000071322943,0.000006937531,0.000028255996,0.000005502807,0.84056664,0.00015823952,0.15799917,0.0011161932,0.000009073894],"about_ca_topic_score_codex":0.0029213405,"about_ca_topic_score_gemma":0.003835738,"teacher_disagreement_score":0.004611756,"about_ca_system_score_codex":0.0014948915,"about_ca_system_score_gemma":0.0009575699,"threshold_uncertainty_score":0.015427887},"labels":[],"label_agreement":null},{"id":"W4320931091","doi":"10.5281/zenodo.7015039","title":"Deliverable 1.8 Data Management Plan V2","year":2021,"lang":"en","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Horizon 2020 Framework Programme","keywords":"Deliverable; Plan (archaeology); Computer science; Operations management; Engineering; Geology; Systems engineering","score_opus":0.5504077258314425,"score_gpt":0.394461531706845,"score_spread":0.15594619412459748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320931091","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009897903,0.0006593965,0.05264435,0.0068688616,0.0018718553,0.0036937313,0.56441945,0.03827583,0.33057666],"genre_scores_gemma":[0.0054745288,0.0010371278,0.058711227,0.0026577832,0.0003923456,0.007673532,0.6968655,0.0153051745,0.21188277],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9910266,0.0018663415,0.00077528413,0.0010157748,0.004461799,0.00085424364],"domain_scores_gemma":[0.9864107,0.002331656,0.00072317733,0.0023961298,0.007126418,0.0010120001],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.012651005,0.0016973797,0.00093656743,0.0054438994,0.0017894402,0.008831451,0.004102397,0.002238279,0.3482256],"category_scores_gemma":[0.029167771,0.0012301257,0.0012784804,0.006215605,0.000772409,0.0059265164,0.0059816483,0.0028961916,0.33437017],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006203661,0.000028548857,0.00026802922,0.00038049775,0.000011467971,0.000040240837,0.00016294837,0.0006587575,0.00034035282,0.010833218,0.9422145,0.044999428],"study_design_scores_gemma":[0.000019866102,0.000013618517,0.00030168812,0.00019193022,0.0000044594435,0.000020477515,0.0001023739,0.00022279908,0.00027121484,0.0033621066,0.99546945,0.000020018038],"about_ca_topic_score_codex":0.030813627,"about_ca_topic_score_gemma":0.01447865,"teacher_disagreement_score":0.3482256,"about_ca_system_score_codex":0.00521249,"about_ca_system_score_gemma":0.012105824,"threshold_uncertainty_score":0.92967707},"labels":[],"label_agreement":null},{"id":"W4366150443","doi":"10.2139/ssrn.4411115","title":"Measuring Discrete Risks on Infinite Domains: Theoretical Foundations, Conditional Five Number Summaries, and Data Analyses","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Econometrics; Statistics; Mathematics; Statistical physics; Physics","score_opus":0.3801287071028057,"score_gpt":0.4715815128302072,"score_spread":0.0914528057274015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366150443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03169703,0.0008737769,0.9637583,0.0012296297,0.00006197675,0.000069178175,0.00033190617,0.00014681689,0.0018314511],"genre_scores_gemma":[0.68070966,0.0013589467,0.31443235,0.00036558602,0.00045166115,0.0005689779,0.00092543697,0.00008237018,0.0011049644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9692309,0.020612579,0.0021048633,0.00372112,0.0037329844,0.0005976256],"domain_scores_gemma":[0.6546294,0.30442047,0.015062828,0.018686833,0.0055945106,0.0016059341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.043657884,0.0012617565,0.0029086347,0.0061038365,0.0011400013,0.008125658,0.0026217313,0.0020696751,0.0026654329],"category_scores_gemma":[0.23352374,0.0007655618,0.0020739085,0.0058987783,0.00663616,0.01646341,0.0042672395,0.0043334602,0.00029489302],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012005954,0.000107030304,0.007480521,0.00032490137,0.00021325199,0.000074452946,0.0005862856,0.036274347,0.00028624196,0.9007641,0.001215178,0.052553568],"study_design_scores_gemma":[0.000012599467,0.0000573349,0.0011290106,0.00008093278,0.000042451647,0.000056372734,0.00013296977,0.09467623,0.0002687688,0.9028086,0.0007042155,0.000030409126],"about_ca_topic_score_codex":0.0011482894,"about_ca_topic_score_gemma":0.00058616535,"teacher_disagreement_score":0.043657884,"about_ca_system_score_codex":0.002283759,"about_ca_system_score_gemma":0.002711253,"threshold_uncertainty_score":0.23088771},"labels":[],"label_agreement":null},{"id":"W4366809734","doi":"10.5220/0006108300001482","title":"A Surveillance Application of Satellite AIS","year":2017,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada); Defence Research and Development Canada","funders":"","keywords":"Remote sensing; Satellite; Computer science; Environmental science; Geology; Aerospace engineering; Engineering","score_opus":0.2541899468411294,"score_gpt":0.4263073034113857,"score_spread":0.17211735657025629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366809734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7977736,0.0013946515,0.10694811,0.0028388754,0.00064468937,0.00047654676,0.0027306431,0.0035824138,0.08361041],"genre_scores_gemma":[0.9325779,0.00067522086,0.05648363,0.0001913651,0.00011524654,0.000033741668,0.0011361194,0.00007355827,0.008713259],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996407,0.00008898732,0.000018052784,0.0000628621,0.00014870173,0.00004076371],"domain_scores_gemma":[0.9993273,0.00021415627,0.000031051735,0.00009241877,0.00027805506,0.000056956505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050077075,0.00048025628,0.00031321665,0.0007961995,0.0005684147,0.0011291251,0.00051291654,0.00079165935,0.0034322094],"category_scores_gemma":[0.0012865149,0.00013062984,0.00031822975,0.0013338634,0.00021620982,0.0005619898,0.00040906976,0.0003258618,0.00051962276],"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":[0.001522618,0.0010694556,0.07414734,0.0009468777,0.000403621,0.005462125,0.0018765011,0.18057193,0.07881634,0.014678465,0.041988816,0.598516],"study_design_scores_gemma":[0.00012242721,0.00076826673,0.048197746,0.00009936598,0.0002057527,0.0013596752,0.0019627223,0.867933,0.03149496,0.005595758,0.042188983,0.00007134424],"about_ca_topic_score_codex":0.02377498,"about_ca_topic_score_gemma":0.025896467,"teacher_disagreement_score":0.02377498,"about_ca_system_score_codex":0.000697435,"about_ca_system_score_gemma":0.0005660119,"threshold_uncertainty_score":0.04727322},"labels":[],"label_agreement":null},{"id":"W4378420534","doi":"10.1007/978-3-031-33743-7","title":"Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)","year":2023,"lang":"en","type":"book","venue":"Lecture notes in networks and systems","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":3,"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":"Lomonosov Moscow State University; Consejo Superior de Investigaciones Científicas; Politechnika Koszalińska; Universität Potsdam; Klaipedos Universitetas; Cardiff Metropolitan University; Central Queensland University; University of South Australia; Conservatoire National des Arts et Métiers; Trent University; Australian Catholic University; York University; King's College London; Nottingham Trent University; University of West Florida; Syddansk Universitet; University of Patras; Rutgers Cancer Institute of New Jersey; Università degli Studi Mediterranea di Reggio Calabria; University of Nicosia; Arab Academy for Science, Technology and Maritime Transport; German University in Cairo; Princeton University","keywords":"Political science; Engineering ethics; Library science; Computer science; Engineering","score_opus":0.2557063377403421,"score_gpt":0.42384406917994427,"score_spread":0.16813773143960214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378420534","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010326643,0.052288022,0.08599902,0.027408587,0.29380438,0.0006970135,0.0071288906,0.0043340633,0.51801336],"genre_scores_gemma":[0.015691482,0.017875174,0.021803144,0.0029029786,0.015695702,0.00032509287,0.009017454,0.0018063532,0.9148826],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980762,0.0004271917,0.00007693081,0.00026603483,0.00092945027,0.0002241366],"domain_scores_gemma":[0.9959941,0.0004609076,0.000114621354,0.00039477766,0.001687265,0.0013483387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034661319,0.0011977736,0.0018261316,0.0012196138,0.000954528,0.0059791,0.0012041229,0.001667295,0.15846221],"category_scores_gemma":[0.0032163404,0.00038308557,0.0008921352,0.0018227468,0.00077279995,0.0021826853,0.0022651465,0.003038329,0.11374616],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016239837,0.000092434166,0.00019283517,0.0001769901,0.000020441095,0.000042816988,0.000034827328,0.0003964526,0.0015604077,0.0037967265,0.879178,0.1143458],"study_design_scores_gemma":[0.000020957747,0.00008207928,0.000899534,0.00009147701,0.000020751448,0.00008841306,0.000046158588,0.0014672456,0.0009480557,0.003137328,0.9931832,0.000014709366],"about_ca_topic_score_codex":0.0036063897,"about_ca_topic_score_gemma":0.00714003,"teacher_disagreement_score":0.15846221,"about_ca_system_score_codex":0.0014795836,"about_ca_system_score_gemma":0.0034832538,"threshold_uncertainty_score":0.5301089},"labels":[],"label_agreement":null},{"id":"W4382878092","doi":"10.23977/acss.2023.070503","title":"Opportunities and Challenges of Technological Innovation in China: Based on the Analysis of China's Big Data Development","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Monopolization; China; Big data; Strengths and weaknesses; State (computer science); Politics; Human capital; Intervention (counseling); Business; Political science; Economic growth; Economics; Market economy; Computer science","score_opus":0.4951525341569511,"score_gpt":0.38684790370311656,"score_spread":0.10830463045383454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382878092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93579596,0.007205929,0.0033858581,0.014681531,0.00009915582,0.0001495627,0.00056095084,0.00003694741,0.038084026],"genre_scores_gemma":[0.9935695,0.00355112,0.00080733636,0.00028710588,0.000040596427,0.000036279518,0.00018721717,0.0000041535905,0.0015166322],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991652,0.0001389515,0.000052303367,0.000086619584,0.00026887504,0.00028820484],"domain_scores_gemma":[0.9984475,0.0004950678,0.00036767605,0.000053777705,0.00036791703,0.00026808813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014425458,0.0003609868,0.0003203563,0.003894993,0.001595331,0.003805461,0.0004373165,0.00063279754,0.0012737844],"category_scores_gemma":[0.0016319179,0.00015075575,0.0005330162,0.005591492,0.0012651731,0.0031627181,0.0018216434,0.00071164494,0.000076189826],"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":[0.00012430917,0.000129016,0.6465641,0.0011622619,0.00018160419,0.005452669,0.015720544,0.013299302,0.0022611474,0.18322895,0.010732459,0.12114371],"study_design_scores_gemma":[0.000021179414,0.000118487405,0.80700064,0.0006319753,0.00017383814,0.000733485,0.03909851,0.032310363,0.0017758042,0.03981973,0.07818263,0.0001333761],"about_ca_topic_score_codex":0.03368648,"about_ca_topic_score_gemma":0.056521773,"teacher_disagreement_score":0.03368648,"about_ca_system_score_codex":0.0061375075,"about_ca_system_score_gemma":0.010995268,"threshold_uncertainty_score":0.06698084},"labels":[],"label_agreement":null},{"id":"W4385407315","doi":"","title":"The problem with data","year":2016,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Computer science; Data science","score_opus":0.732120171553172,"score_gpt":0.6369879614823646,"score_spread":0.09513221007080741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385407315","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025966805,0.008096373,0.04925926,0.73420346,0.0178528,0.00014686174,0.0020570967,0.0010305734,0.18475688],"genre_scores_gemma":[0.16685049,0.021799756,0.086500905,0.49799716,0.033986792,0.0017104005,0.005234127,0.0025420266,0.18337832],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96315455,0.015298778,0.002799944,0.006665343,0.010449161,0.0016323117],"domain_scores_gemma":[0.7935785,0.100681946,0.005900589,0.060747612,0.0333576,0.005733756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03782833,0.00084398634,0.0015527072,0.0026462374,0.006050661,0.012358623,0.0044835424,0.010220485,0.07996296],"category_scores_gemma":[0.21229109,0.0010536892,0.0015492102,0.004559248,0.0120975915,0.032619547,0.010066757,0.014145403,0.04903074],"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":[0.00016388057,0.00003738062,0.0013264081,0.00045770334,0.000043575612,0.00020873858,0.00041441797,0.0004426748,0.0002097466,0.2900794,0.58214384,0.124472305],"study_design_scores_gemma":[0.000040313353,0.000013501092,0.00030637108,0.00064676726,0.000013425967,0.0005237791,0.00049775647,0.0012011465,0.00026173386,0.27951583,0.7169465,0.000032855263],"about_ca_topic_score_codex":0.0065358775,"about_ca_topic_score_gemma":0.0049670106,"teacher_disagreement_score":0.07996296,"about_ca_system_score_codex":0.0044266125,"about_ca_system_score_gemma":0.008852914,"threshold_uncertainty_score":0.26750278},"labels":[],"label_agreement":null},{"id":"W4387295506","doi":"10.5281/zenodo.8403803","title":"Small Data projects/Big Data research: contemporary problems and historical solutions","year":2023,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Lethbridge","funders":"","keywords":"Big data; Computer science; Data science; Data mining","score_opus":0.9396265901188896,"score_gpt":0.4017279743574359,"score_spread":0.5378986157614537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387295506","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004051107,0.2646968,0.025180422,0.4549302,0.03446776,0.00026741665,0.0033666915,0.0010981804,0.21194139],"genre_scores_gemma":[0.063282005,0.51088876,0.03499968,0.03360106,0.036920622,0.0005258282,0.004394756,0.0026116194,0.31277567],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98869574,0.0034217986,0.000706414,0.0012622806,0.005441574,0.00047216093],"domain_scores_gemma":[0.92223746,0.04190383,0.002690845,0.0071303183,0.020564202,0.0054733558],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.026547922,0.0007600341,0.00096806494,0.0060355472,0.0030716206,0.01465446,0.0018365574,0.0044760564,0.059924476],"category_scores_gemma":[0.0477081,0.000746051,0.00042041528,0.018597592,0.008151501,0.019826135,0.0057212366,0.0054201637,0.023866935],"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":[0.00009247347,0.000048312664,0.0006665186,0.002145506,0.00001600091,0.000107853855,0.0004945007,0.00026573852,0.00038698796,0.16561022,0.53306526,0.2971006],"study_design_scores_gemma":[0.000008955512,0.000019998988,0.0006494765,0.0007941162,0.0000046637665,0.000117637355,0.0005416691,0.00018441414,0.00029639082,0.05520353,0.9421623,0.000016857308],"about_ca_topic_score_codex":0.0027937097,"about_ca_topic_score_gemma":0.0042517255,"teacher_disagreement_score":0.9969284,"about_ca_system_score_codex":0.0040397886,"about_ca_system_score_gemma":0.008102149,"threshold_uncertainty_score":0.20046735},"labels":[{"model":"gemma","categories":["sts"],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["sts"],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W4387656228","doi":"10.23977/jeis.2023.080405","title":"Development of Electric Power Information Communication in the Era of Big Data","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Electric power; Information security; Context (archaeology); Data science; Power (physics); Computer science; Telecommunications; Computer security; Data mining; Geography","score_opus":0.180039499419098,"score_gpt":0.3769273172819031,"score_spread":0.1968878178628051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387656228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048784785,0.053342998,0.39253512,0.17361094,0.006765866,0.00026735297,0.00068169995,0.0009110382,0.3231003],"genre_scores_gemma":[0.7271525,0.08686341,0.12160981,0.014448339,0.008353119,0.0002924433,0.000716162,0.00032688625,0.040237263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99797255,0.0007304668,0.000111160836,0.00021202846,0.0008128293,0.00016110383],"domain_scores_gemma":[0.99101895,0.0041274633,0.00079187914,0.001023417,0.0025336533,0.00050462194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033391772,0.00052837614,0.00035265472,0.0017228371,0.0011902966,0.0054024244,0.001128889,0.0024443134,0.007475283],"category_scores_gemma":[0.009033417,0.00030698176,0.0004703316,0.003266488,0.0019473666,0.014286176,0.0027545937,0.0031354243,0.0017349719],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008881071,0.000059092796,0.004496986,0.00066097657,0.000032643104,0.00056429807,0.0009921123,0.0039056784,0.002446514,0.6867685,0.031910036,0.26807424],"study_design_scores_gemma":[0.000020021149,0.00015237514,0.0030443005,0.0010361312,0.000036283778,0.0011155171,0.0024005112,0.021384904,0.0048171775,0.31625262,0.64966357,0.00007651899],"about_ca_topic_score_codex":0.0006688588,"about_ca_topic_score_gemma":0.00061586767,"teacher_disagreement_score":0.007475283,"about_ca_system_score_codex":0.0012682998,"about_ca_system_score_gemma":0.0016606529,"threshold_uncertainty_score":0.025007308},"labels":[],"label_agreement":null},{"id":"W4388105430","doi":"10.18280/ts.400525","title":"Secure Image Retrieval and Sharing Technologies for Digital Inclusive Finance: Methods and Applications","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Image (mathematics); Information retrieval; Computer vision","score_opus":0.1128725283278505,"score_gpt":0.42201654756679235,"score_spread":0.30914401923894186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388105430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01292645,0.0072739716,0.97098285,0.0011882175,0.00015009758,0.000163143,0.000054944347,0.00052450894,0.006735827],"genre_scores_gemma":[0.29864264,0.01947826,0.66794294,0.00039660142,0.000477982,0.0002704682,0.0001643223,0.00011375338,0.012512993],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993038,0.0001534252,0.00003835604,0.00009656014,0.00035629806,0.00005150434],"domain_scores_gemma":[0.99915874,0.0003370011,0.00009599288,0.00020118295,0.00017492201,0.000032065767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003358,0.00057908165,0.0005650247,0.0013940777,0.00044877332,0.0016956999,0.00084192125,0.0013604196,0.002725803],"category_scores_gemma":[0.0019504321,0.0003345834,0.0005519702,0.0015906782,0.0013740787,0.0023369128,0.0012258126,0.0013208921,0.0016624167],"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":[0.00018445858,0.00016642462,0.00082625716,0.00075519684,0.000049736056,0.00028011895,0.0005322795,0.016009772,0.06519898,0.12277728,0.004449823,0.7887696],"study_design_scores_gemma":[0.00007840808,0.0005112591,0.0016030165,0.00027161618,0.00007435537,0.0028258828,0.00061685464,0.591883,0.18216497,0.14178322,0.07798118,0.0002062817],"about_ca_topic_score_codex":0.0004898781,"about_ca_topic_score_gemma":0.0003274566,"teacher_disagreement_score":0.002725803,"about_ca_system_score_codex":0.0007359406,"about_ca_system_score_gemma":0.0004973339,"threshold_uncertainty_score":0.009118736},"labels":[],"label_agreement":null},{"id":"W4388543182","doi":"10.1007/978-3-031-46402-7","title":"Data Enclaves","year":2023,"lang":"es","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Economic and Social Research Council; Social Sciences and Humanities Research Council of Canada; Copenhagen Business School","keywords":"Big data; Business; Internet privacy; Computer science; Data science; Data mining","score_opus":0.6079265092522445,"score_gpt":0.4614987018091529,"score_spread":0.14642780744309153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388543182","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060807383,0.018645586,0.10678878,0.009700793,0.010399024,0.00046084143,0.007500221,0.03212431,0.8082997],"genre_scores_gemma":[0.0139797265,0.0062367027,0.0117524285,0.001890035,0.0006082909,0.00015118123,0.0042043403,0.006982048,0.95419514],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990152,0.00012330248,0.000057812045,0.00016824031,0.0005395121,0.000096001066],"domain_scores_gemma":[0.99768305,0.0005345982,0.00008440659,0.0010890561,0.00042128703,0.0001875339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009925796,0.00083879026,0.0005688729,0.0016660541,0.0018022868,0.006339249,0.0013827606,0.000904745,0.124489315],"category_scores_gemma":[0.0046611964,0.0008083069,0.00070215965,0.0027608278,0.0009923985,0.0093336115,0.0053585265,0.002777765,0.07706903],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068824425,0.000046082303,0.00028408293,0.00030919453,0.000022293787,0.00021129419,0.00062608457,0.000781767,0.0023218733,0.13592023,0.6469245,0.21248375],"study_design_scores_gemma":[0.0000019996598,0.000004681856,0.00004980401,0.00003637514,0.0000015609066,0.0000619513,0.000028007784,0.00016966491,0.00045709682,0.0033254009,0.99585956,0.000003821974],"about_ca_topic_score_codex":0.0022829378,"about_ca_topic_score_gemma":0.003225575,"teacher_disagreement_score":0.124489315,"about_ca_system_score_codex":0.0010590481,"about_ca_system_score_gemma":0.0012046627,"threshold_uncertainty_score":0.41645825},"labels":[],"label_agreement":null},{"id":"W4388998246","doi":"10.23977/jaip.2023.060707","title":"Artificial intelligence for satellite communications and geophysics: current and future trends","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Communications satellite; Process (computing); Boom; Field (mathematics); Telecommunications; Computer science; Data science; Artificial intelligence; Engineering; Satellite","score_opus":0.42613064045003063,"score_gpt":0.4966793961804116,"score_spread":0.070548755730381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388998246","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024757504,0.9102715,0.0076243533,0.04877664,0.0019865865,0.000029107283,0.000050926785,0.000111885645,0.028673273],"genre_scores_gemma":[0.029217103,0.94366455,0.012253438,0.005776366,0.0044153724,0.000045666195,0.00010413117,0.00004258567,0.004480737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979006,0.00085440744,0.00014567771,0.00018904217,0.0007694982,0.00014065191],"domain_scores_gemma":[0.99078965,0.005708148,0.00053295697,0.00033392108,0.0019427736,0.0006924821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051288186,0.0006178142,0.0007868284,0.0031426672,0.0008303484,0.004717901,0.0014373412,0.0035993867,0.0040067066],"category_scores_gemma":[0.004478233,0.00028736214,0.0005143875,0.0064508617,0.0038032965,0.00985966,0.0018831688,0.0039308583,0.0016401283],"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":[0.00008492485,0.00017958527,0.0026315358,0.0035916574,0.00004982662,0.00013474541,0.000696418,0.0010193647,0.000951788,0.16936503,0.05364354,0.7676517],"study_design_scores_gemma":[0.00001899444,0.00014088162,0.0031867654,0.0037564095,0.00004704017,0.00041744078,0.001676367,0.004259916,0.0005479356,0.12303155,0.86285454,0.00006211085],"about_ca_topic_score_codex":0.0015720213,"about_ca_topic_score_gemma":0.0017290934,"teacher_disagreement_score":0.0051288186,"about_ca_system_score_codex":0.0019331475,"about_ca_system_score_gemma":0.003569812,"threshold_uncertainty_score":0.027124107},"labels":[],"label_agreement":null},{"id":"W4390539753","doi":"10.4324/9781003388418","title":"Global Digital Data Governance","year":2024,"lang":"en","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Centre for Global Cooperation Research; Universität Duisburg-Essen; Wissenschaftszentrum Berlin für Sozialforschung; York University; Alexander von Humboldt-Stiftung","keywords":"Data governance; Corporate governance; Business; Computer science; Data quality; Finance","score_opus":0.3330702791145367,"score_gpt":0.41508136882333235,"score_spread":0.08201108970879567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390539753","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014934314,0.00614913,0.011075217,0.008740013,0.0009640342,0.00007885611,0.00031350928,0.0002773571,0.9709085],"genre_scores_gemma":[0.06464645,0.021522857,0.015795331,0.009797906,0.0012738572,0.0003078695,0.001691899,0.00070614857,0.8842576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986292,0.00033125243,0.000058598638,0.00026517254,0.000564192,0.00015169675],"domain_scores_gemma":[0.9993149,0.00021576203,0.00004225469,0.00020689286,0.00014152798,0.000078633864],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012050535,0.00050141127,0.00032479237,0.001432482,0.0016441584,0.0099535175,0.00070097996,0.0014914364,0.027189076],"category_scores_gemma":[0.0024573104,0.00033178687,0.00043327015,0.004292455,0.003234012,0.008259876,0.0044268626,0.0018206579,0.0102467155],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003333992,0.000006101844,0.00017994478,0.00007025093,0.0000032965768,0.000040807245,0.0010269037,0.00038912374,0.00014417399,0.8305529,0.11294913,0.054634064],"study_design_scores_gemma":[8.710039e-7,0.0000022223014,0.00011606783,0.000076348726,0.0000011213463,0.000052253235,0.0002991395,0.00017135547,0.000055299897,0.0492122,0.95001054,0.0000025862603],"about_ca_topic_score_codex":0.0035848098,"about_ca_topic_score_gemma":0.0039410414,"teacher_disagreement_score":0.9900465,"about_ca_system_score_codex":0.003130526,"about_ca_system_score_gemma":0.003122029,"threshold_uncertainty_score":0.09095651},"labels":[],"label_agreement":null},{"id":"W4390982406","doi":"10.23977/acss.2023.071115","title":"Design and Research of Data-driven Scientific Research Management Platform","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Yunnan Provincial Department of Education","keywords":"Research data; Data management; Data science; Computer science; Management science; Knowledge management; Engineering management; Systems engineering; Engineering; Database; Data curation","score_opus":0.7113757234689406,"score_gpt":0.5370656310658665,"score_spread":0.17431009240307416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390982406","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03708364,0.0004151022,0.9440029,0.0008102874,0.00024022855,0.001089147,0.00020009292,0.0026198851,0.013538804],"genre_scores_gemma":[0.482378,0.00078715634,0.4964134,0.000576003,0.00017093585,0.0018679183,0.0012240082,0.00019355958,0.01638894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975083,0.000393253,0.00029121045,0.00054746185,0.0009278837,0.0003318745],"domain_scores_gemma":[0.9988242,0.00012693663,0.00011182693,0.00021204594,0.0005130174,0.00021193961],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0025309524,0.0005207599,0.0005318107,0.0015937657,0.0012620968,0.0044769812,0.0023717089,0.0011648795,0.0022434252],"category_scores_gemma":[0.002803056,0.00051606,0.0007654932,0.0013033523,0.00058528717,0.0047432026,0.0028726195,0.0008852219,0.0010023386],"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":[0.00084540877,0.00079168647,0.01919125,0.001263518,0.0003199401,0.0023418115,0.0027721655,0.043824904,0.0872697,0.3423,0.0187185,0.48036107],"study_design_scores_gemma":[0.00036908797,0.0008692485,0.005926816,0.00028461782,0.0004012503,0.0018355368,0.0014744536,0.6628867,0.073655225,0.061993904,0.19000553,0.00029767476],"about_ca_topic_score_codex":0.002320273,"about_ca_topic_score_gemma":0.00097463786,"teacher_disagreement_score":0.99746907,"about_ca_system_score_codex":0.001177608,"about_ca_system_score_gemma":0.0043910355,"threshold_uncertainty_score":0.013385117},"labels":[],"label_agreement":null},{"id":"W4392600544","doi":"10.5194/egusphere-egu24-6798","title":"Novel environmental big data grid integration and interoperability model","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"College of Family Physicians of Canada; General Electric (Canada)","funders":"","keywords":"Interoperability; Computer science; Big data; Grid; Data science; Data integration; Database; Data mining; World Wide Web; Geography","score_opus":0.5400495817871738,"score_gpt":0.4070020343157101,"score_spread":0.1330475474714637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392600544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00804143,0.00017441869,0.9637095,0.0016575244,0.000115742434,0.00022872722,0.00073363294,0.0013991604,0.023939868],"genre_scores_gemma":[0.44426268,0.0008101947,0.5335636,0.0010036927,0.00025960186,0.0009068646,0.0055778986,0.00044973532,0.013165675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965029,0.000808994,0.0003407917,0.0007219657,0.0012938116,0.0003316007],"domain_scores_gemma":[0.9978771,0.0004661548,0.00020632897,0.0007158348,0.0005690524,0.0001655811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003702886,0.0006536268,0.00062323565,0.0019179025,0.0013273868,0.0053005675,0.0029037294,0.0015672895,0.0034825038],"category_scores_gemma":[0.0058829593,0.00044330402,0.0016673179,0.0033146918,0.0015004328,0.010695815,0.0061228042,0.002155413,0.0010970191],"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":[0.00006527563,0.00010333173,0.002790738,0.00008570751,0.000063377694,0.0005169125,0.00035280822,0.10256198,0.000948536,0.83540106,0.011885807,0.045224443],"study_design_scores_gemma":[0.000028977587,0.000024272298,0.0005624107,0.00004407133,0.00003491453,0.00020980749,0.00019279365,0.6118034,0.0012841749,0.34097075,0.044815417,0.000029038172],"about_ca_topic_score_codex":0.0070626414,"about_ca_topic_score_gemma":0.0044258516,"teacher_disagreement_score":0.0070626414,"about_ca_system_score_codex":0.0020708917,"about_ca_system_score_gemma":0.0031240925,"threshold_uncertainty_score":0.019582927},"labels":[],"label_agreement":null},{"id":"W4392733155","doi":"10.1017/9781108874144.012","title":"Overview of analytic methods for multivariate GFs","year":2024,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Multivariate statistics; Computer science; Mathematics; Statistics","score_opus":0.37811485258637656,"score_gpt":0.4235050655656161,"score_spread":0.045390212979239564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392733155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017245561,0.04779231,0.80003035,0.0022178653,0.0013189961,0.0000797311,0.0011971071,0.0021844634,0.14345455],"genre_scores_gemma":[0.06496609,0.10587092,0.67351115,0.002002545,0.0052910536,0.0004916657,0.003529129,0.0035534787,0.14078401],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989235,0.00023797766,0.00007469619,0.0001317626,0.00056217716,0.00006987295],"domain_scores_gemma":[0.99900097,0.0005117404,0.000041485473,0.00016118803,0.00024678084,0.00003789227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014916471,0.0012920605,0.001005014,0.0053832885,0.000744897,0.002751585,0.0013324027,0.0009380861,0.029652638],"category_scores_gemma":[0.0038095415,0.0006306256,0.0014141949,0.0044077463,0.0012608616,0.0038231504,0.0018604881,0.0029385723,0.015393964],"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":[0.0000095599735,0.000025099534,0.0001966752,0.00046066046,0.000027529428,0.0001097715,0.00016338397,0.0028517167,0.00096575473,0.78202844,0.04143164,0.17172974],"study_design_scores_gemma":[0.0000047027097,0.000009690778,0.0003086034,0.00028169923,0.0000108031245,0.0003049179,0.000049260645,0.010291622,0.0005620715,0.6453165,0.34283283,0.000027339007],"about_ca_topic_score_codex":0.0016314541,"about_ca_topic_score_gemma":0.0015678303,"teacher_disagreement_score":0.029652638,"about_ca_system_score_codex":0.0015081584,"about_ca_system_score_gemma":0.0009410609,"threshold_uncertainty_score":0.099197924},"labels":[],"label_agreement":null},{"id":"W4392749861","doi":"10.23977/acss.2024.080113","title":"Research on Information Management and Decision Support System Based on Big Data","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Data science; Computer science; Decision support system; Knowledge management; Data mining","score_opus":0.3332079687068855,"score_gpt":0.44813486115973833,"score_spread":0.11492689245285281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392749861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058655445,0.07519532,0.7585523,0.026928978,0.0028336362,0.00048386274,0.0006474111,0.0009382908,0.075764686],"genre_scores_gemma":[0.7534244,0.067727014,0.16256487,0.0029585948,0.0029896242,0.00048433713,0.0009816563,0.000092097966,0.008777337],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970782,0.0008459078,0.00021414674,0.0004988757,0.0011688025,0.0001940938],"domain_scores_gemma":[0.9961981,0.0021295282,0.00031505525,0.0003791734,0.00078271964,0.00019542442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003038635,0.0006293974,0.00092356466,0.002451844,0.0011636002,0.0050715995,0.0015859561,0.0012217142,0.0018030909],"category_scores_gemma":[0.0059619728,0.00034503257,0.000917702,0.0034005095,0.0015809422,0.009896605,0.0016888992,0.0017845248,0.00043935224],"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":[0.00016752095,0.00025829757,0.008826177,0.0031065936,0.00036049078,0.00043961412,0.0015904098,0.03597682,0.004444938,0.5530381,0.016980719,0.37481028],"study_design_scores_gemma":[0.00007322551,0.00028053293,0.0062029334,0.0010971217,0.00030087752,0.00061288633,0.0024609081,0.2831778,0.007894388,0.5239924,0.17371747,0.00018942603],"about_ca_topic_score_codex":0.001998401,"about_ca_topic_score_gemma":0.001035248,"teacher_disagreement_score":0.0050715995,"about_ca_system_score_codex":0.0019401737,"about_ca_system_score_gemma":0.0028996016,"threshold_uncertainty_score":0.016070008},"labels":[],"label_agreement":null},{"id":"W4393178192","doi":"10.1051/0004-6361/202348239","title":"Galaxy merger challenge: A comparison study between machine learning-based detection methods","year":2024,"lang":"en","type":"preprint","venue":"Astronomy and Astrophysics","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Victoria","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Science and Technology Facilities Council; European Commission; Rijksuniversiteit Groningen; Comunidad de Madrid","keywords":"Galaxy; Computer science; Artificial intelligence; Astrophysics; Physics","score_opus":0.17749497253984287,"score_gpt":0.42132149206836367,"score_spread":0.2438265195285208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393178192","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7314854,0.15080866,0.05475049,0.00642483,0.0024637897,0.0011860486,0.017085344,0.011619536,0.024175799],"genre_scores_gemma":[0.84456813,0.007862021,0.082400545,0.0024512985,0.0017464302,0.00037395878,0.054810613,0.0008873432,0.004899671],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98383534,0.005337974,0.0014537985,0.0032811665,0.0053674765,0.0007242832],"domain_scores_gemma":[0.9729672,0.016833406,0.0021059897,0.003135349,0.0037830274,0.0011750843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02035401,0.0034012967,0.0020904983,0.01116878,0.0013224072,0.0039844,0.004017274,0.0042691366,0.0012576531],"category_scores_gemma":[0.03242942,0.0005997245,0.0020446368,0.0043941154,0.0011229496,0.004338386,0.003954132,0.002094528,0.001868304],"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":[0.0040252637,0.0015408758,0.29611373,0.004885071,0.0064316043,0.0006243071,0.0009977798,0.06372154,0.0055678785,0.0043565733,0.093956396,0.51777893],"study_design_scores_gemma":[0.0009489542,0.0039371075,0.25829,0.0012174093,0.0017130327,0.0030228316,0.0019879583,0.62136656,0.020429673,0.010796546,0.07567761,0.0006122662],"about_ca_topic_score_codex":0.0075055216,"about_ca_topic_score_gemma":0.006858764,"teacher_disagreement_score":0.02035401,"about_ca_system_score_codex":0.0022038145,"about_ca_system_score_gemma":0.0011835644,"threshold_uncertainty_score":0.107643604},"labels":[],"label_agreement":null},{"id":"W4393435570","doi":"10.54097/2z61sv46","title":"Preface: 5th International Conference on Computer Science and Intelligent Communication (CSIC 2023)","year":2024,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":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; Engineering","score_opus":0.10577708866243837,"score_gpt":0.3596516828673061,"score_spread":0.2538745942048677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393435570","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002188919,0.044622432,0.016669022,0.04724449,0.54452616,0.0010451226,0.006850661,0.0025369804,0.33431625],"genre_scores_gemma":[0.00860194,0.026448881,0.0057490906,0.006699689,0.060985893,0.00043754172,0.011552524,0.0015821214,0.8779424],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99792445,0.00026100487,0.00012445927,0.00031202225,0.0011156601,0.00026241704],"domain_scores_gemma":[0.9892893,0.0005387111,0.00018979402,0.00037255246,0.007043356,0.0025662787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00285399,0.00154646,0.0014646385,0.003542815,0.0024471553,0.008875058,0.0019491536,0.0024041005,0.27263954],"category_scores_gemma":[0.006181455,0.000396217,0.00094453356,0.0027406553,0.0006904652,0.0042790924,0.002606984,0.004492819,0.21990353],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027869339,0.000013162709,0.00007760983,0.00010950295,0.0000037267175,0.000027062424,0.00001720798,0.000051488358,0.0002550321,0.00059703,0.9703785,0.028441768],"study_design_scores_gemma":[0.000005562213,0.000021892178,0.00031725498,0.0001297902,0.000006020768,0.00006176689,0.000050865132,0.00018291504,0.00017945124,0.00055928994,0.99847394,0.000011353809],"about_ca_topic_score_codex":0.007909106,"about_ca_topic_score_gemma":0.010959594,"teacher_disagreement_score":0.27263954,"about_ca_system_score_codex":0.0027320657,"about_ca_system_score_gemma":0.0041902605,"threshold_uncertainty_score":0.91207016},"labels":[],"label_agreement":null},{"id":"W4393450344","doi":"10.5281/zenodo.7488070","title":"Human pseudoDB: simulated database of human genetic variants","year":2022,"lang":"pt","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Database; Computer science; Information retrieval","score_opus":0.20754057116717498,"score_gpt":0.3577449677550974,"score_spread":0.15020439658792242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393450344","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011400708,0.00032305697,0.0023599442,0.0004361369,0.00008885143,0.000058095742,0.98247415,0.0012486288,0.0016103846],"genre_scores_gemma":[0.015192843,0.00011739542,0.0027299095,0.00018243263,0.000010327621,0.00013095431,0.9809926,0.00006828735,0.0005752267],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994703,0.00016755446,0.000049236165,0.00015343129,0.00011400567,0.000045493558],"domain_scores_gemma":[0.99817884,0.00090232166,0.00008961826,0.00043121996,0.00020508816,0.00019292762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009332133,0.0009165433,0.00075212226,0.0011232381,0.0003814806,0.00095755165,0.0022061644,0.0015580263,0.013112648],"category_scores_gemma":[0.0058009923,0.00039902228,0.00089084235,0.002192082,0.00031634912,0.00043642442,0.00077079795,0.0011890382,0.0064236578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017537079,0.00029483574,0.0175549,0.001198927,0.00043550096,0.00058903167,0.00007354716,0.024002725,0.0015055774,0.0035919,0.9331451,0.015854146],"study_design_scores_gemma":[0.0056703915,0.0006179737,0.050068993,0.0006108195,0.0006135253,0.0038820202,0.00036003607,0.114307575,0.008599479,0.026636666,0.7884672,0.0001653356],"about_ca_topic_score_codex":0.010529692,"about_ca_topic_score_gemma":0.015609849,"teacher_disagreement_score":0.013112648,"about_ca_system_score_codex":0.0008410508,"about_ca_system_score_gemma":0.0016748745,"threshold_uncertainty_score":0.043866158},"labels":[],"label_agreement":null},{"id":"W4393550518","doi":"10.5281/zenodo.4733386","title":"DJIN model of aging synthetic dataset","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Computer science","score_opus":0.44214195677123425,"score_gpt":0.417017978966971,"score_spread":0.02512397780426323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393550518","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008615732,0.0005583218,0.0024753192,0.00087715004,0.00027735255,0.0001748887,0.9820104,0.0017196817,0.0032910826],"genre_scores_gemma":[0.014854534,0.00022072291,0.0040397854,0.0004230771,0.0000521204,0.00047077294,0.97609067,0.00017151519,0.00367683],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999432,0.00020896363,0.00004727974,0.00015016075,0.00008283638,0.00007871677],"domain_scores_gemma":[0.998713,0.00047742648,0.00008237436,0.00026874623,0.0003262275,0.00013226354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017061513,0.0013490764,0.0007133975,0.0012851867,0.0005553166,0.0009129259,0.002792294,0.0017691448,0.025685228],"category_scores_gemma":[0.0064386274,0.0004362217,0.0014109046,0.0014856537,0.00036185764,0.00061204634,0.0009855288,0.0018830792,0.022473427],"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":[0.00022882553,0.0001781859,0.004876047,0.00039119876,0.00009701075,0.00010332898,0.000039993207,0.011156268,0.00014195248,0.0014599313,0.9719747,0.009352618],"study_design_scores_gemma":[0.0012082076,0.00030692588,0.024434226,0.0004978192,0.00017641396,0.0005707819,0.00020844632,0.07568406,0.0011619739,0.011189919,0.8844383,0.00012286997],"about_ca_topic_score_codex":0.023313265,"about_ca_topic_score_gemma":0.04623014,"teacher_disagreement_score":0.025685228,"about_ca_system_score_codex":0.0014101644,"about_ca_system_score_gemma":0.0014639797,"threshold_uncertainty_score":0.08592564},"labels":[],"label_agreement":null},{"id":"W4393733384","doi":"10.5281/zenodo.7488069","title":"Human pseudoDB: simulated database of human genetic variants","year":2022,"lang":"pt","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Database; Computer science","score_opus":0.20754057116717498,"score_gpt":0.3577449677550974,"score_spread":0.15020439658792242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393733384","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011400708,0.00032305697,0.0023599442,0.0004361369,0.00008885143,0.000058095742,0.98247415,0.0012486288,0.0016103846],"genre_scores_gemma":[0.015192843,0.00011739542,0.0027299095,0.00018243263,0.000010327621,0.00013095431,0.9809926,0.00006828735,0.0005752267],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994703,0.00016755446,0.000049236165,0.00015343129,0.00011400567,0.000045493558],"domain_scores_gemma":[0.99817884,0.00090232166,0.00008961826,0.00043121996,0.00020508816,0.00019292762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009332133,0.0009165433,0.00075212226,0.0011232381,0.0003814806,0.00095755165,0.0022061644,0.0015580263,0.013112648],"category_scores_gemma":[0.0058009923,0.00039902228,0.00089084235,0.002192082,0.00031634912,0.00043642442,0.00077079795,0.0011890382,0.0064236578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017537079,0.00029483574,0.0175549,0.001198927,0.00043550096,0.00058903167,0.00007354716,0.024002725,0.0015055774,0.0035919,0.9331451,0.015854146],"study_design_scores_gemma":[0.0056703915,0.0006179737,0.050068993,0.0006108195,0.0006135253,0.0038820202,0.00036003607,0.114307575,0.008599479,0.026636666,0.7884672,0.0001653356],"about_ca_topic_score_codex":0.010529692,"about_ca_topic_score_gemma":0.015609849,"teacher_disagreement_score":0.013112648,"about_ca_system_score_codex":0.0008410508,"about_ca_system_score_gemma":0.0016748745,"threshold_uncertainty_score":0.043866158},"labels":[],"label_agreement":null},{"id":"W4394055437","doi":"10.5281/zenodo.4733385","title":"DJIN model of aging synthetic dataset","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Computer science","score_opus":0.27945551284278286,"score_gpt":0.356692318225389,"score_spread":0.07723680538260613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394055437","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012817115,0.0007326298,0.003273077,0.00091974344,0.00031615366,0.00020742053,0.9766248,0.0018757953,0.0032333254],"genre_scores_gemma":[0.017420584,0.00024974698,0.004515699,0.00043588938,0.000056984627,0.000501863,0.97300905,0.0001641601,0.0036460443],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999451,0.00019890105,0.000044154964,0.00014893123,0.00007915292,0.000077859724],"domain_scores_gemma":[0.99895537,0.00035045377,0.000072723305,0.0002292391,0.00027672725,0.00011535817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016201787,0.0013791523,0.00075394387,0.0012087104,0.00053314574,0.00088272657,0.0027817595,0.0017379492,0.018096747],"category_scores_gemma":[0.0054303957,0.0004250144,0.0014153888,0.0013865947,0.0003637827,0.0005659873,0.0009606174,0.0018281509,0.017030176],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002964494,0.00024504735,0.0066342587,0.00044836433,0.00013479426,0.00014420327,0.000046030877,0.016137123,0.00022016952,0.0017711503,0.9628399,0.011082488],"study_design_scores_gemma":[0.0013668694,0.00036820327,0.028730638,0.00049930264,0.00021577117,0.00073127734,0.00021714495,0.10545417,0.0015249635,0.012173878,0.84858197,0.00013576484],"about_ca_topic_score_codex":0.021689218,"about_ca_topic_score_gemma":0.041859828,"teacher_disagreement_score":0.021689218,"about_ca_system_score_codex":0.0013783862,"about_ca_system_score_gemma":0.0014955172,"threshold_uncertainty_score":0.060539663},"labels":[],"label_agreement":null},{"id":"W4394316133","doi":"10.6084/m9.figshare.4010619","title":"PHEME dataset of rumours and non-rumours","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":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":0.2710647247861676,"score_gpt":0.40385188843202674,"score_spread":0.13278716364585913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394316133","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00758498,0.0010071304,0.0011286864,0.0007097335,0.00047871118,0.00013900765,0.98221004,0.002400922,0.0043407464],"genre_scores_gemma":[0.008585232,0.00022809536,0.0018156338,0.00016023846,0.00009183757,0.0001569316,0.986779,0.00008569588,0.0020972618],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984273,0.00045915513,0.00016151945,0.00037503216,0.00039513875,0.00018184796],"domain_scores_gemma":[0.99766564,0.0008445266,0.00019123589,0.00058210496,0.0005550489,0.00016142963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010272313,0.0022214444,0.0012411559,0.0029111484,0.0014460029,0.0020622301,0.0021952884,0.0026859331,0.017423192],"category_scores_gemma":[0.006525509,0.00042098074,0.0011790965,0.0030564854,0.00047419185,0.0021992705,0.001991629,0.0019275721,0.024982782],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036299863,0.00019940485,0.0047448706,0.0011143924,0.000119100965,0.00022163989,0.00015721956,0.0014441431,0.0006738838,0.001041461,0.97371805,0.016202921],"study_design_scores_gemma":[0.00035722853,0.00022037573,0.020441214,0.0004843453,0.00010769877,0.0006867902,0.00088800833,0.01424537,0.002369051,0.002836907,0.9572107,0.00015220717],"about_ca_topic_score_codex":0.014888015,"about_ca_topic_score_gemma":0.029559938,"teacher_disagreement_score":0.017423192,"about_ca_system_score_codex":0.0009784806,"about_ca_system_score_gemma":0.0010877339,"threshold_uncertainty_score":0.05828637},"labels":[],"label_agreement":null},{"id":"W4394712518","doi":"10.1109/icdt61202.2024.10489227","title":"Comparing the Supersonic Cloud Computing Model to Enhance the Networking and Security in Traditional Data Centers","year":2024,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Thomson Reuters (Canada)","funders":"","keywords":"Cloud computing; Computer science; Cloud computing security; Computer security; Operating system","score_opus":0.44394505898415615,"score_gpt":0.4134127683132938,"score_spread":0.030532290670862328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394712518","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8494223,0.00087096606,0.06292112,0.0015325386,0.00024383707,0.00046337812,0.00036465246,0.00040549968,0.08377577],"genre_scores_gemma":[0.9818702,0.00037066307,0.014874698,0.00019778483,0.000020314737,0.00007592008,0.00017579172,0.000021517868,0.0023931041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896836,0.00032461283,0.00003373616,0.00011521267,0.00035638319,0.0002017417],"domain_scores_gemma":[0.998635,0.00024792372,0.00009230068,0.00025899673,0.0005695264,0.0001962426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012167583,0.0003599284,0.00019800855,0.0005296337,0.0005684058,0.0013981255,0.0008240356,0.000332262,0.0028976074],"category_scores_gemma":[0.001588964,0.00012326757,0.0004072098,0.0010146875,0.00045063376,0.0022103975,0.0007954384,0.0005730827,0.00034462067],"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":[0.003101355,0.0019714257,0.056117177,0.0008177788,0.0003374652,0.0003998838,0.0009298077,0.426793,0.030427584,0.16250029,0.01971378,0.2968905],"study_design_scores_gemma":[0.00027535215,0.0028623836,0.037777673,0.000117162235,0.00022803908,0.00019486847,0.0015542116,0.87743366,0.017395066,0.02069542,0.04135298,0.00011318701],"about_ca_topic_score_codex":0.021390276,"about_ca_topic_score_gemma":0.035267316,"teacher_disagreement_score":0.021390276,"about_ca_system_score_codex":0.0035374877,"about_ca_system_score_gemma":0.0029883075,"threshold_uncertainty_score":0.04253155},"labels":[],"label_agreement":null},{"id":"W4394912101","doi":"10.1136/bmjsem-2024-001994","title":"Big data. Big potential. Big problems?","year":2024,"lang":"en","type":"editorial","venue":"BMJ Open Sport & Exercise Medicine","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Calgary","funders":"","keywords":"Big data; Data science; Computer science; Data mining","score_opus":0.33112917276274684,"score_gpt":0.4532192369718727,"score_spread":0.12209006420912588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394912101","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013509963,0.012129064,0.0001808953,0.07181191,0.91431004,0.000069441834,0.00022595315,0.00010758552,0.0011516056],"genre_scores_gemma":[0.00022589,0.005970257,0.00018021274,0.0380353,0.9514459,0.0001001032,0.000075417294,0.00007018692,0.0038967126],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96688974,0.01178425,0.0047130547,0.0018866247,0.013620676,0.0011056862],"domain_scores_gemma":[0.7647561,0.15438133,0.012120813,0.006971444,0.046795275,0.01497508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.040013313,0.0069758086,0.01167112,0.012066386,0.00529927,0.023591833,0.0063900766,0.03357372,0.048509292],"category_scores_gemma":[0.18257557,0.0033090978,0.006773048,0.0069174552,0.0067252214,0.011245446,0.0043385327,0.03242434,0.030168045],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046834022,0.000008034341,0.000016414035,0.0007456539,0.000061783125,0.000027817632,0.00001130747,0.0000140437105,0.00001275636,0.00033484053,0.9951245,0.0035961021],"study_design_scores_gemma":[0.00080036605,0.000050335537,0.00031583352,0.0051037422,0.00039480266,0.00015412517,0.00008443575,0.00039128933,0.000096051335,0.009286011,0.9832587,0.00006427026],"about_ca_topic_score_codex":0.003947611,"about_ca_topic_score_gemma":0.00926627,"teacher_disagreement_score":0.048509292,"about_ca_system_score_codex":0.0071741985,"about_ca_system_score_gemma":0.012187759,"threshold_uncertainty_score":0.21161318},"labels":[],"label_agreement":null},{"id":"W4399471733","doi":"10.23977/jeis.2024.090209","title":"Privacy Protection in Information and Communication Technology Applications Based on Big Data","year":2024,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Computer science; Internet privacy; Computer security; Data science; Data mining","score_opus":0.14111671371730938,"score_gpt":0.37104989226007556,"score_spread":0.22993317854276618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399471733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1068425,0.003825752,0.86410815,0.0052176137,0.00031912245,0.00034219705,0.00046847164,0.00058368046,0.018292451],"genre_scores_gemma":[0.93241215,0.0020050495,0.062825404,0.0005108894,0.00019246557,0.0002043731,0.00027352682,0.000043364365,0.0015327106],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9856135,0.0069975522,0.00088836934,0.0013347906,0.004316071,0.00084968784],"domain_scores_gemma":[0.9840432,0.007551391,0.0016823708,0.0046196445,0.0018117531,0.00029159314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009259064,0.0006432548,0.0007638981,0.0016170102,0.0020141734,0.004314393,0.0013858837,0.0015997187,0.001082179],"category_scores_gemma":[0.021452757,0.0004142936,0.0010821342,0.0025267198,0.0026189638,0.007155918,0.0040489463,0.0022478823,0.00032914046],"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":[0.00094802445,0.0002965597,0.026483767,0.0013667439,0.0003590839,0.0012180627,0.0031262815,0.06641559,0.02876519,0.52805036,0.008875733,0.33409455],"study_design_scores_gemma":[0.00010408914,0.00050909555,0.009569263,0.00042206465,0.00021977998,0.0018438948,0.0019572454,0.44062775,0.061184082,0.4367322,0.046665523,0.00016501705],"about_ca_topic_score_codex":0.00088514853,"about_ca_topic_score_gemma":0.00053592224,"teacher_disagreement_score":0.009259064,"about_ca_system_score_codex":0.0014386778,"about_ca_system_score_gemma":0.0025917077,"threshold_uncertainty_score":0.048967183},"labels":[],"label_agreement":null},{"id":"W4400485157","doi":"10.61091/jcmcc120-22","title":"Construction of Financial Bill Recognition Model Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Social Science Planning Project of Shandong Province","keywords":"Artificial intelligence; Deep learning; Computer science; Finance; Business","score_opus":0.07861500679816442,"score_gpt":0.3208441827489801,"score_spread":0.24222917595081567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400485157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09071619,0.00084567466,0.89201885,0.0011185333,0.00026489823,0.00014050376,0.0011990009,0.0065434673,0.0071528526],"genre_scores_gemma":[0.84234256,0.00080488174,0.13318925,0.00086290453,0.00010513035,0.00037433492,0.0047780126,0.00020116335,0.017341753],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997695,0.000020080346,0.0000150878395,0.00008328766,0.000062997555,0.000049004564],"domain_scores_gemma":[0.9998528,0.00003146158,0.000017074348,0.00001739025,0.00006669085,0.000014607625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003029835,0.0008155392,0.00078358623,0.0009080198,0.00032491406,0.00078091345,0.0013847571,0.0008188187,0.0031332374],"category_scores_gemma":[0.0006717428,0.00040644675,0.0010040164,0.00066092913,0.00030867042,0.0013704618,0.00070103677,0.001285194,0.0014003089],"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":[0.00028366936,0.00031366383,0.006197749,0.0001534212,0.0001270006,0.00031698126,0.000068136105,0.47524703,0.014295722,0.006668176,0.015222624,0.48110586],"study_design_scores_gemma":[0.000004624173,0.000013388543,0.00027313863,0.0000042356123,0.000010499551,0.000019343426,0.0000052628166,0.9968665,0.0013997761,0.0009297152,0.00046818797,0.000005290559],"about_ca_topic_score_codex":0.01578222,"about_ca_topic_score_gemma":0.012566942,"teacher_disagreement_score":0.01578222,"about_ca_system_score_codex":0.0009501074,"about_ca_system_score_gemma":0.0012089176,"threshold_uncertainty_score":0.031380713},"labels":[],"label_agreement":null},{"id":"W4401467332","doi":"10.62051/3a6dex21","title":"Research on User Profile and User Behavior of Integrating Big Data Platforms","year":2024,"lang":"en","type":"article","venue":"Transactions on Economics Business and Management Research","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Computer science; Behavioral analysis; Electric power; Process (computing); Human–computer interaction; Power (physics); Portrait; Behavioral pattern; Database; Data mining; Software engineering; Operating system","score_opus":0.6172003303845578,"score_gpt":0.4800867582924546,"score_spread":0.13711357209210318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401467332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8752774,0.0006961797,0.10571401,0.0009496194,0.00006287705,0.0002519122,0.000637509,0.00045420197,0.015956203],"genre_scores_gemma":[0.981006,0.00026924958,0.01660972,0.00008203711,0.000021384298,0.0000910642,0.00029783865,0.00003301519,0.0015895964],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99734735,0.001156982,0.00015284045,0.00042277706,0.00074461184,0.00017533994],"domain_scores_gemma":[0.9886001,0.005677206,0.0012115116,0.0012317678,0.0027619102,0.00051747577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025812811,0.00046992136,0.00035736169,0.0022672822,0.0009566429,0.002180605,0.0005240126,0.00048251433,0.0014184809],"category_scores_gemma":[0.015382757,0.0002752668,0.0005622148,0.0022082406,0.00049683446,0.0050523775,0.0009288653,0.000747651,0.0005041881],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005563283,0.00040676387,0.591237,0.00061563833,0.00020298616,0.0003027486,0.017186182,0.0040836195,0.011244513,0.0169656,0.0031080837,0.3540905],"study_design_scores_gemma":[0.000022853967,0.0006959044,0.74595326,0.0003502364,0.0002916422,0.0012326005,0.038268927,0.1421056,0.018220566,0.02055572,0.032035626,0.00026705788],"about_ca_topic_score_codex":0.0035422796,"about_ca_topic_score_gemma":0.004370492,"teacher_disagreement_score":0.0035422796,"about_ca_system_score_codex":0.00086591544,"about_ca_system_score_gemma":0.00064951344,"threshold_uncertainty_score":0.013651311},"labels":[],"label_agreement":null},{"id":"W4402095270","doi":"10.23977/jeis.2024.090306","title":"Research on the path of building smart society from the perspective of big data","year":2024,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Perspective (graphical); Big data; Path (computing); Data science; Sociology; Computer science; Architectural engineering; Engineering ethics; Engineering; Artificial intelligence; Data mining","score_opus":0.3590658384373373,"score_gpt":0.46441015289265336,"score_spread":0.10534431445531606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402095270","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105086274,0.061138142,0.23697063,0.23143238,0.0031170116,0.00025644814,0.0007628676,0.00032949154,0.36090675],"genre_scores_gemma":[0.8318474,0.06744699,0.06749543,0.0062877536,0.00082138996,0.00022581953,0.00033653076,0.00008464503,0.025454132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99923575,0.0002515482,0.00002820323,0.00015700165,0.00020463445,0.00012286517],"domain_scores_gemma":[0.99786466,0.0008871336,0.00019162185,0.00017533856,0.00054752635,0.00033368173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015412896,0.00041993102,0.000300549,0.0016682921,0.001853436,0.0053231157,0.0007293351,0.0017766764,0.005612442],"category_scores_gemma":[0.0030931968,0.00021556659,0.00043131463,0.002754912,0.0036562108,0.012692407,0.0020330525,0.002639036,0.00093304255],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017559873,0.00004106394,0.0025145037,0.00024205499,0.000016463535,0.00011137996,0.0010913842,0.0011512494,0.0003200618,0.95073116,0.0068623456,0.0369008],"study_design_scores_gemma":[0.000008873212,0.00005779862,0.0026312561,0.00042473932,0.00002560663,0.00020213817,0.0066220113,0.009626942,0.0009353908,0.841538,0.13788545,0.000041872197],"about_ca_topic_score_codex":0.0030240375,"about_ca_topic_score_gemma":0.0030392983,"teacher_disagreement_score":0.005612442,"about_ca_system_score_codex":0.0025109237,"about_ca_system_score_gemma":0.0037226772,"threshold_uncertainty_score":0.018775463},"labels":[],"label_agreement":null},{"id":"W4402592572","doi":"10.1109/tsc.2024.3463431","title":"Towards Auditable and Privacy-Preserving Online Medical Diagnosis Service Over Cloud","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 New Brunswick","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Cloud computing; Computer security; Internet privacy; Information privacy","score_opus":0.10680652969072472,"score_gpt":0.3745486295845845,"score_spread":0.2677420998938598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402592572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06028184,0.00088091835,0.9283919,0.002247289,0.00018067729,0.0005810683,0.0002609429,0.0018279157,0.005347513],"genre_scores_gemma":[0.8922915,0.00043316666,0.10390351,0.00042833615,0.00011695361,0.00016797801,0.00021334014,0.000039820654,0.0024053366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952042,0.0015121568,0.0003596239,0.0006687435,0.0016168822,0.000638436],"domain_scores_gemma":[0.99317026,0.0017037397,0.0010384503,0.0023726043,0.0011211772,0.0005937504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034621186,0.0004922366,0.0010116107,0.0009674403,0.0018186105,0.0029078382,0.0023884191,0.0018177062,0.002463232],"category_scores_gemma":[0.007516515,0.00034741402,0.0007699651,0.0017174123,0.0012215083,0.004725585,0.00614718,0.0023051624,0.00075469795],"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":[0.0042111063,0.0012308153,0.012589559,0.00081572443,0.00025423677,0.0033574286,0.0019483208,0.10605877,0.06748639,0.33295015,0.017993186,0.45110434],"study_design_scores_gemma":[0.00021610921,0.0002529145,0.001202583,0.00007614808,0.00009747682,0.0016345506,0.00033686607,0.8680575,0.025161132,0.089922905,0.0129456725,0.00009610058],"about_ca_topic_score_codex":0.001692815,"about_ca_topic_score_gemma":0.0011453955,"teacher_disagreement_score":0.0034621186,"about_ca_system_score_codex":0.0012199123,"about_ca_system_score_gemma":0.0046282876,"threshold_uncertainty_score":0.018309653},"labels":[],"label_agreement":null},{"id":"W4402594205","doi":"10.1109/sds60720.2024.00011","title":"Mining and Forecasting Energy Consumption Based on Weather Data","year":2024,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Northern British Columbia; University of Manitoba","funders":"University of Manitoba","keywords":"Energy consumption; Weather forecasting; Computer science; Consumption (sociology); Meteorology; Engineering; Geography","score_opus":0.5665341613022853,"score_gpt":0.4204674190196236,"score_spread":0.1460667422826617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402594205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62454176,0.00089915557,0.34310758,0.0016201811,0.00025411986,0.00018238687,0.019040536,0.0034075622,0.006946778],"genre_scores_gemma":[0.8999288,0.0005555073,0.08831014,0.000078149504,0.0000692939,0.00006987149,0.010089479,0.00008089689,0.00081790163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997366,0.00003786629,0.000038329785,0.000084286505,0.00007814952,0.000024665293],"domain_scores_gemma":[0.9992481,0.00025583646,0.00012920529,0.00013756979,0.00019871886,0.000030517509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038145043,0.0007610455,0.0005312838,0.0016027038,0.00022834573,0.0006051704,0.0005184292,0.00051896565,0.00096338947],"category_scores_gemma":[0.0022131768,0.00027895047,0.00052980584,0.0022996692,0.00015356562,0.001362239,0.00031535962,0.0006216517,0.00048495032],"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":[0.000401377,0.0003381239,0.18679169,0.00036370126,0.00033686846,0.00041525543,0.00015368864,0.5257857,0.009357142,0.006887943,0.011153409,0.25801513],"study_design_scores_gemma":[0.000010608966,0.000025134912,0.014472719,0.000019713103,0.000024753042,0.00003822961,0.000085410495,0.973886,0.0031201309,0.0058598556,0.002445082,0.000012297053],"about_ca_topic_score_codex":0.0075622248,"about_ca_topic_score_gemma":0.014634643,"teacher_disagreement_score":0.0075622248,"about_ca_system_score_codex":0.00035524333,"about_ca_system_score_gemma":0.000412868,"threshold_uncertainty_score":0.015036404},"labels":[],"label_agreement":null},{"id":"W4403116494","doi":"10.57745/p0khag","title":"Insurance dataset","year":2024,"lang":"en","type":"other","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Business","score_opus":0.07562978095177517,"score_gpt":0.3188520873582011,"score_spread":0.24322230640642592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403116494","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026485082,0.00027937646,0.0003375251,0.00027245635,0.00005300488,0.00005415649,0.99350435,0.00036509716,0.002485553],"genre_scores_gemma":[0.002154205,0.00009402367,0.0007437853,0.00010978694,0.000012251759,0.00008502622,0.9959401,0.000026939106,0.0008338588],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998611,0.00026102804,0.00020470827,0.00030770554,0.000456238,0.00015918844],"domain_scores_gemma":[0.99745435,0.0008727531,0.00026822148,0.00051217945,0.00065099675,0.0002414204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013396855,0.001110469,0.00077806524,0.0034928487,0.00084777334,0.0018063491,0.0019308452,0.002153543,0.025983907],"category_scores_gemma":[0.0074726865,0.00031014593,0.0011907348,0.004973713,0.00031392757,0.0010657385,0.0014341105,0.0016156958,0.021147657],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020755208,0.00016374703,0.008317746,0.0006596853,0.000113741735,0.00012611295,0.0000550103,0.001396109,0.00034784395,0.0021773966,0.9746976,0.011737489],"study_design_scores_gemma":[0.00035209843,0.00007016869,0.024632439,0.00031877556,0.00008403332,0.00036088485,0.00021108675,0.0036503002,0.00074734085,0.0031053943,0.9664138,0.000053727494],"about_ca_topic_score_codex":0.020049812,"about_ca_topic_score_gemma":0.033609536,"teacher_disagreement_score":0.025983907,"about_ca_system_score_codex":0.0013255469,"about_ca_system_score_gemma":0.0023859593,"threshold_uncertainty_score":0.08692479},"labels":[],"label_agreement":null},{"id":"W4403487475","doi":"10.3233/faia240772","title":"A Federated Large Language Model for Long-Term Time Series Forecasting","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Concordia University","funders":"","keywords":"Term (time); Series (stratigraphy); Computer science; Time series; Econometrics; Mathematics; Machine learning; Geology; Physics","score_opus":0.203687782524059,"score_gpt":0.3686355930938267,"score_spread":0.1649478105697677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403487475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010079861,0.0005962176,0.98396,0.00048522346,0.00012348236,0.000028829874,0.00046492557,0.002960063,0.0013014475],"genre_scores_gemma":[0.45077747,0.0010509114,0.53136253,0.00075515494,0.00030207983,0.00031229993,0.0028899927,0.0006559946,0.011893567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946684,0.00018074803,0.000034664357,0.00015048166,0.000112323345,0.000054955966],"domain_scores_gemma":[0.9986041,0.00085262203,0.00007469759,0.00019754641,0.00021579205,0.00005532261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015185797,0.00100486,0.00115095,0.00052616105,0.00048680077,0.0014969697,0.0018139025,0.0012663044,0.0026803457],"category_scores_gemma":[0.0038813562,0.0004983933,0.0011698356,0.0011867479,0.00044376595,0.0021422072,0.0012248877,0.0025195924,0.0017631131],"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":[0.00014565577,0.00010945858,0.0009885916,0.00007701079,0.00008725018,0.000110384004,0.00007073872,0.82962257,0.0022362997,0.0124564,0.008858027,0.1452376],"study_design_scores_gemma":[0.0000031920295,0.0000068005115,0.00003565229,0.0000027652889,0.0000042603865,0.000008240886,0.0000036339366,0.9948143,0.00026193043,0.004367539,0.0004884373,0.0000033788808],"about_ca_topic_score_codex":0.007458137,"about_ca_topic_score_gemma":0.0109457085,"teacher_disagreement_score":0.007458137,"about_ca_system_score_codex":0.0010197127,"about_ca_system_score_gemma":0.0013427014,"threshold_uncertainty_score":0.014829457},"labels":[],"label_agreement":null},{"id":"W4403514561","doi":"10.5267/j.ac.2024.7.001","title":"An application of TOPSIS and BWM for portfolio allocation","year":2024,"lang":"en","type":"article","venue":"Accounting","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Portfolio; TOPSIS; Computer science; Portfolio allocation; Business; Operations research; Engineering; Finance","score_opus":0.12198145361857976,"score_gpt":0.4074572582407403,"score_spread":0.28547580462216054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403514561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007279101,0.0010905986,0.97570497,0.00040287495,0.00012309528,0.00060533424,0.00031213232,0.0004139232,0.014067955],"genre_scores_gemma":[0.09617566,0.0011874475,0.8990674,0.00006562052,0.000037301903,0.0006782773,0.0003170586,0.00005206698,0.0024192058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98943895,0.0047202334,0.00069181446,0.00063370477,0.0041989977,0.00031631434],"domain_scores_gemma":[0.997343,0.001456495,0.00025091326,0.00015472063,0.00073232665,0.0000625302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068850815,0.0018088787,0.0015853213,0.00952864,0.0014139102,0.0044046356,0.0013373145,0.000992738,0.005697473],"category_scores_gemma":[0.011633613,0.00064342556,0.0020306034,0.013935116,0.00087384635,0.002082957,0.002401251,0.0014651618,0.0009748427],"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":[0.00010760749,0.00015003617,0.002787712,0.0015004382,0.00071690977,0.0004265858,0.0011840817,0.10469935,0.0043280898,0.11323744,0.005228266,0.76563346],"study_design_scores_gemma":[0.00008158215,0.0005461963,0.0058704927,0.000939032,0.00044230765,0.0006044375,0.002617895,0.64789945,0.0052267774,0.27741003,0.05815392,0.0002079434],"about_ca_topic_score_codex":0.0056262915,"about_ca_topic_score_gemma":0.006228841,"teacher_disagreement_score":0.00952864,"about_ca_system_score_codex":0.0021794373,"about_ca_system_score_gemma":0.0035741006,"threshold_uncertainty_score":0.03641224},"labels":[],"label_agreement":null},{"id":"W4404988426","doi":"10.14778/3749646.3749657","title":"TabulaX: Leveraging Large Language Models for Multi-Class Table Transformations","year":2025,"lang":"en","type":"preprint","venue":"Proceedings of the VLDB Endowment","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Table (database); Class (philosophy); Computer science; Programming language; Natural language processing; Theoretical computer science; Artificial intelligence; Database","score_opus":0.20772441500396746,"score_gpt":0.38232798555009745,"score_spread":0.17460357054613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404988426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049084346,0.0003395845,0.92024267,0.00045548557,0.0001106509,0.00022456964,0.0039519486,0.06835503,0.0014115607],"genre_scores_gemma":[0.073786914,0.00045947958,0.8981178,0.0005523128,0.0000835646,0.00060599385,0.01520021,0.008956855,0.002236887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959084,0.0014606578,0.00047289397,0.0008760854,0.0010970971,0.0001849562],"domain_scores_gemma":[0.986997,0.0070384345,0.0007308892,0.00407854,0.0009604462,0.00019465586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051582847,0.0017073164,0.0009623235,0.0022298405,0.0009113707,0.0049305586,0.0030206293,0.0010471591,0.0072131604],"category_scores_gemma":[0.02480926,0.0010820435,0.0032004106,0.0024966386,0.0013493468,0.0071246964,0.004568659,0.0033073544,0.004424698],"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":[0.0007702241,0.0005031665,0.008139218,0.0015075776,0.0004142469,0.00057014445,0.0020181458,0.11126847,0.014927553,0.12804537,0.09813705,0.63369876],"study_design_scores_gemma":[0.00013051665,0.00010790357,0.00056353817,0.0001908525,0.00007644325,0.00022340684,0.0002718546,0.76642114,0.018205106,0.13084206,0.082875684,0.00009149112],"about_ca_topic_score_codex":0.0060846354,"about_ca_topic_score_gemma":0.010521096,"teacher_disagreement_score":0.0072131604,"about_ca_system_score_codex":0.001471956,"about_ca_system_score_gemma":0.0035493725,"threshold_uncertainty_score":0.027279973},"labels":[],"label_agreement":null},{"id":"W4405025969","doi":"10.21810/jicw.v7i2.6735","title":"In-between Spaces: Unconventional Yet Essential Considerations for Defence and Security","year":2024,"lang":"en","type":"article","venue":"The Journal of Intelligence Conflict and Warfare","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Defence Research and Development Canada","funders":"","keywords":"Liminality; Context (archaeology); Generative grammar; Computer science; Order (exchange); Epistemology; Computer security; Point (geometry); Cognitive science; Environmental ethics; Sociology; Psychology; Geography; Business; Mathematics; Artificial intelligence; Philosophy; Archaeology","score_opus":0.2289600932555739,"score_gpt":0.417539378934562,"score_spread":0.1885792856789881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405025969","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015335972,0.03202157,0.052246727,0.70277625,0.010662843,0.000056569363,0.00010480375,0.00016092797,0.18663435],"genre_scores_gemma":[0.8263432,0.015388415,0.026513506,0.066092946,0.0045055333,0.00017400936,0.000095636766,0.00035162727,0.06053509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.990767,0.0055659944,0.00035000336,0.00067326374,0.0015695984,0.0010742054],"domain_scores_gemma":[0.9901244,0.004981766,0.00061131484,0.0010753947,0.001522149,0.0016850117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010441203,0.0008401821,0.0007640499,0.0010710021,0.00933355,0.02103118,0.002096628,0.007137486,0.008826322],"category_scores_gemma":[0.013928021,0.0004847192,0.00060422026,0.0010455113,0.045111645,0.03338384,0.009701167,0.014071392,0.002606079],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004130331,0.000014310065,0.00030694203,0.00008619365,0.000009393456,0.00027137107,0.0095998915,0.00023549309,0.00018898558,0.93905246,0.032248612,0.017945122],"study_design_scores_gemma":[0.0000088269335,0.000028415268,0.00018865096,0.00023300872,0.000005049524,0.0004213646,0.017049428,0.00029234358,0.00017996547,0.6630558,0.31851026,0.000026895388],"about_ca_topic_score_codex":0.0022126872,"about_ca_topic_score_gemma":0.0037283925,"teacher_disagreement_score":0.02103118,"about_ca_system_score_codex":0.005497478,"about_ca_system_score_gemma":0.005378603,"threshold_uncertainty_score":0.055218995},"labels":[],"label_agreement":null},{"id":"W4405113927","doi":"10.1016/j.procs.2024.11.094","title":"Application of Generative Artificial Intelligence in Minimizing Cyber Attacks on Vehicular Networks","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Acadia University","funders":"","keywords":"Computer science; Generative grammar; Artificial intelligence; Computer security; Machine learning","score_opus":0.1429528841429706,"score_gpt":0.38131346463838195,"score_spread":0.23836058049541134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405113927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031926546,0.0003320567,0.9546659,0.00045608034,0.00003011648,0.00007139561,0.000018439774,0.00032163542,0.012177862],"genre_scores_gemma":[0.8515427,0.0003971074,0.14607695,0.00013639689,0.000021134802,0.00007229457,0.000051669256,0.00006576509,0.0016359255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852765,0.0006409347,0.00006760577,0.00020324804,0.00043199063,0.00012855775],"domain_scores_gemma":[0.9969098,0.0019022343,0.00031258614,0.0004884154,0.0002942011,0.00009277131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021768846,0.00052442664,0.00041925287,0.0012555867,0.0007646879,0.0018856908,0.0011244146,0.00079846964,0.0012431265],"category_scores_gemma":[0.006459171,0.00033850956,0.00075920054,0.0006558554,0.0028640127,0.0017402463,0.0023550326,0.0009707771,0.00020288228],"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":[0.000034970923,0.000042033364,0.0025188266,0.0001136611,0.00005506276,0.00015895905,0.00047319336,0.64089566,0.0035842701,0.2664811,0.0005560924,0.08508622],"study_design_scores_gemma":[0.0000075777307,0.000057553,0.00044304668,0.00004795928,0.00002941769,0.000115034614,0.00013295357,0.80322826,0.0035268285,0.18741156,0.004979582,0.000020233027],"about_ca_topic_score_codex":0.0014162875,"about_ca_topic_score_gemma":0.001644192,"teacher_disagreement_score":0.0021768846,"about_ca_system_score_codex":0.0010803281,"about_ca_system_score_gemma":0.0011521402,"threshold_uncertainty_score":0.0115125775},"labels":[],"label_agreement":null},{"id":"W4405363817","doi":"10.1016/j.enbuild.2024.115177","title":"Explaining deep learning-based anomaly detection in energy consumption data by focusing on contextually relevant data","year":2024,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Anomaly detection; Anomaly (physics); Consumption (sociology); Energy consumption; Deep learning; Energy (signal processing); Computer science; Artificial intelligence; Data science; Machine learning; Engineering; Physics; Sociology; Statistics; Mathematics; Social science; Electrical engineering","score_opus":0.16740959644073536,"score_gpt":0.3594454531349252,"score_spread":0.19203585669418985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405363817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11281616,0.00043195306,0.88355106,0.00066871644,0.000036901565,0.00007914512,0.0005995999,0.0010884596,0.00072794035],"genre_scores_gemma":[0.8693611,0.00024171935,0.12798652,0.000116780415,0.000063636326,0.00007417793,0.0015977024,0.000059801096,0.00049847196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881953,0.0003703187,0.000103079255,0.00035251206,0.00026028452,0.00009426323],"domain_scores_gemma":[0.99400073,0.0035579982,0.0009308603,0.0008579643,0.0005478867,0.000104526225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020951969,0.0009196645,0.00071549596,0.002429336,0.0004303751,0.0012235373,0.0010950842,0.0007405586,0.0007111117],"category_scores_gemma":[0.010056522,0.0002676244,0.00095938257,0.0017826257,0.0006610161,0.0021497018,0.0014757526,0.0016531763,0.00012866866],"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":[0.000339279,0.00035487028,0.08975236,0.0003945367,0.00040202963,0.00038903762,0.00069176656,0.49324813,0.007085537,0.026718907,0.003466365,0.37715724],"study_design_scores_gemma":[0.000007409747,0.000033421875,0.0045673014,0.000023848892,0.00003019173,0.00005532741,0.00008910409,0.9701019,0.0022186597,0.021827608,0.0010310545,0.000014149364],"about_ca_topic_score_codex":0.0041137664,"about_ca_topic_score_gemma":0.0059194784,"teacher_disagreement_score":0.0041137664,"about_ca_system_score_codex":0.0010520888,"about_ca_system_score_gemma":0.0011886576,"threshold_uncertainty_score":0.011080563},"labels":[],"label_agreement":null},{"id":"W4406215577","doi":"10.1007/978-3-031-78586-3_14","title":"Post-pandemic Science &amp; Technology Education: Ongoing Challenges to Societies","year":2024,"lang":"en","type":"book-chapter","venue":"Contemporary trends and issues in science education","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pandemic; Political science; Coronavirus disease 2019 (COVID-19); Engineering ethics; Engineering; Medicine","score_opus":0.22849853467110673,"score_gpt":0.43663186259473946,"score_spread":0.20813332792363273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406215577","genre_codex":"commentary","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061629866,0.03168736,0.0026694378,0.6041751,0.0129065635,0.00003765839,0.00015317723,0.00014022071,0.34206745],"genre_scores_gemma":[0.22184816,0.121087916,0.0077941404,0.17026097,0.013176712,0.00018280972,0.00048221328,0.0002510589,0.46491596],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99863106,0.00045204262,0.00003970011,0.00008871154,0.00036765527,0.00042086534],"domain_scores_gemma":[0.99636877,0.0010596632,0.00018313392,0.00013734422,0.00082715653,0.0014238829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038307945,0.00042342336,0.00039697555,0.0007245944,0.0044243415,0.011604125,0.0011544192,0.004687394,0.02579137],"category_scores_gemma":[0.0048660594,0.0001842506,0.00027656314,0.001182763,0.006081046,0.011289192,0.0058299643,0.0066214874,0.006850201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014925318,0.00012847087,0.000662676,0.00026695084,0.000005652621,0.00015077087,0.003732656,0.000354711,0.00018439794,0.39695415,0.417076,0.18046859],"study_design_scores_gemma":[0.0000034507723,0.000021350219,0.0007232916,0.0003897949,0.0000018556867,0.000069748385,0.0056474726,0.00012124446,0.00008962423,0.084643915,0.9082793,0.000008930832],"about_ca_topic_score_codex":0.0062005194,"about_ca_topic_score_gemma":0.012461519,"teacher_disagreement_score":0.02579137,"about_ca_system_score_codex":0.005003063,"about_ca_system_score_gemma":0.021468263,"threshold_uncertainty_score":0.08628076},"labels":[],"label_agreement":null},{"id":"W4406218525","doi":"10.18280/ts.410603","title":"Real-Time Monitoring and Image Recognition System for Abnormal Activities in Financial Markets Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Artificial intelligence; Computer science; Image (mathematics); Business; Computer vision; Finance","score_opus":0.07261815566400061,"score_gpt":0.31790110216115885,"score_spread":0.24528294649715826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406218525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17881672,0.0010291608,0.8040083,0.00067309727,0.00025155378,0.00017259372,0.00042998654,0.0078082616,0.0068103056],"genre_scores_gemma":[0.9120495,0.0004887534,0.0815028,0.00031650535,0.00006952899,0.00012735481,0.00049522676,0.00007843738,0.0048719137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997209,0.000021483445,0.00002360949,0.00007548979,0.00009704023,0.00006147657],"domain_scores_gemma":[0.9997875,0.000036538957,0.000042069238,0.000021261258,0.00008538164,0.000027329275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003745904,0.00070143334,0.00067246787,0.0008557066,0.00024439004,0.00061289186,0.0009151812,0.00067234645,0.0017522592],"category_scores_gemma":[0.0007789273,0.0002967488,0.00061099866,0.00042345686,0.0002465166,0.0011159962,0.0006890443,0.0008496051,0.0005318756],"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":[0.0006849713,0.00055352796,0.012352739,0.00024056704,0.00013711177,0.0008261079,0.00021654186,0.10600194,0.10264387,0.0033758972,0.0139835365,0.7589832],"study_design_scores_gemma":[0.00001675235,0.00006121833,0.0020163134,0.000006562474,0.000019018164,0.0000903461,0.000013915776,0.98485166,0.011391849,0.00073861436,0.0007777565,0.000015945072],"about_ca_topic_score_codex":0.0037963344,"about_ca_topic_score_gemma":0.0032877456,"teacher_disagreement_score":0.0037963344,"about_ca_system_score_codex":0.0004683785,"about_ca_system_score_gemma":0.0005434971,"threshold_uncertainty_score":0.007548511},"labels":[],"label_agreement":null},{"id":"W4407597021","doi":"10.23987/sts.152099","title":"Calvert Jane (2024) A Place of Science and Technology Studies: Observation, Intervention and Collaboration","year":2025,"lang":"en","type":"article","venue":"Science & Technology Studies","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Intervention (counseling); Psychology; Sociology","score_opus":0.1558318137862283,"score_gpt":0.45256897464784035,"score_spread":0.29673716086161206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407597021","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007040666,0.042608887,0.0014541579,0.8931063,0.055492815,0.000049681068,0.00013160639,0.000049760347,0.0064027538],"genre_scores_gemma":[0.026822893,0.06631423,0.007520589,0.75022304,0.031907894,0.00058570376,0.00033560605,0.00028009314,0.116009876],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9911152,0.0039065476,0.0006777656,0.0006610654,0.0028843891,0.0007550198],"domain_scores_gemma":[0.9554834,0.019237569,0.0019429098,0.0011869563,0.012184543,0.009964656],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.012433108,0.00097020617,0.0007503676,0.0028221835,0.006049233,0.0075013954,0.0017731087,0.019717561,0.014644831],"category_scores_gemma":[0.061046574,0.00092753075,0.0009020443,0.002762803,0.0036724128,0.00915298,0.006699292,0.018273115,0.004785392],"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":[0.000042652555,0.000027024671,0.00084369915,0.00019251865,0.000013512961,0.00015199633,0.0019442146,0.000029049479,0.00009507715,0.0076836506,0.92931587,0.05966068],"study_design_scores_gemma":[0.000031312953,0.000023720757,0.0017388124,0.0011894639,0.000022960488,0.0002120035,0.0034814526,0.00006886075,0.00018057258,0.008373778,0.9846377,0.00003931849],"about_ca_topic_score_codex":0.041551482,"about_ca_topic_score_gemma":0.09472048,"teacher_disagreement_score":0.9939508,"about_ca_system_score_codex":0.0036495915,"about_ca_system_score_gemma":0.018588586,"threshold_uncertainty_score":0.08261925},"labels":[],"label_agreement":null},{"id":"W4408581329","doi":"10.1515/9783111488738","title":"Data, Governance and Narrative","year":2025,"lang":"en","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Narrative; Corporate governance; Political science; Business; Literature; Art; Finance","score_opus":0.2804800969015785,"score_gpt":0.4116678361941457,"score_spread":0.13118773929256722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408581329","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004551594,0.022186788,0.018605191,0.025440317,0.00138405,0.00011320455,0.00028613684,0.00012251824,0.92731035],"genre_scores_gemma":[0.2664529,0.041704495,0.026949495,0.012991334,0.0014638086,0.00063909433,0.0011703357,0.0004865303,0.64814204],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971527,0.0016079816,0.000091484835,0.00023295262,0.0007296197,0.0001852461],"domain_scores_gemma":[0.99665964,0.002570894,0.000112623326,0.0002764865,0.00023329441,0.00014700735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028070481,0.0005387253,0.00044333024,0.001696886,0.0046244212,0.012419702,0.0010389349,0.001562757,0.008810301],"category_scores_gemma":[0.00536456,0.00038728447,0.00024492305,0.0031157993,0.017778749,0.013770999,0.00462856,0.0027674458,0.002106561],"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":[0.0000031681313,0.000004165876,0.000058298956,0.000056967092,0.000001126495,0.00004387938,0.00874995,0.00008599379,0.000039185783,0.95682496,0.022506136,0.0116261095],"study_design_scores_gemma":[0.0000021145481,0.000004669503,0.000065100714,0.00022952941,0.0000011557191,0.00009560894,0.005920946,0.000113428585,0.00007381656,0.14340352,0.8500848,0.0000052407345],"about_ca_topic_score_codex":0.0051387823,"about_ca_topic_score_gemma":0.0060149943,"teacher_disagreement_score":0.012419702,"about_ca_system_score_codex":0.0053767823,"about_ca_system_score_gemma":0.0040479754,"threshold_uncertainty_score":0.03901148},"labels":[],"label_agreement":null},{"id":"W4408739103","doi":"10.23977/jaip.2025.080111","title":"The Practice and Application of Machine Learning in Data Analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Data science; Machine learning; Artificial intelligence","score_opus":0.22253898466643873,"score_gpt":0.4846734670592484,"score_spread":0.2621344823928097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408739103","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032882912,0.045292955,0.8285352,0.06980103,0.0020016925,0.000422324,0.00018755096,0.000444727,0.050026257],"genre_scores_gemma":[0.17325112,0.06467577,0.73573416,0.012701784,0.0065797362,0.0018845563,0.00030380985,0.00032532983,0.0045436844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.8819167,0.08567638,0.007817597,0.00654972,0.01702947,0.001010224],"domain_scores_gemma":[0.81543916,0.14984696,0.0051696184,0.019954486,0.008281654,0.0013081561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08527953,0.0018467824,0.0023592296,0.008949197,0.0030919516,0.014546157,0.0038704386,0.006733253,0.0021831444],"category_scores_gemma":[0.1170335,0.0013530763,0.0017857723,0.0092730895,0.03439234,0.012053308,0.008110757,0.015490079,0.0024297487],"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":[0.000037941205,0.0001033649,0.0017993082,0.0013933146,0.00014728626,0.00022495515,0.0028117946,0.005252623,0.000452598,0.87597764,0.008653931,0.10314525],"study_design_scores_gemma":[0.000027501259,0.000053030893,0.00057547697,0.0019574792,0.000026750784,0.00026626184,0.00067274977,0.0098629845,0.00067995326,0.9112793,0.07453278,0.000065709435],"about_ca_topic_score_codex":0.0027505269,"about_ca_topic_score_gemma":0.0013185549,"teacher_disagreement_score":0.08527953,"about_ca_system_score_codex":0.005444509,"about_ca_system_score_gemma":0.009824525,"threshold_uncertainty_score":0.45100665},"labels":[],"label_agreement":null},{"id":"W4408926663","doi":"10.61091/jcmcc125-17","title":"Machine learning and combinatorial analysis-based recognition of sports activity: An investigation using SVM and KNN classifiers","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Support vector machine; Artificial intelligence; Pattern recognition (psychology); Machine learning; Computer science","score_opus":0.09824008008944138,"score_gpt":0.35106266691198096,"score_spread":0.25282258682253955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408926663","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7086224,0.0018816363,0.28008288,0.00026199067,0.0001430894,0.0001748531,0.00031922932,0.00033403616,0.00817992],"genre_scores_gemma":[0.9563683,0.00040809298,0.04224904,0.000026064688,0.000029124885,0.000052476265,0.0001712857,0.0000118899125,0.00068374793],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864084,0.0003707344,0.00010378061,0.00021963191,0.0005866818,0.000078346835],"domain_scores_gemma":[0.9978649,0.0013174999,0.00022685286,0.000106133135,0.00042623762,0.000058420203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015402451,0.00037781202,0.00068410486,0.002175195,0.00023445257,0.00092988677,0.00046855715,0.00041280972,0.0007294126],"category_scores_gemma":[0.0051255594,0.00012711198,0.00043758022,0.0017487828,0.00043739507,0.0010928604,0.00027922,0.00028545235,0.0002981787],"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":[0.000770954,0.0006877276,0.1269436,0.0004888795,0.00026062486,0.00026532673,0.0003628425,0.06942234,0.021528088,0.0060278275,0.0011741011,0.77206767],"study_design_scores_gemma":[0.000017977804,0.00054723735,0.08197065,0.000064758955,0.000068678586,0.00039036255,0.00041194735,0.9030938,0.007916518,0.0038343763,0.0016407565,0.00004291509],"about_ca_topic_score_codex":0.0014256068,"about_ca_topic_score_gemma":0.0012321562,"teacher_disagreement_score":0.002175195,"about_ca_system_score_codex":0.00038875538,"about_ca_system_score_gemma":0.00040308016,"threshold_uncertainty_score":0.00814569},"labels":[],"label_agreement":null},{"id":"W4409337065","doi":"10.9734/jerr/2025/v27i41471","title":"Data-driven Insights Machine Learning Approaches for Netflix Content Analysis and Visualization","year":2025,"lang":"en","type":"article","venue":"Journal of Engineering Research and Reports","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Visualization; Content (measure theory); Data science; Information retrieval; Machine learning; Artificial intelligence; Mathematics","score_opus":0.5210663781089064,"score_gpt":0.4604436965248431,"score_spread":0.06062268158406331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409337065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010737505,0.0005111053,0.9473011,0.001430257,0.00012250674,0.00034684036,0.007797875,0.026474547,0.0052782944],"genre_scores_gemma":[0.13298082,0.00051461684,0.85170233,0.00019195858,0.00011194752,0.0009438428,0.008818284,0.0014470882,0.0032891128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888927,0.00039091308,0.00008995975,0.0002426126,0.00031696138,0.00007038312],"domain_scores_gemma":[0.9952206,0.002955341,0.00034830748,0.0006756297,0.00064913265,0.00015101727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003232784,0.0016594992,0.00060600985,0.0062926025,0.00069601904,0.0045146914,0.0015113386,0.0009932322,0.010984176],"category_scores_gemma":[0.012255305,0.0005678883,0.00118855,0.0037212898,0.00078920997,0.0029162448,0.0023706045,0.0023134318,0.0030068364],"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":[0.0005312639,0.00041270955,0.008310989,0.00100927,0.0003005809,0.0006690509,0.0019399552,0.10498735,0.010321917,0.11592325,0.06393492,0.6916587],"study_design_scores_gemma":[0.00004760681,0.00007534076,0.0036629164,0.00015236183,0.000034371093,0.00012164061,0.00060369534,0.83886945,0.0067863027,0.106712714,0.042873647,0.000059966376],"about_ca_topic_score_codex":0.0051802485,"about_ca_topic_score_gemma":0.006729766,"teacher_disagreement_score":0.010984176,"about_ca_system_score_codex":0.0018039305,"about_ca_system_score_gemma":0.0012911513,"threshold_uncertainty_score":0.036745727},"labels":[],"label_agreement":null},{"id":"W4409603809","doi":"10.61091/jcmcc127b-184","title":"Research on Real-Time Processing and Risk Management Methods of Enterprise Financial Big Data Based on Distributed Computing Framework","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Finance; Computer science; Data science; Business; Data mining","score_opus":0.15561907467103014,"score_gpt":0.4459331289446279,"score_spread":0.29031405427359774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409603809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01119863,0.0019446786,0.98356134,0.00072749145,0.00012821426,0.00009550564,0.00010275793,0.0004327756,0.0018085913],"genre_scores_gemma":[0.6381732,0.0046614464,0.3526023,0.00039107833,0.00054901565,0.00038056358,0.0005699432,0.00009759286,0.0025748992],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99761,0.00061557366,0.00022682108,0.0005817719,0.000787755,0.00017818781],"domain_scores_gemma":[0.9973334,0.0012900353,0.000289473,0.00027113972,0.00067630224,0.00013969635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002982456,0.0010629805,0.0012132626,0.0022242712,0.0007968791,0.0030903015,0.0018627349,0.0008411706,0.0012550331],"category_scores_gemma":[0.0066002063,0.00036438316,0.0013085777,0.0026268724,0.00077369675,0.0050633266,0.0013096737,0.0013361864,0.00025460872],"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":[0.0002541186,0.00031350355,0.013056701,0.00066241226,0.00041705207,0.0003589061,0.00057452335,0.3349339,0.0045846975,0.09426339,0.0070480877,0.5435328],"study_design_scores_gemma":[0.000015425976,0.000036822566,0.001289312,0.00003060169,0.00004663571,0.00007427573,0.00015038406,0.9591687,0.00095851696,0.03567653,0.00253003,0.000022840704],"about_ca_topic_score_codex":0.006340874,"about_ca_topic_score_gemma":0.0034706378,"teacher_disagreement_score":0.006340874,"about_ca_system_score_codex":0.0014100213,"about_ca_system_score_gemma":0.002144857,"threshold_uncertainty_score":0.01577288},"labels":[],"label_agreement":null},{"id":"W4409785123","doi":"10.61091/jcmcc127b-466","title":"A Study of Human-Computer Interaction in Evaluating Students’ Emotional Behavior and Educational Management under Big Data","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Computer science; Psychology; Big data; Applied psychology; Data science; Human–computer interaction; Data mining","score_opus":0.26248944465343654,"score_gpt":0.4691247796666692,"score_spread":0.20663533501323267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409785123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97999346,0.00044252817,0.016405594,0.00026311204,0.000030826275,0.00016023793,0.00007793482,0.00008036366,0.002545855],"genre_scores_gemma":[0.9926152,0.00017042196,0.0067825713,0.00003774605,0.000013394182,0.00007434946,0.000038814094,0.0000054362267,0.00026203154],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979115,0.0012286477,0.00014489064,0.00021687239,0.00039983774,0.00009823586],"domain_scores_gemma":[0.99070704,0.0070242593,0.00063229003,0.0003533126,0.00090655196,0.00037653645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025439346,0.00036255585,0.00029063705,0.001180729,0.0005078415,0.0010244338,0.0002943074,0.00044527865,0.0005408002],"category_scores_gemma":[0.0102993,0.00015355977,0.00040210577,0.00084224745,0.00043341971,0.001264552,0.00040668415,0.00036798627,0.00008615291],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011562811,0.001874275,0.59188807,0.0009627289,0.0005422177,0.00082740246,0.016653107,0.005852275,0.019908164,0.0027392148,0.00221601,0.3553803],"study_design_scores_gemma":[0.000052889307,0.004643448,0.87916857,0.00018526144,0.00029741827,0.0009828273,0.013677457,0.08502553,0.009843773,0.0017964435,0.004207328,0.00011906973],"about_ca_topic_score_codex":0.0015457409,"about_ca_topic_score_gemma":0.0016292018,"teacher_disagreement_score":0.0025439346,"about_ca_system_score_codex":0.00040242038,"about_ca_system_score_gemma":0.00036637363,"threshold_uncertainty_score":0.013453782},"labels":[],"label_agreement":null},{"id":"W4409787914","doi":"10.61091/jcmcc127a-472","title":"Research on intelligent financial statement analysis and anomaly identification technology based on machine learning","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Identification (biology); Financial statement; Financial statement analysis; Statement (logic); Anomaly (physics); Computer science; Artificial intelligence; Accounting; Business; Financial analysis; Epistemology; Philosophy; Physics","score_opus":0.1043036350811106,"score_gpt":0.4084930354796187,"score_spread":0.30418940039850806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06477838,0.005747971,0.9206237,0.0018246695,0.00016845061,0.000058500424,0.00010659405,0.0013307114,0.0053611333],"genre_scores_gemma":[0.78553784,0.0046632425,0.20620175,0.0004433324,0.00031513037,0.00006510753,0.00029343503,0.000057935344,0.0024221349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998718,0.00024598572,0.00009062781,0.00034712412,0.0004995099,0.00009872961],"domain_scores_gemma":[0.9978836,0.0009769358,0.0003531623,0.00022966327,0.00050063425,0.000055873414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014282152,0.0006879272,0.0007793679,0.002471198,0.00032985696,0.0019427607,0.0011077009,0.0008672911,0.0006547627],"category_scores_gemma":[0.004510165,0.00028420263,0.00080860127,0.0025985676,0.0009241165,0.003573973,0.0005745148,0.0011083373,0.00031668015],"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":[0.00010156355,0.00021108415,0.021656562,0.00034729348,0.00019315456,0.00021092738,0.000256,0.12227707,0.009958451,0.05255117,0.0031206433,0.7891161],"study_design_scores_gemma":[0.00000968748,0.000079403515,0.004933722,0.000052933752,0.00004472816,0.00023404765,0.00008294881,0.9447495,0.009264365,0.035082355,0.005427904,0.000038368624],"about_ca_topic_score_codex":0.0020027452,"about_ca_topic_score_gemma":0.0009676763,"teacher_disagreement_score":0.002471198,"about_ca_system_score_codex":0.0010873822,"about_ca_system_score_gemma":0.00092050695,"threshold_uncertainty_score":0.007889569},"labels":[],"label_agreement":null},{"id":"W4409794953","doi":"10.61091/jcmcc127b-460","title":"Artificial Intelligence-based Financial Big Data Information Security and Local Risk Prevention and Control","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Control (management); Big data; Computer science; Risk prevention; Security controls; Business; Finance; Computer security; Artificial intelligence; Risk analysis (engineering); Data mining","score_opus":0.09069703440964802,"score_gpt":0.34080531989619994,"score_spread":0.25010828548655195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409794953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057419505,0.0015775561,0.9225419,0.0029201063,0.00016432382,0.00027748654,0.00022820043,0.00072902505,0.014141911],"genre_scores_gemma":[0.94079006,0.0009069054,0.055316776,0.00037992158,0.00010609834,0.00014184561,0.0001886815,0.00002886967,0.0021408196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959655,0.0013662446,0.00027215626,0.0006721708,0.0013650947,0.0003588634],"domain_scores_gemma":[0.9957338,0.0014124943,0.0007821191,0.0009987927,0.00085633644,0.00021649202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038710216,0.00059359526,0.00085144676,0.0016320895,0.0010498276,0.0035392414,0.0015974606,0.0011385822,0.0013221382],"category_scores_gemma":[0.008124601,0.00030990446,0.0010427252,0.0012939995,0.0020445064,0.0049341954,0.0026963188,0.0015825394,0.00029444296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046689992,0.0004332188,0.012963582,0.0005387363,0.00034227347,0.00046851463,0.00076491217,0.33200848,0.0077429162,0.36430502,0.009085213,0.27088028],"study_design_scores_gemma":[0.00003774644,0.000115824856,0.001995236,0.00006398567,0.00007116448,0.0001888557,0.000211675,0.85898423,0.0053472538,0.12544923,0.0074860714,0.00004876611],"about_ca_topic_score_codex":0.0026931765,"about_ca_topic_score_gemma":0.0013120015,"teacher_disagreement_score":0.0038710216,"about_ca_system_score_codex":0.0017287913,"about_ca_system_score_gemma":0.0023651302,"threshold_uncertainty_score":0.020472169},"labels":[],"label_agreement":null},{"id":"W4410411773","doi":"10.1016/j.ijrobp.2024.12.047","title":"Which Way to (INDI)GO?","year":2025,"lang":"en","type":"editorial","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Psychology; Computer science","score_opus":0.06531720183050992,"score_gpt":0.41138397248857694,"score_spread":0.346066770658067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410411773","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001256206,0.0025112804,0.00009224244,0.048527204,0.94794995,0.0000076286315,0.000034949764,0.00003171708,0.00083253795],"genre_scores_gemma":[0.00029671277,0.0030883055,0.00016008316,0.043797113,0.9460333,0.00002164722,0.000024980895,0.000048909427,0.006528992],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9919893,0.0016984664,0.0010747308,0.00090926705,0.0037166262,0.00061161525],"domain_scores_gemma":[0.9512135,0.022040624,0.0026483682,0.0014565781,0.014363523,0.008277366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015577876,0.002945692,0.004077677,0.0038151066,0.004511812,0.015427239,0.0037062808,0.0200347,0.02814966],"category_scores_gemma":[0.05814525,0.0011960405,0.0027678248,0.002126977,0.003997689,0.0075165196,0.0024960926,0.031232482,0.019659707],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018822222,0.000008833517,0.000019605679,0.00014707107,0.000012518529,0.000036412675,0.000010528748,0.000013421984,0.000022897872,0.0006015597,0.9957632,0.003345141],"study_design_scores_gemma":[0.00007372076,0.000023936867,0.00018374616,0.00062649074,0.00006589783,0.00015786268,0.00008765583,0.0001389935,0.00008130447,0.0035002718,0.9950335,0.000026708623],"about_ca_topic_score_codex":0.0023271113,"about_ca_topic_score_gemma":0.007438977,"teacher_disagreement_score":0.02814966,"about_ca_system_score_codex":0.004146003,"about_ca_system_score_gemma":0.0074950424,"threshold_uncertainty_score":0.094170034},"labels":[],"label_agreement":null},{"id":"W4411097270","doi":"10.1007/978-981-96-2647-2","title":"Data Science and Applications","year":2025,"lang":"en","type":"book","venue":"Lecture notes in networks and systems","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Leibniz-Rechenzentrum; Leibniz-Gemeinschaft; SRM Institute of Science and Technology; Malaviya National Institute of Technology, Jaipur; Indian Institute of Technology Gandhinagar; BMS College of Engineering; Indian Institute of Technology Ropar; Jaypee University of Information Technology; Bayerische Akademie der Wissenschaften; Chandigarh University; National Institute of Technology, Silchar; Taibah University; Anna University; Indian Institute of Technology Roorkee; Université Sidi Mohamed Ben Abdellah; University of Hong Kong; Government of Canada; Gandhi Institute of Technology and Management; Foreign Affairs and International Trade Canada; Charotar University of Science and Technology; Maulana Azad National Institute of Technology; National Institute of Technology Warangal; Imperial College London; Florida International University","keywords":"Computer science","score_opus":0.15253152451517685,"score_gpt":0.3678227501243882,"score_spread":0.21529122560921135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411097270","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059762393,0.0283256,0.06829045,0.006353801,0.011607556,0.00030639078,0.003446882,0.0034037223,0.87766796],"genre_scores_gemma":[0.0022084792,0.012250232,0.013282931,0.0019206129,0.0024564133,0.00013486017,0.002351746,0.0008245817,0.96457005],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989278,0.0000929935,0.000049575916,0.00017561042,0.0007054983,0.000048485716],"domain_scores_gemma":[0.99845815,0.00045574704,0.000048683698,0.00040142992,0.00047593715,0.00016001666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012102068,0.001719983,0.0014657875,0.0033964075,0.0008696143,0.0050525763,0.0011652408,0.0012352619,0.20105214],"category_scores_gemma":[0.0034941002,0.0007588385,0.0007621806,0.0046329387,0.0008572224,0.003676089,0.0027935675,0.0029704648,0.17368524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014187518,0.000024061184,0.00007145729,0.0002686898,0.000012790956,0.000035916317,0.000055850644,0.00039625968,0.000908982,0.031916153,0.71925575,0.24703993],"study_design_scores_gemma":[0.0000024621809,0.000007854612,0.00011013697,0.00007193956,0.000003639242,0.00006477941,0.000015014832,0.0005153745,0.00019160805,0.014427641,0.98458475,0.0000049234063],"about_ca_topic_score_codex":0.0011387788,"about_ca_topic_score_gemma":0.0017871633,"teacher_disagreement_score":0.20105214,"about_ca_system_score_codex":0.0010334635,"about_ca_system_score_gemma":0.0014658769,"threshold_uncertainty_score":0.67258644},"labels":[],"label_agreement":null},{"id":"W4412361741","doi":"10.33564/ijeast.2025.v10i01.005","title":"CRIME DATA ANALYSIS","year":2025,"lang":"en","type":"article","venue":"International Journal of Engineering Applied Sciences and Technology","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science","score_opus":0.12520565214855978,"score_gpt":0.39664871302534854,"score_spread":0.27144306087678877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412361741","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037475638,0.00009295508,0.0014107286,0.00035403765,0.000083050385,0.00034696952,0.98677284,0.0009601333,0.006231705],"genre_scores_gemma":[0.005189011,0.000077504876,0.0034061568,0.000117721094,0.000024871302,0.0005240505,0.98770636,0.00013502325,0.0028192115],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9971565,0.0005397752,0.00045604905,0.0006509603,0.0009235351,0.00027321107],"domain_scores_gemma":[0.9924386,0.0016882309,0.0006230112,0.0014799358,0.0033685737,0.00040168964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016653761,0.0011353212,0.0008569775,0.004889494,0.0009363577,0.0018801455,0.0020387375,0.0010406878,0.029324872],"category_scores_gemma":[0.011232533,0.0003516055,0.0011451363,0.0073154788,0.00036220122,0.0011257249,0.0014460882,0.001805084,0.031120237],"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":[0.000085054184,0.00016298298,0.009222056,0.00034816354,0.000057632158,0.00007363256,0.00010449151,0.0019894086,0.00012487666,0.0027700989,0.9630664,0.021995194],"study_design_scores_gemma":[0.000103757055,0.00006317278,0.029662162,0.00033720935,0.0000397261,0.00016591293,0.0005537944,0.00838291,0.0010238615,0.0031769169,0.95641947,0.00007107206],"about_ca_topic_score_codex":0.026159262,"about_ca_topic_score_gemma":0.03409759,"teacher_disagreement_score":0.029324872,"about_ca_system_score_codex":0.0017221499,"about_ca_system_score_gemma":0.0033592896,"threshold_uncertainty_score":0.09810144},"labels":[],"label_agreement":null},{"id":"W4412819361","doi":"10.1017/s1743921323001394","title":"How do we design data sets for Machine Learning astronomy?","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Astronomical Union","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Canadian Institute for Theoretical Astrophysics","funders":"","keywords":"Computer science; Astronomy; Artificial intelligence; Physics","score_opus":0.26442971159024675,"score_gpt":0.3662506250858025,"score_spread":0.10182091349555572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412819361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027746145,0.0031195693,0.8909162,0.060541775,0.0015232346,0.0009867229,0.0036752305,0.0013650451,0.010126036],"genre_scores_gemma":[0.19150254,0.0012241354,0.7925106,0.0064320173,0.00093424687,0.0024675117,0.0031113334,0.00064146443,0.0011760979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.90885264,0.065122694,0.00648815,0.006420075,0.011916111,0.0012003977],"domain_scores_gemma":[0.6592241,0.23480806,0.016855136,0.05452658,0.029786073,0.0048000882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.087874934,0.0017137342,0.0042395615,0.008315079,0.0034499809,0.018372169,0.0067161084,0.0049516824,0.0065229842],"category_scores_gemma":[0.33778533,0.0022917872,0.0033485484,0.0071901553,0.013208359,0.03289534,0.0107791815,0.011438676,0.0034272727],"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":[0.00044005364,0.00043891152,0.022727776,0.0019039934,0.0007924062,0.00014722151,0.0020342353,0.0361916,0.0017738262,0.7623613,0.0344123,0.13677645],"study_design_scores_gemma":[0.00008687654,0.0000695554,0.0022176653,0.0005657676,0.000050449496,0.00006568163,0.0010700503,0.054127835,0.0011298708,0.9154526,0.025083672,0.00007992786],"about_ca_topic_score_codex":0.0030915826,"about_ca_topic_score_gemma":0.002218604,"teacher_disagreement_score":0.087874934,"about_ca_system_score_codex":0.004159373,"about_ca_system_score_gemma":0.005112829,"threshold_uncertainty_score":0.46473265},"labels":[],"label_agreement":null},{"id":"W4413684012","doi":"10.2196/70066","title":"Integration of Data and Information Systems Into the Health Data Strategy","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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; Czech; Computer science; Data science; World Wide Web","score_opus":0.2641624079056417,"score_gpt":0.4708056528706074,"score_spread":0.20664324496496567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413684012","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02978706,0.026911033,0.41528922,0.27396902,0.002814466,0.001971084,0.0024612073,0.0014379745,0.245359],"genre_scores_gemma":[0.59833276,0.014108758,0.34752947,0.01566725,0.0021504797,0.0014717999,0.0027635053,0.0006140093,0.01736194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9239014,0.050106037,0.006757959,0.0038469387,0.0121562425,0.0032315033],"domain_scores_gemma":[0.9131531,0.050613772,0.0048888703,0.016486496,0.010928687,0.003929093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.069361955,0.001070128,0.0009498517,0.00610147,0.0027920578,0.030495193,0.004073607,0.004505477,0.006709852],"category_scores_gemma":[0.05375656,0.00069025625,0.0013042103,0.012261865,0.010747307,0.025543002,0.020761598,0.0060093123,0.0023724604],"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":[0.000071036244,0.0001470596,0.006297647,0.0013018587,0.00017981093,0.00018042898,0.0025883922,0.0061721248,0.0007088137,0.7778716,0.015714874,0.18876627],"study_design_scores_gemma":[0.00004113233,0.00017541305,0.0036846183,0.0029909117,0.00011378858,0.00037800358,0.0051675634,0.011729226,0.0021844856,0.34243175,0.6309886,0.00011444231],"about_ca_topic_score_codex":0.0065009487,"about_ca_topic_score_gemma":0.0028537062,"teacher_disagreement_score":0.069361955,"about_ca_system_score_codex":0.013022305,"about_ca_system_score_gemma":0.031232033,"threshold_uncertainty_score":0.36682546},"labels":[],"label_agreement":null},{"id":"W4414511365","doi":"10.23977/jeis.2025.100208","title":"Evaluation and Dynamic Optimization of Big Data Technology in Engineering Project Resource Allocation","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Resource (disambiguation); Field (mathematics); Resource allocation; Resource management (computing); Project management","score_opus":0.09487698848080062,"score_gpt":0.38093440424269626,"score_spread":0.28605741576189564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414511365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16425692,0.0018933374,0.82191837,0.0016603576,0.00016772728,0.0003263205,0.00040108163,0.0004224738,0.008953487],"genre_scores_gemma":[0.93630713,0.00068678724,0.061729323,0.00008766931,0.00004604326,0.00020381878,0.00025874263,0.000041397834,0.0006390401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946701,0.0026262903,0.00030379507,0.0006311058,0.0014276246,0.00034103755],"domain_scores_gemma":[0.9934814,0.003675376,0.0007981715,0.0004884087,0.0012913538,0.0002653177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006731002,0.0012840525,0.0012493327,0.002973203,0.00067398266,0.0038628476,0.001135595,0.00084712455,0.0007082556],"category_scores_gemma":[0.014159661,0.00048428396,0.0006963096,0.0038032818,0.0010215839,0.0049549425,0.0017425233,0.0010301464,0.00011985225],"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":[0.00020381597,0.0002027532,0.015043684,0.00026492873,0.00016278833,0.00009899143,0.00011661817,0.8360629,0.002507845,0.023754792,0.0017236125,0.119857274],"study_design_scores_gemma":[0.000007656567,0.00005862053,0.0020742163,0.000018203558,0.000023129382,0.000014236138,0.0000945324,0.987004,0.0015799615,0.008477904,0.0006319044,0.000015736809],"about_ca_topic_score_codex":0.004762282,"about_ca_topic_score_gemma":0.0043755574,"teacher_disagreement_score":0.006731002,"about_ca_system_score_codex":0.0028455309,"about_ca_system_score_gemma":0.002679664,"threshold_uncertainty_score":0.035597384},"labels":[],"label_agreement":null},{"id":"W4414639538","doi":"10.1007/978-3-662-72116-2_2","title":"Data Assetization Journey: Concepts, Principles, and Illustrations","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 TÉLUQ","funders":"","keywords":"Asset (computer security); Obligation; Line (geometry); Transformation (genetics); Corporate governance; Data governance; Data verification; Word (group theory)","score_opus":0.26451014898457226,"score_gpt":0.39850359266860413,"score_spread":0.13399344368403188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414639538","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060390523,0.02680886,0.6167691,0.02392577,0.0019841904,0.0002464072,0.0013444661,0.0019123621,0.32096982],"genre_scores_gemma":[0.09749437,0.0493454,0.6102153,0.0036588355,0.0012354107,0.0005670518,0.0022921336,0.0012826317,0.23390885],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945515,0.00012067242,0.000040300838,0.00008711666,0.00024340166,0.000053386168],"domain_scores_gemma":[0.9991629,0.00032363736,0.000032142565,0.00016409498,0.00018597553,0.00013127673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012567969,0.00083136215,0.00051245495,0.0019848968,0.0015236319,0.008887599,0.0019892373,0.0017575207,0.015818756],"category_scores_gemma":[0.0029067295,0.0006647013,0.0007834511,0.004732163,0.0029428042,0.013893563,0.0037497075,0.004112024,0.0070285583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016056341,0.000032815777,0.0001682038,0.00015440164,0.0000046462887,0.00009662705,0.0006049853,0.0007715313,0.00048265912,0.8593223,0.04499032,0.093355544],"study_design_scores_gemma":[0.0000034465206,0.000016938502,0.000117543554,0.00022172148,0.000004537622,0.0004551169,0.00050938805,0.0035402605,0.000872729,0.41455632,0.57968736,0.0000146655975],"about_ca_topic_score_codex":0.0017293715,"about_ca_topic_score_gemma":0.0015692613,"teacher_disagreement_score":0.015818756,"about_ca_system_score_codex":0.0019663163,"about_ca_system_score_gemma":0.0021953364,"threshold_uncertainty_score":0.05291897},"labels":[],"label_agreement":null},{"id":"W5421139","doi":"10.1044/jshr.1301.41","title":"The Unfinished Revolution: Making Computers Human-Centric","year":2001,"lang":"en","type":"book","venue":"HarperCollins Publishers eBooks","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Quarter (Canadian coin); Focus (optics); Information revolution; Work (physics); Key (lock); Computer science; Information technology; Data science; Political science; Engineering; History; Computer security; Law","score_opus":0.19964764802077406,"score_gpt":0.3605601265355474,"score_spread":0.16091247851477336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5421139","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016381354,0.2087981,0.027289296,0.12491523,0.011235857,0.00008229608,0.00018727114,0.0006886765,0.62516516],"genre_scores_gemma":[0.070045315,0.16944715,0.021520508,0.040685434,0.009898709,0.00022687126,0.00041805205,0.0006791287,0.68707883],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988379,0.00036355847,0.000047120415,0.00012391794,0.000526416,0.000101034755],"domain_scores_gemma":[0.9977775,0.001310324,0.000091938135,0.0002835572,0.0003558178,0.0001809131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001517974,0.0007713124,0.0004486621,0.001177247,0.001484726,0.008883661,0.0009519364,0.002112412,0.01227934],"category_scores_gemma":[0.003794982,0.00047631445,0.0003337637,0.0019496174,0.005381744,0.014952554,0.0019184612,0.004770192,0.00697286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000124842845,0.000017978842,0.00006191977,0.00022318431,0.000008622223,0.00004764458,0.0011384881,0.00032999512,0.00016303406,0.5858809,0.34443307,0.06768273],"study_design_scores_gemma":[0.000004405962,0.0000059218455,0.000043708507,0.000112712885,0.0000029012408,0.000044572327,0.00011759975,0.00014953407,0.00005943042,0.08182664,0.917628,0.000004627949],"about_ca_topic_score_codex":0.001634721,"about_ca_topic_score_gemma":0.0024117532,"teacher_disagreement_score":0.01227934,"about_ca_system_score_codex":0.0024042525,"about_ca_system_score_gemma":0.0032118459,"threshold_uncertainty_score":0.04107845},"labels":[],"label_agreement":null},{"id":"W616817087","doi":"10.1007/978-3-642-40104-6","title":"Algorithms and data structures : 13th international symposium, WADS 2013, London, ON, Canada, August 12-14, 2013 : proceedings","year":2013,"lang":"en","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":5,"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":"Graphics; Computer science; Data structure; Data mining; Algorithm; Theoretical computer science; Computer graphics (images); Programming language","score_opus":0.11244938248607304,"score_gpt":0.3299356116610665,"score_spread":0.21748622917499344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W616817087","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039973296,0.23773074,0.51705503,0.020827806,0.029208586,0.0005541859,0.014302704,0.019593073,0.15673058],"genre_scores_gemma":[0.011079935,0.13682334,0.26273015,0.0022580023,0.006209966,0.0004349959,0.020803737,0.008501708,0.55115825],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99714893,0.00030916856,0.00021203925,0.00041436832,0.0017532688,0.00016215186],"domain_scores_gemma":[0.99360883,0.0016688626,0.00014664054,0.0011206506,0.0030067002,0.00044837696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040209903,0.0024019114,0.0031130128,0.0037930605,0.0016187801,0.010250004,0.0031253654,0.0015817552,0.06761594],"category_scores_gemma":[0.007326367,0.0019058316,0.0015782522,0.011147755,0.0022174835,0.007123506,0.002889077,0.004564027,0.041344218],"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":[0.00005489587,0.000043252694,0.00014391477,0.00052965083,0.000032815416,0.000023426635,0.00008806467,0.0011512891,0.0007895873,0.013499449,0.73095554,0.25268805],"study_design_scores_gemma":[0.000021455658,0.000029331637,0.0005935121,0.00038336785,0.000029469416,0.00019957309,0.00009036262,0.004865848,0.0015695926,0.028904866,0.9632713,0.000041288804],"about_ca_topic_score_codex":0.030112596,"about_ca_topic_score_gemma":0.056302268,"teacher_disagreement_score":0.9698874,"about_ca_system_score_codex":0.0056219124,"about_ca_system_score_gemma":0.010133946,"threshold_uncertainty_score":0.22619784},"labels":[],"label_agreement":null},{"id":"W621215835","doi":"10.1007/978-3-319-09274-4","title":"Artificial General Intelligence: 7th International Conference, AGI 2014, Quebec City, QC, Canada, August 1-4, 2014, Proceedings","year":2014,"lang":"en","type":"book","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Artificial general intelligence; Human intelligence; Field (mathematics); Library science; Engineering ethics; Artificial intelligence; Sociology; Political science; Computer science; Engineering; Mathematics","score_opus":0.17731448731032354,"score_gpt":0.35452701379680784,"score_spread":0.1772125264864843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W621215835","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068776654,0.17938456,0.068228744,0.037238967,0.045383804,0.00060358254,0.021966277,0.010778069,0.62953836],"genre_scores_gemma":[0.0057243602,0.031425707,0.013577715,0.0013628632,0.0012154009,0.000110061155,0.011255559,0.0013767339,0.93395156],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892277,0.00010176933,0.000029230432,0.00012613145,0.0006607662,0.0001593767],"domain_scores_gemma":[0.9978846,0.00020865479,0.00003032398,0.00015081276,0.0014150877,0.00031054093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00287798,0.0019227912,0.0018393653,0.002586114,0.0018509439,0.006672701,0.0026622736,0.0014543649,0.115424775],"category_scores_gemma":[0.0026439326,0.00073266536,0.0007896884,0.004388493,0.0015818961,0.002684757,0.0018192416,0.0026216004,0.05561546],"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":[0.000019747204,0.000023109576,0.0001213035,0.00011178562,0.000008841929,0.000012342609,0.000030186162,0.00025591013,0.00033368872,0.0020566985,0.92314637,0.0738801],"study_design_scores_gemma":[0.000009999311,0.000014193086,0.001172366,0.00016745288,0.000015266623,0.00005156537,0.000080094906,0.001393093,0.0005810827,0.0026649742,0.9938321,0.000017730448],"about_ca_topic_score_codex":0.37572354,"about_ca_topic_score_gemma":0.58608216,"teacher_disagreement_score":0.62427646,"about_ca_system_score_codex":0.011730618,"about_ca_system_score_gemma":0.017406669,"threshold_uncertainty_score":0.7470732},"labels":[],"label_agreement":null},{"id":"W644851440","doi":"","title":"Theoretical high energy physics , MRST 2000, Rochester, New York 8-9 May 2000","year":2000,"lang":"en","type":"book","venue":"American Institute of Physics eBooks","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics; Quantum chromodynamics; Range (aeronautics); Particle physics; Boson; High energy; Engineering physics; Nuclear physics; Engineering","score_opus":0.08244976803001097,"score_gpt":0.3053065286154697,"score_spread":0.22285676058545872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W644851440","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019594047,0.04494411,0.021424025,0.009537312,0.004068531,0.00006997701,0.002111063,0.0022403377,0.9136452],"genre_scores_gemma":[0.003895664,0.013919147,0.0034255043,0.00026417573,0.00034846982,0.000032752854,0.0006877576,0.00040369135,0.97702277],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997806,0.000017998269,0.0000065797362,0.000042902364,0.00013346005,0.000018477402],"domain_scores_gemma":[0.99968314,0.000064244814,0.000022058055,0.00003329813,0.0001210318,0.000076149845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048868044,0.001217157,0.0006698559,0.00094184076,0.0009501998,0.0025457575,0.00086561975,0.0007684306,0.13628495],"category_scores_gemma":[0.0008939293,0.00073048804,0.00023958406,0.0015378073,0.0005845883,0.0018385121,0.0008658783,0.0018208434,0.07763312],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022690885,0.0000158365,0.000077757395,0.00009384817,0.000005591281,0.00005540459,0.000045158027,0.00069477526,0.00044604737,0.027870722,0.9027814,0.067890696],"study_design_scores_gemma":[0.000010439587,0.000013683974,0.00044963224,0.00010742605,0.000008363424,0.000104654995,0.000032253,0.0022546703,0.00044998675,0.009565885,0.9869955,0.0000075644557],"about_ca_topic_score_codex":0.0075479625,"about_ca_topic_score_gemma":0.036212265,"teacher_disagreement_score":0.13628495,"about_ca_system_score_codex":0.0021881943,"about_ca_system_score_gemma":0.0014381675,"threshold_uncertainty_score":0.45591855},"labels":[],"label_agreement":null},{"id":"W6912602371","doi":"10.5281/zenodo.4150596","title":"REGARDER''-Falling (2020)]EN STREAMING FILM FR COMPLET jgx","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Falling (accident); Heading (navigation); Imago","score_opus":0.2364160396682274,"score_gpt":0.3293120416767511,"score_spread":0.09289600200852369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912602371","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006305375,0.0002240635,0.00078453403,0.00087771437,0.0017726438,0.00017664705,0.005393487,0.005472328,0.98466796],"genre_scores_gemma":[0.0028548744,0.00025965917,0.0008753035,0.00054153963,0.00023700162,0.00007510582,0.0030630026,0.0028620437,0.9892314],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998242,0.000013233894,0.0000059174345,0.000025769648,0.00008052358,0.000050218146],"domain_scores_gemma":[0.99945253,0.00005524545,0.00001727293,0.000062489045,0.00022425487,0.00018821386],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002957622,0.0006070458,0.00028778956,0.0006603791,0.0014955335,0.002854537,0.0007693985,0.0009965436,0.89855313],"category_scores_gemma":[0.0014588423,0.00037242746,0.00043718124,0.00065875705,0.00021530161,0.0027993943,0.0019204317,0.001425093,0.7186315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016950547,0.0000080965965,0.000051659998,0.00004211556,6.7584233e-7,0.00003277118,0.000079933256,0.0000055663068,0.00020888569,0.00056577905,0.97852176,0.020465719],"study_design_scores_gemma":[0.0000074436766,0.0000078429175,0.0004539644,0.000035907753,9.630882e-7,0.00004990997,0.00010177643,0.000017361868,0.00014947075,0.00012564968,0.99904484,0.000004694359],"about_ca_topic_score_codex":0.0073249107,"about_ca_topic_score_gemma":0.024896558,"teacher_disagreement_score":0.10144687,"about_ca_system_score_codex":0.0005544926,"about_ca_system_score_gemma":0.00043464437,"threshold_uncertainty_score":0.14470154},"labels":[],"label_agreement":null},{"id":"W6925621578","doi":"10.17910/b7.1387","title":"EmoGoals","year":2021,"lang":"en","type":"dataset","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Zoom; Face (sociological concept); Relation (database); Object (grammar); Perspective (graphical)","score_opus":0.3676709742955122,"score_gpt":0.44506657424098833,"score_spread":0.07739559994547612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925621578","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059311636,0.00058402366,0.0010700107,0.00044877807,0.00018173501,0.00030456603,0.9183301,0.0012209335,0.018548315],"genre_scores_gemma":[0.05304101,0.0002310646,0.0026012077,0.00032454362,0.000046224817,0.0009960874,0.9308065,0.0002012483,0.011752046],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996834,0.0000765499,0.00003877703,0.00008253819,0.000070402195,0.000048438895],"domain_scores_gemma":[0.99847203,0.0005429231,0.00012936351,0.00030703755,0.0003877304,0.0001609654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052010117,0.0008057691,0.000401235,0.0007750785,0.00051128224,0.000629108,0.00075066474,0.00073984783,0.025206383],"category_scores_gemma":[0.0042290543,0.00018966569,0.0007409902,0.00068067503,0.00015442506,0.00074355974,0.0008585001,0.0009224846,0.024304237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017798473,0.0005296335,0.049978677,0.0015937347,0.00015678244,0.0002094095,0.0005195921,0.00078007823,0.00090922834,0.0012407379,0.8982402,0.044062108],"study_design_scores_gemma":[0.00084414484,0.0006420749,0.3238421,0.00069774216,0.00019316046,0.0006936373,0.0016868566,0.0034314787,0.0026047481,0.002998347,0.66226465,0.00010110103],"about_ca_topic_score_codex":0.0111372955,"about_ca_topic_score_gemma":0.03048788,"teacher_disagreement_score":0.025206383,"about_ca_system_score_codex":0.00042506025,"about_ca_system_score_gemma":0.0004134769,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W6949089475","doi":"10.5281/zenodo.1184303","title":"Big Data Schooling","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Big data; Data collection; Work (physics); Period (music)","score_opus":0.4989616570749087,"score_gpt":0.3822047769631415,"score_spread":0.11675688011176721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949089475","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012606169,0.015073962,0.002987935,0.7505697,0.046537086,0.00009571573,0.0029552293,0.0005394492,0.17998034],"genre_scores_gemma":[0.039764777,0.039525185,0.004245078,0.17914742,0.033712752,0.00021534202,0.0047110827,0.000915135,0.6977632],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971322,0.00042236588,0.00018175729,0.00037148548,0.0014323087,0.000459883],"domain_scores_gemma":[0.9753886,0.004965755,0.000806861,0.0021820532,0.0098965885,0.0067602885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055227005,0.000561563,0.00060752995,0.002059696,0.0031978197,0.006786441,0.0011995371,0.0020828687,0.09213721],"category_scores_gemma":[0.025847543,0.0003903515,0.00045073408,0.0025220935,0.0025856614,0.0063574435,0.004251808,0.005302529,0.02257191],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000100773905,0.000013300089,0.00041222645,0.00006560195,0.0000046668733,0.000015405069,0.00006902737,0.000039460672,0.00003902263,0.014836735,0.947028,0.03746669],"study_design_scores_gemma":[0.000007531031,0.0000045252837,0.0010082021,0.00016832231,0.0000044536127,0.000018608416,0.00019170945,0.00009690063,0.00008715956,0.011068089,0.9873376,0.0000069429148],"about_ca_topic_score_codex":0.063641176,"about_ca_topic_score_gemma":0.11205922,"teacher_disagreement_score":0.09213721,"about_ca_system_score_codex":0.0075941626,"about_ca_system_score_gemma":0.03088142,"threshold_uncertainty_score":0.30822968},"labels":[],"label_agreement":null},{"id":"W6950135270","doi":"10.5281/zenodo.6917218","title":"Small Data projects/Big Data research: contemporary problems and historical solutions","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Lethbridge","funders":"","keywords":"Presentation (obstetrics); Minor (academic); Data collection; Small data","score_opus":0.9315210701659241,"score_gpt":0.37772499857736314,"score_spread":0.553796071588561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6950135270","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002243747,0.13856952,0.050066903,0.5254041,0.115502834,0.0002540805,0.0032168985,0.0017294207,0.16301252],"genre_scores_gemma":[0.047458887,0.30684203,0.07819836,0.07127804,0.10050207,0.00094573735,0.005719028,0.005594656,0.3834612],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945911,0.0019673724,0.00040672583,0.00056769856,0.0022208139,0.00024638438],"domain_scores_gemma":[0.982031,0.009980603,0.0006880359,0.0020440759,0.0040133703,0.0012429499],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.013122945,0.0008153582,0.00069371716,0.0032041124,0.0025629986,0.011669707,0.0018280761,0.003143846,0.036606874],"category_scores_gemma":[0.026137631,0.00074416684,0.00051441207,0.00903878,0.008108819,0.014886862,0.0045105824,0.0073003313,0.020134436],"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":[0.000038785834,0.00001478677,0.00017104008,0.00083297124,0.0000067948185,0.00008759749,0.0009876508,0.00009040028,0.00050428516,0.14059311,0.7565339,0.10013863],"study_design_scores_gemma":[0.0000043657874,0.0000115645735,0.00029650304,0.0005746163,0.0000029391574,0.00016311051,0.00091020303,0.00010348465,0.00022723078,0.047747295,0.9499421,0.000016661214],"about_ca_topic_score_codex":0.002401268,"about_ca_topic_score_gemma":0.002739184,"teacher_disagreement_score":0.997437,"about_ca_system_score_codex":0.0027248557,"about_ca_system_score_gemma":0.0037885646,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"opus","categories":["metaresearch"],"domain":"reproducibility","study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W6968682310","doi":"10.5281/zenodo.6857202","title":"Small Data projects/Big Data research: contemporary problems and historical solutions","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Lethbridge","funders":"","keywords":"Presentation (obstetrics); Minor (academic); Data collection; Small data","score_opus":0.9315210701659241,"score_gpt":0.37772499857736314,"score_spread":0.553796071588561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6968682310","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002243747,0.13856952,0.050066903,0.5254041,0.115502834,0.0002540805,0.0032168985,0.0017294207,0.16301252],"genre_scores_gemma":[0.047458887,0.30684203,0.07819836,0.07127804,0.10050207,0.00094573735,0.005719028,0.005594656,0.3834612],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945911,0.0019673724,0.00040672583,0.00056769856,0.0022208139,0.00024638438],"domain_scores_gemma":[0.982031,0.009980603,0.0006880359,0.0020440759,0.0040133703,0.0012429499],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013122945,0.0008153582,0.00069371716,0.0032041124,0.0025629986,0.011669707,0.0018280761,0.003143846,0.036606874],"category_scores_gemma":[0.026137631,0.00074416684,0.00051441207,0.00903878,0.008108819,0.014886862,0.0045105824,0.0073003313,0.020134436],"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":[0.000038785834,0.00001478677,0.00017104008,0.00083297124,0.0000067948185,0.00008759749,0.0009876508,0.00009040028,0.00050428516,0.14059311,0.7565339,0.10013863],"study_design_scores_gemma":[0.0000043657874,0.0000115645735,0.00029650304,0.0005746163,0.0000029391574,0.00016311051,0.00091020303,0.00010348465,0.00022723078,0.047747295,0.9499421,0.000016661214],"about_ca_topic_score_codex":0.002401268,"about_ca_topic_score_gemma":0.002739184,"teacher_disagreement_score":0.9868771,"about_ca_system_score_codex":0.0027248557,"about_ca_system_score_gemma":0.0037885646,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W6969011827","doi":"10.5281/zenodo.8403802","title":"Small Data projects/Big Data research: contemporary problems and historical solutions","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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 Lethbridge","funders":"","keywords":"Small data; Data collection; Context (archaeology); Work (physics); Key (lock)","score_opus":0.944000042099182,"score_gpt":0.3934813743268047,"score_spread":0.5505186677723772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6969011827","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037660622,0.34039536,0.016893333,0.5322066,0.028415,0.00017065107,0.0011503297,0.00039949612,0.07660318],"genre_scores_gemma":[0.07282447,0.715156,0.030913098,0.052291483,0.04566991,0.00039826005,0.0013598124,0.00095973763,0.08042725],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98730797,0.00420242,0.00090517936,0.0016854287,0.0053103906,0.0005886077],"domain_scores_gemma":[0.8850566,0.06667127,0.004288644,0.009089979,0.027692085,0.0072014583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039442953,0.0008688234,0.001221759,0.006315496,0.0038830359,0.01430009,0.002044521,0.005261928,0.025900118],"category_scores_gemma":[0.060690854,0.00085908436,0.0005380554,0.017452769,0.015166049,0.019825222,0.0065646535,0.0077076945,0.008442668],"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":[0.00012519135,0.00008463539,0.0014386445,0.0035842978,0.0000369276,0.00016641263,0.00082319323,0.00032991616,0.00048901734,0.24683838,0.35566613,0.39041728],"study_design_scores_gemma":[0.000016487877,0.0000381539,0.0014119543,0.0018935218,0.000010924761,0.00020990332,0.0014132301,0.00027433998,0.00041491791,0.11782869,0.8764538,0.00003402876],"about_ca_topic_score_codex":0.0028415865,"about_ca_topic_score_gemma":0.004452696,"teacher_disagreement_score":0.039442953,"about_ca_system_score_codex":0.0043936023,"about_ca_system_score_gemma":0.010060913,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"grok","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"opus","categories":["metaresearch"],"domain":"reproducibility","study_design":"theoretical_or_conceptual","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W7008270000","doi":"","title":"Big data policing: Schets van de belangrijkste vraagstukken, partijen en nieuwste trends in de praktijk","year":2023,"lang":"nl","type":"book-chapter","venue":"Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"L'Alliance Boviteq","funders":"","keywords":"Data collection; Context (archaeology); Work (physics); Qualitative research; Class (philosophy)","score_opus":0.16868662022194747,"score_gpt":0.3164468613362046,"score_spread":0.14776024111425712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008270000","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005559722,0.30888155,0.063837595,0.22175556,0.017155452,0.0001391534,0.0017401765,0.00069544854,0.3802353],"genre_scores_gemma":[0.18687701,0.3644262,0.06313211,0.032795787,0.014494788,0.0005012066,0.0027171748,0.0024877503,0.332568],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99387866,0.0018668362,0.0003264021,0.0005264162,0.0029928843,0.00040876522],"domain_scores_gemma":[0.99100935,0.0062444615,0.00042327095,0.0008632783,0.0009499879,0.0005096038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070053814,0.0008782244,0.0011015974,0.0024587836,0.0023389182,0.0250945,0.0018567762,0.0031068898,0.014508725],"category_scores_gemma":[0.015463531,0.0011525529,0.00060563773,0.007128016,0.007913694,0.016925335,0.004810606,0.006177411,0.003760487],"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":[0.00002560148,0.000026790103,0.0003223413,0.0007915818,0.000027244654,0.00007709441,0.0029246758,0.001274464,0.00016069006,0.6212626,0.22997674,0.14313018],"study_design_scores_gemma":[0.0000051842985,0.0000042731467,0.00019673626,0.0010747517,0.000008747206,0.00007227163,0.0013238918,0.0010624321,0.00014871875,0.25599048,0.7400951,0.000017371975],"about_ca_topic_score_codex":0.013259181,"about_ca_topic_score_gemma":0.019165736,"teacher_disagreement_score":0.0250945,"about_ca_system_score_codex":0.00603233,"about_ca_system_score_gemma":0.014461295,"threshold_uncertainty_score":0.04853654},"labels":[],"label_agreement":null},{"id":"W7083824749","doi":"10.5281/zenodo.17238041","title":"Table 2 in Bringing Shadowdragons to light: Neurocordulia (Anisoptera: Corduliidae) systematics","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Table (database); Table of contents; Section (typography); Channel (broadcasting); Systematics","score_opus":0.1292442179463484,"score_gpt":0.3320078628657848,"score_spread":0.2027636449194364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7083824749","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024517037,0.014597702,0.007854605,0.005792918,0.013242367,0.00094088976,0.48531395,0.0031382802,0.4446022],"genre_scores_gemma":[0.090809606,0.016575286,0.048954792,0.0059621027,0.0029738687,0.0014739573,0.42188674,0.0022872705,0.4090764],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995659,0.00007281829,0.00006344797,0.00008423563,0.00015575912,0.000057916775],"domain_scores_gemma":[0.9987203,0.00025583376,0.00013484182,0.00010193508,0.0006670524,0.000120055745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004493075,0.0007153183,0.0004107624,0.0038996432,0.0016873715,0.0009648862,0.00070705655,0.00043211156,0.20902798],"category_scores_gemma":[0.0019906699,0.0003402712,0.00035886574,0.0051123877,0.00046470162,0.001608667,0.0010225504,0.00079361076,0.04527411],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060285314,0.000013071327,0.011000939,0.0009935278,0.000022160706,0.000111758556,0.0012186363,0.000102291684,0.0014408509,0.0024017994,0.8805239,0.10211073],"study_design_scores_gemma":[0.0000063591856,0.000013865982,0.024012366,0.00032705997,0.0000103235725,0.00018248936,0.0007823637,0.000053029744,0.00024301569,0.0006556426,0.97370285,0.0000107249925],"about_ca_topic_score_codex":0.026060898,"about_ca_topic_score_gemma":0.068916276,"teacher_disagreement_score":0.20902798,"about_ca_system_score_codex":0.0007760777,"about_ca_system_score_gemma":0.0012444064,"threshold_uncertainty_score":0.6992682},"labels":[],"label_agreement":null},{"id":"W7099318648","doi":"","title":"Saskatoon By","year":2016,"lang":"en","type":"article","venue":"","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"","score_opus":0.250478889140823,"score_gpt":0.39111953396693544,"score_spread":0.14064064482611244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099318648","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018890249,0.0023796805,0.0010159691,0.0023134877,0.0016988453,0.00008810808,0.007845091,0.000724312,0.9820455],"genre_scores_gemma":[0.0015574456,0.0007500423,0.00037168403,0.0003221271,0.000028116083,0.000023414994,0.0012288509,0.00014851552,0.99556977],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995449,0.000048737988,0.000017862154,0.00015535782,0.00012098852,0.00011215667],"domain_scores_gemma":[0.9992974,0.00011853273,0.000045631372,0.0001276941,0.00022441536,0.00018628211],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044755344,0.0018370217,0.000593276,0.0019360459,0.0017515803,0.0039881035,0.0008727995,0.0013594235,0.8380469],"category_scores_gemma":[0.0012977608,0.00061267987,0.00060366857,0.002968755,0.00059468707,0.001373991,0.0024686053,0.0015843532,0.6750453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017148737,0.000098486955,0.0014237106,0.0002817118,0.000034865992,0.0004969785,0.00011003707,0.0004238708,0.002102432,0.008306561,0.73710805,0.2494418],"study_design_scores_gemma":[0.000014850442,0.000009519722,0.00086227607,0.000085498425,0.0000074120935,0.000054463464,0.00008371593,0.000073356154,0.00022328422,0.00038035933,0.99820054,0.0000046865425],"about_ca_topic_score_codex":0.043219917,"about_ca_topic_score_gemma":0.096068196,"teacher_disagreement_score":0.16195309,"about_ca_system_score_codex":0.0021995353,"about_ca_system_score_gemma":0.0060246238,"threshold_uncertainty_score":0.23100638},"labels":[],"label_agreement":null},{"id":"W7148627237","doi":"10.71465/ajbd780","title":"Big Data for Enhancing Customer Experience in Digital Marketing","year":2023,"lang":"","type":"article","venue":"American Journal Of Big Data","topic":"Big Data Technologies and Applications","field":"Decision Sciences","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":"Big data; Digital marketing; Purchasing; Customer advocacy; Customer intelligence; Personalized marketing; Customer experience; Product (mathematics)","score_opus":0.4337847677042681,"score_gpt":0.42811211275846234,"score_spread":0.005672654945805766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7148627237","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23810694,0.04687217,0.2146651,0.15652238,0.005274412,0.002056461,0.0060492577,0.0020697566,0.3283835],"genre_scores_gemma":[0.8909069,0.010880043,0.080679685,0.007374534,0.0016854095,0.0005772247,0.0012592186,0.00015231816,0.006484718],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996292,0.0020677014,0.0001515981,0.00029702476,0.0009870545,0.00020472273],"domain_scores_gemma":[0.9912124,0.005113915,0.0007751405,0.0009847337,0.0013186928,0.0005951089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051547545,0.0006417448,0.0005707309,0.0019072344,0.0014941158,0.0068330904,0.0011210585,0.001498655,0.00686955],"category_scores_gemma":[0.015719932,0.00029225144,0.00054908416,0.003719886,0.0014822198,0.0115602305,0.0037698613,0.002048195,0.0014368698],"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":[0.0009387752,0.0009484432,0.03147319,0.002287605,0.0002729661,0.000460137,0.005443964,0.006291913,0.004083119,0.2030285,0.08850706,0.6562643],"study_design_scores_gemma":[0.00016500491,0.0008602854,0.030191947,0.0024043154,0.0002817354,0.0006088555,0.016680082,0.057705317,0.008828776,0.58087915,0.30110052,0.00029392718],"about_ca_topic_score_codex":0.00086848985,"about_ca_topic_score_gemma":0.0015631701,"teacher_disagreement_score":0.00686955,"about_ca_system_score_codex":0.0012001678,"about_ca_system_score_gemma":0.0012567586,"threshold_uncertainty_score":0.027261317},"labels":[],"label_agreement":null}]}