{"meta":{"query_hash":"a57dbc4bd326","filters":{"venue":"Information Systems"},"cohort_total":34,"direct_labels_cover":0,"predictions_cover":34,"exported":34,"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/a57dbc4bd326","api":"https://metacan.xera.ac/api/v1/cohort?venue=Information+Systems"},"results":[{"id":"W1974941207","doi":"10.1016/j.is.2010.08.005","title":"Improving the usability of standard schemas","year":2010,"lang":"en","type":"article","venue":"Information Systems","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Usability; Variety (cybernetics); XML; RDF; Data science; Semantic data model; Software engineering; Semantic Web; Information retrieval; World Wide Web; Human–computer interaction; Artificial intelligence","score_opus":0.01202619647189775,"score_gpt":0.23370759808794234,"score_spread":0.22168140161604458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974941207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31619954,0.000027153503,0.6743205,0.00029301931,0.0017241539,0.000250249,0.0000037802513,0.0001166562,0.007064896],"genre_scores_gemma":[0.9959522,4.5250778e-7,0.003960762,0.00003738814,0.00002721016,0.000008712908,5.314953e-7,7.2192137e-7,0.0000120558725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928504,0.000022131624,0.0003189251,0.000049410053,0.00023574539,0.000088761786],"domain_scores_gemma":[0.9990316,0.0001004457,0.0002038848,0.00047028874,0.00017516097,0.000018639903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008880612,0.000045660876,0.000090855836,0.000034269062,0.000068382506,0.00013418173,0.000433242,0.00004002601,0.000002430663],"category_scores_gemma":[0.00024736172,0.000026885948,0.000027968847,0.00012396999,0.000052700685,0.0012459159,0.00008986829,0.000088115914,0.000028037146],"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":[0.000010971292,0.000013039383,0.011945447,0.0003102977,0.000017612841,3.4284744e-7,0.007814871,0.00020192159,0.005528006,0.8763526,0.0011494989,0.096655354],"study_design_scores_gemma":[0.0015556217,0.00025857647,0.046337437,0.000108409484,0.000017710767,0.00015535523,0.00909379,0.5022649,0.090393126,0.0017610806,0.34739384,0.00066016114],"about_ca_topic_score_codex":0.00018238154,"about_ca_topic_score_gemma":0.000010462108,"teacher_disagreement_score":0.8745915,"about_ca_system_score_codex":0.000009822683,"about_ca_system_score_gemma":0.000061405044,"threshold_uncertainty_score":0.12939174},"labels":[],"label_agreement":null},{"id":"W1979660621","doi":"10.1016/j.is.2007.03.001","title":"Indexing schemes for similarity search in datasets of short protein fragments","year":2007,"lang":"en","type":"article","venue":"Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fonterra Co-Operative Group; Victoria University; Victoria University of Wellington; University of Ottawa","keywords":"Search engine indexing; Computer science; Nearest neighbor search; Similarity (geometry); Information retrieval; Data mining; Artificial intelligence","score_opus":0.03226710671312223,"score_gpt":0.3096500319111106,"score_spread":0.2773829251979884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979660621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03761946,0.000024800693,0.9608952,0.000012361463,0.00015691543,0.000712944,0.0001438646,0.00002891566,0.00040553554],"genre_scores_gemma":[0.97814953,8.9782276e-7,0.021443063,0.000027467144,0.000026381183,0.000036010362,0.0003071013,0.0000021554579,0.00000738623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881524,0.000020408026,0.00051211624,0.00009359821,0.0003608697,0.00019778972],"domain_scores_gemma":[0.99934846,0.000047603902,0.0001063214,0.00033775548,0.00011130453,0.00004854455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016292821,0.000066349676,0.0001202761,0.00020099658,0.000054102664,0.0001083002,0.00043506583,0.00006054548,0.0000010179261],"category_scores_gemma":[0.000042732787,0.000058576752,0.000018423101,0.00024230908,0.0000120942,0.0027297265,0.00019619144,0.00007787595,0.0000062485365],"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":[0.00030593903,0.000420341,0.031933468,0.0046047056,0.0001043504,0.00001646584,0.013088853,0.0098152235,0.007388942,0.21894039,0.009799596,0.70358175],"study_design_scores_gemma":[0.00095600047,0.00009284994,0.004776335,0.0004839669,0.0000016607494,0.000007505781,0.00057245325,0.8255023,0.017673254,0.00011833199,0.14956433,0.00025103815],"about_ca_topic_score_codex":0.00014564891,"about_ca_topic_score_gemma":0.0000056937965,"teacher_disagreement_score":0.94053006,"about_ca_system_score_codex":0.000059922528,"about_ca_system_score_gemma":0.00006050164,"threshold_uncertainty_score":0.23886906},"labels":[],"label_agreement":null},{"id":"W1997167079","doi":"10.1016/j.is.2010.08.007","title":"Building a peer-to-peer content distribution network with high performance, scalability and robustness","year":2010,"lang":"en","type":"article","venue":"Information Systems","topic":"Caching and Content Delivery","field":"Computer Science","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":"McGill University","funders":"","keywords":"Computer science; Scalability; Robustness (evolution); Gossip protocol; Computer network; Server; Distributed computing; Locality; Peer-to-peer; Content distribution; Routing protocol; Load balancing (electrical power); Gossip; Routing (electronic design automation); Database","score_opus":0.01253043178690378,"score_gpt":0.20485683220262468,"score_spread":0.1923264004157209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997167079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6089522,0.0000051272327,0.3894215,0.00036181236,0.0007981122,0.00021672112,0.000009621494,0.000105637046,0.00012923917],"genre_scores_gemma":[0.99750966,7.9716995e-7,0.0020882108,0.00008429188,0.00012416339,0.00004741507,0.00003189997,0.0000031482364,0.00011040524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868745,0.00003174679,0.0003425758,0.00015350252,0.0005490496,0.00023565661],"domain_scores_gemma":[0.99857223,0.00004506401,0.00014679672,0.00035869726,0.0007541134,0.00012307167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010768562,0.00012338691,0.00016071339,0.00005992838,0.00023595593,0.00064233167,0.00027468038,0.000067503344,9.403768e-7],"category_scores_gemma":[0.000066859524,0.00009480404,0.000020792075,0.00028895,0.00003185893,0.0021531498,0.00010920459,0.00019312689,0.000021278402],"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":[0.00029826612,0.00010669615,0.13328288,0.00065155746,0.00012960336,0.0000062142303,0.003201784,0.45242703,0.0012874498,0.24889345,0.011966036,0.14774904],"study_design_scores_gemma":[0.00072353263,0.00014568833,0.09095582,0.000151131,0.000009744906,0.00013095624,0.00016991691,0.8802037,0.0001313939,0.00001604331,0.027035924,0.00032612454],"about_ca_topic_score_codex":0.00026300934,"about_ca_topic_score_gemma":0.00001146642,"teacher_disagreement_score":0.4277767,"about_ca_system_score_codex":0.000049361424,"about_ca_system_score_gemma":0.00003878564,"threshold_uncertainty_score":0.61940193},"labels":[],"label_agreement":null},{"id":"W1997651539","doi":"10.1016/j.is.2010.06.006","title":"Temporal data classification using linear classifiers","year":2010,"lang":"en","type":"article","venue":"Information Systems","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Temporal database; Data mining; Database","score_opus":0.09938944203689498,"score_gpt":0.2954013290880879,"score_spread":0.19601188705119293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997651539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01889816,0.000013371401,0.9719565,0.00018630798,0.0015788373,0.0001685653,0.000022374992,0.00018063172,0.0069952533],"genre_scores_gemma":[0.9718392,9.462493e-7,0.027648352,0.00006585105,0.00018625222,0.0000039100373,0.00018055321,0.000003868522,0.00007108739],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988397,0.000026855152,0.00051352626,0.00015619141,0.00030141076,0.00016233158],"domain_scores_gemma":[0.99823546,0.000023526596,0.00039165793,0.0011021323,0.00017348517,0.00007372133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071162946,0.00009117071,0.00012688717,0.0001467807,0.00019204206,0.0005816377,0.0010009344,0.000082733015,0.000008631107],"category_scores_gemma":[0.00006919503,0.00007934318,0.000031783416,0.00040004577,0.000028398128,0.0059891776,0.00023703894,0.00016012693,0.00016735149],"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":[0.000028348415,0.00010887749,0.033569857,0.00040312207,0.00025561103,0.000006935695,0.008273324,0.01872207,0.01088602,0.6177475,0.02142105,0.28857726],"study_design_scores_gemma":[0.00007315209,0.0000055404653,0.00052065746,0.000008257769,0.000004077417,0.000019723682,0.0002984623,0.8426987,0.000018859986,0.00001105859,0.15625456,0.00008694748],"about_ca_topic_score_codex":0.00021956571,"about_ca_topic_score_gemma":0.000013050347,"teacher_disagreement_score":0.952941,"about_ca_system_score_codex":0.000023346956,"about_ca_system_score_gemma":0.000081732505,"threshold_uncertainty_score":0.5608746},"labels":[],"label_agreement":null},{"id":"W2000702636","doi":"10.1016/j.is.2005.02.004","title":"PeCAN: An architecture for users’ privacy-aware electronic commerce contexts on the semantic web","year":2005,"lang":"en","type":"article","venue":"Information Systems","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Saint Mary's University","funders":"","keywords":"Computer science; World Wide Web; Semantic Web; Social Semantic Web; Semantic Web Stack; Web service; Ontology","score_opus":0.02576577915017323,"score_gpt":0.2945594008217358,"score_spread":0.26879362167156257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000702636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7544784,0.00064663123,0.095006205,0.086118296,0.0035790245,0.016089164,0.00069704675,0.0015297993,0.04185544],"genre_scores_gemma":[0.99767035,0.00003245988,0.000026012802,0.0011678188,0.0006104681,0.00021542457,0.000097243086,0.000008183845,0.0001720227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984662,0.0002693326,0.00035037598,0.00011741051,0.0004149061,0.00038179234],"domain_scores_gemma":[0.9988674,0.00020008607,0.00023465953,0.00044454986,0.00016394486,0.00008934541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013861039,0.00012753782,0.00014648835,0.00010679677,0.00093146996,0.00041217514,0.0006408486,0.00012315882,0.000026757016],"category_scores_gemma":[0.00038502825,0.00009471942,0.000061811916,0.000193511,0.00007548227,0.0015501147,0.000053708838,0.00022844973,0.00018700169],"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":[0.00036513558,0.00019334264,0.00078075595,0.000386174,0.00012540567,4.2992124e-7,0.24129166,0.0016626187,0.00015474454,0.5732726,0.09403298,0.08773421],"study_design_scores_gemma":[0.00044507856,0.00014258642,0.00020418367,0.000048851398,0.000008868993,0.0000039110623,0.009406988,0.0062131453,0.000039589606,0.0006387624,0.9826968,0.00015125335],"about_ca_topic_score_codex":0.0009564553,"about_ca_topic_score_gemma":0.0027459469,"teacher_disagreement_score":0.8886638,"about_ca_system_score_codex":0.00024709257,"about_ca_system_score_gemma":0.00021786128,"threshold_uncertainty_score":0.71642077},"labels":[],"label_agreement":null},{"id":"W2006806998","doi":"10.1016/s0306-4379(03)00038-3","title":"Extending object-oriented databases for fuzzy information modeling","year":2003,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":104,"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","funders":"","keywords":"Computer science; Fuzzy logic; Object (grammar); Database; Data mining; Measure (data warehouse); Database model; Data model (GIS); Database design; Theoretical computer science; Information retrieval; Artificial intelligence","score_opus":0.0335790971528764,"score_gpt":0.26375830145697404,"score_spread":0.23017920430409763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006806998","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033225544,0.000038967206,0.97550815,0.000029678835,0.0015971891,0.0006388617,0.0000653522,0.00024017492,0.021549365],"genre_scores_gemma":[0.8806694,0.000036022244,0.11592886,0.00060871034,0.00015997066,0.00044650433,0.0018429226,0.000011839531,0.00029575726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986676,0.000031079715,0.00060382404,0.00010379581,0.00034547757,0.000248217],"domain_scores_gemma":[0.99897975,0.00004189296,0.00025152363,0.00044470027,0.00022266996,0.00005948216],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008120558,0.0001243634,0.00012917144,0.00032779344,0.00021925136,0.0008189568,0.00037103647,0.000030634885,0.0000021307367],"category_scores_gemma":[0.0001541685,0.00011621951,0.00004298429,0.00038521647,0.0000070903047,0.024462812,0.00009421431,0.000052358002,0.00022323467],"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":[0.000003536002,0.000008521586,0.000035163943,0.00017965978,0.00001838586,2.0322767e-7,0.0010072036,0.018182222,0.0000036464055,0.95589554,0.0037689896,0.02089691],"study_design_scores_gemma":[0.00033286863,0.000013353059,0.0000047486315,0.000031970387,0.0000038023104,0.00000525088,0.0007185534,0.6574239,0.00003486315,0.00011962228,0.34119254,0.00011846956],"about_ca_topic_score_codex":0.00003744769,"about_ca_topic_score_gemma":6.6169235e-7,"teacher_disagreement_score":0.9557759,"about_ca_system_score_codex":0.000054375054,"about_ca_system_score_gemma":0.00004210456,"threshold_uncertainty_score":0.9891815},"labels":[],"label_agreement":null},{"id":"W2046904770","doi":"10.1016/s0306-4379(01)00054-0","title":"One-pass evaluation of region algebra expressions","year":2003,"lang":"en","type":"article","venue":"Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Operator (biology); Set (abstract data type); Simple (philosophy); Tree (set theory); Algorithm; Theoretical computer science; Algebra over a field; Programming language; Mathematics","score_opus":0.05425727120654508,"score_gpt":0.28254411237563026,"score_spread":0.22828684116908518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046904770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053709936,0.00017303543,0.9726574,0.000029874798,0.0007536463,0.00045382357,0.000009051337,0.00008281281,0.020469356],"genre_scores_gemma":[0.9916715,0.0000071694476,0.008102325,0.000029907374,0.000023864399,0.000096836615,0.00001970683,0.0000030680458,0.000045645425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982691,0.00021944263,0.0005689056,0.00010027376,0.00071247254,0.0001298],"domain_scores_gemma":[0.99831945,0.000046806963,0.000448895,0.0005575209,0.00057492085,0.000052430165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010862417,0.00008232949,0.0001525803,0.00014089342,0.00009430576,0.00006031653,0.00017966416,0.00005199791,0.0000053875547],"category_scores_gemma":[0.00021263942,0.0000753266,0.00003531865,0.00028359194,0.00001949254,0.0041432586,0.000040707753,0.00004951861,0.00007039039],"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":[0.0000023971797,0.000014675944,0.00010042338,0.00008664942,0.000010757968,1.6707733e-7,0.0018332724,0.0031382723,0.00032930155,0.9860777,0.000722903,0.0076834336],"study_design_scores_gemma":[0.002415945,0.00017295824,0.0022436257,0.0010254078,0.000039924565,0.00013440123,0.0046719383,0.15498342,0.012237091,0.002798183,0.8185586,0.00071853615],"about_ca_topic_score_codex":0.000041928473,"about_ca_topic_score_gemma":0.0000014122571,"teacher_disagreement_score":0.98630047,"about_ca_system_score_codex":0.00006566608,"about_ca_system_score_gemma":0.00012434734,"threshold_uncertainty_score":0.30717295},"labels":[],"label_agreement":null},{"id":"W2047432893","doi":"10.1016/j.is.2014.09.001","title":"Profile Diversity for Query Processing using User Recommendations","year":2014,"lang":"en","type":"article","venue":"Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Information retrieval; Diversity (politics); Web search query; Query optimization; Search engine","score_opus":0.04397560426400225,"score_gpt":0.27852303754054963,"score_spread":0.23454743327654737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047432893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00081311795,0.000015475067,0.9917749,0.0002661966,0.0007837989,0.0006320421,0.00000991176,0.00035214378,0.0053524156],"genre_scores_gemma":[0.9174497,0.0000014531048,0.081879586,0.00023703968,0.00012055062,0.0001221155,0.000027717137,0.0000056241865,0.00015618958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990534,0.00006252888,0.0004130522,0.000115845876,0.0001818986,0.00017325826],"domain_scores_gemma":[0.9990085,0.000053617776,0.00033813197,0.00027622844,0.0002713309,0.000052214506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008139228,0.00009776789,0.00014856887,0.00016094545,0.0005374941,0.0005325836,0.0003970275,0.000068139285,0.0000019062684],"category_scores_gemma":[0.00003167827,0.00008898007,0.000047833473,0.0001938737,0.000009069064,0.0041430844,0.00022032918,0.000053871816,0.000022922162],"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":[0.00001465067,0.00008447734,0.006456701,0.00183906,0.00006360524,3.4398533e-7,0.015163053,0.001031792,0.00019658479,0.4041227,0.10380363,0.4672234],"study_design_scores_gemma":[0.00023151489,0.000032327203,0.00013233251,0.00012380471,0.0000039073907,0.000014929678,0.00028168954,0.5574669,0.00025025412,0.00026996617,0.44102636,0.00016599194],"about_ca_topic_score_codex":0.00019832063,"about_ca_topic_score_gemma":0.0000028449306,"teacher_disagreement_score":0.9166366,"about_ca_system_score_codex":0.00008439468,"about_ca_system_score_gemma":0.00004271095,"threshold_uncertainty_score":0.5135716},"labels":[],"label_agreement":null},{"id":"W2051197250","doi":"10.1016/j.is.2010.01.002","title":"Vocabularies, ontologies, and rules for enterprise and business process modeling and management","year":2010,"lang":"en","type":"article","venue":"Information Systems","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Business process management; Business process; Process (computing); Business process modeling; Process management; Process modeling; Business rule; Artifact-centric business process model; Knowledge management; Data science; Software engineering; Work in process; Business; Programming language","score_opus":0.014124420301371383,"score_gpt":0.2240172048951858,"score_spread":0.20989278459381444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051197250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79044086,0.0009102555,0.20356382,0.0006461912,0.0005435696,0.0008639914,0.000015870026,0.00025858908,0.0027568538],"genre_scores_gemma":[0.9984503,0.00015740107,0.0006902477,0.00023822814,0.00019709047,0.00013563999,0.00008590608,0.0000112792895,0.000033928194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990756,0.0000024936605,0.00038914775,0.00017827381,0.00016905217,0.00018543187],"domain_scores_gemma":[0.9991172,0.00001661536,0.00022054478,0.00013295018,0.0004955029,0.000017213011],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003569989,0.00017095127,0.00022796942,0.00030201598,0.00029365811,0.0012221386,0.00010307227,0.00009028721,0.0000014960352],"category_scores_gemma":[0.00007550279,0.00014557279,0.00001801301,0.00020942635,0.000051359206,0.0033100478,0.000099222685,0.00007914462,0.0000055778783],"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":[0.00062376796,0.0002065593,0.056127127,0.07857148,0.0007133339,0.000009320942,0.004204415,0.05334482,0.00015530473,0.34106666,0.0016218937,0.4633553],"study_design_scores_gemma":[0.0007047006,0.0000028125187,0.000789971,0.00017220122,0.00011045504,0.0000103129105,0.001692227,0.9726994,0.0000015202095,0.0019097248,0.021652324,0.00025435048],"about_ca_topic_score_codex":0.00042976558,"about_ca_topic_score_gemma":0.000044438882,"teacher_disagreement_score":0.91935456,"about_ca_system_score_codex":0.0000057129755,"about_ca_system_score_gemma":0.000011401952,"threshold_uncertainty_score":0.9998147},"labels":[],"label_agreement":null},{"id":"W2053019617","doi":"10.1016/j.is.2009.09.002","title":"Competitive advantage of enterprise resource planning vendors in Iran","year":2009,"lang":"en","type":"article","venue":"Information Systems","topic":"ERP Systems Implementation and Impact","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Enterprise resource planning; Competitive advantage; Business; Context (archaeology); Globe; Process (computing); Process management; Product (mathematics); Resource (disambiguation); Knowledge management; Industrial organization; Computer science; Marketing","score_opus":0.022675814558472313,"score_gpt":0.28088881885195727,"score_spread":0.2582130042934849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053019617","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44731137,0.00013256406,0.0011292604,0.00022735556,0.0005717009,0.0010230293,0.000023545686,0.00014765318,0.5494335],"genre_scores_gemma":[0.9989167,0.0000015192251,0.000011496177,0.0007852394,0.00013998665,0.000009222134,0.00009227519,0.000004660002,0.000038897662],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99877644,0.000017725433,0.0007109447,0.00006539021,0.00026459305,0.00016488669],"domain_scores_gemma":[0.99923414,0.000027152786,0.0005368486,0.00012428871,0.000066542685,0.0000110049905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042755966,0.00010547069,0.00019887048,0.00051450735,0.00004496913,0.00016786242,0.00012296003,0.000035802983,0.0000296789],"category_scores_gemma":[0.000055358247,0.00009849647,0.000040482464,0.0003359192,0.000011961958,0.0028918139,0.000020194522,0.00006708753,0.0001458241],"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":[0.0008482563,0.00033004582,0.49557844,0.0021732356,0.00009760354,0.00003331052,0.032359645,0.010875504,0.0018394392,0.38313374,0.033661224,0.039069556],"study_design_scores_gemma":[0.0031289654,0.000058940528,0.13948837,0.00083817745,0.000008251457,0.000010755814,0.06591675,0.005396744,0.00013454961,0.00004710608,0.7846599,0.000311479],"about_ca_topic_score_codex":0.00019903968,"about_ca_topic_score_gemma":0.000004867054,"teacher_disagreement_score":0.7509987,"about_ca_system_score_codex":0.000039764076,"about_ca_system_score_gemma":0.000008313232,"threshold_uncertainty_score":0.40165696},"labels":[],"label_agreement":null},{"id":"W2073984053","doi":"10.1016/j.is.2010.11.001","title":"Suffix trees for inputs larger than main memory","year":2010,"lang":"en","type":"article","venue":"Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","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 Victoria","funders":"","keywords":"Generalized suffix tree; Compressed suffix array; Suffix tree; Suffix; Computer science; String (physics); String searching algorithm; Auxiliary memory; Algorithm; Data structure; Tree (set theory); Theoretical computer science; Construct (python library); Mathematics; Combinatorics; Programming language","score_opus":0.010174569598353925,"score_gpt":0.23446209226044487,"score_spread":0.22428752266209095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073984053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009201319,0.00001909196,0.98276466,0.0002455945,0.0031796324,0.000438393,0.000073160816,0.00019125568,0.003886893],"genre_scores_gemma":[0.98134273,0.0000021283472,0.017231349,0.0003221008,0.0003428839,0.00012498435,0.00011056898,0.0000059101853,0.000517343],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991078,0.000020140496,0.00033314934,0.00010568874,0.0002504816,0.00018278397],"domain_scores_gemma":[0.9990758,0.00006655017,0.00017140609,0.0004868234,0.0001253538,0.00007408697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052270404,0.00009340697,0.00011689236,0.000113329894,0.00014680815,0.00047240828,0.00054999336,0.00008174511,0.0000065776812],"category_scores_gemma":[0.0000426964,0.000072336334,0.00004383855,0.00013342952,0.000013713457,0.0033695265,0.00012577955,0.00009828924,0.0001831046],"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":[0.00005297985,0.00016084615,0.00143991,0.00053883076,0.00007715009,0.000006064519,0.015764544,0.002032454,0.008189701,0.4679263,0.27769643,0.2261148],"study_design_scores_gemma":[0.0005124853,0.00003764013,0.0012211342,0.000030843446,0.0000020920652,0.00003320888,0.00013943177,0.3827564,0.0009959678,0.00018337973,0.61390483,0.00018257191],"about_ca_topic_score_codex":0.00006046483,"about_ca_topic_score_gemma":0.000013832137,"teacher_disagreement_score":0.9721414,"about_ca_system_score_codex":0.000014400066,"about_ca_system_score_gemma":0.000052253767,"threshold_uncertainty_score":0.45554438},"labels":[],"label_agreement":null},{"id":"W2075468564","doi":"10.1016/s0306-4379(03)00052-8","title":"Designing information systems in social context: a goal and scenario modelling approach","year":2003,"lang":"en","type":"article","venue":"Information Systems","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":156,"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":"Computer science; Context (archaeology); Human–computer interaction; Information system; Data science; Knowledge management; Process management","score_opus":0.05189589636838759,"score_gpt":0.2570927286918884,"score_spread":0.20519683232350078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075468564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016240339,0.0003091408,0.9949603,0.000007406349,0.0005709838,0.0004784804,0.0000024892965,0.00030636674,0.0017407694],"genre_scores_gemma":[0.8347297,0.000007831035,0.1650799,0.00003408808,0.000027872531,0.00009702465,0.000008138323,0.0000049146506,0.00001055696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984069,0.00020514849,0.0006972742,0.00011352406,0.00030565314,0.00027151793],"domain_scores_gemma":[0.999138,0.00020609656,0.00028373097,0.00018959533,0.00013127642,0.000051296563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016537854,0.00015900882,0.0002598687,0.00034855967,0.00015611424,0.00060240284,0.00023984126,0.00013171065,1.048888e-7],"category_scores_gemma":[0.00023906173,0.00015680738,0.000025616038,0.00039262554,0.00002431152,0.007315046,0.000052826155,0.00018720234,0.000017773145],"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":[0.0000028358897,0.0000030192937,0.00007543759,0.00018390296,0.000005294475,3.2469208e-7,0.009358333,0.89181614,0.0000058195196,0.095135145,0.000028698885,0.0033850526],"study_design_scores_gemma":[0.0005006889,0.000017854174,0.000064683685,0.00008648931,0.000002037132,0.000059337064,0.0041110395,0.98840225,0.000037170306,0.00022352308,0.006263735,0.00023121342],"about_ca_topic_score_codex":0.0000903451,"about_ca_topic_score_gemma":3.5719887e-7,"teacher_disagreement_score":0.8331056,"about_ca_system_score_codex":0.0001648165,"about_ca_system_score_gemma":0.00006792188,"threshold_uncertainty_score":0.6394419},"labels":[],"label_agreement":null},{"id":"W2082534709","doi":"10.1016/j.is.2012.08.005","title":"Consistent query answering under spatial semantic constraints","year":2012,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Computer science; Spatial query; Class (philosophy); Set (abstract data type); Data integrity; Semantics (computer science); Information retrieval; Consistency (knowledge bases); Core (optical fiber); Query optimization; Query language; Theoretical computer science; Database; Web query classification; Web search query; Artificial intelligence; Programming language; Search engine","score_opus":0.021003019347201494,"score_gpt":0.2307467823017295,"score_spread":0.209743762954528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082534709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027725792,0.00004081293,0.9740746,0.000116755524,0.0025060775,0.0002249734,0.0000064610176,0.00017574511,0.020081993],"genre_scores_gemma":[0.9981017,0.0000033900228,0.0013489538,0.00025968946,0.00012685843,0.000014627699,0.000027718477,0.0000025242757,0.00011452799],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990241,0.00003068236,0.00034521378,0.00007106919,0.0002848421,0.00024412772],"domain_scores_gemma":[0.9993735,0.000026742413,0.00015224678,0.00030794728,0.000056499204,0.000083078834],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004602696,0.00009118882,0.00010822004,0.00012420875,0.00008289126,0.00049171667,0.0003137668,0.000033216373,0.00001187144],"category_scores_gemma":[0.000012600523,0.000082006045,0.00003142762,0.00013775782,0.000032715314,0.00559173,0.000150677,0.000051849114,0.0008481905],"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":[0.0000037388097,0.00006420144,0.0060357065,0.00038368555,0.00011695151,0.0000039193137,0.0041393163,0.0010811487,0.000070104,0.7935723,0.007661781,0.18686716],"study_design_scores_gemma":[0.0019419357,0.00008161049,0.032826122,0.00035210664,0.000039807313,0.00027971933,0.005782181,0.5754382,0.0005842492,0.00024026548,0.38121262,0.0012211746],"about_ca_topic_score_codex":0.0001085078,"about_ca_topic_score_gemma":0.000001561726,"teacher_disagreement_score":0.99532914,"about_ca_system_score_codex":0.000038533388,"about_ca_system_score_gemma":0.000020891535,"threshold_uncertainty_score":0.9999298},"labels":[],"label_agreement":null},{"id":"W2101496601","doi":"10.1016/j.is.2011.03.008","title":"Ranking uncertain sky: The probabilistic top-k skyline operator","year":2011,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Data mining; Probabilistic logic; Uncertain data; Set (abstract data type); Ranking (information retrieval); Object (grammar); Operator (biology); Algorithm; Artificial intelligence","score_opus":0.034734958614284876,"score_gpt":0.22833759244706478,"score_spread":0.1936026338327799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101496601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020562047,0.00007009943,0.9361157,0.00043373668,0.0020021717,0.0009290347,0.000012361628,0.00031783624,0.0580629],"genre_scores_gemma":[0.9928545,0.0000064050987,0.005331513,0.0008851432,0.00016555419,0.0001345937,0.00003714024,0.0000051602974,0.0005800292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990392,0.00005131128,0.00036450036,0.00009849581,0.00027901502,0.00016751238],"domain_scores_gemma":[0.9991492,0.00002958373,0.00014241016,0.00053407904,0.00010868245,0.000036036003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075820816,0.00009152188,0.000094917,0.00008018212,0.00015856669,0.0006437968,0.0009618654,0.00002674953,0.000013635851],"category_scores_gemma":[0.00004304675,0.000057175053,0.000027405882,0.00031517635,0.000022635428,0.0035222722,0.00020564487,0.00006496299,0.0004926519],"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":[0.0000074621903,0.000035339872,0.0005050311,0.00019633428,0.000048232334,0.0000053010576,0.015959296,0.0012714703,0.00000619853,0.885148,0.014220332,0.082597],"study_design_scores_gemma":[0.00048889156,0.000054155415,0.00088172767,0.00007073329,0.0000096248605,0.00002073129,0.001119983,0.5740582,0.000065456945,0.0006958671,0.42227122,0.00026342846],"about_ca_topic_score_codex":0.00012301037,"about_ca_topic_score_gemma":0.0000022916465,"teacher_disagreement_score":0.99079823,"about_ca_system_score_codex":0.000029197205,"about_ca_system_score_gemma":0.000030322908,"threshold_uncertainty_score":0.63322073},"labels":[],"label_agreement":null},{"id":"W2114146497","doi":"10.1016/j.is.2011.07.002","title":"Folksonomy-based personalized search and ranking in social media services","year":2011,"lang":"en","type":"article","venue":"Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Folksonomy; Computer science; Information retrieval; Social media; Ranking (information retrieval); World Wide Web; Personalized search; Preference; Resource (disambiguation); Annotation; Learning to rank; Topic model; Search engine; Artificial intelligence","score_opus":0.05081110230503755,"score_gpt":0.25439577021257526,"score_spread":0.2035846679075377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114146497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1353473,0.00033623195,0.8239943,0.0002980087,0.0010929637,0.0011262667,0.000012038863,0.00068616396,0.037106715],"genre_scores_gemma":[0.996439,0.0000027348428,0.0033336503,0.00010760825,0.00004435053,0.000057856214,0.000005611794,0.0000030674566,0.0000061183414],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902767,0.00009481952,0.00037901758,0.00009924277,0.00023444106,0.00016481632],"domain_scores_gemma":[0.9995771,0.000047222267,0.00011269418,0.00014242213,0.000079350095,0.000041193452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008056246,0.00008906046,0.0001654384,0.00025859676,0.00009528099,0.00028387294,0.00031082839,0.00007371772,0.0000041744674],"category_scores_gemma":[0.000003915849,0.000079598896,0.000027587563,0.00021344026,0.00001782974,0.0018149201,0.00006707064,0.000077823504,0.000020086234],"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":[0.000049876533,0.00006261122,0.03407508,0.001791636,0.00004593627,0.000010786882,0.4422918,0.00003197785,0.00006094913,0.43704617,0.0007474507,0.08378572],"study_design_scores_gemma":[0.007487247,0.00019023028,0.077051766,0.0012593325,0.000015009271,0.00012468926,0.03015816,0.75160563,0.0019547886,0.0020629787,0.12647521,0.0016149567],"about_ca_topic_score_codex":0.0007694269,"about_ca_topic_score_gemma":0.000033583452,"teacher_disagreement_score":0.8610917,"about_ca_system_score_codex":0.00004185258,"about_ca_system_score_gemma":0.00003966811,"threshold_uncertainty_score":0.3245949},"labels":[],"label_agreement":null},{"id":"W2159100004","doi":"10.1016/j.is.2008.01.005","title":"The complexity and approximation of fixing numerical attributes in databases under integrity constraints","year":2008,"lang":"en","type":"article","venue":"Information Systems","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Database; Data integrity; Conjunctive query; Consistency (knowledge bases); Semantics (computer science); Computational complexity theory; Approximation algorithm; Aggregate (composite); Theoretical computer science; Time complexity; Relational database; Algorithm; Artificial intelligence; Programming language","score_opus":0.10078243575120967,"score_gpt":0.2798962895402359,"score_spread":0.17911385378902622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159100004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06342655,0.00017310656,0.93088144,0.00015227213,0.0002520075,0.00025485473,0.000014497415,0.00005392816,0.004791322],"genre_scores_gemma":[0.9983103,0.000019416337,0.0015931105,0.000025756437,0.000014594068,0.000010698104,0.000016770044,0.0000010237032,0.000008301987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991244,0.00009177178,0.00040144988,0.000073970084,0.000186074,0.00012229719],"domain_scores_gemma":[0.9991965,0.00023714575,0.00020917626,0.00019223448,0.0001310612,0.000033882043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057509914,0.00006858188,0.00013710048,0.00006224618,0.00016639834,0.000089475085,0.00021719145,0.000031536925,0.0000012893381],"category_scores_gemma":[0.00016888358,0.000046197598,0.000018532424,0.00021411655,0.00023503284,0.0013304659,0.00010055597,0.00009088418,0.00001159309],"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":[0.000006915877,0.00003058084,0.030055894,0.000108589215,0.000011220334,9.392084e-7,0.0056991857,0.00022959779,0.000019042684,0.95700955,0.00063710817,0.00619136],"study_design_scores_gemma":[0.0011919106,0.00007217216,0.18662572,0.00021839415,0.0000043463033,0.00035734905,0.0041321116,0.79437417,0.00052789605,0.0026297239,0.009531344,0.00033487388],"about_ca_topic_score_codex":0.00017700171,"about_ca_topic_score_gemma":0.000014273162,"teacher_disagreement_score":0.95437986,"about_ca_system_score_codex":0.00003653421,"about_ca_system_score_gemma":0.000060770457,"threshold_uncertainty_score":0.18838833},"labels":[],"label_agreement":null},{"id":"W2264573896","doi":"10.1016/j.is.2013.01.004","title":"Forward to the special issue on vocabularies, ontologies and rules for the enterprise","year":2013,"lang":"en","type":"article","venue":"Information Systems","topic":"Collaboration in agile enterprises","field":"Business, Management and Accounting","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Information retrieval; Data science; Knowledge management","score_opus":0.011383458715812289,"score_gpt":0.2225070611590267,"score_spread":0.2111236024432144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2264573896","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11731465,0.0010735906,0.23401707,0.17220607,0.04465761,0.037096977,0.0003278142,0.0014502181,0.39185598],"genre_scores_gemma":[0.9788945,0.000012222096,0.00007525564,0.0074357083,0.0109335305,0.0014447827,0.000052529133,0.0000122494785,0.0011392728],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992353,0.000008317223,0.00032337886,0.00007655686,0.00021479194,0.00014166924],"domain_scores_gemma":[0.9990674,0.00018786543,0.00020128112,0.00022394345,0.00031059617,0.000008876167],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028653725,0.00010888374,0.00010963708,0.00011271415,0.00036174376,0.0020737313,0.00023809595,0.000036679885,0.000096760305],"category_scores_gemma":[0.0003410771,0.000059368012,0.000029520088,0.00016459437,0.00003184955,0.0020114651,0.00009396279,0.000048791455,0.0019484002],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055020588,0.000007473019,0.0012425127,0.00011798208,0.000027347183,9.586912e-8,0.0013587705,0.0017329525,0.0000014167211,0.026764167,0.9272236,0.04146862],"study_design_scores_gemma":[0.0001999442,0.0000118229555,0.0024573687,0.000031789386,0.000010900187,8.632179e-7,0.0048782956,0.008745646,0.0000035111198,0.00013939898,0.9834398,0.000080642945],"about_ca_topic_score_codex":0.00054549635,"about_ca_topic_score_gemma":0.00010285194,"teacher_disagreement_score":0.8615798,"about_ca_system_score_codex":0.000025950254,"about_ca_system_score_gemma":0.000011537738,"threshold_uncertainty_score":0.9989622},"labels":[],"label_agreement":null},{"id":"W2532696200","doi":"10.1016/j.is.2016.10.004","title":"HarVis: An integrated social media content analysis framework for YouTube platform","year":2016,"lang":"en","type":"article","venue":"Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Influencer marketing; Popularity; Social media; Entertainment; World Wide Web; Content analysis; Visualization; Vocabulary; Social network analysis; Exploratory analysis; User-generated content; Globe; Data science; Multimedia","score_opus":0.09981051094323912,"score_gpt":0.29717849900780147,"score_spread":0.19736798806456235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532696200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010196715,0.000020352663,0.9880557,0.00020960007,0.00084212545,0.00023749434,0.00004570225,0.0001470244,0.00024526464],"genre_scores_gemma":[0.9888011,0.000004535153,0.010455799,0.00015807919,0.00022674196,0.000116910225,0.00016013942,0.000004690067,0.00007197456],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998471,0.00003707849,0.0006995318,0.00016074353,0.00040507995,0.00022652664],"domain_scores_gemma":[0.99847895,0.00023918398,0.00042995,0.00032027377,0.00043844513,0.00009318967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006576584,0.00013306845,0.000309894,0.00047092838,0.00018429013,0.0006188109,0.0005118022,0.00011667439,0.000026789525],"category_scores_gemma":[0.00015035561,0.00008324069,0.00021413514,0.0008471121,0.000021193704,0.0039607384,0.000049118276,0.000052105668,0.00012376196],"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":[0.000042274965,0.000047002275,0.0032444582,0.000039297327,0.0012477553,5.4568017e-7,0.024578938,0.0003444483,0.00009239517,0.77984697,0.0024988768,0.18801704],"study_design_scores_gemma":[0.0028243186,0.0001924343,0.010147815,0.0002790168,0.00054007804,0.0000057617053,0.027011922,0.791619,0.0008366255,0.0017995775,0.16360305,0.0011403792],"about_ca_topic_score_codex":0.00003810559,"about_ca_topic_score_gemma":0.00001202403,"teacher_disagreement_score":0.97860444,"about_ca_system_score_codex":0.00008324795,"about_ca_system_score_gemma":0.000040234227,"threshold_uncertainty_score":0.59672076},"labels":[],"label_agreement":null},{"id":"W2549141685","doi":"10.1016/j.is.2017.01.002","title":"Upscaledb: Efficient integer-key compression in a key-value store using SIMD instructions","year":2017,"lang":"en","type":"article","venue":"Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Computer science; SIMD; Byte; Data compression; Key (lock); Associative array; Parallel computing; Compression (physics); Compression ratio; Database; Operating system; Algorithm; Programming language","score_opus":0.02574336979932961,"score_gpt":0.28147636981620056,"score_spread":0.25573300001687094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549141685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13460289,0.00005564319,0.85618824,0.00007747059,0.0034857914,0.00042322246,0.000026134996,0.00013622116,0.0050044106],"genre_scores_gemma":[0.9909252,0.0000041756084,0.008853837,0.000039274128,0.00010436889,0.00002137701,0.0000192785,0.000005474914,0.000027033968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836963,0.00007836588,0.00062133267,0.00019765506,0.00048113428,0.00025187264],"domain_scores_gemma":[0.9979994,0.000036330293,0.0005679476,0.0011287557,0.0001623632,0.00010517993],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00046855686,0.00015956616,0.00022432255,0.00028383563,0.0007159567,0.0012394711,0.0011259145,0.00011152669,0.0000030111078],"category_scores_gemma":[0.00007560633,0.00013288081,0.000050407136,0.00018049344,0.000055730034,0.0044109765,0.0006376918,0.00020289367,0.00009189754],"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":[0.000047921316,0.00019872095,0.006317354,0.00038221708,0.0000364072,0.000016866787,0.019022627,0.75562704,0.0008453743,0.13468374,0.003769106,0.07905264],"study_design_scores_gemma":[0.00051166146,0.00001903634,0.0038191797,0.00039557705,0.0000021541039,0.000047629357,0.0003530925,0.9707604,0.00007779621,0.00004947537,0.023797192,0.00016681012],"about_ca_topic_score_codex":0.0011727451,"about_ca_topic_score_gemma":0.000011901792,"teacher_disagreement_score":0.8563223,"about_ca_system_score_codex":0.00016764448,"about_ca_system_score_gemma":0.00008730156,"threshold_uncertainty_score":0.99979734},"labels":[],"label_agreement":null},{"id":"W2742330620","doi":"10.1016/j.is.2017.08.002","title":"Decentralized enforcement of document lifecycle constraints","year":2017,"lang":"en","type":"article","venue":"Information Systems","topic":"Access Control and Trust","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Workflow; Scalability; Encryption; Process (computing); Artifact (error); Software engineering; Key (lock); Software; Point (geometry); Hash function; Computer security; Database; Programming language","score_opus":0.02204375242083717,"score_gpt":0.33013193308135197,"score_spread":0.3080881806605148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742330620","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039423466,0.000078732024,0.0031172396,0.00046266353,0.0016818322,0.0008686106,0.000029701954,0.00006114552,0.9542766],"genre_scores_gemma":[0.9995502,0.000044968623,0.00002652433,0.000044492503,0.00006206703,0.000024225097,0.000006551879,0.0000014182752,0.00023950341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900067,0.000043317836,0.00037160327,0.000039079696,0.00038648056,0.00015882039],"domain_scores_gemma":[0.9989953,0.000028233862,0.0005410567,0.00020344305,0.00015599681,0.00007598659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061570137,0.000052448217,0.00013025162,0.000039791692,0.0005402335,0.00041277325,0.00029521354,0.000039426766,0.0003160722],"category_scores_gemma":[0.00016511214,0.000045546527,0.000040694194,0.00002632002,0.00021331814,0.0018392636,0.000030972282,0.000030209669,0.00012854396],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026571643,0.000013947608,0.011901828,0.00006538049,0.000050218,6.4279357e-7,0.011826891,0.00014503152,0.000006876224,0.9389621,0.0010430389,0.035957452],"study_design_scores_gemma":[0.0024729755,0.000028851224,0.0079280315,0.00011499853,0.00001996369,0.0000014917824,0.014612692,0.00055291073,0.00015308982,0.0005653145,0.9733669,0.00018275388],"about_ca_topic_score_codex":0.007300522,"about_ca_topic_score_gemma":0.00009426735,"teacher_disagreement_score":0.9723239,"about_ca_system_score_codex":0.000053696076,"about_ca_system_score_gemma":0.0001043502,"threshold_uncertainty_score":0.99930996},"labels":[],"label_agreement":null},{"id":"W2909965602","doi":"10.1016/j.is.2019.01.004","title":"Introduction to the Special Issue on Integrating Process-oriented and Event-based Systems","year":2019,"lang":"en","type":"article","venue":"Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Process (computing); Event (particle physics); Software engineering; Process management; Data science; Programming language; Engineering","score_opus":0.005499075885879379,"score_gpt":0.2368060791239883,"score_spread":0.2313070032381089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909965602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009509402,0.000046211746,0.9735835,0.0017582502,0.009250883,0.0017097592,0.00003949025,0.00018162106,0.003920853],"genre_scores_gemma":[0.98569196,0.0000022613322,0.0014604522,0.0005573905,0.010821279,0.00031385024,0.0000914087,0.000013241516,0.0010481345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851847,0.00008732985,0.00053722324,0.00021276188,0.00045177547,0.00019244489],"domain_scores_gemma":[0.99874437,0.000058736656,0.00030649148,0.00055029773,0.00026882268,0.00007128224],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000643595,0.00015255933,0.00019185258,0.00016229432,0.00018927478,0.00034798807,0.00023161939,0.000047714082,0.000008845569],"category_scores_gemma":[0.0001101196,0.0000994752,0.00002429272,0.00043897048,0.000015409907,0.0022775284,0.000057431374,0.00012205798,0.0009623901],"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":[0.0001042363,0.00003866144,0.00053794426,0.0011666775,0.000037695307,0.0000020581338,0.016885525,0.25251305,0.00019228877,0.6409543,0.06172366,0.0258439],"study_design_scores_gemma":[0.00021208705,0.00011479926,0.000050596525,0.00017435981,0.0000014737157,0.000023345689,0.0038599223,0.12432095,0.00016760167,0.0000021474914,0.8709446,0.00012808222],"about_ca_topic_score_codex":0.000093662406,"about_ca_topic_score_gemma":0.00000684983,"teacher_disagreement_score":0.9761826,"about_ca_system_score_codex":0.00008522325,"about_ca_system_score_gemma":0.000056203375,"threshold_uncertainty_score":0.99981546},"labels":[],"label_agreement":null},{"id":"W2912763210","doi":"10.1016/j.is.2019.01.007","title":"From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring","year":2019,"lang":"en","type":"article","venue":"Information Systems","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":58,"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 New Brunswick; University of Toronto","funders":"Eesti Teadusagentuur","keywords":"Computer science; Encoding (memory); Process (computing); Data mining; Event (particle physics); Dimension (graph theory); Feature (linguistics); Representation (politics); Partition (number theory); Machine learning; Artificial intelligence","score_opus":0.02969100307698083,"score_gpt":0.2777349318460065,"score_spread":0.24804392876902567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912763210","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96360195,0.00013040642,0.019738175,0.00026857594,0.002241799,0.0008014526,0.00007571788,0.00030906615,0.012832832],"genre_scores_gemma":[0.99795365,0.0000028061422,0.000103392566,0.00008434782,0.0013634197,0.00006391296,0.00031440338,0.00001688952,0.00009719857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863416,0.000012229192,0.0005344655,0.00029045847,0.0002827924,0.00024590825],"domain_scores_gemma":[0.9986211,0.000036699334,0.0003470207,0.00046841567,0.0005033426,0.000023412538],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032494214,0.0002049534,0.00032488877,0.00066545716,0.00010603736,0.000824979,0.0005317444,0.00011807629,0.000019846248],"category_scores_gemma":[0.00010418923,0.00018663252,0.000039284983,0.0010013193,0.000009688442,0.008298428,0.00031800842,0.00020341674,0.0011172536],"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":[0.00020036296,0.00015851605,0.28742018,0.0044244747,0.00028819457,0.00007035066,0.020827241,0.65385896,0.00022286928,0.00034237673,0.005583538,0.026602916],"study_design_scores_gemma":[0.00047021234,0.000005007859,0.00072032027,0.0010823709,0.00004586636,0.000009127786,0.013686805,0.9725068,0.000013036152,0.000020987836,0.011170689,0.00026874247],"about_ca_topic_score_codex":0.0017075314,"about_ca_topic_score_gemma":0.000066769884,"teacher_disagreement_score":0.31864786,"about_ca_system_score_codex":0.00007842464,"about_ca_system_score_gemma":0.000037806174,"threshold_uncertainty_score":0.9996605},"labels":[],"label_agreement":null},{"id":"W2936018264","doi":"10.1016/j.is.2019.04.004","title":"Context-aware temporal network representation of event logs: Model and methods for process performance analysis","year":2019,"lang":"en","type":"article","venue":"Information Systems","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":13,"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":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Process mining; Bottleneck; Event (particle physics); Context (archaeology); Representation (politics); Process (computing); Data mining; Process modeling; Machine learning; Pairwise comparison; Probabilistic logic; Artificial intelligence; Business process; Business process management; Work in process","score_opus":0.026173696744640453,"score_gpt":0.31558864883407284,"score_spread":0.2894149520894324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936018264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31508267,0.000120662946,0.68355584,0.0000583515,0.00014848296,0.0004583905,0.00000534517,0.000054004286,0.0005162379],"genre_scores_gemma":[0.9981578,0.00001020354,0.001133549,0.0001565412,0.00012588022,0.0000945601,0.00022583985,0.000008544028,0.000087101325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875766,0.000011706831,0.000705834,0.00015059879,0.00021249171,0.00016170848],"domain_scores_gemma":[0.99802196,0.000040659022,0.00084732403,0.00019172483,0.0008879084,0.000010451908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083856395,0.00013306916,0.00042720028,0.00041524155,0.00011032533,0.00021437257,0.00012868858,0.00007494381,0.000008007179],"category_scores_gemma":[0.000043723136,0.00011479402,0.00011514981,0.0011717036,0.000019348847,0.0033582873,0.000037501817,0.00005049005,0.000012601726],"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":[0.000041798077,0.000008500305,0.053468168,0.0016943786,0.00017136338,1.1634664e-8,0.0002123624,0.9295907,0.0000049050545,0.0010173117,0.00007817418,0.013712303],"study_design_scores_gemma":[0.00038659674,0.00000568982,0.0008912068,0.00009924899,0.00033876637,3.5131936e-7,0.0012414192,0.9957276,0.0000137085,0.00022264897,0.00093343895,0.00013930387],"about_ca_topic_score_codex":0.000301023,"about_ca_topic_score_gemma":0.000009107569,"teacher_disagreement_score":0.68307513,"about_ca_system_score_codex":0.0000141979945,"about_ca_system_score_gemma":0.00002587669,"threshold_uncertainty_score":0.46811643},"labels":[],"label_agreement":null},{"id":"W2979389171","doi":"10.1016/j.is.2019.101444","title":"Speed prediction in large and dynamic traffic sensor networks","year":2019,"lang":"en","type":"article","venue":"Information Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Horizon 2020; Ministero dell’Istruzione, dell’Università e della Ricerca; Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico; University of Alberta","keywords":"Computer science; Context (archaeology); Wireless sensor network; Real-time computing; Distributed computing; Data mining; Computer network","score_opus":0.0029473387324165457,"score_gpt":0.17828583326221475,"score_spread":0.17533849452979822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979389171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8298014,0.00018729235,0.14028761,0.000018877809,0.0019454696,0.0011511928,0.00003609714,0.005594333,0.020977773],"genre_scores_gemma":[0.9996597,0.00007541815,0.00003400671,0.000023870514,0.00002017818,0.000016195274,0.000059483602,0.0000070155543,0.000104104656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999432,0.000011389516,0.00028349814,0.000049569884,0.00009606966,0.00012743231],"domain_scores_gemma":[0.999819,0.000008461644,0.000033500288,0.00009718018,0.000014692542,0.000027173344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019281001,0.00007633767,0.00009773316,0.00019832452,0.000015529933,0.000064759755,0.000038945083,0.00007673118,0.0000067661317],"category_scores_gemma":[0.0000023322193,0.000078466976,0.000013942951,0.00012813025,0.0000044447515,0.00071154395,0.000010356329,0.000090644615,0.00009879633],"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":[0.000005482936,0.0000058533356,0.0015501843,0.00022171717,0.000014150517,4.3558336e-7,0.0006624254,0.9855208,0.000025109632,0.00051655463,0.0051482455,0.0063290372],"study_design_scores_gemma":[0.00040775115,0.0000126713085,0.009576938,0.000050874776,0.0000023772623,0.0000060274397,0.00042972152,0.96112233,0.0000018185752,3.0022966e-7,0.028321372,0.00006782625],"about_ca_topic_score_codex":0.00000461921,"about_ca_topic_score_gemma":0.0000053301383,"teacher_disagreement_score":0.16985838,"about_ca_system_score_codex":0.000060606122,"about_ca_system_score_gemma":0.0000023052846,"threshold_uncertainty_score":0.31997904},"labels":[],"label_agreement":null},{"id":"W3046491668","doi":"10.1016/j.is.2020.101608","title":"Privacy-aware data cleaning-as-a-service","year":2020,"lang":"en","type":"article","venue":"Information Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":17,"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; McMaster University","funders":"","keywords":"Computer science; Service provider; Cloud computing; Data publishing; Service (business); Data as a service; Database; Data modeling; Information privacy; Anonymity; Computer security; Consistency (knowledge bases); Big data; Data mining; Publishing","score_opus":0.07813621063965932,"score_gpt":0.28514965826695304,"score_spread":0.20701344762729373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046491668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001726868,0.000096327145,0.90374774,0.08193434,0.0010089001,0.0005668299,0.00019972636,0.0034279611,0.0072913007],"genre_scores_gemma":[0.942595,0.00003160373,0.047916554,0.008567203,0.0001868764,0.000040775325,0.0006288061,0.000016732616,0.000016419779],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980721,0.00005256373,0.0006222811,0.0003505651,0.00060352363,0.00029897137],"domain_scores_gemma":[0.9875538,0.00009099068,0.0003557258,0.011646454,0.00021688972,0.00013617588],"candidate_categories":["metaresearch","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.00049448444,0.00017157379,0.00020604584,0.00011457822,0.00013867344,0.00088921835,0.054321546,0.00013925873,0.000012606955],"category_scores_gemma":[0.0087904725,0.00016398224,0.000022644896,0.00097036775,0.000024615532,0.01063124,0.11123155,0.00025789908,0.0036038414],"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":[0.000008462938,0.000010955908,0.00025093145,0.00035858984,0.000032671134,0.00000775283,0.0021869438,0.00017282787,0.000040871753,0.013061494,0.96652275,0.017345753],"study_design_scores_gemma":[0.00020412447,0.000028361745,0.00006179176,0.000047582675,0.0000026294197,0.00002805814,0.00046160823,0.68345755,0.00010864136,0.00079074956,0.31463674,0.00017217682],"about_ca_topic_score_codex":0.00017195239,"about_ca_topic_score_gemma":0.0000019741583,"teacher_disagreement_score":0.94086814,"about_ca_system_score_codex":0.00005622177,"about_ca_system_score_gemma":0.00012585174,"threshold_uncertainty_score":0.9995589},"labels":[],"label_agreement":null},{"id":"W3110997203","doi":"10.1016/j.is.2020.101687","title":"Efficient top-k recently-frequent term querying over spatio-temporal textual streams","year":2020,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"H2020 Marie Skłodowska-Curie Actions; Norges Forskningsråd","keywords":"Computer science; Index (typography); Window (computing); Task (project management); Social media; Data mining; Information retrieval; Term (time); Data stream mining; Mobile device; Data stream; World Wide Web","score_opus":0.019253021858057336,"score_gpt":0.2338663340570475,"score_spread":0.21461331219899016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110997203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032894284,0.000074598975,0.9391429,0.00084021693,0.0017433808,0.0008966379,0.000060599647,0.00062931015,0.023718117],"genre_scores_gemma":[0.99680597,0.0000044260014,0.0018920536,0.00075221516,0.00019138855,0.000029269862,0.00023563954,0.000005562171,0.00008350092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983691,0.000044065873,0.0005767677,0.00019466181,0.00056694786,0.0002484861],"domain_scores_gemma":[0.9990585,0.000021558877,0.00029028376,0.00040367356,0.000085867054,0.000140152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030369157,0.00015326677,0.00016649363,0.00012356527,0.000117531294,0.0010012119,0.00071176316,0.000046708676,0.000023491975],"category_scores_gemma":[0.000031950687,0.00013658412,0.000052738236,0.00040586715,0.000016567094,0.0021149681,0.00030943708,0.00009575304,0.00069826364],"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":[0.00006119105,0.0002014407,0.020667244,0.0012664063,0.00021584178,0.000053439555,0.03240594,0.06579607,0.00009551537,0.27329588,0.097365186,0.50857586],"study_design_scores_gemma":[0.0004933562,0.00005297497,0.000856237,0.000053807544,0.0000045715424,0.000004209027,0.0007513235,0.8116585,0.00006981578,0.0000072820235,0.18582486,0.00022307654],"about_ca_topic_score_codex":0.00013500436,"about_ca_topic_score_gemma":0.0000014263998,"teacher_disagreement_score":0.96391165,"about_ca_system_score_codex":0.000076304736,"about_ca_system_score_gemma":0.000040998806,"threshold_uncertainty_score":0.96547097},"labels":[],"label_agreement":null},{"id":"W3121217868","doi":"10.1016/j.is.2021.101718","title":"Multi-label legal document classification: A deep learning-based approach with label-attention and domain-specific pre-training","year":2021,"lang":"en","type":"article","venue":"Information Systems","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Multi-label classification; Domain (mathematical analysis); Training (meteorology); Machine learning; Training set","score_opus":0.03582749930198761,"score_gpt":0.2545681606306928,"score_spread":0.2187406613287052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121217868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014680444,0.00037036775,0.9808335,0.00080717984,0.00021198542,0.00045598924,0.0000021544095,0.0007042507,0.0019341431],"genre_scores_gemma":[0.843629,0.000034998513,0.1554356,0.00008078929,0.000026344238,0.00026816866,0.00009810821,0.000008532191,0.00041845182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819386,0.00012043306,0.00057619,0.00033077187,0.00051423424,0.0002645055],"domain_scores_gemma":[0.99850684,0.000056033823,0.00045828774,0.0005418338,0.00035455136,0.000082459774],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00047992865,0.00019650903,0.0002139572,0.0002374666,0.0003335836,0.0015643698,0.00037753017,0.00012743063,0.000004134928],"category_scores_gemma":[0.000023464372,0.00016947235,0.000029243301,0.0006576794,0.000088719506,0.0031151464,0.00009989538,0.00021799435,0.000055845656],"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":[0.00005427674,0.00029507396,0.0032399087,0.00044538672,0.00010709319,0.0000107400965,0.009492532,0.0043999227,0.003346255,0.7562264,0.00051761343,0.22186475],"study_design_scores_gemma":[0.0038622809,0.00019335999,0.012470156,0.00014050356,0.000013001074,0.00015973118,0.015612348,0.7643647,0.0003320122,0.00011696398,0.20217083,0.0005641083],"about_ca_topic_score_codex":0.000008880105,"about_ca_topic_score_gemma":0.0000029814391,"teacher_disagreement_score":0.82894856,"about_ca_system_score_codex":0.00012042632,"about_ca_system_score_gemma":0.000113254144,"threshold_uncertainty_score":0.9994721},"labels":[],"label_agreement":null},{"id":"W3188393868","doi":"10.1016/j.is.2022.102156","title":"Extending sticky-Datalog<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" id=\"d1e182\" altimg=\"si10.svg\"><mml:msup><mml:mrow/><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:msup></mml:math>via finite-position selection functions: Tractability, algorithms, and optimization","year":2022,"lang":"lv","type":"article","venue":"Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Agencia Nacional de Investigación y Desarrollo","keywords":"Datalog; Computer science; Oracle; MAGIC (telescope); Modulo; Predicate (mathematical logic); Class (philosophy); Function (biology); Selection (genetic algorithm); Algorithm; Theoretical computer science; Discrete mathematics; Programming language; Mathematics; Artificial intelligence","score_opus":0.016549941385575848,"score_gpt":0.23897838599432242,"score_spread":0.22242844460874656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188393868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24942553,0.00083107036,0.7323522,0.00027810296,0.0051336256,0.00024331386,0.0015747168,0.00044787285,0.009713578],"genre_scores_gemma":[0.9827496,0.0003134035,0.009026037,0.00033615224,0.0010203955,0.001234473,0.005060591,0.00013400736,0.00012533228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941091,0.00038321552,0.001894564,0.0009188104,0.0017338921,0.0009604465],"domain_scores_gemma":[0.9955377,0.00075276074,0.0019620738,0.0010742854,0.0002870144,0.0003861848],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018288422,0.0005457078,0.00031037666,0.0004668182,0.0021621285,0.0015234224,0.00072658603,0.00065923453,0.0013212444],"category_scores_gemma":[0.00061918667,0.00081296964,0.0003874531,0.0010554489,0.0003659004,0.007138317,0.0010795374,0.00087266654,0.0006515319],"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":[0.0002604509,0.0001288212,0.000011652588,0.0011955538,0.00026239202,0.000067566456,0.0032324435,0.1216056,0.00027396664,0.8651016,0.0024304397,0.005429565],"study_design_scores_gemma":[0.0008568852,0.00083634804,0.00006397268,0.00050863286,0.00025671703,0.0022769088,0.0040836213,0.97431475,0.004201466,0.000018578516,0.01188985,0.0006922979],"about_ca_topic_score_codex":0.0015898466,"about_ca_topic_score_gemma":0.00012797397,"teacher_disagreement_score":0.865083,"about_ca_system_score_codex":0.00008074551,"about_ca_system_score_gemma":0.00058608776,"threshold_uncertainty_score":0.9995917},"labels":[],"label_agreement":null},{"id":"W4234936942","doi":"10.29085/9781783302437.012","title":"Increasing social connection through a community-of-practice-inspireddesign","year":2018,"lang":"en","type":"book-chapter","venue":"Information Systems","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","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":"Cognition; Protocol analysis; Work (physics); Abstraction; Coding (social sciences); Psychology; Computer science; Cognitive science; Social psychology; Engineering; Sociology; Epistemology; Social science","score_opus":0.0531885258814364,"score_gpt":0.2650689567026203,"score_spread":0.2118804308211839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234936942","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009700251,0.00016006996,0.025036957,0.00018831353,0.0009271486,0.00034272278,0.000022147053,0.0002876406,0.972065],"genre_scores_gemma":[0.9847244,0.000046288784,0.00025878643,0.0015104165,0.004119465,0.00003193549,0.0010404036,0.00007727545,0.008191006],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99792784,0.000042024796,0.0010891833,0.00012592984,0.0006343486,0.00018068495],"domain_scores_gemma":[0.9945274,0.00013095817,0.002605866,0.0003155522,0.0024119192,0.00000833225],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016408719,0.00032016955,0.0005697237,0.0005405145,0.00073703995,0.00068447704,0.0003068941,0.0004456558,0.00013037221],"category_scores_gemma":[0.00031201044,0.00031665288,0.00018004257,0.00021547492,0.00011103151,0.0074178157,0.00012922061,0.00047014482,0.0009819741],"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":[0.0005898386,0.00013991988,0.00008245822,0.012792396,0.0014658144,0.0000050247,0.012067545,0.0026010063,0.000044555927,0.90578914,0.041234028,0.023188297],"study_design_scores_gemma":[0.00047355724,0.00001452621,0.000008203072,0.001006252,0.000550803,0.000019386629,0.004734007,0.011169429,0.0000035266048,0.0057076733,0.97578466,0.00052798004],"about_ca_topic_score_codex":0.0057911077,"about_ca_topic_score_gemma":0.000016299333,"teacher_disagreement_score":0.9837544,"about_ca_system_score_codex":0.00007429978,"about_ca_system_score_gemma":0.00006588981,"threshold_uncertainty_score":0.99992853},"labels":[],"label_agreement":null},{"id":"W4388154056","doi":"10.1016/j.is.2023.102312","title":"Measuring rule-based LTLf process specifications: A probabilistic data-driven approach","year":2023,"lang":"en","type":"article","venue":"Information Systems","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","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":"York University","funders":"","keywords":"Computer science; Probabilistic logic; Data mining; Process (computing); Event (particle physics); Process mining; Context (archaeology); Conformance checking; Temporal logic; Key (lock); Isolation (microbiology); Linear temporal logic; Software engineering; Programming language; Work in process; Artificial intelligence; Business process; Business process modeling","score_opus":0.16879239059271806,"score_gpt":0.26119074343866955,"score_spread":0.09239835284595149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388154056","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16025104,0.00038376026,0.6925821,0.0030528766,0.002670685,0.0046296814,0.00028086008,0.008600081,0.12754895],"genre_scores_gemma":[0.9958369,0.000003495966,0.000187005,0.00018826978,0.0006102911,0.0002607805,0.002815439,0.000020524923,0.00007726562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981068,0.000011545874,0.00069664227,0.0002713615,0.0006361747,0.00027748075],"domain_scores_gemma":[0.99805135,0.000029603842,0.0005043213,0.0006858735,0.00071104727,0.000017829845],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00089092087,0.00018902181,0.00025555358,0.0007574039,0.00032546613,0.0012257711,0.00069648115,0.00007873831,0.000013337646],"category_scores_gemma":[0.00026920703,0.00016768467,0.00005142962,0.0019670015,0.00003505296,0.00556732,0.00012156954,0.00011326373,0.0022141587],"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":[0.000015493199,0.000052371382,0.001722919,0.0041393507,0.000042945714,8.074965e-7,0.00021661802,0.98361725,0.000006844275,0.0042160214,0.0027097755,0.0032596106],"study_design_scores_gemma":[0.0003000856,9.789359e-7,0.00047101922,0.00013214897,0.000057274126,0.0000012451063,0.0012210954,0.9794169,0.0000014326379,0.00017949544,0.017997539,0.0002207991],"about_ca_topic_score_codex":0.00021837886,"about_ca_topic_score_gemma":0.0000037560594,"teacher_disagreement_score":0.8355859,"about_ca_system_score_codex":0.000038888997,"about_ca_system_score_gemma":0.000081349615,"threshold_uncertainty_score":0.99981105},"labels":[],"label_agreement":null},{"id":"W4389989770","doi":"10.1016/j.is.2023.102339","title":"Foundations and practice of binary process discovery","year":2023,"lang":"en","type":"article","venue":"Information Systems","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","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":"Minnow Environmental (Canada)","funders":"","keywords":"Computer science; Process (computing); Binary number; Data science; Business process discovery; Programming language; Work in process; Business process; Business process modeling; Arithmetic; Engineering; Mathematics","score_opus":0.02833699263932253,"score_gpt":0.2800358933909709,"score_spread":0.25169890075164836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389989770","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8600827,0.00031415842,0.038444974,0.0043644207,0.0012275418,0.00077923946,0.00003819349,0.00096749945,0.09378128],"genre_scores_gemma":[0.99927545,0.000021183669,0.000019374833,0.00020106042,0.00015766235,0.000029061133,0.00015484718,0.000005440605,0.0001359465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992435,0.0000056421377,0.00035285458,0.000067266876,0.00023599376,0.00009471943],"domain_scores_gemma":[0.99890465,0.00006425309,0.00039127812,0.00010309681,0.0005317017,0.0000050247427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049479015,0.000071384835,0.0001241886,0.00043257527,0.00012732112,0.0005282609,0.00008073949,0.000034460256,0.0000032460196],"category_scores_gemma":[0.00038235716,0.00006178323,0.000023975794,0.0011079754,0.000025866684,0.012712648,0.000046889687,0.000037986327,0.00019794173],"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":[0.000396661,0.00028978288,0.03153621,0.03156898,0.00063076685,0.000010994789,0.008256553,0.5982115,0.0014452835,0.2671564,0.02132755,0.039169285],"study_design_scores_gemma":[0.00054902595,0.0000073572146,0.00275257,0.00032418311,0.0001601732,0.000010008945,0.020181142,0.82660615,0.000010314773,0.00062750175,0.1484681,0.0003034349],"about_ca_topic_score_codex":0.00033370472,"about_ca_topic_score_gemma":0.0000014679459,"teacher_disagreement_score":0.2665289,"about_ca_system_score_codex":0.0000061104115,"about_ca_system_score_gemma":0.000020595464,"threshold_uncertainty_score":0.9216363},"labels":[],"label_agreement":null},{"id":"W4402217170","doi":"10.1016/j.is.2024.102459","title":"Tri-AL: An open source platform for visualization and analysis of clinical trials","year":2024,"lang":"en","type":"article","venue":"Information Systems","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Sleep Foundation; National Science Foundation","keywords":"Computer science; Visualization; Open source; Data science; Human–computer interaction; Information retrieval; Data mining; Operating system; Software","score_opus":0.23930306441577254,"score_gpt":0.5279758031200584,"score_spread":0.2886727387042859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402217170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020080198,0.00013073736,0.9770061,0.00020482203,0.00083998195,0.0010666398,0.00004153356,0.00017130897,0.00045872977],"genre_scores_gemma":[0.99685043,0.000017205732,0.002357018,0.0002780734,0.0000854357,0.000094460986,0.00021361664,0.0000069156863,0.00009683281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696827,0.00045384868,0.002011389,0.00017636963,0.0002762865,0.00011384524],"domain_scores_gemma":[0.99716306,0.0014573816,0.00072121475,0.0003428818,0.00022218774,0.000093269955],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.014000763,0.00008563315,0.00059965655,0.000515464,0.00008118449,0.0011007839,0.0005060013,0.00008558687,0.000004207984],"category_scores_gemma":[0.0014220078,0.00006858969,0.00010703159,0.0009329992,0.000015125296,0.0035903628,0.00013638496,0.00008789468,0.0000074183745],"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":[0.00008018966,0.000030365925,0.008475353,0.0013134803,0.00064872287,4.6797564e-7,0.013735705,0.016485633,0.0000059883923,0.442407,0.0029413544,0.5138757],"study_design_scores_gemma":[0.00027826685,0.00014827789,0.0015108127,0.000060031605,0.000066873356,0.0000029006944,0.00027637943,0.88276356,0.000004785188,0.000057580583,0.114761434,0.00006908036],"about_ca_topic_score_codex":0.00035969788,"about_ca_topic_score_gemma":0.0000109019875,"teacher_disagreement_score":0.9767702,"about_ca_system_score_codex":0.000024333403,"about_ca_system_score_gemma":0.00012665693,"threshold_uncertainty_score":0.99993616},"labels":[],"label_agreement":null},{"id":"W4402898151","doi":"10.1016/j.is.2024.102460","title":"Data Lakehouse: A survey and experimental study","year":2024,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Survey research; Data science; Psychology","score_opus":0.4319128260972868,"score_gpt":0.47713784578125534,"score_spread":0.045225019683968526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402898151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88636225,0.003334749,0.046899572,0.0005554418,0.009997536,0.0037933434,0.008423107,0.0007818808,0.039852105],"genre_scores_gemma":[0.99886745,0.0000030247875,0.000015526839,0.00007908206,0.000038154292,0.000020905247,0.00029121723,0.0000028302982,0.0006818258],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977377,0.0003096543,0.0006779349,0.0002066723,0.0009724983,0.00009554423],"domain_scores_gemma":[0.9984728,0.00043092002,0.00009256496,0.0008833306,0.00006949369,0.00005085908],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010299285,0.00007268968,0.00013353673,0.00021265581,0.00008388504,0.0030339882,0.0007045482,0.00002174519,0.000047580565],"category_scores_gemma":[0.00050336454,0.00005103362,0.000010453565,0.00039296955,0.000024462883,0.0050950996,0.0006615904,0.000048494454,0.001781812],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046832527,0.00022516707,0.010690324,0.00012809614,0.00013313226,0.000015352834,0.027606232,0.00019881518,0.000006657941,0.016399203,0.9057835,0.038766727],"study_design_scores_gemma":[0.00020673097,0.000071118666,0.008069685,0.000015660773,0.000003842513,0.000007753752,0.037201148,0.018985482,0.0000028711277,0.000028669414,0.935314,0.00009305626],"about_ca_topic_score_codex":0.00061143516,"about_ca_topic_score_gemma":0.00020618402,"teacher_disagreement_score":0.11250517,"about_ca_system_score_codex":0.00001766996,"about_ca_system_score_gemma":0.000025873413,"threshold_uncertainty_score":0.9989954},"labels":[],"label_agreement":null},{"id":"W4403471969","doi":"10.1016/j.is.2024.102475","title":"Explaining results of path queries on graphs: Single-path results for context-free path queries","year":2024,"lang":"en","type":"article","venue":"Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","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":"McMaster University","funders":"","keywords":"Path (computing); Computer science; Context (archaeology); Path expression; Longest path problem; Theoretical computer science; Graph; Data mining; Shortest path problem; Query language; Computer network; Geography","score_opus":0.030239839670588683,"score_gpt":0.24362238565523436,"score_spread":0.21338254598464568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403471969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047200015,0.00087967125,0.9147851,0.0017158348,0.0096544605,0.002556787,0.0052303434,0.0015923168,0.058865514],"genre_scores_gemma":[0.9913693,0.00004506467,0.0066707646,0.00019296614,0.00016782538,0.0001698891,0.0006563237,0.000014206122,0.00071367464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99765897,0.00006115508,0.0011680879,0.0002794186,0.00055093365,0.00028144167],"domain_scores_gemma":[0.99799085,0.00033182176,0.00046945104,0.0009209434,0.00022353847,0.00006338715],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013832131,0.00021052128,0.00028213204,0.00037620927,0.0001518227,0.0012041562,0.0009039534,0.0000760938,5.2609875e-7],"category_scores_gemma":[0.00044006374,0.00017567625,0.00010237098,0.00050101767,0.00005682242,0.0057080006,0.00025262774,0.000097306925,0.000046715122],"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":[0.0002637179,0.000039833547,0.000010199657,0.0010260175,0.00007920244,0.000009262326,0.021914251,0.00027768058,0.000034468474,0.75870126,0.135264,0.08238013],"study_design_scores_gemma":[0.0024579775,0.00088943465,0.000070324015,0.0019895448,0.000016436445,0.0000115972025,0.008820411,0.14580698,0.0006645699,0.0013161718,0.83751005,0.0004464844],"about_ca_topic_score_codex":0.00014009206,"about_ca_topic_score_gemma":0.000009593414,"teacher_disagreement_score":0.9866493,"about_ca_system_score_codex":0.000055125296,"about_ca_system_score_gemma":0.000060226368,"threshold_uncertainty_score":0.9998327},"labels":[],"label_agreement":null}]}