{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":10,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":10,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"9fb287f59369","filters":{"venue":"International Journal of Big Data Intelligence"}},"results":[{"id":"W2062172883","doi":"10.1504/ijbdi.2014.063835","title":"Health big data analytics: current perspectives, challenges and potential solutions","year":2014,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":131,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Big data; Data science; Computer science; Process (computing); Analytics; Guideline; Health data; Data mining; Knowledge management; Health care; Medicine; Political science","authors":[{"name":"Mu Hsing Kuo","is_ca":true},{"name":"Tony Sahama","is_ca":false},{"name":"André Kushniruk","is_ca":true},{"name":"Elizabeth M. Borycki","is_ca":true},{"name":"Daniel Grunwell","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.611332550204909,"gpt":0.5254841188079858,"spread":0.08584843139692311,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00504339,0.0002529717,0.0005053256,0.0004931731,0.0005409125,0.00006456317,0.004161527,0.0001467101,0.0001071717],"category_scores_gemma":[0.003980908,0.0002256271,0.00006984652,0.0001815625,0.0003096848,0.000809412,0.002688822,0.001409734,0.0001459015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003842281,"about_ca_system_score_gemma":0.00127792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009925746,"about_ca_topic_score_gemma":0.002623631,"domain_scores_codex":[0.9947824,0.0008857493,0.001863899,0.000715774,0.001125896,0.0006263201],"domain_scores_gemma":[0.9934524,0.001018995,0.001492743,0.001625357,0.001972072,0.0004384305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009706969,0.0001860118,0.00119118,0.0001088399,0.0001372214,0.00002142044,0.003745042,0.00007988493,0.00001117229,0.01328797,0.005619483,0.9755147],"study_design_scores_gemma":[0.0007666247,0.001134161,0.0115624,0.005347401,0.0002325103,0.0009575888,0.08064929,0.1388932,0.00007415937,0.0575534,0.7016083,0.001220986],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006892046,0.113827,0.7391255,0.09160311,0.04317574,0.000967241,0.002749463,0.000104251,0.001555693],"genre_scores_gemma":[0.9067336,0.08180599,0.001603878,0.000494859,0.008998738,0.000005198928,0.0002738274,0.00003187067,0.00005205665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9742937,"threshold_uncertainty_score":0.9200804,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2215860650","doi":"10.1504/ijbdi.2015.070597","title":"Unstructured data mining: use case for CouchDB","year":2015,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Mitacs","keywords":"NoSQL; Computer science; Semi-structured data; Unstructured data; Database; Append; Storage model; Relational database; Big data; Relational database management system; Data mining; Information retrieval; Data science; World Wide Web","authors":[{"name":"Richard K. Lomotey","is_ca":true},{"name":"Ralph Deters","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4767861738691988,"gpt":0.4121020276593292,"spread":0.06468414620986962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001104695,0.0001310367,0.0001714068,0.0001826046,0.00006030107,0.0006901196,0.01075168,0.00004837808,0.000006067055],"category_scores_gemma":[0.001737461,0.0001130618,0.00003776314,0.0002136895,0.00007558167,0.003284123,0.002404793,0.0001495203,0.00001251549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005750417,"about_ca_system_score_gemma":0.0004064577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009281583,"about_ca_topic_score_gemma":0.00005613086,"domain_scores_codex":[0.998105,0.00003267076,0.0006294092,0.0004406165,0.0006176372,0.0001746039],"domain_scores_gemma":[0.995622,0.0003984052,0.0005096206,0.002041116,0.001214373,0.0002144843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002852128,0.00008004662,0.00008921429,0.000002925079,0.0001139928,0.0006580077,0.0002962612,0.0001191827,0.00003373489,0.004723037,0.06281628,0.9310388],"study_design_scores_gemma":[0.0004165287,0.0001521089,0.00005047309,0.00007661673,0.00003978828,0.01573718,0.0004255878,0.5276442,0.0007520785,0.003952853,0.4504905,0.0002621909],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001653427,0.0001603619,0.9895169,0.001792431,0.003541765,0.00009441769,0.003170023,0.00002218633,0.00004846613],"genre_scores_gemma":[0.181352,0.00009427936,0.8161466,0.0003915185,0.001263594,0.000003455619,0.0006527897,0.00001302031,0.00008268427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9307766,"threshold_uncertainty_score":0.9946007,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2071678891","doi":"10.1504/ijbdi.2015.067567","title":"Terms analytics service for CouchDB: a document-based NoSQL","year":2015,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"NoSQL; Analytics; Computer science; Database; Service (business); World Wide Web; Data science; Big data; Data mining; Business","authors":[{"name":"Richard K. Lomotey","is_ca":true},{"name":"Ralph Deters","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.236714938024969,"gpt":0.3786380129374237,"spread":0.1419230749124548,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009885702,0.0001474933,0.000217966,0.000223862,0.0000406476,0.0001991666,0.003955383,0.00004156661,0.00001000549],"category_scores_gemma":[0.0006204048,0.0001212407,0.00006943835,0.0002544263,0.00004660383,0.001924207,0.0006378769,0.0001387722,0.00003387173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001197428,"about_ca_system_score_gemma":0.0004534335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007053337,"about_ca_topic_score_gemma":0.00008391288,"domain_scores_codex":[0.9979122,0.0000388072,0.0007045201,0.0002919892,0.0008593972,0.0001930654],"domain_scores_gemma":[0.996273,0.0002349917,0.000624252,0.0008259385,0.001846621,0.0001952258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007804744,0.0005680912,0.00149241,0.0001251167,0.0007374216,0.0006199988,0.001280906,0.03269546,0.001008358,0.2920396,0.04718741,0.6214647],"study_design_scores_gemma":[0.0009503675,0.0003879027,0.000053114,0.000380571,0.00003680858,0.0004711626,0.0003105027,0.264877,0.01021874,0.01457263,0.707339,0.0004021485],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003006472,0.0001917813,0.9915003,0.003498737,0.003878784,0.0001085869,0.0003591891,0.00002143707,0.0001404916],"genre_scores_gemma":[0.294167,0.0001040911,0.6996516,0.003490977,0.002063238,0.00001107493,0.0003134159,0.00002574087,0.0001728002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6601517,"threshold_uncertainty_score":0.7350152,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4242118997","doi":"10.1504/ijbdi.2018.094996","title":"Resource management for deadline constrained MapReduce jobs for minimising energy consumption","year":2018,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Cloud computing; Energy consumption; Workload; Service-level agreement; Scheduling (production processes); Distributed computing; Data center; Database; Operating system; Operations management","authors":[{"name":"Adam Gregory","is_ca":true},{"name":"Shikharesh Majumdar","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1240927433748498,"gpt":0.3422785098239938,"spread":0.218185766449144,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001091372,0.0001562149,0.0001863375,0.0003236718,0.0001333664,0.0002838303,0.004065866,0.00004722571,0.00001089744],"category_scores_gemma":[0.0001982751,0.0001414968,0.0001162077,0.0001371551,0.0001457291,0.0001179526,0.0009328781,0.00008434079,0.000007483818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008437759,"about_ca_system_score_gemma":0.00005524372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000799362,"about_ca_topic_score_gemma":0.000006218733,"domain_scores_codex":[0.9980482,0.00004408512,0.0007106737,0.0004101497,0.0005351334,0.0002517319],"domain_scores_gemma":[0.9975954,0.0003951681,0.0005534515,0.000623005,0.000735836,0.00009714608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001733394,0.0001177787,0.00003118827,0.00003320005,0.0003055543,0.00002841668,0.0001851562,0.0009509001,0.0001762979,0.04470736,0.0116038,0.941687],"study_design_scores_gemma":[0.0009308717,0.0005211587,0.0001196234,0.0005490581,0.00007865295,0.0003167434,0.0002651442,0.4101366,0.007587389,0.009292594,0.5698661,0.0003361165],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002755843,0.000204513,0.9905003,0.002542796,0.00305918,0.000169388,0.00005839444,0.00003385799,0.0006756954],"genre_scores_gemma":[0.7032936,0.00009033665,0.2916057,0.001199182,0.003129138,0.00001028822,0.00005671937,0.00001887007,0.0005961744],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9413509,"threshold_uncertainty_score":0.7555458,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3012323650","doi":"10.1504/ijbdi.2020.10027766","title":"Combining the richness of GIS techniques with visualisation tools to better understand the spatial distribution of data - a case study of Chicago City crime analysis","year":2020,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Visualization; Crime analysis; Spatial analysis; Data science; Data visualization; Distribution (mathematics); Computer science; Geography; Cartography; Data mining; Remote sensing; Psychology; Mathematics; Criminology","authors":[{"name":"M. Omair Shafiq","is_ca":true},{"name":"Omar Ibrahim Bani Taha","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4316311108539355,"gpt":0.4667827832985019,"spread":0.03515167244456646,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00168017,0.00008434209,0.0002379091,0.0001208147,0.0001204572,0.0001001728,0.002391737,0.00002951939,0.0001117193],"category_scores_gemma":[0.0007194913,0.0000516697,0.00008001638,0.0006122706,0.0002571594,0.0005800904,0.0005562595,0.0001661839,4.122901e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004913767,"about_ca_system_score_gemma":0.0001067071,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01669918,"about_ca_topic_score_gemma":0.008676982,"domain_scores_codex":[0.9977489,0.0002807215,0.0007808963,0.0001881388,0.0009129068,0.00008840006],"domain_scores_gemma":[0.9972579,0.0003859505,0.0009165238,0.0005035221,0.0008828603,0.00005326283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001821969,0.004730646,0.2528364,0.0001014381,0.01076539,0.0004029436,0.1747636,0.00143068,0.002147819,0.004784852,0.005626013,0.5405883],"study_design_scores_gemma":[0.001746629,0.00725597,0.1558268,0.001457492,0.007135645,0.00040466,0.7543414,0.01969173,0.03931456,0.001743988,0.01005763,0.001023479],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.572101,0.00001879059,0.4241584,0.002413654,0.0001028178,0.0001745148,0.0009873392,0.000003255824,0.00004025425],"genre_scores_gemma":[0.9992555,0.0000275584,0.0002719867,0.0001023984,0.0001886684,0.000001862864,0.0001456547,0.000004015373,0.000002333266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5795778,"threshold_uncertainty_score":0.9898487,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2775790523","doi":"10.1504/ijbdi.2018.10009550","title":"Resource management for deadline constrained MapReduce jobs for minimising energy consumption","year":2017,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Energy consumption; Consumption (sociology); Resource (disambiguation); Resource consumption; Distributed computing; Computer network; Engineering","authors":[{"name":"Shikharesh Majumdar","is_ca":true},{"name":"Adam Gregory","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1560523121209957,"gpt":0.3578123481504218,"spread":0.2017600360294261,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001147463,0.0001569758,0.0002025861,0.0002381314,0.0003006338,0.000747707,0.007122379,0.00004780562,0.000005402514],"category_scores_gemma":[0.0003606316,0.0001428422,0.0001357882,0.00004254784,0.0001263179,0.0001887314,0.001483121,0.00009823999,0.000003715674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007566607,"about_ca_system_score_gemma":0.00005206607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001472146,"about_ca_topic_score_gemma":0.000006993327,"domain_scores_codex":[0.9981372,0.00003339045,0.0006593346,0.0004028719,0.0005284272,0.0002388107],"domain_scores_gemma":[0.9969798,0.0003408674,0.0009725783,0.001118655,0.0004901775,0.00009787286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001260448,0.0001025636,0.00007742779,0.00003973407,0.0002995989,0.00005328145,0.00009572052,0.001888941,0.00009940618,0.04634129,0.005864193,0.9450118],"study_design_scores_gemma":[0.001485658,0.0003171045,0.0006759376,0.0008880677,0.0001136569,0.0003559026,0.0002398196,0.4763243,0.004892929,0.01395488,0.5002942,0.0004576114],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002597006,0.0002173131,0.9886484,0.004434939,0.003050874,0.0001729589,0.0000704936,0.00002450466,0.0007835097],"genre_scores_gemma":[0.7992094,0.0001506274,0.1976011,0.000599431,0.001656642,0.00001103553,0.0000458584,0.00001698241,0.0007089063],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9445542,"threshold_uncertainty_score":0.9982496,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996041985","doi":"10.1504/ijbdi.2014.066323","title":"Innesto: a multi-attribute searchable consistent key/value store","year":2014,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scalability; Key (lock); Consistency (knowledge bases); Cloud computing; Database; Lock (firearm); Associative array; Data consistency; Set (abstract data type); Cloud storage; Value (mathematics); Data mining; Operating system","authors":[{"name":"Mahdi Tayarani Najaran","is_ca":true},{"name":"Norman C. Hutchinson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1330310764661534,"gpt":0.3300014259137139,"spread":0.1969703494475604,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001993072,0.0001590617,0.000224215,0.0003084925,0.00009478649,0.0003375464,0.006874348,0.00004833893,0.00001171887],"category_scores_gemma":[0.000721578,0.0001319743,0.0001040477,0.0002628937,0.0001027619,0.0001702888,0.002565703,0.0003369467,0.00008256018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170799,"about_ca_system_score_gemma":0.000142288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000865816,"about_ca_topic_score_gemma":0.00001602381,"domain_scores_codex":[0.9974318,0.0001632201,0.000657854,0.0003795705,0.001099728,0.0002678581],"domain_scores_gemma":[0.9973623,0.0003093611,0.0004766344,0.0009517433,0.0007368255,0.0001631845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004150613,0.000551343,0.00282828,0.00002988626,0.0004582753,0.0003748157,0.001100738,0.04951698,0.0002347397,0.0430771,0.009445476,0.8923408],"study_design_scores_gemma":[0.0004883747,0.0002668721,0.002021687,0.000347158,0.00002471691,0.0006808976,0.0001772516,0.7714441,0.001387783,0.001632302,0.2212113,0.0003174783],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01447228,0.0002807332,0.9760728,0.004817088,0.003919805,0.00007242997,0.00001810823,0.00003942446,0.0003072974],"genre_scores_gemma":[0.9416189,0.00007792202,0.05617112,0.0008334941,0.0009225172,0.000001227341,0.0000116322,0.00001100504,0.0003522148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9271466,"threshold_uncertainty_score":0.9984989,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4239041421","doi":"10.1504/ijbdi.2019.098891","title":"Extended results from the measurement and analysis of safety in a large city","year":2019,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Seriousness; Suspect; Index (typography); Police department; Criminology; Crime statistics; Psychology; Statistics; Computer science; Mathematics; Political science; Law","authors":[{"name":"Rami Ibrahim","is_ca":true},{"name":"M. Omair Shafiq","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2507318528765463,"gpt":0.4149727737503622,"spread":0.1642409208738159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003555819,0.0000580852,0.0001802932,0.0002110106,0.00004319575,0.00006443347,0.001321764,0.00003247989,0.000652569],"category_scores_gemma":[0.00109606,0.00004222028,0.0001080525,0.0003186781,0.00008719868,0.0002823244,0.0002471329,0.0001519869,0.000005683328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009955817,"about_ca_system_score_gemma":0.0001040477,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006766396,"about_ca_topic_score_gemma":0.0236456,"domain_scores_codex":[0.9979912,0.0001386888,0.0007012886,0.0001615552,0.0009034422,0.0001037957],"domain_scores_gemma":[0.9982779,0.0003340358,0.0004657505,0.000296975,0.0005832623,0.00004207332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001837082,0.001230352,0.5856544,0.000009350058,0.005145414,0.00004493749,0.03114991,0.0004943958,0.0008429628,0.01283179,0.003213594,0.3575459],"study_design_scores_gemma":[0.000797351,0.0001073796,0.9127594,0.0005264617,0.0003865149,0.000003269472,0.01136019,0.005511148,0.0007113115,0.00309217,0.0645638,0.0001809631],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402457,0.0009902788,0.04037723,0.005921736,0.002582315,0.00021977,0.004385868,0.000005902618,0.005271189],"genre_scores_gemma":[0.9987292,0.0007441477,0.0001652728,0.0001029669,0.0001503493,3.533204e-7,0.00006012328,0.000002141619,0.00004542208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3573649,"threshold_uncertainty_score":0.9998477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2916825936","doi":"10.1504/ijbdi.2019.10019310","title":"Extended results from the measurement and analysis of safety in a large city","year":2019,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Forensic engineering; Computer science; Engineering","authors":[{"name":"Rami Ibrahim","is_ca":true},{"name":"M. Omair Shafiq","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2507318528765463,"gpt":0.4149727737503622,"spread":0.1642409208738159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003555819,0.0000580852,0.0001802932,0.0002110106,0.00004319575,0.00006443347,0.001321764,0.00003247989,0.000652569],"category_scores_gemma":[0.00109606,0.00004222028,0.0001080525,0.0003186781,0.00008719868,0.0002823244,0.0002471329,0.0001519869,0.000005683328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009955817,"about_ca_system_score_gemma":0.0001040477,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006766396,"about_ca_topic_score_gemma":0.0236456,"domain_scores_codex":[0.9979912,0.0001386888,0.0007012886,0.0001615552,0.0009034422,0.0001037957],"domain_scores_gemma":[0.9982779,0.0003340358,0.0004657505,0.000296975,0.0005832623,0.00004207332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001837082,0.001230352,0.5856544,0.000009350058,0.005145414,0.00004493749,0.03114991,0.0004943958,0.0008429628,0.01283179,0.003213594,0.3575459],"study_design_scores_gemma":[0.000797351,0.0001073796,0.9127594,0.0005264617,0.0003865149,0.000003269472,0.01136019,0.005511148,0.0007113115,0.00309217,0.0645638,0.0001809631],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402457,0.0009902788,0.04037723,0.005921736,0.002582315,0.00021977,0.004385868,0.000005902618,0.005271189],"genre_scores_gemma":[0.9987292,0.0007441477,0.0001652728,0.0001029669,0.0001503493,3.533204e-7,0.00006012328,0.000002141619,0.00004542208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3573649,"threshold_uncertainty_score":0.9998477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4235093540","doi":"10.1504/ijbdi.2019.097398","title":"An insight into mobile advertising and its impact on the resources of handheld devices: a survey","year":2019,"lang":"en","type":"article","venue":"International Journal of Big Data Intelligence","topic":"Green IT and Sustainability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mobile device; Computer science; World Wide Web; Seriousness; Internet privacy; Work (physics); Mobile Web; Advertising; Multimedia; Mobile technology; Business; Engineering","authors":[{"name":"Abdurhman Albasir","is_ca":true},{"name":"Maazen Alsabaan","is_ca":false},{"name":"Kshirasagar Naik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04833223642941808,"gpt":0.3267239040051741,"spread":0.278391667575756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001012415,0.0001111593,0.0001714486,0.000142064,0.00002371673,0.00007755024,0.001188239,0.00004299702,0.00007760424],"category_scores_gemma":[0.0002896868,0.00006974456,0.00004243538,0.0001201939,0.00004377609,0.0004932854,0.0001274255,0.0002113878,0.000007497381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007123025,"about_ca_system_score_gemma":0.00004774503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002731981,"about_ca_topic_score_gemma":0.0002192707,"domain_scores_codex":[0.9988696,0.00009219372,0.0004147196,0.00012817,0.0003899795,0.0001053485],"domain_scores_gemma":[0.9985043,0.0004714726,0.0001544859,0.0003476797,0.0004570996,0.00006490661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001257909,0.0004086898,0.4401425,0.0002754367,0.001090688,0.00008177562,0.01458326,0.09621464,0.01171704,0.0005867436,0.0005447947,0.4330965],"study_design_scores_gemma":[0.0007192175,0.002380453,0.5781056,0.001553395,0.00007638925,0.0002644228,0.004560141,0.328521,0.06671692,0.002345505,0.01392927,0.0008276442],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954309,0.001145529,0.00253839,0.00004363216,0.0005052285,0.00009662369,0.00008493233,0.00000756577,0.0001472361],"genre_scores_gemma":[0.9995,0.000255993,0.00006410034,0.00003403068,0.0001100118,8.142e-7,0.00001713068,0.000009949604,0.000008016108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4322689,"threshold_uncertainty_score":0.2844101,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}