{"meta":{"query_hash":"e0fdac2723aa","filters":{"topic":"Data Management and Algorithms"},"cohort_total":1256,"direct_labels_cover":2,"predictions_cover":1256,"exported":1256,"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/e0fdac2723aa","api":"https://metacan.xera.ac/api/v1/cohort?topic=Data+Management+and+Algorithms"},"results":[{"id":"W100764105","doi":"10.46298/dmtcs.3484","title":"Around the root of random multidimensional quadtrees","year":2006,"lang":"en","type":"article","venue":"Discrete Mathematics & Theoretical Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Arity; Root (linguistics); Mathematics; Point (geometry); Computer science; Statistics; Algorithm; Combinatorics; Geometry","score_opus":0.010048296120503342,"score_gpt":0.2434762852402676,"score_spread":0.23342798911976426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W100764105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8667239,0.00085525756,0.12951644,0.00030685886,0.00003539097,0.0000218703,0.00023453755,0.00027303759,0.0020326928],"genre_scores_gemma":[0.98772174,0.0002517434,0.010745155,0.000045181612,0.000026002988,0.000027059,0.00027157203,0.00009341951,0.00081815175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99944085,0.00017481828,0.00001891604,0.00013180832,0.00012420009,0.00010940548],"domain_scores_gemma":[0.98626167,0.009044,0.0019275096,0.00083066314,0.0013493818,0.0005868244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016178595,0.00027014746,0.0005542313,0.0013911314,0.00053543336,0.0011211444,0.0011975562,0.0007707554,0.0017427763],"category_scores_gemma":[0.016463295,0.0004502218,0.00039781432,0.00091572595,0.0018152108,0.0018548311,0.0010786349,0.0010083781,0.00028881122],"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":[0.0008221993,0.000099728015,0.048606608,0.00045784411,0.000116205774,0.0021467905,0.0011782798,0.43189016,0.057866346,0.4154854,0.0060932552,0.035237305],"study_design_scores_gemma":[0.00005342439,0.00010667006,0.009470437,0.00006825151,0.000028495995,0.00089664926,0.0002851629,0.8666974,0.005749533,0.115190886,0.0013982769,0.00005479594],"about_ca_topic_score_codex":0.0011016075,"about_ca_topic_score_gemma":0.0010796351,"teacher_disagreement_score":0.0017427763,"about_ca_system_score_codex":0.0007548256,"about_ca_system_score_gemma":0.000300803,"threshold_uncertainty_score":0.008556187},"labels":[],"label_agreement":null},{"id":"W101537656","doi":"","title":"Distance Between Graphs Using Graph Labelings.","year":2005,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Combinatorics; Edge-graceful labeling; Graph; Line graph; Discrete mathematics; Graph power","score_opus":0.020169486850561948,"score_gpt":0.2535486037510895,"score_spread":0.23337911690052757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W101537656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025109166,0.0012888368,0.95350665,0.0010726068,0.0002797363,0.00013520652,0.002279424,0.0022849634,0.014043327],"genre_scores_gemma":[0.24755879,0.0015947495,0.7287124,0.00041118232,0.00036091317,0.00034368905,0.006701992,0.0010062743,0.013310014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99550205,0.0014285217,0.0004971546,0.0012568138,0.0011075244,0.00020789812],"domain_scores_gemma":[0.9828997,0.009069415,0.0015674884,0.004566289,0.001229068,0.0006679126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026223862,0.0009641144,0.0012693162,0.007974724,0.0017088816,0.00556206,0.0021754773,0.0019783974,0.0075108046],"category_scores_gemma":[0.026658198,0.0008465383,0.0011798588,0.009373926,0.0020643175,0.012864317,0.0043028267,0.0029698196,0.0032633722],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002920111,0.0002033789,0.0035845505,0.0005859145,0.00012920573,0.00017053158,0.00075649074,0.025729084,0.002937822,0.65824276,0.019492626,0.28787568],"study_design_scores_gemma":[0.00003069555,0.000081507074,0.0009507076,0.00017088339,0.000068369685,0.00041269764,0.0002873441,0.09042323,0.004230952,0.86510354,0.038190797,0.0000493637],"about_ca_topic_score_codex":0.0018568895,"about_ca_topic_score_gemma":0.002565096,"teacher_disagreement_score":0.007974724,"about_ca_system_score_codex":0.0021765055,"about_ca_system_score_gemma":0.0014061012,"threshold_uncertainty_score":0.02512616},"labels":[],"label_agreement":null},{"id":"W102758216","doi":"","title":"Query Algebra and Query Optimization for Concept Assertion Retrieval.","year":2010,"lang":"en","type":"article","venue":"","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":"University of Waterloo","funders":"","keywords":"Computer science; Query expansion; Query optimization; Sargable; Query language; Information retrieval; Query by Example; Assertion; Web search query; Search engine indexing; Relational algebra; Boolean conjunctive query; Web query classification; Extension (predicate logic); RDF query language; Relational database; Search engine; Programming language","score_opus":0.008842400117279256,"score_gpt":0.23565499863371173,"score_spread":0.22681259851643248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W102758216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053175976,0.00031398892,0.98703927,0.00071240735,0.00008273536,0.00025365816,0.00033585503,0.0014895903,0.00445491],"genre_scores_gemma":[0.21695799,0.0005152592,0.77331173,0.00062575814,0.00046954202,0.00052839145,0.0019099104,0.0003970697,0.005284399],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9873099,0.0046477476,0.0014717157,0.0014235711,0.004488073,0.0006590522],"domain_scores_gemma":[0.98672837,0.005989283,0.0010543175,0.003350898,0.0023938415,0.00048339312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010155805,0.0006293887,0.0011258636,0.001810454,0.0014360402,0.0051990687,0.0025460785,0.0009312379,0.007686967],"category_scores_gemma":[0.026450532,0.00066604215,0.0018715318,0.0026618037,0.0027620029,0.012759956,0.003827233,0.0028760305,0.0026507012],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000295445,0.00018654198,0.0005744511,0.00024194417,0.00006489756,0.00014848585,0.000322707,0.016895548,0.0063781994,0.8756892,0.011845643,0.08735696],"study_design_scores_gemma":[0.00010078259,0.00014007483,0.00024647426,0.0000640753,0.00010069574,0.00039289403,0.00015853136,0.36862475,0.013002628,0.5874781,0.029625367,0.00006564562],"about_ca_topic_score_codex":0.0028332516,"about_ca_topic_score_gemma":0.0025126499,"teacher_disagreement_score":0.010155805,"about_ca_system_score_codex":0.0016335555,"about_ca_system_score_gemma":0.0029554304,"threshold_uncertainty_score":0.053709686},"labels":[],"label_agreement":null},{"id":"W103048715","doi":"10.1007/978-3-540-72029-4_13","title":"UMapIT© (Unrestricted Mapping Interactive Tool): Merging the datacube paradigm with an occurrence-based approach to support on-demand web mapping","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Data Management and Algorithms","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":"Université Laval","funders":"","keywords":"Computer science; Data cube; Object (grammar); Flexibility (engineering); Online analytical processing; Representation (politics); Field (mathematics); The Internet; Data mining; Data science; Information retrieval; World Wide Web; Artificial intelligence; Data warehouse; Mathematics","score_opus":0.021537560409844777,"score_gpt":0.22339032754616805,"score_spread":0.20185276713632327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W103048715","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046461704,0.0001887588,0.9077137,0.00022127821,0.00013817831,0.00010367066,0.002097703,0.0676392,0.017251357],"genre_scores_gemma":[0.10712073,0.0008012877,0.84967226,0.00025457985,0.0001438202,0.00035667612,0.006665805,0.014312158,0.020672662],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996257,0.00007304661,0.000028871038,0.0000669882,0.00016800425,0.00003739954],"domain_scores_gemma":[0.99931204,0.00028023616,0.000028263761,0.00024929852,0.000067721034,0.00006257447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008347816,0.0007604431,0.0005755164,0.0012174769,0.0005016391,0.0022888766,0.0019183077,0.00066079944,0.019074826],"category_scores_gemma":[0.0022321565,0.00066197244,0.00062073994,0.0018055805,0.000480408,0.0038905074,0.003201847,0.0013023913,0.0057437182],"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":[0.0004982483,0.00022324924,0.0018285455,0.0007102635,0.00011862219,0.0007304391,0.001712847,0.027803995,0.027020272,0.15563497,0.16528288,0.6184357],"study_design_scores_gemma":[0.00013379136,0.000069475536,0.00095637346,0.00014548047,0.000059282822,0.00059266033,0.00026203296,0.26388863,0.028531434,0.11484598,0.5904338,0.000081103535],"about_ca_topic_score_codex":0.0033210514,"about_ca_topic_score_gemma":0.0040793815,"teacher_disagreement_score":0.019074826,"about_ca_system_score_codex":0.00038296013,"about_ca_system_score_gemma":0.0006790468,"threshold_uncertainty_score":0.06381166},"labels":[],"label_agreement":null},{"id":"W10346826","doi":"10.1007/3-540-26886-3_7","title":"Multi-Dimensional Interpolations with Fuzzy Sets","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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":"Simon Fraser University","funders":"","keywords":"Interpolation (computer graphics); Fuzzy set; Fuzzy logic; Fuzzy set operations; Multivariate interpolation; Geographic information system; Data mining; Representation (politics); Fuzzy number; Computer science; Defuzzification; Fuzzy classification; Mathematics; Artificial intelligence; Geography; Cartography; Computer vision; Bilinear interpolation","score_opus":0.02525651062524525,"score_gpt":0.24263581385269528,"score_spread":0.21737930322745003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W10346826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013375048,0.0023791823,0.9819602,0.00012204616,0.0003151464,0.000018573675,0.00006215759,0.00029021077,0.013515008],"genre_scores_gemma":[0.04565178,0.0030735224,0.92899483,0.00007903306,0.00026081296,0.00006981784,0.00015906834,0.00016778852,0.021543385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995358,0.00009836227,0.000037738468,0.000068882204,0.00024293801,0.000016224845],"domain_scores_gemma":[0.9994011,0.00029808414,0.000028469507,0.00014579188,0.00011237359,0.000014224568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010144714,0.00057555805,0.0010217704,0.0012831918,0.00053818896,0.0011635859,0.0012428067,0.0005187332,0.009577746],"category_scores_gemma":[0.0026890659,0.00047073598,0.0009820726,0.0024039368,0.0010048551,0.0019048897,0.0011580195,0.0015338558,0.0020239863],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004782991,0.00001839865,0.00013655667,0.00036503846,0.000033434986,0.000063707346,0.00017837204,0.033545285,0.0023362583,0.5333132,0.0091084335,0.4208535],"study_design_scores_gemma":[0.000013644038,0.000042575957,0.00025514606,0.00016958553,0.0000411441,0.00028804364,0.000065204644,0.31499818,0.004673449,0.56470734,0.11470649,0.000039237628],"about_ca_topic_score_codex":0.00075554533,"about_ca_topic_score_gemma":0.00092546025,"teacher_disagreement_score":0.009577746,"about_ca_system_score_codex":0.0005691211,"about_ca_system_score_gemma":0.00036359075,"threshold_uncertainty_score":0.032040775},"labels":[],"label_agreement":null},{"id":"W105892904","doi":"","title":"Approximate Orthogonal Range Search using Patricia Tries","year":2005,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Rectangle; Range query (database); Combinatorics; Range (aeronautics); Mathematics; Boundary (topology); Set (abstract data type); Trie; Range tree; Binary logarithm; Computational geometry; Data structure; Discrete mathematics; Algorithm; Computer science; Tree structure; Geometry; Binary tree; Sargable; Search engine; Interval tree; Mathematical analysis","score_opus":0.05859047501254531,"score_gpt":0.2789679369922952,"score_spread":0.22037746197974992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W105892904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11629083,0.00069897703,0.868032,0.00039571148,0.000083893596,0.00033234656,0.0005470067,0.00583169,0.007787583],"genre_scores_gemma":[0.41957667,0.00027492552,0.57525563,0.00022052691,0.000055420318,0.00044986737,0.0015148501,0.00023847044,0.0024136305],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963387,0.0011012655,0.0004244253,0.00067015545,0.0010605023,0.00040502573],"domain_scores_gemma":[0.99386966,0.0025040086,0.00064176164,0.0018784516,0.0009249789,0.00018114499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022781424,0.00095164357,0.0017975672,0.0025313545,0.00090425153,0.0021349855,0.0024784128,0.0013003083,0.0033749135],"category_scores_gemma":[0.015855344,0.00058300636,0.001169679,0.004025708,0.0010192251,0.0038908676,0.0035006145,0.00096285454,0.0017133312],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019084358,0.00041506952,0.017208444,0.0005524839,0.00032457954,0.00068430783,0.0011282634,0.33401662,0.016478613,0.082705304,0.01658289,0.527995],"study_design_scores_gemma":[0.0001483863,0.00029561887,0.00072936254,0.000028446757,0.000046443394,0.00057863555,0.00020894317,0.9619691,0.008695387,0.021927176,0.0053298348,0.000042633124],"about_ca_topic_score_codex":0.0021530269,"about_ca_topic_score_gemma":0.0024966816,"teacher_disagreement_score":0.0033749135,"about_ca_system_score_codex":0.00062182354,"about_ca_system_score_gemma":0.0014380374,"threshold_uncertainty_score":0.012048125},"labels":[],"label_agreement":null},{"id":"W107444495","doi":"","title":"Looking Backward, Forward, and All Around: Temporal, Spatial, and Spatio-Temporal Data Mining","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"","keywords":"Generalization; Computer science; Data mining; Temporal database; Process (computing); Data modeling; Set (abstract data type); Domain (mathematical analysis); Data set; Software; Spatial analysis; Current (fluid); Artificial intelligence; Geography; Remote sensing; Mathematics; Geology; Database","score_opus":0.05579234385690988,"score_gpt":0.26314808233418185,"score_spread":0.20735573847727198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W107444495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020580929,0.004340679,0.95988744,0.0030462143,0.000121895166,0.00014880883,0.002173492,0.0022167852,0.00748376],"genre_scores_gemma":[0.17507584,0.0047092587,0.8109431,0.0009963008,0.00011139848,0.00018806035,0.0036881529,0.0002627255,0.004025168],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981651,0.0006403269,0.00017366104,0.0003530018,0.0005517652,0.00011623834],"domain_scores_gemma":[0.9954602,0.0021661494,0.0006340785,0.00082687056,0.00076499785,0.0001476271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003271738,0.00066180783,0.0006455683,0.003373828,0.0008337187,0.0027620988,0.0016289481,0.00093220157,0.0027547458],"category_scores_gemma":[0.009771632,0.00048703587,0.0010842114,0.007614833,0.0013915224,0.0045137466,0.0015654913,0.0011824156,0.0010444282],"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":[0.0002383556,0.00015574721,0.03755633,0.0014084444,0.00033678123,0.0008152131,0.0013076427,0.044662103,0.005458245,0.1419285,0.02539467,0.740738],"study_design_scores_gemma":[0.000045665274,0.00013055302,0.01608439,0.00065912254,0.0002520484,0.003182807,0.002547878,0.37641233,0.013419,0.34746525,0.23964056,0.00016035364],"about_ca_topic_score_codex":0.013375257,"about_ca_topic_score_gemma":0.022583744,"teacher_disagreement_score":0.013375257,"about_ca_system_score_codex":0.0011119134,"about_ca_system_score_gemma":0.0023197567,"threshold_uncertainty_score":0.026594818},"labels":[],"label_agreement":null},{"id":"W107722637","doi":"10.1137/1.9781611972764.41","title":"Cluster Description Formats, Problems and Algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Interpretability; Computer science; Cluster analysis; Heuristic; Cluster (spacecraft); Data mining; Code (set theory); State (computer science); Information retrieval; Theoretical computer science; Algorithm; Artificial intelligence; Programming language","score_opus":0.016970937114307572,"score_gpt":0.20509833950533765,"score_spread":0.18812740239103007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W107722637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002626819,0.00075845665,0.98910236,0.0012881545,0.00011020109,0.00044054503,0.0016776478,0.0014372979,0.0025585587],"genre_scores_gemma":[0.037899744,0.0012674822,0.94991356,0.0003477062,0.00018498774,0.000880722,0.005809003,0.00046175302,0.003235006],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98596317,0.005248116,0.0022284114,0.0022507454,0.003848778,0.00046078913],"domain_scores_gemma":[0.9705664,0.01765052,0.0016426186,0.0054594926,0.0040936335,0.00058736425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015428832,0.0017451613,0.0015087927,0.0064748204,0.0024116887,0.010861299,0.00708277,0.003335347,0.00642707],"category_scores_gemma":[0.04955092,0.0015797882,0.002470728,0.012728393,0.002886818,0.014743027,0.005387495,0.0042732065,0.002980099],"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":[0.00032728314,0.00025096064,0.0021809568,0.0013897619,0.00010817271,0.00036810632,0.0007435449,0.07565894,0.0013971054,0.5094697,0.03758844,0.37051702],"study_design_scores_gemma":[0.00009233499,0.00006635169,0.00034124273,0.0003073172,0.00005443319,0.0006695247,0.00076028815,0.32601282,0.005778821,0.6140967,0.051721517,0.00009870084],"about_ca_topic_score_codex":0.0034656825,"about_ca_topic_score_gemma":0.0024171015,"teacher_disagreement_score":0.015428832,"about_ca_system_score_codex":0.003689591,"about_ca_system_score_gemma":0.003936256,"threshold_uncertainty_score":0.081596434},"labels":[],"label_agreement":null},{"id":"W110213072","doi":"","title":"Distributed predictive and descriptive data mining","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Data mining; Variety (cybernetics); Data stream mining; Domain (mathematical analysis); Knowledge extraction; Constraint (computer-aided design); Data science; Artificial intelligence; Engineering","score_opus":0.03084286755736358,"score_gpt":0.2324296577191919,"score_spread":0.20158679016182834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W110213072","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010934579,0.00036697456,0.9851504,0.00038933748,0.000039829185,0.00010885241,0.00030289884,0.00086021563,0.0018469375],"genre_scores_gemma":[0.50554377,0.0009714264,0.48660505,0.00038576676,0.0002326286,0.0003566817,0.0020035652,0.00017707428,0.0037239767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964133,0.0007301418,0.00019748838,0.0010901936,0.0013664505,0.00020239857],"domain_scores_gemma":[0.9922305,0.0031211032,0.0006300251,0.0028118605,0.0010311882,0.00017544137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004306111,0.0010660326,0.0017297255,0.0025738704,0.0012403481,0.0026525676,0.004169793,0.0009019111,0.001700142],"category_scores_gemma":[0.012968017,0.00073447806,0.0012928335,0.0044634277,0.0015270218,0.0044732867,0.0026240193,0.0018218281,0.00064044696],"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":[0.0005785585,0.0005805582,0.017955622,0.00048484188,0.000346411,0.000633216,0.0006148852,0.3868652,0.0066115404,0.13880655,0.009644416,0.43687826],"study_design_scores_gemma":[0.00003244434,0.000062829764,0.00092373986,0.00002718764,0.00008131419,0.00021529736,0.000120514764,0.8856001,0.003795145,0.10429692,0.004822078,0.000022408136],"about_ca_topic_score_codex":0.0028695508,"about_ca_topic_score_gemma":0.0054659867,"teacher_disagreement_score":0.004306111,"about_ca_system_score_codex":0.0013333445,"about_ca_system_score_gemma":0.0021256742,"threshold_uncertainty_score":0.022773147},"labels":[],"label_agreement":null},{"id":"W111494185","doi":"","title":"The total number of maximal k-independent sets in the generalized lexicographical product of graphs.","year":2005,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lexicographical order; Mathematics; Product (mathematics); Combinatorics; Discrete mathematics; Geometry","score_opus":0.012366653337287141,"score_gpt":0.2557627499748185,"score_spread":0.24339609663753137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W111494185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84603757,0.0016867451,0.10056873,0.0014813278,0.00017925035,0.00012066764,0.004238457,0.0004924296,0.045194853],"genre_scores_gemma":[0.93807185,0.0012978505,0.045958742,0.00021182676,0.00018184456,0.00019199488,0.002644212,0.000219437,0.011222324],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998692,0.00036666257,0.00010780405,0.00028767274,0.00032226328,0.00022358767],"domain_scores_gemma":[0.99010897,0.0062817764,0.0009961595,0.0010162547,0.00081085647,0.00078596827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013894559,0.0006273139,0.00096406287,0.0021885869,0.0014436549,0.0045781606,0.0018035166,0.0010471311,0.008627868],"category_scores_gemma":[0.00849421,0.00069975783,0.0006937982,0.0023901558,0.002234036,0.005331755,0.0012726311,0.0010289792,0.0012314222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011776828,0.00018983123,0.01270799,0.0011406374,0.00029914713,0.0007803682,0.0010555823,0.0212556,0.021370867,0.8604686,0.014878611,0.06467501],"study_design_scores_gemma":[0.000084463805,0.0001168511,0.0046504717,0.00011377744,0.00018312164,0.0017394415,0.0005153451,0.046937454,0.010485293,0.9241102,0.011003023,0.00006057006],"about_ca_topic_score_codex":0.00058632623,"about_ca_topic_score_gemma":0.0010953498,"teacher_disagreement_score":0.008627868,"about_ca_system_score_codex":0.0009859415,"about_ca_system_score_gemma":0.000957511,"threshold_uncertainty_score":0.028863072},"labels":[],"label_agreement":null},{"id":"W11219696","doi":"10.1007/978-3-540-36122-0_8","title":"GTSOM: Game Theoretic Self-organizing Maps","year":2006,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science","score_opus":0.055725829606105885,"score_gpt":0.3077881833873966,"score_spread":0.2520623537812907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W11219696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073693763,0.0012583649,0.94750655,0.0008307462,0.00040584215,0.00009769264,0.00039646085,0.0017978994,0.040337063],"genre_scores_gemma":[0.3345192,0.0030271139,0.60666984,0.0005542711,0.00035324422,0.0006448002,0.0011483346,0.0006298575,0.052453443],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983156,0.000042926407,0.0000070886185,0.000026361082,0.00007446495,0.00001755166],"domain_scores_gemma":[0.9998161,0.000080253914,0.000011839429,0.00003155032,0.000031842017,0.0000284838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023828076,0.000482922,0.0004808978,0.0005098114,0.00039346307,0.0013841003,0.0011931583,0.00060759333,0.005848458],"category_scores_gemma":[0.0011203218,0.00020347972,0.00035792342,0.00081688055,0.0007707077,0.0012778491,0.0010840513,0.0012295054,0.001620005],"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":[0.000030716143,0.000040821946,0.00011713237,0.00018922955,0.000027557206,0.00007905545,0.0001765178,0.0558217,0.0019548414,0.7337749,0.03287561,0.17491189],"study_design_scores_gemma":[0.000016151942,0.00001653674,0.000104050574,0.000022606406,0.000006206886,0.00008434738,0.000037905786,0.18646963,0.00083054585,0.7689541,0.04344668,0.000011270883],"about_ca_topic_score_codex":0.0012076998,"about_ca_topic_score_gemma":0.0013346754,"teacher_disagreement_score":0.005848458,"about_ca_system_score_codex":0.0005086331,"about_ca_system_score_gemma":0.00068049505,"threshold_uncertainty_score":0.019565046},"labels":[],"label_agreement":null},{"id":"W112839659","doi":"","title":"Memory-Bounded D* Lite","year":2008,"lang":"en","type":"article","venue":"","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":"University of Windsor","funders":"","keywords":"Pathfinding; Bounded function; Computer science; Queue; Priority queue; Path (computing); Parallel computing; Theoretical computer science; Algorithm; Shortest path problem; Operating system; Mathematics; Computer network; Graph","score_opus":0.02356485116860079,"score_gpt":0.21581872589124257,"score_spread":0.19225387472264177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112839659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010650562,0.00089277024,0.9666171,0.0005057085,0.00012743268,0.00022443032,0.00081797247,0.0077707535,0.012393155],"genre_scores_gemma":[0.09602927,0.00051388965,0.88987297,0.000531476,0.000041971165,0.0005434796,0.0018356061,0.0010088168,0.009622494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881697,0.000273138,0.00014520797,0.00026793272,0.0003383055,0.00015842999],"domain_scores_gemma":[0.99670804,0.0016239906,0.00017502178,0.00087716273,0.0005071441,0.00010868293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012240969,0.0011474245,0.0010216039,0.0015804584,0.0010611983,0.0021396584,0.0034689973,0.0013715527,0.019513946],"category_scores_gemma":[0.0074779117,0.0006711397,0.0013119245,0.0022884933,0.0009386489,0.0036224045,0.0051341965,0.0016424279,0.005597418],"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":[0.00076339,0.00030766655,0.0013107085,0.0010718488,0.000107682594,0.00023324593,0.00036525767,0.05465237,0.0110489195,0.06361368,0.04691421,0.819611],"study_design_scores_gemma":[0.0004475107,0.0007650554,0.00077305856,0.00027871793,0.0001410534,0.0010353109,0.0005181264,0.67528796,0.027690444,0.16462845,0.12828572,0.00014862504],"about_ca_topic_score_codex":0.0028520839,"about_ca_topic_score_gemma":0.0052921693,"teacher_disagreement_score":0.019513946,"about_ca_system_score_codex":0.0006526949,"about_ca_system_score_gemma":0.0018131005,"threshold_uncertainty_score":0.06528062},"labels":[],"label_agreement":null},{"id":"W114151515","doi":"10.5751/es-00371-060102","title":"A Fractal Landscape Realizer for Generating Synthetic Maps","year":2002,"lang":"en","type":"article","venue":"Conservation Ecology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fractal; Geography; Computer science; Cartography; Mathematics","score_opus":0.03244642651610551,"score_gpt":0.23967366990808417,"score_spread":0.20722724339197865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W114151515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01413101,0.00006119001,0.9564398,0.00007915095,0.0000711053,0.00024017708,0.0005977089,0.025013689,0.0033662196],"genre_scores_gemma":[0.12752181,0.0000971634,0.86245376,0.000072499235,0.000021852582,0.0011309424,0.0014262937,0.0036797016,0.003595962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951327,0.00013007669,0.000039416293,0.00007590519,0.00020855972,0.000032756132],"domain_scores_gemma":[0.99861217,0.00077492755,0.0000746878,0.0002642257,0.00022522618,0.000048749844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014909684,0.00076071016,0.00041774067,0.0006482512,0.00034869567,0.0006600479,0.0014220596,0.0005943191,0.009955793],"category_scores_gemma":[0.0044378187,0.0005168045,0.0006868656,0.00039663794,0.00054015213,0.0007194435,0.0013368102,0.00097395934,0.0016046169],"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":[0.0007852003,0.00025421125,0.0045221755,0.001271674,0.00024839683,0.0015009503,0.0014687224,0.2553885,0.13724405,0.10318885,0.04644612,0.4476812],"study_design_scores_gemma":[0.00013252728,0.00017335908,0.0009822552,0.000045296925,0.000029965977,0.00043355912,0.000042524083,0.8920623,0.05192409,0.010880935,0.043208912,0.00008436054],"about_ca_topic_score_codex":0.0006704962,"about_ca_topic_score_gemma":0.00066669565,"teacher_disagreement_score":0.009955793,"about_ca_system_score_codex":0.00045889476,"about_ca_system_score_gemma":0.00035709742,"threshold_uncertainty_score":0.033305466},"labels":[],"label_agreement":null},{"id":"W117904809","doi":"10.3166/isi.10.1.33-57","title":"Recherche d'information. Analyse des résultats de différents systèmes réalisant la même tâche","year":2005,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.06425829610738738,"score_gpt":0.2950916923317034,"score_spread":0.23083339622431603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117904809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4903984,0.10860149,0.28208417,0.007517093,0.0022709344,0.0021614898,0.039323818,0.014039801,0.05360282],"genre_scores_gemma":[0.74498016,0.015857594,0.17176443,0.001053225,0.0010361479,0.0011994839,0.044314496,0.0023806973,0.017413735],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.95117855,0.022227127,0.0026288123,0.0056385077,0.017514803,0.00081221113],"domain_scores_gemma":[0.8752502,0.08439618,0.0045994725,0.018690547,0.015500507,0.0015631437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032420732,0.002501817,0.0027688213,0.008292269,0.0009242314,0.00609303,0.0017710186,0.002295612,0.00566761],"category_scores_gemma":[0.09639737,0.0007145467,0.0030985228,0.007426643,0.0019541495,0.0064581484,0.0021690838,0.0020317014,0.008720494],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031647466,0.0009968207,0.05778699,0.0075719985,0.004108341,0.00048234416,0.0040531843,0.008747258,0.06224106,0.0070018345,0.043231286,0.8006142],"study_design_scores_gemma":[0.0010085237,0.010979563,0.29481867,0.0015077348,0.008896146,0.0047161556,0.00519804,0.085981585,0.28213188,0.026973771,0.27690604,0.0008818857],"about_ca_topic_score_codex":0.003395152,"about_ca_topic_score_gemma":0.0019357225,"teacher_disagreement_score":0.032420732,"about_ca_system_score_codex":0.0013340458,"about_ca_system_score_gemma":0.0016804609,"threshold_uncertainty_score":0.17145932},"labels":[],"label_agreement":null},{"id":"W119401213","doi":"10.1007/3-540-31305-2_5","title":"Distributed Geospatial Information Service","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"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":"Geospatial analysis; Web Coverage Service; Service (business); Geography; Computer science; Data science; World Wide Web; Business; Remote sensing; Web service; Web 2.0","score_opus":0.009089489171984272,"score_gpt":0.18648355310654147,"score_spread":0.1773940639345572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W119401213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058981404,0.002263426,0.7730752,0.002464225,0.0013752853,0.00028460557,0.0020321677,0.01156949,0.20103742],"genre_scores_gemma":[0.1261168,0.0075222757,0.3813808,0.0013786228,0.00079918647,0.00042966424,0.008827723,0.0020159497,0.47152886],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999742,0.000024836983,0.000012924985,0.00004694632,0.00015137398,0.000022016395],"domain_scores_gemma":[0.9996538,0.000069833004,0.000010998218,0.0001420892,0.000090643036,0.000032570708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004971711,0.00047544396,0.000572304,0.0010417774,0.0007576625,0.002055882,0.0012511833,0.0006995761,0.019203411],"category_scores_gemma":[0.0009962554,0.0003084004,0.00026536259,0.0026195478,0.00054076617,0.0021563875,0.0013807063,0.0010129015,0.009040575],"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":[0.00010233719,0.00009901027,0.00043307155,0.00018238375,0.000026794423,0.00015825794,0.00031346103,0.006614888,0.007036954,0.25204724,0.22611006,0.5068756],"study_design_scores_gemma":[0.000039417388,0.000023514962,0.00050863094,0.000055418044,0.000021819082,0.00032876735,0.00010226588,0.03625647,0.003649492,0.11503169,0.8439646,0.000017894325],"about_ca_topic_score_codex":0.003984883,"about_ca_topic_score_gemma":0.003801647,"teacher_disagreement_score":0.019203411,"about_ca_system_score_codex":0.0011757431,"about_ca_system_score_gemma":0.0015087998,"threshold_uncertainty_score":0.06424183},"labels":[],"label_agreement":null},{"id":"W121085945","doi":"","title":"The PKD-tree for Orthogonal d-Dimensional Range Search.","year":2006,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Range tree; Tree (set theory); Combinatorics; R-tree; Search engine indexing; K-ary tree; Mathematics; Pyramid (geometry); Tree structure; Interval tree; Computer science; Algorithm; Binary tree; Spatial database; Artificial intelligence; Statistics; Spatial analysis; Geometry","score_opus":0.02736035179704939,"score_gpt":0.24675582092588252,"score_spread":0.21939546912883312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W121085945","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075303195,0.0024552967,0.97576207,0.0005630026,0.00023512307,0.00026502716,0.0020025794,0.0061230497,0.0050636367],"genre_scores_gemma":[0.066345334,0.00097004254,0.92472965,0.0003889768,0.000119489734,0.00062464824,0.0044902116,0.00047066034,0.001861],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980902,0.00045374304,0.00028725518,0.0003158,0.0006977686,0.00015517803],"domain_scores_gemma":[0.99479276,0.001575019,0.0004191426,0.002090147,0.0008782434,0.00024466796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017748247,0.00076262595,0.0012999356,0.0029398648,0.0011145746,0.002301913,0.0026817431,0.0013672762,0.0067775366],"category_scores_gemma":[0.013587013,0.0006551217,0.00096793036,0.006782678,0.0012147172,0.0058808546,0.0043611084,0.0017390688,0.0054333378],"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":[0.00075793744,0.00023080553,0.004293916,0.001233404,0.00015265238,0.0003381052,0.0005363156,0.03833725,0.011882352,0.10362388,0.08084053,0.7577728],"study_design_scores_gemma":[0.00038433392,0.0005490672,0.0020104328,0.00036134524,0.00014467028,0.0025918,0.00045688034,0.5574421,0.020180127,0.2176916,0.19796614,0.00022146959],"about_ca_topic_score_codex":0.0025248888,"about_ca_topic_score_gemma":0.0029886202,"teacher_disagreement_score":0.0067775366,"about_ca_system_score_codex":0.0010296853,"about_ca_system_score_gemma":0.0021575524,"threshold_uncertainty_score":0.02267307},"labels":[],"label_agreement":null},{"id":"W125162825","doi":"10.1142/s0218213005002053","title":"CLUSTERING SPATIAL DATA IN THE PRESENCE OF OBSTACLES","year":2005,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Cluster analysis; Computer science; Preprocessor; Constraint (computer-aided design); Data mining; Clustering high-dimensional data; Computation; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.1338871831955201,"score_gpt":0.3589904498955765,"score_spread":0.2251032667000564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W125162825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057179566,0.0003344456,0.93956834,0.00026373618,0.000051254472,0.00014569174,0.0005554653,0.0007565452,0.001145006],"genre_scores_gemma":[0.33187512,0.00041415886,0.66262776,0.0001203213,0.000054490512,0.00024280688,0.0029003064,0.00022518521,0.0015398923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670225,0.00067279185,0.00027486382,0.0006979738,0.001421131,0.00023095605],"domain_scores_gemma":[0.993814,0.0026938212,0.00069677207,0.0010987897,0.0014753548,0.00022131577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018385348,0.0010131728,0.0016405634,0.0032063855,0.0015909555,0.002196279,0.0023605735,0.0013926028,0.0009067566],"category_scores_gemma":[0.010194283,0.0008333852,0.0012526965,0.004941827,0.0011795121,0.0023781417,0.003107497,0.0011073695,0.00048782828],"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":[0.0005433247,0.00013260792,0.013667616,0.0006767865,0.00038507985,0.0009363413,0.00089500746,0.7069403,0.01895468,0.01854572,0.0061512496,0.23217128],"study_design_scores_gemma":[0.000022619508,0.000045843066,0.0021853338,0.000036065776,0.00004000247,0.00021738544,0.00039567694,0.96752787,0.010095532,0.012200163,0.007181476,0.00005202365],"about_ca_topic_score_codex":0.011160925,"about_ca_topic_score_gemma":0.014438655,"teacher_disagreement_score":0.011160925,"about_ca_system_score_codex":0.0009459452,"about_ca_system_score_gemma":0.002370837,"threshold_uncertainty_score":0.022191882},"labels":[],"label_agreement":null},{"id":"W1292837","doi":"","title":"Complexity of Octagonal and Rectangular Cartograms.","year":2005,"lang":"en","type":"article","venue":"Pneumonologia i Alergologia Polska","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"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":"Adjacency list; Rectangle; Computer science; Slicing; Population; Type (biology); Combinatorics; Mathematics; Geometry; Computer graphics (images); Geology","score_opus":0.030175102056890477,"score_gpt":0.24214459905200506,"score_spread":0.21196949699511458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1292837","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47559622,0.0028237887,0.46371073,0.0057869367,0.0002209518,0.00049399305,0.006550858,0.0014947132,0.043321818],"genre_scores_gemma":[0.7903852,0.0014489115,0.18864648,0.00048519566,0.00014310385,0.00036871314,0.005862618,0.0004916121,0.012168243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822766,0.000401299,0.00010637189,0.00045649518,0.00050814496,0.0003001068],"domain_scores_gemma":[0.990171,0.0068561165,0.0009833563,0.0009522339,0.00052399014,0.0005133305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090726133,0.0006936041,0.0010966015,0.0008223472,0.001010698,0.00356573,0.0020233826,0.0015921133,0.012239983],"category_scores_gemma":[0.011552125,0.000650649,0.0011893145,0.0020077475,0.0014899472,0.0070174485,0.0022240311,0.0016434377,0.0010453291],"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":[0.00077581085,0.00025056105,0.007820594,0.0012195572,0.00021074069,0.0008229033,0.00087132066,0.555374,0.009200974,0.29985523,0.024620673,0.09897769],"study_design_scores_gemma":[0.0001044604,0.0001086295,0.0021560364,0.000066594635,0.00008143953,0.0009050508,0.0004952376,0.5156608,0.004467905,0.4611067,0.014796957,0.00005010946],"about_ca_topic_score_codex":0.0029890623,"about_ca_topic_score_gemma":0.0034960283,"teacher_disagreement_score":0.012239983,"about_ca_system_score_codex":0.0016578693,"about_ca_system_score_gemma":0.0012364668,"threshold_uncertainty_score":0.04094684},"labels":[],"label_agreement":null},{"id":"W138750640","doi":"10.1007/978-3-642-03668-2_26","title":"Holonic Modelling of Large Scale Geographic Environments","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Situated; Computer science; Geographic information system; Representation (politics); Scale (ratio); Data science; Artificial intelligence; Geography; Cartography","score_opus":0.01631348630134387,"score_gpt":0.22174833510947373,"score_spread":0.20543484880812984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W138750640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013386683,0.0002755482,0.9737265,0.00015375491,0.0000744488,0.000024938223,0.00028859914,0.00038571793,0.011683864],"genre_scores_gemma":[0.7484709,0.0018166364,0.21820253,0.00014482415,0.00015349232,0.00018276664,0.0012123053,0.0005536252,0.02926293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997881,0.000059358073,0.000015325051,0.000040212657,0.00006838884,0.000028586928],"domain_scores_gemma":[0.99952483,0.00019204154,0.000059016398,0.00009283282,0.000079293175,0.00005208328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025769812,0.00070604624,0.0007325006,0.0008407712,0.00054632616,0.0021141497,0.0018858245,0.0009571083,0.005190496],"category_scores_gemma":[0.001366274,0.00060569803,0.00078660005,0.0014590793,0.0011197244,0.0019628783,0.0016993654,0.000968011,0.0008613622],"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":[0.000016964856,0.000010772542,0.00036826005,0.00005914522,0.00002284866,0.00008565034,0.00012041644,0.9151407,0.0008167409,0.06963813,0.0011208287,0.012599538],"study_design_scores_gemma":[0.0000034948562,0.000004585262,0.000109412234,0.000005597572,0.000004708147,0.000022529708,0.00002407012,0.9712451,0.00015452511,0.025986193,0.0024336788,0.000006092656],"about_ca_topic_score_codex":0.014441851,"about_ca_topic_score_gemma":0.014723889,"teacher_disagreement_score":0.014441851,"about_ca_system_score_codex":0.0008798531,"about_ca_system_score_gemma":0.0006357178,"threshold_uncertainty_score":0.02871555},"labels":[],"label_agreement":null},{"id":"W140589133","doi":"10.1007/b10143","title":"Topics in Spatial Stochastic Processes","year":2003,"lang":"en","type":"book","venue":"Lecture notes in mathematics","topic":"Data Management and Algorithms","field":"Computer Science","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 Ottawa","funders":"","keywords":"Mathematics; Stochastic process; Subject (documents); Mathematical economics; Set (abstract data type); Statistical physics; Statistics; Library science; Computer science; Physics","score_opus":0.015668743634088014,"score_gpt":0.24110294985917782,"score_spread":0.2254342062250898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W140589133","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017928608,0.13185799,0.3762004,0.009847279,0.0079164645,0.000070483715,0.0007828549,0.00063893164,0.454757],"genre_scores_gemma":[0.27378666,0.14336681,0.054922305,0.001396333,0.013179086,0.00021381138,0.00131359,0.00055869913,0.51126283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99981636,0.000038244263,0.000011483908,0.000035581477,0.000082549406,0.000015778347],"domain_scores_gemma":[0.99975556,0.00010444205,0.000017893268,0.00003045716,0.000057014342,0.000034708017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033941536,0.0009818391,0.00095057243,0.0011640054,0.0004996053,0.0020823404,0.0004666034,0.0006456988,0.010725465],"category_scores_gemma":[0.001030863,0.00029071074,0.0006836177,0.0030863672,0.0011054714,0.0019586904,0.0007999136,0.0012380641,0.0026794167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006017822,0.000013490361,0.000124803,0.00013249795,0.000014524232,0.000040454048,0.000109121924,0.0044919844,0.000385186,0.9104959,0.040938973,0.04324704],"study_design_scores_gemma":[0.0000066448433,0.000015606562,0.00046423447,0.00006794223,0.00001886819,0.00011583591,0.00008216747,0.015077561,0.00020723534,0.8017686,0.18216841,0.0000069056173],"about_ca_topic_score_codex":0.0015255938,"about_ca_topic_score_gemma":0.0013127789,"teacher_disagreement_score":0.010725465,"about_ca_system_score_codex":0.0010295758,"about_ca_system_score_gemma":0.0008322959,"threshold_uncertainty_score":0.035880268},"labels":[],"label_agreement":null},{"id":"W144578322","doi":"10.1007/0-387-23667-8_13","title":"Parallel Computation, Co-operation, Tabu Search","year":2005,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Tabu search; Computation; Computer science; Focus (optics); Guided Local Search; Point (geometry); Mathematical optimization; Theoretical computer science; Management science; Data science; Algorithm; Mathematics; Engineering","score_opus":0.03662927356086902,"score_gpt":0.2816171157799109,"score_spread":0.24498784221904185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W144578322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034606077,0.020769747,0.8884946,0.00089816516,0.00042096718,0.00020543516,0.0002790632,0.0032848443,0.082186535],"genre_scores_gemma":[0.06952317,0.018529512,0.8605455,0.00050712825,0.00033844874,0.00072245614,0.00092096825,0.0012279465,0.047684837],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994863,0.00010693336,0.000025502226,0.00011864404,0.0002188997,0.00004373783],"domain_scores_gemma":[0.99951446,0.00018329435,0.0000288879,0.0001504937,0.000097267926,0.000025745847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050911115,0.0013931692,0.0009742002,0.00103103,0.0007400259,0.0022288966,0.0016878383,0.0009606458,0.0145344045],"category_scores_gemma":[0.0021482417,0.00048206837,0.0005079892,0.005659841,0.0010778193,0.0025180087,0.0010998165,0.0014575003,0.00910675],"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":[0.00009071129,0.00009857724,0.00027209977,0.0011453173,0.000049291888,0.00011048289,0.00016528775,0.04317544,0.0049205846,0.2639857,0.056422714,0.62956375],"study_design_scores_gemma":[0.000067507244,0.00009687153,0.0003577668,0.00025583245,0.00005970285,0.00086619664,0.00009980436,0.16606075,0.007383942,0.50250643,0.3222051,0.00004017655],"about_ca_topic_score_codex":0.0015381764,"about_ca_topic_score_gemma":0.0018852224,"teacher_disagreement_score":0.0145344045,"about_ca_system_score_codex":0.0009310388,"about_ca_system_score_gemma":0.00127176,"threshold_uncertainty_score":0.04862243},"labels":[],"label_agreement":null},{"id":"W1480779956","doi":"10.1109/mdm.2015.49","title":"A Mixed Breadth-Depth First Search Strategy for Sequenced Group Trip Planning Queries","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Sequence (biology); Point of interest; Focus (optics); Group (periodic table); Euclidean distance; Euclidean geometry; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.20483533165271003,"score_gpt":0.3292976793257234,"score_spread":0.12446234767301337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480779956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058899883,0.0013516091,0.9290437,0.00052938814,0.00006501896,0.0004226694,0.0007972989,0.001596531,0.0072938884],"genre_scores_gemma":[0.3580855,0.00036600075,0.63592947,0.00031274624,0.00004455127,0.00032793955,0.0013169526,0.000244231,0.003372561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894255,0.00037368157,0.00005734496,0.00026228774,0.00023731453,0.00012671908],"domain_scores_gemma":[0.998607,0.0009823539,0.00007906948,0.00010972142,0.00013919103,0.00008279546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012056803,0.0012383019,0.0015268212,0.0010966256,0.0006950794,0.0012306285,0.002285868,0.0016774918,0.0042003985],"category_scores_gemma":[0.0033599858,0.0006792766,0.0009491824,0.0017317737,0.0005777871,0.0024513376,0.001509328,0.000953977,0.00079811446],"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":[0.00061112904,0.000404147,0.0015443239,0.0006998845,0.0001631037,0.0004158593,0.0006597185,0.732534,0.0086207995,0.023986751,0.011213938,0.2191464],"study_design_scores_gemma":[0.00006402273,0.00012079542,0.000121691286,0.000013421661,0.000019514178,0.000094832125,0.00010969855,0.98563194,0.00075692585,0.010559462,0.0024969897,0.000010690766],"about_ca_topic_score_codex":0.008099716,"about_ca_topic_score_gemma":0.0098380735,"teacher_disagreement_score":0.008099716,"about_ca_system_score_codex":0.0009914435,"about_ca_system_score_gemma":0.001683154,"threshold_uncertainty_score":0.016105115},"labels":[],"label_agreement":null},{"id":"W1481682349","doi":"10.1007/11425274_58","title":"A Distance-Based Algorithm for Clustering Database User Sessions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Cluster analysis; Benchmark (surveying); Computation; Workload; Data mining; Online transaction processing; Database; Algorithm; Database transaction; Artificial intelligence; Transaction processing","score_opus":0.0233684434842335,"score_gpt":0.2653069685680108,"score_spread":0.24193852508377728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481682349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0106780045,0.00029640438,0.9839841,0.000100525605,0.000111961686,0.00028734223,0.00040041297,0.002773315,0.0013679314],"genre_scores_gemma":[0.057824764,0.0001851138,0.93518496,0.000054810305,0.000081477294,0.00026378816,0.0013398734,0.00018802068,0.0048771924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996323,0.00051215413,0.00030913026,0.00083105324,0.0017921218,0.00023253071],"domain_scores_gemma":[0.99678516,0.000858783,0.00016671592,0.0005262151,0.0014439864,0.00021909922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021022994,0.0010540165,0.002019211,0.004859963,0.0018996665,0.00222348,0.004105472,0.001616556,0.005528992],"category_scores_gemma":[0.006524449,0.00061836385,0.0010523068,0.0062384475,0.0006947784,0.0023368406,0.0020697303,0.001297822,0.004352605],"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":[0.0005049717,0.00037780558,0.0019534582,0.00013742421,0.00009703817,0.00003489555,0.00024298966,0.025503134,0.011938149,0.00632979,0.0093353335,0.94354504],"study_design_scores_gemma":[0.00016258954,0.00032282656,0.0035496245,0.00003166065,0.00009376011,0.00040234657,0.00028293583,0.948221,0.017811311,0.011556873,0.01744999,0.000115040195],"about_ca_topic_score_codex":0.011427995,"about_ca_topic_score_gemma":0.015400522,"teacher_disagreement_score":0.011427995,"about_ca_system_score_codex":0.0015896403,"about_ca_system_score_gemma":0.0027660974,"threshold_uncertainty_score":0.02272296},"labels":[],"label_agreement":null},{"id":"W1481798977","doi":"10.1007/11424857_20","title":"A New and Efficient K-Medoid Algorithm for Spatial Clustering","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Medoid; k-medoids; Cluster analysis; Computer science; Algorithm; Canopy clustering algorithm; Computation; CURE data clustering algorithm; Correlation clustering; Data mining; Artificial intelligence","score_opus":0.013327264064766016,"score_gpt":0.23704973927285192,"score_spread":0.2237224752080859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481798977","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078084134,0.00018613602,0.99802256,0.000038226248,0.00006578375,0.000034584635,0.000060468898,0.00048978574,0.00032169276],"genre_scores_gemma":[0.009586876,0.00019351963,0.9882962,0.000053152926,0.00004346042,0.00010638897,0.00030269613,0.00015855553,0.0012590684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704677,0.00063550746,0.00023527966,0.00058008154,0.0013594747,0.0001428052],"domain_scores_gemma":[0.9974037,0.00084938883,0.00012536733,0.00052241393,0.0009854286,0.000113766895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019201373,0.001650956,0.0030645542,0.0025875533,0.0017350387,0.0020115909,0.0046841647,0.002775328,0.0052889897],"category_scores_gemma":[0.0067621786,0.0013363454,0.0023814486,0.0051438236,0.0010323515,0.0032140345,0.0039098416,0.0024393003,0.0054953117],"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":[0.0003617152,0.0001203137,0.0005265024,0.0003097944,0.00022023785,0.000107954904,0.0002855497,0.15651347,0.011432739,0.011766694,0.014677779,0.8036772],"study_design_scores_gemma":[0.000050012142,0.00005980135,0.00025642518,0.000030751115,0.000047072204,0.00031965505,0.00010048288,0.9616148,0.006183655,0.016944923,0.014319615,0.000072706396],"about_ca_topic_score_codex":0.008633177,"about_ca_topic_score_gemma":0.010368362,"teacher_disagreement_score":0.008633177,"about_ca_system_score_codex":0.0010412852,"about_ca_system_score_gemma":0.00223774,"threshold_uncertainty_score":0.0176934},"labels":[],"label_agreement":null},{"id":"W148229027","doi":"","title":"The Counting Series for Unlabeled Linear Acyclic Hypergraphs.","year":2006,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Series (stratigraphy); Combinatorics; Biology","score_opus":0.008401725942124443,"score_gpt":0.2225427401329127,"score_spread":0.21414101419078826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W148229027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08002012,0.0038286457,0.73334986,0.004672194,0.0014694688,0.0001358239,0.0021598348,0.001900374,0.17246357],"genre_scores_gemma":[0.7063925,0.0053531677,0.17002624,0.0017069505,0.0025322372,0.000499759,0.0028073413,0.0011383516,0.10954345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983151,0.0004295335,0.00013682092,0.00032741477,0.00058454956,0.00020649673],"domain_scores_gemma":[0.9911289,0.005309007,0.00076756923,0.0010421037,0.0011108625,0.0006415655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023228873,0.00072265003,0.00090601307,0.0059237387,0.002194904,0.005230687,0.0016761379,0.0011242235,0.01392243],"category_scores_gemma":[0.020094644,0.0006625544,0.0008685899,0.005982176,0.0021599887,0.009331707,0.0017416195,0.0029662312,0.0028496222],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025133431,0.000018573934,0.00037073952,0.00006870375,0.000013264739,0.00006905609,0.00011508443,0.0022118497,0.000274308,0.96408993,0.00791564,0.024827624],"study_design_scores_gemma":[0.000006852429,0.000007858169,0.00019270134,0.00003873471,0.000015109636,0.00019011204,0.000036529156,0.022281101,0.00042414252,0.9652244,0.011568715,0.000013765945],"about_ca_topic_score_codex":0.0018814658,"about_ca_topic_score_gemma":0.0027844117,"teacher_disagreement_score":0.01392243,"about_ca_system_score_codex":0.0030415575,"about_ca_system_score_gemma":0.0014821028,"threshold_uncertainty_score":0.04657513},"labels":[],"label_agreement":null},{"id":"W1483181615","doi":"10.1007/978-3-540-68825-9_22","title":"Predicting User Preferences Via Similarity-Based Clustering","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Research and Productivity Council; University of New Brunswick","funders":"","keywords":"Computer science; Cluster analysis; Similarity (geometry); Data mining; Artificial intelligence; Information retrieval","score_opus":0.026412242445519492,"score_gpt":0.23842959414335174,"score_spread":0.21201735169783226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483181615","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75375766,0.0013757546,0.23631577,0.00029423513,0.000080007994,0.00021281996,0.0022511596,0.0011541368,0.004558555],"genre_scores_gemma":[0.92037946,0.00038034882,0.07442295,0.00006508956,0.0000656713,0.00007156883,0.0021533715,0.00005765224,0.0024037708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917275,0.0002456063,0.000050679995,0.00015898402,0.00028375746,0.00008833566],"domain_scores_gemma":[0.9978485,0.0013462702,0.00012317128,0.00016927398,0.00040067895,0.00011218768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006933827,0.00059454713,0.0009518865,0.0024930942,0.00038433587,0.00081338873,0.0006653005,0.00081362785,0.0018431338],"category_scores_gemma":[0.0040553585,0.00023754669,0.0008057883,0.002786687,0.0001852789,0.001229455,0.0004061642,0.0007401599,0.0015897236],"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":[0.0034714756,0.001994231,0.16093522,0.00036366648,0.0008591283,0.00032470928,0.00046604878,0.08143805,0.023854263,0.0031217607,0.013432602,0.70973885],"study_design_scores_gemma":[0.00003747385,0.00038864522,0.036103074,0.000018439407,0.000107810854,0.00035711497,0.00022350009,0.9535305,0.004796911,0.0034225758,0.00095563463,0.000058333186],"about_ca_topic_score_codex":0.0076365704,"about_ca_topic_score_gemma":0.012577514,"teacher_disagreement_score":0.0076365704,"about_ca_system_score_codex":0.00046285038,"about_ca_system_score_gemma":0.00029838772,"threshold_uncertainty_score":0.015184283},"labels":[],"label_agreement":null},{"id":"W1483563006","doi":"10.1007/11766247_12","title":"Intelligent Information Personalization Leveraging Constraint Satisfaction and Association Rule Methods","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Personalization; Computer science; Consistency (knowledge bases); Information filtering system; Constraint (computer-aided design); Information retrieval; Constraint satisfaction problem; Relevance (law); Set (abstract data type); Constraint satisfaction; Association rule learning; Recommender system; Data mining; World Wide Web; Artificial intelligence; Mathematics; Probabilistic logic","score_opus":0.017660564034717406,"score_gpt":0.26557442543268184,"score_spread":0.24791386139796442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483563006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066372966,0.0002928257,0.98997784,0.00017705509,0.000033689037,0.000111636364,0.00015595728,0.0011168243,0.0014967817],"genre_scores_gemma":[0.10501493,0.0005021846,0.8900732,0.00015554698,0.0000860707,0.00014531851,0.0008514262,0.0002238211,0.0029474986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957289,0.001030479,0.00045030916,0.0007231146,0.0018628023,0.00020437379],"domain_scores_gemma":[0.99161506,0.004490881,0.00044758734,0.0017746371,0.001501981,0.0001699615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034721524,0.0009885277,0.002208392,0.0026682687,0.00091557193,0.0027191758,0.0028548192,0.00091569225,0.0040564835],"category_scores_gemma":[0.011881778,0.00077362446,0.0016179149,0.004424919,0.00077895256,0.0046749413,0.002210913,0.0019657332,0.0012537123],"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":[0.00028642325,0.00041175957,0.0024480105,0.00042840253,0.00028005656,0.00028179312,0.00037750692,0.07903643,0.0078277,0.027246444,0.010986151,0.8703893],"study_design_scores_gemma":[0.000041882067,0.0000665905,0.0005165399,0.000048808633,0.00013509038,0.0002738623,0.000101073485,0.936635,0.011081844,0.042113926,0.008945115,0.00004032506],"about_ca_topic_score_codex":0.0055521755,"about_ca_topic_score_gemma":0.009067275,"teacher_disagreement_score":0.0055521755,"about_ca_system_score_codex":0.0005924216,"about_ca_system_score_gemma":0.0015781628,"threshold_uncertainty_score":0.018362701},"labels":[],"label_agreement":null},{"id":"W1485709273","doi":"10.3390/fi7020110","title":"Dynamis: Effective Context-Aware Web Service Selection Using Dynamic Attributes","year":2015,"lang":"en","type":"article","venue":"Future Internet","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Skyline; Web service; Ranking (information retrieval); Data mining; Quality of service; Set (abstract data type); Context (archaeology); Matching (statistics); Service (business); Database; Information retrieval; World Wide Web; Computer network","score_opus":0.01869549164567102,"score_gpt":0.2578691115358681,"score_spread":0.2391736198901971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1485709273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22265057,0.002407758,0.7550018,0.00095408026,0.00021153109,0.0003777461,0.0012418485,0.010994243,0.006160508],"genre_scores_gemma":[0.7651508,0.0004483613,0.23103842,0.00019803127,0.00011047418,0.00014754488,0.001128426,0.00016368357,0.0016142438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990675,0.00019616233,0.000080330916,0.00018022682,0.00036796575,0.000107828135],"domain_scores_gemma":[0.998606,0.0006561041,0.0001307535,0.00026487524,0.00020084092,0.00014148871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012799754,0.0007200214,0.0012723724,0.002276072,0.001259612,0.0018274576,0.0013064427,0.00061482785,0.0011667951],"category_scores_gemma":[0.0030804644,0.00037891918,0.00053306395,0.0025606772,0.00048974715,0.0019508721,0.0016753314,0.000720518,0.00040202678],"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":[0.0013440574,0.00059653196,0.02308094,0.00025549976,0.00019681828,0.00031714523,0.00040165655,0.158509,0.024569029,0.011761404,0.015345363,0.7636225],"study_design_scores_gemma":[0.00007010821,0.00008141859,0.0019761263,0.0000108153245,0.000027517102,0.00020537792,0.00015318533,0.98218864,0.005448369,0.00626837,0.0035438624,0.00002622027],"about_ca_topic_score_codex":0.0057163644,"about_ca_topic_score_gemma":0.00755235,"teacher_disagreement_score":0.0057163644,"about_ca_system_score_codex":0.00093972357,"about_ca_system_score_gemma":0.0013773856,"threshold_uncertainty_score":0.0113661885},"labels":[],"label_agreement":null},{"id":"W1486636865","doi":"10.1007/3-540-45749-6_28","title":"Geometric Algorithms for Density-Based Data Clustering","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Approximation algorithm; Cluster analysis; Algorithm; Computer science; Quadratic equation; Function (biology); Mathematics; Artificial intelligence","score_opus":0.06445117085016047,"score_gpt":0.2762087730906237,"score_spread":0.21175760224046322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486636865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044826046,0.0006257218,0.99753535,0.00007315963,0.00004267025,0.000028525019,0.000042751308,0.0005270221,0.0006764417],"genre_scores_gemma":[0.025224216,0.0015624961,0.9692193,0.00008686301,0.00013439858,0.00020933636,0.00056603673,0.000324323,0.0026729652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976362,0.000653279,0.00013425978,0.0003946792,0.0010658412,0.00011572371],"domain_scores_gemma":[0.9970794,0.0011681403,0.00017390944,0.0008082413,0.00068925996,0.00008113698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018576777,0.0019273482,0.0024431543,0.0035255973,0.0014177766,0.0025129488,0.005074268,0.0020819886,0.006466329],"category_scores_gemma":[0.010500143,0.0015203868,0.0017747697,0.0072398717,0.002009137,0.003765362,0.0045657107,0.0033397437,0.00484372],"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":[0.00010230659,0.00007704403,0.00063270674,0.00044710407,0.00013196937,0.00006207333,0.00022319978,0.22144896,0.0027538675,0.17263255,0.020602059,0.58088607],"study_design_scores_gemma":[0.00003236083,0.000046512836,0.00037519014,0.0000562108,0.00004069356,0.00026366021,0.00007668348,0.74223614,0.0030174903,0.2288593,0.024945756,0.00004997981],"about_ca_topic_score_codex":0.0039590816,"about_ca_topic_score_gemma":0.0037752017,"teacher_disagreement_score":0.006466329,"about_ca_system_score_codex":0.0017023878,"about_ca_system_score_gemma":0.0013720035,"threshold_uncertainty_score":0.021632016},"labels":[],"label_agreement":null},{"id":"W1487521718","doi":"10.1007/978-3-642-19173-2_7","title":"A Folksonomy-Based Recommendation System for the Sensor Web","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Folksonomy; Computer science; Terabyte; Tag system; Geospatial analysis; Key (lock); Information retrieval; World Wide Web; Sensor web; Web resource; Data mining","score_opus":0.03169044304548255,"score_gpt":0.24056679311994608,"score_spread":0.20887635007446353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1487521718","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075131156,0.0040001804,0.5997271,0.0013847207,0.0006650586,0.0011911355,0.03953624,0.26023003,0.018134393],"genre_scores_gemma":[0.1805713,0.0024476266,0.7029472,0.0010459442,0.00026353376,0.000638069,0.06462841,0.0035699855,0.04388789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995042,0.000043296295,0.00006669139,0.00014065628,0.00020802586,0.00003703823],"domain_scores_gemma":[0.9987704,0.00025174706,0.00006727909,0.00033022894,0.0004090317,0.00017138736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087977434,0.0008095117,0.0015675885,0.003670775,0.0010286082,0.0016247613,0.0017809294,0.0012426901,0.010739093],"category_scores_gemma":[0.0023308664,0.00058860687,0.000781918,0.0037177538,0.0001457626,0.0030516637,0.0010256628,0.0008225529,0.010194364],"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":[0.0014555266,0.0006876135,0.006758919,0.0006976836,0.00042965743,0.0006256489,0.00022533671,0.0039838376,0.04493252,0.0023307565,0.1840622,0.75381035],"study_design_scores_gemma":[0.00050254434,0.00059871754,0.026296515,0.00026514294,0.00077865366,0.0030793543,0.000535045,0.66216063,0.095054425,0.015521479,0.19469504,0.0005124476],"about_ca_topic_score_codex":0.010560085,"about_ca_topic_score_gemma":0.024364095,"teacher_disagreement_score":0.010739093,"about_ca_system_score_codex":0.0005183993,"about_ca_system_score_gemma":0.00071474933,"threshold_uncertainty_score":0.035925806},"labels":[],"label_agreement":null},{"id":"W1488044967","doi":"10.1109/easic.1990.207976","title":"A framework for performance and knowledge-based control of basic designs","year":2002,"lang":"en","type":"article","venue":"","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Control (management); Design knowledge; Process (computing); Quality (philosophy); Object (grammar); Expert system; Software engineering; Artificial intelligence; Programming language","score_opus":0.04736273604541713,"score_gpt":0.2582569875414832,"score_spread":0.21089425149606605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488044967","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005724713,0.00013537846,0.9940451,0.00026964283,0.000026926622,0.00006887649,0.00002567267,0.00029795774,0.0045579644],"genre_scores_gemma":[0.11214914,0.0007757849,0.8793962,0.00028295247,0.00018503479,0.0008624684,0.0002083235,0.00017143581,0.0059686657],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931415,0.0021561747,0.00056454155,0.0011542498,0.0025417616,0.0004419218],"domain_scores_gemma":[0.99385554,0.0029194283,0.00063113496,0.0014867692,0.0008456048,0.00026158543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009641498,0.0018604224,0.0012156912,0.0020770768,0.0011775212,0.007119578,0.005111507,0.002697795,0.0068546142],"category_scores_gemma":[0.012588854,0.0008886419,0.0023510442,0.0015746625,0.007694748,0.0065948437,0.0033713675,0.003937131,0.0014579181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019662128,0.00003264057,0.0000846812,0.00012439155,0.000025218182,0.00006615086,0.00023231267,0.065750316,0.0008246728,0.9059593,0.0008407051,0.026039962],"study_design_scores_gemma":[0.000044378532,0.00008177554,0.00006764163,0.00012153034,0.000029003877,0.00006403576,0.000064319014,0.23980357,0.0014604394,0.7297732,0.028457524,0.00003261967],"about_ca_topic_score_codex":0.0057051363,"about_ca_topic_score_gemma":0.0035389927,"teacher_disagreement_score":0.009641498,"about_ca_system_score_codex":0.0037401807,"about_ca_system_score_gemma":0.003978865,"threshold_uncertainty_score":0.050989747},"labels":[],"label_agreement":null},{"id":"W1489209457","doi":"","title":"Tuning QoD in stream processing engines","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scheduling (production processes); Quality of service; Exploit; Stream processing; Response time; Database; Real-time computing; Distributed computing; Computer network; Operating system; Mathematical optimization","score_opus":0.010733023995767059,"score_gpt":0.23641036869278612,"score_spread":0.22567734469701906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1489209457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47338706,0.0014302594,0.51429695,0.0003675116,0.00012238622,0.00032878778,0.00019544415,0.007312589,0.002558971],"genre_scores_gemma":[0.877638,0.00020277171,0.12088136,0.00009893394,0.000025524962,0.00009175251,0.00012092808,0.00016106061,0.0007796948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838626,0.00037380308,0.00019329741,0.00037542038,0.00048502642,0.00018619392],"domain_scores_gemma":[0.99658513,0.0017492062,0.0002796554,0.0005249784,0.00062663766,0.000234278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00335039,0.00074021285,0.0008345776,0.00074267527,0.0005271216,0.0015692792,0.0018867088,0.0007253741,0.0010873657],"category_scores_gemma":[0.0086599365,0.00046955672,0.00032840855,0.00089037605,0.0005632833,0.0018190866,0.0007146598,0.00070039165,0.00036339552],"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":[0.0028651096,0.0008263651,0.012079173,0.0005254885,0.00018615667,0.0003553698,0.0005728383,0.4665908,0.17769448,0.010635615,0.0040059616,0.32366282],"study_design_scores_gemma":[0.00008959858,0.00014753372,0.0006522897,0.000006992984,0.000022948469,0.000035657533,0.000029151357,0.9669504,0.02884107,0.0018758562,0.0013318594,0.00001662338],"about_ca_topic_score_codex":0.0044829017,"about_ca_topic_score_gemma":0.0026367716,"teacher_disagreement_score":0.0044829017,"about_ca_system_score_codex":0.0014542636,"about_ca_system_score_gemma":0.0015175388,"threshold_uncertainty_score":0.017718792},"labels":[],"label_agreement":null},{"id":"W1490413238","doi":"10.1007/978-3-642-12098-5_5","title":"Dynamic Skyline Queries in Large Graphs","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":36,"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":"Skyline; Computer science; Pruning; Euclidean space; Graph; Set (abstract data type); Query optimization; Data mining; Spatial query; Metric space; Theoretical computer science; Algorithm; Web search query; Sargable; Information retrieval; Search engine; Mathematics; Discrete mathematics; Combinatorics","score_opus":0.008112831594243572,"score_gpt":0.23515278624070535,"score_spread":0.22703995464646176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490413238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13354102,0.007364078,0.7960331,0.0045208167,0.0004499034,0.00028927665,0.005721937,0.010367559,0.04171227],"genre_scores_gemma":[0.5841846,0.005251787,0.3626633,0.00065264874,0.000722696,0.0002858971,0.011847261,0.0026617975,0.031730015],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998857,0.00024163013,0.00006723361,0.00026171035,0.00042199277,0.00015033405],"domain_scores_gemma":[0.99609536,0.0022985262,0.00016888072,0.0009214127,0.00030361157,0.000212202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011587373,0.0007671582,0.0013201797,0.0012325434,0.0011198332,0.002445367,0.0019477514,0.0012557893,0.0086489115],"category_scores_gemma":[0.0053383214,0.00074043893,0.00060114654,0.0043503987,0.0008023515,0.00865107,0.0021250208,0.0018305863,0.0019628007],"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":[0.0010202031,0.00035228155,0.0031835702,0.0014883211,0.00014708728,0.0005606394,0.0009033557,0.12180924,0.020616032,0.26499897,0.19404723,0.39087304],"study_design_scores_gemma":[0.00019651663,0.00013242867,0.0012028109,0.00008665269,0.000083799525,0.0011080664,0.00046019338,0.4920593,0.011817679,0.41558713,0.0772175,0.000047928286],"about_ca_topic_score_codex":0.0015635935,"about_ca_topic_score_gemma":0.0023444647,"teacher_disagreement_score":0.0086489115,"about_ca_system_score_codex":0.0012227835,"about_ca_system_score_gemma":0.00067535124,"threshold_uncertainty_score":0.028933465},"labels":[],"label_agreement":null},{"id":"W1491134956","doi":"","title":"Mobile services discovery and selection in the publish/subscribe paradigm","year":2004,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Roaming; Matching (statistics); Service discovery; ENCODE; Service (business); Preference; Encoding (memory); Publication; Value (mathematics); Selection (genetic algorithm); Information retrieval; Personalization; Service provider; Computer network; World Wide Web; Web service; Artificial intelligence; Machine learning; Mathematics","score_opus":0.06744222156279532,"score_gpt":0.37136164871072375,"score_spread":0.3039194271479284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491134956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016878402,0.0006995074,0.97539014,0.0006889234,0.00012848775,0.0003321228,0.00018156382,0.0013245804,0.0043762787],"genre_scores_gemma":[0.24830069,0.0012631427,0.73721737,0.0003580027,0.00036424978,0.0005743576,0.00063321536,0.0001841851,0.011104743],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947743,0.0014769747,0.00030185402,0.0007070813,0.002347361,0.00039243948],"domain_scores_gemma":[0.9961358,0.0020458223,0.00031581376,0.0008308666,0.00041405586,0.00025753165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040654377,0.0007792342,0.0025278593,0.0018616661,0.0025005438,0.004207503,0.0020979634,0.0018932483,0.0032967487],"category_scores_gemma":[0.009502259,0.001007152,0.0013223668,0.004894239,0.0018431909,0.005509269,0.0028538434,0.0016059887,0.0024481206],"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":[0.0014695177,0.0003884458,0.0030739922,0.00045132064,0.00024608607,0.0013971671,0.0009227827,0.0916422,0.016372513,0.24720143,0.0158555,0.620979],"study_design_scores_gemma":[0.0003879561,0.00024711157,0.0008618611,0.000041447718,0.00011588018,0.0012762342,0.0004474841,0.660207,0.015696317,0.28408268,0.036535356,0.000100581135],"about_ca_topic_score_codex":0.003935779,"about_ca_topic_score_gemma":0.004428191,"teacher_disagreement_score":0.004207503,"about_ca_system_score_codex":0.00160473,"about_ca_system_score_gemma":0.001896033,"threshold_uncertainty_score":0.021500349},"labels":[],"label_agreement":null},{"id":"W1491296979","doi":"10.1007/11935148_17","title":"Integrating Data from Maps on the World-Wide Web","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Completeness (order theory); Data mining; Precision and recall; Process (computing); Information retrieval; Data integration; Mathematics","score_opus":0.033943154934804716,"score_gpt":0.25342368495600515,"score_spread":0.21948053002120044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491296979","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12812412,0.007870822,0.6886536,0.004159135,0.0010800249,0.00047036962,0.030378299,0.035685144,0.103578575],"genre_scores_gemma":[0.4559531,0.008958347,0.46932212,0.00039153927,0.00031543637,0.00016657883,0.04341837,0.0031353624,0.01833914],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988059,0.00022220334,0.000074926895,0.00016039406,0.0006732202,0.00006340185],"domain_scores_gemma":[0.99788195,0.00090140314,0.00009303844,0.00052413024,0.0004972814,0.00010223332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011006749,0.0007337001,0.0006759604,0.00806325,0.00078876107,0.006632113,0.0009999326,0.0007282103,0.0054642917],"category_scores_gemma":[0.0075976746,0.0006031374,0.00089680473,0.014652092,0.0005241762,0.0073744003,0.0034210782,0.0009259899,0.002797292],"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":[0.00022646699,0.0002151898,0.017996239,0.0013385717,0.0005729017,0.00107055,0.002339244,0.017924828,0.0084942095,0.03450368,0.05769822,0.8576199],"study_design_scores_gemma":[0.000050669893,0.000090877955,0.023264268,0.0006998306,0.00095486885,0.001090666,0.005565538,0.13455753,0.038383033,0.2247621,0.5703423,0.00023837721],"about_ca_topic_score_codex":0.012036109,"about_ca_topic_score_gemma":0.02204082,"teacher_disagreement_score":0.012036109,"about_ca_system_score_codex":0.00055398466,"about_ca_system_score_gemma":0.0009834503,"threshold_uncertainty_score":0.0239321},"labels":[],"label_agreement":null},{"id":"W1492345494","doi":"10.24297/ijct.v14i4.1962","title":"An Ameliorated Methodology for Ranking the Tuple","year":2015,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Ranking (information retrieval); Computer science; Tuple; Information retrieval; Web search query; Query optimization; Sorting; Task (project management); Sargable; Web query classification; Query expansion; Query language; View; Database; Search engine; Database design; Mathematics","score_opus":0.0873100512517525,"score_gpt":0.362472441262074,"score_spread":0.2751623900103215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492345494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035588313,0.00030798322,0.99339795,0.00013142345,0.00008149521,0.0002068098,0.00023230219,0.00075117045,0.0013319749],"genre_scores_gemma":[0.052563645,0.00027908417,0.9416296,0.000114338494,0.00008654749,0.00023559814,0.00046316147,0.00010443444,0.004523601],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9904963,0.0020845314,0.0010534825,0.0021371804,0.003784013,0.00044452064],"domain_scores_gemma":[0.9951256,0.00095150835,0.00040067564,0.0011475903,0.0022252498,0.0001493259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047785705,0.0013033014,0.0015486534,0.005384836,0.0014705195,0.0041452297,0.0027692416,0.0013924688,0.0042162365],"category_scores_gemma":[0.010009081,0.00061578315,0.0015127676,0.0049655307,0.0011538541,0.0035050323,0.0020269696,0.0016745485,0.0026955097],"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":[0.00027682912,0.00032508915,0.0067869737,0.00069730997,0.00024627705,0.00022396723,0.0008303841,0.025719134,0.02775693,0.112728186,0.009224375,0.8151845],"study_design_scores_gemma":[0.00010325098,0.00095722364,0.005905769,0.00020036925,0.00040899654,0.001886573,0.0007191049,0.69238573,0.052014627,0.12866779,0.116475366,0.00027510812],"about_ca_topic_score_codex":0.0036976594,"about_ca_topic_score_gemma":0.0056018145,"teacher_disagreement_score":0.005384836,"about_ca_system_score_codex":0.0015293028,"about_ca_system_score_gemma":0.0025487742,"threshold_uncertainty_score":0.025271773},"labels":[],"label_agreement":null},{"id":"W1493904710","doi":"10.5539/cis.v8n3p119","title":"Handling Uncertainty in Database: An Introduction and Brief Survey","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Uncertain data; Key (lock); Database; Data administration; Data mining; Data management; Search engine indexing; Database design; Information retrieval; Data science; Database model","score_opus":0.037896907907580026,"score_gpt":0.274460504861961,"score_spread":0.23656359695438098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493904710","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014711656,0.91687936,0.069822125,0.0027132796,0.0013619203,0.00008162229,0.00021423855,0.00012036688,0.007335978],"genre_scores_gemma":[0.018357895,0.92466754,0.046628814,0.0016011498,0.0056239986,0.00013987329,0.00042547518,0.00005338525,0.0025018991],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99671495,0.0007103685,0.00050883397,0.00059261825,0.0013091594,0.00016420249],"domain_scores_gemma":[0.9970047,0.0020358851,0.00017553831,0.00019212339,0.0004916807,0.00010001548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028039212,0.0010861218,0.001626453,0.0049866876,0.001151908,0.004440166,0.002674822,0.0028810427,0.0029839552],"category_scores_gemma":[0.0040965057,0.0011709979,0.0011429595,0.013455711,0.0020268017,0.010899834,0.0020951012,0.0037769955,0.0013394405],"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":[0.00015174001,0.00024627557,0.0033944198,0.014049158,0.00019543355,0.00095431274,0.0010242994,0.007372417,0.0025047674,0.16486555,0.047545236,0.7576963],"study_design_scores_gemma":[0.000021677008,0.00024248322,0.0024355222,0.0031189641,0.00015522848,0.004531676,0.0006768792,0.014283146,0.0014358116,0.10618293,0.8667638,0.00015192297],"about_ca_topic_score_codex":0.0021058666,"about_ca_topic_score_gemma":0.00097161595,"teacher_disagreement_score":0.0049866876,"about_ca_system_score_codex":0.0014242458,"about_ca_system_score_gemma":0.0012594338,"threshold_uncertainty_score":0.014828742},"labels":[],"label_agreement":null},{"id":"W1494905438","doi":"10.1007/978-3-540-85128-8_6","title":"Constructive Path Planning for Natural Phenomena Modeling","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Motion planning; Constructive; Randomness; Computer science; Artificial intelligence; Path (computing); Robotics; Any-angle path planning; Roulette; Mathematical optimization; Shortest path problem; Theoretical computer science; Graph; Process (computing); Algorithm; Robot; Mathematics","score_opus":0.14382780630123568,"score_gpt":0.35566330468604984,"score_spread":0.21183549838481416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1494905438","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063530536,0.0021103641,0.97748613,0.00030063928,0.00010774538,0.000039455073,0.00014169983,0.00043251488,0.018746173],"genre_scores_gemma":[0.05460318,0.0044944137,0.9238228,0.0001584722,0.00016755825,0.00023679557,0.0005289429,0.00032077788,0.015666928],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954045,0.00015106691,0.000024757395,0.00006990558,0.00019368021,0.000020223786],"domain_scores_gemma":[0.99935406,0.0004360164,0.000023848645,0.00008070132,0.00008536212,0.000019979949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061308494,0.0012120999,0.00090810633,0.0008610004,0.00063158316,0.0017227969,0.0023019384,0.0010399083,0.0094882315],"category_scores_gemma":[0.001820878,0.00072852825,0.0010698722,0.0018112944,0.001980989,0.002125297,0.0012462722,0.002664343,0.0022607688],"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":[0.000009964278,0.000019668629,0.0000689803,0.0003550348,0.00002585659,0.000074610885,0.00015883583,0.13279729,0.0006774652,0.7335336,0.011920899,0.12035784],"study_design_scores_gemma":[0.0000067958767,0.000010096734,0.000034735785,0.00008599584,0.000016406271,0.00006249956,0.000040452036,0.25512823,0.00064188906,0.69725835,0.046699982,0.000014557596],"about_ca_topic_score_codex":0.004983958,"about_ca_topic_score_gemma":0.0068917754,"teacher_disagreement_score":0.0094882315,"about_ca_system_score_codex":0.0013522318,"about_ca_system_score_gemma":0.0013900475,"threshold_uncertainty_score":0.03174126},"labels":[],"label_agreement":null},{"id":"W1495160560","doi":"10.1007/978-3-540-30466-1_3","title":"Modeling Geospatial Databases with Plug-Ins for Visual Languages: A Pragmatic Approach and the Impacts of 16 Years of Research and Experimentations on Perceptory","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Geospatial analysis; Rotation formalisms in three dimensions; Computer science; Unified Modeling Language; Database; Geospatial PDF; Plug-in; Data science; Software engineering; Programming language; Geography; Software; Cartography","score_opus":0.04313467398315612,"score_gpt":0.3443264711299999,"score_spread":0.3011917971468438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495160560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0135097625,0.00017780327,0.97606736,0.0010057308,0.000048379323,0.00010114244,0.00027291174,0.004415747,0.0044012545],"genre_scores_gemma":[0.18072452,0.0005340533,0.81056863,0.00038783293,0.00004602844,0.00018220267,0.0005746199,0.0019740178,0.00500814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969584,0.0013599442,0.00028186184,0.00040796303,0.000783947,0.00020795222],"domain_scores_gemma":[0.99295104,0.004095934,0.00032669483,0.00178162,0.0005668081,0.0002779326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075970097,0.0009966063,0.0009047799,0.0009677274,0.0011778522,0.012209748,0.0041397004,0.0019510578,0.0037869937],"category_scores_gemma":[0.014391268,0.0017478267,0.0021072868,0.0013542274,0.0035858678,0.0153678525,0.004801561,0.0032285366,0.00087251054],"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":[0.00049839565,0.00026118086,0.0036040605,0.0007211056,0.0002055388,0.00045627792,0.0041492805,0.080990575,0.009270056,0.80341387,0.0075270743,0.088902645],"study_design_scores_gemma":[0.0001325587,0.00014671525,0.0005385756,0.0003527103,0.00023834595,0.0004286434,0.0016949255,0.47325757,0.019568412,0.41665515,0.08685169,0.00013470836],"about_ca_topic_score_codex":0.006152718,"about_ca_topic_score_gemma":0.009814621,"teacher_disagreement_score":0.012209748,"about_ca_system_score_codex":0.0015053385,"about_ca_system_score_gemma":0.0019941593,"threshold_uncertainty_score":0.040177345},"labels":[],"label_agreement":null},{"id":"W1496437917","doi":"","title":"Modeling location-based services with subject spaces","year":2003,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pager; Publication; Location-based service; Context (archaeology); Mobile computing; Mobile device; Ubiquitous computing; Computer network; World Wide Web; Human–computer interaction","score_opus":0.08792687399602565,"score_gpt":0.3692779066140036,"score_spread":0.28135103261797795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496437917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049866375,0.0013239974,0.9238225,0.0017510591,0.00021370302,0.0002060659,0.0004855773,0.0009506304,0.021380056],"genre_scores_gemma":[0.68115294,0.003926502,0.28916422,0.0002871945,0.00042339,0.00071303744,0.0011586021,0.0002178284,0.022956336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984829,0.00073374,0.00013304211,0.000175298,0.00032078763,0.00015422885],"domain_scores_gemma":[0.9982253,0.00079257746,0.00019591977,0.00036605672,0.00028807105,0.00013215608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017009338,0.0006515687,0.0006272718,0.0011139611,0.0010037486,0.0045236666,0.0016324344,0.0018130558,0.0027999657],"category_scores_gemma":[0.003840841,0.0005105287,0.0011488026,0.0019793417,0.0016729262,0.005853292,0.0027909626,0.0013177545,0.0015127533],"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":[0.000103183156,0.00010238154,0.0026583767,0.00015063629,0.000050496597,0.000382548,0.0013102932,0.25723556,0.00191069,0.70402807,0.002894394,0.029173378],"study_design_scores_gemma":[0.000039224346,0.000059049507,0.00034958412,0.00004709238,0.000060213297,0.00016011784,0.00036422518,0.71200836,0.0011489883,0.24172845,0.044008706,0.000026023783],"about_ca_topic_score_codex":0.009990623,"about_ca_topic_score_gemma":0.006058151,"teacher_disagreement_score":0.009990623,"about_ca_system_score_codex":0.0013785857,"about_ca_system_score_gemma":0.0017858605,"threshold_uncertainty_score":0.019864976},"labels":[],"label_agreement":null},{"id":"W1497284170","doi":"","title":"Spatio-Temporal Approximate Reasoning over Complex Objects","year":2005,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Domain (mathematical analysis); Context (archaeology); Artificial intelligence; Theoretical computer science; Data mining; Mathematics; Geography","score_opus":0.02291557565412861,"score_gpt":0.2590175512023282,"score_spread":0.23610197554819962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497284170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011554786,0.000830583,0.9851231,0.00027518484,0.000039823106,0.00002194849,0.00016433463,0.00024555126,0.0017447205],"genre_scores_gemma":[0.46505094,0.0028240678,0.5277828,0.00015852814,0.00016647388,0.0001152402,0.0009178821,0.000080224374,0.0029038559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983492,0.00039937234,0.00018269068,0.0003772447,0.00057123165,0.000120255514],"domain_scores_gemma":[0.99537116,0.002655139,0.0005690597,0.0009396096,0.0003519127,0.000113046706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022057563,0.0008577556,0.0009242459,0.0014075561,0.0007001491,0.0031558687,0.0020624334,0.0011477612,0.0025705905],"category_scores_gemma":[0.011083976,0.00041779262,0.0012531099,0.0026908412,0.0015950394,0.007658203,0.0021474333,0.001335729,0.00042537437],"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":[0.00023725042,0.000082605744,0.0020046416,0.00051495526,0.00020670048,0.0006803075,0.00071101624,0.51331896,0.0037145566,0.3139472,0.003079712,0.16150214],"study_design_scores_gemma":[0.000010176104,0.00003298001,0.00040223615,0.00003212166,0.000042284424,0.0001771539,0.000118537806,0.70153487,0.001769451,0.29143384,0.004428539,0.00001779403],"about_ca_topic_score_codex":0.0047065937,"about_ca_topic_score_gemma":0.0037713912,"teacher_disagreement_score":0.0047065937,"about_ca_system_score_codex":0.0012316926,"about_ca_system_score_gemma":0.00063325546,"threshold_uncertainty_score":0.011665285},"labels":[],"label_agreement":null},{"id":"W1497709229","doi":"","title":"Seeking stable clusters in the blogosphere","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":66,"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; University of Toronto","funders":"","keywords":"Blogosphere; Computer science; Popularity; Intuition; Context (archaeology); Set (abstract data type); Geolocation; Event (particle physics); Information retrieval; Data mining; Theoretical computer science; Data science; World Wide Web; The Internet","score_opus":0.014809310064884687,"score_gpt":0.24113365580255747,"score_spread":0.2263243457376728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497709229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6781037,0.002826093,0.3054141,0.0036596577,0.00012140793,0.00029178892,0.001618152,0.0008158518,0.007149201],"genre_scores_gemma":[0.8996055,0.0011675374,0.089589484,0.0004259745,0.00025793488,0.00027638086,0.0028201633,0.0002186507,0.0056383824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837095,0.0004332496,0.000087960165,0.0006172034,0.00025498908,0.0002357679],"domain_scores_gemma":[0.9894699,0.006444183,0.0012642813,0.00065831566,0.0014445337,0.00071881415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025003536,0.00073382107,0.001644311,0.0060244836,0.0036632163,0.0042551183,0.0017617188,0.0021162643,0.0030516956],"category_scores_gemma":[0.021578752,0.0009020228,0.0009130791,0.0052863588,0.001508132,0.0037934536,0.003006379,0.0009769589,0.0010452559],"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":[0.002982524,0.00074017426,0.13683434,0.0018584302,0.0011978255,0.002090462,0.013068597,0.22501361,0.0151782,0.23404957,0.07708929,0.28989697],"study_design_scores_gemma":[0.00026244932,0.0002551618,0.017666413,0.00017420364,0.00021784015,0.0006312148,0.0058967336,0.5925038,0.0032433488,0.36517915,0.013826332,0.00014343548],"about_ca_topic_score_codex":0.00673025,"about_ca_topic_score_gemma":0.008900841,"teacher_disagreement_score":0.00673025,"about_ca_system_score_codex":0.0018055779,"about_ca_system_score_gemma":0.0013766629,"threshold_uncertainty_score":0.013382196},"labels":[],"label_agreement":null},{"id":"W1497909917","doi":"10.1007/978-3-540-45227-0_59","title":"Efficient Indexing of High Dimensional Normalized Histograms","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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 Alberta","funders":"","keywords":"Search engine indexing; Histogram; Computer science; Nearest neighbor search; R-tree; Similarity (geometry); Tree (set theory); k-nearest neighbors algorithm; Indexation; Feature (linguistics); Constraint (computer-aided design); Data structure; Feature vector; Data mining; Pattern recognition (psychology); Information retrieval; Artificial intelligence; Spatial analysis; Image (mathematics); Mathematics; Spatial database; Combinatorics","score_opus":0.012396481116063937,"score_gpt":0.22114698413863004,"score_spread":0.2087505030225661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1497909917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033526134,0.0030414811,0.93442863,0.0002894129,0.00045511773,0.00019601699,0.0037895818,0.013775474,0.01049809],"genre_scores_gemma":[0.20142703,0.002095305,0.7651944,0.0001985895,0.00035129683,0.00032398186,0.014275039,0.0016277382,0.014506669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988856,0.00014321246,0.000102775295,0.00015554552,0.00055263715,0.00016026877],"domain_scores_gemma":[0.9976707,0.00046586886,0.00012699795,0.0009821109,0.00061347894,0.0001408321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063380104,0.0007858374,0.0019179563,0.003753336,0.0010881792,0.004062666,0.0027262517,0.00073808734,0.01818921],"category_scores_gemma":[0.00472733,0.0006276742,0.0007290106,0.010308125,0.0006878882,0.00541219,0.0033101267,0.0010385875,0.00666806],"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":[0.00046753566,0.0001937877,0.0011762446,0.000384224,0.000043821565,0.00010272923,0.00016777069,0.008973086,0.026497794,0.047641706,0.041567646,0.8727836],"study_design_scores_gemma":[0.00024593744,0.0003819182,0.0041498574,0.00018520564,0.00011420379,0.0013853925,0.0006141919,0.51463026,0.09079118,0.2773703,0.109929,0.00020252769],"about_ca_topic_score_codex":0.0024890539,"about_ca_topic_score_gemma":0.004213337,"teacher_disagreement_score":0.01818921,"about_ca_system_score_codex":0.001100817,"about_ca_system_score_gemma":0.0013968959,"threshold_uncertainty_score":0.06084901},"labels":[],"label_agreement":null},{"id":"W1499353052","doi":"10.1007/978-3-642-12156-2_31","title":"Ordered Polyline Trees for Efficient Search of Objects Moving on a Graph","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Rectangle; Computer science; Graph; Combinatorics; Object (grammar); Data structure; Line segment; Interval (graph theory); Bounding overwatch; Mathematics; Algorithm; Artificial intelligence; Geometry","score_opus":0.019174590125179785,"score_gpt":0.25854840263300716,"score_spread":0.23937381250782738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499353052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01653576,0.0006472792,0.9740918,0.0001500435,0.000065818,0.00016575595,0.0008629748,0.002814036,0.0046665524],"genre_scores_gemma":[0.10062431,0.00056966353,0.8899335,0.000080594924,0.000050177143,0.00027146158,0.0027213735,0.0006184441,0.0051304004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995627,0.000087506174,0.000035675013,0.00008274645,0.00016375659,0.00006770515],"domain_scores_gemma":[0.9990121,0.0005150721,0.00006790304,0.00020097164,0.00015238358,0.000051527673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036996332,0.0007658945,0.0011565803,0.0017179039,0.000767182,0.0013732217,0.0017095227,0.0010461292,0.008141576],"category_scores_gemma":[0.002335037,0.00059380237,0.0007021858,0.0038022522,0.0006312161,0.0027322033,0.001352624,0.0015266285,0.002020947],"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":[0.0006234891,0.0003116512,0.000901273,0.0010644849,0.00007565997,0.00025012976,0.00052098144,0.15724392,0.02019163,0.17439532,0.04558713,0.5988343],"study_design_scores_gemma":[0.00011735774,0.00016443332,0.00040165597,0.000068620844,0.000044049797,0.00020335465,0.00014655049,0.8539368,0.007348043,0.11503543,0.022498159,0.000035469347],"about_ca_topic_score_codex":0.0036684233,"about_ca_topic_score_gemma":0.006616549,"teacher_disagreement_score":0.008141576,"about_ca_system_score_codex":0.0007963747,"about_ca_system_score_gemma":0.00084738934,"threshold_uncertainty_score":0.027236283},"labels":[],"label_agreement":null},{"id":"W1500551073","doi":"10.1007/978-3-642-10649-1_4","title":"Connecting the Dots: Constructing Spatiotemporal Episodes from Events Schemas","year":2009,"lang":"en","type":"book-chapter","venue":"Transactions on computational science","topic":"Data Management and Algorithms","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 Alberta","funders":"","keywords":"Computer science; Event (particle physics); Consistency (knowledge bases); Data mining; Path (computing); Tree (set theory); Hierarchical database model; Constraint (computer-aided design); Interval (graph theory); Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.026211271901109872,"score_gpt":0.25680099901123066,"score_spread":0.2305897271101208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500551073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005524439,0.00024784543,0.9868592,0.00019572502,0.000038150065,0.00013073572,0.0012588039,0.0034309821,0.002314136],"genre_scores_gemma":[0.074282244,0.000574772,0.91625273,0.0001332382,0.000024532246,0.00017953524,0.005518148,0.0009468777,0.0020877968],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915147,0.00023320061,0.00012204602,0.00027149104,0.00016965577,0.00005211156],"domain_scores_gemma":[0.99730897,0.0014349088,0.00017248622,0.00070607814,0.00025699806,0.000120483484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001855663,0.0008720917,0.0008681559,0.0019231142,0.0008850684,0.0043605682,0.0021452112,0.0012516243,0.00799343],"category_scores_gemma":[0.010478243,0.001188048,0.0017278723,0.003511697,0.0015076222,0.009959191,0.004187114,0.0021508397,0.0025681867],"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":[0.00035543303,0.00014372823,0.0046056747,0.00077851175,0.00017589357,0.0006943132,0.0058067446,0.04082538,0.005278486,0.48104846,0.022983681,0.43730372],"study_design_scores_gemma":[0.00006442335,0.00007323789,0.0008099551,0.00026503604,0.00017418026,0.0003486831,0.0021395902,0.3058929,0.0121867275,0.58904237,0.08893652,0.00006627045],"about_ca_topic_score_codex":0.0061418717,"about_ca_topic_score_gemma":0.007410695,"teacher_disagreement_score":0.00799343,"about_ca_system_score_codex":0.0007804032,"about_ca_system_score_gemma":0.0010978997,"threshold_uncertainty_score":0.02674067},"labels":[],"label_agreement":null},{"id":"W1500796099","doi":"10.1007/3-540-36151-0_12","title":"An Efficient Fixed Parameter Tractable Algorithm for 1-Sided Crossing Minimization","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minification; Constant (computer programming); Algorithm; Combinatorics; Approx; Running time; Computer science; Mathematics; Discrete mathematics; Mathematical optimization","score_opus":0.024134354590971633,"score_gpt":0.2595262473964646,"score_spread":0.235391892805493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500796099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010551915,0.0002649267,0.9660384,0.00045901167,0.00012725077,0.00022236632,0.00043175646,0.004079108,0.017825307],"genre_scores_gemma":[0.09157305,0.00017027075,0.89230865,0.00030764905,0.00008673346,0.0005065263,0.0014311937,0.0012322884,0.012383568],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99864024,0.00030476807,0.00006531888,0.00034140507,0.0004209571,0.00022731879],"domain_scores_gemma":[0.9984995,0.0008039823,0.000077244404,0.00037298794,0.00016991461,0.000076436925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013276044,0.0022937874,0.0022810434,0.0012246385,0.0011448222,0.0025085672,0.0038295137,0.0025718086,0.025243916],"category_scores_gemma":[0.0051205265,0.0010276821,0.0016601533,0.0024808224,0.0011401598,0.003395223,0.003479472,0.003676657,0.007352989],"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":[0.00054104865,0.0005187987,0.00057241356,0.00037676896,0.000101866695,0.00018118341,0.0002082437,0.18396568,0.008458475,0.09338838,0.05295555,0.65873164],"study_design_scores_gemma":[0.00025913378,0.00014478837,0.0003431194,0.000045648663,0.000055217508,0.00019312564,0.00008988983,0.8156357,0.0031059333,0.17165574,0.008434324,0.000037414233],"about_ca_topic_score_codex":0.0033300126,"about_ca_topic_score_gemma":0.0060134293,"teacher_disagreement_score":0.025243916,"about_ca_system_score_codex":0.0017635202,"about_ca_system_score_gemma":0.0026009688,"threshold_uncertainty_score":0.08444929},"labels":[],"label_agreement":null},{"id":"W1507246359","doi":"10.1016/b978-012088469-8.50128-5","title":"Efficient Constraint Processing for Highly Personalized Location Based Services","year":2004,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Constraint (computer-aided design); Engineering; Mechanical engineering","score_opus":0.015810502446586456,"score_gpt":0.2379889543604717,"score_spread":0.22217845191388524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507246359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008323347,0.0004153135,0.98045564,0.00035241587,0.00006742242,0.00012903265,0.0007983596,0.0020592427,0.007399222],"genre_scores_gemma":[0.16498601,0.0006258267,0.8195123,0.00017579344,0.00013078804,0.0002452531,0.002010566,0.00054635835,0.011767142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897265,0.0002329302,0.00006285844,0.00014973915,0.0004470718,0.0001347783],"domain_scores_gemma":[0.99818987,0.00105424,0.00008602816,0.00037430527,0.00023548875,0.00006005046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068629434,0.0010976237,0.0013788175,0.00081249664,0.0009996626,0.0023728758,0.0021471628,0.0011040458,0.018426577],"category_scores_gemma":[0.0039312113,0.00070414727,0.0008582899,0.0033539361,0.0006681478,0.0022991989,0.001744296,0.0018313035,0.002800678],"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":[0.00034216663,0.00020603205,0.00062012195,0.0005061812,0.00009713664,0.00033576397,0.00024052948,0.3692324,0.011483393,0.08822124,0.053506643,0.4752084],"study_design_scores_gemma":[0.000033566543,0.000028516555,0.00014417189,0.000015520647,0.000016890748,0.00009709161,0.00007146051,0.9353886,0.003146838,0.05272112,0.00831943,0.000016860491],"about_ca_topic_score_codex":0.018696355,"about_ca_topic_score_gemma":0.025180953,"teacher_disagreement_score":0.018696355,"about_ca_system_score_codex":0.0009947713,"about_ca_system_score_gemma":0.0016410094,"threshold_uncertainty_score":0.061643004},"labels":[],"label_agreement":null},{"id":"W1507738160","doi":"10.1109/icde.1992.213182","title":"Distance-associated join indices for spatial range search","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Join (topology); Search engine indexing; Computer science; Index (typography); Range (aeronautics); Data mining; Spatial database; Spatial analysis; Range query (database); Information retrieval; Theoretical computer science; Mathematics; Statistics; Search engine; Combinatorics; World Wide Web; Engineering","score_opus":0.02856134253859242,"score_gpt":0.2641502726120932,"score_spread":0.23558893007350076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507738160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006828942,0.0007737899,0.9854134,0.00008708225,0.00012400726,0.0001917898,0.00037505952,0.0020506303,0.004155284],"genre_scores_gemma":[0.07356246,0.0007460537,0.9183285,0.00011684719,0.00018255164,0.00037767366,0.002031233,0.0004050284,0.004249619],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729913,0.0004204399,0.00027908923,0.00022066997,0.0016492928,0.00013125585],"domain_scores_gemma":[0.99450266,0.0017979611,0.00033155235,0.0014612434,0.0017197242,0.00018676695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002141393,0.00070143404,0.00097427174,0.0025503363,0.0010666237,0.0025347099,0.0023493348,0.0008089707,0.00525164],"category_scores_gemma":[0.010745918,0.0005149389,0.0006419937,0.0040260023,0.00059934874,0.0050334907,0.0022127272,0.0015455871,0.0034530032],"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":[0.00072600663,0.00052624865,0.0034947146,0.000558738,0.00013408369,0.00025442723,0.00050728285,0.06546142,0.034320746,0.25490916,0.033412673,0.6056944],"study_design_scores_gemma":[0.00016179756,0.00057745393,0.0011006763,0.000104570616,0.00013227655,0.0008641128,0.00017769275,0.78131497,0.037874382,0.092270054,0.08528421,0.00013775469],"about_ca_topic_score_codex":0.0021088286,"about_ca_topic_score_gemma":0.002876478,"teacher_disagreement_score":0.00525164,"about_ca_system_score_codex":0.0010051219,"about_ca_system_score_gemma":0.0020323223,"threshold_uncertainty_score":0.017568529},"labels":[],"label_agreement":null},{"id":"W1508458705","doi":"10.1007/11766247_13","title":"On the Quality and Quantity of Random Decisions in Stochastic Local Search for SAT","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","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 British Columbia","funders":"","keywords":"Computer science; Quality (philosophy); Artificial intelligence; Theoretical computer science","score_opus":0.05624935494597458,"score_gpt":0.3175180439768304,"score_spread":0.26126868903085587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508458705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13426879,0.007268319,0.8311091,0.0069802287,0.00039732925,0.00018225874,0.0005987786,0.00071121205,0.018483927],"genre_scores_gemma":[0.9153781,0.0022400212,0.0732747,0.000871091,0.00071385223,0.00030848844,0.0004891446,0.0007088866,0.006015706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9873198,0.008711986,0.0005290901,0.0009213874,0.0018520455,0.0006656154],"domain_scores_gemma":[0.70388067,0.27632537,0.0064597563,0.006145169,0.004182638,0.0030063584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026727939,0.0014226916,0.0046529453,0.0032658968,0.0014160116,0.0052380986,0.004147514,0.004352287,0.0071913498],"category_scores_gemma":[0.1556743,0.0024024895,0.002172518,0.0043256,0.008433404,0.0105230175,0.004303274,0.005836659,0.00035965504],"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":[0.00056697364,0.00009198789,0.002359378,0.00037554093,0.0002001865,0.00007803293,0.00028716074,0.49114048,0.00067829195,0.4858411,0.0030819695,0.015298883],"study_design_scores_gemma":[0.00006403945,0.00007042774,0.0005715411,0.00006408918,0.000036633348,0.00003113916,0.0000330063,0.6231365,0.0001741696,0.37545153,0.00034027948,0.000026579039],"about_ca_topic_score_codex":0.0038910913,"about_ca_topic_score_gemma":0.004362502,"teacher_disagreement_score":0.026727939,"about_ca_system_score_codex":0.0051088505,"about_ca_system_score_gemma":0.002371884,"threshold_uncertainty_score":0.1413526},"labels":[],"label_agreement":null},{"id":"W1508670223","doi":"10.1016/b978-012088469-8.50127-3","title":"A-ToPSS","year":2004,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.01574671368467904,"score_gpt":0.22397339214054648,"score_spread":0.20822667845586745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508670223","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00068690424,0.0010535655,0.005002131,0.0005911844,0.0006686053,0.000031963915,0.00059681234,0.0013216215,0.9900473],"genre_scores_gemma":[0.0017397008,0.00052241894,0.0015605675,0.00017273225,0.000081707556,0.000016687896,0.00038610073,0.00021903707,0.9953009],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996947,0.00002844823,0.000014905969,0.000082460996,0.00014391648,0.000035540626],"domain_scores_gemma":[0.9994748,0.00009307469,0.000020953721,0.00014342861,0.00017937298,0.000088451816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030792298,0.0010662235,0.0006532372,0.0018904347,0.0013902321,0.0042404192,0.0008433524,0.0016740224,0.6279201],"category_scores_gemma":[0.001065188,0.0005262533,0.0005329128,0.0023842335,0.0006519206,0.0034026932,0.001862626,0.0013209677,0.56504196],"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":[0.00003255692,0.0000513919,0.0002667279,0.00015579171,0.0000071647505,0.00011950547,0.0001216832,0.00032759304,0.0014438907,0.038122192,0.40278253,0.556569],"study_design_scores_gemma":[0.000004126356,0.000012377268,0.00024009561,0.000060292285,0.0000035180976,0.00014467562,0.000037167392,0.00023939666,0.00032258613,0.0056678923,0.99326354,0.00000434239],"about_ca_topic_score_codex":0.0028478722,"about_ca_topic_score_gemma":0.004451827,"teacher_disagreement_score":0.6279201,"about_ca_system_score_codex":0.0008014491,"about_ca_system_score_gemma":0.0010350458,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1509825211","doi":"10.14778/2733085.2733099","title":"Generating top-k packages via preference elicitation","year":2014,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Recommender system; Preference elicitation; Ranking (information retrieval); Function (biology); Preference; Rank (graph theory); Information retrieval; Variety (cybernetics); Learning to rank; Preference learning; Machine learning; Data mining; Artificial intelligence; Mathematics","score_opus":0.01761425993665048,"score_gpt":0.21582282076499093,"score_spread":0.19820856082834046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509825211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02666762,0.00038364544,0.9643417,0.000426911,0.00005709253,0.0005581886,0.001187642,0.0039786147,0.0023985389],"genre_scores_gemma":[0.16687642,0.0002836219,0.823087,0.00031374014,0.00008941913,0.0007406676,0.0047445986,0.00043284745,0.0034316748],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940918,0.0023474584,0.00049274263,0.0012127407,0.0015436746,0.00031159524],"domain_scores_gemma":[0.9816551,0.010766596,0.000882314,0.003455147,0.002766119,0.00047458755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054607713,0.0024298944,0.0031432065,0.003350758,0.0015039708,0.0019975016,0.0038376737,0.0023518775,0.007231285],"category_scores_gemma":[0.027782915,0.0011976366,0.002585514,0.0053527295,0.0010243874,0.004325232,0.0035470661,0.0025834218,0.004808981],"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":[0.0014929227,0.0008791267,0.008956404,0.0012807335,0.00033391773,0.00039279554,0.0014388309,0.10616814,0.010401131,0.021759849,0.038723964,0.8081721],"study_design_scores_gemma":[0.00021755714,0.00030390284,0.0013665376,0.0000922474,0.0001344349,0.0004244465,0.00043580902,0.9248314,0.0070997034,0.054846223,0.010126392,0.00012127299],"about_ca_topic_score_codex":0.003431275,"about_ca_topic_score_gemma":0.009142269,"teacher_disagreement_score":0.007231285,"about_ca_system_score_codex":0.0011418979,"about_ca_system_score_gemma":0.0026039418,"threshold_uncertainty_score":0.028879642},"labels":[],"label_agreement":null},{"id":"W1510049598","doi":"","title":"Fusing social network data with hard data","year":2015,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Computer science; Dissemination; Event (particle physics); Fuse (electrical); Social media; Social network (sociolinguistics); Identification (biology); Class (philosophy); Process (computing); Data mining; Attack patterns; Microblogging; Sensor fusion; Computer security; Data science; Information retrieval; World Wide Web; Artificial intelligence; Intrusion detection system; Engineering","score_opus":0.17921186500788264,"score_gpt":0.3274271580848725,"score_spread":0.14821529307698986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510049598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18516615,0.0009616477,0.8076088,0.0010191089,0.0003233011,0.00022977707,0.0008909042,0.0012172689,0.002583097],"genre_scores_gemma":[0.8104374,0.0005039104,0.18457301,0.00027413285,0.00042013606,0.00012696505,0.0017254077,0.0000976467,0.0018414654],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803954,0.0004706967,0.00018659135,0.00039527542,0.0007104035,0.00019750987],"domain_scores_gemma":[0.9941841,0.0027757322,0.0006854075,0.001374713,0.0007919302,0.00018820973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002226623,0.0020972237,0.0014484953,0.0040694843,0.0006202512,0.001708858,0.0012646924,0.0015019608,0.0011788292],"category_scores_gemma":[0.011283019,0.00061827304,0.0011215151,0.0034403298,0.0008186849,0.003684928,0.0027809897,0.0013857874,0.00068538846],"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":[0.00076469564,0.00053327397,0.028690903,0.00044332634,0.0005249788,0.0008820335,0.0005038912,0.30648053,0.025160667,0.004379014,0.003616696,0.62802],"study_design_scores_gemma":[0.000012748949,0.00015684744,0.006537361,0.000025386249,0.000058356392,0.00015599266,0.00024988322,0.96584487,0.011045957,0.013344161,0.0025315476,0.000036827772],"about_ca_topic_score_codex":0.00232815,"about_ca_topic_score_gemma":0.0024765967,"teacher_disagreement_score":0.0040694843,"about_ca_system_score_codex":0.0005350245,"about_ca_system_score_gemma":0.0005140944,"threshold_uncertainty_score":0.011775613},"labels":[],"label_agreement":null},{"id":"W1510767022","doi":"10.1007/11733836_42","title":"Query Optimization for a Graph Database with Visual Queries","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Graph database; Query optimization; Graph; Information retrieval; View; Database; Theoretical computer science; Database design","score_opus":0.012275470845136827,"score_gpt":0.2388625836253793,"score_spread":0.22658711278024246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510767022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038560543,0.00073206035,0.9439599,0.00076448126,0.00008556592,0.00026246146,0.0018164959,0.008399332,0.0054192157],"genre_scores_gemma":[0.36568037,0.0004593629,0.62060124,0.00034351987,0.00014404907,0.00026512195,0.0040103123,0.0013694707,0.0071265306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997414,0.00043054327,0.00022173376,0.0005110856,0.0011225154,0.0003000713],"domain_scores_gemma":[0.9977029,0.0008838338,0.00012004319,0.0007516357,0.0003900342,0.00015160171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015729945,0.0009658539,0.0023059314,0.0017490225,0.00087399036,0.003975295,0.003895189,0.0013568016,0.007177283],"category_scores_gemma":[0.0071118297,0.0006740505,0.0014370059,0.0035529968,0.0008656419,0.004496053,0.0028064211,0.0011230411,0.0015685555],"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":[0.0029062198,0.0008010884,0.0033583387,0.000867447,0.0002432762,0.00044428447,0.0003926627,0.26643214,0.028144116,0.08987127,0.061802164,0.54473704],"study_design_scores_gemma":[0.00022864058,0.00017451915,0.00040220504,0.000019713481,0.00008325736,0.00023129162,0.00016559171,0.908426,0.007998162,0.07632017,0.0059117023,0.000038734073],"about_ca_topic_score_codex":0.0075613693,"about_ca_topic_score_gemma":0.009151584,"teacher_disagreement_score":0.0075613693,"about_ca_system_score_codex":0.0013660535,"about_ca_system_score_gemma":0.0012416561,"threshold_uncertainty_score":0.02401042},"labels":[],"label_agreement":null},{"id":"W1516469158","doi":"10.1016/b978-155860869-6/50073-1","title":"The Generalized MDL Approach for Summarization","year":2002,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Automatic summarization; Minimum description length; Online analytical processing; Aggregate (composite); Generalization; Computer science; Data cube; Cube (algebra); Data mining; Information retrieval; Theoretical computer science; Mathematics; Algorithm; Combinatorics; Data warehouse","score_opus":0.02701062889003689,"score_gpt":0.22721061869485687,"score_spread":0.20019998980481998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516469158","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007782251,0.0016003167,0.9901962,0.00040532267,0.000109790715,0.00007715965,0.00043939077,0.0015392231,0.004854273],"genre_scores_gemma":[0.024820007,0.0017000093,0.96060205,0.00020334857,0.0002293061,0.00026255046,0.001689697,0.0003530281,0.010140005],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983706,0.00047200645,0.0002194159,0.00026495045,0.0006074275,0.00006558634],"domain_scores_gemma":[0.99832124,0.00081891485,0.0000978896,0.00035356262,0.00036914655,0.000039175844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530987,0.0009416766,0.0014184208,0.002412282,0.00067145925,0.0030268002,0.0022844959,0.0011291046,0.015276284],"category_scores_gemma":[0.0051786467,0.0005355587,0.0013143934,0.003148352,0.00089291757,0.003908971,0.0015534045,0.0014786806,0.004758561],"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":[0.00005415823,0.00004208782,0.00014805661,0.000696461,0.000066490466,0.0001368852,0.00015333778,0.032176077,0.0024885368,0.109589905,0.028115159,0.82633287],"study_design_scores_gemma":[0.000039043396,0.00006570805,0.00019653626,0.00018605328,0.00007916333,0.00035150314,0.000112814945,0.3852286,0.004805952,0.4986208,0.110259555,0.000054265034],"about_ca_topic_score_codex":0.0020000737,"about_ca_topic_score_gemma":0.0025619662,"teacher_disagreement_score":0.015276284,"about_ca_system_score_codex":0.0009769994,"about_ca_system_score_gemma":0.0011463211,"threshold_uncertainty_score":0.051104248},"labels":[],"label_agreement":null},{"id":"W1517348552","doi":"","title":"Maximal vector computation in large data sets","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":373,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Skyline; Curse of dimensionality; Convex hull; Computer science; Set (abstract data type); Computation; Field (mathematics); Algorithm; Running time; Mathematics; Regular polygon; Data mining; Artificial intelligence","score_opus":0.04080861114137971,"score_gpt":0.2986350357834028,"score_spread":0.2578264246420231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1517348552","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064741515,0.0011132221,0.91894734,0.0011728724,0.00014003509,0.0002041287,0.001327781,0.007660994,0.0046921577],"genre_scores_gemma":[0.27259633,0.0005385151,0.7191778,0.00023736565,0.000191397,0.00040330438,0.0034603572,0.00061369763,0.0027813676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99516666,0.0013760428,0.00058846024,0.0010797342,0.0014128927,0.00037621503],"domain_scores_gemma":[0.98843515,0.0060158656,0.0007450515,0.0031436868,0.0011972953,0.00046292896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043506487,0.001203317,0.0023993875,0.0025308945,0.00218912,0.0045137955,0.0028816096,0.001303433,0.0054798303],"category_scores_gemma":[0.023372682,0.001121279,0.0014346411,0.0064532333,0.0019733938,0.010081322,0.0055571613,0.002073049,0.0018878496],"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":[0.0012754226,0.00029603564,0.009585002,0.00118958,0.00035347685,0.00044948322,0.0011954011,0.285795,0.00723701,0.12801787,0.037294455,0.52731127],"study_design_scores_gemma":[0.00010621913,0.0000813274,0.00081349426,0.00006178276,0.000039837796,0.00019618277,0.0003152908,0.8186747,0.0065077366,0.16546354,0.00771369,0.000026238777],"about_ca_topic_score_codex":0.0032338211,"about_ca_topic_score_gemma":0.0051670177,"teacher_disagreement_score":0.0054798303,"about_ca_system_score_codex":0.0015848496,"about_ca_system_score_gemma":0.002128107,"threshold_uncertainty_score":0.023008704},"labels":[],"label_agreement":null},{"id":"W1518152488","doi":"10.1109/icde.2015.7113341","title":"Size-Constrained Weighted Set Cover","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cover (algebra); Generalization; Set cover problem; Constraint (computer-aided design); Set (abstract data type); Fraction (chemistry); Approximation algorithm; Mathematics; Computer science; Algorithm; Mathematical optimization; Discrete mathematics","score_opus":0.03126983876728152,"score_gpt":0.25166055383354863,"score_spread":0.22039071506626712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518152488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028840864,0.0007168461,0.9628964,0.00060413964,0.000079450496,0.00031993035,0.0009027928,0.00059211336,0.0050473977],"genre_scores_gemma":[0.37243143,0.001099187,0.61671424,0.0004895876,0.00020600135,0.0008857689,0.0027282794,0.0004262057,0.0050193644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9946075,0.0013285856,0.00031394773,0.0011648221,0.002085212,0.00049988413],"domain_scores_gemma":[0.99327797,0.0036956028,0.00053100684,0.0017017581,0.00056352396,0.00023008355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023745415,0.001321186,0.0019288603,0.0018935574,0.0010513273,0.0024202552,0.0034225006,0.0015472694,0.0043571475],"category_scores_gemma":[0.012500602,0.0008658329,0.0016690588,0.004860823,0.0012524544,0.006038669,0.0035091615,0.0016644544,0.0006010363],"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":[0.00063370646,0.00034154582,0.00337662,0.0011546713,0.00032349493,0.00057584903,0.0004572175,0.5823296,0.012348572,0.12146759,0.013962954,0.26302826],"study_design_scores_gemma":[0.000072424504,0.00020581788,0.0008910558,0.00012670956,0.000101170524,0.0010183532,0.0001900408,0.78421533,0.007733817,0.18860534,0.01679065,0.000049279693],"about_ca_topic_score_codex":0.0018380388,"about_ca_topic_score_gemma":0.0024112207,"teacher_disagreement_score":0.0043571475,"about_ca_system_score_codex":0.0018975341,"about_ca_system_score_gemma":0.0015086656,"threshold_uncertainty_score":0.014576137},"labels":[],"label_agreement":null},{"id":"W1518384768","doi":"10.1109/vlhcc.2004.7","title":"An Automatic Layout Algorithm for Lograph","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Debugger; Computer science; Graph; Graph rewriting; Interpreter; Graph Layout; Programming language; Theoretical computer science; Query optimization; Algorithm; Parallel computing; Debugging; Information retrieval; Graph drawing","score_opus":0.014129201752913574,"score_gpt":0.2685554322895135,"score_spread":0.2544262305365999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518384768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017416102,0.0000954376,0.98136395,0.000080214675,0.00002881819,0.00007143108,0.00015192268,0.015346399,0.0011201425],"genre_scores_gemma":[0.029220171,0.00009341643,0.9646843,0.000102874066,0.000019343463,0.00018043412,0.0008114709,0.0020881547,0.002799871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988188,0.00026293792,0.000109147455,0.00031451124,0.00038243653,0.00011211485],"domain_scores_gemma":[0.99832135,0.00063327746,0.00013816713,0.0004180441,0.00042911654,0.00005995193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009713363,0.0011701895,0.0007021263,0.0017560424,0.0008971923,0.0019675724,0.0021570309,0.001024126,0.015124789],"category_scores_gemma":[0.0044022524,0.0008615387,0.00080474705,0.0016334187,0.0009741287,0.003118189,0.0019512557,0.0013535357,0.005414909],"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":[0.00029814054,0.00014189152,0.0010104362,0.0004348,0.000035183974,0.00021385336,0.0004175332,0.02635549,0.027250005,0.041243233,0.028507596,0.87409174],"study_design_scores_gemma":[0.00027969028,0.00032459712,0.00076831295,0.00015083527,0.00006175809,0.0008784557,0.0003880583,0.61431557,0.07816069,0.13217017,0.17233904,0.00016285479],"about_ca_topic_score_codex":0.0029763426,"about_ca_topic_score_gemma":0.0043220585,"teacher_disagreement_score":0.015124789,"about_ca_system_score_codex":0.0010521675,"about_ca_system_score_gemma":0.0017875377,"threshold_uncertainty_score":0.05059749},"labels":[],"label_agreement":null},{"id":"W1518500743","doi":"10.1007/978-3-642-03730-6_22","title":"High Performance Analytics with the R3-Cache","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Cache; Analytics; Parallel computing; Database","score_opus":0.014388152473746488,"score_gpt":0.21484261997778403,"score_spread":0.20045446750403756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518500743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027117973,0.010954696,0.77934396,0.0015774792,0.0010539269,0.0003178167,0.0044526467,0.11183723,0.06334428],"genre_scores_gemma":[0.23787257,0.0035491819,0.69751245,0.0014595562,0.0006033951,0.00043163434,0.011430324,0.0058578397,0.041283105],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984224,0.0002888518,0.00012718311,0.0002344712,0.0007626666,0.00016440301],"domain_scores_gemma":[0.9973744,0.0006201762,0.00010380689,0.0011114896,0.000640435,0.00014965609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014100228,0.0016918393,0.0013882298,0.0014911352,0.00086711056,0.0033463163,0.005063216,0.0011463905,0.026320387],"category_scores_gemma":[0.0046516955,0.0008658873,0.0010627011,0.004449387,0.00069240143,0.0045333635,0.0028079906,0.0021975697,0.014138662],"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":[0.0027867323,0.00030515643,0.0027132607,0.0010454468,0.0003686246,0.0004276365,0.0002718215,0.02499793,0.02873057,0.056627546,0.32312748,0.55859786],"study_design_scores_gemma":[0.000632788,0.00065429445,0.00146304,0.00028845225,0.00030727786,0.001071252,0.00020346254,0.51390636,0.08997541,0.12915902,0.26208958,0.00024904846],"about_ca_topic_score_codex":0.0036970482,"about_ca_topic_score_gemma":0.004463322,"teacher_disagreement_score":0.026320387,"about_ca_system_score_codex":0.0009514943,"about_ca_system_score_gemma":0.0016453257,"threshold_uncertainty_score":0.088050485},"labels":[],"label_agreement":null},{"id":"W151938044","doi":"10.1137/1.9781611972757.5","title":"Summarizing and Mining Skewed Data Streams","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Crohn's and Colitis Foundation of Canada","keywords":"Skew; Skewness; Data stream mining; Computer science; Data mining; Data stream; Space (punctuation); Zipf's law; Point (geometry); Algorithm; Mathematics; Statistics","score_opus":0.04579868955532916,"score_gpt":0.2686245177712225,"score_spread":0.2228258282158933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W151938044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.116591185,0.001063474,0.87576246,0.0007775279,0.00013546826,0.00015808178,0.002143906,0.0026007474,0.00076708995],"genre_scores_gemma":[0.51579124,0.0011390753,0.47319523,0.00021528179,0.00045332452,0.00024382646,0.0077032656,0.00020690402,0.0010519305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997635,0.0005164801,0.00034525275,0.00044932586,0.0008919263,0.00016196995],"domain_scores_gemma":[0.9903419,0.0045358473,0.0013389952,0.0015519098,0.0019345778,0.0002967235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031055373,0.001117254,0.0017433754,0.004433203,0.0007644242,0.0021013732,0.0014296726,0.0008866058,0.00058686384],"category_scores_gemma":[0.019708881,0.0005009691,0.0007487283,0.0048272423,0.00056568964,0.0037752592,0.0015119826,0.0010591026,0.0005536935],"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":[0.00085559604,0.0002820239,0.041912217,0.00055909046,0.00028144073,0.0007286753,0.0009032156,0.2966869,0.011720534,0.023736099,0.013919591,0.60841465],"study_design_scores_gemma":[0.000029937726,0.00007240969,0.0028872334,0.00003074109,0.000030918087,0.00019941866,0.00030786404,0.94290406,0.0071273223,0.04210357,0.004282346,0.00002407026],"about_ca_topic_score_codex":0.00094103086,"about_ca_topic_score_gemma":0.0012535761,"teacher_disagreement_score":0.004433203,"about_ca_system_score_codex":0.00053453184,"about_ca_system_score_gemma":0.0006635269,"threshold_uncertainty_score":0.016423821},"labels":[],"label_agreement":null},{"id":"W1521873349","doi":"10.1007/978-3-642-16321-0_8","title":"Range Queries over Untangled Chains","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Partition (number theory); Computer science; Range (aeronautics); Representation (politics); Monotonic function; Data structure; Set (abstract data type); Space (punctuation); Algorithm; Combinatorics; Discrete mathematics; Mathematics","score_opus":0.01298931434922809,"score_gpt":0.2331379272691236,"score_spread":0.22014861291989551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521873349","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08352583,0.0023405666,0.87648845,0.0014358425,0.00025573332,0.0002384729,0.0023547432,0.0028404114,0.030519918],"genre_scores_gemma":[0.6171151,0.0027006709,0.3442376,0.0005835752,0.0006250784,0.00031820644,0.0059835487,0.0013955906,0.027040675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943212,0.0010231362,0.00061146246,0.0009291517,0.0025039995,0.0006110296],"domain_scores_gemma":[0.9855979,0.00907786,0.0006421372,0.00347492,0.0008627627,0.00034444532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002718656,0.0009541657,0.002108321,0.002203043,0.0012728658,0.004720244,0.0021109108,0.0020992784,0.013290781],"category_scores_gemma":[0.015131815,0.0011102632,0.0011327729,0.005459321,0.0018550406,0.018403925,0.008050553,0.002931815,0.00289977],"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":[0.0011621672,0.0002501248,0.0020716318,0.0010470552,0.0001388386,0.0006658923,0.00097169273,0.05866795,0.015606719,0.64206326,0.016459152,0.26089555],"study_design_scores_gemma":[0.00008854126,0.00007809983,0.000248161,0.00012354767,0.00005391821,0.000499083,0.00022156234,0.19475304,0.008351806,0.78040737,0.015132997,0.00004185625],"about_ca_topic_score_codex":0.0009460809,"about_ca_topic_score_gemma":0.001087011,"teacher_disagreement_score":0.013290781,"about_ca_system_score_codex":0.0010330968,"about_ca_system_score_gemma":0.0007557422,"threshold_uncertainty_score":0.044462144},"labels":[],"label_agreement":null},{"id":"W1524550501","doi":"10.1007/978-3-540-30116-5_41","title":"Density-Based Spatial Clustering in the Presence of Obstacles and Facilitators","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Cluster analysis; Computer science; Preprocessor; Cluster (spacecraft); Data mining; Spatial analysis; Artificial intelligence; k-means clustering; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.01611851001436791,"score_gpt":0.23139271342903908,"score_spread":0.21527420341467118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524550501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06764469,0.0006905803,0.9274479,0.00029653232,0.00004977126,0.0001201692,0.00015173406,0.0005859029,0.0030126653],"genre_scores_gemma":[0.65051323,0.0008079026,0.33964097,0.00008661865,0.00008989083,0.00018285705,0.0005522089,0.00018099973,0.0079453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989655,0.00036451008,0.000047025875,0.00018254545,0.0002921124,0.00014832745],"domain_scores_gemma":[0.9931746,0.0041030226,0.000520689,0.0005635819,0.0013145452,0.00032356594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021452908,0.0007455375,0.0017880634,0.0026371179,0.001471748,0.0012785213,0.0047642803,0.001762165,0.0016227708],"category_scores_gemma":[0.008421208,0.0012511249,0.0010626409,0.003187802,0.001359566,0.0022660615,0.002369377,0.00095078524,0.00061005016],"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":[0.0002157349,0.00007858589,0.002088065,0.0001365865,0.00007178264,0.00007460784,0.00029535507,0.92735577,0.001356367,0.01995619,0.0019534582,0.04641758],"study_design_scores_gemma":[0.00001120141,0.000015932337,0.00041534647,0.0000073553456,0.000011580161,0.000038039332,0.00005370845,0.99235946,0.00033843322,0.0064142207,0.0003165484,0.000018247085],"about_ca_topic_score_codex":0.030774418,"about_ca_topic_score_gemma":0.03173611,"teacher_disagreement_score":0.030774418,"about_ca_system_score_codex":0.002204662,"about_ca_system_score_gemma":0.0018792669,"threshold_uncertainty_score":0.061190546},"labels":[],"label_agreement":null},{"id":"W1524865704","doi":"10.1007/3-540-44466-1_13","title":"Maintaining Horizontally Partitioned Warehouse Views","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Data warehouse; Aggregate (composite); Tuple; Scheme (mathematics); Warehouse; Computer science; Database; Set (abstract data type); Materialized view; Base (topology); Data mining; Information retrieval; Mathematics; View; Geography","score_opus":0.02446178647882132,"score_gpt":0.24570998442426253,"score_spread":0.2212481979454412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524865704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22762421,0.0025996335,0.7063411,0.00083620683,0.00034132434,0.0003295542,0.00704045,0.034857493,0.020030055],"genre_scores_gemma":[0.44613117,0.0015006978,0.5108413,0.00029432072,0.0001384078,0.0001921133,0.022714369,0.0047249505,0.013462745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866617,0.00016283453,0.00012764607,0.00026904978,0.0006020613,0.00017225147],"domain_scores_gemma":[0.99558496,0.00093095604,0.00022807642,0.0024920497,0.00057660457,0.00018737219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012787356,0.0009968037,0.0011857637,0.0017897221,0.000879613,0.0044277622,0.0028656921,0.000933328,0.005795456],"category_scores_gemma":[0.0052875397,0.0018110435,0.0010224583,0.0057343966,0.00060758844,0.00789814,0.0040036896,0.0014681107,0.0019167444],"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":[0.0013763952,0.00047857797,0.009238359,0.00068571436,0.00044032276,0.0010019707,0.0013965119,0.03757332,0.09497331,0.03491761,0.06381502,0.7541029],"study_design_scores_gemma":[0.00048495922,0.0006843092,0.010033885,0.0003222002,0.0010060724,0.001816118,0.0024503556,0.5244283,0.21831761,0.11980029,0.120365836,0.00029008102],"about_ca_topic_score_codex":0.0039207186,"about_ca_topic_score_gemma":0.0071194633,"teacher_disagreement_score":0.005795456,"about_ca_system_score_codex":0.00078608794,"about_ca_system_score_gemma":0.001331703,"threshold_uncertainty_score":0.019387722},"labels":[],"label_agreement":null},{"id":"W1526157805","doi":"10.1007/11424918_14","title":"A Comparative Study of Two Density-Based Spatial Clustering Algorithms for Very Large Datasets","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"University of Regina","funders":"","keywords":"DBSCAN; Computer science; Cluster analysis; Data mining; Spatial analysis; Algorithm; Artificial intelligence; CURE data clustering algorithm; Correlation clustering; Pattern recognition (psychology); Machine learning; Mathematics; Statistics","score_opus":0.038952807120862576,"score_gpt":0.3084190218575835,"score_spread":0.26946621473672094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1526157805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56072927,0.017401768,0.40421954,0.0025582856,0.0006357015,0.00073201704,0.0021375136,0.0033955302,0.008190322],"genre_scores_gemma":[0.58909696,0.00405245,0.39874846,0.0002499509,0.00019852063,0.0002878509,0.0043415744,0.0003681722,0.0026559778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9898929,0.004331544,0.0007257991,0.000987129,0.0036815805,0.00038089987],"domain_scores_gemma":[0.91555053,0.0671279,0.001574675,0.0047687213,0.010284101,0.00069399935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01508126,0.001355303,0.0021769218,0.00698055,0.0022045046,0.004018028,0.0038792843,0.0028226585,0.0018654055],"category_scores_gemma":[0.05653102,0.00073470135,0.001855498,0.009371974,0.0013372035,0.00671069,0.0025937892,0.0011310988,0.0006575164],"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":[0.0060340683,0.0010261305,0.037407838,0.0019241782,0.0020999685,0.00014614628,0.0017270155,0.25130013,0.003513557,0.018676165,0.0123294275,0.6638153],"study_design_scores_gemma":[0.0003653391,0.0007350352,0.020188626,0.00013806128,0.0005045665,0.00037312752,0.0019427445,0.9565544,0.0039158943,0.01115719,0.00398255,0.00014256124],"about_ca_topic_score_codex":0.024912085,"about_ca_topic_score_gemma":0.0285912,"teacher_disagreement_score":0.024912085,"about_ca_system_score_codex":0.0038375098,"about_ca_system_score_gemma":0.0025804355,"threshold_uncertainty_score":0.07975829},"labels":[],"label_agreement":null},{"id":"W1527829338","doi":"10.1023/a:1022870110853","title":"Ordering Points for Incremental TIN Construction from DEMs","year":2003,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Triangulation; Mathematics; Approximation error; Point (geometry); Triangulated irregular network; Terrain; Mean squared error; Elevation (ballistics); Surface (topology); Geometry; Mathematical analysis; Combinatorics; Statistics; Geography","score_opus":0.012531027467926862,"score_gpt":0.21720300360272352,"score_spread":0.20467197613479665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527829338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015503028,0.00016160443,0.9764673,0.00006755487,0.00008008885,0.00028267305,0.00084655534,0.0040090107,0.002582099],"genre_scores_gemma":[0.09678504,0.00019716602,0.89627135,0.000031402844,0.00002180986,0.00018349646,0.0036581864,0.00069295766,0.002158573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988424,0.0001513994,0.00009036869,0.00021270158,0.00057786424,0.00012522853],"domain_scores_gemma":[0.99725026,0.00081956096,0.00014764932,0.00070723915,0.0009415221,0.00013379034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014664165,0.001183397,0.001545384,0.004712457,0.0012280453,0.001962231,0.0024999732,0.0009253968,0.010062984],"category_scores_gemma":[0.008092494,0.0010465528,0.001159143,0.0050973827,0.0009128642,0.002114537,0.0037070168,0.0016299712,0.0028091834],"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":[0.00043677725,0.000201772,0.005028472,0.000540665,0.0001009023,0.0003727192,0.00066903303,0.16349536,0.008063179,0.030246058,0.01833844,0.77250665],"study_design_scores_gemma":[0.000042210377,0.000100437624,0.001133338,0.00007455439,0.000063626205,0.00015977681,0.00031243978,0.92738193,0.012494029,0.038012557,0.020186225,0.00003894217],"about_ca_topic_score_codex":0.009894943,"about_ca_topic_score_gemma":0.026368285,"teacher_disagreement_score":0.010062984,"about_ca_system_score_codex":0.00085863954,"about_ca_system_score_gemma":0.0018689192,"threshold_uncertainty_score":0.033664048},"labels":[],"label_agreement":null},{"id":"W1528801041","doi":"10.1109/tai.1996.560458","title":"Attribute-oriented induction using domain generalization graphs","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Generalization; Domain (mathematical analysis); Theoretical computer science; Mathematics","score_opus":0.026651408239762753,"score_gpt":0.2574127937498282,"score_spread":0.23076138551006542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528801041","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017299464,0.00006930037,0.9786174,0.000096371994,0.000014963907,0.0001350444,0.00026522033,0.0018471915,0.0016549767],"genre_scores_gemma":[0.19881062,0.00015915556,0.7961905,0.00016191787,0.00003221127,0.00030947954,0.0021279764,0.00038181109,0.001826417],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983028,0.00054430903,0.00010238185,0.00040765002,0.00053879945,0.00010409831],"domain_scores_gemma":[0.99452543,0.0029606891,0.00031552187,0.0011409767,0.0009342402,0.0001232571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018796658,0.0005165504,0.0006012429,0.0017167708,0.00053127843,0.000773919,0.0011755739,0.00035936557,0.00164421],"category_scores_gemma":[0.007073143,0.00033216097,0.0009984794,0.001784433,0.0008458888,0.0018948574,0.0014318172,0.0011189599,0.0006035704],"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":[0.00022697833,0.00033037184,0.005710661,0.00025501297,0.000109702414,0.00033452292,0.00047301088,0.15403122,0.02574486,0.105604045,0.009890346,0.69728917],"study_design_scores_gemma":[0.000046254503,0.00009335418,0.0012962942,0.000033722412,0.000051865856,0.00020375344,0.00009490319,0.81117904,0.02347336,0.1530185,0.010471943,0.000036965757],"about_ca_topic_score_codex":0.0014381121,"about_ca_topic_score_gemma":0.0020799737,"teacher_disagreement_score":0.0018796658,"about_ca_system_score_codex":0.00068411464,"about_ca_system_score_gemma":0.0009452457,"threshold_uncertainty_score":0.009940743},"labels":[],"label_agreement":null},{"id":"W1529172590","doi":"10.1007/978-3-540-76888-3_43","title":"Issues in Location-Based Indexing for Co-operating Mobile Information Systems","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","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 Lethbridge","funders":"","keywords":"Computer science; Search engine indexing; Context (archaeology); World Wide Web; Tourism; Information system; Mobile device; Information access; Mobile computing; Computer network; Geography; Engineering","score_opus":0.020834333248542877,"score_gpt":0.28227208832324885,"score_spread":0.26143775507470596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529172590","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035985913,0.042173874,0.78876245,0.033448447,0.0033590815,0.00049843796,0.0010894261,0.005329981,0.08935234],"genre_scores_gemma":[0.6079048,0.017643413,0.3253268,0.0018299057,0.005262999,0.0004615594,0.001367528,0.0014818683,0.03872122],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9854977,0.0043502143,0.0015912816,0.0012938138,0.005528214,0.0017388564],"domain_scores_gemma":[0.9311668,0.03823498,0.0021712892,0.016699668,0.010237654,0.001489534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018077683,0.00097040355,0.0035076346,0.004239463,0.0060196775,0.024455102,0.010130034,0.008803928,0.021595376],"category_scores_gemma":[0.06925945,0.0019098059,0.0013699182,0.012197091,0.005280385,0.047047038,0.0068693124,0.0047650584,0.008362668],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029355843,0.00030505852,0.0020079874,0.0005477969,0.000059420923,0.00030514738,0.0014154577,0.016826503,0.002393385,0.5832512,0.05237028,0.34022412],"study_design_scores_gemma":[0.00009836256,0.00022471094,0.0019653966,0.00045415113,0.0001437995,0.0018237823,0.0046111187,0.21030411,0.008904848,0.6453938,0.12576877,0.00030715868],"about_ca_topic_score_codex":0.0120512815,"about_ca_topic_score_gemma":0.010328542,"teacher_disagreement_score":0.024455102,"about_ca_system_score_codex":0.005743089,"about_ca_system_score_gemma":0.0045878254,"threshold_uncertainty_score":0.095605075},"labels":[],"label_agreement":null},{"id":"W15302468","doi":"","title":"Synthetic and Real Spatiotemporal Datasets.","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Benchmarking; Computer science; Context (archaeology); Data science; Data mining; Synthetic data; Artificial intelligence","score_opus":0.014107196131605347,"score_gpt":0.23331914805024065,"score_spread":0.2192119519186353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W15302468","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39991382,0.0025404687,0.16827996,0.0058038216,0.0020964136,0.0022189077,0.39538318,0.0044755703,0.019287933],"genre_scores_gemma":[0.4631725,0.0011368664,0.1256968,0.0006435932,0.00029981032,0.0017909348,0.40455037,0.00023705346,0.002472006],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958859,0.0018341062,0.00055688166,0.0005834512,0.0009656621,0.00017398845],"domain_scores_gemma":[0.9833717,0.007562736,0.0011802794,0.004611071,0.0027678288,0.0005064162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004844191,0.00055542483,0.00040474164,0.0025578425,0.00069074216,0.0010953007,0.0015784026,0.0011050917,0.002830202],"category_scores_gemma":[0.023018833,0.00022590668,0.00080701854,0.005728109,0.0007417215,0.0016928682,0.0013230331,0.0008943549,0.0010729429],"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":[0.002009897,0.0023788617,0.08587642,0.003912221,0.0009776991,0.0026866107,0.0012523807,0.31887972,0.010740093,0.057307083,0.3536394,0.1603396],"study_design_scores_gemma":[0.0005955087,0.0013079427,0.07362221,0.0005708559,0.0003088024,0.004403749,0.0041808966,0.5002371,0.018923659,0.07171774,0.32382825,0.00030326546],"about_ca_topic_score_codex":0.0042920536,"about_ca_topic_score_gemma":0.00797211,"teacher_disagreement_score":0.004844191,"about_ca_system_score_codex":0.0009325747,"about_ca_system_score_gemma":0.0012806632,"threshold_uncertainty_score":0.025618851},"labels":[],"label_agreement":null},{"id":"W1533258823","doi":"10.1007/978-3-540-39985-8_4","title":"Query Selectivity Estimation via Data Mining","year":2004,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Joins; Join (topology); Computer science; Query optimization; Data mining; Simple (philosophy); Database; Mathematics; Programming language","score_opus":0.046532383626247655,"score_gpt":0.2599893478918166,"score_spread":0.21345696426556895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533258823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045476556,0.0009831014,0.99036205,0.00023928858,0.000046019486,0.00005714113,0.00016119156,0.0021197558,0.0014838721],"genre_scores_gemma":[0.18432721,0.0018813764,0.8020033,0.0002891994,0.0003238784,0.0002231165,0.0014311426,0.00045944357,0.009061297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711645,0.00060166785,0.00016753569,0.00043826966,0.0014584579,0.0002177063],"domain_scores_gemma":[0.9938899,0.0036496343,0.00027311788,0.0012310628,0.00086313795,0.000093174625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029998815,0.0010028391,0.002423866,0.0029001462,0.00068327703,0.0022678594,0.0024731914,0.0007633905,0.0039286898],"category_scores_gemma":[0.01204021,0.0008125915,0.00093527307,0.003816821,0.0007829805,0.0032648877,0.0016679657,0.0020919153,0.001980041],"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":[0.00036840013,0.00014532628,0.0032280814,0.00027631313,0.00013414117,0.00008685992,0.00010073882,0.02997421,0.007823603,0.033258904,0.016115084,0.90848833],"study_design_scores_gemma":[0.000036315403,0.00006442417,0.0010767623,0.000035993675,0.000088400535,0.00034661987,0.000057470137,0.8955457,0.016766913,0.075549394,0.010400858,0.000031182983],"about_ca_topic_score_codex":0.002415342,"about_ca_topic_score_gemma":0.0027464295,"teacher_disagreement_score":0.0039286898,"about_ca_system_score_codex":0.0010689094,"about_ca_system_score_gemma":0.0014428488,"threshold_uncertainty_score":0.015865088},"labels":[],"label_agreement":null},{"id":"W1536212463","doi":"10.1007/978-3-540-77092-3_43","title":"Extraction and Classification of User Behavior","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"St. Francis Xavier University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Markov chain; Generator (circuit theory); Artificial neural network; User group; Artificial intelligence; Human–computer interaction; Machine learning; Multimedia; Power (physics)","score_opus":0.041530658813898766,"score_gpt":0.29418579905001263,"score_spread":0.25265514023611385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536212463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43655634,0.0044073644,0.45831525,0.0011592102,0.00039112393,0.00089747744,0.053980574,0.020574113,0.023718547],"genre_scores_gemma":[0.68075955,0.0018824508,0.24168172,0.00019973819,0.0002784829,0.00044882458,0.050437555,0.00051086105,0.023800755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929595,0.000117687436,0.00006582003,0.0001826593,0.00024073165,0.00009702702],"domain_scores_gemma":[0.99845505,0.0006350846,0.0001554177,0.0002139418,0.00043450345,0.00010596706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055589835,0.0007818755,0.00074030913,0.0055500646,0.00044215992,0.0011152168,0.00061067584,0.0005707045,0.0030888752],"category_scores_gemma":[0.002313534,0.00021153898,0.0008359298,0.003266457,0.00018041601,0.0010288601,0.0005128185,0.00058893906,0.004855417],"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":[0.00042838548,0.0004570397,0.075009175,0.00042468382,0.00010874586,0.00021392027,0.00044244734,0.0014474938,0.030419143,0.0016344712,0.0216342,0.86778027],"study_design_scores_gemma":[0.00006531375,0.0008441558,0.45025447,0.00047786772,0.0005052702,0.0028517793,0.001827003,0.35455716,0.0977488,0.012737517,0.07790703,0.00022367528],"about_ca_topic_score_codex":0.005793841,"about_ca_topic_score_gemma":0.010605451,"teacher_disagreement_score":0.005793841,"about_ca_system_score_codex":0.00035849834,"about_ca_system_score_gemma":0.00067778566,"threshold_uncertainty_score":0.011520207},"labels":[],"label_agreement":null},{"id":"W1539879039","doi":"10.1007/978-3-540-85836-2_12","title":"Processing Aggregate Queries on Spatial OLAP Data","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Online analytical processing; Computer science; Spatial query; Query optimization; Spatial database; Set (abstract data type); Database; Aggregate (composite); Spatial analysis; Data mining; Query language; Information retrieval; Sargable; Web search query; Data warehouse; Search engine; Geography","score_opus":0.03638146454789528,"score_gpt":0.26065759763387575,"score_spread":0.22427613308598046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539879039","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28138626,0.0013449236,0.6634459,0.0010032762,0.00031086744,0.00033653135,0.0044920407,0.028176656,0.019503558],"genre_scores_gemma":[0.58206,0.001134115,0.38380218,0.00034081386,0.00034576285,0.00030455872,0.010959426,0.0023003237,0.018752865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986046,0.00012686323,0.00012364313,0.00015588915,0.00079912465,0.00018986994],"domain_scores_gemma":[0.9973354,0.0012218261,0.00014330486,0.00077728234,0.00040124054,0.00012092664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011451031,0.0012008031,0.001522518,0.0014553609,0.0012647532,0.0037559757,0.0014768456,0.00092799927,0.005318579],"category_scores_gemma":[0.00390071,0.0007156265,0.0009669026,0.0042437506,0.0006288724,0.004682936,0.0027505986,0.0012140219,0.0022788274],"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":[0.002016433,0.0004598999,0.011285795,0.0011003944,0.00037573458,0.0012936248,0.0022144886,0.07952418,0.123896874,0.04109807,0.07007899,0.6666556],"study_design_scores_gemma":[0.00009922624,0.0003002681,0.0039124074,0.00007477671,0.0001761655,0.00070535793,0.0018850036,0.73707414,0.108558275,0.10935084,0.037777025,0.0000865863],"about_ca_topic_score_codex":0.003997287,"about_ca_topic_score_gemma":0.0051573697,"teacher_disagreement_score":0.005318579,"about_ca_system_score_codex":0.00070072414,"about_ca_system_score_gemma":0.0008094153,"threshold_uncertainty_score":0.017792404},"labels":[],"label_agreement":null},{"id":"W1540196280","doi":"10.1007/978-1-4939-2864-4_256","title":"Nearest Neighbor Interchange and Related Distances","year":2016,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"k-nearest neighbors algorithm; Geography; Computer science; Artificial intelligence","score_opus":0.011835362371953729,"score_gpt":0.21984664656121664,"score_spread":0.2080112841892629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540196280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005917746,0.068123594,0.6633696,0.0019831157,0.0047900337,0.00016325229,0.00061015383,0.00043206904,0.2546104],"genre_scores_gemma":[0.12878987,0.08082681,0.64084524,0.0018615292,0.004557079,0.000585502,0.0026425135,0.00067568163,0.1392158],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978916,0.00052230625,0.00013531993,0.00037263523,0.0009890267,0.00008902744],"domain_scores_gemma":[0.998792,0.00049042446,0.000109071276,0.0002555883,0.0003153236,0.000037668546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012058716,0.00073131034,0.0013158495,0.00299335,0.001214206,0.0032068978,0.002380059,0.0016502001,0.009554674],"category_scores_gemma":[0.006567522,0.00038043867,0.0006931052,0.008530152,0.0020184135,0.0054688076,0.0022762213,0.002395141,0.004731889],"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":[0.000021281157,0.00002876497,0.00016393077,0.00031264292,0.000016881115,0.00008246511,0.00016327438,0.0038196011,0.00035363287,0.7511264,0.020488482,0.22342275],"study_design_scores_gemma":[0.000006679771,0.000023497103,0.00021449587,0.0001758259,0.000017990702,0.0004860532,0.00013372098,0.01463367,0.00058502005,0.81555915,0.16813666,0.00002727399],"about_ca_topic_score_codex":0.0014803734,"about_ca_topic_score_gemma":0.0014575488,"teacher_disagreement_score":0.009554674,"about_ca_system_score_codex":0.0014484692,"about_ca_system_score_gemma":0.0008486531,"threshold_uncertainty_score":0.031963587},"labels":[],"label_agreement":null},{"id":"W1542773558","doi":"10.1007/978-3-540-74767-3_38","title":"Distributed Multi-source Regular Path Queries","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Path (computing); Distributed computing; Theoretical computer science; Programming language","score_opus":0.023895276126301032,"score_gpt":0.24997032842705894,"score_spread":0.22607505230075792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542773558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044530787,0.0005814525,0.92468214,0.0007819308,0.00019251967,0.00029869028,0.0011625801,0.013129529,0.014640302],"genre_scores_gemma":[0.5668521,0.0006231077,0.40176708,0.00033004844,0.00023571377,0.00032757994,0.0038739743,0.0019096703,0.02408074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983279,0.0002945328,0.0001255589,0.0003594972,0.00071306963,0.00017940874],"domain_scores_gemma":[0.9955035,0.0016867233,0.00015043812,0.0021759088,0.00034756126,0.00013585082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017251915,0.0007872446,0.0015603543,0.0010235026,0.0010483025,0.002064516,0.0024004185,0.0013440754,0.007954014],"category_scores_gemma":[0.0055431467,0.0006029667,0.00058377313,0.002405431,0.0008400213,0.0046593896,0.003939048,0.0014222438,0.0023810316],"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":[0.0029990044,0.0005863651,0.0025302132,0.001004192,0.00019625718,0.0005874185,0.00082779425,0.11147699,0.055240363,0.15301381,0.060697004,0.61084056],"study_design_scores_gemma":[0.00041025138,0.00023211057,0.0006133194,0.000047688554,0.000108298926,0.0008044675,0.00028699165,0.7417774,0.035862483,0.18161319,0.038178664,0.00006502607],"about_ca_topic_score_codex":0.0007698719,"about_ca_topic_score_gemma":0.0011744876,"teacher_disagreement_score":0.007954014,"about_ca_system_score_codex":0.0008564999,"about_ca_system_score_gemma":0.00081615965,"threshold_uncertainty_score":0.026608765},"labels":[],"label_agreement":null},{"id":"W1544455437","doi":"","title":"Proceedings of the 17th ACM conference on Information and knowledge management","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":407,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Information retrieval; World Wide Web; Data science","score_opus":0.028285685752529027,"score_gpt":0.22942912088786532,"score_spread":0.2011434351353363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544455437","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007974539,0.16373315,0.09885882,0.08244898,0.12199646,0.0013153612,0.009456987,0.0054466105,0.5087691],"genre_scores_gemma":[0.041267686,0.12662673,0.06440855,0.016344314,0.023623953,0.0010614318,0.03158807,0.0016377568,0.69344157],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952212,0.0012473521,0.0005290806,0.0006112553,0.0020598473,0.00033128064],"domain_scores_gemma":[0.9914799,0.0023431892,0.00038815924,0.00151916,0.0027291786,0.0015403677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005278061,0.0014245523,0.0025034596,0.0025425255,0.0017049506,0.011529329,0.0027323542,0.003031617,0.1526206],"category_scores_gemma":[0.012631453,0.00061277737,0.0010978915,0.003622644,0.0018068718,0.010712243,0.0046742517,0.0052470905,0.08739667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010573613,0.00013343665,0.0009564154,0.00047796307,0.000104930594,0.00012745809,0.00024369746,0.00019413438,0.00090442505,0.010889776,0.77194244,0.21391958],"study_design_scores_gemma":[0.000010502195,0.000026854152,0.00043216915,0.00021835738,0.00002897278,0.00011030686,0.0001217812,0.00053104205,0.00017589856,0.0045095957,0.9938186,0.000015944446],"about_ca_topic_score_codex":0.0035960928,"about_ca_topic_score_gemma":0.0038040024,"teacher_disagreement_score":0.1526206,"about_ca_system_score_codex":0.0013660379,"about_ca_system_score_gemma":0.0044437847,"threshold_uncertainty_score":0.5105668},"labels":[],"label_agreement":null},{"id":"W1546191538","doi":"10.1109/icc.2015.7249049","title":"Hidden Markov model based graph matching for calibration of localization maps","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Matching (statistics); Computer science; Graph; Map matching; Hidden Markov model; Pattern recognition (psychology); Markov chain; Algorithm; Scale (ratio); Artificial intelligence; Variance (accounting); 3-dimensional matching; Markov process; Data mining; Theoretical computer science; Mathematics; Bipartite graph; Machine learning; Statistics","score_opus":0.0300206110872125,"score_gpt":0.2515043478573134,"score_spread":0.22148373677010091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546191538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006817064,0.00006322654,0.992088,0.00008102993,0.000020055208,0.000011625564,0.000038268383,0.0005470231,0.0003336741],"genre_scores_gemma":[0.67347926,0.0002740155,0.32360607,0.00014087337,0.00005075384,0.000089877365,0.00050604617,0.0002655525,0.0015875914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879825,0.00046974368,0.00005066564,0.00030114988,0.00027165632,0.00010842264],"domain_scores_gemma":[0.9973008,0.0015296289,0.00028869908,0.00047908814,0.0003158329,0.000085999745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016520025,0.00074791606,0.0009420001,0.0014238119,0.000573991,0.00088146375,0.0017595758,0.0011624375,0.0015599175],"category_scores_gemma":[0.01022354,0.0005830528,0.0007505289,0.0020819718,0.0008928729,0.0022416157,0.0015253368,0.0014281723,0.0007704235],"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":[0.0000898353,0.00003772321,0.00079745153,0.000049293052,0.000044789987,0.000056675657,0.000072292576,0.89398766,0.0024587659,0.013418057,0.0010721314,0.08791532],"study_design_scores_gemma":[0.000004250111,0.000009585549,0.00011327088,0.0000028428292,0.000003682566,0.000014452838,0.000006954283,0.98962224,0.0010131163,0.008939467,0.00026421927,0.0000059175673],"about_ca_topic_score_codex":0.007625676,"about_ca_topic_score_gemma":0.005494619,"teacher_disagreement_score":0.007625676,"about_ca_system_score_codex":0.0011410585,"about_ca_system_score_gemma":0.0013143872,"threshold_uncertainty_score":0.015162587},"labels":[],"label_agreement":null},{"id":"W1552526860","doi":"10.3334/ornldaac/386","title":"BOREAS TGB-05 Fire History of Manitoba 1980 to 1991 in Raster Format","year":2012,"lang":"en","type":"dataset","venue":"NASA Technical Reports Server (NASA)","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":true,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Raster graphics; Atmosphere (unit); Taiga; Trace gas; Environmental science; Biogeochemistry; Boreal; TRACE (psycholinguistics); Scale (ratio); Geography; Physical geography; Meteorology; Remote sensing; Forestry; Geology; Archaeology; Cartography; Oceanography; Computer science; Computer graphics (images)","score_opus":0.025183729470557695,"score_gpt":0.2515476213803319,"score_spread":0.22636389190977418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552526860","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034325607,0.00003800774,0.00004247229,0.00003552651,0.000013450731,0.000020351257,0.9956762,0.0001614247,0.00057997735],"genre_scores_gemma":[0.0038813294,0.00004324612,0.0002126206,0.000015151716,0.0000034219552,0.00004955299,0.99499255,0.000026860964,0.0007751222],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99971753,0.000015725249,0.000028867493,0.00007031677,0.00010274766,0.000064821725],"domain_scores_gemma":[0.9984114,0.00008396328,0.00016439776,0.00023463347,0.0008923225,0.00021331801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004022361,0.0013689754,0.00088734616,0.004506202,0.00089031586,0.0007812191,0.0023029898,0.00059052045,0.009217152],"category_scores_gemma":[0.0015250462,0.0006046633,0.0005482811,0.009509915,0.00035509723,0.000389434,0.0006799514,0.0008863249,0.007654525],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027034627,0.00012347537,0.032899667,0.00082511373,0.00016666531,0.00018541086,0.00025433186,0.0021498909,0.0012202818,0.0005893103,0.9540767,0.007238821],"study_design_scores_gemma":[0.0003790088,0.000046075555,0.3786101,0.0004565608,0.0001245575,0.00018698598,0.0010409179,0.001949802,0.002164098,0.00051212107,0.61444837,0.000081324266],"about_ca_topic_score_codex":0.7418497,"about_ca_topic_score_gemma":0.8702243,"teacher_disagreement_score":0.25815028,"about_ca_system_score_codex":0.004706967,"about_ca_system_score_gemma":0.009078785,"threshold_uncertainty_score":0.5193411},"labels":[],"label_agreement":null},{"id":"W1558205009","doi":"10.1137/1.9781611972764.22","title":"Joint Cluster Analysis of Attribute Data and Relationship Data: the Connected <i>k</i>-Center Problem","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data mining; Heuristic; Identification (biology); Completeness (order theory); Data type; Cluster (spacecraft); Cluster analysis; Approximation algorithm; Algorithm; Artificial intelligence; Mathematics","score_opus":0.07804125867314685,"score_gpt":0.2735365913257415,"score_spread":0.19549533265259464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558205009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039337307,0.00031643006,0.95606077,0.0010455914,0.000041297677,0.00019451624,0.0008952377,0.00049971463,0.0016091459],"genre_scores_gemma":[0.36227548,0.00043626403,0.62938756,0.00036303574,0.00018508888,0.00046508762,0.0037501222,0.0003463609,0.0027910194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954491,0.0014141768,0.00025091253,0.001660427,0.00087510125,0.00035031117],"domain_scores_gemma":[0.9853034,0.008924206,0.001425929,0.0022105102,0.0015113574,0.00062469667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005833948,0.0014503151,0.0030178663,0.003849226,0.0027224375,0.003984069,0.0046871495,0.0029141605,0.0025896488],"category_scores_gemma":[0.020968186,0.000953079,0.0023967775,0.007598049,0.0024064963,0.006357131,0.003849133,0.0026689593,0.00080140785],"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":[0.0010593912,0.00080198987,0.014096463,0.00057354535,0.0006182144,0.0005136584,0.00080453546,0.62094057,0.0031857616,0.12247561,0.018190455,0.21673982],"study_design_scores_gemma":[0.000042404426,0.000039674214,0.0009611968,0.000018956116,0.000052902054,0.0001450651,0.00019541003,0.8713181,0.0016275793,0.123826474,0.0017394413,0.000032755415],"about_ca_topic_score_codex":0.007681048,"about_ca_topic_score_gemma":0.0062784795,"teacher_disagreement_score":0.007681048,"about_ca_system_score_codex":0.0029660733,"about_ca_system_score_gemma":0.0034926678,"threshold_uncertainty_score":0.030853271},"labels":[],"label_agreement":null},{"id":"W1561051430","doi":"10.1007/11599289_14","title":"Adaptive Path Finding for Moving Objects","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Path (computing); Computer vision; Artificial intelligence; Computer graphics (images); Programming language","score_opus":0.02642836210097145,"score_gpt":0.2506438747937748,"score_spread":0.22421551269280338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561051430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051914826,0.00053199194,0.9919551,0.00003869185,0.000051372404,0.000015462056,0.000026753743,0.0005069767,0.0016822763],"genre_scores_gemma":[0.1043752,0.0012101829,0.88309056,0.000039154023,0.000058556372,0.00007599614,0.00024575644,0.00020610077,0.01069861],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997987,0.000019837124,0.000007740354,0.000053336822,0.00010212479,0.00001822704],"domain_scores_gemma":[0.9996706,0.00017017491,0.000022267468,0.00005661291,0.00006738445,0.000012927959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023128848,0.0006557148,0.00059593766,0.0009242852,0.00038819673,0.00045617044,0.0019094034,0.0008794969,0.003137777],"category_scores_gemma":[0.0014925885,0.0004980464,0.00044240398,0.0014752313,0.00047336446,0.0011644801,0.0010814231,0.0010399568,0.00081171206],"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":[0.00011018593,0.000043551645,0.00041661414,0.0002520125,0.00005124698,0.00014954634,0.00017680858,0.21251386,0.027108448,0.03709782,0.007822919,0.71425694],"study_design_scores_gemma":[0.000017096125,0.000047586982,0.0003320571,0.000017581899,0.000023353308,0.00023205772,0.000029940293,0.95007896,0.0068178736,0.034756094,0.0076252357,0.000022123715],"about_ca_topic_score_codex":0.0031957305,"about_ca_topic_score_gemma":0.0029472527,"teacher_disagreement_score":0.0031957305,"about_ca_system_score_codex":0.00035269893,"about_ca_system_score_gemma":0.00037206497,"threshold_uncertainty_score":0.010496855},"labels":[],"label_agreement":null},{"id":"W1563424464","doi":"10.1007/978-3-642-37487-6_17","title":"Indexing Reverse Top-k Queries in Two Dimensions","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"University of Victoria","funders":"","keywords":"Search engine indexing; Computer science; Polygon (computer graphics); Tuple; Information retrieval; Combinatorics; Theoretical computer science; Discrete mathematics; Mathematics","score_opus":0.01701251877270165,"score_gpt":0.25371234571853657,"score_spread":0.2366998269458349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563424464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17681763,0.008115676,0.6967491,0.0039855167,0.0017786977,0.0007020928,0.01171433,0.023344213,0.0767928],"genre_scores_gemma":[0.4623479,0.0023687957,0.4835871,0.0010436542,0.00054180383,0.0003258609,0.017499322,0.002480191,0.029805422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958931,0.0005366727,0.0006503526,0.00059553015,0.0016953793,0.00062890886],"domain_scores_gemma":[0.9916276,0.0020062653,0.0002968487,0.004639693,0.0012308363,0.00019885188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015492338,0.0009257274,0.002801881,0.0024436996,0.0016937105,0.005217125,0.002977571,0.0019271215,0.014577138],"category_scores_gemma":[0.010197073,0.0008479992,0.0012415366,0.0074289665,0.0017968417,0.010231238,0.0045107156,0.002638755,0.0070841787],"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":[0.0018093972,0.00072335795,0.00416368,0.0021501023,0.00020552815,0.0006423996,0.00096507353,0.02855754,0.03690728,0.15492941,0.13717794,0.63176835],"study_design_scores_gemma":[0.00041405155,0.00040241494,0.0019241364,0.00028187825,0.00025791227,0.0038213485,0.0013610172,0.33532646,0.0502053,0.47855988,0.1272098,0.00023576396],"about_ca_topic_score_codex":0.0023436474,"about_ca_topic_score_gemma":0.0042358795,"teacher_disagreement_score":0.014577138,"about_ca_system_score_codex":0.001525259,"about_ca_system_score_gemma":0.0022592011,"threshold_uncertainty_score":0.04876542},"labels":[],"label_agreement":null},{"id":"W1564560516","doi":"10.1007/978-3-540-77294-1","title":"Combinatorial and algorithmic aspects of networking : 4th Workshop, CAAN 2007, Halifax, Canada, August 14, 2007 : revised papers","year":2007,"lang":"en","type":"book","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Luck; Scheduling (production processes); Routing (electronic design automation); Heuristic; Distributed computing; Theoretical computer science; Computer network; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.011964064160898189,"score_gpt":0.21824244442250584,"score_spread":0.20627838026160766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564560516","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007678162,0.15584765,0.20822068,0.03669182,0.03561235,0.00031723705,0.0066146576,0.0032999075,0.54571766],"genre_scores_gemma":[0.03525017,0.06929561,0.11845623,0.0018505409,0.006465067,0.00022312684,0.0049581616,0.0016580066,0.7618432],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933225,0.000072225215,0.000023876388,0.00014062875,0.00034567446,0.00008528921],"domain_scores_gemma":[0.9986498,0.00033866765,0.00004040585,0.00016774709,0.00063737837,0.00016610598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011659303,0.0015805765,0.0012783117,0.0021538404,0.0018071883,0.006909326,0.0022062568,0.001137507,0.046595145],"category_scores_gemma":[0.0021273587,0.0009948455,0.0007997436,0.0046537956,0.0018591258,0.0031930734,0.00090075814,0.0026952315,0.010482902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038621263,0.000043491105,0.00021872592,0.00027730802,0.00002021722,0.000056259134,0.00007772858,0.0045910343,0.0005977587,0.10009553,0.78483,0.1091533],"study_design_scores_gemma":[0.000020819458,0.000021945862,0.0009964396,0.00029348987,0.00003382111,0.00029088478,0.00013235603,0.014659939,0.0011999804,0.12035612,0.8619541,0.000040160645],"about_ca_topic_score_codex":0.07198721,"about_ca_topic_score_gemma":0.17895766,"teacher_disagreement_score":0.9280128,"about_ca_system_score_codex":0.010135649,"about_ca_system_score_gemma":0.007139053,"threshold_uncertainty_score":0.15587634},"labels":[],"label_agreement":null},{"id":"W1565853284","doi":"","title":"Knowledgeable agents for search and choice support in e-commerce: a decision support systems approach","year":2000,"lang":"en","type":"article","venue":"MacSphere (McMaster University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Sociology; Computer science; Management science; Political science; Operations research; Library science; Data science; Engineering","score_opus":0.03240099171009096,"score_gpt":0.24520913847436532,"score_spread":0.21280814676427434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565853284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020009255,0.0112425415,0.842606,0.030861119,0.00054387393,0.0005230569,0.0004784703,0.00067370385,0.093062],"genre_scores_gemma":[0.51126665,0.010024561,0.45432183,0.0012713167,0.00074444414,0.0006098715,0.0005870581,0.00009910073,0.021075182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99743015,0.0014238794,0.00021665826,0.00025613373,0.0005242847,0.00014899297],"domain_scores_gemma":[0.9945754,0.0040979837,0.00035370007,0.00036874728,0.00038737844,0.00021687274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051523442,0.00062348397,0.0011788836,0.0032393348,0.0014899145,0.010554805,0.003079905,0.0044952016,0.010760563],"category_scores_gemma":[0.012530999,0.0007227864,0.0009463549,0.0030503715,0.0028593305,0.012386503,0.002643143,0.002655478,0.0016100132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001577239,0.00028045676,0.0009531492,0.0005080126,0.00018005766,0.00042634076,0.0009978178,0.06291217,0.00062311627,0.7728345,0.013295448,0.1468312],"study_design_scores_gemma":[0.0000936426,0.00006041687,0.00034144663,0.00029529855,0.00008957991,0.00010890947,0.0006725173,0.23155898,0.00077391986,0.7233892,0.042556718,0.00005928283],"about_ca_topic_score_codex":0.005049921,"about_ca_topic_score_gemma":0.0074150735,"teacher_disagreement_score":0.010760563,"about_ca_system_score_codex":0.002687161,"about_ca_system_score_gemma":0.0024463884,"threshold_uncertainty_score":0.03599769},"labels":[],"label_agreement":null},{"id":"W1569796140","doi":"10.1007/978-3-642-00672-2_18","title":"Rank Aggregation to Combine QoS in Web Search","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Ranking (information retrieval); Rank (graph theory); Information retrieval; Quality (philosophy); Process (computing); Learning to rank; Web page; Data mining; World Wide Web; Mathematics; Programming language","score_opus":0.018621470445311105,"score_gpt":0.2562007237998797,"score_spread":0.2375792533545686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1569796140","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05868839,0.0048687984,0.9246965,0.00056925253,0.00053498975,0.00014500435,0.0005758056,0.0039288616,0.005992261],"genre_scores_gemma":[0.6798478,0.0011709682,0.31019202,0.00017010032,0.00085413625,0.00010351007,0.00069355965,0.00030908413,0.0066588074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756193,0.0007249396,0.00016087145,0.00020597367,0.001078736,0.00026758813],"domain_scores_gemma":[0.99581957,0.0020577803,0.00026952612,0.0007302874,0.0009234137,0.00019939279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030911388,0.000837506,0.0018352895,0.0025279159,0.000732457,0.0020021468,0.0011527714,0.00072493806,0.002959332],"category_scores_gemma":[0.008957732,0.00037545292,0.0005931629,0.0043984856,0.0004288408,0.0029224535,0.0010449318,0.0011756982,0.0009963566],"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":[0.00068892544,0.00036139978,0.0037580119,0.00030253414,0.00019927809,0.00009988294,0.00021531199,0.1402035,0.010281801,0.024595983,0.016285617,0.8030078],"study_design_scores_gemma":[0.000047448564,0.00018909019,0.0014727372,0.000025377025,0.000100493926,0.00012091839,0.000067723195,0.9592419,0.0052952464,0.0289612,0.0044321227,0.00004579488],"about_ca_topic_score_codex":0.003872136,"about_ca_topic_score_gemma":0.006893336,"teacher_disagreement_score":0.003872136,"about_ca_system_score_codex":0.0008777702,"about_ca_system_score_gemma":0.0008390097,"threshold_uncertainty_score":0.016347706},"labels":[],"label_agreement":null},{"id":"W1571329508","doi":"10.1007/978-3-540-89197-0_71","title":"Probabilistic Nogood Store as a Heuristic","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Heuristics; Probabilistic logic; Computer science; Heuristic; Binary number; Artificial intelligence; Machine learning; Theoretical computer science; Mathematics","score_opus":0.018029281322692662,"score_gpt":0.23571535280376482,"score_spread":0.21768607148107216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1571329508","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025003316,0.00077968714,0.9402479,0.0010199976,0.00038260588,0.00017338224,0.00040166063,0.0025761584,0.02941534],"genre_scores_gemma":[0.32756218,0.0005745837,0.644803,0.00031602633,0.00020626136,0.00021115111,0.00056874164,0.0008773007,0.024880702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986904,0.00034913307,0.000075814074,0.00022482684,0.00048122273,0.00017857943],"domain_scores_gemma":[0.99624455,0.001946291,0.0001162679,0.0011295028,0.00040350054,0.00015982674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017625735,0.00077277276,0.0016788298,0.001485158,0.0020849332,0.0030661456,0.004514404,0.0017531934,0.019072842],"category_scores_gemma":[0.0075759897,0.0008442928,0.001415647,0.0025874095,0.0014541473,0.0057323496,0.002287331,0.0024423967,0.0019169654],"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":[0.000676064,0.00032015078,0.0004142352,0.00031791136,0.00010171126,0.00013066114,0.00018226444,0.18378387,0.0025839393,0.5564587,0.023485128,0.23154539],"study_design_scores_gemma":[0.000067763336,0.000065314205,0.00007868801,0.00003509242,0.00008076472,0.00015112816,0.000051987972,0.47817472,0.002221224,0.5097593,0.009269498,0.00004450806],"about_ca_topic_score_codex":0.0043614334,"about_ca_topic_score_gemma":0.009804672,"teacher_disagreement_score":0.019072842,"about_ca_system_score_codex":0.0019081478,"about_ca_system_score_gemma":0.003648508,"threshold_uncertainty_score":0.063804984},"labels":[],"label_agreement":null},{"id":"W1572714866","doi":"10.1002/atr.1207","title":"A methodology for schedule‐based paths recommendation in multimodal public transportation networks","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Schedule; Transport engineering; Public transport; Computer science; Flow network; Operations research; Engineering; Mathematical optimization; Mathematics","score_opus":0.06148860993418383,"score_gpt":0.3239089192985081,"score_spread":0.2624203093643243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572714866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025019785,0.000047313366,0.99612576,0.00004064653,0.000009358569,0.00006286674,0.000088464185,0.00029298928,0.00083064556],"genre_scores_gemma":[0.10961684,0.00014690546,0.88656324,0.000029792816,0.00002721905,0.00026890592,0.00037036746,0.00010167733,0.0028751655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899524,0.00026946873,0.00006342591,0.0002566247,0.000335588,0.0000795892],"domain_scores_gemma":[0.998914,0.00050685456,0.00010383753,0.00015395635,0.00027468335,0.00004668822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014682428,0.00065608986,0.00083881983,0.0019820489,0.00072345213,0.0011128517,0.0019860845,0.000971242,0.0061549433],"category_scores_gemma":[0.003863811,0.0005255716,0.0013024687,0.002423112,0.00044185988,0.0011721792,0.00085597124,0.0008524367,0.0010500111],"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":[0.000044998513,0.00009616918,0.00087532686,0.00013887139,0.00008614484,0.000108931876,0.00013166398,0.691655,0.0026911937,0.039735243,0.003276024,0.2611605],"study_design_scores_gemma":[0.0000074123195,0.000014323964,0.000103437706,0.000007393497,0.0000100641055,0.000025071178,0.00002306427,0.9890336,0.00057190936,0.007666686,0.0025306086,0.000006364312],"about_ca_topic_score_codex":0.014771224,"about_ca_topic_score_gemma":0.015393697,"teacher_disagreement_score":0.014771224,"about_ca_system_score_codex":0.0016081156,"about_ca_system_score_gemma":0.0016697199,"threshold_uncertainty_score":0.029370487},"labels":[],"label_agreement":null},{"id":"W1573698156","doi":"10.1007/3-540-45754-2_13","title":"Immersed Visual Data Mining: Walking the Walk","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Data warehouse; Architecture; Visualization; XML; Human–computer interaction; Interactive visual analysis; Data visualization; Data mining; World Wide Web","score_opus":0.039145002991682026,"score_gpt":0.2825056599917915,"score_spread":0.24336065700010948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573698156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073757605,0.006248656,0.94974506,0.0051131905,0.0010276656,0.00010782493,0.0005216836,0.0041742353,0.025685841],"genre_scores_gemma":[0.1684822,0.0088782925,0.7812586,0.0017226315,0.0006438412,0.00023086523,0.0015575306,0.0022648303,0.034961175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992454,0.00023573889,0.000039452207,0.00021629522,0.0002115051,0.000051556268],"domain_scores_gemma":[0.9978115,0.001058705,0.000073710864,0.0004523864,0.00038541082,0.00021830022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012597488,0.0008866911,0.00090021204,0.0015684933,0.0009692559,0.004380446,0.0022394634,0.001913252,0.019418621],"category_scores_gemma":[0.007999421,0.00089476653,0.00092657725,0.0016292413,0.0016314689,0.007606196,0.004488203,0.0018351951,0.007874847],"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":[0.00031586143,0.00010765077,0.00097457005,0.0005702383,0.000090053385,0.00016049725,0.001011609,0.0062102275,0.0044728983,0.05893969,0.06402132,0.8631254],"study_design_scores_gemma":[0.00008316784,0.00016535912,0.0012012774,0.00064216286,0.00008760934,0.0007551248,0.0022308382,0.13966584,0.0077139684,0.593432,0.25390276,0.000119940574],"about_ca_topic_score_codex":0.002405202,"about_ca_topic_score_gemma":0.0028201912,"teacher_disagreement_score":0.019418621,"about_ca_system_score_codex":0.0005530938,"about_ca_system_score_gemma":0.0007481185,"threshold_uncertainty_score":0.06496179},"labels":[],"label_agreement":null},{"id":"W1581079409","doi":"10.1007/3-540-35589-8_28","title":"A Quantitative Similarity Measure for Maps","year":2006,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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":"Simon Fraser University","funders":"","keywords":"Measure (data warehouse); Typification; Cartographic generalization; Computer science; Similarity (geometry); Generalization; Similarity measure; Scale (ratio); Quality (philosophy); Data mining; Selection (genetic algorithm); Artificial intelligence; Information retrieval; Mathematics; Geography; Cartography; Image (mathematics)","score_opus":0.053712895015598346,"score_gpt":0.2621101739334201,"score_spread":0.20839727891782178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581079409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041600266,0.0020572266,0.98541343,0.00026875763,0.00021636409,0.00007094931,0.00026511503,0.00033073899,0.007217325],"genre_scores_gemma":[0.11464397,0.0023443552,0.87113345,0.00017218216,0.00046342047,0.00029019229,0.0008847272,0.0003135821,0.009754064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729484,0.000520351,0.0002688636,0.00047078144,0.001350446,0.00009464356],"domain_scores_gemma":[0.99733305,0.0010878621,0.00020484248,0.000496728,0.0007544129,0.00012320845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020186612,0.0007893152,0.0012880376,0.006615562,0.00095799466,0.003785524,0.0018543422,0.001356892,0.0043549077],"category_scores_gemma":[0.009068341,0.00048740883,0.00094803894,0.0076175723,0.0019505677,0.008067045,0.002438741,0.0017158524,0.0014635713],"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":[0.00006218028,0.00005365047,0.0006743809,0.0005134083,0.00007826484,0.00010242551,0.0003795287,0.014194306,0.0072703576,0.54909515,0.014777161,0.41279918],"study_design_scores_gemma":[0.000016364478,0.00013217064,0.0015996316,0.00015154117,0.000069343565,0.0010989149,0.00028634787,0.15383242,0.0065572853,0.774716,0.06145766,0.000082292325],"about_ca_topic_score_codex":0.000972738,"about_ca_topic_score_gemma":0.00083116733,"teacher_disagreement_score":0.006615562,"about_ca_system_score_codex":0.0012920599,"about_ca_system_score_gemma":0.00071632897,"threshold_uncertainty_score":0.014568567},"labels":[],"label_agreement":null},{"id":"W1581306068","doi":"10.1007/3-540-44732-6_22","title":"A New Geometric Tool for Pattern Recognition - An Algorithm for Real Time Insertion of Layered Segment Trees","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Carleton University","funders":"","keywords":"Computer science; Line segment; Tree (set theory); Sorting; Data structure; Tree structure; Process (computing); Segment tree; Image processing; Algorithm; Line (geometry); Path (computing); Time complexity; Scheme (mathematics); Image (mathematics); Artificial intelligence; Binary tree; Interval tree; Mathematics","score_opus":0.027390248709639648,"score_gpt":0.2532196908368677,"score_spread":0.22582944212722803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581306068","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096029526,0.00010996722,0.99533194,0.000032264114,0.000058538542,0.00002092013,0.00005339366,0.0026372522,0.0007953718],"genre_scores_gemma":[0.01141455,0.00016737294,0.98493665,0.00003996839,0.000040961382,0.00006013416,0.00024982914,0.0006666413,0.0024238853],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933016,0.00006544047,0.000061437895,0.00017828972,0.00031115694,0.000053542488],"domain_scores_gemma":[0.9991142,0.00026575537,0.000083233615,0.00024866036,0.00022987546,0.000058258855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054351625,0.0015597593,0.0012232553,0.0018265967,0.00067421334,0.0020576231,0.0027278843,0.0011209084,0.0126104355],"category_scores_gemma":[0.0023602017,0.001046133,0.0010500421,0.002564819,0.0010562022,0.003741151,0.0018291048,0.002297291,0.0063947923],"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":[0.00018429113,0.00005254296,0.00032774117,0.00029707322,0.000037610647,0.00014805343,0.00020054737,0.014641029,0.052405044,0.04993524,0.014299019,0.8674718],"study_design_scores_gemma":[0.00010066932,0.00038919394,0.00080881553,0.00013055773,0.0001344381,0.0016907959,0.00014829967,0.6470065,0.107964806,0.09475314,0.14671734,0.00015542516],"about_ca_topic_score_codex":0.001061714,"about_ca_topic_score_gemma":0.001372604,"teacher_disagreement_score":0.0126104355,"about_ca_system_score_codex":0.00058282167,"about_ca_system_score_gemma":0.00063525327,"threshold_uncertainty_score":0.04218608},"labels":[],"label_agreement":null},{"id":"W1582070848","doi":"10.1007/978-3-642-17461-2_32","title":"Approximate Ellipsoid in the Streaming Model","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Ellipsoid; Computation; Ball (mathematics); Bounded function; Computer science; Approximation algorithm; Factor (programming language); Algorithm; Mathematics; Mathematical analysis; Physics","score_opus":0.019580363951233283,"score_gpt":0.23865807748270756,"score_spread":0.2190777135314743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582070848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017726753,0.001415673,0.9704216,0.00066864316,0.000113184666,0.000029488894,0.00033965698,0.0001601542,0.009124807],"genre_scores_gemma":[0.57138205,0.0051220157,0.38195333,0.00048085576,0.00078467414,0.00027296488,0.0017856777,0.00053227803,0.037686143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99927956,0.00029176593,0.000028274382,0.00009902838,0.00023598359,0.00006549378],"domain_scores_gemma":[0.9961438,0.002635389,0.00021024903,0.00034333378,0.00043090465,0.00023622798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001647922,0.00076645525,0.0015670297,0.000996395,0.00048485975,0.0016470781,0.0023949826,0.0017553446,0.006124798],"category_scores_gemma":[0.0117377285,0.00061524456,0.000658565,0.0016742813,0.0010671803,0.0037946287,0.0021712035,0.0025068126,0.0008807976],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013084091,0.000042567037,0.0005346274,0.00020208307,0.00003524391,0.000086668224,0.00008139483,0.40845174,0.00090442883,0.5425852,0.010631491,0.03631377],"study_design_scores_gemma":[0.000005843842,0.000008804722,0.00008029726,0.000010775124,0.0000035875878,0.000043262913,0.000011254411,0.8208404,0.00011345617,0.17751342,0.0013614767,0.0000073824554],"about_ca_topic_score_codex":0.004818575,"about_ca_topic_score_gemma":0.0032415155,"teacher_disagreement_score":0.006124798,"about_ca_system_score_codex":0.0013486833,"about_ca_system_score_gemma":0.0007466026,"threshold_uncertainty_score":0.020489454},"labels":[],"label_agreement":null},{"id":"W1588404442","doi":"10.1007/978-3-540-73580-9_27","title":"An Analysis of Map-Based Abstraction and Refinement","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"University of Alberta","funders":"","keywords":"Testbed; Abstraction; Variety (cybernetics); Computer science; Pathfinding; Theoretical computer science; Programming language; Artificial intelligence; World Wide Web; Graph","score_opus":0.02182059924059719,"score_gpt":0.2736472474865811,"score_spread":0.2518266482459839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588404442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02720521,0.00038353674,0.95884895,0.00035011137,0.00005867443,0.00012627087,0.0001319597,0.0009617863,0.011933607],"genre_scores_gemma":[0.5455446,0.0009232669,0.43749645,0.0002610316,0.00016229658,0.00028968332,0.00047686565,0.0010968334,0.013749046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99549955,0.0007813239,0.00023691918,0.00041282177,0.0023373496,0.0007320914],"domain_scores_gemma":[0.9846933,0.00803943,0.00076633075,0.003944981,0.002201655,0.00035439132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045278,0.0008391092,0.0011727588,0.0026362776,0.0018354912,0.0027733739,0.0039176326,0.0014093051,0.010280065],"category_scores_gemma":[0.028492108,0.0012232809,0.0025850344,0.0038105657,0.003526688,0.0077031883,0.005128624,0.0030721757,0.0011278243],"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":[0.00019401162,0.0000624315,0.0019495819,0.00023841795,0.000074523945,0.0002030132,0.00079523784,0.060906146,0.0031764258,0.8574531,0.0031728847,0.071774095],"study_design_scores_gemma":[0.00003656686,0.00006804475,0.00093761215,0.0000591465,0.00012017125,0.0001875972,0.00025135835,0.35738674,0.005079012,0.628233,0.007601293,0.000039454208],"about_ca_topic_score_codex":0.011223332,"about_ca_topic_score_gemma":0.0071224472,"teacher_disagreement_score":0.011223332,"about_ca_system_score_codex":0.0021845035,"about_ca_system_score_gemma":0.0024841535,"threshold_uncertainty_score":0.03439027},"labels":[],"label_agreement":null},{"id":"W1590149829","doi":"","title":"Concurrent Parallel Shortest Path Computation.","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Computer science; Shortest path problem; Parallel computing; Constrained Shortest Path First; Computation; K shortest path routing; Path (computing); Topology (electrical circuits); Theoretical computer science; Algorithm; Mathematics; Combinatorics; Computer network; Graph","score_opus":0.02065145057853855,"score_gpt":0.26182881408960784,"score_spread":0.2411773635110693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590149829","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03304307,0.0012321,0.9386174,0.00088275934,0.00053838117,0.00031086276,0.00079259416,0.003302219,0.021280447],"genre_scores_gemma":[0.51963645,0.0007831402,0.4620096,0.00023107098,0.0002170767,0.00044356764,0.0015798798,0.00047463583,0.0146245435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998774,0.0002236903,0.00008946498,0.00028554056,0.00042885522,0.00019846477],"domain_scores_gemma":[0.9967224,0.0010854772,0.00017059105,0.0011018845,0.00067269476,0.0002468384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011948273,0.0007618451,0.0009307572,0.0008387135,0.0015384693,0.0021229025,0.0024752014,0.00073463656,0.00863736],"category_scores_gemma":[0.00884228,0.0005494341,0.000561744,0.0016886507,0.00088589266,0.0029327981,0.0023794058,0.0013587498,0.0018809125],"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":[0.0027663063,0.0005616068,0.003547681,0.0008325764,0.00027716838,0.00051878754,0.000538205,0.1907803,0.019093415,0.2508721,0.05471927,0.47549254],"study_design_scores_gemma":[0.00034145452,0.00012959115,0.00033286112,0.000044358098,0.00011320465,0.00024436426,0.00011893711,0.6217467,0.0136382235,0.3326241,0.03063543,0.000030802472],"about_ca_topic_score_codex":0.0044610337,"about_ca_topic_score_gemma":0.007348953,"teacher_disagreement_score":0.00863736,"about_ca_system_score_codex":0.0009876075,"about_ca_system_score_gemma":0.002520466,"threshold_uncertainty_score":0.028894901},"labels":[],"label_agreement":null},{"id":"W1590505239","doi":"10.1007/978-3-540-74472-6_7","title":"Shortest Path Queries Between Geometric Objects on Surfaces","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Shortest path problem; Combinatorics; Surface (topology); Path (computing); Euclidean geometry; Face (sociological concept); Mathematics; Genus; Computer science; Geometry","score_opus":0.03260386802131227,"score_gpt":0.2616404646236558,"score_spread":0.22903659660234355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590505239","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2248439,0.0030142877,0.71708745,0.0012501103,0.00030859842,0.00027204317,0.004552682,0.0062508383,0.04242015],"genre_scores_gemma":[0.5177777,0.002681158,0.4458062,0.00015307439,0.00018957804,0.00022496424,0.01087128,0.0010012882,0.021294774],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99906725,0.00009456704,0.00006093549,0.00016476166,0.00052014797,0.00009242221],"domain_scores_gemma":[0.99905723,0.0005873554,0.000046529727,0.00016209277,0.00009576296,0.000051014384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000339898,0.00091150176,0.0015577207,0.0012885487,0.0007925017,0.0018125017,0.0018435743,0.0018658343,0.0071936115],"category_scores_gemma":[0.0028102922,0.0005529135,0.00069866533,0.0039991573,0.00073152187,0.0046887733,0.002987728,0.0012922942,0.0015583565],"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":[0.0006925777,0.00021527425,0.0016282424,0.0014119061,0.00011247656,0.0007377494,0.0011058877,0.22606172,0.045311797,0.21188183,0.043619502,0.46722105],"study_design_scores_gemma":[0.00012369825,0.00019673436,0.0009764023,0.00009649187,0.000051082643,0.00075088337,0.00071593415,0.5525301,0.022439325,0.35281736,0.06923999,0.00006205328],"about_ca_topic_score_codex":0.002118751,"about_ca_topic_score_gemma":0.0019887516,"teacher_disagreement_score":0.0071936115,"about_ca_system_score_codex":0.00082305586,"about_ca_system_score_gemma":0.00040506723,"threshold_uncertainty_score":0.024065077},"labels":[],"label_agreement":null},{"id":"W1591816673","doi":"10.1007/978-3-540-39658-1_40","title":"I/O-Efficient Undirected Shortest Paths","year":2003,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Combinatorics; Mathematics; Computer science","score_opus":0.012924077728041194,"score_gpt":0.23852948296908064,"score_spread":0.22560540524103945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591816673","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26134124,0.00131009,0.6428105,0.0014462607,0.00038923204,0.00062222034,0.0034595283,0.0064529,0.08216801],"genre_scores_gemma":[0.6285536,0.00064709893,0.3415829,0.00025723758,0.00010557174,0.00025614366,0.002895448,0.00088487845,0.02481716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937326,0.00009456289,0.000030928823,0.00016632162,0.00015946894,0.00017533757],"domain_scores_gemma":[0.99842095,0.00063052116,0.00018116603,0.0004155566,0.00019682085,0.0001549573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036352404,0.0011761318,0.0009890622,0.0011833238,0.0009630233,0.0013679855,0.0022034235,0.00085673906,0.010738313],"category_scores_gemma":[0.003311669,0.00042297153,0.0005852696,0.0024957957,0.00051639776,0.0020012541,0.0019835324,0.0010072538,0.0017140125],"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":[0.0015020969,0.00071343506,0.0017695692,0.0008288645,0.00007459879,0.0003096121,0.00031388944,0.35791913,0.031587128,0.048989564,0.044976275,0.5110158],"study_design_scores_gemma":[0.00024317058,0.00031529006,0.0008946966,0.000058266232,0.000088052155,0.00022522571,0.0002970324,0.86854047,0.016717907,0.10062054,0.011961254,0.000038123915],"about_ca_topic_score_codex":0.0033136036,"about_ca_topic_score_gemma":0.0075074364,"teacher_disagreement_score":0.010738313,"about_ca_system_score_codex":0.0009816113,"about_ca_system_score_gemma":0.0015970629,"threshold_uncertainty_score":0.035923243},"labels":[],"label_agreement":null},{"id":"W1591856724","doi":"10.1007/978-3-540-30570-5_23","title":"First-Order Query Rewriting for Inconsistent Databases","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Rewriting; Class (philosophy); Conjunctive query; Query language; Query optimization; Boolean conjunctive query; Database query; Database; Sargable; Data integrity; Order (exchange); Information retrieval; Web search query; Programming language; Relational database; Search engine; Artificial intelligence","score_opus":0.033056032078094426,"score_gpt":0.2629440349701871,"score_spread":0.22988800289209266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591856724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011958516,0.0013729982,0.97559774,0.0006793591,0.00020536753,0.000106401516,0.00023363897,0.0019566382,0.007889336],"genre_scores_gemma":[0.25287434,0.0014736161,0.7261115,0.0005555591,0.00033812074,0.00017275455,0.0014168122,0.0016569283,0.015400369],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9920723,0.0019529767,0.0008758521,0.0007466297,0.0037970995,0.0005551292],"domain_scores_gemma":[0.98784876,0.0060209236,0.00041585453,0.003850318,0.0016531497,0.00021110891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059215017,0.00074455206,0.0017895655,0.001330097,0.0013231364,0.0035151746,0.0033943001,0.001434812,0.0040169763],"category_scores_gemma":[0.015396532,0.001267993,0.0020144063,0.002306728,0.002560346,0.0066677216,0.0028142785,0.0041837534,0.0011344618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017547703,0.00012536008,0.000925652,0.000668268,0.000118921365,0.0006620092,0.0017893229,0.016187672,0.009927501,0.786996,0.016156185,0.16626765],"study_design_scores_gemma":[0.000059019712,0.000071357616,0.00026573575,0.000072652205,0.00014066305,0.0008742398,0.0002786029,0.0903557,0.015347556,0.85718054,0.0352814,0.0000724982],"about_ca_topic_score_codex":0.0022701328,"about_ca_topic_score_gemma":0.0020446603,"teacher_disagreement_score":0.0059215017,"about_ca_system_score_codex":0.0016680292,"about_ca_system_score_gemma":0.0015149789,"threshold_uncertainty_score":0.03131622},"labels":[],"label_agreement":null},{"id":"W1592532873","doi":"10.1109/icde.2015.7113307","title":"Bi-temporal Timeline Index: A data structure for Processing Queries on bi-temporal data","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Timeline; Computer science; Temporal database; Benchmark (surveying); SQL; Implementation; Exploit; Index (typography); Data mining; Database; Information retrieval; Programming language","score_opus":0.14040817073326656,"score_gpt":0.33820071225299186,"score_spread":0.1977925415197253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592532873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021706246,0.0017643373,0.9181532,0.0006282107,0.00039710442,0.00052182167,0.013295671,0.036942564,0.0065908507],"genre_scores_gemma":[0.10559669,0.0013523473,0.85761136,0.00051383214,0.00037076356,0.000712415,0.024703337,0.0031160577,0.0060233176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987355,0.00014035564,0.0002459661,0.00020342611,0.0005721834,0.00010254819],"domain_scores_gemma":[0.9952466,0.00082866446,0.0006692715,0.0018641046,0.0011059443,0.00028544228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017340388,0.00088549306,0.0008718232,0.002909459,0.0010450836,0.0024566045,0.0022167114,0.0007380567,0.0063382876],"category_scores_gemma":[0.0069806227,0.00056092755,0.0007155123,0.0058972547,0.0007815902,0.006077044,0.0026775314,0.0011438003,0.0036890428],"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":[0.0022589925,0.0004924008,0.010457038,0.0014255345,0.00022416917,0.00041043182,0.0011864918,0.013912465,0.05152947,0.08680991,0.18576945,0.6455236],"study_design_scores_gemma":[0.00043211316,0.0012783331,0.00649315,0.00032500055,0.00018631361,0.0017881475,0.0007199665,0.29149464,0.095191464,0.09042828,0.5112697,0.00039296277],"about_ca_topic_score_codex":0.0041870805,"about_ca_topic_score_gemma":0.0053999093,"teacher_disagreement_score":0.0063382876,"about_ca_system_score_codex":0.00091182144,"about_ca_system_score_gemma":0.0024017594,"threshold_uncertainty_score":0.021203697},"labels":[],"label_agreement":null},{"id":"W1594349942","doi":"10.1007/978-3-642-19668-3_12","title":"Uncertainty in Rank Join","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Join (topology); Ranking (information retrieval); Rank (graph theory); Result set; sort; Operator (biology); Set (abstract data type); Information retrieval; Domain (mathematical analysis); Aggregate (composite); Sort-merge join; Semantics (computer science); Theoretical computer science; Core (optical fiber); Learning to rank; Query optimization; Data mining; Programming language","score_opus":0.024074171957326612,"score_gpt":0.23653624849568025,"score_spread":0.21246207653835364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594349942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020843,0.004574351,0.9430518,0.0020245723,0.00045155603,0.00010640687,0.00043098276,0.0008535274,0.02766382],"genre_scores_gemma":[0.61237884,0.003626996,0.31989977,0.00088175345,0.0034629737,0.00043682754,0.0019495228,0.0011218109,0.056241624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99147195,0.0029783212,0.00036214048,0.0010150317,0.003629814,0.00054274546],"domain_scores_gemma":[0.9854798,0.010369197,0.0006576406,0.002038977,0.00095060724,0.00050369796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064208186,0.00093928806,0.0030673412,0.0019822933,0.0021632728,0.004763858,0.0025648386,0.0023181161,0.0129744895],"category_scores_gemma":[0.024050698,0.0009445403,0.0011194828,0.0033798346,0.0028986256,0.0077425647,0.0050026476,0.0050436384,0.0029901923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046812167,0.00009640367,0.0007294141,0.00023169616,0.000076313816,0.00010190921,0.00028196274,0.07300659,0.0010965986,0.78708065,0.016142199,0.120688125],"study_design_scores_gemma":[0.00003077683,0.00007089443,0.0001896913,0.000032272117,0.000026046675,0.00009472123,0.00005368122,0.22018436,0.00078248716,0.770118,0.008394102,0.000022999444],"about_ca_topic_score_codex":0.001207548,"about_ca_topic_score_gemma":0.0013380857,"teacher_disagreement_score":0.0129744895,"about_ca_system_score_codex":0.0018655085,"about_ca_system_score_gemma":0.0012512977,"threshold_uncertainty_score":0.043404043},"labels":[],"label_agreement":null},{"id":"W1595500906","doi":"10.1007/11880592_5","title":"A Supervised Learning Approach to Entity Search","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Key (lock); Baseline (sea); Artificial intelligence; Search engine; Supervised learning; Machine learning; Web search query; Data mining; Artificial neural network","score_opus":0.024429425519479422,"score_gpt":0.2428645211463592,"score_spread":0.2184350956268798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595500906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029904493,0.00042261914,0.99304634,0.00021721379,0.00005032786,0.000064324355,0.00045324332,0.0012745475,0.0014808407],"genre_scores_gemma":[0.08657357,0.0006573657,0.8991507,0.00025019483,0.00021426764,0.00022401796,0.0033089796,0.00030405456,0.009316829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980787,0.000633231,0.00016189663,0.00047507414,0.000559941,0.00009113054],"domain_scores_gemma":[0.99611425,0.0020873554,0.00017184671,0.00090059446,0.0006323214,0.00009368662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020969103,0.0007410284,0.001430658,0.0029277217,0.001321118,0.0016876712,0.0037030599,0.0016353194,0.0047100107],"category_scores_gemma":[0.006794982,0.00065031805,0.0013120407,0.004632846,0.0009220154,0.004496584,0.002179399,0.0020822424,0.0023859208],"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":[0.00020168832,0.00048898015,0.0019957647,0.0002833913,0.00022459656,0.00019323711,0.00023201037,0.09615974,0.003631642,0.08912649,0.04111355,0.7663489],"study_design_scores_gemma":[0.000021439615,0.000034245186,0.00039210866,0.000023398197,0.000045363995,0.00014948014,0.000048643546,0.8878868,0.0020496014,0.101158015,0.008169504,0.00002137765],"about_ca_topic_score_codex":0.006330049,"about_ca_topic_score_gemma":0.014165759,"teacher_disagreement_score":0.006330049,"about_ca_system_score_codex":0.00087472977,"about_ca_system_score_gemma":0.0015669999,"threshold_uncertainty_score":0.015756607},"labels":[],"label_agreement":null},{"id":"W1595627082","doi":"10.1007/978-3-642-02982-0_26","title":"Efficient Evaluation of Static and Dynamic Optimal Route Queries","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Graph; Shortest path problem; Dynamic problem; Enhanced Data Rates for GSM Evolution; Set (abstract data type); Algorithm; Path (computing); Dynamic programming; Mathematical optimization; Theoretical computer science; Mathematics","score_opus":0.01677437801556516,"score_gpt":0.2665094323699007,"score_spread":0.24973505435433555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595627082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2813674,0.003487812,0.66507214,0.0008246684,0.0003438449,0.00042731874,0.0018007492,0.020880492,0.025795583],"genre_scores_gemma":[0.76588196,0.00048195128,0.22231361,0.00012318851,0.0001284726,0.00011762109,0.0022870712,0.0011316938,0.007534412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99543446,0.0008657705,0.0002491698,0.000527062,0.0023045018,0.00061900035],"domain_scores_gemma":[0.99461055,0.003101874,0.00020027939,0.0008627092,0.0010102795,0.00021436144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020378209,0.001241474,0.0021767386,0.0013710303,0.0008219449,0.0035562092,0.0026454583,0.0011139442,0.008590837],"category_scores_gemma":[0.009171225,0.00065832713,0.00066938874,0.002019015,0.00085486105,0.0037836367,0.0018120274,0.0008136236,0.0016839163],"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":[0.0057963687,0.00048151318,0.005336454,0.0005632228,0.0001945786,0.0003464775,0.00038220335,0.21716966,0.037496034,0.029821392,0.029054323,0.6733577],"study_design_scores_gemma":[0.00014442347,0.00015527791,0.0009787752,0.000013834847,0.000053204094,0.00013362287,0.00014555869,0.972705,0.012829644,0.0097054215,0.00311059,0.000024718734],"about_ca_topic_score_codex":0.00889954,"about_ca_topic_score_gemma":0.011370973,"teacher_disagreement_score":0.00889954,"about_ca_system_score_codex":0.0023133624,"about_ca_system_score_gemma":0.002380966,"threshold_uncertainty_score":0.028739274},"labels":[],"label_agreement":null},{"id":"W1596527724","doi":"10.1007/978-3-540-24571-1_73","title":"Similarity Search and Dimensionality Reduction: Not All Dimensions Are Equally Useful","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"University of Alberta","funders":"","keywords":"Dimensionality reduction; Search engine indexing; Computer science; Curse of dimensionality; Reduction (mathematics); Speedup; Nearest neighbor search; Similarity (geometry); Overhead (engineering); Diffusion map; Space (punctuation); Range query (database); Range (aeronautics); Process (computing); Similitude; Data mining; Algorithm; Theoretical computer science; Nonlinear dimensionality reduction; Artificial intelligence; Mathematics; Information retrieval; Search engine; Parallel computing; Web search query","score_opus":0.04871447997598213,"score_gpt":0.27806901145447543,"score_spread":0.2293545314784933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596527724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028443448,0.052863922,0.8274187,0.017471693,0.002516205,0.00012803056,0.00079288334,0.0012103537,0.069154836],"genre_scores_gemma":[0.30203247,0.044415843,0.59881383,0.003262569,0.003626733,0.0003380448,0.0013337102,0.00047011266,0.045706723],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984163,0.0004362591,0.000114747236,0.0002450847,0.0007231854,0.000064495674],"domain_scores_gemma":[0.99769104,0.0009843785,0.00015414668,0.000671202,0.0004121164,0.00008718586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001111989,0.0007648271,0.0016693487,0.0013827448,0.00082296843,0.0031836093,0.0007825665,0.0010151211,0.005935682],"category_scores_gemma":[0.00725139,0.00041199828,0.0007196001,0.004253793,0.001820969,0.006664381,0.0020600792,0.001704613,0.0028402412],"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":[0.00009622244,0.000059899165,0.0009573297,0.00069577846,0.00013025725,0.00008749563,0.00034151462,0.0044642566,0.0034005735,0.19931348,0.05726461,0.7331886],"study_design_scores_gemma":[0.00003243571,0.00017684698,0.0018582642,0.00029614265,0.0001492891,0.0012800923,0.00048656957,0.043182615,0.0052274927,0.8257591,0.12146496,0.00008618185],"about_ca_topic_score_codex":0.00072589,"about_ca_topic_score_gemma":0.0008710494,"teacher_disagreement_score":0.005935682,"about_ca_system_score_codex":0.0005423119,"about_ca_system_score_gemma":0.0008327461,"threshold_uncertainty_score":0.01985681},"labels":[],"label_agreement":null},{"id":"W1596588646","doi":"10.1007/978-3-642-38562-9_55","title":"Probabilistic Graph Summarization","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Automatic summarization; Computer science; Scalability; Probabilistic logic; Graph; Theoretical computer science; Algorithm; Data mining; Artificial intelligence; Database","score_opus":0.014928164622190572,"score_gpt":0.2209521364385043,"score_spread":0.20602397181631374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596588646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004246234,0.0018941725,0.958844,0.0008292445,0.00031970209,0.00021644734,0.002672824,0.0097176405,0.021259679],"genre_scores_gemma":[0.092855476,0.0027957456,0.8237064,0.00046510788,0.0005440294,0.00030646104,0.021727929,0.0031705322,0.054428257],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99911267,0.00016956766,0.00006110761,0.000323639,0.0002813938,0.0000515746],"domain_scores_gemma":[0.9986904,0.00033740545,0.00008628992,0.00046956708,0.00037054744,0.000045789697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006964682,0.0011111822,0.0009523877,0.0028642484,0.00082672224,0.0016649615,0.0019210011,0.0010323053,0.021032326],"category_scores_gemma":[0.002994111,0.0006683249,0.0011489125,0.003529263,0.00053065264,0.0034292422,0.0016119827,0.0015521271,0.010934285],"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":[0.000110766174,0.00008336737,0.00023177995,0.0006167839,0.0001063902,0.00014517235,0.00016078312,0.026010172,0.011440855,0.0520801,0.090070866,0.8189429],"study_design_scores_gemma":[0.00005034638,0.0001630337,0.0010879975,0.0002270926,0.0002493531,0.0006936016,0.00025701692,0.3191159,0.03797718,0.35190713,0.28818145,0.00008995058],"about_ca_topic_score_codex":0.0012119602,"about_ca_topic_score_gemma":0.0027094535,"teacher_disagreement_score":0.021032326,"about_ca_system_score_codex":0.0006588958,"about_ca_system_score_gemma":0.0007319734,"threshold_uncertainty_score":0.07036018},"labels":[],"label_agreement":null},{"id":"W1597331078","doi":"10.5555/963600.963624","title":"A new histogram method for sparse attributes: the averaged rectangular attribute cardinality map","year":2003,"lang":"en","type":"article","venue":"Proceedings of the 1st international symposium on Information and communication technologies","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Histogram; Cardinality (data modeling); Query optimization; Oracle; Computer science; Set (abstract data type); Data mining; Algorithm; Result set; Mathematics; Pattern recognition (psychology); Artificial intelligence; Image (mathematics)","score_opus":0.018469531074071448,"score_gpt":0.25767338078929375,"score_spread":0.2392038497152223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597331078","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006681984,0.00026156072,0.9904067,0.000055380562,0.000038719754,0.000058336656,0.0001873293,0.0018003819,0.0005095111],"genre_scores_gemma":[0.1460752,0.00032990784,0.85077953,0.000087471904,0.00010629561,0.00019375295,0.0007226899,0.00037555082,0.001329493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862146,0.00026573503,0.00009022401,0.00029670654,0.00063603936,0.00008985521],"domain_scores_gemma":[0.9967944,0.0012788447,0.00031518008,0.0006651429,0.0008106001,0.0001358074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011361575,0.0006665886,0.001053906,0.0024896814,0.00051164255,0.0015510063,0.0018632332,0.0005140469,0.0024481171],"category_scores_gemma":[0.0068175467,0.00041179548,0.00052919186,0.0036215682,0.0005574485,0.00299128,0.0012627881,0.0010662153,0.0009278017],"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":[0.00055992755,0.0001359992,0.0055232733,0.00021170708,0.00009756229,0.000064829605,0.00018167129,0.08541482,0.018535517,0.017395237,0.0074603274,0.8644193],"study_design_scores_gemma":[0.000068336754,0.00018754929,0.0023496298,0.000019402283,0.000042797587,0.0003161771,0.00010641957,0.95126903,0.021788651,0.0122353565,0.011534722,0.00008202342],"about_ca_topic_score_codex":0.004209427,"about_ca_topic_score_gemma":0.0031127748,"teacher_disagreement_score":0.004209427,"about_ca_system_score_codex":0.000787583,"about_ca_system_score_gemma":0.0012584003,"threshold_uncertainty_score":0.008369863},"labels":[],"label_agreement":null},{"id":"W1599696396","doi":"","title":"Anytime measures for top-k algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Probabilistic logic; Computation; Ranking (information retrieval); Algorithm; Identification (biology); Point (geometry); Data mining; Asset (computer security); Theoretical computer science; Information retrieval; Artificial intelligence; Mathematics","score_opus":0.025895331435677,"score_gpt":0.27623642975701723,"score_spread":0.2503410983213402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599696396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048053388,0.0046613454,0.93464833,0.002139838,0.00040587108,0.00015897142,0.00090575597,0.0017011341,0.0073254965],"genre_scores_gemma":[0.5922623,0.0020721091,0.39630553,0.0006904778,0.0013913678,0.0006591306,0.0013370601,0.0010611792,0.0042207637],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97492933,0.007093258,0.0021131646,0.004671261,0.009451532,0.0017415322],"domain_scores_gemma":[0.7676024,0.16161221,0.018064959,0.030547723,0.015519189,0.0066535496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020391887,0.0026064138,0.0031556168,0.007619353,0.0030906808,0.009941927,0.005342648,0.0034689265,0.006069779],"category_scores_gemma":[0.15589435,0.0010664962,0.0021446776,0.0069725593,0.0054928157,0.020656094,0.0048172176,0.006379555,0.0015031963],"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":[0.0010489412,0.0002392626,0.008439756,0.0010941152,0.00024534462,0.00022512529,0.00088090374,0.14185977,0.0050500897,0.70659494,0.00712099,0.12720077],"study_design_scores_gemma":[0.000046963818,0.00043664873,0.0021007466,0.00013909693,0.000107830536,0.0003764036,0.00022515979,0.4585861,0.0038440337,0.52769625,0.0063323053,0.000108488726],"about_ca_topic_score_codex":0.0014750242,"about_ca_topic_score_gemma":0.0015915063,"teacher_disagreement_score":0.020391887,"about_ca_system_score_codex":0.0054769833,"about_ca_system_score_gemma":0.002753932,"threshold_uncertainty_score":0.107843876},"labels":[],"label_agreement":null},{"id":"W1601136399","doi":"10.1007/978-3-540-39406-8_11","title":"L-ToPSS – Push-Oriented Location-Based Services","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":32,"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","score_opus":0.01013617173920688,"score_gpt":0.22494480688487498,"score_spread":0.2148086351456681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601136399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01462919,0.0012027947,0.889795,0.0011759005,0.0011425263,0.00028578006,0.00059086474,0.02301902,0.068159014],"genre_scores_gemma":[0.45699382,0.003773134,0.39193928,0.00214935,0.001059544,0.00042307738,0.0024767008,0.00260254,0.13858253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993259,0.00009356888,0.00005371062,0.00009855802,0.00031518567,0.000113048096],"domain_scores_gemma":[0.9992416,0.00009910474,0.00005425201,0.0003131727,0.00020335185,0.000088623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006338312,0.00071430777,0.00060804666,0.0009544584,0.0007159635,0.0027378597,0.0017273411,0.0012277506,0.006773924],"category_scores_gemma":[0.0015016705,0.0004896643,0.0005683727,0.0013608484,0.00089673896,0.0029467777,0.0028755043,0.0014623746,0.009715052],"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":[0.00047177932,0.00018225732,0.0012412067,0.0006979412,0.00008074749,0.0007304696,0.000572184,0.008960593,0.049544744,0.19849114,0.069842756,0.6691842],"study_design_scores_gemma":[0.00010039087,0.00032710907,0.0008819767,0.000175638,0.00012565218,0.0021647867,0.00037533778,0.11553234,0.07183571,0.10086708,0.70747274,0.0001412519],"about_ca_topic_score_codex":0.0018920305,"about_ca_topic_score_gemma":0.0013490828,"teacher_disagreement_score":0.006773924,"about_ca_system_score_codex":0.0006968586,"about_ca_system_score_gemma":0.0010287719,"threshold_uncertainty_score":0.02266103},"labels":[],"label_agreement":null},{"id":"W1604896632","doi":"10.1007/3-540-44886-1_15","title":"Discovering Temporal/Causal Rules: A Comparison of Methods","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Preprocessor; Data mining; Artificial intelligence; Domain (mathematical analysis); Tetrad; Data pre-processing; Machine learning; Mathematics","score_opus":0.04298376201132736,"score_gpt":0.3364625450889709,"score_spread":0.2934787830776435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604896632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024914676,0.036331005,0.9193707,0.001360982,0.00036169722,0.0009148038,0.0013359928,0.005547357,0.009862813],"genre_scores_gemma":[0.07977392,0.019909728,0.89338726,0.00043997628,0.0002541191,0.00061917567,0.0018282718,0.0009963944,0.002791148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98412955,0.0055524725,0.0019284945,0.0021204785,0.005834968,0.00043404225],"domain_scores_gemma":[0.88884926,0.0985634,0.0016164707,0.0047367862,0.0051930877,0.0010410064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022482986,0.0025681516,0.004532789,0.011431787,0.0011854074,0.0074298987,0.006343242,0.0030528668,0.006689967],"category_scores_gemma":[0.05410697,0.001679263,0.003117764,0.007337511,0.0015558923,0.008702614,0.003159402,0.003402045,0.002491905],"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":[0.0015463788,0.00040846074,0.0060791676,0.0026387882,0.0013020363,0.00007642609,0.0006456162,0.011811103,0.0011378189,0.011375607,0.004051221,0.95892733],"study_design_scores_gemma":[0.0016180562,0.0015734184,0.021025253,0.002561628,0.004594373,0.0019995149,0.0029287734,0.7453388,0.011243695,0.15685615,0.049683575,0.0005766618],"about_ca_topic_score_codex":0.008706576,"about_ca_topic_score_gemma":0.014019081,"teacher_disagreement_score":0.022482986,"about_ca_system_score_codex":0.0016083001,"about_ca_system_score_gemma":0.003926662,"threshold_uncertainty_score":0.11890286},"labels":[],"label_agreement":null},{"id":"W16075771","doi":"10.4018/978-1-59140-560-3.ch015","title":"Component-Based Generalized Database Index Model","year":2005,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Database; Computer science; Index (typography); Data mining; Database index; Database design; Nearest neighbor search; Similarity (geometry); Database model; Component (thermodynamics); Quality (philosophy); Information retrieval; Search engine indexing; Artificial intelligence","score_opus":0.030647775382697875,"score_gpt":0.2526843373543047,"score_spread":0.22203656197160682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W16075771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017188484,0.001686114,0.95214283,0.00088914816,0.00019991645,0.00039822984,0.001791608,0.0025746543,0.02312894],"genre_scores_gemma":[0.57475877,0.0039441376,0.36865816,0.0005391951,0.00030906312,0.0009768177,0.0046594585,0.0005238083,0.045630626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773633,0.0003130035,0.00019141402,0.00055219105,0.0009493963,0.00025765924],"domain_scores_gemma":[0.99744403,0.0004697172,0.00018400607,0.0007593071,0.0009982856,0.00014451661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015985507,0.0010463115,0.0014172676,0.0022006857,0.0009544634,0.005753246,0.007290061,0.0020713352,0.01111905],"category_scores_gemma":[0.0049195616,0.00066319655,0.0013367974,0.0054798904,0.0012509068,0.0080093,0.0027392518,0.0016995716,0.0047176233],"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":[0.00020519618,0.00009860422,0.0016132244,0.0003063904,0.00010140569,0.0006022467,0.0002908026,0.32633844,0.002649253,0.56971943,0.016176973,0.08189794],"study_design_scores_gemma":[0.000036713045,0.000057720117,0.00026089977,0.000022806431,0.00005547338,0.0003600695,0.00005657872,0.8630518,0.00075500837,0.11097603,0.024336852,0.000030089737],"about_ca_topic_score_codex":0.0118089905,"about_ca_topic_score_gemma":0.0060716174,"teacher_disagreement_score":0.0118089905,"about_ca_system_score_codex":0.0023972394,"about_ca_system_score_gemma":0.0021714328,"threshold_uncertainty_score":0.037196875},"labels":[],"label_agreement":null},{"id":"W1614173561","doi":"10.1287/moor.2014.0706","title":"Euclidean Networks with a Backbone and a Limit Theorem for Minimum Spanning Caterpillars","year":2015,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Euclidean minimum spanning tree; Spanning tree; Minimum spanning tree; Combinatorics; Euclidean geometry; Minimum weight; Discrete mathematics; Shortest path problem; Subadditivity; Euclidean shortest path; Limit (mathematics); Shortest-path tree; Minimum degree spanning tree; Graph; Longest path problem; Geometry; Mathematical analysis","score_opus":0.1421796790829535,"score_gpt":0.36571574975285154,"score_spread":0.22353607066989803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1614173561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08884427,0.0022833275,0.88781625,0.0016000506,0.0001160784,0.00006488182,0.00034819142,0.00023674012,0.018690279],"genre_scores_gemma":[0.80825716,0.0036286837,0.16495684,0.00076518394,0.0004688138,0.0005064066,0.0007240517,0.00029798993,0.020394795],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99882334,0.00041804832,0.00006358346,0.00029498094,0.00023284274,0.00016709857],"domain_scores_gemma":[0.9922855,0.004836227,0.0009901305,0.00045900993,0.0007723067,0.0006568737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003277894,0.00081001635,0.0012192944,0.0024216035,0.0012644497,0.0024375904,0.0028280886,0.0019009318,0.0057673897],"category_scores_gemma":[0.016322525,0.00063334964,0.0013925477,0.001478383,0.002546804,0.008438969,0.0026756653,0.0026669113,0.0007898126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037373964,0.00002118768,0.00046771776,0.00008330082,0.000024546702,0.000090682894,0.000113888804,0.0405123,0.0007611255,0.9492763,0.0014860064,0.0071256305],"study_design_scores_gemma":[0.000019729527,0.000057204543,0.00040846257,0.00004611803,0.000020052514,0.00016102103,0.000073802046,0.26152116,0.00041126474,0.73436415,0.002891238,0.00002576513],"about_ca_topic_score_codex":0.0016371679,"about_ca_topic_score_gemma":0.0015959366,"teacher_disagreement_score":0.0057673897,"about_ca_system_score_codex":0.0021215363,"about_ca_system_score_gemma":0.0006552525,"threshold_uncertainty_score":0.019293785},"labels":[],"label_agreement":null},{"id":"W1618713168","doi":"10.1016/s1571-0661(04)81007-7","title":"Three approaches to partiality in the sketch data model1 1Research partially supported by the Australian Research Council and NSERC Canada.","year":2003,"lang":"en","type":"article","venue":"Electronic Notes in Theoretical Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mount Allison University","funders":"","keywords":"Sketch; Equivalence (formal languages); Computer science; Information retrieval; Theoretical computer science; Mathematics; Algorithm; Discrete mathematics","score_opus":0.25763518615005127,"score_gpt":0.336122033931163,"score_spread":0.07848684778111176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1618713168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008266649,0.00077444303,0.9801524,0.004083841,0.000107968415,0.0002058446,0.0004298471,0.00057414785,0.005404863],"genre_scores_gemma":[0.23828845,0.0016899918,0.744426,0.002069271,0.00056486356,0.0012053509,0.001569067,0.00039326362,0.009793692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9793937,0.007099773,0.0019037649,0.003226391,0.0070742927,0.0013020354],"domain_scores_gemma":[0.9525878,0.01989312,0.0032349082,0.016273146,0.0059807636,0.0020302788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01833852,0.0014488996,0.0017871514,0.0063261576,0.0025688782,0.010024506,0.0067569637,0.0050459458,0.0074877036],"category_scores_gemma":[0.06030394,0.002354177,0.004555757,0.0061562383,0.014363003,0.029274197,0.01983576,0.008238649,0.0013802042],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010080982,0.000050107687,0.0009134116,0.00019294769,0.000046196197,0.00009329379,0.0011082533,0.0031859544,0.00067665963,0.9640732,0.0020783406,0.02748077],"study_design_scores_gemma":[0.000054639862,0.00013211269,0.0005134688,0.00015905983,0.000080902486,0.00048744353,0.0006211637,0.039157405,0.0017948548,0.93066895,0.026195744,0.0001341132],"about_ca_topic_score_codex":0.003659707,"about_ca_topic_score_gemma":0.0028093464,"teacher_disagreement_score":0.01833852,"about_ca_system_score_codex":0.003724811,"about_ca_system_score_gemma":0.0029213782,"threshold_uncertainty_score":0.096984506},"labels":[],"label_agreement":null},{"id":"W163910197","doi":"","title":"Updatable Indices for Efficient, Generalised Top-k Queries.","year":2011,"lang":"en","type":"article","venue":"Advances in Databases and Information Systems","topic":"Data Management and Algorithms","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 Victoria","funders":"","keywords":"Computer science; Information retrieval; World Wide Web","score_opus":0.02580553964115974,"score_gpt":0.2594260641727355,"score_spread":0.23362052453157578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W163910197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037995547,0.0059465533,0.9073148,0.0012255869,0.000647158,0.00047263,0.005246034,0.021697327,0.01945441],"genre_scores_gemma":[0.3354838,0.0016903211,0.63754416,0.0006511147,0.00033528372,0.00037122326,0.008403672,0.002280601,0.013239821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996738,0.0008564879,0.00050780154,0.00044150176,0.0010386478,0.00041752937],"domain_scores_gemma":[0.9905475,0.003031332,0.00036542807,0.004965833,0.0008758965,0.00021395189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002171376,0.0010379776,0.0020589072,0.0017903927,0.0014761798,0.0039224452,0.0035535558,0.0015565384,0.008868001],"category_scores_gemma":[0.016751025,0.0009402278,0.0008642487,0.004183537,0.0016865002,0.009311134,0.0053828545,0.002282573,0.0068121403],"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":[0.002720744,0.00041238827,0.004061964,0.0014940943,0.00027955952,0.00080417714,0.0012429365,0.028044708,0.020063998,0.18325037,0.14530945,0.61231554],"study_design_scores_gemma":[0.00047604062,0.0003559325,0.0014047752,0.0003145248,0.000229111,0.0018022894,0.0007026037,0.22168918,0.021012388,0.6507335,0.10109679,0.00018293221],"about_ca_topic_score_codex":0.0022954294,"about_ca_topic_score_gemma":0.0047430857,"teacher_disagreement_score":0.008868001,"about_ca_system_score_codex":0.0008807812,"about_ca_system_score_gemma":0.0017628394,"threshold_uncertainty_score":0.029666424},"labels":[],"label_agreement":null},{"id":"W165396895","doi":"","title":"K-Nearest Neighbor Search using the Pyramid Technique.","year":2006,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Pyramid (geometry); k-nearest neighbors algorithm; RADIUS; Best bin first; Nearest neighbor graph; Nearest neighbor search; Nearest-neighbor chain algorithm; Range (aeronautics); Point (geometry); Range query (database); Mathematics; Computer science; Pattern recognition (psychology); Algorithm; Artificial intelligence; Combinatorics; Geometry; Search engine; Information retrieval; Cluster analysis; Web search query","score_opus":0.04234192041526704,"score_gpt":0.2690653430377264,"score_spread":0.22672342262245934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W165396895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006244716,0.0009355714,0.9884239,0.00007547032,0.00009808308,0.00014408086,0.0001679548,0.0012181547,0.002692143],"genre_scores_gemma":[0.09126136,0.0007484317,0.9045633,0.000103530794,0.00005018698,0.00014428461,0.00069468986,0.00007187712,0.0023624185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986866,0.00017176746,0.00009272542,0.00023599049,0.00072229275,0.00009063568],"domain_scores_gemma":[0.9990482,0.00024187582,0.00008091386,0.00026100513,0.00033044178,0.000037614587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006602325,0.00062172516,0.0014123122,0.0020603568,0.001032145,0.0011339692,0.0015084672,0.0009742359,0.003121241],"category_scores_gemma":[0.0035777637,0.00047118132,0.0009836471,0.0037996324,0.00044081925,0.0025871955,0.0016926982,0.0008369819,0.0022687423],"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":[0.0002850586,0.00026394668,0.002405299,0.0006171166,0.00039691426,0.0004275312,0.00030239465,0.08260993,0.022727525,0.021348111,0.018898856,0.8497174],"study_design_scores_gemma":[0.00013564591,0.00033376866,0.0021396384,0.000080027734,0.00017228651,0.002052462,0.00025479688,0.8985834,0.019608172,0.039785128,0.036725044,0.00012963264],"about_ca_topic_score_codex":0.008304523,"about_ca_topic_score_gemma":0.009829401,"teacher_disagreement_score":0.008304523,"about_ca_system_score_codex":0.0005232315,"about_ca_system_score_gemma":0.0014719127,"threshold_uncertainty_score":0.016512394},"labels":[],"label_agreement":null},{"id":"W1657379609","doi":"10.1109/ipdpsw.2015.21","title":"Towards a Combined Grouping and Aggregation Algorithm for Fast Query Processing in Columnar Databases with GPUs","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Computer science; Hash function; Parallel computing; Aggregate (composite); Algorithm; sort; Database; Data structure; Operating system","score_opus":0.03857750052534887,"score_gpt":0.26820241688198154,"score_spread":0.22962491635663268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1657379609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0833738,0.0006698401,0.9032948,0.0002880806,0.00011114952,0.00019439083,0.00020894907,0.009490772,0.0023682069],"genre_scores_gemma":[0.20272051,0.00017817185,0.79429835,0.00013983814,0.000034231834,0.00010815484,0.00045587463,0.00025740734,0.0018073885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934846,0.000091661284,0.000070434275,0.00011499992,0.00027752764,0.00009687992],"domain_scores_gemma":[0.99905723,0.00015860358,0.000064764456,0.00031013117,0.0003355365,0.00007372278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054933754,0.0006695811,0.00086199894,0.0010093258,0.00072756415,0.0016024467,0.0019091998,0.00071567734,0.0018427497],"category_scores_gemma":[0.0018030341,0.00043580338,0.0004984076,0.0018638656,0.00046557904,0.0016613527,0.001231996,0.0007062046,0.00089096325],"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":[0.0016650966,0.000514348,0.008625471,0.00033253388,0.00021229737,0.00032644035,0.0006989357,0.08013029,0.19814803,0.01364181,0.018871585,0.6768332],"study_design_scores_gemma":[0.00010583169,0.00024998822,0.0014299003,0.000016817088,0.000044718894,0.00021185435,0.00018726077,0.9189437,0.061181467,0.005416862,0.012175545,0.000036031157],"about_ca_topic_score_codex":0.008146611,"about_ca_topic_score_gemma":0.0070617218,"teacher_disagreement_score":0.008146611,"about_ca_system_score_codex":0.00080648775,"about_ca_system_score_gemma":0.0009840201,"threshold_uncertainty_score":0.016198397},"labels":[],"label_agreement":null},{"id":"W166953216","doi":"","title":"Vertex-Neighbor-Integrity of Composition Graphs of Paths.","year":2008,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Vertex (graph theory); Composition (language); Combinatorics; Graph; Art","score_opus":0.017667599210157293,"score_gpt":0.22980220846005123,"score_spread":0.21213460924989394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W166953216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21307515,0.0007593595,0.71737313,0.0038560815,0.00033370312,0.00032636579,0.003350765,0.0024210012,0.058504432],"genre_scores_gemma":[0.82425636,0.00055501837,0.15278572,0.0005007586,0.0002687914,0.00017200236,0.0038857472,0.0006216836,0.016953912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994406,0.0014175911,0.00047449133,0.0011866319,0.0018173221,0.0006978357],"domain_scores_gemma":[0.96894574,0.012533367,0.0034710513,0.008841191,0.0047747535,0.0014340231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038942955,0.00063146005,0.0010419536,0.0019763205,0.0028619736,0.0049212426,0.0024617643,0.0019765764,0.009154364],"category_scores_gemma":[0.032724414,0.00088644703,0.0009389311,0.0024283505,0.0030630396,0.015410112,0.0042033014,0.003382317,0.0019374542],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089947094,0.00025135287,0.00909613,0.00043777432,0.00015157109,0.0006182847,0.001477587,0.017529683,0.005548035,0.83726513,0.011099446,0.11562559],"study_design_scores_gemma":[0.000040535586,0.00007839174,0.0010652622,0.00006322883,0.000114013084,0.00062675425,0.00059128203,0.0431909,0.009998803,0.930934,0.013269153,0.000027709382],"about_ca_topic_score_codex":0.0023116264,"about_ca_topic_score_gemma":0.0022642154,"teacher_disagreement_score":0.009154364,"about_ca_system_score_codex":0.0013609229,"about_ca_system_score_gemma":0.0021028765,"threshold_uncertainty_score":0.03062433},"labels":[],"label_agreement":null},{"id":"W1669943","doi":"","title":"The Height Distribution of Nodes in Non-Crossing Trees.","year":2003,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Distribution (mathematics); Statistics; Mathematical analysis","score_opus":0.009088075525755093,"score_gpt":0.23055360921991788,"score_spread":0.22146553369416278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1669943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7290401,0.002617133,0.23494232,0.00083468534,0.00034773766,0.00018830082,0.015332865,0.002580168,0.014116738],"genre_scores_gemma":[0.9314799,0.0007333121,0.05236638,0.00011182703,0.000111836416,0.000120360695,0.010816901,0.00025917482,0.004000315],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99865854,0.00017919895,0.00012028785,0.00040643246,0.000399687,0.00023590802],"domain_scores_gemma":[0.99153686,0.0042950083,0.001138719,0.0013574229,0.0011309739,0.0005409909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018947094,0.00035559657,0.0005889609,0.004147181,0.0007984156,0.0017235252,0.001409204,0.0008242862,0.0052954336],"category_scores_gemma":[0.01422054,0.00034028981,0.0005485867,0.0034211758,0.0008408573,0.0023153478,0.0011691343,0.0007410082,0.0016327975],"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":[0.0021295731,0.00033962523,0.25994748,0.0010972755,0.00034863863,0.0020805849,0.0025916519,0.12086359,0.034329735,0.11764731,0.029637365,0.42898712],"study_design_scores_gemma":[0.0001681603,0.0005833695,0.12050346,0.00032268107,0.0002463235,0.008287792,0.0019457059,0.47545248,0.015683763,0.32047722,0.056194834,0.00013420262],"about_ca_topic_score_codex":0.0013532947,"about_ca_topic_score_gemma":0.0026774602,"teacher_disagreement_score":0.0052954336,"about_ca_system_score_codex":0.00080180966,"about_ca_system_score_gemma":0.00046326526,"threshold_uncertainty_score":0.017714977},"labels":[],"label_agreement":null},{"id":"W1674016448","doi":"10.1017/cbo9780511546570.008","title":"Advances in Point Counting","year":2005,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Point (geometry); Computer science; Mathematics; Geometry","score_opus":0.014408470542076083,"score_gpt":0.19580663870416343,"score_spread":0.18139816816208734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1674016448","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043243705,0.1451701,0.47005382,0.00788329,0.009184723,0.0000913466,0.0013995436,0.0027755557,0.35911724],"genre_scores_gemma":[0.10640793,0.18358523,0.41490528,0.004001775,0.016952176,0.0003971695,0.0041245883,0.004107722,0.26551816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972554,0.000514314,0.00016649801,0.00060883217,0.0012616717,0.00019321992],"domain_scores_gemma":[0.99528706,0.002096771,0.000115036986,0.0013451657,0.0009460154,0.00020992682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032011776,0.0014925053,0.002166065,0.0047086785,0.0013925259,0.0052710953,0.0027705901,0.0014130172,0.027946958],"category_scores_gemma":[0.011553443,0.001019665,0.0016792117,0.009085406,0.002600578,0.010420079,0.0037575462,0.005520797,0.02461255],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020887353,0.000021967368,0.00020589812,0.0004049797,0.000020644029,0.000037218662,0.000080778715,0.002533077,0.00028997604,0.6921909,0.09976119,0.20443255],"study_design_scores_gemma":[0.0000059121635,0.0000135171995,0.00023362566,0.0001860361,0.000015513915,0.00018749913,0.000027642462,0.0063793794,0.00039628166,0.69379526,0.2987356,0.000023844523],"about_ca_topic_score_codex":0.002385697,"about_ca_topic_score_gemma":0.0018723123,"teacher_disagreement_score":0.027946958,"about_ca_system_score_codex":0.0031482007,"about_ca_system_score_gemma":0.0015808714,"threshold_uncertainty_score":0.09349185},"labels":[],"label_agreement":null},{"id":"W1685824674","doi":"10.48550/arxiv.1308.4469","title":"External Memory Algorithms For Path Traversal in Graphs","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Tree traversal; Graph traversal; Polygon mesh; Data structure; Computer science; Path (computing); Representation (politics); Binary tree; Bounded function; Planar graph; Tree (set theory); Longest path problem; Algorithm; Shortest path problem; Theoretical computer science; Mathematics; Graph; Combinatorics","score_opus":0.0707405642959143,"score_gpt":0.19577988601862475,"score_spread":0.12503932172271046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1685824674","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011522585,0.00047705387,0.97273207,0.0003983027,0.00007448626,0.000172127,0.0008193154,0.00515575,0.008648249],"genre_scores_gemma":[0.1371537,0.0007200793,0.845297,0.00028694407,0.00008853753,0.000595915,0.0040059136,0.0016717152,0.010180228],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980021,0.0003645133,0.00022299642,0.00044321475,0.00068707124,0.00028013394],"domain_scores_gemma":[0.9952679,0.0021173574,0.00032351032,0.0016562615,0.0005178804,0.000116984906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011156938,0.0013263134,0.0010625118,0.0019753387,0.0013222138,0.0038056676,0.0030529855,0.0013004247,0.015447816],"category_scores_gemma":[0.008283611,0.00073717703,0.0016609171,0.0038272822,0.0013842705,0.008711912,0.00498926,0.0025179128,0.004535208],"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":[0.0005050223,0.00021972047,0.0012824099,0.00078600814,0.00009000995,0.00021394341,0.00090847106,0.09325447,0.0059635523,0.38095546,0.029329827,0.4864911],"study_design_scores_gemma":[0.0001639786,0.00012394175,0.0002664368,0.0001595224,0.000057172103,0.000270493,0.00037590566,0.39056006,0.0071234265,0.55699486,0.04384661,0.00005747864],"about_ca_topic_score_codex":0.00426015,"about_ca_topic_score_gemma":0.0061084055,"teacher_disagreement_score":0.015447816,"about_ca_system_score_codex":0.0023848212,"about_ca_system_score_gemma":0.0016348836,"threshold_uncertainty_score":0.05167812},"labels":[],"label_agreement":null},{"id":"W1689383259","doi":"","title":"Assessment of the Regionalization of Precipitation in Two Canadian Climate Regions: A Fuzzy Clustering Approach","year":2015,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Precipitation; Fuzzy logic; Fuzzy clustering; Cluster analysis; Environmental science; Geography; Climatology; Computer science; Meteorology; Geology; Artificial intelligence","score_opus":0.1251690301853281,"score_gpt":0.3350170189990403,"score_spread":0.2098479888137122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1689383259","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86266154,0.000730448,0.126625,0.00037806993,0.000015737221,0.00033857374,0.0012806474,0.0001815737,0.007788436],"genre_scores_gemma":[0.9253485,0.00020509593,0.0728913,0.00002073016,0.0000050651183,0.00005691497,0.0005903609,0.000018950761,0.000863012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914,0.00015816718,0.000051148687,0.00015237185,0.00038595893,0.0001123976],"domain_scores_gemma":[0.9985525,0.00030424123,0.00014583238,0.00006331273,0.0008624868,0.000071570095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018193161,0.00044624598,0.00043782475,0.0053577763,0.0015154429,0.0018535587,0.00087526464,0.0003982797,0.00046253938],"category_scores_gemma":[0.0041841986,0.00022480612,0.0007363892,0.0038099724,0.000533558,0.00043580876,0.00065354863,0.00032359257,0.000054633332],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070345995,0.00014269253,0.22992663,0.00041303394,0.0010044193,0.00034044785,0.0039440305,0.3760063,0.023980465,0.014094931,0.003034147,0.3464095],"study_design_scores_gemma":[0.000035600155,0.000087504624,0.36314943,0.00006740014,0.0003026618,0.00012187991,0.003911548,0.6172076,0.007564687,0.0033253657,0.004080934,0.00014542084],"about_ca_topic_score_codex":0.79030246,"about_ca_topic_score_gemma":0.8505351,"teacher_disagreement_score":0.20969754,"about_ca_system_score_codex":0.008828166,"about_ca_system_score_gemma":0.008039246,"threshold_uncertainty_score":0.421865},"labels":[],"label_agreement":null},{"id":"W1696916719","doi":"10.3233/ida-2003-7202","title":"Iceberg-cube algorithms: An empirical evaluation on synthetic and real data","year":2003,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Crossover; Pruning; Algorithm; Point (geometry); Cube (algebra); Computer science; Set (abstract data type); Function (biology); Mathematics; Artificial intelligence; Combinatorics; Geometry","score_opus":0.19531162735217378,"score_gpt":0.40852110797410435,"score_spread":0.21320948062193057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1696916719","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8597104,0.006612657,0.10415038,0.0018839098,0.0005395467,0.0012313864,0.009359032,0.007513079,0.008999566],"genre_scores_gemma":[0.60735035,0.0017678862,0.37117773,0.00052916317,0.0001297503,0.00081981265,0.016409038,0.0005574462,0.0012588544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98943007,0.004354293,0.0012211562,0.0012945072,0.0032315014,0.0004685252],"domain_scores_gemma":[0.9314313,0.054113276,0.0016761596,0.005192518,0.006606987,0.0009796491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020126823,0.0022505792,0.0022722278,0.004294194,0.0016831052,0.0025902241,0.0038215308,0.0025649385,0.0013417635],"category_scores_gemma":[0.054178577,0.000679586,0.0012060583,0.0068650194,0.0017322518,0.005280395,0.0022793696,0.0019245021,0.0006086807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002548167,0.0024164335,0.02003376,0.0016531046,0.00086396746,0.00027822514,0.0005415792,0.71600944,0.001851526,0.006533066,0.027095139,0.22017555],"study_design_scores_gemma":[0.00027976037,0.00048789472,0.0019439947,0.000065449116,0.00007314987,0.00016151808,0.00023602984,0.9862027,0.0030077337,0.004782867,0.0027215115,0.000037357346],"about_ca_topic_score_codex":0.010623152,"about_ca_topic_score_gemma":0.010806204,"teacher_disagreement_score":0.020126823,"about_ca_system_score_codex":0.0023061123,"about_ca_system_score_gemma":0.0026100508,"threshold_uncertainty_score":0.106442094},"labels":[],"label_agreement":null},{"id":"W1710084411","doi":"10.1007/978-3-540-75256-1_62","title":"A Qualitative Hidden Markov Model for Spatio-temporal Reasoning","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Hidden Markov model; Construct (python library); Artificial intelligence; Representation (politics); Markov process; Markov model; Markov chain; Maximum-entropy Markov model; Machine learning; Variable-order Markov model; Hidden semi-Markov model; Theoretical computer science; Programming language; Mathematics","score_opus":0.052698351172976124,"score_gpt":0.3273504401116056,"score_spread":0.27465208893862947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1710084411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029387139,0.00014823266,0.99495196,0.00024780378,0.000027832499,0.00003424151,0.00038238152,0.00021899454,0.0010498273],"genre_scores_gemma":[0.44953358,0.000743245,0.54090524,0.0003402845,0.00011338706,0.000498612,0.0017141318,0.00018561087,0.0059658578],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852306,0.0005856517,0.00013693822,0.00026617068,0.00036390085,0.00012431857],"domain_scores_gemma":[0.9925045,0.0057688234,0.00040696553,0.0005894337,0.0005313073,0.00019887234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031284795,0.00069644366,0.00117599,0.0013872114,0.00079457177,0.0023744758,0.0032126997,0.001489255,0.0068491367],"category_scores_gemma":[0.011370705,0.00085928245,0.0024989934,0.0017503418,0.0020294045,0.0045161163,0.0020388472,0.0024965447,0.0010774785],"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":[0.000114158174,0.00006122854,0.00086867134,0.00019296435,0.00008275622,0.0001978141,0.00043751684,0.32034978,0.0009783729,0.6444466,0.00198738,0.030282784],"study_design_scores_gemma":[0.000013449441,0.00000949444,0.000076019256,0.000017147835,0.000018625535,0.000027531694,0.000026261107,0.69861865,0.00019387128,0.3000888,0.0008986996,0.000011511304],"about_ca_topic_score_codex":0.015938647,"about_ca_topic_score_gemma":0.014668174,"teacher_disagreement_score":0.015938647,"about_ca_system_score_codex":0.0025956417,"about_ca_system_score_gemma":0.0021602907,"threshold_uncertainty_score":0.03169173},"labels":[],"label_agreement":null},{"id":"W1714480694","doi":"10.3233/hsn-2006-282","title":"Destination-driven shortest path tree algorithms","year":2006,"lang":"en","type":"article","venue":"Journal of High Speed Networks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Path (computing); Tree (set theory); Algorithm; Shortest path problem; Theoretical computer science; Computer network; Mathematics; Combinatorics; Graph","score_opus":0.00937035923181781,"score_gpt":0.21665481260163844,"score_spread":0.20728445336982063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1714480694","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006155931,0.00033546437,0.9894838,0.00015854968,0.000056236982,0.00012411994,0.00015525559,0.000541132,0.0029895424],"genre_scores_gemma":[0.17164437,0.0007138588,0.8216901,0.00015358525,0.00007600114,0.0003829538,0.0009346549,0.00016275047,0.0042416924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900526,0.0003102597,0.000073310475,0.00016276713,0.00033231106,0.00011607165],"domain_scores_gemma":[0.9986279,0.0006254007,0.00012413706,0.00021315743,0.00033880514,0.00007056374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010792238,0.00088398077,0.0011414237,0.0011279075,0.0009280532,0.0012791905,0.00213229,0.0011000355,0.0034197161],"category_scores_gemma":[0.0037233727,0.00042555787,0.00065293536,0.0018246198,0.00036623515,0.0020508312,0.0018301682,0.001022624,0.00132709],"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":[0.00023089854,0.00015438782,0.0010299862,0.0003045621,0.00011554714,0.00011230293,0.00021527079,0.6111614,0.0039329305,0.06668167,0.010383882,0.30567718],"study_design_scores_gemma":[0.000059718437,0.000069217145,0.00011244131,0.000020300065,0.000027281316,0.00012933106,0.000051957115,0.93868184,0.0022316014,0.049616423,0.008982814,0.000017085764],"about_ca_topic_score_codex":0.0008966509,"about_ca_topic_score_gemma":0.0015001183,"teacher_disagreement_score":0.0034197161,"about_ca_system_score_codex":0.0008025167,"about_ca_system_score_gemma":0.0018118238,"threshold_uncertainty_score":0.011440098},"labels":[],"label_agreement":null},{"id":"W174827044","doi":"10.1007/978-3-642-14755-5_3","title":"A General Approach to the Fuzzy Modeling of Spatial Relationships","year":2010,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Bounding overwatch; Snapshot (computer storage); Duality (order theory); Position (finance); Dual (grammatical number); Computer science; Mathematics; Object (grammar); Algorithm; Combinatorics; Artificial intelligence","score_opus":0.0951757116457981,"score_gpt":0.28587772403228356,"score_spread":0.19070201238648546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W174827044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013901858,0.0010214404,0.9839369,0.000930792,0.00010449398,0.00004131735,0.00020710063,0.00009869421,0.012269038],"genre_scores_gemma":[0.12484958,0.004774513,0.8520458,0.0007837021,0.0006384007,0.00038465697,0.00045346475,0.00008680539,0.015983082],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993266,0.0001830393,0.00005871074,0.00014624333,0.00024148403,0.000043997898],"domain_scores_gemma":[0.99945253,0.00029230444,0.00004006513,0.00009399772,0.000094511706,0.000026703796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012782308,0.0009950503,0.0012663371,0.0020573568,0.0010411305,0.0030734653,0.003906378,0.0020520526,0.005987256],"category_scores_gemma":[0.0023545194,0.0006516479,0.0026093742,0.003657877,0.0029270018,0.004593528,0.0017437174,0.003090958,0.0012472614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040048267,0.000010614806,0.00008707225,0.00007440435,0.000023065997,0.000061944826,0.00018111072,0.02114585,0.00042332048,0.9625621,0.0016493024,0.0137771275],"study_design_scores_gemma":[0.0000040574228,0.00001062415,0.00008474565,0.000040943596,0.000016096328,0.00010005229,0.00004607435,0.059941802,0.00017143058,0.9226221,0.016950479,0.000011581694],"about_ca_topic_score_codex":0.0071130493,"about_ca_topic_score_gemma":0.0066541866,"teacher_disagreement_score":0.0071130493,"about_ca_system_score_codex":0.0015133669,"about_ca_system_score_gemma":0.0012522342,"threshold_uncertainty_score":0.020029366},"labels":[],"label_agreement":null},{"id":"W175226406","doi":"","title":"k-Geodomination in Graphs","year":2002,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Combinatorics","score_opus":0.015649248580041072,"score_gpt":0.21926343233824622,"score_spread":0.20361418375820514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W175226406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44307977,0.0015072589,0.46875882,0.0050430377,0.00036184714,0.0002121948,0.0010567546,0.0015808506,0.0783993],"genre_scores_gemma":[0.8656618,0.00090695947,0.09480548,0.00077122805,0.00015565899,0.0001600645,0.001001015,0.00048279506,0.03605511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972435,0.0008141798,0.0002246984,0.00079687644,0.00042607903,0.00049464987],"domain_scores_gemma":[0.9917497,0.0036455942,0.00082305336,0.002056983,0.00071573275,0.0010088888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015962663,0.0007888836,0.001487225,0.00200576,0.0040622554,0.0059433,0.0022819564,0.0018449015,0.008849479],"category_scores_gemma":[0.0076295417,0.0010306337,0.0012535403,0.003952254,0.0035239644,0.0102414265,0.0060414546,0.002908289,0.002485664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001786436,0.00007907774,0.0018209992,0.0001874,0.000025764835,0.00034414875,0.0013225644,0.0029862677,0.0015619013,0.9563875,0.0049157836,0.03018986],"study_design_scores_gemma":[0.000028705319,0.000025066778,0.00037779653,0.000040998573,0.00002867227,0.00058088737,0.00044325378,0.00818461,0.0014346646,0.97708786,0.011747443,0.000020050058],"about_ca_topic_score_codex":0.0015543138,"about_ca_topic_score_gemma":0.0020408032,"teacher_disagreement_score":0.008849479,"about_ca_system_score_codex":0.0014529403,"about_ca_system_score_gemma":0.001014889,"threshold_uncertainty_score":0.029604435},"labels":[],"label_agreement":null},{"id":"W1752875563","doi":"10.1007/978-3-540-71703-4_3","title":"Improving Quality and Convergence of Genetic Query Optimizers","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"IBM (Canada)","funders":"","keywords":"Computer science; Heuristics; Convergence (economics); Query optimization; Genetic algorithm; Mathematical optimization; Quality (philosophy); Join (topology); Population; Genetic programming; Selection (genetic algorithm); Data mining; Machine learning; Mathematics","score_opus":0.033527309144610655,"score_gpt":0.27695763267335344,"score_spread":0.24343032352874278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1752875563","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25923944,0.0019446133,0.72739923,0.00088515674,0.0002293282,0.00015574957,0.0001272467,0.0021889703,0.00783027],"genre_scores_gemma":[0.72305673,0.0004296716,0.27232096,0.0002832951,0.00015583787,0.00011931231,0.00029743087,0.0006930485,0.002643729],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9943868,0.0020407895,0.00026667715,0.0005473014,0.002203565,0.0005549],"domain_scores_gemma":[0.9776513,0.015384367,0.0010057314,0.001842267,0.003707162,0.00040910073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008733372,0.0012520427,0.001687782,0.00199504,0.00084592303,0.0020093254,0.002671236,0.0026994555,0.002193641],"category_scores_gemma":[0.048730895,0.00081469695,0.00088782224,0.0017097952,0.0016325503,0.00257473,0.0017665261,0.0022757407,0.0006130855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010353691,0.00026769625,0.0052001164,0.00012066799,0.0001318059,0.00009415637,0.0002903018,0.820679,0.008201633,0.013301808,0.003532471,0.14714491],"study_design_scores_gemma":[0.00007468724,0.00009288181,0.0005107967,0.000013080753,0.000029113173,0.000040527404,0.00004277796,0.99194825,0.0028954006,0.0040320884,0.00031166704,0.000008838631],"about_ca_topic_score_codex":0.006186326,"about_ca_topic_score_gemma":0.0036819268,"teacher_disagreement_score":0.008733372,"about_ca_system_score_codex":0.0019759396,"about_ca_system_score_gemma":0.0018592364,"threshold_uncertainty_score":0.046187043},"labels":[],"label_agreement":null},{"id":"W1758661201","doi":"10.1007/978-3-319-24462-4_2","title":"Extended Spearman and Kendall Coefficients for Gene Annotation List Correlation","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Annotation; Correlation; Spearman's rank correlation coefficient; Gene ontology; Gene Annotation; Computer science; Key (lock); Correlation coefficient; Pearson product-moment correlation coefficient; Data mining; Machine learning; Gene; Statistics; Artificial intelligence; Mathematics; Biology; Genetics; Genome","score_opus":0.02809335195549204,"score_gpt":0.26892840053091677,"score_spread":0.24083504857542473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1758661201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009246578,0.0035860299,0.97524625,0.00043254826,0.0008780932,0.00014919337,0.0035784368,0.002354472,0.0045284717],"genre_scores_gemma":[0.1940559,0.0036071676,0.7743512,0.00048867177,0.0017863469,0.0013526931,0.010544314,0.0023181378,0.011495693],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9815077,0.0082503045,0.0014512874,0.002961143,0.0051029907,0.0007264611],"domain_scores_gemma":[0.93091816,0.04488502,0.0031452253,0.010948933,0.009334072,0.0007686027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01487456,0.002313511,0.0024079608,0.0098685175,0.00146076,0.0042926976,0.004205088,0.0015345352,0.0061797565],"category_scores_gemma":[0.07686129,0.0011102146,0.0026110031,0.011684202,0.00223151,0.005630396,0.0025456487,0.00405438,0.0056315577],"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":[0.00047096933,0.0002123392,0.030448701,0.0015317413,0.0012404963,0.0006550864,0.0009073385,0.040494464,0.0054457397,0.3132354,0.07153772,0.53382003],"study_design_scores_gemma":[0.000059178048,0.0002338023,0.017080257,0.00040646637,0.00040668232,0.0012574993,0.00035099842,0.29948118,0.0065356498,0.59487236,0.07898749,0.00032843443],"about_ca_topic_score_codex":0.003671246,"about_ca_topic_score_gemma":0.0051373807,"teacher_disagreement_score":0.01487456,"about_ca_system_score_codex":0.0016101712,"about_ca_system_score_gemma":0.0040341173,"threshold_uncertainty_score":0.07866514},"labels":[],"label_agreement":null},{"id":"W1760299170","doi":"10.1007/978-3-540-24840-8_7","title":"Finding Interesting Summaries in GenSpace Graphs Efficiently","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Traverse; Automatic summarization; Computer science; Pruning; Graph; Data mining; Theoretical computer science; Set (abstract data type); Algorithm; Artificial intelligence","score_opus":0.023736585282035712,"score_gpt":0.25130724938362103,"score_spread":0.22757066410158533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1760299170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29588455,0.0043211225,0.59979755,0.0047723027,0.00046555547,0.0012509779,0.032423876,0.029700752,0.031383365],"genre_scores_gemma":[0.37578577,0.0016152309,0.55950725,0.0004250154,0.00024274706,0.00022556064,0.03654193,0.0023744246,0.023282122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999108,0.00016398037,0.000075319,0.00021603997,0.00032518656,0.00011156615],"domain_scores_gemma":[0.9958319,0.0024310527,0.00031889416,0.00081141765,0.00044075347,0.00016602236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072034285,0.0010119139,0.0017689276,0.0037650238,0.0012133307,0.002718749,0.0011737071,0.0010894829,0.011551135],"category_scores_gemma":[0.0057613226,0.0007347433,0.0013148448,0.0054345,0.00069161865,0.006680468,0.0018572914,0.0013365096,0.002819202],"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":[0.0011221938,0.00040546863,0.005831817,0.0020693368,0.0002792374,0.00085975503,0.0016852792,0.032918893,0.021996431,0.061782558,0.119478665,0.7515704],"study_design_scores_gemma":[0.00038320536,0.0004950845,0.0030880256,0.0002693691,0.0005812109,0.001545817,0.0030060268,0.27094376,0.0342014,0.57105666,0.1143215,0.00010788676],"about_ca_topic_score_codex":0.0024842918,"about_ca_topic_score_gemma":0.008139659,"teacher_disagreement_score":0.011551135,"about_ca_system_score_codex":0.00084008207,"about_ca_system_score_gemma":0.0012720308,"threshold_uncertainty_score":0.038642406},"labels":[],"label_agreement":null},{"id":"W1760549314","doi":"10.1007/3-540-46146-9_70","title":"Query Optimization via Empty Joins","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"York University","funders":"","keywords":"Joins; Computer science; Query optimization; Tuple; Cartesian product; Join (topology); Relational database; Materialized view; Sargable; Data mining; Information retrieval; Theoretical computer science; Product (mathematics); Database; Web search query; View; Programming language; Search engine; Database design; Mathematics; Discrete mathematics","score_opus":0.017438079444344196,"score_gpt":0.22306605696522241,"score_spread":0.20562797752087822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1760549314","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01813362,0.0019824852,0.9570702,0.00069861155,0.00018705524,0.00007213548,0.0003090562,0.0038956243,0.017651279],"genre_scores_gemma":[0.37246573,0.0014250927,0.5917705,0.0005608626,0.0005601704,0.00015188177,0.0017885716,0.0026987342,0.028578322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951558,0.0012394884,0.00034857454,0.0006912698,0.0020326988,0.0005322094],"domain_scores_gemma":[0.9956683,0.0018226709,0.00019097903,0.0017514277,0.0004347933,0.00013188194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027247728,0.0010225129,0.002487728,0.0013657322,0.0012673542,0.0035908802,0.0024837826,0.0009829673,0.007988773],"category_scores_gemma":[0.008215519,0.0009183909,0.0012146832,0.002917131,0.0021276472,0.00810405,0.004518752,0.0029699649,0.0022580773],"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":[0.001646455,0.00023507907,0.00082158635,0.0006494419,0.00017188856,0.00019491657,0.00045832118,0.031350207,0.016182842,0.47224692,0.03485975,0.44118264],"study_design_scores_gemma":[0.00009027483,0.0001513319,0.00031595823,0.00004834497,0.000107484506,0.00036150278,0.000109674715,0.2276982,0.023674382,0.7239855,0.023404451,0.000052859403],"about_ca_topic_score_codex":0.00073129113,"about_ca_topic_score_gemma":0.0008183109,"teacher_disagreement_score":0.007988773,"about_ca_system_score_codex":0.0008646981,"about_ca_system_score_gemma":0.0007791533,"threshold_uncertainty_score":0.026725113},"labels":[],"label_agreement":null},{"id":"W1763623545","doi":"10.1007/11424918_21","title":"Towards an Ontology-Based Spatial Clustering Framework","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","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 Regina","funders":"","keywords":"Cluster analysis; Computer science; Ontology; Data mining; Spatial analysis; Information retrieval; Artificial intelligence; Geography; Remote sensing","score_opus":0.02057685886093921,"score_gpt":0.2650080722306205,"score_spread":0.2444312133696813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1763623545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011260877,0.00013220287,0.99528044,0.00025543477,0.000040364186,0.0000725723,0.00027513504,0.00095710193,0.0018606025],"genre_scores_gemma":[0.016231515,0.0003564076,0.9795741,0.00013037966,0.00002971687,0.00017616009,0.0015303894,0.0002279442,0.0017433955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99775654,0.00045570594,0.00031820897,0.0004322263,0.0008822829,0.00015501642],"domain_scores_gemma":[0.9985233,0.00026290477,0.000115631155,0.0003711209,0.0005524599,0.00017470928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028928898,0.00081071985,0.0013010752,0.0033992815,0.0022664797,0.0059579248,0.00463617,0.0017111241,0.0027664576],"category_scores_gemma":[0.0033740748,0.0009459755,0.0032298984,0.00560692,0.0019800984,0.006587278,0.005666538,0.0028077876,0.0017305146],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006141329,0.00017301917,0.0010489376,0.00042448947,0.00018466162,0.0002949002,0.0013902938,0.035152555,0.004321047,0.7845882,0.013707237,0.15865323],"study_design_scores_gemma":[0.000031919793,0.000036915135,0.00067037245,0.00023045154,0.00017804805,0.00040824304,0.0010816049,0.2564082,0.0040176436,0.55990547,0.17695074,0.00008043913],"about_ca_topic_score_codex":0.02108806,"about_ca_topic_score_gemma":0.030056143,"teacher_disagreement_score":0.02108806,"about_ca_system_score_codex":0.002201023,"about_ca_system_score_gemma":0.004982277,"threshold_uncertainty_score":0.041930616},"labels":[],"label_agreement":null},{"id":"W1775567208","doi":"","title":"Structure choices for two-dimensional histogram construction","year":2004,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Histogram; Computer science; Data mining; Context (archaeology); Dimension (graph theory); Pattern recognition (psychology); Artificial intelligence; Mathematics; Image (mathematics)","score_opus":0.07778592883821964,"score_gpt":0.39176021727559773,"score_spread":0.31397428843737807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1775567208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070871282,0.00010141129,0.991211,0.000082410246,0.000024707895,0.000061363586,0.00008790103,0.0006552431,0.0006888593],"genre_scores_gemma":[0.14933456,0.00020069099,0.8479991,0.00011573363,0.000047824153,0.00024161504,0.0006497318,0.00032201238,0.0010887308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971961,0.0011539175,0.00020667797,0.00037196823,0.0008609464,0.00021038343],"domain_scores_gemma":[0.9906369,0.0039947666,0.0004145567,0.002738314,0.0018645991,0.00035100142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037042054,0.0005473003,0.0006834669,0.0018899479,0.00086221984,0.0022605269,0.0016611646,0.0012401975,0.0045116814],"category_scores_gemma":[0.018199254,0.0007888336,0.00074029854,0.002912332,0.0011707474,0.0034417443,0.002759741,0.0015644484,0.0018795048],"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":[0.00064567983,0.00027600152,0.004761955,0.00045244413,0.00007883443,0.00020628056,0.001052765,0.06438699,0.03414647,0.14893633,0.012848343,0.7322079],"study_design_scores_gemma":[0.00025193454,0.00019827667,0.0021540914,0.00010196097,0.000044436863,0.0005902205,0.00044934123,0.717169,0.041813985,0.21017699,0.026898356,0.00015142323],"about_ca_topic_score_codex":0.0007676263,"about_ca_topic_score_gemma":0.00086453115,"teacher_disagreement_score":0.0045116814,"about_ca_system_score_codex":0.00073334674,"about_ca_system_score_gemma":0.0008252351,"threshold_uncertainty_score":0.01958996},"labels":[],"label_agreement":null},{"id":"W1779695964","doi":"","title":"A Java based Linkage Disequilibrium plotter","year":2006,"lang":"en","type":"article","venue":"UWA Profiles and Research Repository (University of Western Australia)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Centre for Global Health Research","funders":"","keywords":"Plotter; Java; Computer science; Linkage (software); Java applet; Computer graphics (images); Programming language; Database; Biology","score_opus":0.053537481099952565,"score_gpt":0.28483619546359723,"score_spread":0.23129871436364466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1779695964","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053792754,0.0001822086,0.42702395,0.00017814884,0.00018332081,0.00037987434,0.03929387,0.51990736,0.007472167],"genre_scores_gemma":[0.07741568,0.0004768336,0.7063583,0.0007236309,0.00016412139,0.0027902436,0.07308321,0.10841845,0.030569462],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990165,0.00014741303,0.00014331526,0.00031005763,0.0003071452,0.000075563476],"domain_scores_gemma":[0.99556315,0.0026039016,0.00034818653,0.0007218048,0.00053061836,0.00023235714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00236651,0.0015720213,0.0015674812,0.0038072984,0.0007368143,0.002771954,0.0022921013,0.00088145497,0.09063683],"category_scores_gemma":[0.008390719,0.0015241425,0.0010961462,0.0037990347,0.00026614172,0.0019345538,0.0017056293,0.0012670573,0.025731197],"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":[0.0032000416,0.00060068775,0.012003952,0.0016307515,0.0006407555,0.0009821358,0.00067206245,0.010422859,0.026900733,0.00942369,0.39180148,0.5417209],"study_design_scores_gemma":[0.0029745058,0.00040851336,0.018660864,0.00045535644,0.00065011356,0.0022153496,0.0003250436,0.23793332,0.084029905,0.05124261,0.60057265,0.00053178787],"about_ca_topic_score_codex":0.0017865043,"about_ca_topic_score_gemma":0.0024123439,"teacher_disagreement_score":0.09063683,"about_ca_system_score_codex":0.00044758501,"about_ca_system_score_gemma":0.0010825596,"threshold_uncertainty_score":0.30321038},"labels":[],"label_agreement":null},{"id":"W178322037","doi":"","title":"Geosemantic Proximity to Improve Geospatial Information Discovery in a Wireless Environment","year":2003,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.005039637914213342,"score_gpt":0.1893679564771393,"score_spread":0.18432831856292597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W178322037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025588032,0.00068574917,0.9682192,0.00036024099,0.000039361446,0.00008271947,0.00018168724,0.0018374743,0.0030054688],"genre_scores_gemma":[0.26476172,0.0012297352,0.7267873,0.00015245486,0.000058136375,0.00020081576,0.00086523953,0.00024481193,0.0056996616],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99788886,0.00050565624,0.00016110568,0.00036664947,0.0009712228,0.000106555555],"domain_scores_gemma":[0.99657035,0.001806248,0.00021088058,0.0007938764,0.000532253,0.00008637107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022251816,0.0004942639,0.0007832588,0.002539376,0.0011819426,0.0031983333,0.0012900844,0.0012120445,0.0024717306],"category_scores_gemma":[0.010074895,0.00050996884,0.0006756947,0.0043471637,0.00072500366,0.005371743,0.0044855005,0.0009480674,0.00095976016],"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":[0.00043141918,0.00030209505,0.011140149,0.00082942477,0.00022564246,0.00067024375,0.005865071,0.06551139,0.039325703,0.11498092,0.0071701435,0.7535478],"study_design_scores_gemma":[0.00010683135,0.00031702328,0.007489985,0.0002793583,0.00035390895,0.0023340657,0.00431441,0.63250846,0.101440996,0.12819892,0.12246738,0.0001886388],"about_ca_topic_score_codex":0.004303797,"about_ca_topic_score_gemma":0.0067283474,"teacher_disagreement_score":0.004303797,"about_ca_system_score_codex":0.0006088623,"about_ca_system_score_gemma":0.0011528938,"threshold_uncertainty_score":0.011767983},"labels":[],"label_agreement":null},{"id":"W1800174300","doi":"10.3233/fun-2006-70405","title":"Indexing Schemes for Similarity Search: an Illustrated Paradigm","year":2006,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Search engine indexing; Computer science; Nearest neighbor search; Similarity (geometry); Information retrieval; Data mining; Artificial intelligence","score_opus":0.043228175001902375,"score_gpt":0.27945411912867,"score_spread":0.23622594412676765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1800174300","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055491608,0.00080443715,0.9884934,0.0013587474,0.00009601165,0.00013585898,0.00015876755,0.00014043816,0.0032632786],"genre_scores_gemma":[0.16559602,0.002180829,0.82688,0.0005377496,0.00048373695,0.00061625254,0.00046263836,0.00012315868,0.003119703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926547,0.0030218984,0.00091871613,0.0008780357,0.0022033022,0.00032341937],"domain_scores_gemma":[0.9875974,0.004911228,0.0006919561,0.005247691,0.0011255345,0.00042629484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008271027,0.0007039902,0.0013713496,0.0043666363,0.0018863649,0.006316543,0.003609002,0.0027625877,0.004232819],"category_scores_gemma":[0.024285525,0.00064031914,0.0016739157,0.0080155,0.0060935295,0.017938543,0.007531328,0.0033840116,0.0017959458],"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":[0.000028259794,0.000019364725,0.00015086708,0.00006261852,0.000009112839,0.00004163292,0.0001671342,0.003119667,0.00087108,0.9767411,0.0009711415,0.017818004],"study_design_scores_gemma":[0.00002484996,0.00007192872,0.00011479628,0.0000334211,0.000009489726,0.00022640232,0.00008187207,0.043803193,0.001106126,0.9419493,0.012551581,0.000026954267],"about_ca_topic_score_codex":0.00091020524,"about_ca_topic_score_gemma":0.00047680555,"teacher_disagreement_score":0.008271027,"about_ca_system_score_codex":0.0021256285,"about_ca_system_score_gemma":0.0013394776,"threshold_uncertainty_score":0.04374188},"labels":[],"label_agreement":null},{"id":"W1804029757","doi":"10.1007/978-3-642-20152-3_27","title":"Indexing for Vector Projections","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Search engine indexing; Computer science; Artificial intelligence; Information retrieval","score_opus":0.036854241327270804,"score_gpt":0.25773242166584637,"score_spread":0.22087818033857556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1804029757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007153097,0.0044713877,0.89465296,0.00064260326,0.0012705477,0.00021849028,0.0031638618,0.009347883,0.07907913],"genre_scores_gemma":[0.13461787,0.0065321457,0.724919,0.0006767289,0.0018063838,0.000831151,0.020384496,0.004164924,0.106067255],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981488,0.00029836778,0.00020891675,0.0003454398,0.0008216629,0.000176886],"domain_scores_gemma":[0.9986071,0.0002432652,0.00007438375,0.0006673395,0.0003359042,0.00007197224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010021323,0.0010069276,0.0016191485,0.0027699657,0.0014415797,0.005051215,0.0016006945,0.0008410476,0.038287643],"category_scores_gemma":[0.0038744558,0.0007258737,0.0012991435,0.0061381124,0.0012058963,0.0069945655,0.00341929,0.0022775203,0.02420191],"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":[0.000106061016,0.00008228536,0.00022647786,0.0002680194,0.0000276046,0.000064398984,0.00016948784,0.0018758735,0.0033876484,0.4237815,0.06802516,0.50198543],"study_design_scores_gemma":[0.00002989115,0.00007837596,0.00041344124,0.00013520653,0.000034832898,0.000552185,0.00012472545,0.029894123,0.006467674,0.8017939,0.16041899,0.000056724894],"about_ca_topic_score_codex":0.0009907099,"about_ca_topic_score_gemma":0.0008469158,"teacher_disagreement_score":0.038287643,"about_ca_system_score_codex":0.00090134755,"about_ca_system_score_gemma":0.0011564961,"threshold_uncertainty_score":0.1280849},"labels":[],"label_agreement":null},{"id":"W1812036194","doi":"10.3233/fun-2008-892-304","title":"Preferential Regular Path Queries","year":2008,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Concordia University","funders":"","keywords":"Path (computing); Computer science; Theoretical computer science; Mathematics; Programming language","score_opus":0.021314384049354696,"score_gpt":0.20625042352430084,"score_spread":0.18493603947494613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1812036194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022053597,0.00042158508,0.96704626,0.0008878092,0.00010433768,0.00027337685,0.0009895598,0.0010931415,0.0071303435],"genre_scores_gemma":[0.35915107,0.0009139389,0.62532866,0.0013053926,0.00030333243,0.0006057512,0.002921184,0.00058535254,0.008885223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98495966,0.004911719,0.0016960221,0.0028352768,0.004645127,0.00095217914],"domain_scores_gemma":[0.97701216,0.011677545,0.0013565948,0.0055380594,0.0037882237,0.00062734867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008802709,0.0008634038,0.0012613565,0.0019666792,0.0014606968,0.0038624443,0.0027282462,0.0015059357,0.005668126],"category_scores_gemma":[0.030331122,0.00063455745,0.0013587136,0.0042115306,0.002783849,0.017815148,0.004108735,0.0024285677,0.001250715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030557546,0.00009092991,0.0015002843,0.0003378485,0.00005862501,0.0004588162,0.0012979911,0.0117783435,0.004391636,0.88070476,0.0066417735,0.092433475],"study_design_scores_gemma":[0.00004328638,0.00013954705,0.0002799067,0.000057899175,0.0000723218,0.0007514618,0.0005455532,0.07194947,0.0060114334,0.87639695,0.043673698,0.00007839512],"about_ca_topic_score_codex":0.0024827337,"about_ca_topic_score_gemma":0.0021442233,"teacher_disagreement_score":0.008802709,"about_ca_system_score_codex":0.0014075086,"about_ca_system_score_gemma":0.0018386338,"threshold_uncertainty_score":0.04655373},"labels":[],"label_agreement":null},{"id":"W181282468","doi":"10.1007/978-3-642-31900-6_25","title":"A Spatial Clustering Method for Points-with-Directions","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Cluster analysis; Computer science; Trajectory; Point (geometry); Set (abstract data type); Data set; Data mining; Data point; Correlation clustering; Artificial intelligence; Algorithm; Mathematics","score_opus":0.024455545978639578,"score_gpt":0.27550299358854374,"score_spread":0.25104744760990416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181282468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073190726,0.0000862328,0.99777526,0.00002178137,0.00004904254,0.000034975266,0.00006939534,0.0006963119,0.0005351231],"genre_scores_gemma":[0.010798411,0.00012171688,0.98557335,0.000026244868,0.00004281361,0.000097518765,0.00037093263,0.00028181952,0.0026871872],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99853504,0.00018962275,0.00008123754,0.00031940866,0.0007871583,0.00008757549],"domain_scores_gemma":[0.9986873,0.00025430697,0.000059431248,0.00033206306,0.00060833915,0.00005858104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011087856,0.0012585459,0.0015118222,0.0025418245,0.0014965108,0.0016217097,0.004052336,0.0014237759,0.007164376],"category_scores_gemma":[0.002836724,0.0009478913,0.002307874,0.0036417176,0.00092916534,0.001456477,0.0024648272,0.0017948219,0.0047328006],"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":[0.00021645504,0.00012510945,0.0008792134,0.00037916077,0.00019535288,0.000109825145,0.00023123056,0.08007433,0.024037126,0.04932839,0.015587666,0.8288362],"study_design_scores_gemma":[0.000048991376,0.000074688185,0.00071764993,0.00003587124,0.00008486799,0.00038367818,0.00008815934,0.9186481,0.014282821,0.033995237,0.03155309,0.0000867525],"about_ca_topic_score_codex":0.007799975,"about_ca_topic_score_gemma":0.011389455,"teacher_disagreement_score":0.007799975,"about_ca_system_score_codex":0.0007138001,"about_ca_system_score_gemma":0.0016418564,"threshold_uncertainty_score":0.023967266},"labels":[],"label_agreement":null},{"id":"W1813751874","doi":"10.1109/ideas.1999.787246","title":"Query result size estimation using a novel histogram-like technique: the rectangular attribute cardinality map","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Histogram; Cardinality (data modeling); Probability density function; Mathematics; Density estimation; Algorithm; Set (abstract data type); Probability distribution; Mathematical optimization; Computer science; Image (mathematics); Statistics; Data mining; Artificial intelligence","score_opus":0.033764081521882545,"score_gpt":0.2678671898074362,"score_spread":0.23410310828555364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1813751874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018635139,0.00033678612,0.9787279,0.00007825305,0.000025386638,0.000052191437,0.00019606267,0.0012004421,0.0007478425],"genre_scores_gemma":[0.5022496,0.00058526127,0.49495122,0.00008998978,0.00011326217,0.00016674807,0.0007304841,0.0001848047,0.00092857395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981336,0.00042592527,0.000100118596,0.00028040636,0.00094035466,0.00011970863],"domain_scores_gemma":[0.99390405,0.002957003,0.00060509593,0.0011817559,0.0011791402,0.0001728887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013267164,0.0006219853,0.0008513234,0.0022730595,0.0004323301,0.0013781893,0.0017077642,0.00059255096,0.0015907836],"category_scores_gemma":[0.012767167,0.00036429908,0.00048186188,0.0031771753,0.00062398094,0.0030572717,0.0014952117,0.00090702536,0.0005765727],"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":[0.0008840843,0.00021192948,0.023610143,0.00037824892,0.00019325847,0.00027046466,0.00052692305,0.3068675,0.036375813,0.041539293,0.007867057,0.5812753],"study_design_scores_gemma":[0.0000139818,0.00006979196,0.0021863792,0.000012607652,0.000021255366,0.00024011054,0.00008721459,0.97720075,0.01120575,0.005933483,0.0029950284,0.000033646673],"about_ca_topic_score_codex":0.003458822,"about_ca_topic_score_gemma":0.0021140962,"teacher_disagreement_score":0.003458822,"about_ca_system_score_codex":0.000851736,"about_ca_system_score_gemma":0.00082106976,"threshold_uncertainty_score":0.0070164204},"labels":[],"label_agreement":null},{"id":"W1814173068","doi":"10.1007/978-94-015-9518-6_15","title":"Prototype Validation of the Trapezoidal Attribute Cardinality Map for Query Optimization in Database Systems","year":2000,"lang":"en","type":"book-chapter","venue":"Enterprise Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Query optimization; Cardinality (data modeling); Computer science; Query plan; Database; Histogram; Online aggregation; Data mining; View; Set (abstract data type); Algorithm; Sargable; Database design; Information retrieval; Search engine; Web search query; Artificial intelligence; Programming language","score_opus":0.02005550705722954,"score_gpt":0.231608512032307,"score_spread":0.21155300497507745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1814173068","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26278034,0.00065933797,0.68656623,0.00076224405,0.000293876,0.00069929904,0.0009957632,0.039193057,0.008049823],"genre_scores_gemma":[0.61344194,0.0001340762,0.38218516,0.0001806615,0.000017963679,0.00021001707,0.0009401686,0.0010550296,0.0018350136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99589866,0.0012734,0.0003225825,0.0004166001,0.0018332144,0.00025555782],"domain_scores_gemma":[0.9933072,0.0030652096,0.00016086486,0.0018019627,0.0014648428,0.00019992844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034321083,0.0006118349,0.0010943037,0.0006010944,0.0006806954,0.002178078,0.003965934,0.00104832,0.005542285],"category_scores_gemma":[0.013501452,0.0005228833,0.0005289216,0.0009115188,0.00084960624,0.0021495915,0.0018661825,0.0011055424,0.00089888053],"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":[0.0058411555,0.0017852633,0.006802412,0.0016782709,0.0003646196,0.00080473453,0.0020988951,0.23081894,0.13383862,0.04353867,0.037464272,0.5349642],"study_design_scores_gemma":[0.00026834614,0.0002992239,0.00074006803,0.000035846904,0.000036358462,0.00011421974,0.00024427907,0.9299738,0.05822259,0.0041680513,0.005863064,0.00003425984],"about_ca_topic_score_codex":0.0077840895,"about_ca_topic_score_gemma":0.0038397834,"teacher_disagreement_score":0.0077840895,"about_ca_system_score_codex":0.0009451281,"about_ca_system_score_gemma":0.0017656527,"threshold_uncertainty_score":0.01854074},"labels":[],"label_agreement":null},{"id":"W181609631","doi":"10.1007/978-3-319-05810-8_28","title":"Monitoring Probabilistic Threshold SUM Query Processing in Uncertain Streams","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Probabilistic logic; Scalability; Data stream mining; Probability distribution; Data mining; Stream processing; Computation; Theoretical computer science; Algorithm; Distributed computing; Artificial intelligence; Database","score_opus":0.025054234627576583,"score_gpt":0.25891524561035056,"score_spread":0.23386101098277398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181609631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16182123,0.0012891513,0.8318472,0.0005587929,0.00014612367,0.00009530772,0.00033131897,0.0020319277,0.0018790074],"genre_scores_gemma":[0.92967784,0.00036509076,0.067614354,0.00011090986,0.00020827953,0.00007035306,0.00040740223,0.00016384272,0.0013820098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952335,0.00080221577,0.00031573657,0.00086797844,0.00239362,0.00038698543],"domain_scores_gemma":[0.98908573,0.0072064055,0.00089867914,0.0010522462,0.0013648098,0.00039218322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035438638,0.00096513086,0.0019288723,0.0010424714,0.00087887567,0.0030043814,0.0028525873,0.0014280357,0.0014123047],"category_scores_gemma":[0.018399153,0.000715644,0.0004276787,0.0023366248,0.0009915498,0.0045994706,0.0022954894,0.0018523292,0.00043536632],"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":[0.004274934,0.00047540004,0.0133460695,0.00042959608,0.0002033352,0.0005414784,0.00043847895,0.6525105,0.046938956,0.029050939,0.006829335,0.24496093],"study_design_scores_gemma":[0.000010057883,0.000041390856,0.0003234136,0.0000035259313,0.000009845433,0.00007187445,0.000023439086,0.9881467,0.0038039393,0.007364476,0.00019543277,0.000005848611],"about_ca_topic_score_codex":0.0017190418,"about_ca_topic_score_gemma":0.0012644526,"teacher_disagreement_score":0.0035438638,"about_ca_system_score_codex":0.0013020178,"about_ca_system_score_gemma":0.0010160906,"threshold_uncertainty_score":0.018741965},"labels":[],"label_agreement":null},{"id":"W181730724","doi":"10.1007/978-3-7908-1752-2_7","title":"Using Fuzzy Spatial Relations to Control Movement Behavior of Mobile Objects in Spatially Explicit Ecological Models","year":2002,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Movement (music); Fuzzy logic; Control (management); Computer science; Geography; Spatial relation; Ecology; Artificial intelligence; Biology; Art; Aesthetics","score_opus":0.08341092247206316,"score_gpt":0.30594639524891337,"score_spread":0.2225354727768502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181730724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06013927,0.0007152317,0.92378193,0.0005340031,0.000044835142,0.000027009295,0.0000812907,0.00018461658,0.01449178],"genre_scores_gemma":[0.7921591,0.0013535747,0.196328,0.0000780857,0.00004320601,0.00010510616,0.000106798405,0.00005537227,0.009770809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998153,0.000056113324,0.000011900662,0.000034398945,0.00006645535,0.000015804413],"domain_scores_gemma":[0.9995435,0.00030213286,0.00005282153,0.000038550454,0.000040317274,0.000022732647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005326487,0.000380652,0.00034851523,0.000311687,0.00039411886,0.0012016303,0.000928937,0.00058991567,0.0011123928],"category_scores_gemma":[0.0015149544,0.00024743757,0.0004843931,0.00044704814,0.0012563805,0.0015650137,0.00056461577,0.0006952672,0.00015554273],"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":[0.00002711314,0.000016516968,0.00042094474,0.000037099086,0.00001725526,0.000060042057,0.00035969279,0.74423724,0.0020224638,0.23643342,0.0005396449,0.015828582],"study_design_scores_gemma":[0.0000057651073,0.000011930154,0.00014388448,0.000009282383,0.000009200893,0.000014600717,0.000026870457,0.9350706,0.00040890384,0.0625589,0.0017309687,0.000009091593],"about_ca_topic_score_codex":0.010739433,"about_ca_topic_score_gemma":0.010168223,"teacher_disagreement_score":0.010739433,"about_ca_system_score_codex":0.0010486242,"about_ca_system_score_gemma":0.00068339356,"threshold_uncertainty_score":0.02135384},"labels":[],"label_agreement":null},{"id":"W1824170323","doi":"10.1109/ftcsh.1995.532623","title":"A Compendium of Robust Data Structures","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Compendium; Computer science; Geography","score_opus":0.06700451719216276,"score_gpt":0.274010257755401,"score_spread":0.20700574056323823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1824170323","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006222993,0.021801291,0.95288706,0.0021800464,0.002825534,0.00023759449,0.0018308326,0.0086797215,0.008935569],"genre_scores_gemma":[0.0054501086,0.029371781,0.93021715,0.0012932019,0.0025497936,0.00042021475,0.006894654,0.004547123,0.019255996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939032,0.0007243729,0.0012420577,0.0005813599,0.0033738958,0.00017504132],"domain_scores_gemma":[0.980681,0.004592879,0.00074315007,0.008320883,0.005177002,0.00048511467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006445222,0.0020282315,0.0022808458,0.0057454556,0.0010028358,0.0052401167,0.005267637,0.0022475754,0.020243779],"category_scores_gemma":[0.021956844,0.0022401668,0.0019095142,0.0071774935,0.0018861252,0.009240217,0.0037319472,0.005109561,0.019930823],"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":[0.00006145577,0.000058832018,0.00015624378,0.0013688217,0.000049889528,0.00007805587,0.00009273414,0.0056818086,0.0037432783,0.062277567,0.17838071,0.7480506],"study_design_scores_gemma":[0.000020314566,0.00007126974,0.00017272719,0.00048708948,0.000046497935,0.0004435834,0.000031927677,0.014114512,0.0044826,0.06696291,0.91309667,0.000069971684],"about_ca_topic_score_codex":0.0012275547,"about_ca_topic_score_gemma":0.0014474094,"teacher_disagreement_score":0.020243779,"about_ca_system_score_codex":0.001096363,"about_ca_system_score_gemma":0.0034187867,"threshold_uncertainty_score":0.06772226},"labels":[],"label_agreement":null},{"id":"W1826195249","doi":"10.1007/978-1-4899-7993-3_947-2","title":"Query Expansion for Information Retrieval","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Information retrieval; Query expansion; Computer science","score_opus":0.0207098328871844,"score_gpt":0.2462056284886811,"score_spread":0.2254957956014967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1826195249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054066917,0.060329247,0.813923,0.003554172,0.0019167234,0.00039495886,0.001952709,0.0075528966,0.10496967],"genre_scores_gemma":[0.11705056,0.040568713,0.6716165,0.0028700456,0.0031083687,0.0007144418,0.0089887725,0.0020982327,0.15298435],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998123,0.00045951843,0.00013292849,0.000335766,0.00081449846,0.00013432503],"domain_scores_gemma":[0.9991572,0.0003222769,0.000032121327,0.00026788856,0.00019697275,0.000023532199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011998883,0.0011406178,0.0014596366,0.0020823267,0.00069044216,0.00261831,0.0020099126,0.0009915223,0.022406507],"category_scores_gemma":[0.0034817297,0.00046865025,0.00074963825,0.0036931287,0.001179734,0.0053867265,0.0019855516,0.0020503064,0.016900139],"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":[0.00010986602,0.000086155334,0.0001304226,0.0010032903,0.000044308974,0.00011423071,0.0001701565,0.0042852354,0.006936932,0.18098608,0.17764992,0.6284833],"study_design_scores_gemma":[0.000048779,0.00010017605,0.00057204615,0.00034440262,0.00009637411,0.001104845,0.0001682361,0.10688339,0.009888849,0.40961415,0.471104,0.000074757554],"about_ca_topic_score_codex":0.0015773404,"about_ca_topic_score_gemma":0.0013962534,"teacher_disagreement_score":0.022406507,"about_ca_system_score_codex":0.0011267709,"about_ca_system_score_gemma":0.00073331,"threshold_uncertainty_score":0.07495725},"labels":[],"label_agreement":null},{"id":"W1841647363","doi":"","title":"k-Nearest Neighbors Queries in Time-Dependent Road Networks","year":2012,"lang":"en","type":"article","venue":"Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Correctness; Pruning; k-nearest neighbors algorithm; Speedup; Effi; Data mining; Constant (computer programming); Algorithm; Artificial intelligence; Database","score_opus":0.009278389424483256,"score_gpt":0.22709201674419396,"score_spread":0.2178136273197107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1841647363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36555654,0.0025416426,0.6223951,0.0014064417,0.00012969227,0.00024414292,0.0018255344,0.0010663338,0.0048345537],"genre_scores_gemma":[0.835033,0.0007191397,0.15986499,0.0001315293,0.000083572275,0.00009133126,0.0018396012,0.00009262198,0.002144195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972791,0.00077943556,0.0002968018,0.00077424373,0.0006282778,0.00024205341],"domain_scores_gemma":[0.9920644,0.0056847157,0.0006861027,0.0007416332,0.0006212459,0.00020198387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016010177,0.00057169725,0.0015798128,0.0015338806,0.0014267002,0.0019735957,0.0018257275,0.001860728,0.0013364429],"category_scores_gemma":[0.011144718,0.00049712963,0.00081106374,0.0034251204,0.00074660184,0.0053473786,0.0012714621,0.0008213671,0.000434482],"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":[0.0010392624,0.00028119565,0.008539466,0.00047099253,0.00021872802,0.0010060846,0.00091857085,0.82351834,0.0058926134,0.029096385,0.0068290457,0.12218934],"study_design_scores_gemma":[0.000033191227,0.000053226257,0.0009036395,0.000015885627,0.00003136183,0.00028316805,0.00050138455,0.9652869,0.002430551,0.027927097,0.0025125332,0.00002099216],"about_ca_topic_score_codex":0.009872768,"about_ca_topic_score_gemma":0.009498845,"teacher_disagreement_score":0.009872768,"about_ca_system_score_codex":0.0011901831,"about_ca_system_score_gemma":0.00084039674,"threshold_uncertainty_score":0.019630611},"labels":[],"label_agreement":null},{"id":"W1842291153","doi":"","title":"Comparison of uncertainty representations for missing data in information retrieval","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Missing data; Computer science; Randomness; Data mining; Information retrieval; Representation (politics); Data retrieval; Domain (mathematical analysis); Machine learning; Mathematics; Statistics","score_opus":0.08646012484158463,"score_gpt":0.3722946330535117,"score_spread":0.285834508211927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1842291153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20729819,0.004244583,0.7822829,0.0013785085,0.00010525279,0.00028870153,0.0002824164,0.00030722827,0.0038122616],"genre_scores_gemma":[0.9200207,0.0013338183,0.07759454,0.0001266332,0.00008777515,0.00015416616,0.0003197341,0.00005457906,0.00030814725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9767875,0.016377967,0.0010593702,0.0008791665,0.0042375578,0.00065842376],"domain_scores_gemma":[0.8011438,0.18244466,0.0041892044,0.0073965825,0.004093313,0.00073241245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037859052,0.0010194615,0.0020369727,0.0039718905,0.0008464746,0.0039365026,0.0018586895,0.0026449226,0.0010835314],"category_scores_gemma":[0.13522264,0.00051936205,0.0017704143,0.0030180328,0.0025887291,0.008410615,0.0029505529,0.0018461505,0.00016008616],"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":[0.0009387303,0.00015031864,0.0024609726,0.0004702256,0.0002549988,0.00012450186,0.00028950316,0.87854373,0.001065012,0.05509653,0.00046725245,0.060138155],"study_design_scores_gemma":[0.000060362316,0.0004488487,0.0014158324,0.00011426485,0.000084619925,0.00013301504,0.00021253756,0.9514938,0.001615807,0.043931395,0.00040863192,0.00008093655],"about_ca_topic_score_codex":0.0018345027,"about_ca_topic_score_gemma":0.0009652854,"teacher_disagreement_score":0.037859052,"about_ca_system_score_codex":0.0025135428,"about_ca_system_score_gemma":0.0013554585,"threshold_uncertainty_score":0.20022023},"labels":[],"label_agreement":null},{"id":"W1860119121","doi":"10.1007/978-3-540-85654-2_12","title":"On the Evaluation of Large and Sparse Graph Reachability Queries","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Reachability; Bounded function; Search engine indexing; Theoretical computer science; Graph; Graph database; Treewidth; Mathematics; Information retrieval; Pathwidth","score_opus":0.040333803899718405,"score_gpt":0.2734266014825541,"score_spread":0.2330927975828357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1860119121","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50100946,0.0049439715,0.3994349,0.007877753,0.0010023122,0.001001351,0.003750736,0.031621065,0.049358502],"genre_scores_gemma":[0.8267784,0.00072214083,0.15632598,0.0009441964,0.00039116206,0.00027748448,0.004722869,0.0022836104,0.0075540943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9756796,0.0073667187,0.0013383741,0.0022099828,0.01090184,0.0025034684],"domain_scores_gemma":[0.89835423,0.08596526,0.0016611004,0.008473454,0.0041715316,0.001374403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009203785,0.0022179447,0.0031392255,0.0027281397,0.0019978837,0.0051014274,0.005034427,0.0034543986,0.011829503],"category_scores_gemma":[0.06090453,0.0010477393,0.0015297579,0.0046583745,0.003980103,0.013945655,0.006454334,0.0026228589,0.0017861556],"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":[0.010213932,0.0012400904,0.013411187,0.0015295937,0.00045001376,0.0008169285,0.0014703562,0.22734588,0.03035927,0.112967744,0.08536121,0.5148338],"study_design_scores_gemma":[0.0003847749,0.00036269773,0.0016060384,0.0000704474,0.00012376941,0.00026101698,0.0006207477,0.8733992,0.009465449,0.10908468,0.0045698206,0.00005132793],"about_ca_topic_score_codex":0.013600759,"about_ca_topic_score_gemma":0.02143293,"teacher_disagreement_score":0.013600759,"about_ca_system_score_codex":0.0044679916,"about_ca_system_score_gemma":0.004599447,"threshold_uncertainty_score":0.04867488},"labels":[],"label_agreement":null},{"id":"W1864072479","doi":"10.22201/icat.16656423.2012.10.6.353","title":"Performance of a New Enhanced Topological Decision-Rule Map-Matching Algorithm for Transportation Applications","year":2012,"lang":"en","type":"article","venue":"Journal of Applied Research and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico","keywords":"Map matching; Matching (statistics); Global Positioning System; Computer science; Algorithm; Data mining; Blossom algorithm; Digital mapping; Sequence (biology); Geography; Mathematics; Cartography","score_opus":0.032872435224295865,"score_gpt":0.33303229440369975,"score_spread":0.3001598591794039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1864072479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16481031,0.00026073182,0.8297686,0.00013796982,0.00011608135,0.000109051245,0.00013978546,0.0029255534,0.0017319599],"genre_scores_gemma":[0.4680516,0.00007135394,0.5297693,0.000062361556,0.000029737203,0.00006912729,0.00043144586,0.000074043106,0.001440946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886644,0.00018750223,0.00010955574,0.00027922678,0.0004464321,0.00011084736],"domain_scores_gemma":[0.99795234,0.0006193256,0.00011439046,0.00037943703,0.00085511856,0.00007927934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001590193,0.0005354893,0.00085932505,0.0008552778,0.00045480856,0.0010364737,0.0017632078,0.0009128598,0.0026133934],"category_scores_gemma":[0.0055221743,0.00027825797,0.0003554915,0.0010101353,0.00029375262,0.0015674103,0.00087710255,0.00061415083,0.00068918447],"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":[0.0011318995,0.00036864443,0.005340987,0.00010277853,0.00011890508,0.00013008442,0.0000932616,0.21511035,0.028529188,0.0035933668,0.0027568066,0.7427237],"study_design_scores_gemma":[0.00004739716,0.000066660425,0.0007595445,0.0000023888458,0.000015570326,0.00005920991,0.000012408206,0.98655206,0.010926452,0.000650722,0.000897207,0.000010416681],"about_ca_topic_score_codex":0.0060596154,"about_ca_topic_score_gemma":0.0040416364,"teacher_disagreement_score":0.0060596154,"about_ca_system_score_codex":0.00068047945,"about_ca_system_score_gemma":0.0013184657,"threshold_uncertainty_score":0.012048721},"labels":[],"label_agreement":null},{"id":"W1869095669","doi":"10.5623/cig2015-303","title":"An intelligent spatial proximity SYSTEM USING NEUROFUZZY CLASSIFIERS AND CONTEXTUAL INFORMATION","year":2015,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Fuzzy logic; Artificial intelligence; Artificial neural network; Classifier (UML); Machine learning; Data mining; Expert system; Possibility theory; Spatial intelligence; Neuro-fuzzy; Fuzzy set; Fuzzy control system","score_opus":0.042990595635349384,"score_gpt":0.2569579641857549,"score_spread":0.21396736855040552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1869095669","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06820482,0.000514715,0.92249626,0.00019019662,0.0002580866,0.00012461841,0.000395263,0.0045595937,0.003256521],"genre_scores_gemma":[0.57091945,0.00026791685,0.4227086,0.0001637496,0.00012194349,0.000174329,0.0004708641,0.00007183237,0.005101328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996136,0.000028572565,0.00002725454,0.00015835551,0.00013284772,0.000039320865],"domain_scores_gemma":[0.99953043,0.00007711045,0.000038697217,0.000050358274,0.00026760602,0.000035818644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004623428,0.000503178,0.0008739304,0.0013228233,0.0010789067,0.0010184321,0.0012710982,0.0009580234,0.002089654],"category_scores_gemma":[0.0013046582,0.00032760802,0.0004538841,0.0012377582,0.00023245931,0.0014008371,0.0009805005,0.00047481013,0.0009939455],"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":[0.0005780066,0.00031687345,0.006559396,0.00023893056,0.0001564217,0.00027994218,0.00023508143,0.050956365,0.05015228,0.003940774,0.0076025813,0.8789833],"study_design_scores_gemma":[0.000052974825,0.00019909174,0.0046524424,0.00003598049,0.00018345144,0.0002602258,0.00014905394,0.95782703,0.02629371,0.0032283096,0.0070605255,0.000057214856],"about_ca_topic_score_codex":0.008143818,"about_ca_topic_score_gemma":0.014008941,"teacher_disagreement_score":0.008143818,"about_ca_system_score_codex":0.00059169676,"about_ca_system_score_gemma":0.0009260124,"threshold_uncertainty_score":0.016192794},"labels":[],"label_agreement":null},{"id":"W1871629701","doi":"10.37236/380","title":"Logconcave Random Graphs","year":2010,"lang":"en","type":"article","venue":"The Electronic Journal of Combinatorics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Combinatorics; Mathematics; Random graph; Random regular graph; Bounded function; Graph; Discrete mathematics; Unit cube; Chordal graph; 1-planar graph","score_opus":0.004298915434445819,"score_gpt":0.21050635183446573,"score_spread":0.2062074364000199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1871629701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1261376,0.0011422759,0.8276941,0.006580409,0.00022762029,0.00032388073,0.002501803,0.0012467004,0.0341456],"genre_scores_gemma":[0.9095447,0.0011645107,0.057728413,0.0012322366,0.00043821527,0.0008748252,0.0015238288,0.00020689501,0.02728639],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99721223,0.0013488553,0.00007061991,0.00058353966,0.00044755155,0.00033724084],"domain_scores_gemma":[0.99141437,0.004355405,0.0011722855,0.0017148816,0.00070397765,0.00063896243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026444043,0.0011797757,0.0015658172,0.0015835667,0.0010852917,0.0026064746,0.0040619043,0.0022611916,0.009375577],"category_scores_gemma":[0.014118384,0.0007533101,0.0010125672,0.0020712635,0.0028323776,0.00613934,0.0020715324,0.0027255374,0.0017166409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006793304,0.000040924326,0.00055200525,0.000038840422,0.000020403071,0.00015925877,0.0000892122,0.07003868,0.0003206095,0.92141426,0.003913488,0.003344389],"study_design_scores_gemma":[0.000056762176,0.000031615513,0.00032144747,0.000013788724,0.000015636895,0.000134106,0.00003878663,0.44967315,0.00019327182,0.5453204,0.0041806153,0.000020524576],"about_ca_topic_score_codex":0.003683799,"about_ca_topic_score_gemma":0.004092456,"teacher_disagreement_score":0.009375577,"about_ca_system_score_codex":0.0026433505,"about_ca_system_score_gemma":0.0007906039,"threshold_uncertainty_score":0.03136444},"labels":[],"label_agreement":null},{"id":"W1875872827","doi":"10.1007/978-3-540-24627-5_7","title":"Skyline Cardinality for Relational Processing","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":120,"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":"Skyline; Cardinality (data modeling); Computer science; Online analytical processing; Tuple; Extension (predicate logic); Data mining; Theoretical computer science; Database; Mathematics; Discrete mathematics; Data warehouse; Programming language","score_opus":0.030965914320897212,"score_gpt":0.2674929519471953,"score_spread":0.2365270376262981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1875872827","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063741636,0.0012062566,0.9597185,0.00058228686,0.00026537466,0.000116188516,0.0017869733,0.009447668,0.020502592],"genre_scores_gemma":[0.08926879,0.0020552634,0.86825436,0.0004078266,0.0005787067,0.0003643184,0.0060952906,0.0049382807,0.02803722],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99832875,0.00029673727,0.00018944748,0.0002979958,0.00074934366,0.00013778418],"domain_scores_gemma":[0.9970458,0.0008691669,0.00013051809,0.0014947765,0.00035990356,0.00009985967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013661893,0.00085925293,0.0012994095,0.0013230828,0.0011325468,0.003494833,0.0021116373,0.00073261786,0.022914281],"category_scores_gemma":[0.004677312,0.00094971736,0.0010437429,0.004299388,0.0012459969,0.011263498,0.002680843,0.0024665745,0.006366547],"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":[0.00026703722,0.00007745981,0.0005055419,0.0005044775,0.000042551226,0.00008751986,0.000401526,0.008779222,0.005894229,0.54583746,0.075929545,0.36167347],"study_design_scores_gemma":[0.0000559047,0.00007343132,0.00043386276,0.00015994685,0.000053783227,0.00036384072,0.00015568601,0.061352506,0.013761453,0.68341696,0.24011092,0.000061657476],"about_ca_topic_score_codex":0.0011682281,"about_ca_topic_score_gemma":0.001736941,"teacher_disagreement_score":0.022914281,"about_ca_system_score_codex":0.0010563667,"about_ca_system_score_gemma":0.0009636943,"threshold_uncertainty_score":0.076655924},"labels":[],"label_agreement":null},{"id":"W1887036206","doi":"10.1007/978-3-642-31155-0_7","title":"On Minimum Sum of Radii and Diameters Clustering","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Combinatorics; Euclidean geometry; Approximation algorithm; Metric (unit); Cluster analysis; Shortest path problem; Polynomial-time approximation scheme; Euclidean distance; Mathematics; Plane (geometry); Graph; Singleton; Path (computing); Polynomial; Discrete mathematics; Computer science; Mathematical analysis; Geometry","score_opus":0.018562734337131095,"score_gpt":0.23256725870375436,"score_spread":0.21400452436662326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1887036206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010413836,0.007794301,0.95223904,0.0022678804,0.0008144872,0.000106135536,0.0004952143,0.0007188428,0.025150174],"genre_scores_gemma":[0.18483125,0.010455592,0.76134497,0.0010722352,0.0029766057,0.0004979673,0.0018772123,0.0017222768,0.035221938],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965643,0.0012165392,0.00018301392,0.0006525572,0.0011724106,0.00021107808],"domain_scores_gemma":[0.9939306,0.0034709298,0.00038608335,0.0010377271,0.0008753635,0.00029938825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002644686,0.0025682026,0.0037452374,0.003419459,0.002211798,0.0036145896,0.006914321,0.0034844654,0.0071563586],"category_scores_gemma":[0.01650593,0.0015336989,0.0019653805,0.008086165,0.003106732,0.008248325,0.005457217,0.004944125,0.002841988],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031750553,0.000084151776,0.00060007046,0.00072284776,0.000121117555,0.00008623698,0.00043433593,0.14575231,0.0026352387,0.61860216,0.041038964,0.18960512],"study_design_scores_gemma":[0.000025853715,0.000049265873,0.00045278177,0.00009204365,0.000043486554,0.00025806355,0.00012515047,0.2324763,0.0012310744,0.74722016,0.017981043,0.000044812405],"about_ca_topic_score_codex":0.002881303,"about_ca_topic_score_gemma":0.0027056425,"teacher_disagreement_score":0.0071563586,"about_ca_system_score_codex":0.0041028527,"about_ca_system_score_gemma":0.001297426,"threshold_uncertainty_score":0.029768467},"labels":[],"label_agreement":null},{"id":"W1890828259","doi":"","title":"A trajectory splitting model for efficient spatio-temporal indexing","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Search engine indexing; Trajectory; Bounding overwatch; Computer science; Range (aeronautics); Set (abstract data type); Dynamic programming; Range query (database); Algorithm; Linear programming; Tree (set theory); Mathematical optimization; Mathematics; Combinatorics; Search engine; Artificial intelligence","score_opus":0.03033275801181353,"score_gpt":0.2600813327572613,"score_spread":0.22974857474544777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1890828259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01460256,0.00010979004,0.98381585,0.00015965676,0.00001125123,0.00004497034,0.00012473654,0.00019656985,0.00093463773],"genre_scores_gemma":[0.50289917,0.0004268173,0.49267414,0.00008313257,0.000068731,0.0002903267,0.00064908183,0.00013580151,0.0027727787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881834,0.00030959136,0.00007972421,0.00027630612,0.00038274247,0.00013324479],"domain_scores_gemma":[0.99785644,0.0009957183,0.00026501383,0.0004537912,0.0003075276,0.00012152627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015068658,0.0005177921,0.00086055294,0.0005737796,0.00063123804,0.0014302289,0.0017777212,0.0011879038,0.0027235707],"category_scores_gemma":[0.0055114035,0.00038641764,0.00057882833,0.001622631,0.00085890386,0.00466273,0.0011089187,0.0015660356,0.0008326553],"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":[0.00016736399,0.00009597743,0.0010006749,0.000079171,0.000023916567,0.00013515497,0.0002238179,0.8221907,0.008465356,0.11581445,0.0022328792,0.049570538],"study_design_scores_gemma":[0.000006606295,0.000024218163,0.00006865271,0.0000035801734,0.000003889267,0.000036553378,0.000018993143,0.9843433,0.0008091929,0.013819066,0.00086072605,0.0000053682734],"about_ca_topic_score_codex":0.004383138,"about_ca_topic_score_gemma":0.0033678976,"teacher_disagreement_score":0.004383138,"about_ca_system_score_codex":0.0015308231,"about_ca_system_score_gemma":0.0013770538,"threshold_uncertainty_score":0.011106968},"labels":[],"label_agreement":null},{"id":"W1899304629","doi":"10.1109/icde.1990.113479","title":"An attribute-oriented approach for learning classification rules from relational databases","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Relational database; Database; Information retrieval; Data mining","score_opus":0.12090143033010475,"score_gpt":0.28025421181607696,"score_spread":0.1593527814859722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899304629","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010425617,0.00028942435,0.99666756,0.00013856095,0.000030843472,0.00011353731,0.00015690234,0.0005954816,0.0009650207],"genre_scores_gemma":[0.016952952,0.0005785931,0.98019195,0.00016717361,0.000052168518,0.00022181062,0.0007973563,0.00004758077,0.000990384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99615073,0.0008513578,0.00045699027,0.0005902219,0.0018374026,0.000113334914],"domain_scores_gemma":[0.9963076,0.0017964612,0.00026549582,0.0006522868,0.0008729997,0.00010527279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034694918,0.0007450723,0.0009197749,0.0044036843,0.0007542712,0.0034502198,0.0032348838,0.0010082691,0.0030780288],"category_scores_gemma":[0.009440249,0.0005768591,0.001598107,0.0055296556,0.0009467283,0.004197464,0.001363687,0.0022508984,0.002260926],"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":[0.000093676965,0.00030567954,0.0024317114,0.00043988266,0.00017028431,0.00026630942,0.00036111264,0.036640402,0.0039834585,0.14096928,0.009762589,0.80457574],"study_design_scores_gemma":[0.000054416618,0.00012313378,0.0011208871,0.0001907657,0.00014043355,0.0008729806,0.00016519916,0.5761728,0.012657792,0.3435882,0.06482602,0.00008729927],"about_ca_topic_score_codex":0.0021293357,"about_ca_topic_score_gemma":0.0022344952,"teacher_disagreement_score":0.0044036843,"about_ca_system_score_codex":0.0009676772,"about_ca_system_score_gemma":0.0017153494,"threshold_uncertainty_score":0.018348694},"labels":[],"label_agreement":null},{"id":"W189941673","doi":"","title":"Data Cube Approximation and Mining using Probabilistic Modeling","year":2007,"lang":"en","type":"article","venue":"CogPrints (University of Southampton)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Université du Québec en Outaouais","funders":"","keywords":"Online analytical processing; Data mining; Computer science; Dimension (graph theory); Set (abstract data type); Data warehouse; Abstraction; Data cube; Aggregate (composite); Probabilistic logic; Data set; Outlier; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence","score_opus":0.08750062848913254,"score_gpt":0.2543261015456471,"score_spread":0.16682547305651457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W189941673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006068796,0.00018853298,0.99227965,0.0003524901,0.000016368107,0.000029289376,0.00010771769,0.0003510486,0.0006061187],"genre_scores_gemma":[0.24891214,0.0010736512,0.74722534,0.00020472534,0.000109285094,0.00028618594,0.00083351013,0.00012117782,0.0012339386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684274,0.0012052912,0.00021711625,0.00040583548,0.0011618065,0.00016724998],"domain_scores_gemma":[0.9925868,0.0044737635,0.00057634065,0.0012960896,0.00094307365,0.0001239722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032149344,0.00087355246,0.0015810657,0.0022743877,0.00080778875,0.004140327,0.0022756422,0.0011561448,0.001391564],"category_scores_gemma":[0.017850937,0.00083328626,0.00201845,0.0043448256,0.0010243137,0.0050382116,0.002641575,0.0017363391,0.00052956527],"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":[0.00013677806,0.00006731819,0.00363512,0.00017458547,0.00013278444,0.00011588989,0.00032108393,0.77856845,0.0012363157,0.10105507,0.0029716901,0.111584835],"study_design_scores_gemma":[0.0000043569135,0.000007921158,0.000090196234,0.000010087391,0.000006668153,0.00002977072,0.000031473006,0.96973485,0.00025970733,0.029048938,0.00077026675,0.0000057913426],"about_ca_topic_score_codex":0.0060599083,"about_ca_topic_score_gemma":0.005069314,"teacher_disagreement_score":0.0060599083,"about_ca_system_score_codex":0.001497168,"about_ca_system_score_gemma":0.0020474526,"threshold_uncertainty_score":0.017002404},"labels":[],"label_agreement":null},{"id":"W1901357871","doi":"10.1002/jgt.21655","title":"Transversals in Trees","year":2012,"lang":"en","type":"article","venue":"Journal of Graph Theory","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University; Social Sciences and Humanities Research Council; Natural Sciences and Engineering Research Council of Canada","funders":"McGill University","keywords":"Library science; Computer science; Operations research; Engineering","score_opus":0.013102022792360887,"score_gpt":0.238538950887151,"score_spread":0.2254369280947901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901357871","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5568699,0.0029905927,0.41806847,0.0017341472,0.00015477622,0.00025380586,0.0024715213,0.0014778487,0.015979009],"genre_scores_gemma":[0.8210892,0.001885298,0.16375604,0.00046133282,0.0002646007,0.00035859388,0.0028152864,0.0004829685,0.008886692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995808,0.0012026355,0.0003745639,0.0008877197,0.0009997716,0.00072734995],"domain_scores_gemma":[0.96034294,0.024387348,0.004317061,0.0057021026,0.002764222,0.0024861824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037130322,0.0007363282,0.0013100561,0.0028181712,0.0024234636,0.003441489,0.0014462906,0.0014667622,0.006439564],"category_scores_gemma":[0.023706945,0.0012063328,0.0013080898,0.0027635496,0.0031521297,0.007421688,0.003359004,0.0026596866,0.0011006834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080699736,0.0001947932,0.019023687,0.0005805997,0.000120481294,0.0006651335,0.0023457431,0.032819342,0.015174873,0.8446819,0.010232964,0.07335347],"study_design_scores_gemma":[0.00008840632,0.00039114297,0.003861779,0.00014844406,0.0000895939,0.0028095376,0.0009446497,0.09559735,0.0071920045,0.8651041,0.023707177,0.00006576783],"about_ca_topic_score_codex":0.0015390195,"about_ca_topic_score_gemma":0.0022420017,"teacher_disagreement_score":0.006439564,"about_ca_system_score_codex":0.0021325257,"about_ca_system_score_gemma":0.0011124663,"threshold_uncertainty_score":0.02154249},"labels":[],"label_agreement":null},{"id":"W1915173029","doi":"10.1139/x2012-063","title":"Route selection for best distances in road databases based on drivers’ and customers’ preferences","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Selection (genetic algorithm); Payment; Shortest path problem; Path (computing); Quality (philosophy); Integer programming; Computer science; Database; Operations research; Mathematics; Algorithm; World Wide Web; Artificial intelligence; Graph","score_opus":0.10682296863295443,"score_gpt":0.34814309555168726,"score_spread":0.24132012691873284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1915173029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55842316,0.0008110805,0.4285726,0.0005521668,0.00003506647,0.0004110583,0.0016283501,0.00080895965,0.008757598],"genre_scores_gemma":[0.7640459,0.00028846308,0.23070194,0.00006637511,0.000017294355,0.00021192264,0.0021908176,0.00014481186,0.0023324925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986172,0.0004695936,0.00009589932,0.00032430442,0.0003094546,0.0001835169],"domain_scores_gemma":[0.9972759,0.0015272993,0.00025706997,0.000206786,0.0005557135,0.00017731662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002347009,0.00076958514,0.0014292874,0.0031976982,0.00071554567,0.0022866805,0.0016859119,0.00093423465,0.0033807263],"category_scores_gemma":[0.0063310154,0.0006869134,0.0007692973,0.0035046807,0.00030611144,0.002809926,0.0010030838,0.00064785406,0.0007431095],"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":[0.0012616715,0.00070981507,0.023197206,0.00043333083,0.00025113142,0.00030346334,0.0007246621,0.6084698,0.0070294593,0.012299515,0.008871365,0.3364485],"study_design_scores_gemma":[0.00006760653,0.00013986642,0.00422914,0.000029474948,0.00006378987,0.00013716023,0.0005596358,0.9808929,0.003176732,0.008151889,0.0025089656,0.00004276442],"about_ca_topic_score_codex":0.006868353,"about_ca_topic_score_gemma":0.010633206,"teacher_disagreement_score":0.006868353,"about_ca_system_score_codex":0.001205731,"about_ca_system_score_gemma":0.0013027732,"threshold_uncertainty_score":0.013656795},"labels":[],"label_agreement":null},{"id":"W192218065","doi":"10.1007/978-3-642-37087-8_2","title":"A Strategy for Optimizing a Multi-site Query in a Distributed Spatial Database","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Database; Distributed database; Query optimization; Spatiotemporal database; Information retrieval; Spatial database; Database theory; View; Spatial query; Data mining; Database design; Sargable; Database tuning; Spatial analysis; Web search query; Search engine","score_opus":0.036795136319182964,"score_gpt":0.27006818067291993,"score_spread":0.23327304435373697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W192218065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037649557,0.00016551575,0.9553847,0.00037477122,0.000053234162,0.00023074324,0.00012882466,0.002623582,0.0033890805],"genre_scores_gemma":[0.3321789,0.00012745727,0.6620033,0.00016630936,0.000059842147,0.00019004704,0.00027707446,0.00037445474,0.0046226224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982564,0.00041418892,0.00013683586,0.00028602907,0.0006823769,0.00022418392],"domain_scores_gemma":[0.99807525,0.0006080136,0.00007948459,0.0007529897,0.0003451896,0.00013911372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015889193,0.0010977774,0.0018438945,0.001150831,0.0016527027,0.0026557257,0.0033516448,0.0018921862,0.0044943416],"category_scores_gemma":[0.0047739875,0.00085100025,0.0008747294,0.0023759007,0.0011222,0.0030133228,0.002888893,0.001335332,0.001176134],"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":[0.0017631066,0.0010449265,0.005780877,0.00034876348,0.00028326758,0.00080301525,0.0011651434,0.2369526,0.124448225,0.108796805,0.02244989,0.49616334],"study_design_scores_gemma":[0.000109393,0.00017464388,0.000510569,0.0000077964505,0.00009143292,0.00025894865,0.00029065306,0.9583479,0.014358125,0.022792786,0.0030163068,0.000041417323],"about_ca_topic_score_codex":0.005331806,"about_ca_topic_score_gemma":0.0073191565,"teacher_disagreement_score":0.005331806,"about_ca_system_score_codex":0.0009496179,"about_ca_system_score_gemma":0.0017152604,"threshold_uncertainty_score":0.015035033},"labels":[],"label_agreement":null},{"id":"W1935986945","doi":"10.3233/ida-2003-7406","title":"Measuring the interestingness of discovered knowledge: A principled approach","year":2003,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Data science","score_opus":0.14380152833480334,"score_gpt":0.31391007904092466,"score_spread":0.17010855070612133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1935986945","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02514235,0.00069740845,0.96957594,0.001066529,0.000027688591,0.00044374348,0.00031676618,0.0004351425,0.0022944321],"genre_scores_gemma":[0.23060076,0.000701546,0.76681453,0.00018203062,0.00010163252,0.0007593432,0.0003851996,0.000066350614,0.00038859335],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98637253,0.0040136706,0.001017443,0.0017260541,0.0066299886,0.00024022706],"domain_scores_gemma":[0.95529646,0.03354724,0.0031822172,0.0042693885,0.003171867,0.0005327948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0144217275,0.0015934047,0.0019736164,0.01707756,0.0014946267,0.0070358966,0.0033644615,0.0019244573,0.0010856236],"category_scores_gemma":[0.04682202,0.0013129683,0.0024179076,0.0072929394,0.0062471563,0.0066305893,0.004134196,0.0028932518,0.00028659683],"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":[0.00047833342,0.0007581926,0.020464808,0.0021933862,0.0012196954,0.0005565252,0.0040600877,0.12441985,0.016864793,0.29827586,0.0028485314,0.52785987],"study_design_scores_gemma":[0.000121212346,0.0004458962,0.006873746,0.00034147658,0.00036516113,0.0009123462,0.0010454964,0.38538286,0.00790676,0.5898452,0.006569999,0.00018991793],"about_ca_topic_score_codex":0.0011111806,"about_ca_topic_score_gemma":0.0013147603,"teacher_disagreement_score":0.01707756,"about_ca_system_score_codex":0.0018337832,"about_ca_system_score_gemma":0.002248827,"threshold_uncertainty_score":0.07627034},"labels":[],"label_agreement":null},{"id":"W1959603133","doi":"10.1609/socs.v6i1.18345","title":"Moving Target Search with Subgoal Graphs","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Path (computing); Robotics; Abstraction; Artificial intelligence; Algorithm; Mathematical optimization; Theoretical computer science; Mathematics; Robot; Programming language","score_opus":0.01248162264710213,"score_gpt":0.24000576043663643,"score_spread":0.22752413778953431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1959603133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048535768,0.00017331692,0.9430218,0.00019910415,0.000031587657,0.00012586596,0.00030774868,0.0027941058,0.0048106667],"genre_scores_gemma":[0.36429667,0.00026626405,0.62915057,0.00015243203,0.000015231932,0.0001963529,0.001500856,0.00039984463,0.0040217633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965036,0.000083650084,0.000017320166,0.000091404356,0.00009762008,0.00005970903],"domain_scores_gemma":[0.999461,0.00023055912,0.000057586203,0.000102154285,0.00009968221,0.00004899757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037574634,0.0007756012,0.00062177045,0.0007859947,0.0005096017,0.00065416115,0.0010963649,0.00057203905,0.0038090292],"category_scores_gemma":[0.0014707918,0.00029557475,0.0006770042,0.00090879493,0.0004541955,0.0018666259,0.0014321608,0.0007916099,0.0006910622],"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":[0.00039860012,0.00016919014,0.0013571488,0.00024883208,0.00006131589,0.00018782355,0.00027773192,0.59687454,0.013086355,0.04380633,0.008403155,0.3351289],"study_design_scores_gemma":[0.000040407638,0.00007909764,0.00033607057,0.000012080952,0.000015173181,0.000054737633,0.00006122939,0.95832205,0.0045579975,0.030992422,0.0055176998,0.000010972235],"about_ca_topic_score_codex":0.007603721,"about_ca_topic_score_gemma":0.0063554863,"teacher_disagreement_score":0.007603721,"about_ca_system_score_codex":0.0007150154,"about_ca_system_score_gemma":0.0011309071,"threshold_uncertainty_score":0.015118957},"labels":[],"label_agreement":null},{"id":"W1960442958","doi":"10.1109/icde.2000.839489","title":"DISIMA: an object-oriented approach to developing an image database system","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Salient; Semantics (computer science); Artificial intelligence; Natural language processing; Set (abstract data type); Focus (optics); Information retrieval; Object (grammar); Similarity (geometry); Query language; Image (mathematics); Programming language","score_opus":0.038223262927435726,"score_gpt":0.2520166150604092,"score_spread":0.21379335213297346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1960442958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018674083,0.00027222437,0.97508097,0.00024017667,0.00006218618,0.00036904632,0.0005159511,0.017637398,0.0039546327],"genre_scores_gemma":[0.026898954,0.0006608237,0.9596394,0.00046273533,0.00008313717,0.00048033858,0.0032361767,0.0020770377,0.006461372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983935,0.00022556007,0.00021686977,0.00028991586,0.0007654257,0.000108713066],"domain_scores_gemma":[0.99827313,0.00042842113,0.00013034046,0.0004048365,0.0005331416,0.00023011384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003031584,0.0010884472,0.0009830316,0.0016628458,0.00078734435,0.0052066357,0.00468336,0.001054264,0.005452716],"category_scores_gemma":[0.004076254,0.001432553,0.0013660112,0.0016734455,0.000991923,0.0052029938,0.0028262818,0.0023878636,0.003064521],"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":[0.0008520314,0.0005162512,0.0034302876,0.0018125778,0.000383929,0.0010257182,0.0015982586,0.030403176,0.046591543,0.18819974,0.10790277,0.6172838],"study_design_scores_gemma":[0.00018651418,0.00034501505,0.0013009636,0.00028365702,0.0002306807,0.0012484178,0.00048102246,0.25461677,0.04619472,0.08186203,0.613071,0.00017919061],"about_ca_topic_score_codex":0.002664609,"about_ca_topic_score_gemma":0.0033537021,"teacher_disagreement_score":0.005452716,"about_ca_system_score_codex":0.001080005,"about_ca_system_score_gemma":0.0018175868,"threshold_uncertainty_score":0.018241167},"labels":[],"label_agreement":null},{"id":"W1961651351","doi":"10.3233/ifs-130935","title":"A simulated annealing algorithm for routing problems with fuzzy constrains","year":2014,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Simulated annealing; Computer science; Fuzzy logic; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02341158619173813,"score_gpt":0.251242004933188,"score_spread":0.22783041874144985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1961651351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005282739,0.00017199549,0.9919918,0.00007643384,0.000023060393,0.000037612637,0.00001778263,0.0001511293,0.0022474295],"genre_scores_gemma":[0.23562837,0.00033783994,0.7598063,0.00007878635,0.00003281076,0.0004003948,0.00011294635,0.00014167269,0.003460895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996388,0.00017940291,0.000015826085,0.000054711458,0.00008076427,0.000030447041],"domain_scores_gemma":[0.99959975,0.0002839199,0.000028592885,0.00002473536,0.000048707207,0.000014277769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006149682,0.0007460216,0.000868266,0.0005085279,0.00051241805,0.00055849936,0.0007498658,0.0009670513,0.0022152744],"category_scores_gemma":[0.0015646799,0.0005936196,0.0008018291,0.00065329764,0.00061495235,0.0006057965,0.000740554,0.0010248311,0.00037362674],"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":[0.000023244615,0.000013165487,0.00010814426,0.000030741325,0.000025129108,0.000017856988,0.000042005748,0.9658519,0.0010647282,0.010500041,0.00037127332,0.02195189],"study_design_scores_gemma":[0.000008175042,0.000015915892,0.000030368408,0.0000049059545,0.0000047192566,0.0000074793747,0.000004605532,0.99511826,0.0002660873,0.0034974366,0.0010386851,0.0000033938147],"about_ca_topic_score_codex":0.0039548175,"about_ca_topic_score_gemma":0.0033363614,"teacher_disagreement_score":0.0039548175,"about_ca_system_score_codex":0.0006964858,"about_ca_system_score_gemma":0.00094748545,"threshold_uncertainty_score":0.007863641},"labels":[],"label_agreement":null},{"id":"W1963523624","doi":"10.1145/1281192.1281248","title":"Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cluster analysis; A priori and a posteriori; Computer science; Data mining; Cluster (spacecraft); Probabilistic logic; Theoretical computer science; Artificial intelligence","score_opus":0.08205287164865702,"score_gpt":0.3089478544620917,"score_spread":0.2268949828134347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963523624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029599171,0.00027731806,0.9680282,0.00017572173,0.000019666793,0.00013963792,0.00040262865,0.00044280326,0.00091494026],"genre_scores_gemma":[0.40761122,0.00032443166,0.5874411,0.00010610596,0.00006985834,0.0003712362,0.0021146815,0.00019365846,0.001767738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99472165,0.0013766558,0.00029767276,0.0016519572,0.0016113197,0.0003408731],"domain_scores_gemma":[0.98964953,0.0049175834,0.001054304,0.002226588,0.0018976096,0.0002542625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004639218,0.001136974,0.0016139762,0.004753905,0.0016270671,0.002686232,0.0029647988,0.001783262,0.0013012543],"category_scores_gemma":[0.015489003,0.00070553203,0.0013803665,0.00461838,0.0018273193,0.0033507226,0.003553159,0.0017653166,0.0007315093],"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":[0.0011246856,0.00036559327,0.038353268,0.0007479542,0.00069819926,0.00051875674,0.0022756774,0.4083145,0.01669686,0.13464558,0.009120097,0.38713887],"study_design_scores_gemma":[0.0000204829,0.00006046618,0.003154589,0.00003599362,0.000060189337,0.00015990857,0.00033281927,0.9237138,0.004986187,0.06297782,0.004447665,0.00005003318],"about_ca_topic_score_codex":0.004913981,"about_ca_topic_score_gemma":0.004467201,"teacher_disagreement_score":0.004913981,"about_ca_system_score_codex":0.0014015391,"about_ca_system_score_gemma":0.0018863856,"threshold_uncertainty_score":0.024534822},"labels":[],"label_agreement":null},{"id":"W1964744646","doi":"10.1057/palgrave.ivs.9500069","title":"A Pivot-Based Routine for Improved Parent-Finding in Hybrid MDS","year":2004,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Computer science; Interpolation (computer graphics); Set (abstract data type); Heuristic; Algorithm; Reduction (mathematics); Computational complexity theory; Curse of dimensionality; Multidimensional scaling; Theoretical computer science; Data mining; Artificial intelligence; Mathematics; Machine learning; Programming language","score_opus":0.020986857059478125,"score_gpt":0.2926540428330248,"score_spread":0.2716671857735467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964744646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00969368,0.000069190515,0.98418474,0.00005016259,0.000036034064,0.00009036002,0.000105978004,0.005136566,0.0006332643],"genre_scores_gemma":[0.04403327,0.000029745694,0.9542274,0.00002838733,0.00001081721,0.00019116249,0.00024671445,0.00045746984,0.0007750795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998528,0.00044075603,0.00012345775,0.00031969533,0.00046659255,0.00012148994],"domain_scores_gemma":[0.9963452,0.001717305,0.00024610577,0.0009535235,0.0005766342,0.00016116863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003697911,0.0014625738,0.0015768297,0.0027968544,0.0017466681,0.0014149117,0.0029105477,0.0014071864,0.008053927],"category_scores_gemma":[0.009552746,0.00090079074,0.0010633705,0.0025612533,0.0012982293,0.0016442171,0.003761075,0.0015947819,0.0026929432],"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":[0.001123631,0.00029392086,0.003838829,0.0003720394,0.00020157469,0.00022054445,0.0012867831,0.120490804,0.025499538,0.038096108,0.008037113,0.8005391],"study_design_scores_gemma":[0.00028255073,0.00023611983,0.000917693,0.0000483879,0.00005642259,0.00023406328,0.00021021513,0.94699186,0.018001487,0.020811036,0.012117553,0.00009275157],"about_ca_topic_score_codex":0.0054182257,"about_ca_topic_score_gemma":0.010629865,"teacher_disagreement_score":0.008053927,"about_ca_system_score_codex":0.0009337875,"about_ca_system_score_gemma":0.0022227743,"threshold_uncertainty_score":0.026943088},"labels":[],"label_agreement":null},{"id":"W1964998447","doi":"10.4236/ijg.2015.61007","title":"An OWL-Based Mobile GeoBI Context Ontology Enabling Location-Based and Context-Based Reasoning and Supporting Contextual Business Analysis","year":2015,"lang":"en","type":"article","venue":"International Journal of Geosciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec; Université Laval","funders":"","keywords":"Computer science; Ontology; Context (archaeology); Geospatial analysis; Context model; Context awareness; Context analysis; Web Ontology Language; Data science; Taxonomy (biology); Knowledge management; World Wide Web; Semantic Web; Artificial intelligence; Geography","score_opus":0.02194034940470553,"score_gpt":0.3048498517611198,"score_spread":0.28290950235641427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964998447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022229899,0.00088134897,0.9439652,0.0015091791,0.0004379759,0.0005956764,0.0036397502,0.0031290608,0.023611829],"genre_scores_gemma":[0.18115994,0.0019066256,0.7960034,0.00087871595,0.00014083703,0.00053983147,0.006961515,0.00033315766,0.0120759625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99912256,0.00012487659,0.00012590452,0.0000984631,0.0004169836,0.000111219546],"domain_scores_gemma":[0.9994854,0.00007731175,0.000060056875,0.00012241628,0.00019106542,0.00006369712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008874734,0.00036237028,0.00040805127,0.0011946071,0.0009250882,0.00192445,0.0010719126,0.00068727555,0.0016915196],"category_scores_gemma":[0.001852824,0.0003782086,0.000996499,0.0013671474,0.0006078798,0.0028618348,0.00204424,0.0012634264,0.0006962055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026009683,0.0004483086,0.0048799794,0.0011554634,0.0001800962,0.0020681883,0.0018396146,0.018946271,0.035135534,0.56228364,0.037497453,0.3353054],"study_design_scores_gemma":[0.00009326307,0.00012622826,0.005577425,0.00073118776,0.00027186243,0.002702526,0.0018485622,0.1758233,0.01770576,0.14347664,0.65149623,0.00014710633],"about_ca_topic_score_codex":0.016718565,"about_ca_topic_score_gemma":0.025016174,"teacher_disagreement_score":0.016718565,"about_ca_system_score_codex":0.00094694487,"about_ca_system_score_gemma":0.0030302699,"threshold_uncertainty_score":0.033242524},"labels":[],"label_agreement":null},{"id":"W1965167435","doi":"10.1016/s0924-2716(00)00015-0","title":"Taxonomy of space tessellation","year":2000,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"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","funders":"Hong Kong Polytechnic University","keywords":"Raster graphics; Tessellation (computer graphics); Partition (number theory); Computer science; Raster data; Feature vector; Space partitioning; Pixel; Theoretical computer science; Data mining; Artificial intelligence; Pattern recognition (psychology); Algorithm; Computer graphics (images); Mathematics; Combinatorics","score_opus":0.016095039798276995,"score_gpt":0.2288803512093513,"score_spread":0.2127853114110743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965167435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036097653,0.058194757,0.6366619,0.0025128406,0.0014508808,0.00065787276,0.0058421255,0.003453942,0.25512815],"genre_scores_gemma":[0.23961243,0.051566966,0.66079885,0.0006403325,0.00088566635,0.00048726404,0.008928822,0.00083126256,0.036248516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987722,0.0001959931,0.00018592071,0.0002609283,0.0003921696,0.00019265499],"domain_scores_gemma":[0.99768555,0.0006326081,0.00018854614,0.0005096794,0.00080231094,0.00018128155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095284556,0.0008726378,0.001033816,0.006114175,0.0016227537,0.007396258,0.0028250539,0.0015947805,0.010852737],"category_scores_gemma":[0.0041732932,0.0006710837,0.0014402275,0.012433167,0.0023951489,0.0059354007,0.00234947,0.0018399135,0.0036472464],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005094068,0.00003921332,0.0022880172,0.00057064166,0.00003618127,0.00018106066,0.0006392831,0.009945767,0.0009427034,0.7431397,0.018169355,0.22399715],"study_design_scores_gemma":[0.000013767127,0.00009329107,0.0025847016,0.0004664033,0.00004179618,0.0022066333,0.0009027032,0.028138792,0.0014098445,0.5273707,0.43669447,0.000076991964],"about_ca_topic_score_codex":0.0072135846,"about_ca_topic_score_gemma":0.004513005,"teacher_disagreement_score":0.010852737,"about_ca_system_score_codex":0.002206256,"about_ca_system_score_gemma":0.0014950032,"threshold_uncertainty_score":0.036305964},"labels":[],"label_agreement":null},{"id":"W1966404343","doi":"10.4018/jdwm.2012100103","title":"Efficient and Effective Aggregate Keyword Search on Relational Databases","year":2012,"lang":"en","type":"article","venue":"International Journal of Data Warehousing and Mining","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Coquitlam College","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Aggregate (composite); Ranking (information retrieval); Keyword search; Relational database; Information retrieval; Keyword density; Database; Data mining","score_opus":0.08742456037480173,"score_gpt":0.3419586676621541,"score_spread":0.2545341072873524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966404343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10703275,0.0038535357,0.875906,0.00037797133,0.000049930455,0.00026266137,0.0014652646,0.006005408,0.0050464566],"genre_scores_gemma":[0.44296896,0.0020976658,0.54986715,0.0001516284,0.0001054058,0.00015810208,0.0020192168,0.00027337292,0.002358442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994187,0.0015360916,0.0007908103,0.00055369013,0.0024600765,0.0004723164],"domain_scores_gemma":[0.99243414,0.003424372,0.00059292454,0.001904334,0.0014175447,0.00022662834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023575176,0.0007905403,0.0023357593,0.0035198021,0.0008455189,0.003401106,0.0015020903,0.0009522798,0.0015011879],"category_scores_gemma":[0.01090043,0.0005239225,0.00088868814,0.0073063946,0.00055990275,0.0069503644,0.0022488195,0.0005708922,0.0013434516],"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":[0.0011705186,0.0003653518,0.005327688,0.001119127,0.00030430438,0.0005932949,0.0008493076,0.12219805,0.06369399,0.046306536,0.018364483,0.7397074],"study_design_scores_gemma":[0.00013011473,0.00040385756,0.0025739279,0.000060754082,0.0002194038,0.00095415383,0.00079224206,0.8793516,0.049768604,0.051010616,0.014637496,0.00009726351],"about_ca_topic_score_codex":0.0048674126,"about_ca_topic_score_gemma":0.0051170858,"teacher_disagreement_score":0.0048674126,"about_ca_system_score_codex":0.0008739654,"about_ca_system_score_gemma":0.0017213853,"threshold_uncertainty_score":0.012467861},"labels":[],"label_agreement":null},{"id":"W1966445797","doi":"10.1145/1620432.1620453","title":"Efficient keyword proximity search using a frontier-reduce strategy based on<i>d</i>-distance graph index","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Graph; Set (abstract data type); Efficient frontier; Space (punctuation); Theoretical computer science; Index (typography); Frontier; World Wide Web","score_opus":0.03283030230392023,"score_gpt":0.2784831588872907,"score_spread":0.24565285658337047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966445797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058387224,0.00063428644,0.9319644,0.00033354398,0.00003230232,0.00030876326,0.0005782759,0.002176532,0.0055846255],"genre_scores_gemma":[0.23773268,0.00027511246,0.7584862,0.00010157062,0.000025540148,0.00020420343,0.0010902655,0.00012176531,0.0019626706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935025,0.00013710826,0.00004951205,0.00014413166,0.0002438324,0.00007506975],"domain_scores_gemma":[0.9992631,0.00030367164,0.000068598274,0.00018747816,0.00013145313,0.00004565527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004261132,0.0005955793,0.0012994058,0.0025475272,0.0007903742,0.0011543004,0.0011107387,0.0006888118,0.0018989596],"category_scores_gemma":[0.002299481,0.0002897716,0.0007462686,0.0032412321,0.00049086206,0.001978419,0.0014502178,0.00043093858,0.0007420113],"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":[0.00057311566,0.0005484329,0.0030385952,0.0007983663,0.00015735989,0.00074305787,0.0009311763,0.11142281,0.069950886,0.05700981,0.018291228,0.73653513],"study_design_scores_gemma":[0.00015568348,0.00050056336,0.0020742067,0.000040957388,0.00014540525,0.0013300494,0.00068281585,0.8486691,0.038359974,0.09077103,0.017186783,0.0000833824],"about_ca_topic_score_codex":0.004221621,"about_ca_topic_score_gemma":0.005322841,"teacher_disagreement_score":0.004221621,"about_ca_system_score_codex":0.00072193384,"about_ca_system_score_gemma":0.0014669591,"threshold_uncertainty_score":0.0083940625},"labels":[],"label_agreement":null},{"id":"W1967241189","doi":"10.1002/atr.5670390205","title":"Counting the different efficient paths for transportation networks and its applications","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simple (philosophy); Heuristic; Time complexity; Computer science; Floyd–Warshall algorithm; Mathematical optimization; Flow network; Polynomial; Algorithm; Travel time; Matrix (chemical analysis); Mathematics; Theoretical computer science; Shortest path problem; Graph; Engineering","score_opus":0.0070851890042968714,"score_gpt":0.23572634358927508,"score_spread":0.22864115458497822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967241189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025144987,0.00060059637,0.969283,0.00046650265,0.00004305328,0.0000868485,0.00020762072,0.00027254398,0.0038948718],"genre_scores_gemma":[0.24903753,0.0012801143,0.74536836,0.00010384081,0.00015261529,0.00022421339,0.00073960127,0.00017016928,0.002923544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983966,0.00044217907,0.00013720294,0.00047023254,0.00041663888,0.00013711373],"domain_scores_gemma":[0.9931925,0.004903655,0.00039229126,0.0007613614,0.0005899908,0.00016026184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012917571,0.0010611604,0.0010417204,0.0022345772,0.0008795132,0.001835548,0.0022243368,0.0011996055,0.0046644476],"category_scores_gemma":[0.011248204,0.00053589395,0.0011137559,0.0043720626,0.001242002,0.005018068,0.0019487592,0.0020026478,0.000677558],"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":[0.0002192805,0.00014571013,0.0021687364,0.00044450542,0.000063165426,0.00016263957,0.00030356902,0.35179216,0.003948704,0.38100338,0.0052791643,0.25446892],"study_design_scores_gemma":[0.00002179384,0.000044826083,0.00030697256,0.00002378893,0.000024386862,0.00016287292,0.00007651718,0.71462125,0.001897549,0.27711117,0.005687904,0.000020939771],"about_ca_topic_score_codex":0.0033024647,"about_ca_topic_score_gemma":0.0027729522,"teacher_disagreement_score":0.0046644476,"about_ca_system_score_codex":0.001543035,"about_ca_system_score_gemma":0.0011798004,"threshold_uncertainty_score":0.015604138},"labels":[],"label_agreement":null},{"id":"W1967654850","doi":"10.14778/1687627.1687642","title":"Measure-driven keyword-query expansion","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Pruning; Information retrieval; Query expansion; Web search query; Context (archaeology); Set (abstract data type); Process (computing); Domain (mathematical analysis); Measure (data warehouse); Focus (optics); Query optimization; Data mining; Word (group theory); Search engine; Mathematics","score_opus":0.012523221015441705,"score_gpt":0.21224724329768602,"score_spread":0.1997240222822443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967654850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05700483,0.00062108744,0.93738055,0.00021703732,0.00002723077,0.0003342178,0.0003773574,0.0021253692,0.0019123307],"genre_scores_gemma":[0.4322627,0.0002971892,0.5631849,0.00020237023,0.00006438388,0.00030963853,0.0013052908,0.0002005766,0.0021730089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965932,0.001110728,0.00027596572,0.0006044864,0.0011831922,0.00023242655],"domain_scores_gemma":[0.994218,0.0033182385,0.00041440266,0.0009573521,0.000966171,0.00012588855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027734411,0.00095277664,0.0024637056,0.0020163064,0.0006842415,0.0013754902,0.0025318782,0.0012141435,0.0022160849],"category_scores_gemma":[0.012136029,0.0005287729,0.0011661052,0.0033137202,0.00087081874,0.002724686,0.0024395776,0.0010481557,0.0010295224],"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":[0.001182579,0.0005762913,0.005710719,0.00075316895,0.00019031692,0.0004945683,0.00087842334,0.30837682,0.04444588,0.02566034,0.00971801,0.6020129],"study_design_scores_gemma":[0.000040001967,0.000114475224,0.000634531,0.000011307547,0.000029793608,0.00032412584,0.00010251467,0.97998637,0.0074210055,0.010049246,0.001259642,0.000026997208],"about_ca_topic_score_codex":0.0035818713,"about_ca_topic_score_gemma":0.00531679,"teacher_disagreement_score":0.0035818713,"about_ca_system_score_codex":0.0010919226,"about_ca_system_score_gemma":0.001294963,"threshold_uncertainty_score":0.014667511},"labels":[],"label_agreement":null},{"id":"W1968799289","doi":"10.1145/2808194.2809493","title":"Pooling for User-Oriented Evaluation Measures","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Pooling; Automatic summarization; Computer science; Information retrieval; Relevance (law); Focus (optics); Rank (graph theory); Order (exchange); World Wide Web; Data mining; Artificial intelligence","score_opus":0.12017179713478407,"score_gpt":0.32649510122844744,"score_spread":0.20632330409366337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968799289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053108104,0.0014244604,0.93387014,0.000534377,0.00013164905,0.0012876563,0.00083492906,0.001838634,0.0069699856],"genre_scores_gemma":[0.74813855,0.00031414497,0.24417114,0.00037447398,0.00038631054,0.0018965713,0.0016565836,0.00034245002,0.002719703],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.958465,0.025348378,0.0031065114,0.0045882026,0.007058783,0.0014331647],"domain_scores_gemma":[0.9102951,0.055939484,0.0061924295,0.016289622,0.009457757,0.0018255431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.057646044,0.002883157,0.002492372,0.0053021186,0.0012184045,0.0047763963,0.0027028092,0.0021263245,0.004299282],"category_scores_gemma":[0.13377824,0.00076920865,0.0017674841,0.0035128638,0.0018260864,0.008245225,0.0043786783,0.0020589845,0.0010039556],"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":[0.0032129572,0.0013104227,0.02555662,0.0012391785,0.0020032227,0.00031509073,0.0014703735,0.17447169,0.014197327,0.089619115,0.012882576,0.67372143],"study_design_scores_gemma":[0.00015900032,0.0028772368,0.013449575,0.00019886605,0.0006182493,0.0003193733,0.00032439342,0.8125026,0.017942296,0.14213464,0.0092295585,0.00024419362],"about_ca_topic_score_codex":0.0015828573,"about_ca_topic_score_gemma":0.0011770295,"teacher_disagreement_score":0.057646044,"about_ca_system_score_codex":0.0023803068,"about_ca_system_score_gemma":0.0020228415,"threshold_uncertainty_score":0.30486512},"labels":[],"label_agreement":null},{"id":"W1969872322","doi":"10.1016/j.dam.2004.06.007","title":"Measures on Boolean polynomials and their applications in data mining","year":2004,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Extension (predicate logic); Value (mathematics); Bonferroni correction; Type (biology); Binary number; Discrete mathematics; Statistics; Computer science; Arithmetic","score_opus":0.04596786842187776,"score_gpt":0.2692105800670464,"score_spread":0.22324271164516865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969872322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031285845,0.010633409,0.9517712,0.0021267,0.00034483604,0.00003870315,0.00030120622,0.0002205404,0.0032775656],"genre_scores_gemma":[0.65565413,0.011195864,0.32590184,0.0006664858,0.002544642,0.00022134192,0.00044826287,0.00013778103,0.0032296667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99499905,0.0023316317,0.0003684961,0.0006137571,0.0014688654,0.00021818165],"domain_scores_gemma":[0.9539279,0.037186846,0.0037903702,0.0016849663,0.002398037,0.0010118551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067515,0.00081733207,0.0015003913,0.0056047826,0.0007950106,0.003942861,0.0016342844,0.0012688466,0.0019697149],"category_scores_gemma":[0.04573915,0.0005699649,0.0008489127,0.009613878,0.0039457083,0.0066038934,0.002623031,0.0025332067,0.0002813621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094043826,0.00004549823,0.0017520832,0.00029677994,0.00006719587,0.00007091263,0.00022670323,0.022366766,0.0009685885,0.8834046,0.0018666942,0.0888402],"study_design_scores_gemma":[0.000028377553,0.00007365197,0.0011574135,0.00010648694,0.000044933673,0.00020201101,0.000096626456,0.17403054,0.0007186492,0.81867826,0.004813109,0.000049853486],"about_ca_topic_score_codex":0.001387489,"about_ca_topic_score_gemma":0.0009680187,"teacher_disagreement_score":0.0067515,"about_ca_system_score_codex":0.0021337902,"about_ca_system_score_gemma":0.0009114232,"threshold_uncertainty_score":0.035705745},"labels":[],"label_agreement":null},{"id":"W1970296249","doi":"10.1007/s10115-011-0379-3","title":"Aggregate keyword search on large relational databases","year":2011,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Aggregate (composite); Information retrieval; Tuple; Set (abstract data type); Keyword density; Relational database; Keyword search; Matching (statistics); Data mining; Table (database); Focus (optics); Database; Mathematics","score_opus":0.06452476657364406,"score_gpt":0.26599489127396836,"score_spread":0.2014701247003243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970296249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3098049,0.009358137,0.64986145,0.0020152319,0.00028704555,0.00036235398,0.007928794,0.012066835,0.008315222],"genre_scores_gemma":[0.65268636,0.0031362555,0.32792452,0.00035348753,0.0003384192,0.00026368708,0.0075423573,0.0006213183,0.0071336576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934994,0.0013764652,0.0008330661,0.0007347416,0.0030047223,0.00055163587],"domain_scores_gemma":[0.9855137,0.008071089,0.0007477979,0.0033933034,0.0017052514,0.0005688084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034998413,0.0008962854,0.0038381678,0.00650467,0.001738496,0.0056625092,0.0022932813,0.0013388653,0.004895737],"category_scores_gemma":[0.019153943,0.0011228877,0.0012954368,0.014570538,0.0010241508,0.012638628,0.0045952224,0.0011653068,0.0021864998],"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":[0.003238755,0.0006049239,0.013749818,0.0018559156,0.00072634144,0.0010540867,0.0013731569,0.15339872,0.021497715,0.06445933,0.047334284,0.69070685],"study_design_scores_gemma":[0.00018551611,0.000303434,0.0029263163,0.000089590285,0.00043143536,0.0007381408,0.0010320254,0.807201,0.012337389,0.16375433,0.010931452,0.00006939976],"about_ca_topic_score_codex":0.0067887036,"about_ca_topic_score_gemma":0.013997144,"teacher_disagreement_score":0.0067887036,"about_ca_system_score_codex":0.0015252184,"about_ca_system_score_gemma":0.0027428586,"threshold_uncertainty_score":0.01850915},"labels":[],"label_agreement":null},{"id":"W1971004146","doi":"10.1145/1966478.1966480","title":"CTS 2010 workshop report","year":2011,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Science and engineering; Library science; Operations research; Engineering management; Data science; Engineering ethics; Engineering","score_opus":0.04689999787022253,"score_gpt":0.23958626505780756,"score_spread":0.19268626718758503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971004146","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004002892,0.004851025,0.006280245,0.059970975,0.041552406,0.002142243,0.10066499,0.0034641216,0.77707106],"genre_scores_gemma":[0.013758001,0.0029805438,0.0058455192,0.006805968,0.0033641525,0.001675723,0.09701161,0.002069009,0.8664895],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955278,0.00068108825,0.0002943564,0.00059393793,0.0020095727,0.000893318],"domain_scores_gemma":[0.99009776,0.0004736162,0.00023654908,0.0006500319,0.004054627,0.0044874],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0061263684,0.0014178785,0.0008951664,0.0026166106,0.0032524634,0.0077272714,0.0038628276,0.00331139,0.50430185],"category_scores_gemma":[0.007939797,0.0005820994,0.0013271448,0.00279788,0.00046884283,0.0031925633,0.005693285,0.003199623,0.34666213],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009224262,0.0000596921,0.00015271403,0.00011306503,0.0000048493775,0.000061224084,0.00006388016,0.000094219235,0.00023841391,0.0015868112,0.9811989,0.016333977],"study_design_scores_gemma":[0.0000165603,0.000020836762,0.00033111128,0.00006635602,0.0000040200603,0.000037991267,0.00011638783,0.000044926142,0.00020618936,0.00035624375,0.9987913,0.000008056841],"about_ca_topic_score_codex":0.026996931,"about_ca_topic_score_gemma":0.031647295,"teacher_disagreement_score":0.50430185,"about_ca_system_score_codex":0.0057600252,"about_ca_system_score_gemma":0.023996843,"threshold_uncertainty_score":0.70705324},"labels":[],"label_agreement":null},{"id":"W1972857993","doi":"10.1145/1242572.1242836","title":"Visualizing structural patterns in web collections","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; World Wide Web; Visualization; Information retrieval; Data mining","score_opus":0.014869418158442055,"score_gpt":0.28473958028463964,"score_spread":0.2698701621261976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972857993","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5559842,0.0071067857,0.3481654,0.0026737894,0.00044971108,0.00087982387,0.021217622,0.027396327,0.03612636],"genre_scores_gemma":[0.6253651,0.0056555127,0.32552955,0.00013720772,0.00021956436,0.0003271543,0.023546437,0.002153897,0.017065601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996257,0.00007417408,0.000036378537,0.00007329014,0.00015421039,0.000036306585],"domain_scores_gemma":[0.997521,0.0011551635,0.00026929696,0.00045526391,0.00032984948,0.00026941742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060091686,0.00048408224,0.00054360926,0.0064093424,0.00083867763,0.0029023327,0.00065505924,0.0005736483,0.015414019],"category_scores_gemma":[0.0040115104,0.00047512187,0.00043291118,0.008272775,0.00046052536,0.0026645507,0.0015320138,0.0006603166,0.0026454118],"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":[0.0008955902,0.00047647386,0.039539553,0.0027608927,0.0003363095,0.0014011092,0.004057423,0.016946495,0.082021594,0.02021909,0.073894955,0.75745064],"study_design_scores_gemma":[0.00040526298,0.0009708356,0.1711599,0.0015948853,0.00095328357,0.004783554,0.0074795927,0.3133206,0.096516155,0.14341475,0.2590972,0.0003039619],"about_ca_topic_score_codex":0.0037343735,"about_ca_topic_score_gemma":0.0066998317,"teacher_disagreement_score":0.015414019,"about_ca_system_score_codex":0.00033055712,"about_ca_system_score_gemma":0.000645298,"threshold_uncertainty_score":0.05156505},"labels":[],"label_agreement":null},{"id":"W1972876322","doi":"10.1016/s0304-3975(00)00080-3","title":"Sokoban: improving the search with relevance cuts","year":2001,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Data Management and Algorithms","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 Alberta","funders":"","keywords":"Pruning; Relevance (law); Computer science; Context (archaeology); Reduction (mathematics); Sequence (biology); Current (fluid); Measure (data warehouse); Incremental heuristic search; State (computer science); Artificial intelligence; Algorithm; Search algorithm; Theoretical computer science; Mathematics; Machine learning; Beam search; Data mining","score_opus":0.011189001344637992,"score_gpt":0.2382632445281641,"score_spread":0.22707424318352612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972876322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05426351,0.008058981,0.89893895,0.003170159,0.0012248632,0.0004874498,0.0017196656,0.011659117,0.020477286],"genre_scores_gemma":[0.29043785,0.0016069473,0.6858249,0.0014010712,0.0006444589,0.00050122064,0.0041064788,0.0024696907,0.013007389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99521506,0.0022284272,0.00024424485,0.00064432196,0.0013328367,0.00033511312],"domain_scores_gemma":[0.9929913,0.0035617757,0.00033400676,0.0016651857,0.001133606,0.00031406656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032709234,0.0018305978,0.0036701108,0.0046944898,0.0017857357,0.0034478828,0.0032171002,0.0034951246,0.015770352],"category_scores_gemma":[0.021728119,0.0011374679,0.0016423701,0.005222402,0.0014419085,0.0074722623,0.0041431203,0.0037050066,0.0048299255],"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":[0.0060289195,0.0008961363,0.0016729773,0.0013067699,0.00040308796,0.00022959485,0.00039396677,0.072877645,0.011639153,0.0895101,0.08860991,0.72643185],"study_design_scores_gemma":[0.0012101711,0.0005738437,0.00077710464,0.00017597881,0.00028260058,0.00041308414,0.00021784368,0.7212631,0.009411044,0.2392192,0.026357768,0.00009826459],"about_ca_topic_score_codex":0.0027388597,"about_ca_topic_score_gemma":0.0039922344,"teacher_disagreement_score":0.015770352,"about_ca_system_score_codex":0.0011063152,"about_ca_system_score_gemma":0.00282534,"threshold_uncertainty_score":0.052757084},"labels":[],"label_agreement":null},{"id":"W1972924401","doi":"10.14778/1920841.1920947","title":"Building ranked mashups of unstructured sources with uncertain information","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mashup; Computer science; Ranking (information retrieval); Information retrieval; Rank (graph theory); Probabilistic logic; Information extraction; Semantics (computer science); Database; World Wide Web; Data mining; Web service; Artificial intelligence; Web modeling","score_opus":0.00427333096314046,"score_gpt":0.19202117821881115,"score_spread":0.1877478472556707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972924401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024237052,0.00015179979,0.9717838,0.00021563869,0.000019547519,0.00013662405,0.000295886,0.0017793247,0.0013802178],"genre_scores_gemma":[0.2378239,0.00027862913,0.7576227,0.00013834864,0.00006152817,0.00024863554,0.0013815673,0.00037276238,0.0020719313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678636,0.00094451476,0.00022365234,0.0006373064,0.0011926505,0.00021549068],"domain_scores_gemma":[0.99457955,0.0028237356,0.00048892037,0.001318713,0.0005619757,0.00022708901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027312343,0.0010949888,0.0010676103,0.0015574655,0.0012063414,0.0025262006,0.0017419992,0.0009075924,0.0015057792],"category_scores_gemma":[0.010407234,0.0008647961,0.001200409,0.0023550526,0.0011404254,0.005428982,0.0038529632,0.0014753584,0.00074027386],"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":[0.0011629236,0.00051600364,0.005620344,0.0009827641,0.0006406984,0.002572374,0.0032812299,0.4397057,0.07857477,0.13966972,0.013482036,0.31379148],"study_design_scores_gemma":[0.000062795356,0.00014025315,0.00054434146,0.000039779872,0.00011928483,0.00025554118,0.0006132044,0.8559306,0.032505155,0.095473774,0.014250877,0.00006442395],"about_ca_topic_score_codex":0.002048713,"about_ca_topic_score_gemma":0.003776427,"teacher_disagreement_score":0.0027312343,"about_ca_system_score_codex":0.00064361695,"about_ca_system_score_gemma":0.0010153252,"threshold_uncertainty_score":0.014444292},"labels":[],"label_agreement":null},{"id":"W1973582313","doi":"10.1145/2525314.2525347","title":"A parallel spatial data analysis infrastructure for the cloud","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cloud computing; Computer science; Multi-core processor; Distributed computing; Range (aeronautics); Spatial analysis; Parallel processing; Confluence; Data mining; Computational science; Parallel computing; Remote sensing; Operating system","score_opus":0.025265773279392792,"score_gpt":0.25936779789960634,"score_spread":0.23410202462021357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973582313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025290215,0.0016611224,0.8404772,0.0038954453,0.0009952312,0.0013571859,0.009351049,0.08900727,0.02796524],"genre_scores_gemma":[0.26184466,0.0017990827,0.6891741,0.0013471225,0.00046125636,0.0010438734,0.029346976,0.0037470045,0.011235928],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981926,0.00019037932,0.0001599732,0.00030876778,0.0008273329,0.0003209382],"domain_scores_gemma":[0.99721634,0.00021340858,0.00013627883,0.001070482,0.00088141055,0.00048218155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016064476,0.00084093056,0.00088849757,0.0011817006,0.001569369,0.0027483422,0.0039271186,0.0006647634,0.011528975],"category_scores_gemma":[0.0037604694,0.0005954618,0.0010475497,0.0031632953,0.0007029397,0.0033418683,0.004552523,0.0016164284,0.0068865553],"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":[0.0017543343,0.00070828263,0.007664364,0.00055207626,0.00028065452,0.0014359263,0.00037104648,0.06227396,0.032882173,0.07201239,0.46386063,0.35620415],"study_design_scores_gemma":[0.00067219633,0.0002493068,0.004051387,0.00013737104,0.00011183319,0.0008526532,0.00027499438,0.6769276,0.01905799,0.051056147,0.2464446,0.00016381982],"about_ca_topic_score_codex":0.0112061,"about_ca_topic_score_gemma":0.007899638,"teacher_disagreement_score":0.011528975,"about_ca_system_score_codex":0.0017297635,"about_ca_system_score_gemma":0.004997286,"threshold_uncertainty_score":0.03856826},"labels":[],"label_agreement":null},{"id":"W1973664142","doi":"10.1007/s00224-012-9382-7","title":"(Approximate) Uncertain Skylines","year":2012,"lang":"en","type":"article","venue":"Theory of Computing Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Skyline; Point (geometry); Probability distribution; Set (abstract data type); Simple (philosophy); Computer science; Lying; Construct (python library); Mathematical optimization; Algorithm; Mathematics; Data mining; Statistics; Geometry","score_opus":0.031101281482397936,"score_gpt":0.2640454652165077,"score_spread":0.23294418373410974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973664142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034893367,0.0011139951,0.95764476,0.00082273653,0.00012416809,0.000031488467,0.0009452645,0.0003627452,0.004061527],"genre_scores_gemma":[0.7780836,0.001966416,0.20988701,0.00026581384,0.00043519028,0.000118595824,0.0022039118,0.00018361544,0.006855916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985159,0.00041142825,0.000090605325,0.00038528146,0.00046897537,0.00012782372],"domain_scores_gemma":[0.99328095,0.0040418976,0.0006774959,0.0009672609,0.00083580386,0.00019656458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018302902,0.00078741676,0.0013530537,0.0020933582,0.0006880626,0.002805951,0.0019581593,0.0020575644,0.0066347746],"category_scores_gemma":[0.020258032,0.0008924336,0.000833068,0.0031199756,0.0015251167,0.0068449997,0.0018110581,0.002036271,0.00074145786],"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":[0.0003326747,0.00002924564,0.0033661544,0.00034521444,0.000088272674,0.00018385643,0.00017147702,0.7182695,0.0014026753,0.21757548,0.0063118376,0.051923636],"study_design_scores_gemma":[0.0000126235855,0.000015667141,0.00047611183,0.000020790278,0.000012458954,0.000095971955,0.00003515693,0.87619305,0.00036111104,0.12037065,0.0023937088,0.000012758169],"about_ca_topic_score_codex":0.0039116303,"about_ca_topic_score_gemma":0.0037285767,"teacher_disagreement_score":0.0066347746,"about_ca_system_score_codex":0.0015434875,"about_ca_system_score_gemma":0.0006945921,"threshold_uncertainty_score":0.022195518},"labels":[],"label_agreement":null},{"id":"W1976774657","doi":"10.1117/12.541894","title":"&lt;title&gt;A randomized heuristic approach for multidimensional association in target tracking&lt;/title&gt;","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Heuristic; Computational complexity theory; Computer science; Relaxation (psychology); Mathematical optimization; Tracking (education); Tree (set theory); Algorithm; Artificial intelligence; Mathematics","score_opus":0.010099061955353825,"score_gpt":0.21920683848387093,"score_spread":0.20910777652851711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976774657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008579499,0.0003922798,0.98614776,0.0003584666,0.000099178105,0.00012189591,0.00005821995,0.0004113775,0.0038313656],"genre_scores_gemma":[0.18595928,0.00032019021,0.8076454,0.00038620067,0.00011762325,0.0003320644,0.00022471354,0.00020804816,0.0048064874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984139,0.0009186339,0.00005272723,0.00019999921,0.00027062444,0.00014403727],"domain_scores_gemma":[0.9974184,0.0016199704,0.00024376731,0.0003573173,0.00025048465,0.000110043366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020636998,0.00061878416,0.0010548447,0.000750693,0.00073943444,0.0011635548,0.0016091893,0.0013083135,0.0058127134],"category_scores_gemma":[0.0041553658,0.0004049246,0.00075339084,0.0012348056,0.00100659,0.0010819724,0.00087913155,0.0013791043,0.001436462],"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":[0.00028577304,0.00025830374,0.0007512756,0.00017173361,0.000088493834,0.00014608963,0.000059811948,0.7820049,0.005165528,0.044789307,0.009989876,0.15628894],"study_design_scores_gemma":[0.000032087308,0.00006909403,0.00008440675,0.000011905445,0.000009872747,0.00003176872,0.000009525236,0.9906708,0.00109856,0.0058960686,0.0020728654,0.000013044536],"about_ca_topic_score_codex":0.0031407527,"about_ca_topic_score_gemma":0.0060645253,"teacher_disagreement_score":0.0058127134,"about_ca_system_score_codex":0.001190284,"about_ca_system_score_gemma":0.0017755572,"threshold_uncertainty_score":0.01944542},"labels":[],"label_agreement":null},{"id":"W1977646040","doi":"10.1145/1167350.1167389","title":"On using conditional rotations and randomized heuristics for self-organizing ternary search tries","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Heuristics; Binary search tree; Trie; Ternary search tree; Computer science; Heuristic; Binary number; A priori and a posteriori; Theoretical computer science; Invariant (physics); Search problem; Set (abstract data type); Algorithm; Search algorithm; Binary tree; Search tree; Mathematics; Data structure; Mathematical optimization; Artificial intelligence; Interval tree; Arithmetic","score_opus":0.024937798078680195,"score_gpt":0.2852842621220211,"score_spread":0.26034646404334094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977646040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067584775,0.0006839833,0.9253488,0.00016981649,0.00005077257,0.0002681188,0.0000777458,0.0013939494,0.0044220327],"genre_scores_gemma":[0.53440034,0.00038766096,0.46172264,0.00023963065,0.0000583835,0.00046420886,0.0002657427,0.00026706484,0.0021943154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986745,0.00064621127,0.000093145114,0.00016766312,0.00024084409,0.00017772912],"domain_scores_gemma":[0.99419147,0.0034982618,0.0004756742,0.00109242,0.0004971758,0.00024489974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020155993,0.00056986825,0.0011330238,0.0013985238,0.0007893175,0.0012446333,0.0018981375,0.00089127704,0.0023588627],"category_scores_gemma":[0.010295932,0.00042184547,0.0005348827,0.0019609784,0.0016267606,0.002474009,0.0015828491,0.0007203532,0.0006880954],"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":[0.0008106967,0.00022372154,0.0022522006,0.00024899992,0.00009692286,0.00009802683,0.00043489295,0.5460045,0.008017858,0.100603096,0.0032291848,0.3379799],"study_design_scores_gemma":[0.0001070723,0.00024747936,0.00029018158,0.00003009128,0.000035056615,0.00008863359,0.00008186213,0.97349894,0.003393335,0.019646982,0.0025364663,0.000043850665],"about_ca_topic_score_codex":0.003079084,"about_ca_topic_score_gemma":0.0047505423,"teacher_disagreement_score":0.003079084,"about_ca_system_score_codex":0.00093061395,"about_ca_system_score_gemma":0.0020281265,"threshold_uncertainty_score":0.010659695},"labels":[],"label_agreement":null},{"id":"W1977733667","doi":"10.1093/comjnl/bxt106","title":"A New and Effective Approach to GML Documents Compression","year":2013,"lang":"en","type":"article","venue":"The Computer Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; XML; Compression ratio; Information retrieval; Data mining; Compression (physics); Encoding (memory); Tree (set theory); Data compression; Pattern matching; Database; Artificial intelligence; World Wide Web","score_opus":0.008118702872697147,"score_gpt":0.22243529010725135,"score_spread":0.2143165872345542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977733667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017105028,0.000959467,0.9751061,0.00040101496,0.00023208588,0.00019302679,0.00026626268,0.002666356,0.0030705377],"genre_scores_gemma":[0.13939537,0.0016624494,0.8490546,0.00035947748,0.00022044142,0.00030932823,0.0012460763,0.00031004948,0.0074423263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913955,0.00006951039,0.0000886579,0.00011785039,0.0005369884,0.0000475521],"domain_scores_gemma":[0.9992292,0.00011755955,0.00007181503,0.00022793577,0.00032993668,0.000023477269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002958691,0.0006652685,0.0004706373,0.00201911,0.0005138299,0.0009962427,0.0009880945,0.0005861678,0.0020129306],"category_scores_gemma":[0.0018545948,0.00022778187,0.0004304625,0.002691009,0.000546383,0.0020364956,0.0011083691,0.00078261463,0.0011956117],"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":[0.00020529616,0.00008102994,0.00080687745,0.00030731907,0.00003399346,0.00038983137,0.00026951416,0.009431145,0.099701926,0.01782436,0.006971594,0.863977],"study_design_scores_gemma":[0.000116691044,0.00039406543,0.0022694306,0.000117732285,0.00010072184,0.0034888082,0.0003221897,0.4683184,0.39000356,0.024691572,0.1100792,0.00009766179],"about_ca_topic_score_codex":0.0009623619,"about_ca_topic_score_gemma":0.00094242196,"teacher_disagreement_score":0.00201911,"about_ca_system_score_codex":0.00040957294,"about_ca_system_score_gemma":0.0006549359,"threshold_uncertainty_score":0.006733954},"labels":[],"label_agreement":null},{"id":"W1977873546","doi":"10.1007/s11590-006-0032-3","title":"On pitfalls in computing the geodetic number of a graph","year":2006,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Geodetic datum; Combinatorics; Mathematics; Shortest path problem; Graph; Geodesic; Discrete mathematics","score_opus":0.004860542944742337,"score_gpt":0.20948864178880877,"score_spread":0.20462809884406644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977873546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11017132,0.0036105784,0.86737853,0.007332401,0.0006277123,0.00012589987,0.0006143494,0.0018621129,0.0082770875],"genre_scores_gemma":[0.502406,0.0016379069,0.488871,0.00095919,0.0006516591,0.00016713963,0.00084379787,0.00077736605,0.0036859126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99290717,0.0033410524,0.000436987,0.0010479374,0.0018003338,0.0004665624],"domain_scores_gemma":[0.95686305,0.03276382,0.0013167472,0.0064016986,0.0019139291,0.0007406445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072887084,0.0013846324,0.002661586,0.0034769713,0.0028196094,0.0040361374,0.003294778,0.0024178214,0.0047890088],"category_scores_gemma":[0.05751175,0.0013448463,0.0011607617,0.0070275003,0.004176221,0.01331789,0.005559639,0.004248525,0.001089318],"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":[0.0012001416,0.00018906075,0.007457885,0.00034263372,0.00017719215,0.00027435567,0.0005293478,0.5308233,0.0022387155,0.20157824,0.018671755,0.23651746],"study_design_scores_gemma":[0.000056449,0.000058569512,0.0005865515,0.000039074126,0.000028083768,0.00008551529,0.00010435171,0.73969793,0.0011865678,0.25577077,0.0023623488,0.000023666344],"about_ca_topic_score_codex":0.011695214,"about_ca_topic_score_gemma":0.016859071,"teacher_disagreement_score":0.011695214,"about_ca_system_score_codex":0.0024840366,"about_ca_system_score_gemma":0.0022457372,"threshold_uncertainty_score":0.03854686},"labels":[],"label_agreement":null},{"id":"W1979383090","doi":"10.1109/icassp.2014.6854782","title":"Probabilistic ranking of multi-attribute items using indifference curve","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Computer science; Rank (graph theory); Probabilistic logic; Preference; Process (computing); Quality (philosophy); Selection (genetic algorithm); Machine learning; Competition (biology); Data mining; Information retrieval; Artificial intelligence; Statistics; Mathematics","score_opus":0.054665929588310194,"score_gpt":0.27217448058857674,"score_spread":0.21750855100026656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979383090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012184671,0.0005314037,0.98585504,0.00012843155,0.000023302491,0.000087651635,0.00010184256,0.00020584342,0.0008817107],"genre_scores_gemma":[0.6009905,0.0010455974,0.3930718,0.0002048229,0.00015157113,0.0004494587,0.0007801819,0.00013175172,0.0031743143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98434424,0.0072078197,0.000866433,0.0021565342,0.0048104674,0.0006145073],"domain_scores_gemma":[0.97711563,0.015630059,0.0015758855,0.0021392046,0.0030225606,0.00051668956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011750496,0.0016032166,0.0037285043,0.007069932,0.0010682452,0.0034938697,0.005591378,0.0017966682,0.0028542764],"category_scores_gemma":[0.034595642,0.00089235744,0.002391045,0.006454921,0.0015171668,0.006979162,0.002652058,0.0026865606,0.001141171],"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":[0.00040497386,0.00027635528,0.010476048,0.00042316565,0.0005352637,0.00029010756,0.00032987774,0.636692,0.002050039,0.06267417,0.0026902356,0.28315783],"study_design_scores_gemma":[0.00001898831,0.00013909397,0.0011643904,0.000021478687,0.000043183467,0.00014733386,0.000039427814,0.96372694,0.0008762731,0.032581918,0.0011719335,0.00006900764],"about_ca_topic_score_codex":0.0029602465,"about_ca_topic_score_gemma":0.0016756711,"teacher_disagreement_score":0.011750496,"about_ca_system_score_codex":0.0018497074,"about_ca_system_score_gemma":0.0013419624,"threshold_uncertainty_score":0.062143266},"labels":[],"label_agreement":null},{"id":"W1979503369","doi":"10.1109/itsc.2007.4357638","title":"Improving Vehicle Positioning and Visual Feature Estimates through Mutual Constraint","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Position (finance); Global Positioning System; Constraint (computer-aided design); Computer science; Feature (linguistics); Artificial intelligence; Visualization; Computer vision; Bayesian probability; Engineering","score_opus":0.00919495397747208,"score_gpt":0.2609781876478673,"score_spread":0.25178323367039523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979503369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070839715,0.00010338255,0.99107563,0.00014030183,0.000016943404,0.000019543108,0.00005456186,0.0002719529,0.0012336473],"genre_scores_gemma":[0.56342226,0.00029030602,0.43214697,0.000190754,0.00010364401,0.00017800553,0.00051551225,0.00018957465,0.0029629671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967885,0.0009629143,0.00015944042,0.0006566605,0.001182344,0.00025010837],"domain_scores_gemma":[0.99426466,0.0029187005,0.00083521864,0.0009555408,0.00091739174,0.00010850618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028792117,0.0018231415,0.0016065203,0.0014578244,0.0008133821,0.0015166735,0.002758606,0.0017463189,0.0019196568],"category_scores_gemma":[0.019255625,0.0010762986,0.001064378,0.0023776102,0.0012920204,0.004605487,0.0045703463,0.0018220104,0.000961358],"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":[0.00026570074,0.000089125584,0.0016932826,0.00015425419,0.00014501844,0.00012781269,0.00031682552,0.7404643,0.008735491,0.034682557,0.0018276424,0.21149802],"study_design_scores_gemma":[0.000028125845,0.000072293624,0.0007097571,0.000018067321,0.00003274332,0.00007525575,0.000032905973,0.97759104,0.0040416,0.0156135755,0.0017446764,0.000039964787],"about_ca_topic_score_codex":0.009666558,"about_ca_topic_score_gemma":0.0071783056,"teacher_disagreement_score":0.009666558,"about_ca_system_score_codex":0.0010308385,"about_ca_system_score_gemma":0.0017078479,"threshold_uncertainty_score":0.01922059},"labels":[],"label_agreement":null},{"id":"W1980185632","doi":"10.14778/2350229.2350241","title":"Fundamentals of order dependencies","year":2012,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Functional dependency; Lexicographical order; Axiom; Dependency theory (database theory); Tuple; Computer science; Inference; Dependency (UML); Set (abstract data type); Query optimization; Theoretical computer science; Mathematics; Data mining; Artificial intelligence; Discrete mathematics; Relational database; Programming language; Combinatorics","score_opus":0.015635755890120978,"score_gpt":0.22162146837522673,"score_spread":0.20598571248510575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980185632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008927558,0.006125013,0.9011064,0.0048802136,0.0010478097,0.00021899983,0.002230905,0.00088880997,0.074574254],"genre_scores_gemma":[0.279914,0.015019823,0.65918493,0.0042169928,0.0035118188,0.0010708921,0.003837719,0.0008717074,0.03237215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994282,0.0012267185,0.00074984547,0.0012684814,0.0019738583,0.00049912214],"domain_scores_gemma":[0.99017626,0.0057252753,0.0005882928,0.0016193341,0.0016501345,0.00024064412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048755934,0.0012849596,0.0010253678,0.003275057,0.0029505685,0.0044853473,0.0024598646,0.0022559906,0.009872815],"category_scores_gemma":[0.01207822,0.0013843088,0.0024940227,0.004229862,0.0072171506,0.012880397,0.004340912,0.005852221,0.00369793],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014314301,0.00001471411,0.00023406083,0.00011084408,0.000012622884,0.000101242615,0.0002147517,0.0016236231,0.00040281788,0.9773286,0.0038158458,0.016126627],"study_design_scores_gemma":[0.000012581411,0.000016179058,0.00014522653,0.00005568294,0.00001832637,0.00024109842,0.000048239504,0.0038569197,0.000734066,0.9276745,0.06717586,0.000021365973],"about_ca_topic_score_codex":0.005617953,"about_ca_topic_score_gemma":0.0034436975,"teacher_disagreement_score":0.009872815,"about_ca_system_score_codex":0.0027054497,"about_ca_system_score_gemma":0.0027499553,"threshold_uncertainty_score":0.033027887},"labels":[],"label_agreement":null},{"id":"W1980418511","doi":"10.1109/icde.2007.367933","title":"Evaluating Proximity Relations Under Uncertainty","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Relation (database); Position (finance); Object (grammar); Computer science; Point (geometry); Process (computing); Data mining; Algorithm; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.0748826486360854,"score_gpt":0.3539822720212371,"score_spread":0.2790996233851517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980418511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15817769,0.0013445307,0.8368769,0.00044495132,0.000037144255,0.00013141759,0.0004005282,0.00038882752,0.0021979497],"genre_scores_gemma":[0.8298075,0.00057418016,0.16791266,0.00006355501,0.000110179935,0.0001487962,0.00063924224,0.000073531155,0.0006704001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98973596,0.0032241845,0.00077005057,0.0022334873,0.0035346232,0.00050170167],"domain_scores_gemma":[0.9420113,0.04833639,0.0039463295,0.0027141084,0.0022784858,0.0007133981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075770514,0.0010384682,0.0019240909,0.005518002,0.0014532711,0.004139812,0.0019579802,0.002460397,0.0016564984],"category_scores_gemma":[0.07862502,0.0007222979,0.00092472904,0.0040238807,0.0022488825,0.007887789,0.0046015256,0.001432476,0.00032798556],"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":[0.00070891675,0.000120276105,0.03159944,0.00033118366,0.00023644806,0.0005142154,0.0011412713,0.7331229,0.0029979749,0.057188764,0.0013144693,0.1707242],"study_design_scores_gemma":[0.000013852062,0.00012014509,0.0038218657,0.000034355664,0.00003631032,0.00028650247,0.0003262305,0.94335085,0.0017026179,0.0492319,0.001039539,0.00003580009],"about_ca_topic_score_codex":0.005653166,"about_ca_topic_score_gemma":0.0030480523,"teacher_disagreement_score":0.0075770514,"about_ca_system_score_codex":0.0021538313,"about_ca_system_score_gemma":0.001113638,"threshold_uncertainty_score":0.040071726},"labels":[],"label_agreement":null},{"id":"W1981953140","doi":"10.1007/s10700-006-0020-1","title":"A concept geometry for conceptual spaces","year":2006,"lang":"en","type":"article","venue":"Fuzzy Optimization and Decision Making","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Mathematics; Hypercube; Object (grammar); Space (punctuation); Similarity (geometry); Algebra over a field; Scalar (mathematics); Computer science; Pure mathematics; Theoretical computer science; Artificial intelligence; Geometry; Discrete mathematics; Image (mathematics)","score_opus":0.017281373451054325,"score_gpt":0.27140670863036326,"score_spread":0.2541253351793089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981953140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01918076,0.0028599673,0.9557176,0.0024856904,0.0003700827,0.000070764414,0.00031305882,0.000112863694,0.018889174],"genre_scores_gemma":[0.46420854,0.003340054,0.5176358,0.00086048385,0.0009090454,0.00035775243,0.0006804264,0.00017287185,0.011835028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976393,0.0010247483,0.00019181862,0.0005104768,0.00049658306,0.00013709338],"domain_scores_gemma":[0.9972909,0.0013230422,0.00023809409,0.00033194455,0.00054025376,0.0002756803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026404131,0.0010432383,0.0012443347,0.003706189,0.0023326133,0.005518538,0.0016645605,0.0017775121,0.0048906687],"category_scores_gemma":[0.0063453973,0.0007857707,0.0023015935,0.0033981705,0.0067239674,0.009254087,0.0042944164,0.004223819,0.0007930566],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057406105,0.000003627387,0.000037868096,0.000023300683,0.000006394429,0.000027248223,0.00014071622,0.00080092024,0.00017461585,0.9951715,0.00044938375,0.0031585502],"study_design_scores_gemma":[0.00000453153,0.000016966203,0.0000629922,0.0000142160025,0.000006359548,0.00006132151,0.000077983226,0.0049661333,0.0000989302,0.98700094,0.007681252,0.000008423089],"about_ca_topic_score_codex":0.0021121576,"about_ca_topic_score_gemma":0.0009860322,"teacher_disagreement_score":0.005518538,"about_ca_system_score_codex":0.0021315252,"about_ca_system_score_gemma":0.0008477204,"threshold_uncertainty_score":0.016360939},"labels":[],"label_agreement":null},{"id":"W1982250524","doi":"10.1007/s00453-006-0112-x","title":"Large Deviations for the Weighted Height of an Extended Class of Trees","year":2006,"lang":"en","type":"article","venue":"Algorithmica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":49,"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":"Mathematics; Combinatorics; Binary search tree; Class (philosophy); Weight-balanced tree; Intersection (aeronautics); Constant (computer programming); Theory of computation; Binary tree; Tree (set theory); Random binary tree; Limit (mathematics); Plane (geometry); Discrete mathematics; Large deviations theory; Statistics; Algorithm; Geometry; Mathematical analysis; Computer science","score_opus":0.010497652059589604,"score_gpt":0.24524001371447793,"score_spread":0.23474236165488832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982250524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45652416,0.0025453523,0.5160569,0.005524198,0.00049312337,0.00006730059,0.0007430605,0.0006729439,0.017372895],"genre_scores_gemma":[0.92063,0.001623422,0.064159155,0.00086859846,0.0011410507,0.00026661216,0.0009426367,0.00043294206,0.009935651],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975504,0.0007863,0.000094743955,0.00052237976,0.00069038576,0.00035564153],"domain_scores_gemma":[0.9596056,0.029769044,0.0025705574,0.0029789312,0.002008091,0.0030678771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072801705,0.0010504149,0.0018782234,0.0032184846,0.0018180825,0.003966077,0.0041721184,0.0028795814,0.0066061993],"category_scores_gemma":[0.04630668,0.00084044156,0.0013618467,0.0024135811,0.005446051,0.008567233,0.004159292,0.00637178,0.00070869614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011072207,0.000053289026,0.002318203,0.00009173564,0.00006954078,0.00010313643,0.00022420952,0.031678632,0.0012844714,0.9528452,0.0029489049,0.008272006],"study_design_scores_gemma":[0.000031475167,0.00002649171,0.00085949746,0.0000257885,0.000019837136,0.000100484045,0.00005772344,0.1672105,0.00030600736,0.83047765,0.0008583077,0.000026149806],"about_ca_topic_score_codex":0.001318808,"about_ca_topic_score_gemma":0.0017321167,"teacher_disagreement_score":0.0072801705,"about_ca_system_score_codex":0.002229887,"about_ca_system_score_gemma":0.0011417983,"threshold_uncertainty_score":0.03850168},"labels":[],"label_agreement":null},{"id":"W1982600687","doi":"10.1016/j.cor.2004.11.021","title":"Time-dependent shortest paths through a fixed sequence of nodes: application to a travel planning problem","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Sequence (biology); Computer science; Computation; Travel time; Mathematical optimization; Route planning; Decomposition; Plan (archaeology); Shortest path problem; Mathematics; Algorithm; Theoretical computer science; Graph; Transport engineering","score_opus":0.08730844122553108,"score_gpt":0.3832802464060155,"score_spread":0.2959718051804844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982600687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53153497,0.0010146886,0.4575513,0.0014217593,0.00015853906,0.00024594853,0.0009795928,0.0004812945,0.0066118673],"genre_scores_gemma":[0.79054165,0.0006878172,0.2027275,0.000057763933,0.0000720338,0.00019563179,0.00071102614,0.00011976432,0.0048869136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995153,0.00021023519,0.000022960077,0.00010804728,0.00008520069,0.000058275684],"domain_scores_gemma":[0.9914698,0.0072921934,0.00031654703,0.00014362774,0.00038936432,0.00038850788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019884014,0.0010986635,0.0015077837,0.0017606198,0.0014510737,0.0012541852,0.002732388,0.0029384382,0.0034526621],"category_scores_gemma":[0.010623533,0.0011044182,0.0012515265,0.0038485809,0.0013299977,0.0020106398,0.0012957661,0.0018294096,0.00017422193],"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":[0.00008195656,0.00008140497,0.00032381513,0.00006605339,0.000021862536,0.000084491236,0.00005788238,0.9888359,0.0002431428,0.0045948485,0.0004889863,0.005119631],"study_design_scores_gemma":[0.000024450443,0.000024176745,0.00009070305,0.0000022315853,0.000009497193,0.000008492956,0.000019413,0.9972857,0.00008084756,0.0023373268,0.00011200962,0.000005090671],"about_ca_topic_score_codex":0.052293647,"about_ca_topic_score_gemma":0.030202992,"teacher_disagreement_score":0.052293647,"about_ca_system_score_codex":0.0025722354,"about_ca_system_score_gemma":0.0025522995,"threshold_uncertainty_score":0.103978574},"labels":[],"label_agreement":null},{"id":"W1982732476","doi":"10.1109/edoc.2012.29","title":"Efficient QoS-based Service Selection with Consideration of User Requirements","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skyline; Computer science; Quality of service; Ranking (information retrieval); Selection (genetic algorithm); Process (computing); Service (business); Data mining; Computation; Quality (philosophy); Machine learning; Distributed computing; Database; Computer network; Algorithm","score_opus":0.02757498345983851,"score_gpt":0.25533282575544985,"score_spread":0.22775784229561136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982732476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047553744,0.00044147394,0.9467674,0.0002962681,0.000046636706,0.00016856074,0.00020015823,0.0028942286,0.0016315194],"genre_scores_gemma":[0.6426744,0.0003309256,0.3540196,0.000119166165,0.000099670615,0.00015099168,0.00091733475,0.0002592488,0.0014285077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99749845,0.0008282167,0.0001690417,0.00033649005,0.0009153423,0.0002523436],"domain_scores_gemma":[0.9967487,0.0013838001,0.00020743691,0.0005838082,0.0008568189,0.00021942536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019126675,0.0012084991,0.0019383802,0.0017621231,0.001299529,0.0015365047,0.001260146,0.00080608664,0.0019478515],"category_scores_gemma":[0.0058974614,0.00043623708,0.00069939217,0.002965144,0.0004333972,0.00278274,0.0010241835,0.00071508624,0.0009874306],"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":[0.0013034595,0.00046399355,0.0071387426,0.00031507673,0.00015700252,0.00031369666,0.00036876058,0.28433788,0.0493346,0.012430021,0.01836228,0.6254745],"study_design_scores_gemma":[0.00003611541,0.00006189868,0.0007457234,0.0000039045917,0.000016128737,0.00011480507,0.00007339891,0.9891459,0.004324793,0.004210208,0.0012486033,0.000018492607],"about_ca_topic_score_codex":0.0057830806,"about_ca_topic_score_gemma":0.0049117384,"teacher_disagreement_score":0.0057830806,"about_ca_system_score_codex":0.0010295773,"about_ca_system_score_gemma":0.0015078491,"threshold_uncertainty_score":0.011498868},"labels":[],"label_agreement":null},{"id":"W1983018368","doi":"10.1109/icde.2012.124","title":"Efficient Top-k Keyword Search in Graphs with Polynomial Delay","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Keyword search; Set (abstract data type); Ranking (information retrieval); Graph; Theoretical computer science; Information retrieval","score_opus":0.014786198395387352,"score_gpt":0.24040256443500058,"score_spread":0.22561636603961321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983018368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03958798,0.0006387619,0.91919243,0.00063521235,0.00005904559,0.00022740639,0.0014581704,0.034883764,0.0033172695],"genre_scores_gemma":[0.2860291,0.00033298708,0.706476,0.00019710137,0.00005612088,0.00019586971,0.0022601397,0.001010579,0.0034421163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981172,0.0005728859,0.00020506336,0.00044719447,0.0004194706,0.0002382575],"domain_scores_gemma":[0.9946583,0.0030807313,0.0002748202,0.0011300449,0.0005977475,0.0002583184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015766601,0.0011640525,0.0017648141,0.0021717171,0.0014085236,0.0025798352,0.0022520786,0.0017417115,0.0066528814],"category_scores_gemma":[0.009063564,0.0006518162,0.0009354259,0.00391471,0.0007017737,0.0054495153,0.0025555964,0.0010469407,0.00416485],"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":[0.0028143206,0.00041358682,0.0030398318,0.0011739875,0.00017939926,0.00064380764,0.00085137866,0.12267336,0.061528787,0.030484393,0.050113812,0.7260833],"study_design_scores_gemma":[0.00031545828,0.00023887641,0.0006222619,0.00003550551,0.00007357115,0.0005473863,0.00036069282,0.8751311,0.029985799,0.08008212,0.012514452,0.00009279153],"about_ca_topic_score_codex":0.0064807767,"about_ca_topic_score_gemma":0.009100904,"teacher_disagreement_score":0.0066528814,"about_ca_system_score_codex":0.0016131994,"about_ca_system_score_gemma":0.001918358,"threshold_uncertainty_score":0.022256136},"labels":[],"label_agreement":null},{"id":"W1983171268","doi":"10.1239/aap/1025131226","title":"Density approximation and exact simulation of random variables that are solutions of fixed-point equations","year":2002,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Fixed point; Probabilistic logic; Random variable; Applied mathematics; Class (philosophy); Limit (mathematics); Sampling (signal processing); Discrete mathematics; Mathematical analysis; Statistics","score_opus":0.03694009101673632,"score_gpt":0.2498786806779737,"score_spread":0.21293858966123738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983171268","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027378043,0.00005890728,0.9965252,0.000045258654,0.000016389162,0.000022863378,0.0000136293465,0.00009898021,0.00048078928],"genre_scores_gemma":[0.2701196,0.00049544824,0.7240732,0.00014643425,0.000101996884,0.000625685,0.00032022333,0.0002549493,0.0038623968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976954,0.0010745687,0.00009866304,0.00027075046,0.00068242574,0.0001780736],"domain_scores_gemma":[0.98843354,0.0087387515,0.0006751167,0.0009533828,0.0009325615,0.0002666727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042557125,0.0011842264,0.0015513181,0.0017414114,0.0008568619,0.0016570158,0.0027905672,0.0019481621,0.0031986178],"category_scores_gemma":[0.028945263,0.0008826228,0.0016187755,0.0015916463,0.0025696065,0.0027874785,0.0026088036,0.002632966,0.0007660529],"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":[0.000105016436,0.000051078085,0.0010662347,0.00008392074,0.000055182772,0.00012786478,0.00013522289,0.57519853,0.0011779765,0.39995858,0.00070465606,0.021335833],"study_design_scores_gemma":[0.000011130141,0.000011954491,0.00004532857,0.000010350002,0.000004724588,0.000034132525,0.000006418219,0.9453334,0.00042734298,0.053577516,0.00052813516,0.000009475016],"about_ca_topic_score_codex":0.0040265587,"about_ca_topic_score_gemma":0.0020137813,"teacher_disagreement_score":0.0042557125,"about_ca_system_score_codex":0.0017308779,"about_ca_system_score_gemma":0.0013466239,"threshold_uncertainty_score":0.022506654},"labels":[],"label_agreement":null},{"id":"W1984043155","doi":"10.1145/2189403.2189405","title":"CTS 2011 Workshop Report: the Fourth International Workshop on Computational Transportation Science","year":2012,"lang":"en","type":"article","venue":"SIGSPATIAL Special","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":"University of Calgary","funders":"","keywords":"Library science; Computer science; Operations research; Engineering","score_opus":0.02560025034052508,"score_gpt":0.2774527309373553,"score_spread":0.2518524805968302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984043155","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011107503,0.015673252,0.2500031,0.14650643,0.13108084,0.0020301985,0.083852574,0.010170979,0.34957516],"genre_scores_gemma":[0.04228461,0.01332741,0.08851323,0.005033747,0.014570157,0.00121255,0.13487287,0.005675035,0.6945104],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99601763,0.00091125845,0.00020273423,0.00050376763,0.0018651024,0.0004996018],"domain_scores_gemma":[0.987673,0.0014439414,0.00020942859,0.0011650716,0.006304754,0.0032038342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01026695,0.0021019252,0.0019480814,0.002918023,0.0028270106,0.010519111,0.0037450131,0.0033896675,0.15638292],"category_scores_gemma":[0.009009536,0.0007374947,0.0020479672,0.0036527745,0.0010963869,0.0058172727,0.004595702,0.005740645,0.07438727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007422858,0.000051227707,0.0002638293,0.000105324754,0.000023343964,0.00004756808,0.000059322403,0.0016234798,0.00041208984,0.005940305,0.9685472,0.022852076],"study_design_scores_gemma":[0.000035028537,0.00005243464,0.00051642174,0.000116292395,0.000024666866,0.000059393,0.00021196918,0.0034744248,0.0013252017,0.005469787,0.9886764,0.00003802551],"about_ca_topic_score_codex":0.026980558,"about_ca_topic_score_gemma":0.038151685,"teacher_disagreement_score":0.15638292,"about_ca_system_score_codex":0.0053721247,"about_ca_system_score_gemma":0.01459716,"threshold_uncertainty_score":0.52315295},"labels":[],"label_agreement":null},{"id":"W1984054760","doi":"10.1007/s00236-004-0157-8","title":"A deterministic skip list for k-dimensional range search","year":2005,"lang":"en","type":"article","venue":"Acta Informatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Nova Scotia Community College","funders":"","keywords":"Range (aeronautics); Data structure; Commutative property; Mathematics; Set (abstract data type); Theory of computation; Discrete mathematics; Combinatorics; Cartesian product; Extension (predicate logic); Associative property; Computer science; Algorithm; Pure mathematics","score_opus":0.021941126642092315,"score_gpt":0.26816769603582175,"score_spread":0.24622656939372944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984054760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018368147,0.0006741803,0.9722053,0.00026610374,0.00011384237,0.00017664414,0.0007908507,0.0035157753,0.0038891337],"genre_scores_gemma":[0.20102648,0.0003964143,0.78709495,0.0002812185,0.00009645784,0.00051167025,0.0022927807,0.00056108856,0.0077389097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785775,0.0005266558,0.00023984064,0.00032290653,0.0007265023,0.00032643034],"domain_scores_gemma":[0.99405056,0.0027911875,0.0002512995,0.0016040008,0.0010496407,0.00025321898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022039728,0.0008178362,0.0020092318,0.0017664983,0.0016116258,0.0020306024,0.0035253912,0.0015501744,0.012990009],"category_scores_gemma":[0.0105914455,0.00085018855,0.0009545448,0.0032069907,0.00091509614,0.003880775,0.00430724,0.0015190447,0.0035985457],"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":[0.002549252,0.00041349354,0.002082944,0.0008723914,0.00012763483,0.00021717671,0.00031987592,0.20779175,0.007596912,0.075417094,0.029284881,0.67332655],"study_design_scores_gemma":[0.00022464791,0.0002933221,0.00028831887,0.00007081377,0.00006889961,0.00012959374,0.000078875906,0.8929481,0.00398152,0.094604105,0.007252585,0.000059122838],"about_ca_topic_score_codex":0.0043357466,"about_ca_topic_score_gemma":0.007821101,"teacher_disagreement_score":0.012990009,"about_ca_system_score_codex":0.001101098,"about_ca_system_score_gemma":0.0038513348,"threshold_uncertainty_score":0.0434559},"labels":[],"label_agreement":null},{"id":"W1986041862","doi":"10.14778/1920841.1920982","title":"An access cost-aware approach for object retrieval over multiple sources","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Overhead (engineering); Probabilistic logic; Object (grammar); Source code; Selection (genetic algorithm); Data mining; Information retrieval; Data source; Database; Artificial intelligence; Programming language","score_opus":0.024767759603829508,"score_gpt":0.279069790039691,"score_spread":0.2543020304358615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986041862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011602663,0.00039846747,0.9861456,0.00021231279,0.000021244596,0.00010898451,0.00009745097,0.0006288899,0.0007844221],"genre_scores_gemma":[0.2986858,0.00059046934,0.6960201,0.00018639822,0.000182547,0.00031788534,0.0005868316,0.00023905333,0.0031908266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961377,0.0007155513,0.00028942194,0.0006456359,0.0019584065,0.00025337993],"domain_scores_gemma":[0.9928005,0.002949278,0.0005798573,0.001965022,0.0014970471,0.00020842628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028619163,0.0010559638,0.0022174362,0.0045147724,0.0012621406,0.0029663045,0.0037889064,0.0015330104,0.0022786236],"category_scores_gemma":[0.011820064,0.0007677681,0.0014289335,0.006404118,0.0009971847,0.006970945,0.003256321,0.0014054392,0.0007588859],"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":[0.0006988843,0.00050610455,0.004183117,0.0003937241,0.00030966508,0.00038135977,0.000568464,0.23961498,0.031368665,0.056859888,0.0069395434,0.6581755],"study_design_scores_gemma":[0.000057330388,0.00015776455,0.0007560142,0.000013634053,0.00011024038,0.00043047112,0.000114516915,0.9551309,0.009184999,0.03017065,0.0038197995,0.000053766133],"about_ca_topic_score_codex":0.003236054,"about_ca_topic_score_gemma":0.004235825,"teacher_disagreement_score":0.0045147724,"about_ca_system_score_codex":0.0013806531,"about_ca_system_score_gemma":0.0018311954,"threshold_uncertainty_score":0.015135467},"labels":[],"label_agreement":null},{"id":"W1987170162","doi":"10.1016/j.engappai.2014.04.004","title":"FIA5: A customized Fuzzy Interval Algebra for modeling spatial relevancy in urban context-aware systems","year":2014,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"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 Laval","funders":"","keywords":"Computer science; Context (archaeology); Fuzzy logic; Interval (graph theory); Theoretical computer science; Artificial intelligence; Algebra over a field; Data mining; Mathematics","score_opus":0.02274273637091325,"score_gpt":0.25329204964011764,"score_spread":0.2305493132692044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987170162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006328368,0.00013290443,0.98908174,0.00005167076,0.00004896318,0.000055960812,0.0006286532,0.0011438536,0.0025278612],"genre_scores_gemma":[0.33826715,0.00031335512,0.655249,0.00011765518,0.000085477346,0.0002410219,0.0018375552,0.00021470526,0.0036740026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926347,0.00015014573,0.00007451873,0.00017223027,0.0002717466,0.000067833644],"domain_scores_gemma":[0.9994406,0.00014884221,0.00006434836,0.0001357152,0.00016303727,0.00004750777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011144753,0.000718714,0.00074829283,0.0010452091,0.0006629868,0.0017804117,0.0021275815,0.0007704022,0.0040542665],"category_scores_gemma":[0.002633398,0.00031850958,0.0016003129,0.0012376003,0.0004821164,0.0020118572,0.0010660655,0.0013261335,0.00086858746],"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":[0.00018983624,0.00015880166,0.0023269895,0.0002450729,0.00015566392,0.0002890031,0.00035858536,0.6189576,0.009965327,0.2204223,0.0077232122,0.13920762],"study_design_scores_gemma":[0.00001053252,0.00003112408,0.00023611827,0.000015736585,0.000026324935,0.00005976413,0.000032276632,0.9491174,0.0020525388,0.038127366,0.010270462,0.000020303414],"about_ca_topic_score_codex":0.011820782,"about_ca_topic_score_gemma":0.012642817,"teacher_disagreement_score":0.011820782,"about_ca_system_score_codex":0.00091987004,"about_ca_system_score_gemma":0.0013836438,"threshold_uncertainty_score":0.02350396},"labels":[],"label_agreement":null},{"id":"W1987268029","doi":"10.1007/s11042-007-0103-1","title":"Flexible integration of multimedia sub-queries with qualitative preferences","year":2007,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Flexibility (engineering); Set (abstract data type); Matching (statistics); Rank (graph theory); Object (grammar); Function (biology); Information retrieval; Similarity (geometry); Monotone polygon; Theoretical computer science; Data mining; Image (mathematics); Artificial intelligence; Programming language","score_opus":0.05049170897294804,"score_gpt":0.3214820700270039,"score_spread":0.2709903610540559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987268029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050829124,0.00043098678,0.9313956,0.0005840554,0.00006639872,0.00038225995,0.001266371,0.009051446,0.005993645],"genre_scores_gemma":[0.5595325,0.0004618904,0.42944747,0.0005552324,0.00009471398,0.00046253417,0.0027219753,0.0017567701,0.004966924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954454,0.0012822376,0.0004704597,0.0004926782,0.002055401,0.0002538097],"domain_scores_gemma":[0.9907862,0.005012275,0.00029365905,0.0023265919,0.0012573027,0.00032403017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046584653,0.0011300854,0.0014776953,0.0016846331,0.0004988154,0.0035527947,0.0022649826,0.0009824202,0.005580211],"category_scores_gemma":[0.018015249,0.0005945499,0.0010619396,0.0020321433,0.00064122264,0.005808626,0.0043296227,0.0012682581,0.001380713],"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":[0.003297953,0.0008603946,0.008317441,0.0014275984,0.00041440755,0.0013724634,0.00334442,0.03983497,0.10193655,0.07587558,0.018502207,0.74481595],"study_design_scores_gemma":[0.00023557278,0.0004031719,0.0027323093,0.00018766675,0.00034530865,0.0008508776,0.002572478,0.67390484,0.12456797,0.14334512,0.050642647,0.00021210562],"about_ca_topic_score_codex":0.0012835047,"about_ca_topic_score_gemma":0.0018380134,"teacher_disagreement_score":0.005580211,"about_ca_system_score_codex":0.0006539795,"about_ca_system_score_gemma":0.00059208774,"threshold_uncertainty_score":0.024636567},"labels":[],"label_agreement":null},{"id":"W1987634983","doi":"10.1145/2068984.2068987","title":"Crowd-sourced carpool recommendation based on simple and efficient trajectory grouping","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Carpool; Trajectory; Computer science; Simple (philosophy); Data mining; Transport engineering; Engineering","score_opus":0.033034306958350176,"score_gpt":0.22181301588456653,"score_spread":0.18877870892621634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987634983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080431536,0.00026490062,0.91039896,0.00015679057,0.00007431833,0.00024022943,0.0011862662,0.0045400043,0.0027069408],"genre_scores_gemma":[0.51714,0.00021744678,0.47366875,0.00004617856,0.00007918977,0.00024611322,0.0029922768,0.00020585798,0.0054041687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991787,0.00011765994,0.000055194614,0.00024304676,0.00032777927,0.00007765641],"domain_scores_gemma":[0.9984748,0.00032069816,0.0001614284,0.0004009987,0.00052937306,0.00011285846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004954835,0.00083206204,0.001361232,0.0032775952,0.0007894999,0.00095450657,0.001855814,0.00078666565,0.002257771],"category_scores_gemma":[0.0030143948,0.00052104297,0.0007558643,0.0031725906,0.0003780709,0.0017476478,0.0011241388,0.00058965234,0.0015904224],"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":[0.00079970324,0.00045597815,0.01681216,0.00031900313,0.00034622327,0.00040948432,0.0008420819,0.26336512,0.014874997,0.004913672,0.017722232,0.6791394],"study_design_scores_gemma":[0.000041761083,0.000090860885,0.0029951686,0.000017840828,0.000038488593,0.00017130388,0.00017541926,0.98465616,0.004767719,0.00259677,0.004387308,0.00006113995],"about_ca_topic_score_codex":0.029002134,"about_ca_topic_score_gemma":0.037138805,"teacher_disagreement_score":0.029002134,"about_ca_system_score_codex":0.0006554839,"about_ca_system_score_gemma":0.0011890052,"threshold_uncertainty_score":0.05766666},"labels":[],"label_agreement":null},{"id":"W1987759864","doi":"10.4018/jaeis.2010070102","title":"A Systematic Approach for Managing the Risk Related to Semantic Interoperability between Geospatial Datacubes","year":2010,"lang":"en","type":"article","venue":"International Journal of Agricultural and Environmental Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Geospatial analysis; Computer science; Interoperability; Information retrieval; Database; Geospatial metadata; Data science; Metadata; World Wide Web; Geography; Metadata repository; Remote sensing; Meta Data Services","score_opus":0.006775708503710019,"score_gpt":0.2040943667207618,"score_spread":0.1973186582170518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987759864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020163015,0.00012905053,0.996332,0.0003137659,0.000012895895,0.00023116017,0.000024358738,0.00015610829,0.00078434066],"genre_scores_gemma":[0.051843904,0.00024828486,0.94670165,0.00009729756,0.000023806098,0.00034973997,0.00013154659,0.000046681253,0.00055702985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97912484,0.007853429,0.002273731,0.0018786952,0.00823232,0.0006370171],"domain_scores_gemma":[0.97971034,0.0066286903,0.0019893108,0.0068358276,0.0042020995,0.00063371164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016606301,0.0012273302,0.0016514426,0.004214013,0.0024785083,0.0077274935,0.004895161,0.0031087513,0.0014119523],"category_scores_gemma":[0.029804088,0.0017111356,0.0029560288,0.0047180736,0.004257956,0.011314981,0.009500541,0.004071696,0.00044222732],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014007039,0.0003537934,0.004248427,0.0006768348,0.0004160662,0.00094063167,0.002059262,0.15122968,0.008826765,0.5863507,0.004029946,0.2407279],"study_design_scores_gemma":[0.000074168325,0.00020926572,0.00072467665,0.00042534724,0.00028961408,0.0010398076,0.001377355,0.58141494,0.012104868,0.362114,0.040081237,0.00014475962],"about_ca_topic_score_codex":0.0031872655,"about_ca_topic_score_gemma":0.003563667,"teacher_disagreement_score":0.016606301,"about_ca_system_score_codex":0.0024785178,"about_ca_system_score_gemma":0.0062965727,"threshold_uncertainty_score":0.08782357},"labels":[],"label_agreement":null},{"id":"W1987916013","doi":"10.2316/journal.206.2009.3.206-3274","title":"A QUALITATIVE MODEL OF DYNAMIC SCENE ANALYSIS AND INTERPRETATION IN AMBIENT INTELLIGENCE SYSTEMS","year":2009,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpretation (philosophy); Computer science; Ambient intelligence; Spatial intelligence; Artificial intelligence; Machine learning","score_opus":0.020338160905148554,"score_gpt":0.3319495610846746,"score_spread":0.31161140017952604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987916013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059679765,0.00032198394,0.98170173,0.0008066501,0.00004430787,0.000038072165,0.0001959659,0.00024115367,0.010682069],"genre_scores_gemma":[0.65432155,0.0008916487,0.33352152,0.00032649527,0.00012959506,0.0002982263,0.00047055018,0.00015963269,0.009880789],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992374,0.00024736713,0.000044148645,0.00011084803,0.0002881295,0.00007206013],"domain_scores_gemma":[0.9988741,0.0005102014,0.00010440491,0.00019852481,0.00023892804,0.00007375736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012067708,0.0005138872,0.00049107405,0.0010056659,0.00088030554,0.0031850424,0.0019298898,0.0011316242,0.004319382],"category_scores_gemma":[0.0034025398,0.00050960295,0.0010159425,0.0009642591,0.0039425315,0.005158669,0.0017903041,0.0012155888,0.00074438006],"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":[0.000030071078,0.00001795746,0.00023367586,0.00007147812,0.000015079488,0.00015123881,0.0005300215,0.055836312,0.0012222193,0.9293054,0.0012339829,0.011352636],"study_design_scores_gemma":[0.000016655076,0.000025668573,0.00017023337,0.000037400194,0.00001673149,0.00008790484,0.00018443428,0.24264897,0.0006486192,0.7482934,0.007848967,0.000020929729],"about_ca_topic_score_codex":0.0075469343,"about_ca_topic_score_gemma":0.004174661,"teacher_disagreement_score":0.0075469343,"about_ca_system_score_codex":0.00167402,"about_ca_system_score_gemma":0.0012575813,"threshold_uncertainty_score":0.015006006},"labels":[],"label_agreement":null},{"id":"W1991341889","doi":"10.1109/ccece.2014.6901019","title":"Multiscale navigation in large trace data","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"TRACE (psycholinguistics); Computer science","score_opus":0.026631703787659708,"score_gpt":0.27759460036045625,"score_spread":0.2509628965727965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991341889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03880381,0.0001882128,0.95614314,0.00031387905,0.0000284338,0.000054013886,0.00036372434,0.003338591,0.0007662216],"genre_scores_gemma":[0.28786513,0.000284753,0.7090684,0.000102394275,0.000032472894,0.00013188498,0.0009142514,0.00038309905,0.0012175774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992865,0.00020532827,0.000054083972,0.00017797935,0.00022025776,0.000055894943],"domain_scores_gemma":[0.9973916,0.0014150531,0.00022415853,0.00050392305,0.00032327534,0.00014205102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011092166,0.00052214466,0.0006751048,0.0019866792,0.0007003863,0.0017981585,0.000825713,0.00063965307,0.0026873809],"category_scores_gemma":[0.0064771245,0.00046113835,0.0005881391,0.0021168485,0.0006872493,0.0028234692,0.0027174773,0.0009577747,0.0003192024],"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":[0.00054000807,0.00032794772,0.01709258,0.0006321012,0.00018599546,0.0012608202,0.0052259397,0.25524345,0.063215934,0.12615676,0.0140062915,0.5161122],"study_design_scores_gemma":[0.00005158639,0.000095136864,0.0037241122,0.00006317128,0.000033485416,0.00021191304,0.00079569843,0.8695734,0.0140004745,0.0922877,0.019091982,0.000071237235],"about_ca_topic_score_codex":0.008362344,"about_ca_topic_score_gemma":0.012047796,"teacher_disagreement_score":0.008362344,"about_ca_system_score_codex":0.00069340155,"about_ca_system_score_gemma":0.0007565269,"threshold_uncertainty_score":0.016627371},"labels":[],"label_agreement":null},{"id":"W1991782162","doi":"10.1111/1467-9671.00127","title":"A Perspective on the Fundamentals of Fuzzy Sets and their Use in Geographic Information Systems","year":2003,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fuzzy logic; Data mining; Computer science; Geographic information system; Fuzzy set; Sophistication; Perspective (graphical); Relation (database); AM/FM/GIS; Fuzzy set operations; GIS applications; Defuzzification; Artificial intelligence; Fuzzy number; Geography; Cartography","score_opus":0.021448065113886586,"score_gpt":0.23594112615745103,"score_spread":0.21449306104356444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991782162","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044735824,0.19028649,0.53736377,0.053411562,0.005882383,0.00023076826,0.00041877854,0.00020574791,0.20772694],"genre_scores_gemma":[0.23868015,0.27847198,0.43101412,0.011744609,0.013364148,0.0008495341,0.0003010072,0.00010899515,0.025465518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99734586,0.0013473274,0.00020510431,0.00022664969,0.00076803187,0.00010710922],"domain_scores_gemma":[0.9964502,0.0027488724,0.00017273128,0.00014692024,0.00039179705,0.00008953201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038545083,0.0011049239,0.0010914656,0.004424022,0.002113849,0.00603394,0.0018151911,0.0038877493,0.003958506],"category_scores_gemma":[0.004703948,0.0005845638,0.0010896744,0.004630731,0.009778911,0.006115534,0.0016052221,0.0051184404,0.0012677135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046568675,0.000012104618,0.000078888756,0.0001702324,0.0000067438555,0.00012281591,0.00062899257,0.001606699,0.00015548516,0.9760759,0.003725812,0.017411653],"study_design_scores_gemma":[0.000004699218,0.00003934791,0.00022577758,0.0005870067,0.0000066389725,0.00025470628,0.00054279924,0.002928678,0.00016289002,0.8421155,0.153107,0.000025045614],"about_ca_topic_score_codex":0.0046698563,"about_ca_topic_score_gemma":0.003076143,"teacher_disagreement_score":0.00603394,"about_ca_system_score_codex":0.003556578,"about_ca_system_score_gemma":0.0019278306,"threshold_uncertainty_score":0.025804937},"labels":[],"label_agreement":null},{"id":"W1992628046","doi":"10.1145/375663.375666","title":"Iceberg-cube computation with PC clusters","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Computer science; Computation; Cuboid; Cube (algebra); Parallel computing; Load balancing (electrical power); Algorithm; Mathematics","score_opus":0.01430538454407141,"score_gpt":0.231174238206917,"score_spread":0.21686885366284558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992628046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16917352,0.00068889436,0.7920598,0.0008519301,0.00018074077,0.00020277189,0.000649658,0.0043707266,0.031821992],"genre_scores_gemma":[0.51114875,0.0004950985,0.478226,0.00015799161,0.00005495709,0.00022717143,0.0010962515,0.00025449833,0.008339352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914014,0.00016689862,0.00004670529,0.00018165218,0.00031385064,0.00015071694],"domain_scores_gemma":[0.9983442,0.00060665247,0.00007147142,0.0005238501,0.00034941832,0.00010437013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007190586,0.00059460546,0.0009952242,0.0005698435,0.0011027273,0.0019711542,0.0024502769,0.00063569937,0.0051492215],"category_scores_gemma":[0.0033662831,0.00041646196,0.0005188504,0.002476187,0.00075793546,0.0034114055,0.0018299678,0.00087758276,0.0013546828],"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":[0.0012262984,0.0002753,0.0050098198,0.00032269646,0.00014331106,0.00035739772,0.000503054,0.65974134,0.008634706,0.1011276,0.024363868,0.19829462],"study_design_scores_gemma":[0.000049286587,0.000053729407,0.00028534076,0.0000068757176,0.000013147246,0.000058725465,0.00011544486,0.96198565,0.0051922947,0.026710246,0.0055178786,0.0000113642855],"about_ca_topic_score_codex":0.015449815,"about_ca_topic_score_gemma":0.016770292,"teacher_disagreement_score":0.015449815,"about_ca_system_score_codex":0.0012654304,"about_ca_system_score_gemma":0.0017804047,"threshold_uncertainty_score":0.030719817},"labels":[],"label_agreement":null},{"id":"W1992764338","doi":"10.1109/colcom.2006.361843","title":"How to Fit In Another Meeting","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Ackermann function; Duration (music); Binary logarithm; Combinatorics; Schedule; Interval (graph theory); Computer science; Graph; Euclidean geometry; Mathematics; Discrete mathematics; Inverse; Geometry","score_opus":0.01784500369562987,"score_gpt":0.22244885637587505,"score_spread":0.20460385268024517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992764338","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17002146,0.0017448592,0.6825719,0.019853493,0.0023815224,0.0014206527,0.0022011602,0.004901659,0.114903234],"genre_scores_gemma":[0.43794036,0.0012520289,0.48820314,0.0017716306,0.00045864904,0.0006448575,0.0030410406,0.0013284772,0.06535981],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973091,0.0008120031,0.0001624855,0.0009441903,0.00039288413,0.00037933062],"domain_scores_gemma":[0.99660456,0.00083008443,0.0003030194,0.0011216687,0.00036751328,0.00077320036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023301302,0.00096888613,0.0011102896,0.00071150693,0.0032088903,0.003322709,0.0027899393,0.002418438,0.048450675],"category_scores_gemma":[0.01076309,0.00070936105,0.0014697997,0.0011369366,0.00092785864,0.008620204,0.0035389438,0.001930091,0.019249287],"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":[0.0015224123,0.0011443858,0.011096333,0.0007872145,0.00019056369,0.0013334661,0.0032340558,0.040487558,0.01314185,0.10548839,0.11735223,0.70422167],"study_design_scores_gemma":[0.0003140016,0.0012508734,0.004100858,0.00022819931,0.00029842812,0.0025554895,0.014566754,0.16230078,0.013519509,0.21815808,0.5824272,0.00027978345],"about_ca_topic_score_codex":0.0034360997,"about_ca_topic_score_gemma":0.004521844,"teacher_disagreement_score":0.048450675,"about_ca_system_score_codex":0.00079240697,"about_ca_system_score_gemma":0.0016189449,"threshold_uncertainty_score":0.16208369},"labels":[],"label_agreement":null},{"id":"W1992931136","doi":"10.3166/ria.26.393-408","title":"Extraction et visualisation de structures arborescentes à partir de treillis de concepts","year":2012,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.07556293189651259,"score_gpt":0.3633979581320241,"score_spread":0.2878350262355115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992931136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3845878,0.0047434745,0.5756217,0.0017278303,0.0003173473,0.00043868247,0.0066039516,0.014829925,0.011129252],"genre_scores_gemma":[0.4388564,0.0029327392,0.54444873,0.00014537001,0.00011289212,0.00023834938,0.005966799,0.0011324192,0.0061662765],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999635,0.000043645825,0.000023280061,0.00009896682,0.00014317194,0.000056012752],"domain_scores_gemma":[0.99849725,0.000833623,0.00013009836,0.00014641459,0.00029272964,0.000099970996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064414646,0.001277267,0.0006813805,0.0050542597,0.0010501697,0.0028238075,0.000610794,0.0013238613,0.0051877243],"category_scores_gemma":[0.002837674,0.00053448725,0.00081236893,0.0032462506,0.0006409184,0.0020323494,0.0010053609,0.0014497897,0.0012793203],"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":[0.0015496525,0.00029119052,0.009265761,0.002384707,0.00022901266,0.0021817412,0.004261681,0.03229456,0.29860628,0.03281153,0.022997685,0.5931261],"study_design_scores_gemma":[0.00026667817,0.0005331038,0.028980022,0.00085835723,0.00035808925,0.004586168,0.0029706198,0.6192261,0.16986063,0.054341342,0.11782976,0.00018911685],"about_ca_topic_score_codex":0.0069184443,"about_ca_topic_score_gemma":0.008146144,"teacher_disagreement_score":0.0069184443,"about_ca_system_score_codex":0.00058188586,"about_ca_system_score_gemma":0.0012406689,"threshold_uncertainty_score":0.017354667},"labels":[],"label_agreement":null},{"id":"W1993378086","doi":"10.1145/2806416.2806493","title":"A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":467,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Nvidia","keywords":"Computer science; Encoder; Generative grammar; Context (archaeology); Artificial intelligence; Generative model; Natural language processing","score_opus":0.06969257043282508,"score_gpt":0.30214913904282525,"score_spread":0.2324565686100002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993378086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023466835,0.00090281747,0.9681705,0.0005671237,0.00010229057,0.00009551403,0.00058882346,0.0041504027,0.0019557225],"genre_scores_gemma":[0.64526737,0.00069984834,0.34115723,0.00057477254,0.00017183856,0.000322549,0.0017516176,0.00044395053,0.0096108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934703,0.00021016753,0.000046267156,0.00017881497,0.00014271826,0.00007508806],"domain_scores_gemma":[0.9976405,0.0016245064,0.00010359067,0.00021687575,0.00032624474,0.000088401874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009703735,0.00089718885,0.0012237693,0.00064523006,0.0004118071,0.0008598656,0.0021381634,0.0013703371,0.0029389844],"category_scores_gemma":[0.005184024,0.0006977128,0.0007715976,0.0009170408,0.000640537,0.0015741985,0.0010117819,0.0016705365,0.0016455905],"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":[0.0009740118,0.00034463927,0.002943976,0.00039284152,0.00016890385,0.0005417179,0.0005381213,0.56124467,0.028922293,0.034777638,0.014362788,0.35478836],"study_design_scores_gemma":[0.000015562278,0.000029685028,0.00007393767,0.0000051306956,0.000016392769,0.000046548103,0.000007740244,0.99347913,0.0016660013,0.003977383,0.0006729155,0.000009546519],"about_ca_topic_score_codex":0.012366796,"about_ca_topic_score_gemma":0.019351834,"teacher_disagreement_score":0.012366796,"about_ca_system_score_codex":0.00092941325,"about_ca_system_score_gemma":0.0016248913,"threshold_uncertainty_score":0.024589658},"labels":[],"label_agreement":null},{"id":"W1993926838","doi":"10.1109/services.2013.60","title":"Towards Smarter Task Applications","year":2013,"lang":"en","type":"article","venue":"","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":"University of Victoria","funders":"","keywords":"Computer science; Task (project management); Context (archaeology); Data science; Human–computer interaction; Mobile device; Software; World Wide Web; Systems engineering; Engineering","score_opus":0.009335746972947208,"score_gpt":0.21681894130196908,"score_spread":0.20748319432902187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993926838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028329741,0.0051608975,0.87486196,0.014271537,0.0012936094,0.0004952352,0.00065239327,0.011048105,0.063886605],"genre_scores_gemma":[0.14126565,0.0065152873,0.7770638,0.006654696,0.000853018,0.00079002924,0.0028471055,0.0017657126,0.062244795],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972453,0.00066951395,0.00017938792,0.0006094166,0.000962045,0.00033426678],"domain_scores_gemma":[0.996042,0.001020551,0.00015147147,0.0014599713,0.0009050052,0.00042088822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035729017,0.0012553948,0.00087498315,0.0011801246,0.0009802198,0.0050427006,0.0037334634,0.004571492,0.01862283],"category_scores_gemma":[0.010681354,0.001056013,0.00091600773,0.0013027497,0.0017513178,0.012954463,0.007352881,0.004784731,0.016026169],"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":[0.000561232,0.0006187004,0.0024454065,0.0011181454,0.00006835278,0.0003985342,0.0027385633,0.015728151,0.027950617,0.25268167,0.10306515,0.59262544],"study_design_scores_gemma":[0.00010955389,0.00023374685,0.0009967707,0.00032095765,0.000052525247,0.0004630446,0.0010672159,0.101710215,0.009362407,0.28941533,0.596185,0.00008325753],"about_ca_topic_score_codex":0.0013550952,"about_ca_topic_score_gemma":0.0018210124,"teacher_disagreement_score":0.01862283,"about_ca_system_score_codex":0.0007774519,"about_ca_system_score_gemma":0.0012806107,"threshold_uncertainty_score":0.06229961},"labels":[],"label_agreement":null},{"id":"W1994103787","doi":"10.1016/j.camwa.2011.07.047","title":"On the semantics of top-<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>k</mml:mi></mml:math>ranking for objects with uncertain data","year":2011,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Object (grammar); Semantics (computer science); Rank (graph theory); Mathematics; Context (archaeology); Algorithm; Theoretical computer science; Computer science; Discrete mathematics; Artificial intelligence; Combinatorics; Programming language","score_opus":0.03597014975348904,"score_gpt":0.25429496065205887,"score_spread":0.2183248108985698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994103787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012059239,0.0019517121,0.9488146,0.0049862727,0.0004820292,0.00022569083,0.0030057495,0.001459898,0.027014956],"genre_scores_gemma":[0.3271188,0.0023019877,0.6382516,0.002309223,0.0011012353,0.00058704126,0.006028707,0.0011418756,0.021159451],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9926893,0.0023394465,0.0010602073,0.0012353408,0.0021084838,0.00056720746],"domain_scores_gemma":[0.9873941,0.006025687,0.00076647586,0.002094083,0.0030845674,0.00063497527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074587474,0.0010763336,0.0013939396,0.004015515,0.0036857652,0.010711352,0.0033151533,0.002240348,0.008693228],"category_scores_gemma":[0.014674964,0.0010890793,0.0023848724,0.005941202,0.0054383934,0.019278254,0.005488988,0.0041110436,0.0029858937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066153116,0.000039773695,0.00027545667,0.00018336004,0.00004125387,0.00011950017,0.0007407236,0.0039038365,0.00052737916,0.9689435,0.007152349,0.018006634],"study_design_scores_gemma":[0.00002953734,0.00003580357,0.00022883982,0.00009517464,0.00005190069,0.00015763927,0.00029332994,0.017919207,0.0010251842,0.9451518,0.03496361,0.000047896032],"about_ca_topic_score_codex":0.012781917,"about_ca_topic_score_gemma":0.016986256,"teacher_disagreement_score":0.012781917,"about_ca_system_score_codex":0.0038536864,"about_ca_system_score_gemma":0.0033163119,"threshold_uncertainty_score":0.039446115},"labels":[],"label_agreement":null},{"id":"W1994414840","doi":"10.1007/s10458-015-9285-5","title":"Privacy preserving existence recognition and construction of hypertree agent organization","year":2015,"lang":"en","type":"article","venue":"Autonomous Agents and Multi-Agent Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Probabilistic logic; Constraint (computer-aided design); Hyperbolic tree; Scheme (mathematics); Tree (set theory); Private information retrieval; Theoretical computer science; Adjacency list; Artificial intelligence; Computer security; Mathematics; Algorithm","score_opus":0.10166313198612774,"score_gpt":0.2630290996846375,"score_spread":0.16136596769850975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994414840","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11271315,0.00011884603,0.8832496,0.00026667424,0.000026247459,0.000069972,0.00021761018,0.0005421478,0.0027957896],"genre_scores_gemma":[0.64882296,0.00015023774,0.34721106,0.00008717652,0.00002201546,0.00011066802,0.00085881364,0.00012009223,0.00261703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988483,0.0002785002,0.0000889217,0.0002958166,0.0003299833,0.00015844147],"domain_scores_gemma":[0.9961778,0.0009821677,0.000368151,0.0016295096,0.000563927,0.0002784446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090303586,0.00021406279,0.0005936428,0.0010844495,0.0011941941,0.0019722034,0.001398986,0.0007882238,0.0014100751],"category_scores_gemma":[0.0041408134,0.00045396676,0.0008101429,0.0013094416,0.001262223,0.0034275185,0.003388255,0.0015581035,0.00038596845],"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":[0.00051330175,0.000294403,0.009800439,0.0002314354,0.00009434817,0.00050030113,0.0021664742,0.12548268,0.04036725,0.48328853,0.0070353774,0.33022547],"study_design_scores_gemma":[0.000019858197,0.00008616675,0.0014587914,0.000034994435,0.000039201477,0.0003215301,0.00063164375,0.72697186,0.018495707,0.24561456,0.0062948107,0.000030938547],"about_ca_topic_score_codex":0.0012776587,"about_ca_topic_score_gemma":0.0013597782,"teacher_disagreement_score":0.0019722034,"about_ca_system_score_codex":0.0006907251,"about_ca_system_score_gemma":0.0011764808,"threshold_uncertainty_score":0.005011618},"labels":[],"label_agreement":null},{"id":"W1995622714","doi":"10.1109/cisis.2007.16","title":"Compact Hilbert Indices for Multi-Dimensional Data","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hilbert curve; Hilbert R-tree; Dimension (graph theory); Hilbert space; Cardinality (data modeling); Grid; Computer science; Representation (politics); Theoretical computer science; Mathematics; Algorithm; Reproducing kernel Hilbert space; Rigged Hilbert space; Mathematical analysis; Pure mathematics; Data mining; Geometry","score_opus":0.1259849650363655,"score_gpt":0.35420101087050176,"score_spread":0.22821604583413627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995622714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0152312275,0.0012038521,0.9775081,0.00037039779,0.0002118125,0.00006137459,0.00024272081,0.00056295865,0.0046075564],"genre_scores_gemma":[0.3217964,0.0019348646,0.66798717,0.0003774735,0.0007164654,0.0004942968,0.00087265583,0.0006556827,0.0051650293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955824,0.0010775158,0.0006133142,0.0004699343,0.0019562978,0.00030052196],"domain_scores_gemma":[0.9883022,0.004039891,0.0012122905,0.0038203152,0.001725882,0.00089938607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035403061,0.00080094143,0.0012725318,0.0030355686,0.001121037,0.0056107095,0.0014303133,0.001139756,0.0026245995],"category_scores_gemma":[0.018186843,0.00037995624,0.00076440786,0.0047919685,0.0037799533,0.012513417,0.004133493,0.002954201,0.001343699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057973044,0.000025984305,0.00036480857,0.000084782,0.000009285582,0.00007523305,0.00032293965,0.010157335,0.0021686787,0.94259435,0.0023401193,0.041798543],"study_design_scores_gemma":[0.00002198675,0.00009351684,0.00020021886,0.00005790086,0.000009112729,0.0002299765,0.00014209021,0.11766145,0.004154697,0.84160477,0.03577192,0.00005248156],"about_ca_topic_score_codex":0.00047960918,"about_ca_topic_score_gemma":0.0003264952,"teacher_disagreement_score":0.0056107095,"about_ca_system_score_codex":0.0017070157,"about_ca_system_score_gemma":0.0011927396,"threshold_uncertainty_score":0.01872313},"labels":[],"label_agreement":null},{"id":"W1998792254","doi":"10.1145/1247480.1247613","title":"URank","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Semantics (computer science); Ranking (information retrieval); Probabilistic logic; Information retrieval; Query language; Query optimization; Theoretical computer science; Artificial intelligence; Programming language","score_opus":0.009667932734316846,"score_gpt":0.23642622257327722,"score_spread":0.22675828983896038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998792254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056387796,0.0010322636,0.91165745,0.0011358181,0.00026347395,0.0004586967,0.005995653,0.0535769,0.020240942],"genre_scores_gemma":[0.12565783,0.0013850769,0.82871073,0.0011410577,0.00027366585,0.00051949895,0.015305036,0.005209849,0.021797244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99096537,0.0022456113,0.0012916403,0.0015788341,0.003338878,0.0005796194],"domain_scores_gemma":[0.9872335,0.003288144,0.0006273672,0.0058168997,0.0027287374,0.00030537258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062711737,0.0013140643,0.0019186337,0.0029381434,0.0017300491,0.008346739,0.004475774,0.001986302,0.024033178],"category_scores_gemma":[0.029233059,0.00092661876,0.0015089752,0.0038103415,0.0012889446,0.013010518,0.006865338,0.0023534892,0.020029627],"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":[0.0011193095,0.00021219034,0.00249383,0.0016386325,0.0002309179,0.00052704534,0.00074395764,0.019775396,0.0055649937,0.31454524,0.14314698,0.5100015],"study_design_scores_gemma":[0.00013727196,0.0001823384,0.0005931011,0.00029542157,0.00013856923,0.0011599726,0.00042564125,0.23600367,0.01940315,0.423782,0.31768277,0.00019612264],"about_ca_topic_score_codex":0.0026693349,"about_ca_topic_score_gemma":0.0034829725,"teacher_disagreement_score":0.024033178,"about_ca_system_score_codex":0.0012331817,"about_ca_system_score_gemma":0.00280694,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1999824796","doi":"10.1145/1102351.1102385","title":"Near-optimal sensor placements in Gaussian processes","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":467,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Intel Corporation","keywords":"Mutual information; Computer science; Gaussian; Entropy (arrow of time); Gaussian process; Algorithm; Global Positioning System; Variance (accounting); Task (project management); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.014486650433944056,"score_gpt":0.25045457089195855,"score_spread":0.23596792045801449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999824796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109054476,0.00023563368,0.88888025,0.0003327377,0.000013093525,0.00005481399,0.00011306798,0.00042701047,0.00088899495],"genre_scores_gemma":[0.7302139,0.00015640294,0.26803744,0.000109971144,0.000029006698,0.000118192176,0.0002557847,0.00009739726,0.0009819046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998719,0.00054831244,0.00006182167,0.00028925244,0.00019577419,0.0001859468],"domain_scores_gemma":[0.9958846,0.003032771,0.00044673873,0.0002636493,0.00020300585,0.00016923093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030524286,0.00080760865,0.001641386,0.0010906159,0.0008673994,0.0009927267,0.0012934413,0.001409755,0.0012003514],"category_scores_gemma":[0.007892799,0.000799922,0.0007297714,0.0016894926,0.0017524651,0.0018011901,0.0018675457,0.0010685174,0.00023695246],"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":[0.00011602363,0.00004641084,0.0007915313,0.000030973246,0.000021128768,0.00006347725,0.00010265575,0.97038054,0.0011855375,0.0088030165,0.0005138315,0.017944919],"study_design_scores_gemma":[0.00001901387,0.000023871173,0.00013842038,0.0000027844437,0.000003679257,0.000014433186,0.000022830227,0.98749846,0.00047876828,0.011666964,0.00012619447,0.0000045238803],"about_ca_topic_score_codex":0.0051932493,"about_ca_topic_score_gemma":0.0069638677,"teacher_disagreement_score":0.0051932493,"about_ca_system_score_codex":0.0013797272,"about_ca_system_score_gemma":0.0014876086,"threshold_uncertainty_score":0.016142964},"labels":[],"label_agreement":null},{"id":"W2000053898","doi":"10.1109/cloud.2013.78","title":"HGrid: A Data Model for Large Geospatial Data Sets in HBase","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"NoSQL; Computer science; Scalability; Geospatial analysis; Cloud computing; Data mining; Grid; Database; Big data; Tree (set theory)","score_opus":0.10653666167588431,"score_gpt":0.3196813446547444,"score_spread":0.21314468297886008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000053898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009610437,0.00023318274,0.9788367,0.0006468447,0.00012065331,0.0002749412,0.0028315918,0.005045596,0.0023999265],"genre_scores_gemma":[0.11057801,0.00054605026,0.8757084,0.0004252086,0.00008231807,0.0008024869,0.008566116,0.00097423105,0.0023171308],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850285,0.0003192988,0.00026168392,0.00021038808,0.00059237884,0.00011343796],"domain_scores_gemma":[0.9977623,0.0005472478,0.0001337789,0.0008318527,0.0005743239,0.0001503325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033401134,0.0005625857,0.00079099013,0.0013316821,0.0009842765,0.003722041,0.0044333357,0.0010553895,0.0014452608],"category_scores_gemma":[0.0067949444,0.00059831596,0.0016877113,0.0032211014,0.00095534365,0.005265618,0.0021978072,0.0021079378,0.00090560224],"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":[0.00043806445,0.0003366925,0.010527255,0.00055012654,0.00020548845,0.0005281728,0.0014119225,0.3666201,0.0072883433,0.43106264,0.048014876,0.13301645],"study_design_scores_gemma":[0.000058290534,0.00010932301,0.0010466747,0.00008166088,0.000037774247,0.00023862452,0.0002851732,0.8426908,0.003286356,0.07607491,0.07601374,0.00007665428],"about_ca_topic_score_codex":0.012500243,"about_ca_topic_score_gemma":0.01315231,"teacher_disagreement_score":0.012500243,"about_ca_system_score_codex":0.001223764,"about_ca_system_score_gemma":0.0020925335,"threshold_uncertainty_score":0.024855018},"labels":[],"label_agreement":null},{"id":"W2001088180","doi":"10.5555/338219.338659","title":"Computing contour trees in all dimensions","year":2000,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Simple (philosophy); Computer science; Contour line; Algorithm; Artificial intelligence; Cartography; Geography","score_opus":0.02725043140270391,"score_gpt":0.26435506847606716,"score_spread":0.23710463707336324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001088180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016892904,0.00028372539,0.9765013,0.000112138914,0.00006990969,0.000046267713,0.00036736825,0.0020217323,0.0037045674],"genre_scores_gemma":[0.10307609,0.00033442958,0.8926978,0.0000627118,0.000053533793,0.00009375272,0.0011134283,0.00041700053,0.0021512476],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99876237,0.00012213865,0.00015682077,0.00029054287,0.00050616614,0.00016203488],"domain_scores_gemma":[0.9965463,0.001168017,0.00029805157,0.0011538577,0.00068662997,0.00014712948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007823548,0.0010250376,0.0013623227,0.0025646738,0.0014199634,0.0035674858,0.0015850132,0.0013308289,0.008127303],"category_scores_gemma":[0.009273987,0.0008414043,0.0013287643,0.003932793,0.0012494172,0.007595608,0.0036771896,0.0019317258,0.0030314843],"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":[0.00041924077,0.00013836715,0.0050690873,0.00049290515,0.000121992634,0.00059385074,0.0008670875,0.1284083,0.023979085,0.27319542,0.01922727,0.5474874],"study_design_scores_gemma":[0.00006356046,0.00009415663,0.0010906223,0.00008009433,0.000067129185,0.0004490651,0.00019238927,0.5227549,0.01569329,0.4307282,0.028718153,0.000068369554],"about_ca_topic_score_codex":0.0023782798,"about_ca_topic_score_gemma":0.003563682,"teacher_disagreement_score":0.008127303,"about_ca_system_score_codex":0.00066589867,"about_ca_system_score_gemma":0.0008701975,"threshold_uncertainty_score":0.02718854},"labels":[],"label_agreement":null},{"id":"W2002457770","doi":"10.1016/j.ijar.2003.07.004","title":"P-FCM: a proximity-based fuzzy clustering for user-centered web applications","year":2003,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; The Internet; Information retrieval; World Wide Web; Similarity (geometry); Interface (matter); User interface; Search engine; Fuzzy logic; Cluster analysis; Artificial intelligence","score_opus":0.021751857493087553,"score_gpt":0.2813758141385941,"score_spread":0.2596239566455065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002457770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075899106,0.00020144967,0.9893056,0.00006140968,0.00003478709,0.00007865241,0.00019646075,0.0018600504,0.00067163847],"genre_scores_gemma":[0.12328892,0.00018147985,0.87324727,0.000074404634,0.000042294414,0.0001494717,0.0005198505,0.00019837091,0.002297826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985019,0.00023077607,0.000083797924,0.00029789007,0.0007816389,0.00010401288],"domain_scores_gemma":[0.9990374,0.00023216837,0.00006282044,0.0002138923,0.00039977307,0.000054037293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288147,0.00087204925,0.0014337247,0.002595817,0.0016933341,0.0013633325,0.00323612,0.0018150726,0.0028357287],"category_scores_gemma":[0.0042123343,0.0004568521,0.0014227445,0.0030006117,0.0006130472,0.002132981,0.0015344949,0.0011494369,0.0014334652],"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":[0.0005048645,0.00023204055,0.0015460144,0.00024397313,0.0001563371,0.0001501237,0.00030370566,0.12661959,0.012554717,0.010032156,0.008615393,0.8390411],"study_design_scores_gemma":[0.000019416464,0.000057202586,0.0006668048,0.000017047247,0.000035824505,0.00012768219,0.00006559259,0.97855717,0.008451013,0.008153332,0.0038141133,0.00003477081],"about_ca_topic_score_codex":0.022631396,"about_ca_topic_score_gemma":0.023251783,"teacher_disagreement_score":0.022631396,"about_ca_system_score_codex":0.0015416105,"about_ca_system_score_gemma":0.0019412026,"threshold_uncertainty_score":0.04499936},"labels":[],"label_agreement":null},{"id":"W2002910581","doi":"10.1002/int.1031","title":"Conceptual design of fuzzy object-oriented databases using extended entity-relationship model","year":2001,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Conceptual schema; Entity–relationship model; Fuzzy logic; Database schema; Database design; Data mining; Conceptual model; Relational database; Schema (genetic algorithms); Database; Fuzzy set; Imperfect; Object (grammar); Database model; Information retrieval; Artificial intelligence","score_opus":0.1350884247380204,"score_gpt":0.3359440521008348,"score_spread":0.20085562736281437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002910581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009371331,0.0006106476,0.98570466,0.00045449164,0.00006794544,0.00013560626,0.000077915654,0.0002526213,0.0033247515],"genre_scores_gemma":[0.123462856,0.000937984,0.87267846,0.00023591543,0.000052625594,0.00023176472,0.00031960921,0.000038454666,0.0020422593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979328,0.0006064881,0.00031742523,0.00027276092,0.00076247944,0.000108165485],"domain_scores_gemma":[0.9986129,0.00031383228,0.00017460434,0.00026153063,0.0005014382,0.00013565793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036701963,0.00031792195,0.0005380751,0.0011609929,0.00071526313,0.0042442586,0.0020217656,0.0010962852,0.0016100139],"category_scores_gemma":[0.0052283676,0.00043341887,0.0009178004,0.0015007512,0.0010492571,0.004465369,0.0015047066,0.0009315703,0.00050951046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007611592,0.00007811336,0.00071643636,0.00030888745,0.00007779398,0.00049766834,0.0011244955,0.028016604,0.006327982,0.86215615,0.003173053,0.097446606],"study_design_scores_gemma":[0.0001229751,0.0001645686,0.00065665646,0.00025586342,0.00019811445,0.0011442755,0.00078307604,0.37869912,0.009766212,0.4696217,0.13847902,0.00010836598],"about_ca_topic_score_codex":0.0012752624,"about_ca_topic_score_gemma":0.0011546039,"teacher_disagreement_score":0.0042442586,"about_ca_system_score_codex":0.0008097552,"about_ca_system_score_gemma":0.0012277745,"threshold_uncertainty_score":0.019410133},"labels":[],"label_agreement":null},{"id":"W2002932370","doi":"10.1145/1061318.1061324","title":"Concise descriptions of subsets of structured sets","year":2005,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Automatic summarization; Theoretical computer science; Set (abstract data type); Minimum description length; Online analytical processing; Hierarchy; Cover (algebra); Context (archaeology); Representation (politics); Data mining; Algorithm; Artificial intelligence; Data warehouse","score_opus":0.0319806076430219,"score_gpt":0.26392649524481654,"score_spread":0.23194588760179463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002932370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07561506,0.0022776527,0.9070789,0.0015665243,0.000101353624,0.00021281933,0.0035971568,0.00078893604,0.00876161],"genre_scores_gemma":[0.46506375,0.0020317049,0.515063,0.00036826634,0.00021976203,0.0005280953,0.007841733,0.00035356602,0.0085301455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99712735,0.0009212605,0.00034685564,0.00044658274,0.0009473841,0.00021059053],"domain_scores_gemma":[0.9936215,0.0034808111,0.00086413225,0.0011127458,0.00070964976,0.00021115379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022118103,0.00064115424,0.0010261646,0.002618103,0.0007449456,0.003211886,0.0017840117,0.00091766665,0.0041692504],"category_scores_gemma":[0.011796633,0.0007064867,0.0010027918,0.0041648815,0.0012099728,0.00964782,0.0019796018,0.001231166,0.0007707657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021817422,0.00008357257,0.0012826456,0.0006732363,0.00008319858,0.00056547305,0.0014247168,0.075238675,0.0047289445,0.7949336,0.009184256,0.11158349],"study_design_scores_gemma":[0.000047852925,0.00014885595,0.0005138396,0.0001562373,0.00007466788,0.00053334516,0.0007957797,0.2015044,0.005666014,0.7435764,0.046930987,0.000051695726],"about_ca_topic_score_codex":0.0011068818,"about_ca_topic_score_gemma":0.0016557068,"teacher_disagreement_score":0.0041692504,"about_ca_system_score_codex":0.0013782969,"about_ca_system_score_gemma":0.00087727554,"threshold_uncertainty_score":0.0139475465},"labels":[],"label_agreement":null},{"id":"W2003043371","doi":"10.1080/03461230601110447","title":"On composite lognormal-Pareto models","year":2007,"lang":"en","type":"article","venue":"Scandinavian Actuarial Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":182,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pareto principle; Log-normal distribution; Mathematics; Econometrics; Composite number; Pareto distribution; Statistics; Applied mathematics; Computer science; Mathematical optimization; Mathematical economics; Algorithm","score_opus":0.017291858383935446,"score_gpt":0.2500035017044091,"score_spread":0.23271164332047367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003043371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023500387,0.00045948767,0.97241795,0.00046151094,0.000064538726,0.00012517435,0.00030673467,0.00023420957,0.002430115],"genre_scores_gemma":[0.6036789,0.00213703,0.37560838,0.00069304544,0.00043681788,0.0008363895,0.001943003,0.00027917256,0.0143872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99404764,0.003136539,0.00025648205,0.0007963433,0.0012734478,0.0004896574],"domain_scores_gemma":[0.9685665,0.023093283,0.0017654386,0.0026063637,0.0032910472,0.0006774954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018568862,0.0018931979,0.0022047982,0.0035581412,0.0010954066,0.0031848187,0.004781204,0.0025007175,0.003505132],"category_scores_gemma":[0.038456544,0.0009594662,0.0025802378,0.0030868966,0.0023273318,0.005930041,0.0038077252,0.004571043,0.0016560378],"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":[0.0002281391,0.00010103608,0.0032296858,0.00007991864,0.00009203265,0.00017160513,0.00016691713,0.83788073,0.00036898823,0.13124375,0.0018655944,0.02457168],"study_design_scores_gemma":[0.000013175414,0.000028382516,0.00024037408,0.000012856846,0.00000997263,0.000043785432,0.000019337887,0.9550942,0.00009622398,0.04381814,0.00060731155,0.000016233229],"about_ca_topic_score_codex":0.0078548705,"about_ca_topic_score_gemma":0.0062278905,"teacher_disagreement_score":0.018568862,"about_ca_system_score_codex":0.0021451367,"about_ca_system_score_gemma":0.0015372795,"threshold_uncertainty_score":0.098202705},"labels":[],"label_agreement":null},{"id":"W2003540235","doi":"","title":"Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Library science; Operations research; Database; Mathematics","score_opus":0.039512182558560004,"score_gpt":0.23424321879366222,"score_spread":0.1947310362351022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003540235","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015122531,0.20960008,0.30531928,0.06560926,0.14110598,0.0036178874,0.023560638,0.021396928,0.21466747],"genre_scores_gemma":[0.047314204,0.14940077,0.18902656,0.018274691,0.042852126,0.002133078,0.05687817,0.007845416,0.48627502],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98919886,0.0027332988,0.0010515931,0.0013040953,0.0051903934,0.0005216769],"domain_scores_gemma":[0.980899,0.0056585344,0.0008412277,0.0037934356,0.0054245186,0.0033833135],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010180622,0.0021297552,0.0038703175,0.0035968586,0.0024708607,0.0140728345,0.002934571,0.0025197626,0.087246515],"category_scores_gemma":[0.021457506,0.001950589,0.0015481293,0.0046139257,0.002505275,0.013738625,0.006266778,0.009449286,0.059592634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023184746,0.0002522659,0.0013565996,0.0005046148,0.00017791557,0.00008360226,0.00023477488,0.0005649402,0.0018051744,0.007297864,0.8250883,0.16240208],"study_design_scores_gemma":[0.00004537703,0.00010911836,0.0010770449,0.00026120327,0.000059844635,0.00028378068,0.00013948134,0.001976815,0.0008330579,0.008845701,0.9863215,0.000047143534],"about_ca_topic_score_codex":0.003905265,"about_ca_topic_score_gemma":0.0047219153,"teacher_disagreement_score":0.91275346,"about_ca_system_score_codex":0.0031795262,"about_ca_system_score_gemma":0.00619831,"threshold_uncertainty_score":0.29186863},"labels":[],"label_agreement":null},{"id":"W2003765267","doi":"10.1145/2534921.2534923","title":"The price of generality in spatial indexing","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Search engine indexing; Generality; Computer science; Overhead (engineering); Database index; Tree (set theory); Index (typography); Data mining; Big data; Information retrieval; Task (project management); Database; Programming language","score_opus":0.010309212865929103,"score_gpt":0.21389175394483334,"score_spread":0.20358254107890425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003765267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12574828,0.007690356,0.781823,0.020373834,0.00083191786,0.0007278086,0.0019495697,0.026739232,0.03411602],"genre_scores_gemma":[0.46623492,0.006762791,0.50084984,0.0041316897,0.0009908823,0.00060057425,0.002911715,0.004876866,0.012640776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.978741,0.003958236,0.0030978667,0.0029741845,0.0093777925,0.001850973],"domain_scores_gemma":[0.90398484,0.023384014,0.003803884,0.062004205,0.0050986586,0.0017244674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017157817,0.00200281,0.0024590865,0.0025994775,0.0030124378,0.0119288005,0.010990892,0.0028788175,0.0048835934],"category_scores_gemma":[0.05925611,0.0027032038,0.0040614298,0.010071976,0.007498697,0.030072521,0.012806425,0.0077989968,0.0051256325],"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":[0.0016664702,0.00046431817,0.024927983,0.002262087,0.0006056272,0.001128094,0.0036793465,0.056460653,0.036160767,0.4260586,0.052226085,0.39436007],"study_design_scores_gemma":[0.0006799289,0.0010583223,0.02331511,0.00087011023,0.0010460977,0.0057633845,0.003416448,0.17430054,0.046626043,0.48124954,0.26093814,0.00073632144],"about_ca_topic_score_codex":0.01056457,"about_ca_topic_score_gemma":0.007893094,"teacher_disagreement_score":0.017157817,"about_ca_system_score_codex":0.004498021,"about_ca_system_score_gemma":0.003964983,"threshold_uncertainty_score":0.09074032},"labels":[],"label_agreement":null},{"id":"W2003931981","doi":"10.3166/isi.12.2.75-95","title":"Vers l'interopérabilité des modèles de localisation géométrique et sémantique au travers de l'utilisation de métadonnées","year":2007,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.02773951814302394,"score_gpt":0.261107776105936,"score_spread":0.23336825796291205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003931981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014060168,0.0013358413,0.9733429,0.002023507,0.00022702121,0.00010258296,0.00023076986,0.0014203977,0.007256733],"genre_scores_gemma":[0.29713935,0.003631659,0.6853066,0.0009377549,0.0004336019,0.0007443381,0.00151926,0.0013415859,0.008945773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9753983,0.010348026,0.0022020624,0.003049198,0.008064459,0.00093797705],"domain_scores_gemma":[0.9805108,0.008429016,0.0009645393,0.006958401,0.002689406,0.00044779232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024937088,0.0024970316,0.0020483609,0.004087924,0.0026229648,0.020851761,0.0041592056,0.005582794,0.003431469],"category_scores_gemma":[0.038817927,0.0022165205,0.0053072916,0.0033328547,0.0068785963,0.029963698,0.0074829743,0.009518546,0.0016076756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024290517,0.00017948306,0.0020075927,0.00043642154,0.00033772754,0.00043182028,0.0031419834,0.044672746,0.004490874,0.8789293,0.0017603957,0.06336867],"study_design_scores_gemma":[0.00016262413,0.00024541302,0.0014302253,0.0009353313,0.00039300937,0.00082455407,0.0018147371,0.42890298,0.015781324,0.46327156,0.08599498,0.00024329705],"about_ca_topic_score_codex":0.016495988,"about_ca_topic_score_gemma":0.0074933292,"teacher_disagreement_score":0.024937088,"about_ca_system_score_codex":0.005113433,"about_ca_system_score_gemma":0.0038289768,"threshold_uncertainty_score":0.13188154},"labels":[],"label_agreement":null},{"id":"W2004773941","doi":"10.1007/s11634-009-0046-7","title":"Parsimonious cluster systems","year":2009,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Paul University","funders":"","keywords":"Bijection; Set (abstract data type); Cluster (spacecraft); Cluster analysis; Phylogenetic tree; Computer science; Combinatorics; Tree (set theory); Mathematics; Theoretical computer science; Biology; Artificial intelligence; Genetics","score_opus":0.029910501458001896,"score_gpt":0.30810436773482447,"score_spread":0.2781938662768226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004773941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06363475,0.0007909833,0.911894,0.0011046387,0.00027802904,0.0003211832,0.0008778848,0.0016367078,0.019461732],"genre_scores_gemma":[0.4594582,0.00052540906,0.50524706,0.00040919715,0.00019917625,0.0004650775,0.0024841216,0.00068116677,0.03053056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99776626,0.00070141145,0.00012729739,0.00058911525,0.0005636182,0.0002522292],"domain_scores_gemma":[0.9936168,0.0015095872,0.0002974354,0.0026367821,0.0013938722,0.00054554245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00206463,0.0007526736,0.0014439876,0.0026241953,0.0044756,0.0030837536,0.00315229,0.0018454451,0.017795635],"category_scores_gemma":[0.01265275,0.0008135227,0.0009691936,0.0034807422,0.002141717,0.0042084,0.005456557,0.0023998225,0.003996974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007157352,0.00011368116,0.00324291,0.0002923218,0.00014099306,0.00027600167,0.001112958,0.061152674,0.005251992,0.7358726,0.018845996,0.17298214],"study_design_scores_gemma":[0.000080467566,0.000110823916,0.0013702114,0.000042820124,0.00005878577,0.00033226953,0.00029024074,0.26435083,0.0020812934,0.709029,0.02220149,0.000051804447],"about_ca_topic_score_codex":0.0018478772,"about_ca_topic_score_gemma":0.0036627306,"teacher_disagreement_score":0.017795635,"about_ca_system_score_codex":0.0012208789,"about_ca_system_score_gemma":0.0016363302,"threshold_uncertainty_score":0.059532285},"labels":[],"label_agreement":null},{"id":"W2005048938","doi":"10.1145/1516360.1516459","title":"Interactive query refinement","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Query optimization; Information retrieval; Query language","score_opus":0.009324530882337508,"score_gpt":0.24426485920332866,"score_spread":0.23494032832099115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005048938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010377047,0.00041019355,0.9842047,0.0005243094,0.00004193621,0.0003009211,0.00013660222,0.0019233811,0.0020809546],"genre_scores_gemma":[0.2509477,0.000536281,0.7407108,0.0008243979,0.00021082457,0.00047311105,0.0010965766,0.0011922892,0.004008032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9376021,0.023350688,0.0043136165,0.0090054,0.022443343,0.0032848672],"domain_scores_gemma":[0.84896284,0.09540972,0.005590187,0.03701202,0.011432478,0.0015927526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03868237,0.0025576684,0.0029237736,0.0028476394,0.0021906924,0.004121261,0.008799552,0.0028526075,0.0066206967],"category_scores_gemma":[0.113507554,0.0019331346,0.0042752563,0.003226136,0.005938482,0.013621042,0.01151789,0.005406121,0.0019225981],"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":[0.002082543,0.00090650364,0.013916296,0.0018332724,0.0007573516,0.001533898,0.009874343,0.11278442,0.04798695,0.3122905,0.018084671,0.47794938],"study_design_scores_gemma":[0.00032586866,0.00077311666,0.0016685685,0.00030666808,0.00041257252,0.0016341917,0.0017288064,0.58144206,0.043484766,0.3087793,0.059188675,0.0002553399],"about_ca_topic_score_codex":0.0057101157,"about_ca_topic_score_gemma":0.0049267355,"teacher_disagreement_score":0.03868237,"about_ca_system_score_codex":0.0020680567,"about_ca_system_score_gemma":0.0028194538,"threshold_uncertainty_score":0.2045744},"labels":[],"label_agreement":null},{"id":"W2005698680","doi":"10.1109/aina.2014.132","title":"Efficient Distributed Spatial Semijoins and Their Application in Multiple-Site Queries","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Western Canada Research Grid; Compute Canada","keywords":"Computer science; Distributed database; Distributed computing; Spatial query; Transmission (telecommunications); Data transmission; Spatial analysis; Join (topology); Distributed algorithm; Distributed data store; Data mining; Computer network; Information retrieval; Search engine","score_opus":0.0075535646363497815,"score_gpt":0.1985708475267403,"score_spread":0.19101728289039052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005698680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044499606,0.0002950103,0.9515784,0.00017686909,0.00003735165,0.00015008594,0.00009866183,0.0020809898,0.0010830236],"genre_scores_gemma":[0.25596777,0.00017494283,0.74172884,0.000059919978,0.000051805502,0.00018160183,0.000261629,0.00023918599,0.0013342933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976145,0.0007172319,0.00019846477,0.00029189824,0.001033308,0.00014460241],"domain_scores_gemma":[0.99291915,0.003949653,0.00049896946,0.0015667379,0.00077662326,0.00028888218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004529324,0.0005644417,0.0010857825,0.00084666174,0.0009853861,0.0013500352,0.002282321,0.001172846,0.0018492162],"category_scores_gemma":[0.007902187,0.000485193,0.00057292456,0.0014969505,0.001032032,0.0024833668,0.0022262586,0.0009831242,0.0005363853],"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":[0.0028184876,0.00068499113,0.0071142646,0.00037283316,0.00015373561,0.00035642993,0.0015621237,0.29077065,0.05855746,0.060454845,0.006427684,0.57072645],"study_design_scores_gemma":[0.00021417472,0.0002577908,0.0008187013,0.000013205843,0.000029612072,0.0002877897,0.00023850802,0.95588505,0.017869048,0.020518167,0.0038361163,0.000031886404],"about_ca_topic_score_codex":0.001955105,"about_ca_topic_score_gemma":0.0024537714,"teacher_disagreement_score":0.004529324,"about_ca_system_score_codex":0.0005811814,"about_ca_system_score_gemma":0.0010726547,"threshold_uncertainty_score":0.023953676},"labels":[],"label_agreement":null},{"id":"W2005846137","doi":"","title":"Proceedings of the Eighth ACM International Workshop on Data Engineering for Wireless and Mobile Access","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Computer science; Welsh; Library science; Mobile computing; Event (particle physics); World Wide Web; Telecommunications; History","score_opus":0.04307025708030411,"score_gpt":0.30081736227153577,"score_spread":0.2577471051912317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005846137","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0087072905,0.0906766,0.5768081,0.047879733,0.09009181,0.0011351212,0.0027779525,0.0068312045,0.17509216],"genre_scores_gemma":[0.05349635,0.0800184,0.23604943,0.010796588,0.026127513,0.0010199356,0.013745634,0.0031925223,0.5755536],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99546874,0.0011640765,0.00049914035,0.0005615466,0.0019525482,0.00035398605],"domain_scores_gemma":[0.99383545,0.0014512903,0.00018765166,0.001427571,0.002227608,0.00087049976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005658457,0.0013495217,0.0015448108,0.0014537382,0.0011267918,0.0066888737,0.0026386494,0.0021499812,0.053567294],"category_scores_gemma":[0.010236941,0.0007930241,0.0012733672,0.0016252355,0.00133446,0.007213027,0.004106398,0.0058421977,0.028261293],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016431356,0.00020580078,0.0005649079,0.0004341212,0.000079854144,0.00026789441,0.00050430885,0.00069869374,0.00337459,0.02894436,0.6833781,0.28138316],"study_design_scores_gemma":[0.000012675795,0.000051065625,0.0003363866,0.00016896594,0.000020020485,0.00030145608,0.00013189348,0.0019067043,0.00079107645,0.0086964285,0.9875636,0.000019809331],"about_ca_topic_score_codex":0.0020963098,"about_ca_topic_score_gemma":0.0027341777,"teacher_disagreement_score":0.053567294,"about_ca_system_score_codex":0.00092955586,"about_ca_system_score_gemma":0.0020629163,"threshold_uncertainty_score":0.17920041},"labels":[],"label_agreement":null},{"id":"W2006707333","doi":"10.1016/s0925-7721(02)00102-5","title":"Fast approximations for sums of distances, clustering and the Fermat–Weber problem","year":2002,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":90,"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":"Fermat's Last Theorem; Mathematics; Cluster analysis; Dimension (graph theory); Combinatorics; Randomized algorithm; Euclidean geometry; Preprocessor; Constant (computer programming); Set (abstract data type); Point (geometry); Approximation algorithm; Discrete mathematics; Algorithm; Computer science; Geometry; Statistics; Artificial intelligence","score_opus":0.01932622633743269,"score_gpt":0.22757928364946964,"score_spread":0.20825305731203694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006707333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023434578,0.0012308438,0.97049004,0.0006486406,0.00022591154,0.000042537205,0.00013173874,0.00044256359,0.003353079],"genre_scores_gemma":[0.22043274,0.0011426419,0.7671581,0.00022867967,0.00028438753,0.00018834801,0.0007160047,0.000544909,0.009304246],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980763,0.0006129551,0.00011806847,0.00031850286,0.00070648396,0.00016765331],"domain_scores_gemma":[0.993288,0.0046142354,0.0004265078,0.00079531444,0.0005520589,0.00032388815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003812445,0.0014790742,0.0023288215,0.0028921117,0.001678145,0.0031461124,0.0038325272,0.0026296861,0.005599998],"category_scores_gemma":[0.02762563,0.0011554587,0.0012977485,0.0041204174,0.0025128913,0.008760001,0.004670608,0.0036135865,0.0015638906],"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":[0.00045669093,0.000103336,0.0009899592,0.00040935774,0.00010713605,0.00013810252,0.00039525927,0.3209707,0.0020263111,0.5017212,0.011531632,0.16115026],"study_design_scores_gemma":[0.000022289307,0.000021959273,0.00014878517,0.000025049641,0.00001291413,0.00007934944,0.000056687724,0.6866023,0.0006340368,0.30977187,0.0026050939,0.00001965093],"about_ca_topic_score_codex":0.0039249444,"about_ca_topic_score_gemma":0.0047795367,"teacher_disagreement_score":0.005599998,"about_ca_system_score_codex":0.0026076671,"about_ca_system_score_gemma":0.0015273053,"threshold_uncertainty_score":0.020162404},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068496317,0.00039796397,0.98975813,0.00021113244,0.000054205924,0.000054138556,0.000111252826,0.0006023777,0.0019610836],"genre_scores_gemma":[0.16038753,0.0011701273,0.83539265,0.00023381707,0.0001240936,0.00013032646,0.0006181472,0.00014549786,0.0017978183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760395,0.00066377874,0.00046410187,0.00026245337,0.0008813445,0.00012436116],"domain_scores_gemma":[0.995854,0.0015515157,0.0002770313,0.0015504705,0.00064553303,0.00012131665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039253565,0.0006044939,0.001037176,0.001829677,0.00076331844,0.004622918,0.0024472764,0.0011389756,0.0019673167],"category_scores_gemma":[0.009761606,0.00063847605,0.0018179631,0.0032012095,0.0009248169,0.0070257937,0.0026077293,0.0014629323,0.00080753554],"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":[0.00016670227,0.00020374342,0.0024754417,0.00043107322,0.00021206785,0.00068036583,0.0011119088,0.06487148,0.004231891,0.63009554,0.0037637549,0.29175606],"study_design_scores_gemma":[0.000045818408,0.00006420215,0.00029866298,0.00013335461,0.00015933266,0.00033667538,0.00016019992,0.3846144,0.0043807793,0.5688387,0.040909395,0.000058330592],"about_ca_topic_score_codex":0.0031707725,"about_ca_topic_score_gemma":0.0026038392,"teacher_disagreement_score":0.004622918,"about_ca_system_score_codex":0.0006764531,"about_ca_system_score_gemma":0.0008661176,"threshold_uncertainty_score":0.020759523},"labels":[],"label_agreement":null},{"id":"W2007822373","doi":"10.1109/icra.2014.6907710","title":"Multi-agent rendezvous on street networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Rendezvous; Shortest path problem; Computer science; Task (project management); Path (computing); Mathematical optimization; Distributed computing; Computer network; Mathematics; Theoretical computer science; Engineering; Graph","score_opus":0.02206231649215098,"score_gpt":0.2390775722026118,"score_spread":0.21701525571046082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007822373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063167505,0.00029022296,0.9304844,0.0002154706,0.000030270112,0.0001343252,0.0001934279,0.0008835213,0.004600816],"genre_scores_gemma":[0.5428614,0.0002358298,0.44962266,0.00007085507,0.00002322698,0.000246803,0.00049773324,0.00018367318,0.0062578367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910337,0.00030743392,0.000039699076,0.000217001,0.00019477945,0.00013777857],"domain_scores_gemma":[0.9985362,0.0008052704,0.00018166605,0.00022001979,0.00015749078,0.00009931701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010167675,0.00072760135,0.0011262686,0.0010779189,0.0013671239,0.0012631484,0.002099683,0.0013216195,0.0032714596],"category_scores_gemma":[0.0036621718,0.00049013854,0.0008159691,0.0014296263,0.0009848417,0.0026917628,0.002469458,0.000812463,0.00070188136],"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":[0.0001336581,0.000036197038,0.000640068,0.00006095974,0.00003833688,0.00014741339,0.0001250696,0.9517789,0.0016105396,0.018779954,0.00090922834,0.02573973],"study_design_scores_gemma":[0.000021828368,0.000028590637,0.00010102493,0.000006070202,0.000006464747,0.000047961876,0.00005840844,0.986482,0.0009889527,0.010530649,0.0017214937,0.0000065063173],"about_ca_topic_score_codex":0.0051231724,"about_ca_topic_score_gemma":0.005490751,"teacher_disagreement_score":0.0051231724,"about_ca_system_score_codex":0.0012111425,"about_ca_system_score_gemma":0.00082282926,"threshold_uncertainty_score":0.010944128},"labels":[],"label_agreement":null},{"id":"W2007853611","doi":"10.1016/j.comgeo.2006.03.003","title":"Computing homotopic shortest paths efficiently","year":2006,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":48,"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":"Disjoint sets; Randomized algorithm; Mathematics; Deterministic algorithm; Combinatorics; Floyd–Warshall algorithm; Point (geometry); Plane (geometry); Running time; Shortest path problem; Algorithm; Discrete mathematics; Computer science; K shortest path routing; Geometry; Graph","score_opus":0.008324284205473856,"score_gpt":0.22607426931660687,"score_spread":0.21774998511113303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007853611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17345369,0.00038809804,0.80257016,0.00043581575,0.00013362263,0.00020708,0.0013777602,0.0037466013,0.017687105],"genre_scores_gemma":[0.4353046,0.00041403147,0.5495431,0.00008912803,0.000052981348,0.00014378,0.0031893724,0.0006463335,0.010616627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935347,0.00006979225,0.000044175882,0.00018628598,0.00024224202,0.00010407091],"domain_scores_gemma":[0.9989881,0.00042526377,0.000065263914,0.00031920086,0.00013450699,0.00006766798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041204368,0.0009151502,0.0013720863,0.0018284075,0.00095761276,0.0018107546,0.0015256418,0.0007406708,0.015906317],"category_scores_gemma":[0.0030303346,0.0005464934,0.00074150873,0.0030148863,0.0005882333,0.0036328563,0.0024404167,0.001181318,0.0023473261],"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":[0.00078900985,0.00030365287,0.0023184374,0.0004979083,0.00014186015,0.00041522164,0.00045588953,0.28536892,0.017520437,0.104681805,0.020905608,0.56660116],"study_design_scores_gemma":[0.00010791279,0.00013050463,0.0009773792,0.00003715684,0.00005627723,0.00024578188,0.0004165431,0.76645803,0.010729846,0.21021563,0.0106000565,0.00002485317],"about_ca_topic_score_codex":0.0035929198,"about_ca_topic_score_gemma":0.007664877,"teacher_disagreement_score":0.015906317,"about_ca_system_score_codex":0.0009719942,"about_ca_system_score_gemma":0.0013020012,"threshold_uncertainty_score":0.053211868},"labels":[],"label_agreement":null},{"id":"W2008632807","doi":"10.1109/icde.2012.38","title":"Relevance Matters: Capitalizing on Less (Top-k Matching in Publish/Subscribe)","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Search engine indexing; Personalization; Cluster analysis; Tree (set theory); Data mining; Matching (statistics); Hierarchical clustering; Theoretical computer science; Information retrieval; Index (typography); Relevance (law); Machine learning; World Wide Web","score_opus":0.028357691564821663,"score_gpt":0.2513707586421479,"score_spread":0.22301306707732627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008632807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14541924,0.0035730565,0.814003,0.0029773426,0.0006185533,0.00058994466,0.0009832435,0.008277931,0.023557616],"genre_scores_gemma":[0.57377905,0.0012774444,0.40714213,0.0012221511,0.0005304723,0.0002628301,0.001381895,0.00069112965,0.013712953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949319,0.0009578741,0.00040259204,0.0010604606,0.0021295573,0.00051768235],"domain_scores_gemma":[0.9874626,0.0036121733,0.0008313328,0.006344984,0.0012387892,0.00051011855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002799855,0.0006984543,0.001842854,0.0020160722,0.0018343262,0.0050043617,0.003021955,0.001923214,0.007641718],"category_scores_gemma":[0.020261098,0.00068081776,0.0007792335,0.0051780446,0.0015298772,0.01418549,0.0038291428,0.0016162036,0.0042311554],"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":[0.001162261,0.00081356434,0.006452177,0.00061754923,0.00010202101,0.00044763432,0.00060268765,0.025085513,0.043434698,0.06773987,0.021417651,0.8321244],"study_design_scores_gemma":[0.00035043387,0.001440251,0.0065670367,0.00013957362,0.00038971665,0.0035073902,0.0010417361,0.47792122,0.10437817,0.32657212,0.07745037,0.00024195889],"about_ca_topic_score_codex":0.0020637626,"about_ca_topic_score_gemma":0.0030679477,"teacher_disagreement_score":0.007641718,"about_ca_system_score_codex":0.0012351313,"about_ca_system_score_gemma":0.0028955678,"threshold_uncertainty_score":0.025564015},"labels":[],"label_agreement":null},{"id":"W2009319587","doi":"10.1145/1557626.1557665","title":"General spanning trees and reachability query evaluation","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"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 Winnipeg","funders":"","keywords":"Reachability; Spanning tree; Computer science; Combinatorics; Graph; Tree (set theory); Path (computing); Node (physics); Minimum spanning tree; Enhanced Data Rates for GSM Evolution; Trémaux tree; Routing (electronic design automation); Theoretical computer science; Directed graph; Discrete mathematics; Mathematics; Pathwidth; Line graph; Computer network; Artificial intelligence","score_opus":0.028690230338999687,"score_gpt":0.286801518287136,"score_spread":0.2581112879481363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009319587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0748813,0.002100158,0.90543634,0.0007692586,0.00009582273,0.00048435142,0.002332691,0.005139478,0.008760608],"genre_scores_gemma":[0.47358254,0.0010916563,0.5151948,0.00023950874,0.0001242129,0.0004437752,0.005672262,0.0006348716,0.0030164786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937941,0.0017693236,0.00058877195,0.0010558399,0.002162252,0.0006296996],"domain_scores_gemma":[0.99379575,0.0038061414,0.00038818014,0.00079254847,0.0010203803,0.00019693805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003123754,0.0010430928,0.0012960266,0.003212139,0.00096949557,0.0020910178,0.0019965293,0.0011730884,0.004144055],"category_scores_gemma":[0.015329514,0.00047783196,0.0011711016,0.0050439015,0.0010396675,0.0074989884,0.0023433696,0.00093001034,0.0007767669],"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":[0.0013315973,0.00036752014,0.0052159084,0.0013623306,0.00025468794,0.00059647695,0.00061934034,0.28731802,0.013803159,0.17269439,0.021958869,0.49447778],"study_design_scores_gemma":[0.00007682719,0.00017328125,0.00071629847,0.00006797275,0.00007074616,0.00034050777,0.00026796307,0.8203038,0.006434804,0.1635093,0.008002544,0.000036010377],"about_ca_topic_score_codex":0.0063609206,"about_ca_topic_score_gemma":0.0058379294,"teacher_disagreement_score":0.0063609206,"about_ca_system_score_codex":0.0018715684,"about_ca_system_score_gemma":0.0019581134,"threshold_uncertainty_score":0.016520202},"labels":[],"label_agreement":null},{"id":"W2009688537","doi":"10.1145/1391729.1391730","title":"A survey of top- <i>k</i> query processing techniques in relational database systems","year":2008,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":834,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Relational database; Database; Information retrieval; Dimension (graph theory); XML database; XML; Query optimization; Query language; Sargable; Domain (mathematical analysis); View; Database design; Web search query; World Wide Web; Search engine","score_opus":0.1129110638023711,"score_gpt":0.3416350848872731,"score_spread":0.22872402108490203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009688537","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053591393,0.8102048,0.16307104,0.0029827233,0.00064672536,0.00028225666,0.00038278566,0.00095312245,0.016117366],"genre_scores_gemma":[0.033656765,0.7828611,0.17483918,0.0011110384,0.0016374672,0.00021129528,0.0010151449,0.0001921203,0.0044758753],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962909,0.0005272554,0.000469872,0.00056922383,0.0018840769,0.0002586307],"domain_scores_gemma":[0.9953159,0.002492228,0.0002361555,0.00049915514,0.0013430881,0.000113477225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032624975,0.0010374547,0.0016566481,0.004693703,0.0010644901,0.0031602338,0.0035671636,0.0016538147,0.004043497],"category_scores_gemma":[0.0065186345,0.0010654716,0.0014508271,0.0143214185,0.00095795083,0.0074465824,0.0013012431,0.0021388321,0.0042961333],"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":[0.000076970166,0.00018257479,0.0015308595,0.008231716,0.000101357815,0.00018919416,0.00029071877,0.0028006043,0.00315598,0.030065948,0.027218964,0.9261552],"study_design_scores_gemma":[0.00007558941,0.00045988083,0.0046723,0.003618283,0.000329653,0.005452487,0.0008208776,0.03236962,0.009090717,0.060771517,0.8821216,0.0002174287],"about_ca_topic_score_codex":0.0023964858,"about_ca_topic_score_gemma":0.0017496921,"teacher_disagreement_score":0.004693703,"about_ca_system_score_codex":0.0014045654,"about_ca_system_score_gemma":0.0025937497,"threshold_uncertainty_score":0.017253995},"labels":[],"label_agreement":null},{"id":"W2010342401","doi":"10.1080/13658810601079759","title":"A shortest path algorithm with novel heuristics for dynamic transportation networks","year":2007,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MRF Geosystems (Canada)","funders":"","keywords":"Heuristics; Computer science; Shortest path problem; Path (computing); Process (computing); Yen's algorithm; Algorithm; Object (grammar); Mathematical optimization; Dijkstra's algorithm; K shortest path routing; Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.007295755120132437,"score_gpt":0.24335346984247644,"score_spread":0.236057714722344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010342401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013516959,0.00039935883,0.9828759,0.00011464994,0.00007879775,0.00009378594,0.000095501535,0.0010911336,0.0017338516],"genre_scores_gemma":[0.1269031,0.00031824573,0.8707378,0.000067087785,0.00003822226,0.00017978123,0.00030788078,0.000088237386,0.0013596851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997136,0.00008851903,0.000021824912,0.00006607311,0.00007619183,0.00003378059],"domain_scores_gemma":[0.99932754,0.00039057655,0.000068766385,0.00006872988,0.000100838675,0.000043651842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004788022,0.00064694183,0.0005995896,0.0012180749,0.0006556151,0.00084147044,0.0012351879,0.00081858115,0.0011962195],"category_scores_gemma":[0.0020800252,0.00040552372,0.0005412901,0.0018642773,0.00041670934,0.0013990572,0.00071545504,0.0007839253,0.00038075793],"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":[0.00014977236,0.00020668494,0.00093939423,0.00017279662,0.00009183611,0.00016095866,0.00010169296,0.61480886,0.0054156496,0.027387049,0.00778287,0.34278244],"study_design_scores_gemma":[0.000042976135,0.00008587218,0.00018173351,0.000009031254,0.000018593739,0.00012885434,0.00002848367,0.98417664,0.0013514938,0.008791404,0.0051647727,0.000020057896],"about_ca_topic_score_codex":0.006446355,"about_ca_topic_score_gemma":0.005641946,"teacher_disagreement_score":0.006446355,"about_ca_system_score_codex":0.00059232174,"about_ca_system_score_gemma":0.0015853831,"threshold_uncertainty_score":0.012817681},"labels":[],"label_agreement":null},{"id":"W2010507851","doi":"10.1007/s13740-012-0001-1","title":"On Modeling and Querying Concept Evolution","year":2012,"lang":"en","type":"article","venue":"Journal on Data Semantics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Scalability; Exploit; RDF; Theoretical computer science; Artificial intelligence; Semantic Web; Database","score_opus":0.059410692376369395,"score_gpt":0.2917034509951963,"score_spread":0.2322927586188269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010507851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019294359,0.0007773762,0.97417146,0.001549896,0.00010280893,0.00017018437,0.00047895935,0.0009998991,0.0024551183],"genre_scores_gemma":[0.24421377,0.0011478263,0.74875426,0.0005770568,0.00019828859,0.0002434353,0.0019964767,0.00039858316,0.0024703667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9905902,0.0035457516,0.0012975826,0.0015614793,0.0025689257,0.00043618478],"domain_scores_gemma":[0.97353405,0.01723675,0.00087326416,0.0057602082,0.0020652632,0.0005303643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010045235,0.0010760512,0.002112981,0.0033764457,0.002035059,0.007204984,0.0049209157,0.0029924049,0.0032840972],"category_scores_gemma":[0.046017937,0.0014538254,0.0035555065,0.005537932,0.004336109,0.02244143,0.006911784,0.0043942113,0.0005452979],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035670752,0.00024233278,0.005219183,0.0003535787,0.00023289575,0.0004624894,0.002185831,0.1558813,0.002277406,0.6658861,0.0059847473,0.16091749],"study_design_scores_gemma":[0.0000248758,0.000031253778,0.00026420658,0.000056933022,0.00007081592,0.00013893026,0.00024106707,0.51358247,0.0011394925,0.4788329,0.0055843876,0.00003270299],"about_ca_topic_score_codex":0.02155307,"about_ca_topic_score_gemma":0.015991421,"teacher_disagreement_score":0.02155307,"about_ca_system_score_codex":0.0028595345,"about_ca_system_score_gemma":0.0025631385,"threshold_uncertainty_score":0.053124905},"labels":[],"label_agreement":null},{"id":"W2010916971","doi":"10.1145/1132863.1132868","title":"Integrating XML data sources using approximate joins","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Joins; XML validation; XML; Efficient XML Interchange; Document Structure Description; XML Schema (W3C); XML database; Information retrieval; Streaming XML; Data mining; Set (abstract data type); Tree (set theory); XML Encryption; Database; Programming language; World Wide Web","score_opus":0.06748389564404962,"score_gpt":0.2828255905583581,"score_spread":0.2153416949143085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010916971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014708839,0.00036875784,0.98290294,0.0001556621,0.00002253231,0.000094855524,0.00015531245,0.0009198168,0.0006712509],"genre_scores_gemma":[0.15698422,0.0004293297,0.84011525,0.00012063934,0.000067847206,0.0001603939,0.0011033483,0.00022004689,0.00079888257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9802822,0.0062700724,0.0015100472,0.0030749722,0.00837563,0.00048711346],"domain_scores_gemma":[0.977114,0.011195478,0.0025240916,0.006072334,0.0026196546,0.000474393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013593591,0.0012680612,0.0023426497,0.0047071683,0.001813167,0.007316389,0.0036772161,0.0019972145,0.0011685237],"category_scores_gemma":[0.042752165,0.0012276745,0.0020082414,0.010205037,0.0016512673,0.011064926,0.00909385,0.002113866,0.0008928873],"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":[0.000540636,0.00026710518,0.0132048745,0.00041290987,0.00042221908,0.0007013024,0.002189641,0.34809458,0.015415243,0.15034911,0.0043847375,0.46401763],"study_design_scores_gemma":[0.0000320051,0.00014600491,0.0008764026,0.000044096498,0.000064311345,0.00036835135,0.0004017719,0.903037,0.0081827715,0.08020989,0.006586715,0.000050680563],"about_ca_topic_score_codex":0.003814023,"about_ca_topic_score_gemma":0.003764135,"teacher_disagreement_score":0.013593591,"about_ca_system_score_codex":0.001571089,"about_ca_system_score_gemma":0.0025510434,"threshold_uncertainty_score":0.07189059},"labels":[],"label_agreement":null},{"id":"W2012848182","doi":"10.1007/s00778-009-0176-8","title":"Supporting ranking queries on uncertain and incomplete data","year":2010,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":78,"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":"Ranking (information retrieval); Computer science; Data mining; Uncertain data; Set (abstract data type); Rank (graph theory); Probabilistic logic; Ranking SVM; Information retrieval; Semantics (computer science); Learning to rank; Artificial intelligence; Mathematics","score_opus":0.04986115690612704,"score_gpt":0.32384594359330166,"score_spread":0.27398478668717463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012848182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16858363,0.0030971472,0.8067501,0.0046329326,0.00048925355,0.00036472038,0.005317983,0.0055292714,0.0052349167],"genre_scores_gemma":[0.6873715,0.0013158369,0.29714623,0.0006839785,0.00090490154,0.00014594123,0.008768324,0.0005362898,0.00312701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98735166,0.0033253543,0.0013591655,0.0017074202,0.0052174386,0.0010389147],"domain_scores_gemma":[0.9317869,0.05248009,0.0025351318,0.0075056003,0.0045707333,0.0011215868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008457639,0.0016923775,0.004651676,0.0030702369,0.002000741,0.008426222,0.0045914548,0.00370967,0.0046893433],"category_scores_gemma":[0.054085717,0.0013751837,0.0017379492,0.007149325,0.0014943484,0.017354956,0.0040271785,0.0032918903,0.0016107337],"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":[0.0033913173,0.0010921528,0.013101767,0.0027254391,0.0006572658,0.0026059775,0.0016166144,0.39401138,0.020698128,0.07969754,0.050311036,0.43009138],"study_design_scores_gemma":[0.000096647076,0.00019117535,0.00050887995,0.000052467385,0.00010251575,0.00048714294,0.00043582983,0.8932379,0.0052572573,0.09556155,0.004028921,0.00003970831],"about_ca_topic_score_codex":0.0047969674,"about_ca_topic_score_gemma":0.007261544,"teacher_disagreement_score":0.008457639,"about_ca_system_score_codex":0.0012163868,"about_ca_system_score_gemma":0.0025015671,"threshold_uncertainty_score":0.044728816},"labels":[],"label_agreement":null},{"id":"W2012990590","doi":"10.1109/grc.2005.1547337","title":"Using contextual information to improve retrieval performance","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Information retrieval; Weighting; Divergence-from-randomness model; Query expansion; Construct (python library); Context (archaeology); Granularity; Index (typography); Paragraph; Probabilistic logic; Data mining; Artificial intelligence; World Wide Web","score_opus":0.023033424523372464,"score_gpt":0.25603084098202233,"score_spread":0.23299741645864988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012990590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07434469,0.0073479465,0.9028412,0.00062562164,0.00016355824,0.0002892383,0.00036229202,0.009564282,0.004461126],"genre_scores_gemma":[0.52612835,0.0030283793,0.46575993,0.00036634292,0.0005873815,0.00017822553,0.0015068868,0.0006214308,0.0018231524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996029,0.0013906049,0.00037181983,0.00064133323,0.0012663533,0.00030094796],"domain_scores_gemma":[0.9923993,0.00448391,0.0003936187,0.0014620782,0.0011318879,0.000129194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004416629,0.001602417,0.0024261177,0.0035549912,0.0009701724,0.001945127,0.0012821974,0.0013787872,0.0027530915],"category_scores_gemma":[0.020983702,0.00059331756,0.00085172546,0.0030717726,0.00052553497,0.006864275,0.0021022563,0.001130206,0.0016286551],"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":[0.00075412483,0.00044308093,0.0071942834,0.0009131562,0.00039146843,0.00027728963,0.0004568656,0.085104726,0.061115976,0.010217604,0.010137016,0.8229945],"study_design_scores_gemma":[0.00019323369,0.0011091627,0.007311934,0.00011233298,0.0009519871,0.00096452434,0.00028538267,0.8744283,0.070472926,0.016932502,0.026947463,0.00029031807],"about_ca_topic_score_codex":0.003838835,"about_ca_topic_score_gemma":0.004386556,"teacher_disagreement_score":0.004416629,"about_ca_system_score_codex":0.0005880718,"about_ca_system_score_gemma":0.0008817196,"threshold_uncertainty_score":0.02335763},"labels":[],"label_agreement":null},{"id":"W2013001036","doi":"10.1016/s1386-5056(02)00126-0","title":"Integrating gis components with knowledge discovery technology for environmental health decision support","year":2003,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre hospitalier universitaire de Québec; Institut National de Santé Publique du Québec; Université Laval","funders":"","keywords":"Online analytical processing; GIS and public health; Decision support system; Computer science; Geographic information system; Data science; Knowledge extraction; Field (mathematics); Knowledge management; Data mining; Data warehouse; Geography; Cartography","score_opus":0.01732169662344831,"score_gpt":0.3056542075351189,"score_spread":0.2883325109116706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013001036","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016522594,0.0004017999,0.96861637,0.0008379609,0.00008327424,0.00016634792,0.00053711294,0.0079478575,0.0048866947],"genre_scores_gemma":[0.15140948,0.0006284982,0.8433147,0.00028979,0.00006499648,0.00014949897,0.001188695,0.0002857092,0.00266872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990427,0.0002799368,0.0001433875,0.000115268405,0.00037582382,0.000042823707],"domain_scores_gemma":[0.9948887,0.0032599478,0.00019561924,0.00076931645,0.00077809556,0.00010834486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002250608,0.0008052729,0.0010234568,0.002590362,0.00052104716,0.0036957227,0.0016149723,0.0008188526,0.0050489176],"category_scores_gemma":[0.0076499013,0.0007297005,0.00092871656,0.002756766,0.0004189746,0.0037862812,0.0017183315,0.001027963,0.0013968518],"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":[0.00066040555,0.0005460167,0.009152635,0.001000406,0.00095874985,0.0007590088,0.0007615566,0.04851066,0.023847057,0.029063577,0.010875674,0.8738643],"study_design_scores_gemma":[0.0001764584,0.00028922112,0.004986486,0.0002486759,0.0014443429,0.0012363164,0.00061352435,0.7376219,0.074649036,0.0941704,0.08441461,0.00014916179],"about_ca_topic_score_codex":0.0043820497,"about_ca_topic_score_gemma":0.0053469595,"teacher_disagreement_score":0.0050489176,"about_ca_system_score_codex":0.00044249446,"about_ca_system_score_gemma":0.0010436723,"threshold_uncertainty_score":0.016890347},"labels":[],"label_agreement":null},{"id":"W2013230316","doi":"10.3166/isi.15.1.113-138","title":"Requêtes arbres régulières pour l'analyse de dépendances entre vues et mises à jour de documents XML","year":2010,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"XPath; Class (philosophy); XML; Computer science; Tree (set theory); Combinatorics; Database query; Time complexity; PSPACE; Independence (probability theory); Mathematics; Theoretical computer science; Discrete mathematics; Information retrieval; Computational complexity theory; Algorithm; Artificial intelligence; XML database; World Wide Web; Statistics","score_opus":0.01622366735193683,"score_gpt":0.2679519267846842,"score_spread":0.2517282594327474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013230316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34080994,0.0014547484,0.65055454,0.0007426153,0.00006096607,0.00019120674,0.0010512982,0.0023515637,0.0027830831],"genre_scores_gemma":[0.78752846,0.0006015136,0.20598082,0.000117585805,0.00011519849,0.00018856398,0.001873711,0.0005545802,0.0030395703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98986465,0.0025820853,0.00048517704,0.0018985299,0.0046238583,0.0005456399],"domain_scores_gemma":[0.9520669,0.039337568,0.0028612055,0.002709466,0.0023991459,0.00062563916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008274219,0.0010897751,0.0011046171,0.0025905124,0.0008104159,0.0038527513,0.001795961,0.0016385878,0.0024703767],"category_scores_gemma":[0.041690376,0.0009445767,0.0012919854,0.0020519726,0.0015396945,0.005452266,0.0012775584,0.0024860199,0.0004356972],"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":[0.0021605885,0.0006041791,0.048735052,0.00093567185,0.0003375303,0.000853979,0.0014783336,0.5146687,0.05214408,0.05941667,0.003369921,0.31529534],"study_design_scores_gemma":[0.000056764,0.0002720811,0.007890129,0.0000355998,0.00009115713,0.00040576668,0.00033808456,0.95069087,0.01817683,0.019050637,0.0029455938,0.000046565525],"about_ca_topic_score_codex":0.009932817,"about_ca_topic_score_gemma":0.008719821,"teacher_disagreement_score":0.009932817,"about_ca_system_score_codex":0.002013062,"about_ca_system_score_gemma":0.0013322853,"threshold_uncertainty_score":0.04375875},"labels":[],"label_agreement":null},{"id":"W2014133121","doi":"10.1007/s11009-011-9246-7","title":"Generating Functions of Waiting Times and Numbers of Visits for Random Walks on Graphs","year":2011,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Random walk; Cover (algebra); Class (philosophy); Combinatorics; Discrete mathematics; Order (exchange); Statistics; Computer science; Artificial intelligence","score_opus":0.10535835796620976,"score_gpt":0.29521330892928965,"score_spread":0.1898549509630799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014133121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42000532,0.0008733547,0.5688448,0.0016038745,0.00014302882,0.00027925195,0.0017839004,0.001309548,0.005156893],"genre_scores_gemma":[0.9291042,0.0011973021,0.052821405,0.00032434452,0.00036288195,0.0010548515,0.0028894069,0.00078352273,0.011462083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983524,0.0006163979,0.0000852455,0.0003078028,0.00032443122,0.00031380908],"domain_scores_gemma":[0.92461985,0.062288344,0.004494704,0.0028203207,0.0033852048,0.0023916585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076097073,0.0013697187,0.0016501491,0.00669968,0.0013006063,0.0034177778,0.004440919,0.0028333687,0.009440025],"category_scores_gemma":[0.050493937,0.0013805019,0.0019788276,0.00367272,0.0032103383,0.0052519985,0.0018265261,0.0029723549,0.0011056657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021484528,0.00015124142,0.0062486497,0.00020324292,0.000076111864,0.00024485425,0.0010313586,0.15502016,0.0020859863,0.81803596,0.004806538,0.011881002],"study_design_scores_gemma":[0.0000543586,0.000030508438,0.0018312214,0.00006111649,0.000041263433,0.00022218627,0.00018525042,0.675048,0.0007410366,0.32098657,0.00073056825,0.00006795033],"about_ca_topic_score_codex":0.0030466253,"about_ca_topic_score_gemma":0.0030847066,"teacher_disagreement_score":0.009440025,"about_ca_system_score_codex":0.0026481156,"about_ca_system_score_gemma":0.0014265595,"threshold_uncertainty_score":0.04024446},"labels":[],"label_agreement":null},{"id":"W2015391423","doi":"10.1145/2462356.2462378","title":"Kinetic data structures for all nearest neighbors and closest pair in the plane","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Combinatorics; k-nearest neighbors algorithm; Plane (geometry); Mathematics; Algebraic number; Function (biology); Amortized analysis; Binary logarithm; Degree (music); Trajectory; Kinetic energy; Discrete mathematics; Data structure; Algorithm; Physics; Computer science; Mathematical analysis; Geometry; Artificial intelligence","score_opus":0.04426051183283414,"score_gpt":0.27019164940629237,"score_spread":0.22593113757345823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015391423","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012856211,0.00049396267,0.9773456,0.0007709754,0.00013947267,0.00018011415,0.0033146327,0.0023581062,0.0025409167],"genre_scores_gemma":[0.1733408,0.0009168896,0.80780196,0.00028582825,0.00017749358,0.000560943,0.012097982,0.00053096283,0.00428724],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99647313,0.0004845852,0.00063344906,0.0006397828,0.0015222252,0.00024663977],"domain_scores_gemma":[0.99118364,0.0014866809,0.0010668148,0.0041820817,0.0016711975,0.0004095276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024165886,0.001073405,0.0025417334,0.0032434436,0.0023442095,0.0048135705,0.0048345556,0.0014652922,0.007259932],"category_scores_gemma":[0.016862122,0.0010072164,0.0017784084,0.0066330647,0.001493753,0.015101863,0.0049739624,0.0033536989,0.0036936777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009200002,0.00031646155,0.0031935163,0.00078099023,0.00014055466,0.000210529,0.0005965929,0.14080143,0.0043307343,0.5714453,0.030894762,0.24636912],"study_design_scores_gemma":[0.00014185018,0.00016931033,0.00047950743,0.00008210277,0.00004844419,0.0003586358,0.00030315295,0.40765154,0.005307894,0.5499854,0.035381027,0.00009130533],"about_ca_topic_score_codex":0.0032374556,"about_ca_topic_score_gemma":0.0031630653,"teacher_disagreement_score":0.007259932,"about_ca_system_score_codex":0.0017690051,"about_ca_system_score_gemma":0.0028533398,"threshold_uncertainty_score":0.024286926},"labels":[],"label_agreement":null},{"id":"W2015414733","doi":"10.1002/asi.22648","title":"Modeling geographic, temporal, and proximity contexts for improving geotemporal search","year":2012,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Query expansion; Web search query; Probabilistic logic; Context (archaeology); Data mining; Search engine; Artificial intelligence; Geography","score_opus":0.0190929325820931,"score_gpt":0.2779477077519878,"score_spread":0.2588547751698947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015414733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26341873,0.008261847,0.7163982,0.0006687617,0.00010231624,0.0002455927,0.0013249599,0.0023054674,0.0072741187],"genre_scores_gemma":[0.878564,0.0017572968,0.116341,0.00013862988,0.00013590862,0.000086718785,0.0010913907,0.00010877449,0.0017762879],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883384,0.0003799276,0.00008960303,0.00026691478,0.00035461778,0.00007512948],"domain_scores_gemma":[0.99836296,0.00072778185,0.0003330165,0.00029090792,0.0002292544,0.000056106677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096543843,0.0008094794,0.00080625305,0.0026101896,0.0005358541,0.001520869,0.000784423,0.0008569938,0.0013523902],"category_scores_gemma":[0.0059620235,0.00037418708,0.00081052986,0.0040816646,0.0004086247,0.0046314015,0.0014148268,0.0007504702,0.0007034152],"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":[0.00091428,0.00054014596,0.045337573,0.0009277868,0.00043734425,0.00060406426,0.0009981947,0.37389013,0.035865817,0.03012023,0.007745218,0.5026192],"study_design_scores_gemma":[0.000043126507,0.0002883562,0.010098907,0.00003685933,0.00019366246,0.0005303769,0.00031013647,0.9637379,0.00437447,0.012999763,0.007321469,0.00006491738],"about_ca_topic_score_codex":0.009920876,"about_ca_topic_score_gemma":0.019393064,"teacher_disagreement_score":0.009920876,"about_ca_system_score_codex":0.00064708525,"about_ca_system_score_gemma":0.0009112578,"threshold_uncertainty_score":0.019726217},"labels":[],"label_agreement":null},{"id":"W2017629483","doi":"10.1145/1071246.1071248","title":"Efficient constraint processing for location-aware computing","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Constraint (computer-aided design); Set (abstract data type); Relation (database); Position (finance); Distributed computing; Wireless ad hoc network; Mobile computing; Matching (statistics); Point (geometry); Wireless; Theoretical computer science; Computer network; Data mining; Mathematics","score_opus":0.019338926336859567,"score_gpt":0.2695487969445762,"score_spread":0.2502098706077166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017629483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049151513,0.00045330226,0.9894308,0.00044051505,0.00004561773,0.0001089728,0.00034505402,0.0010538162,0.0032068803],"genre_scores_gemma":[0.12815927,0.0006343585,0.8668748,0.00016549115,0.000083444305,0.00040454703,0.0009541179,0.0002871613,0.00243678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983015,0.0006136041,0.00012258442,0.000281858,0.0005450506,0.0001353621],"domain_scores_gemma":[0.9966022,0.0021285024,0.00019255567,0.00055966846,0.00043531734,0.00008173943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001198234,0.0012164725,0.0013159956,0.0009021324,0.0010316549,0.0019757543,0.0019488705,0.0011281306,0.0076506254],"category_scores_gemma":[0.008017961,0.0006054692,0.0008158759,0.0032645124,0.0008363306,0.0026398946,0.0017810159,0.0017657762,0.0016789257],"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":[0.000281562,0.00015282969,0.0005788467,0.0005715729,0.00011507192,0.00024609128,0.00021977261,0.58040375,0.005790751,0.08671493,0.019767787,0.30515712],"study_design_scores_gemma":[0.000036166733,0.000019008014,0.00007286711,0.000013660023,0.000010630566,0.000042481526,0.000042554435,0.9441152,0.0014194257,0.048991527,0.0052243313,0.0000121508665],"about_ca_topic_score_codex":0.0093797725,"about_ca_topic_score_gemma":0.011256692,"teacher_disagreement_score":0.0093797725,"about_ca_system_score_codex":0.000998247,"about_ca_system_score_gemma":0.0015769041,"threshold_uncertainty_score":0.025593877},"labels":[],"label_agreement":null},{"id":"W2017668371","doi":"10.3166/ria.22.137-159","title":"Génération de plan de site web pour les non-voyants par des fourmis artificielles","year":2008,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Biology; Philosophy","score_opus":0.12354991509025708,"score_gpt":0.27668443620145355,"score_spread":0.15313452111119646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017668371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063201405,0.00012588808,0.9312536,0.000156794,0.000035349225,0.00013097982,0.00014450382,0.002423462,0.0025280234],"genre_scores_gemma":[0.2654375,0.00015407856,0.7272815,0.000062626365,0.000013330161,0.0002037759,0.00056905684,0.00032961552,0.005948476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949944,0.00011379944,0.00003028654,0.00013309858,0.00018384341,0.00003955515],"domain_scores_gemma":[0.9988945,0.0005208065,0.00007692049,0.00024016174,0.00021039034,0.000057190922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076927536,0.0007257347,0.0005146715,0.000704645,0.0005759032,0.0010994173,0.000895039,0.0009997131,0.004029731],"category_scores_gemma":[0.0029370876,0.00038851346,0.0007597853,0.00047725116,0.00077294727,0.0011071522,0.001061025,0.00070984795,0.00094175996],"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":[0.00045727697,0.00023316655,0.008247341,0.00049036817,0.00011955352,0.0010698307,0.0017477171,0.3501885,0.08400363,0.031945188,0.005327931,0.5161695],"study_design_scores_gemma":[0.000049852675,0.0002376293,0.0016822424,0.00005060047,0.000048983267,0.00042624318,0.00053078926,0.9180792,0.04777963,0.016203305,0.014868485,0.000042986347],"about_ca_topic_score_codex":0.0043431036,"about_ca_topic_score_gemma":0.004602574,"teacher_disagreement_score":0.0043431036,"about_ca_system_score_codex":0.000477753,"about_ca_system_score_gemma":0.00096199487,"threshold_uncertainty_score":0.0134807825},"labels":[],"label_agreement":null},{"id":"W2017733151","doi":"10.1109/iccit.2007.148","title":"Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers","year":2007,"lang":"en","type":"article","venue":"2007 International Conference on Convergence Information Technology (ICCIT 2007)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Naive Bayes classifier; Artificial intelligence; k-nearest neighbors algorithm; Computer science; Machine learning; Bayes error rate; Classifier (UML); Bayes classifier; Random subspace method; Pattern recognition (psychology); Bayesian probability; Bayes' theorem; Data mining; Support vector machine","score_opus":0.024900131717299163,"score_gpt":0.26046504088334227,"score_spread":0.2355649091660431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017733151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40799195,0.029043477,0.52071804,0.003364038,0.0020249733,0.0010355046,0.0017204485,0.0024473479,0.031654246],"genre_scores_gemma":[0.77948624,0.0031602485,0.21121487,0.00041924647,0.00046506262,0.00024048796,0.0014320785,0.00020890828,0.0033728566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.972265,0.009156005,0.002022311,0.0034004005,0.012219149,0.00093716406],"domain_scores_gemma":[0.9158292,0.061080836,0.0028181085,0.0036463363,0.015876478,0.00074896455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024978561,0.0017160461,0.0024701804,0.0043599885,0.0018942847,0.003885977,0.0019543234,0.0028710726,0.001898506],"category_scores_gemma":[0.10162636,0.0006303834,0.001129974,0.0032327026,0.0012517548,0.007553869,0.0010593983,0.0016107376,0.0011827209],"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":[0.0027377983,0.00071206247,0.03973011,0.0012196886,0.0009415638,0.00022954737,0.00084799,0.24045718,0.002506417,0.02304417,0.012110934,0.6754625],"study_design_scores_gemma":[0.000077883546,0.0006781075,0.0085443,0.00016241384,0.00017442151,0.00023166009,0.00048473087,0.96362764,0.0030334292,0.018525274,0.0043460513,0.00011403094],"about_ca_topic_score_codex":0.01523003,"about_ca_topic_score_gemma":0.011999207,"teacher_disagreement_score":0.024978561,"about_ca_system_score_codex":0.002820689,"about_ca_system_score_gemma":0.0022467182,"threshold_uncertainty_score":0.13210082},"labels":[],"label_agreement":null},{"id":"W2018416407","doi":"10.1145/2133803.2330083","title":"A topological sorting algorithm for large graphs","year":2012,"lang":"en","type":"article","venue":"ACM Journal of Experimental Algorithmics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Topological sorting; Reachability; Computer science; Sorting; Algorithm; Sorting algorithm; Vertex (graph theory); sort; Directed acyclic graph; Graph; Theoretical computer science; Mathematics","score_opus":0.03450389665994346,"score_gpt":0.314868580395484,"score_spread":0.2803646837355405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018416407","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0175224,0.00022114653,0.9660055,0.0002256793,0.00008270534,0.00016400896,0.00087731984,0.009614041,0.0052872812],"genre_scores_gemma":[0.10560294,0.0002204593,0.8861035,0.00012621276,0.00004668577,0.0002519132,0.002523874,0.00062609283,0.004498352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942553,0.00008271726,0.0000668786,0.00011806132,0.00023338562,0.000073437346],"domain_scores_gemma":[0.998565,0.00044245424,0.00013980031,0.00041360105,0.0003503897,0.00008870178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005790706,0.0007705079,0.0005440185,0.002135213,0.0007965523,0.0014105983,0.0015409008,0.000657149,0.008235252],"category_scores_gemma":[0.0032158468,0.00041698077,0.000797211,0.002599507,0.00063123956,0.0025898372,0.0018189523,0.00085502554,0.0020231004],"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":[0.00041661842,0.00015472558,0.0017508259,0.0005189176,0.000090522066,0.00015130505,0.00026816712,0.076609224,0.023790017,0.064227045,0.025724562,0.8062981],"study_design_scores_gemma":[0.00031316633,0.0005266816,0.0013581873,0.00012833343,0.00011964923,0.00076613156,0.00035905367,0.6629214,0.055084173,0.17615348,0.10216026,0.00010941613],"about_ca_topic_score_codex":0.0023269907,"about_ca_topic_score_gemma":0.00336359,"teacher_disagreement_score":0.008235252,"about_ca_system_score_codex":0.0009538366,"about_ca_system_score_gemma":0.0015786483,"threshold_uncertainty_score":0.027549624},"labels":[],"label_agreement":null},{"id":"W2018601220","doi":"10.1007/s11042-013-1740-1","title":"Knowledge management technologies for semantic multimedia services","year":2013,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Data Management and Algorithms","field":"Computer Science","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; Semantics (computer science); Ubiquitous computing; Database transaction; Context (archaeology); Domain (mathematical analysis); World Wide Web; Domain knowledge; Service (business); Knowledge management; Human–computer interaction; Database","score_opus":0.0194616780833261,"score_gpt":0.2600597032365578,"score_spread":0.2405980251532317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018601220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012273085,0.003251098,0.9297403,0.00480838,0.00041409262,0.00020188442,0.0007615555,0.00297395,0.04557571],"genre_scores_gemma":[0.31400597,0.0072581223,0.6539707,0.001399025,0.0005785022,0.00035500724,0.0038507804,0.00034855207,0.018233167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983663,0.0003623338,0.00022710477,0.00024476924,0.0006201163,0.00017943891],"domain_scores_gemma":[0.9981951,0.0005788373,0.00015286027,0.0005843692,0.00033843034,0.00015041608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002188006,0.00058626285,0.0007457266,0.003161185,0.0013484599,0.0072640944,0.0024458834,0.0019134306,0.0051612435],"category_scores_gemma":[0.004650671,0.00044489576,0.00095778104,0.0044418443,0.0016407078,0.011980261,0.0040620198,0.002319538,0.0021192853],"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":[0.000103254504,0.00018388034,0.00040086973,0.0003748806,0.000063426385,0.00040455413,0.00081040646,0.006214825,0.0033452231,0.730557,0.015522802,0.24201888],"study_design_scores_gemma":[0.000031736687,0.00002813638,0.00029698588,0.0002760537,0.000084699546,0.00037185298,0.0005796818,0.055279803,0.005538385,0.8059184,0.131553,0.000041223637],"about_ca_topic_score_codex":0.0032366079,"about_ca_topic_score_gemma":0.003054308,"teacher_disagreement_score":0.0072640944,"about_ca_system_score_codex":0.0018515703,"about_ca_system_score_gemma":0.0016123288,"threshold_uncertainty_score":0.017266095},"labels":[],"label_agreement":null},{"id":"W2019985064","doi":"10.1109/bigdata.congress.2014.121","title":"Towards Efficient KNN Joins on Data Streams","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Joins; Computer science; Snapshot (computer storage); Data mining; Data stream mining; Cluster analysis; Big data; Database; Artificial intelligence","score_opus":0.039897047600310674,"score_gpt":0.2677724367088479,"score_spread":0.22787538910853725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019985064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042395994,0.00140862,0.9477068,0.00056276255,0.00021221559,0.00024828553,0.0008201839,0.0043859784,0.0022590598],"genre_scores_gemma":[0.14959224,0.00071768364,0.8431245,0.0002813265,0.0003413939,0.0002583533,0.0028899657,0.00053366745,0.0022608605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9902036,0.0018053498,0.0010493676,0.0015474933,0.004703182,0.0006910062],"domain_scores_gemma":[0.9844787,0.006151982,0.0014805378,0.003813932,0.0033839871,0.0006909989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006913296,0.0018521361,0.0031669573,0.0033032512,0.002287406,0.004697222,0.0038708213,0.0019105732,0.0018343367],"category_scores_gemma":[0.024573127,0.0014264338,0.0013797219,0.0072552767,0.0015728747,0.01070117,0.0057940683,0.0025134734,0.0017723908],"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":[0.0026689672,0.0010712856,0.010189302,0.0010688736,0.00040781221,0.00057434,0.0024345203,0.30370936,0.029919595,0.057814594,0.030517831,0.5596235],"study_design_scores_gemma":[0.00013525954,0.00023916103,0.0005804249,0.000039532402,0.000040871204,0.00021995428,0.00052082667,0.91187006,0.011410169,0.06707196,0.007830693,0.000041126837],"about_ca_topic_score_codex":0.0046531283,"about_ca_topic_score_gemma":0.0052106725,"teacher_disagreement_score":0.006913296,"about_ca_system_score_codex":0.0012027479,"about_ca_system_score_gemma":0.0026604875,"threshold_uncertainty_score":0.03656143},"labels":[],"label_agreement":null},{"id":"W2020019218","doi":"10.1111/1467-9671.00111","title":"Spatio‐Temporal Object‐Oriented Data Model for Disaggregate Travel Behavior","year":2002,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval; Université de Montréal","funders":"","keywords":"Computer science; Exploit; Object (grammar); Domain (mathematical analysis); Data model (GIS); Hierarchy; Field (mathematics); Travel behavior; Conceptual model; Process (computing); Focus (optics); Data mining; Database; Artificial intelligence; Engineering; Transport engineering; Mathematics","score_opus":0.08436288203277122,"score_gpt":0.29173461727934974,"score_spread":0.20737173524657854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020019218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008464742,0.00011113251,0.98578703,0.00033649424,0.000050631228,0.00012566679,0.0015494095,0.0011403032,0.0024345953],"genre_scores_gemma":[0.23287274,0.0005649107,0.7543894,0.0002362018,0.00006664983,0.0009279832,0.00564928,0.0002550921,0.005037696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918,0.00017972017,0.00013264731,0.0001659501,0.000278308,0.00006323952],"domain_scores_gemma":[0.9982243,0.00057252194,0.00016230275,0.00051558996,0.00044282706,0.00008247724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020796866,0.0005891027,0.0005833466,0.0015075612,0.0006696293,0.003270897,0.0019910831,0.0010560785,0.002133917],"category_scores_gemma":[0.003846459,0.0004385411,0.0012631286,0.0025619369,0.00074373547,0.0036154327,0.0010286709,0.0011963602,0.0010118595],"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":[0.00020064642,0.00019150553,0.007883855,0.00025010697,0.00012069922,0.0005637987,0.0012706394,0.38662392,0.0043314546,0.52154255,0.009790533,0.067230284],"study_design_scores_gemma":[0.000027814978,0.00003086366,0.00057198846,0.000026701688,0.000057973153,0.00013338577,0.00011394454,0.9043255,0.0012475637,0.05355898,0.039882798,0.000022415792],"about_ca_topic_score_codex":0.018507075,"about_ca_topic_score_gemma":0.017406547,"teacher_disagreement_score":0.018507075,"about_ca_system_score_codex":0.0015443123,"about_ca_system_score_gemma":0.0019041625,"threshold_uncertainty_score":0.036798656},"labels":[],"label_agreement":null},{"id":"W2020493833","doi":"10.1145/1989323.1989390","title":"BE-tree","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Tree (set theory); Mathematics; Combinatorics","score_opus":0.06173774393611898,"score_gpt":0.22555230003446247,"score_spread":0.1638145560983435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020493833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031292096,0.0021995595,0.90043765,0.0010432509,0.00040213275,0.00050882844,0.010847462,0.018721089,0.034547877],"genre_scores_gemma":[0.28241107,0.0017583448,0.66157866,0.0011108901,0.00018214252,0.0008876346,0.026553724,0.002239995,0.02327763],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99921143,0.00009628768,0.00010929965,0.00011429473,0.00037749094,0.00009113457],"domain_scores_gemma":[0.998133,0.00050260854,0.00014736598,0.00059670303,0.0004844892,0.00013571484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006699249,0.00043939622,0.0008402566,0.0013741893,0.00081837404,0.002325094,0.001698167,0.00066483056,0.009170614],"category_scores_gemma":[0.005154752,0.00030315487,0.0005140625,0.0026595464,0.0005041593,0.004894018,0.0020700404,0.0009394282,0.0030151156],"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":[0.000860126,0.00031741854,0.0031490526,0.0009455333,0.00009691859,0.00023388353,0.00039973948,0.022195775,0.021723194,0.26457906,0.12648177,0.55901754],"study_design_scores_gemma":[0.00016014262,0.00052193337,0.0011553323,0.00020641291,0.00007756992,0.0007873094,0.00024781478,0.2811845,0.034989435,0.27581894,0.40472722,0.00012343453],"about_ca_topic_score_codex":0.0016923638,"about_ca_topic_score_gemma":0.0036461828,"teacher_disagreement_score":0.009170614,"about_ca_system_score_codex":0.00080879463,"about_ca_system_score_gemma":0.0013468118,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2020603728","doi":"10.1007/s10796-014-9517-2","title":"Optimization of generic progressive queries based on dependency analysis and materialized views","year":2014,"lang":"en","type":"article","venue":"Information Systems Frontiers","topic":"Data Management and Algorithms","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":"IBM (Canada)","funders":"","keywords":"Materialized view; Computer science; Dependency (UML); Process (computing); Node (physics); Graph; Dependency graph; Set (abstract data type); Greedy algorithm; Data mining; Theoretical computer science; Database; Artificial intelligence; Algorithm; Database design; View","score_opus":0.008966254829263439,"score_gpt":0.2124387450631958,"score_spread":0.20347249023393235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020603728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10749348,0.0004958317,0.8780061,0.00035097453,0.000054115484,0.00028058622,0.0010023275,0.006960927,0.0053557716],"genre_scores_gemma":[0.56470615,0.00027467494,0.4279875,0.00015792777,0.00008333685,0.00021077337,0.0029459062,0.0013437802,0.0022899676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966685,0.0006996088,0.00022993248,0.00054587587,0.0013500035,0.0005060423],"domain_scores_gemma":[0.9949621,0.0024782273,0.00026335733,0.001480417,0.0006420771,0.00017392852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020583253,0.0012799406,0.0019490321,0.0014479154,0.0008431877,0.0025186636,0.0028922148,0.0010858239,0.0050980765],"category_scores_gemma":[0.008623592,0.00072265044,0.0016422848,0.0026825226,0.0011582148,0.0042949077,0.0031432454,0.0015855319,0.0007368357],"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":[0.0026893015,0.00070663093,0.004338804,0.00072981307,0.00031677156,0.00052551425,0.0007230988,0.40843558,0.035551548,0.08328679,0.02180436,0.44089177],"study_design_scores_gemma":[0.00010454695,0.00012748742,0.0005897272,0.00001370445,0.000075856624,0.0000884779,0.000115297495,0.9578082,0.0068596727,0.032379795,0.0018145348,0.000022622287],"about_ca_topic_score_codex":0.0043366547,"about_ca_topic_score_gemma":0.0072573787,"teacher_disagreement_score":0.0050980765,"about_ca_system_score_codex":0.0014298122,"about_ca_system_score_gemma":0.0019968466,"threshold_uncertainty_score":0.017054737},"labels":[],"label_agreement":null},{"id":"W2021494192","doi":"10.1145/1823854.1823873","title":"Synthesizing high fidelity 3D landscapes from GIS data","year":2010,"lang":"en","type":"article","venue":"","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":"Concordia University","funders":"","keywords":"Geospatial analysis; Computer science; Rendering (computer graphics); Fidelity; Digital elevation model; Semantics (computer science); Data mining; Information retrieval; Artificial intelligence; Remote sensing; Geography","score_opus":0.02568987498340746,"score_gpt":0.24922064976506833,"score_spread":0.22353077478166086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021494192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016736776,0.000042613392,0.97376585,0.00012343477,0.000015785115,0.00007919317,0.00058530085,0.0070034647,0.0016476217],"genre_scores_gemma":[0.19016084,0.0001652905,0.8052031,0.000070069014,0.000010221817,0.000091049515,0.0027733548,0.0008028374,0.00072328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990883,0.00015797613,0.00010119038,0.00014493981,0.00046406846,0.000043543278],"domain_scores_gemma":[0.99800843,0.0010405292,0.00010283283,0.0005197365,0.00028373403,0.0000447503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016644187,0.0007898347,0.0006998309,0.003068811,0.0005014445,0.002171557,0.0013135644,0.00083558337,0.0026786968],"category_scores_gemma":[0.005252958,0.00069404976,0.0016206161,0.0015893037,0.0009842152,0.0021554837,0.0017810744,0.00081781985,0.0007363275],"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":[0.00038984892,0.00026589353,0.009937896,0.0009675377,0.00023119999,0.0015384894,0.002440499,0.4203306,0.056619637,0.082638144,0.0075333957,0.41710687],"study_design_scores_gemma":[0.00005011832,0.000067978246,0.0012820268,0.00006223096,0.00008007595,0.00028788095,0.00054034364,0.8975869,0.04271642,0.03912165,0.01815485,0.000049574173],"about_ca_topic_score_codex":0.0036209612,"about_ca_topic_score_gemma":0.0066431826,"teacher_disagreement_score":0.0036209612,"about_ca_system_score_codex":0.0006912658,"about_ca_system_score_gemma":0.00088715844,"threshold_uncertainty_score":0.008961141},"labels":[],"label_agreement":null},{"id":"W2021599742","doi":"10.1117/12.921043","title":"Simulation of large-scale multitarget tracking scenarios using GPUs","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Data Management and Algorithms","field":"Computer Science","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":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Computer science; Central processing unit; Throughput; Conjunction (astronomy); Scale (ratio); Graphics processing unit; Parallel computing; Path (computing); Computational science; Real-time computing; Computer hardware","score_opus":0.019638462857331028,"score_gpt":0.25820028110832594,"score_spread":0.2385618182509949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021599742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5822287,0.00028385746,0.40217432,0.0005538207,0.00010667317,0.00015013402,0.00062132755,0.0017444418,0.012136747],"genre_scores_gemma":[0.92401403,0.00018282184,0.07334396,0.000054063064,0.000009371495,0.00009748247,0.0003542678,0.000093274706,0.0018507526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979573,0.000065581575,0.000014314356,0.000031714204,0.000056315246,0.00003631557],"domain_scores_gemma":[0.9994267,0.0003179344,0.000043152417,0.00006642433,0.00009326053,0.000052481468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003501145,0.0005291881,0.00056601025,0.00035305115,0.0004988106,0.0008499576,0.0009884038,0.00097541587,0.0017520724],"category_scores_gemma":[0.0014978816,0.00036458817,0.00051572925,0.0006464537,0.0005052783,0.0008061627,0.00072857545,0.00072295696,0.0001819017],"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":[0.000034452623,0.000017258057,0.0008658111,0.000012114187,0.000008688131,0.000048258073,0.000023525477,0.9943089,0.0013254706,0.0010864541,0.00022289722,0.002046221],"study_design_scores_gemma":[0.000004402008,0.000005173421,0.00011089462,9.614802e-7,0.000001064702,0.0000061275855,0.000005651816,0.99893504,0.00042048586,0.0003387854,0.00016977017,0.000001665513],"about_ca_topic_score_codex":0.013821722,"about_ca_topic_score_gemma":0.0073901345,"teacher_disagreement_score":0.013821722,"about_ca_system_score_codex":0.0007249287,"about_ca_system_score_gemma":0.0006851299,"threshold_uncertainty_score":0.02748257},"labels":[],"label_agreement":null},{"id":"W2022093457","doi":"10.1007/s10489-009-0206-7","title":"Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation","year":2009,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Skyline; Computer science; Cluster analysis; Data mining; Computation; Task (project management); Process (computing); Multi-objective optimization; Genetic algorithm; Algorithm; Machine learning","score_opus":0.05347847030860073,"score_gpt":0.3203052310956666,"score_spread":0.2668267607870659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022093457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01945605,0.000092575574,0.9784424,0.00008827408,0.00002490564,0.00005709326,0.000032864347,0.0006923458,0.0011135072],"genre_scores_gemma":[0.24114856,0.000077732584,0.75728357,0.00007098648,0.00002873567,0.00013457103,0.00019338592,0.000144913,0.000917647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994293,0.00021610409,0.000028945527,0.000086669585,0.00019082034,0.00004804294],"domain_scores_gemma":[0.99882895,0.00050195894,0.00009882206,0.00014832233,0.00036676816,0.000055225726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013206035,0.00083845184,0.0014696878,0.0015485155,0.00095852325,0.001140116,0.001790166,0.0010242322,0.0016210814],"category_scores_gemma":[0.0037368755,0.0004460181,0.00084023824,0.0020066488,0.0005064672,0.0014409712,0.00084628223,0.0007531375,0.0003939256],"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":[0.0000643765,0.000087288165,0.0007597318,0.00004894825,0.00006270041,0.000027373477,0.00007350513,0.90371317,0.0021642921,0.0029407057,0.0008255199,0.08923239],"study_design_scores_gemma":[0.00000657366,0.000011477816,0.00008634377,0.0000020614123,0.0000050184726,0.000005333025,0.0000087091585,0.99823546,0.00033562217,0.0011443765,0.0001561773,0.0000027812869],"about_ca_topic_score_codex":0.015904361,"about_ca_topic_score_gemma":0.022965487,"teacher_disagreement_score":0.015904361,"about_ca_system_score_codex":0.0013043626,"about_ca_system_score_gemma":0.0017795119,"threshold_uncertainty_score":0.031623602},"labels":[],"label_agreement":null},{"id":"W2022462411","doi":"10.1145/1071246.1071301","title":"Demo","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Object (grammar); Process (computing); Location-based service; Track (disk drive); Computer vision; Wireless; Truck; Video tracking; Tracking (education); Artificial intelligence; Human–computer interaction; Telecommunications; Engineering","score_opus":0.00933770523917123,"score_gpt":0.21795342594348346,"score_spread":0.20861572070431222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022462411","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011158998,0.0022994603,0.027876241,0.0051256376,0.0036828348,0.0004087218,0.24922724,0.008798246,0.6914226],"genre_scores_gemma":[0.11837407,0.0053316304,0.06608448,0.0019638303,0.00078167417,0.0006605523,0.416885,0.0027935382,0.38712528],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996674,0.000059908427,0.000025346946,0.00006950242,0.0001250608,0.000052832613],"domain_scores_gemma":[0.9993161,0.00010873882,0.00003956808,0.00017467716,0.00027374664,0.00008716404],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004846134,0.0007902349,0.00030872045,0.0025443956,0.0012993793,0.0022857122,0.00092670834,0.00071707653,0.20176238],"category_scores_gemma":[0.0023392765,0.00023248924,0.00052732683,0.0034568617,0.00033702172,0.0019103996,0.0014203098,0.00093780056,0.07770209],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017888575,0.00003054499,0.002373005,0.00029932967,0.000027198179,0.00048558437,0.000565229,0.0017902504,0.0004624883,0.021959716,0.85124,0.12058788],"study_design_scores_gemma":[0.000012531222,0.000011357295,0.0013772434,0.00008695116,0.000011561071,0.00028380327,0.00033269412,0.00091998704,0.00033014696,0.004733717,0.99188447,0.000015421869],"about_ca_topic_score_codex":0.020038417,"about_ca_topic_score_gemma":0.032113716,"teacher_disagreement_score":0.7982376,"about_ca_system_score_codex":0.00091404916,"about_ca_system_score_gemma":0.0008561539,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2022630997","doi":"10.1002/jgt.20560","title":"Disjoint Hamilton cycles in the random geometric graph","year":2011,"lang":"en","type":"article","venue":"Journal of Graph Theory","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Combinatorics; Mathematics; Conjecture; Disjoint sets; Hamiltonian path; Pairwise comparison; Discrete mathematics; Line graph; Random graph; Graph","score_opus":0.025574762827060893,"score_gpt":0.22949837930324368,"score_spread":0.2039236164761828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022630997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6545975,0.0009981381,0.32708436,0.0017645238,0.00010136862,0.0001623618,0.00032633875,0.0002687981,0.014696551],"genre_scores_gemma":[0.9488953,0.0005851085,0.043932762,0.00031819017,0.000099420395,0.00021872562,0.00024317231,0.000064726424,0.005642571],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900573,0.00041536445,0.000031326887,0.0002246468,0.0001738771,0.00014909489],"domain_scores_gemma":[0.99629277,0.0016722666,0.00086470664,0.000396487,0.00032181706,0.00045182923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012149845,0.0005593158,0.00064878835,0.001573363,0.0010099164,0.0012508199,0.0012299604,0.0009004257,0.0022586347],"category_scores_gemma":[0.007014877,0.00047605415,0.00059322704,0.00088117784,0.0027546224,0.002624487,0.0015825507,0.0009885645,0.00028640754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014035002,0.000036117573,0.0012758758,0.000065897075,0.000035754743,0.0003062236,0.00025901047,0.05576259,0.0025597059,0.9293511,0.0014224832,0.0087849405],"study_design_scores_gemma":[0.0000796712,0.00007012102,0.00063769124,0.000020084413,0.000023597253,0.00016884129,0.00010829549,0.25196466,0.0013387586,0.7428639,0.0026830873,0.00004122324],"about_ca_topic_score_codex":0.0022532865,"about_ca_topic_score_gemma":0.0022363407,"teacher_disagreement_score":0.0022586347,"about_ca_system_score_codex":0.0015525867,"about_ca_system_score_gemma":0.0007373977,"threshold_uncertainty_score":0.011264801},"labels":[],"label_agreement":null},{"id":"W2022902860","doi":"10.1145/1007568.1007621","title":"Incremental and effective data summarization for dynamic hierarchical clustering","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Automatic summarization; Computer science; Cluster analysis; Data mining; Merge (version control); Hierarchical clustering; Data stream clustering; CURE data clustering algorithm; Fuzzy clustering; Information retrieval; Artificial intelligence","score_opus":0.017484841954494672,"score_gpt":0.2759949414161231,"score_spread":0.2585100994616284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022902860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016825022,0.0003926354,0.9804221,0.00015161354,0.00003953499,0.00012978636,0.00019950807,0.0014119436,0.0004278184],"genre_scores_gemma":[0.17794465,0.00027049892,0.81863016,0.00009729466,0.000097688695,0.00023497632,0.0017207111,0.00019722953,0.0008067947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975495,0.00059975975,0.00023014186,0.000430108,0.0010455862,0.00014496827],"domain_scores_gemma":[0.9940189,0.0020279903,0.0006237486,0.0014536792,0.0016860085,0.00018968873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023104774,0.00080387364,0.0009177759,0.0031680157,0.0007992564,0.0014361598,0.0022045108,0.00094644376,0.0011566877],"category_scores_gemma":[0.012223144,0.0005312934,0.0007967071,0.003477201,0.0006753081,0.003083789,0.0019964618,0.001036294,0.0006948797],"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":[0.00051262614,0.00022057176,0.0033817906,0.00041885438,0.0001405023,0.00027810223,0.0009131127,0.08704744,0.044356238,0.01627923,0.00952921,0.83692235],"study_design_scores_gemma":[0.000080104764,0.0003559575,0.0020996477,0.000035034547,0.00011064308,0.00037206462,0.00035883946,0.91465515,0.044247255,0.023190746,0.014415763,0.00007879797],"about_ca_topic_score_codex":0.00175082,"about_ca_topic_score_gemma":0.002321045,"teacher_disagreement_score":0.0031680157,"about_ca_system_score_codex":0.00068246387,"about_ca_system_score_gemma":0.00090016105,"threshold_uncertainty_score":0.012219131},"labels":[],"label_agreement":null},{"id":"W2023341057","doi":"10.1109/icdim.2009.5356774","title":"The mqr-tree: Improving upon a 2-dimensional spatial access method","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Tree (set theory); Computer science; R-tree; Node (physics); Access method; Set (abstract data type); Tree structure; Data mining; Algorithm; Spatial database; Spatial analysis; Mathematics; Binary tree; Statistics; Database","score_opus":0.01817071787777646,"score_gpt":0.3017663965670973,"score_spread":0.28359567868932084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023341057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006648036,0.0004018393,0.98244876,0.00024133717,0.00011535253,0.00012812347,0.0004844653,0.007528235,0.002003874],"genre_scores_gemma":[0.053629126,0.0003653632,0.9399287,0.00026277214,0.00014224653,0.00017049669,0.0014068829,0.0010889639,0.003005526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950755,0.0010250321,0.0005021139,0.00050587044,0.0025816627,0.00030979174],"domain_scores_gemma":[0.9897814,0.0026866107,0.0005293324,0.0038658518,0.0028063382,0.000330527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029397393,0.00082536566,0.0014838269,0.0031576948,0.0008988527,0.002455045,0.003629068,0.0012773145,0.006056626],"category_scores_gemma":[0.014569605,0.00069263973,0.0016524264,0.0045646057,0.00088655093,0.00763394,0.0050606034,0.0014722303,0.0040512467],"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":[0.00041623964,0.0002375383,0.004411228,0.00050965464,0.00014796003,0.0004360039,0.0008208298,0.028052906,0.018626941,0.04179455,0.028329713,0.8762165],"study_design_scores_gemma":[0.00019354558,0.000567653,0.002379565,0.00015962675,0.00014236003,0.0020477073,0.00032177602,0.78554314,0.02505502,0.047277045,0.13598464,0.0003279293],"about_ca_topic_score_codex":0.004719014,"about_ca_topic_score_gemma":0.005226357,"teacher_disagreement_score":0.006056626,"about_ca_system_score_codex":0.00052978186,"about_ca_system_score_gemma":0.00161389,"threshold_uncertainty_score":0.020261467},"labels":[],"label_agreement":null},{"id":"W2023635708","doi":"10.1145/1183471.1183499","title":"Object localization based on directional information case of 2D vector data","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rectangle; Object (grammar); Raster graphics; Object-based spatial database; Raster data; Computer science; Computation; Spatial analysis; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Spatial database; Geometry","score_opus":0.016382066334276288,"score_gpt":0.23921244194220573,"score_spread":0.22283037560792945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023635708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051829387,0.00043790013,0.9410674,0.00050386565,0.00004080446,0.000053525568,0.00067779137,0.00033195567,0.0050573675],"genre_scores_gemma":[0.7114738,0.0007611275,0.28251237,0.00018065955,0.00009637628,0.00020088955,0.001012155,0.00008708733,0.0036755684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987766,0.00022872598,0.00007384009,0.00030110058,0.00044505403,0.00017462646],"domain_scores_gemma":[0.9980798,0.00095974124,0.00020095611,0.00043146595,0.00024499276,0.000083111765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011207664,0.00039498962,0.0008332919,0.0016030524,0.0005179262,0.0018760158,0.0014877539,0.0014360996,0.0029687225],"category_scores_gemma":[0.0076560173,0.00044750844,0.0011105906,0.0027449357,0.001283509,0.0038197993,0.0015641467,0.0009113495,0.00056654296],"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":[0.0003482242,0.00006568058,0.007563024,0.0003621463,0.00007274721,0.0020601302,0.00089963136,0.5241915,0.007728296,0.3379597,0.0043629697,0.11438597],"study_design_scores_gemma":[0.0000116180045,0.000026916034,0.00104901,0.000014599952,0.000010981573,0.0005105034,0.00007922735,0.95069754,0.0012122536,0.044776104,0.00158913,0.000022143946],"about_ca_topic_score_codex":0.0131777655,"about_ca_topic_score_gemma":0.006113442,"teacher_disagreement_score":0.0131777655,"about_ca_system_score_codex":0.0011780659,"about_ca_system_score_gemma":0.00065001904,"threshold_uncertainty_score":0.026202083},"labels":[],"label_agreement":null},{"id":"W2023889025","doi":"10.2316/journal.206.2013.4.206-3702","title":"SUBDIVISION-BASED CORRIDOR MAP METHOD FOR PATH PLANNING","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subdivision; Path (computing); Computer science; Geography; Archaeology; Computer network","score_opus":0.01748591853544828,"score_gpt":0.3005014314257354,"score_spread":0.2830155128902871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023889025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053103263,0.00013624897,0.9927481,0.00003258112,0.00001640533,0.000037374855,0.00010212569,0.00071029254,0.00090658007],"genre_scores_gemma":[0.15188818,0.00030954758,0.84387916,0.00003261309,0.000021685299,0.00024667344,0.000662723,0.00020369791,0.0027556953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971086,0.000050524297,0.0000135405835,0.000056835852,0.00013647118,0.00003175261],"domain_scores_gemma":[0.99963284,0.00013035107,0.000020324132,0.00004221049,0.0001505022,0.000023759325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030520864,0.00054452487,0.0009883402,0.0012338602,0.00048126926,0.0005536943,0.0014218003,0.0005528927,0.003833217],"category_scores_gemma":[0.0009482499,0.0003701598,0.0007242316,0.0015425112,0.00034510266,0.0006977179,0.0008721016,0.0009715308,0.0008230983],"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":[0.00024321605,0.000120027384,0.00097610947,0.00027421274,0.00008572744,0.00007724139,0.00019632063,0.30696228,0.011042299,0.01632102,0.0067722704,0.6569293],"study_design_scores_gemma":[0.000013963559,0.000029198629,0.00021697556,0.000010708314,0.000013074198,0.000039359493,0.00001711827,0.992542,0.0014667887,0.0031563318,0.002482318,0.0000121471],"about_ca_topic_score_codex":0.02008688,"about_ca_topic_score_gemma":0.017349534,"teacher_disagreement_score":0.02008688,"about_ca_system_score_codex":0.00048530847,"about_ca_system_score_gemma":0.0015499805,"threshold_uncertainty_score":0.03993994},"labels":[],"label_agreement":null},{"id":"W2024336226","doi":"10.1007/s00778-006-0015-0","title":"Histograms based on the minimum description length principle","year":2006,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Computer science; Data mining; Set (abstract data type); Representation (politics); Overhead (engineering); Online analytical processing; Data set; Algorithm; Pattern recognition (psychology); Artificial intelligence; Image (mathematics); Data warehouse","score_opus":0.023451583210226296,"score_gpt":0.22292722325460765,"score_spread":0.19947564004438134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024336226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008013211,0.0005881589,0.9888158,0.00025701817,0.00006814592,0.000027334965,0.00024013226,0.0005421258,0.0014480939],"genre_scores_gemma":[0.4229415,0.0019457036,0.56336635,0.0005369673,0.0006358157,0.00021717571,0.0021247345,0.0007606627,0.0074710706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895346,0.0002712279,0.000050180155,0.00019490722,0.00042391164,0.00010633052],"domain_scores_gemma":[0.9958961,0.002351341,0.00024268393,0.0006529803,0.000651457,0.00020548505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013886852,0.00042422107,0.0012904119,0.0021037993,0.00063211337,0.0019057882,0.0017718382,0.0010319719,0.006366499],"category_scores_gemma":[0.0070232595,0.00059064286,0.0006776342,0.0022365702,0.0010089965,0.004397087,0.0019381212,0.0017505167,0.0014640111],"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":[0.0007357134,0.00013160614,0.0021949697,0.00038191845,0.00011609937,0.00016680613,0.00016134011,0.12949763,0.015868545,0.3271496,0.012955957,0.51063985],"study_design_scores_gemma":[0.000054030475,0.00009874269,0.0010885107,0.000031812957,0.00003216869,0.00015174624,0.00004149525,0.76585686,0.0049499893,0.22130325,0.0063303183,0.000061004004],"about_ca_topic_score_codex":0.0016941306,"about_ca_topic_score_gemma":0.0012527622,"teacher_disagreement_score":0.006366499,"about_ca_system_score_codex":0.0007442914,"about_ca_system_score_gemma":0.00086419436,"threshold_uncertainty_score":0.02129805},"labels":[],"label_agreement":null},{"id":"W2024430332","doi":"10.3138/2773-q553-0483-np77","title":"Modular Web-Based Atlas Information Systems","year":2006,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Modular design; Personalization; Architecture; World Wide Web; Locality; Software engineering; Modularity (biology); Operating system","score_opus":0.006343791936389476,"score_gpt":0.2345362390868176,"score_spread":0.2281924471504281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024430332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014558976,0.0006903204,0.9144053,0.0010990697,0.00021498863,0.0004591706,0.0010387371,0.030644583,0.03688889],"genre_scores_gemma":[0.34737012,0.0025850872,0.58660424,0.0009273136,0.0006397536,0.0007877036,0.0077307904,0.0030331488,0.050321877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99743927,0.00053908856,0.00035276797,0.00045171115,0.0010119041,0.00020528988],"domain_scores_gemma":[0.9949228,0.0011710959,0.0002728698,0.0018857649,0.0013044411,0.00044296632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033512434,0.0007406203,0.00071906066,0.0033268505,0.0012391661,0.0076864003,0.0027265237,0.0017028579,0.008732013],"category_scores_gemma":[0.0065638516,0.00089059025,0.0010367859,0.0035739376,0.0013782885,0.00889328,0.005811873,0.0015228898,0.0075906557],"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":[0.00036958573,0.00032260737,0.0043447497,0.0007964835,0.00033780132,0.0018351802,0.0030482626,0.047674,0.019320903,0.48302382,0.048352674,0.39057392],"study_design_scores_gemma":[0.00010225411,0.00013246188,0.0022791522,0.00028406223,0.00020222599,0.0009959296,0.0006802183,0.20679049,0.02132173,0.28769335,0.47931808,0.00020007575],"about_ca_topic_score_codex":0.0024932246,"about_ca_topic_score_gemma":0.0015153612,"teacher_disagreement_score":0.008732013,"about_ca_system_score_codex":0.0012417868,"about_ca_system_score_gemma":0.0018065056,"threshold_uncertainty_score":0.029211521},"labels":[],"label_agreement":null},{"id":"W2024437575","doi":"10.1155/asp/2006/49073","title":"Information Mining from Multimedia Databases","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multimedia; Database; Information retrieval; World Wide Web","score_opus":0.016361785347271725,"score_gpt":0.27678843063447334,"score_spread":0.2604266452872016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024437575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006553784,0.19264546,0.56049794,0.08396974,0.07167372,0.0017462353,0.020472256,0.009197368,0.053243548],"genre_scores_gemma":[0.059823927,0.17160764,0.4747733,0.030178012,0.06787609,0.0021348319,0.08997519,0.0023028858,0.10132813],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99707365,0.000673811,0.00036128642,0.0004702712,0.0013028737,0.0001180642],"domain_scores_gemma":[0.99153006,0.004144377,0.00045716565,0.00097016216,0.0025334058,0.00036488537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033833361,0.0011153468,0.0016992262,0.008335983,0.00092303153,0.0048912764,0.0022895879,0.0020295205,0.018549694],"category_scores_gemma":[0.013763509,0.00059580134,0.0014406232,0.008645931,0.0006774944,0.0070607555,0.0025320323,0.0030835602,0.008617344],"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":[0.000102946135,0.00008168866,0.0005934801,0.00093336933,0.00014729895,0.00023403554,0.00006397377,0.0013147556,0.0009946715,0.010682145,0.46111906,0.52373266],"study_design_scores_gemma":[0.000046591726,0.00009013025,0.0011150966,0.0006503008,0.00010411581,0.00059448933,0.00017990422,0.019543521,0.0027238338,0.0427522,0.9321444,0.0000554444],"about_ca_topic_score_codex":0.0008071235,"about_ca_topic_score_gemma":0.0010752502,"teacher_disagreement_score":0.018549694,"about_ca_system_score_codex":0.00077536586,"about_ca_system_score_gemma":0.0010086342,"threshold_uncertainty_score":0.062054873},"labels":[],"label_agreement":null},{"id":"W2024678590","doi":"10.1145/602421.602453","title":"Use of the SAND spatial browser for digital government applications","year":2003,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":80,"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":"Computer science; Interface (matter); Spatial analysis; World Wide Web; The Internet; Information retrieval; Web browser; Spatial data infrastructure; Agency (philosophy); Remote sensing; Geography","score_opus":0.06115913605671689,"score_gpt":0.2656214992029471,"score_spread":0.2044623631462302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024678590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019020457,0.0005950134,0.50424534,0.0012982109,0.0002838938,0.0005847484,0.008678502,0.3223422,0.14295171],"genre_scores_gemma":[0.2853117,0.001840712,0.56918377,0.0011669415,0.00020189515,0.0015060846,0.020508243,0.020988036,0.099292636],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988525,0.0002398223,0.00014322903,0.00014356365,0.0004977111,0.00012312061],"domain_scores_gemma":[0.99696976,0.0010447638,0.000107012274,0.0007759066,0.00087601016,0.00022658691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012749236,0.00082243653,0.0006141008,0.0023780875,0.0010481277,0.0028482473,0.0013164944,0.00069268525,0.03429292],"category_scores_gemma":[0.0063449675,0.000630008,0.0005879455,0.0022713654,0.00057251,0.0039804606,0.0032185214,0.0007507932,0.013706866],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00099328,0.00021378185,0.006085733,0.00061035325,0.00011553603,0.0015647156,0.002546755,0.005225084,0.01409286,0.098896235,0.5719946,0.297661],"study_design_scores_gemma":[0.00010025516,0.000030843017,0.0011769013,0.00016768363,0.000056241803,0.0006617409,0.0005599404,0.032120895,0.015609752,0.021511668,0.9279183,0.00008582085],"about_ca_topic_score_codex":0.014863139,"about_ca_topic_score_gemma":0.027266888,"teacher_disagreement_score":0.03429292,"about_ca_system_score_codex":0.0011669352,"about_ca_system_score_gemma":0.0023996753,"threshold_uncertainty_score":0.11472118},"labels":[],"label_agreement":null},{"id":"W2025155398","doi":"10.1016/j.datak.2009.10.001","title":"Skyline queries with constraints: Integrating skyline and traditional query operators","year":2009,"lang":"en","type":"article","venue":"Data & Knowledge Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Skyline; Computer science; Pruning; Class (philosophy); Space (punctuation); Operator (biology); Database; Computation; Information retrieval; Data mining; Theoretical computer science; Algorithm; Artificial intelligence","score_opus":0.027096740140654725,"score_gpt":0.23442299360009852,"score_spread":0.2073262534594438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025155398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014840502,0.0010318325,0.9720415,0.00057678466,0.00007530432,0.00016460716,0.0013283106,0.0075051165,0.0024361562],"genre_scores_gemma":[0.16481128,0.0011451978,0.8260269,0.00039997316,0.00023163577,0.00018301037,0.0035682877,0.0017835874,0.0018501346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949195,0.0012761347,0.00050996226,0.0007555536,0.0022892116,0.00024965676],"domain_scores_gemma":[0.990529,0.0038902625,0.00074825925,0.0028303717,0.0015756412,0.00042641276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051619466,0.0010475712,0.002088022,0.0020678905,0.0008346182,0.0037830002,0.0036125102,0.0011025743,0.00400952],"category_scores_gemma":[0.012761494,0.00073893607,0.00106857,0.0056675966,0.0011302949,0.010911664,0.0045952415,0.00148146,0.000847752],"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":[0.0022727961,0.0006525393,0.009341892,0.0019556708,0.00057659263,0.0008272638,0.0014356626,0.057702146,0.023225473,0.11147872,0.082315505,0.7082158],"study_design_scores_gemma":[0.0003778154,0.0003532865,0.0017907625,0.00016659849,0.00026655933,0.0008587037,0.0006025313,0.78831375,0.018026875,0.11488999,0.07421883,0.0001343086],"about_ca_topic_score_codex":0.0042892382,"about_ca_topic_score_gemma":0.008845294,"teacher_disagreement_score":0.0051619466,"about_ca_system_score_codex":0.0007951144,"about_ca_system_score_gemma":0.0015747674,"threshold_uncertainty_score":0.027299285},"labels":[],"label_agreement":null},{"id":"W2025204875","doi":"10.1109/geoinformatics.2012.6270345","title":"Mobile spatial interaction in the Future Internet of Things","year":2012,"lang":"en","type":"article","venue":"","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":"Institut National de la Recherche Scientifique; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Mobile device; Information overload; Personalization; Spatial contextual awareness; Context (archaeology); Mobile computing; Location-based service; Field (mathematics); The Internet; Clutter; World Wide Web; Human–computer interaction; Radar; Artificial intelligence; Telecommunications","score_opus":0.011242580864961762,"score_gpt":0.24966229524987027,"score_spread":0.2384197143849085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025204875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106457084,0.0037598142,0.84874135,0.0031691738,0.00030764224,0.00013732066,0.00043655402,0.003581493,0.033409595],"genre_scores_gemma":[0.7957081,0.002007604,0.19286433,0.00063823286,0.00015156901,0.00011183867,0.00051691587,0.00013251629,0.007868822],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994887,0.00014248687,0.000023233806,0.000053646418,0.00023253259,0.00005927062],"domain_scores_gemma":[0.9996074,0.00015989238,0.000025790756,0.00007702466,0.00010683907,0.000023053384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005941344,0.00022788005,0.00030763337,0.000453781,0.0005064486,0.0011836715,0.0005157551,0.00062650157,0.0018107573],"category_scores_gemma":[0.0011324277,0.00017554661,0.00029166718,0.000977715,0.0005063554,0.0016714472,0.0010245387,0.00037758212,0.00046505898],"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":[0.0007063799,0.00014924946,0.011127803,0.00048690476,0.000114240094,0.0020655773,0.0021352319,0.15193188,0.04684769,0.25304538,0.04633615,0.48505354],"study_design_scores_gemma":[0.000049228278,0.00023260603,0.0045102695,0.0000639162,0.00006404219,0.0019620203,0.0016003062,0.68657154,0.01573209,0.13468231,0.15443134,0.00010028548],"about_ca_topic_score_codex":0.0034121643,"about_ca_topic_score_gemma":0.0038051412,"teacher_disagreement_score":0.0034121643,"about_ca_system_score_codex":0.00038494746,"about_ca_system_score_gemma":0.0002639825,"threshold_uncertainty_score":0.006784618},"labels":[],"label_agreement":null},{"id":"W2025419780","doi":"10.1109/fskd.2010.5569645","title":"Top-k ranking for uncertain data","year":2010,"lang":"en","type":"article","venue":"2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery","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":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Semantics (computer science); Rank (graph theory); Computer science; Uncertain data; Dimension (graph theory); Information retrieval; Data mining; Space (punctuation); Theoretical computer science; Mathematics; Combinatorics","score_opus":0.08575587457417934,"score_gpt":0.3355655237190078,"score_spread":0.24980964914482845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025419780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015533123,0.0014639576,0.9768174,0.0007624565,0.00015043722,0.0001023804,0.0004889187,0.00031130615,0.004370018],"genre_scores_gemma":[0.42929813,0.0017367883,0.5629573,0.00037654434,0.0004172346,0.00023812511,0.00145611,0.00019155545,0.0033282854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9868255,0.0053060018,0.0012172873,0.0017721774,0.0040421714,0.0008367426],"domain_scores_gemma":[0.9817809,0.009662651,0.0015671405,0.0032382547,0.0030992443,0.0006517729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008591986,0.0012424788,0.002839369,0.005152779,0.0029239545,0.006097709,0.0022974943,0.0015496056,0.0035199663],"category_scores_gemma":[0.02941064,0.00054617313,0.0020428502,0.006936713,0.002687712,0.009690943,0.0027365503,0.0024925629,0.0012192421],"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":[0.00021483614,0.00011005896,0.0037005465,0.0009610812,0.00032152902,0.0004165637,0.0007143171,0.13443686,0.0022603069,0.65314835,0.013285587,0.19042993],"study_design_scores_gemma":[0.00002447037,0.00012387901,0.0009029459,0.000095218325,0.00008026993,0.0004029228,0.0004251336,0.3001309,0.0014253365,0.6849326,0.01135575,0.000100476536],"about_ca_topic_score_codex":0.0024681368,"about_ca_topic_score_gemma":0.0032537035,"teacher_disagreement_score":0.008591986,"about_ca_system_score_codex":0.00272171,"about_ca_system_score_gemma":0.0022347737,"threshold_uncertainty_score":0.045439303},"labels":[],"label_agreement":null},{"id":"W2025843016","doi":"10.1145/2675743.2776761","title":"High performance stream queries in scala","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scala; Latency (audio); Stream processing; Process (computing); Throughput; Data stream; Streaming data; Distributed computing; Data mining; Operating system; Wireless","score_opus":0.021590480583114385,"score_gpt":0.22539422125214284,"score_spread":0.20380374066902845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025843016","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10797885,0.0014632979,0.39612913,0.0032027606,0.0008750568,0.0006342025,0.01819684,0.4383876,0.03313229],"genre_scores_gemma":[0.66125405,0.00083077844,0.24867855,0.0019696685,0.0006106062,0.0010913463,0.038931202,0.031666983,0.014966806],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99534935,0.000959776,0.0004511552,0.0010304212,0.0016540369,0.0005553381],"domain_scores_gemma":[0.99110866,0.0034784777,0.00032947937,0.0032401711,0.001259109,0.00058403815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00472779,0.0023032783,0.001659779,0.0013402987,0.002331932,0.004627765,0.005724311,0.0017882336,0.015980681],"category_scores_gemma":[0.01575969,0.0015062963,0.0017713264,0.0038594955,0.0026199792,0.009353839,0.0060724285,0.0034125566,0.00847189],"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":[0.011116173,0.001235453,0.023623701,0.0017309233,0.0009052371,0.002011312,0.0018114517,0.15071996,0.032856096,0.10103915,0.46596533,0.2069852],"study_design_scores_gemma":[0.0009450499,0.00009872697,0.0022877944,0.000077377445,0.000085581254,0.00016130351,0.00028093066,0.8781136,0.015521888,0.05342463,0.048890557,0.00011248951],"about_ca_topic_score_codex":0.026324494,"about_ca_topic_score_gemma":0.019292124,"teacher_disagreement_score":0.026324494,"about_ca_system_score_codex":0.0034539732,"about_ca_system_score_gemma":0.0028244776,"threshold_uncertainty_score":0.053460717},"labels":[],"label_agreement":null},{"id":"W2026730210","doi":"10.1145/362084.362104","title":"Generating spatiotemporal datasets on the WWW","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Financiadora de Estudos e Projetos; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Search engine indexing; Data mining; Information retrieval; Interface (matter); Database; Operating system","score_opus":0.031612334944660654,"score_gpt":0.2537045766043308,"score_spread":0.22209224165967018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026730210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04150257,0.00043108655,0.85910887,0.0009229874,0.00026050015,0.00035624334,0.02771948,0.046676893,0.023021324],"genre_scores_gemma":[0.19186608,0.001098403,0.7346586,0.00020002948,0.000099064644,0.0007178136,0.06126637,0.0033643504,0.0067293523],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993032,0.00019863433,0.00008426153,0.00013700625,0.00022922404,0.00004765219],"domain_scores_gemma":[0.99817336,0.000439575,0.00007602882,0.0008195263,0.00041471096,0.00007679887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009122008,0.0005084283,0.0005684231,0.0018111953,0.0005293263,0.0025775074,0.0011905766,0.0006370652,0.006268343],"category_scores_gemma":[0.0054282253,0.00040619963,0.0008616031,0.0036592064,0.00030331616,0.003000368,0.0023785797,0.00078782806,0.0037873534],"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":[0.0008240543,0.00023917167,0.010262238,0.0009602007,0.0002328676,0.0015307292,0.0012142909,0.10613359,0.027557017,0.18086872,0.21019432,0.45998284],"study_design_scores_gemma":[0.00015732461,0.00012052958,0.0028913203,0.00013761339,0.00007794795,0.0007507475,0.0007431351,0.46694776,0.047075603,0.10954206,0.37147936,0.000076682634],"about_ca_topic_score_codex":0.0015674423,"about_ca_topic_score_gemma":0.002321025,"teacher_disagreement_score":0.006268343,"about_ca_system_score_codex":0.0004055877,"about_ca_system_score_gemma":0.00069451233,"threshold_uncertainty_score":0.020969748},"labels":[],"label_agreement":null},{"id":"W2026808466","doi":"10.1007/s00778-010-0196-4","title":"Ranking queries on uncertain data","year":2010,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"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":"Ranking (information retrieval); Uncertain data; Rank (graph theory); Computer science; Set (abstract data type); Probabilistic logic; Data mining; Ranking SVM; Information retrieval; Mathematics; Combinatorics; Artificial intelligence","score_opus":0.05671574245012417,"score_gpt":0.29787606197723465,"score_spread":0.24116031952711048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026808466","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14567873,0.009827557,0.82456666,0.0055107255,0.0006015592,0.0002959726,0.0047082407,0.0028998305,0.005910596],"genre_scores_gemma":[0.7564956,0.0036768364,0.22271395,0.0006690067,0.0015730246,0.00018425041,0.008271473,0.00041170383,0.006004146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99091154,0.002909408,0.00091298047,0.0010705506,0.0036422561,0.0005532038],"domain_scores_gemma":[0.97748417,0.016532991,0.0010487298,0.0026723703,0.0016830913,0.0005786863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00537132,0.0012133244,0.0033434615,0.0041478346,0.0011467645,0.0055527734,0.002323704,0.0022199843,0.0039913794],"category_scores_gemma":[0.025816819,0.0007750656,0.0011490899,0.007318537,0.0011054329,0.0085172765,0.0020058956,0.0021636682,0.0013419776],"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":[0.002624922,0.0005547741,0.009920379,0.002154478,0.0006211415,0.0014459379,0.0006204713,0.31488052,0.012539878,0.11517542,0.060696434,0.4787657],"study_design_scores_gemma":[0.00007718947,0.00024320906,0.00091258954,0.00006372145,0.00013646983,0.000604653,0.0002309345,0.83349586,0.004217181,0.15223831,0.0077325525,0.000047374535],"about_ca_topic_score_codex":0.002119123,"about_ca_topic_score_gemma":0.002882676,"teacher_disagreement_score":0.0055527734,"about_ca_system_score_codex":0.0014085786,"about_ca_system_score_gemma":0.0011416429,"threshold_uncertainty_score":0.02840656},"labels":[],"label_agreement":null},{"id":"W2027088687","doi":"10.5539/cis.v2n1p115","title":"A Study On Query Optimization for Federated Database Systems","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Query optimization; Database; Information retrieval; View; Sargable; Web search query; Database design; Search engine","score_opus":0.027325869938409148,"score_gpt":0.276357045068118,"score_spread":0.24903117512970882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027088687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.235723,0.011710553,0.7225605,0.0041880547,0.0003092319,0.00061594363,0.0008465866,0.0025739335,0.021472242],"genre_scores_gemma":[0.7581173,0.0024783793,0.23040168,0.00044916035,0.00026241774,0.00019127273,0.0010550398,0.00061055226,0.0064341063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9843768,0.006568215,0.0009868317,0.0019216473,0.0047203843,0.0014260833],"domain_scores_gemma":[0.9799586,0.013844746,0.0006187497,0.0031331407,0.00207255,0.00037227836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008437186,0.0010378532,0.0024609587,0.0016375063,0.0017547756,0.005348294,0.0030151415,0.001399565,0.0046565444],"category_scores_gemma":[0.03255176,0.000853872,0.0016719454,0.0067215217,0.0014921188,0.0090913195,0.0016675767,0.0022199622,0.0004794494],"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":[0.0017556889,0.001151594,0.009437332,0.0013416342,0.00047979766,0.0004383163,0.0011669245,0.4101875,0.0075029363,0.21056849,0.016057577,0.3399122],"study_design_scores_gemma":[0.00006688019,0.00017806931,0.0012148268,0.0000486105,0.00012664052,0.00024486094,0.00042563074,0.94936985,0.0034487932,0.039750896,0.005099687,0.000025314988],"about_ca_topic_score_codex":0.012539334,"about_ca_topic_score_gemma":0.005165342,"teacher_disagreement_score":0.012539334,"about_ca_system_score_codex":0.003534675,"about_ca_system_score_gemma":0.0035291524,"threshold_uncertainty_score":0.044620633},"labels":[],"label_agreement":null},{"id":"W2028102160","doi":"10.1016/s0167-8655(02)00115-0","title":"Logic-oriented fuzzy clustering","year":2002,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":20,"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 Space Agency","funders":"","keywords":"Cluster analysis; Fuzzy clustering; Correlation clustering; Constrained clustering; CURE data clustering algorithm; Fuzzy logic; Canopy clustering algorithm; Data mining; Mathematics; FLAME clustering; Single-linkage clustering; Computer science; Algorithm; Data stream clustering; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science","score_opus":0.044132254754598435,"score_gpt":0.22689990083145775,"score_spread":0.1827676460768593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028102160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010860904,0.0002447034,0.9768819,0.00017202785,0.000065166576,0.000069311194,0.00009462982,0.0005607527,0.011050598],"genre_scores_gemma":[0.39207283,0.0004565945,0.58986473,0.00029336585,0.000120167126,0.00013264311,0.00077259855,0.00017469496,0.016112356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993906,0.00010719798,0.00003142629,0.00013331388,0.00027751844,0.000059979982],"domain_scores_gemma":[0.99955386,0.000083313214,0.000028855804,0.00008688481,0.00022375182,0.000023292938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069892075,0.00045093693,0.0005634503,0.0017209256,0.0010639,0.0016732798,0.001163019,0.00074465387,0.0047427095],"category_scores_gemma":[0.0015086577,0.00027967183,0.00069010764,0.0013986889,0.0005970041,0.0011088818,0.0009814752,0.00060007296,0.0015547149],"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":[0.00028989447,0.0001191014,0.0010317981,0.0001841355,0.00009391376,0.000117277275,0.0002697034,0.13272142,0.01595654,0.2692848,0.009093592,0.5708378],"study_design_scores_gemma":[0.000028016566,0.000060879185,0.0006955927,0.00002079855,0.000041322008,0.00012028551,0.00008505979,0.8280802,0.0075043854,0.15278363,0.010551996,0.000027881852],"about_ca_topic_score_codex":0.0024734559,"about_ca_topic_score_gemma":0.0027629724,"teacher_disagreement_score":0.0047427095,"about_ca_system_score_codex":0.0011995839,"about_ca_system_score_gemma":0.00090608944,"threshold_uncertainty_score":0.015865922},"labels":[],"label_agreement":null},{"id":"W2028295384","doi":"10.1109/icde.2010.5447757","title":"MashRank: Towards uncertainty-aware and rank-aware mashups","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mashup; Computer science; Ranking (information retrieval); Probabilistic logic; Rank (graph theory); Information retrieval; Set (abstract data type); Web application; World Wide Web; Class (philosophy); Data mining; Web service; Data science; Artificial intelligence; Mathematics; Web modeling; Programming language","score_opus":0.008734477333093486,"score_gpt":0.23043501417259163,"score_spread":0.22170053683949814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028295384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018856794,0.000037516933,0.994402,0.00014501724,0.000010387698,0.000076756165,0.00007352649,0.0027638597,0.00060530467],"genre_scores_gemma":[0.06947811,0.00010897116,0.9282221,0.00010432419,0.000043399094,0.00018315752,0.0003204718,0.00051461544,0.001024822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922869,0.002486055,0.00048056,0.0009911434,0.0032941636,0.00046109263],"domain_scores_gemma":[0.98759264,0.0055788066,0.0013240053,0.0034471038,0.0014172178,0.0006402227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0081068175,0.0012546524,0.0011616256,0.0020657503,0.0015342239,0.0043666153,0.0030214724,0.0015859584,0.0027486624],"category_scores_gemma":[0.022920854,0.0011800252,0.0012333263,0.0024109788,0.002538812,0.007631882,0.006404956,0.0033939711,0.0014075534],"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":[0.0005612927,0.00036013033,0.002332968,0.0007464718,0.0002671447,0.00060265197,0.0022454257,0.23477878,0.023957917,0.32673067,0.019688418,0.38772812],"study_design_scores_gemma":[0.00006198363,0.00007255637,0.00025714407,0.000044489017,0.00004499812,0.00012928185,0.00025967424,0.75207347,0.0155679,0.20543621,0.025984231,0.00006807089],"about_ca_topic_score_codex":0.0031659084,"about_ca_topic_score_gemma":0.004574331,"teacher_disagreement_score":0.0081068175,"about_ca_system_score_codex":0.001139108,"about_ca_system_score_gemma":0.002822735,"threshold_uncertainty_score":0.042873442},"labels":[],"label_agreement":null},{"id":"W2028642351","doi":"10.1016/j.cor.2005.02.040","title":"Road network monitoring: algorithms and a case study","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Ministère des Transports","keywords":"Computer science; Schedule; Global Positioning System; Algorithm; TRACE (psycholinguistics); Plan (archaeology); Interurban; Class (philosophy); Operations research; Flow network; Christian ministry; Real-time computing; Transport engineering; Telecommunications; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.11001924702025198,"score_gpt":0.40173843069173426,"score_spread":0.2917191836714823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028642351","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57398427,0.00088938436,0.4040528,0.002208978,0.00006413487,0.00080233254,0.0005479552,0.0008429385,0.016607346],"genre_scores_gemma":[0.7608514,0.00061912625,0.23481335,0.000057421883,0.00002803883,0.00025292797,0.00026734165,0.000077169505,0.0030332042],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750537,0.0014282777,0.00015476972,0.0002874082,0.00045517777,0.00016899999],"domain_scores_gemma":[0.98548526,0.01173194,0.0004610129,0.0008379187,0.0012815957,0.0002023828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004551568,0.0008527564,0.0006123197,0.001638896,0.0011465017,0.002163414,0.002204385,0.004125167,0.0024605913],"category_scores_gemma":[0.01669119,0.00050883583,0.00084300374,0.0024033836,0.001031225,0.0021746887,0.0009676859,0.0012486338,0.00035294227],"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":[0.0010788704,0.0025361194,0.051872153,0.0005608915,0.000103951585,0.0043399604,0.0013606346,0.5741892,0.0039612907,0.04442956,0.0064058537,0.30916148],"study_design_scores_gemma":[0.00010367524,0.00032939125,0.0034392984,0.000043533848,0.0000565385,0.0015991736,0.00054958335,0.97601515,0.0032283915,0.011618553,0.0029782793,0.000038493523],"about_ca_topic_score_codex":0.009856048,"about_ca_topic_score_gemma":0.0101547735,"teacher_disagreement_score":0.009856048,"about_ca_system_score_codex":0.0017981115,"about_ca_system_score_gemma":0.0009600665,"threshold_uncertainty_score":0.024071336},"labels":[],"label_agreement":null},{"id":"W2028769163","doi":"10.1145/1044731.1044733","title":"Determining approximate shortest paths on weighted polyhedral surfaces","year":2005,"lang":"en","type":"article","venue":"Journal of the ACM","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Discretization; Combinatorics; Face (sociological concept); Mathematics; Shortest path problem; Surface (topology); Euclidean geometry; Polyhedron; Computational geometry; Discrete mathematics; Algorithm; Geometry; Graph; Mathematical analysis","score_opus":0.02130323145929961,"score_gpt":0.2520038338114481,"score_spread":0.23070060235214848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028769163","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05695695,0.00020306832,0.9405017,0.00011152883,0.00002823324,0.00008649098,0.00023137117,0.0007257094,0.0011550144],"genre_scores_gemma":[0.34829003,0.00031318213,0.647987,0.00004522495,0.000030292662,0.00027787706,0.0013080741,0.0002793608,0.001468925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848855,0.00031952173,0.000085507105,0.00034545257,0.00061082223,0.00015013463],"domain_scores_gemma":[0.99749583,0.001453674,0.00023871443,0.00040894,0.00030166752,0.000101056554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088790385,0.0013261498,0.001695771,0.0018894764,0.0007914501,0.0013972677,0.0020477478,0.0014163143,0.0029247997],"category_scores_gemma":[0.007690911,0.00089656975,0.0011365777,0.0023490197,0.0010793051,0.003242007,0.002277142,0.0013733546,0.0006858253],"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":[0.00011826815,0.00003780038,0.000968413,0.00013358702,0.000040747374,0.000110555055,0.0001363805,0.9023156,0.0027962814,0.019492742,0.0013275112,0.072522186],"study_design_scores_gemma":[0.000011728432,0.000020396174,0.000083807856,0.0000074386257,0.000004161147,0.000030451542,0.00005557055,0.9665436,0.0007984279,0.031686325,0.00075244653,0.0000056287645],"about_ca_topic_score_codex":0.0039835735,"about_ca_topic_score_gemma":0.0045135557,"teacher_disagreement_score":0.0039835735,"about_ca_system_score_codex":0.0011524541,"about_ca_system_score_gemma":0.0011080992,"threshold_uncertainty_score":0.00978446},"labels":[],"label_agreement":null},{"id":"W2028936054","doi":"10.1007/s101150050009","title":"An Index Structure for Data Mining and Clustering","year":2000,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Fudan University; National Science Foundation","keywords":"Cluster analysis; Euclidean distance; Data mining; Computer science; Metric (unit); Set (abstract data type); Index (typography); Visualization; Mathematics; Data set; Computation; Approximation error; Algorithm; Pattern recognition (psychology); Artificial intelligence","score_opus":0.02296360961186923,"score_gpt":0.2681822105133437,"score_spread":0.24521860090147446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028936054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005291731,0.0011830876,0.98423946,0.00067642325,0.00029762156,0.00020462487,0.0025564576,0.0026424008,0.0029081213],"genre_scores_gemma":[0.039620485,0.001027263,0.9479585,0.00028422847,0.00030749955,0.0003944876,0.0058113337,0.00044544888,0.0041507618],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99736553,0.00043529237,0.00044292634,0.00034273585,0.0012885011,0.00012506709],"domain_scores_gemma":[0.9931471,0.0016555558,0.00058902113,0.0023578648,0.0018611625,0.0003893021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032931108,0.00071367493,0.0018991486,0.0051311282,0.0023170258,0.004012326,0.0021302,0.0012421245,0.004042132],"category_scores_gemma":[0.015369188,0.00067817234,0.0010799067,0.009693438,0.0010301134,0.007321326,0.0030643255,0.0020420484,0.0035299524],"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":[0.00038531568,0.00027610688,0.0028201106,0.00059304683,0.00011986406,0.00017529227,0.0005992815,0.014592313,0.007878007,0.19348873,0.05655429,0.72251767],"study_design_scores_gemma":[0.00012517223,0.00039381505,0.0020397166,0.00030502825,0.00019180533,0.00088165846,0.0002458033,0.2646913,0.010862662,0.5632091,0.15692483,0.00012900113],"about_ca_topic_score_codex":0.0021788576,"about_ca_topic_score_gemma":0.003615917,"teacher_disagreement_score":0.0051311282,"about_ca_system_score_codex":0.0012498916,"about_ca_system_score_gemma":0.0029214018,"threshold_uncertainty_score":0.017415822},"labels":[],"label_agreement":null},{"id":"W2029626238","doi":"10.1016/s0304-3975(02)00738-7","title":"Mining for empty spaces in large data sets","year":2003,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Focus (optics); Scalability; Computer science; Bounded function; Set (abstract data type); Data structure; Space (punctuation); Mathematics; Algorithm; Data mining; Theoretical computer science","score_opus":0.03465404803737175,"score_gpt":0.30475762553959324,"score_spread":0.27010357750222147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029626238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30917946,0.0031219854,0.6784425,0.0020351193,0.00024150508,0.0002079507,0.0029260633,0.0025780182,0.0012675105],"genre_scores_gemma":[0.5851716,0.000814801,0.4023047,0.00027620752,0.00026064034,0.00020825848,0.008263156,0.0004293523,0.002271354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99251693,0.0015843539,0.0013521605,0.0013923998,0.0024527467,0.00070147956],"domain_scores_gemma":[0.9621026,0.022130972,0.0034178265,0.0066266186,0.0041369097,0.0015851302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065063005,0.001149357,0.003415719,0.0048809703,0.0034123508,0.00568433,0.0049654623,0.0026211354,0.0016168755],"category_scores_gemma":[0.040301487,0.0012824899,0.0026614442,0.006059836,0.0032275382,0.015370697,0.00702293,0.0033427703,0.00090118137],"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":[0.0032230907,0.0012661009,0.1411399,0.002539841,0.00089082046,0.0029455477,0.0053412667,0.07077123,0.015610813,0.08401102,0.021104738,0.6511556],"study_design_scores_gemma":[0.0001032338,0.00052939117,0.0074522705,0.0005014345,0.00037859287,0.0029509782,0.0043396386,0.6203331,0.026667167,0.32116956,0.01543765,0.00013700145],"about_ca_topic_score_codex":0.000900753,"about_ca_topic_score_gemma":0.0016792319,"teacher_disagreement_score":0.0065063005,"about_ca_system_score_codex":0.0006817351,"about_ca_system_score_gemma":0.0023605756,"threshold_uncertainty_score":0.034408987},"labels":[],"label_agreement":null},{"id":"W2029902517","doi":"10.1007/s10115-012-0511-z","title":"How you move reveals who you are: understanding human behavior by analyzing trajectory data","year":2012,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":80,"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","funders":"","keywords":"Computer science; Process (computing); Inference; Field (mathematics); Human behavior; Data science; Trajectory; Artificial intelligence; Knowledge extraction; Business process discovery; Human–computer interaction; Work in process; Business process; Engineering","score_opus":0.07373967419833824,"score_gpt":0.28297431501211756,"score_spread":0.20923464081377932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029902517","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94681036,0.00047268826,0.04586838,0.00088550855,0.000022156912,0.00006524283,0.003136613,0.00032401463,0.0024151192],"genre_scores_gemma":[0.9856839,0.00026797035,0.01249106,0.00003146369,0.000008575304,0.000023303712,0.0010178358,0.000027591046,0.00044822373],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999747,0.00010622821,0.00001665212,0.000062171544,0.000040008734,0.000027912596],"domain_scores_gemma":[0.9976271,0.0013067155,0.00043888175,0.00023524647,0.0002755587,0.00011662861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004530261,0.00033594243,0.00027540172,0.0015895133,0.0004420284,0.0010800579,0.0003502307,0.0006866451,0.0010166672],"category_scores_gemma":[0.0058079944,0.00016068028,0.00019611775,0.0021658498,0.0004174779,0.001553057,0.00046165776,0.000502735,0.000554823],"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":[0.000555159,0.00026172635,0.74750894,0.00026146753,0.00022301769,0.00049785443,0.0116380025,0.020906523,0.008871657,0.0038475704,0.006051277,0.1993768],"study_design_scores_gemma":[0.000026504695,0.0003781372,0.5899519,0.0002358457,0.00021477029,0.000865067,0.034136042,0.32379425,0.0066437465,0.027251331,0.016366262,0.00013621301],"about_ca_topic_score_codex":0.024865828,"about_ca_topic_score_gemma":0.031651583,"teacher_disagreement_score":0.024865828,"about_ca_system_score_codex":0.0003945993,"about_ca_system_score_gemma":0.00059963303,"threshold_uncertainty_score":0.049442172},"labels":[],"label_agreement":null},{"id":"W2031979200","doi":"10.1016/j.ins.2013.06.019","title":"Weighting tags and paths in XML documents according to their topic generalization","year":2013,"lang":"en","type":"article","venue":"Information Sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Weighting; Computer science; Generalization; Information retrieval; XML; Data mining; Document Structure Description; Database; Mathematics; World Wide Web","score_opus":0.014341667468508033,"score_gpt":0.24544668063568473,"score_spread":0.2311050131671767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031979200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46906817,0.0033139014,0.50520176,0.0011420966,0.00047325678,0.0010628382,0.010418454,0.0044653756,0.004854149],"genre_scores_gemma":[0.48099378,0.0024260296,0.497242,0.00022874841,0.0002693759,0.00052352267,0.01184624,0.000872356,0.005597945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780196,0.0005117883,0.00029802098,0.0005396069,0.00066915015,0.00017946596],"domain_scores_gemma":[0.99037373,0.0052560735,0.00071887893,0.00083519315,0.0023913675,0.00042481985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003039313,0.0008106418,0.0007687209,0.010593924,0.001094692,0.0026398292,0.00078963756,0.0011857705,0.0022826553],"category_scores_gemma":[0.014715638,0.00055747025,0.0011976281,0.008340877,0.0008329282,0.0048050988,0.001601623,0.001655107,0.0009448373],"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":[0.0025289308,0.00051713677,0.1236347,0.0019161252,0.0006019353,0.00078290195,0.0064363345,0.023981694,0.07042203,0.037143927,0.015339213,0.7166952],"study_design_scores_gemma":[0.0005851669,0.0014538174,0.1288785,0.0010832755,0.0036986968,0.0033033234,0.008490203,0.3743203,0.07835824,0.26199308,0.1373865,0.0004488308],"about_ca_topic_score_codex":0.0075602275,"about_ca_topic_score_gemma":0.013862051,"teacher_disagreement_score":0.010593924,"about_ca_system_score_codex":0.0012805089,"about_ca_system_score_gemma":0.0023342841,"threshold_uncertainty_score":0.016073644},"labels":[],"label_agreement":null},{"id":"W2032871197","doi":"10.1139/l00-106","title":"Analyse orientée-objet et totalement désagrégée des données d'enquêtes ménages origine-destination","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; TRIPS architecture; Database; Survey data collection; Visualization; Data model (GIS); Object (grammar); Geography; Data mining; Statistics; Mathematics; Artificial intelligence","score_opus":0.018582823856837946,"score_gpt":0.2216172389986528,"score_spread":0.20303441514181486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032871197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18472409,0.0030631376,0.79241085,0.00060765416,0.00008743709,0.00025866192,0.010564605,0.0033152283,0.0049682637],"genre_scores_gemma":[0.45632687,0.0028304695,0.50970376,0.00010862784,0.000072866016,0.0006947751,0.02266722,0.0006824245,0.0069129863],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98937464,0.0030163946,0.0007526631,0.002165586,0.0042837565,0.0004068961],"domain_scores_gemma":[0.99019974,0.0057615726,0.00088223,0.0012804539,0.0016748826,0.00020114602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071790228,0.0018053767,0.0017360061,0.0114831375,0.0010593205,0.0073191053,0.0014381723,0.0011416489,0.0023831031],"category_scores_gemma":[0.021111244,0.0008581182,0.0029411973,0.011566934,0.000933413,0.003336211,0.0020591384,0.0011173732,0.0013795655],"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":[0.0003933651,0.00029655796,0.19974278,0.0024830317,0.0021353029,0.0009057718,0.007612909,0.07346709,0.013816035,0.021399241,0.009083605,0.66866434],"study_design_scores_gemma":[0.00013356838,0.00055406947,0.24132311,0.00071011344,0.0013727673,0.0035019086,0.015322579,0.44623226,0.038530607,0.04931286,0.20260368,0.0004024327],"about_ca_topic_score_codex":0.056634407,"about_ca_topic_score_gemma":0.06268461,"teacher_disagreement_score":0.056634407,"about_ca_system_score_codex":0.0017735629,"about_ca_system_score_gemma":0.003164058,"threshold_uncertainty_score":0.112609506},"labels":[],"label_agreement":null},{"id":"W2033152755","doi":"10.1145/1995412.1995419","title":"Lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Search engine indexing; Metric (unit); Tree (set theory); Similarity (geometry); Computer science; Nearest neighbor search; Metric space; Mathematics; Theoretical computer science; Data mining; Discrete mathematics; Combinatorics; Artificial intelligence; Engineering","score_opus":0.05797500141741404,"score_gpt":0.27117219149028254,"score_spread":0.2131971900728685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033152755","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103223704,0.016333686,0.84367055,0.0066336053,0.00060845964,0.00052667584,0.0018136721,0.003194376,0.023995247],"genre_scores_gemma":[0.7458687,0.0060110553,0.23509154,0.0015345406,0.0011341765,0.0009973075,0.0023617086,0.0010288697,0.005972174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9733854,0.007926595,0.0021409942,0.003047472,0.010122127,0.003377466],"domain_scores_gemma":[0.84286976,0.1185936,0.006667807,0.02312436,0.0062102415,0.002534287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020000624,0.0026210581,0.0044557215,0.0037597083,0.0023552645,0.007596794,0.0062254774,0.0043042796,0.0082668075],"category_scores_gemma":[0.136964,0.0011242293,0.0020373836,0.007791584,0.0044843704,0.018817063,0.008163828,0.0055819075,0.0032386295],"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":[0.0034447522,0.0008244528,0.0064865495,0.0016080581,0.0002714016,0.0002838823,0.0006993404,0.4970869,0.017618908,0.25066116,0.018112183,0.2029024],"study_design_scores_gemma":[0.00009624252,0.00043063072,0.0011838784,0.00013452246,0.00006764498,0.00035699792,0.00012619319,0.8855935,0.004563067,0.1047004,0.0026717226,0.00007521016],"about_ca_topic_score_codex":0.0020904993,"about_ca_topic_score_gemma":0.0022447458,"teacher_disagreement_score":0.020000624,"about_ca_system_score_codex":0.0073933117,"about_ca_system_score_gemma":0.005291908,"threshold_uncertainty_score":0.10577464},"labels":[],"label_agreement":null},{"id":"W2033937100","doi":"10.1145/2433140.2433151","title":"A framework to compute statistics of system parameters from very large trace files","year":2013,"lang":"en","type":"article","venue":"ACM SIGOPS Operating Systems Review","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Granularity; TRACE (psycholinguistics); Hierarchy; Interval (graph theory); Architecture; Data mining; Theoretical computer science; Algorithm; Operating system","score_opus":0.027823030164328938,"score_gpt":0.2784168744414443,"score_spread":0.2505938442771154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033937100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012709616,0.00011374181,0.9914922,0.000083301515,0.000021444439,0.00006602909,0.00046621775,0.0062411823,0.0002449505],"genre_scores_gemma":[0.09748248,0.0003976625,0.8961862,0.00013676599,0.00010865942,0.00043820453,0.003689753,0.0007153356,0.0008449812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977062,0.00024198531,0.00022049765,0.0003753939,0.0013124726,0.00014351026],"domain_scores_gemma":[0.9940801,0.002237497,0.00053956313,0.0018008386,0.0010869217,0.00025507706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026446432,0.0016769991,0.0017823211,0.0048199636,0.0009949977,0.0033626074,0.003452357,0.0012778449,0.0024930313],"category_scores_gemma":[0.013178825,0.0009440032,0.0012729907,0.0039434605,0.0011579887,0.0055908053,0.0026411945,0.002599679,0.0020831453],"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":[0.00047369854,0.0004191796,0.009076408,0.0006148739,0.00037486074,0.0005095374,0.00051714014,0.26260203,0.03002315,0.12458253,0.025285095,0.5455215],"study_design_scores_gemma":[0.000059002767,0.00013174648,0.0017953808,0.000065955806,0.000050949984,0.00037642164,0.00011591667,0.89673996,0.014678635,0.06346145,0.022396455,0.0001280908],"about_ca_topic_score_codex":0.008569662,"about_ca_topic_score_gemma":0.005829417,"teacher_disagreement_score":0.008569662,"about_ca_system_score_codex":0.0015271388,"about_ca_system_score_gemma":0.0024163497,"threshold_uncertainty_score":0.017039537},"labels":[],"label_agreement":null},{"id":"W2034856714","doi":"10.1145/2618243.2618262","title":"Skew-resistant parallel in-memory spatial join","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Skew; Computer science; Partition (number theory); Join (topology); Spatial analysis; Parallel computing; Spatial database; Theoretical computer science; Mathematics","score_opus":0.011469557123735248,"score_gpt":0.2175001016848722,"score_spread":0.20603054456113695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034856714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19560167,0.0009969824,0.78388774,0.00039143,0.00033337073,0.0002697375,0.0007480354,0.008044716,0.009726348],"genre_scores_gemma":[0.7283851,0.00047870804,0.26036423,0.00026651344,0.00019339571,0.00027291666,0.0024418265,0.0008325318,0.0067647346],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968329,0.00049192604,0.0002581625,0.0004675678,0.0015456269,0.0004038016],"domain_scores_gemma":[0.99429506,0.0010448805,0.00036738443,0.0031074947,0.00097126106,0.00021393549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021160045,0.0010075312,0.001285,0.0008011017,0.0015599167,0.0016235373,0.0025007164,0.0005856041,0.0028729488],"category_scores_gemma":[0.0060754283,0.0004927689,0.0006677996,0.0019904976,0.0009072471,0.0025307382,0.0036684303,0.0009132692,0.0015321443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060963463,0.0008275717,0.012063106,0.0008019945,0.00040371146,0.0011009419,0.001704661,0.21441361,0.18945956,0.032283947,0.035039414,0.5058052],"study_design_scores_gemma":[0.0003369806,0.00046464088,0.002269172,0.000026424304,0.000108266606,0.0008576266,0.00045767616,0.8317606,0.11112528,0.03463555,0.017905619,0.000052222866],"about_ca_topic_score_codex":0.0019265347,"about_ca_topic_score_gemma":0.003009615,"teacher_disagreement_score":0.0028729488,"about_ca_system_score_codex":0.0006138614,"about_ca_system_score_gemma":0.0017890783,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2035006988","doi":"10.1007/s00778-006-0017-y","title":"Query processing of multi-way stream window joins","year":2006,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Joins; Computer science; Join (topology); Data stream mining; Window (computing); Sliding window protocol; Data stream; Database; Data mining; Aggregate (composite); Distributed computing; Real-time computing; Programming language","score_opus":0.019450964385993072,"score_gpt":0.24129503545532632,"score_spread":0.22184407106933324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035006988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44248486,0.0029142096,0.5424862,0.0006114672,0.0003880504,0.00023390295,0.001090259,0.0050558276,0.0047351937],"genre_scores_gemma":[0.81416327,0.0007515874,0.17857966,0.000100362806,0.00029428795,0.0001425562,0.0016860162,0.00030906184,0.00397324],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982241,0.00029887442,0.00020763862,0.00029708724,0.0007452429,0.00022697976],"domain_scores_gemma":[0.9972784,0.0012372199,0.00019112047,0.0006561905,0.0004107706,0.00022633543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026076846,0.000689307,0.0019312312,0.0015567562,0.0012600704,0.00336873,0.0014707056,0.0008026635,0.0030505732],"category_scores_gemma":[0.005215438,0.0005887022,0.00061546045,0.0027275002,0.0004983344,0.003812851,0.0019567981,0.00086087687,0.0006960512],"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":[0.0130821355,0.00088109093,0.019809823,0.00066544965,0.00057140994,0.0012930846,0.0014558674,0.08008959,0.16143195,0.033519376,0.019856963,0.6673433],"study_design_scores_gemma":[0.0003915028,0.0009022259,0.0044267676,0.000029628301,0.00018216505,0.00070007035,0.0006348787,0.892761,0.06272402,0.027597286,0.0095927855,0.000057691344],"about_ca_topic_score_codex":0.0013754318,"about_ca_topic_score_gemma":0.0019389829,"teacher_disagreement_score":0.00336873,"about_ca_system_score_codex":0.00045648473,"about_ca_system_score_gemma":0.00080369675,"threshold_uncertainty_score":0.013790965},"labels":[],"label_agreement":null},{"id":"W2036524648","doi":"10.4018/jaeis.2010070104","title":"Describing Spatio-Temporal Phenomena for Environmental System Development","year":2010,"lang":"en","type":"article","venue":"International Journal of Agricultural and Environmental Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Temporalities; Temporality; Rotation formalisms in three dimensions; Simple (philosophy); Computer science; Focus (optics); Extension (predicate logic); Field (mathematics); Cognitive science; Presentation (obstetrics); Epistemology; Programming language; Psychology; Mathematics; Philosophy","score_opus":0.011699973528119913,"score_gpt":0.18282299448812928,"score_spread":0.17112302096000936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036524648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031993696,0.0007513176,0.987856,0.0005812959,0.000068190566,0.000074839765,0.0004877316,0.00030977977,0.0066715605],"genre_scores_gemma":[0.13099234,0.0031244867,0.85436696,0.0002097302,0.00013476938,0.0006252168,0.0020204135,0.00021676057,0.008309365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99891114,0.0004912809,0.00013361475,0.00013373944,0.00026792195,0.00006228484],"domain_scores_gemma":[0.9983676,0.00100133,0.00018113614,0.0002469068,0.00014723287,0.000055786808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019170748,0.0009705177,0.0005028881,0.0012018512,0.00070346746,0.0029774169,0.0013443257,0.0010328551,0.006488134],"category_scores_gemma":[0.0048063826,0.00040086132,0.0014991581,0.002327128,0.0012927541,0.004651594,0.0022427153,0.0018473064,0.001145246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001848987,0.000028084869,0.00084702304,0.00031367454,0.000040418767,0.00038304072,0.0006736426,0.19955811,0.0014461968,0.7621203,0.003216058,0.031354997],"study_design_scores_gemma":[0.0000121887115,0.000018806924,0.0002611175,0.00010670275,0.000027271297,0.00018114642,0.0002832369,0.6154669,0.0010582287,0.30153477,0.08102701,0.000022715507],"about_ca_topic_score_codex":0.005441775,"about_ca_topic_score_gemma":0.009482376,"teacher_disagreement_score":0.006488134,"about_ca_system_score_codex":0.0016446993,"about_ca_system_score_gemma":0.0013078725,"threshold_uncertainty_score":0.021705031},"labels":[],"label_agreement":null},{"id":"W2036609507","doi":"10.1145/2379307.2379314","title":"Ranking the answer trees of graph search by both structure and content","year":2012,"lang":"en","type":"article","venue":"","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":"Carleton University","funders":"China Postdoctoral Science Foundation","keywords":"Ranking (information retrieval); Weighting; Computer science; Keyword search; Graph; Information retrieval; Theoretical computer science; Scheme (mathematics); Tree (set theory); Data mining; Mathematics; Combinatorics","score_opus":0.029930659174962196,"score_gpt":0.2391713763056908,"score_spread":0.20924071713072862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036609507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17221044,0.0021789568,0.8177848,0.0004615542,0.00006758402,0.00035978924,0.00067990355,0.001287256,0.00496976],"genre_scores_gemma":[0.5675281,0.00096130895,0.42672527,0.00012680996,0.00011346561,0.00021734068,0.0016237574,0.00025428727,0.0024495802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971872,0.0010624728,0.00018920576,0.0003754513,0.0009793495,0.00020626992],"domain_scores_gemma":[0.99199986,0.005233664,0.0005819273,0.0007078993,0.001242786,0.00023389187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002209588,0.0008312679,0.0017125502,0.006638782,0.0010970649,0.002324844,0.0010615648,0.0012064017,0.0018697807],"category_scores_gemma":[0.014936684,0.0003460906,0.00074727484,0.0047175162,0.0011467099,0.0052739843,0.0014102262,0.0007506498,0.000706576],"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":[0.0008929749,0.00042546433,0.014198499,0.0013789792,0.00022843615,0.00023674988,0.0006620488,0.07344002,0.034506306,0.06973399,0.009075128,0.7952213],"study_design_scores_gemma":[0.00011442999,0.0009769741,0.006465637,0.00012968497,0.00033548276,0.00055510603,0.00053934136,0.76232934,0.028594969,0.1910736,0.008736393,0.00014905055],"about_ca_topic_score_codex":0.0014510591,"about_ca_topic_score_gemma":0.0032516443,"teacher_disagreement_score":0.006638782,"about_ca_system_score_codex":0.00088113756,"about_ca_system_score_gemma":0.0009872429,"threshold_uncertainty_score":0.01168555},"labels":[],"label_agreement":null},{"id":"W2037133727","doi":"10.1145/2463676.2463687","title":"A query answering system for data with evolution relationships","year":2013,"lang":"en","type":"article","venue":"","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":"Statistics Canada","funders":"","keywords":"Computer science; Schema (genetic algorithms); Database schema; Query language; View; Schema evolution; Information retrieval; Probabilistic logic; Database theory; Database; Probabilistic database; Relational database; Database design; Artificial intelligence","score_opus":0.0533440084306374,"score_gpt":0.23527731013378606,"score_spread":0.18193330170314864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037133727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021567093,0.0007524725,0.9042585,0.0023120989,0.00017091098,0.00060328114,0.003826604,0.061981663,0.0045273732],"genre_scores_gemma":[0.24742371,0.00095044594,0.7258273,0.0020067075,0.0004558707,0.0010665371,0.013214596,0.0023229558,0.006731876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99505544,0.0011784033,0.0007306462,0.0010868722,0.001643886,0.00030475415],"domain_scores_gemma":[0.9925988,0.0036346756,0.00051244284,0.0017630461,0.0011502168,0.0003408251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062080193,0.0011299589,0.0017334693,0.0031875158,0.0024141427,0.0046361387,0.0038972911,0.0036733062,0.0059986208],"category_scores_gemma":[0.017408025,0.0011964913,0.0016778264,0.004179379,0.0013221564,0.010922436,0.004739229,0.0022021541,0.0026504237],"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":[0.0025015988,0.0007409398,0.013672875,0.0016455646,0.0006071264,0.0028279417,0.0036833102,0.041412987,0.05607039,0.15205099,0.15033525,0.5744511],"study_design_scores_gemma":[0.00044756246,0.00048644457,0.003034662,0.00018278771,0.00038232046,0.0021523058,0.00078329886,0.671019,0.030840306,0.13317916,0.15725298,0.00023918421],"about_ca_topic_score_codex":0.0047846567,"about_ca_topic_score_gemma":0.003541724,"teacher_disagreement_score":0.0062080193,"about_ca_system_score_codex":0.0015296276,"about_ca_system_score_gemma":0.0017169359,"threshold_uncertainty_score":0.03283155},"labels":[],"label_agreement":null},{"id":"W2038043779","doi":"10.1002/asi.10191","title":"GeoSearcher: Location‐based ranking of search engine results","year":2002,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Woods Hole Oceanographic Institution","keywords":"Geospatial analysis; Computer science; Information retrieval; Ranking (information retrieval); Search engine; Dimension (graph theory); Data mining; Geographic coordinate system; sort; Geography; Mathematics","score_opus":0.025164326394637917,"score_gpt":0.2735954831368768,"score_spread":0.2484311567422389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038043779","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5266586,0.0054863323,0.33458203,0.0014267438,0.0007925749,0.0011659,0.015531953,0.09478627,0.019569557],"genre_scores_gemma":[0.7703943,0.0004202573,0.212486,0.00012348985,0.00022879575,0.00025754684,0.008979149,0.0005928027,0.006517666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633694,0.0010205016,0.0003682086,0.00044706595,0.001543022,0.000284323],"domain_scores_gemma":[0.9923184,0.003151945,0.00083078654,0.0008974801,0.0024025715,0.00039886555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030073181,0.000855548,0.0017571937,0.010639583,0.0006757356,0.0025280253,0.0012997356,0.0009729169,0.008227852],"category_scores_gemma":[0.014928127,0.0002849542,0.0004353001,0.005448267,0.00036883025,0.0021676496,0.0009785696,0.00041760848,0.004677869],"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":[0.0035717161,0.00079014647,0.05047055,0.00093973044,0.00044355085,0.0003664706,0.00029628348,0.037811987,0.02253462,0.0055045113,0.07485569,0.8024148],"study_design_scores_gemma":[0.00047011056,0.0010596697,0.028400756,0.00006587659,0.00015912934,0.0007175703,0.0003680465,0.9177113,0.032811474,0.0048697228,0.013201663,0.00016472142],"about_ca_topic_score_codex":0.0057655685,"about_ca_topic_score_gemma":0.011494672,"teacher_disagreement_score":0.010639583,"about_ca_system_score_codex":0.001060508,"about_ca_system_score_gemma":0.0011096976,"threshold_uncertainty_score":0.027524889},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W2040010874","doi":"10.1007/s00500-004-0411-6","title":"Exploring spatial data through computational intelligence: a joint perspective","year":2004,"lang":"en","type":"article","venue":"Soft Computing","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 Alberta","funders":"","keywords":"Geospatial analysis; Computer science; Data science; Perspective (graphical); Context (archaeology); Soft computing; Geographic information system; Process (computing); Generalization; Spatial analysis; Computational intelligence; Data mining; Artificial intelligence; Cartography; Geography; Remote sensing; Mathematics","score_opus":0.2505455837239068,"score_gpt":0.32932268302722806,"score_spread":0.07877709930332127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040010874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01159651,0.010061943,0.929407,0.016416533,0.00019371479,0.000028957094,0.00013815737,0.0001596649,0.03199758],"genre_scores_gemma":[0.5810598,0.018348096,0.39279717,0.0009802615,0.00126796,0.0001378276,0.00030003168,0.00018657856,0.0049222913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971409,0.0011812437,0.00016485491,0.00036111756,0.0009649621,0.00018697565],"domain_scores_gemma":[0.9902416,0.006146298,0.00048166758,0.0018854066,0.0007448125,0.00050021155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049421107,0.0010119905,0.0021137784,0.004725594,0.001622868,0.016476387,0.0031250387,0.0029709125,0.0035752435],"category_scores_gemma":[0.012609878,0.0013926652,0.0019782325,0.0072111874,0.011072443,0.02268605,0.0068800235,0.0045886207,0.00048336736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001757248,0.00004570377,0.0007062161,0.0001568861,0.00012189206,0.00007173938,0.00042093347,0.016643504,0.00033924746,0.95283383,0.0014284247,0.02721407],"study_design_scores_gemma":[0.0000059147837,0.00001391771,0.0002636956,0.000057748013,0.000040355233,0.00006309386,0.00033426404,0.051710755,0.00024281218,0.9386249,0.00862279,0.000019761332],"about_ca_topic_score_codex":0.0041772383,"about_ca_topic_score_gemma":0.004943166,"teacher_disagreement_score":0.016476387,"about_ca_system_score_codex":0.0018295036,"about_ca_system_score_gemma":0.0023080825,"threshold_uncertainty_score":0.026136637},"labels":[],"label_agreement":null},{"id":"W2040572217","doi":"10.1016/j.ejor.2004.01.032","title":"The shortest path problem with forbidden paths","year":2004,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Shortest path problem; Constrained Shortest Path First; K shortest path routing; Longest path problem; Shortest Path Faster Algorithm; Euclidean shortest path; Yen's algorithm; Distance; Widest path problem; Mathematics; Matching (statistics); Path (computing); Mathematical optimization; Algorithm; Computer science; Graph; Combinatorics; Dijkstra's algorithm","score_opus":0.057786834878117005,"score_gpt":0.31843098541114934,"score_spread":0.26064415053303236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040572217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14736831,0.0023240962,0.81696004,0.0042814026,0.00044429136,0.00025805613,0.0023000182,0.0006991111,0.025364604],"genre_scores_gemma":[0.6127287,0.0028949599,0.3576572,0.0004331409,0.00045506662,0.00039465318,0.0040367227,0.00043344006,0.020966021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984249,0.0006066761,0.00009328832,0.00034602653,0.00030092016,0.00022818756],"domain_scores_gemma":[0.99314696,0.005266498,0.0004041354,0.00044048874,0.00036186463,0.00038008735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013701597,0.0008822822,0.0013428318,0.001023785,0.0013165603,0.0025022917,0.0024925226,0.0023145766,0.007157907],"category_scores_gemma":[0.009853308,0.0008451234,0.001052172,0.0022558651,0.001529666,0.0058473325,0.0024211027,0.0024444354,0.000815251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011379429,0.00028085153,0.0013788585,0.0012590224,0.00021887523,0.00062248664,0.00037793623,0.41875577,0.0031635598,0.4287894,0.02266174,0.12135356],"study_design_scores_gemma":[0.0001668112,0.00009334968,0.00024040247,0.000059313526,0.00005736219,0.00035572206,0.00015101513,0.2660542,0.0011579052,0.7206582,0.010979139,0.000026534977],"about_ca_topic_score_codex":0.0022691004,"about_ca_topic_score_gemma":0.0018827118,"teacher_disagreement_score":0.007157907,"about_ca_system_score_codex":0.0010383892,"about_ca_system_score_gemma":0.0021443518,"threshold_uncertainty_score":0.02394551},"labels":[],"label_agreement":null},{"id":"W2040641513","doi":"10.1145/1148170.1148341","title":"A platform for Okapi-based contextual information retrieval","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Relevance (law); Relevance feedback; Context (archaeology); Information retrieval; Probabilistic logic; Extension (predicate logic); Java; Query expansion; Dual (grammatical number); Artificial intelligence; Human–computer information retrieval; Search engine; Programming language; Image retrieval","score_opus":0.014971571772353453,"score_gpt":0.22610969182340065,"score_spread":0.2111381200510472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040641513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006004203,0.0003360389,0.770038,0.00031928506,0.00024224738,0.0007660364,0.0023164197,0.21392371,0.0060539553],"genre_scores_gemma":[0.09998354,0.0006189166,0.8487471,0.00079495885,0.00031800673,0.0016620059,0.020131228,0.014861081,0.012883266],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998334,0.00022648383,0.00024896028,0.00034461948,0.0006413009,0.00020465694],"domain_scores_gemma":[0.99724543,0.00050989294,0.00019212873,0.00091885566,0.0007572478,0.0003764763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023042667,0.0008651843,0.0011923987,0.0014720325,0.0011910938,0.0025486252,0.0037749847,0.0010231546,0.010886968],"category_scores_gemma":[0.006157758,0.0011373921,0.0010167006,0.0013087528,0.00053668726,0.0033975954,0.0031404824,0.002790205,0.010169144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023717156,0.0014839354,0.004338803,0.001368011,0.0003236509,0.001352095,0.0008966032,0.009525447,0.08580006,0.033743456,0.19536093,0.66343534],"study_design_scores_gemma":[0.0011148442,0.0007811012,0.00884526,0.00041785557,0.00068431615,0.002265114,0.00032375957,0.33899733,0.10165273,0.07612515,0.46790045,0.00089214084],"about_ca_topic_score_codex":0.007273326,"about_ca_topic_score_gemma":0.004846215,"teacher_disagreement_score":0.010886968,"about_ca_system_score_codex":0.000576437,"about_ca_system_score_gemma":0.0017634236,"threshold_uncertainty_score":0.036420524},"labels":[],"label_agreement":null},{"id":"W2041121499","doi":"10.1198/016214508000000517","title":"Combining Registration and Fitting for Functional Models","year":2008,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Computer science; Econometrics; Mathematics; Artificial intelligence","score_opus":0.03196521524814722,"score_gpt":0.25255041528521477,"score_spread":0.22058520003706755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041121499","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035812678,0.000047925765,0.995876,0.00007856029,0.000008684289,0.00002367044,0.000013553037,0.00015303999,0.00021735525],"genre_scores_gemma":[0.1457812,0.00012958699,0.8518436,0.00008089387,0.00006967571,0.00025910736,0.00023358359,0.0004292598,0.0011732451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99115705,0.0054293307,0.00039391953,0.0012048226,0.0015467941,0.00026816825],"domain_scores_gemma":[0.98776776,0.0078163855,0.00087431987,0.0024154792,0.0009806197,0.0001453731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012619506,0.0014069136,0.0023501932,0.0034540256,0.0012663974,0.002796142,0.0021892148,0.002249192,0.0027082285],"category_scores_gemma":[0.037393615,0.0013446439,0.0027034765,0.0035690218,0.003924495,0.003792241,0.0043365373,0.003144837,0.0009813444],"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":[0.00021248705,0.00009952075,0.0026017658,0.00019290476,0.00027748317,0.0001698311,0.00038897272,0.537628,0.006513679,0.20369822,0.0014243914,0.2467928],"study_design_scores_gemma":[0.000014896148,0.000047391262,0.00045539826,0.000013776435,0.000017445383,0.000052975014,0.000032454114,0.8886695,0.0021112529,0.10688958,0.0016686189,0.00002665436],"about_ca_topic_score_codex":0.0034334753,"about_ca_topic_score_gemma":0.003075054,"teacher_disagreement_score":0.012619506,"about_ca_system_score_codex":0.0015845689,"about_ca_system_score_gemma":0.0017415864,"threshold_uncertainty_score":0.06673914},"labels":[],"label_agreement":null},{"id":"W2041277986","doi":"10.1145/2600428.2609527","title":"An enhanced context-sensitive proximity model for probabilistic information retrieval","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relevance (law); Weighting; Term (time); Computer science; Divergence-from-randomness model; Probabilistic logic; Information retrieval; Context (archaeology); Relevance feedback; Term Discrimination; Ranking (information retrieval); Statistical model; Data mining; Context model; Artificial intelligence; Search engine; Image retrieval; Concept search; Web search query; Geography","score_opus":0.017307298973516138,"score_gpt":0.24683986839512623,"score_spread":0.2295325694216101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041277986","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011132228,0.001181937,0.9853382,0.00028911463,0.00007040671,0.00011303023,0.0001499884,0.0003776432,0.0013474758],"genre_scores_gemma":[0.56780094,0.002409265,0.42127377,0.00052139995,0.0005404808,0.0005895729,0.0006909806,0.00015383215,0.006019775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966612,0.0010222961,0.00022367205,0.00070116506,0.0012154827,0.00017616799],"domain_scores_gemma":[0.99701476,0.001397104,0.00026536392,0.0005452111,0.0006802366,0.00009730201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023347128,0.0008899926,0.0015624543,0.0022035702,0.00071386166,0.0017621926,0.0027157627,0.0018395459,0.0023463324],"category_scores_gemma":[0.010714637,0.0006769074,0.0016016978,0.0033893676,0.00088796363,0.005853112,0.0018957928,0.0018544323,0.0011368889],"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":[0.00058514805,0.00038242037,0.0036504385,0.0007522219,0.00032440867,0.00051451847,0.0007900829,0.48866048,0.022222213,0.15152824,0.006697642,0.32389215],"study_design_scores_gemma":[0.00003117891,0.00015970798,0.0005521405,0.000020675841,0.00008005747,0.0002738818,0.000022474576,0.9573205,0.0017003032,0.03611688,0.003671354,0.00005091073],"about_ca_topic_score_codex":0.0044926396,"about_ca_topic_score_gemma":0.0037089633,"teacher_disagreement_score":0.0044926396,"about_ca_system_score_codex":0.0011929731,"about_ca_system_score_gemma":0.001060942,"threshold_uncertainty_score":0.012347281},"labels":[],"label_agreement":null},{"id":"W2041305873","doi":"10.1145/2668064.2677081","title":"(Reverse) <i>k</i> -nearest neighbors for moving objects","year":2014,"lang":"en","type":"article","venue":"","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":"University of Victoria","funders":"","keywords":"Computer science; k-nearest neighbors algorithm; Object (grammar); Simple (philosophy); Trajectory; Nearest neighbor search; Term (time); Artificial intelligence","score_opus":0.0097642879889438,"score_gpt":0.21719981751453704,"score_spread":0.20743552952559324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041305873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007224884,0.0009264976,0.98233503,0.0005384432,0.00033969732,0.00012116537,0.0011402747,0.0013863854,0.005987637],"genre_scores_gemma":[0.16342069,0.0010582225,0.81595147,0.0006216598,0.000329624,0.00028813668,0.0030558968,0.00039220008,0.0148820635],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99763525,0.00035779056,0.00023848632,0.0006158629,0.00096142077,0.00019123609],"domain_scores_gemma":[0.9972934,0.00054530776,0.00023728544,0.0013295822,0.0005063296,0.000088058165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011813747,0.0008343438,0.0010928035,0.00164628,0.00097143213,0.0017907958,0.002850277,0.00179884,0.0069578886],"category_scores_gemma":[0.009258622,0.00048453722,0.0009962094,0.0025115756,0.0010066278,0.0054500964,0.003389452,0.0018701196,0.005809832],"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":[0.00080439635,0.00025360865,0.005338657,0.0011489899,0.00014694074,0.0008142191,0.0008125983,0.054772347,0.022163847,0.23331493,0.070538454,0.609891],"study_design_scores_gemma":[0.00012832084,0.00029299324,0.0020430353,0.00015761245,0.00010968758,0.0033514418,0.00053509383,0.5002324,0.026843643,0.33417502,0.13194342,0.00018737122],"about_ca_topic_score_codex":0.0042370562,"about_ca_topic_score_gemma":0.0069390982,"teacher_disagreement_score":0.0069578886,"about_ca_system_score_codex":0.0005634742,"about_ca_system_score_gemma":0.0007949174,"threshold_uncertainty_score":0.023276448},"labels":[],"label_agreement":null},{"id":"W2041835266","doi":"10.1145/2452376.2452433","title":"Efficient top-k query answering using cached views","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Query optimization; Scalability; Query expansion; Query language; Scope (computer science); Cache; Partition (number theory); Web search query; Information retrieval; Data mining; Theoretical computer science; Search engine; Database; Parallel computing; Programming language","score_opus":0.03926363015225346,"score_gpt":0.2621972053555646,"score_spread":0.22293357520331117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041835266","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06547037,0.003963529,0.91342366,0.00084684155,0.00017881235,0.00031630747,0.0022426757,0.010016785,0.0035409827],"genre_scores_gemma":[0.41107726,0.0010299139,0.5789696,0.00029004394,0.00025135753,0.00021526184,0.0055907136,0.00038468887,0.0021912453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969716,0.0006285734,0.00032838978,0.0007879904,0.0009196543,0.00036377838],"domain_scores_gemma":[0.9938653,0.0024575691,0.00043707882,0.002091125,0.00093342416,0.00021551076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002180046,0.0016124002,0.003912937,0.0027692583,0.0017566258,0.0041046008,0.004069407,0.0020518827,0.002773332],"category_scores_gemma":[0.00867041,0.00097225764,0.0017296294,0.0068085575,0.00080760673,0.0068147825,0.0030910552,0.0020296827,0.0019443951],"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":[0.0017079946,0.0010377795,0.0074621174,0.0010056547,0.0005114398,0.00044775862,0.0009880798,0.1525812,0.043261983,0.021313347,0.050788097,0.71889454],"study_design_scores_gemma":[0.00012018095,0.00018465173,0.0007237761,0.000022755574,0.000097829405,0.0005102975,0.00036932598,0.96212286,0.00861617,0.023239272,0.003942226,0.00005063816],"about_ca_topic_score_codex":0.009678028,"about_ca_topic_score_gemma":0.017074807,"teacher_disagreement_score":0.009678028,"about_ca_system_score_codex":0.0011139261,"about_ca_system_score_gemma":0.0028250753,"threshold_uncertainty_score":0.01924342},"labels":[],"label_agreement":null},{"id":"W2041882671","doi":"10.1145/1142473.1142481","title":"Supporting ad-hoc ranking aggregates","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Computer science; Tuple; Aggregate (composite); Pruning; Rank (graph theory); Schedule; sort; Query optimization; Process (computing); Information retrieval; Tuple space; Theoretical computer science; Data mining; Mathematics","score_opus":0.008520640680822918,"score_gpt":0.2446987088814756,"score_spread":0.23617806820065268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041882671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026971145,0.0001855216,0.9663373,0.0002788626,0.00003273052,0.0001444732,0.00015427245,0.0029208795,0.0029747968],"genre_scores_gemma":[0.41994524,0.00021595642,0.57629126,0.000101417034,0.000097086275,0.00016163055,0.0005267705,0.0003003156,0.002360287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957438,0.0009994307,0.00029796184,0.00052546995,0.0019422347,0.0004910815],"domain_scores_gemma":[0.9943137,0.0019033356,0.00039117108,0.0022206856,0.00078972394,0.00038131818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004150284,0.00073522667,0.0010889901,0.0011085839,0.0024120088,0.0031278816,0.0030460346,0.001016306,0.0018886899],"category_scores_gemma":[0.0069020498,0.0005976585,0.0006971064,0.0014318404,0.0015022127,0.004770063,0.0030733764,0.0016190722,0.00092379126],"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":[0.001380003,0.00070576754,0.009653462,0.0005011426,0.00022322377,0.00064354733,0.0017528875,0.36715534,0.049277414,0.22748336,0.021677239,0.31954664],"study_design_scores_gemma":[0.00011059937,0.00022751941,0.0007843909,0.00002465846,0.00006184032,0.00027499913,0.00035768512,0.8662712,0.021985216,0.08471089,0.025123794,0.00006726738],"about_ca_topic_score_codex":0.004732996,"about_ca_topic_score_gemma":0.0067614713,"teacher_disagreement_score":0.004732996,"about_ca_system_score_codex":0.0008843692,"about_ca_system_score_gemma":0.0026838814,"threshold_uncertainty_score":0.021949053},"labels":[],"label_agreement":null},{"id":"W2042149992","doi":"10.5194/isprsarchives-xxxix-b4-421-2012","title":"A HYBRID PULL-PUSH SYSTEM FOR NEAR REAL-TIME NOTIFICATIONS ON SENSOR WEB","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Canarie; Microsoft Research","keywords":"Sensor web; Computer science; Web service; Publication; Web mapping; Wireless sensor network; Geospatial analysis; World Wide Web; Data Web; Database; Computer network; Key distribution in wireless sensor networks; Telecommunications; Wireless; Remote sensing","score_opus":0.019670595106740154,"score_gpt":0.2541133945000572,"score_spread":0.23444279939331705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042149992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16284122,0.0012376135,0.7158057,0.0011107457,0.00094808254,0.0014957177,0.0008478324,0.1051866,0.010526474],"genre_scores_gemma":[0.8198643,0.000437003,0.15828769,0.0009442533,0.00031154283,0.00070169405,0.0018427803,0.0012820846,0.0163287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983537,0.00024906418,0.00023951699,0.00032784304,0.0006580975,0.00017167092],"domain_scores_gemma":[0.9970438,0.0006220734,0.00016309766,0.00072174164,0.0008956046,0.0005537919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002192971,0.0010302529,0.0014839852,0.001678356,0.0015014004,0.002264128,0.0024232848,0.001794428,0.0054617682],"category_scores_gemma":[0.0030260908,0.00064714655,0.00076530356,0.0007804028,0.00041218096,0.003546158,0.0031184263,0.0013104491,0.002818545],"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":[0.0065277447,0.0031896106,0.026255777,0.0017975882,0.000699382,0.00777199,0.0030055942,0.011630744,0.36886597,0.010828452,0.05508812,0.504339],"study_design_scores_gemma":[0.0007505678,0.0013443396,0.010156824,0.0001638339,0.00056856836,0.0025497267,0.0009723947,0.71078753,0.20093405,0.0070814868,0.06414425,0.0005464259],"about_ca_topic_score_codex":0.0016269634,"about_ca_topic_score_gemma":0.0011730649,"teacher_disagreement_score":0.0054617682,"about_ca_system_score_codex":0.00041084737,"about_ca_system_score_gemma":0.00096804654,"threshold_uncertainty_score":0.018271506},"labels":[],"label_agreement":null},{"id":"W2042301468","doi":"10.1007/s11004-008-9192-2","title":"Collocated Cokriging Based on Merged Secondary Attributes","year":2008,"lang":"en","type":"article","venue":"Mathematical Geosciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"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":"University of Alberta","keywords":"Hydrogeology; Geostatistics; Geology; Statistics; Computer science; Mathematics; Spatial variability; Geotechnical engineering","score_opus":0.03331157812737271,"score_gpt":0.24369631197313998,"score_spread":0.21038473384576728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042301468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06631691,0.00014938803,0.93090713,0.000037993825,0.00007508378,0.000056589633,0.0003589024,0.0008277566,0.0012702063],"genre_scores_gemma":[0.37771952,0.00013748513,0.61887085,0.000023107212,0.00003056261,0.00009632745,0.0008316835,0.00014270474,0.002147725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991929,0.00015729584,0.000041847085,0.00019449135,0.00032398646,0.00008951249],"domain_scores_gemma":[0.99806005,0.0005582723,0.00010900434,0.00055978366,0.0006452368,0.00006770945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000633949,0.00068275596,0.0013215271,0.0018888633,0.000741271,0.0010781572,0.0011795493,0.00082360307,0.0022975665],"category_scores_gemma":[0.003942908,0.00059251423,0.00094657054,0.0042937943,0.00053007127,0.0014534399,0.0011921874,0.00076748437,0.0011919707],"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":[0.00081061356,0.00021221332,0.0065839,0.0002461442,0.00021025757,0.00020606864,0.0005632674,0.49508533,0.044248495,0.009131701,0.0035745285,0.4391275],"study_design_scores_gemma":[0.00001677634,0.000035295503,0.0015621046,0.0000077157365,0.000027216178,0.00005310922,0.00004442061,0.99008054,0.0048917695,0.0019361053,0.0013197472,0.000025162664],"about_ca_topic_score_codex":0.024861483,"about_ca_topic_score_gemma":0.055898726,"teacher_disagreement_score":0.024861483,"about_ca_system_score_codex":0.0005323277,"about_ca_system_score_gemma":0.0017669407,"threshold_uncertainty_score":0.04943353},"labels":[],"label_agreement":null},{"id":"W2042914194","doi":"10.1080/10095020.2012.708150","title":"Abstraction of informed virtual geographic environments","year":2012,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Abstraction; Situated; Geographic information system; Representation (politics); Graph; Theoretical computer science; Human–computer interaction; Data science; Artificial intelligence; Geography; Cartography","score_opus":0.0129804264962671,"score_gpt":0.24330398587524432,"score_spread":0.23032355937897722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042914194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006249321,0.000054047607,0.99108994,0.00007224889,0.000022062255,0.000039590825,0.00012928688,0.0006495199,0.0016939935],"genre_scores_gemma":[0.24145965,0.00021629047,0.7543865,0.00008403324,0.000017697099,0.00015161569,0.0010287818,0.00027835966,0.002377026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998228,0.0006657,0.000106633765,0.00027080358,0.0005901813,0.00013859563],"domain_scores_gemma":[0.99763834,0.0007070567,0.00022774767,0.0010246852,0.00027202695,0.0001302166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012671533,0.0008122656,0.0006455474,0.0015042846,0.0008569901,0.0030301951,0.0016760948,0.00074415415,0.0028919987],"category_scores_gemma":[0.005202251,0.00064746797,0.0013500146,0.0016032195,0.0013018028,0.0041581467,0.00667725,0.0014766129,0.0006085763],"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":[0.00015366309,0.00005837958,0.0021719926,0.0002046366,0.00010944043,0.0006234161,0.0019614703,0.3589385,0.0058221514,0.49708784,0.0055395053,0.12732913],"study_design_scores_gemma":[0.000034994613,0.000045637225,0.0004021819,0.000055930082,0.00005292794,0.00023510317,0.00047163814,0.659225,0.0058498764,0.2855283,0.048045445,0.000052878146],"about_ca_topic_score_codex":0.0040856632,"about_ca_topic_score_gemma":0.0052677663,"teacher_disagreement_score":0.0040856632,"about_ca_system_score_codex":0.0007713028,"about_ca_system_score_gemma":0.0011640071,"threshold_uncertainty_score":0.009674668},"labels":[],"label_agreement":null},{"id":"W2043316303","doi":"10.1109/cjece.2006.259180","title":"Comparing nearest-neighbour search strategies in the SMOTE algorithm","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; k-nearest neighbors algorithm; Nearest neighbour; Algorithm; Nearest-neighbor chain algorithm; Nearest neighbor search; Artificial intelligence; Pattern recognition (psychology); Cluster analysis","score_opus":0.009873257376333613,"score_gpt":0.19071500132972305,"score_spread":0.18084174395338942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043316303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4444569,0.0030679186,0.5384979,0.0005319128,0.00030069132,0.00028780242,0.00019483885,0.00064063654,0.012021472],"genre_scores_gemma":[0.70382655,0.00056180573,0.29172686,0.00018654748,0.000067397625,0.00022029328,0.00039020387,0.00018970431,0.0028307415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975594,0.0011575504,0.00013327421,0.00017119083,0.0008040297,0.00017451798],"domain_scores_gemma":[0.99259794,0.0053847153,0.00017227842,0.00036711496,0.0013272201,0.0001507094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041295593,0.0005617807,0.0017338366,0.002220223,0.00094474957,0.0012125734,0.0016178773,0.0021795316,0.0019824519],"category_scores_gemma":[0.015673865,0.00045789327,0.0008320168,0.0016962383,0.0006390708,0.0022098992,0.0014551136,0.0009387751,0.00060110673],"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":[0.002091965,0.00074995373,0.0054422356,0.00028962543,0.00028983818,0.00005622645,0.00021017194,0.6793353,0.0026962364,0.015963392,0.0025168771,0.29035828],"study_design_scores_gemma":[0.000073321964,0.00020148406,0.000678268,0.000015065243,0.000029382973,0.000020224496,0.00006537768,0.9942684,0.0008122891,0.003319081,0.00050291,0.000014094071],"about_ca_topic_score_codex":0.00719437,"about_ca_topic_score_gemma":0.01292958,"teacher_disagreement_score":0.00719437,"about_ca_system_score_codex":0.0009052925,"about_ca_system_score_gemma":0.0014085579,"threshold_uncertainty_score":0.02183944},"labels":[],"label_agreement":null},{"id":"W204411587","doi":"10.1007/978-3-642-40148-0_12","title":"Distributed B-Tree with Weak Consistency","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Consistency (knowledge bases); Tree (set theory); Search engine indexing; B-tree; Cloud computing; Tree structure; R-tree; Data consistency; Distributed computing; Theoretical computer science; Data mining; Algorithm; Binary tree; Information retrieval; Mathematics; Artificial intelligence","score_opus":0.013474698375992309,"score_gpt":0.21483600062383063,"score_spread":0.20136130224783833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W204411587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023700278,0.0010145251,0.96039987,0.000909708,0.00032173118,0.0001292842,0.00033987255,0.0019728886,0.01121169],"genre_scores_gemma":[0.36545503,0.0008136067,0.6151876,0.0005795287,0.00037387683,0.00032617734,0.0011307192,0.00083939015,0.015294082],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984648,0.00036723213,0.00013246367,0.00024505664,0.000638713,0.00015174424],"domain_scores_gemma":[0.9953147,0.0017870569,0.00017580253,0.0017842378,0.00072444725,0.00021369176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002092941,0.0004411427,0.0013059916,0.0008698845,0.0011298265,0.0019090044,0.0022759736,0.0012776058,0.0050521092],"category_scores_gemma":[0.008457703,0.0005789139,0.00055193494,0.0024921016,0.0010685882,0.0038748777,0.0033148606,0.0023650604,0.0016804849],"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":[0.0014538373,0.00026947955,0.0011705944,0.0007038824,0.000107805194,0.00029116936,0.00031413426,0.052237764,0.015074298,0.46122032,0.034222744,0.43293387],"study_design_scores_gemma":[0.00026666684,0.00015798675,0.00027860134,0.00005834599,0.00007149839,0.00036269886,0.00006459563,0.24838978,0.007737617,0.72210383,0.02047809,0.000030366507],"about_ca_topic_score_codex":0.0005907442,"about_ca_topic_score_gemma":0.000662982,"teacher_disagreement_score":0.0050521092,"about_ca_system_score_codex":0.0007717965,"about_ca_system_score_gemma":0.0012524269,"threshold_uncertainty_score":0.016901016},"labels":[],"label_agreement":null},{"id":"W2044762750","doi":"10.1007/s11390-010-9335-z","title":"Towards Progressive and Load Balancing Distributed Computation: A Case Study on Skyline Analysis","year":2010,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","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":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Skyline; Computer science; Load balancing (electrical power); Scalability; Distributed computing; Computation; Theory of computation; Distributed database; Load management; Data mining; Database; Algorithm","score_opus":0.009547070416137317,"score_gpt":0.2833963986359273,"score_spread":0.27384932821978997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044762750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41765285,0.0008907462,0.5558274,0.0029383765,0.00008277965,0.00026998602,0.00029164428,0.0021435102,0.019902768],"genre_scores_gemma":[0.6816078,0.00047563127,0.31176528,0.00012815831,0.000069643385,0.000053712054,0.000301263,0.00043077595,0.005167638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979996,0.00072119996,0.00009203905,0.0002625056,0.0007222868,0.00020225983],"domain_scores_gemma":[0.9925431,0.004073742,0.00031489116,0.0014308642,0.0012023723,0.00043503474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031354972,0.0006043332,0.00059274724,0.0011611914,0.0024890727,0.00280502,0.0020117317,0.0017166653,0.002563614],"category_scores_gemma":[0.012092459,0.0003516804,0.000592956,0.004005019,0.0020436447,0.003430507,0.0016771746,0.0015042811,0.000491016],"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":[0.0009243846,0.00075130723,0.018669914,0.0008437034,0.0001622177,0.0025236527,0.008725371,0.35019284,0.02423296,0.23051652,0.019615842,0.34284124],"study_design_scores_gemma":[0.0001479988,0.00015477752,0.0037693884,0.00006942745,0.000074200056,0.0010437301,0.0028939878,0.8301032,0.013363873,0.10996288,0.038373485,0.00004308079],"about_ca_topic_score_codex":0.010825074,"about_ca_topic_score_gemma":0.014354182,"teacher_disagreement_score":0.010825074,"about_ca_system_score_codex":0.001538156,"about_ca_system_score_gemma":0.0018682247,"threshold_uncertainty_score":0.021524131},"labels":[],"label_agreement":null},{"id":"W2046087084","doi":"10.3166/isi.13.5.131-154","title":"La notion de division tolérante et son intérêt pour remédier aux réponses vides","year":2008,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"INT; Philosophy; Humanities; Computer science","score_opus":0.03743520473531755,"score_gpt":0.2717675839899965,"score_spread":0.23433237925467895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046087084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027271083,0.005138228,0.9408976,0.0050135395,0.00069940934,0.0001302351,0.000101989484,0.00030770942,0.02044026],"genre_scores_gemma":[0.5947132,0.0041290033,0.38117772,0.0018400246,0.0018699671,0.00035868937,0.00021958571,0.0002162065,0.015475635],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98431814,0.0042965515,0.0010821077,0.0030135687,0.0063675954,0.000922097],"domain_scores_gemma":[0.9790014,0.011809111,0.0022417149,0.0041377842,0.0019831564,0.0008268398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011044162,0.0011999198,0.0015739774,0.0026084075,0.0025312451,0.0059546386,0.003363133,0.004028831,0.0046424298],"category_scores_gemma":[0.026949443,0.00066022406,0.002068222,0.0026322522,0.012228926,0.01594342,0.005129252,0.0074221906,0.0011564267],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020523119,0.00005026846,0.0005866918,0.00027730042,0.00005718664,0.0003667826,0.0013138448,0.008457415,0.005254562,0.9235969,0.0016704707,0.0581633],"study_design_scores_gemma":[0.00007084944,0.0004546543,0.00075254857,0.00024848414,0.000106765336,0.001866871,0.0010171324,0.068351425,0.009053926,0.8533697,0.06456388,0.00014382803],"about_ca_topic_score_codex":0.0010563699,"about_ca_topic_score_gemma":0.0007245526,"teacher_disagreement_score":0.011044162,"about_ca_system_score_codex":0.0023555087,"about_ca_system_score_gemma":0.0016019364,"threshold_uncertainty_score":0.058407784},"labels":[],"label_agreement":null},{"id":"W2046470653","doi":"10.1109/itaic.2014.7065000","title":"Using data mining techniques to improve location based services","year":2014,"lang":"en","type":"article","venue":"","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":"York University","funders":"","keywords":"Computer science; Data mining; Process (computing); Location-based service; Service (business); Location data; Quality (philosophy); Data deduplication; Database","score_opus":0.04931969447216452,"score_gpt":0.30372076210289795,"score_spread":0.25440106763073345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046470653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057611868,0.0026819871,0.9272109,0.0018312951,0.0001986172,0.0003979391,0.0023641067,0.0048046215,0.002898613],"genre_scores_gemma":[0.24875824,0.0019780626,0.74387366,0.00023135699,0.000092158305,0.00025578067,0.0037110273,0.00012131531,0.0009783686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685305,0.00064562407,0.00056781335,0.00052285776,0.0012635583,0.00014709815],"domain_scores_gemma":[0.99266595,0.00312887,0.0008162246,0.0011456342,0.0021082289,0.00013501283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033085004,0.0016477525,0.001589878,0.007908354,0.0009972351,0.0027043433,0.0020048001,0.0013267796,0.0010074636],"category_scores_gemma":[0.014579968,0.0006718673,0.0017214458,0.007941881,0.0004154514,0.0033105623,0.0014653682,0.0015730163,0.0011917085],"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":[0.0002924392,0.00083445886,0.037106846,0.0009006,0.00052451063,0.0007283203,0.00059022976,0.08148947,0.012462557,0.005887645,0.0062690033,0.85291386],"study_design_scores_gemma":[0.000057628127,0.00023806914,0.0077504353,0.00022521886,0.0002637614,0.0006515675,0.0008082953,0.9210724,0.027294276,0.019188363,0.02237061,0.00007937701],"about_ca_topic_score_codex":0.005632578,"about_ca_topic_score_gemma":0.0061590425,"teacher_disagreement_score":0.007908354,"about_ca_system_score_codex":0.00081291253,"about_ca_system_score_gemma":0.0015425899,"threshold_uncertainty_score":0.017497241},"labels":[],"label_agreement":null},{"id":"W2046883796","doi":"10.1016/j.jspi.2003.11.010","title":"Calibrated random imputation for qualitative data","year":2004,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Mathematics; Imputation (statistics); Statistics; Econometrics; Missing data","score_opus":0.09948713495637404,"score_gpt":0.40970737181317,"score_spread":0.310220236856796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046883796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002705417,0.00024263498,0.99507445,0.0004072224,0.00006264338,0.000088607776,0.00036959242,0.0003955368,0.0006538677],"genre_scores_gemma":[0.27500725,0.00053376984,0.7172614,0.00051253405,0.00014108048,0.0012187232,0.0027368448,0.00033119298,0.0022571736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9451732,0.044672325,0.0020151879,0.0043930244,0.002958653,0.0007875841],"domain_scores_gemma":[0.7640462,0.17759086,0.008402776,0.04165396,0.0074289856,0.00087720493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.054819737,0.0010159381,0.0028650817,0.003158408,0.0015505331,0.0035857963,0.00586523,0.003283671,0.009101721],"category_scores_gemma":[0.2866325,0.0019592058,0.0024940039,0.005918226,0.0031360683,0.0048141414,0.004369927,0.0055917543,0.0018805509],"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":[0.0006970561,0.000210225,0.013151704,0.0009338804,0.0011333377,0.00046715286,0.000991719,0.29817203,0.0004298745,0.4093843,0.016875973,0.25755274],"study_design_scores_gemma":[0.00011853342,0.000054091786,0.0014171893,0.0002473072,0.00014257077,0.0001593784,0.00011268698,0.38314855,0.0007581766,0.60939604,0.004388994,0.0000564555],"about_ca_topic_score_codex":0.003956345,"about_ca_topic_score_gemma":0.0039354595,"teacher_disagreement_score":0.054819737,"about_ca_system_score_codex":0.0025231782,"about_ca_system_score_gemma":0.004014417,"threshold_uncertainty_score":0.28991795},"labels":[],"label_agreement":null},{"id":"W2047152207","doi":"10.3390/ijgi2010001","title":"A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property","year":2013,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Canarie; Microsoft Research","keywords":"Computer science; Geospatial analysis; Data mining; Cluster analysis; Information retrieval; Sensor web; Set (abstract data type); Data set; Matching (statistics); Database; Artificial intelligence; Geography; Remote sensing; Mathematics","score_opus":0.04152532853080417,"score_gpt":0.25468914909149054,"score_spread":0.21316382056068636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047152207","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013828729,0.00016122562,0.97960985,0.00011471139,0.00005042887,0.00040802822,0.0004975998,0.004064815,0.001264528],"genre_scores_gemma":[0.07179271,0.00007024723,0.9253785,0.00006300525,0.000017129176,0.00018745461,0.0012948124,0.00016191562,0.0010342187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684024,0.00037292077,0.00034280564,0.000754959,0.0014862948,0.00020280843],"domain_scores_gemma":[0.99708694,0.0007011218,0.0003472708,0.00058977737,0.0011301896,0.000144748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012914907,0.0013244408,0.0013103549,0.0072669187,0.0016498538,0.0026696573,0.0019093106,0.0014419059,0.002560386],"category_scores_gemma":[0.006493894,0.00059182313,0.0016692526,0.0062029497,0.0009062259,0.0024204566,0.0024421238,0.0010776997,0.001765572],"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":[0.00029949137,0.0004803495,0.010286572,0.00043835683,0.0002439152,0.0005225255,0.00097146415,0.022239687,0.069592014,0.009738709,0.0072093084,0.87797767],"study_design_scores_gemma":[0.0000748508,0.00037658098,0.012918976,0.000130907,0.0002456085,0.0010854276,0.0014394178,0.82521814,0.08490084,0.040554043,0.032894436,0.00016076138],"about_ca_topic_score_codex":0.0057576858,"about_ca_topic_score_gemma":0.0086658085,"teacher_disagreement_score":0.0072669187,"about_ca_system_score_codex":0.00090274727,"about_ca_system_score_gemma":0.0017030095,"threshold_uncertainty_score":0.011448324},"labels":[],"label_agreement":null},{"id":"W2047363895","doi":"10.1145/512161.512167","title":"A map generalization model based on algebra mapping transformation","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universiteit Delft; McMaster University","keywords":"Cartographic generalization; Generalization; Transformation (genetics); Intersection (aeronautics); Computer science; Orientation (vector space); Abstraction; Spatial relationship; Mathematics; Artificial intelligence; Topology (electrical circuits); Geography; Cartography; Geometry; Combinatorics","score_opus":0.024899200540549053,"score_gpt":0.23033611384545158,"score_spread":0.20543691330490252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047363895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068929093,0.00012698153,0.9737332,0.00034542647,0.00006136889,0.000057502926,0.000113332986,0.00074275385,0.017926574],"genre_scores_gemma":[0.4466189,0.0008263735,0.5306487,0.00029913217,0.00017858131,0.0003489497,0.0008198993,0.00027705322,0.019982493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990717,0.00022655913,0.0000670273,0.00023596038,0.00032008177,0.00007854684],"domain_scores_gemma":[0.9993808,0.0001433343,0.000057273635,0.00021532709,0.00014844377,0.000054755903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010904127,0.0004179898,0.0003904021,0.0010791256,0.0011017228,0.0018831419,0.0012041338,0.0006171989,0.0059108445],"category_scores_gemma":[0.0020352984,0.00022683782,0.0011498196,0.0013013093,0.001668895,0.0082199685,0.0018029013,0.0011550192,0.0012056322],"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":[0.000023181585,0.00002318028,0.00034371155,0.000036909605,0.000012163444,0.00016463487,0.00042209277,0.016874762,0.0013205919,0.94662774,0.0021925226,0.0319586],"study_design_scores_gemma":[0.000021406297,0.000071342,0.00031241216,0.000034298562,0.000040152197,0.00051225896,0.00030003782,0.27717507,0.0028842418,0.6465074,0.072101265,0.00004007266],"about_ca_topic_score_codex":0.0050253277,"about_ca_topic_score_gemma":0.0026350894,"teacher_disagreement_score":0.0059108445,"about_ca_system_score_codex":0.0008806465,"about_ca_system_score_gemma":0.0012467009,"threshold_uncertainty_score":0.019773722},"labels":[],"label_agreement":null},{"id":"W2049086388","doi":"10.1038/nmeth.2337","title":"Axes, ticks and grids","year":2013,"lang":"en","type":"article","venue":"Nature Methods","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Computer science; Biology; Computational biology; Human–computer interaction","score_opus":0.010128289303103255,"score_gpt":0.33023298427380393,"score_spread":0.32010469497070065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049086388","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016647883,0.012699554,0.9123811,0.0048696063,0.0030344252,0.00008198271,0.0007162709,0.0021143176,0.0474549],"genre_scores_gemma":[0.41130134,0.012896019,0.5008926,0.0019535574,0.0034699119,0.0005767676,0.0017379252,0.0020372488,0.06513463],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982889,0.0006468593,0.00012937261,0.00037056557,0.0004118564,0.0001524187],"domain_scores_gemma":[0.99691427,0.0013790128,0.00033102618,0.0007873956,0.00033561056,0.00025274884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019146294,0.00085740996,0.0011312868,0.0018260516,0.0016865362,0.005786819,0.0010963489,0.001408417,0.01405407],"category_scores_gemma":[0.011603529,0.00071425125,0.0007449204,0.003780223,0.004794703,0.008197698,0.002993448,0.0031914497,0.0032352912],"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":[0.00009900503,0.000010681343,0.00047860533,0.000114976945,0.000013303736,0.00002614224,0.00022316768,0.0048379716,0.00037263066,0.94777834,0.010259377,0.035785828],"study_design_scores_gemma":[0.000029883247,0.000014485476,0.00018111723,0.00003694909,0.000009240594,0.000076923745,0.00013826454,0.009806846,0.00030571304,0.940386,0.048998207,0.000016400112],"about_ca_topic_score_codex":0.002670231,"about_ca_topic_score_gemma":0.0020206922,"teacher_disagreement_score":0.01405407,"about_ca_system_score_codex":0.0007624083,"about_ca_system_score_gemma":0.0010701846,"threshold_uncertainty_score":0.04701549},"labels":[],"label_agreement":null},{"id":"W2049619678","doi":"10.1007/s10115-012-0486-9","title":"Skyline distance: a measure of multidimensional competence","year":2012,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Scalability; Measure (data warehouse); Data mining; sort; Point (geometry); Theoretical computer science; Information retrieval; Database; Mathematics","score_opus":0.01418241925939989,"score_gpt":0.23315883989896102,"score_spread":0.21897642063956113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049619678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23811528,0.001372171,0.73383844,0.0010351879,0.00011741888,0.0001653607,0.0027953738,0.0005358832,0.022024866],"genre_scores_gemma":[0.78090084,0.00067591085,0.2120197,0.00013623708,0.000121369776,0.00024041942,0.0027505392,0.00010289111,0.0030520698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959984,0.0012208062,0.0004407036,0.0006641467,0.0014519225,0.00022396885],"domain_scores_gemma":[0.9871395,0.0056096343,0.002072598,0.0014240608,0.002633033,0.0011211337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025956044,0.0005334052,0.0006600671,0.006403836,0.0009108819,0.0029777396,0.00092489395,0.0009894044,0.004071974],"category_scores_gemma":[0.023727791,0.00021102285,0.00078756985,0.005286068,0.0015551207,0.006757742,0.0028622241,0.001041017,0.00087935687],"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":[0.00084296527,0.00031131605,0.12952425,0.0010448877,0.0006524926,0.0003969467,0.0059788874,0.035674486,0.014756851,0.4361924,0.013689452,0.36093503],"study_design_scores_gemma":[0.00011455277,0.0008391589,0.1518329,0.0003528309,0.0001933929,0.0016026156,0.00529543,0.19074716,0.010449226,0.58685046,0.05140413,0.00031824858],"about_ca_topic_score_codex":0.001711581,"about_ca_topic_score_gemma":0.0014341426,"teacher_disagreement_score":0.006403836,"about_ca_system_score_codex":0.00094522047,"about_ca_system_score_gemma":0.0009461846,"threshold_uncertainty_score":0.013727009},"labels":[],"label_agreement":null},{"id":"W2050175694","doi":"10.1007/s10115-012-0527-4","title":"A new approach for maximizing bichromatic reverse nearest neighbor search","year":2012,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"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":"National Natural Science Foundation of China; National Science Foundation","keywords":"k-nearest neighbors algorithm; Best bin first; Nearest neighbor search; Large margin nearest neighbor; Nearest-neighbor chain algorithm; Computer science; Nearest neighbor graph; Metric space; Metric (unit); Fixed-radius near neighbors; Algorithm; Space (punctuation); Mathematics; Data mining; Artificial intelligence; Discrete mathematics; Cluster analysis","score_opus":0.036402218891202674,"score_gpt":0.26517865462145235,"score_spread":0.22877643573024967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050175694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002720377,0.00021653231,0.9951918,0.00008013338,0.00004214847,0.000037217964,0.00003434721,0.00019669217,0.0014807645],"genre_scores_gemma":[0.07147072,0.00029010812,0.9240741,0.00016719259,0.00009664541,0.00014150774,0.00018216851,0.00020523385,0.0033722285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969015,0.0010045654,0.00017591832,0.00047865388,0.0012548921,0.00018449342],"domain_scores_gemma":[0.99732155,0.0008063291,0.00017549533,0.0006057321,0.0009966777,0.00009427691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029786457,0.0011767829,0.0018417457,0.0020901016,0.00097108324,0.001483731,0.0037042196,0.0017188698,0.0035898944],"category_scores_gemma":[0.009467943,0.0007315173,0.0008486923,0.0026133286,0.000979381,0.0030607705,0.0031545102,0.0016334779,0.0013955488],"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":[0.00040424487,0.00027632262,0.0015816799,0.00041394337,0.00014999221,0.00011609406,0.00023405996,0.2356356,0.01911618,0.13192321,0.011843917,0.5983048],"study_design_scores_gemma":[0.000025938487,0.00006748514,0.00027673866,0.000023451084,0.000035418427,0.00016924457,0.000033998607,0.9572448,0.004143518,0.032692596,0.005260457,0.000026461983],"about_ca_topic_score_codex":0.0028101932,"about_ca_topic_score_gemma":0.0055102487,"teacher_disagreement_score":0.0037042196,"about_ca_system_score_codex":0.0009600619,"about_ca_system_score_gemma":0.0015879438,"threshold_uncertainty_score":0.015752792},"labels":[],"label_agreement":null},{"id":"W2050599442","doi":"10.1023/b:gein.0000034819.57376.92","title":"Development of a Temporal Extension to Query Travel Behavior Time Paths Using an Object-Oriented GIS","year":2004,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","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":"Université Laval; Université de Montréal","funders":"","keywords":"Computer science; Chaining; Temporal database; Set (abstract data type); Extension (predicate logic); Operator (biology); Data mining; Path (computing); Data model (GIS); Object (grammar); Event (particle physics); Spatiotemporal database; Spatial query; Query language; Information retrieval; Database; Database design; View; Artificial intelligence; Web query classification; Web search query; Programming language; Search engine","score_opus":0.02474059615807743,"score_gpt":0.25684331491594886,"score_spread":0.23210271875787142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050599442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010304461,0.00007161358,0.97361135,0.00009480194,0.000030421952,0.00015093829,0.00044714884,0.01410469,0.0011845457],"genre_scores_gemma":[0.09473201,0.00014526775,0.9012968,0.00009581076,0.000020461579,0.00016585352,0.0013483808,0.00068281835,0.0015125095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918324,0.000074220625,0.00011975043,0.00014960398,0.0004124845,0.000060706134],"domain_scores_gemma":[0.99864084,0.0004347862,0.00010965412,0.00026740588,0.00046578582,0.00008148817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014771108,0.0005066446,0.0006668005,0.0012178667,0.0005641556,0.0014279038,0.0016921205,0.0005839468,0.0022781054],"category_scores_gemma":[0.0032611287,0.0006114472,0.0011442973,0.0013198892,0.00046112426,0.0018213348,0.001101578,0.0008505285,0.00081018003],"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":[0.00077972433,0.000641489,0.019717341,0.0011396866,0.0005641384,0.0014198258,0.0018768872,0.1385811,0.124257535,0.070287585,0.02375159,0.6169832],"study_design_scores_gemma":[0.00007638261,0.00013889435,0.00201088,0.00006499091,0.00019981875,0.00040196662,0.00034167228,0.89217854,0.044479378,0.009289148,0.05075178,0.000066491535],"about_ca_topic_score_codex":0.013608516,"about_ca_topic_score_gemma":0.015074419,"teacher_disagreement_score":0.013608516,"about_ca_system_score_codex":0.00054658897,"about_ca_system_score_gemma":0.0012522325,"threshold_uncertainty_score":0.027058601},"labels":[],"label_agreement":null},{"id":"W2051275384","doi":"10.1145/2187836.2187902","title":"A flexible generative model for preference aggregation","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Benchmark (surveying); Pairwise comparison; Inference; Preference; Ranking (information retrieval); Variety (cybernetics); Machine learning; Generative model; Collaborative filtering; Artificial intelligence; Aggregation problem; Probabilistic logic; Preference learning; Face (sociological concept); Bayesian inference; Recommender system; Data mining; Generative grammar; Bayesian probability; Mathematics","score_opus":0.11944296328508364,"score_gpt":0.2918070294794631,"score_spread":0.17236406619437944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051275384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009106753,0.00019214908,0.9851376,0.0006394045,0.000054094555,0.00005936922,0.00038929138,0.00032261605,0.004098752],"genre_scores_gemma":[0.66420126,0.00082909974,0.31197846,0.00096053147,0.0003136251,0.0007259743,0.0018992914,0.00040542116,0.018686365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966217,0.0015132258,0.00015124878,0.00070825085,0.00069273094,0.00031280651],"domain_scores_gemma":[0.993624,0.0038295938,0.00046960157,0.0012616424,0.0005516398,0.00026343996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003822918,0.0009534859,0.001730466,0.0016549637,0.0013781065,0.0026504153,0.0039646477,0.0025349269,0.008412659],"category_scores_gemma":[0.015979849,0.0014265663,0.002967028,0.002863325,0.0024607715,0.005191397,0.0027522657,0.0041312207,0.0026008897],"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":[0.00010284129,0.00007713049,0.0022307925,0.00009845553,0.00013155985,0.0004748621,0.0005326909,0.23710583,0.0013791833,0.7144721,0.005664066,0.03773043],"study_design_scores_gemma":[0.000027684682,0.000018106397,0.00030538786,0.000014561238,0.000026341811,0.00019419243,0.000033514283,0.66569686,0.00019885122,0.3312974,0.0021599864,0.000027131378],"about_ca_topic_score_codex":0.007882932,"about_ca_topic_score_gemma":0.012062635,"teacher_disagreement_score":0.008412659,"about_ca_system_score_codex":0.0020173928,"about_ca_system_score_gemma":0.0016257656,"threshold_uncertainty_score":0.028143167},"labels":[],"label_agreement":null},{"id":"W2051379876","doi":"10.1080/10824000409480666","title":"Personalized Multi-Criteria Decision Strategies in Location-Based Decision Support","year":2004,"lang":"en","type":"article","venue":"Annals of GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Decision support system; Space (punctuation); Outcome (game theory); Decision analysis; Decision model; Operator (biology); Data mining; Machine learning; Mathematics","score_opus":0.07183809637024421,"score_gpt":0.36353243949977015,"score_spread":0.2916943431295259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051379876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17011246,0.0007797364,0.8236016,0.0007727153,0.000053099644,0.00049558847,0.000104963765,0.0002031588,0.0038767199],"genre_scores_gemma":[0.7996766,0.00013354029,0.19906974,0.0000867779,0.00002816562,0.00027436393,0.000072543065,0.00002604713,0.0006323066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98321486,0.01294646,0.0006310148,0.0008467014,0.0018861057,0.00047483438],"domain_scores_gemma":[0.97879374,0.017430322,0.0010542399,0.0004700454,0.0016486659,0.0006029298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014581776,0.0015337697,0.0016488754,0.0020864448,0.00082395005,0.0026473815,0.0013661764,0.0013641397,0.0016781067],"category_scores_gemma":[0.023686813,0.0006384474,0.0007893332,0.001920014,0.0012542234,0.0021357527,0.0016491015,0.0013391962,0.00023144533],"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":[0.0005075383,0.0004599862,0.0027952516,0.00032061702,0.00031312753,0.00032147064,0.00067595276,0.87760824,0.002516234,0.026184365,0.0008670201,0.08743021],"study_design_scores_gemma":[0.000036996997,0.0001271051,0.00033156303,0.0000245082,0.000028364526,0.000025097794,0.000100242796,0.9800889,0.00071349496,0.018109329,0.00039618977,0.000018237333],"about_ca_topic_score_codex":0.00408786,"about_ca_topic_score_gemma":0.002929161,"teacher_disagreement_score":0.014581776,"about_ca_system_score_codex":0.0021921468,"about_ca_system_score_gemma":0.0013074502,"threshold_uncertainty_score":0.07711667},"labels":[],"label_agreement":null},{"id":"W2051586036","doi":"10.1109/iccsit.2009.5234622","title":"A Ranking Theory for Uncertain Data with Constraints","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Polyhedron; Object (grammar); Space (punctuation); Computation; Computer science; Theoretical computer science; Mathematics; Algorithm; Combinatorics; Artificial intelligence","score_opus":0.045467416384539494,"score_gpt":0.290167778197072,"score_spread":0.24470036181253252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051586036","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026321013,0.00043436524,0.9945069,0.00028635687,0.000044514432,0.000035878973,0.0001864485,0.0000967294,0.001776698],"genre_scores_gemma":[0.2195523,0.0019073245,0.769767,0.000619911,0.00066185225,0.00040315092,0.0012822837,0.00019289712,0.005613348],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99276966,0.0023633924,0.00053948356,0.00095143943,0.0028550806,0.000520874],"domain_scores_gemma":[0.9856036,0.00879146,0.0010334991,0.001614616,0.002457773,0.0004991662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060701217,0.0012539051,0.0027012585,0.005480327,0.0019833387,0.0063271224,0.0031184205,0.0018537554,0.0051163086],"category_scores_gemma":[0.019793585,0.00089195074,0.0022608333,0.0075648627,0.0031246063,0.009167426,0.003193424,0.0035764466,0.0016619263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004106963,0.000041835745,0.0008201547,0.00029142664,0.00011161733,0.00015538529,0.00021868871,0.14245962,0.0009123482,0.7844997,0.005169916,0.06527824],"study_design_scores_gemma":[0.0000111724785,0.00006934719,0.00030269724,0.000058169466,0.000030424326,0.00012849974,0.000076302575,0.3565284,0.00047869416,0.63545966,0.0068015493,0.000055066343],"about_ca_topic_score_codex":0.0035389236,"about_ca_topic_score_gemma":0.0032221014,"teacher_disagreement_score":0.0063271224,"about_ca_system_score_codex":0.0029086499,"about_ca_system_score_gemma":0.0020102924,"threshold_uncertainty_score":0.032102287},"labels":[],"label_agreement":null},{"id":"W2051894417","doi":"10.3166/geo.19.211-230","title":"Une architecture orientée service web pour la constitution de minicubes SOLAP pour clients mobiles","year":2009,"lang":"fr","type":"article","venue":"Revue internationale de géomatique","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science","score_opus":0.0182750614185143,"score_gpt":0.26783736690888854,"score_spread":0.24956230549037425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051894417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0127197495,0.00052171585,0.9706609,0.00062488543,0.00017359965,0.0002585547,0.00021505351,0.008728736,0.00609672],"genre_scores_gemma":[0.12374245,0.0013169924,0.84667504,0.00054078805,0.00022178025,0.0008365889,0.001609365,0.0013725489,0.023684485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980008,0.000417712,0.00018174916,0.00030932025,0.0008748462,0.0002155673],"domain_scores_gemma":[0.99804115,0.00043173385,0.000092064045,0.00045254445,0.0007318985,0.00025047737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002949968,0.00095624145,0.001345679,0.0013276688,0.0027831236,0.006258504,0.001937931,0.0028842979,0.005036417],"category_scores_gemma":[0.0037522807,0.0012454891,0.0014892381,0.0018346641,0.0015121383,0.0051994263,0.0024523444,0.003666958,0.0038443129],"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":[0.0011966081,0.0007223386,0.004529402,0.0008207532,0.00024452637,0.0022553147,0.0029798048,0.09501602,0.06800419,0.3393471,0.03484298,0.45004103],"study_design_scores_gemma":[0.0001743922,0.00024311473,0.0012783305,0.00015664246,0.00008847405,0.0012419217,0.00093455554,0.6407179,0.037243214,0.056106497,0.26165023,0.00016470482],"about_ca_topic_score_codex":0.018961163,"about_ca_topic_score_gemma":0.011144975,"teacher_disagreement_score":0.018961163,"about_ca_system_score_codex":0.0020122728,"about_ca_system_score_gemma":0.0038701794,"threshold_uncertainty_score":0.037701607},"labels":[],"label_agreement":null},{"id":"W2051942744","doi":"10.3166/isi.14.1.13-17","title":"Gestion de données de capteurs","year":2009,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scalability; Computer science; Wireless sensor network; Distributed computing; Stream processing; Domain (mathematical analysis); Real-time computing; Computer network; Database; Mathematics","score_opus":0.02480946168187863,"score_gpt":0.23639834988650693,"score_spread":0.2115888882046283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051942744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02510552,0.01210165,0.8097344,0.0082068555,0.0027705694,0.0015717975,0.046105247,0.016611159,0.077792756],"genre_scores_gemma":[0.15728976,0.013436001,0.6944574,0.0019724942,0.0015798014,0.00213478,0.07959738,0.004668802,0.044863608],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9821915,0.005566117,0.0016058868,0.0031106446,0.007009037,0.0005168886],"domain_scores_gemma":[0.9713717,0.01746684,0.0010540594,0.004721683,0.0049294457,0.00045614902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008387355,0.0031099927,0.0023074916,0.008882112,0.0023653496,0.011556276,0.002746935,0.0033646517,0.03248017],"category_scores_gemma":[0.06097131,0.0015364672,0.004497481,0.0072695366,0.0021080633,0.008251911,0.0051206113,0.0046057953,0.016708776],"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":[0.0017617849,0.00032369388,0.005787646,0.005348032,0.0010978488,0.003988159,0.005198058,0.03237528,0.009914318,0.111553654,0.17408706,0.64856446],"study_design_scores_gemma":[0.00031053845,0.00026488706,0.0042582084,0.0025519896,0.0004781158,0.0024702307,0.0031388707,0.097941846,0.01706257,0.10442722,0.76673615,0.0003594272],"about_ca_topic_score_codex":0.019516395,"about_ca_topic_score_gemma":0.014307129,"teacher_disagreement_score":0.03248017,"about_ca_system_score_codex":0.0022108553,"about_ca_system_score_gemma":0.003852638,"threshold_uncertainty_score":0.108657},"labels":[],"label_agreement":null},{"id":"W2052535241","doi":"10.1145/1317331.1317343","title":"Efficient computation of view subsets","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Concordia University; Carleton University","funders":"","keywords":"Computer science; Suite; Table (database); Schema (genetic algorithms); Dimension (graph theory); Computation; Identification (biology); Greedy algorithm; Theoretical computer science; Data mining; Algorithm; Information retrieval; Mathematics","score_opus":0.016935490035266242,"score_gpt":0.268427751283041,"score_spread":0.2514922612477748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052535241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15174167,0.001515029,0.8239106,0.0005546123,0.00013897159,0.00054280815,0.004999572,0.009875537,0.0067211115],"genre_scores_gemma":[0.29418314,0.00056594325,0.6852243,0.00013624736,0.00006821974,0.00028237235,0.016532257,0.0006641835,0.0023433054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791867,0.00040200778,0.00020376453,0.00045206363,0.0006998908,0.00032365383],"domain_scores_gemma":[0.9957164,0.0017779414,0.00027373308,0.0013213098,0.00069516274,0.00021540785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014032745,0.0015001647,0.0020266918,0.0024365832,0.0011586391,0.0033328198,0.00171819,0.0008708427,0.004395365],"category_scores_gemma":[0.007990247,0.00074495166,0.0016751622,0.003219822,0.0006546071,0.0040888195,0.0027203474,0.0011575012,0.001752919],"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":[0.00096218847,0.00030965474,0.010871019,0.0008251544,0.00022915938,0.00048333817,0.0010718992,0.082964234,0.028096521,0.028495478,0.037948105,0.8077432],"study_design_scores_gemma":[0.00018887388,0.0004385887,0.0035978423,0.0001284632,0.00024051203,0.0008760158,0.0013389543,0.82962346,0.03075668,0.10513877,0.027596762,0.00007509818],"about_ca_topic_score_codex":0.0025562977,"about_ca_topic_score_gemma":0.0053445813,"teacher_disagreement_score":0.004395365,"about_ca_system_score_codex":0.0008668503,"about_ca_system_score_gemma":0.0023272675,"threshold_uncertainty_score":0.014703989},"labels":[],"label_agreement":null},{"id":"W2053271839","doi":"10.1145/2451716.2451721","title":"Testing of sensor observation services","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sensor web; Server; Computer science; Web service; Geospatial analysis; Web Coverage Service; Web server; Service (business); Response time; Interface (matter); Database; World Wide Web; Operating system; Remote sensing; The Internet; Web application security; Web development; Wireless; Key distribution in wireless sensor networks","score_opus":0.05635409759770819,"score_gpt":0.25299471952412333,"score_spread":0.19664062192641515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053271839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9749289,0.00010447834,0.01760252,0.00021467269,0.00007237707,0.00036400472,0.0007801989,0.0035694484,0.002363327],"genre_scores_gemma":[0.98649615,0.000057895075,0.010324532,0.000112470174,0.000016040922,0.00021554902,0.0011170951,0.00032912908,0.0013311905],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.986067,0.002964402,0.001244458,0.0017600944,0.0063580014,0.0016060243],"domain_scores_gemma":[0.96352595,0.017327271,0.0019149175,0.0074478583,0.00836429,0.0014196684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054438864,0.0014331158,0.0008656812,0.0007338578,0.00082720217,0.0013543848,0.002974211,0.0015064025,0.003687762],"category_scores_gemma":[0.032204516,0.000636556,0.00060840865,0.0010078931,0.0011745845,0.002775374,0.0017923018,0.0012711725,0.001264089],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013393131,0.0075735995,0.19283648,0.002139418,0.0008446602,0.004437562,0.0050049378,0.18737808,0.36954272,0.011153681,0.015704794,0.18999091],"study_design_scores_gemma":[0.0005333939,0.0033849252,0.049420603,0.00007008739,0.00016421237,0.001036542,0.0018226777,0.5354303,0.39422587,0.002092432,0.011684348,0.00013456325],"about_ca_topic_score_codex":0.0066579445,"about_ca_topic_score_gemma":0.0026164127,"teacher_disagreement_score":0.0066579445,"about_ca_system_score_codex":0.0015765895,"about_ca_system_score_gemma":0.00186435,"threshold_uncertainty_score":0.028790355},"labels":[],"label_agreement":null},{"id":"W2053495332","doi":"10.1145/2487259.2487260","title":"Analysis and optimization for boolean expression indexing","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Regular expression; Theoretical computer science; Search engine indexing; Boolean expression; Tree (set theory); Matching (statistics); Search tree; Data structure; Tree structure; String searching algorithm; Data mining; Algorithm; Pattern matching; Boolean function; Binary tree; Search algorithm; Artificial intelligence; Mathematics","score_opus":0.020552758876912467,"score_gpt":0.24775622824136947,"score_spread":0.227203469364457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053495332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030712394,0.0013236859,0.95371044,0.00058461766,0.00009096297,0.00016407442,0.0006802697,0.0023304906,0.010403055],"genre_scores_gemma":[0.3858693,0.0011505982,0.60258126,0.0003091113,0.00015022229,0.000394422,0.0021345243,0.0007629616,0.006647552],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99615735,0.00070424593,0.00027502654,0.0003671852,0.0020358574,0.0004603751],"domain_scores_gemma":[0.9951615,0.0026894722,0.0003229961,0.00093716744,0.00078330684,0.000105482526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017930187,0.0007183676,0.001065706,0.0014823194,0.00075101183,0.0030088068,0.0019651477,0.00067935744,0.0049359775],"category_scores_gemma":[0.011212532,0.000394244,0.00097509404,0.004083353,0.0011632888,0.0056216894,0.0016661857,0.0014642633,0.0011391459],"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":[0.0005240041,0.0002933208,0.0031861453,0.000523604,0.00007034847,0.00012318243,0.00022885909,0.25457618,0.013799619,0.24985844,0.016724605,0.46009165],"study_design_scores_gemma":[0.000030232175,0.000087155444,0.0003147385,0.000023014301,0.000024287303,0.000077240846,0.000061566265,0.9092592,0.0061206357,0.07774493,0.0062395153,0.000017521454],"about_ca_topic_score_codex":0.0035463027,"about_ca_topic_score_gemma":0.005173323,"teacher_disagreement_score":0.0049359775,"about_ca_system_score_codex":0.0024029857,"about_ca_system_score_gemma":0.0032632498,"threshold_uncertainty_score":0.017435014},"labels":[],"label_agreement":null},{"id":"W2053777878","doi":"10.5555/2133036.2133059","title":"I/O-efficient contour queries on terrains","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Combinatorics; Set (abstract data type); Terrain; Mathematics; sort; Plane (geometry); Triangulation; Function (biology); Connected component; Contour line; Geometry; Computer science; Geography; Arithmetic; Cartography","score_opus":0.04463725236995688,"score_gpt":0.23084366078490784,"score_spread":0.18620640841495095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053777878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31043786,0.0022121556,0.60754925,0.0020935552,0.00017904368,0.0005921391,0.02313644,0.04106191,0.012737613],"genre_scores_gemma":[0.5970647,0.0007036881,0.36945578,0.00036552828,0.00019094552,0.0004628821,0.02522896,0.0018442705,0.004683253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966388,0.00036839338,0.000488893,0.0007858466,0.0012889839,0.00042916986],"domain_scores_gemma":[0.98862284,0.0036509687,0.0012373999,0.0051235855,0.00091702794,0.00044809608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012408249,0.0012051045,0.002044884,0.0024505393,0.0010501806,0.0033317842,0.003781536,0.0017108513,0.0059273704],"category_scores_gemma":[0.014835663,0.00086953427,0.00101296,0.0074936706,0.0011707636,0.009887723,0.0046130307,0.001293992,0.0021787537],"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":[0.005075096,0.00082309404,0.025212731,0.0012425043,0.00017719748,0.0008453595,0.0026262784,0.12951456,0.06751201,0.06287762,0.0951723,0.60892135],"study_design_scores_gemma":[0.0006898696,0.0006786989,0.0061125183,0.00015278182,0.00010983703,0.0008347499,0.0016268622,0.7558254,0.05636314,0.14165536,0.03584301,0.00010784076],"about_ca_topic_score_codex":0.0034578503,"about_ca_topic_score_gemma":0.0032077315,"teacher_disagreement_score":0.0059273704,"about_ca_system_score_codex":0.0011231567,"about_ca_system_score_gemma":0.0010304177,"threshold_uncertainty_score":0.019829094},"labels":[],"label_agreement":null},{"id":"W2054815985","doi":"10.5555/1639809.1639942","title":"Adaptive web navigation","year":2009,"lang":"en","type":"article","venue":"Spring Simulation Multiconference","topic":"Data Management and Algorithms","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Expansive; Task (project management); Field (mathematics); Preprocessor; Web application; Range (aeronautics); The Internet; Data mining; World Wide Web; Artificial intelligence; Engineering; Systems engineering","score_opus":0.03511111319707586,"score_gpt":0.28704466436199266,"score_spread":0.2519335511649168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054815985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011002653,0.0006061194,0.9772329,0.00015277142,0.00010261881,0.000103401166,0.00009111208,0.002702193,0.008006272],"genre_scores_gemma":[0.37217286,0.001694439,0.6054731,0.00028601746,0.00019458127,0.00021006864,0.00059825496,0.00050495623,0.018865725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872535,0.00029852957,0.000054345022,0.000371452,0.00046778627,0.00008259754],"domain_scores_gemma":[0.9976446,0.00082224706,0.000119953096,0.0007677309,0.0005392854,0.00010611244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009598584,0.0005868202,0.00078802963,0.0011253372,0.0007126847,0.001447284,0.0016303422,0.0010578467,0.005252203],"category_scores_gemma":[0.0047556194,0.000402561,0.00066288793,0.0014132452,0.00066808163,0.0022838742,0.0013943306,0.0010995384,0.0023654453],"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":[0.00021134134,0.00040227294,0.006888734,0.0002995432,0.00021196043,0.00022750185,0.0002421897,0.11332703,0.02489421,0.039670948,0.013036454,0.8005879],"study_design_scores_gemma":[0.000043633976,0.00015242511,0.0026272242,0.000045600635,0.00008554345,0.00062294904,0.0001214845,0.91193855,0.009953681,0.037640214,0.036704928,0.00006374327],"about_ca_topic_score_codex":0.003948258,"about_ca_topic_score_gemma":0.0053915055,"teacher_disagreement_score":0.005252203,"about_ca_system_score_codex":0.00046171367,"about_ca_system_score_gemma":0.000858577,"threshold_uncertainty_score":0.017570376},"labels":[],"label_agreement":null},{"id":"W2055028639","doi":"10.1145/1460797.1460801","title":"On domination game analysis for microeconomic data mining","year":2009,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National University of Singapore","keywords":"Nash equilibrium; Game theory; Dimension (graph theory); Product (mathematics); Commodity; Computer science; Point (geometry); Best response; Space (punctuation); Mathematical economics; Economics; Mathematics","score_opus":0.07051834428210217,"score_gpt":0.3199586946775136,"score_spread":0.2494403503954114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055028639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058465484,0.0007105552,0.98895746,0.00054526544,0.000058306538,0.00017899729,0.00014326611,0.00009317749,0.0034664092],"genre_scores_gemma":[0.380694,0.0029500714,0.60568845,0.0008449686,0.00044677078,0.0017647544,0.00063428783,0.00022739364,0.006749227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949528,0.003103144,0.00023418397,0.0005499937,0.0007952742,0.00036464413],"domain_scores_gemma":[0.97059757,0.026434833,0.00088560814,0.00078049104,0.00089348963,0.00040801562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008198864,0.002995659,0.0033913318,0.0041731503,0.001543905,0.0033031336,0.0029941038,0.0019516514,0.0052226395],"category_scores_gemma":[0.028091377,0.0012116439,0.0030329393,0.0055365195,0.004365378,0.0049340995,0.0036478324,0.0036906993,0.00071825867],"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":[0.00006142603,0.000112388596,0.0013417582,0.00021851127,0.00017205601,0.00016083641,0.00018746579,0.6798005,0.00052576134,0.29495156,0.0017992316,0.020668563],"study_design_scores_gemma":[0.000018712011,0.000015105315,0.00008055411,0.000016855778,0.000012827823,0.000020609514,0.000016735774,0.8109585,0.00009537335,0.1876908,0.0010612158,0.000012799057],"about_ca_topic_score_codex":0.010837673,"about_ca_topic_score_gemma":0.0067493008,"teacher_disagreement_score":0.010837673,"about_ca_system_score_codex":0.0043159705,"about_ca_system_score_gemma":0.0033180725,"threshold_uncertainty_score":0.043360233},"labels":[],"label_agreement":null},{"id":"W2055411343","doi":"10.1145/2424321.2424369","title":"Surveying the landscape","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Workload; Variety (cybernetics); Spatial analysis; Spatial database; Resource (disambiguation); Resource planning; Database; Degree (music); Resource allocation; Geography; Remote sensing; Artificial intelligence; Environmental resource management; Operating system","score_opus":0.02892337505620284,"score_gpt":0.24332186297707223,"score_spread":0.21439848792086938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055411343","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50022143,0.0027097408,0.3463668,0.003972117,0.00024522725,0.0002320273,0.007396805,0.003502073,0.13535386],"genre_scores_gemma":[0.89448315,0.0014530874,0.08212111,0.0003112091,0.00005500411,0.000050723123,0.003397912,0.00021259038,0.017915271],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999708,0.00006642016,0.000009562947,0.00009491727,0.000085624844,0.000035590987],"domain_scores_gemma":[0.9996364,0.000095356845,0.00003036259,0.00010526924,0.00008442132,0.00004815878],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020714823,0.0002441504,0.00021913639,0.00060571387,0.0003944801,0.0012353114,0.00041468441,0.00040462322,0.010363969],"category_scores_gemma":[0.0012093673,0.00014691422,0.0002384769,0.0013713947,0.00033257352,0.0012910727,0.00088716694,0.0002578328,0.0024310446],"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":[0.00032572006,0.00012391068,0.049183216,0.00061141746,0.00009953465,0.0009794997,0.0013794797,0.06671954,0.05912863,0.05802564,0.046329074,0.7170943],"study_design_scores_gemma":[0.000043829295,0.00030848716,0.095645465,0.00020655344,0.00006483465,0.0024580967,0.006091645,0.4278245,0.022801124,0.08589429,0.35856518,0.00009604161],"about_ca_topic_score_codex":0.006772528,"about_ca_topic_score_gemma":0.011485471,"teacher_disagreement_score":0.989636,"about_ca_system_score_codex":0.00036520022,"about_ca_system_score_gemma":0.00043715828,"threshold_uncertainty_score":0.03467095},"labels":[],"label_agreement":null},{"id":"W2055567224","doi":"10.3390/s140712990","title":"A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases","year":2014,"lang":"en","type":"article","venue":"Sensors","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Search engine indexing; Data mining; Tree (set theory); Hash table; Trajectory; Database; Access method; NoSQL; Hash function; Temporal database; Data structure; R-tree; Database index; Field (mathematics); Table (database); Spatial database; Big data; Information retrieval; Spatial analysis; Mathematics","score_opus":0.06935513009240445,"score_gpt":0.32216713151688803,"score_spread":0.25281200142448357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055567224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008090142,0.0008374264,0.98489016,0.00016090518,0.000089108675,0.00016483487,0.0011339025,0.0031281223,0.0015054366],"genre_scores_gemma":[0.13104816,0.0009406614,0.85944855,0.00013510039,0.00012050735,0.0003026026,0.0047922265,0.00028155593,0.0029306153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983505,0.00017551899,0.0002471478,0.00035726122,0.0007706348,0.000098960474],"domain_scores_gemma":[0.9982887,0.00026335812,0.0001422755,0.00059102575,0.0006002008,0.00011440676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010028105,0.0006083095,0.0010164285,0.0034725564,0.0011440581,0.001986074,0.0021895014,0.00067962555,0.0026184355],"category_scores_gemma":[0.0029343057,0.0003935019,0.0009722724,0.007849837,0.00041606554,0.0048457063,0.0019490496,0.0007173188,0.001459929],"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":[0.0005438757,0.00029067558,0.004222182,0.0005057272,0.00017513194,0.0002884713,0.00048691765,0.01975402,0.033681776,0.0402722,0.020488549,0.87929046],"study_design_scores_gemma":[0.00019440668,0.00041911306,0.0029910742,0.000099705794,0.000169326,0.0021134506,0.00065063545,0.8242602,0.046885714,0.036131408,0.08587642,0.00020855141],"about_ca_topic_score_codex":0.0064986977,"about_ca_topic_score_gemma":0.0047499156,"teacher_disagreement_score":0.0064986977,"about_ca_system_score_codex":0.00090189907,"about_ca_system_score_gemma":0.0017717219,"threshold_uncertainty_score":0.012921751},"labels":[],"label_agreement":null},{"id":"W2055575272","doi":"10.1016/j.jda.2008.09.011","title":"Counting distance permutations","year":2008,"lang":"en","type":"article","venue":"Journal of Discrete Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Aeronautics and Space Administration","keywords":"Permutation (music); Metric space; Vector space; Mathematics; String (physics); Point (geometry); Combinatorics; Tree (set theory); Metric (unit); Space (punctuation); Computer science; Discrete mathematics; Pure mathematics","score_opus":0.020306791770880833,"score_gpt":0.2551660287123915,"score_spread":0.2348592369415107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055575272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100649506,0.0016917669,0.70739335,0.0065109106,0.0014544509,0.00019987384,0.0011568202,0.0011593085,0.17978401],"genre_scores_gemma":[0.64970213,0.001762791,0.23396716,0.0020164526,0.0016159535,0.00046849975,0.0025040912,0.0010698652,0.10689302],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956145,0.0011006461,0.00029166724,0.0010579875,0.0015313958,0.000403771],"domain_scores_gemma":[0.98735446,0.0060215103,0.0007507202,0.003460033,0.0015249931,0.00088838453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020931081,0.0011691315,0.0019223061,0.0044535645,0.0037744686,0.006705991,0.0033229555,0.0028674463,0.021719921],"category_scores_gemma":[0.020611558,0.0010437882,0.0015755263,0.00648315,0.0035875582,0.013675592,0.0057033785,0.0062570777,0.0041243387],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055656492,0.0000354845,0.0005347132,0.000050293973,0.000014959385,0.000034421664,0.00009413389,0.0014696132,0.0004555307,0.9628034,0.0059059504,0.028545784],"study_design_scores_gemma":[0.000008612962,0.000011116617,0.00012557599,0.000013627316,0.000013423517,0.00011893876,0.000032757514,0.007274285,0.0005445588,0.9853247,0.0065227137,0.000009675414],"about_ca_topic_score_codex":0.0008246039,"about_ca_topic_score_gemma":0.00090626726,"teacher_disagreement_score":0.021719921,"about_ca_system_score_codex":0.0023769294,"about_ca_system_score_gemma":0.001545044,"threshold_uncertainty_score":0.07266039},"labels":[],"label_agreement":null},{"id":"W2056795974","doi":"10.1016/j.patcog.2006.02.006","title":"Geometric visualization of clusters obtained from fuzzy clustering algorithms","year":2006,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Cluster analysis; Fuzzy clustering; Visualization; Fuzzy logic; Computer science; Data mining; Artificial intelligence; Cluster (spacecraft); Object (grammar); FLAME clustering; Tetrahedron; Algorithm; Pattern recognition (psychology); Mathematics; Canopy clustering algorithm","score_opus":0.024314507256502776,"score_gpt":0.24684455813193756,"score_spread":0.2225300508754348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056795974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20639998,0.0015707426,0.74583673,0.0028078207,0.0006175262,0.00044389628,0.0034234503,0.011996436,0.026903411],"genre_scores_gemma":[0.46135375,0.0011122152,0.52958864,0.00017374495,0.00013728926,0.00022608614,0.002046514,0.001465695,0.003896151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995561,0.00008881548,0.000026714308,0.00007147225,0.00015503618,0.00010183688],"domain_scores_gemma":[0.9985856,0.00044970747,0.00012692992,0.00018726315,0.00055564597,0.0000949381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008903714,0.0015181156,0.0007520474,0.0052201725,0.0009784489,0.0032736298,0.0008837083,0.0011955514,0.012117274],"category_scores_gemma":[0.003180368,0.00051115145,0.000765564,0.003697029,0.0008144528,0.001325536,0.0012814666,0.0014370921,0.0013141987],"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":[0.0030382443,0.00026595156,0.007637373,0.0017342329,0.0002711847,0.0012921408,0.0038985738,0.25515243,0.10982128,0.11692045,0.041998044,0.45797008],"study_design_scores_gemma":[0.00028461165,0.00024923045,0.011652786,0.00027393238,0.00014373174,0.0008295277,0.0014838291,0.81262684,0.061656903,0.06903934,0.041486084,0.00027324987],"about_ca_topic_score_codex":0.008687296,"about_ca_topic_score_gemma":0.007355384,"teacher_disagreement_score":0.012117274,"about_ca_system_score_codex":0.00120408,"about_ca_system_score_gemma":0.0017525594,"threshold_uncertainty_score":0.040536284},"labels":[],"label_agreement":null},{"id":"W2057058417","doi":"10.1145/1132863.1132873","title":"Approximation and streaming algorithms for histogram construction problems","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Histogram; Computer science; Algorithm; Approximation algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.02572179603381434,"score_gpt":0.24387751952964024,"score_spread":0.2181557234958259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057058417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004234343,0.00071062933,0.99241626,0.00031790038,0.00007288921,0.00008143284,0.00015684994,0.0006124793,0.0013972052],"genre_scores_gemma":[0.12968315,0.0016713652,0.8623673,0.00024870457,0.0003357001,0.00039395443,0.001289884,0.00035428876,0.0036556663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975446,0.00072869484,0.00018039197,0.0005269393,0.00072234665,0.00029711332],"domain_scores_gemma":[0.99103725,0.006141248,0.00052085787,0.0012659122,0.0007874742,0.00024717365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032294055,0.0017991414,0.0018958513,0.0018293496,0.001014513,0.0025607406,0.0032665161,0.0019017508,0.00675954],"category_scores_gemma":[0.021510726,0.00096067495,0.0015089521,0.0043921946,0.0012676676,0.006773975,0.0028024577,0.0040849163,0.0019205038],"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":[0.0004619743,0.0002885616,0.0020136435,0.00067259505,0.00011951982,0.00011534751,0.00042252964,0.44497287,0.0026408017,0.15888882,0.018935662,0.37046757],"study_design_scores_gemma":[0.000051542167,0.000048812544,0.00014980204,0.000025965246,0.000019187959,0.00008077947,0.00006233665,0.88275576,0.0008467631,0.112909555,0.0030343975,0.00001505862],"about_ca_topic_score_codex":0.004042483,"about_ca_topic_score_gemma":0.003596257,"teacher_disagreement_score":0.00675954,"about_ca_system_score_codex":0.0021763283,"about_ca_system_score_gemma":0.0017730151,"threshold_uncertainty_score":0.022612989},"labels":[],"label_agreement":null},{"id":"W2057222780","doi":"10.1016/s0020-0190(01)00266-6","title":"Signature files and signature trees","year":2002,"lang":"en","type":"article","venue":"Information Processing Letters","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Signature (topology); Search engine indexing; Overhead (engineering); Tree (set theory); Information retrieval; Inefficiency; Inverted index; Data mining; Database; Mathematics; Operating system","score_opus":0.008946689608552622,"score_gpt":0.19419754317582877,"score_spread":0.18525085356727614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057222780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04065147,0.003397292,0.8860234,0.0054962994,0.0010143829,0.0002256168,0.0028962963,0.0036944987,0.056600824],"genre_scores_gemma":[0.588085,0.0057161115,0.2956996,0.0020458512,0.0018850509,0.00041132755,0.0063568917,0.0014437909,0.09835631],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99602145,0.0006225127,0.00036079212,0.00069745316,0.0018565825,0.00044128345],"domain_scores_gemma":[0.99195427,0.002560055,0.0009393957,0.0031591093,0.0010689825,0.0003181672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001699553,0.0007759594,0.0011457136,0.0031641405,0.002475947,0.0074057155,0.0017060805,0.0025927322,0.016441496],"category_scores_gemma":[0.0112419,0.00085589086,0.0006853052,0.008496371,0.0034997116,0.019721668,0.0031230634,0.0029897888,0.0069351858],"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":[0.00015352145,0.000033596807,0.00041581012,0.00007107251,0.00000997059,0.0001252045,0.00020156452,0.0030044285,0.000839439,0.91299665,0.011084807,0.07106389],"study_design_scores_gemma":[0.00001738693,0.000022963008,0.00012853844,0.000028130251,0.00001293284,0.00037179183,0.00010156404,0.010596967,0.0021550676,0.9501308,0.03640985,0.000023922903],"about_ca_topic_score_codex":0.0009710843,"about_ca_topic_score_gemma":0.0006801384,"teacher_disagreement_score":0.016441496,"about_ca_system_score_codex":0.0017412638,"about_ca_system_score_gemma":0.0021067276,"threshold_uncertainty_score":0.055002272},"labels":[],"label_agreement":null},{"id":"W2057310371","doi":"10.1109/cidm.2013.6597220","title":"Discovery of topological relations for spatial Activity Recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Exploit; Computer science; Activity recognition; Wearable computer; Ubiquitous computing; Machine learning; Artificial intelligence; Human–computer interaction; Computer security; Embedded system","score_opus":0.03751113440578365,"score_gpt":0.2561704451345515,"score_spread":0.21865931072876785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057310371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062118102,0.00027151094,0.9315185,0.00018747937,0.00002952767,0.00009527723,0.0010686446,0.0033095265,0.0014014426],"genre_scores_gemma":[0.71673375,0.00027244264,0.27738836,0.000057453082,0.000064327774,0.00016345305,0.004017857,0.00013527865,0.0011670394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990995,0.00023647856,0.00007083796,0.00034212513,0.00018058857,0.000070453396],"domain_scores_gemma":[0.99754375,0.00082395476,0.00038564706,0.00082860276,0.0002891643,0.00012880667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006069463,0.00077364914,0.0009380468,0.0027758991,0.00040763762,0.001006111,0.0012989028,0.0008350219,0.0013899045],"category_scores_gemma":[0.0056459494,0.0003822992,0.00083841867,0.0022229522,0.0006823335,0.0020236233,0.0014470931,0.00075322075,0.0012818151],"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":[0.00036825222,0.00046992223,0.0269413,0.0003333907,0.00018068856,0.0003690172,0.00064834,0.21707876,0.02924389,0.014355318,0.008571434,0.70143974],"study_design_scores_gemma":[0.00000869317,0.00006879773,0.0031126942,0.00001704085,0.000018450679,0.00021095395,0.00013222807,0.9741208,0.0048883096,0.015181161,0.0022219017,0.000018932298],"about_ca_topic_score_codex":0.0027950318,"about_ca_topic_score_gemma":0.005233184,"teacher_disagreement_score":0.0027950318,"about_ca_system_score_codex":0.0005803093,"about_ca_system_score_gemma":0.00058610697,"threshold_uncertainty_score":0.005557537},"labels":[],"label_agreement":null},{"id":"W2058239738","doi":"10.1145/2484838.2484857","title":"A multidimensional data model with subcategories for flexibly capturing summarizability","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Extension (predicate logic); Aggregate (composite); Dimension (graph theory); Hierarchy; Data warehouse; Cube (algebra); Data cube; Property (philosophy); Online analytical processing; Materialized view; Data modeling; Theoretical computer science; Expressive power; Data mining; Database; View; Programming language; Database design; Mathematics","score_opus":0.055817360048969145,"score_gpt":0.26051186430184153,"score_spread":0.2046945042528724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058239738","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068268715,0.00025865252,0.98970807,0.00032690447,0.000020351912,0.000114826624,0.00040751428,0.0006085624,0.0017283494],"genre_scores_gemma":[0.10342361,0.00025060022,0.893288,0.0002592289,0.000037483907,0.00034288483,0.0008682878,0.00013929246,0.0013905542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99600875,0.0013318735,0.0006088766,0.0006464087,0.0012063324,0.0001977568],"domain_scores_gemma":[0.9938871,0.0016189511,0.00049956946,0.002906309,0.00092643494,0.00016172274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038230706,0.00061920064,0.0008458775,0.0020046341,0.00087965233,0.0031458312,0.002378515,0.0013046415,0.0017822634],"category_scores_gemma":[0.009533673,0.000728209,0.00163961,0.0040805344,0.0016248627,0.008844654,0.0035868613,0.0021702142,0.00066403457],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022145649,0.00009922203,0.0042593223,0.00038429582,0.000071757335,0.0003566807,0.0014783667,0.059935696,0.010306415,0.80346644,0.006564512,0.11285581],"study_design_scores_gemma":[0.000037067974,0.00013536161,0.0010437126,0.0001921326,0.00008420278,0.0005550212,0.00060263474,0.51095694,0.012049964,0.39472857,0.0795192,0.00009510374],"about_ca_topic_score_codex":0.0044845236,"about_ca_topic_score_gemma":0.0057216925,"teacher_disagreement_score":0.0044845236,"about_ca_system_score_codex":0.0014684804,"about_ca_system_score_gemma":0.0014278425,"threshold_uncertainty_score":0.020218551},"labels":[],"label_agreement":null},{"id":"W2059398928","doi":"10.1080/13658811003702147","title":"An ontology-based framework for geospatial clustering","year":2010,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina; University of Calgary","funders":"","keywords":"Geospatial analysis; Ontology; Cluster analysis; Computer science; Geography; Information retrieval; Data science; Data mining; Cartography; Artificial intelligence","score_opus":0.01188119275411146,"score_gpt":0.2913445074125316,"score_spread":0.2794633146584201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059398928","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006176483,0.00019652805,0.9945152,0.00047143575,0.000050496266,0.00015645618,0.00028507042,0.00049854576,0.003208661],"genre_scores_gemma":[0.017477077,0.00047015387,0.9792381,0.0001343616,0.00003966165,0.00033501963,0.00097484555,0.00010645718,0.0012244222],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954904,0.0014246845,0.0006545213,0.0005991288,0.0015562895,0.0002750561],"domain_scores_gemma":[0.99768806,0.00065328775,0.00024155919,0.0005563987,0.0006194894,0.00024124402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058162226,0.0010072092,0.0011253796,0.005540703,0.0033742785,0.0066182576,0.0042355293,0.0021590763,0.0026665905],"category_scores_gemma":[0.0054413336,0.0008645428,0.0037745668,0.008578819,0.0033114671,0.006600575,0.0047223615,0.0029006454,0.0011774652],"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":[0.000025566367,0.00007516465,0.0007422385,0.00029656047,0.00010536622,0.00042827,0.0013646453,0.030085253,0.001578398,0.89796275,0.008637315,0.058698487],"study_design_scores_gemma":[0.000030662817,0.000031345066,0.00059757824,0.00027933528,0.00010035329,0.0005859132,0.0011538911,0.17724837,0.0018035785,0.60514295,0.21292412,0.00010188396],"about_ca_topic_score_codex":0.026849682,"about_ca_topic_score_gemma":0.030538356,"teacher_disagreement_score":0.026849682,"about_ca_system_score_codex":0.0033536733,"about_ca_system_score_gemma":0.006804305,"threshold_uncertainty_score":0.053386807},"labels":[],"label_agreement":null},{"id":"W2060325926","doi":"10.1145/1463434.1463486","title":"Efficient search of moving objects on a planar graph","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Search engine indexing; Computer science; Graph; Auxiliary memory; Data structure; Breadth-first search; Planar graph; Search tree; Tree (set theory); Search algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Algorithm; Combinatorics","score_opus":0.027994935155377813,"score_gpt":0.24097151259354685,"score_spread":0.21297657743816903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060325926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41448003,0.0011059613,0.5665939,0.0004358303,0.000043853346,0.0001492985,0.0046831802,0.0057089906,0.006798908],"genre_scores_gemma":[0.6107661,0.00062803325,0.37942663,0.000066610526,0.00002865282,0.00012134005,0.006683767,0.00020932914,0.0020695413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978036,0.000038314796,0.000014143622,0.00005373474,0.0000802659,0.0000331879],"domain_scores_gemma":[0.99939907,0.0002896104,0.000083724415,0.00011891506,0.00007593808,0.00003279471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025684622,0.00035114106,0.0006448578,0.0015426236,0.00043264322,0.0007235073,0.0008543745,0.00042035076,0.0013559484],"category_scores_gemma":[0.0015582014,0.00027040593,0.0003779801,0.0029268668,0.00034952356,0.0017776366,0.0009860623,0.0002688117,0.0003936891],"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":[0.0008048295,0.00016084193,0.00804985,0.00067222427,0.00014020521,0.0005546361,0.00051815616,0.51787466,0.05613508,0.030655596,0.020055017,0.3643789],"study_design_scores_gemma":[0.0000606018,0.0001513479,0.0020879167,0.0000114807235,0.00002950118,0.0002227315,0.00020589223,0.9626776,0.010003853,0.017066298,0.007458153,0.000024641],"about_ca_topic_score_codex":0.0061684395,"about_ca_topic_score_gemma":0.009121965,"teacher_disagreement_score":0.0061684395,"about_ca_system_score_codex":0.0003819839,"about_ca_system_score_gemma":0.0006758316,"threshold_uncertainty_score":0.012265027},"labels":[],"label_agreement":null},{"id":"W2062257519","doi":"","title":"Approximation algorithms for labeling hierarchical taxonomies","year":2008,"lang":"en","type":"article","venue":"CaltechAUTHORS (California Institute of Technology)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Linear programming relaxation; Approximation algorithm; Extension (predicate logic); Mathematics; Algorithm; Linear programming; Steiner tree problem; Discrete mathematics; Combinatorics; Relaxation (psychology); Computer science","score_opus":0.042226861691430896,"score_gpt":0.2626705395891018,"score_spread":0.22044367789767091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062257519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05734639,0.001485687,0.9269434,0.0014538497,0.00010788248,0.00033421104,0.0014883623,0.0024354567,0.008404786],"genre_scores_gemma":[0.18711819,0.0008872242,0.80324423,0.00036092574,0.00010490104,0.00048641433,0.0036136578,0.00037019097,0.0038142288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978109,0.00062436966,0.00013026892,0.00054105226,0.00049381057,0.0003996922],"domain_scores_gemma":[0.995545,0.002785256,0.0003911609,0.00080267666,0.00024420815,0.00023158459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027401468,0.0016970759,0.0018159178,0.001943999,0.0013670165,0.0026657218,0.0038187527,0.0022926386,0.008029241],"category_scores_gemma":[0.009931512,0.001029164,0.0017087798,0.005797418,0.0010173798,0.0071469396,0.0022277462,0.0027749701,0.0013921765],"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":[0.0005613986,0.0006324212,0.0025360049,0.0007821076,0.00014808604,0.00012537217,0.0005798173,0.61929464,0.002287232,0.082679965,0.021979798,0.26839313],"study_design_scores_gemma":[0.0000988517,0.00005722511,0.00030899188,0.00004520192,0.000037819445,0.000081066195,0.00012659529,0.919741,0.000576158,0.07554027,0.003373501,0.000013313531],"about_ca_topic_score_codex":0.008329226,"about_ca_topic_score_gemma":0.015331766,"teacher_disagreement_score":0.008329226,"about_ca_system_score_codex":0.004085778,"about_ca_system_score_gemma":0.002688291,"threshold_uncertainty_score":0.029644549},"labels":[],"label_agreement":null},{"id":"W2064848265","doi":"10.1007/s10115-011-0405-5","title":"Effectiveness of NAQ-tree in handling reverse nearest-neighbor queries in high-dimensional metric space","year":2011,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Pruning; Metric (unit); k-nearest neighbors algorithm; Nearest neighbor search; Euclidean distance; Tree (set theory); Data mining; Metric space; Artificial intelligence; Mathematics","score_opus":0.014143569775840109,"score_gpt":0.2202502816969014,"score_spread":0.20610671192106128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064848265","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52379537,0.009431058,0.43800312,0.002806716,0.0006923896,0.00038491687,0.0018878042,0.0073771654,0.015621519],"genre_scores_gemma":[0.7299175,0.0013074761,0.2626583,0.0003818622,0.00014036919,0.000092864495,0.0026127098,0.00026144233,0.0026274845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99293035,0.0021225165,0.00073616067,0.00065057183,0.0030873984,0.00047313637],"domain_scores_gemma":[0.9753798,0.014516188,0.0008368762,0.004366026,0.0043755793,0.0005255104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055373223,0.0006349573,0.0022266654,0.001962444,0.0016602128,0.00257624,0.0025960577,0.0021616379,0.002400784],"category_scores_gemma":[0.031020323,0.00036298696,0.00066382607,0.0035391876,0.0008575823,0.007635797,0.0018928626,0.00084278936,0.0012945425],"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":[0.0046579763,0.0014436172,0.020762302,0.0010833781,0.0003972519,0.0004623795,0.00095734646,0.2078406,0.02084329,0.025486974,0.038282868,0.67778206],"study_design_scores_gemma":[0.00013487617,0.0002423219,0.0013729981,0.00002725851,0.000069594294,0.0004979912,0.00040331788,0.97652435,0.0054776454,0.011631745,0.0035856103,0.00003230385],"about_ca_topic_score_codex":0.012987172,"about_ca_topic_score_gemma":0.009305545,"teacher_disagreement_score":0.012987172,"about_ca_system_score_codex":0.001244695,"about_ca_system_score_gemma":0.002816612,"threshold_uncertainty_score":0.029284477},"labels":[],"label_agreement":null},{"id":"W2065063350","doi":"10.1109/eesms.2010.5634177","title":"Collaborative web-based architecture for real-time monitoring of sensor data","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Sensor web; Ajax; Variety (cybernetics); Architecture; Web application; Data sharing; World Wide Web; Real-time data; Database; Key distribution in wireless sensor networks; Telecommunications","score_opus":0.0192080971045939,"score_gpt":0.28059002589249227,"score_spread":0.26138192878789834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065063350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02393511,0.00027594727,0.95150274,0.00027106353,0.00009143148,0.00024173677,0.00007307776,0.018914927,0.0046939943],"genre_scores_gemma":[0.4459649,0.00048220676,0.53169143,0.00042387683,0.00015829742,0.0005931055,0.0007888655,0.0006758649,0.0192215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986078,0.0003777626,0.00016251596,0.0002553706,0.00050396245,0.00009264775],"domain_scores_gemma":[0.9976065,0.00045292405,0.00013684343,0.00089890254,0.0006132912,0.0002914228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020924748,0.00054219685,0.0006353602,0.0010276057,0.0011516124,0.0021151167,0.002720403,0.0018992734,0.0026801776],"category_scores_gemma":[0.0023042986,0.00043825025,0.000670407,0.0011690452,0.00063326873,0.0030342499,0.001566348,0.0010660483,0.0013606993],"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":[0.0028577049,0.0032730894,0.011012933,0.0009093168,0.000731921,0.003920589,0.0031860762,0.070290595,0.15230653,0.05385056,0.039762978,0.6578978],"study_design_scores_gemma":[0.00030398858,0.0006097322,0.00426257,0.000069579924,0.0003770175,0.0016662292,0.00030107156,0.8119009,0.08637232,0.023395674,0.07056005,0.00018078236],"about_ca_topic_score_codex":0.0029446993,"about_ca_topic_score_gemma":0.0026411416,"teacher_disagreement_score":0.0029446993,"about_ca_system_score_codex":0.0004959698,"about_ca_system_score_gemma":0.0009723161,"threshold_uncertainty_score":0.011066198},"labels":[],"label_agreement":null},{"id":"W2065714066","doi":"10.1007/s00778-005-0177-1","title":"Optimization and evaluation of shortest path queries","year":2006,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":50,"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":"Constrained Shortest Path First; Shortest path problem; K shortest path routing; Computer science; Yen's algorithm; Dijkstra's algorithm; Shortest Path Faster Algorithm; Euclidean shortest path; Scalability; Pruning; Path (computing); Query optimization; Algorithm; Theoretical computer science; Data mining; Database; Graph; Computer network","score_opus":0.01871367238197218,"score_gpt":0.2452819636881325,"score_spread":0.2265682913061603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065714066","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5108377,0.0062597757,0.45306292,0.0030767578,0.00039485164,0.0004570504,0.0021819498,0.0062534413,0.01747551],"genre_scores_gemma":[0.84324616,0.0006774544,0.15075774,0.0001252694,0.00011718738,0.00010038424,0.0019949963,0.0005094907,0.0024712286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900511,0.0040320465,0.00053707504,0.0010529477,0.0033717537,0.00095502],"domain_scores_gemma":[0.9805335,0.014823851,0.0006311806,0.0014573605,0.0020306641,0.0005233619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061213155,0.0013907241,0.0023825467,0.0018173221,0.0009320736,0.002972526,0.0026832726,0.0016256046,0.0046550217],"category_scores_gemma":[0.026885184,0.0006165013,0.0007899499,0.0031543016,0.001317867,0.004186526,0.0016333953,0.0011788596,0.0006676768],"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":[0.0037478204,0.0007408289,0.005166725,0.00060297095,0.00015841024,0.00016955374,0.00018871987,0.72889125,0.007308987,0.020211522,0.012654789,0.22015841],"study_design_scores_gemma":[0.000078749756,0.00011885915,0.0004863992,0.000006346985,0.000021174905,0.00004067344,0.000054842603,0.98996335,0.0018119243,0.0068546985,0.00055496534,0.0000080722075],"about_ca_topic_score_codex":0.012597493,"about_ca_topic_score_gemma":0.011944941,"teacher_disagreement_score":0.012597493,"about_ca_system_score_codex":0.0034607418,"about_ca_system_score_gemma":0.0042011864,"threshold_uncertainty_score":0.03237301},"labels":[],"label_agreement":null},{"id":"W206965105","doi":"10.1007/978-3-540-76796-1_3","title":"Antimatroids, Betweenness, Convexity","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":25,"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":"Betweenness centrality; Mathematics; Convexity; Combinatorics; Regular polygon; Pure mathematics; Discrete mathematics; Geometry","score_opus":0.027070840514102208,"score_gpt":0.2148578489244075,"score_spread":0.1877870084103053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W206965105","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072638043,0.0149038695,0.34743038,0.0035347117,0.0015290886,0.00005798806,0.00043200134,0.0003878746,0.6244602],"genre_scores_gemma":[0.38369069,0.027960155,0.29140958,0.0021761875,0.0033227452,0.0003920596,0.0016160257,0.0012814477,0.2881511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99924076,0.00019385364,0.000045994948,0.00019073505,0.0002792941,0.00004930384],"domain_scores_gemma":[0.9992182,0.00043460695,0.000040353734,0.00016157511,0.000109291745,0.000035797344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070367096,0.0007884687,0.000948686,0.0015103021,0.0014408387,0.0037191266,0.00090895727,0.0009548141,0.017738933],"category_scores_gemma":[0.0023456586,0.0006493373,0.0010630965,0.002527114,0.004665486,0.0059223096,0.0016886451,0.0044644875,0.005905129],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005720589,0.0000032968335,0.000017649108,0.00006107079,0.0000045831885,0.000011707389,0.000067995155,0.00081127824,0.00017497316,0.97629493,0.006361198,0.016185628],"study_design_scores_gemma":[0.0000020287766,0.000004090229,0.00002518717,0.000021657068,0.000002995945,0.000060248778,0.000031031133,0.0008305194,0.0001090693,0.9618653,0.037043512,0.0000043154114],"about_ca_topic_score_codex":0.0011021364,"about_ca_topic_score_gemma":0.0010875444,"teacher_disagreement_score":0.017738933,"about_ca_system_score_codex":0.0017423774,"about_ca_system_score_gemma":0.0007855724,"threshold_uncertainty_score":0.059342682},"labels":[],"label_agreement":null},{"id":"W2070325534","doi":"10.4018/jdm.2009062501","title":"A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases","year":2009,"lang":"en","type":"article","venue":"Journal of Database Management","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Query optimization; Sargable; Relational database; Online aggregation; Range (aeronautics); Database; Search engine indexing; Data mining; View; Query language; Query expansion; Information retrieval; Selection (genetic algorithm); Relational database management system; Range query (database); Query by Example; Web search query; Search engine; Database design; Artificial intelligence","score_opus":0.060419915738604536,"score_gpt":0.30755272712627224,"score_spread":0.2471328113876677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070325534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009093791,0.0002511583,0.9895622,0.00011557661,0.000011706725,0.000051435225,0.000051190837,0.0004839864,0.00037893673],"genre_scores_gemma":[0.33839923,0.0004296983,0.6596158,0.00010124505,0.00007475273,0.00021120468,0.0003328193,0.00014428933,0.0006909026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662507,0.0011452178,0.00023274787,0.00050562946,0.0012927065,0.00019863888],"domain_scores_gemma":[0.99440753,0.0033944554,0.00045310496,0.00085493736,0.0007750204,0.00011493226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002237245,0.00085328997,0.0014263747,0.0018954914,0.00055917475,0.0016512659,0.0021415895,0.0011728655,0.0013900119],"category_scores_gemma":[0.015220163,0.00055135554,0.0009225409,0.002589,0.0007778091,0.004051632,0.0016679713,0.0012889021,0.00043636354],"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":[0.00032767377,0.00023239494,0.0037656347,0.00021884704,0.00009622802,0.00011927477,0.00022872534,0.66468906,0.010510895,0.027216312,0.0032041124,0.28939077],"study_design_scores_gemma":[0.000010545512,0.000046612076,0.0002599364,0.000006691759,0.000011799774,0.0000654354,0.000025208212,0.9919749,0.0018557492,0.0052525382,0.00047369755,0.00001696169],"about_ca_topic_score_codex":0.0052106855,"about_ca_topic_score_gemma":0.0030091344,"teacher_disagreement_score":0.0052106855,"about_ca_system_score_codex":0.0011631143,"about_ca_system_score_gemma":0.0013406377,"threshold_uncertainty_score":0.01183182},"labels":[],"label_agreement":null},{"id":"W2070508857","doi":"10.1017/s0373463307004043","title":"A Spatial Indexing Approach for High Performance Location Based Services","year":2006,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MRF Geosystems (Canada)","funders":"","keywords":"Quadtree; Search engine indexing; Computer science; Location-based service; Index (typography); Space partitioning; Tree (set theory); R-tree; Data mining; Service (business); Spatial database; Linear subspace; QR decomposition; Database; Information retrieval; Spatial analysis; Algorithm; Computer network; Geography; World Wide Web; Mathematics","score_opus":0.009378618420394,"score_gpt":0.22018880214771663,"score_spread":0.21081018372732263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070508857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02449899,0.0019363553,0.96017355,0.00039145435,0.00015032743,0.00010347708,0.0003934421,0.0028789984,0.009473419],"genre_scores_gemma":[0.250855,0.0016561691,0.73857504,0.00021761774,0.00021953338,0.00022019114,0.001377299,0.00018674927,0.0066925045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993623,0.00008442224,0.000046302735,0.000064270265,0.00037287475,0.00006977736],"domain_scores_gemma":[0.9992211,0.00015698752,0.00004405472,0.00024588112,0.00028829186,0.000043662385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039774945,0.00034622705,0.00050594343,0.0012944913,0.00078208453,0.0013577518,0.0014184726,0.00054469093,0.004082017],"category_scores_gemma":[0.0019692369,0.0001746264,0.00041978195,0.0036338007,0.00041450694,0.0023499685,0.0011527983,0.000479382,0.0016554216],"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":[0.00035194695,0.00025260955,0.0019118125,0.00035143684,0.00006269853,0.00030522357,0.0003300096,0.060701158,0.041739304,0.09226294,0.025619892,0.776111],"study_design_scores_gemma":[0.000103443934,0.00036467088,0.0013885861,0.000043134518,0.00008495137,0.0012269784,0.0002737824,0.83103234,0.026842158,0.0649299,0.07363646,0.0000736425],"about_ca_topic_score_codex":0.0048004515,"about_ca_topic_score_gemma":0.005330471,"teacher_disagreement_score":0.0048004515,"about_ca_system_score_codex":0.00066353986,"about_ca_system_score_gemma":0.0011799217,"threshold_uncertainty_score":0.013655722},"labels":[],"label_agreement":null},{"id":"W2070626052","doi":"10.1504/ijbidm.2014.068366","title":"A hybrid risk-aware design method for spatial datacubes handling spatial vague data: implementation and validation","year":2014,"lang":"en","type":"article","venue":"International Journal of Business Intelligence and Data Mining","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Online analytical processing; Spatial analysis; Vagueness; Data warehouse; Data mining; Spatial database; Database; Information retrieval; Data science; Fuzzy logic; Artificial intelligence","score_opus":0.12201891936327565,"score_gpt":0.39097008713991216,"score_spread":0.2689511677766365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070626052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028687252,0.000020022488,0.99592716,0.0000408234,0.000010384717,0.00009288161,0.000022559729,0.00068293157,0.00033445368],"genre_scores_gemma":[0.05211802,0.000049515063,0.9464182,0.000034170393,0.000008703637,0.0002558323,0.0001123612,0.00021238571,0.00079080526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945128,0.001805513,0.0005543421,0.00062583754,0.0023429818,0.00015853423],"domain_scores_gemma":[0.9902671,0.0036117423,0.00074335915,0.002174909,0.0029669616,0.00023591744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006471812,0.0007912408,0.0005587589,0.0011574549,0.0005715548,0.002032414,0.0021083732,0.0011790015,0.002844792],"category_scores_gemma":[0.014210805,0.0008088493,0.0011115622,0.0007143285,0.001185128,0.0024268369,0.0020731676,0.001218589,0.0006299594],"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":[0.00055446115,0.00047775343,0.0077617993,0.0008883883,0.00032816685,0.0008085347,0.003080688,0.34300968,0.054980908,0.12316337,0.0061598015,0.45878643],"study_design_scores_gemma":[0.00009170171,0.0002328535,0.00051836006,0.00008546374,0.0000648576,0.00041187822,0.00019129054,0.9252652,0.031934615,0.016331028,0.02480633,0.00006638148],"about_ca_topic_score_codex":0.0013290377,"about_ca_topic_score_gemma":0.0009803505,"teacher_disagreement_score":0.006471812,"about_ca_system_score_codex":0.00072895916,"about_ca_system_score_gemma":0.0017523675,"threshold_uncertainty_score":0.034226596},"labels":[],"label_agreement":null},{"id":"W2071248930","doi":"10.3138/carto.48.2.1837","title":"An Information Model for Pedestrian Routing and Navigation Databases Supporting Universal Accessibility","year":2013,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"FP7 Information and Communication Technologies","keywords":"Geospatial analysis; Computer science; Pedestrian; Conceptual model; Database; Data model (GIS); Class (philosophy); Routing (electronic design automation); Information model; Information retrieval; Data mining; Artificial intelligence; Transport engineering; Geography; Engineering","score_opus":0.02075790918732527,"score_gpt":0.3156206710317091,"score_spread":0.2948627618443838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071248930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007012713,0.00043671447,0.98132527,0.000782275,0.000099319186,0.00019392102,0.0016782202,0.0013606438,0.0071109263],"genre_scores_gemma":[0.20593303,0.0015022758,0.7737042,0.00049683347,0.00015481512,0.0007953433,0.007037286,0.0003527683,0.010023442],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672407,0.00080392085,0.0006286465,0.0005677618,0.001038208,0.00023740229],"domain_scores_gemma":[0.9965216,0.0011909074,0.00029022078,0.0009244701,0.00090420526,0.00016867754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032384787,0.00074570556,0.0010053857,0.0033414743,0.0015552427,0.008245533,0.002840314,0.002293422,0.004839888],"category_scores_gemma":[0.007709431,0.0007649132,0.002426936,0.0042475243,0.0013485606,0.009841796,0.0027407242,0.0017150105,0.001903994],"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":[0.0001409538,0.00013985609,0.0020014679,0.00034162845,0.0000682058,0.00090722606,0.00086392777,0.07148592,0.0026640734,0.861249,0.008562299,0.051575445],"study_design_scores_gemma":[0.000087224194,0.00014894569,0.00069939456,0.00036586047,0.00021075903,0.0011631346,0.0004635454,0.5603824,0.005141335,0.24341796,0.18780817,0.00011122257],"about_ca_topic_score_codex":0.011434514,"about_ca_topic_score_gemma":0.0072968383,"teacher_disagreement_score":0.011434514,"about_ca_system_score_codex":0.002404057,"about_ca_system_score_gemma":0.0034825557,"threshold_uncertainty_score":0.022735953},"labels":[],"label_agreement":null},{"id":"W2071465809","doi":"10.1016/j.jmva.2007.01.013","title":"From moments of sum to moments of product","year":2007,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":131,"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":"Mathematics; Product (mathematics); Random variable; Moment (physics); Identity (music); Quadratic equation; Multivariate statistics; Applied mathematics; Algebra of random variables; Sum of normally distributed random variables; Multivariate random variable; Statistics; Geometry","score_opus":0.017624701618862034,"score_gpt":0.29671318869209123,"score_spread":0.2790884870732292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071465809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007985077,0.0026343004,0.9848053,0.0007175889,0.00035290877,0.000012117232,0.000079912745,0.00021772477,0.0031951107],"genre_scores_gemma":[0.4405916,0.009521834,0.52839386,0.0014157965,0.0047018523,0.00015860848,0.0005368248,0.0014448563,0.013234818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99679404,0.0014200012,0.00024630333,0.00056946784,0.00076135446,0.00020882371],"domain_scores_gemma":[0.97898394,0.014842993,0.001341463,0.002842454,0.0013475961,0.00064153963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046960805,0.0013168781,0.001301289,0.003150681,0.0007578824,0.0037864738,0.0017265988,0.0012774866,0.004705013],"category_scores_gemma":[0.038802776,0.0009925375,0.0013183075,0.0027793082,0.0042250566,0.009850633,0.003870132,0.0039756126,0.0012215391],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008843694,0.000022018123,0.00077014527,0.00013133844,0.000049939237,0.00010703289,0.00030728977,0.013483402,0.0015220342,0.9256921,0.0044498877,0.05337633],"study_design_scores_gemma":[0.000006254331,0.00001793562,0.00027539209,0.000023809038,0.000016745456,0.00014149981,0.000035725923,0.049461316,0.0005867908,0.9444413,0.004968215,0.00002497699],"about_ca_topic_score_codex":0.00081328064,"about_ca_topic_score_gemma":0.0006514306,"teacher_disagreement_score":0.004705013,"about_ca_system_score_codex":0.0012286181,"about_ca_system_score_gemma":0.0009124846,"threshold_uncertainty_score":0.024835587},"labels":[],"label_agreement":null},{"id":"W2071604065","doi":"10.3166/isi.9.3-4.161-182","title":"L'inférence des dépendances fonctionnelles","year":2004,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.02603653703257053,"score_gpt":0.2414358269880673,"score_spread":0.21539928995549676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071604065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009881322,0.0004626056,0.98419374,0.0005325869,0.00008013029,0.000053536933,0.00028586417,0.0014498213,0.0030603625],"genre_scores_gemma":[0.3578207,0.003188107,0.6213526,0.0010233679,0.0010637583,0.000325072,0.0028473458,0.0016827987,0.010696238],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.990335,0.002574495,0.00030734445,0.0025235168,0.00364105,0.000618702],"domain_scores_gemma":[0.9780157,0.01752844,0.00055118825,0.0022078366,0.0014824425,0.0002144134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062280763,0.0038033612,0.0019139788,0.0034128665,0.0016894492,0.0048806416,0.003127223,0.0036896635,0.006417744],"category_scores_gemma":[0.035016946,0.0020974833,0.0061803777,0.0016074659,0.0045644366,0.009636483,0.0027699617,0.008364614,0.0020027575],"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":[0.0007720432,0.00027000898,0.007553717,0.0007735784,0.00067542365,0.0010412391,0.00086475874,0.1454299,0.014693788,0.3716975,0.012728307,0.44349965],"study_design_scores_gemma":[0.00006354756,0.00008481857,0.0027343016,0.000108058666,0.00011274826,0.0006938112,0.00012536017,0.7108613,0.012701982,0.2579287,0.014508139,0.00007722244],"about_ca_topic_score_codex":0.013656194,"about_ca_topic_score_gemma":0.009216286,"teacher_disagreement_score":0.013656194,"about_ca_system_score_codex":0.002611223,"about_ca_system_score_gemma":0.0015136437,"threshold_uncertainty_score":0.032937586},"labels":[],"label_agreement":null},{"id":"W2071898607","doi":"10.1145/2405153.2405162","title":"Location-based matching in publish/subscribe revisited","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Publication; Scalability; Event (particle physics); Focus (optics); Context (archaeology); Abstraction; Matching (statistics); Intrusion detection system; Complex event processing; Pattern matching; Distributed computing; Data mining; Data science; Database; Artificial intelligence","score_opus":0.01960999132443506,"score_gpt":0.2462256749626037,"score_spread":0.22661568363816864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071898607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018683195,0.0016471646,0.96611005,0.002122936,0.00032225167,0.0003133538,0.00029939148,0.0011169687,0.009384626],"genre_scores_gemma":[0.56894964,0.0028236425,0.40517116,0.0009165009,0.0010241633,0.00041808683,0.001071489,0.00045361,0.019171664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9889243,0.0031519386,0.0010805927,0.0025446452,0.0033734597,0.00092495984],"domain_scores_gemma":[0.97894627,0.010928393,0.0015443615,0.0061417134,0.00174475,0.0006944207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011099631,0.00080669107,0.003658965,0.0027022916,0.0032340595,0.007967255,0.005769788,0.004369417,0.008832506],"category_scores_gemma":[0.03449185,0.0014400479,0.0016816565,0.0066561317,0.0026318117,0.013375484,0.005856795,0.0031427417,0.0030799592],"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":[0.00048628444,0.00032029918,0.0030072888,0.0004447949,0.00018771828,0.00084295665,0.00075858313,0.13638636,0.003012319,0.64152044,0.012391094,0.20064181],"study_design_scores_gemma":[0.000101112084,0.00008206747,0.00036579967,0.000037986632,0.00008896421,0.0005981972,0.00022884109,0.5135124,0.0034874505,0.46195334,0.019484939,0.00005889963],"about_ca_topic_score_codex":0.0033136634,"about_ca_topic_score_gemma":0.0021469726,"teacher_disagreement_score":0.011099631,"about_ca_system_score_codex":0.003008199,"about_ca_system_score_gemma":0.0026463645,"threshold_uncertainty_score":0.058701217},"labels":[],"label_agreement":null},{"id":"W2072013890","doi":"10.1145/2792838.2800190","title":"Preference-oriented Social Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Homophily; Preference; Exploit; Set (abstract data type); Social network (sociolinguistics); Context (archaeology); Inference; Computer science; Variety (cybernetics); Probabilistic logic; Social group; Social preferences; Social psychology; Psychology; Microeconomics; Artificial intelligence; Economics; Social media; World Wide Web; Computer security","score_opus":0.07941404259286733,"score_gpt":0.25508297883914505,"score_spread":0.17566893624627772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072013890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046405494,0.0007089825,0.9335328,0.0012238979,0.000081198275,0.00057505566,0.0064739194,0.000741159,0.010257505],"genre_scores_gemma":[0.62173724,0.0014696743,0.35887364,0.0004045788,0.00018378576,0.00069433876,0.00851548,0.00008989299,0.008031345],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689305,0.0013542784,0.00017436348,0.00078671233,0.0006699422,0.000121708385],"domain_scores_gemma":[0.9926536,0.004274929,0.0009458019,0.001049963,0.00077772985,0.0002980209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025139577,0.0009116934,0.0008165265,0.0029778685,0.0012640643,0.0022752383,0.0016463185,0.0013013664,0.0060018147],"category_scores_gemma":[0.01484589,0.0005652657,0.0010007037,0.0044999965,0.0007812894,0.0042119524,0.0013992151,0.0014209258,0.0015579201],"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":[0.00031446427,0.00025135546,0.026167823,0.00041351595,0.00033198114,0.00066323834,0.0011128288,0.26771533,0.0015893639,0.45363322,0.021910174,0.2258967],"study_design_scores_gemma":[0.000033298933,0.000032148604,0.0021647767,0.000049291633,0.000045936842,0.0003823139,0.00021567359,0.636196,0.00050113513,0.34575772,0.014590649,0.000031091757],"about_ca_topic_score_codex":0.006673965,"about_ca_topic_score_gemma":0.009926633,"teacher_disagreement_score":0.006673965,"about_ca_system_score_codex":0.0016067573,"about_ca_system_score_gemma":0.0010514656,"threshold_uncertainty_score":0.020078003},"labels":[],"label_agreement":null},{"id":"W2072167856","doi":"10.3166/isi.16.6.11-39","title":"Un modèle UML et des contraintes OCL pour les entrepôts de données spatiales. De la représentation conceptuelle à l'implémentation","year":2011,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Unified Modeling Language; Computer science; Object Constraint Language; Online analytical processing; Programming language; Data integrity; Data warehouse; Context (archaeology); Software engineering; Implementation; Database; Applications of UML; Software; Geography","score_opus":0.05479642006578949,"score_gpt":0.27042710966438294,"score_spread":0.21563068959859344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072167856","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003947174,0.0009125495,0.9826733,0.0015013127,0.00013259731,0.00019944034,0.00059502834,0.0011052431,0.008933494],"genre_scores_gemma":[0.07153548,0.001565278,0.9092316,0.0008096367,0.00009524984,0.0010953643,0.0018684956,0.00060281414,0.013195962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9865667,0.0049583325,0.0016165785,0.0016939184,0.004721065,0.00044354468],"domain_scores_gemma":[0.9905758,0.0046289647,0.000986622,0.0015331344,0.0020242624,0.00025120965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009836351,0.0019234774,0.0008049683,0.0032531454,0.0016396248,0.00740743,0.0028497644,0.0035223137,0.0051436457],"category_scores_gemma":[0.018279538,0.0017773786,0.002658871,0.002457826,0.0030917842,0.0069990517,0.0026031544,0.004261663,0.0020260795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007746215,0.00010775756,0.0016885907,0.00052159233,0.0000927158,0.0006587797,0.0028143073,0.027702885,0.005903729,0.881623,0.005623879,0.07318534],"study_design_scores_gemma":[0.00008140323,0.00011644917,0.0011914158,0.0012310942,0.00014442498,0.0011061851,0.0011822282,0.22557014,0.014445436,0.21241999,0.5423391,0.000172147],"about_ca_topic_score_codex":0.027539605,"about_ca_topic_score_gemma":0.022801429,"teacher_disagreement_score":0.027539605,"about_ca_system_score_codex":0.0045851194,"about_ca_system_score_gemma":0.00618149,"threshold_uncertainty_score":0.05475861},"labels":[],"label_agreement":null},{"id":"W2072526459","doi":"10.1145/1321440.1321494","title":"A fast unified optimal route query evaluation algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Correctness; Algorithm; Pruning; Theoretical computer science","score_opus":0.023470842308094906,"score_gpt":0.2820873122154505,"score_spread":0.2586164699073556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072526459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029686956,0.00023077257,0.9648599,0.00010342473,0.000021033842,0.00013111309,0.00016126955,0.003062875,0.0017426423],"genre_scores_gemma":[0.2746703,0.000116814736,0.72218716,0.00008511049,0.0000296264,0.00018274286,0.0008858418,0.0002767605,0.0015656285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980604,0.0002957902,0.00014563143,0.00043793072,0.0007558056,0.0003045133],"domain_scores_gemma":[0.99806017,0.0005208528,0.00018060349,0.00054040964,0.00060946133,0.00008848673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010031383,0.0008979889,0.0016933299,0.0014635016,0.00085009617,0.0016871972,0.0023144886,0.001001365,0.0032888222],"category_scores_gemma":[0.004863917,0.00042717086,0.00082163204,0.0023338087,0.0005219138,0.0025914009,0.0017673328,0.0009190789,0.0008613786],"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":[0.00065401336,0.0003167489,0.0034008666,0.00017781665,0.00010332946,0.00019331585,0.00025263854,0.27486134,0.021692036,0.026434405,0.012156886,0.6597565],"study_design_scores_gemma":[0.00005397525,0.00007271016,0.0004407752,0.0000063600173,0.000023724288,0.00013452304,0.000059416732,0.98588276,0.0045970585,0.006815112,0.0018961702,0.00001745841],"about_ca_topic_score_codex":0.007488395,"about_ca_topic_score_gemma":0.008138973,"teacher_disagreement_score":0.007488395,"about_ca_system_score_codex":0.0010797442,"about_ca_system_score_gemma":0.0021385846,"threshold_uncertainty_score":0.014889598},"labels":[],"label_agreement":null},{"id":"W2073247434","doi":"10.1007/s00357-003-0011-7","title":"Maximum Split Clustering Under Connectivity Constraints","year":2003,"lang":"en","type":"article","venue":"Journal of Classification","topic":"Data Management and Algorithms","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":"Polytechnique Montréal; Université de Montréal","funders":"","keywords":"Heuristic; Partition (number theory); Mathematics; Combinatorics; Contiguity; Cluster analysis; Set (abstract data type); Algorithm; Computer science; Mathematical optimization","score_opus":0.04863080670212839,"score_gpt":0.2771362547617657,"score_spread":0.2285054480596373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073247434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09671127,0.0011480537,0.8926807,0.0015118963,0.00010048783,0.00026741842,0.0010668058,0.00081969955,0.0056936364],"genre_scores_gemma":[0.5551834,0.00088341703,0.4291569,0.0004787666,0.00039305835,0.0006266503,0.0045864806,0.0005638683,0.008127429],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978085,0.0009412046,0.00012232213,0.00047080207,0.00046963678,0.00018755354],"domain_scores_gemma":[0.9872416,0.008202769,0.0008138771,0.0019143268,0.0012631827,0.0005641967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035664435,0.0011965277,0.0031867581,0.0022601183,0.002188465,0.002448618,0.004996639,0.0032315229,0.0064529646],"category_scores_gemma":[0.018958755,0.001465611,0.0011000617,0.0037124308,0.0016983907,0.006061402,0.0038512873,0.0020606194,0.0011785782],"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":[0.001672325,0.00034323553,0.002998495,0.0007092441,0.00030237276,0.0003530519,0.0007567679,0.61030364,0.005007157,0.14988908,0.025132999,0.20253162],"study_design_scores_gemma":[0.000094602954,0.00007783726,0.0005966058,0.000038214737,0.000042260588,0.000117054915,0.000113554284,0.8158615,0.0012453945,0.17985046,0.0019462416,0.000016222826],"about_ca_topic_score_codex":0.0023703396,"about_ca_topic_score_gemma":0.0038424362,"teacher_disagreement_score":0.0064529646,"about_ca_system_score_codex":0.0015776227,"about_ca_system_score_gemma":0.0017847508,"threshold_uncertainty_score":0.021587372},"labels":[],"label_agreement":null},{"id":"W2073835465","doi":"10.1145/2600212.2600712","title":"A scalable distributed skip list for range queries","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Scalability; Multi-core processor; Range (aeronautics); Parallel computing; Cluster (spacecraft); Data structure; Distributed computing; Range query (database); Computer cluster; Concurrent data structure; Operating system; Search engine; Information retrieval","score_opus":0.013570312600213795,"score_gpt":0.231650254573932,"score_spread":0.21807994197371822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073835465","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014630313,0.00022278445,0.97189325,0.00022395971,0.00008932961,0.00018223812,0.00034503837,0.008401926,0.0040110843],"genre_scores_gemma":[0.23707704,0.0002836102,0.7511054,0.0003177818,0.00011998531,0.0004510671,0.001975564,0.0008805391,0.0077890493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989491,0.00015374445,0.000083742285,0.0001397701,0.00056250795,0.000111151676],"domain_scores_gemma":[0.99836427,0.00040476417,0.00009592753,0.0005931801,0.0004044815,0.00013731867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008517226,0.00044321278,0.0007376531,0.00086951075,0.0010273522,0.001468605,0.0026593907,0.000745895,0.0059797857],"category_scores_gemma":[0.0031061263,0.00032395494,0.00042873656,0.0011571462,0.00061667705,0.0029246663,0.0027523546,0.00093303097,0.0018455031],"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":[0.001520168,0.00040738782,0.0047124303,0.0007721739,0.00013604034,0.0007246553,0.00081475195,0.0722629,0.08886985,0.14766581,0.048240814,0.633873],"study_design_scores_gemma":[0.00040090922,0.0005549875,0.00086388114,0.00006719126,0.000081497674,0.00051801145,0.00025310554,0.7776564,0.060095012,0.060521062,0.09884865,0.00013939317],"about_ca_topic_score_codex":0.002294686,"about_ca_topic_score_gemma":0.003401801,"teacher_disagreement_score":0.0059797857,"about_ca_system_score_codex":0.0006866192,"about_ca_system_score_gemma":0.0012257443,"threshold_uncertainty_score":0.020004332},"labels":[],"label_agreement":null},{"id":"W2074082874","doi":"10.1145/1376616.1376741","title":"Stretch 'n' shrink","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Open source; Query optimization; Information retrieval; Database; Human–computer interaction; Programming language; Software","score_opus":0.02381021578104246,"score_gpt":0.20803536299146164,"score_spread":0.1842251472104192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074082874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015597287,0.0014319975,0.84326994,0.002350546,0.0010425209,0.00062937354,0.0035993445,0.10669703,0.025381964],"genre_scores_gemma":[0.16913208,0.0011326151,0.7477828,0.0041465783,0.0005284145,0.0010035745,0.011041125,0.034717795,0.03051509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9921673,0.0011869144,0.00094389584,0.0014532881,0.0035486931,0.00069986086],"domain_scores_gemma":[0.98541254,0.004031202,0.00047260517,0.006784192,0.0025962838,0.00070315093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006151179,0.0016189906,0.0018892725,0.0014254317,0.0016119619,0.00411071,0.0048976378,0.0019195061,0.028225133],"category_scores_gemma":[0.025450177,0.0011759463,0.0014288041,0.001615649,0.0018106368,0.009598929,0.01067095,0.0026828488,0.011098104],"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":[0.0022288582,0.0003492179,0.004552335,0.0012453601,0.00015406453,0.00039884468,0.0013979026,0.005777209,0.033838887,0.077348895,0.30178377,0.5709247],"study_design_scores_gemma":[0.0003278386,0.0006038789,0.0019474255,0.00028098744,0.00011504564,0.0012765531,0.0008105738,0.084746376,0.058315974,0.098711535,0.7525505,0.0003133966],"about_ca_topic_score_codex":0.0033651204,"about_ca_topic_score_gemma":0.004692988,"teacher_disagreement_score":0.028225133,"about_ca_system_score_codex":0.0011823659,"about_ca_system_score_gemma":0.0019504442,"threshold_uncertainty_score":0.09442246},"labels":[],"label_agreement":null},{"id":"W2075568796","doi":"10.1145/2505515.2505713","title":"CRF framework for supervised preference aggregation","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pairwise comparison; Conditional random field; Benchmark (surveying); Computer science; Preference; Inference; Machine learning; Unary operation; Artificial intelligence; Aggregation problem; Preference learning; Rank (graph theory); Field (mathematics); Data mining; Mathematics; Statistics","score_opus":0.046988036543216316,"score_gpt":0.2551128107390605,"score_spread":0.2081247741958442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075568796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020728975,0.00011913813,0.99548304,0.00009467382,0.000024987647,0.00003766123,0.00034224,0.0007521121,0.0010732432],"genre_scores_gemma":[0.2558737,0.00042471223,0.731765,0.0003056505,0.0002689686,0.0004383807,0.003378012,0.00036266688,0.0071830046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970107,0.0012422435,0.00014749538,0.00061482785,0.00079114444,0.00019365376],"domain_scores_gemma":[0.99607676,0.0015884185,0.00029075315,0.0010975515,0.00079376705,0.00015266307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037231604,0.0007732507,0.001357735,0.0017844032,0.00093294133,0.0012016761,0.0024065878,0.0009813453,0.008162848],"category_scores_gemma":[0.008565896,0.0004892594,0.0011966223,0.002732748,0.0007555021,0.0028302113,0.0013108067,0.0022550893,0.0022047153],"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":[0.00023209011,0.00029441106,0.0021582022,0.00037881368,0.00020949106,0.0002391416,0.00029738928,0.32311666,0.0032962898,0.17452396,0.026916098,0.46833742],"study_design_scores_gemma":[0.000022015365,0.00004386085,0.0004549844,0.00002153444,0.000020425981,0.00010426597,0.000021774655,0.8787633,0.0012418639,0.11202678,0.0072530955,0.000026115238],"about_ca_topic_score_codex":0.005538775,"about_ca_topic_score_gemma":0.010842116,"teacher_disagreement_score":0.008162848,"about_ca_system_score_codex":0.001157203,"about_ca_system_score_gemma":0.002341706,"threshold_uncertainty_score":0.02730745},"labels":[],"label_agreement":null},{"id":"W2075657963","doi":"10.1145/1516360.1516374","title":"Answering aggregate keyword queries on relational databases using minimal group-bys","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Aggregate (composite); Tuple; Information retrieval; Set (abstract data type); Keyword search; Relational database; Matching (statistics); Table (database); Result set; Group (periodic table); Data mining; Database; Mathematics","score_opus":0.0592646243551924,"score_gpt":0.27941395934440943,"score_spread":0.22014933498921704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075657963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23581046,0.001971544,0.7526715,0.0007876709,0.000038658975,0.0004490839,0.0027757816,0.0028908507,0.0026044343],"genre_scores_gemma":[0.50755566,0.0006464795,0.48564118,0.00019647267,0.00010661781,0.00033087595,0.004108429,0.00020116584,0.0012131692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945294,0.0015879041,0.0007197877,0.0011137313,0.0016119004,0.00043722932],"domain_scores_gemma":[0.9867871,0.008049082,0.0012555127,0.002501206,0.0010153673,0.00039165522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027851162,0.0012132216,0.003957796,0.003760927,0.0013188362,0.003262127,0.0021796923,0.0013977495,0.0019941356],"category_scores_gemma":[0.015468063,0.00073188305,0.0014955869,0.0069627278,0.0010315975,0.008404763,0.0038555777,0.00087476394,0.0009295191],"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":[0.0024493025,0.0007428352,0.013316068,0.0016654254,0.0004482889,0.0010552648,0.004097003,0.27854112,0.03144528,0.0711978,0.016810771,0.5782309],"study_design_scores_gemma":[0.00016683944,0.00035050162,0.0011769137,0.00006134982,0.00013482083,0.00067395763,0.0016201761,0.8094094,0.013050477,0.1668251,0.0064609842,0.00006953092],"about_ca_topic_score_codex":0.0032667785,"about_ca_topic_score_gemma":0.0037681514,"teacher_disagreement_score":0.003957796,"about_ca_system_score_codex":0.0010119298,"about_ca_system_score_gemma":0.0015294083,"threshold_uncertainty_score":0.014729261},"labels":[],"label_agreement":null},{"id":"W2075780554","doi":"10.1145/1183471.1183483","title":"Efficient integration of road maps","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Matching (statistics); Novelty; Data integration; Road map; Point (geometry); Data mining; Artificial intelligence; Geography; Cartography; Mathematics","score_opus":0.010254308351370246,"score_gpt":0.2197246140788357,"score_spread":0.20947030572746544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075780554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038221244,0.0009916124,0.9405193,0.0001512911,0.00009321219,0.00033963125,0.0020017554,0.0135011,0.004180854],"genre_scores_gemma":[0.22253627,0.0006327743,0.7652473,0.00006177994,0.000049870672,0.00018157372,0.0077144583,0.00062412786,0.0029517484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966407,0.00042627088,0.00020581094,0.00089444837,0.0015764362,0.00025634893],"domain_scores_gemma":[0.99644035,0.00071443035,0.00040639384,0.0010421551,0.0012929454,0.00010373519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013424546,0.001502362,0.0018185852,0.0075847683,0.0008940609,0.0031807416,0.0022948857,0.0015185463,0.0031821737],"category_scores_gemma":[0.00824822,0.000987369,0.0010946466,0.00901892,0.0004884702,0.004816095,0.0033954326,0.0009627444,0.0035656877],"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":[0.00046018924,0.00023057603,0.01222334,0.0005113972,0.00030749492,0.0004400192,0.0005591384,0.038706094,0.015988098,0.008681912,0.015595641,0.90629613],"study_design_scores_gemma":[0.00008490252,0.00023068885,0.01606367,0.00013725598,0.0002857195,0.0016039605,0.0009700367,0.8182185,0.07063965,0.027927008,0.06366751,0.00017107262],"about_ca_topic_score_codex":0.0069410605,"about_ca_topic_score_gemma":0.006758769,"teacher_disagreement_score":0.0075847683,"about_ca_system_score_codex":0.0006139103,"about_ca_system_score_gemma":0.0011817247,"threshold_uncertainty_score":0.013801277},"labels":[],"label_agreement":null},{"id":"W2075947171","doi":"10.3138/carto.42.2.153","title":"Understanding Spatiotemporal Patterns: Visual Ordering of Space and Time","year":2007,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Set (abstract data type); Row; Matrix (chemical analysis); Space (punctuation); Usability; Task (project management); Component (thermodynamics); Theoretical computer science; Data mining; Human–computer interaction","score_opus":0.023284918307625368,"score_gpt":0.278920532268305,"score_spread":0.25563561396067963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075947171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03574092,0.0010848118,0.9478064,0.0012565743,0.00013872745,0.00013357877,0.0020426034,0.002331253,0.00946506],"genre_scores_gemma":[0.2546227,0.0018657796,0.73815626,0.0002093768,0.000103475024,0.00018937477,0.0017249439,0.00053624035,0.0025917809],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968755,0.000102731836,0.000025024834,0.00006682205,0.000090222115,0.000027667862],"domain_scores_gemma":[0.99892265,0.00054778054,0.00012569422,0.00014886826,0.00016980967,0.00008512646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084786426,0.0006411762,0.0003706925,0.003171734,0.00042091435,0.003511992,0.00052204146,0.0005440087,0.0058792713],"category_scores_gemma":[0.0043442957,0.000348754,0.0006862499,0.0026642682,0.00091050833,0.004928592,0.001979146,0.0009679617,0.0008989199],"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":[0.0006618353,0.000115231735,0.008967866,0.0015555965,0.00020107295,0.0008023042,0.018696593,0.02506207,0.04200658,0.23223096,0.035769466,0.6339303],"study_design_scores_gemma":[0.00013761727,0.00028903713,0.014668067,0.0006386046,0.00018661034,0.0017803062,0.012857472,0.1816491,0.016055314,0.5433293,0.22819558,0.0002129428],"about_ca_topic_score_codex":0.0042534517,"about_ca_topic_score_gemma":0.004401287,"teacher_disagreement_score":0.0058792713,"about_ca_system_score_codex":0.00047288867,"about_ca_system_score_gemma":0.0008991632,"threshold_uncertainty_score":0.019668162},"labels":[],"label_agreement":null},{"id":"W2076172709","doi":"10.1145/765568.765571","title":"The height of a random binary search tree","year":2003,"lang":"en","type":"article","venue":"Journal of the ACM","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Binary search tree; Combinatorics; Binary number; Random binary tree; Mathematics; Binary tree; Tree (set theory); Self-balancing binary search tree; Optimal binary search tree; Discrete mathematics; K-ary tree; Arithmetic; Tree structure","score_opus":0.022067085861600267,"score_gpt":0.2553730957915877,"score_spread":0.2333060099299874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076172709","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68114793,0.0028991466,0.28485572,0.0035060446,0.0002390943,0.00011591356,0.0021959106,0.0010408653,0.023999315],"genre_scores_gemma":[0.9622296,0.00071039214,0.03211674,0.000300273,0.000099156176,0.0000716391,0.00070497906,0.00013191056,0.0036353888],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990163,0.000246831,0.000036274698,0.00017884838,0.0002953572,0.00022632563],"domain_scores_gemma":[0.9939693,0.0039592055,0.0005176853,0.0006426409,0.00048054344,0.00043054053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012768096,0.00018832873,0.0004930252,0.00079281174,0.00050691224,0.0012139266,0.00085552677,0.00076868915,0.0037348736],"category_scores_gemma":[0.015139066,0.00036150627,0.00022858163,0.0008379942,0.00076605444,0.002322009,0.0010539447,0.0006897877,0.00084180414],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015918071,0.00018827917,0.031965498,0.0006123677,0.00019226491,0.00052934187,0.0005745748,0.3476055,0.046138886,0.35968602,0.027470695,0.18344484],"study_design_scores_gemma":[0.0001299445,0.00028179452,0.01065106,0.000089147274,0.00007075324,0.0007613657,0.00023070663,0.758586,0.008502944,0.21135181,0.009274228,0.00007031875],"about_ca_topic_score_codex":0.0012072163,"about_ca_topic_score_gemma":0.0016663229,"teacher_disagreement_score":0.0037348736,"about_ca_system_score_codex":0.0010762613,"about_ca_system_score_gemma":0.0008490996,"threshold_uncertainty_score":0.012494445},"labels":[],"label_agreement":null},{"id":"W2076293176","doi":"10.1080/17538947.2014.927597","title":"Hexagonal connectivity maps for Digital Earth","year":2014,"lang":"en","type":"article","venue":"International Journal of Digital Earth","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Digital Earth; Geospatial analysis; Hexagonal crystal system; Grid; Polyhedron; Search engine indexing; Computer science; Variety (cybernetics); Hexagonal tiling; Theoretical computer science; Geography; Computational science; Data mining; Information retrieval; Mathematics; Cartography; Geometry; Artificial intelligence; Chemistry; Crystallography","score_opus":0.01380105371859322,"score_gpt":0.24483215849202383,"score_spread":0.2310311047734306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076293176","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02963574,0.00084962614,0.9228879,0.0005178037,0.00021045722,0.00022358252,0.0027828203,0.0013031899,0.04158893],"genre_scores_gemma":[0.39556104,0.0014410582,0.5892027,0.00017718211,0.00014513504,0.00035283278,0.0036705795,0.00024538615,0.009204097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965477,0.00007626411,0.000020569318,0.00005468948,0.00015999621,0.000033575976],"domain_scores_gemma":[0.9994246,0.00015166371,0.0000617398,0.00015288006,0.00017420764,0.000034993845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002067467,0.00025097613,0.00023427962,0.0013783481,0.0005586745,0.001215972,0.0005825684,0.0003420532,0.0053249947],"category_scores_gemma":[0.0026627015,0.00013520713,0.00030548868,0.001749558,0.00057028094,0.0011031401,0.0011531498,0.00052024995,0.0012167102],"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":[0.000050604747,0.000017351327,0.0014449721,0.00014584782,0.00001108353,0.00019953313,0.000330826,0.06338817,0.0025675707,0.80287385,0.018125774,0.11084454],"study_design_scores_gemma":[0.000025913825,0.00004102261,0.0016777953,0.000052044717,0.000016943464,0.00056112587,0.000443921,0.35403255,0.0033957427,0.40318814,0.23652028,0.00004445787],"about_ca_topic_score_codex":0.007929257,"about_ca_topic_score_gemma":0.007823861,"teacher_disagreement_score":0.007929257,"about_ca_system_score_codex":0.00054841966,"about_ca_system_score_gemma":0.0004858512,"threshold_uncertainty_score":0.017813861},"labels":[],"label_agreement":null},{"id":"W2076486399","doi":"10.3166/isi.15.1.35-60","title":"Stratégies alternatives pour la recherche des plus proches voisins dans les espaces multidimensionnels","year":2010,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Curse of dimensionality; Nearest neighbor search; Dimension (graph theory); Computer science; Probabilistic logic; Similarity (geometry); Mathematics; Theoretical computer science; Data mining; Artificial intelligence; Combinatorics","score_opus":0.12417799718269645,"score_gpt":0.3132868043507056,"score_spread":0.18910880716800915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076486399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031740118,0.008218711,0.9386896,0.004604729,0.0005870968,0.00017224469,0.00019234131,0.0007777483,0.015017404],"genre_scores_gemma":[0.14939772,0.005380953,0.8279732,0.0008165689,0.00036046203,0.0002946613,0.0004596721,0.00020896111,0.015107773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961218,0.0018103411,0.00028908742,0.0006199236,0.000987027,0.00017181231],"domain_scores_gemma":[0.99183625,0.0054962244,0.00026926928,0.0008352105,0.0013443502,0.00021872115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040779016,0.001552595,0.0014063282,0.002270538,0.0010555484,0.0057302713,0.0015278487,0.0028358004,0.011930504],"category_scores_gemma":[0.012011124,0.00064877566,0.0015036752,0.0016537831,0.0014121984,0.005239249,0.0013595917,0.0018814103,0.0018751775],"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":[0.0012466668,0.00053583353,0.0028336863,0.0015354619,0.00042475713,0.0005002927,0.001319732,0.101444505,0.04921699,0.32856387,0.010785808,0.50159246],"study_design_scores_gemma":[0.00054211944,0.00084921886,0.0016998791,0.0006821222,0.00041372963,0.0007647939,0.0024605724,0.5599284,0.04452707,0.30753028,0.08036929,0.0002324349],"about_ca_topic_score_codex":0.0055860803,"about_ca_topic_score_gemma":0.0072633787,"teacher_disagreement_score":0.011930504,"about_ca_system_score_codex":0.0011837606,"about_ca_system_score_gemma":0.0027172575,"threshold_uncertainty_score":0.03991151},"labels":[],"label_agreement":null},{"id":"W2076647913","doi":"10.3724/sp.j.1246.2012.00034.1","title":"A method for rapid transmission of multi-scale vector river data via the Internet","year":2012,"lang":"en","type":"article","venue":"Geodesy and Geodynamics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bottleneck; Vector map; Computer science; The Internet; Scale (ratio); Data mining; Data transmission; Transmission (telecommunications); Bandwidth (computing); Computer network; Telecommunications; Cartography; Artificial intelligence; World Wide Web; Geography","score_opus":0.03520013781570731,"score_gpt":0.29360843846254775,"score_spread":0.25840830064684045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076647913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00804034,0.00017360301,0.9873979,0.00015040173,0.00009004928,0.00013717082,0.0001555872,0.0022127756,0.001642119],"genre_scores_gemma":[0.103590995,0.0003544066,0.89022785,0.000075629054,0.00009453857,0.00033335102,0.0007080349,0.00018461187,0.004430596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993326,0.00012354733,0.000057262405,0.00014243432,0.00030302705,0.000041011303],"domain_scores_gemma":[0.99897385,0.00020520629,0.00009847887,0.00034313908,0.00032380392,0.000055596694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677952,0.0005377982,0.00046165293,0.0025200646,0.000793472,0.0008011479,0.0010584313,0.00047415547,0.0027913912],"category_scores_gemma":[0.0016564114,0.00036758833,0.00053315563,0.0025057327,0.00047379764,0.002041995,0.0013018765,0.0007186537,0.0011505713],"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":[0.00021379426,0.00021279024,0.0023059458,0.00021037173,0.00011587623,0.00033035417,0.00051334855,0.019522352,0.06089301,0.016382487,0.013419311,0.8858803],"study_design_scores_gemma":[0.0002564622,0.00056422816,0.004979313,0.00007412025,0.00019992895,0.0025123472,0.0006976959,0.76084787,0.08382388,0.018374255,0.1273888,0.00028103538],"about_ca_topic_score_codex":0.0025660845,"about_ca_topic_score_gemma":0.0026657525,"teacher_disagreement_score":0.0027913912,"about_ca_system_score_codex":0.00030175343,"about_ca_system_score_gemma":0.000634715,"threshold_uncertainty_score":0.0093382},"labels":[],"label_agreement":null},{"id":"W2077780773","doi":"10.14778/1920841.1920948","title":"Computing closed skycubes","year":2010,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Linear subspace; Computer science; Representation (politics); Subspace topology; Computation; Theoretical computer science; Formal concept analysis; Closure (psychology); Space (punctuation); Algorithm; Data mining; Mathematics; Artificial intelligence","score_opus":0.006308341498846236,"score_gpt":0.20824132972052734,"score_spread":0.2019329882216811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077780773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057698105,0.00040574395,0.9366391,0.0002673534,0.000052891137,0.00012355713,0.0006141682,0.0009479034,0.0032511563],"genre_scores_gemma":[0.35312858,0.00053919014,0.6394295,0.0001568796,0.00007037857,0.00029064127,0.002866954,0.00039651062,0.0031213837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759346,0.0006650238,0.00020382211,0.0004144141,0.0008190798,0.00030415462],"domain_scores_gemma":[0.9953087,0.0024394866,0.00037990976,0.0009243291,0.0007603918,0.00018708759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017591413,0.0006639026,0.0012198915,0.0016218502,0.0011957432,0.0026061707,0.001559206,0.0007579528,0.005189117],"category_scores_gemma":[0.009843668,0.0005085262,0.0012697097,0.002261128,0.001584271,0.0070981896,0.004009876,0.0011814805,0.00090464536],"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":[0.00052619696,0.00015243221,0.003971327,0.0007978328,0.00015719552,0.00039518852,0.0010277901,0.23999478,0.0143562835,0.5029123,0.009145083,0.22656353],"study_design_scores_gemma":[0.0000599346,0.00012651867,0.0004267893,0.000087361645,0.000027251923,0.00019106249,0.00051292503,0.45839983,0.010935871,0.51519156,0.014005478,0.000035376353],"about_ca_topic_score_codex":0.001800986,"about_ca_topic_score_gemma":0.002457627,"teacher_disagreement_score":0.005189117,"about_ca_system_score_codex":0.00089738553,"about_ca_system_score_gemma":0.0011239965,"threshold_uncertainty_score":0.017359316},"labels":[],"label_agreement":null},{"id":"W2078488084","doi":"10.1117/12.642362","title":"Subjective trajectory characterization: acquisition, matching, and retrieval","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","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":"University of Calgary","funders":"","keywords":"Particle filter; Computer science; Trajectory; Computer vision; Artificial intelligence; Position (finance); Active shape model; Histogram; Representation (politics); Similarity (geometry); Pattern recognition (psychology); Filter (signal processing); Algorithm","score_opus":0.007055292126385822,"score_gpt":0.20653009863989086,"score_spread":0.19947480651350505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078488084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009643218,0.0000521159,0.9875894,0.00003957225,0.000011830535,0.000062218016,0.00019900256,0.0016783838,0.00072421314],"genre_scores_gemma":[0.17736165,0.00022981108,0.81508654,0.00011175026,0.000050399864,0.00024193735,0.0024434782,0.00052084925,0.0039536357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906605,0.000115613824,0.000084534564,0.00020302375,0.00045755642,0.00007320162],"domain_scores_gemma":[0.99810636,0.00023520221,0.0003132892,0.00056037534,0.00067239656,0.00011239424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011770221,0.00065202743,0.00078469317,0.0021015103,0.0005605435,0.0020307957,0.0014136921,0.0006888211,0.0036353252],"category_scores_gemma":[0.005227862,0.00040658665,0.00052721775,0.0018287923,0.0005973684,0.0033036831,0.0015597058,0.0005825712,0.0022693584],"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":[0.00040741853,0.00020890884,0.00954433,0.00020666923,0.000066264365,0.00015126402,0.00045926688,0.03395556,0.05396634,0.022118045,0.0075914203,0.87132454],"study_design_scores_gemma":[0.000032044834,0.00018629825,0.008220303,0.000033736684,0.000031505,0.00046242602,0.00020383985,0.893269,0.06719088,0.014695737,0.015575997,0.00009826313],"about_ca_topic_score_codex":0.004784577,"about_ca_topic_score_gemma":0.005211199,"teacher_disagreement_score":0.004784577,"about_ca_system_score_codex":0.0007586075,"about_ca_system_score_gemma":0.001248349,"threshold_uncertainty_score":0.012161374},"labels":[],"label_agreement":null},{"id":"W2079246093","doi":"10.1109/mesoca.2011.6049032","title":"On supporting dynamic web service selection with histogramming","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"International Business Machines Corporation","keywords":"Skyline; Computer science; Web service; Snapshot (computer storage); World Wide Web; Ranking (information retrieval); Dynamic web page; WS-Policy; Service (business); Information retrieval; Database; Data mining; Web application security; Web development","score_opus":0.016067437867282344,"score_gpt":0.22180531863435166,"score_spread":0.2057378807670693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079246093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03687439,0.0004269203,0.9538836,0.0003069788,0.00004512044,0.00008131351,0.00015887166,0.0060092383,0.0022136224],"genre_scores_gemma":[0.5069309,0.00038525063,0.48944145,0.00022565598,0.00011432548,0.00012806729,0.0004983104,0.0003490701,0.0019269221],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987992,0.00039120452,0.00007349149,0.00017148875,0.00039696813,0.00016768917],"domain_scores_gemma":[0.9932749,0.0038080344,0.0003011069,0.0013503726,0.0009641974,0.00030133815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021693096,0.000599521,0.0011643919,0.0018403621,0.0011076992,0.0016785621,0.0017437174,0.00060158095,0.0025121109],"category_scores_gemma":[0.011239695,0.0003417771,0.00040072107,0.004344025,0.00094489625,0.0032124491,0.0017350066,0.00079778593,0.0010055868],"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":[0.0010416241,0.00030963245,0.0045636524,0.00015178669,0.00004721347,0.0001807047,0.000386938,0.19362803,0.019316513,0.0330456,0.0075767343,0.7397516],"study_design_scores_gemma":[0.000046868918,0.00008146247,0.00066342123,0.000011518092,0.000013939098,0.00012419827,0.00010641248,0.9607648,0.008710251,0.02633978,0.0031035715,0.000033689394],"about_ca_topic_score_codex":0.009037353,"about_ca_topic_score_gemma":0.009036931,"teacher_disagreement_score":0.009037353,"about_ca_system_score_codex":0.0010931279,"about_ca_system_score_gemma":0.0018104756,"threshold_uncertainty_score":0.01796949},"labels":[],"label_agreement":null},{"id":"W2080555007","doi":"10.1145/1806907.1806909","title":"Continuous online index tuning in moving object databases","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Media Development Authority - Singapore","keywords":"Computer science; Granularity; B-tree; Overhead (engineering); Search engine indexing; Tree (set theory); Grid; Data mining; Workload; Set (abstract data type); Object (grammar); Database; Binary tree; Information retrieval; Algorithm; Artificial intelligence; Mathematics","score_opus":0.028133624355503567,"score_gpt":0.2745241931610565,"score_spread":0.2463905688055529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080555007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24833494,0.0062585506,0.7087886,0.00050114864,0.00038419047,0.0004225628,0.0010084196,0.026139518,0.008162039],"genre_scores_gemma":[0.7060131,0.0009675279,0.28805995,0.00026830946,0.00017810205,0.00021605396,0.0016059786,0.0008411545,0.0018499182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971294,0.00042051068,0.00030966444,0.0006414478,0.0012082311,0.00029071196],"domain_scores_gemma":[0.99513704,0.0015299259,0.00042669725,0.0017809386,0.0007201069,0.00040533356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027643365,0.0008555252,0.0014576606,0.0017399283,0.001170492,0.0029372005,0.0039886883,0.00096496625,0.001234371],"category_scores_gemma":[0.011690541,0.0008134115,0.0004406682,0.003571848,0.0008167914,0.0057842457,0.0032309145,0.0010101733,0.0007534404],"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":[0.0018613513,0.0006953197,0.015519371,0.0005334949,0.00019378134,0.0006266456,0.0008018925,0.15864287,0.055576365,0.0150023,0.02525389,0.7252927],"study_design_scores_gemma":[0.00012576205,0.00024497235,0.0029391705,0.000031523647,0.00004676721,0.00037723486,0.0002107677,0.9523668,0.016977448,0.013697742,0.012916772,0.00006496484],"about_ca_topic_score_codex":0.004184304,"about_ca_topic_score_gemma":0.0037411188,"teacher_disagreement_score":0.004184304,"about_ca_system_score_codex":0.0010790012,"about_ca_system_score_gemma":0.0013291815,"threshold_uncertainty_score":0.01461935},"labels":[],"label_agreement":null},{"id":"W2080801059","doi":"10.1109/wi.2004.42","title":"Clustering Web Surfers with Probabilistic Models in a Real Application","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Open Text (Canada); York University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Probabilistic logic; Web mining; Machine learning; Web page; Artificial intelligence; World Wide Web","score_opus":0.019421044491934283,"score_gpt":0.22980754273789455,"score_spread":0.21038649824596029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080801059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6816853,0.00030955535,0.3145402,0.00048342592,0.000027049497,0.000108002525,0.00028239872,0.001952248,0.0006117884],"genre_scores_gemma":[0.8483446,0.00016214712,0.14947931,0.000059089376,0.000037084443,0.000073280295,0.00075270695,0.00009658729,0.0009951915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976767,0.0011467373,0.00013018707,0.0005012469,0.00042721152,0.000117908465],"domain_scores_gemma":[0.989755,0.007610855,0.00051029393,0.0011026155,0.0007729087,0.0002482853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005321816,0.0007489092,0.0010712572,0.0023860605,0.00075083144,0.0013591257,0.0019047,0.002076302,0.0006182816],"category_scores_gemma":[0.013143977,0.000721005,0.00074704166,0.0025645737,0.00064193376,0.0023037614,0.00085703016,0.001103098,0.0005155499],"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":[0.0011339253,0.0008741216,0.051984414,0.00022205921,0.00030278752,0.00043534508,0.0011372777,0.77201074,0.009791165,0.0025380137,0.0023612012,0.157209],"study_design_scores_gemma":[0.000014637379,0.00007970315,0.0034551,0.0000045400516,0.000014743967,0.000062209,0.00012637423,0.9928334,0.001316382,0.0017651782,0.00031316007,0.000014607773],"about_ca_topic_score_codex":0.008605139,"about_ca_topic_score_gemma":0.010233696,"teacher_disagreement_score":0.008605139,"about_ca_system_score_codex":0.00082088157,"about_ca_system_score_gemma":0.00057098665,"threshold_uncertainty_score":0.028144777},"labels":[],"label_agreement":null},{"id":"W2080956388","doi":"10.1080/13658816.2011.558017","title":"Geopot: a Cloud-based geolocation data service for mobile applications","year":2011,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Cloud computing; Geolocation; Scalability; Service (business); Database; Mobile computing; Computer network; World Wide Web; Operating system","score_opus":0.04624366737655682,"score_gpt":0.28504769206839486,"score_spread":0.23880402469183803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080956388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2049903,0.0042544715,0.66288304,0.004018028,0.0015487671,0.0013535784,0.0052173254,0.08302406,0.0327104],"genre_scores_gemma":[0.85191935,0.00095268904,0.12826608,0.0012742304,0.0003778788,0.00034155112,0.0053809457,0.00086788624,0.0106194075],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999283,0.00007347816,0.00006204628,0.000109550434,0.00029765294,0.00017416851],"domain_scores_gemma":[0.99900347,0.00006812727,0.000105132574,0.0002851791,0.00030171487,0.00023643757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048035354,0.00058167544,0.00071216054,0.00089463656,0.00092991197,0.0016332484,0.0030099242,0.000941716,0.00412599],"category_scores_gemma":[0.001101946,0.00030633944,0.00043802324,0.0014310784,0.00048738788,0.0028115483,0.0036540574,0.0010312904,0.0021806944],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031993445,0.0011046105,0.021040674,0.0008802125,0.00025988428,0.003511146,0.0008015496,0.02586972,0.13416275,0.05039785,0.30875343,0.45001882],"study_design_scores_gemma":[0.00053210265,0.00063902576,0.009032328,0.00011967544,0.0001164726,0.0019671707,0.0005588148,0.6932807,0.06282987,0.012363259,0.21833022,0.0002304322],"about_ca_topic_score_codex":0.0043311976,"about_ca_topic_score_gemma":0.0032834588,"teacher_disagreement_score":0.0043311976,"about_ca_system_score_codex":0.00087318337,"about_ca_system_score_gemma":0.0012536466,"threshold_uncertainty_score":0.013802767},"labels":[],"label_agreement":null},{"id":"W2081425277","doi":"10.1109/iri.2010.5558909","title":"Tag grid: Supporting collaborative and fuzzy multidimensional queries of tagged datasets","year":2010,"lang":"en","type":"article","venue":"","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":"Ontario Tech University","funders":"","keywords":"Computer science; Information retrieval; Grid; Fuzzy logic; Tag cloud; Data mining; World Wide Web; Visualization; Artificial intelligence","score_opus":0.006771716228991452,"score_gpt":0.2551415023184189,"score_spread":0.24836978608942747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081425277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019835364,0.00044750265,0.9568012,0.00052492105,0.00011019161,0.0002471964,0.0018157205,0.017498137,0.0027197443],"genre_scores_gemma":[0.2998625,0.00061115366,0.6890052,0.00045365395,0.00013839503,0.0003413035,0.0056980127,0.0007403713,0.0031494275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706715,0.0006673203,0.00044487038,0.0004971163,0.0010827319,0.00024080781],"domain_scores_gemma":[0.9926737,0.0025490222,0.00039750323,0.0030919288,0.0008349895,0.00045282874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037476772,0.00079707586,0.0017884347,0.0030460518,0.0017559447,0.0037571285,0.0031721431,0.0017212606,0.003372786],"category_scores_gemma":[0.010537654,0.0005318064,0.0011988538,0.0062834136,0.001188331,0.007709565,0.005165462,0.0011793278,0.0019627297],"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":[0.0041052997,0.00067741,0.013575584,0.0010480799,0.00043200247,0.0021527018,0.0030828856,0.055268932,0.036450006,0.13642995,0.08077272,0.66600436],"study_design_scores_gemma":[0.00040400348,0.0003290124,0.0020222717,0.0000968551,0.00011987483,0.0011528245,0.0012331514,0.71552837,0.029621294,0.16210046,0.08717349,0.00021829133],"about_ca_topic_score_codex":0.008605441,"about_ca_topic_score_gemma":0.008793457,"teacher_disagreement_score":0.008605441,"about_ca_system_score_codex":0.00086465845,"about_ca_system_score_gemma":0.0014192124,"threshold_uncertainty_score":0.019819796},"labels":[],"label_agreement":null},{"id":"W2081685229","doi":"10.1080/10824000009480539","title":"Spatial Data Warehousing: A Strategy for Integrated Urban Data Management in Support of Decision Making","year":2000,"lang":"en","type":"article","venue":"Annals of GIS","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":"University of Calgary","funders":"","keywords":"Data warehouse; Spatial analysis; Architecture; Computer science; Data management; Spatial database; Decision support system; Dimensional modeling; Data science; Data mining; Database; Geography","score_opus":0.19953909378020154,"score_gpt":0.39153546540331025,"score_spread":0.1919963716231087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081685229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015118226,0.0012460018,0.96186066,0.004874309,0.00014635015,0.00027546636,0.000263618,0.0016625396,0.014552915],"genre_scores_gemma":[0.1761532,0.0017025541,0.8166597,0.0006336965,0.00018941765,0.0002926177,0.00070491986,0.00010203913,0.003561966],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983725,0.00067323743,0.00012183764,0.00012374592,0.00061050145,0.00009815077],"domain_scores_gemma":[0.99846137,0.00050928706,0.000100296,0.00035642623,0.00040201776,0.00017058027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033524716,0.00049262145,0.0004622959,0.002012016,0.0007364966,0.0046001063,0.0013304777,0.0008876823,0.0030777727],"category_scores_gemma":[0.0031757501,0.0003420654,0.0005944893,0.0025425858,0.0011956686,0.004340055,0.0022713146,0.0008611533,0.0009853514],"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":[0.0001858252,0.00024183925,0.0012417877,0.00057858357,0.00016258973,0.00054748065,0.0008239878,0.04627702,0.005271634,0.3630117,0.020286635,0.56137085],"study_design_scores_gemma":[0.00010463614,0.00032851676,0.0013115737,0.0005024447,0.00013438481,0.0008060614,0.0015242201,0.3113688,0.011864229,0.42138004,0.2505855,0.00008960559],"about_ca_topic_score_codex":0.0015106724,"about_ca_topic_score_gemma":0.0012197125,"teacher_disagreement_score":0.0046001063,"about_ca_system_score_codex":0.00073256687,"about_ca_system_score_gemma":0.0025630046,"threshold_uncertainty_score":0.017729819},"labels":[],"label_agreement":null},{"id":"W2082021920","doi":"10.1023/b:mach.0000033118.09057.80","title":"Subquadratic Approximation Algorithms for Clustering Problems in High Dimensional Spaces","year":2004,"lang":"en","type":"article","venue":"Machine Learning","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Dimension (graph theory); Clustering high-dimensional data; CURE data clustering algorithm; Mathematics; Quadratic equation; Correlation clustering; Computer science; Constrained clustering; Approximation algorithm; Data stream clustering; Algorithm; Data mining; Pattern recognition (psychology); Artificial intelligence; Combinatorics","score_opus":0.018086759246439765,"score_gpt":0.24490751639547292,"score_spread":0.22682075714903316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082021920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054878145,0.0012711526,0.99012554,0.00045554867,0.000101893405,0.000045361772,0.00008624619,0.0006714699,0.0017549596],"genre_scores_gemma":[0.17057195,0.002013927,0.81367344,0.0006780019,0.0005268273,0.0005087648,0.0010945239,0.00072679354,0.01020576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960841,0.0016427864,0.00019376182,0.00055682065,0.0011657579,0.0003566229],"domain_scores_gemma":[0.9879418,0.007538616,0.00062311045,0.0020177288,0.001406049,0.0004726216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049185487,0.001850116,0.003168558,0.0022607225,0.0015214825,0.0033612093,0.0048559657,0.0027606362,0.0070733973],"category_scores_gemma":[0.02263485,0.0012585747,0.001271353,0.005916768,0.0018272881,0.0057258476,0.0036819864,0.0056383996,0.0032331129],"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":[0.00066875323,0.00045465003,0.001242012,0.0006984206,0.00015159995,0.000086127584,0.0003449535,0.46984896,0.0020891316,0.11089335,0.029775275,0.38374683],"study_design_scores_gemma":[0.00003027989,0.000029848368,0.00014615439,0.000014510479,0.00001556799,0.000033418066,0.00003305334,0.9408546,0.0003282929,0.05680186,0.0017032514,0.0000091229485],"about_ca_topic_score_codex":0.0065651448,"about_ca_topic_score_gemma":0.007394312,"teacher_disagreement_score":0.0070733973,"about_ca_system_score_codex":0.0026921532,"about_ca_system_score_gemma":0.0021911855,"threshold_uncertainty_score":0.026012063},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059212632,0.0014388865,0.9244428,0.0050075017,0.00018194165,0.00021240464,0.0018402331,0.0023232717,0.005340219],"genre_scores_gemma":[0.61084634,0.0008216907,0.3776884,0.0013903532,0.0004402499,0.000291972,0.0054695783,0.00045426047,0.0025971662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97326905,0.010626813,0.0025789784,0.0034203553,0.008542567,0.0015622824],"domain_scores_gemma":[0.9436292,0.03878568,0.001829058,0.009406417,0.005698624,0.00065104075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012425798,0.0009732539,0.003095025,0.0031128398,0.00241791,0.0077587953,0.005411184,0.003629387,0.004793877],"category_scores_gemma":[0.06338204,0.0017532127,0.0018865786,0.0071494165,0.0037363379,0.01759817,0.007204987,0.0036981094,0.0008399488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018372148,0.0006073358,0.0068140477,0.0013563124,0.0006043934,0.0014050718,0.0020403357,0.13435616,0.01189039,0.59885967,0.03006033,0.21016873],"study_design_scores_gemma":[0.00018761936,0.000064034684,0.0004203294,0.00004893835,0.0002011271,0.00033958154,0.0006923791,0.37781715,0.006202701,0.6084172,0.0055607464,0.000048111764],"about_ca_topic_score_codex":0.008964719,"about_ca_topic_score_gemma":0.007472067,"teacher_disagreement_score":0.012425798,"about_ca_system_score_codex":0.0020551435,"about_ca_system_score_gemma":0.0044549117,"threshold_uncertainty_score":0.06571466},"labels":[],"label_agreement":null},{"id":"W2083283415","doi":"10.1145/2694413.2694425","title":"Report on the First International Workshop on Exploratory Search in Databases and the Web (ExploreDB 2014)","year":2014,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Database; World Wide Web; Information retrieval","score_opus":0.0560095100511173,"score_gpt":0.2822348339987544,"score_spread":0.22622532394763706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083283415","genre_codex":"review","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017980199,0.7049705,0.011725719,0.076226495,0.066078015,0.0020947857,0.010539444,0.001112753,0.12545428],"genre_scores_gemma":[0.009824626,0.58508366,0.015273345,0.035210792,0.020363266,0.0019971954,0.024315396,0.0015188227,0.30641288],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944311,0.0013343828,0.00049240695,0.0004898118,0.0027217893,0.00053050957],"domain_scores_gemma":[0.9750748,0.005003347,0.0010656047,0.0014913165,0.014398841,0.0029660068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010622045,0.001090392,0.0017410758,0.008596987,0.0008926755,0.0057589603,0.0018413048,0.002550642,0.13861053],"category_scores_gemma":[0.02031806,0.0006455397,0.0021253566,0.008365041,0.0006472298,0.0073048137,0.004978236,0.002651387,0.07325722],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074877804,0.00004795553,0.00020793857,0.0030618848,0.000055246717,0.00004532335,0.000093126786,0.00007052927,0.0006141204,0.0014630146,0.83258975,0.16167624],"study_design_scores_gemma":[0.000014830706,0.000031836575,0.00044085656,0.0018238794,0.000048908554,0.00005082771,0.0000819902,0.000026669139,0.0002189796,0.0006914757,0.9965593,0.000010413591],"about_ca_topic_score_codex":0.0060100984,"about_ca_topic_score_gemma":0.011382837,"teacher_disagreement_score":0.13861053,"about_ca_system_score_codex":0.00308098,"about_ca_system_score_gemma":0.009591543,"threshold_uncertainty_score":0.4636984},"labels":[],"label_agreement":null},{"id":"W2083392507","doi":"10.1068/b31159","title":"A Precategorical Spatial-Data Metamodel","year":2006,"lang":"en","type":"article","venue":"Environment and Planning B Planning and Design","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":"University of Waterloo; Wilfrid Laurier University","funders":"","keywords":"Formalism (music); Metamodeling; Computer science; Spatial analysis; Graph; Graph theory; Land use; Data mining; Theoretical computer science; Geography; Mathematics; Engineering; Software engineering; Remote sensing","score_opus":0.062284283753351766,"score_gpt":0.24230444754764313,"score_spread":0.18002016379429137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083392507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029052738,0.00009474735,0.98938984,0.0010545269,0.00006390343,0.00006161472,0.00048474153,0.00038603923,0.0055592908],"genre_scores_gemma":[0.15137209,0.00049272,0.83702755,0.0006350306,0.000101267084,0.00042220746,0.0018266717,0.00019412681,0.007928309],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971317,0.0009160465,0.0004192683,0.00062421034,0.0007430181,0.00016573348],"domain_scores_gemma":[0.9964856,0.0011252544,0.00019874903,0.001383902,0.0005914697,0.00021502377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004410151,0.0005344317,0.000605655,0.0021332968,0.0010223784,0.0054122596,0.0024915447,0.0016100077,0.00402622],"category_scores_gemma":[0.0057980996,0.0005305209,0.0015881116,0.0024003603,0.0032204615,0.0072475085,0.003076728,0.0025672195,0.00125097],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000121214325,0.000015262198,0.00030793686,0.000047781345,0.00000981616,0.000052693085,0.00013563254,0.0077338014,0.00048728354,0.9808606,0.0009609073,0.009376016],"study_design_scores_gemma":[0.00002153338,0.000039968538,0.0001680357,0.00011882719,0.000040302613,0.00029049406,0.00019843772,0.11318242,0.0028078076,0.773149,0.109955475,0.000027629796],"about_ca_topic_score_codex":0.0028326293,"about_ca_topic_score_gemma":0.0030944056,"teacher_disagreement_score":0.0054122596,"about_ca_system_score_codex":0.0017545945,"about_ca_system_score_gemma":0.003295126,"threshold_uncertainty_score":0.023323357},"labels":[],"label_agreement":null},{"id":"W2085392097","doi":"10.3166/isi.10.4.59-88","title":"Indexation multidimensionnelle de bases de données capteur temps-réel et spatio-temporelles","year":2005,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.027069286593507026,"score_gpt":0.24500815515966298,"score_spread":0.21793886856615596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085392097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055309296,0.0013277781,0.93631595,0.00036237208,0.00008462265,0.00019384989,0.0013715604,0.0025318926,0.0025026982],"genre_scores_gemma":[0.2715115,0.0009654893,0.7191305,0.00011122813,0.0001011105,0.00041829475,0.0033237366,0.0004116063,0.0040264362],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99265224,0.0013536132,0.0009562789,0.0012075483,0.0035223814,0.00030799574],"domain_scores_gemma":[0.98362213,0.007061885,0.0013383824,0.0032017084,0.004424522,0.00035129162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043193446,0.0012898159,0.0020846038,0.00497851,0.0012620762,0.0064886264,0.0018483583,0.0012927534,0.0026134453],"category_scores_gemma":[0.024603542,0.00088931917,0.0017240389,0.0054659583,0.00095243356,0.003798055,0.0032055276,0.0016240072,0.0010167239],"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":[0.000742884,0.00024019796,0.022900887,0.0012858697,0.00052855094,0.0003944665,0.0030008415,0.0880996,0.06473583,0.0170316,0.004695147,0.79634416],"study_design_scores_gemma":[0.00011158991,0.0005395513,0.027134476,0.00037224634,0.0003210005,0.0013283066,0.00236003,0.8062572,0.08445846,0.024140706,0.05266014,0.000316346],"about_ca_topic_score_codex":0.010953718,"about_ca_topic_score_gemma":0.011556025,"teacher_disagreement_score":0.010953718,"about_ca_system_score_codex":0.0013665857,"about_ca_system_score_gemma":0.0022531492,"threshold_uncertainty_score":0.022843122},"labels":[],"label_agreement":null},{"id":"W2085403470","doi":"10.1155/s1173912601000177","title":"A statistical comparison of three goodness-of-fit criteria used inmodelling distances","year":2001,"lang":"en","type":"article","venue":"Journal of Applied Mathematics and Decision Sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Goodness of fit; Mean absolute error; Statistics; Mean squared error; Mathematics; Computer science","score_opus":0.14961168221071405,"score_gpt":0.38839721651434433,"score_spread":0.23878553430363028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085403470","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64924264,0.0014771696,0.3417733,0.0003922134,0.00015783389,0.00034112655,0.002123492,0.0012903069,0.003201887],"genre_scores_gemma":[0.93995965,0.00019645423,0.055860896,0.00007355065,0.000033414435,0.00042701763,0.00274621,0.0002768006,0.00042604745],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9696032,0.018549481,0.0022204989,0.004045281,0.004683892,0.0008976629],"domain_scores_gemma":[0.79207295,0.17502739,0.00823451,0.015647106,0.007452351,0.0015656404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.037214134,0.0015706702,0.001759993,0.0051373537,0.0010367675,0.0022811713,0.0022067209,0.001667949,0.002279068],"category_scores_gemma":[0.13827436,0.000357566,0.002696362,0.004941032,0.002762547,0.0031869444,0.0025436166,0.0020495357,0.00069681276],"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":[0.0023970997,0.00055553543,0.48798928,0.0012602017,0.0048640273,0.00074656506,0.0021775535,0.2479572,0.0051631276,0.018282788,0.005243637,0.22336306],"study_design_scores_gemma":[0.00023604614,0.0039653564,0.40381253,0.00035646322,0.0009898702,0.0010587407,0.004151703,0.53747123,0.010419275,0.027523004,0.009569615,0.00044623218],"about_ca_topic_score_codex":0.0032004334,"about_ca_topic_score_gemma":0.0037297548,"teacher_disagreement_score":0.037214134,"about_ca_system_score_codex":0.0009886242,"about_ca_system_score_gemma":0.0015466927,"threshold_uncertainty_score":0.19680947},"labels":[],"label_agreement":null},{"id":"W2085596541","doi":"10.1016/j.dam.2012.08.006","title":"Spanning cactus of a graph: Existence, extension, optimization, and approximation","year":2012,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cactus; Mathematics; Extension (predicate logic); Combinatorics; Graph; Approximation algorithm; Discrete mathematics; Botany; Computer science","score_opus":0.02074061584606133,"score_gpt":0.2399834890233004,"score_spread":0.2192428731772391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085596541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46407184,0.0017289591,0.51213944,0.0019502195,0.00011171183,0.00015560913,0.0006030391,0.00051502493,0.018724134],"genre_scores_gemma":[0.7793918,0.0015625905,0.20733549,0.00027088626,0.000226837,0.00017940672,0.0010742893,0.00017941922,0.009779277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941003,0.00015783422,0.000028943368,0.00018226755,0.00013043752,0.00009049],"domain_scores_gemma":[0.99274915,0.005120422,0.0006276018,0.00053089095,0.00045145588,0.0005204927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013821516,0.0008652424,0.0014053729,0.0027170004,0.0011686394,0.0017973456,0.0019018052,0.0020535851,0.0052092765],"category_scores_gemma":[0.013938896,0.0007852789,0.00097302167,0.002911295,0.0018925105,0.004937899,0.0018501697,0.0022287895,0.0003237169],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031071345,0.00028657055,0.0053214403,0.00038542863,0.00008951367,0.0003302492,0.00060668774,0.27371934,0.0033553932,0.60874677,0.0073918016,0.09945611],"study_design_scores_gemma":[0.000026059817,0.00008766466,0.00095637643,0.000065774555,0.00003793945,0.00031732008,0.0001377702,0.61553055,0.0011250377,0.37913275,0.0025645148,0.00001817082],"about_ca_topic_score_codex":0.0020956227,"about_ca_topic_score_gemma":0.0022004438,"teacher_disagreement_score":0.0052092765,"about_ca_system_score_codex":0.0011821222,"about_ca_system_score_gemma":0.00084208284,"threshold_uncertainty_score":0.01742673},"labels":[],"label_agreement":null},{"id":"W2086954434","doi":"10.1145/2063576.2063821","title":"Adding structure to top-k","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Information retrieval; Set (abstract data type); Web search query; Query expansion; Web query classification; Result set; Categorical variable; Search engine; Hierarchy; Interface (matter); Theoretical computer science; Machine learning","score_opus":0.026697683445214806,"score_gpt":0.22539785437844223,"score_spread":0.19870017093322742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086954434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04618158,0.0014545927,0.9262322,0.0008473308,0.00017505966,0.00074257836,0.0052901036,0.0134909665,0.0055855745],"genre_scores_gemma":[0.16488715,0.00047940464,0.8189635,0.00036102824,0.00013777088,0.00041938937,0.010471236,0.0007151363,0.0035654232],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961438,0.00052336993,0.00041453354,0.0011088295,0.0013383483,0.00047108866],"domain_scores_gemma":[0.99193853,0.002342024,0.00066967175,0.0032022025,0.0015402853,0.00030726485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020646171,0.0017807413,0.0022320175,0.00523343,0.0027883267,0.0041897874,0.003675431,0.0023150921,0.0056575797],"category_scores_gemma":[0.014415556,0.0012183227,0.0026882386,0.0081472,0.0016221073,0.0067668245,0.0038834694,0.0028139667,0.0051616845],"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":[0.00094639155,0.0008652683,0.012743899,0.0013021849,0.00019740628,0.00047307822,0.0014363909,0.06914356,0.024852049,0.037178554,0.058950383,0.79191077],"study_design_scores_gemma":[0.0002006782,0.00043058328,0.002747006,0.00014030118,0.00020650294,0.001141933,0.0010023661,0.7650097,0.023337878,0.17134614,0.034292895,0.00014403806],"about_ca_topic_score_codex":0.008514989,"about_ca_topic_score_gemma":0.021084374,"teacher_disagreement_score":0.008514989,"about_ca_system_score_codex":0.0019739885,"about_ca_system_score_gemma":0.0036307252,"threshold_uncertainty_score":0.018926501},"labels":[],"label_agreement":null},{"id":"W2087307849","doi":"10.1109/icif.2007.4408208","title":"Situation analysis for decision support: A formal approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Decision support system; Terrain; Situation awareness; Operations research; Decision maker; Situation analysis; Planner; Artificial intelligence; Engineering; Geography; Cartography","score_opus":0.025725439577604384,"score_gpt":0.2781522388994416,"score_spread":0.2524267993218372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087307849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010469246,0.0018426698,0.9809249,0.002867564,0.00013988177,0.00014345991,0.00023270813,0.00020358192,0.012598326],"genre_scores_gemma":[0.102864526,0.003574581,0.8861293,0.000781969,0.00057135726,0.00092378695,0.0007447598,0.00014700099,0.0042627617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9920414,0.0046828254,0.00087503885,0.0007360903,0.0013163364,0.00034829782],"domain_scores_gemma":[0.98166233,0.014487715,0.0007646986,0.0014134027,0.0012456285,0.00042615543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016404772,0.0019430325,0.0013893897,0.005911027,0.0029803584,0.013027803,0.0046688216,0.0026608442,0.008996662],"category_scores_gemma":[0.017145278,0.0014179493,0.0032891755,0.005058448,0.012144601,0.015413158,0.005687444,0.0053470335,0.0018847595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008365157,0.000020081823,0.00010282507,0.00013998752,0.000018996145,0.00010138346,0.0006389966,0.0065109935,0.000097854805,0.9802931,0.0015470615,0.010520268],"study_design_scores_gemma":[0.000014243896,0.000013043264,0.00005212919,0.00019477279,0.000017023487,0.000088817775,0.00040103696,0.024349937,0.00012574252,0.9432621,0.031461474,0.000019673265],"about_ca_topic_score_codex":0.008105349,"about_ca_topic_score_gemma":0.0052420134,"teacher_disagreement_score":0.016404772,"about_ca_system_score_codex":0.0056096106,"about_ca_system_score_gemma":0.0046841986,"threshold_uncertainty_score":0.08675778},"labels":[],"label_agreement":null},{"id":"W2089406675","doi":"10.1080/13658816.2012.743663","title":"LOST-Tree: a spatio-temporal structure for efficient sensor data loading in a sensor web browser","year":2012,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canarie; Microsoft Research","keywords":"Sensor web; Wireless sensor network; Computer science; Mashup; Geography; Data mining; Real-time computing; World Wide Web; Web service; Computer network; Telecommunications; Key distribution in wireless sensor networks; Web 2.0","score_opus":0.02486267327264885,"score_gpt":0.28015399351693054,"score_spread":0.2552913202442817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089406675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009241398,0.00041780362,0.8825596,0.0003243001,0.00015636721,0.00031756205,0.0026521399,0.10191509,0.0024157385],"genre_scores_gemma":[0.15895861,0.0007337802,0.8044415,0.000463945,0.00011424293,0.000792327,0.012633414,0.016252939,0.0056092395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852777,0.00024591634,0.00023938314,0.0002349199,0.00061770494,0.00013431198],"domain_scores_gemma":[0.9942849,0.0012438982,0.0003157029,0.0022165931,0.001314361,0.00062444055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028000309,0.0012277032,0.0011380629,0.001660412,0.0008675411,0.0027597058,0.0036097222,0.0011196182,0.0044889683],"category_scores_gemma":[0.008854901,0.001141071,0.0010356441,0.0017614437,0.0009775404,0.005493694,0.0045424104,0.002101313,0.0028202487],"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":[0.0034467566,0.00090806646,0.017506408,0.0021094,0.00072374864,0.0022076466,0.003964895,0.059689876,0.07965919,0.06971106,0.23770557,0.5223674],"study_design_scores_gemma":[0.00038621525,0.00026145118,0.0030462518,0.00021045111,0.00014666797,0.00086140906,0.0003993763,0.70507234,0.055297796,0.034455754,0.19957829,0.00028403016],"about_ca_topic_score_codex":0.0060734507,"about_ca_topic_score_gemma":0.009106303,"teacher_disagreement_score":0.0060734507,"about_ca_system_score_codex":0.0009246516,"about_ca_system_score_gemma":0.002045872,"threshold_uncertainty_score":0.015017092},"labels":[],"label_agreement":null},{"id":"W2089906307","doi":"10.1145/1807167.1807196","title":"Processing proximity relations in road networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Ranging; Track (disk drive); Set (abstract data type); Real-time computing; Data mining; Telecommunications","score_opus":0.010472758763183808,"score_gpt":0.23524127692069863,"score_spread":0.2247685181575148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089906307","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36316425,0.006411666,0.6075327,0.001313533,0.0002784429,0.00029843772,0.006129136,0.0035945752,0.011277347],"genre_scores_gemma":[0.8903866,0.0017648924,0.096050106,0.000120260695,0.0002562361,0.00013813333,0.0066513084,0.0001279064,0.004504625],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983608,0.0002594853,0.00015168128,0.00048171764,0.00055964064,0.0001867205],"domain_scores_gemma":[0.9977307,0.0011824659,0.00029727787,0.00032153763,0.00036321473,0.00010492246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006120728,0.00073583156,0.001108094,0.0035423639,0.0010625123,0.002170846,0.0011850676,0.0010907177,0.0035431061],"category_scores_gemma":[0.007840062,0.00046336598,0.0006470719,0.0047387974,0.00046965003,0.0039208974,0.0024190939,0.0008571367,0.001179843],"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":[0.00149131,0.00036779649,0.029133026,0.0011829076,0.00034203992,0.0018410917,0.0019235922,0.34050807,0.016393775,0.055481862,0.024684412,0.5266501],"study_design_scores_gemma":[0.00005670556,0.00019947722,0.0076216874,0.000062508494,0.000104496416,0.0008118642,0.0012716297,0.87027395,0.005502566,0.09518625,0.018859092,0.00004974642],"about_ca_topic_score_codex":0.008229992,"about_ca_topic_score_gemma":0.01122978,"teacher_disagreement_score":0.008229992,"about_ca_system_score_codex":0.0006562996,"about_ca_system_score_gemma":0.0005194046,"threshold_uncertainty_score":0.016364217},"labels":[],"label_agreement":null},{"id":"W2090407107","doi":"10.1016/j.jal.2005.06.005","title":"Efficient spatio-temporal data mining with GenSpace graphs","year":2005,"lang":"en","type":"article","venue":"Journal of Applied Logic","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Data mining; Ranking (information retrieval); Process (computing); Path (computing); Temporal database; Machine learning; Information retrieval","score_opus":0.033887326577623,"score_gpt":0.2521135207993993,"score_spread":0.2182261942217763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090407107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08140638,0.0006345969,0.90786725,0.00066441594,0.00006707279,0.0001937099,0.0026257394,0.0042660786,0.0022747286],"genre_scores_gemma":[0.37613264,0.00039704557,0.61661917,0.00017613785,0.000039807743,0.00011941474,0.0042801234,0.00023741358,0.001998205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943227,0.00014075391,0.000046904483,0.00015327717,0.0001684636,0.00005823734],"domain_scores_gemma":[0.99815327,0.00084208284,0.00014549214,0.00057295256,0.00020156613,0.000084549756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054675597,0.00052036747,0.00085632724,0.0017110491,0.00076685817,0.0013112238,0.001387711,0.0006211823,0.0018069631],"category_scores_gemma":[0.0033526246,0.0003821287,0.0010820172,0.0032464757,0.0005663961,0.002907306,0.0019090207,0.0007844379,0.00053184817],"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":[0.0006334687,0.00026101293,0.010365807,0.0004926508,0.00022579398,0.00055398856,0.0004966897,0.20782897,0.01268615,0.055140026,0.016684387,0.69463116],"study_design_scores_gemma":[0.000048999245,0.00008157025,0.0010259439,0.000031147774,0.000045999986,0.00030338604,0.00030920032,0.88429683,0.0056817685,0.10140086,0.006758624,0.000015567315],"about_ca_topic_score_codex":0.007059636,"about_ca_topic_score_gemma":0.014363098,"teacher_disagreement_score":0.007059636,"about_ca_system_score_codex":0.0005675331,"about_ca_system_score_gemma":0.0012771507,"threshold_uncertainty_score":0.014037073},"labels":[],"label_agreement":null},{"id":"W2090539590","doi":"10.1145/375551.375575","title":"Querying partially sound and complete data sources","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Probabilistic logic; Consistency (knowledge bases); Semantics (computer science); Set (abstract data type); Information retrieval; Possible world; Data quality; Data collection; Data mining; Theoretical computer science; Artificial intelligence; Programming language; Mathematics; Metric (unit)","score_opus":0.10321980569761678,"score_gpt":0.2845998366516957,"score_spread":0.1813800309540789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090539590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039627425,0.0005014552,0.95378774,0.0018334354,0.00004445853,0.00021466504,0.0014763318,0.0008708722,0.0016435923],"genre_scores_gemma":[0.3207577,0.00053993956,0.67209136,0.0005420523,0.00017397013,0.00044885784,0.003853629,0.00032078114,0.0012717703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9645989,0.013529142,0.004047927,0.004814421,0.01190211,0.0011075924],"domain_scores_gemma":[0.86988056,0.104094446,0.0048627327,0.0120726945,0.007874214,0.0012154039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02456359,0.0012936292,0.0032103963,0.0046132407,0.0028347205,0.010374152,0.004969481,0.0033847033,0.0018538638],"category_scores_gemma":[0.0991565,0.0019205468,0.00353018,0.006584757,0.005743806,0.016544167,0.008749788,0.0034613835,0.00036463194],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001079329,0.00034595077,0.011421723,0.0013999373,0.000906784,0.002779383,0.005330571,0.17535666,0.011363868,0.6760923,0.0076498343,0.10627372],"study_design_scores_gemma":[0.00017079584,0.00009303267,0.00089056004,0.00017274274,0.00022491558,0.0006867141,0.0011158849,0.35194433,0.009511975,0.6274415,0.0076322523,0.00011528599],"about_ca_topic_score_codex":0.0049118944,"about_ca_topic_score_gemma":0.0065247184,"teacher_disagreement_score":0.02456359,"about_ca_system_score_codex":0.0028469597,"about_ca_system_score_gemma":0.0039059957,"threshold_uncertainty_score":0.12990624},"labels":[],"label_agreement":null},{"id":"W2090656321","doi":"10.5539/cis.v1n1p12","title":"Speedy Algorithm of Public Traffic Route Selection Based on Adaptive Backbone Network","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Backbone network; Selection (genetic algorithm); Computation; Core (optical fiber); Algorithm; Service (business); Selection algorithm; Computer network; Telecommunications; Artificial intelligence","score_opus":0.02675363555684376,"score_gpt":0.22272681670471225,"score_spread":0.1959731811478685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090656321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05236438,0.00021477489,0.94271487,0.00016766005,0.000087655564,0.0001481549,0.00007199659,0.0008951652,0.0033352487],"genre_scores_gemma":[0.57541186,0.00028467065,0.4171158,0.00009395592,0.000099114935,0.00031407172,0.0004221113,0.00010268315,0.006155742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993931,0.000097356526,0.00003918907,0.00016121118,0.00022414295,0.000085081156],"domain_scores_gemma":[0.99925,0.00020089839,0.000088380664,0.00010498707,0.000313516,0.000042146145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071092474,0.00046110034,0.0005796261,0.0011223909,0.0007185562,0.00082594866,0.0011460312,0.0004830122,0.0024334998],"category_scores_gemma":[0.0023812435,0.00023853511,0.00025910043,0.0010007251,0.000460039,0.0013558193,0.00087651523,0.00043063838,0.00038562604],"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":[0.0005129549,0.00011100783,0.0056827012,0.00011883104,0.00008241789,0.000118118434,0.00031336307,0.36284626,0.011530919,0.023512516,0.010557014,0.5846139],"study_design_scores_gemma":[0.00010731791,0.00006903097,0.0008705382,0.000007273778,0.000023658535,0.00012802216,0.00006347973,0.98386973,0.004035039,0.006751501,0.004053902,0.000020516445],"about_ca_topic_score_codex":0.006564159,"about_ca_topic_score_gemma":0.0043128384,"teacher_disagreement_score":0.006564159,"about_ca_system_score_codex":0.0008404424,"about_ca_system_score_gemma":0.0013708605,"threshold_uncertainty_score":0.013051927},"labels":[],"label_agreement":null},{"id":"W2091097976","doi":"10.1068/b33118","title":"Dynamic accessibility analysis in location-based service using an incremental parallel algorithm","year":2008,"lang":"en","type":"article","venue":"Environment and Planning B Planning and Design","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MRF Geosystems (Canada)","funders":"","keywords":"Dijkstra's algorithm; Computer science; Voronoi diagram; k-nearest neighbors algorithm; Path (computing); Shortest path problem; Pathfinding; Algorithm; Euclidean distance; Service (business); Data mining; Computer network; Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.05445833725858438,"score_gpt":0.2741563962339289,"score_spread":0.21969805897534453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091097976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022580652,0.00007992296,0.97405267,0.000089521425,0.00001948863,0.000059545247,0.00004254549,0.0008393603,0.0022363374],"genre_scores_gemma":[0.4888626,0.0001504613,0.50783145,0.000042591764,0.000040471168,0.00017272326,0.00024881176,0.00010950009,0.0025414075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994398,0.00009446166,0.000024812669,0.000117489726,0.00022708785,0.000096421994],"domain_scores_gemma":[0.9992448,0.00029815888,0.00008482323,0.00010493727,0.00021518325,0.0000522241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057208183,0.00067084614,0.0010440681,0.0013910912,0.00081405824,0.0010033227,0.0016787603,0.0005763093,0.002411183],"category_scores_gemma":[0.0025570153,0.00047499643,0.0007287567,0.0015396622,0.00062033656,0.0015311736,0.0012302458,0.0005333816,0.0004814455],"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":[0.000116539784,0.000081888524,0.0014262347,0.00005533668,0.000032355816,0.00012561133,0.000085637184,0.84425545,0.001984985,0.0129138725,0.0015350096,0.13738708],"study_design_scores_gemma":[0.000008942005,0.000011761422,0.000081635626,0.0000011889189,0.000006377041,0.00003035934,0.000010705711,0.99575704,0.00037063562,0.0032911694,0.00042706306,0.0000031189493],"about_ca_topic_score_codex":0.014598978,"about_ca_topic_score_gemma":0.010036414,"teacher_disagreement_score":0.014598978,"about_ca_system_score_codex":0.0011838829,"about_ca_system_score_gemma":0.0017196309,"threshold_uncertainty_score":0.029027998},"labels":[],"label_agreement":null},{"id":"W2091334245","doi":"10.1559/152304003100010947","title":"Representation of Generalized Map Series Using Semi-Structured Data Models","year":2003,"lang":"en","type":"article","venue":"Cartography and Geographic Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Computer science; Representation (politics); Schema (genetic algorithms); Information retrieval; Original equipment manufacturer; Data mining; Database","score_opus":0.0408222443581195,"score_gpt":0.2735583114638165,"score_spread":0.232736067105697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091334245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005216327,0.00021590176,0.98944944,0.00032319882,0.000045407727,0.0001131015,0.0011475803,0.0013584682,0.0021305773],"genre_scores_gemma":[0.1309658,0.0009702254,0.8579494,0.0001423764,0.00007479213,0.0004940906,0.0064809965,0.00025053433,0.0026717596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99754447,0.00071424624,0.00041829937,0.00032273892,0.0009071434,0.000093127244],"domain_scores_gemma":[0.9957425,0.0015365486,0.00049193285,0.001355088,0.0007619154,0.00011193573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027005547,0.00083466107,0.00089081307,0.0027580054,0.00072035805,0.0059401374,0.002204446,0.0010513589,0.002685973],"category_scores_gemma":[0.008713852,0.0005690031,0.0020647785,0.0047907634,0.0011417102,0.0077756196,0.0022944238,0.0014949014,0.0010165529],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013753053,0.00009355933,0.0017644784,0.0004437389,0.00010713493,0.00059547456,0.0016863117,0.24803463,0.0022235073,0.6184521,0.0089165475,0.117545016],"study_design_scores_gemma":[0.00003412074,0.000044388664,0.00018892504,0.000086252694,0.000040063747,0.00019720387,0.00031487748,0.69079006,0.0019612468,0.25606036,0.05024705,0.000035484492],"about_ca_topic_score_codex":0.0057406123,"about_ca_topic_score_gemma":0.0063682576,"teacher_disagreement_score":0.0059401374,"about_ca_system_score_codex":0.0013560803,"about_ca_system_score_gemma":0.0016928465,"threshold_uncertainty_score":0.014282048},"labels":[],"label_agreement":null},{"id":"W2092019692","doi":"10.1145/500933.500936","title":"Parallel traversal of signature trees for fast CBIR","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tree traversal; Computer science; Signature (topology); Histogram; Binary tree; Tree (set theory); k-d tree; Image (mathematics); Parallel computing; Theoretical computer science; Artificial intelligence; Algorithm; Mathematics","score_opus":0.016909529304104363,"score_gpt":0.24115249871248032,"score_spread":0.22424296940837596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092019692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023792021,0.00056613196,0.96509403,0.00016933128,0.000054238557,0.00016490958,0.00017296917,0.005916415,0.0040699025],"genre_scores_gemma":[0.1165388,0.00023567013,0.8797755,0.00008241333,0.00002602715,0.00013255951,0.00048561956,0.00040753323,0.002315786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991786,0.00013326514,0.00007360624,0.00013294158,0.00037526642,0.000106289386],"domain_scores_gemma":[0.99847287,0.0005586022,0.00009948076,0.00043177622,0.00035604698,0.00008117401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008128854,0.00067256566,0.00085064716,0.0017092559,0.0011822089,0.001059368,0.0014809684,0.00072149676,0.0069608185],"category_scores_gemma":[0.0032276148,0.000523922,0.0008504506,0.0023007526,0.0005376719,0.0026031074,0.0013565865,0.0008580062,0.0022590323],"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":[0.00051123596,0.00018689402,0.0010770784,0.00035778643,0.000068225665,0.0002035475,0.00036281903,0.025839321,0.07663665,0.020480836,0.008806642,0.865469],"study_design_scores_gemma":[0.00029001525,0.00036719957,0.0012329916,0.00005776328,0.000094460454,0.0011230682,0.00027465072,0.80203736,0.09460189,0.05899994,0.040832818,0.0000878969],"about_ca_topic_score_codex":0.0068834745,"about_ca_topic_score_gemma":0.010906705,"teacher_disagreement_score":0.0069608185,"about_ca_system_score_codex":0.0009224236,"about_ca_system_score_gemma":0.0020379226,"threshold_uncertainty_score":0.023286223},"labels":[],"label_agreement":null},{"id":"W2092044923","doi":"10.3166/geo.21.8-42","title":"Approche géosémantique intégrée pour les cubes évolutifs de données géospatiales","year":2011,"lang":"fr","type":"article","venue":"Revue internationale de géomatique","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"INT; Humanities; Art; Philosophy; Computer science; Programming language","score_opus":0.08373906001639302,"score_gpt":0.2584708038021653,"score_spread":0.17473174378577228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092044923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068205064,0.0003131151,0.98371524,0.00036442996,0.0000601878,0.000104405204,0.00027786108,0.0033406415,0.00500367],"genre_scores_gemma":[0.12872842,0.0005860965,0.85530835,0.00031431962,0.00005313688,0.00040414653,0.0018709311,0.00063271535,0.012101807],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99788743,0.00034591943,0.0001745717,0.0003276977,0.001140004,0.00012437942],"domain_scores_gemma":[0.9989945,0.00026535863,0.00006221286,0.0002730311,0.00035658322,0.000048410686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013452402,0.0008071293,0.0005902036,0.0007513947,0.0007417967,0.003106739,0.0017920161,0.0012315606,0.0061688893],"category_scores_gemma":[0.002645334,0.0006181193,0.0014445976,0.00096513995,0.0006300867,0.0025336274,0.002786014,0.0019413659,0.0019433451],"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":[0.0005950816,0.0002636381,0.0046705776,0.0008887198,0.0003681938,0.0014592626,0.002099125,0.24331751,0.0948275,0.1251119,0.023530556,0.50286794],"study_design_scores_gemma":[0.00008462478,0.00011381471,0.0011202139,0.00013567398,0.00010526843,0.0006201093,0.00058262324,0.758605,0.042464126,0.041988418,0.1541033,0.00007687147],"about_ca_topic_score_codex":0.0072213574,"about_ca_topic_score_gemma":0.0111588845,"teacher_disagreement_score":0.0072213574,"about_ca_system_score_codex":0.0009831557,"about_ca_system_score_gemma":0.0014578494,"threshold_uncertainty_score":0.020637035},"labels":[],"label_agreement":null},{"id":"W2092318232","doi":"10.1109/icra.2012.6224657","title":"Efficient on-line data summarization using extremum summaries","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Automatic summarization; Computer science; Sample (material); Constraint (computer-aided design); Task (project management); Focus (optics); Selection (genetic algorithm); Data mining; Line (geometry); Time constraint; Artificial intelligence; Machine learning; Engineering; Mathematics","score_opus":0.15976209607818245,"score_gpt":0.3241721104130834,"score_spread":0.16441001433490096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092318232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017834444,0.0010586096,0.97464705,0.00029558083,0.000083510466,0.00017044293,0.0009545598,0.0044123754,0.00054333836],"genre_scores_gemma":[0.17013091,0.00056141906,0.8197447,0.0001501681,0.00026706175,0.0002727345,0.006840583,0.0004714082,0.0015610381],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976676,0.00059412,0.00029581453,0.0006737136,0.0006145698,0.0001542273],"domain_scores_gemma":[0.9935527,0.0028875489,0.0010499386,0.0013701593,0.00094145787,0.00019817484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020146538,0.0022488357,0.0026285355,0.0038057722,0.0011428001,0.0027845518,0.0021143025,0.0015320386,0.0020478608],"category_scores_gemma":[0.010239034,0.0007829309,0.0011358027,0.0037502383,0.00051448046,0.0034790342,0.0018495648,0.0016862603,0.0017933806],"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":[0.0008666938,0.000297929,0.002055466,0.00054313743,0.00019629588,0.00033070592,0.0004432479,0.10342104,0.027359404,0.0043140906,0.013948599,0.8462235],"study_design_scores_gemma":[0.00010433091,0.0003556934,0.0014789151,0.000045445202,0.00009774109,0.00035917782,0.0003556154,0.94923306,0.024673423,0.014407961,0.008831456,0.000057226604],"about_ca_topic_score_codex":0.0020050786,"about_ca_topic_score_gemma":0.002755022,"teacher_disagreement_score":0.0038057722,"about_ca_system_score_codex":0.00066955393,"about_ca_system_score_gemma":0.0011997507,"threshold_uncertainty_score":0.010654628},"labels":[],"label_agreement":null},{"id":"W2092683383","doi":"10.1145/584792.584867","title":"Discovering approximate keys in XML data","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; XML; Key (lock); Information retrieval; Document Structure Description; Set (abstract data type); Representation (politics); XML validation; XML database; Well-formed document; Streaming XML; Search engine indexing; XML Encryption; Data mining; Theoretical computer science; World Wide Web; Programming language; Computer security","score_opus":0.08292524527261654,"score_gpt":0.25329512341279864,"score_spread":0.17036987814018212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092683383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055645443,0.0014442493,0.9400022,0.0006052309,0.000036602723,0.00013817042,0.0010402696,0.00062376814,0.0004641033],"genre_scores_gemma":[0.28264377,0.001587858,0.7114523,0.0001982805,0.00009359303,0.00029532862,0.002656014,0.0001212684,0.0009515771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99406415,0.0014201046,0.0010102282,0.00090161856,0.0023009707,0.00030301846],"domain_scores_gemma":[0.98093843,0.011887842,0.002525003,0.0030735305,0.0013747036,0.00020047145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037605409,0.0007052103,0.0014385771,0.004134993,0.00097135716,0.003061572,0.0015831136,0.0014499968,0.00074660865],"category_scores_gemma":[0.030792238,0.00086600124,0.0011186231,0.0063486695,0.0015144733,0.0120371925,0.003271945,0.0015308165,0.000520682],"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":[0.0010197399,0.00017377744,0.020625124,0.0015224904,0.0002506489,0.0020006213,0.0031089354,0.187805,0.028418459,0.1853377,0.007581877,0.56215566],"study_design_scores_gemma":[0.00006684147,0.00019989941,0.0022543438,0.00017939863,0.000121249504,0.0019751145,0.00108377,0.6136177,0.026686357,0.33293542,0.020786533,0.000093346964],"about_ca_topic_score_codex":0.0013975751,"about_ca_topic_score_gemma":0.0014594739,"teacher_disagreement_score":0.004134993,"about_ca_system_score_codex":0.0009059596,"about_ca_system_score_gemma":0.0011435623,"threshold_uncertainty_score":0.019887924},"labels":[],"label_agreement":null},{"id":"W2093885745","doi":"10.1145/1370256.1370277","title":"Revisiting 2DR-tree insertion","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Tree (set theory); Tree structure; Computer science; Node (physics); Space (punctuation); Data structure; Theoretical computer science; Algorithm; Mathematics; Binary tree; Engineering; Combinatorics","score_opus":0.029364105228486256,"score_gpt":0.23050544469329656,"score_spread":0.2011413394648103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093885745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014480103,0.0014862408,0.96711785,0.0012600805,0.0007309937,0.00034702418,0.00062669313,0.0034208437,0.010530104],"genre_scores_gemma":[0.050538566,0.0009314795,0.9401253,0.00092369854,0.00025987285,0.00011381504,0.0011965944,0.0010159415,0.004894835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922733,0.0019737387,0.0007641401,0.001000283,0.0033509328,0.0006375688],"domain_scores_gemma":[0.98026305,0.0054717003,0.0010529947,0.0058178687,0.007041599,0.00035277518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004665344,0.0011098476,0.0016116711,0.0022820907,0.0013742626,0.0035366458,0.0052608326,0.0023221516,0.007057831],"category_scores_gemma":[0.020685121,0.0010263617,0.0021506678,0.0042233956,0.0015427757,0.009204681,0.0042980677,0.0023799152,0.0050224913],"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":[0.00039784217,0.00036986655,0.0058467113,0.0009076694,0.0001230379,0.0014704862,0.0010267891,0.03225845,0.026405893,0.11782593,0.02926075,0.7841065],"study_design_scores_gemma":[0.00021536602,0.000848339,0.0031114824,0.00057386153,0.000245737,0.0069309706,0.0008925635,0.43079254,0.056863222,0.16296582,0.3360982,0.0004618567],"about_ca_topic_score_codex":0.0032443532,"about_ca_topic_score_gemma":0.0048026824,"teacher_disagreement_score":0.007057831,"about_ca_system_score_codex":0.0010976336,"about_ca_system_score_gemma":0.0022571308,"threshold_uncertainty_score":0.024672985},"labels":[],"label_agreement":null},{"id":"W2094263052","doi":"10.1139/x07-036","title":"Improved road network design models with the consideration of various link patterns and road design elements","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Terrain; Grid; Computer science; Network planning and design; Graph; Minimum spanning tree; Link (geometry); Mathematical optimization; Grid cell; Tree (set theory); Representation (politics); Steiner tree problem; Graph theory; Field (mathematics); Path (computing); Algorithm; Mathematics; Theoretical computer science; Geography; Cartography","score_opus":0.07844955858372361,"score_gpt":0.2989638394534982,"score_spread":0.2205142808697746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094263052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016749136,0.00013534323,0.9767096,0.00010816632,0.000021896998,0.00004936344,0.00023156608,0.0001672457,0.005827634],"genre_scores_gemma":[0.43208554,0.0007978558,0.55527663,0.00006962921,0.00003458351,0.0005064974,0.00082441117,0.0001749799,0.010229934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994672,0.00016792298,0.00003361289,0.000085509324,0.0001881093,0.000057709607],"domain_scores_gemma":[0.9992679,0.0003216828,0.000106826315,0.00008535989,0.0001953082,0.000022890605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064970995,0.0009052413,0.0007246137,0.0008897917,0.00029390783,0.0010549593,0.0014910153,0.00092759967,0.0031275533],"category_scores_gemma":[0.002011904,0.00066224945,0.00112623,0.0014474253,0.00037764947,0.002055934,0.000567203,0.0009931996,0.00066099403],"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":[0.0000049071105,0.0000065892236,0.00009767647,0.00001554649,0.0000035918279,0.000011811624,0.0000072278517,0.99221957,0.00032950443,0.0035993545,0.00012642021,0.0035779017],"study_design_scores_gemma":[0.0000031935783,0.000007637398,0.000060568083,0.000003537964,0.0000054447623,0.000010870481,0.000005024664,0.99601114,0.00024053053,0.0027396886,0.00090850564,0.000003846108],"about_ca_topic_score_codex":0.00866218,"about_ca_topic_score_gemma":0.018455224,"teacher_disagreement_score":0.00866218,"about_ca_system_score_codex":0.0011901848,"about_ca_system_score_gemma":0.0011836715,"threshold_uncertainty_score":0.017223537},"labels":[],"label_agreement":null},{"id":"W2094536036","doi":"10.1007/978-3-540-79299-4","title":"Transactions on Computational Science I","year":2008,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Pleasure; Computer science; Volume (thermodynamics); Series (stratigraphy); Data science; Psychology; Geology","score_opus":0.016204287122543144,"score_gpt":0.24590869321671666,"score_spread":0.2297044060941735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094536036","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035625536,0.052511074,0.11630778,0.030520601,0.049363866,0.00024399963,0.0031967843,0.0018602352,0.7424331],"genre_scores_gemma":[0.04584423,0.047168862,0.04878039,0.003950553,0.010840414,0.00046487642,0.00509043,0.001315851,0.8365444],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986436,0.00027160128,0.00009291174,0.00027540707,0.00062257913,0.0000939254],"domain_scores_gemma":[0.9976701,0.0005288665,0.000073570096,0.0010300281,0.0005336059,0.0001637236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017131364,0.0012006464,0.0021311184,0.001350124,0.0008744848,0.0048066746,0.0016662072,0.0015911191,0.18638444],"category_scores_gemma":[0.005376843,0.00052907737,0.0010132104,0.0033721,0.001909337,0.0031894147,0.0026852025,0.003938311,0.09623902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007407288,0.00005807835,0.00036234604,0.00045027223,0.00006176827,0.000041632473,0.00007663777,0.001565261,0.00067411235,0.16184454,0.5628595,0.2719319],"study_design_scores_gemma":[0.00003073636,0.000026461374,0.0003515534,0.00017813992,0.000037028025,0.00009571604,0.00006511803,0.0032672319,0.0003485599,0.120613106,0.8749757,0.000010756959],"about_ca_topic_score_codex":0.0017326486,"about_ca_topic_score_gemma":0.0016429011,"teacher_disagreement_score":0.18638444,"about_ca_system_score_codex":0.001569904,"about_ca_system_score_gemma":0.0021872104,"threshold_uncertainty_score":0.6235181},"labels":[],"label_agreement":null},{"id":"W2096830858","doi":"10.1007/s00224-014-9539-7","title":"Probabilistic k-Median Clustering in Data Streams","year":2014,"lang":"en","type":"article","venue":"Theory of Computing Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Probabilistic logic; Mathematics; Cluster analysis; Representation (politics); Metric (unit); Probability distribution; Euclidean geometry; Point (geometry); Probabilistic analysis of algorithms; Combinatorics; Statistics","score_opus":0.03428720038795635,"score_gpt":0.25713929499946075,"score_spread":0.2228520946115044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096830858","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015937317,0.00037361626,0.9826132,0.0002474271,0.000053881686,0.000037167458,0.000118688295,0.00023999944,0.00037875428],"genre_scores_gemma":[0.62828666,0.0012173016,0.3640312,0.00015621557,0.00039989353,0.0002812068,0.0010717507,0.00025525448,0.004300567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964414,0.0011288924,0.00031544102,0.00075461157,0.0010686835,0.00029081607],"domain_scores_gemma":[0.98837435,0.0074186553,0.00095698214,0.0011208782,0.0017960314,0.00033310914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005619589,0.00072395353,0.002510605,0.0025975555,0.0013947337,0.0029370289,0.0033535012,0.0022514914,0.0015715461],"category_scores_gemma":[0.024687503,0.0011543673,0.0013943837,0.004352227,0.0015614413,0.0044815037,0.002188617,0.0018748741,0.0005373481],"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":[0.0005072544,0.00009071727,0.0028812794,0.00022281265,0.00011710291,0.00010999331,0.0002718801,0.8565718,0.0013718596,0.06582125,0.002330668,0.06970332],"study_design_scores_gemma":[0.000007724105,0.000011987983,0.00021682866,0.000007941034,0.000006783321,0.000023200939,0.000028075625,0.96792555,0.0003042706,0.031069512,0.00038912575,0.000008908764],"about_ca_topic_score_codex":0.004731608,"about_ca_topic_score_gemma":0.0037739181,"teacher_disagreement_score":0.005619589,"about_ca_system_score_codex":0.002183562,"about_ca_system_score_gemma":0.0015475368,"threshold_uncertainty_score":0.029719591},"labels":[],"label_agreement":null},{"id":"W2096934584","doi":"10.1109/icde.2009.91","title":"Keyword Search over Dynamic Categorized Information","year":2009,"lang":"en","type":"article","venue":"Proceedings - International Conference on Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"ASTER","funders":"","keywords":"Computer science; Categorization; Focus (optics); Information retrieval; Keyword search; Data mining; Contrast (vision); Artificial intelligence","score_opus":0.03711792081511415,"score_gpt":0.2881913891656117,"score_spread":0.25107346835049754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096934584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36861232,0.006633928,0.60408664,0.001147796,0.00017282012,0.0005379022,0.0048176493,0.007450863,0.0065401075],"genre_scores_gemma":[0.77593446,0.0008623524,0.21651292,0.00024151582,0.00008541222,0.00018560588,0.003873834,0.00014029628,0.0021636882],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964394,0.0006163516,0.00048506467,0.00092359155,0.0011167937,0.00041867365],"domain_scores_gemma":[0.98878646,0.005558055,0.0012963625,0.0022206865,0.0017176485,0.00042084153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020588778,0.00094494177,0.002372018,0.005527687,0.0008768301,0.0031240568,0.0028255144,0.0014428317,0.0015725543],"category_scores_gemma":[0.015576662,0.0007467285,0.00077773794,0.009586702,0.0008025465,0.005865124,0.0026307967,0.0006549562,0.0013744936],"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":[0.0050280257,0.00069173303,0.02687939,0.00291496,0.0006131736,0.004287046,0.0030581544,0.17092027,0.086074874,0.037322644,0.014934382,0.64727527],"study_design_scores_gemma":[0.00019294578,0.0008861517,0.0041889013,0.00012342336,0.000237883,0.002872994,0.0013183804,0.8620007,0.047544297,0.06514835,0.015312782,0.00017327396],"about_ca_topic_score_codex":0.0043084812,"about_ca_topic_score_gemma":0.003951457,"teacher_disagreement_score":0.005527687,"about_ca_system_score_codex":0.0013747514,"about_ca_system_score_gemma":0.0016467951,"threshold_uncertainty_score":0.010888517},"labels":[],"label_agreement":null},{"id":"W2097016224","doi":"10.1109/icccyb.2010.5491218","title":"Real-time collaborative intelligent services for sensor networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sensor web; Computer science; Wireless sensor network; Web service; Ajax; Architecture; Service (business); Variety (cybernetics); World Wide Web; Computer network; Database; Key distribution in wireless sensor networks; Telecommunications","score_opus":0.008386407147198568,"score_gpt":0.2473887321151258,"score_spread":0.23900232496792723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097016224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01763608,0.0018384136,0.9501084,0.0012648541,0.00037158246,0.00023104293,0.00014691683,0.0055670757,0.02283567],"genre_scores_gemma":[0.5632944,0.0022731256,0.40797108,0.00047372316,0.0003915534,0.0004291247,0.0008155018,0.00056765083,0.02378384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983347,0.0005645862,0.00012307556,0.00016090642,0.00067757576,0.00013918466],"domain_scores_gemma":[0.9985978,0.00043034327,0.00010474399,0.00042606646,0.00027174223,0.00016925689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015810491,0.00064675964,0.0005459134,0.00054357434,0.0009975662,0.00246707,0.0017416517,0.0014554738,0.0045807734],"category_scores_gemma":[0.0035809353,0.00024826347,0.00047860463,0.0009202048,0.0007665224,0.00303778,0.0017425681,0.0010053689,0.0013653283],"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":[0.00097315386,0.0004512129,0.001460136,0.0008901448,0.00012962114,0.0013513957,0.0017773592,0.0651233,0.03812415,0.409681,0.046054147,0.43398443],"study_design_scores_gemma":[0.00017938895,0.0002172206,0.0007675019,0.00008470219,0.00008377497,0.0009959848,0.00038142916,0.60095346,0.01877144,0.18455254,0.1929364,0.00007612586],"about_ca_topic_score_codex":0.0026062897,"about_ca_topic_score_gemma":0.0026842437,"teacher_disagreement_score":0.0045807734,"about_ca_system_score_codex":0.0008459601,"about_ca_system_score_gemma":0.00073982816,"threshold_uncertainty_score":0.015324175},"labels":[],"label_agreement":null},{"id":"W2097402427","doi":"10.5555/545538.545541","title":"View updates in a semantic data modelling paradigm","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Allison University","funders":"","keywords":"Sketch; Computer science; Categorical variable; Schema (genetic algorithms); Scope (computer science); Data modeling; Database schema; Theoretical computer science; Information retrieval; Database; Database design; Programming language; Algorithm; Machine learning","score_opus":0.08291259319403696,"score_gpt":0.28249692409287197,"score_spread":0.199584330898835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097402427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064279316,0.00016958803,0.9900242,0.00071940484,0.00005249515,0.000073347175,0.00010280131,0.000361637,0.0020685662],"genre_scores_gemma":[0.2362294,0.000462324,0.7576448,0.0005327873,0.00016692783,0.00043732882,0.0005925303,0.00023428212,0.003699735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9863708,0.0046825064,0.0014685125,0.0020759797,0.0047926116,0.0006096238],"domain_scores_gemma":[0.9816211,0.007759553,0.00095793436,0.007172055,0.0020282408,0.00046112476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013216732,0.0006775722,0.0010869043,0.002440283,0.0018318893,0.007981683,0.003908258,0.0034808614,0.0022180344],"category_scores_gemma":[0.02867828,0.0010707618,0.0022681789,0.003171172,0.007848054,0.027336115,0.009451956,0.0053283726,0.0004919954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040565512,0.000013766175,0.00034151133,0.00005631539,0.00001634797,0.00011568731,0.00087689725,0.007542102,0.0006350084,0.9728555,0.00080326915,0.016703023],"study_design_scores_gemma":[0.000028506478,0.000039878298,0.00008914253,0.000047056372,0.000035185127,0.00027856923,0.00040038847,0.08667167,0.002723118,0.88464105,0.025006777,0.000038517945],"about_ca_topic_score_codex":0.0028378654,"about_ca_topic_score_gemma":0.002112178,"teacher_disagreement_score":0.013216732,"about_ca_system_score_codex":0.0022730906,"about_ca_system_score_gemma":0.0024034474,"threshold_uncertainty_score":0.06989759},"labels":[],"label_agreement":null},{"id":"W2097783272","doi":"10.1016/j.jvlc.2011.01.001","title":"Formal classification of integrity constraints in spatiotemporal database applications","year":2011,"lang":"en","type":"article","venue":"Journal of Visual Languages & Computing","topic":"Data Management and Algorithms","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":"Natural Resources Canada; Université Laval; Natural Sciences and Engineering Research Council","funders":"","keywords":"Data integrity; Computer science; Terminology; Constraint (computer-aided design); Database; Data mining","score_opus":0.05527544016951537,"score_gpt":0.34337204731802734,"score_spread":0.288096607148512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097783272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09653747,0.0014240098,0.88573074,0.003340945,0.00023599669,0.00039703792,0.0012422882,0.0007132017,0.010378206],"genre_scores_gemma":[0.7078463,0.0013399154,0.28047425,0.0007153992,0.0007679806,0.00048086827,0.0030969242,0.00029507995,0.0049832133],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99029726,0.0022343034,0.0019722253,0.0011530672,0.0034345514,0.0009087342],"domain_scores_gemma":[0.9613654,0.02063323,0.004383685,0.005478023,0.0071171164,0.001022569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082446905,0.00069351064,0.0012467258,0.0042129266,0.0018245131,0.009036589,0.0032413797,0.0021576202,0.0035305643],"category_scores_gemma":[0.032567684,0.0012244694,0.0020452647,0.004525213,0.0035085713,0.014693889,0.003324162,0.004181706,0.0004720062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008975913,0.00014672612,0.0026452104,0.00020418088,0.00005865332,0.00029066656,0.00062048144,0.012129885,0.0016028726,0.95189595,0.002485341,0.027830267],"study_design_scores_gemma":[0.000050048122,0.00005395147,0.00091732066,0.0001409132,0.00009602978,0.00039257942,0.00038267576,0.13762091,0.002405172,0.8464253,0.011470835,0.000044218137],"about_ca_topic_score_codex":0.005086806,"about_ca_topic_score_gemma":0.004357257,"teacher_disagreement_score":0.009036589,"about_ca_system_score_codex":0.0022276826,"about_ca_system_score_gemma":0.0040592467,"threshold_uncertainty_score":0.043602645},"labels":[],"label_agreement":null},{"id":"W2098416578","doi":"10.1145/335191.335419","title":"Efficient and extensible algorithms for multi query optimization","year":2000,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":409,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Query optimization; Heuristics; Benchmark (surveying); Overhead (engineering); Greedy algorithm; Heuristic; Algorithm; Data mining; Artificial intelligence","score_opus":0.04290867931345283,"score_gpt":0.2807725920124819,"score_spread":0.23786391269902907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098416578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004667192,0.00056318997,0.9861907,0.00017247215,0.000051220395,0.00020325306,0.00017112051,0.0055048056,0.0024759239],"genre_scores_gemma":[0.07820176,0.00054705853,0.9176114,0.00017988861,0.00009716884,0.00048460503,0.0006516078,0.00065620756,0.0015702947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99578613,0.0009963744,0.0005028958,0.0007386413,0.0015119823,0.00046399987],"domain_scores_gemma":[0.9935731,0.0035087087,0.00044126844,0.0016773692,0.0006742534,0.00012519065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003566887,0.0029344202,0.0017107454,0.0025227533,0.0010040456,0.0026525422,0.0045367344,0.001996858,0.0048064147],"category_scores_gemma":[0.011892577,0.0011619609,0.0017486123,0.0042608213,0.0012537069,0.0043402384,0.0033920587,0.0031181625,0.0023914773],"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":[0.000404842,0.00047695445,0.0016505545,0.00036258128,0.00013017678,0.00014419055,0.00023688046,0.3925352,0.006174304,0.038432084,0.014707178,0.54474515],"study_design_scores_gemma":[0.00013008706,0.00007580968,0.00021414887,0.000024916668,0.000037327627,0.00010265397,0.00006868371,0.9586068,0.0028698472,0.032729834,0.0051128343,0.00002704596],"about_ca_topic_score_codex":0.0053370357,"about_ca_topic_score_gemma":0.0062771523,"teacher_disagreement_score":0.0053370357,"about_ca_system_score_codex":0.0017277885,"about_ca_system_score_gemma":0.0023004024,"threshold_uncertainty_score":0.018863738},"labels":[],"label_agreement":null},{"id":"W2099328971","doi":"","title":"Ad-hoc top-k query answering for data streams","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Tuple; Search engine indexing; Pruning; Set (abstract data type); Information retrieval; Data mining; Wireless ad hoc network; Data stream mining; Representation (politics); Spatial query; Database; Theoretical computer science; Sargable; Web search query; Search engine","score_opus":0.05186536901822806,"score_gpt":0.3103343436705761,"score_spread":0.258468974652348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099328971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09928675,0.00092142203,0.89394057,0.0003957828,0.00006368192,0.00028666228,0.00040420383,0.0031519083,0.0015490152],"genre_scores_gemma":[0.5688633,0.0004971026,0.4274039,0.00011317652,0.00015968668,0.00022822723,0.0010129807,0.00022288423,0.001498767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99451774,0.0013610143,0.00061642827,0.001092116,0.0019541646,0.00045858035],"domain_scores_gemma":[0.9893393,0.0046590804,0.0011739284,0.0030920696,0.001310932,0.00042473894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044544972,0.0011085791,0.0026889774,0.001782568,0.001655186,0.0035596632,0.003780178,0.0013240719,0.0015449543],"category_scores_gemma":[0.013762636,0.00082474377,0.0010782629,0.00410455,0.001614677,0.0070808027,0.0025380102,0.0014256346,0.0006158122],"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":[0.0026241178,0.0007675314,0.011898175,0.0007666901,0.00042655706,0.0006645742,0.001651725,0.39663118,0.03964942,0.059688225,0.013084601,0.47214714],"study_design_scores_gemma":[0.000101206744,0.00033237785,0.0009829188,0.000016026985,0.000088490546,0.0006150891,0.0004658375,0.9275182,0.019154387,0.046860754,0.0038135464,0.00005115997],"about_ca_topic_score_codex":0.0030254119,"about_ca_topic_score_gemma":0.0026388564,"teacher_disagreement_score":0.0044544972,"about_ca_system_score_codex":0.0015402678,"about_ca_system_score_gemma":0.0018064969,"threshold_uncertainty_score":0.023557901},"labels":[],"label_agreement":null},{"id":"W2100161618","doi":"10.1109/icde.2005.226","title":"High-Dimensional Similarity Searches Using A Metric Pseudo-Grid","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Metric (unit); Computer science; Similarity (geometry); Grid; Artificial intelligence; Mathematics; Engineering; Geometry","score_opus":0.04889620410546126,"score_gpt":0.2785726413087025,"score_spread":0.22967643720324127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100161618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08887284,0.0005472386,0.9040447,0.00015365503,0.00004907963,0.0001817564,0.00049047114,0.0028072712,0.0028529386],"genre_scores_gemma":[0.3323101,0.00021716065,0.66462356,0.000064375745,0.000021340125,0.00023057559,0.001033504,0.00011615417,0.0013832616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984402,0.00040266805,0.0001398087,0.00026266684,0.00064977387,0.00010483069],"domain_scores_gemma":[0.998243,0.0005010381,0.00017640654,0.0006670459,0.000323605,0.000089015724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000661369,0.00039858394,0.0008757556,0.002012016,0.00067122636,0.0015446541,0.0013451012,0.00068750314,0.002103393],"category_scores_gemma":[0.004383862,0.00032604544,0.00055028027,0.004435258,0.0006065467,0.0025121504,0.0021726524,0.00044897743,0.0012038224],"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":[0.0013812861,0.00042174343,0.009730874,0.00061895,0.0002461519,0.000675499,0.0010990372,0.10419753,0.08716348,0.076250374,0.01396093,0.70425415],"study_design_scores_gemma":[0.00021172699,0.00082181324,0.004186723,0.00003761288,0.000030385394,0.0014085697,0.0006136692,0.88203657,0.03556829,0.053183753,0.021814415,0.00008644048],"about_ca_topic_score_codex":0.0020423604,"about_ca_topic_score_gemma":0.0024679054,"teacher_disagreement_score":0.002103393,"about_ca_system_score_codex":0.00042738102,"about_ca_system_score_gemma":0.0008800325,"threshold_uncertainty_score":0.007036507},"labels":[],"label_agreement":null},{"id":"W2100235843","doi":"10.5555/1182635.1164146","title":"Relaxing join and selection queries","year":2006,"lang":"en","type":"article","venue":"National University of Singapore","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Joins; Computer science; Traverse; Query optimization; Theoretical computer science; Relaxation (psychology); Join (topology); Lattice (music); Selection (genetic algorithm); Information retrieval; Data mining; Mathematics; Combinatorics; Machine learning","score_opus":0.008028305870935474,"score_gpt":0.17630563071848537,"score_spread":0.1682773248475499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100235843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09177588,0.0006240854,0.8990702,0.0016899762,0.00009170002,0.00053378363,0.00056740124,0.0020736493,0.0035733765],"genre_scores_gemma":[0.35603058,0.00027328302,0.63866735,0.0006250377,0.00020368703,0.00043974334,0.0012142027,0.0004599202,0.002086168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.957993,0.019382775,0.0034078374,0.004505541,0.012009622,0.002701133],"domain_scores_gemma":[0.92699033,0.043305486,0.0052312375,0.017331481,0.005060997,0.002080508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027885035,0.0016404837,0.0032731069,0.0019145783,0.0027771033,0.004769934,0.004623576,0.0029858383,0.0045137503],"category_scores_gemma":[0.07891474,0.0015997544,0.0022974121,0.0025004393,0.0055974657,0.012538206,0.0076816226,0.006531983,0.0012185536],"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":[0.0035637082,0.0009286675,0.013872755,0.0012384742,0.0004635395,0.0011572331,0.0067494726,0.3544259,0.07775334,0.2203816,0.011915519,0.30754977],"study_design_scores_gemma":[0.0004117861,0.0011486657,0.0014776402,0.000116952564,0.0001683905,0.0010307188,0.0021959003,0.7705643,0.025256649,0.17279744,0.024649838,0.00018177702],"about_ca_topic_score_codex":0.0030487624,"about_ca_topic_score_gemma":0.0038610667,"teacher_disagreement_score":0.027885035,"about_ca_system_score_codex":0.0018406608,"about_ca_system_score_gemma":0.004221964,"threshold_uncertainty_score":0.1474719},"labels":[],"label_agreement":null},{"id":"W2100247763","doi":"10.5555/1182635.1164182","title":"Similarity search: a matching based approach","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Nearest neighbor search; Similarity (geometry); Computer science; Matching (statistics); Object (grammar); Set (abstract data type); Curse of dimensionality; k-nearest neighbors algorithm; Data mining; Information retrieval; Mathematics; Artificial intelligence; Image (mathematics)","score_opus":0.027808764142922257,"score_gpt":0.2324174349816486,"score_spread":0.20460867083872636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100247763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035820175,0.0033825876,0.9820062,0.0009715826,0.00020912112,0.00027328436,0.00015993234,0.0005422798,0.008872991],"genre_scores_gemma":[0.16457254,0.0051254313,0.81366515,0.0009206874,0.0007053725,0.00041429987,0.0009156212,0.0002298735,0.013450932],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9929507,0.0018455196,0.0004914786,0.0014302257,0.0029928857,0.00028931236],"domain_scores_gemma":[0.99749076,0.0010138411,0.0001645704,0.0005713793,0.00062640617,0.00013307885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028525046,0.0009557569,0.002428279,0.006248432,0.0016744855,0.0039128307,0.0052672843,0.0037962785,0.006787254],"category_scores_gemma":[0.010094174,0.00067510706,0.0017816238,0.009226812,0.0022966275,0.009454555,0.0041527245,0.0019829366,0.0033857925],"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":[0.00026230552,0.00041043793,0.0017253933,0.00086268387,0.0002870971,0.0004027182,0.0004946709,0.04807879,0.006806426,0.35487553,0.019647315,0.56614655],"study_design_scores_gemma":[0.00010364137,0.00040292327,0.0009687631,0.00018504146,0.000173262,0.0018933078,0.00041920788,0.4531498,0.006211148,0.45542866,0.08094673,0.00011749987],"about_ca_topic_score_codex":0.0030733617,"about_ca_topic_score_gemma":0.0016241318,"teacher_disagreement_score":0.006787254,"about_ca_system_score_codex":0.001854427,"about_ca_system_score_gemma":0.001817591,"threshold_uncertainty_score":0.022705555},"labels":[],"label_agreement":null},{"id":"W2100598684","doi":"","title":"Location-based indexing for mobile context-aware access to a digital library","year":2007,"lang":"en","type":"article","venue":"Research Commons (University of Waikato)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Search engine indexing; World Wide Web; Digital library; Information access; Context (archaeology); Mobile device; Mobile computing; Tourism; Telecommunications; Geography","score_opus":0.07120690979862587,"score_gpt":0.3309801712535136,"score_spread":0.2597732614548877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100598684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0646998,0.0021309305,0.9215353,0.0007876788,0.00021342575,0.0002835284,0.0009055525,0.0028828792,0.0065609114],"genre_scores_gemma":[0.50467724,0.0012127428,0.4868245,0.00021587285,0.00022686324,0.00026653765,0.0017709229,0.0001184623,0.0046868683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989126,0.00026141433,0.00012347868,0.00017119905,0.0003933849,0.00013792342],"domain_scores_gemma":[0.99849546,0.00049714075,0.00012731452,0.0005041619,0.00028443398,0.00009133533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009341746,0.000310324,0.0008398732,0.0024478072,0.0012380718,0.0018456227,0.001325468,0.0009808545,0.003877617],"category_scores_gemma":[0.006140661,0.00028808083,0.00050121587,0.0036594868,0.0005787412,0.0038689186,0.0024944877,0.00063797145,0.0018214561],"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":[0.00049917033,0.00033079943,0.005555495,0.00035585815,0.00006656588,0.00035700877,0.00089014013,0.05894443,0.020389276,0.087160036,0.020351723,0.8050995],"study_design_scores_gemma":[0.00007332403,0.00021067023,0.0026334622,0.000049652383,0.00006729611,0.0008336652,0.0005697342,0.9089272,0.007750476,0.05064183,0.02817022,0.000072530136],"about_ca_topic_score_codex":0.007747336,"about_ca_topic_score_gemma":0.0146406535,"teacher_disagreement_score":0.007747336,"about_ca_system_score_codex":0.00088808924,"about_ca_system_score_gemma":0.0012155923,"threshold_uncertainty_score":0.015404463},"labels":[],"label_agreement":null},{"id":"W2100732567","doi":"10.1109/wise.2000.882860","title":"An extended conceptual model for fuzzy data modeling","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conceptual model; Fuzzy set; Computer science; Fuzzy logic; Data modeling; Data mining; Conceptual schema; Data model (GIS); Artificial intelligence; Theoretical computer science; Database","score_opus":0.19636446026420123,"score_gpt":0.31601500321513476,"score_spread":0.11965054295093352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100732567","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015384823,0.0006458279,0.9922104,0.00064260187,0.00015758302,0.00008252717,0.00027535096,0.00026887475,0.0041783727],"genre_scores_gemma":[0.104589194,0.0016409135,0.886629,0.00066291506,0.00033428552,0.0006154694,0.0013216836,0.000109507644,0.0040970235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953147,0.0017639982,0.0005783175,0.0007051523,0.0013909665,0.0002469378],"domain_scores_gemma":[0.9966275,0.0011673843,0.00030483186,0.00089977216,0.0008000405,0.00020040592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005217529,0.00088165986,0.00088178343,0.0030998457,0.0011818007,0.0050956225,0.0027637004,0.001905352,0.0044460446],"category_scores_gemma":[0.008388244,0.00059046136,0.0021810771,0.0042727944,0.0020497425,0.009112965,0.0032737164,0.0029474404,0.0010695221],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002482808,0.000019113297,0.00025554682,0.00018078917,0.000044389213,0.00028027597,0.00047507323,0.008775012,0.0007847838,0.9511462,0.0030512176,0.034962654],"study_design_scores_gemma":[0.000032779357,0.00007636483,0.00024387806,0.00026660613,0.00008069672,0.0006829874,0.00026485123,0.13135137,0.0010503008,0.7200338,0.14584634,0.00006996492],"about_ca_topic_score_codex":0.0032760687,"about_ca_topic_score_gemma":0.0024374141,"teacher_disagreement_score":0.005217529,"about_ca_system_score_codex":0.0017674834,"about_ca_system_score_gemma":0.0020430486,"threshold_uncertainty_score":0.027593255},"labels":[],"label_agreement":null},{"id":"W2100928221","doi":"10.1142/s0218195905001683","title":"GEOMETRIC ALGORITHMS FOR DENSITY-BASED DATA CLUSTERING","year":2005,"lang":"en","type":"article","venue":"International Journal of Computational Geometry & Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Cluster analysis; Approximation algorithm; Algorithm; Mathematics; Quadratic equation; Data point","score_opus":0.046781824903133894,"score_gpt":0.33619353800168617,"score_spread":0.2894117130985523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100928221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007393097,0.0003549548,0.9979151,0.000095555835,0.000026722644,0.000036909263,0.000045134497,0.00034848994,0.0004378607],"genre_scores_gemma":[0.04272176,0.0011200015,0.953919,0.00010355579,0.00010718346,0.00026177126,0.0005808929,0.00017041905,0.0010154713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954035,0.0012204612,0.00030851967,0.00074243563,0.0021177542,0.00020728423],"domain_scores_gemma":[0.9940181,0.0025686275,0.0005381897,0.0014909579,0.0012522055,0.0001319643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033342692,0.0017337649,0.0022537883,0.0043659564,0.0014107939,0.0026670839,0.004281228,0.0022043062,0.0035056218],"category_scores_gemma":[0.018170351,0.0013111326,0.0018571615,0.006156098,0.0020525586,0.005089718,0.0044358387,0.0034055873,0.0026777822],"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":[0.000105998275,0.00007032574,0.0010393971,0.00038902398,0.0001216864,0.00007267377,0.00026041392,0.5768158,0.0015593991,0.15794806,0.0074401367,0.25417715],"study_design_scores_gemma":[0.000023448652,0.000028189468,0.00021557366,0.000035396853,0.000015554526,0.00010802089,0.00005575774,0.881256,0.0012200513,0.108546324,0.008467405,0.000028234559],"about_ca_topic_score_codex":0.0055854656,"about_ca_topic_score_gemma":0.0036597957,"teacher_disagreement_score":0.0055854656,"about_ca_system_score_codex":0.0030499552,"about_ca_system_score_gemma":0.0020583812,"threshold_uncertainty_score":0.022129059},"labels":[],"label_agreement":null},{"id":"W2100934002","doi":"10.1007/978-3-642-03761-0_20","title":"Exploiting User Navigation to Improve Focused Retrieval","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information retrieval; Boosting (machine learning); Ranking (information retrieval); XML; Key (lock); Process (computing); Data mining; Machine learning; World Wide Web","score_opus":0.017349114839110186,"score_gpt":0.24645950088438867,"score_spread":0.22911038604527847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100934002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12775128,0.007251634,0.8304285,0.00034415175,0.00044208512,0.00020768798,0.00093252637,0.021113588,0.011528559],"genre_scores_gemma":[0.4436168,0.0033995893,0.5238686,0.000575512,0.00040388393,0.00017804677,0.0033546358,0.0014732855,0.023129726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.00010614009,0.000023185436,0.0000844689,0.00015883797,0.00005493195],"domain_scores_gemma":[0.9988569,0.00046898387,0.000044893804,0.00021361599,0.00037133342,0.000044209763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006797363,0.0011654089,0.001158892,0.001615282,0.0005550124,0.0011967038,0.0010984506,0.0012309074,0.008388855],"category_scores_gemma":[0.0032348684,0.00042536325,0.0006462328,0.001904807,0.00030022225,0.0027137024,0.0012008391,0.00076830573,0.005193929],"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":[0.0011078101,0.00041824524,0.0022281716,0.00045092346,0.00013043903,0.00015583677,0.0003604286,0.008718247,0.10612942,0.0030201161,0.025410848,0.8518695],"study_design_scores_gemma":[0.0003386334,0.002068443,0.0063624876,0.00013774326,0.0007717804,0.002199745,0.00040285836,0.7364069,0.18589228,0.01912093,0.0460893,0.00020884955],"about_ca_topic_score_codex":0.003910516,"about_ca_topic_score_gemma":0.006798018,"teacher_disagreement_score":0.008388855,"about_ca_system_score_codex":0.00027716736,"about_ca_system_score_gemma":0.0005707771,"threshold_uncertainty_score":0.028063476},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025695747,0.0007250173,0.9656394,0.00048541432,0.00012175734,0.00009080386,0.00087554916,0.0018631609,0.0045031584],"genre_scores_gemma":[0.51760334,0.0008010139,0.47198722,0.00026404476,0.00028478008,0.00014166573,0.0025065728,0.0005685415,0.0058428454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851876,0.00046776354,0.0000766398,0.00026242508,0.0005092515,0.00016519532],"domain_scores_gemma":[0.99784243,0.00088097696,0.00014145132,0.0005150247,0.00046605524,0.00015409246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018060653,0.00075525406,0.0015466661,0.0012814831,0.0009371686,0.0019830274,0.002259243,0.0012577226,0.00816028],"category_scores_gemma":[0.006874705,0.00041889827,0.0008904736,0.0023660767,0.0007839021,0.004737747,0.0022187382,0.0014022774,0.0014839343],"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":[0.0016253127,0.00025684954,0.004160427,0.0005859976,0.0001722797,0.00021998544,0.00028215864,0.3279897,0.0069037224,0.10304973,0.049441587,0.5053122],"study_design_scores_gemma":[0.000051229214,0.00009808829,0.0004343964,0.000017684886,0.000026919672,0.00013492261,0.00007030936,0.9042034,0.001727576,0.08794363,0.0052674576,0.00002436758],"about_ca_topic_score_codex":0.0037461447,"about_ca_topic_score_gemma":0.0054668393,"teacher_disagreement_score":0.00816028,"about_ca_system_score_codex":0.0006566031,"about_ca_system_score_gemma":0.0014138926,"threshold_uncertainty_score":0.027298927},"labels":[],"label_agreement":null},{"id":"W2101997973","doi":"10.1109/icde.2007.369000","title":"On MBR Approximation of Histories for Historical Queries: Expectations and Limitations","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristic; Bounding overwatch; Index (typography); Set (abstract data type); Pairwise comparison; Filter (signal processing); Volume (thermodynamics); Search engine indexing; Algorithm; Mathematical optimization; Data mining; Mathematics; Information retrieval; Artificial intelligence","score_opus":0.0429416170546344,"score_gpt":0.2591871809807201,"score_spread":0.21624556392608568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101997973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06009584,0.012443796,0.9118227,0.005592058,0.00023283514,0.00030651622,0.0009009369,0.0018923307,0.006713097],"genre_scores_gemma":[0.5041302,0.0076998067,0.48187092,0.00092244573,0.0006833719,0.00039794244,0.0016538297,0.00051391905,0.0021276113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9843286,0.007284709,0.0012416483,0.0014334513,0.0048484015,0.00086310157],"domain_scores_gemma":[0.8338482,0.13111883,0.004639727,0.019145733,0.009299773,0.0019477549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01787435,0.0013964156,0.0028956737,0.0025954936,0.0015100345,0.005298282,0.004397847,0.002268278,0.002668873],"category_scores_gemma":[0.13484271,0.0011588703,0.0011456334,0.0052010496,0.0022169687,0.018212698,0.0031900287,0.003016198,0.0012710771],"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":[0.0020334406,0.00037762863,0.013854187,0.0010891778,0.00020352827,0.00028933183,0.0014879289,0.4978117,0.0050489064,0.09835571,0.01566997,0.36377847],"study_design_scores_gemma":[0.000044075252,0.00019256366,0.0008961411,0.00011375783,0.000028690929,0.00025653944,0.0003612101,0.9618608,0.0015629194,0.030012488,0.0046319654,0.00003888485],"about_ca_topic_score_codex":0.013892421,"about_ca_topic_score_gemma":0.006335459,"teacher_disagreement_score":0.01787435,"about_ca_system_score_codex":0.002754768,"about_ca_system_score_gemma":0.0028970272,"threshold_uncertainty_score":0.09452969},"labels":[],"label_agreement":null},{"id":"W2103049742","doi":"10.1016/j.artint.2013.01.001","title":"Predicting the size of IDA*ʼs search tree","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"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 Regina; University of Alberta","funders":"","keywords":"Preprocessor; Computation; Truncation (statistics); Computer science; Tree (set theory); Line (geometry); Algorithm; Chen; Mathematics; Artificial intelligence; Machine learning; Combinatorics","score_opus":0.05447862698732074,"score_gpt":0.2857127171165383,"score_spread":0.23123409012921753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103049742","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9362918,0.0016625044,0.052484874,0.0007890666,0.0001662028,0.00007014399,0.002478457,0.0016734756,0.004383561],"genre_scores_gemma":[0.96436065,0.00021973948,0.03125419,0.00005752471,0.000047389254,0.000037673854,0.0026642024,0.000056159737,0.001302556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941134,0.0001107098,0.000047493057,0.0001595736,0.00017683463,0.00009407761],"domain_scores_gemma":[0.9893785,0.007519494,0.000457353,0.00052159326,0.0016707898,0.0004522886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001401314,0.00025021538,0.00044306193,0.002172143,0.00058336183,0.0010583597,0.000990827,0.0008799067,0.0017277243],"category_scores_gemma":[0.011713123,0.00021804092,0.00043537025,0.0015559737,0.0002576303,0.0013824406,0.00038282803,0.00084811513,0.0008064182],"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":[0.0017005472,0.0006477476,0.3082903,0.00041196393,0.00023751706,0.00030201674,0.00021808474,0.31567726,0.01748385,0.008797134,0.02689927,0.31933436],"study_design_scores_gemma":[0.000017628085,0.00009761775,0.0094995005,0.000013111142,0.000027501335,0.00010854276,0.00004489592,0.98454165,0.0029003134,0.0018066366,0.0009333184,0.000009133163],"about_ca_topic_score_codex":0.004470302,"about_ca_topic_score_gemma":0.007526925,"teacher_disagreement_score":0.004470302,"about_ca_system_score_codex":0.00094962545,"about_ca_system_score_gemma":0.0011772403,"threshold_uncertainty_score":0.008888543},"labels":[],"label_agreement":null},{"id":"W2103287597","doi":"10.1109/glocom.2008.ecp.75","title":"Coverage-Based Sensor Association Rules for Wireless Vehicular Ad Hoc and Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Wireless sensor network; Computer science; Wireless ad hoc network; Process (computing); Set (abstract data type); Computer network; Association rule learning; Data mining; Base station; Key distribution in wireless sensor networks; Association (psychology); Vehicular ad hoc network; Property (philosophy); Wireless; Distributed computing; Real-time computing; Wireless network; Telecommunications","score_opus":0.013679947225468343,"score_gpt":0.21376686958549648,"score_spread":0.20008692236002815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103287597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016024658,0.00046728746,0.9816472,0.0001913949,0.000041153446,0.00012369646,0.00016025045,0.00031521608,0.0010292601],"genre_scores_gemma":[0.49009377,0.0011121632,0.50552833,0.00020820124,0.00009538276,0.00047066362,0.0010885478,0.000059117905,0.001343774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943462,0.0018390606,0.00060235296,0.00072669034,0.0022679737,0.00021767324],"domain_scores_gemma":[0.9863821,0.008443388,0.0013970112,0.0019613525,0.0015968024,0.00021925828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041715894,0.00054836815,0.0010977612,0.0022929376,0.0008825265,0.0016937476,0.0017612677,0.0009425558,0.0008843645],"category_scores_gemma":[0.0212241,0.00045526336,0.0009891212,0.0033827913,0.0010283901,0.0024405282,0.0012658758,0.0010028945,0.0003336628],"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":[0.0003732604,0.0001671217,0.0077273236,0.00029816173,0.0002978616,0.0005232632,0.00044723146,0.5648296,0.003489228,0.07508882,0.0028454696,0.3439127],"study_design_scores_gemma":[0.000029937892,0.00007487454,0.00069408165,0.000031163512,0.0000643718,0.00030699672,0.000077297445,0.9406378,0.0025147262,0.05158805,0.003956276,0.00002432256],"about_ca_topic_score_codex":0.0028162904,"about_ca_topic_score_gemma":0.002871195,"teacher_disagreement_score":0.0041715894,"about_ca_system_score_codex":0.00065348914,"about_ca_system_score_gemma":0.0011476863,"threshold_uncertainty_score":0.022061765},"labels":[],"label_agreement":null},{"id":"W2103552971","doi":"10.1145/2507157.2507159","title":"Orthogonal query recommendation","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Web query classification; Web search query; Information retrieval; Query expansion; Query optimization; Sargable; Query language; Set (abstract data type); Result set; Search engine; Matching (statistics); Ranking (information retrieval); RDF query language; Spatial query; Mathematics; Programming language","score_opus":0.008742361205546412,"score_gpt":0.19987272210056514,"score_spread":0.19113036089501872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103552971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038726322,0.0083174165,0.9134443,0.0017591596,0.00071138114,0.0013226055,0.0039230804,0.0057359827,0.026059704],"genre_scores_gemma":[0.3269932,0.006480274,0.6233018,0.0015541996,0.00076424144,0.0008207685,0.012629029,0.0005349069,0.026921472],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99047136,0.0026851974,0.00090047164,0.0016974222,0.003558817,0.00068673916],"domain_scores_gemma":[0.9894203,0.0024155066,0.00043240044,0.004483372,0.0029553606,0.0002931474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004880788,0.0013913567,0.0021805512,0.0033558796,0.0011546122,0.0026124625,0.0027143839,0.0016447274,0.01182816],"category_scores_gemma":[0.016089318,0.0006558929,0.0014574794,0.0066210954,0.0011022247,0.0061056414,0.0031596455,0.0015217172,0.0075298175],"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":[0.0017083331,0.0012728618,0.0056310603,0.0011655011,0.0003913501,0.00030939098,0.000395272,0.024363106,0.02083348,0.05586286,0.075194955,0.81287193],"study_design_scores_gemma":[0.0005893909,0.001221668,0.003289894,0.00020179228,0.0004888725,0.0019046012,0.0008588776,0.69709635,0.020591898,0.1410193,0.1324786,0.000258776],"about_ca_topic_score_codex":0.007999815,"about_ca_topic_score_gemma":0.010397166,"teacher_disagreement_score":0.01182816,"about_ca_system_score_codex":0.000992607,"about_ca_system_score_gemma":0.0025377763,"threshold_uncertainty_score":0.03956914},"labels":[],"label_agreement":null},{"id":"W2103615139","doi":"10.14778/1453856.1453955","title":"Keyword query cleaning","year":2008,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Ontario Tech University","funders":"","keywords":"Computer science; Query optimization; Query expansion; Web query classification; Web search query; Sargable; Query language; Information retrieval; Online aggregation; View; Set (abstract data type); Context (archaeology); Database; Query by Example; Data mining; Search engine","score_opus":0.016461420648945766,"score_gpt":0.19089173340955867,"score_spread":0.1744303127606129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103615139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019597169,0.002480983,0.9621047,0.0007570855,0.00028587316,0.0006551513,0.002136894,0.0074214623,0.004560646],"genre_scores_gemma":[0.18961003,0.001962342,0.78983134,0.001141436,0.0002727875,0.00060252304,0.007815471,0.002445092,0.0063191284],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9873661,0.0023895397,0.0019199775,0.0027757487,0.0045550456,0.0009936746],"domain_scores_gemma":[0.9793956,0.0059635495,0.0012226908,0.0074739805,0.005598207,0.0003460422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004937681,0.0016732535,0.003820275,0.0035549812,0.0025916079,0.0047119088,0.0042400225,0.0024649843,0.005817516],"category_scores_gemma":[0.029346311,0.0010337067,0.0026037365,0.0066194898,0.0016084074,0.008339137,0.005772573,0.0025313713,0.0059205526],"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":[0.0015723181,0.00048803416,0.0073494622,0.0024524576,0.00044624702,0.0010919417,0.0017855704,0.026083695,0.07403389,0.039281342,0.06947888,0.7759361],"study_design_scores_gemma":[0.00031492603,0.0008275412,0.005487939,0.0004726626,0.0006274961,0.008258673,0.0044745654,0.39245567,0.20262761,0.13633709,0.24763837,0.00047742258],"about_ca_topic_score_codex":0.0032325184,"about_ca_topic_score_gemma":0.0024737972,"teacher_disagreement_score":0.005817516,"about_ca_system_score_codex":0.0013563127,"about_ca_system_score_gemma":0.0038431478,"threshold_uncertainty_score":0.026113272},"labels":[],"label_agreement":null},{"id":"W2104550444","doi":"10.14778/1453856.1453967","title":"Efficient skyline querying with variable user preferences on nominal attributes","year":2008,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Chinese University of Hong Kong; National Natural Science Foundation of China","keywords":"Skyline; Computer science; Preference; Order (exchange); Tree (set theory); Variable (mathematics); Data mining; Mathematics; Statistics","score_opus":0.022724689187778764,"score_gpt":0.2061796146940327,"score_spread":0.18345492550625395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104550444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32215056,0.0012506908,0.64757866,0.0008736514,0.00004452351,0.00039470292,0.0031423823,0.015319609,0.009245206],"genre_scores_gemma":[0.668922,0.0002594148,0.32281396,0.00014296554,0.00004595333,0.00016255368,0.0043900963,0.0004829592,0.0027800007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976966,0.0007007855,0.00019715035,0.00030999366,0.0007801435,0.00031538925],"domain_scores_gemma":[0.99614173,0.0015902364,0.0003001579,0.0011980204,0.00060294155,0.00016689759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018684062,0.00078478,0.0015068041,0.0010025406,0.0007355697,0.0019901576,0.0015022176,0.00083475653,0.003074718],"category_scores_gemma":[0.0054949936,0.00038032353,0.0005282953,0.0024907838,0.00047219344,0.00457098,0.0016039083,0.0004980673,0.0010221424],"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":[0.0061618695,0.0008045915,0.019354325,0.0011638787,0.0002541036,0.0008666403,0.002058187,0.11754274,0.11929967,0.032725986,0.053801347,0.6459667],"study_design_scores_gemma":[0.00027237448,0.00049444695,0.0032681404,0.0000363577,0.00005924936,0.0007944634,0.0010793254,0.92105126,0.038572315,0.022991937,0.011316477,0.00006363928],"about_ca_topic_score_codex":0.0020817271,"about_ca_topic_score_gemma":0.0038495169,"teacher_disagreement_score":0.003074718,"about_ca_system_score_codex":0.00057753717,"about_ca_system_score_gemma":0.00087930413,"threshold_uncertainty_score":0.010285914},"labels":[],"label_agreement":null},{"id":"W2104992338","doi":"10.1109/ccece.2004.1347728","title":"Analysis of distance based indexing methods for similarity search","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Search engine indexing; Computer science; Similarity (geometry); Nearest neighbor search; Metric (unit); Data mining; Database index; Metric space; Tree (set theory); Access method; Data structure; Similitude; Space (punctuation); Tree structure; Information retrieval; Artificial intelligence; Mathematics; Database","score_opus":0.054382943945520186,"score_gpt":0.3891997797608845,"score_spread":0.3348168358153643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104992338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034230888,0.0072715958,0.9504037,0.0005022324,0.0001296268,0.00025799387,0.0003027548,0.000813473,0.0060877097],"genre_scores_gemma":[0.30354545,0.003442659,0.68645126,0.00017741742,0.00033952107,0.00055777037,0.0014910441,0.00031842716,0.0036764112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9862098,0.003322991,0.0008374563,0.00083692337,0.008437256,0.00035557288],"domain_scores_gemma":[0.964567,0.02584533,0.0012299881,0.003372347,0.0046608187,0.00032453047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067002196,0.00079299917,0.0013964276,0.0066176336,0.0009804019,0.0024627305,0.0024669112,0.0012653129,0.0052698883],"category_scores_gemma":[0.042123113,0.00039004424,0.00090414344,0.008138004,0.0011481844,0.007405208,0.0017022695,0.0011582181,0.0017064422],"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":[0.00061504974,0.00037219134,0.004040615,0.0010158095,0.00019210986,0.000079015976,0.00039682523,0.054907303,0.009931329,0.1981114,0.0053872555,0.7249512],"study_design_scores_gemma":[0.00017086485,0.0006117194,0.0029145046,0.00013291267,0.00010737319,0.0007708254,0.00032116912,0.8216965,0.020155614,0.1330057,0.020011954,0.00010082854],"about_ca_topic_score_codex":0.0013466388,"about_ca_topic_score_gemma":0.0010801362,"teacher_disagreement_score":0.0067002196,"about_ca_system_score_codex":0.002230961,"about_ca_system_score_gemma":0.001292199,"threshold_uncertainty_score":0.035434544},"labels":[],"label_agreement":null},{"id":"W2105283873","doi":"10.1007/978-3-540-30076-2_13","title":"A Partial Pre-aggregation Scheme for HOLAP Engines","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Aggregate (composite); Overhead (engineering); Scheme (mathematics); Online aggregation; Cube (algebra); Greedy algorithm; Algorithm; Theoretical computer science; Search engine; Information retrieval; Web search query; Sargable; Mathematics; Programming language","score_opus":0.017649390009643866,"score_gpt":0.24943202107536577,"score_spread":0.23178263106572192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105283873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027332101,0.00047223608,0.9481598,0.00033039277,0.00026620997,0.00030067546,0.0008549842,0.010301966,0.011981661],"genre_scores_gemma":[0.3108224,0.00038598065,0.65856606,0.00024674565,0.00023440477,0.00034381662,0.0020043843,0.0014894545,0.025906801],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798006,0.00030354413,0.00036874603,0.00025675906,0.000763016,0.00032782037],"domain_scores_gemma":[0.9943334,0.00055180205,0.00017863943,0.004139297,0.00057807274,0.00021883562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032490056,0.00066976075,0.0012512691,0.0013190454,0.0020808568,0.004830327,0.0028544758,0.0010510049,0.010825646],"category_scores_gemma":[0.0038877733,0.0009834765,0.0010473387,0.0023628406,0.0013446918,0.0075665456,0.0047373087,0.0022344496,0.0030571392],"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":[0.0013249519,0.00028114454,0.0017836746,0.0005625163,0.00011751063,0.00025626007,0.0008680144,0.018187286,0.018201966,0.48487988,0.02287078,0.45066607],"study_design_scores_gemma":[0.00023788527,0.00049290736,0.0013449583,0.00018022384,0.00032945024,0.0005823314,0.0004562458,0.25705507,0.06413532,0.55084455,0.12411658,0.00022456724],"about_ca_topic_score_codex":0.001530474,"about_ca_topic_score_gemma":0.0025258667,"teacher_disagreement_score":0.010825646,"about_ca_system_score_codex":0.0011695215,"about_ca_system_score_gemma":0.0019307524,"threshold_uncertainty_score":0.036215365},"labels":[],"label_agreement":null},{"id":"W2105814260","doi":"10.1109/cmpsac.1990.139357","title":"A bi-level object-oriented data model for GIS applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Geographic information system; Object-based spatial database; Object (grammar); Data model (GIS); Data modeling; Representation (politics); Geometric modeling; Set (abstract data type); Spatial analysis; Object-oriented programming; Geometric networks; Function (biology); Data mining; Theoretical computer science; Information retrieval; Spatial database; Database; Artificial intelligence; Programming language; Geography; Mathematics; World Wide Web; Remote sensing","score_opus":0.15955316621612123,"score_gpt":0.2996829217535909,"score_spread":0.14012975553746967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105814260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000703949,0.0002193769,0.9892018,0.00087311346,0.000110446905,0.00029425274,0.0011693921,0.004056858,0.0033707428],"genre_scores_gemma":[0.0233258,0.00084594684,0.9575104,0.0013165307,0.00016401632,0.0011819721,0.007339309,0.0010835179,0.0072324034],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939978,0.0011510606,0.0012237899,0.00065944076,0.002588775,0.00037913886],"domain_scores_gemma":[0.99636996,0.00067149627,0.00024198132,0.0012095651,0.0011904583,0.00031652063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005727965,0.0010846298,0.0013424002,0.0027487709,0.0019941488,0.011252685,0.0060105543,0.0032391194,0.0053184624],"category_scores_gemma":[0.007759479,0.0011274376,0.0019463106,0.0056103054,0.0014855533,0.010012634,0.004157408,0.00437841,0.0070023825],"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":[0.00034155475,0.00028794727,0.0027460074,0.0010352196,0.00012944671,0.0009784568,0.0019835383,0.036937628,0.011021222,0.67911804,0.092702545,0.17271836],"study_design_scores_gemma":[0.00008077206,0.00011844213,0.0005490174,0.00026714584,0.000076843586,0.0007070591,0.00029377185,0.1398153,0.005220477,0.16267914,0.69005835,0.00013377548],"about_ca_topic_score_codex":0.008641008,"about_ca_topic_score_gemma":0.006101531,"teacher_disagreement_score":0.011252685,"about_ca_system_score_codex":0.0022465696,"about_ca_system_score_gemma":0.0038827558,"threshold_uncertainty_score":0.03029269},"labels":[],"label_agreement":null},{"id":"W2106261787","doi":"","title":"On the Suitability of Skyline Queries for Data Exploration","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Strategiske Forskningsråd","keywords":"Skyline; Computer science; Data mining; Data science; Information retrieval","score_opus":0.10389612201650028,"score_gpt":0.2952690435094551,"score_spread":0.19137292149295482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106261787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1536235,0.0039264276,0.822964,0.0052370466,0.00012753317,0.0004777792,0.00065216044,0.002045663,0.010945824],"genre_scores_gemma":[0.62269634,0.0013099279,0.37231913,0.0006611158,0.0002122327,0.0003149473,0.0007224949,0.00070786214,0.001056037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96057796,0.02807944,0.001837005,0.0027473106,0.0056781103,0.0010802352],"domain_scores_gemma":[0.5402881,0.42599344,0.007335747,0.015082313,0.008882005,0.002418399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039076574,0.0015763965,0.0022135612,0.0033080364,0.002298626,0.0060558515,0.0026190735,0.0037161345,0.0041444465],"category_scores_gemma":[0.25572467,0.0010513754,0.001513331,0.005420423,0.0063930596,0.023455612,0.005178406,0.0040254174,0.0008402406],"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":[0.00479418,0.0009316758,0.022391994,0.0013034504,0.0003253504,0.00051809545,0.006437302,0.34730172,0.009296775,0.32382312,0.017188475,0.26568782],"study_design_scores_gemma":[0.00019683906,0.0005946228,0.0012744217,0.00012797548,0.00005643617,0.00039003382,0.000875475,0.8517653,0.002396323,0.13827501,0.0039830445,0.000064389096],"about_ca_topic_score_codex":0.007607943,"about_ca_topic_score_gemma":0.0047538457,"teacher_disagreement_score":0.039076574,"about_ca_system_score_codex":0.0022876237,"about_ca_system_score_gemma":0.002655002,"threshold_uncertainty_score":0.2066592},"labels":[],"label_agreement":null},{"id":"W2107717699","doi":"10.1109/icpr.2006.947","title":"Probabilistic Relaxation using the Heat Equation","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Laplacian matrix; Probabilistic logic; Heat equation; Mathematics; Graph; Computer science; Discrete mathematics; Mathematical analysis; Artificial intelligence","score_opus":0.03669928004500724,"score_gpt":0.24581203043714367,"score_spread":0.20911275039213642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107717699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028977322,0.0002654516,0.9931879,0.00038146172,0.00007796207,0.000026012634,0.000036454534,0.00006431862,0.003062646],"genre_scores_gemma":[0.3704931,0.0021406144,0.60116386,0.0007213184,0.00086223095,0.00062776543,0.00037516878,0.00052504835,0.023090908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983877,0.0006027617,0.000066489185,0.00031381776,0.00049007515,0.00013906388],"domain_scores_gemma":[0.997303,0.0017150112,0.00023672335,0.0003491538,0.0002723305,0.00012376506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002972342,0.0008352391,0.0013141694,0.0010544484,0.0008681125,0.0021898325,0.0023154037,0.0018048511,0.006257457],"category_scores_gemma":[0.008339772,0.0007725386,0.0017430071,0.0012377871,0.0027369186,0.006250452,0.00263135,0.0031913999,0.0009194915],"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":[0.000035325214,0.000024913055,0.00017031885,0.00009617473,0.000025016096,0.000063050924,0.00015879021,0.17627077,0.001167839,0.80405617,0.0014408281,0.01649073],"study_design_scores_gemma":[0.0000143798425,0.000023650158,0.000097942226,0.000018379053,0.000011959411,0.000049686998,0.000030279922,0.6514699,0.00043640198,0.34353122,0.004290904,0.000025284193],"about_ca_topic_score_codex":0.0030927071,"about_ca_topic_score_gemma":0.0016439604,"teacher_disagreement_score":0.006257457,"about_ca_system_score_codex":0.0018746369,"about_ca_system_score_gemma":0.0013392919,"threshold_uncertainty_score":0.02093321},"labels":[],"label_agreement":null},{"id":"W2108523040","doi":"10.1109/icpads.2000.857695","title":"Efficient query result retrieval over the Web","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Web query classification; Web search query; Database; Interface (matter); Web server; Query optimization; Set (abstract data type); Application server; Information retrieval; Operating system; The Internet; Search engine; Programming language","score_opus":0.02038459448792331,"score_gpt":0.22420434647119358,"score_spread":0.20381975198327026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108523040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06551436,0.0017016002,0.9108558,0.0007795922,0.00006460159,0.00018721368,0.00035728503,0.0100875,0.010451994],"genre_scores_gemma":[0.40770966,0.0016124644,0.57842904,0.00029951843,0.00013113924,0.00021741587,0.0015887149,0.0008875674,0.00912444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985409,0.0002942402,0.00015000968,0.00016326283,0.00068593986,0.00016571021],"domain_scores_gemma":[0.998014,0.0008025895,0.00010414185,0.00050455454,0.0005172694,0.0000575183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015220093,0.0006798543,0.0013588398,0.0014846828,0.0009214064,0.0026195778,0.0015618303,0.0010231526,0.003718637],"category_scores_gemma":[0.0051074256,0.0004502261,0.00042355293,0.002733533,0.00069922785,0.0054187654,0.0021101276,0.0005821022,0.002951848],"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":[0.0013698067,0.00057320006,0.0031430016,0.0007468834,0.00017058797,0.002043723,0.001035033,0.065774664,0.12962323,0.03313711,0.023542628,0.73884016],"study_design_scores_gemma":[0.000330348,0.00037145917,0.0018741967,0.00006427252,0.00017001842,0.0023009458,0.0009033774,0.73568124,0.15390976,0.058565456,0.04569422,0.00013474096],"about_ca_topic_score_codex":0.0034568713,"about_ca_topic_score_gemma":0.002146463,"teacher_disagreement_score":0.003718637,"about_ca_system_score_codex":0.0005529789,"about_ca_system_score_gemma":0.00075657526,"threshold_uncertainty_score":0.012440085},"labels":[],"label_agreement":null},{"id":"W2109010760","doi":"10.1109/ssdbm.2006.6","title":"A Disc-based Approach to Data Summarization and Privacy Preservation","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Automatic summarization; Computer science; Data mining; Heuristic; Constraint (computer-aided design); Set (abstract data type); Cluster analysis; Exploit; Algorithm; Theoretical computer science; Information retrieval; Artificial intelligence; Mathematics","score_opus":0.04332015707856461,"score_gpt":0.24894069578372094,"score_spread":0.20562053870515634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109010760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037443805,0.000536358,0.99297017,0.0005181138,0.00006491829,0.00012608129,0.0002566139,0.00044655,0.001336762],"genre_scores_gemma":[0.1390048,0.00089992955,0.8550583,0.00039780245,0.00035891106,0.0004321992,0.0012621416,0.00016862217,0.002417315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99210393,0.0028707462,0.0009839472,0.0011594865,0.002609525,0.00027225763],"domain_scores_gemma":[0.97993726,0.007269791,0.0019635933,0.0072657014,0.0031837635,0.00037993622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064074933,0.0009806504,0.0017646083,0.004388091,0.001387175,0.0035788657,0.0028115138,0.0018790998,0.0022850218],"category_scores_gemma":[0.028098626,0.0005059617,0.0012956082,0.005901217,0.0022455363,0.006400976,0.0039429697,0.0024530946,0.0011116788],"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":[0.0006650124,0.00017804283,0.002126597,0.0008275968,0.00017409437,0.00025625993,0.001012507,0.1182398,0.014269559,0.25491118,0.013708299,0.593631],"study_design_scores_gemma":[0.00012349605,0.00085249176,0.0012123459,0.00021587075,0.000179206,0.0013227863,0.00056354003,0.5310396,0.0428775,0.34965283,0.07184046,0.000119974524],"about_ca_topic_score_codex":0.00062203186,"about_ca_topic_score_gemma":0.00048381105,"teacher_disagreement_score":0.0064074933,"about_ca_system_score_codex":0.0014951306,"about_ca_system_score_gemma":0.0012972134,"threshold_uncertainty_score":0.033886492},"labels":[],"label_agreement":null},{"id":"W2109416465","doi":"","title":"A Combinatorial Approach to Nearly Uncoupled Markov Chains","year":2011,"lang":"en","type":"dissertation","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National University of Ireland; University of Regina","keywords":"Markov chain; Mathematics; Additive Markov chain; Markov chain mixing time; Invariant (physics); Markov property; Combinatorics; Disjoint sets; Markov process; State space; Complement (music); Markov model; Discrete mathematics; Variable-order Markov model; Markov renewal process; Random variable; Statistics","score_opus":0.015594422460048744,"score_gpt":0.23615201266788502,"score_spread":0.22055759020783627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109416465","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01580009,0.0008683993,0.95556337,0.0007112275,0.00015011428,0.00007523842,0.0004633053,0.00024910446,0.026119187],"genre_scores_gemma":[0.5893428,0.0027662558,0.3664791,0.00087718177,0.0010232104,0.00080688874,0.001521414,0.00047150176,0.03671173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979329,0.0007502669,0.00011391599,0.00048786285,0.00051724317,0.00019775884],"domain_scores_gemma":[0.99359953,0.003583527,0.0007492393,0.0008785873,0.0007188514,0.00047020393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015956705,0.001097586,0.0011968856,0.0025486667,0.0012281535,0.003874627,0.0026718418,0.0014023981,0.0100389775],"category_scores_gemma":[0.007701519,0.0010026891,0.0017632607,0.0023327854,0.0029320272,0.0046606325,0.0034374327,0.0034735645,0.0013946985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009388415,0.000011314712,0.00017192325,0.000034349185,0.000014490974,0.00008206815,0.000072982344,0.02497304,0.00020580993,0.97036386,0.00053166266,0.003529157],"study_design_scores_gemma":[0.000007917897,0.000010404762,0.000071564005,0.000019609808,0.000007716413,0.00004514079,0.000021519709,0.14405723,0.00010830251,0.8520405,0.0035969478,0.000013264003],"about_ca_topic_score_codex":0.0029200257,"about_ca_topic_score_gemma":0.0029683402,"teacher_disagreement_score":0.0100389775,"about_ca_system_score_codex":0.0027573865,"about_ca_system_score_gemma":0.0014187138,"threshold_uncertainty_score":0.0335837},"labels":[],"label_agreement":null},{"id":"W2109557983","doi":"","title":"The Last Descent In Samples Of Geometric Random Variables And Permutations.","year":2007,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Descent (aeronautics); Combinatorics; Statistics; Geography","score_opus":0.013036842164318543,"score_gpt":0.23518992439154165,"score_spread":0.2221530822272231,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109557983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114391744,0.002186233,0.8611405,0.0035402456,0.00047063344,0.000108851906,0.001088924,0.0010697189,0.016003126],"genre_scores_gemma":[0.74746305,0.00147422,0.21410926,0.0014623376,0.0010806742,0.0004861346,0.0027217614,0.0007922896,0.030410307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952566,0.0025666116,0.00019436916,0.0006951092,0.0007703731,0.00051693013],"domain_scores_gemma":[0.9827381,0.01116268,0.00094194897,0.0032541465,0.0010570558,0.00084618543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060230703,0.0007031581,0.0014202961,0.0020193146,0.0016185999,0.003871393,0.0023484642,0.0017187388,0.007237],"category_scores_gemma":[0.040802564,0.0008323245,0.0011415249,0.0018488712,0.004374286,0.005716893,0.0027300501,0.0031554247,0.001878725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002685594,0.000062434396,0.002786714,0.00011075689,0.00006919055,0.00017269279,0.00025257998,0.021985713,0.0007151831,0.9281751,0.009493766,0.035907228],"study_design_scores_gemma":[0.0000525815,0.000067376146,0.0011692018,0.000062816755,0.000025181333,0.00019972854,0.00007621057,0.13805853,0.000935957,0.8533585,0.0059670336,0.000026915308],"about_ca_topic_score_codex":0.0024481986,"about_ca_topic_score_gemma":0.004923952,"teacher_disagreement_score":0.007237,"about_ca_system_score_codex":0.002103605,"about_ca_system_score_gemma":0.0016736797,"threshold_uncertainty_score":0.031853378},"labels":[],"label_agreement":null},{"id":"W2110175519","doi":"10.1145/1938551.1938576","title":"(Approximate) uncertain skylines","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Army Research Office; Division of Information and Intelligent Systems; Division of Computer and Network Systems; Division of Computing and Communication Foundations; National Institutes of Health; National Science Foundation","keywords":"Computer science","score_opus":0.061871102603712466,"score_gpt":0.24358545929449738,"score_spread":0.1817143566907849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110175519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05113971,0.0007395895,0.9435565,0.0005637702,0.000052532647,0.000076718774,0.0012785622,0.001006224,0.001586385],"genre_scores_gemma":[0.59429026,0.0008076224,0.3984527,0.00019621369,0.0002672382,0.00019665579,0.0034376576,0.00025398258,0.0020977207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965509,0.000634885,0.0002528836,0.0010366273,0.0012161982,0.0003083541],"domain_scores_gemma":[0.9919505,0.0045297532,0.0010087582,0.0013634564,0.0008954028,0.00025210946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027426602,0.0010428017,0.0020888308,0.00215375,0.0010010422,0.00343427,0.0031128118,0.0022224467,0.003626653],"category_scores_gemma":[0.019949378,0.0012009162,0.0013930312,0.0042126365,0.0012714759,0.0069901,0.0025460033,0.0017867762,0.00081579806],"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":[0.00048962206,0.000053248194,0.007392439,0.00026322782,0.0001376161,0.00029725741,0.00034391411,0.8747613,0.0025289336,0.049060974,0.0054928763,0.059178688],"study_design_scores_gemma":[0.000025556963,0.000028024031,0.0007902378,0.000021161033,0.000019244973,0.00014656248,0.000066584405,0.9636098,0.0010421281,0.032391414,0.0018419204,0.000017371816],"about_ca_topic_score_codex":0.008046429,"about_ca_topic_score_gemma":0.007868717,"teacher_disagreement_score":0.008046429,"about_ca_system_score_codex":0.002079564,"about_ca_system_score_gemma":0.0012528349,"threshold_uncertainty_score":0.015999138},"labels":[],"label_agreement":null},{"id":"W2110557355","doi":"10.1007/s007780100049","title":"Approximate query processing using wavelets","year":2001,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":477,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Wavelet; Query optimization; Data mining; Tuple; Histogram; Algorithm; Artificial intelligence; Mathematics; Image (mathematics)","score_opus":0.0412518917975479,"score_gpt":0.2701677799750619,"score_spread":0.22891588817751402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110557355","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022355795,0.00068945996,0.974838,0.00027325953,0.00012115079,0.000029796667,0.000090928326,0.00071146514,0.00089010736],"genre_scores_gemma":[0.42440933,0.0011854653,0.56917,0.0002105767,0.00037578974,0.00014521516,0.0008243181,0.00026693684,0.0034123156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99653023,0.00091922,0.00029131278,0.0004194638,0.0015174467,0.0003223026],"domain_scores_gemma":[0.9946956,0.002182281,0.0002738486,0.0017452196,0.00093295757,0.0001701513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002698724,0.0007566524,0.0024333296,0.0019333527,0.00080616045,0.0029193496,0.0014089892,0.0010848101,0.0027945547],"category_scores_gemma":[0.012947684,0.0007078713,0.0011208508,0.0040124995,0.0010219909,0.0040418915,0.0021986857,0.0020015684,0.0011878127],"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":[0.0018782353,0.00027786853,0.0030081454,0.0002992157,0.00027901892,0.0002583879,0.0003515654,0.25006658,0.034418013,0.113969214,0.012597296,0.5825965],"study_design_scores_gemma":[0.000028691284,0.00006702182,0.00019830924,0.0000073175224,0.000020802765,0.00007342384,0.00004600806,0.9685407,0.0033226814,0.026220975,0.0014624267,0.000011567985],"about_ca_topic_score_codex":0.0024025308,"about_ca_topic_score_gemma":0.0017799778,"teacher_disagreement_score":0.0029193496,"about_ca_system_score_codex":0.0008298015,"about_ca_system_score_gemma":0.001185185,"threshold_uncertainty_score":0.014272392},"labels":[],"label_agreement":null},{"id":"W2110610722","doi":"10.1145/872757.872798","title":"Mapping data in peer-to-peer systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":253,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Semantic mapping; Data mapping; Semantics (computer science); Peer-to-peer; Information retrieval; Data sharing; Listing (finance); Data exchange; Theoretical computer science; Data mining; Database; Programming language; World Wide Web","score_opus":0.061956020897478724,"score_gpt":0.28126520072606803,"score_spread":0.2193091798285893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110610722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015461235,0.0011256544,0.97810286,0.0013031149,0.00014816041,0.00031020783,0.00031659924,0.00070788007,0.002524306],"genre_scores_gemma":[0.21160132,0.0018845754,0.7814501,0.00029021318,0.00017695465,0.00046705912,0.0009061909,0.00018211827,0.0030415158],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9896694,0.0040690308,0.0011461569,0.0015925954,0.0030363807,0.0004864686],"domain_scores_gemma":[0.9807365,0.011977876,0.0012677608,0.0040496294,0.0013385142,0.0006297193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008936899,0.0010174084,0.0022409651,0.0028319184,0.004329105,0.0073712417,0.004666781,0.0037891183,0.0034158733],"category_scores_gemma":[0.033099726,0.0014158391,0.0013524913,0.0076789185,0.0033114892,0.017509196,0.0075791953,0.0029240518,0.001123237],"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":[0.00025799844,0.00016358703,0.0028658633,0.0009366572,0.00021533141,0.0015650425,0.0020827819,0.24869095,0.0032519267,0.58755976,0.007767876,0.14464216],"study_design_scores_gemma":[0.00006781786,0.000075575954,0.000279759,0.00009521344,0.00008119546,0.0005461407,0.0008289728,0.30149403,0.0046983655,0.64803797,0.04371766,0.0000772629],"about_ca_topic_score_codex":0.003745782,"about_ca_topic_score_gemma":0.002539515,"teacher_disagreement_score":0.008936899,"about_ca_system_score_codex":0.001762503,"about_ca_system_score_gemma":0.0025859664,"threshold_uncertainty_score":0.047263384},"labels":[],"label_agreement":null},{"id":"W2111811740","doi":"10.14778/2021017.2021018","title":"On pruning for top-k ranking in uncertain databases","year":2011,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Alberta","funders":"","keywords":"Tuple; Pruning; Ranking (information retrieval); Rank (graph theory); Computer science; Parameterized complexity; Key (lock); Range (aeronautics); Learning to rank; Semantics (computer science); Function (biology); Computation; Ranking SVM; Task (project management); Database; Information retrieval; Artificial intelligence; Data mining; Mathematics; Algorithm; Programming language; Combinatorics; Discrete mathematics","score_opus":0.06770205945613778,"score_gpt":0.2641757924783974,"score_spread":0.19647373302225962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111811740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022787083,0.0016371345,0.97204405,0.0005344452,0.000049739032,0.0000964724,0.00016355247,0.00044735344,0.0022401304],"genre_scores_gemma":[0.34979042,0.0016003407,0.64497125,0.00030245987,0.00019447062,0.00019696461,0.0006811132,0.00026535403,0.0019975703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99177635,0.003349777,0.0005920557,0.0008798662,0.0026746246,0.0007273653],"domain_scores_gemma":[0.97834957,0.015825745,0.0008704213,0.0032153407,0.0013860471,0.00035297094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073932623,0.0012907118,0.0030591688,0.0026043581,0.00265284,0.004163521,0.0026200563,0.0023154246,0.0018962395],"category_scores_gemma":[0.031850133,0.0006858756,0.0017777779,0.004120819,0.0028467742,0.0078783985,0.0030600692,0.0029922414,0.0005819621],"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":[0.0005935527,0.00022381099,0.0028343578,0.00046121617,0.00019600593,0.000503913,0.00078962033,0.48991328,0.004731545,0.25029814,0.006229629,0.2432249],"study_design_scores_gemma":[0.00003638778,0.00010165837,0.00044018158,0.00007059588,0.00006126146,0.0002861362,0.00015811197,0.7340154,0.0027642213,0.25847912,0.0035417126,0.00004516723],"about_ca_topic_score_codex":0.0051340745,"about_ca_topic_score_gemma":0.006152779,"teacher_disagreement_score":0.0073932623,"about_ca_system_score_codex":0.0018631296,"about_ca_system_score_gemma":0.0023532698,"threshold_uncertainty_score":0.039099753},"labels":[],"label_agreement":null},{"id":"W2111838425","doi":"10.1007/11504894_87","title":"The Coordination of Parallel Search with Common Components","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science","score_opus":0.021478151606717767,"score_gpt":0.2510388984967862,"score_spread":0.22956074689006842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111838425","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030832293,0.00072475267,0.94158554,0.0004646927,0.00016037143,0.00012047172,0.00007195465,0.0015701937,0.024469716],"genre_scores_gemma":[0.6059838,0.0007225058,0.37541312,0.0001704786,0.00016615793,0.00045907174,0.00030122747,0.00071715907,0.016066464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978161,0.00074107514,0.00014072856,0.00046187342,0.0005479053,0.00029235287],"domain_scores_gemma":[0.99429554,0.002497172,0.00030086658,0.0018686373,0.00064687233,0.00039104046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021852425,0.0008752367,0.0014434163,0.0012327443,0.001705349,0.004190193,0.0033764585,0.0013781218,0.008845682],"category_scores_gemma":[0.01182894,0.001042757,0.0006968181,0.0021711644,0.0022669781,0.005098957,0.003946936,0.0017475446,0.0016282379],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006478106,0.00010841168,0.0007757938,0.00022318475,0.00011296988,0.00019361949,0.00057508633,0.15416214,0.007187089,0.6989187,0.009616719,0.12747845],"study_design_scores_gemma":[0.00012624831,0.000080271275,0.0002578883,0.00003248134,0.000058217927,0.00013042442,0.00013710622,0.49458048,0.005339719,0.4846008,0.014621874,0.000034420358],"about_ca_topic_score_codex":0.002484469,"about_ca_topic_score_gemma":0.0023711731,"teacher_disagreement_score":0.008845682,"about_ca_system_score_codex":0.0013993996,"about_ca_system_score_gemma":0.0019373721,"threshold_uncertainty_score":0.029591799},"labels":[],"label_agreement":null},{"id":"W2111878549","doi":"10.1109/aiccsa.2005.1387094","title":"A user modeling approach to improving estimation accuracy in location-tracking applications","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Wilfrid Laurier University; University of Guelph","funders":"","keywords":"Computer science; Focus (optics); Estimation; Creatures; Location tracking; Trajectory; Point (geometry); Real-time computing; Object (grammar); Sampling (signal processing); Tracking (education); Variance (accounting); Tracking system; Data mining; Artificial intelligence; Computer vision; Kalman filter","score_opus":0.027350263711973098,"score_gpt":0.2779662463896305,"score_spread":0.25061598267765745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111878549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01758284,0.0002986532,0.9795607,0.00031202205,0.000039458915,0.000042191245,0.00005865131,0.0011924161,0.0009131065],"genre_scores_gemma":[0.61597925,0.0006925785,0.3799813,0.00020597712,0.00018120093,0.00017224104,0.00026786173,0.00027959907,0.0022399882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945334,0.0029862754,0.00033246182,0.00058478164,0.0012752431,0.000287915],"domain_scores_gemma":[0.9777174,0.013633816,0.0009170506,0.0036955131,0.0036910858,0.00034514366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005337311,0.0013159378,0.0015450786,0.0011343446,0.0008912613,0.0016348866,0.0023428958,0.0018432243,0.0019673822],"category_scores_gemma":[0.028727306,0.00092111895,0.00110219,0.0017215967,0.0006314513,0.003812478,0.0018552294,0.002030389,0.0010866983],"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":[0.0008158067,0.0003892343,0.01208179,0.00021035729,0.0002969127,0.00023360038,0.00064013805,0.6188438,0.010988418,0.012128678,0.0042992802,0.33907205],"study_design_scores_gemma":[0.0000067065635,0.000047012985,0.00039444433,0.0000069452494,0.000030244235,0.00005394822,0.000019822495,0.995754,0.001487058,0.0014283007,0.0007554691,0.000016084343],"about_ca_topic_score_codex":0.008618513,"about_ca_topic_score_gemma":0.007944279,"teacher_disagreement_score":0.008618513,"about_ca_system_score_codex":0.00093774474,"about_ca_system_score_gemma":0.0011249369,"threshold_uncertainty_score":0.028226733},"labels":[],"label_agreement":null},{"id":"W2112007194","doi":"10.14778/2536274.2536316","title":"IPS","year":2013,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Flexibility (engineering); Computer science; Set (abstract data type); Task (project management); Point of interest; Human–computer interaction; Engineering; Artificial intelligence; Systems engineering","score_opus":0.0070832293950941465,"score_gpt":0.17799929477527485,"score_spread":0.1709160653801807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112007194","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014441143,0.00048454816,0.4698322,0.00071934104,0.00074203307,0.0005073824,0.023178658,0.26334646,0.22674814],"genre_scores_gemma":[0.24068321,0.0012833419,0.4738337,0.0009329145,0.00042212658,0.0013547218,0.07859316,0.024688534,0.17820829],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992974,0.00010779146,0.000047269677,0.0002131096,0.00024328388,0.000091171365],"domain_scores_gemma":[0.99865437,0.00026619376,0.000066002416,0.0006032939,0.00028983402,0.0001202261],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009284746,0.0011107341,0.000571706,0.0014526732,0.00091947964,0.0032006877,0.001912624,0.00090143684,0.1176783],"category_scores_gemma":[0.0034307956,0.0006135087,0.00069127465,0.001654454,0.00038386002,0.0034891248,0.0026151736,0.0011953014,0.06546022],"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":[0.0006793531,0.00013963995,0.0036149432,0.0004946201,0.00006446804,0.000461779,0.0007982898,0.006412411,0.0060801124,0.042743143,0.4596569,0.4788543],"study_design_scores_gemma":[0.00006804213,0.00012213664,0.001914713,0.00008479714,0.000049835406,0.00067764486,0.00033102775,0.03885493,0.012177488,0.02140662,0.92424506,0.00006777337],"about_ca_topic_score_codex":0.002648898,"about_ca_topic_score_gemma":0.0030581206,"teacher_disagreement_score":0.8823217,"about_ca_system_score_codex":0.000464173,"about_ca_system_score_gemma":0.0008989076,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2112064847","doi":"","title":"Indexing mixed types for approximate retrieval","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Search engine indexing; Computer science; Trie; Edit distance; Categorical variable; String (physics); Data mining; Access method; Inverted index; Data structure; Theoretical computer science; Information retrieval; Algorithm; Mathematics; Database; Machine learning","score_opus":0.022312491443381526,"score_gpt":0.254048192522594,"score_spread":0.23173570107921246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112064847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004242754,0.0007716419,0.98860884,0.0003608417,0.00015366919,0.00019237118,0.00036053572,0.000817811,0.004491536],"genre_scores_gemma":[0.084141545,0.0012674963,0.9041572,0.00045857305,0.0003091287,0.0009248445,0.0017685551,0.000503662,0.0064689303],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99089456,0.0021721586,0.0013057628,0.000927341,0.0042854305,0.00041478992],"domain_scores_gemma":[0.97565436,0.0073373434,0.0013885719,0.0120649105,0.003242279,0.00031249205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062292665,0.0008236066,0.0022967872,0.0045009516,0.0018266854,0.0071163224,0.0031740433,0.0021607329,0.011060706],"category_scores_gemma":[0.033392824,0.00078595977,0.0017056486,0.011443556,0.0026326329,0.01632532,0.0059636263,0.002615792,0.0052161356],"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":[0.00045755997,0.00012797382,0.0010939483,0.00058343925,0.00007545179,0.0002253001,0.00067852304,0.016485926,0.0060881875,0.5550918,0.01209279,0.40699908],"study_design_scores_gemma":[0.00010839806,0.00021570073,0.00033511996,0.00019862718,0.00008388104,0.0010296465,0.00044369133,0.19065551,0.012415467,0.707204,0.08719643,0.00011352849],"about_ca_topic_score_codex":0.0011871521,"about_ca_topic_score_gemma":0.0012746061,"teacher_disagreement_score":0.011060706,"about_ca_system_score_codex":0.0018819834,"about_ca_system_score_gemma":0.0023721252,"threshold_uncertainty_score":0.03700179},"labels":[],"label_agreement":null},{"id":"W2112268371","doi":"10.1109/aina.2009.82","title":"Network Voronoi Diagram Based Range Search","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Voronoi diagram; Computer science; Range (aeronautics); Weighted Voronoi diagram; Euclidean distance; Spatial network; Data mining; Diagram; Computational geometry; Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Database","score_opus":0.022307822049841636,"score_gpt":0.2482409210875101,"score_spread":0.22593309903766848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112268371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018896224,0.0016439611,0.972924,0.00017469241,0.00006497482,0.00009686059,0.00019581142,0.00039207726,0.005611488],"genre_scores_gemma":[0.5847939,0.001932633,0.40719914,0.00009107459,0.00007380786,0.00026369092,0.00082517177,0.000068340836,0.004752214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991067,0.00021759959,0.000041235948,0.000132829,0.0004268299,0.000074744035],"domain_scores_gemma":[0.99922264,0.0003839265,0.000063337815,0.0000821709,0.00020790818,0.000040073875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039836476,0.0003633744,0.001047185,0.0014037685,0.00047480155,0.0012482468,0.0011584669,0.0006026944,0.0019539702],"category_scores_gemma":[0.0031308671,0.00021395466,0.00039641626,0.0019342905,0.0004056714,0.0021529899,0.0015459432,0.00034137705,0.00061700743],"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":[0.00048834545,0.00009773542,0.0030523064,0.00047898953,0.0000942351,0.0003721441,0.0003591976,0.422346,0.015679294,0.1371437,0.008900115,0.410988],"study_design_scores_gemma":[0.00003661608,0.00005182091,0.00037355776,0.000016177766,0.00001860828,0.0003526508,0.0000673767,0.96225303,0.0039492114,0.021607684,0.011248643,0.000024687044],"about_ca_topic_score_codex":0.004510105,"about_ca_topic_score_gemma":0.0034055535,"teacher_disagreement_score":0.004510105,"about_ca_system_score_codex":0.0006560054,"about_ca_system_score_gemma":0.00078661286,"threshold_uncertainty_score":0.008967698},"labels":[],"label_agreement":null},{"id":"W2112729204","doi":"10.1007/s00236-008-0069-0","title":"Weighted height of random trees","year":2008,"lang":"en","type":"article","venue":"Acta Informatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Weight-balanced tree; Combinatorics; Binary search tree; Theory of computation; Extension (predicate logic); Tree (set theory); Set (abstract data type); Random binary tree; Binary logarithm; Discrete mathematics; Ternary search tree; Constant (computer programming); Binary tree; Algorithm; Tree structure; Computer science; Interval tree","score_opus":0.012954597852491713,"score_gpt":0.20640523636354366,"score_spread":0.19345063851105196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112729204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39311495,0.0028700093,0.5726547,0.0026003367,0.00031223884,0.00009628805,0.0011346766,0.0009021804,0.026314674],"genre_scores_gemma":[0.8792141,0.0016160536,0.09685416,0.00052814453,0.00053541735,0.0001665158,0.0015246905,0.00052927446,0.019031757],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983668,0.0005406999,0.000077223085,0.0002623759,0.0004680676,0.00028479059],"domain_scores_gemma":[0.9836909,0.01141921,0.00111337,0.0014250713,0.0010007092,0.001350727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023275444,0.00061512145,0.0014434336,0.0031510317,0.0013869954,0.0031783143,0.0024541472,0.0019910622,0.00845368],"category_scores_gemma":[0.021699982,0.0009766202,0.00091397733,0.0029254993,0.0019643072,0.0054092035,0.0026089263,0.0024437145,0.0011020892],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022865973,0.00005726817,0.0015914973,0.00017533053,0.00006020266,0.00013290167,0.00025472083,0.06176869,0.0021785235,0.8979176,0.0052053784,0.030429171],"study_design_scores_gemma":[0.00003437914,0.000041671818,0.00061246514,0.00003134474,0.00003426507,0.00014593135,0.000056472374,0.1850951,0.0006357037,0.81074303,0.0025489903,0.000020638532],"about_ca_topic_score_codex":0.0015511025,"about_ca_topic_score_gemma":0.0024904038,"teacher_disagreement_score":0.00845368,"about_ca_system_score_codex":0.0017871079,"about_ca_system_score_gemma":0.0008843725,"threshold_uncertainty_score":0.028280318},"labels":[],"label_agreement":null},{"id":"W2113284000","doi":"10.1109/icci.1993.315397","title":"Analysis of windowing operations on R/sup +/ trees","year":2002,"lang":"en","type":"article","venue":"","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":"University of Alberta","funders":"","keywords":"Object (grammar); Tree (set theory); Computer science; Domain (mathematical analysis); Combinatorics; Theoretical computer science; Discrete mathematics; Data mining; Mathematics; Artificial intelligence","score_opus":0.030251858218655878,"score_gpt":0.24305257663812696,"score_spread":0.21280071841947107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113284000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38794374,0.0016136174,0.6000162,0.0002691973,0.000031822336,0.00013184395,0.00038962532,0.0014134495,0.008190543],"genre_scores_gemma":[0.8120944,0.001033183,0.18190119,0.00006804573,0.00008629394,0.00014890617,0.0006889194,0.0005896308,0.0033895005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978301,0.00040966267,0.00016573713,0.0002622581,0.00087844755,0.0004538156],"domain_scores_gemma":[0.9879836,0.007972865,0.0012335227,0.0014519466,0.0010796286,0.00027851583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002139403,0.0006414066,0.00080524484,0.0015070905,0.0005332324,0.0021618519,0.0014049822,0.00049609365,0.003250206],"category_scores_gemma":[0.012768313,0.00045184826,0.0006520899,0.002382457,0.0009599961,0.0057583763,0.000875155,0.00071252615,0.0005435826],"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":[0.0013434476,0.00021642636,0.007464919,0.00046712105,0.0000962287,0.00046572942,0.0006802306,0.3910333,0.054878794,0.34270433,0.00625453,0.19439504],"study_design_scores_gemma":[0.000025283487,0.000103231265,0.0013884187,0.000017819752,0.000026498206,0.00018935744,0.000081025915,0.9515773,0.008630175,0.035513267,0.0024286725,0.000018925984],"about_ca_topic_score_codex":0.0030053786,"about_ca_topic_score_gemma":0.0014602329,"teacher_disagreement_score":0.003250206,"about_ca_system_score_codex":0.0014508568,"about_ca_system_score_gemma":0.0010801593,"threshold_uncertainty_score":0.011314392},"labels":[],"label_agreement":null},{"id":"W2114278236","doi":"10.1109/cvpr.1993.341021","title":"Efficient serial associative memory","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Information Technology Research Centre","keywords":"Computer science; Pruning; Set (abstract data type); Curse of dimensionality; Probabilistic logic; Result set; Tree (set theory); Feature vector; Data mining; Computation; Feature (linguistics); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics","score_opus":0.019253451064887125,"score_gpt":0.21592706978416104,"score_spread":0.1966736187192739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114278236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026039513,0.0030982674,0.9342527,0.0004358337,0.0005250762,0.00024939334,0.0010536787,0.013563219,0.020782355],"genre_scores_gemma":[0.30492666,0.0019546228,0.63799655,0.0007744102,0.00040348948,0.000828247,0.0040776976,0.0004988898,0.048539415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992403,0.00008492581,0.00006724017,0.00017538747,0.0003308579,0.00010133432],"domain_scores_gemma":[0.9987245,0.00026381275,0.00011684536,0.0004455508,0.00039209516,0.000057216646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006736067,0.0010137265,0.0008456341,0.0014092172,0.0010438348,0.00202775,0.0036138508,0.0006547381,0.02059485],"category_scores_gemma":[0.00187654,0.0004845562,0.00043136478,0.0027124472,0.000618246,0.0041820295,0.001849437,0.0008029176,0.0067997053],"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":[0.0007026275,0.00034750986,0.0015786565,0.00039475976,0.00010810322,0.00029104037,0.00013519284,0.03390603,0.012368036,0.05065176,0.05583749,0.8436788],"study_design_scores_gemma":[0.00025972785,0.0006006304,0.0011170793,0.00011327716,0.00018939514,0.001615402,0.00017832048,0.67451763,0.048332762,0.16827047,0.10467342,0.00013189798],"about_ca_topic_score_codex":0.0027446616,"about_ca_topic_score_gemma":0.005397449,"teacher_disagreement_score":0.02059485,"about_ca_system_score_codex":0.0008665184,"about_ca_system_score_gemma":0.0014860994,"threshold_uncertainty_score":0.06889671},"labels":[],"label_agreement":null},{"id":"W2114454762","doi":"","title":"Social Network Opinion and Posts Mining for Community Preference Discovery","year":2013,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preference; Data science; Computer science; World Wide Web; Internet privacy; Public relations; Business; Political science; Economics","score_opus":0.05535008070530805,"score_gpt":0.2448965180359668,"score_spread":0.18954643733065873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114454762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083101384,0.00045038617,0.90323704,0.0003951295,0.00008371822,0.00053691765,0.00300004,0.0029750164,0.006220328],"genre_scores_gemma":[0.5464669,0.00032425026,0.44285446,0.00013673108,0.00018239288,0.00039763947,0.0047839573,0.00014560056,0.0047081704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906665,0.00020580922,0.00006785872,0.00023170447,0.00035006358,0.00007793919],"domain_scores_gemma":[0.99837756,0.0006164551,0.00026143034,0.00017053701,0.0004984865,0.00007552862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000858804,0.00076185673,0.00058412715,0.0032885938,0.00060157984,0.0007136389,0.0006630929,0.00051333837,0.0023915956],"category_scores_gemma":[0.0039294623,0.00021654984,0.0009727033,0.0023183345,0.00021346296,0.0011744603,0.0005088297,0.0005556597,0.0016988331],"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":[0.00038405537,0.0005580522,0.024505027,0.00038563207,0.00038410004,0.00036657968,0.00043900337,0.028890183,0.024314525,0.009433734,0.01570427,0.89463484],"study_design_scores_gemma":[0.000033159,0.00019145926,0.010159102,0.000037313646,0.00012545456,0.00029522128,0.00023091993,0.95357114,0.012432594,0.012684529,0.010204663,0.000034427794],"about_ca_topic_score_codex":0.0033859739,"about_ca_topic_score_gemma":0.007641873,"teacher_disagreement_score":0.0033859739,"about_ca_system_score_codex":0.0005677801,"about_ca_system_score_gemma":0.00047722293,"threshold_uncertainty_score":0.0080007315},"labels":[],"label_agreement":null},{"id":"W2114523181","doi":"","title":"Effective sampling and learning for mallows models with pairwise-preference data","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pairwise comparison; Ranking (information retrieval); Inference; Sampling (signal processing); Machine learning; Computer science; Artificial intelligence; Preference learning; Preference; Parametric statistics; Data mining; Mathematics; Statistics","score_opus":0.0924687703155178,"score_gpt":0.266705355704623,"score_spread":0.1742365853891052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114523181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060448814,0.0002762808,0.99257857,0.00024795334,0.000019918394,0.00009673889,0.00016489915,0.0003612871,0.00020949256],"genre_scores_gemma":[0.17405404,0.0006420653,0.81829387,0.0005067794,0.00030336453,0.0011152269,0.0023000413,0.0002886584,0.0024958337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98553306,0.009049449,0.0007759375,0.002588577,0.0015870897,0.0004658634],"domain_scores_gemma":[0.8954736,0.08738445,0.0033873627,0.0099984445,0.0025444976,0.0012117112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024671216,0.0028018993,0.0038755215,0.003985746,0.002035993,0.0041096346,0.008450772,0.0042833793,0.0047339713],"category_scores_gemma":[0.098585606,0.00316932,0.0038235537,0.0046093385,0.0047898176,0.011620505,0.006202033,0.008519931,0.0015810905],"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":[0.0010814958,0.0004470589,0.0103447605,0.00062099035,0.00068211,0.0003909911,0.0010699225,0.44069898,0.0019759354,0.37853,0.005632761,0.15852495],"study_design_scores_gemma":[0.0000497944,0.0000687922,0.00030844702,0.00004293123,0.000031257,0.000060102935,0.000051282674,0.80476964,0.00056024926,0.19307047,0.0009555024,0.0000316032],"about_ca_topic_score_codex":0.005473384,"about_ca_topic_score_gemma":0.010595258,"teacher_disagreement_score":0.024671216,"about_ca_system_score_codex":0.0032383066,"about_ca_system_score_gemma":0.002676807,"threshold_uncertainty_score":0.1304754},"labels":[],"label_agreement":null},{"id":"W2115425457","doi":"10.1109/aina.2003.1192959","title":"An ontology-based method for querying the Web data","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong University of Science and Technology; University of Toronto","keywords":"Computer science; Information retrieval; Search engine; Ontology; World Wide Web; Web search query; The Internet; Web search engine; Web query classification","score_opus":0.08787829871693748,"score_gpt":0.36697855642997357,"score_spread":0.2791002577130361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115425457","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005879239,0.00012571608,0.99550205,0.0003783567,0.00008198769,0.00016090098,0.0003625467,0.0014916871,0.0013088345],"genre_scores_gemma":[0.008718804,0.0002947131,0.9870487,0.00017179745,0.000040202347,0.00031983544,0.0009637753,0.00023162627,0.002210534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939476,0.0010499018,0.000843887,0.0007194304,0.0032477283,0.0001914556],"domain_scores_gemma":[0.9971584,0.0009864033,0.00016442899,0.0007490628,0.00079186476,0.00014985843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053662118,0.0010910206,0.0012512739,0.005289371,0.0020089047,0.0053724716,0.0028674954,0.0019673514,0.0045192474],"category_scores_gemma":[0.011061515,0.0009037796,0.0028103536,0.006257994,0.0016378001,0.007954492,0.0036298404,0.003257244,0.0028029068],"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":[0.00018669273,0.0003517556,0.001563127,0.0008501983,0.00026869503,0.0009072064,0.002072151,0.008242904,0.024262192,0.3101308,0.042539056,0.60862523],"study_design_scores_gemma":[0.00011917033,0.000092370195,0.0009745149,0.00030756023,0.0002464716,0.0020040954,0.0008411473,0.27858043,0.025419438,0.21520424,0.47588307,0.00032744862],"about_ca_topic_score_codex":0.00879283,"about_ca_topic_score_gemma":0.011443654,"teacher_disagreement_score":0.00879283,"about_ca_system_score_codex":0.0015101518,"about_ca_system_score_gemma":0.0035027978,"threshold_uncertainty_score":0.02837956},"labels":[],"label_agreement":null},{"id":"W2115687261","doi":"10.1109/icde.2011.5767929","title":"Jackpine: A benchmark to evaluate spatial database performance","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Database; Spatial database; Geocoding; Benchmark (surveying); Spatial analysis; Data mining; Search engine indexing; Relational database management system; Relational database; Information retrieval","score_opus":0.04291799912277831,"score_gpt":0.2519456535039992,"score_spread":0.20902765438122092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115687261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.704669,0.005144736,0.11998611,0.0015965817,0.0011462425,0.0015380987,0.03833713,0.08361212,0.04397],"genre_scores_gemma":[0.76117355,0.0014277203,0.14521536,0.0005067013,0.00015595938,0.0009127554,0.08062939,0.004224235,0.0057543493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912149,0.0018214588,0.0014615537,0.001136663,0.0036463917,0.0007190584],"domain_scores_gemma":[0.98158836,0.006687326,0.0010806117,0.0033362096,0.0063706688,0.00093690376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005006012,0.0025894826,0.0008830501,0.0031835365,0.0008788288,0.002125329,0.004761925,0.0010936981,0.0023726977],"category_scores_gemma":[0.017999854,0.00058665354,0.0007428172,0.006101875,0.0009236675,0.0035445641,0.0018653114,0.0012754033,0.00097630016],"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":[0.0049073454,0.0037378496,0.060909443,0.0048748334,0.0012287588,0.0011507745,0.0013604187,0.26807585,0.04945388,0.022485085,0.2631852,0.31863046],"study_design_scores_gemma":[0.00068467006,0.0020602096,0.02463812,0.00018145282,0.00025125314,0.00083259994,0.00082537724,0.8023514,0.08893274,0.0075008506,0.07152149,0.00021979216],"about_ca_topic_score_codex":0.017766023,"about_ca_topic_score_gemma":0.010630301,"teacher_disagreement_score":0.017766023,"about_ca_system_score_codex":0.0019917171,"about_ca_system_score_gemma":0.0020745974,"threshold_uncertainty_score":0.03532523},"labels":[],"label_agreement":null},{"id":"W2116138848","doi":"10.1109/cit.2010.130","title":"Two Novel Semantics of Top-k Queries Processing in Uncertain Database","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"St. Francis Xavier University","funders":"","keywords":"Tuple; Semantics (computer science); Computer science; Ranking (information retrieval); Database; Rank (graph theory); Information retrieval; Theoretical computer science; Algorithm; Mathematics; Discrete mathematics; Combinatorics; Programming language","score_opus":0.02265649673199035,"score_gpt":0.28789280062074507,"score_spread":0.26523630388875474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116138848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015628517,0.00032560047,0.9815715,0.0003664539,0.00005484064,0.00018664451,0.00038081908,0.0005402304,0.00094545225],"genre_scores_gemma":[0.29681543,0.00052058714,0.69958574,0.00029769583,0.00018548853,0.00035761864,0.0010659622,0.00011545024,0.0010560043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99007016,0.0020274108,0.002297033,0.0015470043,0.0035938516,0.0004644823],"domain_scores_gemma":[0.9918133,0.0027865618,0.0012379069,0.0018813732,0.0018894862,0.00039142102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036182767,0.0011797711,0.0016532006,0.0028266462,0.0015324528,0.005052157,0.0028157467,0.0021386896,0.0011469158],"category_scores_gemma":[0.013792186,0.0005768704,0.0019551234,0.0041971267,0.002666929,0.012501173,0.0035105327,0.0023953721,0.00040040622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013450094,0.00048850256,0.008953568,0.0010796658,0.0003414629,0.0019285859,0.0045679132,0.12527855,0.022882957,0.56489915,0.008841734,0.2593929],"study_design_scores_gemma":[0.0001148731,0.00027404228,0.001342304,0.00009628306,0.00017367434,0.001916797,0.0012574991,0.5731998,0.015149757,0.39186192,0.014396369,0.00021675698],"about_ca_topic_score_codex":0.0018104721,"about_ca_topic_score_gemma":0.0017831586,"teacher_disagreement_score":0.005052157,"about_ca_system_score_codex":0.0012175465,"about_ca_system_score_gemma":0.0018956936,"threshold_uncertainty_score":0.019135475},"labels":[],"label_agreement":null},{"id":"W2116595874","doi":"10.1007/11562214_25","title":"Document Clustering with Grouping and Chaining Algorithms","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Chaining; Cluster analysis; Document clustering; Event (particle physics); Backward chaining; Set (abstract data type); Data mining; Algorithm; Canopy clustering algorithm; Correlation clustering; Artificial intelligence; Information retrieval; Expert system; Programming language","score_opus":0.014362959641179852,"score_gpt":0.2329629422753225,"score_spread":0.21859998263414265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116595874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002250296,0.0005286386,0.99379057,0.00006319429,0.00008708013,0.000096205426,0.0001382779,0.0016492087,0.0013965891],"genre_scores_gemma":[0.014866257,0.00034975694,0.97992146,0.0000347998,0.0000952577,0.00009649801,0.0005883044,0.00025552086,0.0037922258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720925,0.00067240745,0.00028198655,0.0007251212,0.0009873087,0.00012392903],"domain_scores_gemma":[0.99575114,0.0014741648,0.00015132259,0.0015487751,0.0009594504,0.00011505375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028775611,0.0013308205,0.0022581029,0.0044436203,0.0018577803,0.0026052238,0.0033773219,0.0018854206,0.0071365116],"category_scores_gemma":[0.008142639,0.0011519333,0.0018736102,0.011127752,0.00094309385,0.004225528,0.0022807699,0.0017344729,0.0059033795],"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":[0.00017587871,0.00012584958,0.000539314,0.00027279777,0.00014062651,0.000044140714,0.00014719622,0.042249616,0.0048632557,0.015136373,0.012443696,0.9238613],"study_design_scores_gemma":[0.00009934912,0.0001874921,0.00093463523,0.00007139121,0.00021180353,0.0005001649,0.00012384886,0.8108919,0.024360837,0.11963026,0.04289793,0.0000903521],"about_ca_topic_score_codex":0.003929589,"about_ca_topic_score_gemma":0.005064665,"teacher_disagreement_score":0.0071365116,"about_ca_system_score_codex":0.0009187336,"about_ca_system_score_gemma":0.0015087704,"threshold_uncertainty_score":0.023874044},"labels":[],"label_agreement":null},{"id":"W2117379916","doi":"10.1109/icde.2005.92","title":"Monitoring k-Nearest Neighbor Queries over Moving Objects","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":307,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Search engine indexing; Computer science; Scalability; k-nearest neighbors algorithm; Grid; Data mining; Tree (set theory); Best bin first; Nearest neighbor search; R-tree; Object (grammar); Spatial database; Artificial intelligence; Spatial analysis; Database; Mathematics","score_opus":0.013368878396575917,"score_gpt":0.2457943660586419,"score_spread":0.232425487662066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117379916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41080943,0.0016268289,0.5795722,0.00048312393,0.000105061605,0.00021510605,0.0009803242,0.0036549913,0.002552978],"genre_scores_gemma":[0.85918605,0.000534317,0.13833418,0.000060951206,0.000060216174,0.0000905707,0.0010778728,0.00007325949,0.00058262056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768,0.0005079486,0.0002297509,0.0005687528,0.00086112437,0.00015242018],"domain_scores_gemma":[0.9921846,0.0038265106,0.0010182519,0.0016203934,0.0010755315,0.00027479266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019027502,0.00059583964,0.0015062619,0.001164935,0.00095383707,0.0012656286,0.002065306,0.0011968582,0.00046554866],"category_scores_gemma":[0.0115928585,0.00036955014,0.00030515742,0.0030746842,0.00048258813,0.003604764,0.0015670432,0.0006475918,0.0004424706],"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":[0.0014579289,0.00067482097,0.053753328,0.0005090922,0.0002889951,0.0006635211,0.0007734673,0.40616882,0.041908775,0.0068426277,0.008329983,0.47862867],"study_design_scores_gemma":[0.00003808688,0.00012255076,0.005335782,0.000009903931,0.000028767316,0.00033904667,0.00028903398,0.9756243,0.010763876,0.0054369234,0.0019812009,0.000030413563],"about_ca_topic_score_codex":0.005444654,"about_ca_topic_score_gemma":0.0064345165,"teacher_disagreement_score":0.005444654,"about_ca_system_score_codex":0.0006015016,"about_ca_system_score_gemma":0.00078737846,"threshold_uncertainty_score":0.010825932},"labels":[],"label_agreement":null},{"id":"W2117538523","doi":"10.1111/j.1467-9671.2012.01353.x","title":"Measuring Dynamic Interaction in Movement Data","year":2012,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Movement (music); Computer science; Scope (computer science); Object (grammar); Statistic; Sophistication; Data mining; Interval (graph theory); Data science; Artificial intelligence; Mathematics; Statistics","score_opus":0.07975381783471722,"score_gpt":0.29703634588748234,"score_spread":0.21728252805276513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117538523","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6820294,0.00046992613,0.30377063,0.00036434838,0.00006419488,0.00026019354,0.0047278306,0.0008207946,0.007492592],"genre_scores_gemma":[0.9441596,0.00009651752,0.052277345,0.000036320766,0.00003200846,0.00015169472,0.002750344,0.000025345536,0.00047087023],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942306,0.0018405609,0.0005210272,0.0010672107,0.0020793846,0.00026127766],"domain_scores_gemma":[0.9791094,0.0138671165,0.0032861675,0.0014118965,0.0019690874,0.00035629002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029259652,0.00047429584,0.00059424964,0.0050665657,0.00053004024,0.0014917792,0.00069592916,0.00051972404,0.0012331464],"category_scores_gemma":[0.017038671,0.0002445278,0.00033535715,0.0067038946,0.00071493356,0.0017081236,0.0018417422,0.0007149946,0.0003219263],"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":[0.0007876623,0.00043382763,0.6099184,0.0007486125,0.0006872855,0.00043104435,0.0017913226,0.11205885,0.0238415,0.013517336,0.004302289,0.23148182],"study_design_scores_gemma":[0.000042105446,0.00048144133,0.38912514,0.000094633404,0.00012953924,0.00065725757,0.0019356252,0.56732666,0.013287873,0.016613109,0.0101703005,0.00013635673],"about_ca_topic_score_codex":0.0033306787,"about_ca_topic_score_gemma":0.0033121707,"teacher_disagreement_score":0.0050665657,"about_ca_system_score_codex":0.0008693641,"about_ca_system_score_gemma":0.00069477677,"threshold_uncertainty_score":0.015474141},"labels":[],"label_agreement":null},{"id":"W2117913411","doi":"10.1145/1247480.1247545","title":"Adaptive location constraint processing","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint (computer-aided design); Computer science; Search engine indexing; Set (abstract data type); Constellation; Time constraint; Position (finance); Matching (statistics); Point (geometry); Algorithm; Theoretical computer science; Distributed computing; Artificial intelligence; Mathematics","score_opus":0.023754973195826038,"score_gpt":0.2578563954654535,"score_spread":0.2341014222696275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117913411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031461146,0.00046976033,0.95879155,0.00033426576,0.00009143321,0.00018964146,0.00055606605,0.0038347123,0.0042714425],"genre_scores_gemma":[0.35273013,0.0005520532,0.6375126,0.00031214085,0.00017382384,0.00030004725,0.0019716509,0.00057625084,0.0058712037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982742,0.00025637515,0.00013875189,0.0004725509,0.0006964754,0.00016162626],"domain_scores_gemma":[0.9954619,0.0014200656,0.0003372167,0.0016401225,0.001016806,0.00012392382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089604326,0.0007663443,0.0012960654,0.001106556,0.0011113521,0.0022421435,0.0031140475,0.0010687395,0.0052654757],"category_scores_gemma":[0.0062016803,0.00050514756,0.00057007844,0.0035038963,0.0007455553,0.0031171339,0.0024800496,0.0013295949,0.001575512],"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":[0.00075425976,0.0002970744,0.003834457,0.00038103666,0.00013284001,0.00064932957,0.0004985476,0.1640892,0.07176143,0.02606231,0.02611407,0.7054254],"study_design_scores_gemma":[0.00007830998,0.000100139456,0.00115566,0.000017740294,0.00004033329,0.00044630666,0.0002769877,0.9330208,0.025910944,0.021659695,0.017233217,0.00005988931],"about_ca_topic_score_codex":0.0071981284,"about_ca_topic_score_gemma":0.0069510727,"teacher_disagreement_score":0.0071981284,"about_ca_system_score_codex":0.0008600849,"about_ca_system_score_gemma":0.0013518481,"threshold_uncertainty_score":0.017614782},"labels":[],"label_agreement":null},{"id":"W2118142335","doi":"10.1109/ismvl.2002.1011089","title":"On functions defined on free Boolean algebras","year":2002,"lang":"en","type":"article","venue":"","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":"Université de Montréal","funders":"","keywords":"Boolean function; Boolean algebra; Mathematics; Ternary operation; Two-element Boolean algebra; Binary number; Discrete mathematics; Binary relation; Boolean expression; Stone's representation theorem for Boolean algebras; Conjunction (astronomy); Parity function; Complete Boolean algebra; Boolean algebras canonically defined; Computer science; Algebra over a field; Arithmetic; Pure mathematics","score_opus":0.024512687309902668,"score_gpt":0.2053565651084609,"score_spread":0.18084387779855823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118142335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1196969,0.00313941,0.8539281,0.00066192,0.00020571235,0.00011176197,0.0005316017,0.00032470448,0.021399872],"genre_scores_gemma":[0.78055626,0.0027579917,0.20334464,0.0005271385,0.000724586,0.0004222279,0.00083417475,0.00027396766,0.010558965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957332,0.0011000122,0.00030895727,0.00067489035,0.0016703043,0.0005126103],"domain_scores_gemma":[0.99341655,0.0036334682,0.0009497795,0.00053946656,0.00094620435,0.00051452016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028876788,0.0013207663,0.0010345756,0.0043969075,0.0014357378,0.004568182,0.0012749217,0.0010427306,0.0033061143],"category_scores_gemma":[0.0096841995,0.0005872096,0.0016069532,0.004195509,0.0034221204,0.011256703,0.0023481704,0.0018581625,0.00071850856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024994484,0.00001798691,0.000400788,0.000060673985,0.000021914473,0.00009456409,0.00023219622,0.002646287,0.001037588,0.9850138,0.00041333612,0.010035823],"study_design_scores_gemma":[0.000008832832,0.000030404546,0.0003096597,0.000043699867,0.000017397799,0.00020369505,0.0000736267,0.013686308,0.0010479279,0.9803073,0.0042448635,0.000026363277],"about_ca_topic_score_codex":0.0011080538,"about_ca_topic_score_gemma":0.00081302534,"teacher_disagreement_score":0.004568182,"about_ca_system_score_codex":0.002605841,"about_ca_system_score_gemma":0.0007304024,"threshold_uncertainty_score":0.018906772},"labels":[],"label_agreement":null},{"id":"W2118182253","doi":"10.1613/jair.1081","title":"An Analysis of Phase Transition in NK Landscapes","year":2002,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Infinity; Phase transition; Mathematics; Upper and lower bounds; Polynomial; Perspective (graphical); Phase (matter); Discrete mathematics; Applied mathematics; Statistical physics; Mathematical analysis; Physics; Quantum mechanics","score_opus":0.17904413690257273,"score_gpt":0.4374027383675618,"score_spread":0.2583586014649891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118182253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29200453,0.00073954347,0.6831886,0.0016046993,0.00007349757,0.00015273747,0.00021229516,0.00028016896,0.021743817],"genre_scores_gemma":[0.96271205,0.00033270035,0.03190973,0.0002616206,0.00005148791,0.00014881721,0.00018292735,0.00007640994,0.004324198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992211,0.0002187667,0.000029838437,0.00016406368,0.00023333011,0.00013294279],"domain_scores_gemma":[0.99649054,0.0022990282,0.0003910887,0.0002767192,0.00028246886,0.0002601982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012012263,0.00029818155,0.0007491562,0.0008057077,0.00062904623,0.0014315874,0.0014742475,0.0010788143,0.0065172887],"category_scores_gemma":[0.0100517,0.0002867737,0.0007190696,0.00061653485,0.0017634756,0.004381355,0.0012928064,0.0014835917,0.00036347503],"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":[0.00014423407,0.00010832116,0.0017528259,0.00014458664,0.000037505724,0.00022247649,0.00023928752,0.23688893,0.0052925814,0.74154776,0.0017763873,0.011845045],"study_design_scores_gemma":[0.000028829261,0.000048471753,0.00045268866,0.000013021237,0.000011476503,0.00009292746,0.000057358662,0.7413581,0.0010971325,0.25527066,0.0015557561,0.000013561653],"about_ca_topic_score_codex":0.0015147771,"about_ca_topic_score_gemma":0.000668718,"teacher_disagreement_score":0.0065172887,"about_ca_system_score_codex":0.0012866638,"about_ca_system_score_gemma":0.0006216238,"threshold_uncertainty_score":0.021802485},"labels":[],"label_agreement":null},{"id":"W2118265701","doi":"10.1145/1189769.1189772","title":"Adaptive rank-aware query optimization in relational databases","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":56,"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":"Computer science; Query optimization; Query plan; Ranking (information retrieval); Rank (graph theory); Cardinality (data modeling); Key (lock); Query language; Relational database; Join (topology); Operator (biology); Sargable; Theoretical computer science; Database; Data mining; Information retrieval; Web search query; Search engine","score_opus":0.038032297023068214,"score_gpt":0.24930291190045173,"score_spread":0.21127061487738352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118265701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09871926,0.0015516743,0.89006954,0.0003835795,0.000033055694,0.00016329435,0.00028995713,0.0065246425,0.002264976],"genre_scores_gemma":[0.58750033,0.00048960384,0.40963784,0.00016390841,0.00004255056,0.000085222906,0.0004544175,0.00041090138,0.0012152055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965239,0.00087426836,0.00024337384,0.0006374824,0.0013735977,0.0003472264],"domain_scores_gemma":[0.99738175,0.0012261283,0.0002494766,0.0006344712,0.0003996836,0.00010849378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002333811,0.0010045992,0.0010021677,0.00078645727,0.0008179749,0.0016426853,0.0021251491,0.0007602485,0.00078309025],"category_scores_gemma":[0.004435072,0.0004756686,0.00074252696,0.0014474225,0.00092571165,0.0027237092,0.0015790609,0.0012279664,0.00032237874],"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":[0.0007820624,0.0004634802,0.0048808963,0.00030347417,0.00013401009,0.00023676112,0.00047147987,0.68791324,0.050939184,0.022223985,0.0059607774,0.2256907],"study_design_scores_gemma":[0.00002664925,0.000098046694,0.00046305632,0.0000050590033,0.00002669086,0.000069724454,0.00006931829,0.98139703,0.009056041,0.007243744,0.0015229634,0.000021618958],"about_ca_topic_score_codex":0.009449322,"about_ca_topic_score_gemma":0.010027917,"teacher_disagreement_score":0.009449322,"about_ca_system_score_codex":0.0011179572,"about_ca_system_score_gemma":0.0017901848,"threshold_uncertainty_score":0.018788636},"labels":[],"label_agreement":null},{"id":"W2119234548","doi":"10.1145/1321440.1321491","title":"Extending query translation to cross-language query expansion with markov chain models","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Query expansion; Cross-language information retrieval; Query language; Translation (biology); Natural language processing; Artificial intelligence; Query optimization; RDF query language; Graph; Relation (database); Web query classification; Information retrieval; Web search query; Theoretical computer science; Data mining; Search engine","score_opus":0.02034364459976031,"score_gpt":0.2843728339785337,"score_spread":0.2640291893787734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119234548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012402687,0.00039160685,0.98432934,0.00019329143,0.000033028868,0.00014787824,0.00009768004,0.0015155125,0.0008890447],"genre_scores_gemma":[0.36713874,0.00081464095,0.62711805,0.00042226716,0.00019003736,0.0005099792,0.000820882,0.00038171478,0.002603707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734783,0.0014474827,0.00015228053,0.00042739097,0.0004960681,0.00012900188],"domain_scores_gemma":[0.99090844,0.007007901,0.00034348015,0.0009181808,0.00072377204,0.000098170065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036587354,0.001191124,0.0013810516,0.0015146434,0.00065932743,0.0010668422,0.0015768124,0.001290045,0.0018139931],"category_scores_gemma":[0.012860969,0.0006451034,0.001425237,0.002399399,0.00096653425,0.0029400606,0.0012819391,0.0015615559,0.0008371135],"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":[0.00047628637,0.00040401725,0.0029288174,0.00051756954,0.00025306875,0.0004161137,0.00073575357,0.52183175,0.016999478,0.036006212,0.006810689,0.41262022],"study_design_scores_gemma":[0.000025666259,0.000041407107,0.0001389829,0.000005427707,0.00002373589,0.000066636596,0.000016086015,0.989224,0.0019472936,0.0074436264,0.0010520753,0.00001502886],"about_ca_topic_score_codex":0.012884727,"about_ca_topic_score_gemma":0.010052108,"teacher_disagreement_score":0.012884727,"about_ca_system_score_codex":0.0011287922,"about_ca_system_score_gemma":0.0015472105,"threshold_uncertainty_score":0.025619507},"labels":[],"label_agreement":null},{"id":"W2119339035","doi":"10.6010/geoinformatics.12.58","title":"Spatial Prediction Models and Applications.","year":2001,"lang":"en","type":"article","venue":"GEOINFORMATICS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Computer science","score_opus":0.014819490109537878,"score_gpt":0.2121460048233019,"score_spread":0.19732651471376403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119339035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005041901,0.073273115,0.8842958,0.009360837,0.0019843406,0.0000912347,0.003032616,0.0040202467,0.018899864],"genre_scores_gemma":[0.32476258,0.110882156,0.49846554,0.0016412365,0.005015069,0.00062584743,0.012897778,0.0006947852,0.045014944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985037,0.0005504776,0.00015528496,0.0002832272,0.0004360313,0.00007126971],"domain_scores_gemma":[0.9938059,0.0037776232,0.00029507896,0.0008763708,0.0010676121,0.00017738952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030136767,0.0011030051,0.0013860837,0.0021163821,0.00052852876,0.0028192608,0.002005381,0.0019892873,0.009411905],"category_scores_gemma":[0.015729165,0.0009073452,0.0007987345,0.004028261,0.0009984728,0.0035025715,0.0016820279,0.0023840035,0.006119283],"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":[0.000096121796,0.00014236408,0.0053626006,0.00076294795,0.00042949006,0.0002167659,0.00019294953,0.12426055,0.0005021408,0.19669183,0.1673701,0.5039721],"study_design_scores_gemma":[0.000022459302,0.00004480867,0.0013731349,0.0002541207,0.00013555832,0.0002570483,0.0001535128,0.33703008,0.0006225587,0.5539606,0.1061096,0.000036473615],"about_ca_topic_score_codex":0.0068593714,"about_ca_topic_score_gemma":0.008060448,"teacher_disagreement_score":0.009411905,"about_ca_system_score_codex":0.0009814383,"about_ca_system_score_gemma":0.0014226129,"threshold_uncertainty_score":0.031485975},"labels":[],"label_agreement":null},{"id":"W2119339514","doi":"10.1145/2462356.2462395","title":"Competitive query strategies for minimising the ply of the potential locations of moving points","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Representation (politics); Computer science; Query optimization; Quality (philosophy); Upper and lower bounds; Measure (data warehouse); Sargable; Data mining; Information retrieval; Web search query; Search engine; Mathematics","score_opus":0.01311500691766539,"score_gpt":0.2292175367494753,"score_spread":0.2161025298318099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119339514","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11279105,0.0026865518,0.8750381,0.0019077931,0.000101581514,0.00032715104,0.00079492514,0.00082974584,0.005523073],"genre_scores_gemma":[0.73816115,0.00092785637,0.25331324,0.000600424,0.00031876317,0.00045281881,0.0013955858,0.00038064425,0.0044495794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945834,0.0018736919,0.00032424874,0.001279324,0.0012712557,0.0006680453],"domain_scores_gemma":[0.9705064,0.023187077,0.001773686,0.0017606906,0.0017130241,0.0010591153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007338143,0.0024756936,0.004322888,0.0025427577,0.001460719,0.0037599192,0.009321493,0.0047771917,0.0044266353],"category_scores_gemma":[0.035162054,0.0013307863,0.0011584433,0.005210549,0.0023505667,0.009204855,0.003929813,0.002382941,0.0009353265],"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":[0.0012822713,0.00043762728,0.003392373,0.000613001,0.00026065088,0.00031579632,0.00065263134,0.79323655,0.0042684698,0.07770357,0.010343036,0.10749411],"study_design_scores_gemma":[0.00010192002,0.00025846934,0.00028182004,0.000019148012,0.000048086436,0.00017703067,0.00012346597,0.96300143,0.0008424287,0.0339134,0.001209123,0.000023670947],"about_ca_topic_score_codex":0.0068342593,"about_ca_topic_score_gemma":0.005752406,"teacher_disagreement_score":0.009321493,"about_ca_system_score_codex":0.0032170883,"about_ca_system_score_gemma":0.0023524906,"threshold_uncertainty_score":0.038808227},"labels":[],"label_agreement":null},{"id":"W2119462844","doi":"10.1109/icde.2001.914848","title":"Spatial clustering in the presence of obstacles","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cluster analysis; Computer science; Data stream clustering; Pruning; CURE data clustering algorithm; Function (biology); Canopy clustering algorithm; Data mining; Scalability; Space (punctuation); Correlation clustering; Obstacle; Artificial intelligence; Geography","score_opus":0.03443696842166376,"score_gpt":0.23324899908527733,"score_spread":0.19881203066361358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119462844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14627394,0.0006606448,0.8490898,0.0006441377,0.0000716071,0.00010290204,0.00019008815,0.0007558931,0.0022109812],"genre_scores_gemma":[0.5518482,0.00048702417,0.444242,0.000111032125,0.00007469345,0.00012936087,0.0005760434,0.0000922049,0.0024395746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823606,0.0004891599,0.00010071153,0.0004066788,0.0005980084,0.00016940676],"domain_scores_gemma":[0.9938646,0.0033665025,0.0006913731,0.0010955695,0.000756063,0.00022601262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020186873,0.00067700516,0.0011878619,0.0014759038,0.0016293832,0.001297153,0.0016425898,0.0012809895,0.00068790687],"category_scores_gemma":[0.008334194,0.0005781413,0.0007282003,0.0025377716,0.001292085,0.002518314,0.0027012907,0.00092104875,0.00028280576],"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":[0.0003390431,0.000066818684,0.00556229,0.00018308556,0.00011197528,0.00054085185,0.00040404417,0.8726261,0.006123309,0.029049173,0.0025454902,0.08244785],"study_design_scores_gemma":[0.000017524115,0.000040008876,0.000936528,0.000011803255,0.00002099888,0.00016521166,0.00013640437,0.97434944,0.0039902795,0.017793693,0.0025197407,0.000018321698],"about_ca_topic_score_codex":0.0061568744,"about_ca_topic_score_gemma":0.007538028,"teacher_disagreement_score":0.0061568744,"about_ca_system_score_codex":0.0011840025,"about_ca_system_score_gemma":0.0015205069,"threshold_uncertainty_score":0.012242079},"labels":[],"label_agreement":null},{"id":"W2119565875","doi":"10.1002/j.2158-1592.2001.tb00009.x","title":"A HYBRID TRAVEL DISTANCE APPROXIMATION FOR A GIS‐BASED DECISION SUPPORT SYSTEM","year":2001,"lang":"en","type":"article","venue":"Journal of Business Logistics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Geographic information system; Decision support system; Computer science; Shortest path problem; Operations research; Travel time; Information system; Global Positioning System; Transport engineering; Data mining; Geography; Mathematics; Engineering; Remote sensing; Telecommunications","score_opus":0.03737388903082799,"score_gpt":0.2631662904065242,"score_spread":0.22579240137569617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119565875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014228571,0.00012954426,0.984177,0.000084907384,0.00002553582,0.00002385213,0.000080557846,0.00054277154,0.00070726144],"genre_scores_gemma":[0.5730479,0.0002584332,0.42372727,0.00006265073,0.000039056627,0.00017684103,0.0003476864,0.00010841327,0.0022317993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991467,0.0002471004,0.00006122601,0.00017161403,0.00031036758,0.00006299128],"domain_scores_gemma":[0.9986461,0.0008066395,0.00009661482,0.00010854495,0.00029106505,0.00005095333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011305113,0.0006244822,0.0007737675,0.0006398506,0.0004155001,0.0014530115,0.0012204731,0.0008257885,0.002288537],"category_scores_gemma":[0.004092007,0.00048603365,0.00057862257,0.00091759325,0.0003921083,0.0015024565,0.0007446465,0.0011234767,0.00062250363],"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":[0.000103780556,0.000021032385,0.00046070595,0.00003376695,0.000031285785,0.000031428724,0.000036364137,0.9596203,0.0012545598,0.004542586,0.00042944934,0.03343474],"study_design_scores_gemma":[0.000003388117,0.000009402483,0.00004547305,0.0000015282919,0.0000035910496,0.0000075428034,0.000002467052,0.99867105,0.00018568076,0.0007391715,0.00032822287,0.000002507399],"about_ca_topic_score_codex":0.013172198,"about_ca_topic_score_gemma":0.0054258653,"teacher_disagreement_score":0.013172198,"about_ca_system_score_codex":0.0013913116,"about_ca_system_score_gemma":0.0011002441,"threshold_uncertainty_score":0.026191056},"labels":[],"label_agreement":null},{"id":"W2121433213","doi":"10.1145/1386118.1386119","title":"Probabilistic top- <i>k</i> and ranking-aggregate queries","year":2008,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ranking (information retrieval); Probabilistic logic; Aggregate (composite); Uncertain data; Semantics (computer science); Tuple; Probabilistic database; Search engine indexing; Materialized view; Dimension (graph theory); Information retrieval; Online aggregation; Data mining; Theoretical computer science; Web search query; Sargable; Search engine; Relational database; Artificial intelligence; View; Database theory; Programming language","score_opus":0.027337698147204898,"score_gpt":0.23315272567843237,"score_spread":0.20581502753122746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121433213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008490557,0.00046477557,0.9854857,0.0006847719,0.00005451708,0.0002391189,0.000633522,0.0010550366,0.0028919748],"genre_scores_gemma":[0.26549426,0.0008102531,0.72831506,0.0004174823,0.00023394081,0.0003359421,0.0012919975,0.0002454158,0.0028556823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99116546,0.0022345618,0.0012521595,0.0014616789,0.0033079342,0.00057830644],"domain_scores_gemma":[0.98819566,0.004778282,0.0016434215,0.003834311,0.0012288284,0.00031946346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068583973,0.001092663,0.0021339983,0.00230493,0.0017601036,0.006600272,0.003480271,0.0021810532,0.0042334986],"category_scores_gemma":[0.02158022,0.00096074044,0.0022894866,0.005715603,0.0022658166,0.014321743,0.0036583706,0.0024149066,0.0013850861],"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":[0.00052433397,0.00035198792,0.0049428917,0.0010320343,0.00021796717,0.000429306,0.0010223333,0.15808032,0.0063776327,0.5143227,0.020195452,0.2925031],"study_design_scores_gemma":[0.00005626035,0.00014416914,0.0008622931,0.00006395539,0.00008845164,0.0008789226,0.00040758812,0.60600805,0.005140896,0.37075728,0.015475266,0.00011685164],"about_ca_topic_score_codex":0.0029567287,"about_ca_topic_score_gemma":0.004636779,"teacher_disagreement_score":0.0068583973,"about_ca_system_score_codex":0.0015839745,"about_ca_system_score_gemma":0.002713371,"threshold_uncertainty_score":0.036271095},"labels":[],"label_agreement":null},{"id":"W2121804177","doi":"10.1145/2666310.2666485","title":"Towards knowledge-enriched path computation","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Closeness; Probabilistic logic; Measure (data warehouse); Path (computing); Geospatial analysis; Ground truth; Routing (electronic design automation); Shortest path problem","score_opus":0.01594114754581812,"score_gpt":0.2600668622141003,"score_spread":0.24412571466828217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121804177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0154478755,0.00023034669,0.9799478,0.00024529,0.000032128224,0.00008966463,0.00095847074,0.0015256017,0.0015227165],"genre_scores_gemma":[0.19388106,0.00034529355,0.7980314,0.00016554257,0.00006442396,0.0002599199,0.0047635473,0.0003246518,0.0021641029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860746,0.00031356758,0.00009126764,0.00045349007,0.00039553427,0.00013860533],"domain_scores_gemma":[0.99660575,0.001831718,0.00023994326,0.00066479953,0.0005445165,0.00011323842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009842602,0.0017144033,0.001597171,0.004115466,0.0010313424,0.0024597978,0.0028309356,0.0024179595,0.004172045],"category_scores_gemma":[0.010561032,0.00079889665,0.0015785061,0.0054416046,0.0010367127,0.0040354934,0.0034412583,0.0016663292,0.0013006323],"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":[0.00019021265,0.00015624159,0.002205279,0.0003334225,0.00011544707,0.0002094579,0.00032162917,0.74223715,0.0017866669,0.03670926,0.0075596236,0.20817555],"study_design_scores_gemma":[0.000016561842,0.00001734387,0.00019460825,0.000022031327,0.000018960485,0.000043860724,0.000088959074,0.94253,0.0008360754,0.053404085,0.0028170021,0.000010607769],"about_ca_topic_score_codex":0.012845232,"about_ca_topic_score_gemma":0.018509977,"teacher_disagreement_score":0.012845232,"about_ca_system_score_codex":0.0017168672,"about_ca_system_score_gemma":0.002443152,"threshold_uncertainty_score":0.025540888},"labels":[],"label_agreement":null},{"id":"W2122098189","doi":"10.1109/iasp.2009.5054570","title":"Research on optimized spatial data query algorithm in the spatial database","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Object-based spatial database; Spatial database; Spatial analysis; Spatial query; Computer science; Data mining; Object (grammar); Spatiotemporal database; Database; Information retrieval; View; Database design; Sargable; Web search query; Artificial intelligence; Search engine; Database tuning; Geography; Remote sensing","score_opus":0.13490279937498753,"score_gpt":0.38551729720262573,"score_spread":0.2506144978276382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122098189","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018225433,0.0016729843,0.9768763,0.00030285266,0.000046337118,0.00005254485,0.00007984977,0.0005365604,0.0022070212],"genre_scores_gemma":[0.24344504,0.0023698993,0.7491775,0.00020288493,0.00013297146,0.000117069016,0.00054451055,0.00024204045,0.003768013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981546,0.00035631572,0.00015366643,0.00047635203,0.00072658603,0.00013261107],"domain_scores_gemma":[0.99851626,0.0005198777,0.00009230027,0.00026284697,0.0005716837,0.000037106325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016428541,0.0006806565,0.0014265556,0.0012189994,0.0007680887,0.0020896322,0.0015450948,0.0007551393,0.0016185767],"category_scores_gemma":[0.004189081,0.0004873586,0.0007745993,0.002986429,0.00065382273,0.0047193277,0.00073967513,0.0010569426,0.0004587082],"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":[0.00043246523,0.00019989563,0.0065192627,0.0004194472,0.00018548197,0.00014028807,0.00042749618,0.27972665,0.018062163,0.13866204,0.011000325,0.54422456],"study_design_scores_gemma":[0.000038346607,0.00007606043,0.00073171174,0.000019844692,0.000052013896,0.00019336441,0.00012089558,0.9612247,0.010447904,0.019843653,0.0072271475,0.000024417071],"about_ca_topic_score_codex":0.008502576,"about_ca_topic_score_gemma":0.0037285483,"teacher_disagreement_score":0.008502576,"about_ca_system_score_codex":0.0015316424,"about_ca_system_score_gemma":0.001759163,"threshold_uncertainty_score":0.016906142},"labels":[],"label_agreement":null},{"id":"W2122137272","doi":"10.1109/icde.1995.380399","title":"Context-dependent interpretations of linguistic terms in fuzzy relational databases","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Interpretation (philosophy); Tuple; Fuzzy logic; Context (archaeology); Computer science; Term (time); Natural language processing; Meaning (existential); Data mining; Relational database; Artificial intelligence; Fuzzy set; Information retrieval; Theoretical computer science; Mathematics; Discrete mathematics; Programming language","score_opus":0.050671667147898496,"score_gpt":0.26537370476837646,"score_spread":0.21470203762047796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122137272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014541846,0.0024784636,0.97644436,0.00081236014,0.00016328816,0.00014650532,0.00019630547,0.0004142037,0.004802836],"genre_scores_gemma":[0.190469,0.001609539,0.80415964,0.00035342548,0.00030177686,0.0002522181,0.00039853205,0.00011696212,0.0023389459],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9920082,0.0030841855,0.0011780538,0.0011735293,0.002231449,0.00032449482],"domain_scores_gemma":[0.99567586,0.0020019957,0.00048659675,0.0007753321,0.0008543673,0.00020579454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065734847,0.0010816642,0.0009187891,0.0032368985,0.0014898097,0.0058494587,0.0024067184,0.0018846589,0.0021774543],"category_scores_gemma":[0.011719169,0.0011804928,0.0020433997,0.0035339866,0.004141187,0.009418296,0.003037649,0.0035881565,0.00064857135],"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":[0.000109711415,0.00004962277,0.00050235697,0.00027175233,0.00005424369,0.00068756164,0.004074645,0.010033018,0.0050824303,0.91789454,0.002042055,0.05919791],"study_design_scores_gemma":[0.0000422908,0.00008167897,0.0003810527,0.00021656082,0.00010684273,0.0006072075,0.0011293509,0.06625833,0.006329006,0.89106643,0.033663336,0.00011795448],"about_ca_topic_score_codex":0.0014094079,"about_ca_topic_score_gemma":0.001921141,"teacher_disagreement_score":0.0065734847,"about_ca_system_score_codex":0.0018943975,"about_ca_system_score_gemma":0.0012308969,"threshold_uncertainty_score":0.03476429},"labels":[],"label_agreement":null},{"id":"W2122464844","doi":"10.1109/hicss.1988.11924","title":"Fuzzy information management using the roster model","year":2003,"lang":"en","type":"article","venue":"","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":"Queen's University","funders":"","keywords":"Unix; Fuzzy logic; Computer science; Fuzzy set; Set (abstract data type); Data mining; Artificial intelligence; Theoretical computer science; Programming language","score_opus":0.025261781740607423,"score_gpt":0.23675120627429572,"score_spread":0.2114894245336883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122464844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009378136,0.00040605856,0.96048796,0.0013467566,0.0000881753,0.0001739139,0.0003085237,0.0011355611,0.026674906],"genre_scores_gemma":[0.3184183,0.0011590065,0.65223604,0.0005683716,0.00024714202,0.0005598977,0.0010111546,0.00016621627,0.025633896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99725854,0.000659919,0.00027403282,0.0005209502,0.0011010009,0.00018552515],"domain_scores_gemma":[0.9978783,0.00062440196,0.00024456516,0.00066388724,0.0004788883,0.00010994386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024550005,0.0005514028,0.0007721426,0.0013988628,0.0010844552,0.005127335,0.0020355433,0.0012136573,0.0099702785],"category_scores_gemma":[0.0050831614,0.0004025856,0.0014346384,0.0012753761,0.0014900217,0.00738541,0.0024194366,0.0014832569,0.0019075278],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057945545,0.000038173283,0.00025842973,0.00006642045,0.000035549696,0.00017850903,0.0003195478,0.040342987,0.0010849824,0.90946335,0.003894982,0.04425905],"study_design_scores_gemma":[0.00005270633,0.00006320665,0.00014848856,0.000070985756,0.000046375204,0.00016976145,0.0001464657,0.4149167,0.002637371,0.5308949,0.050803248,0.000049830916],"about_ca_topic_score_codex":0.0048406157,"about_ca_topic_score_gemma":0.0032876406,"teacher_disagreement_score":0.0099702785,"about_ca_system_score_codex":0.001994184,"about_ca_system_score_gemma":0.0018849389,"threshold_uncertainty_score":0.033353925},"labels":[],"label_agreement":null},{"id":"W2122471363","doi":"10.1109/tfuzz.2010.2040479","title":"Fuzzy Clustering with Viewpoints","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Viewpoints; Cluster analysis; Computer science; Data mining; Fuzzy clustering; Perspective (graphical); Fuzzy logic; Domain knowledge; Artificial intelligence; Domain (mathematical analysis); Machine learning; Mathematics","score_opus":0.014409782342732585,"score_gpt":0.22634982170703005,"score_spread":0.21194003936429748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122471363","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045913183,0.00014924427,0.9928283,0.000098330915,0.00002210053,0.000043293814,0.000048413764,0.000078995436,0.002139968],"genre_scores_gemma":[0.119900316,0.00034852672,0.8770812,0.00007606424,0.000051624702,0.00012185406,0.00030637992,0.000080997066,0.0020330916],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668556,0.001280622,0.00017434449,0.0007060972,0.0009838394,0.00016969071],"domain_scores_gemma":[0.9978707,0.00080440793,0.00018100024,0.0006122435,0.00044052303,0.00009114196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032610248,0.0011206976,0.0011971792,0.00179572,0.0011443392,0.002696238,0.0020252012,0.0013757338,0.0022660466],"category_scores_gemma":[0.0070125023,0.00070768245,0.0023127252,0.0024568967,0.0017241422,0.0031298553,0.0031618478,0.0017440586,0.0008612317],"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":[0.00016614822,0.00005425783,0.0012941859,0.000309556,0.000120466066,0.00016422162,0.0010611174,0.2546918,0.009087704,0.5634621,0.0023786963,0.16720968],"study_design_scores_gemma":[0.000030326139,0.0001273111,0.0005440606,0.00008055357,0.000060187594,0.00022201861,0.0002521135,0.72540593,0.005491674,0.2501049,0.017609732,0.00007114118],"about_ca_topic_score_codex":0.003677941,"about_ca_topic_score_gemma":0.002741106,"teacher_disagreement_score":0.003677941,"about_ca_system_score_codex":0.002070076,"about_ca_system_score_gemma":0.0013238329,"threshold_uncertainty_score":0.017246187},"labels":[],"label_agreement":null},{"id":"W2123193503","doi":"10.3233/jcs-2007-15401","title":"Parity-based inference control for multi-dimensional range sum queries","year":2007,"lang":"en","type":"article","venue":"Journal of Computer Security","topic":"Data Management and Algorithms","field":"Computer Science","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":"Concordia University","funders":"National Science Foundation","keywords":"Inference; Computer science; Range (aeronautics); Parity (physics); Statistical inference; Subtraction; Control (management); Theoretical computer science; Discrete mathematics; Mathematics; Statistics; Artificial intelligence; Arithmetic","score_opus":0.02731855282240219,"score_gpt":0.29240019902335673,"score_spread":0.26508164620095453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123193503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033703256,0.0003693132,0.9600653,0.0006637932,0.000071759234,0.00016964987,0.0002462701,0.002456048,0.0022545832],"genre_scores_gemma":[0.66680956,0.000319718,0.328038,0.0007793042,0.00028748493,0.00036301423,0.000747452,0.0005952173,0.0020601903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9771752,0.0059614684,0.0020282865,0.004266733,0.008728404,0.0018399945],"domain_scores_gemma":[0.90881145,0.06599919,0.0043990687,0.013098495,0.0066715986,0.0010201852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015108389,0.0010219135,0.0019344868,0.002191587,0.0016740065,0.003943206,0.005187104,0.0014077774,0.0033494208],"category_scores_gemma":[0.07667094,0.0010047087,0.0017790918,0.002035116,0.0038095831,0.010464883,0.0057729096,0.0038541004,0.0006644866],"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":[0.0027349151,0.00060214975,0.01766119,0.00086935936,0.00042663154,0.0008448465,0.0016543453,0.16083074,0.029725788,0.29535744,0.008841534,0.48045105],"study_design_scores_gemma":[0.00017630096,0.00015400842,0.0008750737,0.000040885374,0.00014289531,0.0002999917,0.00013413114,0.78682554,0.02490075,0.18352771,0.0028390877,0.00008367712],"about_ca_topic_score_codex":0.0049889497,"about_ca_topic_score_gemma":0.0033878784,"teacher_disagreement_score":0.015108389,"about_ca_system_score_codex":0.0026706336,"about_ca_system_score_gemma":0.0037255774,"threshold_uncertainty_score":0.079901755},"labels":[],"label_agreement":null},{"id":"W2123567890","doi":"10.1007/978-3-540-69839-5_54","title":"Deriving and Mining Spatiotemporal Event Schemas in In-Situ Sensor Data","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Granularity; Computer science; Data mining; Construct (python library); Event (particle physics); Hierarchical database model; Tree (set theory); Tree structure; Axiom; Schema (genetic algorithms); Theoretical computer science; Algorithm; Binary tree; Information retrieval; Mathematics","score_opus":0.04023980359721857,"score_gpt":0.2652922379323274,"score_spread":0.22505243433510885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123567890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037740484,0.000672937,0.9543532,0.00023784119,0.00006269876,0.0002091444,0.0034008701,0.0021714936,0.0011513205],"genre_scores_gemma":[0.20455034,0.0016565298,0.7753571,0.00011577447,0.000049391307,0.00015309289,0.016806824,0.00029790576,0.0010130598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985397,0.00020633252,0.0002500188,0.00034467306,0.0005740529,0.00008523044],"domain_scores_gemma":[0.9960995,0.001777568,0.0004280786,0.00096419523,0.0006398064,0.00009086356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002443689,0.0010449756,0.0010184,0.0032574122,0.00056366326,0.0029753072,0.00267479,0.0011020745,0.0010000155],"category_scores_gemma":[0.009112798,0.00098807,0.002296538,0.004798841,0.0007208442,0.0045828894,0.00198374,0.0011583962,0.0007924349],"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":[0.00033765857,0.00056534825,0.04028316,0.0022695512,0.00079875893,0.0029871743,0.0029353192,0.1977681,0.041909654,0.062514454,0.010979192,0.6366517],"study_design_scores_gemma":[0.00004771316,0.00017893009,0.0072469693,0.0003292789,0.00058642175,0.002054354,0.0024657228,0.8080017,0.065306254,0.08268818,0.031003235,0.000091299866],"about_ca_topic_score_codex":0.0036101528,"about_ca_topic_score_gemma":0.006396974,"teacher_disagreement_score":0.0036101528,"about_ca_system_score_codex":0.0007141965,"about_ca_system_score_gemma":0.0014788278,"threshold_uncertainty_score":0.012923598},"labels":[],"label_agreement":null},{"id":"W2123914025","doi":"","title":"Approximating Range-Aggregate Queries using Coresets","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Rectangle; Combinatorics; Range (aeronautics); Ball (mathematics); Aggregate (composite); Set (abstract data type); RADIUS; Function (biology); Point (geometry); Mathematics; Approximation algorithm; Value (mathematics); Computer science; Geometry; Statistics","score_opus":0.02188332451805042,"score_gpt":0.2593523324219685,"score_spread":0.23746900790391806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123914025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18562974,0.001976551,0.8001675,0.00077276654,0.00018200064,0.00019107458,0.0012573263,0.0035737832,0.0062492276],"genre_scores_gemma":[0.6777344,0.000749928,0.31504905,0.0002215738,0.00017063685,0.00026290567,0.0023592045,0.00026804677,0.0031842894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971584,0.00061925384,0.00028715158,0.0004936103,0.00109082,0.00035061126],"domain_scores_gemma":[0.9940942,0.003143893,0.00041524446,0.0014277761,0.0006993008,0.00021957414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002454968,0.0012526709,0.0022098203,0.0023735939,0.0007233556,0.0026543734,0.0024542103,0.0011820433,0.002884992],"category_scores_gemma":[0.013354156,0.00064619805,0.00091393595,0.0048278314,0.0008121801,0.0054708333,0.0027752405,0.0012752577,0.00080640055],"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":[0.002027573,0.00036036238,0.0068012057,0.00030220824,0.00013535129,0.00024978098,0.00037543548,0.70221347,0.0073970165,0.042056598,0.012727731,0.22535327],"study_design_scores_gemma":[0.00002855097,0.000054980665,0.00018693328,0.000011149076,0.000007416206,0.00006925442,0.000064233005,0.9837053,0.0014048925,0.0135084605,0.00095305603,0.0000057316956],"about_ca_topic_score_codex":0.0044967784,"about_ca_topic_score_gemma":0.004888318,"teacher_disagreement_score":0.0044967784,"about_ca_system_score_codex":0.0014047595,"about_ca_system_score_gemma":0.0011030608,"threshold_uncertainty_score":0.012983263},"labels":[],"label_agreement":null},{"id":"W2123965785","doi":"10.1109/cmpsac.1997.625000","title":"Concurrent access to point data","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Concurrency; Concurrency control; Multiversion concurrency control; Non-lock concurrency control; Lock (firearm); Tree (set theory); Concurrent computing; Programming language; B-tree; Theoretical computer science; Distributed computing; Parallel computing; Data structure; Distributed concurrency control; Mathematics","score_opus":0.19836140510693248,"score_gpt":0.3377557586632647,"score_spread":0.1393943535563322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123965785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13400151,0.00077520084,0.84948456,0.0002588098,0.00014156663,0.00029237824,0.00022065028,0.007745912,0.007079448],"genre_scores_gemma":[0.75330323,0.0005768517,0.23641545,0.00018214947,0.00019483094,0.00038507226,0.0006121593,0.00055447785,0.0077756913],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948796,0.0008052558,0.00040068047,0.00075218035,0.0025104429,0.00065179367],"domain_scores_gemma":[0.9809461,0.004998663,0.00089215563,0.008633177,0.0035916122,0.00093822146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029897543,0.00068810326,0.0012891251,0.0012936932,0.0015534785,0.0038579374,0.0038478773,0.00086345844,0.0031918685],"category_scores_gemma":[0.013918241,0.00079558505,0.00088825723,0.0022211063,0.001916346,0.0045280387,0.007522053,0.0016192016,0.0010357827],"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":[0.0049173553,0.00070018263,0.01624414,0.0007240442,0.00032191098,0.0012471896,0.003333673,0.054021753,0.13410902,0.15183353,0.011177216,0.62136996],"study_design_scores_gemma":[0.0008009386,0.00087151706,0.0022485373,0.00007657459,0.00027576962,0.0013833303,0.0005436623,0.68823326,0.13898832,0.12465746,0.041708324,0.00021230856],"about_ca_topic_score_codex":0.0023821783,"about_ca_topic_score_gemma":0.0017828607,"teacher_disagreement_score":0.0038579374,"about_ca_system_score_codex":0.0006827062,"about_ca_system_score_gemma":0.0014507137,"threshold_uncertainty_score":0.015811503},"labels":[],"label_agreement":null},{"id":"W2124547665","doi":"10.3138/carto.48.2.1836","title":"Detecting Level-of-Detail Inconsistencies in Volunteered Geographic Information Data Sets","year":2013,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Volunteered geographic information; Generalization; Computer science; Data mining; Information retrieval; Data science; Mathematics","score_opus":0.054007837470390375,"score_gpt":0.2972475775605441,"score_spread":0.24323974009015373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124547665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3812633,0.0008862861,0.61070466,0.00038627136,0.00007647185,0.00032122518,0.0023953537,0.002043622,0.0019228102],"genre_scores_gemma":[0.70207626,0.00021948869,0.29303876,0.00006581358,0.000029251818,0.00012389832,0.0037697395,0.00027123984,0.00040547305],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99143136,0.0018755128,0.0010320905,0.0015863706,0.0035976549,0.00047694717],"domain_scores_gemma":[0.9684752,0.014094206,0.005571529,0.0066063413,0.0048364424,0.00041617837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008274955,0.0005241748,0.0011526758,0.007011729,0.0012350419,0.0038511814,0.0018956512,0.0012634913,0.00055350567],"category_scores_gemma":[0.040835153,0.0006947208,0.0009818844,0.00694369,0.0011988357,0.0045911213,0.0039948216,0.0010710964,0.00019454751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001254456,0.00043881653,0.36709774,0.0018261987,0.000749652,0.0036123923,0.016275708,0.08369568,0.03979758,0.053169124,0.008176738,0.42390582],"study_design_scores_gemma":[0.0001218357,0.00033620128,0.14676604,0.00057201216,0.00056632207,0.0039335177,0.011909758,0.58420134,0.08445505,0.111230016,0.05557532,0.00033262555],"about_ca_topic_score_codex":0.0040260786,"about_ca_topic_score_gemma":0.0054266783,"teacher_disagreement_score":0.008274955,"about_ca_system_score_codex":0.0010038739,"about_ca_system_score_gemma":0.0012117772,"threshold_uncertainty_score":0.043762684},"labels":[],"label_agreement":null},{"id":"W2124894008","doi":"10.1109/mwscas.2007.4488662","title":"Exploring different methods for 2DR-tree binary search on a FPGA","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Implementation; Field-programmable gate array; Computer science; Software implementation; Software; Binary tree; Data structure; Tree (set theory); Binary data; Computer architecture; Binary number; Parallel computing; Theoretical computer science; Embedded system; Operating system; Software engineering; Programming language; Arithmetic; Mathematics","score_opus":0.2558867416882275,"score_gpt":0.378630708343344,"score_spread":0.1227439666551165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124894008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056118146,0.0014201764,0.9322974,0.0002794696,0.00006489097,0.000098193006,0.00006509216,0.0016971175,0.007959489],"genre_scores_gemma":[0.20014822,0.0005370692,0.7949199,0.00011144524,0.000045871355,0.00011975831,0.00012591072,0.00018932071,0.0038024231],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999355,0.00018442422,0.00005765109,0.00008448364,0.00024526508,0.000073115196],"domain_scores_gemma":[0.9991755,0.00044517848,0.000049771752,0.00015266292,0.00015475262,0.000022171002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057258015,0.0004327006,0.0004560687,0.0010158443,0.00040397112,0.000820708,0.0011114575,0.0006302939,0.0049305144],"category_scores_gemma":[0.0021233237,0.00027220522,0.00047353352,0.0014352171,0.00037993828,0.0019519466,0.0005840712,0.00033477705,0.0008373784],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004626993,0.00014834311,0.0014106774,0.00039804482,0.00007603211,0.0001281068,0.0002693604,0.08653527,0.02177233,0.055373717,0.0031847535,0.83024067],"study_design_scores_gemma":[0.0002742059,0.0003117498,0.001038023,0.00006606561,0.000053103227,0.00044187982,0.00017511127,0.9344045,0.022665666,0.025076933,0.015439685,0.0000530517],"about_ca_topic_score_codex":0.002609713,"about_ca_topic_score_gemma":0.006050372,"teacher_disagreement_score":0.0049305144,"about_ca_system_score_codex":0.00058941916,"about_ca_system_score_gemma":0.00071418285,"threshold_uncertainty_score":0.016494274},"labels":[],"label_agreement":null},{"id":"W2126080031","doi":"10.1109/icassp.2004.1326474","title":"Hierarchical annealing for scientific models","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Simulated annealing; Computational complexity theory; Theoretical computer science; Convergence (economics); Computational science; Algorithm","score_opus":0.049093765891834,"score_gpt":0.2666565142974884,"score_spread":0.21756274840565437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126080031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008111643,0.0006932134,0.9824829,0.0005807425,0.000080461374,0.00008330831,0.00015505684,0.000604338,0.0072083855],"genre_scores_gemma":[0.3693199,0.0010645232,0.6182752,0.00050358806,0.0001690271,0.0009797883,0.00069206196,0.0005157538,0.008480109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985643,0.00073347235,0.00006153665,0.00019358464,0.00036017987,0.00008686256],"domain_scores_gemma":[0.99678874,0.0021019243,0.00017105562,0.00060225767,0.0002084362,0.00012761148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020196605,0.0005939201,0.0011628125,0.00079947885,0.0009462031,0.0014733212,0.0015552382,0.0016709786,0.0067024953],"category_scores_gemma":[0.008928536,0.0007230572,0.0013593517,0.0008900999,0.0017284004,0.0018293364,0.0021040577,0.0022434592,0.0012112493],"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":[0.00003304021,0.000027291871,0.00025789635,0.00010611242,0.000035029025,0.000036684414,0.000090712405,0.6161439,0.0008836645,0.3648149,0.0023652497,0.015205405],"study_design_scores_gemma":[0.000010814145,0.000010230554,0.00006132148,0.00000754384,0.000005027088,0.000008438771,0.000006797603,0.83700883,0.00016437718,0.16014823,0.002562391,0.000006013332],"about_ca_topic_score_codex":0.0047829337,"about_ca_topic_score_gemma":0.006200187,"teacher_disagreement_score":0.0067024953,"about_ca_system_score_codex":0.0024381715,"about_ca_system_score_gemma":0.0014741296,"threshold_uncertainty_score":0.022422016},"labels":[],"label_agreement":null},{"id":"W2126220026","doi":"10.1007/s10619-009-7043-x","title":"Continuously monitoring top-k uncertain data streams: a probabilistic threshold method","year":2009,"lang":"en","type":"article","venue":"Distributed and Parallel Databases","topic":"Data Management and Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Data stream mining; Computer science; Probabilistic logic; Uncertain data; Quantile; Data mining; Data structure; Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.07410263996446681,"score_gpt":0.33747829743305896,"score_spread":0.26337565746859215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126220026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003978627,0.00017406272,0.9952342,0.0000863295,0.000028258812,0.000019152587,0.000029753497,0.00021058317,0.00023905202],"genre_scores_gemma":[0.4719142,0.00061205,0.524624,0.00018237748,0.00028290635,0.00015563151,0.0002432449,0.00019151231,0.0017941537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968671,0.0005829437,0.0002857062,0.0008765584,0.0011154978,0.00027213985],"domain_scores_gemma":[0.9928019,0.0044576004,0.00056759996,0.00085528195,0.0010315066,0.00028603012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038330804,0.0009202566,0.002570082,0.0016785525,0.0008430126,0.0027603002,0.0042544194,0.0017054002,0.00165694],"category_scores_gemma":[0.01366929,0.00080945116,0.001399651,0.0028563482,0.0013104716,0.0049600936,0.002616903,0.0024531411,0.00039469995],"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":[0.000903664,0.00019251734,0.0041209804,0.00032578877,0.00035838285,0.00016365823,0.00024572274,0.5255739,0.010735191,0.044198092,0.002801724,0.41038042],"study_design_scores_gemma":[0.000010785816,0.000024752679,0.00015338151,0.000005665315,0.000029942003,0.00003307959,0.0000105895315,0.98849213,0.0010614841,0.009845556,0.00032237783,0.000010188254],"about_ca_topic_score_codex":0.0040516607,"about_ca_topic_score_gemma":0.002489069,"teacher_disagreement_score":0.0042544194,"about_ca_system_score_codex":0.0013972656,"about_ca_system_score_gemma":0.0017986806,"threshold_uncertainty_score":0.02027154},"labels":[],"label_agreement":null},{"id":"W2126431245","doi":"10.1007/s00778-006-0029-7","title":"Algorithms and analyses for maximal vector computation","year":2006,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":177,"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":"Skyline; Curse of dimensionality; Computation; Field (mathematics); Computer science; Running time; Divide and conquer algorithms; Algorithm; Mathematics; Discrete mathematics; Theoretical computer science; Combinatorics; Artificial intelligence; Data mining; Pure mathematics","score_opus":0.04801050073090831,"score_gpt":0.3160797680561167,"score_spread":0.2680692673252084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126431245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063389577,0.0019542282,0.97920144,0.00089791196,0.00021511008,0.000079900565,0.0003003806,0.0015231587,0.009488952],"genre_scores_gemma":[0.31471542,0.0036274523,0.65375775,0.0011498927,0.002264948,0.00067938,0.0021533633,0.0019801646,0.019671755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99328953,0.0022342661,0.0004683542,0.0010047501,0.002230083,0.00077310443],"domain_scores_gemma":[0.9749116,0.016975673,0.0010672213,0.0040946384,0.002336722,0.0006141624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058178934,0.0024986477,0.0021530841,0.005575067,0.0025338058,0.00698099,0.005808397,0.0020489225,0.013877985],"category_scores_gemma":[0.038814344,0.0013354854,0.0028446068,0.0073616947,0.0051719025,0.017230734,0.0055537107,0.0067913216,0.004388889],"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":[0.00031379831,0.00015826907,0.0009700437,0.00032523889,0.00008653073,0.00004961489,0.00022508496,0.02553238,0.0008970145,0.85293186,0.011886465,0.106623635],"study_design_scores_gemma":[0.000019391284,0.00001893421,0.00016515146,0.00004202341,0.000037356618,0.000030408915,0.000035125406,0.092816055,0.000847549,0.9024708,0.0034977805,0.000019297917],"about_ca_topic_score_codex":0.0029166138,"about_ca_topic_score_gemma":0.0029885834,"teacher_disagreement_score":0.013877985,"about_ca_system_score_codex":0.0034800463,"about_ca_system_score_gemma":0.0030176626,"threshold_uncertainty_score":0.046426475},"labels":[],"label_agreement":null},{"id":"W2126574345","doi":"10.1109/93.978352","title":"Spatial object clustering and buffering","year":2002,"lang":"en","type":"article","venue":"IEEE Multimedia","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Cluster analysis; Object (grammar); Polygon (computer graphics); Spatial analysis; Geographic information system; Data mining; Database; Object-based spatial database; Information retrieval; Data science; Spatial database; Artificial intelligence","score_opus":0.025362723315093275,"score_gpt":0.22610200808944578,"score_spread":0.2007392847743525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126574345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0885158,0.0020010534,0.88320696,0.0008697657,0.00030453276,0.00036572764,0.00072150404,0.008047896,0.015966766],"genre_scores_gemma":[0.44828358,0.0011043021,0.53612155,0.00026853537,0.00019662465,0.0002532845,0.0011940568,0.0005006916,0.012077358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856853,0.00022766845,0.00008666632,0.00034221733,0.0005554126,0.00021942363],"domain_scores_gemma":[0.99725634,0.0007991078,0.00023266886,0.00096049946,0.0005876737,0.00016375467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015206446,0.0011361873,0.0009490711,0.0030211692,0.0019094469,0.002724042,0.003260476,0.0011955719,0.006493663],"category_scores_gemma":[0.0066433344,0.00068738166,0.0005688472,0.005839349,0.0010222533,0.004168518,0.0025753826,0.000629111,0.0015830639],"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":[0.0010786927,0.00027485294,0.0067144446,0.00033639875,0.00013591428,0.00022209468,0.0008724226,0.19521478,0.031407006,0.10561009,0.018570762,0.6395626],"study_design_scores_gemma":[0.00016729407,0.00042436994,0.0035321931,0.0000795052,0.00020215962,0.0008084958,0.00082120585,0.7810691,0.09591981,0.063465886,0.053342067,0.000167847],"about_ca_topic_score_codex":0.013171956,"about_ca_topic_score_gemma":0.011773115,"teacher_disagreement_score":0.013171956,"about_ca_system_score_codex":0.0017492473,"about_ca_system_score_gemma":0.0018207602,"threshold_uncertainty_score":0.026190579},"labels":[],"label_agreement":null},{"id":"W2126593637","doi":"10.1111/j.0016-7363.2005.00676.x","title":"Extending the SAND Spatial Database System for the Visualization of Three‐Dimensional Scientific Data","year":2005,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"University of California, Davis; U.S. Department of Energy; National Science Foundation","keywords":"Voronoi diagram; Computer science; Data mining; Visualization; Context (archaeology); Spatial analysis; Preprocessor; Spatial database; Scientific visualization; Focus (optics); Spatial query; Database; Information retrieval; Artificial intelligence; Mathematics; Geography; Web search query","score_opus":0.034978365885814294,"score_gpt":0.2849930023201093,"score_spread":0.250014636434295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126593637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013864969,0.001285825,0.85213846,0.00096077536,0.00040935437,0.00038296558,0.006212645,0.099869736,0.024875248],"genre_scores_gemma":[0.13051853,0.002398808,0.82710636,0.0006029403,0.00018328744,0.00060542196,0.013349639,0.0035157376,0.021719228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989716,0.00014138926,0.00014112794,0.00010264093,0.0005547844,0.00008850143],"domain_scores_gemma":[0.9973748,0.00057064526,0.00016289746,0.0010416259,0.0006425777,0.00020739151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014044377,0.0006493738,0.0008138357,0.0027020872,0.0008874362,0.0036689844,0.001766553,0.0006608422,0.011521995],"category_scores_gemma":[0.0051551703,0.00050818705,0.00084778626,0.0040007075,0.0006070799,0.0031617142,0.0037025604,0.0012164204,0.004722971],"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":[0.0012856512,0.00018996732,0.006376084,0.00096339505,0.00025913137,0.0014673934,0.0015778461,0.016969897,0.028503018,0.10812389,0.194212,0.6400717],"study_design_scores_gemma":[0.00016960746,0.000094766634,0.0020060402,0.00014251073,0.00012376979,0.0013183106,0.00036622185,0.09873161,0.019731628,0.04800192,0.829129,0.00018466741],"about_ca_topic_score_codex":0.009759573,"about_ca_topic_score_gemma":0.015477192,"teacher_disagreement_score":0.011521995,"about_ca_system_score_codex":0.0008498916,"about_ca_system_score_gemma":0.0021942318,"threshold_uncertainty_score":0.038544893},"labels":[],"label_agreement":null},{"id":"W2127094475","doi":"10.1007/s10115-004-0189-y","title":"Optimizing complex queries based on similarities of subqueries","year":2005,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"International Business Machines Corporation","keywords":"Computer science; Query optimization; Joins; Key (lock); Similarity (geometry); Information retrieval; Graph; Database; Query expansion; Query language; Data mining; Theoretical computer science; Artificial intelligence","score_opus":0.021749295487071173,"score_gpt":0.24014789906072057,"score_spread":0.2183986035736494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127094475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5585521,0.0018244023,0.42886448,0.00093654956,0.00014781795,0.00047862812,0.0006744443,0.004070262,0.0044513494],"genre_scores_gemma":[0.78566873,0.00040602687,0.20927785,0.00020345776,0.0002521379,0.00015891099,0.001480659,0.00072801125,0.0018242385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9885958,0.0024250757,0.0011142934,0.0019537706,0.005129637,0.000781434],"domain_scores_gemma":[0.9681253,0.022058742,0.0021108652,0.0037264512,0.003234958,0.00074372994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042685303,0.001362877,0.0038502524,0.0040926323,0.0013013397,0.0039841505,0.0030557748,0.0019846805,0.0047929217],"category_scores_gemma":[0.035014752,0.0009880578,0.0014182127,0.00553178,0.0013184368,0.009306245,0.0033037832,0.0024838403,0.00081256364],"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":[0.005373107,0.001522611,0.03359206,0.0007263157,0.0008318726,0.000503324,0.001158137,0.25069138,0.041364092,0.0281405,0.009393995,0.62670255],"study_design_scores_gemma":[0.00015222126,0.0005630767,0.004200055,0.000021019769,0.00020457235,0.00024236686,0.00033518235,0.9504631,0.009574764,0.03251589,0.0016882614,0.00003946913],"about_ca_topic_score_codex":0.0037800507,"about_ca_topic_score_gemma":0.0059034103,"teacher_disagreement_score":0.0047929217,"about_ca_system_score_codex":0.0019910745,"about_ca_system_score_gemma":0.001935298,"threshold_uncertainty_score":0.022574425},"labels":[],"label_agreement":null},{"id":"W2127114148","doi":"10.1109/icde.2011.5767832","title":"Decomposing DAGs into spanning trees: A new way to compress transitive closures","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Reachability; Digraph; Transitive closure; Combinatorics; Computer science; Transitive relation; Spanning tree; Graph; Transitive reduction; Mathematics; Directed acyclic graph; Theoretical computer science; Discrete mathematics; Line graph; Voltage graph","score_opus":0.04140842396028783,"score_gpt":0.26187042712011094,"score_spread":0.22046200315982312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127114148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046666104,0.0012355403,0.9416853,0.0005502479,0.00019081868,0.00038690027,0.0032052503,0.0030584682,0.0030212898],"genre_scores_gemma":[0.19337559,0.0011609389,0.7958642,0.00017408321,0.00016593306,0.00034055242,0.0056368355,0.0005652719,0.0027165427],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984339,0.0002546387,0.0002303749,0.0003041407,0.0005892262,0.00018755245],"domain_scores_gemma":[0.9947601,0.0017155395,0.000527388,0.0020421925,0.00070917676,0.00024566366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223922,0.0011346386,0.0014188273,0.0062291864,0.00093753793,0.0022631697,0.0013746527,0.0007164041,0.0031908748],"category_scores_gemma":[0.009526111,0.00062196254,0.0014615337,0.0076386416,0.000778465,0.006972153,0.0025785863,0.0016199921,0.0010218131],"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":[0.00043328654,0.0003045943,0.0047366964,0.00063589885,0.00018406055,0.00059314474,0.0013715407,0.09276487,0.017549513,0.09239253,0.01615615,0.77287775],"study_design_scores_gemma":[0.00011615631,0.00035472043,0.0020559276,0.00020315364,0.0001692085,0.0009512912,0.0010271838,0.6023072,0.015681956,0.32355264,0.053470124,0.00011045567],"about_ca_topic_score_codex":0.0048144683,"about_ca_topic_score_gemma":0.007211939,"teacher_disagreement_score":0.0062291864,"about_ca_system_score_codex":0.0010279347,"about_ca_system_score_gemma":0.0018095529,"threshold_uncertainty_score":0.010674536},"labels":[],"label_agreement":null},{"id":"W2127465748","doi":"10.1109/i-span.2009.98","title":"Recommend Touring Routes to Travelers According to Their Sequential Wandering Behaviours","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Set (abstract data type); Sequential Pattern Mining; Matching (statistics); Information retrieval; Data mining; Mathematics; Programming language","score_opus":0.035371413579015645,"score_gpt":0.2705757647283549,"score_spread":0.23520435114933924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127465748","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7113042,0.0010721337,0.2803485,0.00069781666,0.00007434814,0.00026879687,0.002082541,0.0010850991,0.0030665658],"genre_scores_gemma":[0.7976391,0.0010303356,0.19234699,0.00007476635,0.00007813948,0.000120677185,0.003486192,0.00011918904,0.0051046186],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998049,0.000033698536,0.00001644142,0.00007934945,0.000043914635,0.00002175558],"domain_scores_gemma":[0.9991773,0.00037079226,0.00014299426,0.00007018633,0.0001786513,0.000059946135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003635516,0.00063581695,0.00056553894,0.0028070735,0.00039576186,0.0006123156,0.00073383714,0.0006910725,0.0014854973],"category_scores_gemma":[0.002350324,0.0003651163,0.0005110499,0.001984519,0.0002067329,0.00078485295,0.0002906545,0.0004320454,0.00079125323],"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":[0.0009620917,0.0006473791,0.2514202,0.00078693475,0.00066963775,0.0006519913,0.0012672837,0.21639267,0.011377378,0.0035084435,0.016449673,0.4958663],"study_design_scores_gemma":[0.00012669049,0.00054528646,0.064072266,0.000087342974,0.00028974144,0.00080723024,0.0013137531,0.91218346,0.003270633,0.006725627,0.010493239,0.00008472623],"about_ca_topic_score_codex":0.011761316,"about_ca_topic_score_gemma":0.031684734,"teacher_disagreement_score":0.011761316,"about_ca_system_score_codex":0.00028338013,"about_ca_system_score_gemma":0.0004962835,"threshold_uncertainty_score":0.023385704},"labels":[],"label_agreement":null},{"id":"W2127782281","doi":"","title":"Catching the best views of skyline: a semantic approach based on decisive subspaces","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":245,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Linear subspace; Subspace topology; Computer science; Semantics (computer science); Set (abstract data type); Theoretical computer science; Object (grammar); Space (punctuation); Algorithm; Data mining; Mathematics; Artificial intelligence; Pure mathematics; Programming language","score_opus":0.03357830335054178,"score_gpt":0.27134072372388834,"score_spread":0.23776242037334655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127782281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015920768,0.00024520984,0.9803453,0.00026758073,0.000025199828,0.00005904797,0.00027299518,0.0004479066,0.0024159432],"genre_scores_gemma":[0.3204093,0.00039157315,0.676333,0.00010750541,0.00008949673,0.00014090665,0.000816063,0.0001819748,0.001530137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834573,0.0004846017,0.00014834081,0.0004015285,0.00043367868,0.0001862137],"domain_scores_gemma":[0.9971378,0.00072927837,0.00044328446,0.0007603325,0.00065911585,0.00027026847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016464705,0.0006794675,0.0010811635,0.0038281057,0.0016809034,0.0033415982,0.0015410585,0.0010205285,0.0027479082],"category_scores_gemma":[0.0055880332,0.00052221783,0.0016544131,0.004188828,0.002842488,0.011238571,0.0034915064,0.0015458569,0.00049702823],"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":[0.00030045107,0.00007694202,0.0035308257,0.00029348847,0.00011297963,0.0002532271,0.0020561889,0.050190665,0.0059405407,0.80230933,0.0048999065,0.13003545],"study_design_scores_gemma":[0.00004316587,0.000115531075,0.0011149475,0.000088468296,0.000088781686,0.0002685823,0.001433993,0.37894085,0.0059752506,0.5873163,0.024527144,0.000087100045],"about_ca_topic_score_codex":0.0035682248,"about_ca_topic_score_gemma":0.0038025952,"teacher_disagreement_score":0.0038281057,"about_ca_system_score_codex":0.0010035832,"about_ca_system_score_gemma":0.0011161849,"threshold_uncertainty_score":0.009192705},"labels":[],"label_agreement":null},{"id":"W2128207581","doi":"10.1109/cmpsac.1991.170198","title":"Dynamic spatial query language: a customized query language for object-oriented database systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Query language; Object Query Language; RDF query language; Data control language; Object (grammar); Query optimization; Query expansion; Query by Example; Spatial query; Information retrieval; Object-based spatial database; View; Sargable; Database; Spatial database; Programming language; Web search query; Database design; Web query classification; Spatial analysis; Artificial intelligence; Search engine","score_opus":0.0105628418730422,"score_gpt":0.2470393899691693,"score_spread":0.2364765480961271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128207581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092849904,0.00027780957,0.97872156,0.00056277687,0.00010143536,0.00022574492,0.0008415662,0.015153018,0.0031875682],"genre_scores_gemma":[0.033658244,0.0010369776,0.94030124,0.001979068,0.0002897276,0.0008571444,0.0059839296,0.0046740784,0.011219521],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618286,0.00094816077,0.000750701,0.0004352832,0.0014326362,0.00025032758],"domain_scores_gemma":[0.99683374,0.00085028325,0.00022652488,0.0008547468,0.00095734856,0.0002773848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038044401,0.0011460449,0.0011657776,0.0018423086,0.0010085992,0.0046420796,0.004100539,0.001490287,0.009073609],"category_scores_gemma":[0.0062684976,0.0010339884,0.0012790255,0.0028140696,0.001987671,0.0072298194,0.004134139,0.0022673695,0.0057347636],"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":[0.0005361461,0.00018085683,0.001130253,0.0012863849,0.00016560245,0.0011830814,0.0022555143,0.015278873,0.030400975,0.41242403,0.285853,0.24930526],"study_design_scores_gemma":[0.00016304504,0.0001318357,0.00029042034,0.00019267693,0.00008575784,0.0015260457,0.00039618433,0.08793597,0.015776888,0.08683239,0.80650145,0.00016726417],"about_ca_topic_score_codex":0.0031902364,"about_ca_topic_score_gemma":0.004301624,"teacher_disagreement_score":0.009073609,"about_ca_system_score_codex":0.0013380998,"about_ca_system_score_gemma":0.0021784708,"threshold_uncertainty_score":0.030354261},"labels":[],"label_agreement":null},{"id":"W2128277764","doi":"10.1109/icpr.1998.711921","title":"Matching spatial relations using DB-tree for image retrieval","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Tree (set theory); Matching (statistics); Computer science; Image retrieval; Spatial relation; Image (mathematics); Segment tree; K-ary tree; Spatial relationship; Artificial intelligence; Interval tree; Tree structure; Pattern recognition (psychology); Algorithm; Mathematics; Binary tree; Combinatorics","score_opus":0.04019237647552226,"score_gpt":0.2627413511290969,"score_spread":0.22254897465357465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128277764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017674541,0.0013921653,0.975686,0.00020869588,0.000072579176,0.00013503415,0.00046353583,0.0026091367,0.0017583714],"genre_scores_gemma":[0.10849495,0.0010824229,0.88607085,0.00014131148,0.00005217492,0.00017724294,0.0021656202,0.0002007185,0.0016147099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862766,0.00027536412,0.00014638709,0.00022520647,0.0006173721,0.00010803011],"domain_scores_gemma":[0.9984665,0.0004795754,0.00015463722,0.00047113613,0.00036281816,0.00006539053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009532393,0.00058022165,0.001284157,0.0039654216,0.0011358843,0.0015282145,0.0014241895,0.0009966481,0.0031324867],"category_scores_gemma":[0.0049091685,0.00041791075,0.00066627003,0.006864877,0.00058059674,0.0047255964,0.0018773985,0.00059556484,0.0022340901],"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":[0.0003540061,0.00012920405,0.0023089736,0.0003631647,0.00009936603,0.00018379964,0.00036022792,0.034772724,0.023459194,0.030480994,0.012903617,0.89458483],"study_design_scores_gemma":[0.000113053,0.00031936527,0.002328193,0.00007454482,0.000128599,0.001437128,0.000528705,0.7711745,0.04581818,0.10617251,0.07178524,0.0001199832],"about_ca_topic_score_codex":0.0050768806,"about_ca_topic_score_gemma":0.0048083356,"teacher_disagreement_score":0.0050768806,"about_ca_system_score_codex":0.0010685262,"about_ca_system_score_gemma":0.0011945975,"threshold_uncertainty_score":0.010479212},"labels":[],"label_agreement":null},{"id":"W2128445650","doi":"10.1145/967900.968050","title":"Signature file hierarchies and signature graphs","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Signature (topology); Hierarchy; Graph; Theoretical computer science; Data structure; Mathematics; Programming language","score_opus":0.006163335959788518,"score_gpt":0.20201633530746868,"score_spread":0.19585299934768016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128445650","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01661565,0.0011221865,0.9694418,0.0009906492,0.00021679503,0.00014617707,0.00083281565,0.0026833427,0.007950667],"genre_scores_gemma":[0.24464484,0.0021742643,0.73831606,0.00095378386,0.0005237374,0.00025429289,0.0033152623,0.00094444427,0.008873401],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963864,0.00066327787,0.00034535432,0.0005572965,0.0016932209,0.0003544722],"domain_scores_gemma":[0.9907917,0.0028028688,0.0009954072,0.0031373072,0.001724615,0.0005480086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017017485,0.00054198445,0.0007261418,0.003073454,0.0015272043,0.0032865,0.0023135538,0.0012791815,0.004127048],"category_scores_gemma":[0.008290172,0.0005094592,0.0007219277,0.0059621288,0.0020100053,0.015309071,0.0029797447,0.0018493201,0.0013635831],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023289019,0.0001377627,0.002328125,0.00036360565,0.00006603211,0.0002890705,0.00050370605,0.021122009,0.0067166705,0.6755311,0.019489419,0.27321956],"study_design_scores_gemma":[0.00007961487,0.00018294742,0.0011351412,0.0001154928,0.00011439203,0.0009872464,0.0004726353,0.121430054,0.01607232,0.6768585,0.18239458,0.00015706616],"about_ca_topic_score_codex":0.0044988883,"about_ca_topic_score_gemma":0.0049221576,"teacher_disagreement_score":0.0044988883,"about_ca_system_score_codex":0.0016887601,"about_ca_system_score_gemma":0.0022268242,"threshold_uncertainty_score":0.013806403},"labels":[],"label_agreement":null},{"id":"W2128759130","doi":"10.3166/isi.14.5.35-58","title":"Base de données de capteurs à localisation mobile Modèle et langage","year":2009,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Humanities; Philosophy","score_opus":0.03758550440697143,"score_gpt":0.25080257766455516,"score_spread":0.21321707325758374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128759130","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03469697,0.0036309054,0.9229942,0.0006522535,0.00018385798,0.0003214924,0.014505592,0.0128464755,0.010168259],"genre_scores_gemma":[0.31552824,0.0049629873,0.6143332,0.00032768087,0.00019665934,0.0008950824,0.049138352,0.0018745243,0.012743313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99493486,0.0010298876,0.0005593517,0.001072841,0.0022081076,0.00019496992],"domain_scores_gemma":[0.9897334,0.005893209,0.00046806448,0.0019688422,0.0017576129,0.0001789429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030278019,0.0022833354,0.0022484558,0.004766024,0.0012181405,0.0074442704,0.0022781119,0.0028197623,0.013415828],"category_scores_gemma":[0.022792524,0.001479582,0.0037537212,0.0044746543,0.0013673166,0.0069355797,0.0018501317,0.0022651432,0.00887891],"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":[0.0020547286,0.00054229086,0.018916318,0.004415986,0.0011811422,0.00544575,0.0039889924,0.3093463,0.034096863,0.051339,0.03757235,0.53110033],"study_design_scores_gemma":[0.00030085252,0.00036749078,0.010115824,0.000762716,0.00053431053,0.0032050565,0.001197448,0.68262285,0.038962662,0.044870928,0.21653265,0.00052724173],"about_ca_topic_score_codex":0.03439596,"about_ca_topic_score_gemma":0.02258959,"teacher_disagreement_score":0.03439596,"about_ca_system_score_codex":0.0015520164,"about_ca_system_score_gemma":0.0019183507,"threshold_uncertainty_score":0.0683915},"labels":[],"label_agreement":null},{"id":"W2128780455","doi":"10.1109/sitis.2008.31","title":"Core Labeling: A New Way to Compress Transitive Closure","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Reachability; Transitive closure; Combinatorics; Graph; Bounded function; Tree (set theory); Computer science; Mathematics; Discrete mathematics","score_opus":0.09017840366944073,"score_gpt":0.27481552004055315,"score_spread":0.1846371163711124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128780455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05224166,0.0011980011,0.9027521,0.0010343634,0.00026967263,0.0007797367,0.0076554576,0.028584853,0.005484182],"genre_scores_gemma":[0.21344349,0.00074179156,0.7626705,0.00056066917,0.00017373178,0.00077896635,0.015717233,0.002103774,0.0038097734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99652076,0.00042032733,0.00044766854,0.0007903881,0.0014959795,0.00032495035],"domain_scores_gemma":[0.9913442,0.002367498,0.0007316596,0.0041950755,0.0010694158,0.00029219865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014692954,0.0015775085,0.0015171501,0.0044394075,0.0011630047,0.0024763083,0.0023823404,0.0010682747,0.005446335],"category_scores_gemma":[0.010584097,0.0008591654,0.001798364,0.0051869783,0.0011123266,0.010310922,0.0035819036,0.0020677557,0.0017479596],"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":[0.0018264039,0.00050962693,0.003892928,0.0010019365,0.00016505008,0.0005280706,0.0010839367,0.05599423,0.0570823,0.07261453,0.043570843,0.7617301],"study_design_scores_gemma":[0.00031559315,0.0006889708,0.0015225007,0.00021715868,0.00018418612,0.0009659402,0.00059372606,0.69261503,0.074382104,0.15412606,0.074218616,0.00017015729],"about_ca_topic_score_codex":0.0057124007,"about_ca_topic_score_gemma":0.0073760236,"teacher_disagreement_score":0.0057124007,"about_ca_system_score_codex":0.0018972071,"about_ca_system_score_gemma":0.0032256346,"threshold_uncertainty_score":0.018219829},"labels":[],"label_agreement":null},{"id":"W2129129103","doi":"10.1109/hpcs.2007.25","title":"Parallel Computation of Skyline Queries","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Skyline; Computer science; Context (archaeology); Set (abstract data type); Property (philosophy); Computation; Data mining; Information retrieval; Data set; Database; Theoretical computer science; Algorithm; Artificial intelligence; Programming language; Geography","score_opus":0.017167190101722848,"score_gpt":0.26872648909164093,"score_spread":0.2515592989899181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129129103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17658217,0.0015952805,0.7936356,0.0014010689,0.00030200885,0.00032954398,0.0020621598,0.007972456,0.016119674],"genre_scores_gemma":[0.592846,0.00068346626,0.39570487,0.00025497386,0.00021927319,0.00035326602,0.0042577693,0.00064981513,0.005030542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743587,0.00046333272,0.00023364583,0.0006109242,0.00087707106,0.000379241],"domain_scores_gemma":[0.9956864,0.001753247,0.00029692208,0.0011572219,0.0008981175,0.00020800467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019200534,0.0009963964,0.0021262304,0.0013634689,0.0015758646,0.0035202473,0.0019842433,0.0009650978,0.0055309697],"category_scores_gemma":[0.00989768,0.0006365033,0.0011794083,0.0037365134,0.0010431196,0.0050025554,0.0024549784,0.0011379361,0.001470646],"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":[0.002033692,0.00043016975,0.010471844,0.0007696898,0.00041538978,0.00091944344,0.0013287168,0.4538298,0.01842021,0.117491364,0.04094482,0.35294488],"study_design_scores_gemma":[0.00015938956,0.0000956294,0.00092758244,0.000017234226,0.000049889106,0.00017531699,0.00024347875,0.8827002,0.0053846305,0.10305217,0.0071719843,0.000022480071],"about_ca_topic_score_codex":0.0072260145,"about_ca_topic_score_gemma":0.009050533,"teacher_disagreement_score":0.0072260145,"about_ca_system_score_codex":0.0011891746,"about_ca_system_score_gemma":0.0018533901,"threshold_uncertainty_score":0.01850301},"labels":[],"label_agreement":null},{"id":"W2130076146","doi":"10.3138/carto.46.3.160","title":"Toward the Spatial and Temporal Management of Documents: The GéoTopia Platform","year":2011,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Thumbnail; Timeline; Computer science; Search engine indexing; Zoom; Information retrieval; Georeference; World Wide Web; Thematic map; Feature (linguistics); Cartography; Geography; Artificial intelligence","score_opus":0.025317449545030304,"score_gpt":0.2668007782623878,"score_spread":0.2414833287173575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130076146","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011532514,0.0010988513,0.9449986,0.0030147908,0.00063177955,0.00029853915,0.0010266268,0.018294055,0.019104285],"genre_scores_gemma":[0.06518351,0.0015374332,0.91142887,0.00050185865,0.0005528239,0.0003753791,0.0024495283,0.002343119,0.015627429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969168,0.0008293702,0.0002916093,0.00042011443,0.0012394806,0.0003025933],"domain_scores_gemma":[0.99103934,0.0012004006,0.0006549066,0.0031893398,0.0026249697,0.0012910982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00613144,0.0005998068,0.00082048774,0.0034030639,0.0020862115,0.014565972,0.0034630634,0.0014982297,0.0068533714],"category_scores_gemma":[0.0149037875,0.00076027855,0.0009745428,0.004565758,0.0023061761,0.013022457,0.0088963695,0.0028892679,0.0058175237],"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":[0.0006130271,0.00024828984,0.0033695248,0.00058424263,0.00006650403,0.0011401939,0.005441164,0.0059432467,0.018724082,0.34085664,0.11130931,0.5117037],"study_design_scores_gemma":[0.00010958662,0.0001568038,0.0016818913,0.0005549796,0.000082753686,0.0012953178,0.0027150742,0.079471394,0.02123199,0.12974147,0.7627876,0.00017122418],"about_ca_topic_score_codex":0.006771701,"about_ca_topic_score_gemma":0.004403636,"teacher_disagreement_score":0.014565972,"about_ca_system_score_codex":0.0011180275,"about_ca_system_score_gemma":0.0045620343,"threshold_uncertainty_score":0.032426536},"labels":[],"label_agreement":null},{"id":"W2130696419","doi":"10.1145/1189769.1189774","title":"Towards multidimensional subspace skyline analysis","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Linear subspace; Subspace topology; Computer science; Semantics (computer science); Theoretical computer science; Space (punctuation); Object (grammar); Online analytical processing; Algorithm; Data mining; Mathematics; Artificial intelligence; Pure mathematics; Programming language","score_opus":0.017999956148877616,"score_gpt":0.25174250827514694,"score_spread":0.23374255212626932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130696419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061091236,0.00028143433,0.9918046,0.00013727398,0.000017352171,0.000038139715,0.00024656282,0.0003751121,0.0009903526],"genre_scores_gemma":[0.22323057,0.00092459214,0.7713571,0.00018223272,0.00014793516,0.0002849384,0.0017450209,0.00032944404,0.0017982549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986136,0.00048369524,0.00008812942,0.00024077842,0.0004206132,0.0001531998],"domain_scores_gemma":[0.99735594,0.0010461592,0.00036124294,0.00043484257,0.00066338974,0.000138462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014808063,0.0011514407,0.0014396845,0.0028712703,0.00089652237,0.0028612346,0.0012787916,0.0010370555,0.003505152],"category_scores_gemma":[0.0062027206,0.0005003476,0.0017621464,0.004168312,0.0014085002,0.004678326,0.0028762291,0.0025467372,0.0009227031],"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":[0.0002268368,0.000094208015,0.004138349,0.00035189788,0.00014873958,0.00027018166,0.00074235053,0.41420624,0.0055502406,0.4169173,0.009528934,0.14782473],"study_design_scores_gemma":[0.00001043261,0.000029522083,0.00030350307,0.000025538762,0.000010923683,0.00003888863,0.00013771078,0.8286785,0.0009101667,0.16532436,0.0045128944,0.000017546725],"about_ca_topic_score_codex":0.0046133236,"about_ca_topic_score_gemma":0.002814696,"teacher_disagreement_score":0.0046133236,"about_ca_system_score_codex":0.0012027251,"about_ca_system_score_gemma":0.0012347426,"threshold_uncertainty_score":0.011725843},"labels":[],"label_agreement":null},{"id":"W2131197449","doi":"10.1142/s0218195905001853","title":"ON MULTI-LEVEL k-RANGES FOR RANGE SEARCH","year":2005,"lang":"en","type":"article","venue":"International Journal of Computational Geometry & Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Range (aeronautics); Mathematics; Combinatorics; Binary logarithm; Tree (set theory); Materials science","score_opus":0.05640688424490003,"score_gpt":0.350634776905452,"score_spread":0.29422789266055194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131197449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16211337,0.0014148287,0.815691,0.0007348166,0.000060073216,0.00029807154,0.0005189138,0.008773865,0.010395041],"genre_scores_gemma":[0.38214046,0.00023931605,0.61418974,0.00017497252,0.000035742265,0.00027055768,0.0007064585,0.00035724664,0.001885435],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99643505,0.00089362706,0.00036791837,0.00051848084,0.001310338,0.00047462707],"domain_scores_gemma":[0.9885747,0.0051096855,0.00084732793,0.003867712,0.0012621298,0.00033848104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002608171,0.00060905266,0.0012505399,0.0020634965,0.0009381151,0.002276998,0.0041187275,0.0013563663,0.0072025047],"category_scores_gemma":[0.014427633,0.0007354684,0.0010081788,0.004061011,0.0012582432,0.008062659,0.0048213475,0.001284641,0.002675589],"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":[0.0019147103,0.0007888764,0.01139524,0.0007403902,0.00015957859,0.00034539757,0.0013511324,0.1424027,0.023814239,0.08923468,0.010820275,0.71703285],"study_design_scores_gemma":[0.0003549271,0.00096931733,0.0023648643,0.000118859614,0.00007215803,0.0007925822,0.0004876926,0.8935603,0.019957121,0.06942324,0.011744215,0.00015481946],"about_ca_topic_score_codex":0.002899032,"about_ca_topic_score_gemma":0.0037951604,"teacher_disagreement_score":0.0072025047,"about_ca_system_score_codex":0.00097331277,"about_ca_system_score_gemma":0.0012642478,"threshold_uncertainty_score":0.02409476},"labels":[],"label_agreement":null},{"id":"W2131209872","doi":"10.1109/icdew.2010.5452725","title":"Graphical models for dependencies and queries in uncertain data","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Conjunctive query; Hypergraph; Dependency (UML); Graphical model; Relational database; Probabilistic logic; Probabilistic database; Dependency theory (database theory); Functional dependency; Theoretical computer science; Uncertain data; Relational model; Query optimization; Data mining; Information retrieval; Database theory; Artificial intelligence; Mathematics","score_opus":0.06086420917569164,"score_gpt":0.29119215558275907,"score_spread":0.23032794640706744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131209872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048913476,0.0007102965,0.9906781,0.0010478892,0.00003580129,0.000059051912,0.0005776559,0.0003740277,0.001625826],"genre_scores_gemma":[0.45953858,0.003820712,0.52684087,0.0011876508,0.00043496833,0.0006213479,0.00273853,0.00031116884,0.0045063016],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9929624,0.00342917,0.00043235515,0.0011334718,0.0017052182,0.0003372863],"domain_scores_gemma":[0.9779056,0.016566962,0.0018590322,0.002041074,0.0012618755,0.00036545625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047835256,0.0012776959,0.0012367965,0.003341414,0.00084729487,0.004218862,0.0030922897,0.0022810292,0.0036360642],"category_scores_gemma":[0.025238799,0.0008956353,0.001994089,0.00428979,0.0029538907,0.010081184,0.0021578704,0.0030919793,0.00074238825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116355295,0.000053578668,0.001096119,0.00023768804,0.00009739034,0.00024065742,0.00049245596,0.27483732,0.00098678,0.6836772,0.0039709746,0.03419349],"study_design_scores_gemma":[0.000017951179,0.000022708935,0.00021015336,0.00003304666,0.00003453244,0.00009314863,0.000052788775,0.44813418,0.00026248672,0.54795104,0.0031625126,0.000025470958],"about_ca_topic_score_codex":0.0064968937,"about_ca_topic_score_gemma":0.007387969,"teacher_disagreement_score":0.0064968937,"about_ca_system_score_codex":0.0026911972,"about_ca_system_score_gemma":0.0012568753,"threshold_uncertainty_score":0.025298},"labels":[],"label_agreement":null},{"id":"W2131795689","doi":"10.1109/mdm.2008.34","title":"Mobile User Profile Acquisition through Network Observables and Explicit User Queries","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Focus (optics); Set (abstract data type); sort; Service (business); Mobile device; User profile; Mobile phone; Ask price; World Wide Web; Phone; Recommender system; Human–computer interaction; Information retrieval; Telecommunications","score_opus":0.025683388428905655,"score_gpt":0.23483874456364917,"score_spread":0.2091553561347435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131795689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22276159,0.00036891055,0.75770044,0.00059236673,0.00004025728,0.00056362624,0.0032742224,0.009630416,0.005068154],"genre_scores_gemma":[0.8720394,0.00019042328,0.12213467,0.00014653182,0.000060864502,0.00019577258,0.0028888935,0.00012440005,0.0022191324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750865,0.0007582025,0.0001622153,0.0005128447,0.0008922659,0.00016571092],"domain_scores_gemma":[0.9945326,0.0024430347,0.00082365505,0.0010012335,0.0009257432,0.0002737609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017502643,0.00079301244,0.001156311,0.0018375104,0.00034884762,0.0016391901,0.0010540079,0.00056751445,0.0009701285],"category_scores_gemma":[0.009881035,0.0005061417,0.00034900277,0.0013958185,0.0002840948,0.0035045375,0.0016136841,0.0012526028,0.0010170358],"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":[0.0012399658,0.001607269,0.14274876,0.00044037242,0.00024030978,0.00053614227,0.0031490447,0.038276788,0.045594282,0.006559218,0.0073852073,0.7522226],"study_design_scores_gemma":[0.0000408355,0.00039540508,0.06060219,0.00004291576,0.000055163444,0.00041246047,0.00088816346,0.8995184,0.019854832,0.008403729,0.009683233,0.00010274015],"about_ca_topic_score_codex":0.005918221,"about_ca_topic_score_gemma":0.0102106305,"teacher_disagreement_score":0.005918221,"about_ca_system_score_codex":0.0005787155,"about_ca_system_score_gemma":0.0007226067,"threshold_uncertainty_score":0.011767566},"labels":[],"label_agreement":null},{"id":"W2131811358","doi":"10.37236/2541","title":"Common Edges in Rooted Trees and Polygonal Triangulations","year":2013,"lang":"en","type":"article","venue":"The Electronic Journal of Combinatorics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Mathematics; Combinatorics; Rotation (mathematics); Polygon mesh; Computation; Subdivision; Enhanced Data Rates for GSM Evolution; Time complexity; Distribution (mathematics); Similarity (geometry); Geometry; Algorithm; Computer science; Image (mathematics); Mathematical analysis","score_opus":0.008062309830648522,"score_gpt":0.22125009576519425,"score_spread":0.21318778593454574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131811358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32563263,0.0011014909,0.6660051,0.00033543873,0.00004038026,0.0000859708,0.00071885536,0.00031443784,0.0057657715],"genre_scores_gemma":[0.86787975,0.0006465518,0.12675872,0.0000902514,0.00006885065,0.0001546596,0.0013541094,0.00018827307,0.0028588928],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967842,0.00073120126,0.00021369076,0.00074773526,0.0011524404,0.00037068833],"domain_scores_gemma":[0.9826805,0.010325889,0.0029289292,0.0019280119,0.0012568727,0.0008798073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017913261,0.0004206456,0.00094063906,0.003675078,0.0015208349,0.0025960107,0.0016190098,0.001417649,0.0032663622],"category_scores_gemma":[0.02626666,0.0008325418,0.00064843654,0.0051436475,0.003449156,0.0046869623,0.0033299476,0.0014161674,0.00048669387],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004469841,0.00006499984,0.011945107,0.00021260131,0.00007241008,0.0006182837,0.001127525,0.22549905,0.006475727,0.6846209,0.002498634,0.06641783],"study_design_scores_gemma":[0.000043347343,0.00006599093,0.0028034619,0.000052739102,0.000020860696,0.0006213157,0.00043551833,0.34609637,0.00325516,0.64112496,0.005438202,0.00004217547],"about_ca_topic_score_codex":0.0021470934,"about_ca_topic_score_gemma":0.0020583037,"teacher_disagreement_score":0.003675078,"about_ca_system_score_codex":0.0017964066,"about_ca_system_score_gemma":0.0004956694,"threshold_uncertainty_score":0.0130339265},"labels":[],"label_agreement":null},{"id":"W2132611668","doi":"10.1109/icpr.2006.879","title":"Object Localization Based on Directional Information: Case of 2D Raster Data","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Raster graphics; Object (grammar); Computer science; Raster data; Space (punctuation); Algorithm; Spatial analysis; Artificial intelligence; Image (mathematics); Computer vision; Theoretical computer science; Mathematics","score_opus":0.017476329888502912,"score_gpt":0.23910420837921367,"score_spread":0.22162787849071075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132611668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023277288,0.0003479709,0.9742493,0.00033882644,0.000027873653,0.000030438317,0.00031936783,0.00033465584,0.0010742184],"genre_scores_gemma":[0.46723858,0.001058053,0.5286605,0.00010866116,0.00009819975,0.00013510505,0.00074347656,0.00011400283,0.0018434392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987268,0.0003440335,0.00007035019,0.0002576616,0.00043129953,0.00016985266],"domain_scores_gemma":[0.99648917,0.0020300914,0.00029312973,0.00083401514,0.00027240659,0.00008114032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015296319,0.0006393724,0.0011505017,0.0016087712,0.0006316981,0.0015628791,0.0018130222,0.0019629186,0.0015563086],"category_scores_gemma":[0.010794314,0.0005414635,0.0009989953,0.0046928567,0.0010946414,0.0039073094,0.0016329025,0.0009135076,0.0005438389],"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":[0.00032423288,0.000061356375,0.006145379,0.00023044623,0.00006224549,0.0012292428,0.00027557035,0.79250896,0.003928278,0.055438522,0.0039128643,0.13588294],"study_design_scores_gemma":[0.000009401945,0.000013181546,0.00041107548,0.0000059123918,0.000008349012,0.0003237935,0.000044601526,0.98072535,0.0010502894,0.016195027,0.0012019442,0.000011032474],"about_ca_topic_score_codex":0.020273596,"about_ca_topic_score_gemma":0.01352896,"teacher_disagreement_score":0.020273596,"about_ca_system_score_codex":0.0010458829,"about_ca_system_score_gemma":0.0008983001,"threshold_uncertainty_score":0.040311158},"labels":[],"label_agreement":null},{"id":"W2132671464","doi":"","title":"Aspects of metric spaces in computation","year":2008,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Metric space; Equidistant; Metric (unit); Mathematics; Similarity (geometry); Set (abstract data type); Curse of dimensionality; Space (punctuation); Constraint (computer-aided design); Theoretical computer science; Search engine indexing; Nearest neighbor search; Computer science; Discrete mathematics; Data mining; Information retrieval; Artificial intelligence; Geometry; Statistics","score_opus":0.012183718721649289,"score_gpt":0.2075819277995772,"score_spread":0.19539820907792793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132671464","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008925384,0.10563963,0.67414635,0.03506024,0.0027561856,0.00014175908,0.0006696714,0.00042078845,0.17223999],"genre_scores_gemma":[0.423275,0.08165248,0.4393333,0.0055556153,0.0099639,0.0010344802,0.001004515,0.00058019895,0.037600473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939057,0.0031047438,0.00042686422,0.00078768906,0.0015210423,0.00025388907],"domain_scores_gemma":[0.99152726,0.0057521155,0.00027531257,0.0012818588,0.00089827704,0.00026516654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004346631,0.0013030681,0.001286714,0.0030778896,0.0026003243,0.009563703,0.0015952353,0.0025181547,0.0065059857],"category_scores_gemma":[0.015094332,0.00071756355,0.001572451,0.0071925926,0.013000267,0.014572221,0.003978478,0.0053834105,0.0020108838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035225742,0.0000027663586,0.00005152881,0.000049043436,0.000005275996,0.000015477066,0.00011853983,0.00092906115,0.000040329644,0.9902864,0.0022673425,0.0062308493],"study_design_scores_gemma":[0.0000026726916,0.0000044620137,0.00006065726,0.00002676815,0.0000021834894,0.00003249125,0.000048065867,0.0022306761,0.000039931012,0.9730858,0.024460483,0.000005810352],"about_ca_topic_score_codex":0.004248533,"about_ca_topic_score_gemma":0.002860855,"teacher_disagreement_score":0.009563703,"about_ca_system_score_codex":0.004403655,"about_ca_system_score_gemma":0.0019999812,"threshold_uncertainty_score":0.03195089},"labels":[],"label_agreement":null},{"id":"W2132707173","doi":"10.1109/ccece.2003.1226258","title":"Maintaining quality of service for adaptive mobile map clients","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Heuristics; Proxy (statistics); Set (abstract data type); Proxy server; Quality of experience; Generalization; Data mining; Quality of service; Real-time computing; Database; Computer network; Machine learning; Operating system","score_opus":0.05211768313122967,"score_gpt":0.3164084153244743,"score_spread":0.2642907321932446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132707173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31346968,0.0005206163,0.6704291,0.0006436456,0.00008180745,0.0003232212,0.00010718922,0.0099351425,0.004489678],"genre_scores_gemma":[0.93386877,0.00015446631,0.06377044,0.00008477804,0.00005747086,0.000097067335,0.00015728951,0.00023798006,0.0015716566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971222,0.00044648544,0.0001535564,0.00044593183,0.0014288434,0.000402833],"domain_scores_gemma":[0.9894601,0.0032216758,0.0007628062,0.0028904497,0.0029443866,0.00072060747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037782178,0.0007263875,0.001103847,0.0008969594,0.0015105121,0.0030405123,0.00322466,0.0014089925,0.0023870755],"category_scores_gemma":[0.014369735,0.00059766055,0.00034336923,0.001185347,0.00096313574,0.00368441,0.0017051527,0.0018566066,0.0009245138],"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":[0.0033279182,0.0009160704,0.035550542,0.00045983755,0.00029419782,0.0011871809,0.0022780162,0.16006455,0.24342035,0.027011517,0.0060268003,0.51946294],"study_design_scores_gemma":[0.00014258352,0.00034104954,0.006286318,0.000024771345,0.00009238601,0.00035605568,0.00037712438,0.91989875,0.056071192,0.008467669,0.007841459,0.00010071075],"about_ca_topic_score_codex":0.0056514395,"about_ca_topic_score_gemma":0.003718317,"teacher_disagreement_score":0.0056514395,"about_ca_system_score_codex":0.0018285573,"about_ca_system_score_gemma":0.0014662036,"threshold_uncertainty_score":0.019981384},"labels":[],"label_agreement":null},{"id":"W2133246278","doi":"10.14778/1453856.1453895","title":"Efficient search for the top-k probable nearest neighbors in uncertain databases","year":2008,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":149,"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":"Computer science; Data mining; Query optimization; Online aggregation; k-nearest neighbors algorithm; Query language; Semantics (computer science); Information retrieval; Sargable; Object (grammar); Point (geometry); Database; Feature (linguistics); Web query classification; Web search query; Search engine; Artificial intelligence","score_opus":0.05346318793657699,"score_gpt":0.2715280562480217,"score_spread":0.21806486831144467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133246278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17573752,0.0031427033,0.81504804,0.0012193901,0.00006339722,0.00019182921,0.0012278561,0.001691636,0.0016777643],"genre_scores_gemma":[0.48442835,0.00061501854,0.51118755,0.00018718434,0.000103656675,0.00013810962,0.0022349204,0.00018329892,0.0009218142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953053,0.0012680939,0.0006558885,0.0010161038,0.0014244618,0.0003301212],"domain_scores_gemma":[0.98712057,0.009335232,0.00079833774,0.001407169,0.0010495082,0.00028920427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039843055,0.0009378948,0.0033646068,0.0044468977,0.0016462222,0.0033508583,0.0038666788,0.0023076038,0.0016353374],"category_scores_gemma":[0.025117988,0.0010143458,0.0009179089,0.0063813482,0.00092770316,0.00732142,0.0024272026,0.0012199462,0.00058644],"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":[0.0017395514,0.0006127961,0.015211785,0.0006535663,0.00033741727,0.0005832411,0.0011358742,0.45957366,0.0069668246,0.023601962,0.015731843,0.47385144],"study_design_scores_gemma":[0.00005500209,0.000070828595,0.00076031557,0.000018278224,0.000045162742,0.00024574014,0.00034344784,0.9706444,0.0021153642,0.024754303,0.0009205624,0.000026660076],"about_ca_topic_score_codex":0.0071888925,"about_ca_topic_score_gemma":0.01182296,"teacher_disagreement_score":0.0071888925,"about_ca_system_score_codex":0.001354977,"about_ca_system_score_gemma":0.0018274914,"threshold_uncertainty_score":0.021071255},"labels":[],"label_agreement":null},{"id":"W2133443743","doi":"10.1007/s10619-007-7023-y","title":"PnP: sequential, external memory, and parallel iceberg cube computation","year":2008,"lang":"en","type":"article","venue":"Distributed and Parallel Databases","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":"Dalhousie University; Concordia University; Carleton University","funders":"","keywords":"Computer science; Speedup; Cube (algebra); Scalability; Iceberg; Auxiliary memory; Data cube; Computation; Parallel computing; Pruning; Data structure; Algorithm; Data mining; Mathematics; Computer hardware; Database; Operating system; Geology; Combinatorics","score_opus":0.04727396136730296,"score_gpt":0.2760956854569131,"score_spread":0.22882172408961013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133443743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027323527,0.0008679874,0.94570416,0.0011996208,0.00045327062,0.000094609546,0.0006018129,0.0028608611,0.020894172],"genre_scores_gemma":[0.43474853,0.0013182582,0.54185563,0.00047536925,0.0004100715,0.0003111874,0.0016013172,0.00067866594,0.018601062],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994141,0.0001250216,0.00002928832,0.000092135495,0.000252494,0.00008705304],"domain_scores_gemma":[0.9990388,0.00025735132,0.00004377695,0.00033601926,0.00024089267,0.00008302947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008272703,0.00051446253,0.0007908778,0.00062199956,0.0010025233,0.0019354518,0.0017979104,0.000610955,0.004949782],"category_scores_gemma":[0.003623422,0.0003409097,0.0004900451,0.0016717361,0.0011544114,0.0030580638,0.0028675445,0.0014541253,0.0012192648],"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":[0.00063801144,0.000117147836,0.0016161753,0.00021857394,0.000055533543,0.00018995423,0.0001839041,0.20341241,0.00277407,0.50001436,0.052880727,0.23789914],"study_design_scores_gemma":[0.000047065936,0.00002976144,0.00021565963,0.000015251333,0.000014909337,0.00008510893,0.00003935875,0.6142029,0.0023657575,0.36864862,0.014322256,0.000013201232],"about_ca_topic_score_codex":0.0042358832,"about_ca_topic_score_gemma":0.0045939963,"teacher_disagreement_score":0.004949782,"about_ca_system_score_codex":0.0008661708,"about_ca_system_score_gemma":0.0017616005,"threshold_uncertainty_score":0.016558647},"labels":[],"label_agreement":null},{"id":"W2133623058","doi":"10.14778/1687627.1687729","title":"Creating competitive products","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Skyline; Dominance (genetics); Set (abstract data type); Computer science; Competitive advantage; Data mining; Business; Marketing","score_opus":0.011451827037985965,"score_gpt":0.21806801338273515,"score_spread":0.20661618634474918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133623058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15363796,0.001433546,0.7893676,0.0013285042,0.0002757921,0.0023531753,0.0025393083,0.0021378992,0.046926208],"genre_scores_gemma":[0.18490776,0.0005147007,0.8044261,0.00027076187,0.000077477096,0.0007952509,0.002989127,0.00036429625,0.005654551],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99585956,0.0011134993,0.00023797559,0.00086353556,0.001622912,0.00030246505],"domain_scores_gemma":[0.9918058,0.0036253682,0.00071900274,0.0017924708,0.0015934462,0.00046397324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003877573,0.0019201135,0.0016765745,0.004045139,0.0020191695,0.003294736,0.002877159,0.0019947658,0.011833327],"category_scores_gemma":[0.01363522,0.0010809607,0.0025300486,0.0036770606,0.0012123758,0.0055595366,0.0036614777,0.0017477892,0.0028796992],"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":[0.00080767856,0.0014877585,0.013894099,0.0017969043,0.00044622968,0.001410111,0.0015092157,0.11107848,0.021305118,0.13114223,0.038356483,0.6767656],"study_design_scores_gemma":[0.00043823812,0.0014122688,0.0038437585,0.00034563668,0.0005014093,0.0031706563,0.0018901689,0.59085816,0.03267386,0.2233596,0.1412832,0.0002230631],"about_ca_topic_score_codex":0.0012228948,"about_ca_topic_score_gemma":0.0022197128,"teacher_disagreement_score":0.011833327,"about_ca_system_score_codex":0.0010500887,"about_ca_system_score_gemma":0.0019397951,"threshold_uncertainty_score":0.039586484},"labels":[],"label_agreement":null},{"id":"W2133735693","doi":"10.5267/j.msl.2012.03.007","title":"A new weighted fuzzy grammar on object oriented database queries","year":2012,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Object (grammar); Database; Grammar; Fuzzy logic; Information retrieval; Programming language; Data mining; Artificial intelligence; Linguistics","score_opus":0.012793631825862621,"score_gpt":0.23719592732618044,"score_spread":0.22440229550031782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133735693","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00925247,0.0001857182,0.98216516,0.0005791726,0.00013379894,0.00011336314,0.00021387779,0.0005265366,0.006829869],"genre_scores_gemma":[0.20979014,0.000552389,0.7775844,0.0008415331,0.00026619527,0.00041685515,0.0005774276,0.0005185672,0.00945257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962955,0.0009079486,0.0004435853,0.00062384066,0.0014961634,0.00023297309],"domain_scores_gemma":[0.9974674,0.0010808128,0.00014669246,0.00046103683,0.00071119034,0.00013286219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031179993,0.0006241683,0.00085922924,0.0018512609,0.0010804851,0.0029305138,0.0019739582,0.0016435792,0.003022899],"category_scores_gemma":[0.0063185333,0.0006240565,0.0014254989,0.0023207422,0.0029396329,0.0065763,0.003155086,0.0020688018,0.0009338698],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036292346,0.000030023606,0.00026757963,0.000063618805,0.000015724849,0.0003300749,0.0005684343,0.0097364625,0.00293071,0.953379,0.0023160689,0.030326147],"study_design_scores_gemma":[0.000028922395,0.00004128199,0.00012696518,0.000039884326,0.00002399528,0.00029594172,0.000114537674,0.09407577,0.0018178208,0.88285476,0.02054058,0.000039556362],"about_ca_topic_score_codex":0.0029094492,"about_ca_topic_score_gemma":0.0019003429,"teacher_disagreement_score":0.0031179993,"about_ca_system_score_codex":0.0013164248,"about_ca_system_score_gemma":0.0014647605,"threshold_uncertainty_score":0.016489744},"labels":[],"label_agreement":null},{"id":"W2134150651","doi":"10.1068/a3722","title":"Reliability of Sequence-Alignment Analysis of Social Processes: Monte Carlo Tests of Clustalg Software","year":2006,"lang":"en","type":"article","venue":"Environment and Planning A Economy and Space","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; St. Mary's University; Canada Mortgage and Housing Corporation","funders":"","keywords":"Computer science; Sequence (biology); Set (abstract data type); Reliability (semiconductor); Software; Data mining; Event (particle physics); Matching (statistics); Monte Carlo method; Theoretical computer science; Algorithm; Mathematics; Statistics; Biology","score_opus":0.011528319454089048,"score_gpt":0.21570735415246117,"score_spread":0.20417903469837212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134150651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70549005,0.0011302957,0.2859865,0.0006135371,0.00016940746,0.000665354,0.0006114232,0.0012258531,0.0041076937],"genre_scores_gemma":[0.9317376,0.000139777,0.066178754,0.00010906845,0.00004825122,0.00043873323,0.000823036,0.00027604203,0.0002486598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.8946425,0.08118671,0.005643025,0.007030477,0.009947065,0.0015501309],"domain_scores_gemma":[0.29927245,0.63844424,0.018251589,0.031276844,0.01090119,0.0018538104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12543483,0.0011613856,0.0022215988,0.0062008426,0.00214359,0.0030101428,0.0035356327,0.0039544194,0.0018310176],"category_scores_gemma":[0.44111526,0.00090780616,0.002566166,0.004721286,0.006610691,0.0038802994,0.00302023,0.0038661647,0.00056436023],"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":[0.006695901,0.0011350824,0.2629237,0.0012855479,0.0039302385,0.0010583744,0.003864283,0.56157905,0.008790217,0.064197406,0.0036491656,0.08089102],"study_design_scores_gemma":[0.00013669347,0.0007699836,0.020317307,0.00014240685,0.00019643131,0.0003469324,0.0004205494,0.9425203,0.0046428405,0.029315755,0.001085562,0.00010518022],"about_ca_topic_score_codex":0.0029418252,"about_ca_topic_score_gemma":0.0019373816,"teacher_disagreement_score":0.12543483,"about_ca_system_score_codex":0.001764542,"about_ca_system_score_gemma":0.0018806126,"threshold_uncertainty_score":0.6633707},"labels":[],"label_agreement":null},{"id":"W2134161546","doi":"10.1007/s10707-005-4576-7","title":"Continuous Query Processing of Spatio-Temporal Data Streams in PLACE","year":2005,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Scalability; Spatial query; Data stream mining; Variety (cybernetics); Query language; Global Positioning System; Temporal database; Sliding window protocol; Semantics (computer science); Database; Distributed computing; Window (computing); Web search query; Web query classification; Data mining; Information retrieval; Operating system; Search engine; Artificial intelligence; Programming language","score_opus":0.019862812702019037,"score_gpt":0.2529512089406807,"score_spread":0.23308839623866165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134161546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.257112,0.0014599655,0.7324326,0.0007593012,0.0002589895,0.0002524297,0.0019239354,0.0035570192,0.0022437442],"genre_scores_gemma":[0.89175946,0.00050563103,0.103245564,0.00008339464,0.000171452,0.00011654606,0.0024433422,0.00012067043,0.0015539697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973894,0.00031717314,0.0002269808,0.0004880049,0.0013290332,0.00024944922],"domain_scores_gemma":[0.9948249,0.0024940115,0.00043881126,0.0009305905,0.00092249486,0.00038922075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023740917,0.00091053534,0.0021050521,0.0022241129,0.001224073,0.0040022014,0.0025899198,0.0015054259,0.0019445523],"category_scores_gemma":[0.01051579,0.00062687165,0.0007366198,0.005098321,0.0009757929,0.0046477937,0.0025583326,0.0011641585,0.0005902287],"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":[0.006928498,0.0014261858,0.026396623,0.0011828735,0.00054404733,0.0023547907,0.002702354,0.34122488,0.06296802,0.044768162,0.01768574,0.49181792],"study_design_scores_gemma":[0.00009367617,0.00020823083,0.002187513,0.000020488045,0.000062982435,0.00027852677,0.00064325123,0.968478,0.010169404,0.014803707,0.0030250954,0.000029057353],"about_ca_topic_score_codex":0.0061869617,"about_ca_topic_score_gemma":0.0058268006,"teacher_disagreement_score":0.0061869617,"about_ca_system_score_codex":0.0008620761,"about_ca_system_score_gemma":0.00095877534,"threshold_uncertainty_score":0.012555599},"labels":[],"label_agreement":null},{"id":"W2134820668","doi":"10.1002/asi.10344","title":"Spatialization of Web sites using a weighted frequency model of navigation data","year":2003,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; McMaster University","funders":"","keywords":"Spatialization; A priori and a posteriori; Computer science; Probabilistic logic; Information geometry; Metric (unit); Sensitivity (control systems); Data mining; Artificial intelligence; Algorithm; Mathematics; Geometry; Curvature","score_opus":0.036770052962693076,"score_gpt":0.2922916368838619,"score_spread":0.2555215839211688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134820668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19198343,0.00013199005,0.8058687,0.0004102495,0.00003182796,0.00008692485,0.00020074131,0.00029134357,0.0009947442],"genre_scores_gemma":[0.9313798,0.00014471787,0.066348135,0.000048458147,0.0000561339,0.00012001962,0.00046714343,0.00009851243,0.0013369839],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954301,0.002302178,0.00023723749,0.0010415311,0.00071354985,0.00027544211],"domain_scores_gemma":[0.97659916,0.015557528,0.0027685033,0.0023475487,0.0024405743,0.0002866472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008135298,0.00062381,0.00085843384,0.003227133,0.0006541038,0.0024217085,0.0021837302,0.0012925405,0.0013985584],"category_scores_gemma":[0.035028145,0.00066919863,0.0012593475,0.0030329996,0.0015471611,0.0036845815,0.0014404304,0.0012082447,0.00033738514],"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":[0.00013851165,0.00007503892,0.016665302,0.000060686827,0.00015942761,0.00016576092,0.00026894498,0.88107216,0.0015734264,0.061326098,0.0005503206,0.037944343],"study_design_scores_gemma":[0.000004722036,0.000011529563,0.0011425737,0.000003486729,0.000007958469,0.000021198266,0.000016486256,0.989202,0.00014373215,0.0092808865,0.00015744602,0.000007939834],"about_ca_topic_score_codex":0.022781769,"about_ca_topic_score_gemma":0.015422894,"teacher_disagreement_score":0.022781769,"about_ca_system_score_codex":0.0023752926,"about_ca_system_score_gemma":0.0013574258,"threshold_uncertainty_score":0.045298338},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"split"},{"id":"W2134971776","doi":"10.1109/ideas.2000.880582","title":"Query result size estimation using the Trapezoidal Attribute Cardinality Map","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Cardinality (data modeling); Histogram; Function (biology); Probability density function; Query optimization; Computer science; Algorithm; Density estimation; Estimation; Heuristic; Mathematics; Mathematical optimization; Data mining; Image (mathematics); Artificial intelligence; Statistics","score_opus":0.05725909767975521,"score_gpt":0.2640166818967036,"score_spread":0.20675758421694837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134971776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052806783,0.00048497014,0.9439751,0.000081305676,0.000020840062,0.000042903263,0.00022219947,0.0014999129,0.0008660199],"genre_scores_gemma":[0.64514035,0.0004362197,0.35313126,0.00005127868,0.000030124285,0.000085825435,0.00044434832,0.00014272823,0.00053790025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842846,0.00038035007,0.000112174544,0.00021684283,0.00074967294,0.000112497546],"domain_scores_gemma":[0.9928357,0.003945235,0.00052766927,0.0013529038,0.0011668511,0.00017162759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015567235,0.00039962205,0.0008042503,0.002093698,0.00037787543,0.0013831523,0.0013184375,0.00042416426,0.00096942857],"category_scores_gemma":[0.0143958535,0.00029951552,0.0004053703,0.002344824,0.00042927536,0.0025934957,0.0010797185,0.0005674241,0.00031474026],"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":[0.00094553863,0.00010650008,0.020814147,0.00025245175,0.00013534835,0.00016889832,0.00046704823,0.39802992,0.02813708,0.023460383,0.0038596026,0.52362305],"study_design_scores_gemma":[0.00001038175,0.00003691977,0.0015747306,0.0000071996546,0.000012544858,0.000107558655,0.00005799019,0.98495376,0.0072559533,0.004944288,0.0010170672,0.00002152698],"about_ca_topic_score_codex":0.0049853106,"about_ca_topic_score_gemma":0.0031457772,"teacher_disagreement_score":0.0049853106,"about_ca_system_score_codex":0.0007556393,"about_ca_system_score_gemma":0.0008223174,"threshold_uncertainty_score":0.00991255},"labels":[],"label_agreement":null},{"id":"W2135362144","doi":"10.1109/icpp.2006.5","title":"A Coarse Grained Parallel Algorithm for Hausdorff Voronoi Diagrams","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Voronoi diagram; Hausdorff space; Algorithm; Computer science; Parallel algorithm; Diagram; Hausdorff distance; Combinatorics; Mathematics; Discrete mathematics; Artificial intelligence; Geometry","score_opus":0.01095114095527749,"score_gpt":0.22522925614175776,"score_spread":0.21427811518648027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135362144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059808176,0.00012472943,0.990672,0.00006226431,0.000036443595,0.00006655288,0.00009105527,0.0012080023,0.0017581041],"genre_scores_gemma":[0.07299169,0.00010638364,0.92452943,0.000041879204,0.000029572057,0.000110685854,0.00029823114,0.00019676096,0.0016953673],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895597,0.000121623874,0.00007321631,0.0002768127,0.00045194395,0.00012041964],"domain_scores_gemma":[0.9990314,0.00029659856,0.000054453725,0.00035395028,0.00019647578,0.00006708112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006541936,0.0006289267,0.0011392322,0.0013516402,0.00086835737,0.0015315681,0.0015389696,0.0006683204,0.0053261416],"category_scores_gemma":[0.002891215,0.00048470518,0.00089822855,0.0017794576,0.0008331918,0.0021290702,0.0017879189,0.0010493686,0.0013967056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021450057,0.000115889314,0.0010804205,0.00026628614,0.0000902806,0.0001359937,0.00023225689,0.2851412,0.016162574,0.11139996,0.008480613,0.57668006],"study_design_scores_gemma":[0.00007417402,0.00005330076,0.00025596737,0.000012608302,0.000021706717,0.00012147756,0.000036945432,0.8950237,0.0070303013,0.084431164,0.012915741,0.000022922162],"about_ca_topic_score_codex":0.0055087125,"about_ca_topic_score_gemma":0.0070892517,"teacher_disagreement_score":0.0055087125,"about_ca_system_score_codex":0.0013045016,"about_ca_system_score_gemma":0.0016960431,"threshold_uncertainty_score":0.017817736},"labels":[],"label_agreement":null},{"id":"W2135472213","doi":"10.1109/icdar.1995.602012","title":"Four directional adjacency graphs (FDAG) and their application in locating fields in forms","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adjacency list; Computer science; Adjacency matrix; Horizontal and vertical; Graph; Theoretical computer science; Mathematics; Algorithm; Geometry","score_opus":0.01908069347569015,"score_gpt":0.21079948447227598,"score_spread":0.19171879099658584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135472213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028531825,0.00061771047,0.961151,0.00039637453,0.00008359468,0.0001799441,0.0015004154,0.0019726295,0.0055664503],"genre_scores_gemma":[0.16285786,0.00062545313,0.83191603,0.00010912846,0.000047110774,0.00023948222,0.002000003,0.00016292736,0.002041918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905664,0.00027902247,0.0000817209,0.00023468617,0.0002912286,0.000056799796],"domain_scores_gemma":[0.99763894,0.001132028,0.00025706962,0.0004832557,0.00038974226,0.000098987315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005215257,0.0005470604,0.0005282281,0.0035458345,0.0010697183,0.0014216981,0.0009815318,0.00083093776,0.0034691093],"category_scores_gemma":[0.0046815937,0.00033119338,0.0007027789,0.004908537,0.0008783294,0.0023902513,0.0011349795,0.0006765924,0.00089798303],"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":[0.00022745594,0.00009692789,0.006640217,0.0005388394,0.0000816393,0.00042599635,0.00078802236,0.07435229,0.013076825,0.21039498,0.011373182,0.6820036],"study_design_scores_gemma":[0.000060351205,0.00024048891,0.005376493,0.00019400512,0.00016775035,0.0014182387,0.00097477087,0.49374762,0.016543197,0.37496805,0.10612574,0.00018330693],"about_ca_topic_score_codex":0.005230568,"about_ca_topic_score_gemma":0.007272015,"teacher_disagreement_score":0.005230568,"about_ca_system_score_codex":0.00080268196,"about_ca_system_score_gemma":0.000853523,"threshold_uncertainty_score":0.011605322},"labels":[],"label_agreement":null},{"id":"W2135843591","doi":"10.1145/511446.511489","title":"Probabilistic query expansion using query logs","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":427,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Sargable; Query optimization; Query expansion; Computer science; Probabilistic logic; Query language; RDF query language; Web search query; Web query classification; Query by Example; Boolean conjunctive query; Information retrieval; Data mining; Artificial intelligence; Search engine","score_opus":0.0558195065342248,"score_gpt":0.24033600455912468,"score_spread":0.1845164980248999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135843591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028994985,0.0012045515,0.95981175,0.00047594914,0.00006916995,0.00044092882,0.0004770813,0.006747653,0.0017779485],"genre_scores_gemma":[0.524208,0.0012920946,0.46632195,0.00038983533,0.0003239198,0.0009959579,0.002919786,0.00047535548,0.0030730574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99403244,0.00239364,0.00040017613,0.0007283003,0.0022219745,0.00022348712],"domain_scores_gemma":[0.98367727,0.011699706,0.0008690848,0.0017372408,0.0018181098,0.0001986673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041308342,0.0015296544,0.0016063276,0.0034547825,0.00071799656,0.0015859861,0.0018181587,0.0009773071,0.0018436611],"category_scores_gemma":[0.02310297,0.00074213016,0.00096535153,0.0034035393,0.0008387431,0.0049883937,0.0015348793,0.0015834725,0.001023743],"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":[0.0014885054,0.0008097534,0.0083987275,0.00076826505,0.00023446472,0.00069485634,0.0008410193,0.11657125,0.06439256,0.017956387,0.016679198,0.771165],"study_design_scores_gemma":[0.00010599267,0.00022142362,0.0018066701,0.000030737767,0.000091111055,0.00056360697,0.00012932214,0.96041584,0.015265495,0.013999028,0.007277913,0.00009295334],"about_ca_topic_score_codex":0.003298987,"about_ca_topic_score_gemma":0.0030446462,"teacher_disagreement_score":0.0041308342,"about_ca_system_score_codex":0.0006054761,"about_ca_system_score_gemma":0.0011942965,"threshold_uncertainty_score":0.021846235},"labels":[],"label_agreement":null},{"id":"W2135886882","doi":"10.1109/icips.1997.669344","title":"2D-h trees: an index scheme for content-based retrieval of images in multimedia systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Search engine indexing; Scheme (mathematics); Index (typography); Hierarchy; Object (grammar); Salient; Tree (set theory); Information retrieval; Image retrieval; Feature (linguistics); Database index; Content-based image retrieval; Multimedia database; Object-based spatial database; Content (measure theory); Visual Word; Spatial relationship; Image (mathematics); Artificial intelligence; Spatial database; Spatial analysis; Mathematics; World Wide Web; Geography; Remote sensing","score_opus":0.06512474589860029,"score_gpt":0.26336411403584836,"score_spread":0.19823936813724807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135886882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021174002,0.0034069654,0.961214,0.000562899,0.0003332666,0.00069150527,0.0015657833,0.004829242,0.006222399],"genre_scores_gemma":[0.21550883,0.00264778,0.76957977,0.00037139453,0.00035199831,0.0006675154,0.0034259774,0.00037643072,0.007070365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909544,0.00019614126,0.00010603614,0.00009596611,0.00039805347,0.00010838397],"domain_scores_gemma":[0.9986833,0.00033168666,0.00012605457,0.00043656287,0.0003077278,0.000114679446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013036273,0.0005081657,0.0011466444,0.0031231793,0.001374527,0.0021384484,0.0014733621,0.0011504386,0.0036702522],"category_scores_gemma":[0.003347635,0.00040891577,0.00044383277,0.004794266,0.00081006845,0.0047535435,0.001853115,0.0007314378,0.0026350392],"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":[0.0007915933,0.00027003742,0.001912455,0.00071625714,0.00011231398,0.0003177531,0.00072604325,0.030214213,0.049576156,0.09398857,0.06207487,0.75929976],"study_design_scores_gemma":[0.00034529495,0.0009604487,0.0032340297,0.00017783417,0.00016483135,0.0018258821,0.00036441142,0.57922757,0.052406557,0.14438064,0.21659027,0.00032221575],"about_ca_topic_score_codex":0.0027397652,"about_ca_topic_score_gemma":0.0031046073,"teacher_disagreement_score":0.0036702522,"about_ca_system_score_codex":0.0011881341,"about_ca_system_score_gemma":0.001337487,"threshold_uncertainty_score":0.012278199},"labels":[],"label_agreement":null},{"id":"W2137206963","doi":"10.1145/1370256.1370286","title":"Answering preference queries with bit-sliced index arithmetic","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Preference; Skyline; Arithmetic; Index (typography); Bit (key); Theoretical computer science; SQL; Database; Algorithm; Information retrieval; Data mining; Mathematics; Programming language; Statistics","score_opus":0.03508904335849693,"score_gpt":0.19882240925477937,"score_spread":0.16373336589628246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137206963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10231125,0.0006002873,0.8828038,0.00030735804,0.000081951155,0.00018249583,0.0012275248,0.0065677483,0.005917657],"genre_scores_gemma":[0.47655421,0.0004099002,0.5178219,0.00023024828,0.00009703836,0.00017288388,0.001781018,0.0004599372,0.002472788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672204,0.000648832,0.0005678839,0.0003925941,0.0013785524,0.0002901225],"domain_scores_gemma":[0.9945102,0.0019484962,0.00040911706,0.002047529,0.000912776,0.00017192443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026420902,0.0007402756,0.0012674268,0.0017099767,0.00060386164,0.002572661,0.0014029104,0.0006908683,0.004150359],"category_scores_gemma":[0.008366276,0.0004665293,0.00064391847,0.00338623,0.0008091539,0.008088476,0.0025060067,0.00091592956,0.001287078],"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":[0.0040635206,0.00038129,0.010224588,0.0011405417,0.00024043393,0.0005976481,0.0014834824,0.038940925,0.10063014,0.14562848,0.017427076,0.6792419],"study_design_scores_gemma":[0.00040560387,0.0011142071,0.0033711905,0.00014791904,0.00021804353,0.0017105524,0.0011585711,0.5643131,0.14629446,0.24347219,0.037547696,0.00024648968],"about_ca_topic_score_codex":0.0010296842,"about_ca_topic_score_gemma":0.0012215147,"teacher_disagreement_score":0.004150359,"about_ca_system_score_codex":0.0006910586,"about_ca_system_score_gemma":0.00086378347,"threshold_uncertainty_score":0.013972878},"labels":[],"label_agreement":null},{"id":"W2137819616","doi":"10.1145/1277741.1277980","title":"Focused ranking in a vertical search engine","year":2007,"lang":"en","type":"article","venue":"","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":"Minnow Environmental (Canada)","funders":"","keywords":"Spamdexing; Information retrieval; Computer science; Search engine; PageRank; Ranking (information retrieval); World Wide Web; Hyperlink; Web search engine; Metasearch engine; Semantic search; Web crawler; Search analytics; Search engine optimization; Domain (mathematical analysis); Web page; Web search query","score_opus":0.021885780121617564,"score_gpt":0.2664371022391155,"score_spread":0.24455132211749792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137819616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06358548,0.0019378157,0.8900685,0.0013385823,0.00023112299,0.00076343445,0.0018837599,0.0076029347,0.03258841],"genre_scores_gemma":[0.28029916,0.0009747611,0.6826735,0.0006712648,0.00023620884,0.0004629742,0.0027965528,0.0004303541,0.03145521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967713,0.00096546765,0.0002050708,0.0003580348,0.0013462083,0.0003539675],"domain_scores_gemma":[0.9958638,0.0012558396,0.00025036532,0.0011491473,0.0012569084,0.0002238109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035188508,0.00073086336,0.0011398542,0.002814953,0.0009457569,0.0028358514,0.0015747667,0.0014490113,0.00916304],"category_scores_gemma":[0.008282969,0.0006701771,0.00073910895,0.0041607185,0.0005301764,0.004720277,0.0024880168,0.0007935835,0.0052992622],"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":[0.00086020696,0.00075511145,0.005738701,0.0005085966,0.00020943447,0.000785655,0.00043878693,0.054966427,0.022227518,0.26197323,0.05749479,0.5940415],"study_design_scores_gemma":[0.00035767053,0.0007827198,0.0019464691,0.00014898193,0.00016948729,0.0011000382,0.00023113358,0.73593754,0.012460074,0.17250694,0.07421503,0.00014396783],"about_ca_topic_score_codex":0.005414901,"about_ca_topic_score_gemma":0.009463293,"teacher_disagreement_score":0.00916304,"about_ca_system_score_codex":0.0008878122,"about_ca_system_score_gemma":0.002172086,"threshold_uncertainty_score":0.030653477},"labels":[],"label_agreement":null},{"id":"W2138160905","doi":"10.1109/csicc.2009.5349326","title":"A unified framework for evaluating deductive databases with uncertainty","year":2009,"lang":"en","type":"article","venue":"2009 14th International CSI Computer Conference","topic":"Data Management and Algorithms","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Certainty; Computer science; Deductive database; Annotation; Database; Database theory; Artificial intelligence; Relational database; Mathematics","score_opus":0.08610360806311745,"score_gpt":0.35261431963211093,"score_spread":0.2665107115689935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138160905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038410563,0.0002986324,0.993548,0.00017036036,0.000024863366,0.00014516032,0.0001241699,0.0008305049,0.0010173579],"genre_scores_gemma":[0.09923031,0.000323701,0.8991049,0.00009757716,0.00007849313,0.00025355324,0.00035008838,0.00013881929,0.00042270098],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9709246,0.007682355,0.003755331,0.0028878646,0.013667768,0.001082128],"domain_scores_gemma":[0.97087336,0.014179828,0.002610554,0.0042960094,0.0070683435,0.0009719492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022386093,0.0020098812,0.003059566,0.0074178674,0.0013497408,0.011896483,0.005267944,0.0024214839,0.0028719436],"category_scores_gemma":[0.06548661,0.0010291846,0.0030385384,0.005452315,0.0037576477,0.014010667,0.0061043943,0.0029776334,0.0006826167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039171934,0.000283985,0.0032927569,0.00078687747,0.00033304747,0.00054287555,0.0010100917,0.22385792,0.0082598915,0.5314895,0.0035572322,0.22619413],"study_design_scores_gemma":[0.000059786762,0.00020653351,0.0004182888,0.00017480995,0.00016835246,0.00019942476,0.00022994995,0.81416625,0.006977075,0.17013106,0.0071705407,0.0000979492],"about_ca_topic_score_codex":0.005857869,"about_ca_topic_score_gemma":0.0037283145,"teacher_disagreement_score":0.022386093,"about_ca_system_score_codex":0.002882506,"about_ca_system_score_gemma":0.0038951575,"threshold_uncertainty_score":0.11839038},"labels":[],"label_agreement":null},{"id":"W2138271690","doi":"10.1109/icde.2007.367935","title":"Top-k Query Processing in Uncertain Databases","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":428,"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":"Tuple; Computer science; Semantics (computer science); Probabilistic logic; Construct (python library); Database; Query language; Materialized view; Query optimization; Space (punctuation); Theoretical computer science; Information retrieval; Data mining; View; Programming language; Artificial intelligence; Mathematics; Database design","score_opus":0.038690036601686045,"score_gpt":0.30718534086678395,"score_spread":0.26849530426509793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138271690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015806805,0.0003544264,0.9806315,0.00065537065,0.00003488864,0.00008037227,0.00023047507,0.0008718481,0.0013342627],"genre_scores_gemma":[0.5107709,0.0006143379,0.48574707,0.0003089826,0.00014871787,0.00012901364,0.0005900173,0.00022445087,0.0014665697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99167913,0.002702104,0.0010515057,0.0014744885,0.0024125737,0.00068013306],"domain_scores_gemma":[0.9865562,0.00783266,0.0009799878,0.0031920138,0.001143989,0.00029520356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004960571,0.0008964305,0.002473876,0.0012882414,0.002378618,0.0067059877,0.0032745295,0.0019150339,0.0027125427],"category_scores_gemma":[0.021073507,0.0009380365,0.0020042763,0.0037982587,0.0025548355,0.01151896,0.0049178083,0.0027929884,0.00080735626],"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":[0.0008739089,0.00024658395,0.0028224573,0.00076677545,0.00031221262,0.0006709617,0.0015601458,0.3793276,0.008131818,0.350167,0.011408478,0.24371202],"study_design_scores_gemma":[0.000029090665,0.000056676516,0.00021600183,0.000025046218,0.000052308947,0.00026674318,0.00030464932,0.6771826,0.0039729825,0.3150821,0.0027606725,0.000051080155],"about_ca_topic_score_codex":0.0039858916,"about_ca_topic_score_gemma":0.0053907232,"teacher_disagreement_score":0.0067059877,"about_ca_system_score_codex":0.0016699687,"about_ca_system_score_gemma":0.0021750417,"threshold_uncertainty_score":0.02623427},"labels":[],"label_agreement":null},{"id":"W2138414767","doi":"","title":"Probabilistic skylines on uncertain data","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":461,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Uncertain data; Probabilistic logic; Computer science; Data mining; Benchmark (surveying); Object (grammar); Set (abstract data type); Data set; Artificial intelligence","score_opus":0.08583155124540513,"score_gpt":0.32339550275575746,"score_spread":0.2375639515103523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138414767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025318874,0.0014757154,0.9685796,0.0007694929,0.0000436747,0.00012959233,0.0016758399,0.0010395013,0.0009677452],"genre_scores_gemma":[0.37792125,0.0018019463,0.61286515,0.00029764426,0.00032967512,0.00027147587,0.0050874287,0.00024718774,0.0011782629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9940975,0.002262639,0.0004795503,0.00131671,0.0015684156,0.0002752164],"domain_scores_gemma":[0.976772,0.015020208,0.0029327339,0.0030777187,0.0018199185,0.00037730127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044074655,0.0011272545,0.0020708907,0.0039214077,0.0015516853,0.003459882,0.0022978846,0.0019048171,0.0014651421],"category_scores_gemma":[0.02636332,0.0012960186,0.0019416183,0.0070212805,0.0018715336,0.009824344,0.002514064,0.002442348,0.00069843367],"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":[0.0005850208,0.00007539529,0.01592119,0.0007053145,0.00032673153,0.0006908192,0.0008686275,0.70873153,0.0052577513,0.0804782,0.0129694985,0.17338988],"study_design_scores_gemma":[0.00002126274,0.0000497851,0.0019079368,0.000050542585,0.000037340375,0.00031009223,0.00020665364,0.92018795,0.0021704785,0.06769123,0.0073319636,0.00003482667],"about_ca_topic_score_codex":0.0064221118,"about_ca_topic_score_gemma":0.005951278,"teacher_disagreement_score":0.0064221118,"about_ca_system_score_codex":0.0016775259,"about_ca_system_score_gemma":0.0011147966,"threshold_uncertainty_score":0.023309171},"labels":[],"label_agreement":null},{"id":"W2139292246","doi":"10.1109/icde.2005.107","title":"PnP: Parallel and External Memory Iceberg Cube Computation","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Dalhousie University","funders":"","keywords":"Iceberg; Cube (algebra); Computer science; Pruning; Data cube; Auxiliary memory; Computation; Novelty; Parallel computing; Data mining; Algorithm; Computer hardware; Geology; Mathematics; Combinatorics","score_opus":0.014397803639399976,"score_gpt":0.24642858386599562,"score_spread":0.23203078022659565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139292246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013901847,0.00019086203,0.97840834,0.00016902183,0.000052516156,0.000081757484,0.00024111719,0.0033442257,0.0036103602],"genre_scores_gemma":[0.19702698,0.00025606915,0.79584855,0.00016939944,0.00006353528,0.00025503314,0.0012363844,0.00065487996,0.0044891355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990312,0.0001426824,0.00006554929,0.00014925192,0.00047542073,0.00013591291],"domain_scores_gemma":[0.9988951,0.0003354395,0.00005902373,0.00042846653,0.00021922468,0.00006271663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007931462,0.0008675746,0.00095533003,0.0007171913,0.0008890561,0.0014944175,0.0024589216,0.0007175585,0.0039645554],"category_scores_gemma":[0.0027308383,0.0005058839,0.0006813945,0.0016256224,0.0008872194,0.0029881673,0.0033547427,0.0012244345,0.0012783386],"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":[0.00081101997,0.00018643402,0.0030217264,0.000397465,0.00014280765,0.00049476215,0.000507747,0.1918462,0.017857296,0.099782996,0.044396088,0.64055544],"study_design_scores_gemma":[0.000060782528,0.00006730515,0.0002721199,0.000014884804,0.000023163522,0.00016890734,0.00006697762,0.9145762,0.014017903,0.058295358,0.012411637,0.000024770998],"about_ca_topic_score_codex":0.0036064787,"about_ca_topic_score_gemma":0.0045706155,"teacher_disagreement_score":0.0039645554,"about_ca_system_score_codex":0.0006342503,"about_ca_system_score_gemma":0.0011738625,"threshold_uncertainty_score":0.013262689},"labels":[],"label_agreement":null},{"id":"W2139923606","doi":"10.1109/ccece.2013.6567822","title":"Evaluating the spatial indexing of dense point sets","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Search engine indexing; R-tree; Computer science; Tree (set theory); Point (geometry); Benchmark (surveying); Access method; Spatial database; Data mining; Database index; Spatial analysis; Index (typography); Database; Information retrieval; Mathematics; Statistics; Geography; Cartography; Geometry; Combinatorics","score_opus":0.0522531299893662,"score_gpt":0.318124598208659,"score_spread":0.2658714682192928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139923606","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7058141,0.008631706,0.24401164,0.0007230092,0.0004272774,0.0007735319,0.004004309,0.014799259,0.020815121],"genre_scores_gemma":[0.6998471,0.0016865903,0.29024705,0.00014352164,0.00013340093,0.00019246335,0.0053075496,0.00040897404,0.002033389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99212086,0.0013509315,0.00067975855,0.000636194,0.0049074017,0.00030477945],"domain_scores_gemma":[0.97647786,0.013946386,0.0012201916,0.0036468739,0.004141873,0.0005667506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004405397,0.0008494658,0.0015353425,0.0040875124,0.00093298923,0.0019789594,0.0026680976,0.0011709917,0.0031351708],"category_scores_gemma":[0.032282032,0.00040145734,0.00068821176,0.0067678597,0.0010655264,0.006447647,0.0030419985,0.0005255578,0.0009119053],"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":[0.0027975752,0.0010189376,0.022626014,0.0021622314,0.0004378853,0.00056083454,0.0009361074,0.2961526,0.025017614,0.022248236,0.015371052,0.6106709],"study_design_scores_gemma":[0.00021114503,0.0010795515,0.005294517,0.000068805755,0.00009364493,0.0005988022,0.00048855797,0.9618032,0.014150276,0.009287432,0.0068645547,0.000059448765],"about_ca_topic_score_codex":0.010641792,"about_ca_topic_score_gemma":0.009609026,"teacher_disagreement_score":0.010641792,"about_ca_system_score_codex":0.0013284329,"about_ca_system_score_gemma":0.0015479596,"threshold_uncertainty_score":0.023298204},"labels":[],"label_agreement":null},{"id":"W2140060501","doi":"10.1145/1557670.1557680","title":"Query segmentation using conditional random fields","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Conditional random field; Query optimization; Query expansion; Segmentation; Quality (philosophy); Sargable; Data mining; Field (mathematics); Information retrieval; Web query classification; Relational database; Artificial intelligence; Web search query; Search engine","score_opus":0.02237004621615805,"score_gpt":0.2745286333615625,"score_spread":0.25215858714540446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140060501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018999623,0.0007493331,0.9758671,0.0002578487,0.000032972042,0.00008190677,0.00032505763,0.0029640899,0.0007221292],"genre_scores_gemma":[0.6878771,0.0007134589,0.3051723,0.0003877387,0.00021534722,0.00025764076,0.0024375047,0.00039577455,0.0025432005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976954,0.000865808,0.00011740048,0.0006025353,0.00048324704,0.00023566725],"domain_scores_gemma":[0.99264055,0.0055805757,0.00044400504,0.0007084579,0.0004849702,0.00014153896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035584322,0.00086217595,0.0014854968,0.0024177695,0.0007100384,0.0010977864,0.0018533998,0.0013173178,0.0022390478],"category_scores_gemma":[0.010001638,0.00061735447,0.0014944965,0.0027945319,0.00097447314,0.0031880566,0.0009929725,0.0013207997,0.0007014534],"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":[0.0011252201,0.00026769022,0.0061747893,0.00026857434,0.00015978483,0.0003921079,0.0005211755,0.6150312,0.013165849,0.03758425,0.012574385,0.31273505],"study_design_scores_gemma":[0.000017417586,0.000044861958,0.00044087385,0.000007860903,0.000015235866,0.00008041001,0.000019075425,0.982251,0.0013232986,0.01477988,0.0010005094,0.00001952693],"about_ca_topic_score_codex":0.015894275,"about_ca_topic_score_gemma":0.009228011,"teacher_disagreement_score":0.015894275,"about_ca_system_score_codex":0.0017485213,"about_ca_system_score_gemma":0.00181713,"threshold_uncertainty_score":0.031603515},"labels":[],"label_agreement":null},{"id":"W2140190241","doi":"10.5860/choice.49-3305","title":"Data mining: concepts and techniques","year":2012,"lang":"en","type":"article","venue":"Choice Reviews Online","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28877,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data science; Data mining","score_opus":0.15542448392305214,"score_gpt":0.4082003367705969,"score_spread":0.2527758528475448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140190241","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015437381,0.22616439,0.69977665,0.017249621,0.004029798,0.0015865897,0.0026937989,0.0029418508,0.044013582],"genre_scores_gemma":[0.03527581,0.21225458,0.7143649,0.0066977614,0.007685497,0.0040633623,0.005047382,0.0004884694,0.014122338],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.977741,0.007931681,0.0026188933,0.0026495338,0.008520926,0.0005379845],"domain_scores_gemma":[0.9877366,0.008173077,0.0006946042,0.0015053564,0.001592512,0.0002978814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011512614,0.0034448497,0.0039384216,0.009593101,0.0017051088,0.011731264,0.0071473084,0.0044746227,0.00888191],"category_scores_gemma":[0.017872233,0.0018299051,0.0030839823,0.016017908,0.006387398,0.011377424,0.006140475,0.0074458495,0.008215756],"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":[0.00008579978,0.00016619751,0.0015831651,0.007680964,0.00046904344,0.00052158104,0.001560269,0.010604973,0.0008568575,0.40627268,0.122235775,0.44796267],"study_design_scores_gemma":[0.00004794879,0.00007454823,0.0005915709,0.0028009748,0.00007582336,0.0008281257,0.0005025778,0.015356544,0.0005857597,0.53432727,0.44473714,0.00007173193],"about_ca_topic_score_codex":0.0019226238,"about_ca_topic_score_gemma":0.00085805764,"teacher_disagreement_score":0.011731264,"about_ca_system_score_codex":0.0027727266,"about_ca_system_score_gemma":0.0053944136,"threshold_uncertainty_score":0.06088519},"labels":[],"label_agreement":null},{"id":"W2140740564","doi":"","title":"Multidimensional Orthogonal Range Search Using Tries.","year":2003,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Rectangle; Range (aeronautics); Competitive analysis; Set (abstract data type); Algorithm; Space (punctuation); Computer science; Range query (database); Online algorithm; Combinatorics; Search algorithm; Mathematics; Theoretical computer science; Search engine; Information retrieval; Upper and lower bounds; Web search query; Engineering; Geometry; Sargable","score_opus":0.06550581335891824,"score_gpt":0.28024586088074516,"score_spread":0.2147400475218269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140740564","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015537799,0.0015320645,0.9757529,0.00017610745,0.0000938331,0.00017747396,0.00038849996,0.0024066987,0.0039345324],"genre_scores_gemma":[0.1704664,0.0007778404,0.8240158,0.0001726441,0.00007460216,0.00047514838,0.001276163,0.00017945607,0.0025618304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968106,0.00088831905,0.0003262449,0.0004962297,0.0011897112,0.00028886745],"domain_scores_gemma":[0.995867,0.0016451403,0.0005558839,0.0011472938,0.0006275234,0.00015716822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015863917,0.0011095675,0.0017436268,0.0026232318,0.0008560471,0.0024842764,0.0023978576,0.0012253894,0.0037046156],"category_scores_gemma":[0.009683107,0.0007771331,0.0012160676,0.0053470563,0.0009597913,0.006029231,0.00551206,0.0010424466,0.0032625704],"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":[0.0013620249,0.00031158558,0.0072084037,0.0010292528,0.00040871967,0.0004325236,0.00067388994,0.09917375,0.026173066,0.0862332,0.019664926,0.75732857],"study_design_scores_gemma":[0.00024193793,0.0005626256,0.000966527,0.00012707865,0.00014833412,0.0020608997,0.00045187803,0.8775091,0.033568,0.056498364,0.027737316,0.00012782532],"about_ca_topic_score_codex":0.0011856667,"about_ca_topic_score_gemma":0.0015241294,"teacher_disagreement_score":0.0037046156,"about_ca_system_score_codex":0.0004903652,"about_ca_system_score_gemma":0.0014001522,"threshold_uncertainty_score":0.012393117},"labels":[],"label_agreement":null},{"id":"W2140924391","doi":"10.1109/icccyb.2005.1511565","title":"Spatial OLAP modeling: an overview base on spatial objects changing over time","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Online analytical processing; Spatial analysis; Data warehouse; Data mining; Spatial query; Spatial database; Geographic information system; Search engine indexing; Context (archaeology); Data modeling; Field (mathematics); Data model (GIS); Interoperability; Geospatial analysis; Database; Information retrieval; Geography; Cartography; Web query classification; Artificial intelligence","score_opus":0.031990848570380884,"score_gpt":0.2568247464844555,"score_spread":0.22483389791407465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140924391","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006795403,0.013540079,0.9678409,0.0019951619,0.00014782487,0.00013593378,0.00096217537,0.0011687181,0.0074137533],"genre_scores_gemma":[0.162707,0.0584444,0.7673955,0.00059690507,0.00052866014,0.00046585267,0.003119599,0.00042913048,0.0063129733],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99860626,0.00033608975,0.00021232261,0.00029631713,0.00046550637,0.00008355578],"domain_scores_gemma":[0.9985228,0.00042649586,0.00015829335,0.0003813791,0.00040001055,0.00011105392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002443683,0.0011020105,0.0011041899,0.0037275779,0.001154748,0.007956253,0.0032186178,0.0012666652,0.0018909799],"category_scores_gemma":[0.002730663,0.0010313572,0.0018380493,0.008403961,0.0012099515,0.011818655,0.0020004972,0.001788461,0.0010509694],"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":[0.00017550577,0.00014697651,0.009206994,0.00226867,0.0003671395,0.001101436,0.0021255924,0.18194002,0.004045298,0.3123771,0.017884694,0.4683606],"study_design_scores_gemma":[0.000015794296,0.00008708139,0.0018350715,0.0007389603,0.00022040994,0.001395998,0.0013303936,0.59738976,0.0036882288,0.1524847,0.24068017,0.00013349392],"about_ca_topic_score_codex":0.011813099,"about_ca_topic_score_gemma":0.007131696,"teacher_disagreement_score":0.011813099,"about_ca_system_score_codex":0.001558254,"about_ca_system_score_gemma":0.0018566196,"threshold_uncertainty_score":0.0234887},"labels":[],"label_agreement":null},{"id":"W2141296717","doi":"10.1145/1989734.1989741","title":"MoveMine","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Army Research Laboratory; Air Force Office of Scientific Research; Division of Computing and Communication Foundations; National Science Foundation","keywords":"Computer science; Data mining; Object (grammar); Swarm behaviour; Focus (optics); Trajectory; Cluster analysis; Data stream mining; Movement (music); Artificial intelligence","score_opus":0.03626296199891101,"score_gpt":0.23551538622712453,"score_spread":0.19925242422821351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141296717","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008668625,0.002665977,0.14590017,0.002634298,0.0015577022,0.0008609844,0.11365734,0.5144928,0.20956208],"genre_scores_gemma":[0.06268043,0.0028840748,0.2042394,0.005091137,0.00041423025,0.0025073115,0.39135522,0.08381043,0.24701785],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843484,0.00015160328,0.0001157001,0.0005453502,0.0005744055,0.00017809142],"domain_scores_gemma":[0.9985227,0.00027674143,0.00011685495,0.00046850537,0.0004716113,0.00014357608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014285825,0.0016639893,0.0014722099,0.0022373344,0.0011889961,0.0037357162,0.0053944057,0.0024659694,0.15079369],"category_scores_gemma":[0.004899097,0.0012517519,0.0017221819,0.0021570616,0.0006767252,0.0051332805,0.0044148476,0.00204875,0.16011532],"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":[0.00083250867,0.00015637137,0.00307772,0.0012959273,0.00017091083,0.00037243412,0.00036424724,0.0023255113,0.0063390047,0.015585752,0.8117526,0.15772703],"study_design_scores_gemma":[0.00016417289,0.00012180123,0.0015925094,0.00013019958,0.00006167119,0.00044292875,0.0001026549,0.01003466,0.0048544183,0.009231811,0.9731712,0.000092025744],"about_ca_topic_score_codex":0.0036452315,"about_ca_topic_score_gemma":0.006707834,"teacher_disagreement_score":0.15079369,"about_ca_system_score_codex":0.00097189145,"about_ca_system_score_gemma":0.0018562364,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2141646866","doi":"10.24517/00062794","title":"Linear-Space Algorithms for Distance Preserving Embedding","year":2007,"lang":"en","type":"article","venue":"NPARC","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Combinatorics; Distance matrix; Mathematics; Euclidean distance; Euclidean space; Euclidean distance matrix; Embedding; Disjoint sets; Strength of a graph; Discrete mathematics; Linear space; Partition (number theory); Algorithm; Graph; Graph power; Computer science; Line graph; Artificial intelligence","score_opus":0.028730944505920935,"score_gpt":0.3070024107349173,"score_spread":0.27827146622899634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141646866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040684366,0.00074630807,0.98945755,0.0003423622,0.000056745568,0.00008754251,0.00022385463,0.0019243107,0.0030929155],"genre_scores_gemma":[0.10447957,0.00074297114,0.8851188,0.00022097336,0.0001414278,0.000514249,0.0014288648,0.0005090364,0.006844045],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969465,0.0008033514,0.00019809409,0.0006275908,0.0010683464,0.00035609276],"domain_scores_gemma":[0.99547786,0.0022531399,0.00032487942,0.0011922771,0.00062302436,0.0001288207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018266088,0.0021488317,0.0017709503,0.002469358,0.0014158896,0.0031020935,0.0036890113,0.0019332055,0.012047813],"category_scores_gemma":[0.008595695,0.0008085303,0.0013051411,0.0047008484,0.0018498024,0.0069763213,0.004872446,0.0036665224,0.005589037],"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":[0.00049328944,0.00047644638,0.0006965753,0.0005287776,0.00012956302,0.00010296175,0.00038192523,0.2497065,0.0029794108,0.19890608,0.027165687,0.5184328],"study_design_scores_gemma":[0.00011434764,0.00011321081,0.00017838132,0.000032035092,0.000028489965,0.000116525465,0.00013749789,0.647903,0.0024835616,0.33841857,0.010440998,0.00003341046],"about_ca_topic_score_codex":0.0037312792,"about_ca_topic_score_gemma":0.004560482,"teacher_disagreement_score":0.012047813,"about_ca_system_score_codex":0.0023902694,"about_ca_system_score_gemma":0.0016148168,"threshold_uncertainty_score":0.040304005},"labels":[],"label_agreement":null},{"id":"W2142080841","doi":"10.3390/s131013402","title":"GeoCENS: A Geospatial Cyberinfrastructure for the World-Wide Sensor Web","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Jet Propulsion Laboratory; Canarie; Microsoft Research","keywords":"Cyberinfrastructure; Sensor web; Geospatial analysis; Computer science; Architecture; Web Coverage Service; World Wide Web; Web service; Process (computing); Web mapping; Data science; Web modeling; Remote sensing; Geography; Telecommunications; Key distribution in wireless sensor networks","score_opus":0.00877642595381383,"score_gpt":0.21978529625155335,"score_spread":0.21100887029773951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142080841","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020781286,0.0004554428,0.93957,0.0019078299,0.00021167315,0.00032205426,0.00033694654,0.017602941,0.01881186],"genre_scores_gemma":[0.36545783,0.0011024545,0.60607463,0.000855398,0.00019581366,0.00043469665,0.0022415747,0.0013285318,0.022309056],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99937433,0.000121068515,0.000047915157,0.000075643686,0.000334975,0.0000461281],"domain_scores_gemma":[0.9987684,0.00014492022,0.00009924295,0.0004915235,0.00026151273,0.00023437917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012594417,0.00040364772,0.00028617773,0.00078748394,0.00071655057,0.0027470028,0.0014881465,0.000991545,0.0032521896],"category_scores_gemma":[0.002417504,0.00035546455,0.00040274218,0.00079655263,0.001376254,0.0047301897,0.0034121985,0.0015787376,0.0015122052],"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":[0.0003831308,0.0005357963,0.009110397,0.00058458425,0.00013378836,0.001114879,0.0012419908,0.029773869,0.05343563,0.40278062,0.07396952,0.4269357],"study_design_scores_gemma":[0.00010431862,0.00029487387,0.0045222505,0.00016609766,0.00005877174,0.001317378,0.0005249215,0.36712605,0.034364,0.1275064,0.4639126,0.000102291844],"about_ca_topic_score_codex":0.0018374387,"about_ca_topic_score_gemma":0.00263964,"teacher_disagreement_score":0.0032521896,"about_ca_system_score_codex":0.0006009014,"about_ca_system_score_gemma":0.0012263645,"threshold_uncertainty_score":0.010879636},"labels":[],"label_agreement":null},{"id":"W2142162160","doi":"10.1016/j.dam.2014.07.023","title":"Finding the closest ultrametric","year":2014,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Data Management and Algorithms","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":"University of Waterloo","funders":"","keywords":"Ultrametric space; Mathematics; Multiplicative function; Distortion (music); Combinatorics; Pairwise comparison; Monotonic function; Approximation algorithm; Tree (set theory); Constant (computer programming); Discrete mathematics; Metric space; Statistics; Mathematical analysis; Computer science","score_opus":0.016697596064645652,"score_gpt":0.2383872469492028,"score_spread":0.22168965088455714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142162160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109235734,0.0016774349,0.8727265,0.002190763,0.0005820398,0.00012834376,0.0004920687,0.00069065666,0.01227642],"genre_scores_gemma":[0.38360503,0.0011039535,0.6040021,0.00064849074,0.0004611817,0.00023629634,0.0013504208,0.0005478617,0.008044675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99427223,0.0021834231,0.0004296023,0.0013080637,0.0014753286,0.00033138736],"domain_scores_gemma":[0.98969144,0.005599836,0.00070450065,0.0017735204,0.0014818627,0.0007489281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032840148,0.0012927116,0.0024154705,0.0046375543,0.0019846815,0.0040038074,0.0028432398,0.0027203592,0.008409604],"category_scores_gemma":[0.039290406,0.0012580401,0.0021058654,0.0030327803,0.0027284368,0.008961555,0.008397386,0.00561134,0.0020635447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006412717,0.00035658447,0.00649875,0.00084100466,0.0003712575,0.0004188957,0.0012910127,0.04267306,0.008950814,0.53571916,0.018371329,0.38386682],"study_design_scores_gemma":[0.00004763496,0.0003505975,0.0010599735,0.00017055677,0.000079333586,0.000823544,0.0007649392,0.14631042,0.005023136,0.8263752,0.01891983,0.0000748274],"about_ca_topic_score_codex":0.00048241546,"about_ca_topic_score_gemma":0.0005361851,"teacher_disagreement_score":0.008409604,"about_ca_system_score_codex":0.0012140314,"about_ca_system_score_gemma":0.0011490685,"threshold_uncertainty_score":0.028132856},"labels":[],"label_agreement":null},{"id":"W2142560248","doi":"10.5555/1182635.1164151","title":"FIX: feature-based indexing technique for XML documents","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"XPath; Computer science; Search engine indexing; XML; XML database; Twig; Information retrieval; Pruning; Data mining; Feature (linguistics); Document Structure Description; World Wide Web","score_opus":0.00916301481277251,"score_gpt":0.24445006663403096,"score_spread":0.23528705182125845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142560248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0114473775,0.0010553459,0.9768728,0.0002384984,0.00009166075,0.00013708147,0.00058402005,0.007896547,0.0016766805],"genre_scores_gemma":[0.08091587,0.0006571887,0.912469,0.00018731145,0.00013543543,0.00021488804,0.0021143034,0.0006954618,0.002610505],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973074,0.00028095342,0.00024092235,0.00038980765,0.0016046386,0.00017629802],"domain_scores_gemma":[0.99612206,0.0012903499,0.00044157202,0.0012599097,0.0007582769,0.00012776961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019009055,0.00081231975,0.0013031971,0.005892339,0.0011517698,0.0021608933,0.0035191653,0.0012727585,0.0045915637],"category_scores_gemma":[0.007455253,0.0005236758,0.0012337681,0.007558815,0.0011304935,0.0075956546,0.0023930932,0.0013880567,0.0022974452],"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":[0.00040573443,0.00017601733,0.0023586776,0.0005925789,0.00011294412,0.00020943301,0.0005699835,0.008256985,0.03167321,0.043282386,0.018059934,0.8943022],"study_design_scores_gemma":[0.00033532723,0.001447932,0.0047626626,0.00032927233,0.00036488543,0.00531777,0.0010037839,0.5241221,0.16522224,0.1290896,0.16762191,0.00038246304],"about_ca_topic_score_codex":0.0022613357,"about_ca_topic_score_gemma":0.002007669,"teacher_disagreement_score":0.005892339,"about_ca_system_score_codex":0.0010769407,"about_ca_system_score_gemma":0.00108249,"threshold_uncertainty_score":0.015360296},"labels":[],"label_agreement":null},{"id":"W2142595897","doi":"10.1109/tkde.2006.146","title":"On the Signature Tree Construction and Analysis","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Signature (topology); Tree (set theory); Set (abstract data type); Digital signature; Data structure; Data mining; File format; Information retrieval; Theoretical computer science; Database; Programming language; Hash function; Mathematics","score_opus":0.011715846144598278,"score_gpt":0.2185866327377866,"score_spread":0.20687078659318833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142595897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048663286,0.0006757962,0.9913248,0.000252945,0.000058167305,0.00007677974,0.00020242875,0.0005707425,0.0019720325],"genre_scores_gemma":[0.06422652,0.0014360504,0.9289873,0.00017234124,0.00022762174,0.00014897306,0.0014422814,0.0002948702,0.003063974],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968713,0.000726717,0.0002824432,0.00049574167,0.0014232784,0.00020044995],"domain_scores_gemma":[0.99250436,0.0028172287,0.0006533103,0.0018445406,0.0019068186,0.0002737359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020521258,0.0008683396,0.0011568443,0.004942249,0.001540506,0.0029334913,0.0019882454,0.0011780564,0.0041445745],"category_scores_gemma":[0.012572039,0.0006516917,0.0012295751,0.009070652,0.001689327,0.008252941,0.0023037384,0.002337043,0.002916873],"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":[0.00019305159,0.00014609033,0.0022698226,0.00036900153,0.00006202583,0.0003378647,0.00044421115,0.035829578,0.011947421,0.26466912,0.014652949,0.6690788],"study_design_scores_gemma":[0.000052931337,0.00020082701,0.001118241,0.00013223317,0.000071316485,0.0013426539,0.00019970439,0.4917144,0.016757179,0.43731374,0.050989665,0.00010717844],"about_ca_topic_score_codex":0.0028864916,"about_ca_topic_score_gemma":0.0018614932,"teacher_disagreement_score":0.004942249,"about_ca_system_score_codex":0.001233792,"about_ca_system_score_gemma":0.0024451506,"threshold_uncertainty_score":0.013864994},"labels":[],"label_agreement":null},{"id":"W2143085379","doi":"","title":"Efficiently answering top-k typicality queries on large databases","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Discriminative model; Set (abstract data type); Tree (set theory); Measure (data warehouse); Quality (philosophy); Information retrieval; Data mining; Artificial intelligence; Mathematics","score_opus":0.024676018051859223,"score_gpt":0.292098508945551,"score_spread":0.2674224908936918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143085379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2937954,0.0012663878,0.6946444,0.0022841776,0.00006732935,0.0003125689,0.0022064564,0.0029668482,0.0024563735],"genre_scores_gemma":[0.6505179,0.00036962118,0.3441286,0.00026517326,0.000121965684,0.00019439255,0.0033132755,0.00016730043,0.00092171144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99443346,0.0016167366,0.0006598864,0.001640765,0.0012559395,0.00039310424],"domain_scores_gemma":[0.98008186,0.013630933,0.0014847004,0.0031748454,0.0011356863,0.00049193087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035784794,0.0010994965,0.00293924,0.002170656,0.0016702422,0.0036922127,0.0028138517,0.001988517,0.0021011683],"category_scores_gemma":[0.02414353,0.00083337,0.001681373,0.0049382057,0.0012768406,0.009017266,0.0025671232,0.0017700983,0.00072514254],"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":[0.0017880008,0.0008173622,0.03278739,0.0014236538,0.0006765258,0.00084997894,0.002416708,0.36265293,0.02096277,0.0730093,0.024976814,0.4776386],"study_design_scores_gemma":[0.00007830827,0.00013116506,0.0019955244,0.000023586474,0.000063234605,0.0006010546,0.0006501779,0.86371416,0.0040503475,0.12614238,0.0025160885,0.00003397521],"about_ca_topic_score_codex":0.0028087718,"about_ca_topic_score_gemma":0.0033996613,"teacher_disagreement_score":0.0036922127,"about_ca_system_score_codex":0.0012584913,"about_ca_system_score_gemma":0.0013877274,"threshold_uncertainty_score":0.01892507},"labels":[],"label_agreement":null},{"id":"W2143682328","doi":"10.1007/s00778-004-0128-2","title":"Supporting top-k join queries in relational databases","year":2004,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":387,"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":"Joins; Computer science; Join (topology); Sort-merge join; Query optimization; Ranking (information retrieval); Hash join; Database; Relational database; Heuristic; Query language; Sargable; Information retrieval; Rank (graph theory); Heuristics; Relational algebra; Query expansion; Theoretical computer science; Web search query; Search engine; Programming language; Mathematics; Artificial intelligence","score_opus":0.04003804845750331,"score_gpt":0.2958259358899701,"score_spread":0.2557878874324668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143682328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23191631,0.0037001858,0.72542375,0.00212552,0.00042744496,0.00023260979,0.0025119295,0.021904675,0.01175753],"genre_scores_gemma":[0.7734467,0.0010220394,0.21719071,0.00034510173,0.00024879028,0.0000756262,0.002636575,0.0010555355,0.0039789495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9910232,0.0015058549,0.001461646,0.0013549813,0.0034739445,0.0011803731],"domain_scores_gemma":[0.9746523,0.011983865,0.0012878597,0.008777367,0.0023721366,0.0009265894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005228457,0.001087648,0.0026009292,0.0014441856,0.0024318404,0.008435749,0.0050384793,0.0023221492,0.005461701],"category_scores_gemma":[0.024543,0.0014960065,0.0012096213,0.0034806414,0.002048505,0.017407231,0.0064024376,0.0023789587,0.0036280267],"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":[0.009171318,0.0012449906,0.033378214,0.0027905835,0.0007308449,0.0022295604,0.0043582246,0.083032586,0.079179205,0.10421686,0.057633623,0.622034],"study_design_scores_gemma":[0.0005810113,0.0006774622,0.0030556954,0.00018851865,0.0005458107,0.0022114315,0.0019335044,0.6167038,0.068529695,0.27914062,0.026170032,0.0002623973],"about_ca_topic_score_codex":0.003360765,"about_ca_topic_score_gemma":0.004614528,"teacher_disagreement_score":0.008435749,"about_ca_system_score_codex":0.0009942695,"about_ca_system_score_gemma":0.0021836564,"threshold_uncertainty_score":0.027651072},"labels":[],"label_agreement":null},{"id":"W2144152591","doi":"10.4028/www.scientific.net/amm.94-96.1957","title":"An Optimal Identifier-Selection Method Based on Recognition Probability of Expressway Network","year":2011,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","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":"Ministry of Transportation of Ontario","funders":"","keywords":"Identifier; Selection (genetic algorithm); Computer science; Probability model; Data mining; Artificial intelligence; Computer network; Mathematics; Statistics","score_opus":0.04030112681551362,"score_gpt":0.24913007899565576,"score_spread":0.20882895218014214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144152591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071662595,0.00011336489,0.99157023,0.00006272413,0.000032305295,0.000022160437,0.000019797235,0.00024575504,0.000767474],"genre_scores_gemma":[0.5097526,0.0005407217,0.48240808,0.00013021442,0.00022339323,0.00031627787,0.00033008485,0.0002133899,0.006085287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707735,0.0008362644,0.00015932973,0.00079320394,0.0008764348,0.0002574724],"domain_scores_gemma":[0.99658537,0.0014094915,0.00025960623,0.0003613107,0.0012003018,0.00018393029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001962063,0.00081214256,0.0014970532,0.0014009839,0.0008484954,0.0016428587,0.0017956225,0.0009447312,0.002964297],"category_scores_gemma":[0.0067693824,0.00049963885,0.0008616676,0.0012726791,0.0009496979,0.003276894,0.0012108375,0.0011380668,0.0007778665],"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":[0.00048092933,0.00016265409,0.003864647,0.00026891474,0.00013068179,0.00022876059,0.0003402586,0.3683181,0.015377277,0.0833535,0.008038006,0.51943624],"study_design_scores_gemma":[0.000022988675,0.000048128768,0.00048632492,0.000007816313,0.0000251873,0.0001127247,0.000023507459,0.9847189,0.0034513138,0.009665617,0.0014043219,0.00003300588],"about_ca_topic_score_codex":0.003141241,"about_ca_topic_score_gemma":0.0015756343,"teacher_disagreement_score":0.003141241,"about_ca_system_score_codex":0.0010543801,"about_ca_system_score_gemma":0.0018910635,"threshold_uncertainty_score":0.010376513},"labels":[],"label_agreement":null},{"id":"W2144586885","doi":"","title":"On dominating your neighborhood profitably","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Scalability; Data mining; Object (grammar); Index (typography); Spatial database; Query optimization; Quality (philosophy); Information retrieval; Spatial query; Database; Sargable; Web search query; Spatial analysis; Search engine; Artificial intelligence; Mathematics","score_opus":0.016671204942364577,"score_gpt":0.26853565743541613,"score_spread":0.25186445249305156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144586885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2854915,0.005043211,0.63814396,0.0080736745,0.00043545588,0.00040011865,0.0023406453,0.0011662743,0.058905173],"genre_scores_gemma":[0.8337763,0.002105153,0.1365522,0.0011370027,0.0004545806,0.00030053136,0.0018967747,0.00048986176,0.023287568],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758637,0.0007901335,0.000115646006,0.0006320953,0.0005232349,0.00035255932],"domain_scores_gemma":[0.9915064,0.005003187,0.00057483034,0.0012467945,0.0010404615,0.00062841334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024615864,0.00082020985,0.0024296434,0.0014175193,0.0019614338,0.0028894972,0.0022792697,0.0020456715,0.011665566],"category_scores_gemma":[0.018779404,0.000544672,0.0007858954,0.002457391,0.0017959775,0.010375013,0.0039523556,0.0013340495,0.0022902067],"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":[0.0016070426,0.0003204116,0.0053121652,0.00059839844,0.00016162508,0.0006857353,0.001203554,0.14794463,0.0069094063,0.6007229,0.0642734,0.17026073],"study_design_scores_gemma":[0.00015709587,0.0002805899,0.0007317861,0.00008789367,0.000075399716,0.00081102934,0.0007494121,0.49296883,0.004461162,0.46971858,0.029910928,0.000047345806],"about_ca_topic_score_codex":0.0022937395,"about_ca_topic_score_gemma":0.0027948564,"teacher_disagreement_score":0.011665566,"about_ca_system_score_codex":0.0012637922,"about_ca_system_score_gemma":0.0008859209,"threshold_uncertainty_score":0.039025187},"labels":[],"label_agreement":null},{"id":"W2144687917","doi":"10.1145/2063576.2063628","title":"Factorization-based lossless compression of inverted indices","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Inverted index; Lossless compression; Term (time); Computer science; Compression (physics); Algorithm; Factorization; Matrix (chemical analysis); Matrix decomposition; Greedy algorithm; Sparse matrix; Space (punctuation); Data compression; Search engine indexing; Artificial intelligence","score_opus":0.050618940650993836,"score_gpt":0.22700281014987816,"score_spread":0.17638386949888432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144687917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061549716,0.0011708818,0.9302252,0.0005428786,0.00015212808,0.00030573594,0.0006880673,0.0025863524,0.0027789935],"genre_scores_gemma":[0.32229307,0.0008681016,0.669716,0.00033200247,0.00024804182,0.00043464606,0.0016828757,0.00024062578,0.0041846526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983296,0.00028645922,0.0001458339,0.00017649571,0.0008917717,0.00016995796],"domain_scores_gemma":[0.99576676,0.0014202019,0.0003627896,0.0013599325,0.0010053213,0.00008507079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013811653,0.00093026063,0.0010349251,0.0019317588,0.00075844844,0.0012491234,0.0014906725,0.0008582257,0.001876138],"category_scores_gemma":[0.0089140795,0.00039042832,0.0005971671,0.0030863774,0.0008984665,0.0030226144,0.0014847047,0.0010484613,0.0013263905],"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":[0.0009820586,0.0004135386,0.0025002903,0.00033015953,0.000092527385,0.0004891768,0.0004535109,0.10054587,0.0839555,0.028988082,0.019178104,0.76207125],"study_design_scores_gemma":[0.00013221239,0.00035826437,0.0012957682,0.000052019564,0.00005628289,0.00086019625,0.00020507711,0.86845976,0.08801807,0.03149593,0.008995653,0.000070806134],"about_ca_topic_score_codex":0.003141096,"about_ca_topic_score_gemma":0.00532274,"teacher_disagreement_score":0.003141096,"about_ca_system_score_codex":0.00090509414,"about_ca_system_score_gemma":0.0013499382,"threshold_uncertainty_score":0.0073043704},"labels":[],"label_agreement":null},{"id":"W2145284542","doi":"10.1109/time.1998.674148","title":"Generalization for calendar attributes using domain generalization graphs","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Generalization; Pruning; Computer science; Reachability; Graph; Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.054578747798892525,"score_gpt":0.2607013558819321,"score_spread":0.2061226080830396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145284542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022432858,0.00019599748,0.9718917,0.00023336397,0.000022637088,0.00013164975,0.0005849691,0.0023790158,0.0021278635],"genre_scores_gemma":[0.14020564,0.00045495076,0.8536022,0.00018122184,0.000031561805,0.00014002282,0.0029119127,0.00043524292,0.002037325],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981522,0.00042907204,0.00020348826,0.0004859557,0.00060299,0.00012623263],"domain_scores_gemma":[0.99618393,0.0016436413,0.00028167834,0.0014498321,0.00034180866,0.00009911896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020846524,0.00073491974,0.000859348,0.0023640797,0.0009159266,0.0014935227,0.0013649903,0.0005969074,0.0021734857],"category_scores_gemma":[0.0059125344,0.00050320965,0.0023591982,0.002989436,0.0010431226,0.0055849603,0.0022975153,0.0016168054,0.0005358099],"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":[0.00025676758,0.00018883425,0.0070722424,0.00044938235,0.00018901804,0.00063907006,0.0017136818,0.17262794,0.011634235,0.17985322,0.011635923,0.61373967],"study_design_scores_gemma":[0.000043608783,0.00009682545,0.0019272227,0.00019193081,0.00017665113,0.000657427,0.00068255566,0.6134145,0.021244483,0.28602287,0.07543835,0.00010355451],"about_ca_topic_score_codex":0.010558346,"about_ca_topic_score_gemma":0.013369325,"teacher_disagreement_score":0.010558346,"about_ca_system_score_codex":0.0010858482,"about_ca_system_score_gemma":0.0013745774,"threshold_uncertainty_score":0.02099377},"labels":[],"label_agreement":null},{"id":"W2145352743","doi":"10.1002/jcc.20484","title":"An algorithm for three‐dimensional Voronoi S‐network","year":2006,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alexander von Humboldt-Stiftung","keywords":"Voronoi diagram; Delaunay triangulation; SPHERES; Algorithm; Realization (probability); Centroidal Voronoi tessellation; Computer science; Bowyer–Watson algorithm; Computational geometry; Mathematics; Geometry; Physics","score_opus":0.008499045044182228,"score_gpt":0.24048199801596423,"score_spread":0.231982952971782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145352743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011424073,0.00007505781,0.9961391,0.00007473682,0.00003190167,0.000084344145,0.00011181897,0.00096442574,0.0013761289],"genre_scores_gemma":[0.027255902,0.00012395323,0.96948224,0.000036992737,0.000024275936,0.00027136612,0.00052241207,0.00025874213,0.002024175],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99862456,0.00025402606,0.000114132214,0.00027004338,0.0006009148,0.00013631338],"domain_scores_gemma":[0.9985983,0.00055657636,0.00006768461,0.00028555075,0.00041808208,0.00007375836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010373662,0.0009400151,0.0010408602,0.0022981928,0.0014701252,0.0023940657,0.002883641,0.0012593481,0.009642623],"category_scores_gemma":[0.0042866855,0.0006126331,0.0012885794,0.0021657094,0.0010627923,0.0028585177,0.0034411391,0.0014409004,0.0031124507],"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":[0.00017325106,0.0000873577,0.0009810962,0.00023554795,0.000084221814,0.00015944494,0.0002694828,0.23688717,0.00382892,0.24384533,0.0134786265,0.4999696],"study_design_scores_gemma":[0.00005586312,0.00003571845,0.00011425677,0.00003009072,0.0000150511505,0.00015210651,0.000055826007,0.8523157,0.0028734724,0.11734911,0.026972173,0.00003071193],"about_ca_topic_score_codex":0.007255905,"about_ca_topic_score_gemma":0.006604469,"teacher_disagreement_score":0.009642623,"about_ca_system_score_codex":0.0018844698,"about_ca_system_score_gemma":0.0024526266,"threshold_uncertainty_score":0.032257795},"labels":[],"label_agreement":null},{"id":"W2145814045","doi":"10.1145/1097064.1097092","title":"Two ellipse-based pruning methods for group nearest neighbor queries","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ellipse; Pruning; Tree traversal; Computer science; k-nearest neighbors algorithm; Query optimization; Rectangle; Minimum bounding box; Bounding overwatch; Point (geometry); Nearest neighbor search; k-d tree; Data mining; Algorithm; Mathematics; Artificial intelligence; Image (mathematics)","score_opus":0.030422471733267113,"score_gpt":0.35255334716373604,"score_spread":0.3221308754304689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145814045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016288703,0.0018485581,0.97893846,0.0002939055,0.00006208438,0.00016899605,0.000100136014,0.0006478565,0.0016512315],"genre_scores_gemma":[0.13069451,0.0012141226,0.864453,0.0001708611,0.00013664288,0.0003030018,0.00044290407,0.00013488732,0.0024499942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99648774,0.00086435763,0.0003071559,0.00039377433,0.0017641447,0.00018284164],"domain_scores_gemma":[0.9952838,0.0021264774,0.00058836374,0.000927383,0.0009324353,0.00014149905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017957516,0.0008682085,0.0013450179,0.0024727338,0.0009199822,0.0012445392,0.0019986122,0.0012782923,0.0014567169],"category_scores_gemma":[0.010688473,0.00048216354,0.0007764354,0.002654714,0.0006835737,0.0028258949,0.0018449603,0.0010787874,0.0007220292],"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":[0.00050791044,0.00029338358,0.002565723,0.00030441355,0.0001100837,0.00030681075,0.0007958604,0.06273564,0.018718602,0.030123925,0.011871184,0.8716665],"study_design_scores_gemma":[0.00027130145,0.00039225066,0.0029767621,0.000067118846,0.00011585402,0.0021501305,0.000354481,0.9030521,0.027179495,0.018292245,0.0450001,0.00014822377],"about_ca_topic_score_codex":0.0032460608,"about_ca_topic_score_gemma":0.0057579395,"teacher_disagreement_score":0.0032460608,"about_ca_system_score_codex":0.00065227266,"about_ca_system_score_gemma":0.0009533096,"threshold_uncertainty_score":0.009496987},"labels":[],"label_agreement":null},{"id":"W2145957360","doi":"10.1007/s00778-011-0230-1","title":"Maximizing bichromatic reverse nearest neighbor for L p -norm in two- and three-dimensional spaces","year":2011,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"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":"Curse of dimensionality; Computer science; k-nearest neighbors algorithm; Norm (philosophy); Fixed-radius near neighbors; Best bin first; Space (punctuation); Algorithm; Nearest neighbor search; Theoretical computer science; Combinatorics; Mathematics; Data mining; Artificial intelligence; Cluster analysis","score_opus":0.046977370759670206,"score_gpt":0.2579352204925931,"score_spread":0.2109578497329229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145957360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024518851,0.00034081185,0.9735702,0.00016787805,0.000032509037,0.00004076932,0.00009572914,0.00021234326,0.0010208796],"genre_scores_gemma":[0.28076366,0.00038940294,0.7153053,0.00015338295,0.00008752726,0.0001750451,0.0005488069,0.00021525925,0.0023615195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973219,0.0011756208,0.00014274915,0.0004019975,0.0008214145,0.00013628507],"domain_scores_gemma":[0.995,0.002784934,0.0003327577,0.00065398245,0.0010332512,0.00019519814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035908802,0.0009867304,0.0017824688,0.0012548012,0.0008232119,0.0016772497,0.0025091723,0.0016674481,0.001827541],"category_scores_gemma":[0.015311781,0.0005442859,0.0007234583,0.0015086605,0.0012679114,0.0027388195,0.002670983,0.0011762651,0.0006806678],"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":[0.0008845334,0.0002836921,0.0027408453,0.00056415814,0.00013896724,0.00020954212,0.00035389335,0.57099867,0.012304662,0.07155029,0.00767636,0.33229437],"study_design_scores_gemma":[0.000012575376,0.000049397597,0.00020654792,0.000010271958,0.000008167142,0.0000649469,0.00003339589,0.9796363,0.0015352311,0.01794044,0.0004899972,0.000012787584],"about_ca_topic_score_codex":0.0039375667,"about_ca_topic_score_gemma":0.004985914,"teacher_disagreement_score":0.0039375667,"about_ca_system_score_codex":0.0009535821,"about_ca_system_score_gemma":0.0011842219,"threshold_uncertainty_score":0.018990636},"labels":[],"label_agreement":null},{"id":"W2146327344","doi":"10.1007/s10844-010-0141-4","title":"Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods","year":2010,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Uncertain data; Pruning; Probabilistic logic; Bounding overwatch; Data mining; Benchmark (surveying); Object (grammar); Reachability; Algorithm; Artificial intelligence","score_opus":0.08668100124270832,"score_gpt":0.3626044773832188,"score_spread":0.2759234761405105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146327344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052335076,0.0005915838,0.99309045,0.00021645252,0.00002337852,0.00003445336,0.00010927973,0.00025306924,0.00044783927],"genre_scores_gemma":[0.19822422,0.0014512544,0.7958088,0.00015761369,0.00027297015,0.0002214101,0.001118502,0.0003883088,0.0023569677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966546,0.0015460199,0.00017879198,0.0004789846,0.0009388869,0.00020264114],"domain_scores_gemma":[0.97361606,0.020309169,0.0012414419,0.002446726,0.0019392112,0.00044740096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008488044,0.0014972413,0.0036034645,0.003402336,0.0012534722,0.0026567224,0.005596582,0.0030540442,0.002998798],"category_scores_gemma":[0.039223436,0.0021873314,0.0022435328,0.005066263,0.0021572104,0.0084320605,0.0039970498,0.004007502,0.00057517283],"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":[0.00030739087,0.00008130062,0.0015364509,0.00028204112,0.00008413462,0.000086763044,0.00025013209,0.8417814,0.0008055669,0.06498857,0.004721238,0.08507497],"study_design_scores_gemma":[0.00000991277,0.0000097318825,0.00008600319,0.000014853692,0.000007810225,0.000015184983,0.0000077645,0.98523456,0.00015187958,0.014056195,0.00040037313,0.0000058539085],"about_ca_topic_score_codex":0.011831308,"about_ca_topic_score_gemma":0.010662752,"teacher_disagreement_score":0.011831308,"about_ca_system_score_codex":0.0016653966,"about_ca_system_score_gemma":0.002110472,"threshold_uncertainty_score":0.04488963},"labels":[],"label_agreement":null},{"id":"W2146776490","doi":"","title":"RankSQL: supporting ranking queries in relational database management systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Relational database management system; Relational database; Database; SQL; Conjunctive query; Spatial query; Web search query; World Wide Web; Web query classification; Search engine","score_opus":0.021944729448840936,"score_gpt":0.2572904370307163,"score_spread":0.23534570758187537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146776490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013092398,0.0014654577,0.7621414,0.0017175192,0.00026554323,0.00061537436,0.009318277,0.19805026,0.013333669],"genre_scores_gemma":[0.30665562,0.0020898108,0.60274994,0.0019206046,0.0007094618,0.0011755265,0.044162273,0.01939437,0.021142514],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98967487,0.0028237754,0.0012121222,0.0008413522,0.004645604,0.00080230145],"domain_scores_gemma":[0.9909116,0.0036370012,0.0005708044,0.0024313985,0.001988161,0.00046100494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008184495,0.0014299436,0.0011605474,0.0018849309,0.0011157336,0.005456174,0.004538927,0.001730241,0.015165672],"category_scores_gemma":[0.021836936,0.0012732411,0.0010966338,0.00322012,0.0013095614,0.009316956,0.0040748008,0.0021829884,0.009436063],"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":[0.0019163626,0.00051871804,0.005205112,0.0023785306,0.000256128,0.0009444567,0.0015801816,0.034412116,0.019227318,0.11165574,0.41226086,0.40964454],"study_design_scores_gemma":[0.00071541965,0.00043091128,0.0016847545,0.00030528865,0.00016449482,0.00095181697,0.0006222212,0.38305083,0.042526662,0.15760304,0.41160634,0.00033825808],"about_ca_topic_score_codex":0.006044983,"about_ca_topic_score_gemma":0.0040639015,"teacher_disagreement_score":0.015165672,"about_ca_system_score_codex":0.0011395762,"about_ca_system_score_gemma":0.0025544954,"threshold_uncertainty_score":0.050734162},"labels":[],"label_agreement":null},{"id":"W2147033904","doi":"10.14778/1687627.1687702","title":"Power-law based estimation of set similarity join size","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hash function; Exploit; Computer science; Set (abstract data type); Similarity (geometry); Algorithm; Data mining; Signature (topology); Representation (politics); Nearest neighbor search; Mathematics; Theoretical computer science; Pattern recognition (psychology); Artificial intelligence; Law","score_opus":0.013244692484878601,"score_gpt":0.23772138781357566,"score_spread":0.22447669532869705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147033904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042851247,0.00020362556,0.95501137,0.00009615765,0.000028975757,0.000076802906,0.00011132921,0.00076766073,0.0008528099],"genre_scores_gemma":[0.51651496,0.00022743143,0.4816266,0.00007090865,0.00014983742,0.00022804532,0.00040104505,0.00016607049,0.00061513484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939237,0.0010473501,0.0003471848,0.0008231912,0.003647274,0.00021119727],"domain_scores_gemma":[0.9658494,0.022524199,0.0036521247,0.004069834,0.0033381023,0.00056641106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032359632,0.0006586469,0.0011066635,0.0034832868,0.0005188052,0.001414684,0.0027054443,0.0008905126,0.0013998036],"category_scores_gemma":[0.035606533,0.00056318234,0.0005404153,0.0022022862,0.0009131025,0.004504072,0.0017678239,0.001293785,0.00083362736],"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":[0.00090078486,0.0005571353,0.03837159,0.00039175298,0.00020774003,0.00043574977,0.0006937365,0.22919632,0.09769297,0.03956952,0.0033168183,0.58866584],"study_design_scores_gemma":[0.00001718581,0.000098927885,0.0018269913,0.0000111318395,0.000010933701,0.00028524225,0.000040342908,0.9718726,0.016384285,0.00865763,0.0007648937,0.000029751109],"about_ca_topic_score_codex":0.0007166724,"about_ca_topic_score_gemma":0.0007312026,"teacher_disagreement_score":0.0034832868,"about_ca_system_score_codex":0.0007429418,"about_ca_system_score_gemma":0.00071698497,"threshold_uncertainty_score":0.017113626},"labels":[],"label_agreement":null},{"id":"W2147312192","doi":"10.1109/tkde.2006.73","title":"Approximate processing of massive continuous quantile queries over high-speed data streams","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Academy of Space Technology; University of New South Wales","keywords":"Computer science; Data stream mining; STREAMS; Quantile; Data mining; Parallel computing; Computer network; Statistics; Mathematics","score_opus":0.020542173346513697,"score_gpt":0.2531488894735193,"score_spread":0.23260671612700562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147312192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08510664,0.00094511587,0.9092313,0.00033953087,0.00007208404,0.000095753836,0.00046643597,0.0029047334,0.000838409],"genre_scores_gemma":[0.71967405,0.0005522156,0.27661002,0.00014349981,0.00015547492,0.000119813885,0.0014230593,0.00017252886,0.0011493663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797446,0.00032642216,0.00019877242,0.00043597017,0.0008762752,0.00018802963],"domain_scores_gemma":[0.9964187,0.0013386576,0.00045603333,0.00092909776,0.0006894848,0.00016805305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016846556,0.0009746532,0.0016027984,0.0009700097,0.0008390953,0.0016813211,0.0016790639,0.0007162113,0.0010226064],"category_scores_gemma":[0.0072101117,0.00044053802,0.00061550416,0.0029087258,0.000727087,0.0025999385,0.0015322932,0.0009986655,0.00046071477],"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":[0.0023556752,0.00032840206,0.013750646,0.00049116486,0.0003088349,0.0008163706,0.0010284433,0.46941787,0.07152443,0.01625702,0.009933361,0.41378778],"study_design_scores_gemma":[0.00002765065,0.000119935656,0.0015817381,0.0000066765924,0.000025055173,0.00016000307,0.00017768345,0.976901,0.010405037,0.008754604,0.0018258517,0.000014702316],"about_ca_topic_score_codex":0.003641268,"about_ca_topic_score_gemma":0.0032051238,"teacher_disagreement_score":0.003641268,"about_ca_system_score_codex":0.00085206755,"about_ca_system_score_gemma":0.0008900011,"threshold_uncertainty_score":0.008909404},"labels":[],"label_agreement":null},{"id":"W2147851030","doi":"10.1109/time.2003.1214895","title":"Spatio-temporal data mining with expected distribution domain generalization graphs","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Generalization; Computer science; Domain (mathematical analysis); Data mining; Distribution (mathematics); Temporal database; Artificial intelligence; Information retrieval; Data science; Mathematics","score_opus":0.025006692456521137,"score_gpt":0.24285011470838905,"score_spread":0.21784342225186792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147851030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015260826,0.00022989896,0.97944975,0.00026013155,0.000018545385,0.00013186484,0.0017155065,0.0022467063,0.0006867673],"genre_scores_gemma":[0.2002805,0.00026386583,0.79193974,0.0001793433,0.00005791324,0.00028071713,0.006200467,0.00017067845,0.000626829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803656,0.00055684795,0.00018076377,0.00056728465,0.0005755327,0.00008316803],"domain_scores_gemma":[0.9938211,0.0035875696,0.0007074851,0.0011687852,0.00055565796,0.00015946472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023017183,0.00072532985,0.00071212393,0.0034223527,0.0007092438,0.0012110134,0.0014629149,0.0006138673,0.0009851289],"category_scores_gemma":[0.010115475,0.0004186121,0.0014310429,0.0038565302,0.0005259569,0.0031754118,0.0013781296,0.001305348,0.00037401414],"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":[0.00043769923,0.00044942446,0.037182014,0.0007104106,0.00060273596,0.0013638197,0.0013356757,0.26019657,0.010585601,0.07367813,0.022376215,0.59108174],"study_design_scores_gemma":[0.000043361237,0.00006473956,0.0036615056,0.000043383825,0.0000838298,0.00065547705,0.00022116203,0.87366045,0.005100187,0.10136256,0.015057623,0.00004581196],"about_ca_topic_score_codex":0.0040029674,"about_ca_topic_score_gemma":0.008299169,"teacher_disagreement_score":0.0040029674,"about_ca_system_score_codex":0.0007159543,"about_ca_system_score_gemma":0.0010083305,"threshold_uncertainty_score":0.012172818},"labels":[],"label_agreement":null},{"id":"W2149065333","doi":"10.1109/icsmc.1994.400012","title":"Graphical forms for preference representation: considerations for practical application","year":2002,"lang":"en","type":"article","venue":"","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":"Wilfrid Laurier University; Carleton University; University of Waterloo","funders":"","keywords":"Preference; Computer science; Schema (genetic algorithms); Representation (politics); Flexibility (engineering); Process (computing); Preference elicitation; Artificial intelligence; Theoretical computer science; Machine learning; Information retrieval; Data mining; Human–computer interaction; Programming language; Mathematics","score_opus":0.16069616102711543,"score_gpt":0.3428448717452218,"score_spread":0.1821487107181064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149065333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012341483,0.0018244964,0.9703446,0.0065740813,0.00029546244,0.0005101819,0.0003770192,0.0015012156,0.017338766],"genre_scores_gemma":[0.025433905,0.0023607477,0.96362984,0.0009113427,0.0003744538,0.0016220214,0.00048134592,0.00048632553,0.0047000484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96671396,0.026211178,0.0018037173,0.0011367428,0.003691377,0.00044294872],"domain_scores_gemma":[0.8925391,0.08004539,0.0020975056,0.01304458,0.011256601,0.0010168104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031379793,0.0024885035,0.0023177147,0.004074967,0.001870773,0.017629914,0.005455641,0.006100895,0.041378535],"category_scores_gemma":[0.13315248,0.0018445932,0.0025663006,0.008354455,0.0057366537,0.015952278,0.0041378154,0.006001516,0.015183322],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016290731,0.00008430766,0.0004218283,0.0006015201,0.000044053882,0.00019989652,0.0008403222,0.008168427,0.0006615173,0.86506236,0.018099003,0.10565385],"study_design_scores_gemma":[0.00016558237,0.00013090452,0.00033431145,0.0011832538,0.00005956743,0.0007169635,0.0009443155,0.058838025,0.0011170731,0.8190547,0.117317945,0.00013733101],"about_ca_topic_score_codex":0.0032522427,"about_ca_topic_score_gemma":0.0031996167,"teacher_disagreement_score":0.041378535,"about_ca_system_score_codex":0.0023912871,"about_ca_system_score_gemma":0.0029488367,"threshold_uncertainty_score":0.16595417},"labels":[],"label_agreement":null},{"id":"W2151322085","doi":"10.1109/iri.2008.4583036","title":"Fuzzy querying of nested XML","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; XML database; XML; Scope (computer science); Database; XML validation; Information retrieval; Efficient XML Interchange; Fuzzy logic; XML Schema Editor; Database query; Data mining; World Wide Web; Programming language; Artificial intelligence","score_opus":0.02914071064020381,"score_gpt":0.22418479782118206,"score_spread":0.19504408718097824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151322085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030925894,0.0002206682,0.96226734,0.0001515043,0.000032098047,0.00013165771,0.00009131509,0.0031001784,0.0030793473],"genre_scores_gemma":[0.33158866,0.00021830187,0.66421175,0.00020502928,0.00002749925,0.00012624974,0.0004550464,0.0003228184,0.0028445877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995869,0.0009369362,0.00049424276,0.0006058185,0.001905572,0.00018849022],"domain_scores_gemma":[0.9964055,0.0015569308,0.00020704696,0.0009425714,0.00071874785,0.00016928796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037530686,0.00033223414,0.0007053179,0.0006886009,0.00093005114,0.0026623544,0.0020193656,0.00093941076,0.0022628296],"category_scores_gemma":[0.009281412,0.00041408316,0.00078425894,0.0005636869,0.0012188056,0.0041631274,0.0026562698,0.0010572321,0.000581905],"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":[0.0010479013,0.0002820473,0.006832384,0.00074419955,0.00016552291,0.0015720634,0.0074222023,0.034241237,0.11680234,0.43266916,0.005410283,0.39281067],"study_design_scores_gemma":[0.00017416607,0.00037814246,0.0020691373,0.00033308257,0.00015015055,0.0022741298,0.0008702043,0.56740075,0.13515,0.17434375,0.11666041,0.00019613846],"about_ca_topic_score_codex":0.0021387914,"about_ca_topic_score_gemma":0.0017854576,"teacher_disagreement_score":0.0037530686,"about_ca_system_score_codex":0.0007246393,"about_ca_system_score_gemma":0.0009096933,"threshold_uncertainty_score":0.019848347},"labels":[],"label_agreement":null},{"id":"W2151427378","doi":"","title":"Learning a Metric Space for Neighbourhood Topology Estimation: Application to Manifold Learning","year":2013,"lang":"en","type":"article","venue":"Asian Conference on Machine Learning","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; McGill University","funders":"","keywords":"Nonlinear dimensionality reduction; Manifold alignment; Topology (electrical circuits); Manifold (fluid mechanics); Euclidean space; Cluster analysis; Metric (unit); Neighbourhood (mathematics); Density estimation; Computer science; Graph; Mathematics; Euclidean distance; Algorithm; Theoretical computer science; Artificial intelligence; Dimensionality reduction; Mathematical analysis; Combinatorics; Statistics","score_opus":0.013947475986548628,"score_gpt":0.2649391483983309,"score_spread":0.2509916724117823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151427378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021838339,0.00016317489,0.9971317,0.00009536607,0.00001101387,0.000022776761,0.000028458493,0.00017593378,0.00018773759],"genre_scores_gemma":[0.14407477,0.00060525467,0.8538347,0.000060516257,0.000098842334,0.00017024405,0.00030961403,0.00016192568,0.0006841775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785197,0.0010042678,0.00012258743,0.000407314,0.0005601679,0.000053725133],"domain_scores_gemma":[0.9942357,0.0026735445,0.0006066148,0.001129358,0.0011347983,0.00021998081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003063625,0.0010849392,0.001498137,0.0026664485,0.0009932521,0.0015894973,0.0020450288,0.0017715034,0.0011750063],"category_scores_gemma":[0.018889507,0.0005395382,0.0010509724,0.0029427945,0.0018059158,0.0028158352,0.003565403,0.0020252755,0.00061314774],"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":[0.00008277225,0.00010291765,0.004035517,0.00033909085,0.00017189543,0.00016959493,0.00045443492,0.50538063,0.007095889,0.09220375,0.0044423365,0.38552126],"study_design_scores_gemma":[0.000006078255,0.00003980532,0.00038821352,0.000015586247,0.000008257168,0.00008736612,0.000033883683,0.95301247,0.0011961274,0.043397572,0.001791665,0.00002289774],"about_ca_topic_score_codex":0.0029237042,"about_ca_topic_score_gemma":0.002685273,"teacher_disagreement_score":0.003063625,"about_ca_system_score_codex":0.0009798384,"about_ca_system_score_gemma":0.0010035139,"threshold_uncertainty_score":0.016202152},"labels":[],"label_agreement":null},{"id":"W2151749331","doi":"10.1109/tsmcb.2005.848489","title":"MetricMap: An Embedding Technique for Processing Distance-Based Queries in Metric Spaces","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of General Medical Sciences; National Cancer Institute","keywords":"Embedding; Oracle; Euclidean distance; Metric space; Cluster analysis; Dimension (graph theory); Mathematics; Euclidean space; Tree (set theory); Edit distance; Class (philosophy); Distance measures; Computer science; Space (punctuation); Metric (unit); Data mining; Theoretical computer science; Algorithm; Artificial intelligence; Discrete mathematics; Combinatorics","score_opus":0.02385153451401728,"score_gpt":0.2732672958122047,"score_spread":0.24941576129818743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151749331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025466029,0.00028620704,0.9948827,0.00011374143,0.00005611686,0.000068554415,0.00018199749,0.0014225813,0.00044143267],"genre_scores_gemma":[0.08078514,0.00072237174,0.91529405,0.00016905491,0.00013803967,0.00029756434,0.0011337704,0.0002940889,0.0011659579],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968651,0.0007414598,0.00025536466,0.00037787735,0.0016354547,0.00012474794],"domain_scores_gemma":[0.9960949,0.0013552766,0.00031113194,0.0012706161,0.00082302996,0.0001451168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019486507,0.0013941819,0.0017418896,0.0029411176,0.00078343466,0.0018521951,0.0023121885,0.0015620423,0.0026003295],"category_scores_gemma":[0.010960575,0.00067640113,0.0011594285,0.004756554,0.0010299605,0.0076123616,0.0037238928,0.0022477712,0.001997724],"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":[0.00034090542,0.00019409374,0.0015693736,0.000574726,0.00017090137,0.00033033054,0.0006343813,0.043723807,0.01676821,0.04925516,0.019065669,0.8673724],"study_design_scores_gemma":[0.000064921805,0.00047421697,0.0011428995,0.000081845465,0.000058291567,0.0020178955,0.00040429135,0.7883068,0.031640913,0.11774258,0.05788877,0.00017670546],"about_ca_topic_score_codex":0.001382226,"about_ca_topic_score_gemma":0.0012085134,"teacher_disagreement_score":0.0029411176,"about_ca_system_score_codex":0.00060594577,"about_ca_system_score_gemma":0.0008154736,"threshold_uncertainty_score":0.0103055835},"labels":[],"label_agreement":null},{"id":"W2152021196","doi":"10.1109/34.895981","title":"Validity measures for the fuzzy cluster analysis of orientations","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rocscience (Canada)","funders":"","keywords":"Fuzzy set; Cluster analysis; Fuzzy logic; Artificial intelligence; Computer science; Fuzzy clustering; Data mining; Identification (biology); Discontinuity (linguistics); Pattern recognition (psychology); Mathematics","score_opus":0.03971663047023295,"score_gpt":0.29447997799330217,"score_spread":0.25476334752306923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152021196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03032413,0.001254964,0.9574007,0.00048105576,0.00017752615,0.0004490585,0.0008531663,0.00026289662,0.00879647],"genre_scores_gemma":[0.45825064,0.0007095046,0.5342265,0.00026034264,0.00054525654,0.0016616173,0.002098805,0.00040665796,0.001840751],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96848744,0.010226495,0.0034707398,0.0032627832,0.013329369,0.001223234],"domain_scores_gemma":[0.86277145,0.09097202,0.0076441094,0.012270993,0.024545895,0.0017955096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036970846,0.0013913818,0.0019431406,0.017201874,0.0034408711,0.0054590106,0.003539243,0.0020593402,0.0034444048],"category_scores_gemma":[0.17138954,0.00074788544,0.0032998808,0.008292104,0.008450712,0.007661298,0.006613804,0.0034758237,0.0008624512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077054347,0.00021661104,0.024439117,0.00092515815,0.00075630745,0.00019978451,0.0015017652,0.043316755,0.0041613183,0.72990704,0.004584189,0.18922147],"study_design_scores_gemma":[0.00009082349,0.0002797975,0.013607336,0.0004731308,0.00021371518,0.00030215856,0.00069960556,0.23779224,0.006944082,0.7326325,0.006724641,0.00023993196],"about_ca_topic_score_codex":0.0035012935,"about_ca_topic_score_gemma":0.0014448377,"teacher_disagreement_score":0.036970846,"about_ca_system_score_codex":0.003995205,"about_ca_system_score_gemma":0.003337529,"threshold_uncertainty_score":0.19552284},"labels":[],"label_agreement":null},{"id":"W2152170845","doi":"10.1109/icde.2004.1320023","title":"Modeling uncertainties in publish/subscribe systems","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Publication; Dissemination; Computer science; Set (abstract data type); Matching (statistics); Information Dissemination; World Wide Web; Process (computing); Data science; Information retrieval; Telecommunications","score_opus":0.027176091580084896,"score_gpt":0.22911141836129092,"score_spread":0.20193532678120601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152170845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035606675,0.001090696,0.95707923,0.00060628675,0.00010056289,0.00009523191,0.00021555605,0.00021046997,0.00499529],"genre_scores_gemma":[0.9406163,0.0013079857,0.053860676,0.00008391518,0.00016284069,0.00022069903,0.0002283489,0.00003888543,0.0034804344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979996,0.0006546404,0.00015166095,0.00033237503,0.0006151496,0.00024641806],"domain_scores_gemma":[0.9953069,0.0034298105,0.00057685596,0.00018424258,0.00033780726,0.00016431068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002724239,0.0011238283,0.0015060374,0.0011944888,0.0010236558,0.0030940187,0.0017720998,0.0025365248,0.0022074329],"category_scores_gemma":[0.007819576,0.0007574074,0.0010219777,0.0018450253,0.001673753,0.0029901653,0.0019575376,0.0018222063,0.00024850835],"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":[0.000028024739,0.0000130734325,0.00026241696,0.000037132733,0.000020684554,0.000080307655,0.000055701545,0.96025914,0.00021651865,0.03519558,0.00017926077,0.0036522336],"study_design_scores_gemma":[0.000006977821,0.000010027032,0.0000615208,0.000003831159,0.0000075917083,0.000012569555,0.000018045594,0.9704267,0.00011727546,0.028895812,0.00043274762,0.0000069349644],"about_ca_topic_score_codex":0.009141752,"about_ca_topic_score_gemma":0.0035772265,"teacher_disagreement_score":0.009141752,"about_ca_system_score_codex":0.0020841346,"about_ca_system_score_gemma":0.0012734551,"threshold_uncertainty_score":0.018177092},"labels":[],"label_agreement":null},{"id":"W2152729153","doi":"10.1109/ideas.2002.1029674","title":"Clustering spatial data in the presence of obstacles: a density-based approach","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Database Engineering and Applications Symposium","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Constrained clustering; Computer science; CURE data clustering algorithm; Correlation clustering; Constraint (computer-aided design); Data mining; Data stream clustering; Canopy clustering algorithm; Clustering high-dimensional data; Fuzzy clustering; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01807254718542831,"score_gpt":0.23750204376065603,"score_spread":0.21942949657522773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152729153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009222925,0.00043387408,0.98925567,0.00021598673,0.000018106866,0.000092350965,0.00007245531,0.0002014174,0.00048725813],"genre_scores_gemma":[0.2662848,0.0012452658,0.7299296,0.00014085976,0.0001084011,0.000289507,0.0006008739,0.00010242128,0.0012983475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973617,0.0007210601,0.00018387794,0.00045342656,0.001090903,0.00018897808],"domain_scores_gemma":[0.99460477,0.0030439775,0.0005212275,0.00060192926,0.0010228084,0.00020538365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022636575,0.0011046118,0.0022743465,0.005555254,0.0014576967,0.0023556645,0.0038474333,0.0021845296,0.0007225829],"category_scores_gemma":[0.01039234,0.0013612892,0.0016167861,0.0054315627,0.0014847394,0.003350605,0.0032520278,0.0014232532,0.0003676446],"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":[0.0000990932,0.000082498955,0.003306785,0.00018315869,0.00013358402,0.00021990889,0.0003317602,0.87240213,0.0016752194,0.02036018,0.0016765172,0.09952909],"study_design_scores_gemma":[0.000009064782,0.00001936422,0.00040320953,0.000015203586,0.000016027037,0.000098165714,0.00007436342,0.9905877,0.000648601,0.007089433,0.0010190886,0.000019769604],"about_ca_topic_score_codex":0.015603044,"about_ca_topic_score_gemma":0.011984208,"teacher_disagreement_score":0.015603044,"about_ca_system_score_codex":0.0022059313,"about_ca_system_score_gemma":0.0017101233,"threshold_uncertainty_score":0.031024456},"labels":[],"label_agreement":null},{"id":"W2153516438","doi":"10.1109/icde.1998.655809","title":"High dimensional similarity joins: algorithms and performance evaluation","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Joins; Search engine indexing; Computer science; Feature (linguistics); Generalization; Algorithm; Variety (cybernetics); Theoretical computer science; Mathematics; Data mining; Artificial intelligence","score_opus":0.040210212335515305,"score_gpt":0.24321096475871068,"score_spread":0.20300075242319537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153516438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4218591,0.01720204,0.5059983,0.0019689812,0.00085400476,0.0011489041,0.00564294,0.025820773,0.019504827],"genre_scores_gemma":[0.4859989,0.00370333,0.49515933,0.00020553327,0.00038655903,0.0004978981,0.0106788855,0.0009358263,0.0024337461],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98526704,0.0031897647,0.0014435842,0.0016732955,0.007911211,0.0005151683],"domain_scores_gemma":[0.9689892,0.017925136,0.001416232,0.0047175754,0.00613753,0.00081442227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010386672,0.0018752838,0.0028962921,0.005740427,0.0021570974,0.0037928787,0.0041342154,0.003189571,0.003465669],"category_scores_gemma":[0.033443306,0.0005206046,0.0010235943,0.014482473,0.0010640464,0.007501299,0.0025102547,0.0013142087,0.0017129595],"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":[0.0033904305,0.0025380054,0.0115234675,0.0013282651,0.0005616194,0.00020437299,0.00043181135,0.22621572,0.010725903,0.011167138,0.03408306,0.69783014],"study_design_scores_gemma":[0.00034049197,0.0009429758,0.0040329997,0.00003980316,0.000097451324,0.000600712,0.00028470854,0.9579458,0.019108312,0.010114878,0.006406673,0.000085124775],"about_ca_topic_score_codex":0.004339948,"about_ca_topic_score_gemma":0.003021347,"teacher_disagreement_score":0.010386672,"about_ca_system_score_codex":0.0023124032,"about_ca_system_score_gemma":0.0016804081,"threshold_uncertainty_score":0.054930627},"labels":[],"label_agreement":null},{"id":"W2153974857","doi":"10.14778/3402707.3402754","title":"Efficient rank join with aggregation constraints","year":2011,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Join (topology); Computer science; Rank (graph theory); Probabilistic logic; Semantics (computer science); Theoretical computer science; Data mining; Artificial intelligence; Mathematics; Programming language","score_opus":0.01904745898815237,"score_gpt":0.1970963168557973,"score_spread":0.1780488578676449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153974857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011833892,0.00019941317,0.98393285,0.00030173617,0.000039122016,0.00008900212,0.00025777647,0.0016200576,0.0017261156],"genre_scores_gemma":[0.3058473,0.00026434625,0.6888143,0.00031377107,0.00016975305,0.00014220347,0.00094788254,0.00047616073,0.0030242528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9906008,0.0028003452,0.0007139688,0.0011418572,0.0040956032,0.00064747146],"domain_scores_gemma":[0.9857119,0.007363549,0.0009690133,0.0042447387,0.0014202298,0.00029074893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007126879,0.0009387786,0.0014105942,0.0010698283,0.0012705995,0.003643682,0.0021372156,0.0015230044,0.0029140094],"category_scores_gemma":[0.021054652,0.0006025264,0.0009192331,0.0024795413,0.0012349995,0.0053627975,0.003525723,0.0022840742,0.0011940161],"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":[0.00090662297,0.00037514739,0.004607774,0.0005301669,0.00016602957,0.0004337649,0.00072022766,0.275109,0.025691364,0.27213162,0.023639163,0.39568922],"study_design_scores_gemma":[0.00007174754,0.00012618532,0.00042228613,0.000019789517,0.00003602051,0.00036080935,0.00013228056,0.84498894,0.014061573,0.12790069,0.0118231075,0.000056540604],"about_ca_topic_score_codex":0.0020556904,"about_ca_topic_score_gemma":0.004163596,"teacher_disagreement_score":0.007126879,"about_ca_system_score_codex":0.00075603,"about_ca_system_score_gemma":0.0024805735,"threshold_uncertainty_score":0.037690997},"labels":[],"label_agreement":null},{"id":"W2154768509","doi":"10.14778/1453856.1453934","title":"Efficient network aware search in collaborative tagging sites","year":2008,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Popularity; Cluster analysis; Seekers; Context (archaeology); Upper and lower bounds; Heuristic; Space (punctuation); Information retrieval; Data mining; Machine learning; Artificial intelligence; Mathematics; Geography","score_opus":0.018816654655215464,"score_gpt":0.23477065336095104,"score_spread":0.2159539987057356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154768509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34673777,0.0015544937,0.64321226,0.0007131455,0.000045400913,0.00024278507,0.0008934593,0.0018732917,0.004727425],"genre_scores_gemma":[0.7774791,0.00035911234,0.21722426,0.000103844584,0.000065490334,0.00012337025,0.0012889899,0.00018156455,0.0031741671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717194,0.0009333146,0.00021334026,0.00067240925,0.0006092726,0.00039973424],"domain_scores_gemma":[0.989407,0.0063480297,0.001103732,0.001869678,0.0008283364,0.00044322922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033433486,0.00081422663,0.002247597,0.0031864238,0.0017521456,0.002921827,0.0027721478,0.0018065227,0.001515398],"category_scores_gemma":[0.017424155,0.00075781415,0.000850577,0.005506529,0.00119131,0.0054787574,0.0026987444,0.00090648496,0.00095634995],"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":[0.0017021932,0.0006145177,0.029224414,0.0007888292,0.0003021344,0.0005879742,0.0022187154,0.587754,0.023107907,0.045677185,0.011010867,0.2970113],"study_design_scores_gemma":[0.00005298417,0.000107094274,0.0016530032,0.000017741264,0.00005585298,0.00023097666,0.00033383653,0.9607445,0.0040275226,0.031034673,0.0017157829,0.00002599858],"about_ca_topic_score_codex":0.005331591,"about_ca_topic_score_gemma":0.009775597,"teacher_disagreement_score":0.005331591,"about_ca_system_score_codex":0.0015558944,"about_ca_system_score_gemma":0.0016143281,"threshold_uncertainty_score":0.01768148},"labels":[],"label_agreement":null},{"id":"W2154900736","doi":"10.1145/375663.375693","title":"SPARTAN","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Spartan; Computer science; Operating system; Field-programmable gate array","score_opus":0.017262406094311268,"score_gpt":0.22872685475344537,"score_spread":0.2114644486591341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154900736","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01769781,0.0029125358,0.33744457,0.00071847596,0.0011371194,0.00077141775,0.03412099,0.48438525,0.120811835],"genre_scores_gemma":[0.16859247,0.0039019983,0.35250178,0.0023173208,0.0003721066,0.0013239064,0.21858154,0.039073654,0.21333523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881196,0.00009273954,0.00011074326,0.00031675192,0.00054498774,0.0001228597],"domain_scores_gemma":[0.9979736,0.00029646934,0.00010184315,0.0009921198,0.00055319304,0.00008274295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097127527,0.0012912153,0.001042307,0.0017116154,0.00080553,0.0025361762,0.0038099596,0.0009763495,0.0808633],"category_scores_gemma":[0.004001576,0.0007405218,0.00085737684,0.0024248785,0.00050689484,0.0040136618,0.0024218825,0.0014931969,0.05848939],"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":[0.0015430967,0.00021874665,0.0022558938,0.0007431086,0.00017544047,0.00034355358,0.00028751374,0.0066000326,0.012700839,0.024666844,0.42830163,0.5221634],"study_design_scores_gemma":[0.0002544759,0.00029260622,0.0016812565,0.00012585353,0.00010483665,0.0010297666,0.0001561488,0.06527987,0.052816804,0.029001102,0.84911627,0.00014086918],"about_ca_topic_score_codex":0.0025772722,"about_ca_topic_score_gemma":0.0030968515,"teacher_disagreement_score":0.0808633,"about_ca_system_score_codex":0.0008477192,"about_ca_system_score_gemma":0.0017401188,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2155121477","doi":"10.1145/1321440.1321508","title":"Rk-hist","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.009623351704832018,"score_gpt":0.23530943752982364,"score_spread":0.2256860858249916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155121477","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014258973,0.002035508,0.6563934,0.0010526835,0.0014770856,0.00070731033,0.018271722,0.08668727,0.21911599],"genre_scores_gemma":[0.19109076,0.0017008398,0.480805,0.0012208166,0.00042859354,0.00071587856,0.052629676,0.011632333,0.25977612],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991653,0.000110668814,0.000062045605,0.00026032448,0.0002928289,0.00010891222],"domain_scores_gemma":[0.9980903,0.00015753938,0.000065192326,0.0012672979,0.00032522852,0.00009439171],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00065817026,0.00086069235,0.0008350684,0.0011567445,0.0010077313,0.0026591367,0.0030446737,0.0011309007,0.13100573],"category_scores_gemma":[0.002365173,0.00058383524,0.00076400285,0.0015096018,0.0005028644,0.0026735628,0.003045089,0.0010527496,0.12620503],"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":[0.00065215485,0.00019375198,0.0022365688,0.0008779315,0.00011977719,0.0002877021,0.00015224775,0.015068508,0.016573392,0.051907495,0.30657998,0.6053504],"study_design_scores_gemma":[0.00013800208,0.00014128952,0.0019928722,0.0000763955,0.000065153756,0.0012074121,0.00023759475,0.098022126,0.027908131,0.052198544,0.8179224,0.0000901207],"about_ca_topic_score_codex":0.0019071565,"about_ca_topic_score_gemma":0.003819275,"teacher_disagreement_score":0.86899424,"about_ca_system_score_codex":0.00080425985,"about_ca_system_score_gemma":0.0010843797,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2155352045","doi":"10.54419/msztj8","title":"Automated aggregation of geographic objects. A new approach to the conceptual generalisation of geographic databases","year":2003,"lang":"en","type":"book","venue":"Publications on geodesy. New series","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Rijkswaterstaat","keywords":"Computer science; Database; Information retrieval; Data science; Geography","score_opus":0.04685294653383381,"score_gpt":0.2573177431917173,"score_spread":0.21046479665788348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155352045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003348381,0.0015605879,0.9887462,0.0010167479,0.00013443393,0.00015467378,0.0004692514,0.0014566595,0.003113007],"genre_scores_gemma":[0.05799812,0.0012092827,0.93567103,0.00030563414,0.00013219232,0.00019342544,0.001388868,0.00026047183,0.0028409895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927355,0.0021562965,0.0008146374,0.0017728755,0.0022891476,0.00023154652],"domain_scores_gemma":[0.99109787,0.0028061904,0.0009662126,0.003603421,0.0012520544,0.00027431714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007417924,0.000880701,0.0012855582,0.008284253,0.001603609,0.0078124832,0.003105797,0.001599871,0.0022339947],"category_scores_gemma":[0.013469214,0.0011743149,0.0034959887,0.008197903,0.0047297156,0.010443618,0.005884055,0.0030515045,0.0010512342],"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":[0.00008303468,0.00007574619,0.005181684,0.0009821295,0.00028750492,0.00049748644,0.0072987024,0.02407258,0.004743662,0.54337627,0.015241979,0.3981592],"study_design_scores_gemma":[0.000031160453,0.00009232669,0.005062846,0.0005290156,0.00023959248,0.001164942,0.002491117,0.10621664,0.0044331527,0.4888074,0.39080814,0.00012361417],"about_ca_topic_score_codex":0.0101248855,"about_ca_topic_score_gemma":0.009459758,"teacher_disagreement_score":0.0101248855,"about_ca_system_score_codex":0.002472762,"about_ca_system_score_gemma":0.0021461688,"threshold_uncertainty_score":0.039230168},"labels":[],"label_agreement":null},{"id":"W2155858469","doi":"10.5772/13720","title":"Using Markov Models to Mine Temporal and Spatial Data","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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":"ASTER","funders":"École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg; Centre National de la Recherche Scientifique; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche","keywords":"Markov model; Computer science; Markov chain; Data mining; Variable-order Markov model; Software; Markov process; Hidden Markov model; Spatial analysis; Artificial intelligence; Machine learning; Mathematics; Statistics; Programming language","score_opus":0.15771231079294193,"score_gpt":0.28394901365300773,"score_spread":0.1262367028600658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155858469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045889867,0.0001668522,0.9938215,0.00009929222,0.000008436249,0.000039067978,0.00023375892,0.0006188554,0.0004232591],"genre_scores_gemma":[0.15569338,0.0005348122,0.8402676,0.0001364089,0.000034614717,0.00024170282,0.001546185,0.00015103784,0.001394218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99882275,0.0004272302,0.00009090203,0.00028844393,0.00027726556,0.00009344456],"domain_scores_gemma":[0.9936386,0.005316357,0.0003492847,0.00030320548,0.00029751487,0.00009500172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00222985,0.00092878606,0.00080765714,0.0027311407,0.0005835991,0.0016876262,0.0014476231,0.0010651205,0.0019733515],"category_scores_gemma":[0.009702715,0.00073025137,0.0019259361,0.0018242309,0.0007163433,0.0024858569,0.0015035293,0.0013445229,0.00082973985],"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":[0.0002727046,0.000110373185,0.010482923,0.00057178317,0.00035177765,0.0005503696,0.0009878764,0.6250883,0.008763572,0.09535832,0.0039556497,0.2535064],"study_design_scores_gemma":[0.000009364348,0.000025732505,0.00061229535,0.000050608272,0.000027225924,0.00012947788,0.00006366172,0.9414716,0.002354086,0.052792087,0.002437268,0.00002664911],"about_ca_topic_score_codex":0.009232083,"about_ca_topic_score_gemma":0.011575459,"teacher_disagreement_score":0.009232083,"about_ca_system_score_codex":0.0011714391,"about_ca_system_score_gemma":0.0016281705,"threshold_uncertainty_score":0.01835668},"labels":[],"label_agreement":null},{"id":"W2156171684","doi":"10.31979/etd.hwkh-5782","title":"Optimizing a Web Search Engine","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bigram; Search engine; Information retrieval; Search analytics; Computer science; Spamdexing; Metasearch engine; Web search engine; Weighting; Web crawler; Web search query; Relevance (law); Organic search; Word (group theory); Search-oriented architecture; Inverted index; World Wide Web; Phrase search; Search engine indexing; Artificial intelligence; Trigram; Mathematics","score_opus":0.022029956291254336,"score_gpt":0.27377034284325674,"score_spread":0.2517403865520024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156171684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14135453,0.0040578423,0.73757786,0.0018994245,0.00039282237,0.0021087083,0.004085951,0.052200064,0.056322806],"genre_scores_gemma":[0.25128803,0.0011446775,0.7193966,0.0004892994,0.00011949856,0.0007278071,0.00506811,0.0020097066,0.019756306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797314,0.0005292231,0.00018952979,0.00034051813,0.0007281012,0.000239515],"domain_scores_gemma":[0.99779785,0.0006884363,0.000077170036,0.0002777378,0.0010331776,0.00012563234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014879048,0.0010602166,0.0012993483,0.0021757,0.0007079978,0.0021334623,0.0016611852,0.00092465425,0.0074588847],"category_scores_gemma":[0.0064008418,0.0006202579,0.0008071724,0.0035954146,0.00030789713,0.0032985383,0.0009847531,0.0009009797,0.0064371354],"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":[0.001021244,0.0006348551,0.0063945986,0.0010326335,0.0003554771,0.0004040714,0.00023739733,0.08796461,0.046914153,0.009530186,0.08249528,0.76301545],"study_design_scores_gemma":[0.00023085879,0.00026336958,0.0028983268,0.00005058422,0.0002368409,0.00040061292,0.00036669677,0.9224746,0.024163647,0.009342607,0.039513823,0.000057993657],"about_ca_topic_score_codex":0.011487571,"about_ca_topic_score_gemma":0.017408293,"teacher_disagreement_score":0.011487571,"about_ca_system_score_codex":0.0014578967,"about_ca_system_score_gemma":0.0020405059,"threshold_uncertainty_score":0.024952471},"labels":[],"label_agreement":null},{"id":"W2156276760","doi":"","title":"Wavelet-based relative prefix sum methods for range sum queries in data cubes","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"University of Toronto; Acadia University","keywords":"Prefix; Computer science; Range (aeronautics); Wavelet; Cube (algebra); Algorithm; Combinatorics; Mathematics; Data mining; Artificial intelligence","score_opus":0.10394108600485404,"score_gpt":0.3455550852004152,"score_spread":0.2416139991955612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156276760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034795909,0.00029512786,0.99454236,0.00013405587,0.000041133502,0.00005458619,0.00007754955,0.0006249123,0.0007507073],"genre_scores_gemma":[0.06168785,0.0005768447,0.93498325,0.00012217037,0.00018340538,0.00023063058,0.00039895825,0.0002501653,0.0015668088],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963644,0.001031775,0.00034960316,0.00039893168,0.0016874626,0.00016795065],"domain_scores_gemma":[0.9900877,0.0051448066,0.00066787004,0.0025689856,0.0013190514,0.00021154745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031621756,0.0012109078,0.0015766473,0.002598228,0.0008259746,0.0025279918,0.0021902763,0.0012124936,0.005210285],"category_scores_gemma":[0.016843012,0.0006797088,0.0009882282,0.005130761,0.0013576437,0.0062571373,0.0034100828,0.0025313857,0.002788718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061611854,0.00023117634,0.0012212456,0.00036464114,0.0001282016,0.0001406814,0.0003503975,0.115587555,0.016982347,0.08862031,0.0127667785,0.76299053],"study_design_scores_gemma":[0.00009737156,0.00016441225,0.00034363894,0.000034835877,0.00003846497,0.00025556824,0.00013914047,0.90295213,0.0134211,0.07000697,0.012502697,0.000043652326],"about_ca_topic_score_codex":0.0013156878,"about_ca_topic_score_gemma":0.0015816732,"teacher_disagreement_score":0.005210285,"about_ca_system_score_codex":0.0007447799,"about_ca_system_score_gemma":0.0010811336,"threshold_uncertainty_score":0.017430127},"labels":[],"label_agreement":null},{"id":"W2156647424","doi":"10.1109/icde.1994.282996","title":"A query sampling method for estimating local cost parameters in a multidatabase system","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":71,"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":"Computer science; Sampling (signal processing); Query optimization; Class (philosophy); Sample (material); Data mining; Artificial intelligence; Detector","score_opus":0.08598551938416749,"score_gpt":0.32345012324581546,"score_spread":0.23746460386164797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156647424","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013790596,0.000072681665,0.9855678,0.000033627683,0.0000051324887,0.000043223292,0.000030582603,0.00032242006,0.00013402222],"genre_scores_gemma":[0.3838249,0.000117312564,0.61440766,0.00007185056,0.000052252908,0.00030808014,0.00038480724,0.00017430166,0.00065871974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689925,0.0011409089,0.00014569414,0.0004735724,0.0011929038,0.00014761383],"domain_scores_gemma":[0.9944149,0.0029571773,0.00041072827,0.0009170904,0.0011238676,0.00017617356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033533643,0.00069722696,0.0010937315,0.0015325426,0.00065794116,0.00093941827,0.001638693,0.0007398577,0.0011774921],"category_scores_gemma":[0.01254365,0.00044012372,0.000651402,0.0015631425,0.00055683486,0.0024489183,0.00095143355,0.0009212045,0.00031681635],"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":[0.00067827455,0.0003646556,0.017228998,0.0001718691,0.00024465573,0.00017470156,0.00043071917,0.4674081,0.01947812,0.022417583,0.0025990037,0.46880332],"study_design_scores_gemma":[0.00001796473,0.000045765482,0.001197023,0.0000029349283,0.000016037884,0.000050474137,0.000023662684,0.99210745,0.0028612542,0.0030719473,0.0005917974,0.000013674147],"about_ca_topic_score_codex":0.008076265,"about_ca_topic_score_gemma":0.0055455263,"teacher_disagreement_score":0.008076265,"about_ca_system_score_codex":0.001131003,"about_ca_system_score_gemma":0.0010710699,"threshold_uncertainty_score":0.017734468},"labels":[],"label_agreement":null},{"id":"W2156892372","doi":"10.1007/s00778-009-0162-1","title":"Threshold-based probabilistic top-k dominating queries","year":2009,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"","keywords":"Randomized algorithm; Scalability; Probabilistic logic; Computer science; Computation; Uncertain data; Set (abstract data type); Efficient algorithm; Dominating set; Algorithm; Theoretical computer science; Data mining; Artificial intelligence; Database","score_opus":0.015818699428412988,"score_gpt":0.24787564485482708,"score_spread":0.23205694542641409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156892372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07652448,0.0021810078,0.9089671,0.0018172422,0.00030828762,0.00040504945,0.0013325211,0.0018785557,0.006585797],"genre_scores_gemma":[0.80174255,0.000989435,0.18960807,0.0005458335,0.00032009167,0.000290124,0.0014990738,0.000345593,0.004659254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99253535,0.0021005196,0.00066299096,0.0013422925,0.0025370775,0.00082172186],"domain_scores_gemma":[0.98101383,0.011929716,0.0008573743,0.0035230694,0.001817405,0.00085863937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052558016,0.0012679754,0.0042971615,0.001927595,0.0019737545,0.0044767815,0.006446841,0.0024188212,0.0045313193],"category_scores_gemma":[0.028441435,0.0011542948,0.0014158437,0.0049483967,0.0017084009,0.008687461,0.0056463405,0.0022149403,0.0012214826],"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":[0.0044720992,0.0005500252,0.004568313,0.0012160271,0.00030957186,0.00032941764,0.000650089,0.6389386,0.013419838,0.09765514,0.020209271,0.2176817],"study_design_scores_gemma":[0.00013312159,0.00017521497,0.00031850202,0.000023365335,0.00008163668,0.0002834029,0.00011627092,0.9083197,0.0023713345,0.086307704,0.0018364489,0.0000332671],"about_ca_topic_score_codex":0.00154627,"about_ca_topic_score_gemma":0.0026683144,"teacher_disagreement_score":0.006446841,"about_ca_system_score_codex":0.002524327,"about_ca_system_score_gemma":0.0022184344,"threshold_uncertainty_score":0.027795672},"labels":[],"label_agreement":null},{"id":"W2157044362","doi":"10.1007/s00778-008-0128-8","title":"Top-k typicality queries and efficient query answering methods on large databases","year":2009,"lang":"en","type":"article","venue":"The VLDB Journal","topic":"Data Management and Algorithms","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":"Simon Fraser University","funders":"","keywords":"Computer science; Discriminative model; Set (abstract data type); Tree (set theory); Information retrieval; Measure (data warehouse); Quality (philosophy); Series (stratigraphy); Data mining; Artificial intelligence; Mathematics","score_opus":0.03418460099070763,"score_gpt":0.3495786809841878,"score_spread":0.3153940799934802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157044362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0323919,0.0023846347,0.9565058,0.0019881919,0.00014206785,0.00010110847,0.0011776371,0.0035975624,0.0017111569],"genre_scores_gemma":[0.4254284,0.001441173,0.563081,0.0007538561,0.0007796686,0.00027253793,0.0036779416,0.0010612451,0.0035042139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98694485,0.003939845,0.0015677905,0.002749191,0.0038230375,0.0009751743],"domain_scores_gemma":[0.964893,0.021766467,0.0014017401,0.008451535,0.0027607759,0.0007264594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0085006505,0.0015273009,0.004818978,0.00334122,0.0024817996,0.0067157587,0.0070211645,0.0032338707,0.0053056558],"category_scores_gemma":[0.040784586,0.0020138887,0.0025613874,0.009267921,0.0030489392,0.019165365,0.005584576,0.0038118642,0.0020407594],"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":[0.0041401233,0.00049784826,0.00950204,0.0020762193,0.00062863313,0.0005512723,0.0016949342,0.19673789,0.016056048,0.2537173,0.053919364,0.46047837],"study_design_scores_gemma":[0.00014704697,0.00011775712,0.00058805913,0.000040368835,0.00013897334,0.0005980825,0.00031203218,0.5930769,0.0044049625,0.39570838,0.0048061996,0.00006128094],"about_ca_topic_score_codex":0.0027486596,"about_ca_topic_score_gemma":0.0031519618,"teacher_disagreement_score":0.0085006505,"about_ca_system_score_codex":0.0022162537,"about_ca_system_score_gemma":0.0023443778,"threshold_uncertainty_score":0.044956267},"labels":[],"label_agreement":null},{"id":"W2157082519","doi":"10.1007/978-3-319-06028-6_38","title":"Integrating Multiple Resources for Diversified Query Expansion","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Université de Montréal","funders":"","keywords":"Computer science; Query expansion; Information retrieval; Resource (disambiguation); Relevance (law); Query optimization; Sargable; Principal (computer security); Web search query; Web query classification; Data mining; Search engine","score_opus":0.02062620191132638,"score_gpt":0.23480755195424038,"score_spread":0.214181350042914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157082519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05973681,0.0042649773,0.89716905,0.00093230186,0.00030326008,0.0005531318,0.0011191176,0.0203073,0.015614034],"genre_scores_gemma":[0.30535698,0.001645844,0.672298,0.000746879,0.00024225005,0.0003678398,0.004025838,0.002302504,0.013013925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714595,0.0006469258,0.00024395234,0.00043235376,0.0012017239,0.00032903568],"domain_scores_gemma":[0.99684983,0.0011672903,0.00008435637,0.0012564646,0.0005085346,0.00013352124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022370524,0.001253557,0.0020805704,0.0030146225,0.0009107587,0.0025662517,0.0027012776,0.0013599,0.00920212],"category_scores_gemma":[0.0064389138,0.0007398049,0.0011011458,0.004451107,0.00097593566,0.0067887073,0.005081389,0.0018567966,0.0030560032],"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":[0.0011665298,0.0005304347,0.002755574,0.000817257,0.00022878904,0.0010203522,0.0009855548,0.029976746,0.06897085,0.03564153,0.04149955,0.8164069],"study_design_scores_gemma":[0.00022408836,0.00037999416,0.0018804742,0.00026904815,0.0005623908,0.0025428988,0.0009397306,0.6968979,0.10757199,0.084505826,0.10399537,0.00023026936],"about_ca_topic_score_codex":0.0029712983,"about_ca_topic_score_gemma":0.004332872,"teacher_disagreement_score":0.00920212,"about_ca_system_score_codex":0.0009044183,"about_ca_system_score_gemma":0.0010642374,"threshold_uncertainty_score":0.03078419},"labels":[],"label_agreement":null},{"id":"W2158532749","doi":"10.1109/lcn.2003.1243174","title":"Feedback mechanism validation and path query messages in the label distribution protocol","year":2004,"lang":"en","type":"article","venue":"","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":"Concordia University","funders":"","keywords":"Computer science; Path (computing); Path vector protocol; Constraint (computer-aided design); Set (abstract data type); Network topology; Protocol (science); Computer network; Routing (electronic design automation); Node (physics); Distributed computing; Routing protocol; Database; Data mining; Topology (electrical circuits); Static routing; Mathematics","score_opus":0.018175010536126437,"score_gpt":0.26451576445056774,"score_spread":0.2463407539144413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158532749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026153728,0.00040777112,0.960952,0.00092037173,0.00023896666,0.000737234,0.00022005783,0.007564269,0.0028055971],"genre_scores_gemma":[0.70661455,0.00032117154,0.28436884,0.0009130928,0.00018834676,0.0011206866,0.0004708438,0.0007110649,0.0052913516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98822474,0.0045112357,0.0009976366,0.0011284145,0.004111448,0.0010265531],"domain_scores_gemma":[0.94579387,0.033046726,0.0037020883,0.009867542,0.00678228,0.000807493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016448732,0.0011250902,0.0011643686,0.0020755748,0.002248642,0.0028010309,0.004608986,0.0036833405,0.0031471935],"category_scores_gemma":[0.05276693,0.0010012668,0.00063590874,0.0015786152,0.0024681543,0.008229974,0.0039324476,0.0030630992,0.0008936811],"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":[0.005858291,0.0011195339,0.012620957,0.00088704977,0.00020273117,0.001498392,0.0026635157,0.17527984,0.056507863,0.21600068,0.018503452,0.5088578],"study_design_scores_gemma":[0.0005165895,0.00077086024,0.0012845086,0.00013986512,0.00013525588,0.0006861177,0.0002510516,0.84899354,0.054101516,0.06866984,0.024193231,0.00025758473],"about_ca_topic_score_codex":0.004875679,"about_ca_topic_score_gemma":0.0024261489,"teacher_disagreement_score":0.016448732,"about_ca_system_score_codex":0.0024677399,"about_ca_system_score_gemma":0.0032258725,"threshold_uncertainty_score":0.0869903},"labels":[],"label_agreement":null},{"id":"W2158668414","doi":"10.1007/3-540-45244-3_10","title":"Data Mining with Calendar Attributes","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Generalization; Probabilistic logic; Granularity; Data mining; Graph; Theoretical computer science; Probability distribution; Domain (mathematical analysis); Set (abstract data type); Artificial intelligence; Mathematics; Statistics; Programming language","score_opus":0.045830102197593814,"score_gpt":0.26138535238206634,"score_spread":0.21555525018447252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158668414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03356081,0.003246861,0.9288891,0.0010222165,0.0006920545,0.00029468213,0.008837292,0.008295069,0.015161925],"genre_scores_gemma":[0.27084273,0.002822659,0.69395083,0.00035352525,0.0005429971,0.0002607955,0.014793358,0.0003823168,0.016050782],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908304,0.00015790995,0.00013795879,0.0002692838,0.00030357405,0.000048231053],"domain_scores_gemma":[0.99794465,0.0007412484,0.00017794661,0.0007986335,0.0002256202,0.00011177492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014394699,0.00048172916,0.0007200488,0.0022013895,0.0006746729,0.0023372998,0.0011481007,0.00036857402,0.005175346],"category_scores_gemma":[0.006423095,0.00042325025,0.0011186885,0.00568777,0.00029800704,0.0027244785,0.0014120217,0.00081539166,0.0033800786],"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":[0.000404226,0.00016613981,0.012129806,0.0004778213,0.00015772239,0.00022300959,0.00017329254,0.010035351,0.0034172079,0.02716664,0.034235857,0.9114129],"study_design_scores_gemma":[0.000116747455,0.0003289935,0.010529831,0.00029075917,0.0002745048,0.002097808,0.00048852904,0.4166314,0.024703765,0.25203314,0.29241684,0.00008771333],"about_ca_topic_score_codex":0.00089532766,"about_ca_topic_score_gemma":0.0013849766,"teacher_disagreement_score":0.005175346,"about_ca_system_score_codex":0.00029680878,"about_ca_system_score_gemma":0.0008846717,"threshold_uncertainty_score":0.017313302},"labels":[],"label_agreement":null},{"id":"W2160152607","doi":"10.14778/1687627.1687754","title":"Efficient method for maximizing bichromatic reverse nearest neighbor","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; k-nearest neighbors algorithm; Point (geometry); Best bin first; Exponential function; Algorithm; Exponential growth; Theoretical computer science; Mathematics; Artificial intelligence","score_opus":0.014581193934234049,"score_gpt":0.25726412710710916,"score_spread":0.24268293317287512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160152607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013429034,0.0005591736,0.98252904,0.00013040447,0.00004309442,0.00011958795,0.00013182592,0.0010281708,0.0020296804],"genre_scores_gemma":[0.09750411,0.00022960654,0.8994473,0.00008888402,0.000038437687,0.00020120134,0.00040639602,0.00019085256,0.0018932427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764204,0.0005515056,0.0001347962,0.0004520314,0.0010290174,0.00019069249],"domain_scores_gemma":[0.9980293,0.0007052377,0.0001990311,0.00038534516,0.00060479256,0.00007631326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015720602,0.0011313003,0.0018772769,0.0023617866,0.00086056115,0.0010226021,0.0026746388,0.0011679356,0.004400157],"category_scores_gemma":[0.0062883524,0.00063561334,0.0008446203,0.0028124324,0.0006000947,0.0022648457,0.002824784,0.00091342797,0.0018032754],"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":[0.00044815734,0.00023186857,0.0018362644,0.00037556066,0.00008333039,0.00015672174,0.00027931132,0.15288286,0.015917938,0.019222856,0.009857926,0.79870725],"study_design_scores_gemma":[0.00006724329,0.00009956697,0.00057981187,0.000025534579,0.00002845482,0.0003891127,0.000107584994,0.96903056,0.010167378,0.013292927,0.006174479,0.000037310598],"about_ca_topic_score_codex":0.0038927475,"about_ca_topic_score_gemma":0.0061986945,"teacher_disagreement_score":0.004400157,"about_ca_system_score_codex":0.00092426845,"about_ca_system_score_gemma":0.0016593373,"threshold_uncertainty_score":0.014720023},"labels":[],"label_agreement":null},{"id":"W2160372286","doi":"10.1049/cp:20070352","title":"On middleware support for motion awareness","year":2007,"lang":"en","type":"article","venue":"","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":"University of Windsor","funders":"","keywords":"Middleware (distributed applications); Computer science; Motion (physics); Artificial intelligence; Distributed computing","score_opus":0.033786586770695466,"score_gpt":0.2886188044754937,"score_spread":0.25483221770479825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160372286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021160474,0.0039401916,0.9289812,0.0031018867,0.000805678,0.00008360123,0.000094231444,0.004216619,0.03761609],"genre_scores_gemma":[0.64207196,0.005480699,0.32163537,0.0011113497,0.0008780309,0.00021304625,0.00042424092,0.00093460554,0.027250748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998412,0.00029489436,0.00018255762,0.00028072274,0.000569839,0.00025992014],"domain_scores_gemma":[0.99741143,0.00088297675,0.00016431205,0.0008306071,0.0004710698,0.0002396008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019383387,0.0006561374,0.0006332956,0.00079498533,0.0012984502,0.0033589546,0.0021062524,0.0014391718,0.004041126],"category_scores_gemma":[0.0061627473,0.0006447748,0.000791839,0.0008775369,0.0012318621,0.0068738847,0.0044606933,0.0026478511,0.0013488767],"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":[0.00021739415,0.000084894884,0.0016567006,0.00054670597,0.000082980514,0.00086684164,0.0019068161,0.015567694,0.031584088,0.7939668,0.009914202,0.14360489],"study_design_scores_gemma":[0.00007024961,0.00017614108,0.0012356201,0.0004507165,0.0002448331,0.0012509202,0.00080297503,0.14322901,0.03453687,0.32212964,0.49575964,0.00011334907],"about_ca_topic_score_codex":0.0017731407,"about_ca_topic_score_gemma":0.0017582087,"teacher_disagreement_score":0.004041126,"about_ca_system_score_codex":0.00090001523,"about_ca_system_score_gemma":0.0010282927,"threshold_uncertainty_score":0.0135189295},"labels":[],"label_agreement":null},{"id":"W2160600700","doi":"10.1109/sccc.2005.1587861","title":"A Geometric Framework to Visualize Fuzzy-clustered Data","year":2006,"lang":"en","type":"article","venue":"","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":"University of Windsor","funders":"","keywords":"Computer science; Data mining; Fuzzy logic; Cluster analysis; Fuzzy clustering; Visualization; Object (grammar); Artificial intelligence; Scheme (mathematics); Data visualization; Fuzzy set; FLAME clustering; Cluster (spacecraft); Pattern recognition (psychology); Machine learning; Mathematics; CURE data clustering algorithm","score_opus":0.04133289703896613,"score_gpt":0.3064629536711043,"score_spread":0.2651300566321382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160600700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022941348,0.00025595888,0.99377185,0.00032572277,0.000060665945,0.00006097249,0.00028121506,0.0013314402,0.0016179491],"genre_scores_gemma":[0.0451868,0.0005665082,0.95203066,0.000098917175,0.00007560859,0.0001906162,0.0007198427,0.00031229336,0.0008186855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893576,0.00036154923,0.00007834113,0.00016822937,0.0003673899,0.00008877475],"domain_scores_gemma":[0.99857557,0.0004170955,0.0001396387,0.00029144363,0.0004511652,0.00012514647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013709663,0.0016359214,0.00085642765,0.005091343,0.00091725885,0.0032333557,0.0018048339,0.001208868,0.0060811206],"category_scores_gemma":[0.0039036924,0.00066484004,0.001137638,0.0037923849,0.0013110584,0.0031528804,0.002814767,0.001734062,0.0015728268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031870624,0.000115270275,0.0019815378,0.00083802675,0.00019567786,0.0009349368,0.002375921,0.18385479,0.05273857,0.40762302,0.03501589,0.3140076],"study_design_scores_gemma":[0.0000961602,0.00021551676,0.0017722676,0.00020059252,0.00008631204,0.0016928619,0.0009755669,0.5720929,0.027796352,0.2165031,0.17829351,0.00027487255],"about_ca_topic_score_codex":0.0023328953,"about_ca_topic_score_gemma":0.0019544063,"teacher_disagreement_score":0.0060811206,"about_ca_system_score_codex":0.0007203518,"about_ca_system_score_gemma":0.0011336667,"threshold_uncertainty_score":0.020343423},"labels":[],"label_agreement":null},{"id":"W2161529934","doi":"","title":"Core-Preserving Algorithms","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplicative function; Class (philosophy); Algorithm; Ball (mathematics); Core (optical fiber); Computer science; Set (abstract data type); Mathematics; Out-of-core algorithm; Combinatorics; Artificial intelligence; Geometry","score_opus":0.1065928038436099,"score_gpt":0.27847765368349087,"score_spread":0.17188484983988095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161529934","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02011729,0.00026300809,0.9714102,0.00020259598,0.000056697194,0.0001724448,0.00031058004,0.002282508,0.0051846765],"genre_scores_gemma":[0.22086115,0.00045631657,0.7667851,0.00037289353,0.00011623943,0.0004382331,0.0018921517,0.0006565819,0.008421329],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720556,0.0003930435,0.00026740457,0.00079178106,0.0009564698,0.00038573093],"domain_scores_gemma":[0.99369264,0.0014920157,0.00036617782,0.0034219837,0.0007765214,0.00025079245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019312386,0.00096603617,0.0013526961,0.001394399,0.001205879,0.0025150462,0.004342345,0.0010398171,0.006445372],"category_scores_gemma":[0.008473746,0.0007001171,0.0013897126,0.0023884133,0.0012850692,0.0059876475,0.0050819246,0.0022019825,0.0025175249],"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":[0.000723395,0.0005250013,0.0026440963,0.00051989383,0.00013189553,0.00015156648,0.00045012502,0.09946491,0.013254941,0.30456114,0.01802112,0.55955195],"study_design_scores_gemma":[0.0001716377,0.0003629113,0.00055711943,0.00006819051,0.0000667902,0.000513888,0.00019402655,0.5263926,0.023955323,0.41535264,0.032323856,0.00004102916],"about_ca_topic_score_codex":0.0008752779,"about_ca_topic_score_gemma":0.0009232998,"teacher_disagreement_score":0.006445372,"about_ca_system_score_codex":0.00091257156,"about_ca_system_score_gemma":0.0015366678,"threshold_uncertainty_score":0.02156192},"labels":[],"label_agreement":null},{"id":"W2161824006","doi":"10.1504/ijwet.2008.019943","title":"Web services-oriented architectures for mobile SOLAP applications","year":2008,"lang":"en","type":"article","venue":"International Journal of Web Engineering and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Université Laval; Centre de Géomatique du Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Geospatial analysis; Web service; Mobile device; World Wide Web; Popularity; Mobile computing; Mobile Web; Mobile business development; Mobile technology; Telecommunications","score_opus":0.004142416255261322,"score_gpt":0.2174246229963203,"score_spread":0.21328220674105897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161824006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012702263,0.0017732376,0.9591854,0.0017094491,0.00023831295,0.00034125167,0.00014230602,0.0039964546,0.019911183],"genre_scores_gemma":[0.2629403,0.0048587387,0.7093463,0.0009339709,0.0002754711,0.00062898465,0.0010135032,0.000683182,0.019319568],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890053,0.00032496496,0.00010830951,0.00008770185,0.00047031266,0.00010804788],"domain_scores_gemma":[0.999166,0.0002129739,0.000059345355,0.00013822956,0.00034152588,0.000081978564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011734829,0.00060549314,0.0004077725,0.0008616696,0.0009245653,0.0030146313,0.0013780987,0.0015144207,0.0036425139],"category_scores_gemma":[0.002272417,0.00042249923,0.000576795,0.0014151415,0.00068733154,0.002728197,0.0015163363,0.0015239328,0.0023550873],"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":[0.00037209678,0.0003097018,0.0017499313,0.0012008924,0.00014932602,0.0020566704,0.0017140392,0.0682778,0.049425684,0.55846184,0.02648651,0.28979552],"study_design_scores_gemma":[0.00007973192,0.00014554943,0.0008175262,0.00032599305,0.00009206489,0.0011011088,0.0007646753,0.43185443,0.019004446,0.22118853,0.32453468,0.000091307935],"about_ca_topic_score_codex":0.0023967035,"about_ca_topic_score_gemma":0.002614598,"teacher_disagreement_score":0.0036425139,"about_ca_system_score_codex":0.0008851274,"about_ca_system_score_gemma":0.0011735852,"threshold_uncertainty_score":0.012185395},"labels":[],"label_agreement":null},{"id":"W2162148987","doi":"","title":"Clusterability: A Theoretical Study","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":94,"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":"Cluster analysis; Generality; Computer science; Pairwise comparison; Set (abstract data type); Property (philosophy); Data mining; Theoretical computer science; Artificial intelligence","score_opus":0.00920970011625449,"score_gpt":0.25647706827589223,"score_spread":0.24726736815963773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162148987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04371805,0.009104131,0.87196857,0.015665404,0.00032645767,0.0002638858,0.00071356545,0.00029790035,0.057942007],"genre_scores_gemma":[0.8299276,0.008393838,0.14597858,0.0016547261,0.002532156,0.0006952099,0.0015502269,0.00044109384,0.008826506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9814672,0.0075839865,0.00094657775,0.0038798824,0.004926989,0.0011954173],"domain_scores_gemma":[0.8356991,0.13288376,0.007660625,0.011221347,0.009336126,0.0031990735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015664568,0.0016681356,0.00216494,0.009332836,0.005197671,0.010322954,0.005587206,0.003927781,0.012328823],"category_scores_gemma":[0.09896776,0.0014906363,0.0029448252,0.013794282,0.016323835,0.023728741,0.007530558,0.008217606,0.0016945332],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030550418,0.000039632952,0.0016741974,0.00019625152,0.000038568924,0.00006452246,0.0004427217,0.008198379,0.00018256891,0.97631663,0.0024128577,0.010403069],"study_design_scores_gemma":[0.000015788315,0.0000378486,0.0007189725,0.00012474867,0.000030542153,0.00022518859,0.0004268925,0.044389144,0.0005388469,0.94382554,0.009641402,0.000025233456],"about_ca_topic_score_codex":0.0028450922,"about_ca_topic_score_gemma":0.001225878,"teacher_disagreement_score":0.015664568,"about_ca_system_score_codex":0.008427003,"about_ca_system_score_gemma":0.0019131001,"threshold_uncertainty_score":0.082843125},"labels":[],"label_agreement":null},{"id":"W2162526413","doi":"10.1109/icde.1989.47239","title":"Aggregative closure: an extension of transitive closure","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Government of Canada","keywords":"Transitive closure; Closure (psychology); Closure operator; Extension (predicate logic); Computer science; Transitive relation; Variety (cybernetics); Theoretical computer science; Operator (biology); Transitive reduction; Algorithm; Algebra over a field; Mathematics; Discrete mathematics; Artificial intelligence; Programming language; Graph; Pure mathematics; Closed set; Combinatorics","score_opus":0.01820417521875536,"score_gpt":0.24533103106362364,"score_spread":0.22712685584486828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162526413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036048403,0.00075766275,0.9824556,0.00037692193,0.0003199005,0.00012110828,0.00022941233,0.00052355486,0.011610943],"genre_scores_gemma":[0.13646685,0.0021611522,0.8487423,0.0006795362,0.0011388949,0.00043977355,0.0007642796,0.00062954385,0.008977612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99298453,0.0016998979,0.0008473991,0.001949224,0.002128773,0.00039007282],"domain_scores_gemma":[0.99183935,0.0039803577,0.0008135749,0.001937719,0.0010675618,0.0003614844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005647015,0.001310416,0.0014671743,0.0035604327,0.001925873,0.0071313274,0.001989345,0.0012755838,0.0055434178],"category_scores_gemma":[0.01172466,0.0007908594,0.003018351,0.0043570194,0.0052956743,0.013157343,0.0051891347,0.003367936,0.0017844688],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004089443,0.00003894545,0.00025305687,0.00018038119,0.000032114236,0.00016711977,0.00052659056,0.0038615428,0.0016409225,0.92624915,0.004521521,0.062487755],"study_design_scores_gemma":[0.000017192831,0.0000683492,0.00017856901,0.00011755286,0.00004071676,0.0006684487,0.00019755519,0.030723058,0.0031138195,0.89681476,0.06801169,0.000048273563],"about_ca_topic_score_codex":0.0012417476,"about_ca_topic_score_gemma":0.0009275873,"teacher_disagreement_score":0.0071313274,"about_ca_system_score_codex":0.0016294391,"about_ca_system_score_gemma":0.0020801825,"threshold_uncertainty_score":0.029864669},"labels":[],"label_agreement":null},{"id":"W2162541870","doi":"10.1109/cts.2010.5478494","title":"SWE-FE: Extending folksonomies to the Sensor Web","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary; Canarie","keywords":"Folksonomy; Geospatial analysis; Computer science; World Wide Web; Suite; Sensor web; The Internet; Key (lock); Information retrieval; Remote sensing; Geography; Computer security; Operating system","score_opus":0.012959577582206982,"score_gpt":0.24332096599077352,"score_spread":0.23036138840856654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162541870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018087339,0.00030582162,0.956998,0.00027785136,0.000069541384,0.00034385108,0.0023529688,0.01738277,0.0041817795],"genre_scores_gemma":[0.07010895,0.00026317182,0.92045206,0.00015043837,0.000033473163,0.00020472636,0.0056057563,0.0007145219,0.002466928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815625,0.0005417968,0.00018389968,0.00032519235,0.00071924966,0.00007346603],"domain_scores_gemma":[0.992856,0.0026995158,0.00032744172,0.003023555,0.00085437833,0.00023893085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026417447,0.0007704227,0.0007276221,0.0035612213,0.0007972346,0.0018052686,0.001446219,0.00077567884,0.0031965717],"category_scores_gemma":[0.0130024385,0.00047480586,0.0008926849,0.0026864544,0.0004818761,0.0057310103,0.002678126,0.0011132451,0.0017665505],"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":[0.00027478932,0.0004546897,0.00819242,0.00069450133,0.00027292536,0.00023457754,0.001445289,0.02147014,0.009987713,0.019901128,0.02450292,0.9125689],"study_design_scores_gemma":[0.000085247986,0.00023024861,0.012036271,0.0003211239,0.000104520455,0.0014523994,0.0011333331,0.63921404,0.035014544,0.090273425,0.21994136,0.00019347834],"about_ca_topic_score_codex":0.0037999537,"about_ca_topic_score_gemma":0.009410496,"teacher_disagreement_score":0.0037999537,"about_ca_system_score_codex":0.0004305965,"about_ca_system_score_gemma":0.0007154929,"threshold_uncertainty_score":0.013971031},"labels":[],"label_agreement":null},{"id":"W2162783807","doi":"10.14778/2021017.2021025","title":"Keyword search in graphs","year":2011,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Substructure; Clique; Computer science; Graph; Combinatorics; Theoretical computer science; Time complexity; Mathematics; Algorithm","score_opus":0.036510813204070874,"score_gpt":0.2195111061961708,"score_spread":0.18300029299209994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162783807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04531558,0.005098478,0.9254759,0.0026069314,0.00017137,0.0004927976,0.0040235603,0.0029681614,0.013847203],"genre_scores_gemma":[0.28606388,0.004916594,0.69141895,0.0008215114,0.00024157985,0.00037560557,0.006168046,0.00051714573,0.009476676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727684,0.000910157,0.00024048227,0.0008716729,0.00045007383,0.00025078538],"domain_scores_gemma":[0.99399275,0.0041166656,0.0005052529,0.0007695441,0.0004229021,0.0001928289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012357202,0.0009957463,0.0016016557,0.0031398495,0.0015225998,0.0036798355,0.0019880033,0.0020240827,0.0069523132],"category_scores_gemma":[0.011150416,0.0009229629,0.001308278,0.0077954847,0.0013177645,0.008909615,0.0024320106,0.0012962526,0.0029225647],"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":[0.00062776945,0.00028094646,0.0031632774,0.0028884711,0.00029710287,0.00085721834,0.00122683,0.21969709,0.011904722,0.31667033,0.055028748,0.38735747],"study_design_scores_gemma":[0.000092065544,0.000105362175,0.0006494442,0.00012692486,0.00007200029,0.0009228826,0.00043345295,0.28435084,0.004845701,0.6707673,0.037582252,0.000051719744],"about_ca_topic_score_codex":0.0047476958,"about_ca_topic_score_gemma":0.0049530873,"teacher_disagreement_score":0.0069523132,"about_ca_system_score_codex":0.0020369252,"about_ca_system_score_gemma":0.0016293918,"threshold_uncertainty_score":0.023257792},"labels":[],"label_agreement":null},{"id":"W2162954778","doi":"10.1109/ssdbm.2007.32","title":"Effective Summarization of Multi-Dimensional Data Streams for Historical Stream Mining","year":2007,"lang":"en","type":"article","venue":"International Conference on Scientific and Statistical Database Management","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Automatic summarization; Computer science; Data stream; Data stream mining; Data mining; ENCODE; Data stream clustering; Cluster analysis; STREAMS; Task (project management); Space (punctuation); Simple (philosophy); Information retrieval; Artificial intelligence","score_opus":0.09385673441405702,"score_gpt":0.34855274837898825,"score_spread":0.25469601396493124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162954778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067723095,0.0026513159,0.9265907,0.0005571908,0.000099929915,0.00015821322,0.00070172583,0.0009714156,0.0005464468],"genre_scores_gemma":[0.4114531,0.002307803,0.5817508,0.00010360675,0.00036467423,0.00021708656,0.0025690317,0.00013621201,0.0010976553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989858,0.00027984905,0.000154159,0.00020409336,0.00031618588,0.000059922077],"domain_scores_gemma":[0.99649197,0.0014649346,0.0006209451,0.0005764302,0.0007015365,0.00014418068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021030318,0.0007973162,0.0014109516,0.0022542763,0.000578806,0.0016415712,0.0010844122,0.0006463213,0.00068731944],"category_scores_gemma":[0.009483124,0.00041009783,0.0005057626,0.003022768,0.0003569902,0.0035828655,0.0008448054,0.0006884742,0.0003655292],"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":[0.00064035645,0.00032086906,0.009650347,0.00095412275,0.00024680418,0.00040019225,0.00087950367,0.24329552,0.034026336,0.019209897,0.008485946,0.6818901],"study_design_scores_gemma":[0.000037361973,0.00034303995,0.002083944,0.000059910966,0.00011586629,0.00023159328,0.0003002361,0.958502,0.014973322,0.016350992,0.006968865,0.000032842403],"about_ca_topic_score_codex":0.00088536146,"about_ca_topic_score_gemma":0.0015455105,"teacher_disagreement_score":0.0022542763,"about_ca_system_score_codex":0.0004837156,"about_ca_system_score_gemma":0.0006739002,"threshold_uncertainty_score":0.011122048},"labels":[],"label_agreement":null},{"id":"W2163010693","doi":"10.1109/ssdbm.2004.60","title":"SaIL: a library for efficient application integration of spatial indices","year":2004,"lang":"en","type":"article","venue":"Statistical and Scientific Database Management","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Esri (Canada)","funders":"","keywords":"Search engine indexing; Computer science; Index (typography); Focus (optics); Spatial analysis; Interface (matter); Data mining; Spatial database; Extensibility; Information retrieval; Range (aeronautics); Data science; World Wide Web; Geography; Engineering; Remote sensing; Programming language","score_opus":0.013910255542295843,"score_gpt":0.2502628843625411,"score_spread":0.23635262882024527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163010693","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011328374,0.000421938,0.6997614,0.00012319504,0.000092137285,0.00020743802,0.0053815055,0.28805003,0.004829483],"genre_scores_gemma":[0.03522102,0.0019024069,0.81068957,0.0005292475,0.00023449799,0.0018464494,0.044087373,0.08742849,0.018060882],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972066,0.0003754182,0.00042853563,0.00038008572,0.0013736142,0.00023566952],"domain_scores_gemma":[0.99505025,0.001420522,0.0003252547,0.0017647004,0.0010882949,0.00035088463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033859145,0.0027085699,0.002563487,0.004822549,0.0016496652,0.0051122257,0.0069236057,0.0015899074,0.027039569],"category_scores_gemma":[0.011265324,0.002640952,0.003306193,0.0067437794,0.0013184733,0.007590836,0.008271746,0.0039559444,0.028913463],"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":[0.001103884,0.00033321042,0.0043047024,0.0024011454,0.000566592,0.0008405462,0.0009328431,0.01958361,0.019170204,0.050864402,0.36385253,0.5360463],"study_design_scores_gemma":[0.00036847446,0.00021938709,0.0021093113,0.00035301378,0.00023558742,0.0011583815,0.00019709386,0.19436124,0.049971394,0.06313199,0.6874344,0.00045982088],"about_ca_topic_score_codex":0.004416403,"about_ca_topic_score_gemma":0.005627967,"teacher_disagreement_score":0.027039569,"about_ca_system_score_codex":0.0013144265,"about_ca_system_score_gemma":0.003126694,"threshold_uncertainty_score":0.09045631},"labels":[],"label_agreement":null},{"id":"W2163814946","doi":"10.1145/1557670.1557681","title":"Keyword query cleaning using hidden Markov models","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Computer science; Hidden Markov model; Query optimization; Probabilistic logic; Web query classification; Data mining; Information retrieval; Keyword spotting; Web search query; Artificial intelligence; Search engine","score_opus":0.0369287846093509,"score_gpt":0.2555249096461961,"score_spread":0.21859612503684522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163814946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010466681,0.00048662143,0.9851569,0.00023345303,0.0000251309,0.000059270475,0.00024373563,0.0029830423,0.00034512684],"genre_scores_gemma":[0.3674482,0.00074093376,0.62652534,0.00036609234,0.000107081054,0.00017276262,0.0018875874,0.00054160954,0.0022103537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961842,0.0011976162,0.0003040927,0.0008468953,0.0012082779,0.0002588823],"domain_scores_gemma":[0.9893778,0.0069154617,0.00063906715,0.0019116192,0.0009669325,0.00018924093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032127898,0.0008873787,0.0021660938,0.0015269353,0.0009381374,0.0023551255,0.0025763395,0.0017177379,0.0016427665],"category_scores_gemma":[0.016497882,0.0009979465,0.001677713,0.0027945666,0.00083705207,0.0043443865,0.0019498346,0.0016397748,0.0013730701],"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":[0.0011403684,0.00025944432,0.0065999846,0.00061318505,0.00037109884,0.00083712285,0.0009752242,0.54877645,0.023203243,0.04162165,0.008937226,0.366665],"study_design_scores_gemma":[0.00002089996,0.000039031373,0.00027934942,0.000009849026,0.000032221422,0.00013121632,0.000060020862,0.9722988,0.005583444,0.019454122,0.002063001,0.000028072067],"about_ca_topic_score_codex":0.00976168,"about_ca_topic_score_gemma":0.007739646,"teacher_disagreement_score":0.00976168,"about_ca_system_score_codex":0.0014440969,"about_ca_system_score_gemma":0.0017426942,"threshold_uncertainty_score":0.019409716},"labels":[],"label_agreement":null},{"id":"W2164125443","doi":"10.1109/sisap.2009.9","title":"Curse of Dimensionality in Pivot Based Indexes","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Curse of dimensionality; Search engine indexing; Sublinear function; Metric (unit); Intrinsic dimension; Dimension (graph theory); Sequence (biology); Similarity (geometry)","score_opus":0.010865020135798393,"score_gpt":0.2455646894210683,"score_spread":0.2346996692852699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164125443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06525227,0.002159143,0.9254026,0.0013312977,0.0001536555,0.00022296848,0.00038803526,0.0010823504,0.004007671],"genre_scores_gemma":[0.5570422,0.0018276119,0.43546405,0.000509954,0.00035386666,0.00060600246,0.00089532195,0.0003988964,0.0029021327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97917765,0.0073963916,0.002123849,0.0023388157,0.007663683,0.001299611],"domain_scores_gemma":[0.8628725,0.07685568,0.007520542,0.043699063,0.0075304857,0.0015217564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017110856,0.0011907411,0.004034679,0.0034925535,0.0026605953,0.008465837,0.003319628,0.002702307,0.0030561322],"category_scores_gemma":[0.1406773,0.0012436955,0.0016650241,0.0065397886,0.005905051,0.015998274,0.0083821295,0.004072769,0.0015982729],"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":[0.0018721437,0.0003785829,0.012379289,0.0007416217,0.00025194825,0.00049645203,0.0011277236,0.16134971,0.010825919,0.6051239,0.008469017,0.1969837],"study_design_scores_gemma":[0.00009860245,0.0005726558,0.0017623319,0.00011305506,0.00007152154,0.00079737767,0.00021149161,0.69857305,0.013661458,0.27773058,0.006302462,0.00010526581],"about_ca_topic_score_codex":0.0015755397,"about_ca_topic_score_gemma":0.00109625,"teacher_disagreement_score":0.017110856,"about_ca_system_score_codex":0.0031858692,"about_ca_system_score_gemma":0.0034577916,"threshold_uncertainty_score":0.09049195},"labels":[],"label_agreement":null},{"id":"W2164178448","doi":"10.5194/isprsarchives-xxxix-b2-157-2012","title":"MULTI-CRITERIA PATH FINDING","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Shortest path problem; Computer science; Flexibility (engineering); Constrained Shortest Path First; Mathematical optimization; Path (computing); Enhanced Data Rates for GSM Evolution; Point (geometry); K shortest path routing; Longest path problem; Rank (graph theory); Algorithm; Graph; Mathematics; Theoretical computer science; Artificial intelligence; Computer network; Statistics; Combinatorics","score_opus":0.028394556550633297,"score_gpt":0.2779082881230395,"score_spread":0.24951373157240622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164178448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016314952,0.00072646793,0.9727816,0.0003263989,0.00010862743,0.00043317,0.00035520364,0.00032176805,0.008631823],"genre_scores_gemma":[0.270893,0.00040014964,0.71949816,0.00017051701,0.00006725731,0.0006730759,0.0007836979,0.00015694014,0.0073571973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967354,0.0012665616,0.00024137802,0.00056239835,0.00093160395,0.00026274653],"domain_scores_gemma":[0.99535096,0.002790081,0.0003502365,0.00021361378,0.0011273,0.00016771839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030441033,0.0018819119,0.002331921,0.0044763563,0.001641032,0.00247834,0.0022346335,0.0025764143,0.012367856],"category_scores_gemma":[0.009424766,0.0007770534,0.0017624267,0.004141181,0.0009404356,0.0020118696,0.0020090684,0.0010231346,0.0016640383],"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":[0.00021147636,0.00019030941,0.003027467,0.0007888365,0.00025824283,0.0002916978,0.00023217787,0.7506379,0.001978164,0.026636427,0.006047089,0.20970027],"study_design_scores_gemma":[0.000032934557,0.00014799529,0.00038165646,0.00006862083,0.000035845784,0.000115238836,0.000087109125,0.97514445,0.0009259104,0.018840704,0.0041930596,0.000026496438],"about_ca_topic_score_codex":0.0033663805,"about_ca_topic_score_gemma":0.0026173424,"teacher_disagreement_score":0.012367856,"about_ca_system_score_codex":0.0017636527,"about_ca_system_score_gemma":0.0016787044,"threshold_uncertainty_score":0.041374564},"labels":[],"label_agreement":null},{"id":"W2164323312","doi":"10.3233/mis-130154","title":"Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra","year":2013,"lang":"en","type":"article","venue":"RMIT Research Repository (RMIT University Library)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Voronoi diagram; Interval (graph theory); Context (archaeology); Computer science; Ubiquitous computing; Range (aeronautics); Theoretical computer science; Mathematics; Geography; Human–computer interaction; Engineering; Combinatorics","score_opus":0.04570867605291263,"score_gpt":0.24192920517675612,"score_spread":0.1962205291238435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164323312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040286314,0.00025611644,0.9577048,0.000112811606,0.00001834015,0.000059354632,0.00014436779,0.00026975287,0.001148122],"genre_scores_gemma":[0.7851949,0.00035456987,0.21288179,0.000044563687,0.00003762741,0.0001226207,0.00029049837,0.00004956498,0.0010239385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827254,0.00061369163,0.00013890973,0.00033761215,0.0005011395,0.00013618621],"domain_scores_gemma":[0.9977448,0.0013578071,0.0002720832,0.00025739844,0.00026849436,0.000099433826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015014518,0.00051446696,0.00087298243,0.0011330136,0.0004963275,0.002414083,0.0018235041,0.00074743194,0.0011574152],"category_scores_gemma":[0.005938355,0.0003405235,0.0011021533,0.0018106463,0.0008442726,0.0033599043,0.0014884518,0.0009193259,0.00028260227],"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":[0.00015684748,0.00007154772,0.0031780952,0.00010807021,0.00005564453,0.00026280066,0.00036382244,0.88315463,0.003939036,0.07466347,0.00053501315,0.033510912],"study_design_scores_gemma":[0.0000043123646,0.000018099183,0.00015171946,0.0000032874857,0.0000066279836,0.00004273445,0.00004659357,0.9901759,0.0004627074,0.008640288,0.00044127373,0.000006470484],"about_ca_topic_score_codex":0.012413334,"about_ca_topic_score_gemma":0.009453795,"teacher_disagreement_score":0.012413334,"about_ca_system_score_codex":0.0011501674,"about_ca_system_score_gemma":0.0011303024,"threshold_uncertainty_score":0.024682164},"labels":[],"label_agreement":null},{"id":"W2164509564","doi":"10.1145/1097064.1097078","title":"Finding corresponding objects when integrating several geo-spatial datasets","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Join (topology); Computer science; Data mining; k-nearest neighbors algorithm; Precision and recall; Algorithm; Artificial intelligence; Mathematics","score_opus":0.01813873062087971,"score_gpt":0.2586099181512564,"score_spread":0.2404711875303767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164509564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09147626,0.0007622645,0.9016306,0.00027814767,0.00007954342,0.0005826921,0.0012342792,0.0022551909,0.0017009095],"genre_scores_gemma":[0.14894071,0.00028404966,0.84565717,0.000066651926,0.000048812635,0.00027038658,0.004096223,0.000104002705,0.0005319672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98826104,0.002208191,0.0012253895,0.0022284728,0.005597648,0.00047931558],"domain_scores_gemma":[0.98492426,0.007141155,0.0018999098,0.0034809418,0.0021290018,0.0004246029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009978901,0.0012772961,0.0019349267,0.010428637,0.0015032737,0.0049388665,0.003141959,0.0027628855,0.0010170528],"category_scores_gemma":[0.03413608,0.0012093632,0.0020451897,0.011096889,0.0010425166,0.007266059,0.005026308,0.0013192665,0.0008368216],"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":[0.0010749357,0.0008419748,0.06901994,0.0011569444,0.0010524334,0.0030517743,0.0036293806,0.12667832,0.03760704,0.021297097,0.0067595853,0.72783065],"study_design_scores_gemma":[0.00013250932,0.00046380082,0.031851523,0.00032568513,0.00094207627,0.0032295424,0.0047482476,0.73735815,0.10264551,0.07260794,0.045407593,0.00028744698],"about_ca_topic_score_codex":0.0028538664,"about_ca_topic_score_gemma":0.0042281128,"teacher_disagreement_score":0.010428637,"about_ca_system_score_codex":0.0008876865,"about_ca_system_score_gemma":0.0013353265,"threshold_uncertainty_score":0.05277413},"labels":[],"label_agreement":null},{"id":"W2165238029","doi":"10.1109/icsmc.1997.633246","title":"Constructing virtual mines for navigation","year":2002,"lang":"en","type":"article","venue":"","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":"Polytechnique Montréal","funders":"","keywords":"Construct (python library); Computer science; Obstacle; Representation (politics); Software; Object (grammar); 3d model; Artificial intelligence; Human–computer interaction; Programming language; Geography","score_opus":0.030351668809439333,"score_gpt":0.24015105468706477,"score_spread":0.20979938587762542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165238029","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01360344,0.000057261226,0.98317575,0.00007940546,0.000019138175,0.000025726107,0.0001648624,0.0010761976,0.0017981638],"genre_scores_gemma":[0.23306926,0.00027937646,0.7627873,0.00006388831,0.000013061229,0.000174882,0.0011242835,0.00046883276,0.002019156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937123,0.00022314118,0.000035538884,0.00011258498,0.00021247551,0.000045092675],"domain_scores_gemma":[0.99908054,0.0003036083,0.000065870634,0.0004020289,0.00009459697,0.000053413714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005583325,0.00066353293,0.0007360167,0.00062936766,0.00058353203,0.0015924226,0.0015095077,0.0009984719,0.0033720199],"category_scores_gemma":[0.002922306,0.00076361856,0.0013414851,0.0007089451,0.0011285347,0.0028977892,0.0031186026,0.0011039785,0.0010860856],"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":[0.00016941865,0.00008273856,0.0030980555,0.0002356474,0.00008824406,0.00041171344,0.00094211596,0.6086307,0.011297583,0.24792558,0.006057596,0.121060655],"study_design_scores_gemma":[0.00003339277,0.000070190676,0.00045862223,0.00005029935,0.000028390314,0.00030767426,0.0002307723,0.8293991,0.0076119364,0.11623629,0.045535095,0.00003814309],"about_ca_topic_score_codex":0.0016422527,"about_ca_topic_score_gemma":0.0026777175,"teacher_disagreement_score":0.0033720199,"about_ca_system_score_codex":0.00045269137,"about_ca_system_score_gemma":0.0006707416,"threshold_uncertainty_score":0.011280537},"labels":[],"label_agreement":null},{"id":"W2166205117","doi":"10.1007/s10707-007-0030-3","title":"PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data","year":2007,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Relational database management system; Search engine indexing; Oracle; Temporal database; Data mining; Space partitioning; Heuristic; Component (thermodynamics); Tree (set theory); Relational database; Algorithm; Information retrieval; Artificial intelligence; Mathematics","score_opus":0.04829495645624118,"score_gpt":0.30978033011690725,"score_spread":0.2614853736606661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166205117","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005693017,0.000622619,0.94832295,0.00019104521,0.00050518377,0.00027112968,0.008280428,0.03335013,0.0027635745],"genre_scores_gemma":[0.054325722,0.001106791,0.91131675,0.00015776818,0.00037208173,0.0005267462,0.024405122,0.0023936706,0.0053954422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822956,0.00012163167,0.00027035808,0.00024907175,0.0010026,0.00012677036],"domain_scores_gemma":[0.99667645,0.00050783803,0.0002146795,0.0016561374,0.0007919026,0.00015301655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016297254,0.0013895484,0.0016522062,0.0070704813,0.0014367254,0.002686485,0.0024699455,0.000845562,0.013605866],"category_scores_gemma":[0.007973992,0.0009296378,0.0014967126,0.011501485,0.0007598932,0.006109636,0.0042103366,0.0014781224,0.009886611],"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":[0.00045482395,0.00013231064,0.002453542,0.00069838436,0.00014285618,0.0003384953,0.0003912591,0.005502801,0.017251046,0.015706765,0.0835228,0.8734049],"study_design_scores_gemma":[0.00041277526,0.00060622994,0.0066759484,0.00029965126,0.00038996933,0.0035778773,0.0008460008,0.37913963,0.091242075,0.09838138,0.41808313,0.00034525915],"about_ca_topic_score_codex":0.0034205597,"about_ca_topic_score_gemma":0.004586062,"teacher_disagreement_score":0.013605866,"about_ca_system_score_codex":0.00064767257,"about_ca_system_score_gemma":0.0019268934,"threshold_uncertainty_score":0.045516133},"labels":[],"label_agreement":null},{"id":"W2166470254","doi":"10.1145/1963192.1963217","title":"CELF++","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":857,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Heuristics; Computer science; Maximization; Greedy algorithm; Simple (philosophy); Node (physics); Set (abstract data type); Mathematical optimization; Monte Carlo method; Quadratic equation; Algorithm; Mathematics","score_opus":0.048721630734645575,"score_gpt":0.1971299117888966,"score_spread":0.14840828105425102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166470254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020683473,0.0016208974,0.6339163,0.0022287022,0.0010108736,0.0012223879,0.017252661,0.21034242,0.111722395],"genre_scores_gemma":[0.113693975,0.00064254826,0.77713627,0.0020901181,0.00025259203,0.0011484149,0.030573552,0.013614512,0.060847998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987896,0.00016407529,0.00006171764,0.00034446636,0.00035706063,0.00028307224],"domain_scores_gemma":[0.9980925,0.0005109937,0.00007868289,0.0007346565,0.00046701948,0.000116240975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090478285,0.0017684974,0.0010504284,0.0014832147,0.0011575756,0.002305102,0.0042295386,0.0013983487,0.06399979],"category_scores_gemma":[0.0042681354,0.0006724781,0.001272904,0.0016163999,0.0006635816,0.0032019522,0.0030699493,0.0015863865,0.041945696],"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":[0.001403927,0.0004684755,0.0014988414,0.0007004419,0.000066415836,0.00027783457,0.00011949983,0.02631439,0.0068509136,0.034630172,0.34100538,0.5866636],"study_design_scores_gemma":[0.00093801855,0.00045445,0.00086614065,0.00012664108,0.000065416745,0.0012406869,0.000099390636,0.2686917,0.018475562,0.05147297,0.65743726,0.00013181569],"about_ca_topic_score_codex":0.005917178,"about_ca_topic_score_gemma":0.0072079753,"teacher_disagreement_score":0.06399979,"about_ca_system_score_codex":0.0017407119,"about_ca_system_score_gemma":0.0018214388,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2167912921","doi":"10.1109/dasfaa.2001.916393","title":"Histogram methods in query optimization: the relation between accuracy and optimality","year":2001,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Histogram; Query optimization; Relation (database); Computer science; Perspective (graphical); Mathematics; Mathematical optimization; Data mining; Pattern recognition (psychology); Artificial intelligence; Image (mathematics)","score_opus":0.0504565396278578,"score_gpt":0.35419588904723354,"score_spread":0.30373934941937575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167912921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03017055,0.0026456895,0.9606269,0.0025712769,0.00008304245,0.00012404149,0.00011989423,0.00034959888,0.003308945],"genre_scores_gemma":[0.59808004,0.0022161694,0.39508486,0.0007335244,0.0005039809,0.00031447242,0.00027442718,0.00038540753,0.0024070302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98184067,0.009057789,0.000996391,0.0021583464,0.0046253772,0.0013213941],"domain_scores_gemma":[0.83211684,0.14485653,0.005000494,0.010745575,0.006285761,0.0009948041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020692868,0.001002971,0.00213695,0.0016460096,0.0013438092,0.0030743014,0.0027454305,0.002928171,0.0032042877],"category_scores_gemma":[0.12866688,0.00090030296,0.0010232803,0.0031908187,0.0045061205,0.012143497,0.0038444134,0.004233187,0.0006063553],"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":[0.0011526019,0.00036708117,0.019228024,0.0005732114,0.00021189425,0.00015262693,0.00081576355,0.41695043,0.0055709346,0.2536881,0.006700929,0.2945884],"study_design_scores_gemma":[0.00007150756,0.0002657099,0.0029001415,0.00005450202,0.000046347373,0.00019617725,0.00019676183,0.8234798,0.0063550733,0.16427714,0.0021160275,0.000040795105],"about_ca_topic_score_codex":0.0039231163,"about_ca_topic_score_gemma":0.0027374798,"teacher_disagreement_score":0.020692868,"about_ca_system_score_codex":0.0030152833,"about_ca_system_score_gemma":0.0027614108,"threshold_uncertainty_score":0.10943568},"labels":[],"label_agreement":null},{"id":"W2168268282","doi":"10.2201/niipi.2012.9.5","title":"Implementation issues of clique enumeration algorithm","year":2012,"lang":"en","type":"article","venue":"Progress in Informatics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Ottawa","keywords":"Enumeration; Computer science; Clique problem; Implementation; Clique; Cluster analysis; Clique percolation method; Clique graph; Graph; Theoretical computer science; Algorithm; Mathematics; Combinatorics; Chordal graph; Artificial intelligence; Programming language; Line graph","score_opus":0.017512544248580602,"score_gpt":0.33306526995043245,"score_spread":0.31555272570185183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168268282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013823027,0.000291777,0.96670103,0.0014400202,0.00018312858,0.0002466051,0.0002661404,0.0044069975,0.012641308],"genre_scores_gemma":[0.16536506,0.00034117076,0.8246034,0.00046453037,0.00010131424,0.00051334186,0.00109084,0.0007674849,0.006752888],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99536884,0.001864627,0.00028869594,0.00057082647,0.0013118639,0.0005951919],"domain_scores_gemma":[0.9921417,0.003296909,0.00020497765,0.0024152223,0.0017330315,0.00020808012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002982259,0.00066599675,0.00085919816,0.0010199434,0.0017175882,0.0037415011,0.0033947178,0.0016477243,0.013190706],"category_scores_gemma":[0.016232055,0.0006675056,0.0008707257,0.0030230426,0.0008442694,0.0058989907,0.002060539,0.0021149504,0.0045378543],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007013155,0.00042864587,0.0024981706,0.00043469077,0.00012386925,0.0002479783,0.00060866016,0.07110402,0.012252455,0.2573418,0.04837372,0.6058847],"study_design_scores_gemma":[0.00023344754,0.00012032457,0.00061286136,0.00010165446,0.00005198443,0.00037192277,0.00035349478,0.7177801,0.017158855,0.2086053,0.05452852,0.00008148831],"about_ca_topic_score_codex":0.0035676619,"about_ca_topic_score_gemma":0.004778997,"teacher_disagreement_score":0.013190706,"about_ca_system_score_codex":0.0015860837,"about_ca_system_score_gemma":0.003991643,"threshold_uncertainty_score":0.044127345},"labels":[],"label_agreement":null},{"id":"W2168503413","doi":"10.1109/icde.2000.839397","title":"DB2 advisor: an optimizer smart enough to recommend its own indexes","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":290,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; IBM (Canada)","funders":"","keywords":"Knapsack problem; Computer science; Index (typography); Selection (genetic algorithm); Quality (philosophy); Relational database management system; Interface (matter); Data mining; World Wide Web; Algorithm; Artificial intelligence; Relational database; Operating system","score_opus":0.04929654506499023,"score_gpt":0.25215798432311193,"score_spread":0.2028614392581217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168503413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051291138,0.001204766,0.77517366,0.0009773376,0.00027241543,0.00039181198,0.0018910767,0.14815651,0.020641264],"genre_scores_gemma":[0.24282525,0.00059455563,0.72487897,0.0007106547,0.00019193268,0.00026824803,0.0034624261,0.0143506685,0.012717259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826765,0.00037065844,0.00012577807,0.00032854933,0.0007798277,0.00012752593],"domain_scores_gemma":[0.9976661,0.0005203184,0.000118641736,0.0010499184,0.00046231292,0.00018266495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028304874,0.0012850257,0.0012515349,0.00082570314,0.00065883115,0.0024933112,0.0017618269,0.0009409102,0.0074017104],"category_scores_gemma":[0.0074396497,0.0008649078,0.00047402168,0.00089201785,0.0004259506,0.0030242053,0.0014075416,0.0017439364,0.0046625193],"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":[0.0026159557,0.0005118069,0.00907537,0.00050912966,0.00032959098,0.00017439912,0.00043317434,0.064967945,0.058279727,0.01139518,0.11852034,0.7331874],"study_design_scores_gemma":[0.00036328865,0.0003197925,0.0035472524,0.000049241276,0.00009919635,0.0002091239,0.000089508125,0.808321,0.07623388,0.0064895237,0.1041486,0.00012959326],"about_ca_topic_score_codex":0.0039078384,"about_ca_topic_score_gemma":0.0041303164,"teacher_disagreement_score":0.0074017104,"about_ca_system_score_codex":0.00056740735,"about_ca_system_score_gemma":0.0011482014,"threshold_uncertainty_score":0.02476114},"labels":[],"label_agreement":null},{"id":"W2169227925","doi":"10.1109/icdm.2002.1184042","title":"Clustering spatial data when facing physical constraints","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Data mining; CURE data clustering algorithm; Constrained clustering; Correlation clustering; Process (computing); Canopy clustering algorithm; Data stream clustering; Artificial intelligence","score_opus":0.05208659589505677,"score_gpt":0.2733264954871608,"score_spread":0.22123989959210405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169227925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09911811,0.0004225459,0.8964009,0.00060249923,0.00005390973,0.00021967757,0.00047444974,0.0006617235,0.002046047],"genre_scores_gemma":[0.42756018,0.00042126916,0.56814754,0.00016737972,0.00006120701,0.00030858416,0.001608818,0.00014149073,0.0015834376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99696046,0.0008344907,0.00020781923,0.00071990164,0.001041077,0.00023619134],"domain_scores_gemma":[0.9886646,0.0075888922,0.000866482,0.0014337506,0.0011892106,0.0002570637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027006625,0.0006847395,0.0014559596,0.0022005716,0.001382434,0.0023601388,0.0021993523,0.0022964037,0.0012224568],"category_scores_gemma":[0.020294836,0.00084884884,0.0009443303,0.0038627235,0.0016173187,0.0030992182,0.0032147416,0.0011165609,0.00042853368],"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":[0.000277392,0.00009104755,0.0077593094,0.00042211602,0.0001244444,0.00061788753,0.00071217684,0.81094843,0.005268305,0.024838666,0.0038718446,0.14506839],"study_design_scores_gemma":[0.000023686034,0.000039336715,0.0011761286,0.000026872065,0.000019044644,0.00026141538,0.0004616025,0.9687641,0.0042773965,0.021175021,0.0037552686,0.00002012874],"about_ca_topic_score_codex":0.00733824,"about_ca_topic_score_gemma":0.007057083,"teacher_disagreement_score":0.00733824,"about_ca_system_score_codex":0.0011118525,"about_ca_system_score_gemma":0.0014310844,"threshold_uncertainty_score":0.014591038},"labels":[],"label_agreement":null},{"id":"W2169459648","doi":"10.1109/icci-cc.2014.6921461","title":"A fuzzy structure similarity algorithm for attributed generalized trees","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Mathematics; Vertex (graph theory); Fuzzy set; Fuzzy number; Fuzzy classification; Fuzzy set operations; Defuzzification; Pattern recognition (psychology); Similarity (geometry); Fuzzy logic; Artificial intelligence; Algorithm; Discrete mathematics; Theoretical computer science; Computer science; Graph; Image (mathematics)","score_opus":0.017784110382046737,"score_gpt":0.24812130767906465,"score_spread":0.2303371972970179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169459648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005428938,0.00008700465,0.9935051,0.00003725871,0.000019410585,0.000069681686,0.0000488043,0.000336234,0.00046743613],"genre_scores_gemma":[0.089843296,0.00009770319,0.9083816,0.000050792703,0.000023810035,0.00013165544,0.00035913888,0.00010018244,0.0010117653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800164,0.00036619743,0.00018995791,0.000526623,0.00080958125,0.00010591561],"domain_scores_gemma":[0.9985056,0.0004895384,0.00014173039,0.0003029987,0.00049200404,0.00006819939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014751456,0.0005055619,0.0012613372,0.0035191902,0.0010681882,0.0017516237,0.0022544586,0.0014703617,0.0024313545],"category_scores_gemma":[0.0055439044,0.00042586753,0.0014646839,0.003732419,0.0008590697,0.003580852,0.0023325367,0.0014950109,0.0006733609],"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":[0.00016021411,0.00013401987,0.001149515,0.00024802334,0.00011667513,0.00015041252,0.00044498526,0.1707057,0.0069156345,0.10911831,0.0026903467,0.7081661],"study_design_scores_gemma":[0.000026959784,0.0000661088,0.00024315187,0.000027625872,0.000027184087,0.00011079885,0.00009106498,0.9122676,0.002960112,0.07945932,0.0046997387,0.000020324402],"about_ca_topic_score_codex":0.0033656834,"about_ca_topic_score_gemma":0.0030640326,"teacher_disagreement_score":0.0035191902,"about_ca_system_score_codex":0.0012944001,"about_ca_system_score_gemma":0.001519619,"threshold_uncertainty_score":0.009391606},"labels":[],"label_agreement":null},{"id":"W2169796379","doi":"10.1109/aiccsa.2003.1227557","title":"Using graph representation in content-based image retrieval","year":2004,"lang":"en","type":"article","venue":"","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":"Université de Sherbrooke","funders":"","keywords":"Computer science; Matching (statistics); Cluster analysis; Content-based image retrieval; Image retrieval; Graph; Pattern recognition (psychology); Representation (politics); Content (measure theory); Artificial intelligence; Blossom algorithm; Image (mathematics); Mathematics; Theoretical computer science","score_opus":0.10213553031064675,"score_gpt":0.31487492935051237,"score_spread":0.2127393990398656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169796379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054389182,0.003339245,0.9815941,0.00063672254,0.00033100543,0.00015401532,0.00020962028,0.0023895262,0.0059068743],"genre_scores_gemma":[0.1280395,0.006659009,0.8500831,0.0004818258,0.000666831,0.00027363293,0.0015037411,0.00055985135,0.011732544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939203,0.00020086141,0.00003721797,0.00011026237,0.00022576241,0.000033868673],"domain_scores_gemma":[0.9990175,0.00050151435,0.000051541563,0.00019592927,0.00020020638,0.00003332531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005540538,0.0006440339,0.0008501313,0.002575494,0.00035670868,0.0015893293,0.0010444575,0.0010785887,0.012637532],"category_scores_gemma":[0.0026089002,0.00025779815,0.0005238596,0.004802318,0.00078008237,0.0023754146,0.00058721995,0.0005213711,0.005754561],"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":[0.00017041643,0.000087855646,0.00034769494,0.0009422628,0.00010236074,0.00029199294,0.00011455541,0.045909412,0.029305039,0.031731676,0.03089172,0.860105],"study_design_scores_gemma":[0.00006604318,0.00027015593,0.0009829868,0.00019733763,0.00020892345,0.001039991,0.00014895685,0.74679685,0.034265794,0.09745866,0.118441895,0.00012231455],"about_ca_topic_score_codex":0.0020516503,"about_ca_topic_score_gemma":0.0017142768,"teacher_disagreement_score":0.012637532,"about_ca_system_score_codex":0.00052085007,"about_ca_system_score_gemma":0.00034148837,"threshold_uncertainty_score":0.04227674},"labels":[],"label_agreement":null},{"id":"W2169813676","doi":"","title":"On distributed robust routing for transportation networks under local information constraints","year":2011,"lang":"en","type":"article","venue":"Lund University Publications (Lund University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Computer science; Routing (electronic design automation); Computer network; Distributed computing; Static routing; Policy-based routing; Routing protocol","score_opus":0.02472950949099545,"score_gpt":0.1785706005424823,"score_spread":0.15384109105148686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169813676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006883732,0.00060513866,0.9886348,0.0004932996,0.00013915828,0.00008076345,0.0001212817,0.00019198567,0.0028497453],"genre_scores_gemma":[0.5628951,0.0028394412,0.41601506,0.0006185341,0.00083161535,0.0006251354,0.0010767523,0.0008640957,0.014234245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972128,0.00106553,0.0001793749,0.0006330595,0.0006191985,0.0002900318],"domain_scores_gemma":[0.9893986,0.007945711,0.0006125825,0.0010369384,0.0007570385,0.00024902145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045041596,0.002561006,0.0038685529,0.0016808633,0.0010448756,0.002410856,0.003432009,0.0028424042,0.0065273554],"category_scores_gemma":[0.016942393,0.0011765525,0.0018189992,0.0026829129,0.0024936765,0.005345953,0.003951079,0.0027331652,0.0007606967],"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":[0.0002478765,0.00005268981,0.00018186127,0.00017046803,0.0001033244,0.000088862645,0.000076939345,0.91946155,0.0011193558,0.047448542,0.0031474528,0.02790106],"study_design_scores_gemma":[0.000025423944,0.00004391322,0.00006785676,0.000015178681,0.00001975556,0.000016940101,0.000018159417,0.9501645,0.00033110444,0.04851757,0.00076839776,0.000011172968],"about_ca_topic_score_codex":0.004423407,"about_ca_topic_score_gemma":0.0034243588,"teacher_disagreement_score":0.0065273554,"about_ca_system_score_codex":0.0026044187,"about_ca_system_score_gemma":0.0016759305,"threshold_uncertainty_score":0.023820579},"labels":[],"label_agreement":null},{"id":"W2169872697","doi":"10.1109/icde.2009.70","title":"Online Interval Skyline Queries on Time Series","year":2009,"lang":"en","type":"article","venue":"Proceedings - International Conference on Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"SFU Community Trust Endowment Fund; Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Skyline; Interval (graph theory); Computer science; Series (stratigraphy); Set (abstract data type); Time series; Data mining; Time complexity; On the fly; Space (punctuation); Algorithm; Mathematics; Combinatorics; Machine learning","score_opus":0.04585186737646731,"score_gpt":0.27924077977895334,"score_spread":0.23338891240248602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169872697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08528644,0.0013055592,0.8950356,0.0008225484,0.000103290346,0.00023062728,0.0046455776,0.009826737,0.0027435166],"genre_scores_gemma":[0.5262347,0.0006499725,0.46175787,0.00021228117,0.00028651766,0.00024803416,0.008314458,0.000617063,0.0016791805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971794,0.00054594554,0.0003718765,0.0007180875,0.001025435,0.00015934878],"domain_scores_gemma":[0.9901426,0.005266715,0.0009459383,0.002457455,0.0008738514,0.00031346225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022841871,0.0010617388,0.001757981,0.0024715178,0.00070310186,0.0025144746,0.0018136814,0.0011142917,0.0037367353],"category_scores_gemma":[0.016905973,0.0005506423,0.0010071626,0.005003792,0.0005973382,0.007434581,0.0020493115,0.0011457768,0.0012201669],"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":[0.0027239814,0.00051825924,0.015773278,0.0013033608,0.00043758744,0.0011420024,0.0025778138,0.156122,0.0586143,0.080068804,0.043307405,0.6374112],"study_design_scores_gemma":[0.00016493772,0.0003128713,0.003003046,0.00005151886,0.00007661828,0.0006654449,0.0007551129,0.87189555,0.020268098,0.07833466,0.024411099,0.00006120169],"about_ca_topic_score_codex":0.001780238,"about_ca_topic_score_gemma":0.0021469726,"teacher_disagreement_score":0.0037367353,"about_ca_system_score_codex":0.0006872444,"about_ca_system_score_gemma":0.00064668665,"threshold_uncertainty_score":0.012500644},"labels":[],"label_agreement":null},{"id":"W2170300677","doi":"10.5267/j.msl.2011.12.024","title":"A fuzzy application on MICE hosting: An Iranian case study for locating suitable areas based on P.L Indexes","year":2012,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Computer science; Business; Data mining; Artificial intelligence","score_opus":0.02597745523792985,"score_gpt":0.2824678375896739,"score_spread":0.2564903823517441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170300677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96140516,0.00023356183,0.02438041,0.00054496917,0.00002507267,0.00021118007,0.0003829042,0.00007043938,0.012746316],"genre_scores_gemma":[0.9678678,0.00020546583,0.029159537,0.000026400845,0.0000073739943,0.00004549431,0.00017474365,0.0000065163335,0.0025067052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996276,0.00015559726,0.000021598365,0.000045489614,0.000080204285,0.00006950495],"domain_scores_gemma":[0.9993709,0.0003351096,0.00006363835,0.00004351138,0.00011627016,0.00007062486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008162576,0.00038382638,0.0002251721,0.0015083782,0.0012618281,0.00087023806,0.00089804246,0.0011018898,0.0018926527],"category_scores_gemma":[0.0015540984,0.00013768746,0.00052310264,0.0020633668,0.0007171821,0.0006180757,0.00066944456,0.00035492887,0.00017783142],"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":[0.0014199222,0.0017689108,0.2729136,0.0011310959,0.00019885904,0.072016105,0.017673207,0.2698911,0.021276766,0.03758086,0.013948648,0.29018092],"study_design_scores_gemma":[0.0002001007,0.0013018175,0.1371342,0.00027466708,0.00027433987,0.011713284,0.07161622,0.70323133,0.01844496,0.011850595,0.04376335,0.0001952377],"about_ca_topic_score_codex":0.024475172,"about_ca_topic_score_gemma":0.040630337,"teacher_disagreement_score":0.024475172,"about_ca_system_score_codex":0.0010682874,"about_ca_system_score_gemma":0.0007941679,"threshold_uncertainty_score":0.048665404},"labels":[],"label_agreement":null},{"id":"W2170892040","doi":"10.1109/icdew.2007.4400999","title":"Self Managing Top-k (Summary, Keyword) Indexes in XML Retrieval","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information retrieval; Relevance (law); XML; Exploit; Keyword search; Index (typography); Data retrieval; XML database; Data mining; Database; World Wide Web","score_opus":0.009753287891364539,"score_gpt":0.24140543600610764,"score_spread":0.2316521481147431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170892040","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49136093,0.003349055,0.48446178,0.0004440752,0.0000781859,0.0005500934,0.00041776904,0.01500853,0.004329612],"genre_scores_gemma":[0.592527,0.0006054752,0.40323514,0.00009158332,0.00007740679,0.00017327811,0.00070523284,0.00039666894,0.00218824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957652,0.0014738637,0.000732293,0.00042635703,0.0013038695,0.00029846138],"domain_scores_gemma":[0.9883117,0.0050026095,0.00082172337,0.0042280382,0.001274597,0.00036122705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050659864,0.0007002318,0.0015569341,0.0019715878,0.0013465502,0.0027747154,0.002056615,0.0010917687,0.00089674356],"category_scores_gemma":[0.016305774,0.00075625733,0.0004887063,0.0035138684,0.0008004565,0.0079178475,0.0016559991,0.00061567314,0.0007101423],"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":[0.0028399033,0.0012466883,0.020088607,0.0007245916,0.00025343645,0.00058611203,0.0018013332,0.110192835,0.07787771,0.015783276,0.012240528,0.75636506],"study_design_scores_gemma":[0.0003354072,0.0011288197,0.0048978683,0.00003602468,0.0003345578,0.00132454,0.0008078879,0.8698314,0.095380664,0.014592773,0.011153675,0.00017633016],"about_ca_topic_score_codex":0.003967202,"about_ca_topic_score_gemma":0.0049918927,"teacher_disagreement_score":0.0050659864,"about_ca_system_score_codex":0.00094324525,"about_ca_system_score_gemma":0.0016453305,"threshold_uncertainty_score":0.02679187},"labels":[],"label_agreement":null},{"id":"W2170896764","doi":"10.1109/icde.2009.102","title":"Ranking with Uncertain Scores","year":2009,"lang":"en","type":"article","venue":"Proceedings - International Conference on Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ranking (information retrieval); Computer science; Rank (graph theory); Data mining; Set (abstract data type); Uncertain data; Probabilistic logic; Learning to rank; Semantics (computer science); Information retrieval; Ranking SVM; Sampling (signal processing); Machine learning; Artificial intelligence; Mathematics","score_opus":0.05920357991017572,"score_gpt":0.27934363750450186,"score_spread":0.22014005759432614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170896764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052650277,0.0005954988,0.94322836,0.000473721,0.00005152548,0.00015934175,0.00047135714,0.00060834346,0.0017614883],"genre_scores_gemma":[0.5306363,0.00061665126,0.4633922,0.00018608246,0.00021938872,0.00025964773,0.0015581809,0.00017705122,0.0029544525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98605865,0.005429043,0.0010333307,0.0017415601,0.005046347,0.00069112045],"domain_scores_gemma":[0.97359055,0.016288076,0.0019404794,0.004793686,0.0028117662,0.00057548395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008682693,0.0011523833,0.0026845005,0.0030308303,0.001158447,0.0048771068,0.0023135648,0.0014738322,0.0026394501],"category_scores_gemma":[0.036935262,0.0007905192,0.0013252831,0.0057229106,0.0013586637,0.007116942,0.0022949053,0.0016595671,0.0009380834],"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":[0.0009446207,0.00029900108,0.009777337,0.00051658024,0.000298607,0.00042002465,0.00057930266,0.48709315,0.006014702,0.158277,0.0069545396,0.32882512],"study_design_scores_gemma":[0.000052007385,0.0002448764,0.0013144049,0.000031919255,0.000060406073,0.00031568555,0.0001839275,0.8849777,0.0031989254,0.105799355,0.0037569671,0.00006383233],"about_ca_topic_score_codex":0.002582267,"about_ca_topic_score_gemma":0.0036294677,"teacher_disagreement_score":0.008682693,"about_ca_system_score_codex":0.0019021165,"about_ca_system_score_gemma":0.0016675362,"threshold_uncertainty_score":0.04591906},"labels":[],"label_agreement":null},{"id":"W2171353987","doi":"","title":"On multi-level k-ranges for range search.","year":2004,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick; University of Victoria","funders":"","keywords":"Range (aeronautics); Combinatorics; Binary logarithm; Tree (set theory); Mathematics; Physics; Materials science","score_opus":0.1099219150007775,"score_gpt":0.29831106541324254,"score_spread":0.18838915041246504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171353987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109768644,0.001934527,0.8660103,0.0007410313,0.00006748193,0.00034914393,0.00060591655,0.008968096,0.011554904],"genre_scores_gemma":[0.3107322,0.0003321059,0.6847064,0.00021843209,0.000040456234,0.00034458475,0.0009677925,0.0003979781,0.0022600414],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963012,0.0009693485,0.00036014957,0.00052441796,0.0013838741,0.00046102243],"domain_scores_gemma":[0.9882464,0.0052594445,0.0008851197,0.0039954395,0.0012511566,0.0003624615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027521085,0.00069455925,0.001217517,0.0021254143,0.0010194363,0.002348041,0.004197444,0.0015282014,0.008235366],"category_scores_gemma":[0.015777828,0.0007744085,0.0011002294,0.0042079403,0.0014333602,0.0092640435,0.005532727,0.0013868555,0.0033901911],"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":[0.0017640615,0.00078769366,0.011688859,0.0008950773,0.00019399893,0.00037392063,0.0014579325,0.13033871,0.0220907,0.09942713,0.0144169275,0.71656513],"study_design_scores_gemma":[0.0004310485,0.0011432677,0.0027851202,0.00017910147,0.000097730044,0.001104892,0.00065226783,0.8599319,0.021050062,0.094208956,0.01822833,0.00018740266],"about_ca_topic_score_codex":0.0033079798,"about_ca_topic_score_gemma":0.0046289777,"teacher_disagreement_score":0.008235366,"about_ca_system_score_codex":0.0010051271,"about_ca_system_score_gemma":0.0014037357,"threshold_uncertainty_score":0.027550042},"labels":[],"label_agreement":null},{"id":"W2176441584","doi":"10.1109/pacrim.2015.7334826","title":"The Area Code Tree for nearest neighbour searching","year":2015,"lang":"en","type":"article","venue":"","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":"University of Lethbridge","funders":"","keywords":"Computer science; Code (set theory); Nearest neighbour; Trie; Tree (set theory); Sequence (biology); k-nearest neighbors algorithm; Nearest neighbor search; Chain code; Algorithm; Theoretical computer science; Data structure; Data mining; Artificial intelligence; Mathematics; Combinatorics; Programming language","score_opus":0.09276382512785833,"score_gpt":0.2980709151642166,"score_spread":0.20530709003635825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2176441584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010160124,0.0011179702,0.9799013,0.00024725514,0.00012943418,0.00012089575,0.0004246026,0.0017557296,0.0061427113],"genre_scores_gemma":[0.17912848,0.0011845141,0.81145346,0.00025010042,0.00011638506,0.00031150162,0.0020866913,0.0003480581,0.0051207524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982546,0.00026623366,0.00011114476,0.00024101758,0.0009983768,0.00012854589],"domain_scores_gemma":[0.9969863,0.0009083577,0.0001786936,0.0006588554,0.0011522311,0.00011549876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074726524,0.00043303438,0.0008259624,0.0021132466,0.0012357456,0.0016175753,0.0017649628,0.0009999541,0.004964555],"category_scores_gemma":[0.0076444955,0.0003132922,0.00059642683,0.003843011,0.00078675785,0.0034061344,0.001844207,0.0009562593,0.0030169568],"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":[0.0003803333,0.000116936986,0.0031621275,0.00035853387,0.000074430325,0.00025943827,0.0004128735,0.07664861,0.010125444,0.09708223,0.024753619,0.7866255],"study_design_scores_gemma":[0.00007736881,0.00025573611,0.001572894,0.00015291279,0.000054402542,0.0010119113,0.0003200327,0.78839135,0.0121892365,0.11361906,0.08224487,0.00011022985],"about_ca_topic_score_codex":0.007168413,"about_ca_topic_score_gemma":0.0056915465,"teacher_disagreement_score":0.007168413,"about_ca_system_score_codex":0.0008747418,"about_ca_system_score_gemma":0.0015152004,"threshold_uncertainty_score":0.016608119},"labels":[],"label_agreement":null},{"id":"W2183598638","doi":"","title":"SOLAP: A NEW TYPE OF USER INTERFACE TO SUPPORT SPATIO-TEMPORAL MULTIDIMENSIONAL DATA EXPLORATION AND ANALYSIS","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Online analytical processing; Data warehouse; Computer science; Exploit; Spatial database; Database; Data mining; Spatial analysis; Decision support system; Component (thermodynamics); Transaction data; Data science; Database transaction; Geography","score_opus":0.09455095475737456,"score_gpt":0.3248288427895295,"score_spread":0.23027788803215493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183598638","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041544354,0.0011672645,0.80095696,0.00090975605,0.00045329455,0.00052342634,0.005569504,0.17121069,0.015054666],"genre_scores_gemma":[0.04588521,0.0020311242,0.8808997,0.0034722353,0.00056853757,0.0017245908,0.015980104,0.021789635,0.027648853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973236,0.0006120893,0.00031306682,0.00036349005,0.0012316797,0.00015618172],"domain_scores_gemma":[0.99397075,0.0028351063,0.00024920877,0.0012001763,0.0012194647,0.00052524154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004605301,0.0020965731,0.0017156117,0.0027395228,0.0007290641,0.0050717364,0.0041396846,0.0026357635,0.035022005],"category_scores_gemma":[0.009900106,0.0013599881,0.0020427657,0.0031022409,0.000945356,0.010678625,0.0072153737,0.0036709043,0.018399585],"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":[0.001904079,0.00030776032,0.0022796735,0.0024736773,0.00031657724,0.0021033164,0.0049224137,0.0034315255,0.02744087,0.044986643,0.43517193,0.4746615],"study_design_scores_gemma":[0.00019070551,0.0002534065,0.0012580708,0.00047514695,0.000102537146,0.0024655827,0.00039148613,0.03393696,0.012763681,0.03104929,0.9168216,0.0002915851],"about_ca_topic_score_codex":0.0010585428,"about_ca_topic_score_gemma":0.0010394849,"teacher_disagreement_score":0.035022005,"about_ca_system_score_codex":0.00046686697,"about_ca_system_score_gemma":0.0012303478,"threshold_uncertainty_score":0.1171602},"labels":[],"label_agreement":null},{"id":"W2184216761","doi":"10.1007/978-3-031-38906-1","title":"Algorithms and Data Structures","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Concordia University","keywords":"Computer science; Data structure; Programming language","score_opus":0.036449678339465635,"score_gpt":0.289143112668365,"score_spread":0.25269343432889935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184216761","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023640213,0.046984438,0.5127898,0.0042926325,0.004491912,0.00018086625,0.0015666572,0.0038924117,0.42343724],"genre_scores_gemma":[0.038401634,0.04073868,0.30597496,0.0022591772,0.004768473,0.0005576024,0.0042967573,0.0033840414,0.5996187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988662,0.00017366622,0.00007829385,0.00022326464,0.00060154905,0.000056947167],"domain_scores_gemma":[0.998868,0.0004569449,0.000038703518,0.00038303263,0.00020222222,0.00005113437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007648909,0.0018853912,0.001949972,0.0026499373,0.0013309739,0.005595141,0.0016284015,0.001071322,0.053255063],"category_scores_gemma":[0.0029419777,0.0010784931,0.0010284069,0.0076245284,0.0023522098,0.006963103,0.0021031038,0.0038738044,0.042478904],"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":[0.00002375081,0.000031226104,0.00011036904,0.0003913142,0.000024518931,0.000034770117,0.00018258192,0.0016029795,0.0009152441,0.51843315,0.16783719,0.31041297],"study_design_scores_gemma":[0.000012498578,0.000019857122,0.00016028294,0.00012529567,0.000019232459,0.00020714322,0.00004468032,0.0040404093,0.0006827811,0.5019397,0.49273503,0.000013052348],"about_ca_topic_score_codex":0.0009096373,"about_ca_topic_score_gemma":0.00092868076,"teacher_disagreement_score":0.053255063,"about_ca_system_score_codex":0.0015477659,"about_ca_system_score_gemma":0.0013283272,"threshold_uncertainty_score":0.17815596},"labels":[],"label_agreement":null},{"id":"W2186003804","doi":"","title":"Using Ontology Based Knowledge Discovery in Location Based Services","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ontology; Computer science; Location-based service; Context (archaeology); Domain (mathematical analysis); Process (computing); Domain knowledge; Data science; Service (business); Raw data; Knowledge extraction; Data mining; Service provider; World Wide Web; Artificial intelligence; Geography","score_opus":0.045304923909569285,"score_gpt":0.3018490734310908,"score_spread":0.2565441495215215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186003804","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019012095,0.0005237355,0.9722019,0.0016094709,0.000067912566,0.00030140806,0.000595083,0.00061068585,0.0050777504],"genre_scores_gemma":[0.19642459,0.0010410507,0.79873323,0.00028320274,0.000041846764,0.00031752946,0.0015119992,0.00006458697,0.001581941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948559,0.0017442412,0.00066240935,0.0005019466,0.0019244773,0.00031113104],"domain_scores_gemma":[0.9957917,0.0025215577,0.0003647891,0.0006464049,0.0005291883,0.00014639113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038357873,0.00049095915,0.0009845031,0.0058163935,0.0017802666,0.004933119,0.0022333486,0.0015496269,0.0013847399],"category_scores_gemma":[0.010679808,0.00062938686,0.0022975313,0.0070361434,0.0017252162,0.0069998205,0.0035293165,0.0014190335,0.00048925256],"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":[0.00025136338,0.0006550683,0.00838292,0.00081518973,0.0005613609,0.0023117636,0.003649005,0.12336416,0.0061718677,0.4161667,0.0068947133,0.43077588],"study_design_scores_gemma":[0.00004873909,0.000043917356,0.0014078632,0.00022220275,0.00018223665,0.00058282755,0.0011805265,0.74212724,0.0056682806,0.22127886,0.027171789,0.00008551921],"about_ca_topic_score_codex":0.025276437,"about_ca_topic_score_gemma":0.02908865,"teacher_disagreement_score":0.025276437,"about_ca_system_score_codex":0.0023722665,"about_ca_system_score_gemma":0.0036141481,"threshold_uncertainty_score":0.050258577},"labels":[],"label_agreement":null},{"id":"W2205395973","doi":"","title":"Spatio-temporal trajectory models for target tracking","year":2014,"lang":"en","type":"article","venue":"International Conference on Information Fusion","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":"University of British Columbia","funders":"","keywords":"Computer science; Trajectory; Hidden Markov model; BitTorrent tracker; Tracking (education); Context model; Markov chain; Artificial intelligence; Context (archaeology); Scale (ratio); Machine learning; Eye tracking","score_opus":0.04446074168811947,"score_gpt":0.2722947473471788,"score_spread":0.2278340056590593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205395973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009529824,0.00049547484,0.98646253,0.00032644268,0.000073882184,0.000028476978,0.00074502855,0.00043839446,0.0018997992],"genre_scores_gemma":[0.7447996,0.0024908409,0.23302048,0.00029421944,0.0002216914,0.00039670622,0.0036856118,0.0002891172,0.014801704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994529,0.00015501735,0.000039709445,0.00014691941,0.00014556866,0.000059918086],"domain_scores_gemma":[0.9986405,0.0007021325,0.00020703439,0.00016936682,0.0002272859,0.000053728425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096517784,0.0008848529,0.0006985136,0.00094874605,0.00044184644,0.0010708445,0.0017998257,0.0012473078,0.002469015],"category_scores_gemma":[0.0039288276,0.00043349943,0.0011331658,0.0017228855,0.0006879767,0.0019835446,0.0009066456,0.0015181184,0.0009190741],"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":[0.000023189408,0.000012140263,0.0006839046,0.000034331228,0.000032282285,0.00006168251,0.0000671942,0.92215914,0.00046514513,0.06537342,0.00091546663,0.010172085],"study_design_scores_gemma":[0.000002302231,0.000004765259,0.000084964064,0.0000044460917,0.0000061512787,0.0000114508475,0.000006571011,0.9746328,0.00007279062,0.024188649,0.0009810284,0.0000040534624],"about_ca_topic_score_codex":0.026326643,"about_ca_topic_score_gemma":0.02030832,"teacher_disagreement_score":0.026326643,"about_ca_system_score_codex":0.0013724001,"about_ca_system_score_gemma":0.0013472089,"threshold_uncertainty_score":0.052346826},"labels":[],"label_agreement":null},{"id":"W2215748439","doi":"10.1145/2820783.2820846","title":"Optimal time-dependent sequenced route queries in road networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"European Social Fund; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Pruning; Computer science; Sequence (biology); Set (abstract data type); Heuristic; Enhanced Data Rates for GSM Evolution; Dependency (UML); Travel time; Speedup; Function (biology); Scheme (mathematics); Algorithm; Theoretical computer science; Mathematical optimization; Mathematics; Artificial intelligence; Parallel computing","score_opus":0.021746509476664955,"score_gpt":0.24062002327376913,"score_spread":0.21887351379710418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215748439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09461504,0.0005361861,0.89948696,0.00023448555,0.000037047506,0.00020646834,0.0006607957,0.002007169,0.0022157899],"genre_scores_gemma":[0.36125496,0.00021729077,0.6340994,0.000078644014,0.00003206271,0.00016745659,0.0018865862,0.00030324608,0.0019603209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986021,0.0003601557,0.00011240439,0.00035740252,0.00035979488,0.00020826489],"domain_scores_gemma":[0.99797696,0.0012595598,0.00017047006,0.0002243654,0.0002688772,0.00009976798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011748228,0.0010538051,0.0014718769,0.0015559929,0.00085359573,0.0017036785,0.0021902795,0.0018132086,0.0024536056],"category_scores_gemma":[0.004366605,0.000741455,0.0009395504,0.0023853271,0.00070660556,0.0025949553,0.001270392,0.0009491439,0.00060772715],"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":[0.00059696875,0.00020425094,0.0016089967,0.00025058375,0.00008909703,0.000222867,0.00042866514,0.8021798,0.008746374,0.015811687,0.0047587967,0.16510184],"study_design_scores_gemma":[0.000048248014,0.00006838154,0.00020952127,0.0000069422663,0.000013782324,0.00006406879,0.00018225532,0.9842822,0.0024934155,0.011094414,0.001523289,0.0000134898655],"about_ca_topic_score_codex":0.010042902,"about_ca_topic_score_gemma":0.008464548,"teacher_disagreement_score":0.010042902,"about_ca_system_score_codex":0.0009165286,"about_ca_system_score_gemma":0.0018573737,"threshold_uncertainty_score":0.019968927},"labels":[],"label_agreement":null},{"id":"W2217207442","doi":"10.1007/978-3-642-32115-3_23","title":"Optimization of Quadtree Representation and Compression","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"McMaster University","funders":"","keywords":"Quadtree; Computer science; Representation (politics); Node (physics); Heuristic; Data compression; Algorithm; Compression (physics); R-tree; Theoretical computer science; Spatial analysis; Artificial intelligence; Spatial database; Mathematics; Statistics","score_opus":0.023680281446400418,"score_gpt":0.26125002521301244,"score_spread":0.23756974376661202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2217207442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02275829,0.00086309534,0.962128,0.00023173208,0.00009925042,0.000058316167,0.00022228995,0.0010668392,0.012572146],"genre_scores_gemma":[0.23108707,0.0008412672,0.7542715,0.000109679495,0.000086325774,0.000121247234,0.00063773827,0.0008550312,0.011990052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958724,0.00006283206,0.000017608074,0.000049914164,0.00022351168,0.000058864436],"domain_scores_gemma":[0.999445,0.00022887602,0.000035934303,0.00011962234,0.00014964276,0.00002088726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004044244,0.00064759015,0.0007167708,0.00088686054,0.00027062235,0.0011755532,0.00096647104,0.0007424817,0.010442376],"category_scores_gemma":[0.0026198258,0.00034325663,0.0004225248,0.0019028779,0.00037811827,0.0015586866,0.00095890823,0.0008138031,0.001687272],"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":[0.00015238655,0.00011712498,0.00037543205,0.00017191318,0.000025860476,0.00006723164,0.000056019417,0.2803176,0.02870157,0.04668885,0.012001484,0.6313246],"study_design_scores_gemma":[0.000014981122,0.000047916874,0.00024725465,0.000016666922,0.0000111744885,0.000108118875,0.000025293173,0.9657965,0.008543985,0.02058926,0.004588607,0.000010312639],"about_ca_topic_score_codex":0.0018894459,"about_ca_topic_score_gemma":0.0030038017,"teacher_disagreement_score":0.010442376,"about_ca_system_score_codex":0.0006487608,"about_ca_system_score_gemma":0.00080793654,"threshold_uncertainty_score":0.03493321},"labels":[],"label_agreement":null},{"id":"W2239313659","doi":"10.11575/prism/17658","title":"The 2DR-tree: a 2-dimensional spatial access method","year":2004,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Node (physics); Algorithm; Computer science; Mathematics; Bounding overwatch; Tree (set theory); Partition (number theory); Time complexity; Binary number; Theoretical computer science; Combinatorics; Artificial intelligence","score_opus":0.012118856456450385,"score_gpt":0.2474575217851173,"score_spread":0.2353386653286669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2239313659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002006929,0.00034832777,0.99026734,0.00016337617,0.00007411554,0.00008675568,0.00041374261,0.0040331045,0.0026062746],"genre_scores_gemma":[0.028101964,0.00048027755,0.96253055,0.00019939052,0.000073057345,0.00023636698,0.0016030459,0.0010989235,0.005676357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971379,0.0006013114,0.00025498768,0.00042849054,0.0013807812,0.00019657768],"domain_scores_gemma":[0.9973067,0.00077134,0.00017569668,0.0009260658,0.0006749327,0.00014521882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016410914,0.00076122535,0.001286842,0.0021702999,0.0011365305,0.003289243,0.0029418364,0.001189778,0.00802186],"category_scores_gemma":[0.0053693308,0.0007236901,0.0013040849,0.003971766,0.0012041,0.006471174,0.0038657866,0.001568504,0.0037021504],"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":[0.00027853643,0.00012833506,0.0015619687,0.00052700436,0.000114847964,0.0003187156,0.00089542434,0.018059237,0.011600766,0.1416767,0.06579239,0.759046],"study_design_scores_gemma":[0.00021992113,0.00020348225,0.0012087255,0.00021208363,0.00011497373,0.0016973884,0.00041897234,0.4260765,0.02609296,0.15793839,0.38557136,0.00024530792],"about_ca_topic_score_codex":0.0034329826,"about_ca_topic_score_gemma":0.005222615,"teacher_disagreement_score":0.00802186,"about_ca_system_score_codex":0.00086465763,"about_ca_system_score_gemma":0.0022766264,"threshold_uncertainty_score":0.0268358},"labels":[],"label_agreement":null},{"id":"W2257535843","doi":"10.1007/s10601-016-9238-x","title":"ℚ-bounds consistency for the spread constraint with variable mean","year":2016,"lang":"en","type":"article","venue":"Constraints","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variable (mathematics); Constraint (computer-aided design); Mathematics; Consistency (knowledge bases); Piecewise; Local consistency; Algorithm; Extension (predicate logic); Function (biology); Mathematical optimization; Computer science; Constraint satisfaction; Statistics; Discrete mathematics","score_opus":0.021146918477015452,"score_gpt":0.2322014977262077,"score_spread":0.21105457924919227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257535843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012546476,0.0007719257,0.97728467,0.0018702511,0.00020045998,0.0000858631,0.0004724334,0.0005370858,0.0062308605],"genre_scores_gemma":[0.38584977,0.0014005377,0.5908739,0.0020809046,0.0014296985,0.0006364134,0.002485177,0.0016022037,0.01364132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9834425,0.005449997,0.001101577,0.003923732,0.0045742122,0.0015081088],"domain_scores_gemma":[0.9119903,0.062454425,0.0031706577,0.013439795,0.0073922644,0.0015525782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013046848,0.0019531809,0.004053639,0.0027518927,0.0026018831,0.0065175607,0.008074513,0.0038154237,0.0103825405],"category_scores_gemma":[0.08957387,0.0022549715,0.0033035951,0.006548978,0.004635072,0.018077455,0.009396043,0.010680196,0.0016751305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007722436,0.00023486605,0.002040977,0.00059752865,0.00035352434,0.00024363094,0.0005931334,0.21513934,0.002134422,0.66460025,0.020442916,0.09284728],"study_design_scores_gemma":[0.00007136581,0.00004676955,0.00027695062,0.000077883975,0.000065669185,0.00009579737,0.000054906242,0.37965477,0.0014415251,0.6148165,0.0033545385,0.000043434786],"about_ca_topic_score_codex":0.004962429,"about_ca_topic_score_gemma":0.0037227906,"teacher_disagreement_score":0.013046848,"about_ca_system_score_codex":0.0030937511,"about_ca_system_score_gemma":0.005177681,"threshold_uncertainty_score":0.06899911},"labels":[],"label_agreement":null},{"id":"W2266752413","doi":"10.3138/cart.50.4.3126","title":"Maps and the Meaning of the Cloud","year":2015,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Workspace; Nexus (standard); Meaning (existential); Computer science; Symbol (formal); Scale (ratio); Data science; Geography; Cartography; Artificial intelligence; Epistemology; Operating system","score_opus":0.014762193782532636,"score_gpt":0.2584982673258402,"score_spread":0.24373607354330756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266752413","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028919851,0.09617256,0.11920056,0.15433654,0.0098425,0.00011245125,0.0020308476,0.0005553932,0.5888293],"genre_scores_gemma":[0.84320194,0.056991056,0.041175876,0.011569505,0.008482799,0.00018861722,0.0007940529,0.0007064245,0.036889855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99790597,0.0009803424,0.00009364226,0.000315,0.00050982164,0.00019518193],"domain_scores_gemma":[0.99693716,0.0016542712,0.00020667752,0.0004369613,0.0005576357,0.00020731355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002095559,0.00061042357,0.00053090916,0.0031479627,0.0054671247,0.012410669,0.001309358,0.002176522,0.008412345],"category_scores_gemma":[0.00861804,0.00033970588,0.0004299292,0.0066860165,0.020876845,0.016858794,0.0043302765,0.0039864443,0.0013775994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008449124,0.0000018824875,0.000092591225,0.000032085638,0.0000020287878,0.000043160424,0.0010969542,0.00030267966,0.000034377987,0.98600566,0.0059950096,0.0063851797],"study_design_scores_gemma":[0.0000075730723,0.000005666455,0.000246969,0.00017058771,0.000003831048,0.00012395982,0.0016203142,0.0009335903,0.00006330808,0.7458897,0.25092188,0.000012565347],"about_ca_topic_score_codex":0.015162096,"about_ca_topic_score_gemma":0.008046753,"teacher_disagreement_score":0.015162096,"about_ca_system_score_codex":0.004360173,"about_ca_system_score_gemma":0.0021483002,"threshold_uncertainty_score":0.031635404},"labels":[],"label_agreement":null},{"id":"W2272548005","doi":"","title":"OPEN DATA EN TRANSPORT URBAIN : QUELLES SONT LES DONNEES MISES A DISPOSITION ? QUELS SONT LES STRATEGIES DES AUTORITES ORGANISATRICES ?","year":2013,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.03676807949528188,"score_gpt":0.2555181290154801,"score_spread":0.21875004952019822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272548005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0975994,0.009288567,0.72095776,0.03842369,0.0021128224,0.0013322545,0.0106885675,0.016182568,0.10341437],"genre_scores_gemma":[0.54593384,0.010867278,0.32701012,0.004694761,0.00094836456,0.0018471546,0.019741232,0.0076906886,0.08126662],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9766989,0.008326499,0.002014277,0.0028084507,0.008670219,0.0014816717],"domain_scores_gemma":[0.9423096,0.018334594,0.002850015,0.01760447,0.01691922,0.001982083],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.02290319,0.0012738912,0.0015676357,0.0051054345,0.0053935926,0.019212328,0.0034213054,0.0035417445,0.011074227],"category_scores_gemma":[0.07112394,0.001101385,0.001267452,0.008427649,0.003954192,0.03252039,0.0077104704,0.0053773327,0.0073638074],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012142516,0.0003132157,0.03257694,0.0019742185,0.00027925518,0.0006686796,0.020178853,0.011241638,0.010802528,0.27558294,0.068992764,0.5761747],"study_design_scores_gemma":[0.00008228249,0.00019081999,0.015127841,0.0018462765,0.00016682723,0.0006207866,0.0222663,0.035030186,0.02700297,0.11273094,0.7844925,0.00044232863],"about_ca_topic_score_codex":0.051401827,"about_ca_topic_score_gemma":0.027429212,"teacher_disagreement_score":0.9965787,"about_ca_system_score_codex":0.0073519135,"about_ca_system_score_gemma":0.011435878,"threshold_uncertainty_score":0.1211251},"labels":[],"label_agreement":null},{"id":"W2274280163","doi":"10.1007/978-3-642-24412-4_34","title":"Learning a Classifier when the Labeling Is Known","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Artificial intelligence; Computer science","score_opus":0.03076799446700004,"score_gpt":0.23714233784831265,"score_spread":0.2063743433813126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274280163","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057913918,0.0013994258,0.91532254,0.007852553,0.0017870257,0.00022446208,0.0014101851,0.0029030188,0.011186912],"genre_scores_gemma":[0.4882809,0.0016590705,0.4670485,0.0031754517,0.003728232,0.0005670212,0.008601577,0.0006909963,0.026248166],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961272,0.0006495155,0.00030007568,0.0016286237,0.00094922335,0.0003453619],"domain_scores_gemma":[0.986953,0.007630982,0.0005183617,0.0032166094,0.001336734,0.00034422605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004220669,0.0011086774,0.0026647826,0.0014539037,0.0014939258,0.0044420487,0.003588987,0.0054531135,0.0047110165],"category_scores_gemma":[0.020170972,0.0010284837,0.0016465945,0.0015549962,0.0014866557,0.011788653,0.002878147,0.005411774,0.00426276],"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":[0.0014087927,0.0005187705,0.010052099,0.0009082608,0.00033977313,0.0011653539,0.000567013,0.040733017,0.013119094,0.03974035,0.08125212,0.8101954],"study_design_scores_gemma":[0.000088534616,0.00016547149,0.0016733335,0.00019114999,0.0001567832,0.0010975031,0.00027403253,0.75913084,0.015023349,0.20380308,0.018337803,0.000058156405],"about_ca_topic_score_codex":0.0014816716,"about_ca_topic_score_gemma":0.00269073,"teacher_disagreement_score":0.0054531135,"about_ca_system_score_codex":0.0010737798,"about_ca_system_score_gemma":0.0016602642,"threshold_uncertainty_score":0.022321284},"labels":[],"label_agreement":null},{"id":"W2277931218","doi":"10.1007/978-3-319-24704-5_18","title":"L-Fuzzy Databases in Arrow Categories","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Arrow; Fuzzy logic; Database; Semantics (computer science); Fuzzy set; Information retrieval; Data mining; Theoretical computer science; Artificial intelligence; Natural language processing; Programming language","score_opus":0.043052184250674363,"score_gpt":0.2719023573771973,"score_spread":0.22885017312652295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277931218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04553838,0.021071548,0.65886843,0.003680791,0.001284307,0.00011960685,0.0009856354,0.00075571146,0.2676956],"genre_scores_gemma":[0.58389866,0.013268956,0.2800099,0.0013181586,0.0012265703,0.00022577848,0.0010187534,0.00023805491,0.118795164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99945575,0.000110435896,0.00006577746,0.00010408067,0.00021452416,0.000049366114],"domain_scores_gemma":[0.9994578,0.00017743326,0.000037911042,0.00009462872,0.00017668911,0.00005556213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066179835,0.0003397313,0.00051495933,0.0019266878,0.0011022036,0.0034140118,0.0009675421,0.0006246679,0.008021721],"category_scores_gemma":[0.0016394813,0.0003762012,0.00040529587,0.003796623,0.0014863902,0.0068696183,0.0018090663,0.0015053169,0.002522211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001840547,0.000008029267,0.00009280885,0.000101781676,0.0000049747096,0.00003535726,0.00021831068,0.00039170694,0.0007581726,0.9494329,0.0051497747,0.043787856],"study_design_scores_gemma":[0.000005204393,0.00001624089,0.00010166891,0.00004606725,0.000006246105,0.00014169293,0.000113356495,0.0020794645,0.00077262725,0.92938864,0.06731531,0.000013426421],"about_ca_topic_score_codex":0.00096247206,"about_ca_topic_score_gemma":0.0006873973,"teacher_disagreement_score":0.008021721,"about_ca_system_score_codex":0.0013016434,"about_ca_system_score_gemma":0.0006849973,"threshold_uncertainty_score":0.026835322},"labels":[],"label_agreement":null},{"id":"W2278525775","doi":"10.4271/2005-01-1599","title":"A Navigation Assistance Agent: Online Access to LBS","year":2005,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Xiamen University; Jilin University","keywords":"Computer science; World Wide Web","score_opus":0.023266095078658476,"score_gpt":0.2917587890252124,"score_spread":0.26849269394655395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278525775","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110264316,0.0006628244,0.7443283,0.0014353015,0.00039693282,0.0010076808,0.0026191296,0.062156826,0.07712861],"genre_scores_gemma":[0.49551937,0.0006684963,0.4111719,0.00051376905,0.00017278967,0.00035296453,0.0036007687,0.00073000416,0.08726997],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976784,0.000032777414,0.00001747127,0.000043132208,0.00009434671,0.00004431393],"domain_scores_gemma":[0.9996189,0.000060322473,0.00003851522,0.00008154309,0.00010574179,0.00009505292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033416707,0.00037963022,0.00051924697,0.0007220577,0.00055576937,0.0010663073,0.0008224361,0.00096130907,0.010770251],"category_scores_gemma":[0.0010325173,0.00017628801,0.00026113752,0.00053472136,0.0002610188,0.0014218247,0.000982043,0.00045828996,0.0049875085],"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":[0.001556051,0.00086685526,0.010393571,0.00069831527,0.00010003324,0.0023105338,0.0014257163,0.008058935,0.08866698,0.04473929,0.12009648,0.7210872],"study_design_scores_gemma":[0.0002456083,0.00050997233,0.0063673905,0.00013142529,0.00016378946,0.0020312583,0.00058589736,0.22154999,0.077544145,0.007107891,0.6836265,0.00013623269],"about_ca_topic_score_codex":0.00530145,"about_ca_topic_score_gemma":0.0054544252,"teacher_disagreement_score":0.010770251,"about_ca_system_score_codex":0.00031046485,"about_ca_system_score_gemma":0.00065044663,"threshold_uncertainty_score":0.036030114},"labels":[],"label_agreement":null},{"id":"W2279033101","doi":"10.1007/978-3-540-85902-4_3","title":"XML Retrieval by Improving Structural Relevance Measures Obtained from Summary Models","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Relevance (law); Information retrieval; Ranking (information retrieval); XML; Rank (graph theory); Data mining; Document Structure Description; World Wide Web","score_opus":0.01870901423863776,"score_gpt":0.22113036841028633,"score_spread":0.20242135417164858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279033101","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08981417,0.0072911642,0.8852141,0.0008103167,0.0003128954,0.00027122468,0.0024408603,0.00952149,0.004323849],"genre_scores_gemma":[0.60882753,0.002784924,0.3700568,0.0003909301,0.001067763,0.00025516155,0.010507062,0.0010765891,0.0050331936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970126,0.00089465716,0.00040255772,0.0005487613,0.00096281664,0.00017853035],"domain_scores_gemma":[0.9923078,0.0043651816,0.0005650405,0.0012581365,0.0013629606,0.00014085737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029030805,0.0013392734,0.0018511158,0.008031961,0.00055816356,0.002411691,0.001505983,0.0016952197,0.0045887493],"category_scores_gemma":[0.017080553,0.0005917986,0.0017924809,0.004502744,0.00045032435,0.006076356,0.0012817956,0.0013003375,0.0029882225],"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":[0.0016217995,0.0003612958,0.0052044364,0.0010435754,0.00052282354,0.0002688666,0.00023627003,0.041397743,0.03649137,0.01141878,0.021021444,0.88041157],"study_design_scores_gemma":[0.00016095968,0.0006642358,0.0038110204,0.00009927509,0.0008873868,0.0005142503,0.0001317481,0.92995316,0.026920285,0.026953291,0.009809843,0.00009445436],"about_ca_topic_score_codex":0.0023497918,"about_ca_topic_score_gemma":0.0035165106,"teacher_disagreement_score":0.008031961,"about_ca_system_score_codex":0.0007873267,"about_ca_system_score_gemma":0.0010311101,"threshold_uncertainty_score":0.015353084},"labels":[],"label_agreement":null},{"id":"W2279569925","doi":"10.1016/j.jlamp.2015.12.006","title":"Dependencies in relational models of databases","year":2016,"lang":"en","type":"article","venue":"Journal of Logical and Algebraic Methods in Programming","topic":"Data Management and Algorithms","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relational database; Computer science; Fuzzy logic; Set (abstract data type); Fuzzy set; Data mining; Relational model; Database; Binary number; Binary relation; Value (mathematics); Database model; Theoretical computer science; Mathematics; Artificial intelligence; Discrete mathematics; Programming language; Machine learning; Arithmetic","score_opus":0.13628566378259063,"score_gpt":0.3716287206328223,"score_spread":0.23534305685023169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279569925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046751175,0.0023830084,0.93143696,0.0029946086,0.00020073536,0.0000812151,0.00087134447,0.0005906972,0.014690211],"genre_scores_gemma":[0.7265599,0.004315971,0.24919118,0.0008332396,0.0006217746,0.00032363483,0.002157795,0.00039687037,0.015599613],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99567586,0.0017391003,0.00050098076,0.0006321028,0.0011675077,0.00028442687],"domain_scores_gemma":[0.9869131,0.00856614,0.00095418765,0.0020251973,0.0010687525,0.00047269737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039020237,0.0007244001,0.0009812869,0.002252457,0.0016881364,0.0051221102,0.0021027795,0.0015487131,0.004866988],"category_scores_gemma":[0.018777436,0.0014281472,0.0015299713,0.0032709995,0.0026592722,0.014718196,0.0033227482,0.0032324076,0.00083249935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027450345,0.00002473373,0.0006066379,0.000064376516,0.00001942204,0.000100493104,0.0003326683,0.009076589,0.00022023205,0.9793107,0.0013789316,0.008837704],"study_design_scores_gemma":[0.000008519729,0.000009071904,0.00008811589,0.000021937047,0.000023497605,0.00005315992,0.000059115413,0.03286756,0.00020025912,0.9624515,0.0042072497,0.0000099928175],"about_ca_topic_score_codex":0.0048390813,"about_ca_topic_score_gemma":0.0042302413,"teacher_disagreement_score":0.0051221102,"about_ca_system_score_codex":0.0017885937,"about_ca_system_score_gemma":0.0019398835,"threshold_uncertainty_score":0.020636082},"labels":[],"label_agreement":null},{"id":"W2279683949","doi":"10.1007/978-3-642-38457-8_28","title":"Preference Thresholds Optimization by Interactive Variation","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Université de Montréal","funders":"","keywords":"Weighting; Cluster analysis; Similarity (geometry); Preference; Computer science; Variation (astronomy); Cluster (spacecraft); Statistics; Data mining; Artificial intelligence; Mathematics; Physics","score_opus":0.017747836409253533,"score_gpt":0.22824435531297613,"score_spread":0.2104965189037226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279683949","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063216677,0.0002644914,0.9856845,0.00013017561,0.0000642952,0.00003370455,0.000032942313,0.00025339043,0.0072148573],"genre_scores_gemma":[0.47134903,0.00046314145,0.5076665,0.00024278126,0.00020866086,0.00034116267,0.00019332099,0.0007934373,0.018741826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987556,0.0005414235,0.000043469165,0.00019539245,0.00031842128,0.00014583088],"domain_scores_gemma":[0.998575,0.0009692716,0.000065544365,0.00016534631,0.00013878004,0.000085999156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016249035,0.0009052456,0.0016645043,0.0008752358,0.0005257309,0.0014426378,0.002313873,0.0010838897,0.00846514],"category_scores_gemma":[0.005890988,0.00068251137,0.001367344,0.0015927576,0.001089415,0.0017233775,0.0025930419,0.0022754287,0.0010582629],"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":[0.00023934759,0.00015990887,0.00035889336,0.00023588327,0.00013280498,0.000096295356,0.00019193458,0.41561535,0.006253901,0.3122532,0.012104577,0.252358],"study_design_scores_gemma":[0.000026408643,0.000063702784,0.00009333026,0.000015132846,0.000015264644,0.000039958202,0.000013015951,0.8997794,0.00079024263,0.09646734,0.0026794581,0.000016776576],"about_ca_topic_score_codex":0.0016911301,"about_ca_topic_score_gemma":0.0013020843,"teacher_disagreement_score":0.00846514,"about_ca_system_score_codex":0.0011842708,"about_ca_system_score_gemma":0.00074417895,"threshold_uncertainty_score":0.028318703},"labels":[],"label_agreement":null},{"id":"W2279848971","doi":"10.1109/wi-iat.2015.173","title":"Querying RDF Data with Imprecise Time Phrases","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"RDF; Computer science; SPARQL; Simple Knowledge Organization System; RDF Schema; Information retrieval; RDF query language; Cwm; Semantic Web; Linked data; Representation (politics); RDF/XML; Data mining; Web search query; Web query classification; Search engine","score_opus":0.07435423628405952,"score_gpt":0.2695548766888717,"score_spread":0.19520064040481216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279848971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021982858,0.0011369027,0.9571505,0.0022093623,0.00016110383,0.00021023366,0.0066755693,0.0022978326,0.008175639],"genre_scores_gemma":[0.22275694,0.002926719,0.7556924,0.00093675184,0.00021714852,0.00039758984,0.012625405,0.000588831,0.0038582194],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99093246,0.0026584535,0.0014173517,0.0011217806,0.0035905915,0.0002794127],"domain_scores_gemma":[0.98777056,0.007308369,0.0010540473,0.0023891942,0.0013469063,0.00013087416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071574426,0.000740155,0.001226868,0.0038373051,0.0014486202,0.005326609,0.0020638048,0.0020529355,0.0023735848],"category_scores_gemma":[0.020290276,0.00058194215,0.0016768107,0.006640622,0.0019498031,0.012578231,0.0028573303,0.0018266402,0.0010360832],"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":[0.00041971667,0.00020179128,0.004193856,0.0013172668,0.0002629896,0.003816403,0.0030513338,0.09896873,0.013412151,0.66596025,0.018394323,0.19000125],"study_design_scores_gemma":[0.00007947221,0.000095303105,0.0010537248,0.0006036781,0.00014731442,0.0021374659,0.002735655,0.25516593,0.025090676,0.5030745,0.20963137,0.00018485967],"about_ca_topic_score_codex":0.0052717933,"about_ca_topic_score_gemma":0.0056280717,"teacher_disagreement_score":0.0071574426,"about_ca_system_score_codex":0.0017321174,"about_ca_system_score_gemma":0.0015950926,"threshold_uncertainty_score":0.037852645},"labels":[],"label_agreement":null},{"id":"W2288975584","doi":"10.1007/s10707-016-0245-2","title":"The direction-constrained k nearest neighbor query","year":2016,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","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":"Simon Fraser University","funders":"Defense Acquisition Program Administration; Agency for Defense Development","keywords":"k-nearest neighbors algorithm; Geography; Best bin first; Nearest neighbor search; Cartography; Mathematics; Computer science; Data mining; Artificial intelligence","score_opus":0.0063903637919549405,"score_gpt":0.19970990491244206,"score_spread":0.19331954112048713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288975584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033407394,0.0024467362,0.9461589,0.001458042,0.0003283222,0.00019603742,0.0032407143,0.0014050665,0.011358778],"genre_scores_gemma":[0.49079436,0.0021287976,0.488804,0.0007476992,0.00026729805,0.0002656571,0.0066238875,0.00025426774,0.010113972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979532,0.00042497113,0.00016264318,0.0004192593,0.0008752493,0.00016468816],"domain_scores_gemma":[0.9982644,0.00050288084,0.00012389664,0.0006295232,0.00039361246,0.00008573807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009131052,0.0004988501,0.0014454253,0.001174476,0.0008994529,0.0017220047,0.0020608313,0.001739366,0.0046437364],"category_scores_gemma":[0.0060051577,0.00041829812,0.00053846324,0.0037604568,0.0007174812,0.0031147522,0.0027863851,0.0011436037,0.001977697],"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":[0.0018558302,0.0003772853,0.0054894537,0.00081033085,0.00014278208,0.0005444314,0.0003276886,0.11647235,0.02035916,0.093509145,0.06766103,0.6924505],"study_design_scores_gemma":[0.00021870775,0.00025885494,0.0017366405,0.00006778863,0.00007243213,0.0015916852,0.00037228037,0.8421176,0.009213408,0.10211116,0.042124372,0.00011507593],"about_ca_topic_score_codex":0.0066291513,"about_ca_topic_score_gemma":0.0076916027,"teacher_disagreement_score":0.0066291513,"about_ca_system_score_codex":0.00073904137,"about_ca_system_score_gemma":0.0020375843,"threshold_uncertainty_score":0.015534878},"labels":[],"label_agreement":null},{"id":"W2290690097","doi":"10.1145/2820783.2820785","title":"Meet-and-go","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point (geometry); Computer science; Aggregate (composite); Simple (philosophy); Impression; World Wide Web; Human–computer interaction; Information retrieval; Mathematics","score_opus":0.03996244385714242,"score_gpt":0.24542442090468836,"score_spread":0.20546197704754593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290690097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13442545,0.000870133,0.7625672,0.007062911,0.00046768345,0.0016266522,0.0111255525,0.0170752,0.06477915],"genre_scores_gemma":[0.3472179,0.00041142592,0.61523825,0.0013272109,0.00010208028,0.00068876956,0.011160538,0.0015948622,0.022259014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983557,0.00036698897,0.00011063526,0.00054916175,0.00032548228,0.0002920065],"domain_scores_gemma":[0.99833375,0.0008291435,0.00011532708,0.00033900733,0.00013014342,0.00025265513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001227601,0.0011762746,0.0013191804,0.0004956968,0.002409267,0.0020743525,0.003436188,0.0036356878,0.03028625],"category_scores_gemma":[0.0052223597,0.00062708196,0.0014705418,0.0010844438,0.0011354154,0.005643942,0.004762714,0.0017886611,0.004909483],"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":[0.0032913205,0.0015347075,0.013305424,0.0028728386,0.0004367455,0.0046987366,0.006236328,0.15965627,0.019438615,0.26901895,0.2609612,0.25854895],"study_design_scores_gemma":[0.00059036113,0.000669021,0.0029578556,0.00011641318,0.00013371962,0.0025457575,0.0070793796,0.46951964,0.01093554,0.26355115,0.24172775,0.00017341676],"about_ca_topic_score_codex":0.0084251985,"about_ca_topic_score_gemma":0.022123277,"teacher_disagreement_score":0.03028625,"about_ca_system_score_codex":0.0008591863,"about_ca_system_score_gemma":0.0011116327,"threshold_uncertainty_score":0.101317585},"labels":[],"label_agreement":null},{"id":"W2292015157","doi":"10.1145/2820783.2820791","title":"Graphast","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Order (exchange); Distributed computing; Data mining","score_opus":0.048982047433589054,"score_gpt":0.24592114263520726,"score_spread":0.1969390952016182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2292015157","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033481205,0.00061211776,0.4424445,0.00072027225,0.00042771586,0.00037138665,0.020340096,0.49287394,0.038861834],"genre_scores_gemma":[0.083978795,0.0025296612,0.438426,0.0018802789,0.00020408706,0.0015582613,0.16973224,0.21729504,0.084395565],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845314,0.00023483156,0.00012505337,0.00033544988,0.0006693943,0.00018209634],"domain_scores_gemma":[0.9975866,0.0006814318,0.00009910434,0.0009183254,0.0005440649,0.0001705066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016932349,0.0019568373,0.0011506776,0.001629678,0.0010224141,0.00387691,0.004514633,0.0014701344,0.055432864],"category_scores_gemma":[0.0065297494,0.0015955757,0.0024694954,0.0018472362,0.0007890987,0.0056781913,0.003967939,0.0027099627,0.037123453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000662593,0.00020937703,0.00215972,0.0013711437,0.00023272003,0.00043834836,0.00069210335,0.014412057,0.010879689,0.07665171,0.7186309,0.1736597],"study_design_scores_gemma":[0.00013717519,0.00009473093,0.0006334619,0.0001319197,0.00007689343,0.00050913275,0.00017903937,0.05511702,0.010918718,0.036335886,0.89575344,0.00011264466],"about_ca_topic_score_codex":0.0119479075,"about_ca_topic_score_gemma":0.013995701,"teacher_disagreement_score":0.055432864,"about_ca_system_score_codex":0.0015053205,"about_ca_system_score_gemma":0.0021059727,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2293475867","doi":"","title":"Towards web search engine scale data mining","year":2009,"lang":"en","type":"article","venue":"Australasian Data Mining Conference","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Adaptability; Search engine; Web mining; Data science; Data mining; Context (archaeology); Web search query; Web crawler; Web search engine; Metasearch engine; Search analytics; Construct (python library); Big data; Information retrieval; World Wide Web; Database; Web service","score_opus":0.12955901543971857,"score_gpt":0.33253224259375386,"score_spread":0.2029732271540353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293475867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022476617,0.0043022516,0.95929426,0.008032251,0.00018871011,0.00024642595,0.0008455352,0.0027528463,0.0018610869],"genre_scores_gemma":[0.11448854,0.0039040237,0.875137,0.0016343464,0.0007722658,0.00051273615,0.0017754423,0.00027885436,0.001496857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99404365,0.0024250562,0.0004136929,0.0010679791,0.0018603642,0.00018934932],"domain_scores_gemma":[0.9732766,0.01633543,0.0011759996,0.0053499155,0.003195586,0.0006663943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010036509,0.0012204511,0.0029840716,0.0045612464,0.0009197942,0.004947164,0.0027648993,0.0025164366,0.0010408211],"category_scores_gemma":[0.04024334,0.0015838818,0.0023870356,0.006794104,0.0016627588,0.009602892,0.0038520922,0.006941668,0.0018604802],"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":[0.00045942658,0.0014804999,0.029384604,0.001874151,0.0010354276,0.0009538346,0.0015770311,0.21984978,0.016577644,0.22938925,0.042003,0.4554153],"study_design_scores_gemma":[0.000059211532,0.00005522839,0.00126737,0.000085811975,0.00004755408,0.00015057367,0.0001984122,0.7455148,0.0021164694,0.23710208,0.0133660175,0.000036511465],"about_ca_topic_score_codex":0.0026758856,"about_ca_topic_score_gemma":0.0029868137,"teacher_disagreement_score":0.010036509,"about_ca_system_score_codex":0.0012974006,"about_ca_system_score_gemma":0.0020275868,"threshold_uncertainty_score":0.05307877},"labels":[],"label_agreement":null},{"id":"W2295016007","doi":"","title":"Bichromatic Line Segment Intersection Counting in O(n sqrt(log n)) Time.","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Intersection (aeronautics); Combinatorics; Line (geometry); Binary logarithm; Point (geometry); Line segment; Reduction (mathematics); Time complexity; Mathematics; Algorithm; Computer science; Discrete mathematics; Geometry","score_opus":0.026179486533430895,"score_gpt":0.22125303109264802,"score_spread":0.19507354455921713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295016007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026978157,0.000861554,0.9347994,0.0003974115,0.00018511266,0.0003395466,0.0016480371,0.019008225,0.015782593],"genre_scores_gemma":[0.15611692,0.0003754328,0.82023394,0.00027190466,0.00008695951,0.00037656413,0.0058127465,0.0013402674,0.015385245],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980876,0.00022969904,0.00014741605,0.00043992512,0.0007399398,0.0003553549],"domain_scores_gemma":[0.9978783,0.00053423166,0.00021168843,0.0007637343,0.0005129697,0.00009905073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047099678,0.0015054975,0.0014488085,0.0023240657,0.0013101902,0.0022776232,0.0032923596,0.0010385574,0.022140864],"category_scores_gemma":[0.0034763059,0.0005737531,0.0008900567,0.0055667143,0.0006573736,0.0042266054,0.0025298537,0.0013767541,0.008830387],"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":[0.00071377767,0.00025576906,0.0020816745,0.0006543873,0.00012264095,0.0003338088,0.00031200695,0.019422099,0.027966177,0.025201045,0.052193187,0.8707435],"study_design_scores_gemma":[0.0004028855,0.0005217411,0.0032095155,0.00016667423,0.00022944975,0.0020069184,0.00080404885,0.67250836,0.09709256,0.12402112,0.09880703,0.00022969737],"about_ca_topic_score_codex":0.0052080806,"about_ca_topic_score_gemma":0.012112823,"teacher_disagreement_score":0.022140864,"about_ca_system_score_codex":0.0010511416,"about_ca_system_score_gemma":0.0018203121,"threshold_uncertainty_score":0.07406855},"labels":[],"label_agreement":null},{"id":"W2295019244","doi":"10.5220/0005463203670373","title":"The mqr-tree for Very Large Object Sets","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Search engine indexing; Computer science; R-tree; Object-based spatial database; Spatial database; Benchmark (surveying); Object (grammar); Tree (set theory); Data mining; Index (typography); Access method; Database index; Point (geometry); Spatial query; Spatial analysis; Information retrieval; Database; Artificial intelligence; Web search query; Search engine; Sargable; Geography; Mathematics; World Wide Web","score_opus":0.03595741419305881,"score_gpt":0.27710284631447935,"score_spread":0.24114543212142053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295019244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062496714,0.0040665623,0.8984028,0.0013591931,0.00027860442,0.0005650783,0.0055384175,0.017205471,0.010087106],"genre_scores_gemma":[0.12375008,0.0009621409,0.86533505,0.00028936408,0.00012650908,0.00036288297,0.0064434074,0.0007525993,0.0019778812],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99313146,0.0011860149,0.000693872,0.0007360149,0.0038902618,0.00036229973],"domain_scores_gemma":[0.98478955,0.0066036675,0.0006960838,0.004328588,0.0031570878,0.00042494238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057644947,0.00081910007,0.0017995815,0.0038964623,0.0013238973,0.0027085505,0.004398572,0.0017209697,0.003324421],"category_scores_gemma":[0.028574182,0.00054918387,0.0013950878,0.008123307,0.0010811512,0.0077798367,0.0035053638,0.0013262735,0.0028260224],"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":[0.0010618143,0.00046153532,0.0069819144,0.0014009292,0.00032065282,0.00063887454,0.0009202378,0.16383879,0.018052261,0.08088374,0.070526235,0.654913],"study_design_scores_gemma":[0.0001311877,0.00029503385,0.00163783,0.0000944333,0.0000805278,0.0007962094,0.0002577762,0.8837615,0.007783192,0.07407878,0.031020124,0.000063344036],"about_ca_topic_score_codex":0.0087943,"about_ca_topic_score_gemma":0.007570292,"teacher_disagreement_score":0.0087943,"about_ca_system_score_codex":0.0013186779,"about_ca_system_score_gemma":0.0022310237,"threshold_uncertainty_score":0.030485928},"labels":[],"label_agreement":null},{"id":"W2295356180","doi":"10.1145/2830567","title":"Adaptive and Approximate Orthogonal Range Counting","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Center for Massive Data Algorithmics; Danmarks Grundforskningsfond","keywords":"Range (aeronautics); Mathematics; Combinatorics; Computer science; Discrete mathematics; Algorithm; Statistics; Artificial intelligence","score_opus":0.02557180926512948,"score_gpt":0.23998807247528925,"score_spread":0.21441626321015977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295356180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032829836,0.000997681,0.94604284,0.0005694762,0.00017371868,0.00015143605,0.0008977335,0.0030931174,0.01524421],"genre_scores_gemma":[0.3597301,0.0010101806,0.6234173,0.0007534077,0.00043110122,0.00063160615,0.0028370244,0.0006740406,0.010515241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942478,0.0006213906,0.00047564218,0.0012826495,0.0026387975,0.0007336882],"domain_scores_gemma":[0.9923409,0.0018385253,0.0006529286,0.004003104,0.00091518287,0.0002494497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013093331,0.0013022405,0.0017829913,0.002090402,0.0010638539,0.003201284,0.0048138145,0.0013340598,0.010085296],"category_scores_gemma":[0.0108287,0.00065913075,0.0015097571,0.005526169,0.0019029306,0.01040689,0.0072296727,0.0028052935,0.0028484736],"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":[0.00087512715,0.00030724044,0.003934962,0.00055693026,0.00011828228,0.0002737943,0.0004405034,0.12125175,0.022713212,0.36557046,0.024194758,0.45976302],"study_design_scores_gemma":[0.00010898184,0.00023114403,0.0010691268,0.00007721576,0.000078008015,0.0006985056,0.00018123297,0.6388398,0.018021166,0.29903185,0.041548967,0.00011400099],"about_ca_topic_score_codex":0.0019464801,"about_ca_topic_score_gemma":0.0018084412,"teacher_disagreement_score":0.010085296,"about_ca_system_score_codex":0.0016150536,"about_ca_system_score_gemma":0.0015719,"threshold_uncertainty_score":0.033738613},"labels":[],"label_agreement":null},{"id":"W2295558846","doi":"10.3166/isi.20.2.9-39","title":"Méthode de factorisation progressive pour accroître l’abstraction d’un modèle de classes","year":2015,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mod; Mathematics; Physics; Combinatorics","score_opus":0.09510193298799904,"score_gpt":0.28081125773537063,"score_spread":0.1857093247473716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295558846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026562435,0.00010245156,0.99217975,0.00007715813,0.00006192455,0.000057958267,0.00009729667,0.003360749,0.0014063607],"genre_scores_gemma":[0.055065963,0.00028477467,0.9323278,0.00010293407,0.00007642497,0.0002852411,0.00074265705,0.0020264525,0.009087799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961223,0.0007178525,0.00028841922,0.0010200368,0.001511954,0.00033937907],"domain_scores_gemma":[0.99503255,0.0023527879,0.00017083439,0.0012200191,0.0011043807,0.00011949114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003592351,0.0029176734,0.0018874408,0.0034909388,0.0012304931,0.004169464,0.002138144,0.0019577406,0.020590074],"category_scores_gemma":[0.010211035,0.0019787985,0.0061603915,0.0017419113,0.0025092366,0.0046698786,0.003053424,0.0052720457,0.0063044066],"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":[0.0012687789,0.00033890476,0.003418698,0.0012817996,0.0004916262,0.00062113424,0.0018903761,0.10504879,0.03933307,0.20186995,0.016647704,0.62778914],"study_design_scores_gemma":[0.0004073845,0.0003040868,0.0021182876,0.00043106952,0.00031356548,0.0006388307,0.0005322206,0.681466,0.058479372,0.1087129,0.14638768,0.00020862442],"about_ca_topic_score_codex":0.024480816,"about_ca_topic_score_gemma":0.017251782,"teacher_disagreement_score":0.024480816,"about_ca_system_score_codex":0.002340118,"about_ca_system_score_gemma":0.0026709791,"threshold_uncertainty_score":0.06888062},"labels":[],"label_agreement":null},{"id":"W2301534275","doi":"10.1111/tgis.12205","title":"A Genetic Algorithm for Tributary Selection with Consideration of Multiple Factors","year":2016,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Tributary; Drainage; Generalization; Drainage network; Selection (genetic algorithm); Fitness function; Genetic algorithm; Computer science; Algorithm; Data mining; Aggregate (composite); Drainage system (geomorphology); Drainage basin; Hydrology (agriculture); Geography; Artificial intelligence; Geology; Cartography; Mathematics; Machine learning; Ecology; Geotechnical engineering","score_opus":0.01776401326274799,"score_gpt":0.23192900213908588,"score_spread":0.2141649888763379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301534275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0508907,0.00027408212,0.94479394,0.00023802703,0.000051711017,0.0001728611,0.000055395663,0.00038982317,0.0031334271],"genre_scores_gemma":[0.44711894,0.00020833878,0.5478503,0.00021391718,0.000048496076,0.00064533297,0.000221574,0.00010486607,0.0035883079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929416,0.00027174508,0.00003894761,0.00015065349,0.00014680786,0.00009766575],"domain_scores_gemma":[0.9987012,0.0007781122,0.00009208453,0.000050975006,0.00031633585,0.00006127211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001740388,0.0013297759,0.0016780093,0.0022654438,0.0008286759,0.0010150863,0.0021772627,0.0018293591,0.0023415224],"category_scores_gemma":[0.0033191547,0.0006818501,0.001018319,0.0019565783,0.0007701416,0.0007575058,0.0010262917,0.00094631576,0.00029796985],"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":[0.000032363467,0.00005076642,0.0009251302,0.000026050522,0.000057516947,0.00007258803,0.00008308135,0.9438094,0.00070680125,0.0024489083,0.0005618487,0.051225606],"study_design_scores_gemma":[0.000016413784,0.000014802564,0.00009144883,0.000004267507,0.00001136283,0.000010122949,0.000009633618,0.9989442,0.0001220704,0.0006018202,0.00017059018,0.0000031966845],"about_ca_topic_score_codex":0.017562447,"about_ca_topic_score_gemma":0.011259771,"teacher_disagreement_score":0.017562447,"about_ca_system_score_codex":0.0014822185,"about_ca_system_score_gemma":0.0021046204,"threshold_uncertainty_score":0.034920454},"labels":[],"label_agreement":null},{"id":"W2304460499","doi":"","title":"A Trajectory Cleaning Framework for Trajectory Clustering","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Trajectory; Computer science; Preprocessor; Data mining; Interpolation (computer graphics); Similarity (geometry); Process (computing); Sample (material); Artificial intelligence; Motion (physics)","score_opus":0.03920083626687594,"score_gpt":0.2799169441624404,"score_spread":0.24071610789556447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2304460499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006416657,0.00012642417,0.9975305,0.00006941314,0.000028641878,0.00007077476,0.00034769782,0.0007630794,0.00042181014],"genre_scores_gemma":[0.025109105,0.0003238964,0.9691776,0.000060240644,0.000074478405,0.0002773851,0.002863767,0.0002829634,0.0018305689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767965,0.0004621779,0.00015398902,0.00056394347,0.00096278574,0.00017751618],"domain_scores_gemma":[0.997693,0.00032121412,0.00021572103,0.00063976407,0.0009965193,0.00013367804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019603139,0.0013053282,0.0016443158,0.0037919544,0.0027032131,0.0030077512,0.0029359595,0.0016319397,0.002755541],"category_scores_gemma":[0.0044512968,0.00066210114,0.0023317426,0.005609877,0.0010011566,0.0026190807,0.0032895945,0.0026762623,0.0024111462],"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":[0.00017900247,0.00023775021,0.0039744917,0.00052800035,0.00038839818,0.00051079056,0.0009964922,0.29300743,0.015285253,0.18374705,0.051083107,0.45006222],"study_design_scores_gemma":[0.000027836031,0.00008957851,0.0019635104,0.00006928873,0.00006754986,0.00046252552,0.00031352456,0.85323966,0.0064747203,0.05591935,0.081255615,0.00011683373],"about_ca_topic_score_codex":0.017346753,"about_ca_topic_score_gemma":0.02077877,"teacher_disagreement_score":0.017346753,"about_ca_system_score_codex":0.0016780364,"about_ca_system_score_gemma":0.003340139,"threshold_uncertainty_score":0.03449154},"labels":[],"label_agreement":null},{"id":"W2309226206","doi":"","title":"Scalable clustering of categorical data and applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cluster analysis; Data mining; Categorical variable; Tuple; Redundancy (engineering); Scalability; Constrained clustering; Correlation clustering; Canopy clustering algorithm; Artificial intelligence; Machine learning; Database; Mathematics","score_opus":0.03283988717391283,"score_gpt":0.26698512357284127,"score_spread":0.23414523639892842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2309226206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008386308,0.0008248515,0.9838801,0.0006077638,0.0000773105,0.00022863461,0.0011034417,0.0032638477,0.0016276868],"genre_scores_gemma":[0.12773335,0.00082937087,0.86290264,0.00028069929,0.00017392387,0.0006501065,0.0050111143,0.0005506512,0.0018681537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924213,0.0022408566,0.000609577,0.0016132976,0.0026846007,0.0004303097],"domain_scores_gemma":[0.98172885,0.0075044893,0.0015859703,0.004769325,0.0038139138,0.0005974278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006070415,0.0013549358,0.0021000176,0.008191299,0.0023633474,0.0041642585,0.0038608173,0.0019390391,0.0029118087],"category_scores_gemma":[0.03205987,0.0009515692,0.0024327522,0.012579409,0.0018444757,0.0050081857,0.0051099826,0.0025510592,0.0019061083],"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":[0.00042812974,0.0002445713,0.010596186,0.0009858909,0.00031337998,0.00030287573,0.0013470473,0.24197929,0.0075834785,0.12482001,0.029235633,0.5821635],"study_design_scores_gemma":[0.000041521293,0.00010288857,0.0030677198,0.00010417328,0.00004914991,0.00022300532,0.00053459936,0.7432346,0.0047775446,0.22717234,0.020585408,0.000107015854],"about_ca_topic_score_codex":0.007187844,"about_ca_topic_score_gemma":0.007564934,"teacher_disagreement_score":0.008191299,"about_ca_system_score_codex":0.0032011182,"about_ca_system_score_gemma":0.0039853444,"threshold_uncertainty_score":0.032103837},"labels":[],"label_agreement":null},{"id":"W2315381121","doi":"10.3166/jesa.43.905-919","title":"Exploration aléatoire de modèles","year":2009,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mod; Computer science; Space (punctuation); Topology (electrical circuits); Theoretical computer science; Mathematics; Combinatorics; Artificial intelligence","score_opus":0.04487176660726525,"score_gpt":0.2726064993243781,"score_spread":0.22773473271711284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315381121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10672138,0.0005065367,0.883589,0.00082912407,0.00004740298,0.00012209525,0.00053292664,0.0010816829,0.0065698046],"genre_scores_gemma":[0.5804116,0.00060821295,0.40492854,0.0003614329,0.00010128022,0.0007600436,0.002076267,0.0006359769,0.010116622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944898,0.002318554,0.00020357649,0.0013722,0.0010658175,0.00055011275],"domain_scores_gemma":[0.9707383,0.024194626,0.00091125333,0.0023901549,0.00096846756,0.0007971101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004048341,0.002437012,0.002109786,0.0033402739,0.0021818827,0.0041564093,0.0027764565,0.0026158255,0.0062817526],"category_scores_gemma":[0.032181296,0.0019104623,0.0035497318,0.0020187804,0.0028556066,0.0061748796,0.006609329,0.0049129087,0.001046127],"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":[0.00084835675,0.00043934857,0.005517978,0.00042466004,0.00030790665,0.00047064765,0.000850434,0.69350505,0.0056089913,0.24033801,0.0034302054,0.04825834],"study_design_scores_gemma":[0.00005607392,0.00007898323,0.00020068458,0.0000303882,0.000025417812,0.000076132055,0.00007486973,0.8889001,0.0013223815,0.10730991,0.0019097234,0.00001538587],"about_ca_topic_score_codex":0.00406921,"about_ca_topic_score_gemma":0.006055303,"teacher_disagreement_score":0.0062817526,"about_ca_system_score_codex":0.0028593605,"about_ca_system_score_gemma":0.0015629824,"threshold_uncertainty_score":0.021409988},"labels":[],"label_agreement":null},{"id":"W2335952259","doi":"10.1007/s12145-016-0260-8","title":"A development of spatiotemporal queries to analyze the simulation outcomes from a voxel automata model","year":2016,"lang":"en","type":"article","venue":"Earth Science Informatics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Geospatial analysis; Computer science; Spatial analysis; Representation (politics); Data mining; Cellular automaton; Process (computing); Raster data; Raster graphics; Geographic information system; Field (mathematics); Theoretical computer science; Artificial intelligence; Cartography; Geography; Remote sensing; Mathematics","score_opus":0.03381438578232409,"score_gpt":0.2786010866259727,"score_spread":0.24478670084364862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2335952259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034677999,0.000023427614,0.9931289,0.00009501458,0.000012733255,0.00007116427,0.0001339803,0.0025151905,0.0005517811],"genre_scores_gemma":[0.16139169,0.00014139574,0.8349347,0.000125934,0.00004398755,0.0003107811,0.0009281191,0.0009500872,0.0011732962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747664,0.0005931323,0.00037476333,0.00041246304,0.0009961014,0.00014689231],"domain_scores_gemma":[0.99319494,0.0036296258,0.00031793842,0.0012515899,0.0013589929,0.00024690115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003670372,0.0008523957,0.0010909282,0.0017766325,0.00080451055,0.003216677,0.0033296722,0.0011845151,0.0042468184],"category_scores_gemma":[0.018543601,0.00067209755,0.0017012286,0.0014889835,0.0013294441,0.003647931,0.0033662224,0.0022224053,0.0011028722],"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":[0.0003363038,0.00054410705,0.01013925,0.00034053912,0.00022127763,0.00067119213,0.0012160938,0.41045618,0.014469394,0.3488001,0.0073347148,0.20547093],"study_design_scores_gemma":[0.000012853311,0.000018532575,0.000117057905,0.000009458999,0.000013453964,0.000033149045,0.000045030574,0.9737196,0.0021175316,0.021796959,0.0021057236,0.0000107017595],"about_ca_topic_score_codex":0.009658187,"about_ca_topic_score_gemma":0.0059488863,"teacher_disagreement_score":0.009658187,"about_ca_system_score_codex":0.0011646365,"about_ca_system_score_gemma":0.002312644,"threshold_uncertainty_score":0.019411027},"labels":[],"label_agreement":null},{"id":"W2336256035","doi":"","title":"Variations of Distance-Based Invariants of Trees","year":2014,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Pure mathematics","score_opus":0.013732561208242114,"score_gpt":0.2380394499397507,"score_spread":0.22430688873150859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336256035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6759266,0.0017079427,0.28169772,0.00066813553,0.0005951748,0.00007044521,0.0005541162,0.00065748574,0.03812235],"genre_scores_gemma":[0.9681149,0.00051624887,0.02317002,0.00013560269,0.00030279404,0.000026687138,0.00028196097,0.00024582964,0.0072058784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990982,0.00016958865,0.000055864482,0.00024668837,0.00030364728,0.00012610141],"domain_scores_gemma":[0.99618703,0.0013254919,0.0008084846,0.00044168084,0.00058464456,0.00065264304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010719569,0.0006157385,0.00077465794,0.0040007187,0.000791908,0.0025590237,0.0018307066,0.00088989513,0.0046578106],"category_scores_gemma":[0.0064719063,0.00041882505,0.00075934373,0.0023139082,0.0023313132,0.0035488987,0.002135593,0.0019214463,0.00055031624],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014691448,0.00008304382,0.0016896253,0.0000763074,0.000027670176,0.00013835682,0.00030114895,0.016746607,0.006081616,0.94284606,0.001808028,0.030054668],"study_design_scores_gemma":[0.000026117967,0.0001607939,0.0035481853,0.00002852299,0.000038400554,0.00036956422,0.0001624696,0.10226503,0.0023127787,0.88389164,0.007129327,0.00006728827],"about_ca_topic_score_codex":0.0009572374,"about_ca_topic_score_gemma":0.0010065626,"teacher_disagreement_score":0.0046578106,"about_ca_system_score_codex":0.0016150106,"about_ca_system_score_gemma":0.00043806166,"threshold_uncertainty_score":0.015581906},"labels":[],"label_agreement":null},{"id":"W2337712459","doi":"10.24297/ijmit.v4i2.4625","title":"P-Path Minimum Distance Connectivity from Head Quarter to the Cities","year":2013,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Path (computing); Head (geology); Distance matrix; Cluster (spacecraft); Minimum distance; Combinatorics; Computer science; Mathematics; Polynomial; Algorithm; Statistics; Geography; Computer network","score_opus":0.006351623077112952,"score_gpt":0.22714145853437212,"score_spread":0.22078983545725916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2337712459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2924216,0.0003025388,0.6822622,0.0015593256,0.000053139534,0.00027473932,0.0016658275,0.00073412294,0.020726549],"genre_scores_gemma":[0.7135373,0.0002555645,0.27398175,0.00017290942,0.000028291097,0.00025599677,0.0016834235,0.00010903637,0.009975732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.00007468502,0.000018055807,0.00011874824,0.000043905893,0.00006999381],"domain_scores_gemma":[0.9994734,0.0002805309,0.000090067915,0.000045920176,0.0000615214,0.000048610847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027171723,0.00035428806,0.00051021465,0.00045378224,0.00081849255,0.0010364628,0.0010554141,0.0009635166,0.007183197],"category_scores_gemma":[0.0016169263,0.00027312574,0.0005239542,0.0012368484,0.00037598138,0.0017241464,0.00090007694,0.0006621381,0.00036821983],"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":[0.00042787893,0.0002718251,0.006281339,0.00051641493,0.00007940435,0.0006908679,0.00040502596,0.75336015,0.006977668,0.07191746,0.014479945,0.144592],"study_design_scores_gemma":[0.00006411198,0.00022113833,0.002196514,0.00003600151,0.000031921256,0.00071745063,0.0004621972,0.898859,0.00601964,0.08243225,0.008934831,0.000024933906],"about_ca_topic_score_codex":0.0040892055,"about_ca_topic_score_gemma":0.0043911315,"teacher_disagreement_score":0.007183197,"about_ca_system_score_codex":0.00079579366,"about_ca_system_score_gemma":0.001016783,"threshold_uncertainty_score":0.024030149},"labels":[],"label_agreement":null},{"id":"W2338354263","doi":"","title":"Toward better support for spatial decision making: defining the characteristics of spatial on-line analytical processing (solap)","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Online analytical processing; Exploit; Data warehouse; Computer science; Spatial analysis; Line (geometry); Decision support system; Data mining; Data science; Geography; Cartography; Mathematics; Remote sensing","score_opus":0.029069845078091877,"score_gpt":0.2903889626372823,"score_spread":0.26131911755919046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338354263","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019973813,0.000913451,0.96836734,0.0031539632,0.00008898957,0.00012156764,0.0002758274,0.0005471308,0.0065579764],"genre_scores_gemma":[0.25537416,0.0014310315,0.7396495,0.00069322326,0.00023184478,0.00019678983,0.0006494421,0.00018927059,0.0015847704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995589,0.0015227933,0.000469605,0.0006182957,0.0015187823,0.00028143064],"domain_scores_gemma":[0.9799579,0.008105144,0.002535018,0.004131444,0.004463924,0.00080663303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006578223,0.0008422727,0.0010631927,0.0023767236,0.0010624547,0.0098748095,0.0021854246,0.0019036878,0.002280747],"category_scores_gemma":[0.024410948,0.0007293382,0.00079294026,0.0045962348,0.0044731232,0.016432708,0.0039953375,0.002985594,0.001209225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000343462,0.00021515305,0.008440039,0.0007831246,0.00011101673,0.00050658686,0.0023385547,0.054686908,0.0061712353,0.67711,0.0072300136,0.24206395],"study_design_scores_gemma":[0.000021250313,0.00011551594,0.0016364602,0.00018282776,0.000030168469,0.00033723292,0.00081403187,0.34179285,0.004928244,0.6237636,0.026325174,0.00005254469],"about_ca_topic_score_codex":0.003536041,"about_ca_topic_score_gemma":0.0015962686,"teacher_disagreement_score":0.0098748095,"about_ca_system_score_codex":0.0013860645,"about_ca_system_score_gemma":0.002602357,"threshold_uncertainty_score":0.034789383},"labels":[],"label_agreement":null},{"id":"W2339325713","doi":"10.1145/2872518.2890487","title":"Open Data for Local Search","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; USable; Originality; Data science; Search engine; Local search (optimization); World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.1692272607389326,"score_gpt":0.36842180668746366,"score_spread":0.19919454594853106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339325713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005262343,0.0064687105,0.8845172,0.022741936,0.0026127056,0.0005345096,0.007811704,0.004733783,0.06531718],"genre_scores_gemma":[0.15763704,0.007035288,0.7693455,0.005832867,0.0029811235,0.0022392047,0.022998165,0.0030410027,0.028889826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.986361,0.006022581,0.0013606957,0.0018972412,0.003758356,0.00060021],"domain_scores_gemma":[0.9341456,0.028342333,0.001954635,0.026778659,0.006840973,0.0019376692],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.012845674,0.00078171684,0.0018199534,0.0052353316,0.0034298778,0.011302244,0.0045451494,0.004286569,0.04875314],"category_scores_gemma":[0.09171167,0.0009995206,0.002205516,0.009041065,0.0049072052,0.029249167,0.01434193,0.007298786,0.017792694],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013921957,0.00007629367,0.00063747365,0.0004301507,0.000043980435,0.00008983309,0.00044127795,0.0040548663,0.00053116115,0.82815224,0.057621844,0.10778166],"study_design_scores_gemma":[0.00003354688,0.000022589662,0.00015795058,0.0002423229,0.000016031825,0.000094288545,0.00025274637,0.014114164,0.0007406374,0.78775257,0.19654213,0.000031016323],"about_ca_topic_score_codex":0.003476002,"about_ca_topic_score_gemma":0.0031493532,"teacher_disagreement_score":0.99545485,"about_ca_system_score_codex":0.0032357045,"about_ca_system_score_gemma":0.0045807147,"threshold_uncertainty_score":0.16309553},"labels":[],"label_agreement":null},{"id":"W2339356764","doi":"10.1145/1862413.1862416","title":"Intrinsic dimensionality","year":2010,"lang":"en","type":"article","venue":"SIGSPATIAL Special","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Curse of dimensionality; Search engine indexing; Similarity (geometry); Computer science; Information retrieval; Workload; Gauge (firearms); Data mining; Theoretical computer science; Artificial intelligence; Geography","score_opus":0.0074448023591275085,"score_gpt":0.22220236922687658,"score_spread":0.21475756686774908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339356764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037164886,0.027623668,0.86809117,0.014296454,0.002827567,0.0004004541,0.004008272,0.0008040839,0.044783376],"genre_scores_gemma":[0.695972,0.020366648,0.2537152,0.0064494936,0.005918828,0.0018145456,0.00320215,0.00033139565,0.012229746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9892776,0.0028097264,0.0011033129,0.0024864825,0.0038931104,0.0004296582],"domain_scores_gemma":[0.9772676,0.009334009,0.0023795865,0.007003785,0.0033360193,0.00067891856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006091996,0.0011713995,0.001713442,0.0030259008,0.0014545594,0.008235813,0.0019574976,0.0020612364,0.0031988458],"category_scores_gemma":[0.03134698,0.00063158735,0.0014985679,0.0027655356,0.008162797,0.011663796,0.005659152,0.0054291105,0.0011797217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095545875,0.000062796185,0.004561439,0.0009272488,0.0001926964,0.00020098689,0.0007368522,0.0052534873,0.0026961993,0.88622737,0.014902266,0.08414322],"study_design_scores_gemma":[0.000019818117,0.00009447952,0.0032998694,0.00029527035,0.000094112074,0.0013912889,0.0002774115,0.021069182,0.0020742565,0.90697676,0.06430405,0.00010354343],"about_ca_topic_score_codex":0.0007688697,"about_ca_topic_score_gemma":0.0003367333,"teacher_disagreement_score":0.008235813,"about_ca_system_score_codex":0.0019025799,"about_ca_system_score_gemma":0.0020598755,"threshold_uncertainty_score":0.03221792},"labels":[],"label_agreement":null},{"id":"W2341947765","doi":"","title":"THE BANFF SYSTEM FOR AUTOMATED EDITING AND IMPUTATION","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Computer science; Data science; Usability; Data collection; Graphical user interface; Software engineering; Human–computer interaction; Missing data; Statistics; Programming language; Machine learning; Mathematics","score_opus":0.008995434753622052,"score_gpt":0.24220406396705502,"score_spread":0.23320862921343297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341947765","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001290954,0.00031642886,0.72793126,0.00045402703,0.0004308761,0.00054431875,0.029030487,0.22949511,0.010506579],"genre_scores_gemma":[0.010653321,0.00043410674,0.86106706,0.0007458756,0.0002388447,0.002178787,0.057912294,0.044814233,0.021955416],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912947,0.0030767617,0.0009133143,0.001499145,0.0027696758,0.00044640148],"domain_scores_gemma":[0.970432,0.013449202,0.0013033709,0.007295059,0.0068909675,0.0006294275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013087416,0.0015529911,0.0022211983,0.0050263726,0.0020805467,0.0038051594,0.004445994,0.0014956354,0.121393226],"category_scores_gemma":[0.051817816,0.0019478385,0.0019311411,0.005158118,0.0008812563,0.0035410176,0.0047250134,0.0030584284,0.07438028],"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":[0.00037760616,0.00008418561,0.0024159984,0.00053055596,0.00019996951,0.00017698569,0.00048236773,0.0025373013,0.0020940872,0.011850994,0.68199724,0.29725263],"study_design_scores_gemma":[0.00027307816,0.00006253387,0.0040225904,0.0002840327,0.00009577483,0.00063532544,0.00013694077,0.04039895,0.008334623,0.033497643,0.91192585,0.00033263682],"about_ca_topic_score_codex":0.03136876,"about_ca_topic_score_gemma":0.04095116,"teacher_disagreement_score":0.121393226,"about_ca_system_score_codex":0.0015083482,"about_ca_system_score_gemma":0.008190195,"threshold_uncertainty_score":0.4061008},"labels":[],"label_agreement":null},{"id":"W234276927","doi":"","title":"Log Analysis of Trip Calculation on a Transit Website","year":2004,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Transit (satellite); Computer science; The Internet; Calculator; Transport engineering; World Wide Web; Function (biology); Information system; Public transport; Engineering","score_opus":0.011395927464232507,"score_gpt":0.23160964708786005,"score_spread":0.22021371962362754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W234276927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91324866,0.00018872431,0.033996657,0.00012542984,0.00007767081,0.00036986265,0.031193545,0.006427979,0.014371562],"genre_scores_gemma":[0.9493383,0.00015874361,0.020754643,0.000023098459,0.000028385455,0.00025998807,0.022468653,0.00028135793,0.0066867485],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99916196,0.0001384566,0.00007913689,0.00015172875,0.00041070074,0.00005810112],"domain_scores_gemma":[0.99526596,0.0019929514,0.00045352094,0.00055373623,0.0015488422,0.0001849829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004890446,0.0003710243,0.00032700298,0.0040251194,0.00030075482,0.00083968666,0.00032771798,0.00019266496,0.0053562797],"category_scores_gemma":[0.0041278987,0.00011105326,0.00027954468,0.0035667466,0.00015026821,0.00041686092,0.00032316064,0.00038591662,0.0020057505],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010392785,0.0004978961,0.47168583,0.0005222074,0.00014350942,0.0007926012,0.0017323545,0.017536169,0.011882412,0.0022464467,0.026006883,0.4659144],"study_design_scores_gemma":[0.000032253596,0.0006712294,0.75628275,0.00006613465,0.00010037536,0.0012224993,0.0018088297,0.18526551,0.026100859,0.0010565643,0.027263226,0.00012979988],"about_ca_topic_score_codex":0.008914518,"about_ca_topic_score_gemma":0.0068974234,"teacher_disagreement_score":0.008914518,"about_ca_system_score_codex":0.00042569972,"about_ca_system_score_gemma":0.000559768,"threshold_uncertainty_score":0.017918527},"labels":[],"label_agreement":null},{"id":"W2344430697","doi":"10.3233/web-160335","title":"Accurate and efficient query clustering via top ranked search results","year":2016,"lang":"en","type":"article","venue":"Web Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Science Foundation","keywords":"Computer science; Cluster analysis; Data mining; Information retrieval; DBSCAN; Search engine; Web search query; Metric (unit); Feature (linguistics); Hierarchical clustering; Similarity (geometry); Query expansion; Fuzzy clustering; CURE data clustering algorithm; Machine learning; Artificial intelligence","score_opus":0.031748576220429654,"score_gpt":0.27678550056573076,"score_spread":0.2450369243453011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344430697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08412556,0.0018041091,0.9000657,0.00039467114,0.00010412027,0.00050919,0.0011549761,0.0077221766,0.004119539],"genre_scores_gemma":[0.40915447,0.00053717877,0.58398503,0.000115724215,0.00010655017,0.00017434603,0.0030863145,0.00036567432,0.0024746705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944939,0.0011823366,0.00047189323,0.00091581064,0.0024897223,0.00044630875],"domain_scores_gemma":[0.9934662,0.0014563639,0.0006072649,0.0021394324,0.0021024328,0.0002283215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002318833,0.0018256598,0.002771639,0.007601652,0.0018965318,0.0040156813,0.002908799,0.0016433086,0.0015472214],"category_scores_gemma":[0.015339817,0.000707758,0.001197927,0.0081995195,0.0006852128,0.0036594954,0.0019675891,0.0011016289,0.002630474],"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":[0.0012408672,0.0009052553,0.011443615,0.00060426723,0.0004269657,0.0002676218,0.00094499456,0.14001037,0.04741715,0.0144404415,0.01993027,0.76236814],"study_design_scores_gemma":[0.00007141317,0.00019788601,0.0039889915,0.00003012104,0.000107282205,0.00050082617,0.00058640086,0.9454324,0.030990632,0.013301439,0.0046868687,0.00010584179],"about_ca_topic_score_codex":0.0154577885,"about_ca_topic_score_gemma":0.018304426,"teacher_disagreement_score":0.0154577885,"about_ca_system_score_codex":0.0017976195,"about_ca_system_score_gemma":0.0032981606,"threshold_uncertainty_score":0.030735612},"labels":[],"label_agreement":null},{"id":"W2344932587","doi":"10.1109/cjece.2015.2463753","title":"Using Spatial Semijoins Over Multiple Sites in Distributed Spatial Query Processing","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Spatial query; Joins; Computer science; Spatial relation; Spatial analysis; Spatial database; Spatial ecology; Query optimization; Distributed computing; Data mining; Information retrieval; Web search query; Geography; Web query classification; Search engine; Artificial intelligence; Ecology; Remote sensing","score_opus":0.012138330186495714,"score_gpt":0.19677261810347263,"score_spread":0.18463428791697692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344932587","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057106763,0.00032130934,0.9397221,0.00016776031,0.000033226854,0.00014153765,0.000054192362,0.00095888576,0.0014941802],"genre_scores_gemma":[0.43448082,0.00016593684,0.5635836,0.00009413473,0.000045850877,0.00015538216,0.00017545672,0.00016735925,0.0011314087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694926,0.000988286,0.00022060686,0.0003943217,0.0012449587,0.00020263146],"domain_scores_gemma":[0.9961966,0.0014521477,0.0004059172,0.001262559,0.00047226567,0.00021050776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003040836,0.0007276311,0.0011149229,0.0007401212,0.0010863695,0.0013173718,0.0019544156,0.00074374396,0.001775766],"category_scores_gemma":[0.004782645,0.0005167641,0.0006465566,0.0013798887,0.0012420329,0.003096105,0.0028938747,0.0010552342,0.0004599598],"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":[0.001723522,0.0008826779,0.008143577,0.0003840751,0.0002535795,0.0005775405,0.0013402662,0.4688398,0.086929135,0.063272454,0.0053831637,0.3622703],"study_design_scores_gemma":[0.00019336725,0.00072751526,0.0010220337,0.000022804892,0.00008668512,0.00036996108,0.00041992287,0.917567,0.035003595,0.037099615,0.007423077,0.00006440668],"about_ca_topic_score_codex":0.001813818,"about_ca_topic_score_gemma":0.0030201755,"teacher_disagreement_score":0.003040836,"about_ca_system_score_codex":0.00043757397,"about_ca_system_score_gemma":0.0012482431,"threshold_uncertainty_score":0.01608169},"labels":[],"label_agreement":null},{"id":"W2345545768","doi":"10.1016/j.ifacol.2015.06.363","title":"Development of business spatial analysis tools: methodology and framework","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Development (topology); Interface (matter); Software engineering; Work (physics); Architecture; Systems engineering; Engineering","score_opus":0.1563682099173584,"score_gpt":0.32886497665634407,"score_spread":0.17249676673898567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345545768","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010695745,0.00034855786,0.9952733,0.00030370406,0.000022712682,0.00041358868,0.0000990958,0.00044227185,0.0020272257],"genre_scores_gemma":[0.015317079,0.0007121266,0.98194134,0.000061494095,0.0000229496,0.00085853407,0.00029962926,0.000086304906,0.00070050836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99289167,0.002851052,0.000848316,0.0009099314,0.002114351,0.0003847964],"domain_scores_gemma":[0.9957065,0.0014118166,0.00046975448,0.00067947624,0.0014451859,0.00028734282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011563953,0.001491187,0.00095954846,0.006565008,0.0016673204,0.007337337,0.0036763828,0.001796227,0.0019417411],"category_scores_gemma":[0.007826086,0.0012455449,0.001534397,0.005147883,0.0036984044,0.0044606593,0.003879297,0.0023402984,0.001735983],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046379457,0.00038936504,0.0028863123,0.0018873201,0.00013095126,0.0005316176,0.002426416,0.0321213,0.00808805,0.67506117,0.0055304,0.2709007],"study_design_scores_gemma":[0.000090577996,0.00037474465,0.0033123116,0.0029926137,0.00014809448,0.0015167659,0.0034861334,0.2184873,0.027058661,0.4459888,0.29626936,0.00027458166],"about_ca_topic_score_codex":0.006491971,"about_ca_topic_score_gemma":0.0037439228,"teacher_disagreement_score":0.011563953,"about_ca_system_score_codex":0.0028080868,"about_ca_system_score_gemma":0.013039606,"threshold_uncertainty_score":0.06115675},"labels":[],"label_agreement":null},{"id":"W2349743712","doi":"","title":"ADAPTIVE APPROXIMATION TREE","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"CAE (Canada)","funders":"","keywords":"Computer science; Tree structure; Data structure; Curse of dimensionality; Space partitioning; Tree (set theory); Partition (number theory); Search tree; k-d tree; Skew; Algorithm; Data mining; Trie; Search engine indexing; Tree traversal; Theoretical computer science; Search algorithm; Artificial intelligence; Mathematics","score_opus":0.037830017527624604,"score_gpt":0.2109307021366941,"score_spread":0.1731006846090695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2349743712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013478196,0.0017131167,0.9806126,0.00024565923,0.000096070515,0.000044497327,0.0001465873,0.00038783657,0.00327542],"genre_scores_gemma":[0.26024663,0.0027886198,0.7293226,0.00021293802,0.00017422973,0.0001763057,0.0009815366,0.00013819268,0.005958964],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923503,0.00014151439,0.000052920677,0.00012024049,0.00037721664,0.000073142815],"domain_scores_gemma":[0.99868613,0.00048082523,0.000108503555,0.00023741092,0.00041489172,0.00007231425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067510735,0.00028162726,0.0009365183,0.0014451204,0.0007154523,0.0014245748,0.0011694869,0.00067187974,0.0027679882],"category_scores_gemma":[0.0044594863,0.0002543855,0.00059772376,0.0028883682,0.0005072375,0.0027044371,0.0010332518,0.0009875491,0.0009846775],"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":[0.00025817315,0.00010946345,0.0038988583,0.00037656192,0.000109231325,0.00025601828,0.00034383667,0.14975764,0.012059747,0.24213742,0.015092536,0.5756005],"study_design_scores_gemma":[0.000023979896,0.00014973334,0.0006415306,0.00003649519,0.000041620788,0.00052948366,0.00007871491,0.89162266,0.0039188503,0.081448,0.021483205,0.00002577338],"about_ca_topic_score_codex":0.0022401453,"about_ca_topic_score_gemma":0.0015559307,"teacher_disagreement_score":0.0027679882,"about_ca_system_score_codex":0.0006974915,"about_ca_system_score_gemma":0.0009453375,"threshold_uncertainty_score":0.00925982},"labels":[],"label_agreement":null},{"id":"W2355085396","doi":"","title":"Research of Indexing for Moving Object","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Search engine indexing; Quadtree; Computer science; Variation (astronomy); Object (grammar); Index (typography); R-tree; Tree (set theory); Data mining; Artificial intelligence; Spatial database; Spatial analysis; Mathematics; Statistics; World Wide Web","score_opus":0.033858118787905414,"score_gpt":0.3266502604672358,"score_spread":0.2927921416793304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2355085396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04235379,0.055537455,0.8789649,0.0009928433,0.0007782084,0.00024303854,0.0005506682,0.0023216244,0.018257465],"genre_scores_gemma":[0.28995138,0.044978064,0.64987046,0.00058952457,0.0015459567,0.0003796934,0.0025536967,0.00046094946,0.009670282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740535,0.00028809477,0.00021386551,0.00050558633,0.0013919502,0.00019512049],"domain_scores_gemma":[0.99605834,0.001047956,0.00024154464,0.0007940484,0.0017134263,0.00014471982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019892363,0.00050061755,0.0016289667,0.003947184,0.001077011,0.0028536005,0.0026259904,0.0009382001,0.0022010796],"category_scores_gemma":[0.0063250368,0.0004321041,0.00074435835,0.009991053,0.0011384463,0.0113888215,0.0013695557,0.0009265618,0.0010457948],"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":[0.00020999149,0.00013629408,0.0028656127,0.0009317943,0.0001066947,0.00015958893,0.0006068183,0.012789557,0.014133674,0.117886394,0.01060999,0.8395636],"study_design_scores_gemma":[0.00016368991,0.0010367468,0.006576045,0.00042104672,0.00041505852,0.0027352893,0.0010313289,0.50298524,0.06311083,0.17159966,0.2495714,0.00035378846],"about_ca_topic_score_codex":0.005065893,"about_ca_topic_score_gemma":0.0014405333,"teacher_disagreement_score":0.005065893,"about_ca_system_score_codex":0.0015694646,"about_ca_system_score_gemma":0.0015171581,"threshold_uncertainty_score":0.011387289},"labels":[],"label_agreement":null},{"id":"W2359946253","doi":"","title":"Lightweight Document clustering Algorithm for Forum FAQ Document","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Document clustering; Cluster analysis; Information retrieval; Data mining; Algorithm; Artificial intelligence","score_opus":0.011945996148695088,"score_gpt":0.24372006109141953,"score_spread":0.23177406494272443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359946253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072423266,0.0005116698,0.9823735,0.00023953071,0.00021176082,0.0003302587,0.0011399892,0.0051326826,0.0028182263],"genre_scores_gemma":[0.06388463,0.00033514004,0.9183146,0.000114736875,0.00017447365,0.0005460818,0.004150991,0.00033750583,0.0121419635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983967,0.00020308461,0.00013320454,0.0003910864,0.0007208741,0.00015508335],"domain_scores_gemma":[0.9981198,0.00031443202,0.0001455118,0.00040085134,0.0009287793,0.00009063012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011807402,0.00070382014,0.001061308,0.0044004745,0.0019111051,0.0017696468,0.00188387,0.0013816489,0.007904965],"category_scores_gemma":[0.0048860963,0.00027326588,0.0009368874,0.0045590536,0.0004622822,0.0018370514,0.0013244495,0.0010420847,0.008078814],"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":[0.00026671359,0.00010984072,0.0012707289,0.00019205055,0.00005980782,0.00013823202,0.00016950615,0.03627138,0.014657355,0.015451192,0.03665641,0.8947567],"study_design_scores_gemma":[0.0001077861,0.00014210529,0.0018738001,0.0000498008,0.000044512297,0.0007128963,0.00018884244,0.8556177,0.019421848,0.034060605,0.087674685,0.000105472034],"about_ca_topic_score_codex":0.0070189605,"about_ca_topic_score_gemma":0.0059967386,"teacher_disagreement_score":0.007904965,"about_ca_system_score_codex":0.0012612477,"about_ca_system_score_gemma":0.0025639273,"threshold_uncertainty_score":0.026444793},"labels":[],"label_agreement":null},{"id":"W2364483700","doi":"","title":"A New Algorithm to Query Closed Cube","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Algorithm; Cube (algebra); Set (abstract data type); Efficient algorithm; Data mining; Mathematics","score_opus":0.012627479874508322,"score_gpt":0.22982930198219909,"score_spread":0.21720182210769076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364483700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008328568,0.0004149681,0.9870647,0.00022567298,0.00010529104,0.00014560862,0.00030220248,0.0019099085,0.0015031394],"genre_scores_gemma":[0.06765843,0.00034649065,0.92777425,0.00020871214,0.000102625905,0.00030788817,0.0011616043,0.00020320454,0.0022368194],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99768794,0.00026048947,0.00026842835,0.0004383459,0.0011733519,0.00017142591],"domain_scores_gemma":[0.99779725,0.000690854,0.00012829206,0.00049173564,0.0007859082,0.00010592988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009725143,0.0006360927,0.0011186481,0.0018354476,0.0011187551,0.0017153616,0.0020278688,0.00094183587,0.0052103],"category_scores_gemma":[0.005475425,0.00037112267,0.0007055498,0.0025282009,0.0006305114,0.0047017382,0.0030333109,0.001103313,0.001856602],"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":[0.000832476,0.0001785514,0.001399198,0.00034788885,0.000078562734,0.00023464813,0.00045353168,0.025509832,0.03963035,0.040748496,0.021636898,0.86894965],"study_design_scores_gemma":[0.0002546927,0.000470855,0.00085551036,0.00006608365,0.00007694455,0.0016062342,0.0003178476,0.81241834,0.045502722,0.065129586,0.07314929,0.00015192264],"about_ca_topic_score_codex":0.0024649643,"about_ca_topic_score_gemma":0.0015520215,"teacher_disagreement_score":0.0052103,"about_ca_system_score_codex":0.000654312,"about_ca_system_score_gemma":0.0014104915,"threshold_uncertainty_score":0.017430246},"labels":[],"label_agreement":null},{"id":"W2380878665","doi":"","title":"Indexing the Uncertain Trajectories of Network-constrained Moving Objects","year":2008,"lang":"en","type":"article","venue":"","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":"CAE (Canada)","funders":"","keywords":"Search engine indexing; Computer science; Index (typography); Track (disk drive); Trajectory; Data mining; Tree (set theory); Database index; Artificial intelligence","score_opus":0.025805158195143715,"score_gpt":0.22838668484579616,"score_spread":0.20258152665065243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2380878665","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073710665,0.0028784487,0.9150759,0.0004954085,0.00015588215,0.00013296775,0.004506558,0.0009678467,0.002076303],"genre_scores_gemma":[0.6714345,0.0033008037,0.3121698,0.00010737557,0.00017269609,0.00020475339,0.0106328335,0.000114688984,0.001862528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982193,0.00018983234,0.00026777186,0.00039351024,0.00075486366,0.00017470705],"domain_scores_gemma":[0.99713993,0.00078029325,0.0005782116,0.0006680127,0.00069779565,0.00013582442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013279926,0.00057650206,0.0017263768,0.0029906514,0.0011784744,0.0024190098,0.0019877004,0.00084920356,0.0009268691],"category_scores_gemma":[0.008390219,0.0003621011,0.0006394531,0.007744268,0.0005875581,0.006052907,0.0022469542,0.0008408292,0.00033050324],"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":[0.0008821882,0.00012065507,0.015468658,0.0007104801,0.00015716907,0.0008295963,0.0010438168,0.40634188,0.008221064,0.1175086,0.012773941,0.43594196],"study_design_scores_gemma":[0.00002077187,0.00009960395,0.0023538365,0.00007645236,0.000059247293,0.0003680719,0.0004797917,0.9078598,0.006340844,0.06586649,0.01640181,0.00007331003],"about_ca_topic_score_codex":0.011273714,"about_ca_topic_score_gemma":0.0073970985,"teacher_disagreement_score":0.011273714,"about_ca_system_score_codex":0.0012009168,"about_ca_system_score_gemma":0.0017003848,"threshold_uncertainty_score":0.022416174},"labels":[],"label_agreement":null},{"id":"W2386814783","doi":"","title":"Observer-Orientation-Based Directional Relations and Query Processing","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Rectangle; Observer (physics); Orientation (vector space); Query optimization; Set (abstract data type); Algorithm; Sargable; Data mining; Theoretical computer science; Information retrieval; Mathematics; Web search query","score_opus":0.008296399862576883,"score_gpt":0.234087723770853,"score_spread":0.2257913239082761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2386814783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006505663,0.00011260798,0.9909769,0.000119646145,0.000017553426,0.000037777343,0.00005235689,0.00031335352,0.0018641916],"genre_scores_gemma":[0.48558688,0.0006289382,0.5066838,0.00015060265,0.000107765314,0.00028067056,0.00055284466,0.00017667064,0.005831697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99650675,0.0008712669,0.00026029945,0.0008752097,0.0011183767,0.00036802137],"domain_scores_gemma":[0.9974554,0.00088341744,0.00027143836,0.0006523856,0.0005951112,0.00014218413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023924592,0.00061002397,0.00083361904,0.0011365316,0.000990321,0.003496871,0.0017497155,0.0009880145,0.0029020566],"category_scores_gemma":[0.0065077986,0.00051762094,0.0013333211,0.0018555985,0.0019378021,0.008071385,0.001953571,0.0014018014,0.0008690796],"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":[0.00028948375,0.00008127235,0.0019976276,0.00016566378,0.00004230193,0.00026472996,0.0011311962,0.05678126,0.015357441,0.84078366,0.0024370977,0.080668315],"study_design_scores_gemma":[0.000047924965,0.0001576817,0.0006273068,0.000023365517,0.000059359747,0.000307263,0.0003265353,0.7688158,0.009887476,0.20683324,0.012830867,0.00008314386],"about_ca_topic_score_codex":0.0069140326,"about_ca_topic_score_gemma":0.0028665657,"teacher_disagreement_score":0.0069140326,"about_ca_system_score_codex":0.0015251632,"about_ca_system_score_gemma":0.0015695109,"threshold_uncertainty_score":0.013747573},"labels":[],"label_agreement":null},{"id":"W23877756","doi":"","title":"SEMANTIC HETEROGENEITY OF GEODATA","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Semantic heterogeneity; Computer science; Semantic grid; Semantic computing; Spatial heterogeneity; Information retrieval; Semantic similarity; Semantic Web","score_opus":0.0402596094850267,"score_gpt":0.23641082905071198,"score_spread":0.19615121956568526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W23877756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0884698,0.004736332,0.8424272,0.019449633,0.0003855031,0.00021556982,0.001346321,0.0009816698,0.04198798],"genre_scores_gemma":[0.89291847,0.0024612718,0.09574001,0.0023220673,0.00041753348,0.000257784,0.0017107928,0.00032023079,0.003851803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97202176,0.010815655,0.00275538,0.0024194932,0.010921908,0.0010658397],"domain_scores_gemma":[0.95696783,0.019199105,0.0037947781,0.01449998,0.004714938,0.0008234007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020683007,0.00042380093,0.0010219436,0.0058265743,0.0032297433,0.012419842,0.0024091972,0.0018013451,0.0022488388],"category_scores_gemma":[0.050315186,0.0008153058,0.001219866,0.008698267,0.007930761,0.025461575,0.011850641,0.0029493372,0.0004827034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003773632,0.000020394684,0.0047345725,0.00019253866,0.00009191382,0.00030281168,0.002792307,0.0043931436,0.0007330137,0.94593036,0.0034003153,0.03737091],"study_design_scores_gemma":[0.000009043193,0.00001932424,0.0016553957,0.00018043953,0.000064645465,0.00055387296,0.0024817358,0.009614502,0.0012653461,0.92179203,0.06232787,0.000035759567],"about_ca_topic_score_codex":0.0037341446,"about_ca_topic_score_gemma":0.0021435379,"teacher_disagreement_score":0.020683007,"about_ca_system_score_codex":0.0036971746,"about_ca_system_score_gemma":0.0034097896,"threshold_uncertainty_score":0.10938352},"labels":[],"label_agreement":null},{"id":"W2388068227","doi":"","title":"An Improved Indexing Method for Moving Objects","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Search engine indexing; Computer science; Trajectory; Index (typography); Convex hull; Property (philosophy); Function (biology); Computer vision; Artificial intelligence; Hull; Regular polygon; Mathematics","score_opus":0.010277079469529183,"score_gpt":0.2975007286245641,"score_spread":0.28722364915503495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2388068227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037755282,0.00090112543,0.99029714,0.000083640494,0.00021516794,0.000084271654,0.00032561136,0.001999161,0.002318343],"genre_scores_gemma":[0.06334294,0.0016713892,0.92267513,0.00012396839,0.0004340373,0.0002256669,0.002464273,0.0005760997,0.008486558],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975516,0.00017651821,0.00020364103,0.00040108612,0.0015232727,0.00014384974],"domain_scores_gemma":[0.9981115,0.0002564698,0.000116258154,0.00049198524,0.0009359757,0.00008788901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009113659,0.0006913422,0.0015521551,0.0044772634,0.0012280324,0.0021132533,0.0018428208,0.0008173925,0.004343353],"category_scores_gemma":[0.0035299691,0.00043318263,0.0009818919,0.007327952,0.00072377693,0.0058495146,0.0015754018,0.0013199275,0.0031283547],"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":[0.0001848717,0.00009318114,0.00084354,0.0002499604,0.00005996601,0.0002125177,0.00030478387,0.009610726,0.028085725,0.055105753,0.018301666,0.8869473],"study_design_scores_gemma":[0.00013977663,0.0002983137,0.002678301,0.00009386987,0.00019376031,0.0018958694,0.0002883178,0.67716485,0.08173027,0.05038886,0.1848007,0.00032713398],"about_ca_topic_score_codex":0.0073780864,"about_ca_topic_score_gemma":0.003496341,"teacher_disagreement_score":0.0073780864,"about_ca_system_score_codex":0.00096729235,"about_ca_system_score_gemma":0.0015321282,"threshold_uncertainty_score":0.014670312},"labels":[],"label_agreement":null},{"id":"W2393892477","doi":"","title":"A Distributed Database Query Optimization Algorithm for Multi-attribute Classification","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Partition (number theory); Query optimization; Data mining; Distributed database; Algorithm; Online aggregation; Hash function; Sargable; Materialized view; Boolean conjunctive query; Database; View; Web search query; Information retrieval; Database design; Search engine","score_opus":0.032279587303237975,"score_gpt":0.2866762524649909,"score_spread":0.2543966651617529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2393892477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056250626,0.00026081386,0.99150264,0.00014180393,0.000036814745,0.000096186544,0.000102019265,0.001477481,0.0007572775],"genre_scores_gemma":[0.14610282,0.00024029444,0.8489046,0.0001557148,0.00009191512,0.00032687056,0.0007686405,0.00020756817,0.003201684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989374,0.0001595244,0.0000689037,0.0002464554,0.00049946294,0.0000881754],"domain_scores_gemma":[0.99934465,0.00016682313,0.000043465614,0.00016395915,0.00023820269,0.000042910986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009520988,0.0005835068,0.0010845616,0.0011914535,0.0010291389,0.0010675951,0.0015944957,0.0007330473,0.002347506],"category_scores_gemma":[0.0018198696,0.00033686752,0.0005164268,0.0020894213,0.000544413,0.0020655517,0.0016307242,0.0012357362,0.0010663653],"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":[0.0007832156,0.00030204668,0.0018598124,0.0001808098,0.00010807287,0.0001251418,0.00024335585,0.08564729,0.023549244,0.031814903,0.024257533,0.83112854],"study_design_scores_gemma":[0.00020346939,0.0001811459,0.00096972665,0.000014169674,0.000047796344,0.00042260636,0.00009823882,0.9408542,0.013992271,0.022443192,0.020726252,0.000046958456],"about_ca_topic_score_codex":0.0031148114,"about_ca_topic_score_gemma":0.0025700429,"teacher_disagreement_score":0.0031148114,"about_ca_system_score_codex":0.0009941099,"about_ca_system_score_gemma":0.0010995881,"threshold_uncertainty_score":0.00785315},"labels":[],"label_agreement":null},{"id":"W2394952598","doi":"","title":"Mitigating the Curse of Dimensionality for Exact kNN Retrieval","year":2014,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Curse of dimensionality; Search engine indexing; Computer science; Benchmark (surveying); Data mining; k-nearest neighbors algorithm; Filter (signal processing); Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.07136814435032705,"score_gpt":0.3757991783019898,"score_spread":0.3044310339516627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394952598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13515267,0.0030919788,0.8409273,0.0013710671,0.00027047907,0.00042226785,0.00080045825,0.010271713,0.0076920283],"genre_scores_gemma":[0.31524178,0.00079214486,0.67996943,0.0003136602,0.00009889156,0.00015937252,0.0013335466,0.00059837016,0.0014927865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99337864,0.0017734381,0.0007089975,0.0013039066,0.0023772155,0.00045779505],"domain_scores_gemma":[0.979807,0.008344628,0.0010394565,0.007840279,0.0026227117,0.00034596495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007566402,0.0014855547,0.0025559766,0.002795836,0.0022461975,0.0047153854,0.003195053,0.0015749234,0.0028422016],"category_scores_gemma":[0.043796774,0.00070130173,0.00095355173,0.0054469756,0.0023454553,0.010056816,0.0041973423,0.0019381922,0.0023162619],"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":[0.00088451174,0.0008007017,0.0177431,0.0011637904,0.00039152583,0.00031311077,0.0014760693,0.18735225,0.031815607,0.031185303,0.031473923,0.6954001],"study_design_scores_gemma":[0.00016290527,0.00040681037,0.003461836,0.00007984997,0.000087087625,0.0009465495,0.0011134695,0.89364654,0.0435521,0.041215222,0.015191256,0.00013635996],"about_ca_topic_score_codex":0.008488835,"about_ca_topic_score_gemma":0.01901579,"teacher_disagreement_score":0.008488835,"about_ca_system_score_codex":0.0020174868,"about_ca_system_score_gemma":0.003764702,"threshold_uncertainty_score":0.04001546},"labels":[],"label_agreement":null},{"id":"W2395766643","doi":"10.1007/978-3-319-13186-3_70","title":"BigSAM: Mining Interesting Patterns from Probabilistic Databases of Uncertain Big Data","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","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 Manitoba","funders":"","keywords":"Computer science; Big data; Uncertain data; Probabilistic logic; Database transaction; Data mining; Process (computing); Database; Artificial intelligence","score_opus":0.1140113605752562,"score_gpt":0.2911063442832601,"score_spread":0.17709498370800392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395766643","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027178923,0.0025047276,0.9549916,0.0007037875,0.00019016123,0.00019089165,0.004121583,0.00808268,0.002035696],"genre_scores_gemma":[0.20670368,0.0022571424,0.77634555,0.00031424695,0.00032417043,0.0002979762,0.009924781,0.0006118587,0.0032205563],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913675,0.00012670418,0.00007810472,0.00021277461,0.00039619103,0.000049547998],"domain_scores_gemma":[0.9976708,0.0014379774,0.00015515336,0.00043856585,0.00020473155,0.00009276415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012124556,0.0012419648,0.0012881149,0.0019271022,0.0005585281,0.0019863397,0.0025961834,0.00094346283,0.0034270268],"category_scores_gemma":[0.006484302,0.00096948683,0.0016078041,0.0030176248,0.00072695466,0.003927003,0.002209341,0.0017266498,0.0013944673],"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":[0.0005677371,0.00025791195,0.009824943,0.0018042044,0.00054088124,0.0013856151,0.0005034236,0.1289048,0.007344864,0.033640902,0.047006745,0.7682179],"study_design_scores_gemma":[0.00005574986,0.00011349318,0.0018488847,0.00008636445,0.00010552691,0.0012250989,0.00016112262,0.8418678,0.005120886,0.13477847,0.014594336,0.00004225263],"about_ca_topic_score_codex":0.00092537334,"about_ca_topic_score_gemma":0.001664013,"teacher_disagreement_score":0.0034270268,"about_ca_system_score_codex":0.00029316873,"about_ca_system_score_gemma":0.0006090794,"threshold_uncertainty_score":0.011464536},"labels":[],"label_agreement":null},{"id":"W2397167913","doi":"","title":"A Data Structure Supporting Exclusion Persistence Range Search.","year":2012,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","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":"University of New Brunswick","funders":"","keywords":"Computer science; Set (abstract data type); Data structure; Range (aeronautics); Block (permutation group theory); Space (punctuation); Persistence (discontinuity); Range query (database); Algorithm; Web search query; Mathematics; Combinatorics; Information retrieval; Sargable; Search engine","score_opus":0.10867846574142782,"score_gpt":0.30237502488695656,"score_spread":0.19369655914552875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397167913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0238511,0.0011630518,0.9509312,0.0012444676,0.00018578045,0.00035869385,0.0039630234,0.012945118,0.005357539],"genre_scores_gemma":[0.30876285,0.0006900268,0.66779864,0.0009402459,0.00025230722,0.0007648477,0.0113315955,0.0010359014,0.008423554],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997912,0.00032268083,0.00032712563,0.00033593105,0.00086856785,0.00023376316],"domain_scores_gemma":[0.992516,0.0017434941,0.0006873271,0.0035991822,0.0010477974,0.0004062234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017133296,0.00056763156,0.00090894726,0.0015069384,0.0015090219,0.0023276971,0.0028228061,0.0014178386,0.008159563],"category_scores_gemma":[0.010236357,0.0006350151,0.0006720373,0.003248558,0.0012546417,0.006274075,0.006321663,0.0016471698,0.0035156305],"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":[0.002506628,0.000519201,0.011358196,0.0009223023,0.0001618809,0.0007449227,0.0016618456,0.044542022,0.039539598,0.16397563,0.10673053,0.62733734],"study_design_scores_gemma":[0.00069691695,0.001315571,0.0033661886,0.00023716179,0.00014345272,0.0025814583,0.001015241,0.45546362,0.06185688,0.15062813,0.32240185,0.00029340308],"about_ca_topic_score_codex":0.0036138457,"about_ca_topic_score_gemma":0.0034596159,"teacher_disagreement_score":0.008159563,"about_ca_system_score_codex":0.0008334638,"about_ca_system_score_gemma":0.0016793495,"threshold_uncertainty_score":0.027296424},"labels":[],"label_agreement":null},{"id":"W2397334329","doi":"10.1007/978-3-319-26154-6_26","title":"LORI: Linguistically Oriented RDF Interface for Querying Fuzzy Temporal Data","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"RDF; Computer science; SPARQL; Linked data; Interface (matter); RDF Schema; Simple Knowledge Organization System; Semantics (computer science); Information retrieval; Semantic Web; Variety (cybernetics); Data model (GIS); Artificial intelligence; Programming language","score_opus":0.07667803835647694,"score_gpt":0.3385215102504843,"score_spread":0.26184347189400736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397334329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003800703,0.00064519705,0.8767592,0.00076013076,0.00021369067,0.0003131571,0.022278136,0.07306238,0.022167476],"genre_scores_gemma":[0.072084494,0.00174218,0.8068031,0.0014967942,0.00017869992,0.00096080377,0.063804366,0.017265292,0.035664216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921966,0.00015692876,0.00012137425,0.00013178695,0.0003144077,0.000055735785],"domain_scores_gemma":[0.9991604,0.00036830053,0.000046577778,0.00019998351,0.0001864318,0.00003835346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019180349,0.0008531839,0.0008947418,0.0024070847,0.0007216195,0.0032152233,0.0019776057,0.0011827766,0.023771942],"category_scores_gemma":[0.0045253546,0.0006701991,0.0013195499,0.0019108297,0.0007750006,0.0056151245,0.0026335658,0.0016200775,0.010452526],"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":[0.00062896457,0.0001539349,0.0010808185,0.0021026589,0.00012911933,0.00090373564,0.0019393667,0.009558686,0.02278407,0.2472619,0.29433382,0.41912287],"study_design_scores_gemma":[0.00008932378,0.00004904819,0.00050802465,0.00054837187,0.00007551112,0.00095265167,0.00045113964,0.06511925,0.03000451,0.12436168,0.77765745,0.00018315365],"about_ca_topic_score_codex":0.0044002,"about_ca_topic_score_gemma":0.0046966467,"teacher_disagreement_score":0.023771942,"about_ca_system_score_codex":0.0011388063,"about_ca_system_score_gemma":0.0008881662,"threshold_uncertainty_score":0.07952505},"labels":[],"label_agreement":null},{"id":"W2397680708","doi":"10.5281/zenodo.3781050","title":"Geocoding: Adding Another Dimension to Non-Spatial Data","year":2013,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geocoding; Dimension (graph theory); Computer science; Geographic information system; Computer graphics (images); Cartography; Geography; Mathematics","score_opus":0.05416432425136568,"score_gpt":0.25296482874479304,"score_spread":0.19880050449342734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397680708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054955487,0.0036201302,0.95667386,0.017152263,0.0024267705,0.00014097022,0.0023543327,0.0013909084,0.010745298],"genre_scores_gemma":[0.0949367,0.008120694,0.8762013,0.004321778,0.0027066155,0.00042197804,0.0042541567,0.0013756744,0.0076610213],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97880733,0.009832161,0.0015948399,0.0020688637,0.0072213435,0.00047544335],"domain_scores_gemma":[0.8742522,0.069625795,0.005669196,0.035639968,0.013290474,0.0015222767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0154435225,0.0010485786,0.001631753,0.010430576,0.0021692077,0.012263504,0.0031241993,0.0024512282,0.0072574234],"category_scores_gemma":[0.0991407,0.00092248636,0.0017127213,0.02351726,0.008459962,0.027813908,0.015946276,0.006001223,0.0028057673],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008282908,0.000052192343,0.0066366517,0.0016716999,0.00019252389,0.0003793011,0.004548667,0.0047952957,0.0013165734,0.62596905,0.032652836,0.32170242],"study_design_scores_gemma":[0.00002135645,0.00004173518,0.002259974,0.00066720176,0.000092704875,0.00078325486,0.0032009576,0.009501416,0.0018687891,0.6298515,0.35158113,0.00012995543],"about_ca_topic_score_codex":0.0044488655,"about_ca_topic_score_gemma":0.0043491847,"teacher_disagreement_score":0.0154435225,"about_ca_system_score_codex":0.0016855428,"about_ca_system_score_gemma":0.0050728046,"threshold_uncertainty_score":0.0816741},"labels":[],"label_agreement":null},{"id":"W2397880522","doi":"","title":"Local PTAS for independent set and vertex cover in location aware unit disk graphs","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Unit disk graph; Vertex cover; Vertex (graph theory); Dominating set; Combinatorics; Bounded function; Approximation algorithm; Embedding; Feedback vertex set; Independent set; Polynomial-time approximation scheme; Unit disk; Constant (computer programming); Edge cover; Graph; Discrete mathematics; Mathematics; Time complexity; Computer science; Wireless network","score_opus":0.020622490485375133,"score_gpt":0.26370231974108216,"score_spread":0.24307982925570704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397880522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07631294,0.00081419287,0.9150941,0.0006022167,0.0000421159,0.00016160388,0.00039555834,0.0017268927,0.004850375],"genre_scores_gemma":[0.6908823,0.0004941754,0.302638,0.00014690391,0.00011626821,0.00035348904,0.00084821146,0.00032350144,0.0041970997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99752,0.0007714138,0.00011194964,0.00047048106,0.0007541342,0.00037204754],"domain_scores_gemma":[0.9914836,0.0045321463,0.00066713506,0.0024988772,0.00043533713,0.00038289835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019976404,0.0010259051,0.0020051245,0.0015981824,0.001440257,0.0029033676,0.003515764,0.0015492802,0.0073295822],"category_scores_gemma":[0.011707142,0.00059894985,0.0013861799,0.0035281978,0.0014475425,0.0071477825,0.004054309,0.002432132,0.0013666943],"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":[0.0020193392,0.00031450513,0.0028478578,0.0006493372,0.00017904922,0.00028900124,0.00074136024,0.70103055,0.007906425,0.08735921,0.009458249,0.187205],"study_design_scores_gemma":[0.000056955476,0.00010453999,0.00034455978,0.000025541098,0.00004969901,0.00016166412,0.00007946334,0.94519985,0.0032723956,0.04906896,0.0016224076,0.000013973135],"about_ca_topic_score_codex":0.0022146683,"about_ca_topic_score_gemma":0.002711275,"teacher_disagreement_score":0.0073295822,"about_ca_system_score_codex":0.0030352166,"about_ca_system_score_gemma":0.0013420345,"threshold_uncertainty_score":0.02451992},"labels":[],"label_agreement":null},{"id":"W2398935836","doi":"","title":"Property Testing on Linked Lists.","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Strategic Research Council; Natural Sciences and Engineering Research Council of Canada; Sino-Danish Center; Tsinghua University; National Natural Science Foundation of China; National Research Foundation; Danmarks Grundforskningsfond; Center for Massive Data Algorithmics; National Science Foundation","keywords":"Property (philosophy); Computer science; Property testing; Combinatorics; Mathematics; Algorithm; Philosophy; Epistemology","score_opus":0.045701287493257935,"score_gpt":0.22887525246382193,"score_spread":0.18317396497056398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398935836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025050113,0.0003197885,0.96753114,0.0020188054,0.00005697452,0.00022799699,0.00039208776,0.0011818624,0.0032212257],"genre_scores_gemma":[0.58112097,0.00065656885,0.40566748,0.002268508,0.00072224234,0.0011009721,0.0017980271,0.00047377375,0.006191382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9815399,0.007259873,0.0013782936,0.0023044527,0.006074552,0.0014430155],"domain_scores_gemma":[0.90358615,0.06501166,0.0071976082,0.017696958,0.00465669,0.0018510001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011652675,0.0012793265,0.0015389632,0.0021119034,0.0017690975,0.005528591,0.0062084165,0.0035805034,0.0053983163],"category_scores_gemma":[0.068704896,0.0010890104,0.0024270758,0.0031044893,0.0054304837,0.030361136,0.0067593004,0.0048713777,0.0014835226],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007799674,0.00048178452,0.009254507,0.0006121182,0.00015478824,0.0008428779,0.00064583303,0.14929761,0.011337112,0.7260276,0.008424929,0.0921408],"study_design_scores_gemma":[0.00006602116,0.00026180345,0.00036911474,0.000064594635,0.000057445304,0.00050346024,0.00012485616,0.48904872,0.007248455,0.49490553,0.007315104,0.000034891218],"about_ca_topic_score_codex":0.0017147696,"about_ca_topic_score_gemma":0.0015020631,"teacher_disagreement_score":0.011652675,"about_ca_system_score_codex":0.002811415,"about_ca_system_score_gemma":0.0025880998,"threshold_uncertainty_score":0.061626017},"labels":[],"label_agreement":null},{"id":"W2400087804","doi":"","title":"Range Counting with Distinct Constraints.","year":2015,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","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":"University of Waterloo","funders":"","keywords":"Range (aeronautics); Range query (database); Computer science; Counting problem; Point (geometry); Data structure; Combinatorics; Mathematics; Algorithm; Sargable; Information retrieval; Search engine; Web search query; Geometry","score_opus":0.04844032645569903,"score_gpt":0.24745062627455283,"score_spread":0.1990102998188538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400087804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07177078,0.001855813,0.9096175,0.0016003533,0.00026773615,0.0004226182,0.0018832034,0.0026586181,0.009923419],"genre_scores_gemma":[0.47953933,0.001169214,0.50631666,0.0012853638,0.0005269925,0.00060649816,0.0036968288,0.0007025592,0.006156427],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98325896,0.003373885,0.0020555528,0.0034447396,0.0063859345,0.0014808782],"domain_scores_gemma":[0.9519247,0.02366216,0.003951311,0.016861193,0.0025700307,0.0010306269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056159366,0.001239257,0.002333629,0.0018114564,0.0016879241,0.005909192,0.00471154,0.0023646308,0.00618138],"category_scores_gemma":[0.038377948,0.0008106963,0.001517429,0.0062990705,0.0031782386,0.020171583,0.0074088504,0.0031510617,0.0016138424],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015365387,0.0005854495,0.01374869,0.0014956736,0.0002763211,0.001989273,0.0017550801,0.05253216,0.025321785,0.50074893,0.024341188,0.37566888],"study_design_scores_gemma":[0.00022355455,0.0007073936,0.0022257695,0.0002217757,0.00021064733,0.005461538,0.00184776,0.38881877,0.037047513,0.466068,0.09690422,0.00026303934],"about_ca_topic_score_codex":0.0022431486,"about_ca_topic_score_gemma":0.0017274858,"teacher_disagreement_score":0.00618138,"about_ca_system_score_codex":0.0010985385,"about_ca_system_score_gemma":0.0018578803,"threshold_uncertainty_score":0.02970028},"labels":[],"label_agreement":null},{"id":"W2400147605","doi":"10.1007/978-3-319-21470-2_52","title":"Efficient Combined Text and Spatial Search","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Full text search; Rank (graph theory); Combinatorics; Suffix; Tree (set theory); Information retrieval; String (physics); Data structure; Suffix array; Search engine; Mathematics","score_opus":0.02579766932172315,"score_gpt":0.25610899734912235,"score_spread":0.2303113280273992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400147605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04397358,0.0034165136,0.88949287,0.000663477,0.00059768965,0.0003055572,0.003862054,0.018085444,0.039602857],"genre_scores_gemma":[0.19067997,0.0012864983,0.7161057,0.00033836352,0.0003909891,0.0002880615,0.0074664727,0.0013029218,0.08214107],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889416,0.000122796,0.00007696009,0.00017610042,0.00058827375,0.00014183152],"domain_scores_gemma":[0.998288,0.0006568497,0.000066148874,0.00043389996,0.0004782413,0.000076813405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004685759,0.0012212959,0.0019640753,0.0027593565,0.0009328435,0.0018639624,0.002720918,0.0014571839,0.03595065],"category_scores_gemma":[0.003517134,0.00050860137,0.0010060798,0.005278682,0.0005129847,0.004495972,0.002683025,0.00065505516,0.014939743],"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":[0.0007668358,0.00023154833,0.00043693813,0.00045739664,0.000079057245,0.00022061291,0.000108390675,0.01827806,0.029189732,0.01696966,0.048947696,0.8843141],"study_design_scores_gemma":[0.00033032463,0.0004452218,0.0011905134,0.00008702471,0.00023479403,0.0012845171,0.0003431336,0.8004153,0.065559074,0.061180014,0.06883767,0.0000924892],"about_ca_topic_score_codex":0.0050129546,"about_ca_topic_score_gemma":0.009953132,"teacher_disagreement_score":0.03595065,"about_ca_system_score_codex":0.0006160822,"about_ca_system_score_gemma":0.0016999277,"threshold_uncertainty_score":0.120266914},"labels":[],"label_agreement":null},{"id":"W2400921732","doi":"10.1007/11011620_5","title":"D-GridMST: Clustering Large Distributed Spatial Databases","year":2005,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Data Management and Algorithms","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":"Cluster analysis; Computer science; Partition (number theory); Data mining; Overhead (engineering); Grid; Distributed database; Spatial database; Spatial analysis; Database; CURE data clustering algorithm; Space (punctuation); Correlation clustering; Artificial intelligence; Mathematics","score_opus":0.11456629510891252,"score_gpt":0.3584500276949944,"score_spread":0.24388373258608187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400921732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068521257,0.00093978125,0.92946833,0.000555667,0.00039034899,0.00022478923,0.0026262573,0.050421797,0.008520914],"genre_scores_gemma":[0.050795104,0.0009500386,0.92467326,0.00027610114,0.00010395506,0.00025288883,0.0066028475,0.0038680302,0.012477844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994462,0.000086917054,0.000040967218,0.00009582218,0.00029571855,0.00003430282],"domain_scores_gemma":[0.999106,0.00020603395,0.00002796446,0.00039183928,0.00018368658,0.000084456304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007588555,0.0009664181,0.0010387364,0.0016513562,0.0007882565,0.0017642254,0.0029501081,0.0007614038,0.010533996],"category_scores_gemma":[0.0024905696,0.00079810526,0.00082861667,0.0038613584,0.00058459165,0.001993163,0.0023159436,0.0011643547,0.005376575],"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":[0.00029925822,0.00008457154,0.0011825653,0.00062531576,0.00013308246,0.00020635144,0.00031630555,0.043868635,0.0126365395,0.0195401,0.24026753,0.68083984],"study_design_scores_gemma":[0.00023469649,0.00013268752,0.0013980347,0.00009840631,0.00008911549,0.000875382,0.00027250597,0.6057685,0.030167324,0.078137554,0.28274688,0.0000789811],"about_ca_topic_score_codex":0.0050760936,"about_ca_topic_score_gemma":0.008248224,"teacher_disagreement_score":0.010533996,"about_ca_system_score_codex":0.0005669165,"about_ca_system_score_gemma":0.001008896,"threshold_uncertainty_score":0.035239697},"labels":[],"label_agreement":null},{"id":"W2401646429","doi":"10.14778/2831360.2831363","title":"Finding Pareto optimal groups","year":2015,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Air Force Office of Scientific Research; Science and Technology Planning Project of Guangdong Province; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Skyline; Computer science; Pruning; Pareto optimal; Scalability; Computation; Point (geometry); Heuristic; Set (abstract data type); Pareto principle; Group (periodic table); Data mining; Algorithm; Theoretical computer science; Mathematics; Mathematical optimization; Artificial intelligence; Database","score_opus":0.036021360278773625,"score_gpt":0.24106280418211742,"score_spread":0.2050414439033438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401646429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09495758,0.00048161918,0.89613044,0.00039717578,0.000034665933,0.0003649867,0.00096036046,0.0007541738,0.0059190053],"genre_scores_gemma":[0.28492212,0.00028651126,0.70703065,0.00017303682,0.00005238554,0.00045319696,0.0032251752,0.0002840534,0.0035729802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804187,0.0004991798,0.00012268116,0.0004940514,0.00056739314,0.00027482907],"domain_scores_gemma":[0.99639636,0.001706921,0.00040118326,0.00050385774,0.00074627943,0.0002453032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019757552,0.0015188602,0.0020292704,0.0051804055,0.0018730816,0.00253376,0.0016628735,0.0015408525,0.005317073],"category_scores_gemma":[0.008365124,0.00072647823,0.0017385399,0.003721265,0.0011155343,0.0032760315,0.0028124724,0.0011111991,0.0012977837],"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":[0.0006513778,0.0004891765,0.0146885,0.00065420987,0.00038552398,0.0004330963,0.0013917387,0.4471321,0.008547134,0.1074646,0.020964572,0.39719802],"study_design_scores_gemma":[0.00010252413,0.00015936243,0.0013945577,0.00007324917,0.00005887819,0.00016304317,0.00065809494,0.8566238,0.0039278064,0.1293202,0.0074857264,0.000032739288],"about_ca_topic_score_codex":0.0034141755,"about_ca_topic_score_gemma":0.0042788787,"teacher_disagreement_score":0.005317073,"about_ca_system_score_codex":0.0014292622,"about_ca_system_score_gemma":0.002158912,"threshold_uncertainty_score":0.017787337},"labels":[],"label_agreement":null},{"id":"W2402160301","doi":"","title":"The Large numbers of 2-independent sets in extra-free forests.","year":2015,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Forestry; Geography","score_opus":0.01919751744235347,"score_gpt":0.2554542964505641,"score_spread":0.23625677900821063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402160301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41109407,0.0043363986,0.49044254,0.0104052825,0.0010213985,0.00024718174,0.002814779,0.0012530714,0.078385256],"genre_scores_gemma":[0.8843045,0.001235523,0.086883195,0.0015801269,0.001033172,0.0003966633,0.001450984,0.00037990516,0.022735849],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959046,0.0014067137,0.00024148294,0.00071443414,0.001122083,0.00061072933],"domain_scores_gemma":[0.9516215,0.036984965,0.0022806167,0.005308003,0.0017788947,0.0020260143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041314755,0.000634188,0.00160045,0.0025870833,0.0032186178,0.0051887357,0.0025222986,0.0021501367,0.011081467],"category_scores_gemma":[0.035984926,0.0014902004,0.0014728169,0.0023803364,0.0038831707,0.012777328,0.004544325,0.0046131616,0.0016944162],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000578232,0.00015351044,0.0040149703,0.00032524357,0.0001047954,0.0006155066,0.0005069029,0.013170488,0.0028835617,0.91854393,0.019454578,0.039648265],"study_design_scores_gemma":[0.00005621579,0.000019424899,0.0008257794,0.00004102016,0.00004027547,0.00051099,0.00008343282,0.022473777,0.0011141577,0.97033596,0.004477176,0.000021624148],"about_ca_topic_score_codex":0.0009168006,"about_ca_topic_score_gemma":0.0020362574,"teacher_disagreement_score":0.011081467,"about_ca_system_score_codex":0.0014549757,"about_ca_system_score_gemma":0.0011668146,"threshold_uncertainty_score":0.03707117},"labels":[],"label_agreement":null},{"id":"W2402181505","doi":"","title":"Orthogonal Range Search using a Distributed Computing Model","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Node (physics); Graph; Message passing; Message Passing Interface; Range (aeronautics); Set (abstract data type); Point (geometry); Combinatorics; Theoretical computer science; Mathematics; Parallel computing","score_opus":0.13569911409660299,"score_gpt":0.28635295350254275,"score_spread":0.15065383940593977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402181505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013046806,0.00019101775,0.9823774,0.00024897853,0.000034903063,0.000062190884,0.000038880476,0.00026624976,0.0037336082],"genre_scores_gemma":[0.44105285,0.00061674276,0.54754496,0.00020103132,0.00011218049,0.0005246372,0.00019249688,0.00011191068,0.009643207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986093,0.0004176304,0.000063048436,0.0002196478,0.00054038584,0.00015001396],"domain_scores_gemma":[0.998611,0.0006158826,0.000099790246,0.0003856933,0.00021909252,0.00006864078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011429094,0.00060398586,0.0011629941,0.0006621028,0.00089482154,0.0018378751,0.0028154228,0.0011842827,0.0038630571],"category_scores_gemma":[0.0033021932,0.0004633578,0.0008513964,0.0015846126,0.0013244434,0.0033902088,0.0025619124,0.00135981,0.0010239824],"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":[0.00022121842,0.000116717754,0.00065117725,0.00013581823,0.00004775874,0.00025264733,0.00017627048,0.74585193,0.0065293964,0.19750427,0.0026351472,0.04587764],"study_design_scores_gemma":[0.000027822274,0.000027563829,0.000028427217,0.0000036178017,0.0000057845004,0.00003783923,0.000015399364,0.97080225,0.0007561,0.02676917,0.0015197606,0.000006206627],"about_ca_topic_score_codex":0.0023942126,"about_ca_topic_score_gemma":0.0023512433,"teacher_disagreement_score":0.0038630571,"about_ca_system_score_codex":0.0011303815,"about_ca_system_score_gemma":0.0012873312,"threshold_uncertainty_score":0.012923241},"labels":[],"label_agreement":null},{"id":"W2402668406","doi":"10.14778/2735461.2735467","title":"Interpretable and informative explanations of outcomes","year":2014,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automatic summarization; Heuristics; Computer science; Construct (python library); Set (abstract data type); Dimension (graph theory); Data mining; Binary classification; Binary number; Machine learning; Sample (material); Artificial intelligence; Mathematics","score_opus":0.0047771768864128625,"score_gpt":0.19223746017089116,"score_spread":0.1874602832844783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402668406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038751516,0.0020284418,0.94562936,0.0023668048,0.000107833584,0.00052153494,0.007343734,0.0019449496,0.0013057507],"genre_scores_gemma":[0.20659043,0.0010983448,0.77749294,0.00035896344,0.00025662684,0.00044483325,0.012553986,0.0002711251,0.0009326946],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942781,0.0025657823,0.00058652414,0.001340446,0.0010356868,0.00019348192],"domain_scores_gemma":[0.9509837,0.038450096,0.003395645,0.0046619284,0.0020701275,0.00043848177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0076865274,0.0019863388,0.0018784577,0.005205008,0.00082376326,0.0037718534,0.0021459982,0.0017262446,0.004442412],"category_scores_gemma":[0.052522507,0.0010619775,0.0018010784,0.004460458,0.0009831814,0.0070347744,0.0020260506,0.0027149364,0.000906672],"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":[0.0016766438,0.00043083768,0.021773947,0.0037702874,0.0010201288,0.0011548192,0.0035885083,0.13866667,0.010337412,0.09333403,0.03208365,0.6921631],"study_design_scores_gemma":[0.00034508426,0.00041105942,0.008354545,0.0007952171,0.0009521783,0.0008443969,0.0021145882,0.46866038,0.016456407,0.47062203,0.03022397,0.00022012122],"about_ca_topic_score_codex":0.0016684263,"about_ca_topic_score_gemma":0.0029703984,"teacher_disagreement_score":0.0076865274,"about_ca_system_score_codex":0.0012864701,"about_ca_system_score_gemma":0.00261763,"threshold_uncertainty_score":0.040650725},"labels":[],"label_agreement":null},{"id":"W2403192553","doi":"","title":"The Crossing Numbers of Cartesian Product of Cone Graph C m + K l with Path P n .","year":2011,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cartesian product; Mathematics; Combinatorics; Graph; Path (computing); Product (mathematics); Geometry; Computer science","score_opus":0.014605114551273985,"score_gpt":0.21321666928822794,"score_spread":0.19861155473695397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403192553","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7461194,0.0008116371,0.10681479,0.00055194425,0.0004106569,0.00008793093,0.0017890995,0.00091244554,0.1425021],"genre_scores_gemma":[0.94990736,0.0003317855,0.029857678,0.00010624839,0.000053112115,0.00007319455,0.0011954021,0.00019968879,0.018275512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99956757,0.0000723288,0.00002324621,0.00011922503,0.000111410096,0.00010618838],"domain_scores_gemma":[0.99845314,0.00043628353,0.00029637513,0.00015942563,0.00028727803,0.0003674727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037341722,0.00038184627,0.00024348461,0.002181674,0.0011573773,0.0021456685,0.0005866728,0.00057804986,0.01285021],"category_scores_gemma":[0.0023647635,0.00027772522,0.00027362746,0.0010950044,0.0009886011,0.0020406991,0.0009209399,0.0008735932,0.0016401799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059853285,0.000121498015,0.009053588,0.00025736858,0.000034954035,0.0009034044,0.0009821851,0.0066145835,0.016083129,0.87181056,0.021871444,0.0716688],"study_design_scores_gemma":[0.00006079777,0.0002878883,0.016501194,0.00020635797,0.0000907734,0.0030039372,0.0014794557,0.052272726,0.024835138,0.8212458,0.07990016,0.00011569674],"about_ca_topic_score_codex":0.0016745493,"about_ca_topic_score_gemma":0.002181565,"teacher_disagreement_score":0.01285021,"about_ca_system_score_codex":0.0008325839,"about_ca_system_score_gemma":0.00056497374,"threshold_uncertainty_score":0.04298818},"labels":[],"label_agreement":null},{"id":"W2404459678","doi":"","title":"(1 + ε)-Approximation for Facility Location in Data Streams∗","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Facility location problem; Approximation algorithm; Simple (philosophy); Space (punctuation); Computer science; Set (abstract data type); Point (geometry); 1-center problem; Streaming algorithm; Decomposition; Mathematical optimization; Algorithm; Mathematics; Upper and lower bounds; Geometry","score_opus":0.16513186807217373,"score_gpt":0.32114308456516505,"score_spread":0.15601121649299132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404459678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061143834,0.0016469478,0.92112035,0.0023099778,0.00030160978,0.00037529715,0.0019498692,0.004400724,0.0067513906],"genre_scores_gemma":[0.4185949,0.0010543662,0.5674414,0.0006091499,0.00039570534,0.00061605894,0.004088263,0.0007221847,0.0064779054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99721885,0.00049207197,0.00021106891,0.0007706522,0.00073607854,0.0005712083],"domain_scores_gemma":[0.99485445,0.0028190897,0.00053738296,0.0009211051,0.0004917285,0.00037623584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025914675,0.0026256535,0.002678325,0.0013906645,0.0011637871,0.002808549,0.0047931946,0.0029044307,0.0071874354],"category_scores_gemma":[0.011582155,0.00087083736,0.0015382335,0.0035132442,0.0010987635,0.007763022,0.00277298,0.003508328,0.0021307454],"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":[0.0019464006,0.0006662558,0.0025030274,0.00084826216,0.00014426501,0.00025474804,0.00026220598,0.7580659,0.0042978507,0.03684206,0.024181927,0.16998704],"study_design_scores_gemma":[0.00009330256,0.00009329117,0.00020497233,0.000020366515,0.000015207508,0.00010718361,0.00006506205,0.96333206,0.0010154078,0.032920722,0.0021216923,0.000010802914],"about_ca_topic_score_codex":0.0054162424,"about_ca_topic_score_gemma":0.004669318,"teacher_disagreement_score":0.0071874354,"about_ca_system_score_codex":0.003134879,"about_ca_system_score_gemma":0.0028015492,"threshold_uncertainty_score":0.024044335},"labels":[],"label_agreement":null},{"id":"W2404694012","doi":"","title":"Ortsbezogene Visualisierung von komplexen relationalen Daten am Beispiel von Food Environment Maps.","year":2013,"lang":"de","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.020645699814456017,"score_gpt":0.21736337065868266,"score_spread":0.19671767084422664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404694012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1455526,0.002088909,0.75763696,0.0013832295,0.00034812858,0.00021203895,0.017047744,0.027324159,0.04840619],"genre_scores_gemma":[0.68849254,0.0019442847,0.28625143,0.00019699273,0.000056567824,0.00014134924,0.006974344,0.0018828529,0.014059711],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977666,0.000039134637,0.0000121937865,0.00004762165,0.00009319501,0.000031255415],"domain_scores_gemma":[0.9993554,0.00029799007,0.00004329984,0.000116523435,0.00014592097,0.00004090892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042646023,0.0006184587,0.00027753145,0.0016291902,0.0002909262,0.0018272498,0.0004969297,0.00064308004,0.016039409],"category_scores_gemma":[0.0021352782,0.0003967098,0.0004281005,0.0015158808,0.00038624599,0.0014579535,0.0013069833,0.0008140272,0.0025270034],"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":[0.0026316745,0.0002853561,0.009977109,0.002306231,0.00033089027,0.00372758,0.005043718,0.09746384,0.12276761,0.049111165,0.068104066,0.63825077],"study_design_scores_gemma":[0.00022403216,0.00030274715,0.022867795,0.0009546015,0.00034301495,0.0047995737,0.005563465,0.4068747,0.11684624,0.08478425,0.35613278,0.00030677012],"about_ca_topic_score_codex":0.005909024,"about_ca_topic_score_gemma":0.005676644,"teacher_disagreement_score":0.016039409,"about_ca_system_score_codex":0.00032745907,"about_ca_system_score_gemma":0.0003740109,"threshold_uncertainty_score":0.053657115},"labels":[],"label_agreement":null},{"id":"W2405459696","doi":"","title":"Searching Circulant Graphs.","year":2011,"lang":"en","type":"article","venue":"Cologne Twente Workshop on Graphs and Combinatorial Optimization","topic":"Data Management and Algorithms","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":"Memorial University of Newfoundland","funders":"","keywords":"Circulant matrix; Computer science; Combinatorics; Mathematics; Algorithm","score_opus":0.025711468339408898,"score_gpt":0.23512952671154555,"score_spread":0.20941805837213665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405459696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1028632,0.0068900455,0.78310466,0.0039846357,0.001026761,0.0003266519,0.0033359223,0.0034973207,0.09497074],"genre_scores_gemma":[0.4419199,0.002642809,0.4956454,0.0015918763,0.00046413625,0.00033867234,0.0062113684,0.0012583542,0.04992753],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994172,0.00018824561,0.000025262329,0.00012880193,0.00013376203,0.00010671632],"domain_scores_gemma":[0.99758136,0.0014697787,0.00019026583,0.0003419886,0.0002138845,0.0002025966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064119144,0.0008657051,0.0010973926,0.0018473669,0.0011055702,0.001467212,0.0013680278,0.0016592576,0.015609344],"category_scores_gemma":[0.007329714,0.00070868136,0.00071416376,0.0018462129,0.0006378806,0.0017086683,0.0016594249,0.001336835,0.0033282104],"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":[0.0006597905,0.00047044875,0.002637104,0.0010193981,0.00022118633,0.00053047773,0.00026472486,0.1194728,0.009009364,0.25057226,0.14864685,0.4664956],"study_design_scores_gemma":[0.00021862003,0.0001586704,0.0010290538,0.0001402552,0.00010072651,0.00040830515,0.0001577765,0.4625903,0.0046625133,0.4940081,0.036484364,0.00004132152],"about_ca_topic_score_codex":0.0027492857,"about_ca_topic_score_gemma":0.008943222,"teacher_disagreement_score":0.015609344,"about_ca_system_score_codex":0.001004135,"about_ca_system_score_gemma":0.0011637793,"threshold_uncertainty_score":0.052218497},"labels":[],"label_agreement":null},{"id":"W2406104576","doi":"","title":"Some Results On Strong Generalized Neighborhood Systems.","year":2012,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics","score_opus":0.023027847055165997,"score_gpt":0.2497194157725645,"score_spread":0.2266915687173985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406104576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16483553,0.008103573,0.5571348,0.0070103155,0.0009927369,0.00019208311,0.0010572877,0.0005405322,0.26013324],"genre_scores_gemma":[0.82465607,0.005338997,0.09484568,0.0017838984,0.0010444634,0.00035657067,0.0014043136,0.00024453958,0.07032532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99896836,0.00029533118,0.000059746533,0.00020396098,0.00032960996,0.00014299108],"domain_scores_gemma":[0.99734515,0.0012765522,0.0002188626,0.0004120537,0.00039823513,0.00034915423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011850602,0.00053284323,0.0007518023,0.0016841487,0.0024639375,0.0024261074,0.0011464598,0.0009248653,0.01252569],"category_scores_gemma":[0.0050385655,0.00041146294,0.0009359866,0.002160692,0.0021135733,0.005635428,0.0036878055,0.0021867892,0.0013381578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001225225,0.00001208438,0.00022402749,0.00006221227,0.000012199478,0.000057249745,0.0001341699,0.0011954652,0.00026816723,0.9875395,0.0036055662,0.0068770596],"study_design_scores_gemma":[0.00001280629,0.000023099541,0.0003083474,0.000028631175,0.000018395527,0.00017700638,0.0001418634,0.008976709,0.00028217048,0.9726236,0.01739739,0.000010060289],"about_ca_topic_score_codex":0.0016852802,"about_ca_topic_score_gemma":0.0023018136,"teacher_disagreement_score":0.01252569,"about_ca_system_score_codex":0.001303808,"about_ca_system_score_gemma":0.0007740359,"threshold_uncertainty_score":0.041902542},"labels":[],"label_agreement":null},{"id":"W2407196879","doi":"10.1007/978-3-319-19066-2_68","title":"Combining Constrained CP-Nets and Quantitative Preferences for Online Shopping","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science","score_opus":0.08148010704042642,"score_gpt":0.3099909860485635,"score_spread":0.2285108790081371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407196879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021537725,0.0002632644,0.970753,0.00040139907,0.00008408171,0.00006905208,0.00037193138,0.00019737931,0.006322122],"genre_scores_gemma":[0.61678064,0.000605035,0.36915866,0.00026723184,0.00023413931,0.00021494951,0.0011169757,0.00021649865,0.0114058545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973206,0.0013333695,0.00015861503,0.00041329404,0.00053738966,0.00023681468],"domain_scores_gemma":[0.9902588,0.0070150346,0.000511427,0.0008840147,0.0007976071,0.00053315243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043852963,0.0009685059,0.0012987859,0.0013045514,0.00065590296,0.0027693692,0.0022871601,0.0013491478,0.010337934],"category_scores_gemma":[0.018177038,0.000890239,0.0014277583,0.002536963,0.0013897186,0.007128182,0.0027743026,0.0029306195,0.000729747],"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":[0.00033585192,0.00020621675,0.0017360135,0.0002591901,0.0000862053,0.00013130756,0.00014036044,0.32262623,0.00069592916,0.5953442,0.0030056874,0.07543278],"study_design_scores_gemma":[0.00001737445,0.000031213644,0.00017824961,0.000021952852,0.00001429426,0.000028698645,0.00003796891,0.56439555,0.00018448966,0.43386352,0.001210004,0.000016650118],"about_ca_topic_score_codex":0.0054242956,"about_ca_topic_score_gemma":0.006626615,"teacher_disagreement_score":0.010337934,"about_ca_system_score_codex":0.0015710442,"about_ca_system_score_gemma":0.0014469988,"threshold_uncertainty_score":0.034583807},"labels":[],"label_agreement":null},{"id":"W2407224220","doi":"","title":"An Algorithm for the Maximum Weight Independent Set Problem onOutersting Graphs.","year":2015,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","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 Saskatchewan","funders":"","keywords":"Maximal independent set; Computer science; Independent set; Algorithm; Set (abstract data type); Theoretical computer science; Chordal graph; Graph; 1-planar graph","score_opus":0.055230565297083936,"score_gpt":0.2744272080274784,"score_spread":0.21919664273039446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407224220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01723709,0.0007955415,0.9572601,0.0011211697,0.0003367698,0.0008085914,0.0013608825,0.004693198,0.016386645],"genre_scores_gemma":[0.0730256,0.00046426314,0.9115185,0.00027484942,0.00017776508,0.0007381109,0.0035202592,0.00061483844,0.009665778],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99844307,0.00035055078,0.00012952765,0.00035946976,0.00044974752,0.0002676976],"domain_scores_gemma":[0.9966413,0.0015655212,0.00018302404,0.0008400066,0.00051045563,0.00025988114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016414236,0.002408419,0.0019118339,0.0026749198,0.001981714,0.002885225,0.0055019376,0.0026209336,0.020583445],"category_scores_gemma":[0.0076606893,0.0011494637,0.0020272532,0.004710974,0.0010246397,0.0060618785,0.00541803,0.0031710437,0.004762047],"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":[0.0008419414,0.0011173681,0.001775045,0.0010167579,0.00022700733,0.00034454884,0.00047271574,0.10447997,0.006741916,0.08442977,0.06359772,0.73495525],"study_design_scores_gemma":[0.00065862347,0.0002976094,0.0011200802,0.0001464148,0.00017359684,0.0004985436,0.00029038623,0.6683125,0.0062325294,0.29690942,0.025283864,0.00007648791],"about_ca_topic_score_codex":0.004909737,"about_ca_topic_score_gemma":0.0098418,"teacher_disagreement_score":0.020583445,"about_ca_system_score_codex":0.0021492601,"about_ca_system_score_gemma":0.0037931092,"threshold_uncertainty_score":0.068858504},"labels":[],"label_agreement":null},{"id":"W2407385737","doi":"10.1109/bigdataservice.2016.42","title":"A Novel Point of Interest (POI) Location Based Recommender System Utilizing User Location and Web Interactions","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point of interest; Computer science; Trajectory; Timestamp; Location-based service; Geographic coordinate system; Mobile device; Point (geometry); World Wide Web; Service (business); Recommender system; Information retrieval; Data mining; Real-time computing; Artificial intelligence; Computer network; Geography","score_opus":0.07356407168206223,"score_gpt":0.27432913570110107,"score_spread":0.20076506401903882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407385737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09703008,0.0032745968,0.88748485,0.0012236424,0.0007611095,0.00032122785,0.0009936746,0.0026456867,0.0062652556],"genre_scores_gemma":[0.59582806,0.0022099747,0.37864295,0.00066087476,0.00052390795,0.00029635458,0.0016458753,0.000085708525,0.020106426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993318,0.00009691749,0.000052832555,0.00025374044,0.00019454806,0.0000702438],"domain_scores_gemma":[0.99920326,0.00016305484,0.00005685203,0.00014543213,0.00035257265,0.00007886945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058881054,0.0007534519,0.0017708341,0.00075497205,0.001027318,0.0010703977,0.0027566024,0.0016046828,0.0023694444],"category_scores_gemma":[0.0012585309,0.00047611637,0.00087548606,0.001491312,0.00029696757,0.0020903423,0.0010462577,0.0010786272,0.0022414483],"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":[0.0009589465,0.0018147443,0.020903595,0.00075524364,0.00080079737,0.0016982106,0.0006343477,0.10633147,0.085488394,0.010631757,0.032657664,0.73732483],"study_design_scores_gemma":[0.00008806185,0.00040638543,0.00255152,0.000020487681,0.00017448176,0.0011056599,0.00009018003,0.97966,0.004890875,0.0012370168,0.0096877385,0.00008748053],"about_ca_topic_score_codex":0.018800225,"about_ca_topic_score_gemma":0.029559314,"teacher_disagreement_score":0.018800225,"about_ca_system_score_codex":0.00056147395,"about_ca_system_score_gemma":0.00095528713,"threshold_uncertainty_score":0.03738159},"labels":[],"label_agreement":null},{"id":"W2407951356","doi":"","title":"On the path-connectivity of lexicographic product graphs.","year":2015,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Lexicographical order; Mathematics; Path (computing); Product (mathematics); Combinatorics; Computer science; Computer network; Geometry","score_opus":0.024398730678705793,"score_gpt":0.23422302795536384,"score_spread":0.20982429727665805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407951356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34816578,0.0039433255,0.4935607,0.008492144,0.00032207067,0.00027441196,0.0034085673,0.0009901221,0.14084293],"genre_scores_gemma":[0.8422279,0.0035200033,0.12943763,0.0011051575,0.0004586805,0.00031862868,0.0035480168,0.00041042047,0.018973684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983595,0.000674113,0.00011073765,0.0003607223,0.00029313486,0.0002018881],"domain_scores_gemma":[0.97547543,0.019637447,0.0013203234,0.0016698731,0.0010374644,0.00085939304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016104417,0.00055214757,0.0007988669,0.0029743807,0.0021000428,0.0033316428,0.0014426344,0.0018002789,0.017988227],"category_scores_gemma":[0.021005714,0.0007659343,0.00063411676,0.0045844754,0.0034526803,0.012377651,0.0031289107,0.0024122589,0.0018081502],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017723395,0.000088906425,0.0024961168,0.00024875428,0.000041125346,0.00037237105,0.00076800183,0.007968294,0.0012117692,0.9340376,0.011214779,0.041375056],"study_design_scores_gemma":[0.000027900856,0.000037619615,0.00069861056,0.00007335112,0.0000293026,0.00035918137,0.00029466435,0.016469123,0.00061706605,0.9734514,0.007925525,0.000016388947],"about_ca_topic_score_codex":0.002136814,"about_ca_topic_score_gemma":0.0027651663,"teacher_disagreement_score":0.017988227,"about_ca_system_score_codex":0.001187462,"about_ca_system_score_gemma":0.0008789519,"threshold_uncertainty_score":0.06017661},"labels":[],"label_agreement":null},{"id":"W2408577007","doi":"","title":"Spanning Eulerian Subgraphs in Generalized Prisms.","year":2012,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Eulerian path; Combinatorics; Geometry; Mathematical analysis; Lagrangian","score_opus":0.01706646239536925,"score_gpt":0.24357836005575406,"score_spread":0.22651189766038482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408577007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27597106,0.0012480994,0.63960433,0.0015075821,0.00020588712,0.00021599823,0.002358137,0.0019089,0.076980054],"genre_scores_gemma":[0.71526766,0.001291788,0.24290527,0.0005095129,0.000086997374,0.00017965349,0.0027620213,0.00034711417,0.03665003],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991673,0.00020652013,0.00006704611,0.0002109411,0.00018958637,0.00015850386],"domain_scores_gemma":[0.99808466,0.00075706583,0.0002936831,0.0004568185,0.0001964795,0.00021144545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006354095,0.00037821312,0.00041878966,0.0010886332,0.001074277,0.0017623621,0.00092611474,0.0005923141,0.009499513],"category_scores_gemma":[0.0034647898,0.0005181318,0.00055465515,0.0021217342,0.0010338529,0.0040943488,0.0025686743,0.0010462382,0.0013973895],"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":[0.00010571304,0.00006849794,0.002038091,0.00027498332,0.00005592382,0.0005560808,0.00055821653,0.022391727,0.003969869,0.881322,0.010250454,0.07840841],"study_design_scores_gemma":[0.000027913788,0.000036594553,0.0009463122,0.00008209828,0.00004604172,0.0006781188,0.00045331588,0.04529004,0.003574986,0.9148531,0.033988398,0.00002298816],"about_ca_topic_score_codex":0.002790282,"about_ca_topic_score_gemma":0.005185778,"teacher_disagreement_score":0.009499513,"about_ca_system_score_codex":0.0009182186,"about_ca_system_score_gemma":0.0008939459,"threshold_uncertainty_score":0.03177905},"labels":[],"label_agreement":null},{"id":"W2414522820","doi":"10.1007/s00453-016-0170-7","title":"Succinct Indices for Path Minimum, with Applications","year":2016,"lang":"en","type":"article","venue":"Algorithmica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Ackermann function; Path (computing); Combinatorics; Mathematics; Range query (database); Constant (computer programming); Discrete mathematics; Tree traversal; Range (aeronautics); Tree (set theory); Data structure; Inverse; Sigma; Node (physics); Computer science; Algorithm","score_opus":0.009513887450547032,"score_gpt":0.2270798350867203,"score_spread":0.21756594763617326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2414522820","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011578252,0.0023762998,0.9607171,0.001620342,0.00073201227,0.00018848835,0.0023305514,0.0026819687,0.017775053],"genre_scores_gemma":[0.13532904,0.0024239253,0.8368814,0.0008896269,0.0009236935,0.0006851779,0.0052001835,0.0015811443,0.016085776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99779284,0.00054660323,0.00018975214,0.00042754703,0.00090132176,0.00014191357],"domain_scores_gemma":[0.99264824,0.0034942378,0.00048096522,0.0023910135,0.00074535346,0.00024013116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024344479,0.0014324663,0.0015645684,0.0033794732,0.0014213292,0.0035411776,0.0027935263,0.0016214201,0.015518109],"category_scores_gemma":[0.021912768,0.0008359194,0.0011554445,0.0063555194,0.0018966971,0.008948148,0.004381355,0.005354408,0.0046427827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005043001,0.0002878429,0.0009655351,0.00057718414,0.000057343874,0.00016106461,0.0003428896,0.037039697,0.0032870423,0.5527117,0.054176327,0.3498889],"study_design_scores_gemma":[0.000068297784,0.00009398731,0.00020134133,0.00012316096,0.000031232164,0.00021484795,0.0000656104,0.09429734,0.0022013339,0.8798495,0.02281783,0.00003567778],"about_ca_topic_score_codex":0.0014593996,"about_ca_topic_score_gemma":0.0029176108,"teacher_disagreement_score":0.015518109,"about_ca_system_score_codex":0.0017713761,"about_ca_system_score_gemma":0.002274591,"threshold_uncertainty_score":0.0519132},"labels":[],"label_agreement":null},{"id":"W2422676813","doi":"10.1109/icde.2016.7498281","title":"Finding the minimum spatial keyword cover","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cover (algebra); Pruning; Scalability; Computer science; Approximation algorithm; Set cover problem; Set (abstract data type); Function (biology); Efficient algorithm; Focus (optics); Time complexity; Theoretical computer science; Algorithm; Database","score_opus":0.019515255777372186,"score_gpt":0.23396203631380036,"score_spread":0.21444678053642818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2422676813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32657626,0.0023465902,0.6508728,0.0014880003,0.00007378836,0.0003020583,0.0038610918,0.0023006895,0.012178641],"genre_scores_gemma":[0.67594653,0.00094705605,0.3150069,0.0002213411,0.00007808386,0.00022746628,0.0037635022,0.00041562237,0.0033935183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985141,0.00028641877,0.00011281653,0.0004220153,0.00041336956,0.0002512436],"domain_scores_gemma":[0.9956352,0.0028577088,0.00037562635,0.0005291493,0.00037983689,0.00022245779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091229985,0.00091235724,0.0017993117,0.0015350247,0.0011509379,0.0022128387,0.0017877938,0.0019487648,0.0050839237],"category_scores_gemma":[0.0090313,0.0006573748,0.000971846,0.0031755085,0.0009766198,0.0056856587,0.002213142,0.0008280488,0.0012408922],"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":[0.0016936829,0.00039881514,0.01050696,0.0021520888,0.0002193454,0.0009510763,0.0016337695,0.62028444,0.030280754,0.052918598,0.027132336,0.25182804],"study_design_scores_gemma":[0.000076411736,0.00017923901,0.0012822181,0.00006422013,0.00006314285,0.00084120955,0.00081051583,0.90258485,0.01136658,0.075401194,0.007298915,0.000031551182],"about_ca_topic_score_codex":0.0026791645,"about_ca_topic_score_gemma":0.0027690767,"teacher_disagreement_score":0.0050839237,"about_ca_system_score_codex":0.0012207649,"about_ca_system_score_gemma":0.0016335999,"threshold_uncertainty_score":0.01700741},"labels":[],"label_agreement":null},{"id":"W2460197302","doi":"10.1145/2948649.2948655","title":"Dynamically ranked top-k spatial keyword search","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Popularity; Computer science; Information retrieval; Relevance (law); Keyword search; Keyword density; World Wide Web; Volume (thermodynamics); Data mining","score_opus":0.011416831503963784,"score_gpt":0.23330730010462822,"score_spread":0.22189046860066444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460197302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4334002,0.013434326,0.505457,0.0019150669,0.0008119033,0.0008118843,0.008757203,0.01068087,0.024731573],"genre_scores_gemma":[0.65421677,0.0016359286,0.3251849,0.00031894541,0.00025695236,0.00019771067,0.0078010918,0.00050740916,0.009880346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738425,0.0005178154,0.00032389446,0.0004452598,0.0008974261,0.00043130797],"domain_scores_gemma":[0.99518096,0.0020217535,0.0003842119,0.00064600323,0.0014005598,0.0003664001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013667165,0.001233973,0.0025550495,0.0048611853,0.0016290925,0.0028733762,0.0027062278,0.0014396084,0.0069530867],"category_scores_gemma":[0.009848977,0.0007176429,0.00095966604,0.0063311406,0.0005620396,0.004039641,0.0018711559,0.000609478,0.0045951786],"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":[0.004661128,0.0012185979,0.024947833,0.0021922444,0.00050054764,0.0011693346,0.00086998777,0.11026408,0.034047123,0.014686569,0.05808116,0.7473615],"study_design_scores_gemma":[0.0002893203,0.0006005041,0.0044052703,0.00012643749,0.00038347178,0.0018745714,0.0013244617,0.9189555,0.020820796,0.035008714,0.016079118,0.00013176439],"about_ca_topic_score_codex":0.00838345,"about_ca_topic_score_gemma":0.02749755,"teacher_disagreement_score":0.00838345,"about_ca_system_score_codex":0.0013211671,"about_ca_system_score_gemma":0.003481169,"threshold_uncertainty_score":0.023260415},"labels":[],"label_agreement":null},{"id":"W2460852158","doi":"10.1007/s11390-016-1654-2","title":"Mining Object Similarity for Predicting Next Locations","year":2016,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Similarity (geometry); Markov chain; Trajectory; Object (grammar); Locality; Data mining; Markov model; Sequence (biology); Hidden Markov model; Artificial intelligence; Theory of computation; Machine learning; Algorithm; Image (mathematics)","score_opus":0.021658267926861267,"score_gpt":0.26099372336252996,"score_spread":0.2393354554356687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460852158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81018645,0.0020294702,0.17756368,0.00035485555,0.00024190739,0.0002815863,0.003092404,0.0014811207,0.004768532],"genre_scores_gemma":[0.9154114,0.0004869445,0.07778232,0.00006439457,0.000116879615,0.00008817346,0.003987944,0.000056921253,0.002004966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991732,0.000060573668,0.00010907254,0.00023255275,0.00032818096,0.00009635914],"domain_scores_gemma":[0.99824524,0.00047127885,0.00030935736,0.0002608506,0.00052462734,0.00018857598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036329296,0.00060148584,0.00095266727,0.007897587,0.0009999702,0.0010547573,0.001473757,0.0010975286,0.0019616026],"category_scores_gemma":[0.0033302943,0.00030073733,0.0009122707,0.0055524283,0.00035661552,0.00232856,0.0011338951,0.00063571543,0.0010657645],"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":[0.0013408498,0.001495585,0.36529735,0.0004686066,0.0005067783,0.0015286371,0.00050719746,0.029034965,0.020626444,0.0073060086,0.01233201,0.5595556],"study_design_scores_gemma":[0.000076590026,0.0007930592,0.08507912,0.00010526079,0.00046086914,0.0022523657,0.0014417765,0.8629402,0.018632378,0.01806322,0.01006766,0.00008756254],"about_ca_topic_score_codex":0.0072022593,"about_ca_topic_score_gemma":0.012689289,"teacher_disagreement_score":0.007897587,"about_ca_system_score_codex":0.0004791762,"about_ca_system_score_gemma":0.0010213957,"threshold_uncertainty_score":0.014320672},"labels":[],"label_agreement":null},{"id":"W2466800257","doi":"10.1111/tgis.12219","title":"A New Combined Assessment of Mixed Uncertainty in Spatial Models: Conceptualization and Implementation","year":2016,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Conceptualization; Computer science; Python (programming language); Uncertainty analysis; Monte Carlo method; Fuzzy logic; Propagation of uncertainty; Uncertainty quantification; Data mining; Machine learning; Artificial intelligence; Algorithm; Mathematics; Simulation; Statistics","score_opus":0.024589368697617468,"score_gpt":0.30199854791787606,"score_spread":0.2774091792202586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466800257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066071413,0.000060369595,0.9916306,0.0001527388,0.000009230129,0.000031644184,0.000032625867,0.00017711372,0.0012984587],"genre_scores_gemma":[0.30528408,0.00017935387,0.6928393,0.00007000081,0.000026201282,0.0002270634,0.00007951512,0.00009594786,0.0011985067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99759537,0.0010969038,0.00011720581,0.00023089947,0.00082087534,0.00013866679],"domain_scores_gemma":[0.9972596,0.0014726582,0.00033011014,0.000281617,0.00049689604,0.00015896391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046006804,0.0010998778,0.001162282,0.0030329174,0.0008153702,0.0041322624,0.0027811301,0.0020203306,0.0022920629],"category_scores_gemma":[0.0071728225,0.0009646105,0.0018900423,0.0019522904,0.0022658296,0.004664403,0.0052179126,0.0019996776,0.0002588787],"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":[0.000038517326,0.000045260625,0.0012065087,0.00006391604,0.00006666734,0.00010096648,0.00018415818,0.83102053,0.0010821884,0.14442533,0.0003925653,0.021373441],"study_design_scores_gemma":[0.000004069567,0.000017129005,0.000062621904,0.000017231325,0.0000090264375,0.000018609839,0.000023949508,0.9739086,0.00031407014,0.024850478,0.00076398597,0.00001022882],"about_ca_topic_score_codex":0.0076775593,"about_ca_topic_score_gemma":0.0066092038,"teacher_disagreement_score":0.0076775593,"about_ca_system_score_codex":0.001841879,"about_ca_system_score_gemma":0.0022602526,"threshold_uncertainty_score":0.024330974},"labels":[],"label_agreement":null},{"id":"W2468149677","doi":"10.22215/etd/2012-09696","title":"Exploiting non-uniform query distributions in data structuring problems","year":2012,"lang":"en","type":"dissertation","venue":"","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":"Carleton University; Canadian Heritage; Library and Archives Canada; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Structuring; Computer science; Information retrieval; Mathematics; Political science","score_opus":0.042721301232802926,"score_gpt":0.28299573974565656,"score_spread":0.24027443851285363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2468149677","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09561652,0.00077305076,0.8987586,0.0016975317,0.0000399455,0.000111862006,0.000118263786,0.0009130939,0.0019711582],"genre_scores_gemma":[0.7103064,0.00075032917,0.28349358,0.00054195256,0.0003073653,0.00024877102,0.0005192617,0.00050858787,0.0033236507],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9863854,0.0072494657,0.00089505885,0.0017463323,0.0027248908,0.0009987756],"domain_scores_gemma":[0.9281865,0.058497436,0.0027754377,0.0070196074,0.0021110761,0.001410005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013530214,0.0009125086,0.002175313,0.0019689323,0.0014147587,0.003485635,0.0031490389,0.0020740554,0.0025164927],"category_scores_gemma":[0.055213857,0.001302756,0.0012719877,0.0032458387,0.002536046,0.010935569,0.005674706,0.0033328654,0.00054672523],"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":[0.0015395388,0.0007405423,0.01016606,0.0005076879,0.00025968414,0.00028865997,0.0011755298,0.476131,0.008907548,0.219153,0.0067479736,0.2743828],"study_design_scores_gemma":[0.00007850077,0.00016540945,0.00035280015,0.000017908043,0.00002743676,0.00011985906,0.00013420226,0.87217146,0.0020632092,0.12367004,0.0011828239,0.000016301074],"about_ca_topic_score_codex":0.0016190623,"about_ca_topic_score_gemma":0.0023893758,"teacher_disagreement_score":0.013530214,"about_ca_system_score_codex":0.0023532733,"about_ca_system_score_gemma":0.0023926243,"threshold_uncertainty_score":0.071555436},"labels":[],"label_agreement":null},{"id":"W2473418787","doi":"10.5623/cig2015-108","title":"An Efficient Parallel Map Visualization Framework for Large Vector Data","year":2015,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China University of Geosciences","keywords":"Visualization; Computer science; Vector map; Graphics; Data mining; Data visualization; Process (computing); Scalable Vector Graphics; Software; Vector graphics; The Internet; Geographic information system; Computer graphics (images); Artificial intelligence; World Wide Web; Geography; Cartography","score_opus":0.06670161258104966,"score_gpt":0.3450724783285144,"score_spread":0.2783708657474647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473418787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062453705,0.00014182983,0.95123667,0.00016188703,0.00006960085,0.000073473544,0.00084648427,0.039260417,0.001964244],"genre_scores_gemma":[0.08277433,0.0002556323,0.9070818,0.00009301452,0.00005616632,0.00021843296,0.0025848756,0.0032685502,0.0036671385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997129,0.000038121347,0.000015754162,0.000043149837,0.0001494143,0.000040616702],"domain_scores_gemma":[0.9995552,0.000101224345,0.000019505447,0.00012002303,0.00014616062,0.00005786514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004545518,0.0014810207,0.00086402684,0.001379831,0.0009346646,0.0018852619,0.0022243625,0.00065462425,0.011606916],"category_scores_gemma":[0.0014122684,0.00067540054,0.001015007,0.0016690127,0.00038387024,0.0016856992,0.0020949256,0.0012791296,0.0023163965],"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":[0.00080583984,0.0003255847,0.0022591718,0.00042920205,0.00028667797,0.0004211276,0.0005619154,0.100307524,0.039263893,0.033911888,0.12625891,0.69516826],"study_design_scores_gemma":[0.00014974509,0.000052341315,0.0004882352,0.000024900197,0.00004507175,0.00016365908,0.00008091373,0.923871,0.015808633,0.028309412,0.030954104,0.000052061092],"about_ca_topic_score_codex":0.014239209,"about_ca_topic_score_gemma":0.02268537,"teacher_disagreement_score":0.014239209,"about_ca_system_score_codex":0.0005454165,"about_ca_system_score_gemma":0.0013714168,"threshold_uncertainty_score":0.03882897},"labels":[],"label_agreement":null},{"id":"W2473523307","doi":"10.5623/cig2015-301","title":"ADVANCES IN GEOSPATIAL STATISTICAL MODELLING, ANALYSIS AND DATA MINING","year":2015,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Simon Fraser University; University of New Brunswick","funders":"","keywords":"Geospatial analysis; Humanities; Geography; Cartography; Philosophy","score_opus":0.05433346667079821,"score_gpt":0.3079598296451203,"score_spread":0.2536263629743221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473523307","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045677046,0.043131135,0.9411038,0.0037519883,0.00062581786,0.000043927375,0.0005333912,0.0010857137,0.0051564807],"genre_scores_gemma":[0.14079393,0.10948937,0.7378745,0.0010335192,0.0029579743,0.00016830297,0.0019279559,0.00044359989,0.0053108972],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967963,0.00095254247,0.00031221006,0.0004856521,0.0013474984,0.00010580757],"domain_scores_gemma":[0.98937106,0.0064862906,0.00050924363,0.0020890892,0.0013589334,0.00018536874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004460299,0.00096001895,0.0018091302,0.00366394,0.0005268867,0.004317333,0.0023240545,0.001206381,0.0032386219],"category_scores_gemma":[0.016998738,0.00062629953,0.0012082579,0.011278919,0.0019491544,0.0051353266,0.0025052596,0.002908123,0.0023252026],"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":[0.00006735483,0.000110470275,0.0038377696,0.0012171222,0.00018755747,0.000096136166,0.00021213984,0.050099734,0.0024761993,0.26081696,0.015938528,0.66493994],"study_design_scores_gemma":[0.000018218701,0.000062854255,0.0026596913,0.00029326437,0.00012140988,0.00031871232,0.0001657746,0.3416707,0.0034769,0.4447286,0.20641626,0.00006752454],"about_ca_topic_score_codex":0.006328178,"about_ca_topic_score_gemma":0.0035662027,"teacher_disagreement_score":0.006328178,"about_ca_system_score_codex":0.0015761799,"about_ca_system_score_gemma":0.0031164389,"threshold_uncertainty_score":0.023588657},"labels":[],"label_agreement":null},{"id":"W2480278432","doi":"10.4018/978-1-60566-172-8.ch014","title":"Retrieval Optimization for Server-Based Repositories in Location-Based Mobile Commerce","year":2009,"lang":"en","type":"book-chapter","venue":"Advances in database research (ADR) book series/Advances in database research series","topic":"Data Management and Algorithms","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Mobile commerce; Scope (computer science); Database transaction; Heuristic; Transactional leadership; Database; World Wide Web","score_opus":0.05327107552315204,"score_gpt":0.3854372046597578,"score_spread":0.33216612913660576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480278432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14373307,0.003715159,0.8364117,0.0007997553,0.00006180737,0.00031633725,0.0002418488,0.0009625326,0.0137577485],"genre_scores_gemma":[0.70209974,0.0016343386,0.2866978,0.00013286318,0.000095742216,0.0002281231,0.00039097617,0.00023956578,0.0084808115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867874,0.00056780287,0.00008595315,0.0001879937,0.00030494787,0.00017463184],"domain_scores_gemma":[0.9970216,0.002177346,0.00021255108,0.0001703421,0.00033798994,0.00008019127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018307659,0.00075445924,0.0020584115,0.0011728995,0.0007861501,0.0025604933,0.001890361,0.0013292637,0.004677876],"category_scores_gemma":[0.0067852484,0.0006720908,0.0006921026,0.0032637739,0.00062332593,0.0032278157,0.0011781439,0.0009369245,0.00094540865],"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":[0.00028616426,0.00022770964,0.0010178178,0.00025020534,0.00006259125,0.00011851504,0.0001685118,0.888394,0.0028954342,0.01599954,0.004727304,0.08585221],"study_design_scores_gemma":[0.000020207242,0.000061598264,0.00018939812,0.0000063295292,0.000015753889,0.00004421217,0.000053036798,0.9954972,0.0007218709,0.0027901132,0.0005918841,0.000008411718],"about_ca_topic_score_codex":0.011074593,"about_ca_topic_score_gemma":0.0069867535,"teacher_disagreement_score":0.011074593,"about_ca_system_score_codex":0.002176492,"about_ca_system_score_gemma":0.001706252,"threshold_uncertainty_score":0.02202028},"labels":[],"label_agreement":null},{"id":"W2480353330","doi":"10.1109/nafips.2001.944277","title":"Multivalued dependencies in extended possibility-based fuzzy relational databases","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Relational database; Functional dependency; Dependency theory (database theory); Fuzzy logic; Computer science; Fuzzy set; Fuzzy set operations; Data mining; Fuzzy classification; Relational model; Theoretical computer science; Axiom; Type-2 fuzzy sets and systems; Equivalence (formal languages); Mathematics; Artificial intelligence; Discrete mathematics","score_opus":0.07438106180287549,"score_gpt":0.2721741915902875,"score_spread":0.197793129787412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2480353330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021345377,0.0027659016,0.96815836,0.0007512161,0.00011687318,0.00007557553,0.00030311188,0.0002715707,0.0062120752],"genre_scores_gemma":[0.48043784,0.0022811706,0.5130924,0.0002874551,0.00033877918,0.00016302495,0.00063451414,0.00005166014,0.002713113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99331516,0.0017417077,0.0008318467,0.0009520431,0.0028714207,0.00028782678],"domain_scores_gemma":[0.99341017,0.0041314038,0.00057234,0.000804384,0.0009073175,0.00017431995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004795964,0.0004272124,0.00086572894,0.0024982698,0.001045007,0.0038378611,0.0017108758,0.0011388839,0.0017318008],"category_scores_gemma":[0.012434198,0.00057683024,0.0012819403,0.0032604872,0.002322567,0.0073416415,0.002431292,0.0020004106,0.0002933152],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009776253,0.00004955092,0.00074212597,0.00020696645,0.00007644312,0.0004846333,0.00066423893,0.020938825,0.0015978172,0.90312344,0.0015574888,0.070460744],"study_design_scores_gemma":[0.000023777035,0.000041802206,0.00039684706,0.000083947045,0.000058671696,0.0004215959,0.000114483926,0.09239631,0.001702682,0.8906111,0.014102166,0.000046521975],"about_ca_topic_score_codex":0.001665182,"about_ca_topic_score_gemma":0.0009695806,"teacher_disagreement_score":0.004795964,"about_ca_system_score_codex":0.0012375312,"about_ca_system_score_gemma":0.0008731168,"threshold_uncertainty_score":0.025363803},"labels":[],"label_agreement":null},{"id":"W2484289422","doi":"","title":"An Object-Oriented Approach for Temporal Data","year":2015,"lang":"en","type":"article","venue":"Hrčak Portal of scientific journals of Croatia (University Computing Centre)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Object (grammar); Class (philosophy); Metaphor; Data model (GIS); Object-oriented programming; Data modeling; Object model; Temporal database; Data mining; Artificial intelligence; Information retrieval; Database; Programming language","score_opus":0.07279038873027313,"score_gpt":0.2838701725139117,"score_spread":0.2110797837836386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484289422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006128929,0.0021246786,0.98422897,0.0019740174,0.000560327,0.00017193482,0.0002979947,0.0006740366,0.009355129],"genre_scores_gemma":[0.015900513,0.0033300528,0.9671804,0.0012590293,0.0008472782,0.00050326093,0.0007593682,0.00033599592,0.009884157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958935,0.00095701945,0.00054544985,0.0007291523,0.001658319,0.00021660955],"domain_scores_gemma":[0.99683315,0.00093713787,0.00027022458,0.0009998125,0.0007171429,0.00024254469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055293483,0.0011186746,0.0010364456,0.0037180027,0.0021872034,0.009746482,0.0046577267,0.0024556406,0.00591651],"category_scores_gemma":[0.006257635,0.0009245208,0.0027162237,0.0054190992,0.0038340313,0.011478116,0.0045446963,0.0050492263,0.0033334943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016271248,0.000032748205,0.00018201197,0.00018276303,0.000027912225,0.0001952776,0.00045563307,0.0018536622,0.00075300405,0.9537561,0.0068056183,0.035738923],"study_design_scores_gemma":[0.000024463148,0.00003178672,0.00014423387,0.0002454659,0.00004920699,0.00051495427,0.00022861671,0.015131805,0.0011674957,0.51529163,0.46712038,0.000049933667],"about_ca_topic_score_codex":0.0044149207,"about_ca_topic_score_gemma":0.0033720857,"teacher_disagreement_score":0.009746482,"about_ca_system_score_codex":0.0025562735,"about_ca_system_score_gemma":0.0034151687,"threshold_uncertainty_score":0.029242337},"labels":[],"label_agreement":null},{"id":"W2485167870","doi":"10.1016/j.tcs.2016.07.039","title":"Dynamic range majority data structures","year":2016,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Dalhousie University","funders":"","keywords":"Data structure; Amortized analysis; Combinatorics; Range (aeronautics); Mathematics; Set (abstract data type); Range query (database); Constant (computer programming); Space (punctuation); Upper and lower bounds; Fraction (chemistry); Discrete mathematics; Computer science; Information retrieval; Search engine; Web search query; Sargable","score_opus":0.017063029502736967,"score_gpt":0.2707024161268978,"score_spread":0.25363938662416086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485167870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087009996,0.0036949562,0.80785865,0.0037583492,0.0007176562,0.0003538441,0.002767169,0.009431952,0.08440739],"genre_scores_gemma":[0.75263995,0.0016094603,0.18388845,0.0018367682,0.00074819353,0.0004969233,0.0038384043,0.0015150269,0.053426847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99739146,0.00039304516,0.00024150143,0.00051993754,0.001061312,0.0003927278],"domain_scores_gemma":[0.9925206,0.0014000565,0.00051957055,0.00417253,0.0010214637,0.00036590517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017289986,0.00061103713,0.0011653029,0.0015024344,0.0018723345,0.0031360162,0.0022386836,0.000989849,0.021848446],"category_scores_gemma":[0.007924457,0.0006218028,0.00059873925,0.002656155,0.0015251806,0.009956718,0.008259778,0.0023781192,0.0066508595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012137407,0.0002244516,0.0017525745,0.00047346394,0.00008293515,0.0001500134,0.0005419875,0.009886152,0.018304272,0.56716424,0.038695306,0.36151087],"study_design_scores_gemma":[0.00018864845,0.00048982294,0.0006832476,0.00016091116,0.00013957557,0.00072619045,0.0003605571,0.04592128,0.055695154,0.77712196,0.11839302,0.0001197204],"about_ca_topic_score_codex":0.000272034,"about_ca_topic_score_gemma":0.00044314665,"teacher_disagreement_score":0.021848446,"about_ca_system_score_codex":0.000749058,"about_ca_system_score_gemma":0.0011555842,"threshold_uncertainty_score":0.073090315},"labels":[],"label_agreement":null},{"id":"W2485262335","doi":"10.22215/etd/2009-06397","title":"A ubiquitous GIS: framework, services and algorithms, development","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Computer science","score_opus":0.010983593163209513,"score_gpt":0.2541352670870325,"score_spread":0.24315167392382298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485262335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003744704,0.0010520903,0.95803654,0.0015898858,0.00016398911,0.00025883218,0.0006856592,0.01664989,0.017818363],"genre_scores_gemma":[0.048949882,0.0017872624,0.9363479,0.00027448367,0.00008565573,0.00019792917,0.0014657408,0.001300341,0.009590756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985461,0.00039666702,0.0001391868,0.00027224355,0.0005409142,0.000104928324],"domain_scores_gemma":[0.99840087,0.0002981885,0.00005862738,0.00051422435,0.00044466116,0.00028336066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026062736,0.00079646887,0.0007296093,0.0022552407,0.0011970385,0.0055598076,0.0020356847,0.0016275451,0.007368713],"category_scores_gemma":[0.0042581153,0.00067032047,0.0009000735,0.0026412283,0.0014278799,0.005470693,0.002990485,0.0014802467,0.0042341016],"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":[0.00013093118,0.00019714361,0.0027340122,0.0006341821,0.00010093965,0.0004222014,0.0017671648,0.013329062,0.009608316,0.29026815,0.076824985,0.6039829],"study_design_scores_gemma":[0.00004431852,0.000116605064,0.0014870563,0.00033109525,0.0000721732,0.0008988282,0.0010054708,0.07471088,0.0090721315,0.12558912,0.78660035,0.000071915754],"about_ca_topic_score_codex":0.008891524,"about_ca_topic_score_gemma":0.0076816664,"teacher_disagreement_score":0.008891524,"about_ca_system_score_codex":0.0012189867,"about_ca_system_score_gemma":0.0033845024,"threshold_uncertainty_score":0.024650812},"labels":[],"label_agreement":null},{"id":"W2485416360","doi":"10.4018/978-1-61692-868-1.ch001","title":"Region-Based Theories of Space","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in geospatial technologies book series","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mereology; Variety (cybernetics); Space (punctuation); Computer science; Domain (mathematical analysis); Field (mathematics); Simplicity; Focus (optics); Theoretical computer science; Epistemology; Mathematics; Artificial intelligence; Pure mathematics","score_opus":0.008732744848662068,"score_gpt":0.22078114838217794,"score_spread":0.21204840353351587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485416360","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011009201,0.036060013,0.32707468,0.009964609,0.0013570443,0.000074273106,0.0007897443,0.00043636243,0.6132341],"genre_scores_gemma":[0.6339002,0.038950324,0.15369071,0.0027138384,0.0022519203,0.00040506374,0.0018864514,0.00036272476,0.16583878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99941266,0.00023289815,0.000028419921,0.00011385665,0.00016278819,0.00004941198],"domain_scores_gemma":[0.9995639,0.00020873224,0.00003627901,0.00007901817,0.00007519006,0.00003697726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086127484,0.0008354353,0.0005457524,0.0016587109,0.0012748508,0.0042660907,0.0013123014,0.0013127889,0.0090361405],"category_scores_gemma":[0.0010460687,0.00037792476,0.0010899039,0.0024859335,0.006805246,0.008629692,0.0016835066,0.002412881,0.0019171679],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.674119e-7,6.0735294e-7,0.000007442229,0.000008789435,8.210224e-7,0.0000050375274,0.00006912404,0.00019693558,0.000015266358,0.99823725,0.000578166,0.00088003674],"study_design_scores_gemma":[0.0000023262503,0.000004060807,0.000049546965,0.000031656906,0.0000035419937,0.00004136639,0.00010977221,0.0010299428,0.00004506392,0.93626356,0.06241551,0.0000035717148],"about_ca_topic_score_codex":0.003194142,"about_ca_topic_score_gemma":0.0031797437,"teacher_disagreement_score":0.0090361405,"about_ca_system_score_codex":0.0029584907,"about_ca_system_score_gemma":0.0011177293,"threshold_uncertainty_score":0.030228972},"labels":[],"label_agreement":null},{"id":"W2489493656","doi":"10.1016/b978-008045374-3/50011-9","title":"A Data Warehouse Strategy for on-Demand Multiscale Mapping","year":2007,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec; Université Laval","funders":"","keywords":"Computer science; Cartographic generalization; Data warehouse; Generalization; Context (archaeology); Representation (politics); Abstraction; Architecture; Data science; Data mining; Database; Geography","score_opus":0.12828918378852253,"score_gpt":0.3107184563530914,"score_spread":0.1824292725645689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2489493656","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008815674,0.00016382999,0.9811131,0.00036733065,0.00005367295,0.00013018574,0.00043895596,0.0022563317,0.0066610076],"genre_scores_gemma":[0.11938871,0.00036089987,0.8704621,0.00024149795,0.000065184766,0.00019043231,0.0020006807,0.00053717947,0.0067533343],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991517,0.00013487818,0.00009252642,0.0001516119,0.00037771394,0.00009155483],"domain_scores_gemma":[0.9980718,0.00027252277,0.00006624748,0.0010070278,0.00045158013,0.00013086939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013409735,0.00079502165,0.001035042,0.002225115,0.0010086347,0.0038758805,0.0026762267,0.0009060265,0.008381096],"category_scores_gemma":[0.003863751,0.00067759224,0.0014389032,0.0041567553,0.00047055114,0.0045484602,0.005048867,0.0009830217,0.002481238],"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":[0.00024786775,0.0004889743,0.0031655654,0.0002653181,0.00020753901,0.000611053,0.00079768244,0.028770275,0.021658506,0.16318344,0.05559558,0.72500825],"study_design_scores_gemma":[0.00007659451,0.00020204938,0.002074426,0.00008395102,0.00020902771,0.0013129282,0.0012739907,0.6299286,0.019849136,0.2588001,0.086090505,0.00009870391],"about_ca_topic_score_codex":0.002422136,"about_ca_topic_score_gemma":0.0043300893,"teacher_disagreement_score":0.008381096,"about_ca_system_score_codex":0.00058465044,"about_ca_system_score_gemma":0.0009950683,"threshold_uncertainty_score":0.028037548},"labels":[],"label_agreement":null},{"id":"W2490017848","doi":"10.2495/dne-v11-n3-186-197","title":"Using entity identification and classification for automated integration of spatial-temporal data","year":2016,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computer science; Artificial intelligence; Data mining; Biology","score_opus":0.06151311774487913,"score_gpt":0.32932349161066005,"score_spread":0.26781037386578094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2490017848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07318797,0.0010647471,0.89987946,0.0007505222,0.00019824127,0.0004125025,0.0057373047,0.015703995,0.0030651325],"genre_scores_gemma":[0.28296602,0.00056130893,0.6868051,0.00025622465,0.000122806,0.00024992772,0.025391137,0.00038056314,0.0032669578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977884,0.0003696067,0.00025479682,0.00073886773,0.0006406404,0.00020766638],"domain_scores_gemma":[0.99509144,0.0016950617,0.00060333556,0.0014463057,0.0009848247,0.00017904521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025277778,0.0012573501,0.0011850761,0.007337681,0.0011102299,0.0026237846,0.0017936204,0.0014940994,0.0014431841],"category_scores_gemma":[0.0068956637,0.00040201988,0.0015121627,0.008396533,0.00047565225,0.0052365186,0.002050282,0.0015920534,0.0025346181],"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":[0.0004014644,0.0006623727,0.051809203,0.00030370432,0.00032699795,0.00073141983,0.00085248396,0.02513399,0.017738163,0.0063494216,0.021316446,0.8743743],"study_design_scores_gemma":[0.000022707804,0.00010051836,0.01717874,0.00008135871,0.00016476092,0.0005924803,0.0009987337,0.91594017,0.025780711,0.0130466,0.026010852,0.000082331084],"about_ca_topic_score_codex":0.012665783,"about_ca_topic_score_gemma":0.017051686,"teacher_disagreement_score":0.012665783,"about_ca_system_score_codex":0.0010095403,"about_ca_system_score_gemma":0.0015434045,"threshold_uncertainty_score":0.025184095},"labels":[],"label_agreement":null},{"id":"W2490635813","doi":"10.4018/987-1-59904-364-7.ch013","title":"Spatial Online Analytical Processing (SOLAP)","year":2007,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Online analytical processing; Geospatial analysis; Computer science; Spatial analysis; Geographic coordinate system; Geolocation; Geographic information system; Point (geometry); Data mining; Quadtree; Component (thermodynamics); Geography; Cartography; Information retrieval; Data warehouse; World Wide Web; Remote sensing; Artificial intelligence; Mathematics","score_opus":0.038005467736327285,"score_gpt":0.2890434116484372,"score_spread":0.25103794391210993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2490635813","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013128526,0.001695547,0.9589121,0.00082798366,0.00018130484,0.0001506803,0.001696558,0.009523579,0.025699286],"genre_scores_gemma":[0.021553531,0.003160902,0.953983,0.0005877629,0.0002910134,0.00037808588,0.0050520655,0.0016332252,0.013360375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758875,0.00074584124,0.00018551826,0.0004447098,0.0009166954,0.00011855767],"domain_scores_gemma":[0.9948107,0.0020773276,0.00023902117,0.001815992,0.00091953226,0.00013733661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030431831,0.0016045428,0.0013515281,0.0039763073,0.0012215414,0.007949933,0.0041653737,0.0011528756,0.020644171],"category_scores_gemma":[0.0062401886,0.0007329035,0.0014257568,0.009560785,0.0016198562,0.006844011,0.0050440645,0.0017163442,0.015965002],"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":[0.000054712997,0.000052688076,0.0005154284,0.0009847542,0.00009216022,0.00024613185,0.0008541336,0.006895287,0.0017783822,0.19175416,0.083830394,0.7129419],"study_design_scores_gemma":[0.000014807551,0.000041608328,0.00045505044,0.0003631736,0.000038684484,0.0006764492,0.00051602145,0.05829682,0.005381997,0.33601806,0.5981421,0.000055266883],"about_ca_topic_score_codex":0.0018820292,"about_ca_topic_score_gemma":0.0013842606,"teacher_disagreement_score":0.020644171,"about_ca_system_score_codex":0.0009960002,"about_ca_system_score_gemma":0.0020333235,"threshold_uncertainty_score":0.06906158},"labels":[],"label_agreement":null},{"id":"W2493539536","doi":"10.4018/978-1-60960-521-6.ch012","title":"Cost Modeling and Range Estimation for Top-k Retrieval in Relational Databases","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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":"Brock University","funders":"","keywords":"Computer science; Query optimization; Information retrieval; Relational database; Data mining; Range (aeronautics); Search engine indexing; Database; Sargable; Range query (database); Online aggregation; View; Query expansion; Relational database management system; Database design; Web search query; Search engine","score_opus":0.09564848466483881,"score_gpt":0.2883735946860542,"score_spread":0.1927251100212154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493539536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00560319,0.0014315288,0.9909116,0.00019733331,0.000025099123,0.000043340275,0.00012059937,0.0004151861,0.0012520769],"genre_scores_gemma":[0.30868387,0.0040495,0.6823075,0.00016383913,0.00021638977,0.00028624904,0.0008574374,0.00037832416,0.0030570102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996944,0.0010363971,0.00020748944,0.00042002834,0.0012402429,0.00015184017],"domain_scores_gemma":[0.9941859,0.0039004358,0.00043835002,0.0007293999,0.0006606477,0.00008521491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002282067,0.0010494615,0.001519705,0.0019573625,0.00053216337,0.002678176,0.00244828,0.0011965825,0.0020119317],"category_scores_gemma":[0.0139052505,0.00058372086,0.0013242794,0.004173945,0.00070252613,0.004961009,0.0013250337,0.0016549707,0.0010277036],"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":[0.00008644256,0.00008345867,0.0021758056,0.00028940264,0.00007317852,0.000107056105,0.00015267488,0.70649064,0.0035301659,0.05094939,0.004785367,0.23127647],"study_design_scores_gemma":[0.000002585051,0.000015458165,0.00029880385,0.0000112068055,0.000010138003,0.00008972454,0.000022554821,0.98289263,0.0007801122,0.014707409,0.0011558717,0.000013486606],"about_ca_topic_score_codex":0.0066496152,"about_ca_topic_score_gemma":0.0034610296,"teacher_disagreement_score":0.0066496152,"about_ca_system_score_codex":0.001988285,"about_ca_system_score_gemma":0.0010266462,"threshold_uncertainty_score":0.014426112},"labels":[],"label_agreement":null},{"id":"W2493714127","doi":"10.1109/icc.2016.7511323","title":"Efficient privacy-preserving circular range search on outsourced spatial data","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Encryption; Computer science; Range query (database); Information privacy; Security token; Database; Outsourcing; Security analysis; Tree (set theory); Web search query; Data mining; Information retrieval; Computer security; Web query classification; Search engine; Mathematics","score_opus":0.06408807659871574,"score_gpt":0.28283181650479217,"score_spread":0.21874373990607643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2493714127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14156891,0.0010264746,0.8493474,0.0004734571,0.000056624816,0.0002061855,0.00076115865,0.0012061745,0.005353513],"genre_scores_gemma":[0.89492345,0.0005029162,0.10074047,0.00013360438,0.0000435198,0.000092214934,0.0006243381,0.00006036315,0.0028790236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702173,0.00052082003,0.00027229966,0.00046003208,0.0011899642,0.00053516787],"domain_scores_gemma":[0.9949515,0.0010978235,0.0005322318,0.0027268503,0.000549497,0.00014219843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012589416,0.00035220952,0.0012400652,0.0008551251,0.00082639384,0.0012318111,0.001608395,0.0007644544,0.0016501052],"category_scores_gemma":[0.0047319965,0.00027447875,0.00081114715,0.0027262114,0.00090450107,0.004094281,0.0033142732,0.00073314615,0.00073216064],"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":[0.003091423,0.00041161443,0.006335797,0.0008269661,0.00025690583,0.0020793593,0.0016911038,0.2481854,0.11446837,0.15119912,0.013216082,0.4582379],"study_design_scores_gemma":[0.00017556554,0.0004468804,0.0013276235,0.00003997923,0.000090694666,0.0022639863,0.0007001564,0.8731219,0.06555954,0.044397272,0.011784856,0.000091537455],"about_ca_topic_score_codex":0.0019736916,"about_ca_topic_score_gemma":0.001478373,"teacher_disagreement_score":0.0019736916,"about_ca_system_score_codex":0.0007343216,"about_ca_system_score_gemma":0.0016847709,"threshold_uncertainty_score":0.0066580176},"labels":[],"label_agreement":null},{"id":"W2494146050","doi":"10.1007/978-3-319-23519-6_1625-1","title":"Clustering of Geospatial Big Data in a Distributed Environment","year":2015,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Data Management and Algorithms","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":"Computer Research Institute of Montréal","funders":"","keywords":"Geospatial analysis; Cluster analysis; Computer science; Big data; Data mining; Data science; Geography; Remote sensing; Artificial intelligence","score_opus":0.05438564057662813,"score_gpt":0.24324921505490563,"score_spread":0.1888635744782775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2494146050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020145888,0.0029995171,0.96514,0.00061512436,0.00032490602,0.00014185945,0.0005184176,0.0014967906,0.008617528],"genre_scores_gemma":[0.24434704,0.0043256846,0.7217893,0.00026301024,0.000479046,0.00028458404,0.0034705638,0.0010325909,0.02400811],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992551,0.0001397841,0.00004559138,0.00017539442,0.00032179747,0.000062356274],"domain_scores_gemma":[0.9987638,0.0004154746,0.000085506865,0.00038403433,0.00026246195,0.00008875567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064983906,0.00066200655,0.0009171945,0.0017647874,0.0011038207,0.0018991267,0.0018892651,0.0006049146,0.0022409887],"category_scores_gemma":[0.0025181402,0.0005611457,0.00085829356,0.0053006527,0.00067096506,0.0020564995,0.0018594143,0.0009260914,0.0011703934],"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":[0.00017170154,0.00014631427,0.0039374586,0.000773199,0.00027884985,0.00034575775,0.0009143069,0.271564,0.011503511,0.08204401,0.06463121,0.56368965],"study_design_scores_gemma":[0.000028266824,0.00007738063,0.0042696795,0.00012104938,0.00006814115,0.0004948713,0.0005891172,0.6725961,0.007526887,0.23547345,0.07870168,0.00005323174],"about_ca_topic_score_codex":0.0045455317,"about_ca_topic_score_gemma":0.0068288236,"teacher_disagreement_score":0.0045455317,"about_ca_system_score_codex":0.0011263271,"about_ca_system_score_gemma":0.0010901822,"threshold_uncertainty_score":0.00903815},"labels":[],"label_agreement":null},{"id":"W2496331279","doi":"10.1016/j.pmcj.2016.06.019","title":"Automatic rule generation using crowdsourcing for better relationship type discovery","year":2016,"lang":"en","type":"article","venue":"Pervasive and Mobile Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Crowdsourcing; Correctness; Data mining; Key (lock); Task (project management); Set (abstract data type); Popularity; Context (archaeology); Data science; Knowledge extraction; Focus (optics); Information retrieval; Machine learning; World Wide Web","score_opus":0.05281647897310366,"score_gpt":0.28722876865682234,"score_spread":0.2344122896837187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496331279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07332111,0.0009828408,0.9030834,0.0011436697,0.0008866779,0.0010105544,0.0060497243,0.007906888,0.005615069],"genre_scores_gemma":[0.28984913,0.0003411697,0.689536,0.00054556376,0.00029775067,0.00048511874,0.012437708,0.00061723567,0.0058903806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99348205,0.0014473015,0.00054003834,0.001713418,0.0024210548,0.0003960411],"domain_scores_gemma":[0.9793981,0.0120036,0.00058766734,0.0039374433,0.0036425763,0.0004306627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048469296,0.0015203586,0.0022322924,0.007607936,0.0021961713,0.0031396705,0.005252148,0.002872591,0.0068029067],"category_scores_gemma":[0.02441094,0.0008338248,0.003086828,0.004992196,0.0009250442,0.0030429487,0.0030855956,0.0031557009,0.004202713],"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":[0.00085763447,0.0011622227,0.01712858,0.00079296477,0.0005634893,0.0021415849,0.0008168314,0.05014173,0.024730738,0.011750422,0.041058376,0.8488555],"study_design_scores_gemma":[0.00015365511,0.00009257301,0.0027280864,0.00014088451,0.00020053769,0.000675176,0.00040822465,0.9223869,0.015251223,0.042933453,0.014951774,0.00007747138],"about_ca_topic_score_codex":0.009878438,"about_ca_topic_score_gemma":0.019071307,"teacher_disagreement_score":0.009878438,"about_ca_system_score_codex":0.00091634993,"about_ca_system_score_gemma":0.0027434716,"threshold_uncertainty_score":0.025633335},"labels":[],"label_agreement":null},{"id":"W2497658231","doi":"","title":"Finding interesting summaries in genspace graphs","year":2005,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","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":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Information retrieval; Online analytical processing; Data mining; Consistency (knowledge bases); Graph; Process (computing); Probability distribution; Set (abstract data type); Data science; Theoretical computer science; Artificial intelligence; Data warehouse; Statistics; Mathematics","score_opus":0.016965238637129173,"score_gpt":0.2275960922733133,"score_spread":0.2106308536361841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2497658231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04182611,0.00045170586,0.95196617,0.00072271895,0.000029709416,0.00022540223,0.001200982,0.0013963326,0.0021808413],"genre_scores_gemma":[0.36287868,0.0008113546,0.62519336,0.0005192699,0.00007769207,0.00038123375,0.005367698,0.00035392985,0.0044168346],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980089,0.00062090607,0.00015702932,0.00054251606,0.00053912593,0.00013146682],"domain_scores_gemma":[0.9895984,0.005905792,0.0013189043,0.0012258642,0.0016897706,0.00026118752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016702892,0.000967027,0.0007705762,0.0032894334,0.0010864513,0.002565307,0.0014351617,0.0012212017,0.0031318136],"category_scores_gemma":[0.013674974,0.0006559204,0.001367962,0.0027171217,0.0010680895,0.0054727853,0.0022284861,0.0012115937,0.0006727112],"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":[0.00051092845,0.00021000342,0.021251414,0.0011924584,0.0004019279,0.0019304318,0.004960637,0.21303546,0.012549951,0.28825036,0.013580372,0.44212607],"study_design_scores_gemma":[0.000057998768,0.00014177612,0.0033839839,0.00013529288,0.000191285,0.00059633766,0.0011012505,0.5284184,0.006975415,0.42872086,0.030209994,0.00006743713],"about_ca_topic_score_codex":0.0052345516,"about_ca_topic_score_gemma":0.0059953067,"teacher_disagreement_score":0.0052345516,"about_ca_system_score_codex":0.0010182512,"about_ca_system_score_gemma":0.0009439412,"threshold_uncertainty_score":0.010476947},"labels":[],"label_agreement":null},{"id":"W2498735781","doi":"10.1109/mdm.2016.44","title":"K-Closest Pairs Queries in Road Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tree traversal; Search engine indexing; Traverse; Pruning; Computer science; Tree (set theory); Metric (unit); Metric space; Breadth-first search; Theoretical computer science; Euclidean space; Algorithm; Mathematics; Combinatorics; Artificial intelligence; Discrete mathematics","score_opus":0.010795592138850882,"score_gpt":0.21231468774939252,"score_spread":0.20151909561054165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498735781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35882196,0.003877424,0.615484,0.0017996426,0.00016860053,0.00071084814,0.005045805,0.004231024,0.009860621],"genre_scores_gemma":[0.68417454,0.000832425,0.30761534,0.00020797134,0.00007308701,0.00021585543,0.004488261,0.00024971453,0.0021427704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945222,0.0016709369,0.0006147039,0.0011607553,0.0015360528,0.0004954036],"domain_scores_gemma":[0.99115366,0.005595885,0.0006644587,0.0014966206,0.00074741844,0.00034185126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003111797,0.0012098968,0.002604597,0.0033915655,0.0021321946,0.0030243376,0.0032065883,0.0029566544,0.0023170677],"category_scores_gemma":[0.017490815,0.0007453621,0.0008740221,0.0063663446,0.0011408416,0.009817963,0.0035728032,0.0011470037,0.00089013047],"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":[0.0022844006,0.0006048047,0.010186347,0.0012110395,0.0003318267,0.0011933631,0.0022376005,0.61901987,0.013642672,0.07741171,0.024129253,0.24774706],"study_design_scores_gemma":[0.00012626678,0.0001794587,0.0013761395,0.000043761695,0.0000556699,0.00070202455,0.0016737692,0.89988303,0.009053424,0.076982886,0.009855933,0.0000675974],"about_ca_topic_score_codex":0.008780943,"about_ca_topic_score_gemma":0.008491063,"teacher_disagreement_score":0.008780943,"about_ca_system_score_codex":0.0016629874,"about_ca_system_score_gemma":0.0015086692,"threshold_uncertainty_score":0.01745969},"labels":[],"label_agreement":null},{"id":"W2499221028","doi":"10.1090/gsm/089/04","title":"Models for the web graph","year":2008,"lang":"en","type":"book-chapter","venue":"Graduate studies in mathematics","topic":"Data Management and Algorithms","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Graph; World Wide Web; Theoretical computer science","score_opus":0.2552848852701486,"score_gpt":0.32188076662289694,"score_spread":0.06659588135274835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2499221028","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018432442,0.0075048953,0.76883894,0.010126128,0.00062015257,0.00015750864,0.0023881951,0.001219677,0.19071199],"genre_scores_gemma":[0.5546975,0.015734842,0.22396712,0.0035275328,0.0020387564,0.0010924755,0.0053836177,0.001768426,0.19178969],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993637,0.000257299,0.000028462498,0.00013495698,0.00015832017,0.0000571952],"domain_scores_gemma":[0.9982849,0.0009772038,0.000108099775,0.00033432402,0.00015612485,0.00013939902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009509995,0.0015347172,0.0012321871,0.0019520331,0.0016296896,0.003822351,0.0023561274,0.002793172,0.017541133],"category_scores_gemma":[0.0033727924,0.0008142107,0.0018137523,0.0027652793,0.0022402606,0.007588042,0.0014716983,0.0042065657,0.0034574857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048925945,0.000010329588,0.000040875122,0.000029914405,0.0000065173,0.000020997733,0.000058047022,0.0057887044,0.0000590394,0.98404515,0.0062569245,0.0036785577],"study_design_scores_gemma":[0.0000024963554,0.0000014754378,0.000016613752,0.000009398452,0.000003045614,0.000013831742,0.000011516135,0.013937987,0.00001906348,0.9806264,0.0053553446,0.0000027192532],"about_ca_topic_score_codex":0.007962763,"about_ca_topic_score_gemma":0.008843446,"teacher_disagreement_score":0.017541133,"about_ca_system_score_codex":0.0031478505,"about_ca_system_score_gemma":0.0010828932,"threshold_uncertainty_score":0.05868095},"labels":[],"label_agreement":null},{"id":"W2499909973","doi":"10.1017/cbo9780511736261.011","title":"A surprising problem in navigation","year":2011,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","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":"McGill University","funders":"","keywords":"Snapshot (computer storage); Excursion; Computer science; Narrative; Mobile robot navigation; Artificial intelligence; Global Positioning System; Computer vision; Geography; Mobile robot; Robot; Art; Telecommunications; Robot control; Political science; Literature","score_opus":0.027710675546539287,"score_gpt":0.1928511416435746,"score_spread":0.1651404660970353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2499909973","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070199133,0.04907605,0.6516546,0.04901787,0.011808747,0.000071609844,0.0009416595,0.0013916492,0.2290178],"genre_scores_gemma":[0.217706,0.056638714,0.315092,0.019689662,0.01618635,0.00049185345,0.0021604837,0.0020587237,0.3699762],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99850464,0.00022990484,0.00007862085,0.0006647,0.00044184373,0.00008022711],"domain_scores_gemma":[0.9980773,0.0010976865,0.000066530854,0.00035267766,0.00033984543,0.00006604874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010127424,0.0010892003,0.00094342284,0.0008506719,0.0027220182,0.004115214,0.0017554674,0.0039355042,0.01365586],"category_scores_gemma":[0.006397747,0.0006206583,0.0010450294,0.0018498923,0.005094709,0.009469366,0.003519948,0.0057467907,0.007976875],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028075823,0.000010766602,0.00016900043,0.00025913658,0.000019320814,0.00009334604,0.00034776688,0.0020443876,0.00031666498,0.83991945,0.062703885,0.094088145],"study_design_scores_gemma":[0.000010107276,0.00001626192,0.00014149644,0.00011010992,0.0000132582845,0.00040823303,0.00012852142,0.0034889828,0.00035957395,0.66504264,0.33026037,0.000020509566],"about_ca_topic_score_codex":0.003500944,"about_ca_topic_score_gemma":0.0024646313,"teacher_disagreement_score":0.01365586,"about_ca_system_score_codex":0.0016174128,"about_ca_system_score_gemma":0.0010979834,"threshold_uncertainty_score":0.045683444},"labels":[],"label_agreement":null},{"id":"W2500302690","doi":"10.4018/978-1-61520-883-8.ch019","title":"Geo-Aware Digital Cultural Heritage","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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":"Library and Archives Canada","funders":"","keywords":"Cultural heritage; Interpretation (philosophy); Object (grammar); Dimension (graph theory); Museology; Visual arts; Geography; World Wide Web; Media studies; Sociology; Computer science; Art; Archaeology","score_opus":0.015813207507177563,"score_gpt":0.23150436444890482,"score_spread":0.21569115694172725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500302690","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03648834,0.01905802,0.17735353,0.00787166,0.0010340506,0.0003094381,0.0016262765,0.005701626,0.750557],"genre_scores_gemma":[0.58870554,0.045855053,0.15464108,0.0023818922,0.0005870814,0.0002528775,0.0040720296,0.0007040303,0.20280051],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962497,0.00006509386,0.000021854798,0.000040965166,0.0001794124,0.00006772448],"domain_scores_gemma":[0.99956554,0.00007423564,0.000024829047,0.00018983075,0.000076178585,0.00006941479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039746577,0.00035906062,0.0002589863,0.00097057,0.0011088009,0.0058306935,0.0010432135,0.0008474555,0.012362591],"category_scores_gemma":[0.0011953248,0.0002954567,0.00039330535,0.0019279509,0.0009840258,0.007350759,0.0049078176,0.0011605313,0.0045991084],"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":[0.000092289374,0.00008933177,0.0028468596,0.0012743727,0.00004099196,0.0014715804,0.007472496,0.0045986883,0.008227165,0.3133562,0.08479383,0.5757362],"study_design_scores_gemma":[0.0000070391256,0.000015152182,0.0013442091,0.00034637353,0.00002467498,0.00077272346,0.003583011,0.0029962512,0.0023036306,0.046192355,0.94238544,0.000029252647],"about_ca_topic_score_codex":0.0047012875,"about_ca_topic_score_gemma":0.008404152,"teacher_disagreement_score":0.012362591,"about_ca_system_score_codex":0.00064767257,"about_ca_system_score_gemma":0.0008774265,"threshold_uncertainty_score":0.04135704},"labels":[],"label_agreement":null},{"id":"W2500435251","doi":"10.1017/cbo9781316091548.016","title":"Dual graphs and cluster algebras","year":2016,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"","keywords":"ENCODE; Dual (grammatical number); Simple (philosophy); Computer science; Planar; Cluster (spacecraft); Shell (structure); Square (algebra); Section (typography); Dual graph; Planar graph; Combinatorics; Topology (electrical circuits); Theoretical computer science; Mathematics; Engineering; Geometry; Computer graphics (images); Computer network; Graph; Chemistry; Epistemology; Philosophy; Linguistics; Mechanical engineering","score_opus":0.01594348044719709,"score_gpt":0.18424682413851579,"score_spread":0.1683033436913187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500435251","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02732562,0.01257813,0.052325375,0.0052755387,0.0012095686,0.000036057918,0.00023918018,0.00030027653,0.90071034],"genre_scores_gemma":[0.540448,0.013556453,0.02853936,0.0030150248,0.0012804356,0.000108243956,0.0005918507,0.00040072063,0.41205996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998074,0.000051437157,0.0000049277182,0.000034617453,0.000072528055,0.000029032602],"domain_scores_gemma":[0.9998677,0.000037246125,0.000010059557,0.000023257162,0.000034928715,0.000026831727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026993302,0.00028015568,0.00025334713,0.00089738553,0.0012764168,0.0020610755,0.00045728975,0.00057205104,0.011338456],"category_scores_gemma":[0.00058661465,0.00017163552,0.00028324264,0.00086142786,0.0019679049,0.0028507742,0.0010978642,0.0013853203,0.001869947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000126969,0.0000018289361,0.000010992554,0.000010046512,6.753164e-7,0.0000049862742,0.000052451596,0.00009680697,0.000073067815,0.99475783,0.0025645646,0.0024255654],"study_design_scores_gemma":[0.0000015388504,0.000001939698,0.000038308775,0.000014026212,0.0000011821436,0.000036894497,0.00006262895,0.00047823405,0.00011297017,0.9503985,0.048851103,0.0000026696782],"about_ca_topic_score_codex":0.001408523,"about_ca_topic_score_gemma":0.0016631949,"teacher_disagreement_score":0.011338456,"about_ca_system_score_codex":0.0016909136,"about_ca_system_score_gemma":0.000718899,"threshold_uncertainty_score":0.037930846},"labels":[],"label_agreement":null},{"id":"W2501089572","doi":"10.4018/978-1-59140-384-5.ch009","title":"An Object-Oriented Approach to Managing Fuzziness in Spatially Explicit Models Coupled to a Geographic Database","year":2005,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; University of Toronto","funders":"","keywords":"Computer science; Data mining; Fuzzy logic; Database; Spatial database; Schema (genetic algorithms); Component (thermodynamics); Geographic information system; Spatial analysis; Information retrieval; Artificial intelligence; Geography; Cartography","score_opus":0.022896336254403012,"score_gpt":0.24664870596226046,"score_spread":0.22375236970785745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2501089572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029972168,0.00047172583,0.99198747,0.00057031924,0.0000355024,0.000038840775,0.000050217226,0.00017160006,0.003677089],"genre_scores_gemma":[0.062235814,0.0013550946,0.93065536,0.00022737333,0.00006151455,0.00016466307,0.00023667989,0.00009312997,0.004970545],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982855,0.00053312787,0.00017318562,0.00026924285,0.00067013106,0.00006891509],"domain_scores_gemma":[0.99757856,0.0014156755,0.00013971496,0.0005990972,0.00019703551,0.00006985358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037562805,0.00066241046,0.000942943,0.0012862969,0.0012726361,0.0066621685,0.0033558975,0.0014761603,0.0024604942],"category_scores_gemma":[0.0060513755,0.000884022,0.0017113968,0.0036311666,0.002289066,0.008126065,0.003840661,0.0031151313,0.00044560968],"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":[0.000023460523,0.00004490363,0.0006465201,0.00016953307,0.00007052916,0.00022704666,0.000978199,0.04818206,0.0016238871,0.86616373,0.0024976563,0.07937243],"study_design_scores_gemma":[0.000016187392,0.000029310911,0.00030979447,0.00011476637,0.00007408468,0.00031864172,0.00035327463,0.27717128,0.002199304,0.6456115,0.07376456,0.00003724885],"about_ca_topic_score_codex":0.0041988255,"about_ca_topic_score_gemma":0.004827703,"teacher_disagreement_score":0.0066621685,"about_ca_system_score_codex":0.0022175848,"about_ca_system_score_gemma":0.0016871281,"threshold_uncertainty_score":0.019865394},"labels":[],"label_agreement":null},{"id":"W2502885977","doi":"10.4018/978-1-59140-527-6.ch009","title":"Imprecise and Uncertain Engineering Information Modeling in Databases","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"IDEF1X; Computer science; Conceptual schema; Database schema; Database; Information engineering; Logical data model; Database model; Database design; Relational database; Data modeling; Entity–relationship model; Relational model; Schema (genetic algorithms); Information system; Data mining; Software engineering; Information retrieval; Engineering","score_opus":0.038241232157997504,"score_gpt":0.2293924445582798,"score_spread":0.19115121240028232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2502885977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004330501,0.02647167,0.9425938,0.0019784046,0.00029350969,0.00007948539,0.0005745262,0.00035944788,0.023318604],"genre_scores_gemma":[0.13607545,0.056082465,0.78769207,0.00084630045,0.0007947846,0.0003187958,0.0016273019,0.0001806437,0.016382135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972867,0.0008454281,0.0002698921,0.0003968591,0.0011172249,0.00008405654],"domain_scores_gemma":[0.9979334,0.0013068755,0.00010978668,0.00040299408,0.00020429796,0.00004271061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026894687,0.0009227023,0.0010301282,0.0020597226,0.000687361,0.006200975,0.0024855942,0.0011368034,0.002884288],"category_scores_gemma":[0.0050604376,0.0007864914,0.0010827829,0.005451795,0.0016886774,0.008909354,0.0020687338,0.0027135245,0.0014113373],"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":[0.000015477703,0.000017150269,0.00027302813,0.00033595142,0.000029538318,0.00016943215,0.00045975245,0.014583466,0.00065464695,0.8965112,0.0075121345,0.07943828],"study_design_scores_gemma":[0.000006925296,0.000019960504,0.00023227166,0.0003314786,0.000032907268,0.00045597614,0.00022532322,0.07632596,0.00094492803,0.7955966,0.12579714,0.000030642626],"about_ca_topic_score_codex":0.0015794646,"about_ca_topic_score_gemma":0.00085763633,"teacher_disagreement_score":0.006200975,"about_ca_system_score_codex":0.0012682998,"about_ca_system_score_gemma":0.000997408,"threshold_uncertainty_score":0.014223397},"labels":[],"label_agreement":null},{"id":"W2504311311","doi":"10.4018/978-1-60566-242-8.ch074","title":"Index and Materialized View Selection in Data Warehouses","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Materialized view; Data warehouse; Heuristics; Selection (genetic algorithm); Computer science; Index (typography); Focus (optics); Data mining; State (computer science); Information retrieval; Data science; World Wide Web; View; Artificial intelligence; Algorithm; Database design","score_opus":0.03430870571903638,"score_gpt":0.2659318365498237,"score_spread":0.23162313083078734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504311311","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02028331,0.010693599,0.9525513,0.00059793174,0.00014468457,0.00016350954,0.0007364919,0.0018193285,0.013009795],"genre_scores_gemma":[0.17624708,0.0077938237,0.8049084,0.00024296506,0.00024331844,0.00020427832,0.0020844517,0.0005037439,0.007771902],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99843913,0.00036835103,0.00013733814,0.0002977294,0.0006466035,0.00011075916],"domain_scores_gemma":[0.9984018,0.0007329049,0.00013831248,0.00043950626,0.00021198156,0.00007546781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016453727,0.00082822784,0.001521289,0.0016127985,0.00076254056,0.004485652,0.0025001168,0.0008950231,0.0037106588],"category_scores_gemma":[0.004054161,0.00094716734,0.0010339706,0.005989299,0.0009935522,0.005371582,0.0020333603,0.0012889047,0.001796032],"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":[0.00060897187,0.00028652913,0.002028843,0.0010669692,0.00012700171,0.0004261335,0.00045730316,0.09524955,0.015819246,0.21295865,0.031119887,0.63985085],"study_design_scores_gemma":[0.00012864041,0.00037029028,0.001363135,0.0002395223,0.00018479436,0.0011972924,0.00031273966,0.49212736,0.019540407,0.38957223,0.094855174,0.000108403816],"about_ca_topic_score_codex":0.00093761657,"about_ca_topic_score_gemma":0.00092623255,"teacher_disagreement_score":0.004485652,"about_ca_system_score_codex":0.0010659635,"about_ca_system_score_gemma":0.0008656865,"threshold_uncertainty_score":0.0124133825},"labels":[],"label_agreement":null},{"id":"W2504924293","doi":"10.4018/978-1-60566-242-8.ch027","title":"The Linkcell Construct and Location-Aware Query Processing for Location-Referent Transactions in Mobile Business","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Variety (cybernetics); Mobile commerce; Transactional leadership; Business; World Wide Web; Management","score_opus":0.01752619576803014,"score_gpt":0.24717807600745698,"score_spread":0.22965188023942684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2504924293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010275717,0.00054471876,0.9651501,0.001469919,0.00015453645,0.000392743,0.00072449306,0.005882573,0.0154052125],"genre_scores_gemma":[0.22187486,0.0011225333,0.7507955,0.0013942875,0.00030227288,0.0010439404,0.0036721798,0.001820878,0.017973524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942497,0.0014289141,0.0004374047,0.0009072095,0.0024714721,0.00050531514],"domain_scores_gemma":[0.9935361,0.0026587893,0.0004852839,0.0019565814,0.0010397199,0.00032353404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042106407,0.0009150133,0.0008726382,0.0051256474,0.0025025108,0.009252933,0.0033403563,0.0024001845,0.014935877],"category_scores_gemma":[0.016281951,0.0009046187,0.0019524223,0.0072576324,0.005997902,0.017071677,0.008154714,0.0032622137,0.006670464],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026913182,0.00022074406,0.0043570283,0.00029931054,0.00005388363,0.00062848843,0.004153154,0.008778374,0.0043304744,0.8400888,0.021816196,0.11500452],"study_design_scores_gemma":[0.00009494282,0.00020280252,0.0020240769,0.00026205156,0.00015458389,0.001086631,0.0036092466,0.2228252,0.016832521,0.48354074,0.26914886,0.00021828427],"about_ca_topic_score_codex":0.013705854,"about_ca_topic_score_gemma":0.012334653,"teacher_disagreement_score":0.014935877,"about_ca_system_score_codex":0.0028527647,"about_ca_system_score_gemma":0.0031246033,"threshold_uncertainty_score":0.0499655},"labels":[],"label_agreement":null},{"id":"W2505904285","doi":"10.4018/978-1-60566-748-5.ch001","title":"The LBF R-Tree","year":2010,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; Data warehouse; Tree (set theory); Trie; Search engine indexing; Data mining; Block (permutation group theory); Terabyte; Construct (python library); R-tree; Data structure; Theoretical computer science; Database; Information retrieval; Mathematics; Spatial database; Spatial analysis; Statistics","score_opus":0.014247343075986806,"score_gpt":0.22819293482676417,"score_spread":0.21394559175077738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505904285","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064511574,0.004474827,0.9626403,0.0011514116,0.00030145614,0.00026361164,0.0010728018,0.0044823317,0.019162094],"genre_scores_gemma":[0.06474535,0.005294836,0.89969784,0.00088137167,0.00026495155,0.00036260497,0.003956518,0.0008118005,0.023984706],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986105,0.00021643008,0.00009857044,0.000224136,0.0007093749,0.00014100106],"domain_scores_gemma":[0.9979966,0.000479692,0.00012854012,0.0005617557,0.00073265564,0.00010080632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011315336,0.0005831726,0.00081978797,0.001890844,0.00093468284,0.0026531364,0.0023131534,0.0016936854,0.0074325404],"category_scores_gemma":[0.0055153645,0.00044576262,0.0007329146,0.003922343,0.00068367337,0.0041036047,0.0017353385,0.0014913426,0.009098802],"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":[0.0001494644,0.00008711549,0.00089340186,0.00045852186,0.000037393445,0.00018685206,0.00017394716,0.019819379,0.0085341325,0.07709339,0.06790702,0.82465935],"study_design_scores_gemma":[0.00008076119,0.0004347146,0.0010396763,0.00033408232,0.000081784674,0.0022723977,0.000249725,0.4000629,0.014955318,0.13893199,0.44142845,0.00012821313],"about_ca_topic_score_codex":0.0032724987,"about_ca_topic_score_gemma":0.0036603527,"teacher_disagreement_score":0.0074325404,"about_ca_system_score_codex":0.0010011919,"about_ca_system_score_gemma":0.0019103223,"threshold_uncertainty_score":0.024864376},"labels":[],"label_agreement":null},{"id":"W2509540125","doi":"","title":"Machine learning methods and models for ranking","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ranking (information retrieval); Computer science; Machine learning; Inference; Artificial intelligence; Field (mathematics); Domain (mathematical analysis); Learning to rank; Variety (cybernetics); Data science; Information retrieval","score_opus":0.03379766816942833,"score_gpt":0.33873471763750324,"score_spread":0.3049370494680749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509540125","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001938034,0.015656294,0.9656236,0.0042331037,0.00054154143,0.0001642645,0.00091088936,0.00049664406,0.010435549],"genre_scores_gemma":[0.20498085,0.052975506,0.68831205,0.0030084322,0.006027595,0.002436723,0.004910786,0.0005759692,0.036772024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99282396,0.0037542747,0.00040242585,0.000937012,0.0018265343,0.000255806],"domain_scores_gemma":[0.98175526,0.014984633,0.00069778546,0.0011602468,0.0012567759,0.00014528148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006786206,0.0021010053,0.002497052,0.0036134801,0.0010551438,0.004552411,0.0038479248,0.0034167045,0.0077649215],"category_scores_gemma":[0.023666691,0.0007781735,0.0018678396,0.007199641,0.0027887842,0.0059794514,0.0019087965,0.0050541516,0.0051476653],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051322968,0.0001227858,0.0018495944,0.0010011484,0.000244219,0.00012509308,0.00020581526,0.1564368,0.00022182362,0.6395757,0.033676825,0.16648887],"study_design_scores_gemma":[0.000023161265,0.00003222936,0.0003273592,0.000118732365,0.000027073354,0.00007189076,0.000038060018,0.25442988,0.0001354768,0.71817935,0.02658475,0.000032083386],"about_ca_topic_score_codex":0.005725345,"about_ca_topic_score_gemma":0.004818134,"teacher_disagreement_score":0.0077649215,"about_ca_system_score_codex":0.0027292648,"about_ca_system_score_gemma":0.0020190745,"threshold_uncertainty_score":0.035889328},"labels":[],"label_agreement":null},{"id":"W2517620127","doi":"10.1145/2970398.2970434","title":"Rank-at-a-Time Query Processing","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ranking (information retrieval); Latency (audio); Rank (graph theory); Information retrieval; Learning to rank; Online aggregation; Query optimization; Query expansion; Sargable; Search engine; Web search query; Theoretical computer science; Data mining; Mathematics","score_opus":0.009759955401003408,"score_gpt":0.21230576778370722,"score_spread":0.2025458123827038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517620127","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014117085,0.0005450246,0.97917134,0.00016849932,0.00005883681,0.0002570254,0.0001293966,0.0027388837,0.0028138568],"genre_scores_gemma":[0.24528432,0.00050915935,0.7459266,0.00025409443,0.00016651403,0.00031260142,0.00055648043,0.0005221223,0.0064680427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954737,0.0011152065,0.00038167078,0.00059364486,0.0019897313,0.00044610238],"domain_scores_gemma":[0.99533683,0.0015899603,0.0003808953,0.001482442,0.0010209874,0.0001888997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030993153,0.0015272778,0.0020881502,0.0016860354,0.0012408302,0.003373155,0.0028766762,0.0012886843,0.006510852],"category_scores_gemma":[0.008022483,0.0005891529,0.0013023801,0.0023247048,0.001412951,0.0049373456,0.002547332,0.0018494573,0.0039373366],"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":[0.001112556,0.000535832,0.0021930018,0.000740071,0.0002146557,0.0003213412,0.00060285965,0.04491121,0.08001478,0.1217216,0.011087287,0.7365447],"study_design_scores_gemma":[0.00026451322,0.0013123115,0.0009591796,0.00005370633,0.0002799129,0.0011021196,0.00035874976,0.72509205,0.10835797,0.13210556,0.02990819,0.00020576201],"about_ca_topic_score_codex":0.0027162444,"about_ca_topic_score_gemma":0.0037456,"teacher_disagreement_score":0.006510852,"about_ca_system_score_codex":0.0010780775,"about_ca_system_score_gemma":0.0019867679,"threshold_uncertainty_score":0.021780968},"labels":[],"label_agreement":null},{"id":"W2519264255","doi":"10.1145/2938503.2938549","title":"The Hilbert PDC-tree","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Carleton University","funders":"Center for Advanced Study, University of Illinois at Urbana-Champaign; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Tree (set theory); Search engine indexing; Overhead (engineering); Hilbert curve; Algorithm; Tree structure; R-tree; Rectangle; Theoretical computer science; Mathematics; Binary tree; Combinatorics; Artificial intelligence; Spatial database; Statistics; Geometry","score_opus":0.01042858524661519,"score_gpt":0.21183685763417232,"score_spread":0.20140827238755712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519264255","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020514678,0.0016825504,0.95368266,0.00093557645,0.00020638868,0.00047763018,0.0036006502,0.0072665624,0.01163337],"genre_scores_gemma":[0.13599765,0.0012929575,0.84405726,0.0006418863,0.00017911068,0.0005409381,0.008239594,0.0007211287,0.008329527],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982297,0.00019835,0.00017423964,0.0003294935,0.0008709058,0.00019731966],"domain_scores_gemma":[0.9943409,0.0009588406,0.00034589294,0.0020317878,0.0019670355,0.00035552823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013461681,0.0004826772,0.000999975,0.0022973423,0.0014131322,0.0023808845,0.0022182518,0.00090759894,0.0039433683],"category_scores_gemma":[0.008108306,0.0005389179,0.00071015244,0.0056084995,0.0009887943,0.0048368727,0.0035634045,0.0011314983,0.002586087],"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":[0.0005288578,0.00020247503,0.006092817,0.0005663512,0.00010646174,0.00035525716,0.0004715279,0.046699487,0.0132672265,0.10397003,0.1004895,0.72725004],"study_design_scores_gemma":[0.0001671906,0.0004113075,0.002195061,0.00011148613,0.000109940825,0.001945769,0.00033729366,0.6084424,0.020164993,0.14921638,0.21674332,0.00015480147],"about_ca_topic_score_codex":0.0063527822,"about_ca_topic_score_gemma":0.005636024,"teacher_disagreement_score":0.0063527822,"about_ca_system_score_codex":0.0014379738,"about_ca_system_score_gemma":0.0041643493,"threshold_uncertainty_score":0.013191819},"labels":[],"label_agreement":null},{"id":"W2520788569","doi":"10.1007/978-3-319-45817-5_7","title":"Spica: A Path Bundling Model for Rational Route Recommendation","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"York University","funders":"","keywords":"Computer science; Heuristic; Rationality; Path (computing); Set (abstract data type); Graph; Artificial intelligence; Operations research; Simulation; Theoretical computer science; Computer network","score_opus":0.032704783676984726,"score_gpt":0.262412621856328,"score_spread":0.2297078381793433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2520788569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006629229,0.00031179062,0.98617536,0.0003151276,0.00010964898,0.00014210677,0.0007131454,0.0015250535,0.0040784846],"genre_scores_gemma":[0.32836774,0.0008809204,0.6476045,0.00028054137,0.00022241657,0.0005394766,0.0020942392,0.0004966693,0.01951348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864715,0.0003953098,0.000077004486,0.00032804353,0.000400297,0.00015220462],"domain_scores_gemma":[0.9977053,0.0010268749,0.00012871603,0.00051535433,0.00047015506,0.00015363652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021768701,0.0010524362,0.0019863567,0.0018419693,0.0010523775,0.0027819446,0.0036952114,0.002087856,0.012134702],"category_scores_gemma":[0.007112958,0.00081379176,0.0015253635,0.0036440524,0.00078425626,0.0041898927,0.0013611349,0.0027410553,0.002896015],"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":[0.00021605412,0.00017805549,0.00055476313,0.00011531637,0.00008470691,0.0000501186,0.00007703735,0.74303836,0.0007622481,0.10094478,0.017379476,0.13659902],"study_design_scores_gemma":[0.000008008981,0.000015911726,0.000024596313,0.0000044101957,0.000007460977,0.000008664803,0.0000046574287,0.9774046,0.00007947242,0.021413257,0.0010234416,0.0000054813795],"about_ca_topic_score_codex":0.031602148,"about_ca_topic_score_gemma":0.02747096,"teacher_disagreement_score":0.031602148,"about_ca_system_score_codex":0.0022578954,"about_ca_system_score_gemma":0.00313774,"threshold_uncertainty_score":0.06283641},"labels":[],"label_agreement":null},{"id":"W2522191305","doi":"","title":"Advanced techniques for high performance query optimization in database systems","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Query optimization; Scalability; Skyline; Online aggregation; View; Data mining; Materialized view; Cardinality (data modeling); Join (topology); Web query classification; Database; Sargable; Query plan; Query language; Key (lock); Task (project management); Query expansion; Result set; Information retrieval; Set (abstract data type); Web search query; Database design; Search engine","score_opus":0.012023784586364208,"score_gpt":0.23800838516904743,"score_spread":0.22598460058268321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522191305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018079182,0.00218176,0.9905571,0.00075394503,0.000111387664,0.00016481742,0.00023413333,0.0018413324,0.0023476956],"genre_scores_gemma":[0.06627021,0.0038296154,0.9220565,0.00046623798,0.0005918798,0.00085317227,0.001110374,0.00060024014,0.004221824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98768336,0.0030601698,0.0011979403,0.0012650425,0.0062053064,0.00058824354],"domain_scores_gemma":[0.9922901,0.003452426,0.00044873985,0.0026339057,0.001035239,0.00013949507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062698605,0.002933229,0.0026638808,0.0032509489,0.0014525019,0.0058081984,0.0038900343,0.0017664882,0.007393727],"category_scores_gemma":[0.01641727,0.0016495225,0.002845072,0.007785202,0.0023560342,0.00894814,0.00469596,0.0066886772,0.0041007353],"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":[0.0004609356,0.0002558326,0.0012919256,0.0012390993,0.00027596226,0.0002181836,0.00060174154,0.11463202,0.011143001,0.4041758,0.03384331,0.43186215],"study_design_scores_gemma":[0.000116266936,0.00014941988,0.0003855198,0.000121532554,0.00006825932,0.00023795576,0.00011820086,0.64088625,0.0057529067,0.30831027,0.04379548,0.000057981346],"about_ca_topic_score_codex":0.003039547,"about_ca_topic_score_gemma":0.0024341804,"teacher_disagreement_score":0.007393727,"about_ca_system_score_codex":0.0026988534,"about_ca_system_score_gemma":0.0020938413,"threshold_uncertainty_score":0.0331586},"labels":[],"label_agreement":null},{"id":"W2523255603","doi":"10.1007/978-0-387-39940-9_2666","title":"Fuzzy Information Retrieval","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Fuzzy logic; Information retrieval; Computer science; Data mining; Artificial intelligence","score_opus":0.012300861670173576,"score_gpt":0.2176548570040507,"score_spread":0.20535399533387713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523255603","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003627089,0.100848466,0.32666287,0.0024737087,0.0031672243,0.00025257576,0.0013022929,0.0033476013,0.5583182],"genre_scores_gemma":[0.04933614,0.049029652,0.22258832,0.0017203393,0.001978328,0.00022161492,0.003536953,0.00051830854,0.6710704],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995566,0.000038160342,0.00003260542,0.000082206134,0.00026650005,0.00002383415],"domain_scores_gemma":[0.9997205,0.00006646807,0.0000138489,0.00006367554,0.00012234482,0.000013153256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004916987,0.00079714763,0.0011742595,0.002561926,0.0007302681,0.0025366154,0.0011828076,0.0009728585,0.042042535],"category_scores_gemma":[0.001192786,0.00037978153,0.00051630917,0.0029660296,0.00066446787,0.0023585383,0.0007740208,0.0011976013,0.03019619],"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":[0.0000345792,0.000044794582,0.00013200434,0.0005538014,0.00003872066,0.000098598706,0.00013254033,0.0013002319,0.0041343397,0.08699613,0.13510142,0.7714329],"study_design_scores_gemma":[0.000012224592,0.000059258004,0.0007449718,0.00035436323,0.00004294857,0.0009753478,0.00008362291,0.01031039,0.005384996,0.105689935,0.87628967,0.000052192765],"about_ca_topic_score_codex":0.0011727443,"about_ca_topic_score_gemma":0.0020028623,"teacher_disagreement_score":0.042042535,"about_ca_system_score_codex":0.00065022754,"about_ca_system_score_gemma":0.0006009781,"threshold_uncertainty_score":0.14064628},"labels":[],"label_agreement":null},{"id":"W2524143095","doi":"10.3233/jcm-160653","title":"Iterated similarity sequences and factorial level similarities in databases","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Methods in Sciences and Engineering","topic":"Data Management and Algorithms","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Iterated function; Similarity (geometry); Constraint (computer-aided design); Simple (philosophy); Mathematics; Database; Computer science; Property (philosophy); Similitude; Data mining; Algorithm; Theoretical computer science; Artificial intelligence","score_opus":0.12776568704729097,"score_gpt":0.3856599203283524,"score_spread":0.2578942332810614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524143095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13566405,0.00084784813,0.8576028,0.00022102417,0.00004266172,0.0001357602,0.00044480784,0.00043959302,0.004601492],"genre_scores_gemma":[0.7471728,0.0004935249,0.2472254,0.00014083619,0.00014511973,0.00033737437,0.0010931832,0.000091904854,0.0032998365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945328,0.0015159246,0.00051457906,0.0013003967,0.0018444401,0.000291923],"domain_scores_gemma":[0.98606306,0.007295944,0.0024831393,0.002191995,0.0014382872,0.0005275843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036668272,0.00040709454,0.0010095071,0.0045871567,0.0007238293,0.002401187,0.0018400301,0.0014286954,0.0044681043],"category_scores_gemma":[0.026154028,0.0003734966,0.00075906527,0.0049707023,0.0019934932,0.006364472,0.0021370854,0.0008992476,0.0010149064],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038905855,0.00017615879,0.0071444674,0.00038655257,0.00009806555,0.0005101041,0.00073036336,0.06672205,0.007525946,0.7654089,0.0015004781,0.1494079],"study_design_scores_gemma":[0.000030089988,0.000208752,0.0024497074,0.00004656452,0.000019996865,0.00054961524,0.00016363224,0.3348791,0.0025783123,0.65615124,0.002881697,0.000041168638],"about_ca_topic_score_codex":0.0006721975,"about_ca_topic_score_gemma":0.00047212432,"teacher_disagreement_score":0.0045871567,"about_ca_system_score_codex":0.0012081803,"about_ca_system_score_gemma":0.00069367286,"threshold_uncertainty_score":0.019392312},"labels":[],"label_agreement":null},{"id":"W2526788202","doi":"","title":"Test semi-paramétrique d'indépendance des composantes d'un vecteur aléatoire","year":2007,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Humanities; Mathematics; Statistical analysis; Combinatorics; Physics; Art; Statistics","score_opus":0.019534161176606224,"score_gpt":0.23329749493406587,"score_spread":0.21376333375745965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526788202","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24924928,0.00017966099,0.74093926,0.00022175146,0.00010274528,0.00006174345,0.0002738197,0.002973222,0.005998552],"genre_scores_gemma":[0.93693244,0.000118153686,0.053741984,0.00008369614,0.00008379222,0.00016674119,0.0006175894,0.0010244193,0.00723126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99433905,0.0016539441,0.00024770095,0.0014613152,0.0017387375,0.00055915536],"domain_scores_gemma":[0.9648476,0.02648091,0.0017741709,0.0028620495,0.0028293084,0.0012059684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031374535,0.0028530727,0.001794294,0.0015811116,0.00093425554,0.004197964,0.0012350713,0.0025348351,0.0070798304],"category_scores_gemma":[0.020737655,0.0009802207,0.0016495829,0.0008766823,0.0028358896,0.0035313815,0.0019446134,0.0036576842,0.0013756171],"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":[0.008969139,0.00055482314,0.023173267,0.0006882132,0.00064097927,0.0021599706,0.001161273,0.5654253,0.14908113,0.099418856,0.0028158864,0.1459112],"study_design_scores_gemma":[0.00013907571,0.00042358844,0.0071495487,0.00006180535,0.00005464967,0.0003533824,0.00014782806,0.93383956,0.040020116,0.01584214,0.0018940335,0.00007424201],"about_ca_topic_score_codex":0.0061562057,"about_ca_topic_score_gemma":0.0036130594,"teacher_disagreement_score":0.0070798304,"about_ca_system_score_codex":0.0017968366,"about_ca_system_score_gemma":0.0011284347,"threshold_uncertainty_score":0.023684442},"labels":[],"label_agreement":null},{"id":"W2527110900","doi":"10.3233/jcm-160652","title":"On the dimension dependence of the level of optimality of certain multidimensional sampling strategies","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Methods in Sciences and Engineering","topic":"Data Management and Algorithms","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Hypersphere; Dimension (graph theory); Computer science; Hypercube; Mathematics; Compatibility (geochemistry); Mathematical optimization; Artificial intelligence; Combinatorics","score_opus":0.14594124310929912,"score_gpt":0.385538836621807,"score_spread":0.23959759351250787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527110900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38931543,0.0039067236,0.5821202,0.0019976515,0.00010263527,0.00031625634,0.00038276892,0.0003554243,0.021502908],"genre_scores_gemma":[0.8336522,0.0013039293,0.16279292,0.000291455,0.00008997901,0.00028597345,0.00025008834,0.00014862361,0.0011848365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99193174,0.004681837,0.0006898396,0.0007658904,0.0012617238,0.00066897843],"domain_scores_gemma":[0.917139,0.07056939,0.0032689993,0.004642677,0.0031607284,0.0012191745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012729088,0.0007922861,0.0014839486,0.00246233,0.0007167674,0.0033653905,0.0013350333,0.002196446,0.0024115532],"category_scores_gemma":[0.094004415,0.0005918401,0.00070312433,0.0015342414,0.00176908,0.0040512006,0.0022423987,0.0019194477,0.0004010394],"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":[0.0016940873,0.00069963624,0.020429144,0.0010554102,0.00041512167,0.00030994782,0.00087701256,0.4104911,0.016235797,0.37912834,0.00288369,0.16578084],"study_design_scores_gemma":[0.00017164096,0.001150597,0.005220817,0.00032960752,0.00014965296,0.00038561717,0.00036113273,0.85716337,0.009806711,0.12311583,0.0020665987,0.00007837295],"about_ca_topic_score_codex":0.00081142195,"about_ca_topic_score_gemma":0.00080028985,"teacher_disagreement_score":0.012729088,"about_ca_system_score_codex":0.0011951147,"about_ca_system_score_gemma":0.001290922,"threshold_uncertainty_score":0.06731868},"labels":[],"label_agreement":null},{"id":"W2529060894","doi":"10.1109/bigdatacongress.2016.19","title":"Enhanced State History Tree (eSHT): A Stateful Data Structure for Analysis of Highly Parallel System Traces","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Stateful firewall; TRACE (psycholinguistics); Scalability; Data structure; Thread (computing); Heap (data structure); Debugging; Tree (set theory); Theoretical computer science; Distributed computing; Parallel computing; Programming language; Database","score_opus":0.03647089403056068,"score_gpt":0.2544424261053332,"score_spread":0.2179715320747725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529060894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0284519,0.0003623471,0.91374415,0.00036186885,0.00008879482,0.00023440772,0.009820156,0.044874385,0.0020619933],"genre_scores_gemma":[0.38778988,0.0005022672,0.58222586,0.00023377572,0.00008044022,0.0006570993,0.022758557,0.0028128887,0.0029392836],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987149,0.00020249517,0.00021641412,0.00023473636,0.00053896947,0.00009245228],"domain_scores_gemma":[0.9941706,0.0022318857,0.0006338979,0.0019663682,0.000773342,0.00022390948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014025023,0.0009005612,0.00070541597,0.0026491333,0.00075225957,0.0018361842,0.0016866663,0.0007805471,0.0040282514],"category_scores_gemma":[0.007936496,0.0005885639,0.000897858,0.0029954067,0.00084756775,0.0041540437,0.0015507451,0.0010963477,0.0011001361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016332489,0.00041674048,0.030235538,0.0013485282,0.00030410397,0.0006302028,0.002167705,0.118644945,0.035199463,0.101047926,0.051892392,0.65647924],"study_design_scores_gemma":[0.00012863438,0.00042121075,0.0051559145,0.00013369687,0.00008220518,0.00040401382,0.00032332068,0.8210695,0.04454057,0.07332832,0.0542518,0.00016090112],"about_ca_topic_score_codex":0.0053323396,"about_ca_topic_score_gemma":0.0063864444,"teacher_disagreement_score":0.0053323396,"about_ca_system_score_codex":0.00086541224,"about_ca_system_score_gemma":0.0019502864,"threshold_uncertainty_score":0.013475895},"labels":[],"label_agreement":null},{"id":"W2531121659","doi":"10.1016/j.jcss.2016.09.011","title":"Computing source-to-target shortest paths for complex networks in RDBMS","year":2016,"lang":"en","type":"article","venue":"Journal of Computer and System Sciences","topic":"Data Management and Algorithms","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":"University of Victoria","funders":"","keywords":"Computer science; Relational database management system; Shortest path problem; Theoretical computer science; Distributed computing; Parallel computing; Relational database; Database; Graph","score_opus":0.02856876478531118,"score_gpt":0.2616750358609549,"score_spread":0.23310627107564375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531121659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34954923,0.0012361592,0.6306647,0.0012761268,0.00019355307,0.00030312836,0.002724775,0.007166172,0.0068861544],"genre_scores_gemma":[0.5787627,0.00035805936,0.4143321,0.000133028,0.000065814216,0.00016273538,0.0024767374,0.00044395524,0.003264945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885285,0.00025968105,0.00012265633,0.00030711305,0.000305008,0.00015264623],"domain_scores_gemma":[0.9955485,0.0026221438,0.00028609938,0.00083334616,0.00044145898,0.00026836724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019432943,0.0009370686,0.0014260216,0.0016138577,0.0012498451,0.0028473632,0.0024214806,0.001496087,0.005488642],"category_scores_gemma":[0.011556265,0.0008043821,0.00082621555,0.0026736837,0.0007781917,0.0043067248,0.0019534857,0.0011131332,0.0009602165],"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":[0.00069291185,0.00021753507,0.0041137515,0.0003865992,0.0001450401,0.00013895874,0.00037901432,0.84174633,0.005040603,0.018763449,0.0063603814,0.12201548],"study_design_scores_gemma":[0.000023284421,0.000028600398,0.0002415635,0.000008417801,0.000013417628,0.000021677486,0.00007119083,0.98003525,0.0011515879,0.017718023,0.0006811075,0.0000057796847],"about_ca_topic_score_codex":0.014892527,"about_ca_topic_score_gemma":0.022896692,"teacher_disagreement_score":0.014892527,"about_ca_system_score_codex":0.002208082,"about_ca_system_score_gemma":0.002784278,"threshold_uncertainty_score":0.029611647},"labels":[],"label_agreement":null},{"id":"W2535219971","doi":"10.1109/plans.1988.195469","title":"Digital map dependent functions of automatic vehicle location systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Computer science; Digital mapping; Representation (politics); Position (finance); Electronic map; Data mining; Computer vision; Information retrieval; Artificial intelligence; Real-time computing; Geography; Cartography; Programming language","score_opus":0.012544003419259297,"score_gpt":0.20949659875716561,"score_spread":0.19695259533790632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535219971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012121237,0.0014294577,0.8907841,0.000702802,0.00038477185,0.0001818919,0.0023278254,0.019511351,0.07255665],"genre_scores_gemma":[0.3461016,0.0031330779,0.5601637,0.000595385,0.00052505,0.0005015617,0.007408036,0.0021214949,0.0794501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987116,0.00028154787,0.00010206649,0.00019922815,0.0006334356,0.0000722269],"domain_scores_gemma":[0.99740785,0.0005814408,0.0001178495,0.000940441,0.00086796517,0.000084445775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085786346,0.0007363577,0.00026450655,0.0026728027,0.00061807304,0.0034743457,0.0019207388,0.00065187586,0.017802848],"category_scores_gemma":[0.005517648,0.00035979715,0.0003342349,0.0032000397,0.00062649953,0.0028784932,0.0015064382,0.00070279866,0.011696542],"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":[0.00029011106,0.00009017416,0.0018161918,0.0002791014,0.00003741276,0.0002973108,0.00036897487,0.018053865,0.011280325,0.17290138,0.056118205,0.738467],"study_design_scores_gemma":[0.00009566854,0.00010702992,0.0017672919,0.00013669401,0.00007079375,0.0008196214,0.00020779835,0.116580196,0.074864075,0.059645522,0.74560773,0.00009762412],"about_ca_topic_score_codex":0.0024572755,"about_ca_topic_score_gemma":0.0013571111,"teacher_disagreement_score":0.017802848,"about_ca_system_score_codex":0.0009806402,"about_ca_system_score_gemma":0.0007881165,"threshold_uncertainty_score":0.059556425},"labels":[],"label_agreement":null},{"id":"W2539639989","doi":"10.1109/clei.2012.6427223","title":"Snapshot of the position of a site in the Internet: Vector space model applied to external pages","year":2012,"lang":"en","type":"article","venue":"","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":"Western University","funders":"","keywords":"Snapshot (computer storage); Computer science; The Internet; Position (finance); Computer graphics (images); World Wide Web; Database","score_opus":0.020221940397885208,"score_gpt":0.23528671233881726,"score_spread":0.21506477194093204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2539639989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.327443,0.0007869866,0.66720575,0.0007856822,0.00010534766,0.00007809207,0.00057119183,0.00034271137,0.0026812556],"genre_scores_gemma":[0.96520907,0.00048282495,0.03059348,0.000048073416,0.00007577933,0.000057994857,0.0005619872,0.000043555894,0.0029271566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997315,0.0000980824,0.000014772963,0.000071960785,0.00003944976,0.00004417856],"domain_scores_gemma":[0.9985297,0.0007525284,0.00018900896,0.00011437055,0.00026086174,0.00015357853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007981555,0.00045861682,0.00065419107,0.0013446624,0.00035683863,0.0016841944,0.0012335159,0.0010845544,0.0016611804],"category_scores_gemma":[0.004197966,0.0002967997,0.0005950755,0.001452849,0.00072128605,0.002287393,0.00072780554,0.0009728539,0.00038221612],"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":[0.00027141185,0.000103939004,0.01836391,0.000092518996,0.00007432622,0.0003892429,0.00033306284,0.9126307,0.0016148385,0.032965284,0.001707955,0.031452816],"study_design_scores_gemma":[0.0000018406232,0.000014525211,0.0007583103,0.000002850628,0.0000039475653,0.000015457848,0.00002443925,0.995691,0.000057389843,0.0033194649,0.00010473303,0.000006069456],"about_ca_topic_score_codex":0.014695312,"about_ca_topic_score_gemma":0.007524637,"teacher_disagreement_score":0.014695312,"about_ca_system_score_codex":0.0008188948,"about_ca_system_score_gemma":0.00047159565,"threshold_uncertainty_score":0.029219568},"labels":[],"label_agreement":null},{"id":"W2539741397","doi":"10.1109/plans.1988.195471","title":"Design concepts for knowledge-based route guidance systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Principal (computer security); Automation; Computer science; Process (computing); Knowledge base; Base (topology); Software engineering; Artificial intelligence; Programming language; Operating system; Engineering","score_opus":0.054137795589016574,"score_gpt":0.2959471589853001,"score_spread":0.24180936339628353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2539741397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014717147,0.0010850367,0.9736621,0.00075608876,0.00013236977,0.00012620313,0.00010190139,0.00048253825,0.02218203],"genre_scores_gemma":[0.10245698,0.0029830115,0.86361563,0.00074773945,0.00022515106,0.0012055784,0.0006101897,0.00024604367,0.027909644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99843615,0.0003920358,0.00019427437,0.0002795055,0.0005593003,0.00013879912],"domain_scores_gemma":[0.9990702,0.00033332963,0.000109959765,0.00015498385,0.00026105792,0.000070500406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017614319,0.00092781096,0.00042083068,0.00090525026,0.0010180309,0.0047281943,0.0019748744,0.0017701953,0.007848003],"category_scores_gemma":[0.0030913167,0.00071875483,0.000952734,0.0009795943,0.0025777684,0.0040173866,0.0021925159,0.0021641438,0.0034578699],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001625172,0.000021207159,0.00009539332,0.00024730805,0.00002305689,0.0001434407,0.0005584594,0.008942902,0.0015088522,0.9422948,0.0040116976,0.0421366],"study_design_scores_gemma":[0.000043970347,0.00005585691,0.000108960885,0.00025672858,0.000053821932,0.0003925872,0.00019364763,0.052426923,0.0028075129,0.6155716,0.32805884,0.000029510411],"about_ca_topic_score_codex":0.0017188577,"about_ca_topic_score_gemma":0.0017456167,"teacher_disagreement_score":0.007848003,"about_ca_system_score_codex":0.0015622408,"about_ca_system_score_gemma":0.0012905471,"threshold_uncertainty_score":0.026254177},"labels":[],"label_agreement":null},{"id":"W2545112008","doi":"10.1109/pvgs.2003.1249038","title":"A parallel framework for simplification of massive meshes","year":2004,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Thrashing; Parallel computing; Workstation; Polygon mesh; Computation; Virtual memory; Virtual machine; Distributed computing; Memory management; Operating system; Algorithm; Computer graphics (images)","score_opus":0.030465405933257448,"score_gpt":0.28800895691640793,"score_spread":0.25754355098315046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545112008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004420458,0.00014317912,0.9883889,0.00009144483,0.000029816667,0.000054380434,0.0001041673,0.004118942,0.002648641],"genre_scores_gemma":[0.08307746,0.0002937422,0.9122911,0.00004733887,0.000042481297,0.00018063793,0.00044340745,0.0007805218,0.0028432945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916005,0.00011410545,0.000040985105,0.00011904215,0.0004989707,0.00006694059],"domain_scores_gemma":[0.99954957,0.0001037691,0.00003125728,0.0001624708,0.00011698003,0.000035943718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070996024,0.0008164953,0.0009170757,0.00084256125,0.0010492111,0.0011429072,0.0023832265,0.00069142785,0.005262979],"category_scores_gemma":[0.0015167717,0.00064179127,0.0013069075,0.0011323737,0.0010473896,0.0014346158,0.0019435819,0.0011628506,0.0014683518],"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":[0.0003286414,0.00014161343,0.0012614505,0.00040761242,0.00014845534,0.0005571571,0.00040944183,0.5436912,0.044391297,0.14788999,0.01809629,0.24267685],"study_design_scores_gemma":[0.00006476584,0.000042692594,0.0002270181,0.000014821817,0.000024479592,0.00019124118,0.000025916084,0.9358982,0.007902291,0.03140865,0.024176575,0.00002335115],"about_ca_topic_score_codex":0.0043467116,"about_ca_topic_score_gemma":0.0053325617,"teacher_disagreement_score":0.005262979,"about_ca_system_score_codex":0.0007655953,"about_ca_system_score_gemma":0.0010757493,"threshold_uncertainty_score":0.017606378},"labels":[],"label_agreement":null},{"id":"W2554481486","doi":"10.1007/978-3-319-17885-1_1603","title":"Integration of Spatial Constraint Databases","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Data Management and Algorithms","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":"Computer Research Institute of Montréal","funders":"","keywords":"Database; Computer science; Constraint (computer-aided design); Spatiotemporal database; Information retrieval; Database design; Mathematics; Database tuning; View; Geometry","score_opus":0.032067260527247704,"score_gpt":0.2643638663434926,"score_spread":0.23229660581624492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554481486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008235844,0.005308355,0.9082112,0.00077842554,0.00033278245,0.00027923504,0.0026935039,0.006256507,0.06790418],"genre_scores_gemma":[0.098456554,0.0069068074,0.8485819,0.00039197685,0.00015394036,0.00026963808,0.015124417,0.0016377554,0.02847702],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997265,0.00032804592,0.00029253156,0.00041273117,0.0015641961,0.00013752125],"domain_scores_gemma":[0.99762815,0.0004874853,0.00010058715,0.0010083894,0.0006798429,0.000095643314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018368746,0.0008481737,0.0013704278,0.0040362044,0.00083602173,0.0072181276,0.0036040468,0.0009615137,0.01677667],"category_scores_gemma":[0.0069526294,0.0010539066,0.0013822345,0.010358772,0.000630735,0.0058575473,0.005244759,0.0015963194,0.005377455],"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":[0.0001327708,0.00015820905,0.001533692,0.0007073142,0.00021309506,0.00042342453,0.0005406966,0.016211752,0.0041435403,0.15217242,0.034202706,0.7895603],"study_design_scores_gemma":[0.00004852382,0.000073739066,0.0014260426,0.00057530415,0.0002514924,0.0013662794,0.00068163115,0.16497889,0.020207902,0.15996438,0.6503288,0.00009700752],"about_ca_topic_score_codex":0.005691557,"about_ca_topic_score_gemma":0.006642736,"teacher_disagreement_score":0.01677667,"about_ca_system_score_codex":0.0010335457,"about_ca_system_score_gemma":0.0023584329,"threshold_uncertainty_score":0.056123614},"labels":[],"label_agreement":null},{"id":"W25572007","doi":"10.1007/978-3-642-22922-0_3","title":"An Ontology-Based Traffic Accident Risk Mapping Framework","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Ontology; Cluster analysis; Traffic accident; Accident (philosophy); Domain (mathematical analysis); Data mining; Point (geometry); Information retrieval; Transport engineering; Artificial intelligence","score_opus":0.024106046252622606,"score_gpt":0.2502911648562512,"score_spread":0.22618511860362858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W25572007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004140833,0.0002936023,0.9821681,0.0003992149,0.000073775605,0.00022772844,0.0018888392,0.0025203023,0.008287722],"genre_scores_gemma":[0.079438634,0.0008962499,0.90784574,0.00021027954,0.00004358999,0.00044845368,0.0058768676,0.00035984558,0.0048803836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987239,0.00021578955,0.00021530545,0.00022952349,0.0005185901,0.00009681689],"domain_scores_gemma":[0.99932563,0.00017818403,0.000071967996,0.00012509235,0.0002270381,0.000072088624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018746163,0.0008747606,0.00087723543,0.003534546,0.0012085612,0.0039439737,0.0022860875,0.0010979595,0.0039767814],"category_scores_gemma":[0.002470327,0.0005692388,0.0025098165,0.0034332257,0.00069015234,0.003919374,0.0028947857,0.0015754296,0.0013406338],"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":[0.00008374704,0.00039341676,0.0023880112,0.0006643336,0.0003415953,0.0008622154,0.0013833065,0.09684814,0.005864711,0.6277159,0.019665439,0.24378917],"study_design_scores_gemma":[0.000035036086,0.000048852875,0.0016273457,0.0003688764,0.00031403286,0.0006198089,0.0007271567,0.36227822,0.0040288926,0.4199847,0.20985529,0.000111716196],"about_ca_topic_score_codex":0.017640743,"about_ca_topic_score_gemma":0.020951321,"teacher_disagreement_score":0.017640743,"about_ca_system_score_codex":0.001491244,"about_ca_system_score_gemma":0.0035129455,"threshold_uncertainty_score":0.03507614},"labels":[],"label_agreement":null},{"id":"W2561314860","doi":"10.48550/arxiv.1611.03736","title":"A Formal Definition for Configuration","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Discovery Air (Canada)","funders":"","keywords":"Computer science; Matching (statistics); Task (project management); Relational database; Information retrieval; Set (abstract data type); Keyword search; Key (lock); Process (computing); Space (punctuation); Data mining; Programming language; Mathematics","score_opus":0.10886080436238679,"score_gpt":0.18953618814353276,"score_spread":0.08067538378114597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2561314860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039374987,0.001991302,0.8643824,0.0032605194,0.000952206,0.0004426943,0.002198973,0.002358895,0.12047561],"genre_scores_gemma":[0.29008573,0.004419624,0.61544347,0.003645206,0.0020880837,0.0029385376,0.006067405,0.0022171137,0.07309482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943763,0.0014145885,0.00076592766,0.0015759595,0.0011101041,0.00075718266],"domain_scores_gemma":[0.9957385,0.0013829501,0.00038660198,0.0012387254,0.00097300566,0.0002802713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038539846,0.0018891979,0.00086233864,0.0030060231,0.0030046944,0.0079958355,0.0045157094,0.003606645,0.029663289],"category_scores_gemma":[0.006981264,0.0010003309,0.001556992,0.0039287847,0.0074688364,0.016214086,0.005413708,0.0043592495,0.0132064875],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011713341,0.000012842132,0.00010632423,0.00006571119,0.0000063073926,0.00011479694,0.00021050172,0.00079574605,0.000292367,0.9833161,0.005837097,0.009230348],"study_design_scores_gemma":[0.000016461257,0.000028938499,0.00009944646,0.000113697984,0.000015253851,0.00047768294,0.00028807568,0.0053701014,0.0007051746,0.73316956,0.25968894,0.000026693027],"about_ca_topic_score_codex":0.0030255457,"about_ca_topic_score_gemma":0.0017392128,"teacher_disagreement_score":0.029663289,"about_ca_system_score_codex":0.0024903764,"about_ca_system_score_gemma":0.0023677915,"threshold_uncertainty_score":0.09923363},"labels":[],"label_agreement":null},{"id":"W2562653017","doi":"10.1145/2996913.2996994","title":"k-Optimal meeting points based on preferred paths","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Exploit; Computer science; Group (periodic table); Order (exchange); Information retrieval; Data mining; Computer security","score_opus":0.019533678003626875,"score_gpt":0.23203482468197165,"score_spread":0.21250114667834477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562653017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13427718,0.0010574639,0.8533792,0.0011234896,0.000110185916,0.0005206495,0.0026167855,0.0016837115,0.005231341],"genre_scores_gemma":[0.6438794,0.000710882,0.34610087,0.00027932323,0.00016263485,0.00032880102,0.005308834,0.00033275518,0.0028964402],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947865,0.0011240587,0.00045420518,0.0012553929,0.0017865966,0.00059316633],"domain_scores_gemma":[0.9924676,0.0038362448,0.00090811955,0.0014163844,0.0009786618,0.00039294778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002368873,0.0013139797,0.0023373421,0.002313588,0.0017175538,0.0023009847,0.0037579176,0.0019049103,0.004703939],"category_scores_gemma":[0.014937848,0.00072472147,0.0014852294,0.004276683,0.0015203389,0.008663769,0.0038696446,0.0021560786,0.001398059],"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":[0.0027282313,0.0004558786,0.0111437375,0.0010128501,0.00026621638,0.0009491165,0.0012112752,0.6368657,0.020811833,0.103771105,0.019489825,0.20129427],"study_design_scores_gemma":[0.00011941717,0.00043474766,0.0011304546,0.000036622638,0.00004432016,0.0009107807,0.000837736,0.889504,0.008091991,0.09258083,0.0062272027,0.000081965016],"about_ca_topic_score_codex":0.0031956332,"about_ca_topic_score_gemma":0.003567155,"teacher_disagreement_score":0.004703939,"about_ca_system_score_codex":0.0015262666,"about_ca_system_score_gemma":0.001730335,"threshold_uncertainty_score":0.015736222},"labels":[],"label_agreement":null},{"id":"W2563307839","doi":"10.1145/2996913.2996991","title":"Fast transportation network traversal with hyperedges","year":2016,"lang":"en","type":"article","venue":"","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":"Esri (Canada)","funders":"","keywords":"Computer science; Scalability; Tree traversal; Enhanced Data Rates for GSM Evolution; Joins; Flow network; Distributed computing; Sorting; Relational database; Database; Algorithm","score_opus":0.007426990026595474,"score_gpt":0.18106129709358407,"score_spread":0.1736343070669886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563307839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030647872,0.00017220477,0.95554173,0.00029395218,0.000055365035,0.00013354246,0.0015974065,0.005690838,0.0058671585],"genre_scores_gemma":[0.22715418,0.00033988943,0.75917685,0.00014658934,0.000021262447,0.00018680881,0.0051339893,0.0008944132,0.0069460683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992434,0.00019961633,0.00006783292,0.00016542165,0.00023493651,0.000088896566],"domain_scores_gemma":[0.99854016,0.00049173157,0.00008733107,0.0005275591,0.0002652066,0.000087946544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010163065,0.0005624828,0.00073576596,0.0014862212,0.0007918144,0.002925235,0.0016367596,0.0010531388,0.008577251],"category_scores_gemma":[0.0038468044,0.00056032074,0.0009564629,0.0028852029,0.000547305,0.00499362,0.0029444937,0.0011139791,0.0018059416],"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":[0.0005230693,0.00021737482,0.005240362,0.0003833366,0.00014929542,0.0004104495,0.0007770666,0.47432443,0.010594223,0.15648326,0.02439668,0.32650048],"study_design_scores_gemma":[0.000050960494,0.000035995658,0.000329385,0.00003483851,0.000021853008,0.00009476179,0.00019394372,0.90773827,0.005726119,0.058724362,0.027027508,0.000021997137],"about_ca_topic_score_codex":0.01088815,"about_ca_topic_score_gemma":0.015838224,"teacher_disagreement_score":0.01088815,"about_ca_system_score_codex":0.0011315186,"about_ca_system_score_gemma":0.0012039887,"threshold_uncertainty_score":0.028693795},"labels":[],"label_agreement":null},{"id":"W2563903707","doi":"10.1504/ijipt.2016.081318","title":"K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns","year":2016,"lang":"en","type":"article","venue":"International Journal of Internet Protocol Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Complex event processing; Leverage (statistics); SQL; Data mining; Event (particle physics); Database; Machine learning; Process (computing)","score_opus":0.040949103624080774,"score_gpt":0.406556510535927,"score_spread":0.36560740691184623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563903707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010648649,0.00011505444,0.99561656,0.00015578151,0.00003128224,0.00007441107,0.0002509466,0.0018314343,0.0008597065],"genre_scores_gemma":[0.104149505,0.0005370447,0.89186275,0.00023914529,0.00008330041,0.00026598282,0.001144631,0.00023665078,0.0014809667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989636,0.0001906216,0.00014486175,0.0002275553,0.00038243196,0.0000909976],"domain_scores_gemma":[0.99838567,0.0006122786,0.00018932733,0.00032204774,0.00036955858,0.00012112928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018506012,0.00084115146,0.00083852455,0.0015283732,0.0006436832,0.0027273449,0.0026419982,0.00091790676,0.002254436],"category_scores_gemma":[0.0039806627,0.0004568136,0.0011758937,0.0016251698,0.00095345685,0.003946563,0.001947402,0.0017193519,0.0009278105],"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":[0.000551207,0.00036003403,0.003753831,0.001132953,0.00033833424,0.00165561,0.0014169364,0.23155408,0.016798725,0.29454982,0.032771993,0.4151164],"study_design_scores_gemma":[0.00004860223,0.000065956214,0.0005229295,0.00009888442,0.00008177247,0.00035845465,0.00020091765,0.8375687,0.006047212,0.11039062,0.04455032,0.00006562919],"about_ca_topic_score_codex":0.005488476,"about_ca_topic_score_gemma":0.005427193,"teacher_disagreement_score":0.005488476,"about_ca_system_score_codex":0.0008031471,"about_ca_system_score_gemma":0.0014483326,"threshold_uncertainty_score":0.010913074},"labels":[],"label_agreement":null},{"id":"W2566486882","doi":"10.1109/iros.2016.7759299","title":"Fast and efficient rendezvous in street networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Rendezvous; Computer science; Scheduling (production processes); Taxis; Schedule; Task (project management); Limiting; Computer network; Operations research; Distributed computing; Real-time computing; Transport engineering; Mathematical optimization; Engineering","score_opus":0.007626359047626582,"score_gpt":0.20224199398305032,"score_spread":0.19461563493542375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566486882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29743382,0.0006861301,0.6958742,0.0002852289,0.000022627451,0.00012374787,0.0004897914,0.0013738464,0.003710596],"genre_scores_gemma":[0.82101,0.00024956552,0.17567234,0.000036562826,0.000012669801,0.0000831707,0.00049759547,0.00014307896,0.0022951185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992361,0.00019572281,0.000034342836,0.00019444672,0.00016602513,0.00017338873],"domain_scores_gemma":[0.9988398,0.0005899041,0.00017543626,0.00019639406,0.00010673148,0.0000918482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010435983,0.0006036078,0.00093875214,0.00085887156,0.00084111653,0.0009954547,0.0013937884,0.0009196553,0.0013654643],"category_scores_gemma":[0.002822437,0.0005091058,0.00044617755,0.001769203,0.00082031224,0.0018495515,0.0012174996,0.0005357639,0.00038146588],"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":[0.00012732089,0.000037652648,0.0012483223,0.000033559998,0.000033995184,0.00008144545,0.00009134358,0.965158,0.003392441,0.005002092,0.00072912907,0.024064798],"study_design_scores_gemma":[0.000016909076,0.000035599638,0.00027714067,0.0000030451563,0.000007041831,0.000048458736,0.00005149927,0.9920358,0.002022376,0.004623584,0.00087260094,0.0000059327185],"about_ca_topic_score_codex":0.007127483,"about_ca_topic_score_gemma":0.009937935,"teacher_disagreement_score":0.007127483,"about_ca_system_score_codex":0.00093149615,"about_ca_system_score_gemma":0.00094154966,"threshold_uncertainty_score":0.014172018},"labels":[],"label_agreement":null},{"id":"W2575197938","doi":"","title":"Extremal cactuses for the Schultz and modified Schultz indices.","year":2016,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Applied mathematics","score_opus":0.023577891500833704,"score_gpt":0.24419312668460322,"score_spread":0.22061523518376952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575197938","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29289445,0.0051829647,0.28671536,0.006015983,0.0016899053,0.00019896099,0.0012497634,0.0014268922,0.4046258],"genre_scores_gemma":[0.8747708,0.0015800032,0.05249036,0.00092163053,0.001310877,0.0003266889,0.00073372497,0.00069012784,0.06717575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986185,0.00032532492,0.000086627515,0.00020195973,0.0005043423,0.00026314904],"domain_scores_gemma":[0.9954991,0.0022644682,0.00052298856,0.0007196757,0.0005307943,0.00046291223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015823955,0.0007266963,0.0009791347,0.0040958486,0.0023547066,0.004406798,0.001701137,0.001538955,0.024928398],"category_scores_gemma":[0.015206147,0.00043380822,0.00073300407,0.002106529,0.0032269335,0.007069624,0.0035613703,0.004106654,0.0040593008],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003319724,0.000017104534,0.00020910369,0.000024053585,0.000005991003,0.000051209452,0.000076319106,0.000549561,0.0001887326,0.98899347,0.0031735084,0.0066777933],"study_design_scores_gemma":[0.00001864763,0.000012036807,0.00025085127,0.000029048668,0.000010010546,0.00016396986,0.00005588357,0.0057568615,0.00045454325,0.9854998,0.0077276886,0.000020748981],"about_ca_topic_score_codex":0.0008028418,"about_ca_topic_score_gemma":0.001149283,"teacher_disagreement_score":0.024928398,"about_ca_system_score_codex":0.0021092964,"about_ca_system_score_gemma":0.0010217879,"threshold_uncertainty_score":0.08339375},"labels":[],"label_agreement":null},{"id":"W2578061732","doi":"","title":"Relationships between distance two labellings and circular distance two labellings by group path covering.","year":2016,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Path (computing); Group (periodic table); Computer science; Computer network; Physics","score_opus":0.01444856507265261,"score_gpt":0.22663817205344708,"score_spread":0.21218960698079448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578061732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27984074,0.002041014,0.59083265,0.0033652827,0.0005074681,0.00021973153,0.0015308678,0.0015521189,0.12011013],"genre_scores_gemma":[0.8198833,0.0010458246,0.15394105,0.00092511886,0.00043124676,0.00022502417,0.0018394643,0.0005763927,0.021132642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99634486,0.0011186932,0.00027743602,0.00088105944,0.00079105946,0.00058689027],"domain_scores_gemma":[0.9599974,0.02864842,0.003335475,0.0043245982,0.0018744263,0.0018196892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022578945,0.00057730795,0.00083624094,0.0026256396,0.0028778706,0.0045066983,0.0018658132,0.0020760086,0.011345438],"category_scores_gemma":[0.02900423,0.0010166614,0.0011528839,0.0043928996,0.0044172606,0.010048355,0.0045695007,0.0034449906,0.0018051044],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019343881,0.000069408496,0.00264354,0.00012413706,0.000039007336,0.00033083934,0.0007059185,0.003739972,0.0010127758,0.9579827,0.006691238,0.02646694],"study_design_scores_gemma":[0.000027570453,0.000040557607,0.00094309024,0.000040898398,0.00005585352,0.00057785056,0.0002962272,0.01370826,0.0010176471,0.96930176,0.013956651,0.000033611832],"about_ca_topic_score_codex":0.0019497678,"about_ca_topic_score_gemma":0.001808951,"teacher_disagreement_score":0.011345438,"about_ca_system_score_codex":0.002016293,"about_ca_system_score_gemma":0.001226785,"threshold_uncertainty_score":0.03795427},"labels":[],"label_agreement":null},{"id":"W2579692817","doi":"10.1109/ism.2016.0044","title":"E-Tourism: Mobile Dynamic Trip Planner","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Planner; TRIPS architecture; Computer science; Tourism; Recommender system; Orienteering; Algorithm design; Mobile computing; Operations research; Mathematical optimization; World Wide Web; Algorithm; Artificial intelligence; Computer network; Engineering; Operating system; Mathematics","score_opus":0.006716947227403578,"score_gpt":0.22665333874691457,"score_spread":0.219936391519511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579692817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023738455,0.00030454568,0.94919205,0.00046643024,0.00015298996,0.00039818097,0.0024784824,0.012132241,0.011136558],"genre_scores_gemma":[0.21546037,0.00029942743,0.7693374,0.00016063322,0.00004182838,0.0002737334,0.0041186376,0.00036532123,0.00994269],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998821,0.000020981823,0.0000064071423,0.000046784575,0.000025564223,0.000018139195],"domain_scores_gemma":[0.9998894,0.000030239238,0.000010341187,0.000025073989,0.00002520946,0.000019815723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020918186,0.00059988396,0.00042198904,0.0004963623,0.00037769406,0.00054624106,0.00095548603,0.00055215013,0.009714448],"category_scores_gemma":[0.0004998063,0.00022179924,0.00031962595,0.00081513217,0.0001735617,0.00080444035,0.00066917733,0.0005734657,0.0025704328],"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":[0.00053917797,0.0002823426,0.0023226952,0.00045521898,0.00007522802,0.00025371593,0.00012437899,0.26082844,0.009729781,0.018878372,0.06110844,0.64540213],"study_design_scores_gemma":[0.000093214745,0.000108615415,0.0005793513,0.00001290708,0.000019375175,0.0001707835,0.00009958996,0.9529988,0.004613499,0.0073150136,0.0339677,0.000021019783],"about_ca_topic_score_codex":0.0047865845,"about_ca_topic_score_gemma":0.007188624,"teacher_disagreement_score":0.009714448,"about_ca_system_score_codex":0.00039256478,"about_ca_system_score_gemma":0.0007796784,"threshold_uncertainty_score":0.03249806},"labels":[],"label_agreement":null},{"id":"W2583219280","doi":"10.1109/bigdata.2016.7840671","title":"Parallel gathering discovery over big trajectory data","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Trajectory; Big data; Computation; Scale (ratio); Urban computing; Distributed computing; Data mining; Data science; Machine learning; Algorithm","score_opus":0.05429180465669192,"score_gpt":0.25997367587873593,"score_spread":0.205681871222044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583219280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17969893,0.00079428154,0.7995736,0.0017805711,0.00020151389,0.00046008322,0.0027455578,0.009935907,0.0048094774],"genre_scores_gemma":[0.4849313,0.00038868093,0.5062271,0.00014813058,0.000105503845,0.00027610286,0.0047561815,0.00034013367,0.0028269333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973072,0.00047932682,0.00019269483,0.00082316366,0.00085388386,0.00034371373],"domain_scores_gemma":[0.9931472,0.0021638304,0.00057550875,0.0024763667,0.0011838323,0.00045324268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031873127,0.0010389581,0.0016583384,0.0016090362,0.002547583,0.0021716056,0.0029554486,0.00079709257,0.00140788],"category_scores_gemma":[0.0083652465,0.00071886985,0.00097147527,0.005726882,0.0009621685,0.0041081617,0.002483552,0.0011769432,0.0006523712],"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":[0.0015759304,0.0005340977,0.013315872,0.000411356,0.00029503275,0.00073300535,0.0007623555,0.6048481,0.018051967,0.023070782,0.031930957,0.3044706],"study_design_scores_gemma":[0.00006172106,0.00003926819,0.0008137753,0.0000044956173,0.000022018867,0.00010656198,0.00016431879,0.97846395,0.0043809046,0.013025491,0.0029045967,0.00001284686],"about_ca_topic_score_codex":0.017163673,"about_ca_topic_score_gemma":0.024107726,"teacher_disagreement_score":0.017163673,"about_ca_system_score_codex":0.0017071147,"about_ca_system_score_gemma":0.0038823574,"threshold_uncertainty_score":0.034127533},"labels":[],"label_agreement":null},{"id":"W2583598520","doi":"10.1109/bigdata.2016.7840879","title":"Implementing trajectory data stream analysis in parallel","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Partition (number theory); Locality; Workflow; Trajectory; Data mining; Stream processing; Big data; Focus (optics); SPARK (programming language); Data stream; Process (computing); Batch processing; Distributed computing; Parallel computing; Database; Operating system; Programming language; Mathematics","score_opus":0.04496775241824208,"score_gpt":0.2930721111915908,"score_spread":0.24810435877334874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583598520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056753505,0.000068677946,0.9121566,0.0005742426,0.00016051832,0.00030894837,0.0004648638,0.024485933,0.005026676],"genre_scores_gemma":[0.3463399,0.00012729313,0.6456941,0.00018343132,0.00008662451,0.0003011484,0.0018937953,0.0014978333,0.0038759434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967073,0.000475905,0.00028580672,0.0009624392,0.0011764128,0.00039220045],"domain_scores_gemma":[0.994592,0.0013125413,0.0002483322,0.0020921093,0.001479357,0.00027576616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028569102,0.0010160814,0.0009995522,0.0009500151,0.0015491887,0.0029247317,0.002288575,0.0006120548,0.002564174],"category_scores_gemma":[0.0074530626,0.0007562678,0.0010971042,0.0019202816,0.0009806976,0.002915994,0.0018773163,0.0018097588,0.0015608498],"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":[0.003076526,0.0011664743,0.03272192,0.0003871152,0.00067982543,0.0008840653,0.0016223731,0.3137919,0.09975513,0.052801836,0.034367125,0.45874575],"study_design_scores_gemma":[0.00018852059,0.00010088393,0.0020443422,0.000013583012,0.00007000953,0.00014674704,0.00032149962,0.90340424,0.054511607,0.024935653,0.014203233,0.00005966377],"about_ca_topic_score_codex":0.019335737,"about_ca_topic_score_gemma":0.011699382,"teacher_disagreement_score":0.019335737,"about_ca_system_score_codex":0.0014461068,"about_ca_system_score_gemma":0.0046329694,"threshold_uncertainty_score":0.038446426},"labels":[],"label_agreement":null},{"id":"W2587250740","doi":"","title":"Generic Integration in Environmental Information and Decision Support Systems","year":2002,"lang":"en","type":"article","venue":"ScholarsArchive  (Brigham Young University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Computer science; Decision support system; Process (computing); Information system; Data science; Management information systems; Software; Risk analysis (engineering); Management science; Knowledge management; Engineering; Data mining; Business","score_opus":0.010228228374316743,"score_gpt":0.17323590012014925,"score_spread":0.1630076717458325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587250740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026404554,0.0022441188,0.9556462,0.001209917,0.00013642301,0.00025762952,0.00043755188,0.0043724612,0.009291139],"genre_scores_gemma":[0.3932603,0.0035704246,0.589187,0.0014396168,0.000261499,0.00038790348,0.0034586405,0.0007816412,0.007652922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952631,0.0015583072,0.00060971314,0.0008672794,0.0013751385,0.00032655083],"domain_scores_gemma":[0.9971878,0.00072278373,0.0002903248,0.0012639513,0.0004196083,0.000115591756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059784227,0.0006517765,0.001085855,0.001785117,0.0010167161,0.0050363597,0.0018085558,0.0018905697,0.002738201],"category_scores_gemma":[0.0074212495,0.0006668589,0.0012794452,0.005003611,0.0023288268,0.008354996,0.0043258895,0.0018362673,0.0015477526],"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":[0.00046207826,0.00025594523,0.008305341,0.0012643292,0.00026939283,0.00065381627,0.001617707,0.050403643,0.009447181,0.4446161,0.0142112635,0.46849325],"study_design_scores_gemma":[0.00008801286,0.0002755632,0.0046112314,0.00038359157,0.00039295322,0.00058086275,0.0005117535,0.28312385,0.01271776,0.5020996,0.19511387,0.000101013444],"about_ca_topic_score_codex":0.0028402293,"about_ca_topic_score_gemma":0.002843794,"teacher_disagreement_score":0.0059784227,"about_ca_system_score_codex":0.0012716964,"about_ca_system_score_gemma":0.0012010072,"threshold_uncertainty_score":0.031617284},"labels":[],"label_agreement":null},{"id":"W2589068952","doi":"10.29173/cais150","title":"Performance in ART1-Like Document Clustering with Variable Similarity Thresholds","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Cluster analysis; Similarity (geometry); Variable (mathematics); Implementation; Massively parallel; Document clustering; Data mining; Information retrieval; Parallel computing; Artificial intelligence; Mathematics","score_opus":0.015078656515238268,"score_gpt":0.21540691538705348,"score_spread":0.2003282588718152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589068952","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9027922,0.0032458513,0.074101694,0.00057440967,0.00019093436,0.0001426035,0.00070432667,0.008475016,0.00977293],"genre_scores_gemma":[0.93538535,0.00036910974,0.05962455,0.00011677327,0.000056170204,0.00005019961,0.0013841648,0.000232064,0.0027815788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99577326,0.0011321469,0.00032101825,0.00086959044,0.0013541473,0.0005498009],"domain_scores_gemma":[0.9870177,0.007265855,0.0006324956,0.0022772655,0.0022257266,0.0005809522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056293947,0.00077159464,0.0014065941,0.0015583029,0.0012627069,0.002275992,0.0026326329,0.0023423042,0.0022788956],"category_scores_gemma":[0.020245127,0.00042984498,0.00040566022,0.0032488373,0.0012076182,0.0035965368,0.0013061097,0.0008834642,0.0015687279],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010782174,0.0013460115,0.0132452585,0.000585968,0.00026978576,0.00022917357,0.0005842287,0.46472675,0.030609312,0.0049293693,0.01191021,0.46078163],"study_design_scores_gemma":[0.00015583345,0.00096084527,0.0041840253,0.000012750313,0.000039242652,0.00016799825,0.00014667021,0.96386856,0.02763873,0.0018727722,0.0009084594,0.000044140135],"about_ca_topic_score_codex":0.011972543,"about_ca_topic_score_gemma":0.0074533625,"teacher_disagreement_score":0.011972543,"about_ca_system_score_codex":0.0019769329,"about_ca_system_score_gemma":0.001464621,"threshold_uncertainty_score":0.029771447},"labels":[],"label_agreement":null},{"id":"W2591095207","doi":"10.3390/ijgi6020054","title":"AHS Model: Efficient Topological Operators for a Sensor Web Publish/Subscribe System","year":2017,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates; Canarie; Microsoft Research","keywords":"Sensor web; Computer science; Wireless sensor network; Geospatial analysis; Interoperability; Publication; Web mapping; Distributed computing; Data mining; Database; Web service; Web modeling; World Wide Web; Key distribution in wireless sensor networks; Computer network; Remote sensing; Geography","score_opus":0.01739969853041448,"score_gpt":0.2761489247987076,"score_spread":0.2587492262682931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591095207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018009603,0.00011669499,0.9648061,0.0002701915,0.00007120365,0.0003372242,0.00055567257,0.013519833,0.0023135324],"genre_scores_gemma":[0.42714435,0.00024080614,0.56501776,0.00022471046,0.00010039883,0.0005441197,0.0021180506,0.0005052462,0.004104529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982337,0.00033912517,0.00022177678,0.00023920402,0.00081525085,0.00015094208],"domain_scores_gemma":[0.9976019,0.00077486335,0.00019840512,0.0007244986,0.0005109801,0.00018934044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002059955,0.0007087171,0.00088886044,0.0014931046,0.0009414523,0.0027507858,0.0024275698,0.0009335592,0.0046416014],"category_scores_gemma":[0.005392925,0.00043451204,0.000972637,0.0014416901,0.00090418267,0.003685814,0.0019027893,0.0009900937,0.0014839317],"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":[0.0012034167,0.00061125384,0.00644487,0.0006250974,0.00016420677,0.0005878744,0.000748191,0.43299073,0.030902129,0.118563086,0.026275113,0.38088405],"study_design_scores_gemma":[0.000041058243,0.0000668691,0.00018973231,0.000007961311,0.000020317826,0.000063354964,0.00007082601,0.97332406,0.0044788956,0.015086066,0.0066318894,0.000018951845],"about_ca_topic_score_codex":0.0067914566,"about_ca_topic_score_gemma":0.0046595642,"teacher_disagreement_score":0.0067914566,"about_ca_system_score_codex":0.001254861,"about_ca_system_score_gemma":0.001996837,"threshold_uncertainty_score":0.015527666},"labels":[],"label_agreement":null},{"id":"W2591955099","doi":"10.1145/3132847.3133012","title":"Efficient Computation of Subspace Skyline over Categorical Domains","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Army Research Office; Qatar Foundation; Microsoft Research; Qatar National Research Fund; National Science Foundation","keywords":"Skyline; Categorical variable; Computer science; Data mining; Exploit; Tuple; Set (abstract data type); Subspace topology; Theoretical computer science; Machine learning; Artificial intelligence; Mathematics","score_opus":0.018838813719897596,"score_gpt":0.28618756179440474,"score_spread":0.26734874807450715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591955099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14668776,0.00078000873,0.83871895,0.0005207363,0.00006111529,0.00016326376,0.003275588,0.006503217,0.0032893901],"genre_scores_gemma":[0.473808,0.00033193713,0.5134811,0.00013216573,0.00008056555,0.00016513596,0.009742912,0.00028495764,0.001973346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889624,0.00020641802,0.00010090758,0.0002477479,0.00035575338,0.00019289623],"domain_scores_gemma":[0.9974304,0.0009914994,0.00028910054,0.00044833386,0.0006414432,0.00019927451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009752361,0.00076591136,0.0011003311,0.0027344038,0.00097452087,0.0022134,0.0011898748,0.0006733907,0.002617467],"category_scores_gemma":[0.0052661803,0.0003524082,0.00084454304,0.004237716,0.00045219297,0.00357842,0.0017378408,0.00081488094,0.001173881],"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":[0.0010512135,0.0004324453,0.026992796,0.0007034432,0.00022245159,0.0004295451,0.00081882096,0.30314445,0.023614153,0.053609654,0.028335078,0.56064594],"study_design_scores_gemma":[0.000042125983,0.00012842512,0.0016242679,0.000018362245,0.000013035592,0.00017049834,0.00032704382,0.96096134,0.005520523,0.027283814,0.0038874696,0.000023138102],"about_ca_topic_score_codex":0.0050777434,"about_ca_topic_score_gemma":0.010198132,"teacher_disagreement_score":0.0050777434,"about_ca_system_score_codex":0.00081326545,"about_ca_system_score_gemma":0.0020692537,"threshold_uncertainty_score":0.010096371},"labels":[],"label_agreement":null},{"id":"W2592110518","doi":"10.1016/j.neucom.2015.12.142","title":"Iceberg Clique queries in large graphs","year":2017,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Natural Science Foundation of Shaanxi Province; National Research Foundation of Korea","keywords":"Computer science; Adjacency matrix; Theoretical computer science; Clique; Graph; Combinatorics; Mathematics","score_opus":0.018554344378203583,"score_gpt":0.27388117163293807,"score_spread":0.2553268272547345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592110518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3554268,0.0043237754,0.58287954,0.024073152,0.00068898144,0.0007675134,0.008974164,0.004989813,0.01787629],"genre_scores_gemma":[0.8323036,0.00095269934,0.14777984,0.0020549858,0.00067025435,0.00029191282,0.006854615,0.0009958589,0.008096299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9924178,0.0027650846,0.00044944417,0.0019369696,0.0015678655,0.0008627929],"domain_scores_gemma":[0.9382119,0.049323134,0.002153656,0.0058723777,0.0022843508,0.0021545147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072860247,0.0010599317,0.0032022307,0.0021552795,0.0028137232,0.005974021,0.004361303,0.004124497,0.007744052],"category_scores_gemma":[0.050482415,0.0013785365,0.0013842816,0.0048725037,0.0023536864,0.016524184,0.0046419674,0.004272097,0.0011386832],"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":[0.003946093,0.0013655904,0.01319262,0.0014659998,0.00052863767,0.0013199775,0.001807575,0.43957922,0.005654181,0.2640142,0.12991276,0.13721314],"study_design_scores_gemma":[0.00021593047,0.000080406986,0.00072804117,0.000040815135,0.00006880782,0.00034458187,0.00041409847,0.6396645,0.0017315128,0.35201898,0.0046669953,0.000025341462],"about_ca_topic_score_codex":0.007167931,"about_ca_topic_score_gemma":0.011115994,"teacher_disagreement_score":0.007744052,"about_ca_system_score_codex":0.003475044,"about_ca_system_score_gemma":0.0032214567,"threshold_uncertainty_score":0.038532674},"labels":[],"label_agreement":null},{"id":"W2593159659","doi":"","title":"Base64Geo: an efficient data structure and transmission format for large, dense, scalar GIS datasets","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Management and Algorithms","field":"Computer Science","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 Victoria","funders":"","keywords":"Scalar (mathematics); Computer science; Data structure; Grid; String (physics); Magnitude (astronomy); Data mining; Tree (set theory); Database; Algorithm; Mathematics; Geometry; Physics; Combinatorics","score_opus":0.012801900714414489,"score_gpt":0.23185445276274097,"score_spread":0.2190525520483265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593159659","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014129746,0.0009739018,0.2972695,0.0013756403,0.00084641617,0.00078866933,0.3853084,0.2805933,0.018714378],"genre_scores_gemma":[0.05948694,0.0010295769,0.2923906,0.0007283536,0.00018074772,0.0018953359,0.60676265,0.029550625,0.007975237],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980427,0.00023535347,0.00030758968,0.00028353088,0.00089234783,0.0002385057],"domain_scores_gemma":[0.9945747,0.0010904175,0.0004145942,0.0023247083,0.0013174572,0.00027808538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021369103,0.001834493,0.0014986843,0.0039068917,0.0011284401,0.003919497,0.0043543642,0.0012390693,0.02853861],"category_scores_gemma":[0.014296522,0.0014283315,0.0013669039,0.009602162,0.0007589083,0.0069775134,0.004703004,0.0021712997,0.023244843],"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":[0.0010859059,0.00014646177,0.004848739,0.001048171,0.0001429791,0.00041593023,0.0006076539,0.00925621,0.0082004545,0.020227252,0.79325503,0.1607651],"study_design_scores_gemma":[0.00052874145,0.00014735722,0.003853568,0.0003100057,0.000060807233,0.00044834928,0.00057235145,0.03828968,0.025498882,0.047907986,0.88210326,0.00027894927],"about_ca_topic_score_codex":0.0067111827,"about_ca_topic_score_gemma":0.0071197464,"teacher_disagreement_score":0.02853861,"about_ca_system_score_codex":0.0014958448,"about_ca_system_score_gemma":0.0027457438,"threshold_uncertainty_score":0.0954712},"labels":[],"label_agreement":null},{"id":"W2594154281","doi":"","title":"Counting rooted nonseparable unicursal planar maps.","year":2017,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Planar; Combinatorics; Computer graphics (images); Computer science","score_opus":0.01683502484614639,"score_gpt":0.25220071106849634,"score_spread":0.23536568622234996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594154281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31582996,0.002401849,0.5496509,0.003007627,0.00033742323,0.00026516386,0.0021598537,0.0020219327,0.12432524],"genre_scores_gemma":[0.8225294,0.0010388056,0.1374573,0.0003701267,0.00022236031,0.00020208133,0.0018774065,0.00041703339,0.035885457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99792284,0.0004162653,0.0001751058,0.00036385693,0.0007318553,0.0003901068],"domain_scores_gemma":[0.99184704,0.0043083965,0.00074589264,0.0013541098,0.0011794345,0.0005651909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098335,0.0006119886,0.0008854475,0.0031540645,0.002003564,0.004745932,0.0021343234,0.0013581411,0.01147865],"category_scores_gemma":[0.0145477485,0.0005386524,0.00057041115,0.0040181438,0.001841502,0.008348717,0.004906642,0.0017852555,0.0023198922],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013834766,0.000056985045,0.0014728332,0.000264204,0.000032782013,0.00016407427,0.0003835152,0.004652366,0.0023268685,0.9217561,0.007998017,0.06075393],"study_design_scores_gemma":[0.000017321432,0.000029426223,0.00053534936,0.00006491442,0.000042741383,0.00038726494,0.0002839331,0.029678052,0.004204725,0.94808567,0.016647227,0.000023487024],"about_ca_topic_score_codex":0.0015295999,"about_ca_topic_score_gemma":0.0024662109,"teacher_disagreement_score":0.01147865,"about_ca_system_score_codex":0.001908197,"about_ca_system_score_gemma":0.00086419145,"threshold_uncertainty_score":0.038399935},"labels":[],"label_agreement":null},{"id":"W2598545891","doi":"10.14313/jamris_1-2017/4","title":"Multi-Strategy Navigation for a Mobile Data Acquisition Platform Using Genetic Algorithms","year":2017,"lang":"en","type":"article","venue":"Journal of Automation Mobile Robotics & Intelligent Systems","topic":"Data Management and Algorithms","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":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Artificial intelligence; Algorithm","score_opus":0.11555074723646186,"score_gpt":0.3635139512787691,"score_spread":0.24796320404230726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598545891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08055527,0.00016181517,0.91567045,0.00010119561,0.000041735988,0.0000597266,0.000014365531,0.00033778968,0.0030576636],"genre_scores_gemma":[0.8014542,0.00009731558,0.19554664,0.000053711257,0.000014661157,0.00016012265,0.000032205477,0.00003531343,0.002605869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998802,0.000023218958,0.000005806361,0.000032973137,0.000037081467,0.000020728972],"domain_scores_gemma":[0.99982196,0.000079880774,0.000022073073,0.000011738314,0.000052705447,0.0000116584115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028466503,0.0006481181,0.000579097,0.00060151337,0.00071271794,0.00057985814,0.00067234255,0.0009612848,0.0008129663],"category_scores_gemma":[0.0006047829,0.00034632575,0.0007147875,0.00043160687,0.0004370737,0.00041096332,0.0005390666,0.00045901374,0.00012725157],"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":[0.000077472665,0.00007309588,0.0011288209,0.000044003937,0.000072042225,0.00010131436,0.00012614405,0.89935136,0.010332389,0.006104735,0.00040824333,0.08218037],"study_design_scores_gemma":[0.000009330384,0.00004076324,0.0001587949,0.0000032403518,0.000012850304,0.000014049216,0.0000094616935,0.9983583,0.0005860842,0.0006509277,0.00015063978,0.000005594524],"about_ca_topic_score_codex":0.013486472,"about_ca_topic_score_gemma":0.008610027,"teacher_disagreement_score":0.013486472,"about_ca_system_score_codex":0.00068143115,"about_ca_system_score_gemma":0.0010478576,"threshold_uncertainty_score":0.02681595},"labels":[],"label_agreement":null},{"id":"W2600424944","doi":"","title":"Flow mapping vs movement mapping: a theoretical and cartographic demonstration of their significance at a world scale","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","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":"Ministère des Transports","funders":"","keywords":"Flow (mathematics); Scale (ratio); Computer science; Dimension (graph theory); Feature (linguistics); Mathematics; Cartography; Linguistics; Geography; Geometry; Pure mathematics; Philosophy","score_opus":0.016143379286897273,"score_gpt":0.21085912871233478,"score_spread":0.1947157494254375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600424944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05583551,0.0034843558,0.8726302,0.0071213357,0.0003085187,0.000083225816,0.0007767319,0.00038590154,0.05937423],"genre_scores_gemma":[0.77297,0.002803511,0.21697232,0.00027679108,0.00039361414,0.00022348756,0.0003895875,0.0002261004,0.00574451],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990196,0.0004196545,0.00004313665,0.00024557425,0.00020170242,0.00007025093],"domain_scores_gemma":[0.990445,0.0072296658,0.00063870795,0.00093668816,0.0005774699,0.00017236655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033904875,0.0005223717,0.000504479,0.0047255876,0.0014749792,0.005666186,0.0010426065,0.0015890683,0.0082919905],"category_scores_gemma":[0.019713745,0.00051321683,0.00066446594,0.0077829254,0.0067060357,0.01004368,0.0025284619,0.001615622,0.00049324025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022853059,0.000009697214,0.002054136,0.0000862932,0.0000114113545,0.000063037005,0.00064023904,0.0075298394,0.00027287507,0.9507121,0.001101809,0.03749563],"study_design_scores_gemma":[0.000014305332,0.00001776947,0.0023210375,0.0000842451,0.000024087714,0.0002194811,0.0007816431,0.0526776,0.00046096605,0.9295562,0.013820914,0.000021820997],"about_ca_topic_score_codex":0.005127381,"about_ca_topic_score_gemma":0.0038645875,"teacher_disagreement_score":0.0082919905,"about_ca_system_score_codex":0.0012426103,"about_ca_system_score_gemma":0.0010677741,"threshold_uncertainty_score":0.027739465},"labels":[],"label_agreement":null},{"id":"W2602074824","doi":"","title":"Complement to a Comparative Analysis of Heuristics for the p-Median Problem","year":2007,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Data Management and Algorithms","field":"Computer Science","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Heuristics; Heuristic; Complement (music); Artificial neural network; Series (stratigraphy); Mathematics; Evolutionary algorithm; Variable neighborhood search; Computer science; Mathematical optimization; Artificial intelligence; Algorithm; Metaheuristic","score_opus":0.030612689729685522,"score_gpt":0.29267806898487475,"score_spread":0.26206537925518925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602074824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16456126,0.005725692,0.7914696,0.00096443156,0.00028634045,0.00030177514,0.0004886224,0.0011936657,0.0350086],"genre_scores_gemma":[0.52088064,0.003593087,0.46778825,0.00036576926,0.00019928203,0.0002878295,0.0007793394,0.000494284,0.005611509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978235,0.0009790879,0.00010032181,0.000345261,0.0005551295,0.00019677231],"domain_scores_gemma":[0.98762417,0.009499826,0.0005585219,0.0012466825,0.0008664567,0.00020445338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037062897,0.0009831439,0.0012781017,0.0025736452,0.0010554333,0.0014194578,0.0015521648,0.0019210936,0.005883232],"category_scores_gemma":[0.016238734,0.0003949834,0.0012981725,0.0035497302,0.0008339154,0.0027475124,0.00095011323,0.0012363943,0.00063142396],"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":[0.0008165078,0.0004758351,0.0027528733,0.0006199999,0.00024616165,0.00007625805,0.00012359113,0.708849,0.0013251833,0.04190935,0.0060656653,0.23673958],"study_design_scores_gemma":[0.00012629885,0.0006203334,0.0017467972,0.000093671784,0.00007661464,0.0002531339,0.00022872144,0.95816934,0.0032627732,0.030027691,0.0053599565,0.000034748486],"about_ca_topic_score_codex":0.003922203,"about_ca_topic_score_gemma":0.0057832394,"teacher_disagreement_score":0.005883232,"about_ca_system_score_codex":0.0019361638,"about_ca_system_score_gemma":0.0017749318,"threshold_uncertainty_score":0.019681394},"labels":[],"label_agreement":null},{"id":"W2603348183","doi":"","title":"Outils de visualisation de données de cartes à puce pour une société de transport collectif","year":2016,"lang":"fr","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.027024478188954686,"score_gpt":0.2647284395829841,"score_spread":0.2377039613940294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603348183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03038733,0.005186526,0.8121976,0.003374441,0.0008805931,0.0006940044,0.022079086,0.09418988,0.03101052],"genre_scores_gemma":[0.14521703,0.004812031,0.80351233,0.0005952294,0.00023903033,0.0007873601,0.01918592,0.010136362,0.015514647],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963176,0.0010471717,0.00026322014,0.00052813516,0.0016595435,0.00018430696],"domain_scores_gemma":[0.9874275,0.0081556775,0.00028149152,0.0016545925,0.0020633175,0.00041748706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041414406,0.00266365,0.0014218153,0.006063735,0.001495728,0.011623644,0.0016031829,0.0021285708,0.030513488],"category_scores_gemma":[0.017617233,0.0012470155,0.0030034161,0.0042897332,0.001137346,0.005800442,0.0037252447,0.003736224,0.00808051],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019445167,0.00044553343,0.008096282,0.0050571808,0.00061045005,0.002746212,0.010013896,0.01901962,0.04495722,0.03761659,0.16103005,0.70846254],"study_design_scores_gemma":[0.00026323015,0.0002917304,0.02041685,0.0025126762,0.00018747788,0.0033652498,0.006058646,0.17423384,0.041504297,0.025071548,0.725445,0.0006495516],"about_ca_topic_score_codex":0.01704383,"about_ca_topic_score_gemma":0.016718227,"teacher_disagreement_score":0.030513488,"about_ca_system_score_codex":0.0010709745,"about_ca_system_score_gemma":0.0020686847,"threshold_uncertainty_score":0.10207778},"labels":[],"label_agreement":null},{"id":"W2603660651","doi":"","title":"Ad-hoc Holistic Ranking Aggregation","year":2012,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Ranking (information retrieval); Post hoc; Information retrieval; Computer science; Medicine; Dentistry","score_opus":0.018332695113920952,"score_gpt":0.21679602768244116,"score_spread":0.19846333256852022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603660651","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01764683,0.0008110002,0.97453034,0.00022120665,0.00010829182,0.00020989793,0.0003380418,0.0009479622,0.005186351],"genre_scores_gemma":[0.28677946,0.0011032529,0.6965164,0.0002246063,0.0002528177,0.00034044386,0.0016696259,0.00028679136,0.01282672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99439764,0.0016135162,0.00037971695,0.0010660446,0.0020680018,0.0004752244],"domain_scores_gemma":[0.99458426,0.0015376935,0.00037451935,0.0018539593,0.0013891135,0.0002604664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003707059,0.00128069,0.002346806,0.0021196976,0.0013471697,0.0031311829,0.001996878,0.0008388358,0.005484565],"category_scores_gemma":[0.00839008,0.0005052894,0.0013134861,0.004518915,0.0007142217,0.0039812224,0.004412438,0.0011984555,0.0018335538],"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":[0.00033282937,0.0002969615,0.0032121814,0.0006067571,0.0003432087,0.00026703285,0.00038760566,0.12854545,0.010791506,0.09018778,0.022074506,0.7429542],"study_design_scores_gemma":[0.000047472964,0.0003678223,0.0019281278,0.000057533576,0.00018856295,0.0005664017,0.0003626611,0.82421386,0.0090187825,0.12882987,0.03434956,0.00006926438],"about_ca_topic_score_codex":0.0013684168,"about_ca_topic_score_gemma":0.0023747298,"teacher_disagreement_score":0.005484565,"about_ca_system_score_codex":0.0009856946,"about_ca_system_score_gemma":0.001824302,"threshold_uncertainty_score":0.0196051},"labels":[],"label_agreement":null},{"id":"W2604861954","doi":"10.3233/fi-2018-1651","title":"On Desirable Semantics of Functional Dependencies over Databases with Incomplete Information","year":2018,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Functional dependency; Axiom; Semantics (computer science); Relational database; Database; Independence (probability theory); Theoretical computer science; Relational model; Possible world; Information retrieval; Programming language; Mathematics","score_opus":0.031046352721690713,"score_gpt":0.2388561724885678,"score_spread":0.2078098197668771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604861954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050706398,0.000543967,0.93892556,0.0026039619,0.00007371677,0.00011571657,0.0006082876,0.00032537314,0.0060969773],"genre_scores_gemma":[0.5500097,0.00083426153,0.44339335,0.0009387909,0.0002842284,0.0005392046,0.0013036034,0.00019144038,0.002505378],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98807085,0.004763945,0.0016022858,0.0016197929,0.0031979117,0.0007452458],"domain_scores_gemma":[0.9668787,0.020549206,0.0023609297,0.004536858,0.0045247334,0.0011495865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013401919,0.0009805172,0.0011304897,0.002875243,0.0019279289,0.0050253132,0.003011202,0.0024475537,0.0022410164],"category_scores_gemma":[0.031285718,0.0011652363,0.0024059662,0.0035786955,0.004919529,0.016761804,0.0051716794,0.004540293,0.0003997192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004648811,0.000047021746,0.00041414966,0.00010158528,0.000022642218,0.00018703184,0.0006058118,0.009762526,0.0009018107,0.97996706,0.00076451607,0.0071792933],"study_design_scores_gemma":[0.000029680315,0.000045022032,0.00015770171,0.000049763516,0.000022364087,0.00018816661,0.00018652948,0.05701925,0.0007793014,0.9384133,0.0030869283,0.000022071308],"about_ca_topic_score_codex":0.0023554997,"about_ca_topic_score_gemma":0.0022561809,"teacher_disagreement_score":0.013401919,"about_ca_system_score_codex":0.0030448472,"about_ca_system_score_gemma":0.0022087472,"threshold_uncertainty_score":0.070877016},"labels":[],"label_agreement":null},{"id":"W2606016433","doi":"10.1007/978-3-319-57351-9_34","title":"Probabilistic TCP-net","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Net (polyhedron); Computer science; Preference; Bayesian network; Probabilistic logic; Set (abstract data type); Graphical model; Preference relation; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.022462443124361207,"score_gpt":0.2503938629422117,"score_spread":0.2279314198178505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606016433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007866408,0.0015194142,0.86784464,0.0017640799,0.0012339787,0.00016165792,0.0012171635,0.006914544,0.111478075],"genre_scores_gemma":[0.4325031,0.005237296,0.35235915,0.0011917222,0.0018637016,0.00076586154,0.0050516264,0.003407093,0.1976204],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988655,0.0002247747,0.000056919594,0.00022961032,0.00050128036,0.00012194001],"domain_scores_gemma":[0.9978295,0.00061978877,0.00011225275,0.00081017223,0.00045301538,0.0001752468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018401825,0.00088394195,0.0007705384,0.0014416347,0.0015204072,0.0032320053,0.0018144258,0.0013800117,0.026852652],"category_scores_gemma":[0.0063393,0.00069174526,0.00063856185,0.0014313904,0.001216494,0.004779385,0.0026358338,0.0029270116,0.010766833],"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":[0.00019692643,0.000081518054,0.00055986614,0.00017079723,0.000033848813,0.000081688166,0.00007341121,0.039461892,0.0021014651,0.7586647,0.05585079,0.14272316],"study_design_scores_gemma":[0.000029826886,0.00004771344,0.0003337073,0.000086993336,0.000040054023,0.00028904786,0.000031714273,0.25220314,0.0039091418,0.6304654,0.112514876,0.00004833576],"about_ca_topic_score_codex":0.0012542568,"about_ca_topic_score_gemma":0.0014472998,"teacher_disagreement_score":0.026852652,"about_ca_system_score_codex":0.0016199551,"about_ca_system_score_gemma":0.0016357087,"threshold_uncertainty_score":0.089830995},"labels":[],"label_agreement":null},{"id":"W2606633748","doi":"10.1007/978-3-319-57351-9_19","title":"Sequence-Based Bidirectional Merge Map-Matching Algorithm for Simplified Road Network","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Computer science; Map matching; Merge (version control); Algorithm; Merge algorithm; Outlier; Blossom algorithm; Matching (statistics); Sequence (biology); Global Positioning System; Artificial intelligence; Mathematics","score_opus":0.03445042403683292,"score_gpt":0.276466503553487,"score_spread":0.2420160795166541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606633748","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021335397,0.0003061976,0.9710569,0.00007819808,0.000109516084,0.00018496382,0.0004455673,0.0026766704,0.0038066152],"genre_scores_gemma":[0.1894713,0.00040862805,0.7971514,0.00007741492,0.000055974593,0.00027763093,0.0028268648,0.00028736237,0.009443494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992576,0.00005253418,0.0000449436,0.00020996602,0.000327473,0.00010748414],"domain_scores_gemma":[0.99954873,0.000056711913,0.000031730055,0.00009514139,0.00023594228,0.000031728272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050563633,0.00097569666,0.0013709428,0.0027168503,0.0012258126,0.0010590053,0.0023902524,0.0008970748,0.008746012],"category_scores_gemma":[0.0014164709,0.000609264,0.0010170261,0.003425366,0.000360066,0.0020858552,0.002051453,0.00080949743,0.0027564713],"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":[0.00044412026,0.00016536664,0.0018066526,0.0002918768,0.00012242308,0.00018385962,0.00021520125,0.06895158,0.024132587,0.0105989035,0.0126328,0.8804546],"study_design_scores_gemma":[0.00007532107,0.000118670614,0.0012733375,0.000021752938,0.00008810201,0.00040718497,0.00021312809,0.9459236,0.026300484,0.013402915,0.012129461,0.000045933863],"about_ca_topic_score_codex":0.012969337,"about_ca_topic_score_gemma":0.013906734,"teacher_disagreement_score":0.012969337,"about_ca_system_score_codex":0.0007992031,"about_ca_system_score_gemma":0.0026285932,"threshold_uncertainty_score":0.029258251},"labels":[],"label_agreement":null},{"id":"W2606648713","doi":"10.1007/978-3-319-57529-2_14","title":"MaP2R: A Personalized Maximum Probability Route Recommendation Method Using GPS Trajectories","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Global Positioning System; Recommender system; Smoothing; Graph; Personalization; Information retrieval; Theoretical computer science; World Wide Web; Computer vision","score_opus":0.05690339408799902,"score_gpt":0.3099706010547597,"score_spread":0.25306720696676066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606648713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024049105,0.0013516647,0.94550455,0.0001872904,0.000320994,0.00019475493,0.0037737542,0.020511394,0.0041064527],"genre_scores_gemma":[0.20869006,0.0009962493,0.76449996,0.00016510913,0.00024001938,0.00022301372,0.0073402836,0.0009152745,0.01692999],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995758,0.00004588566,0.00001822054,0.00014163827,0.0001854262,0.00003307946],"domain_scores_gemma":[0.99964345,0.00009444631,0.000020212838,0.00010862082,0.00011115449,0.000022095624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003822394,0.0010387276,0.0011953696,0.0018519362,0.0005888604,0.00071117515,0.0019479194,0.0009544306,0.0065368246],"category_scores_gemma":[0.0015900999,0.0005181891,0.0009719853,0.002214347,0.00013960678,0.0013178072,0.0006977122,0.0008152149,0.0043077534],"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":[0.0005232268,0.00021979219,0.003386029,0.00030471693,0.00026696397,0.0002665717,0.000087569904,0.054609843,0.008120431,0.0017293652,0.051202077,0.87928337],"study_design_scores_gemma":[0.00009680683,0.00011110151,0.0020861863,0.000025994843,0.00011962236,0.00059092406,0.00005937502,0.9678765,0.0076372433,0.0028510015,0.018471567,0.000073718424],"about_ca_topic_score_codex":0.014340993,"about_ca_topic_score_gemma":0.022483956,"teacher_disagreement_score":0.014340993,"about_ca_system_score_codex":0.0003265964,"about_ca_system_score_gemma":0.0005963387,"threshold_uncertainty_score":0.02851504},"labels":[],"label_agreement":null},{"id":"W2606963904","doi":"","title":"Median of an odd number of permutations","year":2011,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Combinatorics; Mathematics","score_opus":0.028531620440564318,"score_gpt":0.25537196201972845,"score_spread":0.22684034157916413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606963904","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5932103,0.0010146037,0.33872163,0.0023951398,0.00039468822,0.0001717326,0.0020707538,0.0007362964,0.06128485],"genre_scores_gemma":[0.8526757,0.0009267603,0.09795401,0.00061027566,0.0004967931,0.00038078497,0.0017975735,0.00038287404,0.044775374],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99828655,0.00047503714,0.00013504972,0.0003786012,0.00036479044,0.00035996432],"domain_scores_gemma":[0.9932287,0.0035014513,0.0009341696,0.0010307529,0.00050817727,0.00079668645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013820938,0.00086751796,0.0019164335,0.0017674193,0.0018340226,0.004422718,0.001476878,0.0017175531,0.012638603],"category_scores_gemma":[0.0084687825,0.00054795406,0.0010526533,0.0023433743,0.0016889009,0.0033080352,0.0023280338,0.002185802,0.0017204923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002197941,0.00018028478,0.0031474296,0.0004463834,0.00016862198,0.0008722531,0.00045490786,0.040360685,0.014045139,0.85984594,0.0104725985,0.06780771],"study_design_scores_gemma":[0.00013114877,0.00017569088,0.00091802765,0.00005563339,0.00005035145,0.0005239421,0.00018063973,0.05604107,0.003790277,0.9295099,0.008587726,0.00003560978],"about_ca_topic_score_codex":0.00045012226,"about_ca_topic_score_gemma":0.0005937752,"teacher_disagreement_score":0.012638603,"about_ca_system_score_codex":0.0013018561,"about_ca_system_score_gemma":0.001245549,"threshold_uncertainty_score":0.042280316},"labels":[],"label_agreement":null},{"id":"W2608249025","doi":"10.18357/jcs.v37i2.15201","title":"Count on More than Just Counting with Counting Books","year":2012,"lang":"en","type":"article","venue":"Journal of Childhood Studies","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics","score_opus":0.032004503487076334,"score_gpt":0.28147649549977466,"score_spread":0.24947199201269832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608249025","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029736541,0.021025853,0.19068265,0.12723444,0.022608628,0.00032938964,0.006629534,0.006585328,0.5951675],"genre_scores_gemma":[0.18614827,0.014514142,0.15990819,0.029598566,0.019714855,0.0007615907,0.008008752,0.008399542,0.5729461],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9896885,0.0030406949,0.00068755203,0.0014532985,0.004787062,0.00034281088],"domain_scores_gemma":[0.9556955,0.018643182,0.002286817,0.009187275,0.012002892,0.0021842637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003917772,0.0016441842,0.0020983417,0.010611151,0.0045701405,0.013377314,0.003033121,0.002284788,0.10308336],"category_scores_gemma":[0.07607555,0.0010811754,0.0012412788,0.015211369,0.0055607543,0.040698316,0.0055724503,0.0048588812,0.023490086],"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":[0.00012006884,0.00010483364,0.004735413,0.00048183562,0.00009756512,0.00013690634,0.001450033,0.00058323034,0.00054880406,0.37232512,0.3307179,0.28869823],"study_design_scores_gemma":[0.00002742738,0.000047112997,0.002709559,0.0004696489,0.000078997226,0.00046754695,0.0017757873,0.0013475231,0.0010705135,0.33301148,0.65890324,0.00009112525],"about_ca_topic_score_codex":0.007715554,"about_ca_topic_score_gemma":0.010026375,"teacher_disagreement_score":0.10308336,"about_ca_system_score_codex":0.0033149787,"about_ca_system_score_gemma":0.0029182145,"threshold_uncertainty_score":0.34484816},"labels":[],"label_agreement":null},{"id":"W2613838618","doi":"10.1109/iat.2007.39","title":"Agent-based Fault Detection Mechanism in Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Computer network; Distributed computing; Sensor node; Multicast; Energy consumption; Mobile wireless sensor network; Fault detection and isolation; Real-time computing; Wireless; Wireless network; Engineering; Actuator","score_opus":0.012317582839634281,"score_gpt":0.23042815547265302,"score_spread":0.21811057263301875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613838618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017844923,0.00029736877,0.98026294,0.0001776483,0.00004209581,0.00005694661,0.0000139802105,0.00054148235,0.0007625766],"genre_scores_gemma":[0.8062316,0.00035355065,0.1916518,0.00011019772,0.000060044567,0.0002224141,0.00005014477,0.000027003387,0.0012931902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819845,0.00060317817,0.00017153785,0.00030837115,0.00059640425,0.000122040015],"domain_scores_gemma":[0.9963128,0.001982819,0.00054346345,0.0004637685,0.0005928935,0.00010425963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031477443,0.00049683446,0.00079791446,0.0008565762,0.0006781856,0.0012668191,0.0022859462,0.0011938955,0.0005773294],"category_scores_gemma":[0.0073903333,0.00033911035,0.00056863227,0.00063391123,0.001199248,0.0020888466,0.00091664423,0.0010515266,0.00014016475],"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":[0.00018492804,0.00014667134,0.0028983003,0.00016925293,0.0001409227,0.00029603258,0.00039790245,0.7328042,0.007594775,0.15319198,0.0013505942,0.10082439],"study_design_scores_gemma":[0.00001660712,0.000032684664,0.000100286044,0.000006900855,0.000019868692,0.00004028323,0.000010847059,0.9744933,0.0016862197,0.022732735,0.00084954296,0.000010772402],"about_ca_topic_score_codex":0.0016613939,"about_ca_topic_score_gemma":0.0011703902,"teacher_disagreement_score":0.0031477443,"about_ca_system_score_codex":0.00077028514,"about_ca_system_score_gemma":0.0010150461,"threshold_uncertainty_score":0.0166471},"labels":[],"label_agreement":null},{"id":"W2613845756","doi":"10.1109/aina.2017.30","title":"A Compact-Trie-Based Structure for K-Nearest-Neighbour Searching","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Trie; Nearest neighbour; k-nearest neighbors algorithm; Computer science; Grid; Nearest neighbor search; Space (punctuation); Data structure; Space partitioning; Algorithm; Pattern recognition (psychology); Data mining; Mathematics; Artificial intelligence; Geometry","score_opus":0.03684698012904807,"score_gpt":0.3117793133192961,"score_spread":0.274932333190248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613845756","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007339363,0.0005415099,0.9883859,0.00011505806,0.000091271795,0.00012791295,0.00021802055,0.00083883805,0.0023422113],"genre_scores_gemma":[0.111410335,0.00051455654,0.8835533,0.00014134795,0.000075339136,0.00037971753,0.0012118458,0.00019017798,0.0025233934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836415,0.0003629085,0.00015835153,0.0002607302,0.0007392538,0.00011459799],"domain_scores_gemma":[0.9975526,0.0006208658,0.00018842916,0.00066948135,0.00087117375,0.000097398595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007692032,0.0006802282,0.001452544,0.0026630976,0.0011378955,0.0016345653,0.0023598603,0.0013497922,0.005308553],"category_scores_gemma":[0.006616712,0.0005450498,0.0008546354,0.0037702087,0.00082609657,0.0032214797,0.0020754188,0.0010075609,0.0038440241],"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":[0.00057282794,0.00031055493,0.0019954075,0.00069036364,0.00015146603,0.00038378214,0.00077969005,0.17179894,0.024767905,0.06483906,0.01877729,0.71493274],"study_design_scores_gemma":[0.000086769425,0.0003038792,0.0006506081,0.00006195338,0.000033290093,0.0009906946,0.00021738611,0.93777734,0.01072382,0.025321133,0.023731798,0.0001012149],"about_ca_topic_score_codex":0.0033648931,"about_ca_topic_score_gemma":0.0035832555,"teacher_disagreement_score":0.005308553,"about_ca_system_score_codex":0.00066981086,"about_ca_system_score_gemma":0.0010774656,"threshold_uncertainty_score":0.017758846},"labels":[],"label_agreement":null},{"id":"W2613865975","doi":"10.11591/ijai.v6.i1.pp1-7","title":"Fuzzy Information Modeling in a Database System","year":2017,"lang":"en","type":"article","venue":"IAES International Journal of Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Fuzzy logic; Data mining; Fuzzy set operations; Database design; Neuro-fuzzy; Fuzzy classification; Artificial intelligence; Fuzzy set; Fuzzy control system","score_opus":0.07553415624370242,"score_gpt":0.3275671142655698,"score_spread":0.25203295802186737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613865975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012560524,0.00083044136,0.9740113,0.0007690655,0.00012419783,0.00018011905,0.00043277556,0.00168832,0.00940332],"genre_scores_gemma":[0.36337054,0.0017611852,0.62184215,0.00042653293,0.00019083483,0.0003950801,0.0010898326,0.000110960034,0.010812854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827695,0.00048173274,0.00027190815,0.00030826867,0.00058234716,0.00007867499],"domain_scores_gemma":[0.99900144,0.00040135105,0.000061962826,0.0002183539,0.0002624268,0.00005440828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002116792,0.00041797367,0.0006352015,0.0011889781,0.00096568686,0.0045208866,0.0014157109,0.0012564283,0.0032392624],"category_scores_gemma":[0.002965436,0.0002811988,0.0007676263,0.0013972996,0.00064524694,0.003469912,0.0011963664,0.001010685,0.0012057159],"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":[0.00041034006,0.00023275155,0.003002536,0.0006077011,0.00016980359,0.0010725948,0.0014936762,0.18205912,0.016976256,0.55823714,0.011548563,0.22418942],"study_design_scores_gemma":[0.00005647634,0.00012280843,0.00038316144,0.00013761659,0.00012367275,0.0003297613,0.00019573145,0.809207,0.007976804,0.10833253,0.07307608,0.00005834115],"about_ca_topic_score_codex":0.005067299,"about_ca_topic_score_gemma":0.002514008,"teacher_disagreement_score":0.005067299,"about_ca_system_score_codex":0.0009672541,"about_ca_system_score_gemma":0.0012791862,"threshold_uncertainty_score":0.011194766},"labels":[],"label_agreement":null},{"id":"W2614759802","doi":"10.1109/icde.2017.205","title":"Multiple-Query Optimization of Regular Path Queries","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; SPARQL; Path expression; Path (computing); Graph; Query optimization; Query plan; Graph database; Semantic Web; Theoretical computer science; Web search query; Information retrieval; Query language; RDF; Search engine; Sargable; Programming language","score_opus":0.017153646428272697,"score_gpt":0.23521557811791877,"score_spread":0.21806193168964608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614759802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07425299,0.0005490067,0.91138065,0.0007356555,0.00007875848,0.00036565663,0.00081384997,0.005082184,0.006741197],"genre_scores_gemma":[0.47590458,0.00027217466,0.5169026,0.00025597206,0.000051343737,0.00031644737,0.0021432186,0.0013637913,0.0027898406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958261,0.0011939334,0.00029016318,0.0008678553,0.0012255441,0.00059637806],"domain_scores_gemma":[0.99630886,0.0021079427,0.00023989672,0.00068098365,0.0004732827,0.00018896304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003189101,0.0014190259,0.0012569005,0.00082679867,0.00091658795,0.001783983,0.0020499025,0.0009549359,0.0029564183],"category_scores_gemma":[0.008402658,0.0005524897,0.0012717237,0.0012876047,0.001334739,0.0027317149,0.0030170656,0.0017378234,0.00050018664],"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":[0.0006025704,0.0003321717,0.0035915812,0.00033950928,0.00018448819,0.00038474632,0.0006175112,0.787101,0.012627997,0.03720674,0.010833371,0.14617832],"study_design_scores_gemma":[0.00004242865,0.00009892522,0.0003104971,0.000012879533,0.000031087442,0.00006495209,0.00022149844,0.96680015,0.004489307,0.02499528,0.0029164264,0.000016481737],"about_ca_topic_score_codex":0.0077872793,"about_ca_topic_score_gemma":0.0112788025,"teacher_disagreement_score":0.0077872793,"about_ca_system_score_codex":0.0012674503,"about_ca_system_score_gemma":0.0022061928,"threshold_uncertainty_score":0.01686579},"labels":[],"label_agreement":null},{"id":"W2615008404","doi":"10.24166/im.03.2018","title":"Subhypergraphs in non-uniform random hypergraphs","year":2018,"lang":"en","type":"article","venue":"Internet Mathematics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hypergraph; Mathematics; Random graph; Generalization; Graph; Cluster analysis; Binomial (polynomial); Focus (optics); Relation (database); Section (typography); Discrete mathematics; Combinatorics; Data mining; Computer science; Statistics","score_opus":0.012693409263658108,"score_gpt":0.24062307456692916,"score_spread":0.22792966530327105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615008404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15228042,0.0012531708,0.8404855,0.0011428191,0.00003784558,0.00014910586,0.000506341,0.0004698476,0.0036748373],"genre_scores_gemma":[0.82972944,0.0014660321,0.15895106,0.00086545816,0.00030054088,0.00045354394,0.0015253361,0.000275287,0.0064333426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964964,0.0016199128,0.00012759768,0.0010827541,0.0004480298,0.00022528977],"domain_scores_gemma":[0.9557338,0.034309898,0.0040890668,0.0032555612,0.0015127556,0.0010989778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004515626,0.00062848744,0.0015195255,0.0031194612,0.0010690602,0.0024150617,0.0018613233,0.0020580257,0.004479364],"category_scores_gemma":[0.030519139,0.0012443946,0.0011302248,0.0027750996,0.0031895223,0.007947959,0.0019363075,0.002582536,0.00073174917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037477366,0.00014832432,0.021783287,0.0005285181,0.00023131252,0.0011688842,0.0012995848,0.22636063,0.00482005,0.69237566,0.0056618447,0.04524719],"study_design_scores_gemma":[0.000036030895,0.000051046492,0.0039297272,0.00005726529,0.000053192707,0.0005164538,0.0002503807,0.3889419,0.001666775,0.6011342,0.0033296463,0.000033405668],"about_ca_topic_score_codex":0.0018258892,"about_ca_topic_score_gemma":0.0021461055,"teacher_disagreement_score":0.004515626,"about_ca_system_score_codex":0.0015668664,"about_ca_system_score_gemma":0.0005941831,"threshold_uncertainty_score":0.023881257},"labels":[],"label_agreement":null},{"id":"W2615376099","doi":"","title":"Contribution du raisonnement logique à la cartographie des flux","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Ministère des Transports","funders":"","keywords":"Humanities; Philosophy","score_opus":0.014074099325332438,"score_gpt":0.22140698819337684,"score_spread":0.2073328888680444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615376099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021186922,0.0020795134,0.9652435,0.0009408782,0.00022319723,0.0000626195,0.00023276488,0.0006070631,0.009423504],"genre_scores_gemma":[0.40064543,0.004022442,0.58101237,0.00033297355,0.00081385963,0.00021630625,0.0005841251,0.00079765136,0.01157476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979367,0.0009170091,0.000086164626,0.00037942565,0.00056551327,0.000115153205],"domain_scores_gemma":[0.9969369,0.0018481556,0.00021943373,0.00039339,0.0004996234,0.00010248816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025895957,0.00097310374,0.0010038294,0.004440373,0.0015819863,0.0063276235,0.0013688678,0.0008995964,0.0061703078],"category_scores_gemma":[0.008933094,0.00058864354,0.0018398532,0.0040205396,0.0041204975,0.006291932,0.0016231989,0.0021953168,0.0013581191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080836966,0.000035127792,0.003912224,0.0003492578,0.000053512274,0.00028281388,0.0010125245,0.0402799,0.0027029444,0.7839847,0.0032786322,0.16402753],"study_design_scores_gemma":[0.000021478132,0.000043289605,0.0022137386,0.0001127104,0.000039213734,0.00043483268,0.00039474768,0.23846066,0.002353733,0.69177,0.06407758,0.00007800575],"about_ca_topic_score_codex":0.01004165,"about_ca_topic_score_gemma":0.0042573046,"teacher_disagreement_score":0.01004165,"about_ca_system_score_codex":0.0022450534,"about_ca_system_score_gemma":0.001972591,"threshold_uncertainty_score":0.020641744},"labels":[],"label_agreement":null},{"id":"W2615549044","doi":"","title":"Éléments pour une représentation (carto)graphique des matrices de flux","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Ministère des Transports","funders":"","keywords":"Humanities; Art","score_opus":0.028615254127677858,"score_gpt":0.2719356683738202,"score_spread":0.24332041424614237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615549044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021704737,0.00040814088,0.9567971,0.00082575734,0.00038837886,0.000109296314,0.0014432673,0.0013690375,0.016954191],"genre_scores_gemma":[0.24225856,0.0020499371,0.6948533,0.0007550314,0.0008469574,0.0009101991,0.00390579,0.0018127703,0.05260742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984592,0.00047420966,0.000050305265,0.00029799924,0.0005978001,0.00012051999],"domain_scores_gemma":[0.99802065,0.00106095,0.00017434842,0.00021174185,0.00040363224,0.00012866785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010190833,0.0021686153,0.0011033845,0.00317401,0.0011840876,0.0054048463,0.00093626225,0.0017353665,0.021630822],"category_scores_gemma":[0.0045808083,0.0005924036,0.0017826927,0.0022637006,0.0027918413,0.0024702367,0.0009726217,0.005189245,0.006827352],"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":[0.0001662491,0.0001026345,0.000727604,0.000303578,0.00007193052,0.00053025037,0.0012796962,0.030535318,0.012605829,0.84606206,0.015414598,0.092200235],"study_design_scores_gemma":[0.00015727033,0.00015599557,0.002500036,0.00018278247,0.000043084925,0.0014382653,0.0010433038,0.18608105,0.010062567,0.5932218,0.20494685,0.00016702253],"about_ca_topic_score_codex":0.011939422,"about_ca_topic_score_gemma":0.0081028305,"teacher_disagreement_score":0.021630822,"about_ca_system_score_codex":0.0014040051,"about_ca_system_score_gemma":0.0010731453,"threshold_uncertainty_score":0.072362304},"labels":[],"label_agreement":null},{"id":"W2621190814","doi":"10.1145/3019612.3019665","title":"Discovering spatial contrast and common sets with statistically significant co-location patterns","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Contrast (vision); Computer science; Artificial intelligence; Data mining; Statistics; Mathematics","score_opus":0.015223851464387145,"score_gpt":0.268849345548599,"score_spread":0.25362549408421187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621190814","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50036883,0.00075983466,0.49298784,0.0005632663,0.000055791425,0.00022247582,0.0015196367,0.00076129043,0.002761144],"genre_scores_gemma":[0.79655224,0.00019237728,0.19982927,0.00010000496,0.00003912995,0.00017213075,0.0023401722,0.00005090966,0.00072379893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965927,0.0005024289,0.00038058765,0.0011055514,0.0010957934,0.00032275284],"domain_scores_gemma":[0.98824674,0.0070048077,0.0017206396,0.0014341092,0.0012258972,0.0003677823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001874718,0.00081320497,0.0014153591,0.0052616736,0.0011708941,0.0023010909,0.0019770635,0.0013897874,0.0012188874],"category_scores_gemma":[0.013958821,0.000507319,0.0020890904,0.003095019,0.001395198,0.0025963504,0.002799232,0.0012133608,0.00038304523],"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":[0.0014369448,0.0011539769,0.30448025,0.0011916601,0.001267863,0.004780812,0.0030424765,0.108551055,0.043094344,0.05005333,0.0069152303,0.47403207],"study_design_scores_gemma":[0.000098786775,0.00038792432,0.05958428,0.00015366389,0.00035520617,0.0032952598,0.001969833,0.8326747,0.018594632,0.075625956,0.0071493043,0.00011044124],"about_ca_topic_score_codex":0.003596585,"about_ca_topic_score_gemma":0.0049875323,"teacher_disagreement_score":0.0052616736,"about_ca_system_score_codex":0.0007962756,"about_ca_system_score_gemma":0.0013111857,"threshold_uncertainty_score":0.009914577},"labels":[],"label_agreement":null},{"id":"W2688773974","doi":"","title":"A luhn-inspired vector re-weighting approach for improving personalized web search","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Information retrieval; Set (abstract data type); Personalization; Weighting; Similarity (geometry); Vector space model; Personalized search; Web search query; Search engine; Rank (graph theory); Zipf's law; Data mining; Result set; World Wide Web; Artificial intelligence; Mathematics","score_opus":0.07217503258343165,"score_gpt":0.25702363088739844,"score_spread":0.18484859830396677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2688773974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031806186,0.00058032287,0.9655756,0.00020732227,0.000041844498,0.00006568195,0.000053190914,0.00044006555,0.0012297733],"genre_scores_gemma":[0.59076965,0.00050383084,0.40365288,0.00020925673,0.0001303691,0.000209431,0.00027288744,0.00011924953,0.0041323937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986993,0.00058766035,0.000078862235,0.00020301118,0.00033140712,0.0000997125],"domain_scores_gemma":[0.9979411,0.00092730817,0.0001882214,0.00039823752,0.0004849322,0.000060262362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025020812,0.0005225046,0.0013064805,0.0016862842,0.0005014192,0.00083322916,0.0014433613,0.000881733,0.001386759],"category_scores_gemma":[0.0065738806,0.00034981407,0.0006716951,0.002129754,0.0006495948,0.0022788004,0.0009557187,0.0009932938,0.00043470485],"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":[0.00022813244,0.00037156473,0.0030236563,0.00016673184,0.00017639488,0.000076882876,0.0003178122,0.47909892,0.009114535,0.041483544,0.0052475343,0.46069437],"study_design_scores_gemma":[0.000010640955,0.00003774965,0.0001754612,0.000004438252,0.000013682204,0.00001903937,0.000013197584,0.9912445,0.00081258616,0.0069211293,0.0007399387,0.000007628777],"about_ca_topic_score_codex":0.005637122,"about_ca_topic_score_gemma":0.0064826864,"teacher_disagreement_score":0.005637122,"about_ca_system_score_codex":0.0013052511,"about_ca_system_score_gemma":0.0009176308,"threshold_uncertainty_score":0.01323247},"labels":[],"label_agreement":null},{"id":"W2714643361","doi":"10.1016/s0022-0000(02)00034-x","title":"Reachability and connectivity queries in constraint databases","year":2003,"lang":"en","type":"article","venue":"Journal of Computer and System Sciences","topic":"Data Management and Algorithms","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":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Reachability; Computer science; Theoretical computer science; Path (computing); Context (archaeology); Set (abstract data type); Time complexity; Constraint (computer-aided design); Query language; Reachability problem; Datalog; Mathematics; Database; Algorithm; Programming language","score_opus":0.031986980961840494,"score_gpt":0.2640258118051083,"score_spread":0.23203883084326782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2714643361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2514659,0.002737552,0.71330726,0.005921204,0.00017748396,0.00050267397,0.006134624,0.0040303487,0.015723],"genre_scores_gemma":[0.7947304,0.00116555,0.19260852,0.00062994077,0.00021433544,0.00026963823,0.0060636527,0.00043866623,0.0038792887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9920557,0.0022643146,0.0011468751,0.0013496401,0.002522821,0.00066059345],"domain_scores_gemma":[0.94902754,0.042917624,0.0019047331,0.003558184,0.0016732145,0.0009187422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057930024,0.00085114985,0.0021813833,0.0032916074,0.002400078,0.0068544946,0.0036412887,0.0034284913,0.006403293],"category_scores_gemma":[0.04425651,0.0011053655,0.0018647469,0.0075659472,0.0032396952,0.023166163,0.0046465322,0.003246235,0.0006093628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026685938,0.00055185606,0.009666749,0.0010888745,0.00031963643,0.0012697362,0.0022680603,0.18903288,0.004495222,0.63108695,0.017904418,0.13964699],"study_design_scores_gemma":[0.00026874567,0.00012423813,0.00083846063,0.000085808504,0.0001605014,0.000589333,0.0008253187,0.45957857,0.0038226454,0.5270758,0.006557343,0.00007326702],"about_ca_topic_score_codex":0.013197809,"about_ca_topic_score_gemma":0.011574166,"teacher_disagreement_score":0.013197809,"about_ca_system_score_codex":0.0026652275,"about_ca_system_score_gemma":0.0026841024,"threshold_uncertainty_score":0.030636728},"labels":[],"label_agreement":null},{"id":"W2725407719","doi":"","title":"Demo Abstract: A Semantic Notication Approach for IoT-Based Sensory Data","year":2017,"lang":"en","type":"article","venue":"The Internet of Things","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Internet of Things; Semantics (computer science); Presentation (obstetrics); Perception; Sensory system; Analytics; Human–computer interaction; Data science; World Wide Web","score_opus":0.08394379158909927,"score_gpt":0.3056366726760442,"score_spread":0.22169288108694496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2725407719","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053658895,0.0002762094,0.9599295,0.0020314506,0.0003646194,0.0003453039,0.0013784963,0.00475725,0.025551328],"genre_scores_gemma":[0.19553676,0.0009089845,0.7690774,0.0012708167,0.00020040368,0.000619475,0.004542743,0.00093548506,0.026908018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988355,0.0002838939,0.0001326179,0.00020309752,0.00046014477,0.000084798994],"domain_scores_gemma":[0.9986118,0.00032856886,0.00006865858,0.00051545305,0.00037758457,0.0000978968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019674872,0.0007219356,0.00037009738,0.001634338,0.0012457733,0.0036561224,0.0017943257,0.0011277212,0.011787198],"category_scores_gemma":[0.0043149623,0.0003572888,0.0011037545,0.0017462632,0.0014306464,0.0051103313,0.0041941144,0.0017222073,0.002582197],"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":[0.00042408358,0.00023597144,0.0030547385,0.00088904833,0.000117739946,0.002170315,0.0028015207,0.017144285,0.013459353,0.6063758,0.06631538,0.2870118],"study_design_scores_gemma":[0.00006674385,0.00009604491,0.0012295201,0.00035081644,0.00010492484,0.0013369527,0.0016865161,0.19570507,0.023326764,0.2769086,0.4990987,0.000089447225],"about_ca_topic_score_codex":0.0041834614,"about_ca_topic_score_gemma":0.0059032594,"teacher_disagreement_score":0.011787198,"about_ca_system_score_codex":0.0009609959,"about_ca_system_score_gemma":0.0010255099,"threshold_uncertainty_score":0.03943211},"labels":[],"label_agreement":null},{"id":"W2725860187","doi":"","title":"Beyond affinity propagation: message passing algorithms for clustering","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Affinity propagation; Cluster analysis; Inference; Computer science; Propagation of uncertainty; Belief propagation; Set (abstract data type); Benchmark (surveying); Algorithm; Artificial intelligence; Correlation clustering; CURE data clustering algorithm","score_opus":0.03221431082130276,"score_gpt":0.28940464477048916,"score_spread":0.2571903339491864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2725860187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00084042666,0.00035537826,0.99698645,0.0002663025,0.000055293443,0.000037826678,0.00003885615,0.00036505525,0.0010543261],"genre_scores_gemma":[0.094931155,0.0024335522,0.88925457,0.0007458869,0.000628542,0.00052147877,0.0006046702,0.00057062984,0.010309529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582386,0.0016497753,0.00021422765,0.0008970118,0.0011699517,0.00024518915],"domain_scores_gemma":[0.9903105,0.0056751766,0.0006962154,0.0015643662,0.0015436229,0.00021019863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004814279,0.0024408973,0.0025478012,0.0027750758,0.0017460912,0.0050266394,0.0043253307,0.0039952826,0.004907005],"category_scores_gemma":[0.016909886,0.0013255671,0.002104992,0.0055479677,0.0032089255,0.008608016,0.0034883686,0.006051252,0.0031844175],"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":[0.0001211369,0.00013692223,0.0008035427,0.0005168451,0.00027173778,0.000097024276,0.00041688912,0.47727233,0.0020143115,0.31898287,0.009699269,0.18966705],"study_design_scores_gemma":[0.00002162828,0.000032860517,0.000086998116,0.000035596182,0.000023593338,0.0000407415,0.000031522246,0.80524224,0.0013540636,0.18459786,0.00850871,0.000024208115],"about_ca_topic_score_codex":0.0066602724,"about_ca_topic_score_gemma":0.004572744,"teacher_disagreement_score":0.0066602724,"about_ca_system_score_codex":0.0029464387,"about_ca_system_score_gemma":0.0026782013,"threshold_uncertainty_score":0.02546066},"labels":[],"label_agreement":null},{"id":"W2732060891","doi":"10.1109/mdm.2017.36","title":"IBS: An Efficient Stateful Algorithm for Optimal Sequenced Group Trip Planning Queries","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stateful firewall; Stateless protocol; Computer science; Context (archaeology); Theoretical computer science; Group (periodic table); Sequence (biology); State (computer science); Algorithm; Geography; Computer network; Traffic engineering","score_opus":0.044302268834994066,"score_gpt":0.3193233892135157,"score_spread":0.27502112037852167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732060891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020731837,0.00030233047,0.9666226,0.00033133078,0.00006117972,0.00026468572,0.0006518448,0.0075710462,0.003463145],"genre_scores_gemma":[0.26152006,0.00016827101,0.73299646,0.00022016255,0.00004210673,0.0004381438,0.0020950227,0.00040566915,0.002114117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992009,0.00020736227,0.000061819825,0.0001757573,0.00020965756,0.00014451529],"domain_scores_gemma":[0.99830085,0.00096228794,0.000114359274,0.0003416095,0.00021278403,0.00006815445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014806272,0.0011769263,0.001104292,0.00094845047,0.0007784924,0.0015491865,0.0027081307,0.0014420322,0.005309782],"category_scores_gemma":[0.0051202322,0.000694333,0.00084107206,0.0014411014,0.0008754285,0.002704252,0.0022534335,0.0017690682,0.0016660247],"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":[0.00091046764,0.00039372244,0.0015630103,0.0004798935,0.00007855293,0.00015052281,0.0005137972,0.48356634,0.010473687,0.036140576,0.019789644,0.44593975],"study_design_scores_gemma":[0.00009758935,0.000066899185,0.00009802288,0.000013367184,0.00001193183,0.000032091477,0.00004891299,0.9808126,0.0016244886,0.014999584,0.0021834983,0.000011009029],"about_ca_topic_score_codex":0.00952236,"about_ca_topic_score_gemma":0.010875971,"teacher_disagreement_score":0.00952236,"about_ca_system_score_codex":0.0012144268,"about_ca_system_score_gemma":0.0037626135,"threshold_uncertainty_score":0.018933833},"labels":[],"label_agreement":null},{"id":"W2738927942","doi":"10.1007/978-3-319-64367-0_9","title":"Towards Spatially- and Category-Wise k-Diverse Nearest Neighbors Queries","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Skyline; Leverage (statistics); k-nearest neighbors algorithm; Set (abstract data type); Point (geometry); Information retrieval; Variation (astronomy); Data mining; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.02146954906912258,"score_gpt":0.24705711732411095,"score_spread":0.22558756825498838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738927942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058127802,0.0022586428,0.9276413,0.0010358813,0.00023159059,0.00018534508,0.0017332328,0.0020147525,0.0067713903],"genre_scores_gemma":[0.47198507,0.0010472259,0.50969166,0.0006982791,0.0004011775,0.00020930292,0.007568621,0.00047899908,0.007919715],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99343324,0.0014129054,0.0005054289,0.0013991339,0.002651676,0.000597551],"domain_scores_gemma":[0.98955065,0.0047865706,0.00045601677,0.0023787688,0.0024237966,0.00040415552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030929733,0.0012655696,0.0044313767,0.0026688576,0.0021133972,0.004510302,0.005879067,0.0038710416,0.0038685382],"category_scores_gemma":[0.022052577,0.00092412706,0.0015942962,0.0064769033,0.0015043726,0.008075253,0.009826918,0.0022401144,0.0025359953],"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":[0.0019472222,0.0007741681,0.008586309,0.0009921829,0.00033716141,0.00061682996,0.0015793031,0.24346246,0.02154991,0.13926576,0.043756906,0.53713185],"study_design_scores_gemma":[0.00006019176,0.00010800278,0.00056635303,0.00004421225,0.00003875144,0.0004119473,0.0006316465,0.84753376,0.003218239,0.14242537,0.0049194414,0.00004205045],"about_ca_topic_score_codex":0.0057641747,"about_ca_topic_score_gemma":0.009119578,"teacher_disagreement_score":0.005879067,"about_ca_system_score_codex":0.0017135417,"about_ca_system_score_gemma":0.0017734735,"threshold_uncertainty_score":0.016357422},"labels":[],"label_agreement":null},{"id":"W2740924709","doi":"10.1145/3068335","title":"DBSCAN Revisited, Revisited","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2625,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Alberta","funders":"","keywords":"Computer science; DBSCAN; Data mining; Heuristics; Information retrieval; Algorithm; Artificial intelligence; Cluster analysis","score_opus":0.03953632886572851,"score_gpt":0.28752780225235086,"score_spread":0.24799147338662236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740924709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024258574,0.06059712,0.72930074,0.12361847,0.00963214,0.00017070812,0.0011939433,0.004991226,0.04623705],"genre_scores_gemma":[0.31812415,0.027146984,0.6015165,0.022838196,0.005715937,0.00021357174,0.0014516978,0.0016846528,0.021308294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9756469,0.008321157,0.0012472882,0.0027973896,0.011138028,0.0008492405],"domain_scores_gemma":[0.96483886,0.01724369,0.0009839458,0.0059562232,0.009832587,0.0011446313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02027269,0.0013793627,0.0027660355,0.0072846245,0.0027350034,0.014289488,0.0060949414,0.005047181,0.0055761165],"category_scores_gemma":[0.079933025,0.0013494715,0.0013766525,0.021336533,0.0072264904,0.0142529365,0.004312376,0.012469385,0.002477925],"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":[0.00058752944,0.0000773709,0.0018606429,0.0003581035,0.00019219858,0.0001801117,0.00046590125,0.032155957,0.00050224137,0.47007117,0.11061603,0.38293275],"study_design_scores_gemma":[0.00011497132,0.000120984485,0.00071800017,0.00032559194,0.00007723065,0.0009507195,0.0008897463,0.26025936,0.004560767,0.55482525,0.17701255,0.00014494923],"about_ca_topic_score_codex":0.023808187,"about_ca_topic_score_gemma":0.023367932,"teacher_disagreement_score":0.023808187,"about_ca_system_score_codex":0.0075089037,"about_ca_system_score_gemma":0.0070958,"threshold_uncertainty_score":0.1072135},"labels":[],"label_agreement":null},{"id":"W2741563444","doi":"10.1145/3105831.3105836","title":"Quantifying Eventual Consistency For Aggregate Queries","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University; Dalhousie University","funders":"","keywords":"Citation; Consistency (knowledge bases); Computer science; Aggregate (composite); Library science; Information retrieval; World Wide Web; Data science; Artificial intelligence","score_opus":0.09404760168267429,"score_gpt":0.3287245843772686,"score_spread":0.2346769826945943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741563444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08513768,0.003239662,0.8855186,0.0050180117,0.00046096393,0.00054308865,0.0034163618,0.0049607335,0.011704891],"genre_scores_gemma":[0.74732393,0.0016462102,0.23119493,0.0018488558,0.0019554778,0.00063999614,0.0076048006,0.0023840286,0.0054017575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.90333354,0.030837322,0.010758363,0.01248699,0.036934633,0.005649184],"domain_scores_gemma":[0.6309717,0.23798372,0.017024387,0.06762927,0.041165505,0.005225429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04470443,0.0016859913,0.0039000395,0.0066668815,0.004042859,0.0144267185,0.0051360093,0.003209029,0.0052118176],"category_scores_gemma":[0.24248973,0.0021534387,0.0039999913,0.009531723,0.005522287,0.041929327,0.013110193,0.0070591783,0.0015641736],"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":[0.0025981339,0.00049023464,0.03578567,0.0016519817,0.0009263534,0.0007933902,0.002672116,0.14535016,0.006509433,0.6061121,0.02411549,0.17299487],"study_design_scores_gemma":[0.00009380176,0.00021447474,0.0024360176,0.00021401707,0.0002852025,0.00043483835,0.00049606373,0.29198393,0.005192584,0.6900099,0.008526942,0.00011228454],"about_ca_topic_score_codex":0.0065986183,"about_ca_topic_score_gemma":0.0036704894,"teacher_disagreement_score":0.04470443,"about_ca_system_score_codex":0.006672113,"about_ca_system_score_gemma":0.008347541,"threshold_uncertainty_score":0.23642242},"labels":[],"label_agreement":null},{"id":"W2741824765","doi":"10.1007/978-3-319-59511-5_1","title":"GeoComputational Research on Regional Systems","year":2017,"lang":"en","type":"book-chapter","venue":"Advances in geographic information science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Data science; Geographic information system; Heuristics; Computer science; Field (mathematics); Spatial analysis; Context (archaeology); Relation (database); Thematic map; Focus (optics); Geography; Data mining; Cartography","score_opus":0.05994673655828778,"score_gpt":0.35654030714081153,"score_spread":0.2965935705825238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741824765","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005308281,0.097627625,0.13846858,0.022098506,0.005776285,0.000040928993,0.0010502662,0.00067273434,0.7289567],"genre_scores_gemma":[0.17858459,0.23533012,0.13731922,0.0037627786,0.008988039,0.00020785129,0.0025213263,0.0014676146,0.4318184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970216,0.00009130898,0.000015019524,0.000056125813,0.00011436963,0.0000209087],"domain_scores_gemma":[0.99933124,0.00035037607,0.000026254333,0.00013166612,0.00012655712,0.00003397537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047674007,0.0007430253,0.0006209908,0.0019254715,0.00062244525,0.0031353158,0.0009040836,0.00066626305,0.025304079],"category_scores_gemma":[0.0023354648,0.00032783017,0.0004567539,0.006787977,0.0023785129,0.0039433534,0.0011896918,0.001795515,0.005538252],"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":[0.0000017936001,0.0000035154012,0.00008062247,0.000117488366,0.00000580143,0.000009661662,0.00008429371,0.0024544992,0.0000786788,0.8973996,0.056553915,0.04321018],"study_design_scores_gemma":[0.0000018786626,0.0000025746076,0.00019919583,0.0000998969,0.0000061128358,0.00003208427,0.00007767924,0.0026270389,0.00008598209,0.5913686,0.4054947,0.0000042342253],"about_ca_topic_score_codex":0.0064397193,"about_ca_topic_score_gemma":0.00811391,"teacher_disagreement_score":0.025304079,"about_ca_system_score_codex":0.0026012298,"about_ca_system_score_gemma":0.0017263376,"threshold_uncertainty_score":0.084650576},"labels":[],"label_agreement":null},{"id":"W2742419825","doi":"","title":"Multiple criteria decision analysis in autonomous computing: a study on independent and coordinated self-management","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Impromptu; Distributed computing; Cloud computing; Multi-agent system; Multiple-criteria decision analysis; Novelty; Mobile ad hoc network; Autonomous agent; Artificial intelligence; Operations research; Engineering; Computer network","score_opus":0.016505393537613846,"score_gpt":0.28572719870640173,"score_spread":0.2692218051687879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742419825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027947132,0.012852505,0.92417747,0.0035047384,0.00026934326,0.0002789403,0.000045150384,0.000040066156,0.030884665],"genre_scores_gemma":[0.7341512,0.0089913495,0.25133407,0.00061032316,0.0005870562,0.00046929845,0.00006537987,0.00006203569,0.0037293704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98578185,0.009355776,0.0005853212,0.0013114037,0.0023486693,0.0006169749],"domain_scores_gemma":[0.9685546,0.026552962,0.0015748298,0.0007598132,0.001956448,0.00060129166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013914556,0.0011926078,0.0017608067,0.0026981297,0.0014446939,0.006234381,0.0018815384,0.0023047908,0.0016528425],"category_scores_gemma":[0.020076375,0.0008064651,0.0023868096,0.0043172925,0.004569366,0.0035837472,0.0021771528,0.0030167573,0.0001989468],"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":[0.000038436774,0.00022397544,0.0018135195,0.00074189604,0.00027921656,0.00026044453,0.0012552955,0.23775329,0.00064470206,0.7050511,0.0012539608,0.05068405],"study_design_scores_gemma":[0.00003951042,0.00020259696,0.00086401764,0.0004892268,0.000081508435,0.000114983486,0.0007982303,0.49137962,0.00048331797,0.490771,0.014712597,0.00006344506],"about_ca_topic_score_codex":0.003195138,"about_ca_topic_score_gemma":0.0015782859,"teacher_disagreement_score":0.013914556,"about_ca_system_score_codex":0.0036030347,"about_ca_system_score_gemma":0.0034495692,"threshold_uncertainty_score":0.07358807},"labels":[],"label_agreement":null},{"id":"W2748952100","doi":"10.3166/isi.14.5.85-106","title":"Analyse de similarité de trajectoires d'objets mobiles suivant le même itinéraire","year":2009,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Art","score_opus":0.018219911822722576,"score_gpt":0.2434343150336179,"score_spread":0.2252144032108953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748952100","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9316927,0.00083301094,0.06400735,0.000107575885,0.000044538414,0.000075019474,0.0010923783,0.0004342272,0.0017132093],"genre_scores_gemma":[0.9605172,0.0002936159,0.034969203,0.000013248999,0.000022323373,0.00008265626,0.002434469,0.00007997975,0.0015873648],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998705,0.00021702374,0.00009230029,0.00045344833,0.00039288873,0.00013930509],"domain_scores_gemma":[0.9937925,0.0034750695,0.00059691345,0.0005555211,0.0012897837,0.00029025905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017838845,0.00055444107,0.00075198506,0.006613902,0.000765738,0.0016493457,0.00085079344,0.0008690133,0.0027780624],"category_scores_gemma":[0.010968274,0.0002941304,0.001266792,0.0032644984,0.00065336365,0.0017235883,0.0006622331,0.00042679088,0.0004898219],"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":[0.003466858,0.0005804311,0.46117738,0.0011465868,0.0015427313,0.0026820542,0.0035083315,0.13441138,0.04669682,0.012044885,0.0031829625,0.32955953],"study_design_scores_gemma":[0.00010677649,0.0010296003,0.38865575,0.00010065489,0.00035144712,0.0036052503,0.0022732706,0.57398057,0.01471194,0.0079621915,0.0070463284,0.00017627029],"about_ca_topic_score_codex":0.015061456,"about_ca_topic_score_gemma":0.0139741935,"teacher_disagreement_score":0.015061456,"about_ca_system_score_codex":0.0010118783,"about_ca_system_score_gemma":0.00045891464,"threshold_uncertainty_score":0.029947579},"labels":[],"label_agreement":null},{"id":"W2750991217","doi":"10.14778/3137765.3137788","title":"Interactive navigation of open data linkages","year":2017,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scalability; Cloud computing; The Internet; Big data; Interface (matter); Data mining; Linkage (software); Millisecond; Information retrieval; World Wide Web; Database; Operating system","score_opus":0.07352448097533695,"score_gpt":0.3467404140371007,"score_spread":0.2732159330617637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750991217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061721157,0.0009932445,0.75103176,0.00097416947,0.00038835636,0.0005627416,0.009796159,0.157947,0.016585356],"genre_scores_gemma":[0.26139084,0.000746286,0.6988667,0.0005555399,0.00017721459,0.000543736,0.017022729,0.007960881,0.012736018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980883,0.0003549771,0.00014291306,0.0005529305,0.00069847534,0.00016248984],"domain_scores_gemma":[0.99438995,0.0023996697,0.00026548235,0.0017467095,0.0005379831,0.00066025194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024335068,0.0010579554,0.001168158,0.0030892908,0.0017221548,0.004094742,0.0023002196,0.0012486338,0.0120203905],"category_scores_gemma":[0.009570195,0.00085357705,0.0010120663,0.0035983026,0.00079459633,0.0062334323,0.011356927,0.0015967414,0.0033324873],"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":[0.0024568187,0.0008278706,0.019556068,0.0012333754,0.0005359304,0.002573105,0.006143983,0.022959944,0.046810348,0.059502948,0.2085518,0.6288479],"study_design_scores_gemma":[0.0004604929,0.00036307116,0.0060185767,0.00027456955,0.00015028135,0.0011536957,0.0017711533,0.4267363,0.05318553,0.096822426,0.41269377,0.0003701915],"about_ca_topic_score_codex":0.005471734,"about_ca_topic_score_gemma":0.010074406,"teacher_disagreement_score":0.0120203905,"about_ca_system_score_codex":0.0007192351,"about_ca_system_score_gemma":0.0016997003,"threshold_uncertainty_score":0.040212154},"labels":[],"label_agreement":null},{"id":"W2751393422","doi":"10.5623/cig2017-102","title":"An Effective Approach to Estimating Computing Time of Vector Data Spatial Computational Domains in WebGIS","year":2017,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Santa Barbara","keywords":"Computer science; Decision tree; Visualization; Node (physics); Tree (set theory); Data mining; Estimation; Support vector machine; Supercomputer; Machine learning; Engineering; Operating system","score_opus":0.023262413385873055,"score_gpt":0.2976871799196479,"score_spread":0.27442476653377484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751393422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009704768,0.00009605126,0.989239,0.000042058527,0.000017656586,0.00002484965,0.000048224156,0.00041478145,0.00041270637],"genre_scores_gemma":[0.13307141,0.00012636311,0.86555654,0.000034883233,0.000030062625,0.00010472444,0.00014538951,0.0001363705,0.00079428946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983882,0.00058070273,0.0001146565,0.00022709373,0.00058387464,0.000105531166],"domain_scores_gemma":[0.99555355,0.0024364966,0.00024298271,0.0005972306,0.0010403369,0.00012945665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015809918,0.00064731314,0.00072556286,0.0023777436,0.0007101629,0.0014979815,0.0012403269,0.0006320167,0.002510071],"category_scores_gemma":[0.010740075,0.00046479286,0.00055983284,0.0023238135,0.00062408205,0.0021227126,0.001132542,0.0010144148,0.00053237774],"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":[0.00029221043,0.00017148907,0.005831454,0.0002079838,0.0001050225,0.00013154467,0.00035199255,0.4430402,0.012187168,0.05020878,0.0036301902,0.48384196],"study_design_scores_gemma":[0.0000102328795,0.000021318407,0.0006926918,0.000008756253,0.000015873391,0.000048544327,0.00004858138,0.98499596,0.0038796226,0.008638395,0.0016277677,0.000012288065],"about_ca_topic_score_codex":0.0123104295,"about_ca_topic_score_gemma":0.015373251,"teacher_disagreement_score":0.0123104295,"about_ca_system_score_codex":0.0010356683,"about_ca_system_score_gemma":0.0017107527,"threshold_uncertainty_score":0.024477541},"labels":[],"label_agreement":null},{"id":"W2751679866","doi":"10.1007/s10707-017-0306-1","title":"Knowledge extraction from crowdsourced data for the enrichment of road networks","year":2017,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"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":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data mining; Proxy (statistics); Path (computing); Data science; Attractiveness; Information retrieval; Machine learning","score_opus":0.05533295200460093,"score_gpt":0.31959532437666266,"score_spread":0.26426237237206174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751679866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06907076,0.0027490675,0.86632925,0.0014146373,0.00031034352,0.0009473436,0.043151934,0.0054055923,0.010621119],"genre_scores_gemma":[0.43255335,0.0018853892,0.487545,0.00027196444,0.0002023335,0.00087152864,0.071949884,0.0003408151,0.0043797106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685174,0.0006432963,0.00025444842,0.0010099289,0.0009834182,0.0002571153],"domain_scores_gemma":[0.9941262,0.003093305,0.00042929515,0.0013141643,0.00085947925,0.00017759806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018462376,0.0014783167,0.0011712288,0.014561551,0.0014498071,0.0028785015,0.0025554614,0.0020346679,0.0024673198],"category_scores_gemma":[0.013337373,0.00058904797,0.0018502973,0.011527351,0.0008238539,0.00319358,0.0042400486,0.0013444044,0.0028527796],"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":[0.00041018962,0.00076082046,0.027029203,0.0026220023,0.00075573387,0.0015802995,0.0017070727,0.09333446,0.012546938,0.020403024,0.06330973,0.77554053],"study_design_scores_gemma":[0.000100071455,0.00012920368,0.018171595,0.00082816114,0.0005622978,0.00089668704,0.0030881395,0.7088264,0.02631691,0.1289089,0.11198522,0.00018630186],"about_ca_topic_score_codex":0.015783494,"about_ca_topic_score_gemma":0.029221702,"teacher_disagreement_score":0.015783494,"about_ca_system_score_codex":0.001292937,"about_ca_system_score_gemma":0.003301059,"threshold_uncertainty_score":0.031383216},"labels":[],"label_agreement":null},{"id":"W2751694342","doi":"10.14778/3137628.3137630","title":"Trajectory similarity join in spatial networks","year":2017,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":179,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Beijing Nova Program; King Abdullah University of Science and Technology; National Natural Science Foundation of China; Innovationsfonden","keywords":"Join (topology); Computer science; Pruning; Similarity (geometry); Trajectory; Nearest neighbor search; Heuristic; Matching (statistics); Data mining; Scheduling (production processes); Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Mathematical optimization","score_opus":0.017957525548891625,"score_gpt":0.2326463657509419,"score_spread":0.21468884020205029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751694342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06919332,0.0013410475,0.922861,0.00048739646,0.00007873572,0.0002716003,0.001151843,0.001394408,0.003220666],"genre_scores_gemma":[0.50178534,0.00090675673,0.488082,0.0001706108,0.00014885096,0.0003003645,0.00370589,0.00019928208,0.0047009387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99659014,0.00069061894,0.00026821945,0.0009711265,0.0012244239,0.00025546955],"domain_scores_gemma":[0.99618584,0.001619811,0.00052164146,0.000822759,0.0006227158,0.00022717286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023288047,0.0008499649,0.001586289,0.003559474,0.00172811,0.002500695,0.0022682035,0.0013841247,0.0028848972],"category_scores_gemma":[0.010027416,0.00052203273,0.0010924768,0.0058677355,0.0011286174,0.0050805095,0.0036601839,0.0010855193,0.00090601266],"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":[0.00076342176,0.00030915398,0.009325427,0.00040371553,0.00024609655,0.00047993887,0.0007790136,0.5982834,0.0075385193,0.09688129,0.0077548646,0.27723506],"study_design_scores_gemma":[0.00003932925,0.000098985336,0.0011507544,0.00002310965,0.000037664657,0.00025152828,0.00025792126,0.9164852,0.004638363,0.07045581,0.0065377415,0.000023610257],"about_ca_topic_score_codex":0.008056411,"about_ca_topic_score_gemma":0.006782762,"teacher_disagreement_score":0.008056411,"about_ca_system_score_codex":0.0018412189,"about_ca_system_score_gemma":0.0019557108,"threshold_uncertainty_score":0.016019046},"labels":[],"label_agreement":null},{"id":"W2753982414","doi":"10.1145/3126973.3126979","title":"Compressing Trajectory for Trajectory Indexing","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"BC Research (Canada)","funders":"National Research Foundation","keywords":"Trajectory; Computer science; Global Positioning System; Raw data; Volume (thermodynamics); Search engine indexing; Data compression; Big data; Data mining; Artificial intelligence; Telecommunications","score_opus":0.053816577242600604,"score_gpt":0.30010830622253926,"score_spread":0.24629172897993865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753982414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020633804,0.0016133334,0.96088856,0.00041297983,0.00046943288,0.00025285996,0.007562908,0.0047471104,0.0034189445],"genre_scores_gemma":[0.22112727,0.0045518493,0.71953964,0.00020558068,0.00055679225,0.0005162842,0.04496957,0.0007586495,0.0077743735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906987,0.00007329732,0.000106174484,0.00023307395,0.00040239515,0.00011522494],"domain_scores_gemma":[0.9985586,0.00027381108,0.00011587965,0.0005729086,0.00043201845,0.000046922018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042199888,0.0011407982,0.0011346304,0.0038986579,0.0010260463,0.0014012734,0.0012062144,0.00077031925,0.0072622416],"category_scores_gemma":[0.0044015106,0.00033816168,0.00090430677,0.008568937,0.0005610628,0.0030459592,0.0020482205,0.0012588558,0.004879764],"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":[0.00046989438,0.000119556695,0.0026236475,0.00056580384,0.00007146086,0.00043367458,0.000369384,0.054558165,0.018986903,0.02776908,0.030324172,0.8637083],"study_design_scores_gemma":[0.000071391456,0.000283741,0.0038115408,0.00018006717,0.00009578465,0.0018189731,0.0008421979,0.7841866,0.04322471,0.070449725,0.09493188,0.00010334059],"about_ca_topic_score_codex":0.007113375,"about_ca_topic_score_gemma":0.005942694,"teacher_disagreement_score":0.0072622416,"about_ca_system_score_codex":0.0006858076,"about_ca_system_score_gemma":0.0016004315,"threshold_uncertainty_score":0.024294555},"labels":[],"label_agreement":null},{"id":"W2758103571","doi":"","title":"Mining thick skylines over large databases","year":2004,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Pruning; Computer science; Search engine indexing; Computation; Data mining; Representation (politics); Database; Operator (biology); Information retrieval; Algorithm","score_opus":0.03296461356241734,"score_gpt":0.28417163515554805,"score_spread":0.2512070215931307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758103571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36992568,0.0033359108,0.6134121,0.0009391273,0.00010251196,0.00025461832,0.0058055744,0.0041595325,0.0020650628],"genre_scores_gemma":[0.65453655,0.00083183387,0.33270487,0.00014578902,0.00018478403,0.00016739743,0.010193456,0.00018643358,0.0010488895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99661607,0.00061554945,0.00047222915,0.0007592002,0.0012810812,0.00025588417],"domain_scores_gemma":[0.9855321,0.005955862,0.0031166973,0.0019930052,0.0026067195,0.0007954891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024124002,0.0012711412,0.0022375986,0.008439261,0.0016394624,0.0026072022,0.0019864645,0.0012936773,0.000935261],"category_scores_gemma":[0.013723321,0.00091184967,0.0009899357,0.010658825,0.0007013114,0.0055795233,0.001881311,0.0009900748,0.0006288934],"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":[0.0026290736,0.0005696723,0.13409656,0.0014427955,0.0007812293,0.004311205,0.0016729492,0.2581351,0.032005258,0.023016252,0.033807937,0.50753194],"study_design_scores_gemma":[0.00006989876,0.00023356717,0.011805877,0.00011344693,0.00009774832,0.0009557139,0.0007637211,0.9354667,0.011701632,0.030823637,0.0079297805,0.00003830085],"about_ca_topic_score_codex":0.0033179908,"about_ca_topic_score_gemma":0.004472609,"teacher_disagreement_score":0.008439261,"about_ca_system_score_codex":0.000726733,"about_ca_system_score_gemma":0.0010808335,"threshold_uncertainty_score":0.012758136},"labels":[],"label_agreement":null},{"id":"W2760295842","doi":"10.1109/mcg.2017.3621221","title":"ANALYTiC: An Active Learning System for Trajectory Classification","year":2017,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Trajectory; Task (project management); Active learning (machine learning); Artificial intelligence; Process (computing); Set (abstract data type); Machine learning; Annotation; Domain (mathematical analysis); Semi-supervised learning; Semantic Web","score_opus":0.046744834369461016,"score_gpt":0.29522404153133525,"score_spread":0.24847920716187422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760295842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021811642,0.00018041635,0.9384826,0.0002040079,0.0001606497,0.00016202398,0.0025548104,0.054380283,0.0016939798],"genre_scores_gemma":[0.11811618,0.0004866823,0.8552509,0.00045239113,0.0002373982,0.0014498541,0.011260569,0.0029564316,0.009789535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988336,0.00025744963,0.000108689856,0.0003245591,0.00041033834,0.00006533427],"domain_scores_gemma":[0.99833196,0.00074056356,0.00014334072,0.00026067876,0.0004250465,0.000098439625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001974194,0.0018904444,0.0012274969,0.0026671535,0.00086495717,0.0020425245,0.0037165335,0.0017835906,0.0130344145],"category_scores_gemma":[0.0066956477,0.00084426696,0.0009437662,0.002152403,0.00066987507,0.0026571255,0.0026672042,0.0021079187,0.008313612],"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":[0.00096198986,0.00040755517,0.0021051245,0.0004884291,0.00022417406,0.00024327553,0.00035191886,0.0852502,0.009381774,0.015466028,0.090946145,0.79417336],"study_design_scores_gemma":[0.000056957488,0.00006658808,0.00028243143,0.00003164652,0.000028150434,0.00008679664,0.000040766132,0.94797856,0.00721065,0.015398072,0.0287755,0.000043952557],"about_ca_topic_score_codex":0.005966909,"about_ca_topic_score_gemma":0.0065523353,"teacher_disagreement_score":0.0130344145,"about_ca_system_score_codex":0.0009100604,"about_ca_system_score_gemma":0.0017113407,"threshold_uncertainty_score":0.043604434},"labels":[],"label_agreement":null},{"id":"W2763207634","doi":"","title":"Succinct representation of labeled graphs","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Combinatorics; Vertex (graph theory); Adjacency list; Representation (politics); Planar graph; Graph; Theoretical computer science; Mathematics; Algorithm","score_opus":0.018967866383620594,"score_gpt":0.27900091329780147,"score_spread":0.2600330469141809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763207634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015200995,0.00017761663,0.9768559,0.00033235256,0.00006503601,0.0001449033,0.0025050887,0.0023076243,0.0024105385],"genre_scores_gemma":[0.25499788,0.0005489473,0.729868,0.00047546724,0.00008152567,0.000617083,0.008738329,0.0005849716,0.004087752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976515,0.00065649423,0.00024444266,0.00042911273,0.0008205928,0.00019791757],"domain_scores_gemma":[0.9928576,0.0018544047,0.0007322062,0.0031898525,0.0011835158,0.0001825352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089031877,0.00095182215,0.0009235805,0.0015657187,0.00061917974,0.002613449,0.0024734195,0.0013004843,0.005623347],"category_scores_gemma":[0.009467987,0.00065748446,0.00092781807,0.0028264094,0.0009922193,0.0074948515,0.0022517496,0.0017223253,0.0016026365],"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":[0.0008678727,0.00026865205,0.0018904215,0.00075104216,0.000078053665,0.00066131464,0.0008697361,0.2679386,0.018837208,0.3434573,0.021517402,0.34286234],"study_design_scores_gemma":[0.000074817064,0.00013702702,0.00026647403,0.00012684694,0.00004885453,0.0003432126,0.0002775851,0.53515273,0.014849468,0.42337877,0.025276646,0.000067620276],"about_ca_topic_score_codex":0.0021875768,"about_ca_topic_score_gemma":0.0038856056,"teacher_disagreement_score":0.005623347,"about_ca_system_score_codex":0.0011997992,"about_ca_system_score_gemma":0.0012931679,"threshold_uncertainty_score":0.018811941},"labels":[],"label_agreement":null},{"id":"W2763926089","doi":"10.48550/arxiv.1710.02058","title":"Skyline Computation with Noisy Comparisons","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Computation; Artificial intelligence; Data mining; Algorithm","score_opus":0.10133170189627634,"score_gpt":0.20654120294150002,"score_spread":0.10520950104522368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763926089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10731532,0.0011488163,0.8679143,0.0013769377,0.00018048064,0.00028600974,0.0036286023,0.011489073,0.006660465],"genre_scores_gemma":[0.53567684,0.00026231047,0.44994804,0.0004708301,0.00020929024,0.0004104437,0.0068525607,0.0009923885,0.0051772944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950641,0.0010645272,0.00038256703,0.0017844385,0.0011747187,0.00052967906],"domain_scores_gemma":[0.9892868,0.0043027825,0.0010548919,0.004052602,0.0009204129,0.00038240064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022159352,0.0015260202,0.0028141232,0.0020239565,0.0016100844,0.0038622057,0.0042067016,0.0029098059,0.008613719],"category_scores_gemma":[0.017807724,0.0009212962,0.0017207593,0.0040708743,0.00173705,0.008646103,0.005111898,0.002049132,0.0032087027],"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":[0.004652,0.00047158063,0.013641072,0.0012293127,0.00056209514,0.00059265445,0.0012498511,0.44367737,0.015034839,0.11773682,0.04696778,0.3541846],"study_design_scores_gemma":[0.00017376144,0.00017737437,0.0010643812,0.00004841798,0.000056088225,0.00025509438,0.00020648834,0.8591134,0.007285465,0.12575953,0.005812588,0.000047392372],"about_ca_topic_score_codex":0.0034464381,"about_ca_topic_score_gemma":0.0047953925,"teacher_disagreement_score":0.008613719,"about_ca_system_score_codex":0.0019185982,"about_ca_system_score_gemma":0.002125392,"threshold_uncertainty_score":0.028815687},"labels":[],"label_agreement":null},{"id":"W2765471337","doi":"10.5555/3107979.3107989","title":"Generating stochastic data to simulate a twitter user","year":2017,"lang":"en","type":"article","venue":"Communications and Networking Symposium","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Recommender system; Collaborative filtering; Similarity (geometry); Weibull distribution; Information retrieval; Cluster analysis; tf–idf; Data mining; Term (time); Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.12875455410999564,"score_gpt":0.34185262015695855,"score_spread":0.2130980660469629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765471337","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90535337,0.00011597591,0.08472747,0.00068940077,0.0001518372,0.00045397747,0.0034226833,0.0004728792,0.004612311],"genre_scores_gemma":[0.9631393,0.00009179388,0.032151517,0.00011925298,0.000025539588,0.00047509628,0.0020949272,0.000035844645,0.001866809],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991259,0.0003810414,0.00006607686,0.0001395713,0.00017657333,0.00011076803],"domain_scores_gemma":[0.9902388,0.007018991,0.00043748374,0.00075874105,0.0013049359,0.00024100198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019102758,0.0006224699,0.0005155464,0.00073230616,0.00055744156,0.000664479,0.0010974737,0.0014401156,0.0027897246],"category_scores_gemma":[0.009983594,0.00031262566,0.00073829415,0.000946759,0.0005715447,0.0007733587,0.0007653685,0.0013893188,0.0004213883],"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":[0.00034159768,0.00033211606,0.020262767,0.000099248035,0.00007665301,0.00023998531,0.00032128315,0.9528858,0.0019533464,0.01457954,0.0019316686,0.006976043],"study_design_scores_gemma":[0.000028351618,0.00006819022,0.0012139846,0.0000063127463,0.000009961508,0.000018477134,0.00006107927,0.99557793,0.00074025325,0.0017123354,0.00055165583,0.0000115332905],"about_ca_topic_score_codex":0.016027704,"about_ca_topic_score_gemma":0.012416491,"teacher_disagreement_score":0.016027704,"about_ca_system_score_codex":0.0010565695,"about_ca_system_score_gemma":0.0007829805,"threshold_uncertainty_score":0.031868815},"labels":[],"label_agreement":null},{"id":"W2765816511","doi":"10.14778/3151106.3151111","title":"Efficient mining of regional movement patterns in semantic trajectories","year":2017,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Trajectory; Computer science; Focus (optics); Data mining; Movement (music); Scheme (mathematics); Semantics (computer science); Space (punctuation); Key (lock); Artificial intelligence; Mathematics","score_opus":0.023246962939578505,"score_gpt":0.242734101600947,"score_spread":0.21948713866136849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765816511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1807454,0.0008627071,0.806033,0.0005907059,0.00005239976,0.00023600197,0.0062940763,0.0024836406,0.0027020331],"genre_scores_gemma":[0.6544463,0.00064424064,0.33016998,0.00008845474,0.00005523491,0.00022406843,0.01222703,0.00018483821,0.0019598834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990471,0.00014099908,0.00009243727,0.0003726448,0.00022851252,0.00011825321],"domain_scores_gemma":[0.99852717,0.0004374752,0.00033619843,0.00025722195,0.00036318455,0.00007879858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006424194,0.00091191666,0.0010680051,0.0041263015,0.0007822433,0.0010274878,0.0015195763,0.00086508825,0.0012108306],"category_scores_gemma":[0.004652071,0.00040846635,0.001328083,0.0051142653,0.0006393169,0.0022576335,0.0016447251,0.00070711516,0.00072916725],"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":[0.00067189446,0.0003399781,0.08300839,0.00096175715,0.00045431862,0.0018024797,0.001255288,0.34275162,0.017411016,0.03148465,0.016060587,0.50379795],"study_design_scores_gemma":[0.000028193912,0.00008646768,0.008645934,0.00005205558,0.0000820879,0.0006027273,0.00076912035,0.9462473,0.005466589,0.029191932,0.008800244,0.000027332288],"about_ca_topic_score_codex":0.009537203,"about_ca_topic_score_gemma":0.01707091,"teacher_disagreement_score":0.009537203,"about_ca_system_score_codex":0.00068665814,"about_ca_system_score_gemma":0.0014911437,"threshold_uncertainty_score":0.018963337},"labels":[],"label_agreement":null},{"id":"W2766521183","doi":"10.1017/9781316105450.011","title":"Spatio-temporal Graphs","year":2017,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science; Geography","score_opus":0.02772968181758098,"score_gpt":0.20580803190245903,"score_spread":0.17807835008487805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766521183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058468934,0.016538497,0.64005584,0.0039024947,0.0018958384,0.00018883283,0.026593952,0.0044732634,0.30050448],"genre_scores_gemma":[0.1511657,0.04780334,0.37778556,0.0016750513,0.0012764392,0.00039488953,0.06637968,0.0032162566,0.35030308],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99975413,0.000042526266,0.000016530184,0.00009388971,0.00007446941,0.000018430837],"domain_scores_gemma":[0.9994443,0.00020958489,0.000033167977,0.00015233601,0.00011505089,0.00004556367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028284758,0.00078651315,0.00041665294,0.0019178404,0.0006309138,0.0021219451,0.0007642691,0.0005879309,0.055298097],"category_scores_gemma":[0.0014275848,0.0003747438,0.0007130179,0.004608391,0.00058447075,0.0034528784,0.00092808285,0.0009162491,0.017058177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044869084,0.000020395883,0.00041384855,0.00064003875,0.000049937928,0.0002521224,0.0002534105,0.010279558,0.0018019236,0.5812209,0.18568681,0.21933618],"study_design_scores_gemma":[0.000006125382,0.00000998599,0.0005519362,0.00012704736,0.000025292435,0.00037213883,0.000102500904,0.00988334,0.00080041616,0.37852696,0.6095771,0.000017140666],"about_ca_topic_score_codex":0.0055224574,"about_ca_topic_score_gemma":0.0069168326,"teacher_disagreement_score":0.055298097,"about_ca_system_score_codex":0.000972315,"about_ca_system_score_gemma":0.000696156,"threshold_uncertainty_score":0.18499053},"labels":[],"label_agreement":null},{"id":"W2766616325","doi":"10.5194/isprs-annals-iv-4-w2-125-2017","title":"A SPARK BASED COMPUTING FRAMEWORK FOR SPATIAL DATA","year":2017,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Administration of Surveying, Mapping and Geoinformation of China; Canadian Institute of Steel Construction","keywords":"Spatial query; SPARK (programming language); Computer science; Spatial analysis; Raster graphics; Spatial database; Grid; Raster data; Data mining; Range query (database); Layer (electronics); Database; Information retrieval; Computer graphics (images); Remote sensing; Web query classification; Geography; Web search query","score_opus":0.13931688226471753,"score_gpt":0.3723766269351537,"score_spread":0.2330597446704362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766616325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023475825,0.00033962037,0.97485954,0.0004294604,0.0001805777,0.00023058937,0.00094585464,0.015967038,0.004699721],"genre_scores_gemma":[0.08815943,0.0006007332,0.89696956,0.00036196937,0.00015881665,0.0005633945,0.0055780737,0.0019106416,0.005697457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977829,0.00035788343,0.00023900707,0.0003696239,0.001040886,0.00020976036],"domain_scores_gemma":[0.99841905,0.0002768951,0.000080238904,0.00048203158,0.00049008825,0.00025173195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002269705,0.0009247586,0.0012038718,0.001179201,0.0017036864,0.0031096586,0.0046778866,0.0008511767,0.0056453366],"category_scores_gemma":[0.003518122,0.00062177615,0.0015645786,0.0025143523,0.0011106329,0.003307323,0.0041552098,0.0017373676,0.002993418],"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":[0.000971572,0.000327301,0.0027072432,0.0011903723,0.0003286273,0.0012763091,0.0010690748,0.1421222,0.019181913,0.35236487,0.20733976,0.27112076],"study_design_scores_gemma":[0.00016815568,0.00010513604,0.00061052415,0.00007605146,0.000055113484,0.00048902683,0.00029829209,0.62633336,0.008676993,0.1525898,0.2104925,0.00010490928],"about_ca_topic_score_codex":0.008446853,"about_ca_topic_score_gemma":0.008786374,"teacher_disagreement_score":0.008446853,"about_ca_system_score_codex":0.0015159083,"about_ca_system_score_gemma":0.0030895143,"threshold_uncertainty_score":0.018885553},"labels":[],"label_agreement":null},{"id":"W2772601103","doi":"10.1109/tits.2017.2774186","title":"Mass Evidence Accumulation and Traveler Risk Scoring Engine in e-Border Infrastructure","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Defence Research and Development Canada","keywords":"Cover (algebra); Inference; Computer science; Demonstrative; Component (thermodynamics); Estimation; Risk analysis (engineering); Causal inference; Risk assessment; Data science; Operations research; Engineering; Computer security; Systems engineering; Artificial intelligence; Econometrics; Business; Economics","score_opus":0.04974927922742074,"score_gpt":0.32164726835225094,"score_spread":0.2718979891248302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772601103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14968494,0.0004905739,0.84426147,0.0008765201,0.000020414976,0.00026291152,0.00015464853,0.000549145,0.0036993888],"genre_scores_gemma":[0.8283634,0.00019294787,0.17014183,0.0000879138,0.00002002014,0.00011813725,0.000118431904,0.000043860822,0.0009132924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912049,0.0050819926,0.00047856735,0.0013745665,0.0014427191,0.00041718347],"domain_scores_gemma":[0.9323197,0.05494758,0.0044830428,0.0037320692,0.0035593524,0.0009582525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01981083,0.00085262716,0.001399479,0.00394465,0.0009886628,0.0043110657,0.0022615853,0.0020732079,0.0034333658],"category_scores_gemma":[0.082785554,0.0007047951,0.0008859615,0.0019885842,0.0026768083,0.009839583,0.0064937025,0.0024356097,0.0002968153],"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":[0.0013806772,0.00045922628,0.039337337,0.000534599,0.00037869933,0.0007209268,0.0025008905,0.34881026,0.0049707694,0.1986549,0.0016234333,0.40062824],"study_design_scores_gemma":[0.0000856453,0.0005096793,0.0058715674,0.00013545324,0.00013575506,0.00028309468,0.00065990683,0.84897083,0.00634974,0.13533759,0.0015882559,0.00007255636],"about_ca_topic_score_codex":0.0035936043,"about_ca_topic_score_gemma":0.0028703245,"teacher_disagreement_score":0.01981083,"about_ca_system_score_codex":0.0017151339,"about_ca_system_score_gemma":0.002371587,"threshold_uncertainty_score":0.1047709},"labels":[],"label_agreement":null},{"id":"W2772756146","doi":"10.1155/2017/5656323","title":"A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Dijkstra's algorithm; Public transport; Computer science; Router; Graph; Plan (archaeology); Transportation planning; Route planning; Operations research; Motion planning; Routing (electronic design automation); Node (physics); Transport engineering; Routing algorithm; Shortest path problem; Engineering; Robot; Artificial intelligence; Computer network; Routing protocol","score_opus":0.06903255165066252,"score_gpt":0.3468965048434949,"score_spread":0.27786395319283236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772756146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96299785,0.00019083782,0.027548017,0.00041156507,0.000028042743,0.00023144952,0.00035659404,0.00026889177,0.007966754],"genre_scores_gemma":[0.95980364,0.00014232524,0.037117213,0.00002526001,0.000003701676,0.000078399054,0.0003249814,0.000038802576,0.0024656786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952054,0.00020542025,0.00002475596,0.00008746502,0.000046864716,0.000115012925],"domain_scores_gemma":[0.9994362,0.00023056217,0.000048411308,0.00007022695,0.000093720046,0.00012088056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007838036,0.0005664571,0.00032261363,0.00069238397,0.0018748955,0.00095048087,0.0013885993,0.0014526616,0.0028826704],"category_scores_gemma":[0.0012682418,0.00023763801,0.0007132772,0.001438864,0.0006539137,0.0012635274,0.00084805896,0.00072616537,0.00031672063],"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":[0.0010796438,0.001766924,0.038495522,0.00081750535,0.00013151468,0.024265843,0.0071195606,0.7463528,0.008206654,0.03280466,0.008075856,0.13088359],"study_design_scores_gemma":[0.00025263487,0.0010521094,0.035316367,0.00012300856,0.00016615068,0.0033154453,0.02969903,0.865514,0.00880492,0.010081153,0.045494787,0.00018044948],"about_ca_topic_score_codex":0.035426922,"about_ca_topic_score_gemma":0.06071165,"teacher_disagreement_score":0.035426922,"about_ca_system_score_codex":0.0026551322,"about_ca_system_score_gemma":0.001838619,"threshold_uncertainty_score":0.070441425},"labels":[],"label_agreement":null},{"id":"W2775284932","doi":"","title":"An ontology-based method for quality assessment of spatial data bases","year":2004,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Centre de Géomatique du Québec","funders":"","keywords":"Ontology; Consistency (knowledge bases); Computer science; Data mining; Relation (database); Quality (philosophy); Data integrity; Process (computing); Knowledge base; Data quality; Set (abstract data type); Information retrieval; Database; Artificial intelligence; Engineering; Programming language","score_opus":0.058245010735814416,"score_gpt":0.3523763359320248,"score_spread":0.2941313251962104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775284932","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033566821,0.00012965039,0.9951827,0.00010975472,0.000018304128,0.00012221963,0.000071234084,0.0002820244,0.0007275542],"genre_scores_gemma":[0.042588774,0.00014427718,0.9563336,0.000024034183,0.000013108973,0.00015352017,0.00018304281,0.000039334624,0.0005202776],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923822,0.001754109,0.0007755067,0.0005389068,0.004358809,0.00019048057],"domain_scores_gemma":[0.99459165,0.0016687054,0.00050369924,0.0008769822,0.0022156883,0.00014324575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053048055,0.00063139596,0.00084824325,0.005606925,0.001145802,0.0037491764,0.0017869737,0.0010268635,0.0012936064],"category_scores_gemma":[0.013061925,0.00052525307,0.0013287688,0.0039861454,0.0010999464,0.00281506,0.0018039295,0.0011887974,0.00037377034],"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":[0.00015872075,0.00023726854,0.006111124,0.00073138135,0.00027790476,0.0005048312,0.0021820161,0.041538,0.022175059,0.11278822,0.004277843,0.80901754],"study_design_scores_gemma":[0.00009176338,0.00014716451,0.003971396,0.00027049918,0.00030920046,0.0010880488,0.0010897623,0.85080725,0.027096247,0.065332495,0.049593262,0.00020295342],"about_ca_topic_score_codex":0.0065881675,"about_ca_topic_score_gemma":0.006979843,"teacher_disagreement_score":0.0065881675,"about_ca_system_score_codex":0.0017025297,"about_ca_system_score_gemma":0.0027877209,"threshold_uncertainty_score":0.028054774},"labels":[],"label_agreement":null},{"id":"W2777217533","doi":"10.1145/3139958.3140007","title":"Best-Compromise In-Route Nearest Neighbor Queries","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Ciência sem Fronteiras; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Compromise; Skyline; Computer science; Context (archaeology); Point of interest; Point (geometry); k-nearest neighbors algorithm; Path (computing); Algorithm; Theoretical computer science; Mathematical optimization; Data mining; Mathematics; Artificial intelligence","score_opus":0.029459686774400497,"score_gpt":0.28215890065086247,"score_spread":0.252699213876462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777217533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077903,0.001030199,0.91117007,0.00082626875,0.000112659305,0.0002668578,0.0007996459,0.001461239,0.006430086],"genre_scores_gemma":[0.4501649,0.0003610963,0.54186565,0.0003096331,0.00012728748,0.00024282726,0.0019958469,0.00045908283,0.004473609],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995526,0.001713468,0.00033856786,0.0009190344,0.0010662933,0.00043655257],"domain_scores_gemma":[0.9944137,0.0029690082,0.000424242,0.0013580469,0.0005896829,0.00024543479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031724505,0.0014851198,0.002569236,0.0013073575,0.0015013968,0.0022972866,0.003552779,0.002913961,0.0046034763],"category_scores_gemma":[0.014221001,0.0007631061,0.0012171749,0.0026376557,0.0009282226,0.0047294484,0.0030013497,0.0014450406,0.0015310068],"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":[0.0020587645,0.0005704571,0.0036367972,0.00067625067,0.00033450063,0.0004846002,0.00097180536,0.6849453,0.017919784,0.03292721,0.023676842,0.2317977],"study_design_scores_gemma":[0.000091104805,0.0002514645,0.00045129212,0.000021637059,0.00004634679,0.0005728697,0.0003242881,0.95682126,0.005558948,0.030423492,0.0054005673,0.00003665623],"about_ca_topic_score_codex":0.0015279127,"about_ca_topic_score_gemma":0.0034033856,"teacher_disagreement_score":0.0046034763,"about_ca_system_score_codex":0.0011938552,"about_ca_system_score_gemma":0.0009937647,"threshold_uncertainty_score":0.016777754},"labels":[],"label_agreement":null},{"id":"W2780497540","doi":"10.1145/3139958.3140039","title":"MinMax Routing","year":2017,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Minimax; Routing (electronic design automation); Computer science; Mathematical optimization; Set (abstract data type); SAFER; Point (geometry); Path (computing); Focus (optics); Computer network; Mathematics","score_opus":0.028351550689226925,"score_gpt":0.2709557054882775,"score_spread":0.2426041547990506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2780497540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011377007,0.002146708,0.8305796,0.0017000949,0.0008538518,0.000606811,0.0036536907,0.015640276,0.13344197],"genre_scores_gemma":[0.3326497,0.0049727107,0.53163236,0.001961101,0.0006066656,0.0009654042,0.012343446,0.0040399116,0.110828705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837226,0.0003575059,0.00011795152,0.00034881718,0.0005657511,0.00023774452],"domain_scores_gemma":[0.9971239,0.0006092076,0.00020273402,0.0012892835,0.0005783771,0.00019635813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001808251,0.0011576689,0.0009180527,0.0009958684,0.0016236493,0.004911342,0.0032195875,0.0014836157,0.03991894],"category_scores_gemma":[0.0048626224,0.00076009094,0.00081295084,0.0013191126,0.0008937969,0.004544181,0.003157749,0.0014103679,0.013912934],"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":[0.0009178275,0.00021020162,0.0014727062,0.00088050246,0.0001867651,0.0003232756,0.0003511209,0.06476266,0.009257526,0.2471401,0.22383718,0.45066017],"study_design_scores_gemma":[0.00009944355,0.00019654975,0.0005775392,0.00018145291,0.000078383164,0.00066831184,0.00022500353,0.26378426,0.013898515,0.172894,0.5472874,0.00010908548],"about_ca_topic_score_codex":0.002371377,"about_ca_topic_score_gemma":0.003514448,"teacher_disagreement_score":0.03991894,"about_ca_system_score_codex":0.0013087753,"about_ca_system_score_gemma":0.0017868555,"threshold_uncertainty_score":0.13354212},"labels":[],"label_agreement":null},{"id":"W2781981262","doi":"","title":"Regroupement optimal d'objets à l'intérieur d'un nombre imposé de classes de taille égale","year":2013,"lang":"fr","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université Laval","keywords":"Humanities; Limiting; Markov chain; Mathematics; Statistics; Philosophy; Engineering","score_opus":0.017946065131416635,"score_gpt":0.239573740625335,"score_spread":0.22162767549391837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781981262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16655165,0.00031754994,0.8265294,0.0002821483,0.000050836756,0.00018892126,0.00018396029,0.0005269302,0.0053685918],"genre_scores_gemma":[0.44537765,0.00022534285,0.5397337,0.00009530303,0.000053735923,0.0004065303,0.0005684353,0.00026965392,0.01326962],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981116,0.00036565968,0.000121168465,0.0005633589,0.00049529294,0.0003428985],"domain_scores_gemma":[0.99653035,0.0015410793,0.00040977413,0.00059277867,0.0006519938,0.0002741565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032285892,0.0010456215,0.001484205,0.0017300228,0.0012562189,0.0024762952,0.0014151003,0.0010787711,0.0048029507],"category_scores_gemma":[0.0053548194,0.0005364287,0.0015724562,0.0011128809,0.0011575674,0.0023322972,0.0018300968,0.0012223498,0.0012192067],"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":[0.0008143262,0.00037670313,0.013280427,0.00036562714,0.00016193473,0.00014188123,0.0011370878,0.44235176,0.028410267,0.024192683,0.0021979397,0.48656937],"study_design_scores_gemma":[0.00009174769,0.00084569294,0.0074110413,0.00012995307,0.0001277479,0.00020101322,0.0009708743,0.90166754,0.03588621,0.042714342,0.009874659,0.00007918684],"about_ca_topic_score_codex":0.0035417122,"about_ca_topic_score_gemma":0.004746927,"teacher_disagreement_score":0.0048029507,"about_ca_system_score_codex":0.001366609,"about_ca_system_score_gemma":0.001846663,"threshold_uncertainty_score":0.017074645},"labels":[],"label_agreement":null},{"id":"W2788558106","doi":"10.1145/3178876.3186078","title":"Strategies for Geographical Scoping and Improving a Gazetteer","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scope (computer science); Hierarchy; Computer science; Boundary (topology); Baseline (sea); Information retrieval; Probabilistic logic; Quality (philosophy); Pairwise comparison; Volunteered geographic information; Data mining; Geography; Position (finance); Geotagging; Data science; Artificial intelligence; Mathematics","score_opus":0.018918393147419416,"score_gpt":0.26777508309470655,"score_spread":0.24885668994728713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788558106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03257488,0.001379867,0.9533122,0.001009757,0.00008775557,0.0004107664,0.0005510009,0.0061023026,0.004571503],"genre_scores_gemma":[0.25921154,0.0008204624,0.73236036,0.0002948569,0.00008626819,0.00047985016,0.0020115373,0.0012691328,0.0034659682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926932,0.002451823,0.0005772923,0.0018707204,0.002017467,0.00038953964],"domain_scores_gemma":[0.9739425,0.009589489,0.0021687474,0.008186297,0.005445978,0.0006669956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0104264775,0.0021789912,0.0026109212,0.010068301,0.0031377221,0.0039937254,0.004833778,0.0025868472,0.0046646907],"category_scores_gemma":[0.051986802,0.0013682741,0.001567467,0.00652692,0.002224772,0.0103712985,0.010127051,0.0023968962,0.0024791062],"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":[0.00045627827,0.00028570654,0.020289151,0.0009945014,0.000180494,0.0005466848,0.0095294025,0.051599838,0.026028514,0.041571196,0.021035543,0.8274826],"study_design_scores_gemma":[0.00021552993,0.00044598847,0.015407793,0.00079400913,0.00040836688,0.0013129431,0.008196685,0.73427933,0.04731635,0.101956286,0.08921008,0.00045663447],"about_ca_topic_score_codex":0.015248106,"about_ca_topic_score_gemma":0.020236049,"teacher_disagreement_score":0.015248106,"about_ca_system_score_codex":0.0019056122,"about_ca_system_score_gemma":0.0033328068,"threshold_uncertainty_score":0.05514115},"labels":[],"label_agreement":null},{"id":"W2788561287","doi":"10.48550/arxiv.1802.10233","title":"Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources","year":2018,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université TÉLUQ; University of Waterloo","funders":"UT-Battelle; Battelle; U.S. Department of Energy","keywords":"Computer science; Query optimization; Query language; Sargable; View; Extensibility; Architecture; Database; Relational database; Web search query; Programming language; Information retrieval; Database design","score_opus":0.0299497598105161,"score_gpt":0.27958759103575104,"score_spread":0.24963783122523495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788561287","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022678114,0.0004993017,0.8577549,0.00043013095,0.00013310504,0.0005784808,0.0033867867,0.12936468,0.0055848234],"genre_scores_gemma":[0.067222185,0.0013282673,0.868504,0.0005890588,0.0002350401,0.0014088909,0.025649022,0.027754644,0.0073089306],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942263,0.0007680777,0.0006781569,0.0006712713,0.003163417,0.0004927274],"domain_scores_gemma":[0.99482995,0.0017214684,0.00029361897,0.0014597238,0.0012062717,0.00048894994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00662955,0.0021019552,0.0019331449,0.0038128223,0.0019302058,0.0072624716,0.0067290994,0.0016048369,0.007125179],"category_scores_gemma":[0.01296963,0.0019710474,0.0031646611,0.005310938,0.002608935,0.006592541,0.0067339423,0.0040751337,0.004999186],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015290435,0.0004887592,0.006200263,0.0023327607,0.0009509253,0.0014830332,0.0014487259,0.06720733,0.022426594,0.25000417,0.33300194,0.31292647],"study_design_scores_gemma":[0.00070527283,0.00013461805,0.0018699893,0.0003271348,0.00018101464,0.0009978052,0.00026824398,0.47720128,0.018875478,0.14256573,0.35645744,0.00041594272],"about_ca_topic_score_codex":0.021889035,"about_ca_topic_score_gemma":0.016410574,"teacher_disagreement_score":0.021889035,"about_ca_system_score_codex":0.0022799291,"about_ca_system_score_gemma":0.0064658416,"threshold_uncertainty_score":0.043523252},"labels":[],"label_agreement":null},{"id":"W2789770672","doi":"","title":"Counting Rooted Non-separable Nearly Cubic Planar Maps.","year":2018,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Planar; Separable space; Combinatorics; Pure mathematics; Mathematical analysis; Computer graphics (images); Computer science","score_opus":0.010639028018055953,"score_gpt":0.2313903681729526,"score_spread":0.22075134015489667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789770672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37690365,0.002015823,0.46355736,0.0037831503,0.0004463864,0.0002742102,0.0040650615,0.0024967387,0.14645773],"genre_scores_gemma":[0.8283859,0.00086930685,0.12599757,0.00042609466,0.0002140041,0.00017643532,0.0031088116,0.00056845223,0.040253483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984042,0.00022570882,0.000097347234,0.00026653474,0.00065790996,0.00034825294],"domain_scores_gemma":[0.9942674,0.0028281526,0.0005043256,0.0011058593,0.0008362654,0.00045796283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006887187,0.00062111096,0.0008693545,0.0016163222,0.0014587208,0.0036539028,0.0021759847,0.0013652717,0.014225193],"category_scores_gemma":[0.011839015,0.00048147162,0.0005556213,0.003110636,0.001319722,0.00687201,0.004341162,0.00215279,0.002525224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000266291,0.00009257452,0.0019186708,0.00050308544,0.000049520862,0.00021085872,0.0004356151,0.011235867,0.0043491777,0.87562424,0.018436207,0.08687791],"study_design_scores_gemma":[0.000030953815,0.000034051864,0.00077271566,0.00005328205,0.000038550428,0.0003504733,0.00027052016,0.046805345,0.0040134476,0.93222916,0.015376911,0.000024641493],"about_ca_topic_score_codex":0.0028506836,"about_ca_topic_score_gemma":0.004876845,"teacher_disagreement_score":0.014225193,"about_ca_system_score_codex":0.0018052377,"about_ca_system_score_gemma":0.0010670089,"threshold_uncertainty_score":0.04758799},"labels":[],"label_agreement":null},{"id":"W2792994061","doi":"","title":"Strongly Spanning Trailable Graphs with Short Longest Paths.","year":2018,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Combinatorics","score_opus":0.015316116922232373,"score_gpt":0.2302556098121114,"score_spread":0.21493949288987904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792994061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2986905,0.0012471975,0.60672164,0.002560814,0.0003760362,0.00043069615,0.006384899,0.0024093736,0.08117887],"genre_scores_gemma":[0.7624068,0.0011465389,0.1798739,0.00054724555,0.00021451442,0.00028546917,0.0060881916,0.00037864177,0.04905875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99920386,0.00019529942,0.00009303891,0.0001644382,0.00016914397,0.00017423778],"domain_scores_gemma":[0.9939486,0.00267951,0.0009943736,0.0010788996,0.0005839631,0.000714795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079442374,0.00045458344,0.00043306517,0.001149543,0.0012417441,0.0022098152,0.00092816015,0.00075672957,0.011724063],"category_scores_gemma":[0.0068615805,0.00046657422,0.00050854805,0.0023145194,0.0008545634,0.0047523933,0.0021551382,0.001176431,0.002373028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052674004,0.0002757049,0.0033826898,0.00053352886,0.00009098665,0.0009557634,0.00073710084,0.020795535,0.010925039,0.80604035,0.030115401,0.12562124],"study_design_scores_gemma":[0.000058757632,0.00009952179,0.00082399725,0.0000731526,0.000051374933,0.0006965677,0.00034180062,0.03297423,0.0040637953,0.93069714,0.030097058,0.000022584942],"about_ca_topic_score_codex":0.0011732595,"about_ca_topic_score_gemma":0.0018975324,"teacher_disagreement_score":0.011724063,"about_ca_system_score_codex":0.00078923587,"about_ca_system_score_gemma":0.0012672318,"threshold_uncertainty_score":0.03922087},"labels":[],"label_agreement":null},{"id":"W2795112749","doi":"10.1109/icde.2018.00065","title":"Skyline Diagram: Finding the Voronoi Counterpart for Skyline Queries","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Voronoi diagram; Set (abstract data type); Scalability; Data mining; Theoretical computer science; Database; Mathematics; Geometry; Programming language","score_opus":0.02765257858579212,"score_gpt":0.29012473529258176,"score_spread":0.26247215670678964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795112749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01601861,0.0010796163,0.97414094,0.00026254286,0.00009110713,0.00033403386,0.002710968,0.002759248,0.0026029323],"genre_scores_gemma":[0.20988311,0.00087829074,0.7761316,0.0001310557,0.00012661693,0.00039072052,0.009335765,0.00050450664,0.002618295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980445,0.0003645434,0.00014284113,0.0005700874,0.00071204576,0.00016584629],"domain_scores_gemma":[0.99668115,0.0012134732,0.00046913125,0.0006855954,0.0006815327,0.0002691393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009245261,0.001031143,0.00196126,0.0036110138,0.0014026251,0.0029080345,0.0022264842,0.0016957468,0.0051325844],"category_scores_gemma":[0.008176846,0.0006165389,0.0011359244,0.005510502,0.0007761709,0.0060931565,0.0033830935,0.0012228983,0.0023774689],"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":[0.00095886324,0.00038641025,0.015275143,0.001709718,0.0002529823,0.00076596544,0.0011253146,0.16027771,0.016574576,0.13031588,0.06505276,0.6073047],"study_design_scores_gemma":[0.00015118373,0.00016462458,0.0013327732,0.0000995082,0.000044053148,0.000978684,0.00048087302,0.8719382,0.008796387,0.07137601,0.04456306,0.000074615105],"about_ca_topic_score_codex":0.0042559123,"about_ca_topic_score_gemma":0.0064420262,"teacher_disagreement_score":0.0051325844,"about_ca_system_score_codex":0.00097567344,"about_ca_system_score_gemma":0.0021837063,"threshold_uncertainty_score":0.01717019},"labels":[],"label_agreement":null},{"id":"W2795346474","doi":"10.5220/0006794601190128","title":"Elcano: A Geospatial Big Data Processing System based on SparkSQL","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Geospatial analysis; Computer science; Big data; Data science; Database; Data mining; Remote sensing; Geography","score_opus":0.06612404947162556,"score_gpt":0.26461536564690713,"score_spread":0.19849131617528157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795346474","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02998916,0.0007340113,0.20990378,0.001043244,0.00060896436,0.0011490294,0.038155276,0.69567853,0.022737995],"genre_scores_gemma":[0.31846792,0.0011217763,0.41812277,0.0028346193,0.00042310974,0.0025609494,0.18171479,0.051510777,0.023243371],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843675,0.00015534328,0.00012770173,0.0003819688,0.00067785056,0.0002204122],"domain_scores_gemma":[0.99742633,0.0004619015,0.00013231859,0.00068391877,0.0006950816,0.00060039246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002158532,0.0023687086,0.0020637077,0.0027103866,0.0017157708,0.003480051,0.0054032393,0.00074601016,0.016075956],"category_scores_gemma":[0.0051076342,0.0011415058,0.0011115719,0.003476557,0.0012530115,0.0034081973,0.0037582067,0.0022317509,0.0073239156],"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":[0.0055895746,0.0010600054,0.009127911,0.0014227399,0.0010362566,0.0011025373,0.0013443474,0.02759781,0.04328253,0.01995961,0.6882879,0.20018886],"study_design_scores_gemma":[0.0036743337,0.00037935283,0.008749967,0.00016501632,0.0004916687,0.0005243778,0.00078646507,0.5319318,0.047767222,0.05962642,0.34527758,0.0006257794],"about_ca_topic_score_codex":0.024743991,"about_ca_topic_score_gemma":0.014517469,"teacher_disagreement_score":0.024743991,"about_ca_system_score_codex":0.0016260094,"about_ca_system_score_gemma":0.005148228,"threshold_uncertainty_score":0.053779483},"labels":[],"label_agreement":null},{"id":"W2797901955","doi":"10.3138/carto.46.3.1a","title":"Vers une gestion spatiale et temporelle des documents : l'exemple de la plateforme GeoTopia","year":2011,"lang":"fr","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Political science; Geography","score_opus":0.022746606011623555,"score_gpt":0.2916661334305828,"score_spread":0.26891952741895925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797901955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34261933,0.0026572698,0.46293172,0.0076252134,0.00056570227,0.0002086268,0.0028698475,0.0039893882,0.1765328],"genre_scores_gemma":[0.7561067,0.001992937,0.15994401,0.0002939669,0.00026438117,0.00016521926,0.0014109618,0.0005887848,0.07923309],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991736,0.00012992563,0.000033624332,0.00017820873,0.00040716908,0.000077491924],"domain_scores_gemma":[0.9984267,0.00070760533,0.00012995189,0.0002460324,0.00040371463,0.000086034284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056528026,0.00079540844,0.00039999193,0.0018406346,0.001838842,0.006356531,0.0009174201,0.0018360015,0.007454343],"category_scores_gemma":[0.002718467,0.0004014558,0.00083140505,0.004277469,0.003925979,0.0026207927,0.0010308032,0.0018071549,0.0020754682],"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":[0.000525553,0.00015735625,0.014851355,0.00050419755,0.00015669345,0.011245117,0.012104446,0.08174947,0.0657999,0.59927565,0.016299998,0.1973303],"study_design_scores_gemma":[0.00024063973,0.00023369222,0.031463448,0.00026524244,0.00016789109,0.009359018,0.009684321,0.2106138,0.053043485,0.11110521,0.57343024,0.0003930736],"about_ca_topic_score_codex":0.127903,"about_ca_topic_score_gemma":0.05841566,"teacher_disagreement_score":0.127903,"about_ca_system_score_codex":0.0021997227,"about_ca_system_score_gemma":0.0014326901,"threshold_uncertainty_score":0.254317},"labels":[],"label_agreement":null},{"id":"W2798371164","doi":"10.1145/3209978.3210038","title":"Top-k Route Search through Submodularity Modeling of Recurrent POI Features","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Submodular set function; Computer science; Pruning; Feature (linguistics); Variety (cybernetics); Point of interest; Heuristic; Class (philosophy); Space (punctuation); Data mining; Information retrieval; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.06527603556412061,"score_gpt":0.3174099566823212,"score_spread":0.2521339211182006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798371164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061839443,0.0014054382,0.9269832,0.00094292517,0.00006823147,0.00015831647,0.0014727298,0.0006791859,0.006450564],"genre_scores_gemma":[0.69447035,0.0007928108,0.29346365,0.0005132475,0.00011787464,0.00041313402,0.0027554734,0.00033755729,0.0071358657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989309,0.00045351972,0.000039036702,0.00030110686,0.00011190534,0.00016347712],"domain_scores_gemma":[0.9972915,0.0019150623,0.00023417122,0.00020824045,0.0002035895,0.00014738335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018624752,0.0018385034,0.003204472,0.0011592468,0.0009149187,0.0024646453,0.0033949262,0.0020881263,0.0058267233],"category_scores_gemma":[0.0066598714,0.00097148813,0.0018329412,0.0034476975,0.0011166992,0.0038078283,0.0016726253,0.002330005,0.0010660194],"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":[0.00013856818,0.00012835079,0.0010757339,0.00020806545,0.000093237664,0.0001614543,0.00015949813,0.9319057,0.00061101717,0.024868311,0.006155879,0.03449428],"study_design_scores_gemma":[0.00001703287,0.000030220961,0.00008299372,0.000008070657,0.000012211549,0.00002680114,0.000039837232,0.9797106,0.00010075932,0.019401532,0.00056249194,0.0000074114414],"about_ca_topic_score_codex":0.008555661,"about_ca_topic_score_gemma":0.010812036,"teacher_disagreement_score":0.008555661,"about_ca_system_score_codex":0.0021169279,"about_ca_system_score_gemma":0.0015256657,"threshold_uncertainty_score":0.019492269},"labels":[],"label_agreement":null},{"id":"W2799147087","doi":"10.1145/3209978.3210066","title":"Split-Lists and Initial Thresholds for WAND-based Search","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Partition (number theory); Search engine; Rank (graph theory); Information retrieval; Data mining; Mathematics; Combinatorics","score_opus":0.04492788530374531,"score_gpt":0.3256248482940918,"score_spread":0.28069696299034647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799147087","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72375333,0.0049572345,0.2172083,0.0010039601,0.00032812366,0.0007317582,0.0018285147,0.040427487,0.009761291],"genre_scores_gemma":[0.8471639,0.0003290175,0.14813901,0.0001465824,0.00004367493,0.00022304726,0.0019339765,0.0007436462,0.0012771161],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933007,0.0018574337,0.00075477187,0.00083023007,0.0023584429,0.00089849875],"domain_scores_gemma":[0.96531606,0.019585686,0.0014878783,0.006742665,0.005580486,0.0012873014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00853513,0.0012737627,0.0014345169,0.0035326,0.0015194627,0.0033928824,0.0032810115,0.001662784,0.0033208318],"category_scores_gemma":[0.049116753,0.00058873504,0.00062738894,0.0039969417,0.001339887,0.008557352,0.0017702602,0.0017202278,0.0016856919],"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":[0.015114983,0.0028279652,0.036832593,0.0013983924,0.00059581996,0.00040877634,0.0010562703,0.23078044,0.057141308,0.016095644,0.033005547,0.6047422],"study_design_scores_gemma":[0.000721943,0.003254581,0.0095245605,0.00007925885,0.00015515999,0.0003721145,0.00060417945,0.90832615,0.061997402,0.010309776,0.004461628,0.00019312257],"about_ca_topic_score_codex":0.0094574485,"about_ca_topic_score_gemma":0.013369231,"teacher_disagreement_score":0.0094574485,"about_ca_system_score_codex":0.0017914753,"about_ca_system_score_gemma":0.0029728466,"threshold_uncertainty_score":0.045138657},"labels":[],"label_agreement":null},{"id":"W2806337402","doi":"10.1016/j.patcog.2018.05.030","title":"A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data","year":2018,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":170,"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":"State Key Laboratory of Computer Aided Design and Computer Graphics; National Laboratory of Pattern Recognition; Fujian Provincial Department of Science and Technology; Zhejiang University; Huaqiao University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"DBSCAN; Computer science; Pruning; Computation; Cluster analysis; Noise (video); Dimension (graph theory); Algorithm; Search engine indexing; Pattern recognition (psychology); Data mining; Mathematics; Artificial intelligence; CURE data clustering algorithm; Correlation clustering; Combinatorics; Image (mathematics)","score_opus":0.052716270361348704,"score_gpt":0.285088083781462,"score_spread":0.2323718134201133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806337402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005593974,0.00043670947,0.99052656,0.00010098884,0.0001627815,0.00009720369,0.00019875444,0.0022549331,0.0006280711],"genre_scores_gemma":[0.028561372,0.00028211856,0.96806407,0.00008882674,0.000071403156,0.00018220553,0.0009570763,0.00026212764,0.0015308285],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658775,0.00039738943,0.00024257562,0.0005084055,0.0020230808,0.00024077538],"domain_scores_gemma":[0.99719065,0.00054227706,0.00012679335,0.0004737111,0.0015456724,0.0001207541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015269591,0.0015878108,0.0028585952,0.0038819497,0.0026311735,0.0028393,0.0048649055,0.0016062106,0.0033684114],"category_scores_gemma":[0.0051814113,0.00096113596,0.0014419723,0.008219852,0.0010220995,0.002461762,0.002253666,0.0027287016,0.002483765],"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":[0.0005108356,0.00025629954,0.0011191971,0.00025600125,0.00020784212,0.00012785282,0.00023389142,0.06551039,0.015101712,0.013388363,0.014811598,0.8884761],"study_design_scores_gemma":[0.000067444205,0.00014771572,0.0010368049,0.000036569454,0.00008391166,0.00037094616,0.00015647442,0.9480713,0.021443227,0.014997812,0.01347238,0.0001154895],"about_ca_topic_score_codex":0.021785546,"about_ca_topic_score_gemma":0.026540466,"teacher_disagreement_score":0.021785546,"about_ca_system_score_codex":0.0010556304,"about_ca_system_score_gemma":0.0046305642,"threshold_uncertainty_score":0.043317497},"labels":[],"label_agreement":null},{"id":"W2809243187","doi":"10.1007/s12599-018-0548-y","title":"Conceptual Framework for SDSS Development with an Application in the Retail Industry","year":2018,"lang":"en","type":"article","venue":"Business & Information Systems Engineering","topic":"Data Management and Algorithms","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Process (computing); Decision support system; Set (abstract data type); Visualization; Development (topology); Conceptual framework; Software; Data science; Process management; Systems engineering; Software engineering; Data mining; Engineering","score_opus":0.02048923443639808,"score_gpt":0.2254111283261817,"score_spread":0.20492189388978363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809243187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049011167,0.00015733909,0.97087955,0.0017216043,0.00006464004,0.0006135781,0.0002627752,0.0020666583,0.019332822],"genre_scores_gemma":[0.06829212,0.000313758,0.91782826,0.00032471382,0.000036168436,0.000688633,0.00064155227,0.00045132337,0.011423421],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99622214,0.0012337798,0.00051055395,0.00055019197,0.0011013639,0.0003819011],"domain_scores_gemma":[0.99543697,0.0012355304,0.00030223807,0.0007920901,0.001546629,0.0006864934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006730492,0.00093073235,0.00054825057,0.0034304124,0.0021619506,0.010281258,0.0037052138,0.003493946,0.01236608],"category_scores_gemma":[0.0073774583,0.0011085907,0.0018556021,0.00235847,0.004182504,0.0060502137,0.005168263,0.00323992,0.004514031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004074442,0.00017122613,0.0019619353,0.00022778835,0.000030590465,0.0005370182,0.0019875811,0.011205337,0.002471439,0.9365121,0.004106205,0.04074794],"study_design_scores_gemma":[0.00010271309,0.00022670344,0.0012717209,0.0010951863,0.00010039012,0.0012169492,0.0033580638,0.19094406,0.009320946,0.39234033,0.39988154,0.0001413228],"about_ca_topic_score_codex":0.013735814,"about_ca_topic_score_gemma":0.0112388,"teacher_disagreement_score":0.013735814,"about_ca_system_score_codex":0.0029296777,"about_ca_system_score_gemma":0.009204755,"threshold_uncertainty_score":0.041368663},"labels":[],"label_agreement":null},{"id":"W2821470430","doi":"10.1109/mdm.2018.00031","title":"A Semi-Supervised Approach for the Semantic Segmentation of Trajectories","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Trajectory; Minimum description length; Pattern recognition (psychology); Ground truth; Image segmentation; Set (abstract data type); Machine learning","score_opus":0.029725717442451373,"score_gpt":0.26088706462566585,"score_spread":0.2311613471832145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2821470430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040585273,0.000100830264,0.99363565,0.000051084742,0.000017211521,0.0001051167,0.00019749081,0.0013474282,0.00048674355],"genre_scores_gemma":[0.1326054,0.00016045885,0.8624054,0.00010574959,0.00006167978,0.00039593308,0.0022291117,0.00038579688,0.0016504845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755955,0.0006164146,0.00017691801,0.0010382789,0.00047589783,0.00013294049],"domain_scores_gemma":[0.997315,0.0010059985,0.00038967363,0.0005695596,0.00059622596,0.00012345225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015879559,0.0016386284,0.0013702706,0.0029895043,0.0010219383,0.0013304455,0.0025892006,0.0016175483,0.0023148663],"category_scores_gemma":[0.0042328816,0.00068756804,0.0018760016,0.0023879327,0.0014019989,0.0022128315,0.001694509,0.001762941,0.0013917158],"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":[0.00059751724,0.00039765003,0.0034462463,0.0008840224,0.0003724151,0.00040373552,0.0011821389,0.23973775,0.04970865,0.029473936,0.01286803,0.66092795],"study_design_scores_gemma":[0.00002056886,0.00010083078,0.0007128815,0.000046567802,0.000036901245,0.00018828888,0.00013827959,0.96508324,0.010295618,0.0182425,0.0050986353,0.000035715533],"about_ca_topic_score_codex":0.0042379294,"about_ca_topic_score_gemma":0.008571126,"teacher_disagreement_score":0.0042379294,"about_ca_system_score_codex":0.0010173862,"about_ca_system_score_gemma":0.0024706656,"threshold_uncertainty_score":0.008426487},"labels":[],"label_agreement":null},{"id":"W28493347","doi":"10.1111/cid.12492","title":"Hybrid Indexing of Moving Objects with Frequent Updates on Fixed Networks.","year":2007,"lang":"en","type":"article","venue":"Clinical Implant Dentistry and Related Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Search engine indexing; Computer science; Object (grammar); Artificial intelligence","score_opus":0.07329611189131639,"score_gpt":0.3987449713391797,"score_spread":0.3254488594478633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28493347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08467365,0.0029450876,0.90162885,0.0003719284,0.00043981784,0.0003147274,0.0016420794,0.005750822,0.002232999],"genre_scores_gemma":[0.5589127,0.0013690214,0.42975703,0.00013579121,0.00047360326,0.00040377735,0.0043148436,0.00025319736,0.004380122],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982009,0.0003682944,0.00024127461,0.0003985301,0.00063196017,0.0001589891],"domain_scores_gemma":[0.9949222,0.0021921354,0.00044072396,0.0015817814,0.00070228754,0.00016087598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019221514,0.00088807696,0.0015626551,0.004124595,0.0009831022,0.0025973024,0.0023164537,0.0011923704,0.0018278369],"category_scores_gemma":[0.01287127,0.00056937203,0.00071470544,0.0058419155,0.0006419993,0.004548069,0.0018723684,0.0007150178,0.0011627758],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014041789,0.00025724457,0.005519018,0.00024598773,0.00017221701,0.0001813513,0.00030960108,0.10522384,0.010003109,0.009850265,0.0066954624,0.8601377],"study_design_scores_gemma":[0.000054275482,0.00023435484,0.0022091526,0.000029320474,0.00008653113,0.0003086968,0.0001310557,0.964136,0.009777795,0.014939395,0.0080511365,0.00004225822],"about_ca_topic_score_codex":0.009242946,"about_ca_topic_score_gemma":0.009635705,"teacher_disagreement_score":0.009242946,"about_ca_system_score_codex":0.0009744039,"about_ca_system_score_gemma":0.0011189667,"threshold_uncertainty_score":0.018378317},"labels":[],"label_agreement":null},{"id":"W2851970333","doi":"10.1109/mdm.2018.00017","title":"Optimal Meeting Points for Public Transit Users","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aggregate (composite); Exploit; Computer science; Public transport; Space (punctuation); Order (exchange); Group (periodic table); Transit (satellite); Operations research; Transport engineering; Mathematics; Computer security; Engineering; Economics","score_opus":0.0382260637432487,"score_gpt":0.2589547348088772,"score_spread":0.2207286710656285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2851970333","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78629744,0.00090276374,0.19245955,0.002740788,0.00010416965,0.0002467875,0.00628508,0.0021187845,0.008844618],"genre_scores_gemma":[0.93287873,0.00018207224,0.05912709,0.00008578464,0.0000631705,0.000065634384,0.0054196212,0.00013062259,0.002047389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820197,0.00037045346,0.00013236691,0.00057956873,0.00045884188,0.000256847],"domain_scores_gemma":[0.9972722,0.0011436811,0.00042899803,0.0005017258,0.00038765208,0.0002656885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014230954,0.0010158737,0.0012817895,0.0012719944,0.0014342585,0.0017218374,0.0019631006,0.001810448,0.0052322033],"category_scores_gemma":[0.009228272,0.0004550575,0.0007741134,0.0021230753,0.00074638485,0.0032649145,0.002090759,0.0012887451,0.0015618876],"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":[0.006029449,0.00075520074,0.06607627,0.0010698432,0.00025431393,0.0017906466,0.0024845533,0.5312068,0.023117894,0.030874696,0.044488985,0.29185143],"study_design_scores_gemma":[0.00022475724,0.00043498832,0.009072764,0.00005441076,0.00007628406,0.0011328303,0.004550282,0.89443207,0.013246775,0.06790671,0.008790921,0.000077119555],"about_ca_topic_score_codex":0.0039678295,"about_ca_topic_score_gemma":0.005844946,"teacher_disagreement_score":0.0052322033,"about_ca_system_score_codex":0.0014407234,"about_ca_system_score_gemma":0.0011663063,"threshold_uncertainty_score":0.0175035},"labels":[],"label_agreement":null},{"id":"W286790658","doi":"10.1016/j.ejor.2003.10.029","title":"SARPlan: A decision support system for Canadian Search and Rescue Operations","year":2004,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Search and rescue; Decision support system; Computer science; Operations research; Engineering; Artificial intelligence; Robot","score_opus":0.0883167890346906,"score_gpt":0.3564225687883026,"score_spread":0.268105779753612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W286790658","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21937814,0.0011541244,0.24097279,0.0015165762,0.0006747982,0.0015278958,0.07152734,0.41698554,0.04626283],"genre_scores_gemma":[0.64172095,0.0014295791,0.24305491,0.0007729373,0.00017685899,0.0007199029,0.07865787,0.004323197,0.029143764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972934,0.00002674489,0.00001623246,0.000052854688,0.00012720966,0.00004762861],"domain_scores_gemma":[0.9991636,0.00015369419,0.000052174357,0.00008027271,0.0004030309,0.00014722103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006231631,0.001360014,0.0006888795,0.0014898763,0.00087210326,0.001147665,0.0014614468,0.00046413715,0.017055035],"category_scores_gemma":[0.0014663489,0.00039852923,0.00034111712,0.001311132,0.00033616013,0.0011332008,0.00074282807,0.00059222744,0.0028914213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043266737,0.000454959,0.013940067,0.0006510793,0.00026596963,0.00053356786,0.0005428901,0.1368875,0.023671176,0.004653929,0.39707834,0.41699395],"study_design_scores_gemma":[0.00078119483,0.00041975846,0.013016935,0.00009974512,0.00024663855,0.00016998993,0.00036696525,0.8029436,0.018708365,0.0027845837,0.16017333,0.0002888947],"about_ca_topic_score_codex":0.43603542,"about_ca_topic_score_gemma":0.51069546,"teacher_disagreement_score":0.99672335,"about_ca_system_score_codex":0.0032766354,"about_ca_system_score_gemma":0.006951224,"threshold_uncertainty_score":0.86699486},"labels":[],"label_agreement":null},{"id":"W28758523","doi":"10.1080/0142159x.2017.1357805","title":"Top-Down Skiplists.","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Binary number; Combinatorics; Set (abstract data type); Binary logarithm; Amortized analysis; Binary search algorithm; Data structure; Mathematics; Computer science; Search algorithm; Algorithm; Arithmetic","score_opus":0.05040916365503023,"score_gpt":0.17643766280245998,"score_spread":0.12602849914742975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28758523","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047022894,0.0023569344,0.27398854,0.014963367,0.020992806,0.0070832083,0.060295153,0.051851112,0.521446],"genre_scores_gemma":[0.11214503,0.0012075733,0.21971905,0.010233999,0.0018559817,0.007454352,0.03008253,0.018435908,0.59886557],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99312466,0.0021597159,0.0007962825,0.0012387523,0.0019445108,0.00073604984],"domain_scores_gemma":[0.9752367,0.011164861,0.001091159,0.004701463,0.0066753426,0.0011303925],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006932054,0.0016218108,0.0013084643,0.0027859292,0.0032769097,0.0032321482,0.0021261787,0.0016426711,0.31787592],"category_scores_gemma":[0.04116956,0.0006885572,0.0010699339,0.003208904,0.0014795997,0.005383,0.0051472266,0.001995698,0.17715536],"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":[0.0009147176,0.0002554307,0.0027611966,0.0008179263,0.000032807035,0.00030796792,0.0035903025,0.00023244023,0.002580098,0.03911242,0.583436,0.36595872],"study_design_scores_gemma":[0.000060640345,0.00012755176,0.0014023617,0.00037684795,0.00002207484,0.00027320068,0.0013642778,0.0012478734,0.002576276,0.025833642,0.9666571,0.000058114107],"about_ca_topic_score_codex":0.0022434315,"about_ca_topic_score_gemma":0.0050532906,"teacher_disagreement_score":0.31787592,"about_ca_system_score_codex":0.0012603663,"about_ca_system_score_gemma":0.002369157,"threshold_uncertainty_score":0.9729672},"labels":[],"label_agreement":null},{"id":"W2876125642","doi":"10.1109/mdm.2018.00053","title":"Trajectolizer: Interactive Analysis and Exploration of Trajectory Group Dynamics","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trajectory; Computer science; Group (periodic table); Domain (mathematical analysis); Data mining; Tracking (education); Task (project management); Scale (ratio); Data stream mining; Artificial intelligence; Geography; Mathematics; Engineering","score_opus":0.01614914317608099,"score_gpt":0.25583871685633836,"score_spread":0.23968957368025737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2876125642","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031394787,0.00026435434,0.8853074,0.00026398196,0.00006965921,0.00022759187,0.0042929878,0.07534472,0.0028345464],"genre_scores_gemma":[0.21568587,0.0003405808,0.770313,0.00014968678,0.000048937825,0.0005216676,0.006969425,0.002347457,0.0036233312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994653,0.00009014238,0.000028923361,0.00020454153,0.00016757504,0.000043580858],"domain_scores_gemma":[0.9988513,0.00059975154,0.00008970421,0.00023689873,0.000101335165,0.00012105248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089940615,0.00094712485,0.0008662149,0.0016406025,0.0005794396,0.0011189529,0.001972529,0.0007315972,0.006598968],"category_scores_gemma":[0.0036128447,0.00053514994,0.00072495564,0.00134528,0.0005300665,0.0020538433,0.003072332,0.0008531729,0.0016237623],"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":[0.0031033345,0.000530842,0.028893841,0.0013435652,0.00082544464,0.0013168094,0.003592948,0.1197504,0.06766638,0.02494448,0.09327454,0.65475744],"study_design_scores_gemma":[0.00009703629,0.00013913494,0.003437936,0.00004218464,0.00004391205,0.0002630528,0.00025328895,0.9405745,0.013481795,0.012092006,0.029512098,0.000062968924],"about_ca_topic_score_codex":0.0049479855,"about_ca_topic_score_gemma":0.009535271,"teacher_disagreement_score":0.006598968,"about_ca_system_score_codex":0.00043916987,"about_ca_system_score_gemma":0.0010786102,"threshold_uncertainty_score":0.022075772},"labels":[],"label_agreement":null},{"id":"W2884409942","doi":"10.3138/cart.53.2.2017-0004","title":"Location-Based Applications Using Analog Maps for Sustainable Local Tourism Information Services","year":2018,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tourism; Business; Computer science; Geography","score_opus":0.00797224240916463,"score_gpt":0.2716887242889953,"score_spread":0.2637164818798307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884409942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14466013,0.001743821,0.76914775,0.0006440061,0.00016564086,0.00089392427,0.0022208795,0.029451704,0.05107207],"genre_scores_gemma":[0.6600328,0.0013586781,0.3149985,0.0001714061,0.00008155883,0.00044034608,0.0016484263,0.00040843867,0.020859746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997291,0.00009876483,0.000012858552,0.000024155883,0.000111143774,0.000023950375],"domain_scores_gemma":[0.9994843,0.00023396676,0.00002480218,0.000122035795,0.0001033371,0.000031538013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000329293,0.0004414963,0.00023774971,0.0008752796,0.00027892517,0.0010732424,0.00064597913,0.000494242,0.008745157],"category_scores_gemma":[0.0014208063,0.000120592355,0.00024170821,0.001256688,0.0001668641,0.00088213687,0.0009175993,0.00025376174,0.0019656576],"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":[0.00078116823,0.00040156563,0.006395547,0.0013309389,0.000097868244,0.0019015465,0.0030866116,0.015485147,0.07046071,0.01767912,0.03154375,0.85083604],"study_design_scores_gemma":[0.0002342466,0.0008268497,0.021806492,0.00047401048,0.0003923747,0.0031340683,0.0050315796,0.26753566,0.11668246,0.028614148,0.5550072,0.0002609734],"about_ca_topic_score_codex":0.0014357378,"about_ca_topic_score_gemma":0.0019959526,"teacher_disagreement_score":0.008745157,"about_ca_system_score_codex":0.00019952659,"about_ca_system_score_gemma":0.00023816446,"threshold_uncertainty_score":0.02925551},"labels":[],"label_agreement":null},{"id":"W2884886133","doi":"10.5555/3310435.3310618","title":"Exact algorithms and lower bounds for stable instances of euclidean k-means","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Combinatorics; Hamming distance; Euclidean distance; Euclidean geometry; Cluster analysis; Time complexity; Dimension (graph theory); Heuristics; Algorithm; Discrete mathematics; Mathematical optimization","score_opus":0.03704816256138506,"score_gpt":0.17964740890551903,"score_spread":0.14259924634413396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884886133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11817417,0.0043713637,0.8429996,0.005857117,0.00030535972,0.00047401246,0.0019179899,0.002421417,0.023478907],"genre_scores_gemma":[0.5459276,0.0016663986,0.4390355,0.001006696,0.00042251614,0.00061136135,0.003271491,0.001033297,0.007025133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954158,0.0012627308,0.00026217705,0.0012332009,0.0011329026,0.000693253],"domain_scores_gemma":[0.9757214,0.018116172,0.0015149069,0.0026605579,0.0012235247,0.00076341716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045190803,0.002587417,0.0021601203,0.0014797625,0.0015815572,0.0043926286,0.004416455,0.0024848413,0.010551823],"category_scores_gemma":[0.03118191,0.0010054728,0.0026408741,0.0030416022,0.0021731486,0.007998642,0.0036336482,0.005873404,0.001768745],"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":[0.0011447077,0.0005835776,0.004169896,0.0011019821,0.00030914904,0.0002629238,0.00048221872,0.6897437,0.005307888,0.1863847,0.013844615,0.09666464],"study_design_scores_gemma":[0.00008456141,0.00010957941,0.00038055447,0.000045226043,0.000049019094,0.00009406305,0.00008813884,0.8350272,0.0013396043,0.1612061,0.0015560047,0.000019976038],"about_ca_topic_score_codex":0.003607915,"about_ca_topic_score_gemma":0.0056393035,"teacher_disagreement_score":0.010551823,"about_ca_system_score_codex":0.003729957,"about_ca_system_score_gemma":0.0028242103,"threshold_uncertainty_score":0.0352993},"labels":[],"label_agreement":null},{"id":"W2885204832","doi":"10.1007/s10707-017-0293-2","title":"Uncertain Voronoi cell computation based on space decomposition","year":2017,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"German Academic Exchange Service London; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Alberta Innovates - Technology Futures; Research Grants Council, University Grants Committee; Deutscher Akademischer Austauschdienst; National Science Foundation","keywords":"Voronoi diagram; Centroidal Voronoi tessellation; k-nearest neighbors algorithm; Computation; Computer science; Space partitioning; Computational geometry; Object (grammar); Nearest neighbor search; Mathematics; Algorithm; Data mining; Artificial intelligence; Geometry","score_opus":0.014134765706544181,"score_gpt":0.2716939366836894,"score_spread":0.2575591709771452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885204832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014685364,0.00019101275,0.9836932,0.00007459088,0.000028194836,0.000027997681,0.00013225642,0.0002498528,0.0009175658],"genre_scores_gemma":[0.49014586,0.00037736038,0.5065582,0.000051577277,0.000055123288,0.00010042666,0.0007740374,0.00014383384,0.001793622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999199,0.00013941985,0.00004242268,0.00014930617,0.00038357815,0.000086374705],"domain_scores_gemma":[0.9987212,0.00059217506,0.00008057448,0.00018074569,0.00033845534,0.000086814915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046978792,0.0004398285,0.0016291747,0.0016173149,0.00073060323,0.001884894,0.0013743477,0.00065708417,0.0025331757],"category_scores_gemma":[0.0034836016,0.00043633563,0.0009799616,0.0020851207,0.0005692803,0.0018751677,0.0019561446,0.000801619,0.00049474754],"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":[0.0003928795,0.000078830526,0.0028509523,0.00022645424,0.000104527935,0.0002165986,0.00030785205,0.618918,0.0071261167,0.06988432,0.005779495,0.29411402],"study_design_scores_gemma":[0.0000063698044,0.0000092213895,0.00012241623,0.0000059375875,0.000006920241,0.000043181528,0.000025178533,0.98778975,0.0009362238,0.009936712,0.0011107591,0.000007243061],"about_ca_topic_score_codex":0.010064856,"about_ca_topic_score_gemma":0.008305706,"teacher_disagreement_score":0.010064856,"about_ca_system_score_codex":0.00070374843,"about_ca_system_score_gemma":0.001224295,"threshold_uncertainty_score":0.020012558},"labels":[],"label_agreement":null},{"id":"W2887728628","doi":"10.1007/s10844-018-0524-5","title":"REMI: A framework of reusable elements for mining heterogeneous data with missing information","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Computer science; Data mining; Information retrieval; Missing data; Data science; Machine learning","score_opus":0.0448779938772994,"score_gpt":0.29895527494351004,"score_spread":0.25407728106621064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887728628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008510892,0.00008529869,0.98657644,0.000082487444,0.000016037608,0.00015249848,0.00056935305,0.011327648,0.00033913465],"genre_scores_gemma":[0.008371622,0.000105353574,0.986813,0.00007472889,0.000014835636,0.0002515072,0.002558297,0.0011575938,0.0006531152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99451786,0.0015012696,0.0008601147,0.0011013101,0.0017258163,0.00029362764],"domain_scores_gemma":[0.98950654,0.004948091,0.00055194914,0.0036367797,0.0010250654,0.0003316589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010727702,0.0019997815,0.00196536,0.004961996,0.0012727797,0.004279385,0.0075576096,0.0019288531,0.00573899],"category_scores_gemma":[0.029174712,0.0020868608,0.005298679,0.0046205046,0.0014552618,0.006629773,0.0071326885,0.0035139471,0.0032915806],"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":[0.00097212073,0.00046632788,0.007795957,0.0028745166,0.0015575505,0.0017001,0.0031944495,0.044844314,0.015828226,0.16820666,0.04142951,0.7111303],"study_design_scores_gemma":[0.00018697626,0.00024808486,0.0019161821,0.00090035785,0.0007989649,0.0011037025,0.0007352755,0.5758427,0.035252944,0.21509624,0.16766669,0.00025193757],"about_ca_topic_score_codex":0.0052581504,"about_ca_topic_score_gemma":0.008169355,"teacher_disagreement_score":0.010727702,"about_ca_system_score_codex":0.00082101964,"about_ca_system_score_gemma":0.003106045,"threshold_uncertainty_score":0.056734204},"labels":[],"label_agreement":null},{"id":"W2889053599","doi":"10.5555/3236187.3269462","title":"Efficient construction of approximate ad-hoc ML models through materialization and reuse","year":2018,"lang":"en","type":"article","venue":"Very Large Data Bases","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Online analytical processing; Reuse; Dimension (graph theory); Cluster analysis; Data warehouse; Data mining; Construct (python library); Variety (cybernetics); Mixture model; Data modeling; Machine learning; Artificial intelligence; Database; Programming language","score_opus":0.04435581347020066,"score_gpt":0.26769465447678786,"score_spread":0.2233388410065872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889053599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013759134,0.00012380215,0.9825075,0.00024825372,0.000012676904,0.00008296494,0.00013101815,0.0023571386,0.00077748415],"genre_scores_gemma":[0.2288459,0.00019853332,0.7668522,0.00021109308,0.00006104561,0.00024637682,0.0011871207,0.0008229409,0.0015747581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944278,0.0018889277,0.00041980195,0.0009655513,0.0018007179,0.00049721333],"domain_scores_gemma":[0.9821231,0.009989939,0.0009999617,0.005292078,0.0012315968,0.00036341546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005564487,0.0021294847,0.0025678093,0.001993426,0.0013171071,0.0046274224,0.004590313,0.0019059279,0.0033487598],"category_scores_gemma":[0.026633224,0.0018187516,0.0031465027,0.0032994747,0.0022787722,0.008489148,0.0066871885,0.0037005646,0.0016268742],"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":[0.00016978827,0.00019492739,0.0023633768,0.00012327352,0.00011078065,0.00020262609,0.00036859015,0.8129876,0.002688573,0.03874538,0.0039741923,0.13807087],"study_design_scores_gemma":[0.000012505122,0.000018548486,0.000057628542,0.0000040647233,0.000010354343,0.000027586631,0.000051890944,0.97926694,0.0010922985,0.018641096,0.00080889306,0.000008232639],"about_ca_topic_score_codex":0.009461256,"about_ca_topic_score_gemma":0.011842938,"teacher_disagreement_score":0.009461256,"about_ca_system_score_codex":0.002661781,"about_ca_system_score_gemma":0.0038007316,"threshold_uncertainty_score":0.029428124},"labels":[],"label_agreement":null},{"id":"W2889148525","doi":"10.1111/tgis.12464","title":"Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint","year":2018,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Education Department of Henan Province","keywords":"Huffman coding; Trajectory; Computer science; Feature (linguistics); Constraint (computer-aided design); Artificial intelligence; Computer vision; Data compression; Data mining; Mathematics","score_opus":0.02474127967054755,"score_gpt":0.258426339923819,"score_spread":0.2336850602532715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889148525","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16592519,0.0015182123,0.8238834,0.0004002744,0.00030450022,0.00014676791,0.0019118965,0.0023728642,0.0035368202],"genre_scores_gemma":[0.69409984,0.001813536,0.29162028,0.00015641614,0.00020986337,0.00023207837,0.006379919,0.00021795322,0.0052701845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959904,0.000029772678,0.000034946286,0.00006784672,0.00022901282,0.000039376653],"domain_scores_gemma":[0.9993642,0.00011550911,0.00005933064,0.00016692681,0.00026965002,0.000024493831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000204783,0.0006083429,0.00049236417,0.0013996263,0.00030222302,0.0005468795,0.0006588723,0.0003190095,0.0011205107],"category_scores_gemma":[0.0017175489,0.00012881868,0.0003529031,0.0023240226,0.0002276818,0.0012642231,0.00060837914,0.00055737095,0.0004306388],"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":[0.00052772503,0.0000847516,0.0030140139,0.00023473914,0.00007843379,0.00053276255,0.00023829957,0.10977907,0.05499984,0.0066757607,0.008954175,0.8148805],"study_design_scores_gemma":[0.000022640934,0.000087554574,0.0026905641,0.000028737773,0.000042501415,0.0003544872,0.00016590492,0.92923236,0.0558816,0.003374878,0.008093295,0.000025559466],"about_ca_topic_score_codex":0.0052818675,"about_ca_topic_score_gemma":0.003982671,"teacher_disagreement_score":0.0052818675,"about_ca_system_score_codex":0.0003808273,"about_ca_system_score_gemma":0.00078868703,"threshold_uncertainty_score":0.010502219},"labels":[],"label_agreement":null},{"id":"W2889180561","doi":"10.1007/978-3-319-98530-5_15","title":"Continuous k-Nearest Neighbour Strategies Using the mqrtree","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"k-nearest neighbors algorithm; Best bin first; Computer science; Nearest neighbor search; Nearest neighbor graph; Property (philosophy); Nearest neighbour; Data mining; Pattern recognition (psychology); Algorithm; Artificial intelligence","score_opus":0.05135188821761769,"score_gpt":0.2660235016565628,"score_spread":0.2146716134389451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889180561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038624986,0.0005664739,0.9923006,0.00006958998,0.00007919023,0.00004082145,0.00005710793,0.00045254506,0.0025711341],"genre_scores_gemma":[0.10329487,0.0005871325,0.8904213,0.00009565149,0.000075015305,0.00011415245,0.00023777621,0.00027749827,0.0048966324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988476,0.0003281964,0.00007602788,0.00015691612,0.0005154339,0.00007573494],"domain_scores_gemma":[0.9984316,0.0006955371,0.00007143475,0.00034046153,0.0003908309,0.000070031034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012679894,0.0006902392,0.0019286015,0.0013424859,0.0007422189,0.0018490216,0.0037770835,0.0015037025,0.005863846],"category_scores_gemma":[0.00534168,0.00049213594,0.0009451097,0.002052408,0.0007510304,0.002336924,0.0024709078,0.0014614328,0.002428889],"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":[0.00033119283,0.00020885032,0.0005913868,0.00045028242,0.00014407147,0.00016049328,0.00030671706,0.28267726,0.006993239,0.11416726,0.010374754,0.5835945],"study_design_scores_gemma":[0.000032443277,0.000057497888,0.000108068154,0.000030023155,0.000020682586,0.00014580823,0.000044716286,0.95966977,0.0014420828,0.033062313,0.0053596157,0.000027022787],"about_ca_topic_score_codex":0.005614499,"about_ca_topic_score_gemma":0.006007233,"teacher_disagreement_score":0.005863846,"about_ca_system_score_codex":0.00068636663,"about_ca_system_score_gemma":0.00093004934,"threshold_uncertainty_score":0.019616544},"labels":[],"label_agreement":null},{"id":"W2889833276","doi":"10.1109/ficloud.2018.00064","title":"Sensing as a Service Middleware Architecture","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Middleware (distributed applications); Cloud computing; Service (business); Internet of Things; SQL; Wireless sensor network; Service discovery; Architecture; Smart objects; Distributed computing; Message oriented middleware; Service-oriented architecture; Intelligent sensor; Computer network; Web service; Database; World Wide Web; Software architecture; Software; Operating system","score_opus":0.015570528152164044,"score_gpt":0.23856382167238518,"score_spread":0.22299329352022115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889833276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017227218,0.0007671978,0.937311,0.0012104667,0.00045707703,0.00051405234,0.00032027686,0.025770465,0.016422324],"genre_scores_gemma":[0.4405559,0.0012722295,0.5251585,0.0013214152,0.00022641271,0.0008568693,0.002032922,0.0013843651,0.027191382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849415,0.00027750363,0.00023648408,0.0002975661,0.00050249946,0.00019174731],"domain_scores_gemma":[0.99897873,0.00015522318,0.00006915453,0.00026169905,0.0003621397,0.0001730268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013873231,0.0008813915,0.0008069347,0.0007401769,0.001252934,0.0038777625,0.003048548,0.0015618815,0.0039089746],"category_scores_gemma":[0.001816426,0.00053033716,0.0010281007,0.0010843452,0.0008214129,0.0028919878,0.0019554016,0.0015702269,0.0026308242],"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":[0.0016852055,0.001049447,0.005223995,0.0014882685,0.00047918374,0.0014591453,0.0016951138,0.14517224,0.13252005,0.32179448,0.04994669,0.33748618],"study_design_scores_gemma":[0.0001783309,0.00033200302,0.0011427313,0.00009198344,0.00018193592,0.00066157634,0.0003167113,0.7079315,0.05425743,0.056181926,0.17858244,0.00014147391],"about_ca_topic_score_codex":0.0048600733,"about_ca_topic_score_gemma":0.0032383401,"teacher_disagreement_score":0.0048600733,"about_ca_system_score_codex":0.0015313515,"about_ca_system_score_gemma":0.003027475,"threshold_uncertainty_score":0.013076782},"labels":[],"label_agreement":null},{"id":"W2896619815","doi":"","title":"Efficacité d'un test semi-paramétrique d'indépendance entre vecteurs aléatoires","year":2009,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Humanities; Mathematics; Philosophy","score_opus":0.010879621705497501,"score_gpt":0.2130917481783446,"score_spread":0.2022121264728471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896619815","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49018374,0.00042241995,0.49263346,0.00051872793,0.00023166802,0.00006583719,0.0004016236,0.008766275,0.006776119],"genre_scores_gemma":[0.9584596,0.00004394504,0.038545806,0.00008790933,0.000034086843,0.000046902453,0.00034855146,0.00047582784,0.0019574221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99786395,0.0009808968,0.000099473385,0.00035458506,0.00042079517,0.0002803948],"domain_scores_gemma":[0.97727907,0.018084737,0.00059566065,0.001894696,0.0015455604,0.0006002002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020826883,0.0017459225,0.0012626582,0.00086589524,0.0005542627,0.0014718286,0.001367962,0.0021718482,0.009454036],"category_scores_gemma":[0.01255259,0.000458781,0.00096490193,0.00065295584,0.0009916896,0.0015811153,0.001182018,0.0009317319,0.001004905],"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":[0.013075404,0.00039051205,0.006461186,0.00055614277,0.0005334657,0.00046032175,0.00022917689,0.7429239,0.05834769,0.007228923,0.0031317212,0.16666159],"study_design_scores_gemma":[0.0001762944,0.00030411713,0.001412779,0.000017806478,0.00004665629,0.00007393852,0.00004228306,0.9766368,0.019385133,0.0013833238,0.00049515394,0.000025764379],"about_ca_topic_score_codex":0.0065031634,"about_ca_topic_score_gemma":0.0029875138,"teacher_disagreement_score":0.009454036,"about_ca_system_score_codex":0.00082650216,"about_ca_system_score_gemma":0.00082325353,"threshold_uncertainty_score":0.03162694},"labels":[],"label_agreement":null},{"id":"W2897520795","doi":"10.1007/s10707-018-0332-7","title":"Diverse nearest neighbors queries using linear skylines","year":2018,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"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":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Closeness; k-nearest neighbors algorithm; Computer science; Leverage (statistics); Skyline; Set (abstract data type); Euclidean space; Categorical variable; Point (geometry); Space (punctuation); Data mining; Information retrieval; Theoretical computer science; Mathematics; Geography; Combinatorics; Artificial intelligence; Machine learning","score_opus":0.032010437354175386,"score_gpt":0.27436445519887565,"score_spread":0.24235401784470026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897520795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08087458,0.0015515414,0.89829534,0.00089199975,0.0001878806,0.00040221494,0.0044259513,0.007351654,0.006018869],"genre_scores_gemma":[0.566525,0.00067452755,0.41594398,0.00026073094,0.00022146759,0.00025841664,0.0116270995,0.0004284222,0.0040603094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99468166,0.0013822671,0.0004546201,0.0009320303,0.0021825687,0.00036678673],"domain_scores_gemma":[0.9945016,0.002112015,0.0003792684,0.0018781137,0.0008498609,0.00027914392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022839883,0.0010701163,0.0027370448,0.0038990353,0.001601074,0.0029265955,0.0028989343,0.0017118427,0.0051447386],"category_scores_gemma":[0.011154529,0.0007553851,0.0011896827,0.00716054,0.00084831833,0.0067206747,0.004339023,0.0012426133,0.0022105658],"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":[0.0034045016,0.0011288803,0.011424416,0.0007862451,0.00044188573,0.00071336504,0.0008164568,0.27016628,0.018240592,0.056159,0.052412156,0.58430624],"study_design_scores_gemma":[0.00012727814,0.00015748035,0.00071314635,0.000022445196,0.00003494729,0.00028477036,0.00026216294,0.9578514,0.0040252944,0.030321648,0.006169755,0.000029611501],"about_ca_topic_score_codex":0.0056689056,"about_ca_topic_score_gemma":0.009973305,"teacher_disagreement_score":0.0056689056,"about_ca_system_score_codex":0.0012187475,"about_ca_system_score_gemma":0.0011392816,"threshold_uncertainty_score":0.0172109},"labels":[],"label_agreement":null},{"id":"W2897972190","doi":"10.1145/3282825.3282830","title":"Improving Parallel Performance of Temporally Relevant Top-K Spatial Keyword Search","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Exploit; Ranking (information retrieval); Relevance (law); Information retrieval; Keyword search; Similarity (geometry); Parallelism (grammar); Data mining; Artificial intelligence; Parallel computing","score_opus":0.017214488642603876,"score_gpt":0.24317867895437686,"score_spread":0.22596419031177298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897972190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61162883,0.009137126,0.33869377,0.00097269105,0.00070016127,0.00033131553,0.0024701776,0.019310655,0.0167553],"genre_scores_gemma":[0.81658995,0.0014243195,0.17440102,0.00013687075,0.00015813092,0.00014860863,0.0028640733,0.00044252165,0.0038346595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811965,0.0003062339,0.00021236572,0.00038955017,0.0005897934,0.00038231208],"domain_scores_gemma":[0.996843,0.0012143666,0.0001989138,0.0006745139,0.0008144024,0.00025484175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013578009,0.0010995871,0.0015153728,0.0014148416,0.0009949503,0.0016908571,0.0018871517,0.0005869606,0.0033308165],"category_scores_gemma":[0.0069359276,0.00046794384,0.00055901834,0.0041597625,0.0004708471,0.003044555,0.0015003677,0.000653062,0.0016447369],"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":[0.0072692703,0.0011652436,0.014621139,0.0012828051,0.00037176977,0.0006957215,0.0006767477,0.25247517,0.082150005,0.010762857,0.038443692,0.5900856],"study_design_scores_gemma":[0.00030031794,0.00034295424,0.0013330831,0.000014040458,0.00008339091,0.00021851972,0.00017919007,0.9651024,0.020748293,0.0072101783,0.004438564,0.000029140714],"about_ca_topic_score_codex":0.012320355,"about_ca_topic_score_gemma":0.014166298,"teacher_disagreement_score":0.012320355,"about_ca_system_score_codex":0.0010607111,"about_ca_system_score_gemma":0.0035082828,"threshold_uncertainty_score":0.02449727},"labels":[],"label_agreement":null},{"id":"W2899185753","doi":"10.1016/j.trpro.2018.10.054","title":"A robust datawarehouse as a requirement to the increasing quantity and complexity of travel survey data","year":2018,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Adaptation (eye); Travel survey; Computer science; Travel behavior; Survey data collection; Data science; Data processing; Data mining; Transport engineering; Engineering; Database; Mathematics","score_opus":0.5154869028348916,"score_gpt":0.42974384900978907,"score_spread":0.08574305382510256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899185753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069233696,0.00041786997,0.9634633,0.0052492325,0.00032501598,0.00054051646,0.0043628598,0.012402617,0.0063153077],"genre_scores_gemma":[0.05244669,0.00048600978,0.93190324,0.000923725,0.00028230518,0.00064359716,0.007909435,0.0019412881,0.0034637891],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9783197,0.00629041,0.002723862,0.0035416677,0.008668369,0.00045593092],"domain_scores_gemma":[0.8336375,0.034910202,0.005826602,0.101848595,0.021158447,0.002618691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025671924,0.00066094333,0.0014225398,0.0044347295,0.0016937379,0.010466443,0.0050216573,0.002002136,0.009979631],"category_scores_gemma":[0.092778765,0.0013065416,0.001635567,0.006732017,0.0020394868,0.013427156,0.0073687197,0.005045285,0.007633359],"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":[0.00036886337,0.00044105813,0.00891347,0.0009644029,0.00024906447,0.00057666213,0.0018649859,0.01770742,0.017308857,0.23269813,0.092625804,0.62628126],"study_design_scores_gemma":[0.00012621957,0.0002692879,0.0053105745,0.000935618,0.00017214337,0.0012651085,0.0016167156,0.09209475,0.033674005,0.16804218,0.6961991,0.00029433283],"about_ca_topic_score_codex":0.0031163737,"about_ca_topic_score_gemma":0.0020075897,"teacher_disagreement_score":0.025671924,"about_ca_system_score_codex":0.0017841284,"about_ca_system_score_gemma":0.005144822,"threshold_uncertainty_score":0.13576776},"labels":[],"label_agreement":null},{"id":"W2900653001","doi":"10.1145/3274895.3276476","title":"Using biconnected components for efficient identification of upstream features in large spatial networks (GIS cup)","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Upstream (networking); Laptop; Graph; Identification (biology); Data mining; Theoretical computer science; Computer network","score_opus":0.02628290578341722,"score_gpt":0.28414907830178787,"score_spread":0.25786617251837063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900653001","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068904534,0.00072787306,0.92174137,0.0003986586,0.00004237986,0.00043935122,0.0010523578,0.003279877,0.0034136807],"genre_scores_gemma":[0.21788454,0.00033245902,0.7750718,0.00013152928,0.000021718306,0.00029105338,0.0032163733,0.00036359613,0.002686937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989625,0.00024948575,0.00005534806,0.000280918,0.00029537058,0.00015635916],"domain_scores_gemma":[0.99770397,0.0010500673,0.00030169418,0.00030605673,0.0004746257,0.00016352192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093873614,0.0022391586,0.0013857333,0.005686888,0.0018463052,0.0018349645,0.001968505,0.0015574405,0.0036566502],"category_scores_gemma":[0.0073841484,0.0011543543,0.00096766476,0.006630096,0.0011512241,0.0033063798,0.0027580643,0.0013286682,0.001082028],"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":[0.00053844997,0.0003208144,0.011614263,0.00079338264,0.00022221425,0.0005439716,0.0012050855,0.50035566,0.0100481585,0.034339517,0.017201476,0.42281705],"study_design_scores_gemma":[0.000036304446,0.000054734548,0.0012361254,0.000057200763,0.000047393292,0.00014436648,0.0003508111,0.95920616,0.0030929504,0.030502347,0.0052368646,0.000034697987],"about_ca_topic_score_codex":0.045062825,"about_ca_topic_score_gemma":0.06783119,"teacher_disagreement_score":0.045062825,"about_ca_system_score_codex":0.0014199025,"about_ca_system_score_gemma":0.002781564,"threshold_uncertainty_score":0.0896011},"labels":[],"label_agreement":null},{"id":"W2901356348","doi":"10.1145/3274895.3274961","title":"DISTIL","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; SPARK (programming language); Volume (thermodynamics); Throughput; Distributed computing; Distributed database; Real-time computing; Operating system; Wireless","score_opus":0.01122644371644641,"score_gpt":0.23323707337320865,"score_spread":0.22201062965676224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901356348","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013695294,0.0033391546,0.122919805,0.0055196215,0.0034493292,0.00051170593,0.029884463,0.11261283,0.7080678],"genre_scores_gemma":[0.10104032,0.002870377,0.09970369,0.0030835175,0.00069705443,0.00050140265,0.073750004,0.019929696,0.6984239],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988153,0.0001470934,0.000057506648,0.0002693436,0.0005238181,0.00018704186],"domain_scores_gemma":[0.99827826,0.0002268356,0.000057843215,0.00055306376,0.0005668287,0.00031724633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000876043,0.0011655322,0.00074088183,0.0016585443,0.0015540473,0.0038014539,0.0022765691,0.00138151,0.2530947],"category_scores_gemma":[0.0031164137,0.000565726,0.00059875293,0.0016492577,0.0006347386,0.004048835,0.0053676423,0.0020021591,0.16834038],"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":[0.00068547786,0.00019580974,0.0017706588,0.0007095622,0.000035704285,0.00031252942,0.00027983726,0.0019823574,0.0059502805,0.045101326,0.59795547,0.34502104],"study_design_scores_gemma":[0.000045204702,0.000043744927,0.00035552014,0.00005385677,0.0000078929315,0.0002137184,0.00009487532,0.0039663827,0.0038332113,0.0075707673,0.98379,0.000024921874],"about_ca_topic_score_codex":0.002530832,"about_ca_topic_score_gemma":0.004409814,"teacher_disagreement_score":0.2530947,"about_ca_system_score_codex":0.001001636,"about_ca_system_score_gemma":0.0017659956,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2902521181","doi":"10.1109/tkde.2019.2923914","title":"Skyline Diagram: Efficient Space Partitioning for Skyline Queries","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Skyline; Computer science; Voronoi diagram; Set (abstract data type); Scalability; Data mining; Space (punctuation); Theoretical computer science; Database; Mathematics","score_opus":0.0196882249210968,"score_gpt":0.2580137143152098,"score_spread":0.23832548939411302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902521181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010578696,0.0008273261,0.9790178,0.00020135472,0.00007868308,0.0002844175,0.0020054658,0.005122343,0.0018839473],"genre_scores_gemma":[0.13231614,0.00067619217,0.85554034,0.00011746941,0.00006353264,0.0004406252,0.008220297,0.00068936124,0.0019361272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809355,0.0004061127,0.00016880156,0.0004040748,0.0007634034,0.00016401036],"domain_scores_gemma":[0.99759704,0.0007369468,0.00024837372,0.00066723727,0.00054926774,0.00020101145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089049904,0.0012522646,0.0018242829,0.0031193157,0.0013818851,0.0025430722,0.0023250242,0.0013678767,0.0051011033],"category_scores_gemma":[0.005695955,0.0005970815,0.0011902744,0.006293241,0.00060636573,0.005603361,0.0035802976,0.0012762789,0.0025469614],"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":[0.00088466564,0.00034813082,0.005861663,0.001050288,0.00018003145,0.00037519613,0.0009959302,0.14627668,0.018915761,0.05945334,0.07061195,0.69504637],"study_design_scores_gemma":[0.00015577915,0.00015250583,0.000685443,0.00005461139,0.000028569726,0.0004319633,0.00037804784,0.9128619,0.009599573,0.035939872,0.03965133,0.000060422266],"about_ca_topic_score_codex":0.0050651236,"about_ca_topic_score_gemma":0.0085495915,"teacher_disagreement_score":0.0051011033,"about_ca_system_score_codex":0.0010721668,"about_ca_system_score_gemma":0.0020247668,"threshold_uncertainty_score":0.017064929},"labels":[],"label_agreement":null},{"id":"W2903819099","doi":"10.1049/iet-its.2018.5036","title":"Efficient processing of distance–time <i>k</i> th‐order skyline queries in bicriteria networks","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Data Management and Algorithms","field":"Computer Science","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":"Novelis (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Skyline; Computer science; Order (exchange); Combinatorics; Mathematics; Data mining; Economics","score_opus":0.012067479246454347,"score_gpt":0.23528894678985798,"score_spread":0.22322146754340363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903819099","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2885767,0.0008676919,0.7001803,0.00036798173,0.000055632805,0.00023945392,0.00086779124,0.0033338168,0.005510593],"genre_scores_gemma":[0.7860429,0.00027513248,0.2096749,0.0001227336,0.00003209808,0.00012456595,0.0017781069,0.000112029025,0.0018374705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893767,0.00019211552,0.00011907741,0.0002848904,0.00027962294,0.00018659477],"domain_scores_gemma":[0.99871385,0.00047761062,0.0001466963,0.00030940963,0.0002712728,0.0000811751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000584952,0.0006697914,0.0010124891,0.0011024993,0.00087911775,0.0012756385,0.0014597526,0.000770313,0.0013374182],"category_scores_gemma":[0.0026843816,0.0003507882,0.00041445438,0.0021199943,0.0004857928,0.0026979244,0.0011022473,0.00049126626,0.00045928138],"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":[0.0020199693,0.00035350616,0.01076637,0.0004938027,0.00018902711,0.00089134264,0.0012359486,0.3587707,0.082546726,0.017583773,0.016470205,0.5086787],"study_design_scores_gemma":[0.00003508096,0.00014087936,0.0017025406,0.000007944187,0.000018188935,0.00028875985,0.0004187275,0.9764109,0.012356403,0.005612132,0.002982365,0.00002608209],"about_ca_topic_score_codex":0.009994349,"about_ca_topic_score_gemma":0.010311006,"teacher_disagreement_score":0.009994349,"about_ca_system_score_codex":0.00079391256,"about_ca_system_score_gemma":0.001012656,"threshold_uncertainty_score":0.019872367},"labels":[],"label_agreement":null},{"id":"W2904055118","doi":"","title":"Proceedings of the Eleventh International Symposium on Combinatorial Search","year":2018,"lang":"en","type":"article","venue":"Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Eleventh; Computer science; Information retrieval; Physics","score_opus":0.017048061752254713,"score_gpt":0.2690321695104767,"score_spread":0.251984107758222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904055118","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019683454,0.06308723,0.3506184,0.0288804,0.08772915,0.0004469423,0.0034692255,0.002091789,0.4439934],"genre_scores_gemma":[0.16716295,0.04241365,0.26751035,0.0060075945,0.03130816,0.00082249544,0.011839462,0.003012126,0.46992317],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99714166,0.001006789,0.00018408509,0.0004192975,0.0009437946,0.00030425386],"domain_scores_gemma":[0.99364203,0.002541028,0.0001897561,0.0010608219,0.0016878231,0.0008785759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00451938,0.001731613,0.003159731,0.0023525076,0.0013948227,0.009426895,0.0027322748,0.0021243456,0.05848913],"category_scores_gemma":[0.007863143,0.0007131436,0.0017568952,0.0027525902,0.0015570112,0.0028233484,0.0024778848,0.005633649,0.016926423],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084207306,0.00052474183,0.0013168169,0.0006555933,0.00029606154,0.00017522406,0.00015032542,0.013491984,0.0017866446,0.10993805,0.56622046,0.304602],"study_design_scores_gemma":[0.00021491488,0.00027369612,0.0016454877,0.0006101514,0.00022850423,0.00038984587,0.00019734717,0.10113823,0.0027531595,0.14672925,0.7457354,0.0000839975],"about_ca_topic_score_codex":0.0035024416,"about_ca_topic_score_gemma":0.0063763037,"teacher_disagreement_score":0.05848913,"about_ca_system_score_codex":0.0027438148,"about_ca_system_score_gemma":0.0038019738,"threshold_uncertainty_score":0.19566566},"labels":[],"label_agreement":null},{"id":"W2907125844","doi":"10.4230/lipics.icdt.2019.9","title":"Categorical Range Reporting with Frequencies","year":2019,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Range (aeronautics); Categorical variable; Computer science; Machine learning; Engineering","score_opus":0.018562575614069335,"score_gpt":0.2422777644464392,"score_spread":0.22371518883236985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907125844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03607315,0.0013299056,0.9507749,0.0020241388,0.00023989755,0.00021565933,0.0017562038,0.0034684043,0.0041176556],"genre_scores_gemma":[0.4186184,0.0011348166,0.5669502,0.0011619828,0.00074979616,0.00060766976,0.0036305073,0.0009013416,0.0062452927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98005486,0.0057835523,0.0019544032,0.0039806813,0.0065037613,0.0017227393],"domain_scores_gemma":[0.93785787,0.030603714,0.0053476826,0.020337159,0.004604643,0.0012489144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008357789,0.0012502337,0.002549121,0.0027602476,0.001701482,0.0062782057,0.0055940906,0.002659234,0.007932506],"category_scores_gemma":[0.059640832,0.00096903066,0.0019178953,0.0076071215,0.0024115532,0.017907422,0.0067006093,0.0041693435,0.0021087695],"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":[0.0018361753,0.0004886397,0.01341971,0.0011687509,0.00024273558,0.00047783955,0.0017692115,0.120526016,0.011935541,0.35405326,0.025461376,0.46862084],"study_design_scores_gemma":[0.00024365554,0.00047315008,0.0020810445,0.00019701327,0.00016972919,0.0012115708,0.00092199893,0.44500384,0.014913527,0.4972112,0.037376918,0.00019640096],"about_ca_topic_score_codex":0.003298283,"about_ca_topic_score_gemma":0.0015957078,"teacher_disagreement_score":0.008357789,"about_ca_system_score_codex":0.0022713481,"about_ca_system_score_gemma":0.002165761,"threshold_uncertainty_score":0.044200778},"labels":[],"label_agreement":null},{"id":"W2907216425","doi":"10.1145/3282834.3282841","title":"A Performance Study of Big Spatial Data Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Big data; Computer science; Scalability; SPARK (programming language); Spatial analysis; Benchmark (surveying); Field (mathematics); Variety (cybernetics); Volume (thermodynamics); Computer data storage; Data science; Database; Data mining; Artificial intelligence; Operating system","score_opus":0.0771246113023431,"score_gpt":0.2765820328687794,"score_spread":0.1994574215664363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907216425","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9257112,0.0082135955,0.027511967,0.0026321055,0.000603299,0.00044607953,0.0057241297,0.0038017058,0.025355851],"genre_scores_gemma":[0.98310065,0.0012300335,0.009635158,0.00013806266,0.00015886762,0.00012835627,0.004166683,0.00021921683,0.0012230567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99293643,0.0015067921,0.0006998139,0.0009741997,0.0029306808,0.00095213176],"domain_scores_gemma":[0.9811369,0.007803862,0.0015002561,0.0022227268,0.005681269,0.0016549975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004091319,0.0012027709,0.001045603,0.0033549292,0.0016657813,0.0027790954,0.0018677615,0.0008409671,0.002472141],"category_scores_gemma":[0.016254997,0.00040571822,0.000589411,0.0067622145,0.0011317892,0.0047910186,0.0016276924,0.0010216463,0.00070924277],"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":[0.003374844,0.0020237395,0.11821233,0.0040969076,0.0009253561,0.0012321208,0.0029287674,0.5226623,0.03331081,0.04129516,0.0845366,0.18540113],"study_design_scores_gemma":[0.00014944485,0.0017729127,0.05208088,0.00015963246,0.0001553419,0.00073845196,0.0026393153,0.8829492,0.019267337,0.014539531,0.02541662,0.00013128191],"about_ca_topic_score_codex":0.008913552,"about_ca_topic_score_gemma":0.0030835369,"teacher_disagreement_score":0.008913552,"about_ca_system_score_codex":0.0022983197,"about_ca_system_score_gemma":0.0019254907,"threshold_uncertainty_score":0.02163726},"labels":[],"label_agreement":null},{"id":"W2908981684","doi":"10.1109/iemcon.2018.8614984","title":"Strategies for Continuous Region Query Processing in Moving Point Sets","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Workload; Online aggregation; Point (geometry); Web query classification; Query expansion; Query optimization; Location-based service; Sargable; Web search query; Server; Transmission (telecommunications); Spatial query; Mobile device; Response time; Data mining; Real-time computing; Search engine; Information retrieval; Computer network; Operating system; Telecommunications","score_opus":0.026909964421460462,"score_gpt":0.2763185627897786,"score_spread":0.24940859836831816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908981684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01935529,0.00030966022,0.97794557,0.00020190216,0.000028063905,0.00021051924,0.00005915044,0.00064124767,0.0012486652],"genre_scores_gemma":[0.3086888,0.00034408155,0.6876833,0.0001814612,0.00011264732,0.0005131393,0.00029876592,0.00019066418,0.00198726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671435,0.0009913462,0.00022724962,0.00043740848,0.0012789847,0.0003506728],"domain_scores_gemma":[0.9932515,0.0041867024,0.00038061527,0.0009780999,0.0009540747,0.00024889642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026879956,0.0012178795,0.0013759749,0.0017288052,0.0011537151,0.0017778912,0.0037672925,0.0016132877,0.0035337012],"category_scores_gemma":[0.009467272,0.0006683188,0.0009388649,0.0019715033,0.0012171663,0.0033265408,0.0029440261,0.0014122307,0.0010788394],"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":[0.0016064056,0.000837513,0.0023614164,0.0006579353,0.00019658783,0.0006600369,0.0018027419,0.44464487,0.067873195,0.121848166,0.012887801,0.3446234],"study_design_scores_gemma":[0.0001000768,0.00025572302,0.0002880526,0.000017484483,0.000031295247,0.00023513936,0.00023198714,0.9688278,0.007850282,0.018089699,0.004029249,0.000043306296],"about_ca_topic_score_codex":0.0033312568,"about_ca_topic_score_gemma":0.0030809536,"teacher_disagreement_score":0.0037672925,"about_ca_system_score_codex":0.00092506845,"about_ca_system_score_gemma":0.001255329,"threshold_uncertainty_score":0.014215648},"labels":[],"label_agreement":null},{"id":"W2911209624","doi":"10.1109/smc.2018.00714","title":"Extending Conditional Preference Network with User's Genuine Decisions","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Ceteris paribus; Preference; Outcome (game theory); Net (polyhedron); Computer science; Extension (predicate logic); Mathematical economics; Mathematics; Microeconomics; Economics; Statistics","score_opus":0.04382751988778996,"score_gpt":0.25431130348608055,"score_spread":0.2104837835982906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911209624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07572198,0.00016821864,0.91387075,0.0007279643,0.000040217303,0.00019830644,0.001433275,0.001041648,0.006797734],"genre_scores_gemma":[0.7180803,0.0002216972,0.2752652,0.00022559147,0.000037834026,0.00024406437,0.0018234804,0.00011294972,0.003988857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957397,0.002052822,0.00026176113,0.0006828395,0.00096932333,0.0002935883],"domain_scores_gemma":[0.99140173,0.005469171,0.0005501586,0.001496599,0.00073348056,0.00034884358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047045886,0.0006921771,0.00065046153,0.0010875661,0.00061392225,0.0018413565,0.0015933735,0.00076425704,0.005192892],"category_scores_gemma":[0.0136053655,0.00030827458,0.00096390455,0.0020597284,0.0009310902,0.006357646,0.002171462,0.0013355649,0.00046021093],"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":[0.0016568366,0.00040590874,0.012699632,0.0003720463,0.0001636019,0.0007312764,0.0005306731,0.43466607,0.0047642547,0.3135944,0.007215828,0.22319946],"study_design_scores_gemma":[0.000036607857,0.00007938525,0.00073681556,0.000018501887,0.00003488326,0.00013962515,0.00008375156,0.86506855,0.0022108841,0.12665519,0.004907562,0.000028227993],"about_ca_topic_score_codex":0.0065548467,"about_ca_topic_score_gemma":0.008834323,"teacher_disagreement_score":0.0065548467,"about_ca_system_score_codex":0.0020614434,"about_ca_system_score_gemma":0.001322727,"threshold_uncertainty_score":0.024880588},"labels":[],"label_agreement":null},{"id":"W2911355891","doi":"10.1109/bigdata.2018.8621966","title":"Trajectory Cluster Lifecycle Analysis: An Evolutionary Perspective","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster (spacecraft); Perspective (graphical); Computer science; Data science; Trajectory; Artificial intelligence","score_opus":0.014315281322586011,"score_gpt":0.27119591305191043,"score_spread":0.2568806317293244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911355891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040770054,0.0013780593,0.94632244,0.0027986413,0.00008621294,0.00019759574,0.0008095732,0.00024831088,0.007389136],"genre_scores_gemma":[0.42688215,0.0021616335,0.56158525,0.00030714012,0.00017960307,0.00039128013,0.0020519346,0.00030282303,0.006138179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974286,0.0010463048,0.00011136821,0.0005627493,0.00059483753,0.00025609668],"domain_scores_gemma":[0.989931,0.004929967,0.0012024133,0.0011521798,0.0021733507,0.0006110427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005326326,0.00063404813,0.000853525,0.0065814303,0.0021166408,0.0041632284,0.0022903592,0.0012929209,0.0030456493],"category_scores_gemma":[0.019275328,0.0005741376,0.0013733904,0.005652858,0.0030629756,0.006349686,0.00314558,0.0018472422,0.00055189605],"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":[0.00007744466,0.00008509608,0.036882482,0.0003388066,0.00023542815,0.0004144567,0.0029107756,0.14963545,0.0016796684,0.68564695,0.0056572743,0.11643612],"study_design_scores_gemma":[0.000012207738,0.00004407224,0.009019379,0.00017111773,0.000059281934,0.0003697814,0.0022159847,0.53358966,0.0011839676,0.417552,0.035714872,0.000067665685],"about_ca_topic_score_codex":0.012730807,"about_ca_topic_score_gemma":0.01083342,"teacher_disagreement_score":0.012730807,"about_ca_system_score_codex":0.0031949647,"about_ca_system_score_gemma":0.002431034,"threshold_uncertainty_score":0.028168678},"labels":[],"label_agreement":null},{"id":"W2911937761","doi":"10.1109/bigdata.2018.8621964","title":"Fast and Accurate Mining of Node Importance in Trajectory Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Trajectory; Node (physics); Focus (optics); Similarity (geometry); Data mining; Object (grammar); Cluster analysis; Range (aeronautics); Scale (ratio); Artificial intelligence","score_opus":0.01832773874836648,"score_gpt":0.24578005086766594,"score_spread":0.22745231211929945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911937761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2494542,0.0010954625,0.74400365,0.0003553437,0.00006574925,0.00014129374,0.0020860892,0.0014986568,0.0012994889],"genre_scores_gemma":[0.77319866,0.000538963,0.22057225,0.000038381193,0.00006781874,0.00011031442,0.0044202907,0.00007265536,0.0009805926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999337,0.00009189182,0.000050421088,0.00022125167,0.00022466667,0.0000747942],"domain_scores_gemma":[0.99799335,0.0009130507,0.00036338295,0.00019312423,0.0004292444,0.000107940694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007812753,0.0008517721,0.00068755326,0.003042645,0.0006320694,0.0008323697,0.0012876936,0.00070038036,0.0003354366],"category_scores_gemma":[0.005862464,0.00037179815,0.00037858824,0.0030800104,0.00030910096,0.001713984,0.0008180111,0.0006565693,0.000284986],"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":[0.00036072216,0.0001954748,0.059619617,0.0002780164,0.00015538395,0.00060261716,0.0005152023,0.60626584,0.016600968,0.0070268963,0.00552608,0.3028531],"study_design_scores_gemma":[0.0000053695835,0.000021559745,0.003775503,0.000008362797,0.000010638256,0.00008764643,0.00008910684,0.9885888,0.0023009973,0.0040942444,0.0010116502,0.000006078619],"about_ca_topic_score_codex":0.010128069,"about_ca_topic_score_gemma":0.016244395,"teacher_disagreement_score":0.010128069,"about_ca_system_score_codex":0.00083892257,"about_ca_system_score_gemma":0.0007283221,"threshold_uncertainty_score":0.020138264},"labels":[],"label_agreement":null},{"id":"W2912457582","doi":"","title":"Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Uncertain data; Computer science; Data science; Data mining; Probabilistic logic; Scalability; Knowledge extraction; Object (grammar); Artificial intelligence; Database","score_opus":0.06236342699461999,"score_gpt":0.29629887884226885,"score_spread":0.23393545184764886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912457582","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016315468,0.23086475,0.49275357,0.11943079,0.05661042,0.0015983197,0.02226631,0.008443004,0.05171744],"genre_scores_gemma":[0.09285794,0.16051684,0.5247954,0.022191642,0.022129007,0.0020469127,0.10134063,0.0027715852,0.071350016],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9834584,0.0053292285,0.0017592357,0.001990102,0.0066683874,0.0007946837],"domain_scores_gemma":[0.96638393,0.018173657,0.0011177587,0.0055905073,0.006289806,0.0024442538],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.020478997,0.0026127195,0.006137429,0.0065258965,0.0022956042,0.013581013,0.007588538,0.0047914824,0.014011318],"category_scores_gemma":[0.038123664,0.0021386398,0.004415321,0.0057607465,0.003501237,0.016872449,0.008743549,0.0101605235,0.0061560543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005706122,0.0004867485,0.0024200825,0.0014657015,0.0008268373,0.00067882566,0.0006641424,0.011681307,0.00095454813,0.024750143,0.6424092,0.31309178],"study_design_scores_gemma":[0.00017753713,0.0001307127,0.0026922848,0.0012636567,0.00031728597,0.0007715775,0.0006392904,0.044145264,0.0014636636,0.075657375,0.8725541,0.00018724962],"about_ca_topic_score_codex":0.015966246,"about_ca_topic_score_gemma":0.021021252,"teacher_disagreement_score":0.9859887,"about_ca_system_score_codex":0.0043557617,"about_ca_system_score_gemma":0.007669449,"threshold_uncertainty_score":0.10830456},"labels":[],"label_agreement":null},{"id":"W2912701419","doi":"","title":"Proceedings of the First International Workshop on Keyword Search on Structured Data","year":2009,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; World Wide Web; Keyword search; XML; Information retrieval; Data science","score_opus":0.04442472414593395,"score_gpt":0.2881679948879358,"score_spread":0.24374327074200186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912701419","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0168914,0.097681016,0.51451474,0.05102002,0.11799328,0.00218127,0.017998343,0.008690931,0.17302898],"genre_scores_gemma":[0.0633918,0.05892821,0.3470552,0.013546582,0.027115982,0.0016949045,0.07157708,0.0051797717,0.41151047],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99402654,0.0017217794,0.00086200115,0.0011443842,0.0017972544,0.0004481259],"domain_scores_gemma":[0.98961115,0.0042581884,0.0003122634,0.001487025,0.0033408657,0.0009905811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066292267,0.0016897991,0.0026544076,0.0029245939,0.0012495922,0.0072225574,0.002933519,0.0022749873,0.064189546],"category_scores_gemma":[0.01591822,0.00085570454,0.0022974813,0.0034741259,0.0015249476,0.010478268,0.0037267723,0.004214181,0.035163995],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071173225,0.00029888717,0.000496197,0.0011765662,0.00017676615,0.00055101333,0.00065582956,0.0017103981,0.0046417858,0.020347333,0.68920016,0.28003344],"study_design_scores_gemma":[0.000086773536,0.00013110961,0.0004946129,0.00043167407,0.00007262822,0.0004664426,0.00035452,0.004565503,0.0020301717,0.017662307,0.9736517,0.000052610685],"about_ca_topic_score_codex":0.0036487873,"about_ca_topic_score_gemma":0.0036517093,"teacher_disagreement_score":0.064189546,"about_ca_system_score_codex":0.0020456996,"about_ca_system_score_gemma":0.0038613474,"threshold_uncertainty_score":0.21473545},"labels":[],"label_agreement":null},{"id":"W2913227339","doi":"10.5539/mas.v13n2p258","title":"Extracting Interesting Regions and Trips from Taxi Trajectory Data","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Jordan","keywords":"Computer science; TRIPS architecture; Trajectory; Taxis; Artificial intelligence; Region of interest; Set (abstract data type); Identification (biology); Point of interest; Global Positioning System; Computer vision; Data science; Transport engineering","score_opus":0.05857205640769624,"score_gpt":0.2665555504015678,"score_spread":0.20798349399387156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913227339","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74153644,0.0025237107,0.17210524,0.00075220666,0.00016546108,0.0006091622,0.07210942,0.0037379824,0.0064603076],"genre_scores_gemma":[0.78743106,0.0013084621,0.14701986,0.000037920432,0.000071147406,0.00025224668,0.061470836,0.0001275876,0.0022808397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999634,0.00004482046,0.000040103296,0.00011919839,0.00009936714,0.00006245022],"domain_scores_gemma":[0.99898165,0.00036851413,0.00016789888,0.00014266933,0.0002574753,0.000081755774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036867466,0.0007175397,0.00038517526,0.007236308,0.0005247199,0.00090419815,0.00056626496,0.0006250985,0.001053883],"category_scores_gemma":[0.0024085648,0.00021420594,0.0008092009,0.0051601715,0.0002533138,0.00086302875,0.00069527916,0.0004016895,0.0008078542],"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":[0.0008228113,0.0004957783,0.17932095,0.00138352,0.00053777586,0.0037318615,0.002183356,0.10574859,0.019396706,0.00512572,0.027726397,0.65352654],"study_design_scores_gemma":[0.000059161906,0.00034441423,0.263506,0.00031803298,0.0003553166,0.0028336053,0.01001214,0.6258525,0.020614583,0.012167407,0.063786805,0.00015009123],"about_ca_topic_score_codex":0.016926525,"about_ca_topic_score_gemma":0.028971124,"teacher_disagreement_score":0.016926525,"about_ca_system_score_codex":0.00060556905,"about_ca_system_score_gemma":0.0010001884,"threshold_uncertainty_score":0.033656},"labels":[],"label_agreement":null},{"id":"W2917106292","doi":"10.14778/3303753.3303756","title":"Correlation constraint shortest path over large multi-relation graphs","year":2019,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Relation (database); Theoretical computer science; Computer science; Reachability; Vertex (graph theory); Tree traversal; Enhanced Data Rates for GSM Evolution; Shortest path problem; Longest path problem; Mathematics; Discrete mathematics; Graph; Algorithm; Data mining; Artificial intelligence","score_opus":0.010235153334248006,"score_gpt":0.2181015650240609,"score_spread":0.20786641168981287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917106292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.120483294,0.0005549997,0.8736378,0.0010022703,0.00003247619,0.0001633146,0.00125459,0.00097401807,0.0018973973],"genre_scores_gemma":[0.5382194,0.0005780685,0.45610523,0.00025359786,0.000046812835,0.00022729397,0.00231388,0.00027025182,0.001985533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973374,0.000944526,0.00017678755,0.00072939094,0.00058059965,0.00023128049],"domain_scores_gemma":[0.9892029,0.00766222,0.0011237735,0.0010989957,0.0005613024,0.00035069045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017030475,0.0008879293,0.0013327205,0.0014953669,0.0010516609,0.0015479303,0.0018672568,0.0012271485,0.0021864052],"category_scores_gemma":[0.011271031,0.00067295,0.0008618105,0.0048539685,0.0010705761,0.0062541673,0.0019506058,0.0017970351,0.00027433407],"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":[0.00020285508,0.00013407301,0.0025389732,0.0003905185,0.00010247033,0.0008799752,0.00037758108,0.8110193,0.005774776,0.09066295,0.0043819915,0.083534524],"study_design_scores_gemma":[0.000028427668,0.00003552161,0.00048458914,0.00001697136,0.000021469055,0.00022820075,0.00014210149,0.9050405,0.0018883197,0.08996712,0.0021310132,0.000015738437],"about_ca_topic_score_codex":0.006650717,"about_ca_topic_score_gemma":0.009261714,"teacher_disagreement_score":0.006650717,"about_ca_system_score_codex":0.002075499,"about_ca_system_score_gemma":0.001608752,"threshold_uncertainty_score":0.0150588155},"labels":[],"label_agreement":null},{"id":"W2918480433","doi":"10.1007/978-1-4899-7993-3_80680-1","title":"Rank-Aware Query Processing","year":2016,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Rank (graph theory); Computer science; Information retrieval; Mathematics; Combinatorics","score_opus":0.01644125424369736,"score_gpt":0.23057280090675578,"score_spread":0.21413154666305842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918480433","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005301508,0.009163006,0.9376152,0.0008745957,0.00062509364,0.00021773773,0.0018495098,0.0134816235,0.030871633],"genre_scores_gemma":[0.11553742,0.009992309,0.79668003,0.0007943822,0.0010989921,0.0001827161,0.008935003,0.0016497321,0.065129414],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981912,0.00018067872,0.00012251665,0.00031542918,0.0010204362,0.00016968923],"domain_scores_gemma":[0.9987079,0.00028920374,0.00005104681,0.0005168556,0.00038782644,0.000047113743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009364835,0.0012514998,0.0016160767,0.0016408118,0.0007558038,0.0034817213,0.002557014,0.00081202417,0.01714408],"category_scores_gemma":[0.0027233206,0.00061813166,0.0008089569,0.0034668122,0.0004831743,0.0029874058,0.0019098715,0.0018301978,0.015408666],"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":[0.00023032959,0.00017688064,0.0002857836,0.00063964527,0.00008183016,0.000079614285,0.0000858531,0.012394875,0.02300052,0.03372861,0.11617528,0.8131209],"study_design_scores_gemma":[0.000088154695,0.00028023066,0.0010766016,0.00018080356,0.00018287543,0.0012702275,0.000188949,0.46386892,0.06938407,0.15242958,0.31090003,0.00014954917],"about_ca_topic_score_codex":0.0017500994,"about_ca_topic_score_gemma":0.0029664722,"teacher_disagreement_score":0.01714408,"about_ca_system_score_codex":0.0006735749,"about_ca_system_score_gemma":0.0012937697,"threshold_uncertainty_score":0.057352662},"labels":[],"label_agreement":null},{"id":"W2918878758","doi":"10.1007/978-0-387-39940-9_297","title":"Query Processing and Optimization in Object Relational Databases","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Query optimization; Database; Relational database; Sargable; Information retrieval; Object (grammar); Artificial intelligence; Web search query; Search engine","score_opus":0.023954901432926896,"score_gpt":0.2416569673018848,"score_spread":0.2177020658689579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918878758","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00709327,0.04054554,0.9244781,0.0015076498,0.00044122842,0.00012886876,0.00039547426,0.0016975309,0.023712378],"genre_scores_gemma":[0.10608246,0.036198158,0.8117274,0.00067934825,0.0014306551,0.00031916765,0.001977911,0.0009256956,0.040659208],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963826,0.00070705084,0.00031461843,0.00038704634,0.0020453713,0.00016332747],"domain_scores_gemma":[0.9985164,0.00090074784,0.000059696722,0.00028925997,0.0001977622,0.000036096462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002213042,0.0010364767,0.0019011237,0.00114603,0.000670568,0.0050791283,0.002908738,0.0010642927,0.0056668366],"category_scores_gemma":[0.005574837,0.00090599625,0.0009584069,0.0051755,0.0017535982,0.005678536,0.001691638,0.0021833894,0.0024124782],"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":[0.00016502588,0.00016869899,0.00063346885,0.0012375808,0.000107711465,0.00017418542,0.00045634096,0.0449909,0.0032826655,0.33285415,0.054566614,0.5613626],"study_design_scores_gemma":[0.000054340493,0.00008798422,0.00080018764,0.00021738042,0.00009794049,0.0004517227,0.00022100295,0.30195135,0.00468086,0.55269355,0.1386801,0.00006366937],"about_ca_topic_score_codex":0.00381359,"about_ca_topic_score_gemma":0.0026679668,"teacher_disagreement_score":0.0056668366,"about_ca_system_score_codex":0.0014307955,"about_ca_system_score_gemma":0.0014064226,"threshold_uncertainty_score":0.018957436},"labels":[],"label_agreement":null},{"id":"W2919820325","doi":"","title":"Labeling Mycielski Graphs with a Condition at Distance Two.","year":2018,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Combinatorics; Statistics","score_opus":0.009028724248424968,"score_gpt":0.23720384316054777,"score_spread":0.2281751189121228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919820325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3252766,0.0006524641,0.46527016,0.0060777385,0.00096087926,0.0004269367,0.0050899973,0.0047566765,0.19148856],"genre_scores_gemma":[0.7614194,0.00030085954,0.19023842,0.0017551559,0.00021370502,0.00026662202,0.005453799,0.0011010984,0.039250907],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984547,0.00034047881,0.00014892884,0.00036077728,0.00032558438,0.0003695975],"domain_scores_gemma":[0.99374205,0.0027432847,0.00046455592,0.001650264,0.0007848235,0.00061496964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011908987,0.00050876697,0.0006610993,0.0012688398,0.0024242257,0.0030057817,0.0016966753,0.0018805849,0.022528186],"category_scores_gemma":[0.008717299,0.0005670473,0.00074362813,0.0015874584,0.0018767444,0.00706947,0.004359854,0.0029542502,0.004499728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012941032,0.0003644504,0.0024671757,0.00041599217,0.000045589153,0.00035551476,0.00074438826,0.004546962,0.022784673,0.87092334,0.03256372,0.06349418],"study_design_scores_gemma":[0.00013648086,0.00014762279,0.0013708757,0.00011112991,0.000070176786,0.00034161514,0.00067877315,0.017324807,0.021583527,0.912583,0.045589726,0.000062223065],"about_ca_topic_score_codex":0.0022079034,"about_ca_topic_score_gemma":0.0035860345,"teacher_disagreement_score":0.022528186,"about_ca_system_score_codex":0.0017289295,"about_ca_system_score_gemma":0.001614355,"threshold_uncertainty_score":0.07536435},"labels":[],"label_agreement":null},{"id":"W2923556723","doi":"10.1016/j.disopt.2019.03.004","title":"Combinatorial optimization with interaction costs: Complexity and solvable cases","year":2019,"lang":"en","type":"article","venue":"Discrete Optimization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund","keywords":"Parameterized complexity; Combinatorial optimization; Matroid; Mathematics; Quadratic assignment problem; Diagonal; Combinatorics; Matrix (chemical analysis); Mathematical optimization; Intersection (aeronautics); Rank (graph theory); Optimization problem; Computational complexity theory; Algorithm","score_opus":0.014847516591581499,"score_gpt":0.23939515683151302,"score_spread":0.2245476402399315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923556723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14929736,0.002233523,0.7184098,0.007861599,0.00028706394,0.0003558491,0.0013980676,0.00037206837,0.11978469],"genre_scores_gemma":[0.8376357,0.0016542259,0.12985075,0.0006441153,0.0009038901,0.0007495942,0.0010944072,0.0003169,0.027150406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.994221,0.002575626,0.00023848706,0.0006940922,0.0014523393,0.00081854506],"domain_scores_gemma":[0.9553624,0.0392213,0.0017603299,0.0019167616,0.00095700915,0.0007822088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050290865,0.0019360374,0.0028692028,0.0021152887,0.0016229701,0.007898756,0.0039465837,0.0046473104,0.016037527],"category_scores_gemma":[0.040436953,0.0012986636,0.0026762737,0.004772198,0.0034036152,0.008504356,0.0038362027,0.0062046517,0.00094468833],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024031583,0.00038381692,0.0010460359,0.00040480116,0.00014615575,0.00052276836,0.00015918314,0.19455053,0.00042654396,0.7711115,0.009722346,0.021286024],"study_design_scores_gemma":[0.00007301324,0.000034326484,0.0002796338,0.000032674347,0.000045121913,0.00017758408,0.00007171988,0.41204554,0.00024446088,0.58527696,0.0016930348,0.000025879823],"about_ca_topic_score_codex":0.0031408481,"about_ca_topic_score_gemma":0.0026073845,"teacher_disagreement_score":0.016037527,"about_ca_system_score_codex":0.0028695017,"about_ca_system_score_gemma":0.0023059598,"threshold_uncertainty_score":0.053650856},"labels":[],"label_agreement":null},{"id":"W2927487476","doi":"10.3138/cart.54.1.2018-0022","title":"Central Place Indexing: Hierarchical Linear Indexing Systems for Mixed-Aperture Hexagonal Discrete Global Grid Systems","year":2019,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Search engine indexing; Geospatial analysis; Grid; Hexagonal tiling; Computer science; Geodesic; Theoretical computer science; Computational science; Algorithm; Topology (electrical circuits); Data mining; Mathematics; Information retrieval; Geometry; Geography; Remote sensing; Combinatorics","score_opus":0.008742193213408935,"score_gpt":0.2652332684159675,"score_spread":0.2564910752025586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927487476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007020891,0.00039075318,0.9757705,0.00014129635,0.00013811736,0.00016070649,0.0013770314,0.0053175837,0.009683209],"genre_scores_gemma":[0.23722355,0.00056278746,0.74678606,0.00015300521,0.00016780537,0.00036367282,0.005537842,0.0006305638,0.008574756],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998514,0.0003039969,0.00022766601,0.00022732517,0.0005584905,0.00016850443],"domain_scores_gemma":[0.9974827,0.00047147498,0.00026076793,0.0009834619,0.00068247906,0.000119165656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012394075,0.00065929187,0.0010129123,0.0017724786,0.001022548,0.0038906583,0.0019203394,0.0006536589,0.01320194],"category_scores_gemma":[0.006598924,0.000388459,0.0005779174,0.004412354,0.0008838174,0.0038647621,0.002698503,0.0011150367,0.005976966],"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":[0.00046645233,0.00011268899,0.0016787576,0.00046631892,0.000046582863,0.00022330067,0.00053359225,0.096579835,0.008336704,0.30291766,0.039020248,0.5496179],"study_design_scores_gemma":[0.00012564224,0.00021185282,0.0005533101,0.00010499485,0.00003192173,0.00033878497,0.00040006856,0.7284318,0.013932973,0.16134866,0.09442821,0.00009175734],"about_ca_topic_score_codex":0.0055214413,"about_ca_topic_score_gemma":0.005796151,"teacher_disagreement_score":0.01320194,"about_ca_system_score_codex":0.0012278073,"about_ca_system_score_gemma":0.0018165185,"threshold_uncertainty_score":0.044164956},"labels":[],"label_agreement":null},{"id":"W2931608745","doi":"10.1007/s12652-019-01287-1","title":"Developing FIA5 to FSTPR25 for modeling spatio-temporal relevancy in context-aware wayfinding systems","year":2019,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Data Management and Algorithms","field":"Computer Science","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":"Université Laval","funders":"","keywords":"Computer science; Context (archaeology); Fuzzy logic; Interval (graph theory); Process (computing); Computational intelligence; Spatial contextual awareness; Artificial intelligence; Fuzzy set; Data mining; Human–computer interaction; Mathematics; Geography","score_opus":0.06657421554706484,"score_gpt":0.3035039872047331,"score_spread":0.23692977165766826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2931608745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080979474,0.00023646008,0.9051896,0.00019309527,0.0001066411,0.00018947844,0.0012345834,0.006377522,0.005493137],"genre_scores_gemma":[0.5695545,0.00015074003,0.4228672,0.0001193955,0.000026124837,0.00028918346,0.002451094,0.00052805163,0.0040136706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999617,0.0000690581,0.000032400665,0.00013042372,0.00008816784,0.0000629626],"domain_scores_gemma":[0.99937385,0.0002576997,0.000039863724,0.00010263263,0.0001767147,0.000049260914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008118435,0.0009124046,0.00080281077,0.0012292993,0.0009365831,0.0017030694,0.0026144648,0.001380521,0.0035808708],"category_scores_gemma":[0.003248757,0.0006170427,0.001885664,0.00066568615,0.00046603152,0.0016486226,0.0015178785,0.0011539755,0.0009182115],"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":[0.000040818188,0.000072788775,0.002019023,0.00006151183,0.000109499015,0.00008673608,0.00012835693,0.9515637,0.0018623879,0.008013733,0.0014797375,0.03456173],"study_design_scores_gemma":[0.0000018046114,0.0000069943226,0.00011424324,0.000003938718,0.000007053618,0.000005775318,0.000014739299,0.99735343,0.00040075678,0.0012858189,0.0008013909,0.0000041013996],"about_ca_topic_score_codex":0.11912174,"about_ca_topic_score_gemma":0.10948886,"teacher_disagreement_score":0.11912174,"about_ca_system_score_codex":0.0016373753,"about_ca_system_score_gemma":0.002377645,"threshold_uncertainty_score":0.23685676},"labels":[],"label_agreement":null},{"id":"W2932849524","doi":"10.3138/cart.54.1.2018-0010","title":"Geospatial Data Organization Methods with Emphasis on Aperture-3 Hexagonal Discrete Global Grid Systems","year":2019,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; University of Calgary","keywords":"Computer science; Geospatial analysis; Hierarchy; Grid; Emphasis (telecommunications); Scale (ratio); Wavelet; Data mining; Distributed computing; Theoretical computer science; Remote sensing; Telecommunications; Artificial intelligence; Geography","score_opus":0.01403077564004055,"score_gpt":0.31610286823174427,"score_spread":0.3020720925917037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2932849524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022905804,0.0002530535,0.9940301,0.00019368008,0.000065272565,0.000070334645,0.00026136925,0.00029344417,0.0025420957],"genre_scores_gemma":[0.1105492,0.0009267764,0.88326126,0.00014636695,0.00012676333,0.0002500842,0.0011381903,0.0001717008,0.00342964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886155,0.0003693351,0.00010509248,0.0001981858,0.0003881681,0.000077672565],"domain_scores_gemma":[0.99850416,0.00039365067,0.00017065885,0.0005136969,0.00034392334,0.000073900956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011568194,0.0003900515,0.0005347767,0.0014371756,0.00060041994,0.0025220516,0.0011602936,0.00047849154,0.0040342053],"category_scores_gemma":[0.005054341,0.0003046865,0.0006824502,0.004096887,0.0007314079,0.0023544847,0.001994462,0.0010199964,0.0017934231],"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":[0.000074744,0.00009214139,0.0035218508,0.00032518661,0.00007601031,0.00012648001,0.00046793147,0.11107886,0.0039995015,0.41259745,0.019083548,0.44855627],"study_design_scores_gemma":[0.00005044894,0.000070902875,0.0017533039,0.000091866255,0.00003323737,0.00027355234,0.0006152378,0.6865436,0.0041945893,0.19706194,0.109269194,0.00004209575],"about_ca_topic_score_codex":0.004679606,"about_ca_topic_score_gemma":0.004705437,"teacher_disagreement_score":0.004679606,"about_ca_system_score_codex":0.00093632296,"about_ca_system_score_gemma":0.0010796115,"threshold_uncertainty_score":0.013495743},"labels":[],"label_agreement":null},{"id":"W2937254335","doi":"10.11575/prism/31331","title":"Modelling and Design of Generic Semantic Trajectory Data Warehouse","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data warehouse; Trajectory; Data mining; Ontology; Geospatial analysis; Context (archaeology); Semantic data model; Inference; Object (grammar); Information retrieval; Data science; Artificial intelligence","score_opus":0.270021894141485,"score_gpt":0.21350428606604224,"score_spread":0.05651760807544273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937254335","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005811438,0.00007369801,0.9900483,0.0003760482,0.00002951674,0.00019136691,0.0006900479,0.0006578901,0.0021216979],"genre_scores_gemma":[0.10741689,0.0005108848,0.88487625,0.00016774774,0.000029975146,0.0004703922,0.003987789,0.0002070869,0.0023330306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970381,0.0006007171,0.0006417005,0.00058828853,0.00088150264,0.00024965286],"domain_scores_gemma":[0.99805295,0.00036450513,0.00021380058,0.00054863025,0.0006786949,0.00014147646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040203715,0.0005725133,0.0008909336,0.0021639925,0.0011995798,0.006990705,0.0030816623,0.0015195569,0.0014022085],"category_scores_gemma":[0.0057316786,0.0007786011,0.0025264218,0.0040096436,0.0014169707,0.0075528505,0.003719487,0.0016228063,0.00081109424],"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":[0.00013579411,0.00015827715,0.0033323609,0.0004114294,0.00013406418,0.0009852849,0.0017572419,0.25530866,0.007020135,0.670959,0.0056733983,0.05412434],"study_design_scores_gemma":[0.000033141972,0.000044386346,0.00043029772,0.00010787575,0.000068024056,0.00034800725,0.00071271777,0.7734897,0.0067515653,0.15224458,0.065719925,0.00004976314],"about_ca_topic_score_codex":0.009915097,"about_ca_topic_score_gemma":0.0087098945,"teacher_disagreement_score":0.009915097,"about_ca_system_score_codex":0.0025052119,"about_ca_system_score_gemma":0.004150962,"threshold_uncertainty_score":0.02126205},"labels":[],"label_agreement":null},{"id":"W2947007575","doi":"10.5194/isprs-annals-iv-2-w5-543-2019","title":"TRAFFIC EVENT DETECTION USING TWITTER DATA BASED ON ASSOCIATION RULES","year":2019,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ministry of Transportation of Ontario; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geolocation; Social media; Computer science; Event (particle physics); Association rule learning; Association (psychology); Data mining; Microblogging; Data science; Information retrieval; World Wide Web","score_opus":0.07074856228882728,"score_gpt":0.314196317294391,"score_spread":0.24344775500556373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947007575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9034844,0.00085431983,0.0672772,0.0010207294,0.00031545,0.000553401,0.017479261,0.002141052,0.0068740603],"genre_scores_gemma":[0.9526933,0.0003415091,0.03216824,0.00008469146,0.0001332061,0.00020902869,0.012884186,0.00002830361,0.0014575898],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851555,0.00020756637,0.00025241426,0.00039084282,0.0004694164,0.00016411881],"domain_scores_gemma":[0.99641967,0.0013487432,0.0008304909,0.00026282293,0.0009505902,0.00018758349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096797233,0.00080024975,0.0007311009,0.0065791546,0.0006743033,0.0013836109,0.00063953904,0.00081711874,0.00090677396],"category_scores_gemma":[0.004537174,0.00022774497,0.0007584333,0.0036758482,0.00031366965,0.0014063194,0.0007551767,0.0006414219,0.0012768419],"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":[0.0012922545,0.00075448817,0.6701697,0.00071754307,0.00040971313,0.0031882196,0.0006879255,0.03278687,0.017033888,0.0019276217,0.013405754,0.257626],"study_design_scores_gemma":[0.000047850637,0.00031652657,0.19308434,0.00012821783,0.00023071025,0.0011510705,0.0018106167,0.7731527,0.016412899,0.003602193,0.009981098,0.00008171556],"about_ca_topic_score_codex":0.008260367,"about_ca_topic_score_gemma":0.010079871,"teacher_disagreement_score":0.008260367,"about_ca_system_score_codex":0.0006788497,"about_ca_system_score_gemma":0.00074411655,"threshold_uncertainty_score":0.016424596},"labels":[],"label_agreement":null},{"id":"W2947194824","doi":"10.4230/oasics.ldk.2019.13","title":"A Proposal for a Two-Way Journey on Validating Locations in Unstructured and Structured Data","year":2019,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Innovation Cluster (Canada)","funders":"","keywords":"Computer science; Unstructured data; Data science; Database; Data mining; Big data","score_opus":0.027119531376479898,"score_gpt":0.2921384102888953,"score_spread":0.26501887891241543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947194824","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013050775,0.0002638015,0.9720622,0.020226868,0.0005703778,0.00053966703,0.00026131535,0.0013030565,0.0034676339],"genre_scores_gemma":[0.010595142,0.0001935367,0.98289204,0.0024549484,0.000239739,0.0006473262,0.00056908827,0.00032324373,0.0020849344],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.91142905,0.049353518,0.006848261,0.0126541965,0.01756506,0.0021498925],"domain_scores_gemma":[0.80696625,0.10475709,0.0053046434,0.050043993,0.025955487,0.0069725164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0985364,0.0020444714,0.002718669,0.010420369,0.008051131,0.027820839,0.013924342,0.018775227,0.020120787],"category_scores_gemma":[0.18764819,0.0034454144,0.008010269,0.012167574,0.038863327,0.08061048,0.033252243,0.02049734,0.009234827],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010393624,0.0002203228,0.0011069877,0.0006174841,0.00011367202,0.00056354946,0.0038556263,0.002812421,0.0019546465,0.9263434,0.016008882,0.046299115],"study_design_scores_gemma":[0.00012268346,0.00016100633,0.00043795726,0.0008225019,0.000074441086,0.0009695758,0.002362868,0.029744394,0.0025644838,0.81693876,0.1455915,0.00020989132],"about_ca_topic_score_codex":0.0066352016,"about_ca_topic_score_gemma":0.0052717556,"teacher_disagreement_score":0.0985364,"about_ca_system_score_codex":0.0048582517,"about_ca_system_score_gemma":0.016512379,"threshold_uncertainty_score":0.5211165},"labels":[],"label_agreement":null},{"id":"W2947950257","doi":"10.1186/s40537-019-0203-6","title":"Detecting taxi movements using Random Swap clustering and sequential pattern mining","year":2019,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Carleton University","keywords":"Cluster analysis; Computer science; Swap (finance); Silhouette; Data mining; Hierarchical clustering; k-means clustering; Artificial intelligence","score_opus":0.11508057947702388,"score_gpt":0.29573302343564384,"score_spread":0.18065244395861996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947950257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26713526,0.00036257057,0.7183812,0.0003543238,0.000100033205,0.000425864,0.0030625986,0.0067595616,0.0034185674],"genre_scores_gemma":[0.59528756,0.00018656414,0.39819852,0.0000635523,0.000032794822,0.00021242525,0.004375211,0.00014603743,0.001497307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991874,0.0001162518,0.000075009506,0.00027819464,0.00024782037,0.00009522343],"domain_scores_gemma":[0.9989956,0.00020813942,0.00016151324,0.00016584765,0.00040526857,0.000063649524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006399604,0.0008251789,0.000699029,0.0044836295,0.0006652285,0.0007451881,0.0009909585,0.00055530627,0.00085331034],"category_scores_gemma":[0.0022506178,0.00032904383,0.00085627544,0.0036179738,0.00031330952,0.0008793217,0.00072284706,0.0003913816,0.00059487927],"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":[0.00076958496,0.0005236405,0.075676255,0.00042320168,0.0004913834,0.00074659794,0.00065819937,0.2105328,0.020930847,0.005384438,0.013932288,0.66993076],"study_design_scores_gemma":[0.000020671401,0.00007303961,0.012482034,0.000015566298,0.000045679004,0.00022705852,0.00024946444,0.97462964,0.0065736254,0.0027841064,0.0028696496,0.000029454635],"about_ca_topic_score_codex":0.021043578,"about_ca_topic_score_gemma":0.027066119,"teacher_disagreement_score":0.021043578,"about_ca_system_score_codex":0.0006673876,"about_ca_system_score_gemma":0.0010115997,"threshold_uncertainty_score":0.041842163},"labels":[],"label_agreement":null},{"id":"W2948323804","doi":"10.5281/zenodo.3241945","title":"Models and Data for Simple Applications of BERT for Ad Hoc Document Retrieval","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","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":"Simple (philosophy); Computer science; Information retrieval; Post hoc; Medicine","score_opus":0.11441677004267531,"score_gpt":0.3490369527446988,"score_spread":0.2346201827020235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948323804","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009711821,0.0016468697,0.015698468,0.0016217936,0.002143593,0.0007464733,0.8947479,0.058560617,0.015122417],"genre_scores_gemma":[0.01491262,0.0003592466,0.018850366,0.00033409166,0.00019664505,0.0005265539,0.9550624,0.0020403028,0.007717829],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974903,0.00057690585,0.00025125203,0.0006750753,0.00067585567,0.00033053747],"domain_scores_gemma":[0.99400276,0.0013936127,0.0001767183,0.0028876988,0.001255031,0.00028433508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041091684,0.004079988,0.0013864349,0.0035841968,0.0014002162,0.0032056482,0.0046081743,0.002780313,0.059684884],"category_scores_gemma":[0.018003913,0.0010850421,0.0026340145,0.003974863,0.0008179285,0.0031270224,0.0022557105,0.0032602376,0.11634065],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002132765,0.00015677938,0.00093080924,0.0004257426,0.00007450107,0.000040641928,0.000024293502,0.005682025,0.00036590607,0.00094071263,0.96906763,0.022077693],"study_design_scores_gemma":[0.0012561764,0.0004580541,0.008406469,0.00043575704,0.00021204508,0.0005341158,0.00034568927,0.15431182,0.011775083,0.022487031,0.7995348,0.0002429241],"about_ca_topic_score_codex":0.023851342,"about_ca_topic_score_gemma":0.052821886,"teacher_disagreement_score":0.059684884,"about_ca_system_score_codex":0.0023231802,"about_ca_system_score_gemma":0.0028057164,"threshold_uncertainty_score":0.19966584},"labels":[],"label_agreement":null},{"id":"W2949063183","doi":"10.1109/icde.2019.00147","title":"Top-K Frequent Term Queries on Streaming Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Term (time); Information retrieval; Streaming data; Social media; Data mining; World Wide Web","score_opus":0.031126829572152435,"score_gpt":0.2643527266923595,"score_spread":0.23322589712020705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949063183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27256328,0.0063074525,0.6953398,0.0023226682,0.00045223685,0.0007030925,0.011574663,0.008382168,0.002354704],"genre_scores_gemma":[0.55189925,0.0011645155,0.42796698,0.0004740912,0.00066605624,0.0002887782,0.015566505,0.00040142884,0.0015723261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918429,0.0015472203,0.0013485221,0.0021146936,0.0025914225,0.0005552144],"domain_scores_gemma":[0.96864456,0.020356618,0.002310851,0.0044837464,0.0033825522,0.00082177646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005114705,0.0019518241,0.004010516,0.0028845405,0.0014388846,0.0038440025,0.0035865272,0.0027602923,0.0017205292],"category_scores_gemma":[0.033899363,0.0007625387,0.0014824381,0.005721855,0.00089418783,0.008758697,0.0020938674,0.0017864014,0.0011957843],"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":[0.0031002057,0.001110463,0.033764414,0.0027988558,0.00070292375,0.001556049,0.0015609,0.29681987,0.053615946,0.01337472,0.041842163,0.5497535],"study_design_scores_gemma":[0.00008227181,0.0002905711,0.0028292492,0.000037374684,0.000085262436,0.00090690237,0.000628992,0.96134424,0.007258402,0.023160256,0.0033271185,0.0000494341],"about_ca_topic_score_codex":0.0057107816,"about_ca_topic_score_gemma":0.00937688,"teacher_disagreement_score":0.0057107816,"about_ca_system_score_codex":0.0013021319,"about_ca_system_score_gemma":0.0017295353,"threshold_uncertainty_score":0.027049482},"labels":[],"label_agreement":null},{"id":"W2950253786","doi":"10.1137/1.9781611973068.54","title":"Biased Range Trees","year":2009,"lang":"en","type":"article","venue":"Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Range query (database); Data structure; Range (aeronautics); Computer science; Range tree; Preprocessor; Set (abstract data type); Tree (set theory); Tree structure; Query optimization; Segment tree; Constant (computer programming); Interval tree; Data mining; Mathematics; Combinatorics; Sargable; Artificial intelligence; Information retrieval; Web search query; Search engine","score_opus":0.013072420826448742,"score_gpt":0.245268440160445,"score_spread":0.23219601933399625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950253786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025556391,0.0012332456,0.9590648,0.00035002487,0.000111862195,0.0002683537,0.0033561238,0.0049676695,0.005091502],"genre_scores_gemma":[0.2455262,0.001113272,0.7325578,0.0007037293,0.00025279252,0.00086714973,0.011154884,0.0011313093,0.006692927],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976968,0.00039180263,0.00022369104,0.000376826,0.0010645184,0.00024630572],"domain_scores_gemma":[0.9931369,0.002347168,0.00069367007,0.002149858,0.0013743538,0.0002980267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013957589,0.00048433055,0.0012105365,0.0018837606,0.00076344074,0.001772389,0.0017156826,0.000896095,0.0074448483],"category_scores_gemma":[0.012018714,0.00057540723,0.0008710772,0.0034858203,0.00058398745,0.0052093556,0.0026256123,0.001018319,0.0036279853],"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":[0.0016365696,0.00030537363,0.006934832,0.0007318102,0.00015881164,0.0004915607,0.0004347347,0.04123896,0.04429821,0.14867988,0.042770572,0.71231866],"study_design_scores_gemma":[0.00046215588,0.001016127,0.0038288631,0.00031323475,0.00018734897,0.002466013,0.00034278675,0.36409009,0.05196605,0.43783537,0.1372581,0.00023393643],"about_ca_topic_score_codex":0.0009853923,"about_ca_topic_score_gemma":0.0017961381,"teacher_disagreement_score":0.0074448483,"about_ca_system_score_codex":0.00047876593,"about_ca_system_score_gemma":0.0012137428,"threshold_uncertainty_score":0.024905562},"labels":[],"label_agreement":null},{"id":"W2950833175","doi":"10.1145/3299869.3319894","title":"Pessimistic Cardinality Estimation","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Innovates - Technology Futures; National Science Foundation","keywords":"Computer science; Cardinality (data modeling); Benchmark (surveying); Hash function; Tuple; Key (lock); Monotonic function; Parameterized complexity; Partition (number theory); Join (topology); Theoretical computer science; Mathematical optimization; Algorithm; Data mining; Mathematics; Discrete mathematics","score_opus":0.007955252744919091,"score_gpt":0.2270172417928118,"score_spread":0.2190619890478927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950833175","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04614757,0.001144567,0.94388807,0.0012209244,0.00012723221,0.00013754208,0.00046173602,0.002021717,0.004850598],"genre_scores_gemma":[0.6115032,0.0005999864,0.3812525,0.00048318296,0.00026143162,0.00021290757,0.0009321833,0.0010098268,0.0037448495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98066324,0.0060793133,0.0010398668,0.0031022362,0.007951163,0.0011642514],"domain_scores_gemma":[0.9491098,0.030165518,0.0036552446,0.013681981,0.0024978821,0.00088968425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012849707,0.0014383141,0.0021610702,0.0016583124,0.0014319719,0.0052726106,0.0046240855,0.0015511025,0.0048927157],"category_scores_gemma":[0.057006553,0.0013419023,0.0015561426,0.0021378268,0.0023311079,0.011174516,0.0051852562,0.0038425731,0.0010644417],"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":[0.0012646561,0.00027923472,0.013477205,0.00066625286,0.00029870856,0.0003209653,0.0010485847,0.5850462,0.018184826,0.1617606,0.010924068,0.20672877],"study_design_scores_gemma":[0.000037931764,0.00013585246,0.0010277489,0.00008511376,0.000053578333,0.0002417353,0.00025860147,0.8989489,0.007927939,0.084696494,0.0065325988,0.000053590804],"about_ca_topic_score_codex":0.0027780198,"about_ca_topic_score_gemma":0.0037486316,"teacher_disagreement_score":0.012849707,"about_ca_system_score_codex":0.0028423746,"about_ca_system_score_gemma":0.0032665334,"threshold_uncertainty_score":0.06795657},"labels":[],"label_agreement":null},{"id":"W2950930770","doi":"10.48550/arxiv.cs/0211018","title":"Indexing schemes for similarity search: an illustrated paradigm","year":2002,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Marsden Fund; Fonterra Co-Operative Group; Natural Sciences and Engineering Research Council of Canada; Royal Society Te Apārangi; Royal Society; University of Ottawa","keywords":"Search engine indexing; Similarity (geometry); Nearest neighbor search; Metric (unit); Measure (data warehouse); Similarity measure; Computer science; Variety (cybernetics); Index (typography); Data mining; Information retrieval; Artificial intelligence","score_opus":0.24194109308483916,"score_gpt":0.22537138935229437,"score_spread":0.016569703732544794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950930770","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055780136,0.0006259215,0.9889804,0.0014131579,0.00008650952,0.00013927398,0.00016779153,0.00017109048,0.0028378826],"genre_scores_gemma":[0.15297392,0.001627091,0.84040284,0.0005088667,0.00038455328,0.00060319237,0.00043511167,0.00012522274,0.0029392454],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9923495,0.0031834922,0.0009496252,0.000904196,0.0022710662,0.00034216826],"domain_scores_gemma":[0.9870913,0.005063966,0.00066124235,0.0055468837,0.0011695087,0.0004671187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00821635,0.0007244114,0.001373431,0.0043174806,0.0020626562,0.006776693,0.0037810185,0.0027250722,0.004437185],"category_scores_gemma":[0.024943843,0.00065858697,0.0017107689,0.008285636,0.0065150503,0.019673131,0.008033901,0.0036773977,0.0018768231],"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":[0.000030433146,0.000021728005,0.0001974116,0.00005723432,0.000009392865,0.000043881526,0.00016943741,0.003182272,0.0008357622,0.97633535,0.0010853446,0.018031688],"study_design_scores_gemma":[0.000024351457,0.00006633979,0.000113941416,0.000030260477,0.000009549892,0.00021191817,0.000091541486,0.045648225,0.0011522885,0.9404424,0.012183513,0.000025655589],"about_ca_topic_score_codex":0.0009366031,"about_ca_topic_score_gemma":0.0005635792,"teacher_disagreement_score":0.00821635,"about_ca_system_score_codex":0.0022856023,"about_ca_system_score_gemma":0.0014159497,"threshold_uncertainty_score":0.04345274},"labels":[],"label_agreement":null},{"id":"W2951184112","doi":"10.22215/etd/2018-13352","title":"Implementation of Minimum Edge Constraints Sets for Proximity Graphs","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Delaunay triangulation; Computer science; Edge contraction; Enhanced Data Rates for GSM Evolution; Graph; Spanning tree; Constrained Delaunay triangulation; Line graph; Theoretical computer science; Algorithm; Mathematics; Combinatorics; Voltage graph; Artificial intelligence","score_opus":0.025619668222263095,"score_gpt":0.33094518192445954,"score_spread":0.30532551370219646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951184112","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0249333,0.00018452582,0.96361214,0.0001897659,0.0000625281,0.0002406682,0.0006565478,0.005539527,0.004581039],"genre_scores_gemma":[0.14603631,0.00019556214,0.8490335,0.00008217804,0.000020485444,0.0003100274,0.001971488,0.00047632924,0.0018740783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976814,0.00042321862,0.0002218808,0.00031892382,0.0012281974,0.00012630816],"domain_scores_gemma":[0.99568653,0.0014352151,0.000206766,0.0016121315,0.0009484615,0.00011086327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010573892,0.0007247788,0.0006453324,0.0013850168,0.0009308454,0.0019831548,0.0024024954,0.00092953065,0.0071301525],"category_scores_gemma":[0.012421064,0.00059310667,0.0007110185,0.002542089,0.00051627203,0.0027421934,0.0022469575,0.0013090526,0.0017475163],"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":[0.00043684582,0.00033663597,0.002120945,0.0005643614,0.00012359014,0.00019994462,0.0006435149,0.18963762,0.025027746,0.06750442,0.018173777,0.69523054],"study_design_scores_gemma":[0.00012181864,0.00018176214,0.0009754068,0.000077273435,0.000027280303,0.0003272049,0.0002720954,0.86032134,0.05164547,0.0587301,0.027254608,0.00006559776],"about_ca_topic_score_codex":0.0036676133,"about_ca_topic_score_gemma":0.005468896,"teacher_disagreement_score":0.0071301525,"about_ca_system_score_codex":0.001021416,"about_ca_system_score_gemma":0.0018307964,"threshold_uncertainty_score":0.023852706},"labels":[],"label_agreement":null},{"id":"W2951446960","doi":"10.1145/3299869.3300094","title":"Fingerprints for Compressed Columnar Data Search","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Overhead (engineering); Block (permutation group theory); Data compression; Volume (thermodynamics); Compression (physics); Column (typography); Memory management; Compression ratio; Auxiliary memory; Data structure; Parallel computing; Minimax; Data compression ratio; Computer hardware; Semiconductor memory; Algorithm; Image compression; Artificial intelligence; Operating system; Computer network; Mathematics; Engineering","score_opus":0.08198813717934422,"score_gpt":0.3090936588729068,"score_spread":0.22710552169356257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951446960","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10665748,0.004665414,0.86271256,0.0006141113,0.0003107271,0.0005100327,0.0030626862,0.0132130515,0.008253957],"genre_scores_gemma":[0.45043916,0.0012894295,0.5350754,0.0003849501,0.00015117996,0.0003662836,0.0035164296,0.00041476203,0.008362442],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931955,0.00011020182,0.000062296276,0.000096204654,0.00034559408,0.000066186294],"domain_scores_gemma":[0.99742955,0.0008989235,0.00025319878,0.00092046865,0.00039754403,0.00010036143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052657194,0.00044816756,0.00059081655,0.0015827249,0.00041070994,0.0010238508,0.00096023805,0.00052279554,0.0073687653],"category_scores_gemma":[0.0048376764,0.00026997548,0.00020973507,0.0026376697,0.0004822973,0.0018618284,0.001173023,0.00045322935,0.0019311052],"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":[0.0021419004,0.00031185546,0.004113857,0.00059223187,0.00006204847,0.00033781576,0.00024154833,0.017453926,0.09623898,0.030221224,0.023079112,0.8252056],"study_design_scores_gemma":[0.00041422076,0.0021913417,0.0054195384,0.00023786585,0.00010477422,0.0029488627,0.00042451554,0.61304164,0.23348725,0.051315706,0.09026826,0.00014613188],"about_ca_topic_score_codex":0.0012416473,"about_ca_topic_score_gemma":0.0018893039,"teacher_disagreement_score":0.0073687653,"about_ca_system_score_codex":0.0005495965,"about_ca_system_score_gemma":0.0010899527,"threshold_uncertainty_score":0.024650991},"labels":[],"label_agreement":null},{"id":"W2952004427","doi":"10.25417/uic.14869440","title":"Efficient Computation of Subspace Skyline over Categorical Domains","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Army Research Office; Fonds National de la Recherche Luxembourg; Qatar Foundation; Microsoft Research; Qatar National Research Fund; National Science Foundation","keywords":"Skyline; Categorical variable; Computer science; Data mining; Exploit; Tuple; Set (abstract data type); Subspace topology; Computation; Theoretical computer science; Machine learning; Artificial intelligence; Algorithm; Mathematics","score_opus":0.0636550452972275,"score_gpt":0.21273983833349655,"score_spread":0.14908479303626904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952004427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12547016,0.00065381645,0.8430517,0.0010069794,0.00015260305,0.00016643858,0.008808549,0.012389278,0.0083004655],"genre_scores_gemma":[0.5529426,0.00023298715,0.42332226,0.00018391346,0.00010357867,0.00013326542,0.015960569,0.0011850267,0.0059358175],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982765,0.00034843365,0.00011525824,0.00046046125,0.0005083977,0.00029093237],"domain_scores_gemma":[0.9964167,0.0012000779,0.00015960104,0.0013984634,0.000617494,0.00020762073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010773265,0.00080711086,0.0015089221,0.0018378481,0.001124405,0.0031795471,0.0019812218,0.0010297318,0.014239389],"category_scores_gemma":[0.007054133,0.0006272126,0.0015222743,0.003714825,0.0009381844,0.0080675725,0.003882018,0.0018430863,0.0030202616],"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":[0.0016014155,0.00033966932,0.008462569,0.001278532,0.0002559837,0.00029194052,0.001033651,0.08351141,0.014883697,0.3508934,0.07174608,0.46570158],"study_design_scores_gemma":[0.000089525594,0.00014203768,0.0010629911,0.00007172375,0.00006904746,0.00017722823,0.00058992754,0.49262595,0.012330553,0.4730172,0.019768901,0.000054885466],"about_ca_topic_score_codex":0.0034495264,"about_ca_topic_score_gemma":0.0072715813,"teacher_disagreement_score":0.014239389,"about_ca_system_score_codex":0.001531404,"about_ca_system_score_gemma":0.0019751075,"threshold_uncertainty_score":0.047635555},"labels":[],"label_agreement":null},{"id":"W2952068610","doi":"10.1109/icde.2019.00027","title":"A Semi-Supervised Framework of Clustering Selection for De-Duplication","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Correlation clustering; Set (abstract data type); Class (philosophy); Sampling (signal processing); Hash function; Domain (mathematical analysis); Data mining; Mathematics; Artificial intelligence; Algorithm","score_opus":0.013568442786025025,"score_gpt":0.25639397814770176,"score_spread":0.24282553536167673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952068610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022282535,0.00018569521,0.99687225,0.0000932812,0.00001557478,0.00006266111,0.000052545896,0.00022791268,0.00026184178],"genre_scores_gemma":[0.17326769,0.00039607266,0.8214405,0.0002843648,0.00033714803,0.0005212897,0.0007750353,0.00021061084,0.0027672672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99053407,0.004354951,0.00034281347,0.002164976,0.0022696934,0.00033347443],"domain_scores_gemma":[0.98945355,0.0044261264,0.001193294,0.0026218581,0.0020057852,0.00029935577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070575704,0.0011920512,0.0025226464,0.0030956285,0.0013371514,0.0020141716,0.005138636,0.002148104,0.00151825],"category_scores_gemma":[0.012010461,0.00093205634,0.0014933783,0.00359243,0.0021550981,0.0029809847,0.0026939206,0.0023426297,0.00097071374],"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":[0.00031140854,0.00041086372,0.0038189632,0.0005449059,0.00051920064,0.00038494548,0.00066855364,0.46890336,0.011615553,0.091878,0.012079968,0.40886423],"study_design_scores_gemma":[0.000011959665,0.00008139237,0.000413451,0.000015774853,0.000019636916,0.0001265466,0.000038691418,0.96932226,0.0019617507,0.025476161,0.0025091316,0.000023187402],"about_ca_topic_score_codex":0.0022100252,"about_ca_topic_score_gemma":0.0031472025,"teacher_disagreement_score":0.0070575704,"about_ca_system_score_codex":0.0018389726,"about_ca_system_score_gemma":0.0021066642,"threshold_uncertainty_score":0.03732449},"labels":[],"label_agreement":null},{"id":"W2952098125","doi":"10.1137/1.9781611975482.183","title":"Exact Algorithms and Lower Bounds for Stable Instances of Euclidean <i>k</i>-MEANS","year":2019,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Euclidean geometry; Euclidean algorithm; Algorithm; Mathematics; Combinatorics; Computer science; Discrete mathematics; Geometry","score_opus":0.04989408783899186,"score_gpt":0.242237781629213,"score_spread":0.19234369379022115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952098125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11682898,0.002923593,0.849831,0.0049711177,0.00023897162,0.0003711689,0.0013859024,0.0021832269,0.021266028],"genre_scores_gemma":[0.5953716,0.0011257795,0.39266446,0.00088305655,0.0003011435,0.00044311688,0.002285228,0.00088186655,0.006043722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962035,0.0009788197,0.00020119119,0.0011506525,0.0008623678,0.00060346944],"domain_scores_gemma":[0.98044044,0.01375301,0.0016504712,0.0023962467,0.001055344,0.0007046652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038816747,0.0022210015,0.0020110742,0.0011854781,0.0014081053,0.0040595746,0.0042183613,0.0023283472,0.008741566],"category_scores_gemma":[0.025314564,0.00095160457,0.002392357,0.0025557715,0.0022317765,0.006487387,0.0030451673,0.005029757,0.001666806],"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":[0.0009840856,0.00047318483,0.003802697,0.00096066296,0.0002680252,0.00023220746,0.00043859423,0.7135697,0.0062909694,0.16962077,0.012495418,0.09086368],"study_design_scores_gemma":[0.0000784385,0.00011800296,0.0003974973,0.000038877446,0.00004296665,0.000094909585,0.000079484445,0.86000425,0.0019140897,0.13569984,0.001509363,0.00002225826],"about_ca_topic_score_codex":0.0037572514,"about_ca_topic_score_gemma":0.005153637,"teacher_disagreement_score":0.008741566,"about_ca_system_score_codex":0.0038402958,"about_ca_system_score_gemma":0.0028878574,"threshold_uncertainty_score":0.02924341},"labels":[],"label_agreement":null},{"id":"W2952162622","doi":"10.13140/rg.2.2.11166.28489","title":"Random Tessellation Forests","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Mondrian; Computer science; Process (computing); Binary tree; Tree (set theory); Monte Carlo method; Space partitioning; Tessellation (computer graphics); Domain (mathematical analysis); Binary number; Point process; Algorithm; Inference; Theoretical computer science; Mathematics; Artificial intelligence; Computer graphics (images); Statistics","score_opus":0.05804052649545609,"score_gpt":0.1790589830140752,"score_spread":0.1210184565186191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952162622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015069315,0.0007766537,0.9799299,0.0001509982,0.00008708563,0.00010182216,0.0005493588,0.000988825,0.0023460574],"genre_scores_gemma":[0.4055489,0.0008910597,0.58158344,0.00045168897,0.00023480516,0.0004930595,0.0041052396,0.00045076787,0.0062410105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982527,0.00061793247,0.00010205891,0.0004522969,0.00037258063,0.0002023341],"domain_scores_gemma":[0.9961671,0.002207425,0.00025997363,0.0006503008,0.00058026303,0.0001349738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002020047,0.00068349094,0.0014190179,0.0021298442,0.000876582,0.001639649,0.0018263706,0.0013402872,0.0040951637],"category_scores_gemma":[0.008106691,0.0006033056,0.0019733657,0.0019003854,0.00092286704,0.0017804108,0.0013990765,0.0011308263,0.0015192818],"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":[0.00020364176,0.00006976713,0.0046702144,0.00023383705,0.00022058941,0.00023491151,0.0002048959,0.64266616,0.0028546404,0.13881478,0.013022079,0.19680445],"study_design_scores_gemma":[0.000017701435,0.000014113863,0.0003886984,0.000024793904,0.000015160892,0.000109504916,0.000020629062,0.93149036,0.0009111418,0.06292739,0.004065923,0.00001446566],"about_ca_topic_score_codex":0.004967187,"about_ca_topic_score_gemma":0.0076450994,"teacher_disagreement_score":0.004967187,"about_ca_system_score_codex":0.0009107205,"about_ca_system_score_gemma":0.00072338764,"threshold_uncertainty_score":0.01369971},"labels":[],"label_agreement":null},{"id":"W2952780231","doi":"10.1007/978-1-4614-0110-0_16","title":"Counting Plane Graphs: Flippability and Its Applications","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Plane (geometry); Mathematics; Geometry","score_opus":0.022572560065373858,"score_gpt":0.22584076886558146,"score_spread":0.2032682088002076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952780231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015660523,0.026695417,0.60008043,0.0033297124,0.0018042276,0.0001331528,0.00071930676,0.00080649083,0.35077074],"genre_scores_gemma":[0.33343714,0.051051594,0.34379032,0.0022139158,0.002967337,0.0005758859,0.00170028,0.0016417269,0.26262188],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996289,0.000061971296,0.000015939257,0.00011568458,0.00013447767,0.000043096654],"domain_scores_gemma":[0.9987668,0.0006425222,0.000078904195,0.00020385293,0.00025413834,0.000053868524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000518799,0.0010847109,0.00084948965,0.0025107397,0.0011382261,0.002682178,0.0016618384,0.0015035528,0.015628869],"category_scores_gemma":[0.0038447112,0.00072828186,0.0005581947,0.0057895323,0.0022262596,0.00548021,0.001327045,0.002597684,0.0031876839],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014804266,0.000016182068,0.00011869255,0.00015485345,0.000006483908,0.00003177906,0.0000800218,0.004379231,0.0006835244,0.85915744,0.024857422,0.11049954],"study_design_scores_gemma":[0.0000049048963,0.000008275589,0.00011517904,0.00008130131,0.000010084851,0.00013177352,0.000042972162,0.0099252025,0.0008728544,0.92568576,0.06310972,0.000011999012],"about_ca_topic_score_codex":0.002405377,"about_ca_topic_score_gemma":0.0020872063,"teacher_disagreement_score":0.015628869,"about_ca_system_score_codex":0.0016529071,"about_ca_system_score_gemma":0.000893802,"threshold_uncertainty_score":0.052283764},"labels":[],"label_agreement":null},{"id":"W2954173133","doi":"10.22215/etd/2018-12991","title":"Distributed Real-Time Analysis of High-Velocity Many-Dimensional Data Sets","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scalability; Distributed computing; Aggregate (composite); Cloud computing; Key (lock); Set (abstract data type); Isolation (microbiology); Database; High availability; Data mining; Real-time computing; Operating system","score_opus":0.021801141265799035,"score_gpt":0.2853062881420922,"score_spread":0.2635051468762932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954173133","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035428215,0.0010382875,0.95817935,0.000722721,0.00031246716,0.00008034309,0.00016507828,0.0012413366,0.0028322993],"genre_scores_gemma":[0.58239883,0.0016242679,0.40821195,0.00021552853,0.0005792837,0.00026914515,0.0010471612,0.00028890237,0.0053648897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861336,0.00022691263,0.00005939823,0.0003329793,0.0006475983,0.00011972414],"domain_scores_gemma":[0.99710315,0.0014950506,0.0002209218,0.00050182716,0.0005279203,0.000151144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014775406,0.0007479674,0.0009294837,0.0008335614,0.0008513944,0.0015283484,0.0015573173,0.0007702539,0.0016751033],"category_scores_gemma":[0.005501716,0.0003815517,0.0005599831,0.0013735952,0.0010795585,0.0019073634,0.0017564069,0.0013114555,0.0008338117],"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":[0.00069392804,0.00043568827,0.005621388,0.0005152512,0.00024684917,0.00047093438,0.0007680006,0.5388891,0.051935535,0.030057956,0.016262606,0.35410276],"study_design_scores_gemma":[0.0000396768,0.000050919796,0.0012393275,0.000011634417,0.000014017774,0.0000702858,0.00008486871,0.9709188,0.0048473044,0.018858725,0.0038516237,0.000012921392],"about_ca_topic_score_codex":0.0013630664,"about_ca_topic_score_gemma":0.0013610836,"teacher_disagreement_score":0.0016751033,"about_ca_system_score_codex":0.0005750761,"about_ca_system_score_gemma":0.00080121506,"threshold_uncertainty_score":0.00781405},"labels":[],"label_agreement":null},{"id":"W2954304876","doi":"10.5194/ica-adv-1-20-2019","title":"Modelling and Analysis of Semantically Enriched Simplified Trajectories Using Graph Databases","year":2019,"lang":"en","type":"article","venue":"Advances in Cartography and GIScience of the ICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"","keywords":"Computer science; Graph database; Trajectory; Geospatial analysis; Database; Visualization; Semantics (computer science); Graph; Data mining; Data structure; Process (computing); Theoretical computer science; Information retrieval; Algorithm; Programming language","score_opus":0.019525022977099345,"score_gpt":0.26945253905669225,"score_spread":0.2499275160795929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954304876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049586706,0.00024197853,0.93472373,0.00031282144,0.000067291076,0.00018062272,0.0057138535,0.0056395433,0.003533396],"genre_scores_gemma":[0.3945458,0.0009693274,0.58145845,0.00008330912,0.000032282453,0.00025452947,0.019180704,0.00068684295,0.0027887996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990336,0.0001902766,0.0001250323,0.00024360478,0.00034775044,0.0000598217],"domain_scores_gemma":[0.9986118,0.00042535097,0.00017654276,0.00034530307,0.00038615864,0.000054797838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079347537,0.0007699112,0.0005701288,0.003613214,0.0005617935,0.0027995594,0.0015032643,0.00069180917,0.002082194],"category_scores_gemma":[0.0032331238,0.00034945758,0.0016389497,0.0038778859,0.0006253926,0.0026722853,0.0012901692,0.0005990543,0.00063935283],"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":[0.00022868584,0.000117414325,0.0097587425,0.000452982,0.00019433875,0.0011851158,0.0009016298,0.7890235,0.008836715,0.099763,0.006834591,0.08270316],"study_design_scores_gemma":[0.000014083111,0.000028437084,0.0013234918,0.00003243939,0.000041001724,0.00013975363,0.00025551306,0.95927507,0.004288748,0.018029504,0.01654134,0.000030635423],"about_ca_topic_score_codex":0.036201607,"about_ca_topic_score_gemma":0.027387088,"teacher_disagreement_score":0.036201607,"about_ca_system_score_codex":0.0014509714,"about_ca_system_score_gemma":0.0015083762,"threshold_uncertainty_score":0.07198179},"labels":[],"label_agreement":null},{"id":"W2954484802","doi":"10.1007/978-3-030-19738-4_28","title":"Supervised Classification Box Algorithm Based on Graph Partitioning","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Data Management and Algorithms","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":"Concordia University","funders":"","keywords":"Computer science; Graph; Pattern recognition (psychology); Artificial intelligence; Algorithm; Theoretical computer science","score_opus":0.026309398559450994,"score_gpt":0.2597019193474782,"score_spread":0.23339252078802722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954484802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015392598,0.0003707365,0.9775981,0.00016634575,0.00016570008,0.0002197289,0.00026106296,0.0021609014,0.0036649383],"genre_scores_gemma":[0.0838751,0.00024526974,0.9030435,0.00015974457,0.000116029165,0.00026150918,0.0017043564,0.00026570994,0.010328773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940884,0.00011223073,0.000028207927,0.00018640934,0.00021239762,0.00005189375],"domain_scores_gemma":[0.9993759,0.00020420145,0.000040259536,0.00011972829,0.0002296512,0.00003030143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057351193,0.00076675334,0.0014796829,0.0014830027,0.000970651,0.0009877359,0.002105615,0.0011260493,0.00600495],"category_scores_gemma":[0.0010846071,0.00035104365,0.0007661197,0.0013808944,0.0004544896,0.0013223283,0.0007803741,0.0011584123,0.0025006486],"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":[0.0004340481,0.00029745384,0.0011348567,0.0001547165,0.00010765997,0.00008743041,0.00007020456,0.050336838,0.017286923,0.018967018,0.0170857,0.8940372],"study_design_scores_gemma":[0.00006383721,0.000095589014,0.00089852355,0.000021400308,0.00004484108,0.00013896308,0.000037432485,0.96903783,0.00921995,0.013119822,0.007302061,0.00001967978],"about_ca_topic_score_codex":0.0046823556,"about_ca_topic_score_gemma":0.005725171,"teacher_disagreement_score":0.00600495,"about_ca_system_score_codex":0.00053449895,"about_ca_system_score_gemma":0.0013392996,"threshold_uncertainty_score":0.020088613},"labels":[],"label_agreement":null},{"id":"W2961427308","doi":"10.5194/ica-proc-2-128-2019","title":"Semantically Enriched Simplification of Trajectories","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ICA","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":"University of Calgary; University of New Brunswick","funders":"","keywords":"Trajectory; Computer science; Offset (computer science); Algorithm; Raster data; Process (computing); Dimension (graph theory); Data structure; Path (computing); Tracing; Object (grammar); Data point; Data mining; Artificial intelligence; Mathematics; Raster graphics","score_opus":0.006214568058820971,"score_gpt":0.19898912530846222,"score_spread":0.19277455724964124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961427308","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026937418,0.00016805046,0.9572818,0.0001630834,0.00007421996,0.00014357486,0.0045548407,0.006892448,0.003784568],"genre_scores_gemma":[0.23908596,0.00045832296,0.7205708,0.0000948605,0.00005650529,0.00020417539,0.03340181,0.0016362625,0.0044912444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972222,0.0004486795,0.00034378527,0.0005158874,0.0013152922,0.0001542008],"domain_scores_gemma":[0.9962769,0.0006567342,0.0002998793,0.0016267905,0.0010668925,0.00007282413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009828527,0.00075448724,0.00073422474,0.0025371078,0.000820426,0.002162922,0.0013229684,0.00048011,0.0035006478],"category_scores_gemma":[0.0060318555,0.00036026962,0.0013852043,0.004013645,0.0008089638,0.002931722,0.002913491,0.0011556377,0.0014617132],"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":[0.00059913844,0.00016325046,0.0065594506,0.00088658714,0.00024882707,0.0010825741,0.0029697986,0.2698176,0.0343992,0.16042982,0.026288448,0.49655533],"study_design_scores_gemma":[0.000058098554,0.00014030564,0.0029341877,0.00013616603,0.00010181426,0.00057843,0.00097306655,0.6593584,0.069815084,0.07021697,0.1955978,0.000089636975],"about_ca_topic_score_codex":0.008715833,"about_ca_topic_score_gemma":0.008062632,"teacher_disagreement_score":0.008715833,"about_ca_system_score_codex":0.0011878082,"about_ca_system_score_gemma":0.001290528,"threshold_uncertainty_score":0.01733023},"labels":[],"label_agreement":null},{"id":"W2963575846","doi":"10.5555/2627817.2627912","title":"Frozen variables in random boolean constraint satisfaction problems","year":2013,"lang":"en","type":"article","venue":"Symposium on Discrete Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Constraint satisfaction problem; Constraint (computer-aided design); Heuristics; Cluster analysis; Constraint satisfaction; Local consistency; Mathematics; Random variable; Algorithm; Computer science; Hypergraph; Discrete mathematics; Mathematical optimization; Statistics","score_opus":0.010199872324038213,"score_gpt":0.22240178705055139,"score_spread":0.21220191472651317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963575846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31647733,0.0014942206,0.66780555,0.0022007967,0.000060739578,0.00016990752,0.00059083564,0.0007042441,0.010496429],"genre_scores_gemma":[0.9473913,0.000632974,0.047562484,0.0002701696,0.000065430766,0.00023565779,0.0005542738,0.00015404301,0.003133831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997273,0.001158625,0.00008628702,0.0004886623,0.00046707928,0.00052630855],"domain_scores_gemma":[0.9852207,0.012002287,0.0009933023,0.0006408932,0.0005062746,0.0006365601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00340302,0.0010287797,0.001417553,0.0015120138,0.0011544363,0.002350305,0.0026765105,0.0014654694,0.004738858],"category_scores_gemma":[0.020175328,0.0010494621,0.001615166,0.0014155121,0.003076304,0.0046288436,0.0023590187,0.0026959185,0.00032978185],"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":[0.00025768633,0.00012217573,0.002170872,0.00020276805,0.000091449656,0.00024450323,0.0003503628,0.5253381,0.0020194547,0.45498687,0.0033327425,0.010883043],"study_design_scores_gemma":[0.000042183197,0.000039818337,0.00036132798,0.000027626527,0.000016411144,0.000092891445,0.00006391457,0.7327913,0.00070717774,0.26501206,0.0008215411,0.000023730265],"about_ca_topic_score_codex":0.005491907,"about_ca_topic_score_gemma":0.0048090955,"teacher_disagreement_score":0.005491907,"about_ca_system_score_codex":0.0030854295,"about_ca_system_score_gemma":0.0011625665,"threshold_uncertainty_score":0.022386491},"labels":[],"label_agreement":null},{"id":"W2964255032","doi":"10.1109/access.2019.2931005","title":"Scalable Distributed kNN Processing on Clustered Data Streams","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Shandong University","keywords":"Computer science; Scalability; Partition (number theory); Data stream mining; Bounded function; Data mining; Sliding window protocol; Set (abstract data type); Process (computing); Index (typography); Distributed computing; Theoretical computer science; Window (computing); Database; World Wide Web","score_opus":0.061647486904073796,"score_gpt":0.31963422692817056,"score_spread":0.2579867400240968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964255032","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06277063,0.0006438184,0.9283076,0.00040561226,0.00017499572,0.00020878552,0.0010434847,0.004097025,0.002348159],"genre_scores_gemma":[0.4535021,0.00039149838,0.5375555,0.00022685887,0.00016161636,0.0002704276,0.0036475519,0.0002951408,0.0039493693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804246,0.00023432521,0.00017255021,0.0005788459,0.00069399073,0.00027780325],"domain_scores_gemma":[0.9968798,0.0010286157,0.00024013862,0.00068150286,0.0009624112,0.00020747847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013162099,0.0012506173,0.001966219,0.0014000703,0.0012118632,0.0019586727,0.0027667668,0.001121864,0.0025181018],"category_scores_gemma":[0.0067781983,0.0006449027,0.00091117225,0.003528656,0.00065015006,0.0030888093,0.0023250165,0.0012580976,0.0011083706],"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":[0.0012920222,0.00027740412,0.0043639154,0.00033139717,0.00018198714,0.0005001691,0.00054398057,0.6874635,0.016348956,0.010287562,0.011162331,0.26724666],"study_design_scores_gemma":[0.0000204555,0.000022829272,0.0002411184,0.0000035034373,0.000005538347,0.000026907615,0.00007594265,0.9934429,0.001436375,0.0040388126,0.0006789405,0.000006723184],"about_ca_topic_score_codex":0.02196998,"about_ca_topic_score_gemma":0.026344754,"teacher_disagreement_score":0.02196998,"about_ca_system_score_codex":0.0013962162,"about_ca_system_score_gemma":0.0016598745,"threshold_uncertainty_score":0.043684185},"labels":[],"label_agreement":null},{"id":"W2964516965","doi":"","title":"Conception Et Développement D’un Outil De Modification De La Segmentation Routière","year":2019,"lang":"fr","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geography; Cartography; Humanities; Philosophy","score_opus":0.02786156823984626,"score_gpt":0.3051512344656399,"score_spread":0.27728966622579365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964516965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032262962,0.0005455139,0.9500663,0.0006405624,0.00025927764,0.000283119,0.00029166025,0.005079616,0.010570888],"genre_scores_gemma":[0.17694183,0.00061479985,0.8064359,0.00029003667,0.00009349832,0.0002957783,0.0009835315,0.0013882106,0.012956396],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965468,0.00077184604,0.00022557098,0.0008175879,0.0014085568,0.0002295473],"domain_scores_gemma":[0.9929597,0.0026083647,0.00042923164,0.00191882,0.0018233301,0.0002604693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040142704,0.0008667975,0.00085137534,0.0017751592,0.000838865,0.0058765183,0.0016529559,0.0018739464,0.006639921],"category_scores_gemma":[0.009384306,0.0007539213,0.0013417503,0.0011811947,0.0019032394,0.0030901222,0.0020144044,0.0014869665,0.0022177906],"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":[0.00060570374,0.0003615155,0.006777029,0.0010564936,0.00021147811,0.0009081619,0.004465941,0.08727896,0.13401747,0.07527669,0.008807656,0.6802329],"study_design_scores_gemma":[0.0001575961,0.0008080121,0.007918745,0.0003904378,0.00020894504,0.0014666218,0.0013132289,0.42358902,0.2558895,0.021660596,0.28635728,0.00023995223],"about_ca_topic_score_codex":0.009319429,"about_ca_topic_score_gemma":0.00513191,"teacher_disagreement_score":0.009319429,"about_ca_system_score_codex":0.001580362,"about_ca_system_score_gemma":0.0030493087,"threshold_uncertainty_score":0.022212744},"labels":[],"label_agreement":null},{"id":"W2967977841","doi":"10.1007/978-3-030-29029-0_7","title":"Exploring Bit Arrays for Join Processing in Spatial Data Streams","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","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 Lethbridge","funders":"","keywords":"Join (topology); Computer science; STREAMS; Bit (key); Database; Data mining; Mathematics; Computer network","score_opus":0.13167963245567665,"score_gpt":0.301888639843374,"score_spread":0.17020900738769734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967977841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01688291,0.0023369668,0.96951014,0.00048173222,0.00023367496,0.00004805981,0.00022540663,0.0024944257,0.0077867513],"genre_scores_gemma":[0.21072415,0.003254767,0.7742589,0.00034607085,0.00029171465,0.00017771704,0.0007267639,0.00072827266,0.009491589],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999368,0.00016113702,0.00006117618,0.00009154942,0.0002459135,0.000072172574],"domain_scores_gemma":[0.99832386,0.0009952645,0.000075590775,0.00029613476,0.0002432521,0.0000659107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010327972,0.000539403,0.00070985884,0.0006779847,0.0007291086,0.003289454,0.0016220602,0.00071248435,0.0063319327],"category_scores_gemma":[0.0037832248,0.00044322797,0.00061041425,0.0024783823,0.00073488825,0.0046450924,0.0016848946,0.0015885555,0.001886822],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010248908,0.00016807293,0.0015817982,0.0005149864,0.00008299894,0.0002176071,0.00071537314,0.086650304,0.029045032,0.3705738,0.016681127,0.492744],"study_design_scores_gemma":[0.00005677729,0.00015805679,0.00024256589,0.00007747077,0.000043645727,0.00020970457,0.00033706063,0.59729856,0.021850424,0.34087095,0.038809635,0.000045097677],"about_ca_topic_score_codex":0.0009477658,"about_ca_topic_score_gemma":0.0012083868,"teacher_disagreement_score":0.0063319327,"about_ca_system_score_codex":0.00060519454,"about_ca_system_score_gemma":0.0005983188,"threshold_uncertainty_score":0.021182358},"labels":[],"label_agreement":null},{"id":"W2968931659","doi":"","title":"An event-driven approach to spatio-temporal mapping","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Computer science; Geography; Space (punctuation); Cartography; Data science","score_opus":0.017326103128200217,"score_gpt":0.23926069471250966,"score_spread":0.22193459158430945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968931659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000915927,0.00023834202,0.9955968,0.00035905864,0.00009870446,0.000038811246,0.00024516316,0.00026296524,0.0022442483],"genre_scores_gemma":[0.21735272,0.0022658505,0.7651701,0.0004879333,0.00037145455,0.00031861762,0.0015514344,0.0003448513,0.012137076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984962,0.00049344567,0.00009607278,0.0003414164,0.00048230102,0.00009064174],"domain_scores_gemma":[0.99817955,0.0009860634,0.0001319994,0.0003079527,0.00029102262,0.0001033811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001776313,0.00094160973,0.0009073171,0.0015898583,0.0007537783,0.0032596844,0.0035514932,0.0013456559,0.0046574967],"category_scores_gemma":[0.004850275,0.0007365394,0.0017999894,0.002848547,0.0011367183,0.0040378743,0.0025778175,0.002167759,0.0014276223],"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":[0.00012501908,0.00012912412,0.0009951952,0.00028426587,0.00018581428,0.0004552407,0.00034447547,0.38681376,0.0024431478,0.5064194,0.0072839446,0.09452058],"study_design_scores_gemma":[0.00001786765,0.000027532833,0.00016190465,0.000028519087,0.000033283228,0.00016824681,0.000075075186,0.7192189,0.0010417413,0.2581696,0.021024037,0.0000333085],"about_ca_topic_score_codex":0.006629246,"about_ca_topic_score_gemma":0.007370209,"teacher_disagreement_score":0.006629246,"about_ca_system_score_codex":0.0011849998,"about_ca_system_score_gemma":0.0016225065,"threshold_uncertainty_score":0.015580893},"labels":[],"label_agreement":null},{"id":"W2969414045","doi":"10.1007/978-3-030-29948-4_7","title":"Enabling Standard Geospatial Capabilities in Spark for the Efficient Processing of Geospatial Big Data","year":2019,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Geospatial analysis; SPARK (programming language); Computer science; Big data; Geospatial PDF; Data science; Database; Geography; Remote sensing; Data mining","score_opus":0.07821030335994344,"score_gpt":0.30089710571526823,"score_spread":0.2226868023553248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969414045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008118247,0.0055406736,0.8886651,0.0025740613,0.0017276141,0.00015571556,0.001582837,0.01761522,0.0740206],"genre_scores_gemma":[0.10626665,0.0096324,0.83780944,0.0014583137,0.001151179,0.00023803634,0.007435792,0.0056591164,0.030349055],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989172,0.00011102121,0.000085044856,0.00015106906,0.0006152729,0.00012035022],"domain_scores_gemma":[0.99876297,0.00038877732,0.00005153477,0.0003834576,0.00029781056,0.000115508556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014544721,0.0006696089,0.00058352284,0.0009880258,0.0005410701,0.0035956227,0.0020755336,0.0007659828,0.005468559],"category_scores_gemma":[0.003193978,0.00062674977,0.00085217314,0.0029716925,0.00093251123,0.0050571975,0.002971082,0.0025213617,0.0045786407],"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":[0.00026306414,0.00010510299,0.0007267154,0.0010963085,0.00013037922,0.00032005482,0.0007696476,0.010656822,0.029560966,0.4164263,0.24259187,0.2973528],"study_design_scores_gemma":[0.000052811596,0.000047790218,0.00050322484,0.00016207717,0.00003708213,0.0005625839,0.00015304031,0.05163919,0.018781586,0.29716417,0.63083047,0.00006600537],"about_ca_topic_score_codex":0.0012421688,"about_ca_topic_score_gemma":0.0019454012,"teacher_disagreement_score":0.005468559,"about_ca_system_score_codex":0.00052970235,"about_ca_system_score_gemma":0.0012019904,"threshold_uncertainty_score":0.018294096},"labels":[],"label_agreement":null},{"id":"W2975826719","doi":"10.1016/j.asoc.2019.105750","title":"Temporal event searches based on event maps and relationships","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Douglas College","funders":"Fundamental Research Funds for the Central Universities; Innovation and Technology Fund; Education University of Hong Kong; South China University of Technology; Faculty of Liberal Arts and Social Sciences, Education University of Hong Kong; Research Grants Council, University Grants Committee; Natural Science Foundation of Guangdong Province; Innovation and Technology Commission; National Natural Science Foundation of China","keywords":"Computer science; Event (particle physics); Set (abstract data type); Component (thermodynamics); Graph; Complex event processing; Data mining; Artificial intelligence; Information retrieval; Theoretical computer science","score_opus":0.026727579189967116,"score_gpt":0.24629263421161868,"score_spread":0.21956505502165155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975826719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102698125,0.0013492961,0.8817947,0.0004721038,0.00012607043,0.00032303,0.0024578895,0.0029500243,0.007828776],"genre_scores_gemma":[0.7006624,0.000798545,0.29113746,0.00010301111,0.00013990857,0.00017080322,0.0029625301,0.00020703232,0.003818433],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799097,0.00040150125,0.0001852417,0.0004313619,0.000823344,0.00016756228],"domain_scores_gemma":[0.9926806,0.005145928,0.0004838886,0.0007234308,0.0007285591,0.00023755031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001434909,0.00075551,0.0011591563,0.0064672325,0.0010604816,0.0031300231,0.0013946933,0.0010624157,0.0057823434],"category_scores_gemma":[0.0145926,0.0004832556,0.0010729337,0.0060798987,0.0006320703,0.0054897587,0.0023259912,0.0008392364,0.0011015041],"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":[0.0035736202,0.0008349052,0.02358892,0.0011993787,0.0004711361,0.0016548918,0.0013016067,0.12833546,0.022875428,0.15162024,0.016109988,0.6484344],"study_design_scores_gemma":[0.000082393,0.00017539565,0.004610994,0.00008253779,0.00020310172,0.00076362025,0.0004967522,0.8611877,0.012318508,0.109433606,0.010580889,0.000064580905],"about_ca_topic_score_codex":0.0042330106,"about_ca_topic_score_gemma":0.0052553075,"teacher_disagreement_score":0.0064672325,"about_ca_system_score_codex":0.00067628996,"about_ca_system_score_gemma":0.0012673337,"threshold_uncertainty_score":0.019343853},"labels":[],"label_agreement":null},{"id":"W2980271943","doi":"","title":"Using Clustering Methods in Geospatial Information Systems","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Geospatial analysis; Cluster analysis; Geography; Spatial analysis; Field (mathematics); Computer science; Cartography; Data mining; Geographic information system; Space (punctuation); Remote sensing; Artificial intelligence; Mathematics","score_opus":0.029999569720429935,"score_gpt":0.31664505613497257,"score_spread":0.2866454864145426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980271943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031014017,0.009291848,0.97885615,0.0015891985,0.0004290355,0.00014092057,0.00033418165,0.00063417555,0.0056231315],"genre_scores_gemma":[0.14057945,0.015298039,0.8370684,0.00063214457,0.00084736355,0.00038431835,0.0013166589,0.0002441814,0.0036294572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99286616,0.0032524813,0.00045646512,0.0010541587,0.002223823,0.00014680871],"domain_scores_gemma":[0.9950547,0.0026007171,0.00042564326,0.00069437554,0.0011222743,0.00010228452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004273685,0.0009156273,0.0011712823,0.0048817396,0.0014672448,0.003952902,0.0016244064,0.0021787519,0.002390257],"category_scores_gemma":[0.013432173,0.0006285662,0.0011807424,0.011570855,0.0017005865,0.0044380613,0.0021579857,0.0018661657,0.0018403174],"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":[0.00008852902,0.00009397443,0.0037998357,0.0015934365,0.00063482946,0.00024535033,0.00087539566,0.1536112,0.0024360972,0.29647058,0.02374436,0.5164065],"study_design_scores_gemma":[0.000027899205,0.00005451387,0.0022818123,0.0004783878,0.0001104651,0.0003190633,0.00046745595,0.4319677,0.0031895426,0.4842918,0.07668784,0.00012342798],"about_ca_topic_score_codex":0.008883232,"about_ca_topic_score_gemma":0.004878426,"teacher_disagreement_score":0.008883232,"about_ca_system_score_codex":0.0019404144,"about_ca_system_score_gemma":0.0019268813,"threshold_uncertainty_score":0.022601664},"labels":[],"label_agreement":null},{"id":"W2980655200","doi":"10.1016/j.mex.2019.10.001","title":"GrCount: Counting method for uncertain data","year":2019,"lang":"en","type":"article","venue":"MethodsX","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Government of Canada","keywords":"Interval (graph theory); Interval data; Python (programming language); Mathematics; Fuzzy logic; Algorithm; Computer science; Mathematical optimization; Artificial intelligence; Combinatorics","score_opus":0.10611389428347481,"score_gpt":0.40273554624002594,"score_spread":0.2966216519565511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980655200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090984255,0.00020468433,0.9909402,0.00013403044,0.00010938798,0.00011587428,0.0012397995,0.005561016,0.0007852265],"genre_scores_gemma":[0.026601331,0.00027362088,0.96531826,0.0002199476,0.00018885994,0.00055080303,0.0033701188,0.001820613,0.0016564829],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9845031,0.003692041,0.0018877999,0.0024114228,0.00698206,0.0005235938],"domain_scores_gemma":[0.9706819,0.016249707,0.0022813512,0.0060325833,0.0041290023,0.0006254871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008673046,0.0018212134,0.0020745697,0.008128414,0.0014666037,0.0053839395,0.004341122,0.0019959991,0.01433486],"category_scores_gemma":[0.05988196,0.0010933981,0.0026699835,0.0064360006,0.0015187996,0.005764087,0.0047675846,0.0038151247,0.006544558],"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":[0.00056957616,0.00011607818,0.004926034,0.0017237423,0.00039471587,0.0006611492,0.0006325019,0.031959448,0.00826742,0.12975097,0.049626004,0.7713724],"study_design_scores_gemma":[0.00017169473,0.0001405242,0.0015735473,0.0005645578,0.00016601705,0.0019392718,0.00025317256,0.47823957,0.03167099,0.32848963,0.15649071,0.00030036012],"about_ca_topic_score_codex":0.0017980908,"about_ca_topic_score_gemma":0.0017172472,"teacher_disagreement_score":0.01433486,"about_ca_system_score_codex":0.0012822718,"about_ca_system_score_gemma":0.0037488134,"threshold_uncertainty_score":0.047954857},"labels":[],"label_agreement":null},{"id":"W2981816395","doi":"10.22215/etd/2019-13400","title":"A Recommendation System based on Clustering and Classification for Optimal Trajectory Analysis","year":2019,"lang":"en","type":"dissertation","venue":"","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":"Carleton University","funders":"","keywords":"Cluster analysis; Computer science; Naive Bayes classifier; Data mining; Classifier (UML); Traffic analysis; Trajectory; Bayes' theorem; Machine learning; Artificial intelligence; Bayesian probability","score_opus":0.02818883269102794,"score_gpt":0.2775842978771662,"score_spread":0.24939546518613825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981816395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052560613,0.0005352603,0.9245468,0.00045574814,0.0002024762,0.0006020527,0.0015788614,0.01591932,0.003598822],"genre_scores_gemma":[0.28906062,0.00044035204,0.69791436,0.00019210909,0.00010979438,0.00041316994,0.0032147132,0.00022298416,0.0084319385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999057,0.00008908382,0.00009942369,0.00035998318,0.00030440424,0.000090055204],"domain_scores_gemma":[0.99849415,0.00024166031,0.00008984307,0.0003351869,0.000757677,0.000081508144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009118445,0.0007352322,0.0015148246,0.002412119,0.0013562611,0.0009180768,0.001617499,0.001325776,0.0036552607],"category_scores_gemma":[0.0021856513,0.0005051235,0.0011118905,0.00279599,0.00024805102,0.0014828913,0.00045855084,0.00081330305,0.0028257645],"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":[0.0006124655,0.00061654905,0.011863877,0.00023145077,0.00034125353,0.00033314608,0.0002281384,0.048080973,0.02589637,0.0030914743,0.024621068,0.88408315],"study_design_scores_gemma":[0.000084799976,0.00015838222,0.0049362713,0.000023725506,0.00012816167,0.0002831312,0.00011454611,0.973571,0.010882747,0.0015375618,0.00820293,0.00007680997],"about_ca_topic_score_codex":0.049850926,"about_ca_topic_score_gemma":0.060286757,"teacher_disagreement_score":0.049850926,"about_ca_system_score_codex":0.0010251919,"about_ca_system_score_gemma":0.0011559377,"threshold_uncertainty_score":0.09912157},"labels":[],"label_agreement":null},{"id":"W2982627676","doi":"10.5539/jmr.v11n6p21","title":"Algorithm on Trees With Knots and Their Folding","year":2019,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Folding (DSP implementation); Tree (set theory); Combinatorics; Knot (papermaking); Algorithm","score_opus":0.06104297433271652,"score_gpt":0.33323582073118335,"score_spread":0.27219284639846686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982627676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013927863,0.00017892403,0.98319083,0.000080529186,0.0000727821,0.00005228009,0.00006089407,0.000494035,0.0019417531],"genre_scores_gemma":[0.08220333,0.00031521483,0.9135135,0.00009100265,0.000038583396,0.000105774736,0.00028929685,0.00020818261,0.0032351066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999175,0.00020296806,0.000101042075,0.00018614919,0.00023225197,0.000102555074],"domain_scores_gemma":[0.99855655,0.00038875907,0.000099040764,0.00045465562,0.0004082022,0.00009277968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090104784,0.0005795609,0.0007603133,0.0011611173,0.0009862464,0.0009613764,0.0010058978,0.000803384,0.0034290906],"category_scores_gemma":[0.00377078,0.00036686522,0.00089082704,0.0016623908,0.00075292867,0.002782507,0.001929585,0.0013355039,0.0012006926],"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":[0.00032729318,0.00018016988,0.0026650282,0.0004025771,0.00009253827,0.00046882028,0.00090660184,0.07607807,0.032292325,0.34889898,0.00505505,0.5326325],"study_design_scores_gemma":[0.000067120796,0.00031590989,0.0010818794,0.00014370252,0.00008730024,0.0009490099,0.0002565356,0.45164067,0.026390823,0.47379395,0.045191765,0.00008137658],"about_ca_topic_score_codex":0.00079608033,"about_ca_topic_score_gemma":0.00077829283,"teacher_disagreement_score":0.0034290906,"about_ca_system_score_codex":0.00056564267,"about_ca_system_score_gemma":0.0006553887,"threshold_uncertainty_score":0.01147145},"labels":[],"label_agreement":null},{"id":"W2990002113","doi":"10.4230/lipics.isaac.2019.45","title":"Path and Ancestor Queries over Trees with Multidimensional Weight Vectors","year":2019,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ancestor; Path (computing); Computer science; Theoretical computer science; Tree (set theory); Combinatorics; Mathematics; Algorithm; Geography; Computer network","score_opus":0.00610076255544151,"score_gpt":0.20920867595822878,"score_spread":0.20310791340278728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990002113","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31924713,0.0013754535,0.66290015,0.002146282,0.00014513823,0.0003436728,0.0026911062,0.0044719814,0.0066790776],"genre_scores_gemma":[0.6840148,0.0006067734,0.30655596,0.00032764906,0.00014271679,0.00028131308,0.0030414122,0.00032108062,0.0047083967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99504495,0.0011187167,0.0006273727,0.00096787786,0.0015604638,0.0006806482],"domain_scores_gemma":[0.99044496,0.0056819892,0.0008328932,0.0019181843,0.00073310535,0.00038889248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002826114,0.0009891052,0.0016957694,0.00157809,0.0013113219,0.003032994,0.0024359745,0.0017377299,0.0034663542],"category_scores_gemma":[0.016808154,0.0006569001,0.0011321311,0.004548717,0.00095560483,0.013811839,0.0037544842,0.001730102,0.00073252426],"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":[0.00224138,0.00085964927,0.016523236,0.0013667714,0.0002315509,0.0015396435,0.0027383007,0.21376228,0.041966382,0.19163817,0.024753679,0.50237894],"study_design_scores_gemma":[0.00015743132,0.00034615063,0.0012209313,0.000051463703,0.0000655639,0.00070152053,0.0007044284,0.81151015,0.013159677,0.16184261,0.010175524,0.000064553264],"about_ca_topic_score_codex":0.0033151158,"about_ca_topic_score_gemma":0.0036454757,"teacher_disagreement_score":0.0034663542,"about_ca_system_score_codex":0.001391433,"about_ca_system_score_gemma":0.0013404181,"threshold_uncertainty_score":0.014946103},"labels":[],"label_agreement":null},{"id":"W2990751717","doi":"10.1007/s11634-019-00379-2","title":"Is-ClusterMPP: clustering algorithm through point processes and influence space towards high-dimensional data","year":2019,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Cluster analysis; Curse of dimensionality; Algorithm; Data point; Computer science; Subspace topology; Mathematics; Statistical physics; Artificial intelligence; Physics","score_opus":0.0354850183539955,"score_gpt":0.31527472121398364,"score_spread":0.27978970285998817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990751717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025267424,0.00011101668,0.99309945,0.000071649054,0.0000528801,0.0000919236,0.00020842126,0.0031333617,0.00070442946],"genre_scores_gemma":[0.04310918,0.00016818185,0.95166695,0.00007781869,0.0000639659,0.0003566966,0.0011959798,0.0008934099,0.002467859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855477,0.0003041665,0.0000734127,0.00030085488,0.00066154473,0.00010533108],"domain_scores_gemma":[0.99874395,0.0002818114,0.00006124617,0.000295683,0.00055319624,0.000064191256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001543198,0.001473635,0.0017921159,0.002692093,0.0022498874,0.0021220532,0.0038439678,0.0017889062,0.0056795697],"category_scores_gemma":[0.005146992,0.0007726229,0.0019873003,0.0046054386,0.0009175259,0.0018611707,0.0036128156,0.0019359668,0.0037339067],"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":[0.00052293803,0.00021667221,0.0032702463,0.00053824845,0.0004199035,0.00021090366,0.00056732586,0.15987642,0.0112265805,0.03657522,0.031346813,0.75522876],"study_design_scores_gemma":[0.000056533947,0.000064284795,0.00078649103,0.000025499421,0.0000550326,0.00017974009,0.00006965774,0.9575888,0.0087392265,0.019328969,0.0130661735,0.000039516694],"about_ca_topic_score_codex":0.007608857,"about_ca_topic_score_gemma":0.00921559,"teacher_disagreement_score":0.007608857,"about_ca_system_score_codex":0.00092062715,"about_ca_system_score_gemma":0.00239973,"threshold_uncertainty_score":0.019000053},"labels":[],"label_agreement":null},{"id":"W2994742394","doi":"10.1109/tkde.2019.2960347","title":"Group-Based Skyline for Pareto Optimal Groups","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Air Force Office of Scientific Research; National Science Foundation","keywords":"Skyline; Computer science; Pareto principle; Dimension (graph theory); Heuristic; Group (periodic table); Point (geometry); Pareto optimal; Computation; Set (abstract data type); Theoretical computer science; Data mining; Combinatorics; Artificial intelligence; Mathematics; Algorithm; Mathematical optimization","score_opus":0.02137811809518209,"score_gpt":0.2535165900882512,"score_spread":0.23213847199306914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994742394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016906489,0.00015604931,0.97915226,0.00011394274,0.000018274144,0.00014276418,0.00043377362,0.00097493257,0.0021014395],"genre_scores_gemma":[0.16489592,0.00022043342,0.8291133,0.0000895119,0.00004071092,0.0003256565,0.0025914668,0.00032659233,0.0023964613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857235,0.00032820983,0.00010056595,0.00034980045,0.0004855688,0.00016346364],"domain_scores_gemma":[0.99747133,0.0008940536,0.0003484841,0.00040415404,0.00072835165,0.00015363535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012920665,0.0013098345,0.0015032807,0.0032387653,0.0013255033,0.0024796075,0.0016773256,0.0012294059,0.007863597],"category_scores_gemma":[0.0060030664,0.0005334052,0.001736283,0.0029930368,0.00096998893,0.0035263044,0.0023280848,0.0014562135,0.0019475151],"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":[0.0004975038,0.00024276986,0.0054350006,0.00055548904,0.00016050792,0.00027366128,0.0009968724,0.5458623,0.008062344,0.14220314,0.013978197,0.28173223],"study_design_scores_gemma":[0.00003658367,0.00007488485,0.00038817993,0.000034669112,0.000016318238,0.00007071979,0.00013675074,0.9310396,0.0025497298,0.05995379,0.0056797676,0.000019006744],"about_ca_topic_score_codex":0.0058084745,"about_ca_topic_score_gemma":0.005533569,"teacher_disagreement_score":0.007863597,"about_ca_system_score_codex":0.0016622753,"about_ca_system_score_gemma":0.0015605638,"threshold_uncertainty_score":0.02630639},"labels":[],"label_agreement":null},{"id":"W2994986475","doi":"10.1109/ccece47787.2020.9255682","title":"Dynamic Group Trip Planning Queries in Spatial Databases","year":2020,"lang":"en","type":"article","venue":"","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":"University of Lethbridge","funders":"","keywords":"Computer science; Plan (archaeology); Group (periodic table); Set (abstract data type); Database; Information retrieval; Geography","score_opus":0.03873811214761011,"score_gpt":0.2719478572918074,"score_spread":0.23320974514419732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994986475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24584208,0.002902906,0.73462766,0.0017488151,0.00011668504,0.00061428384,0.003491503,0.003979541,0.0066765337],"genre_scores_gemma":[0.79613054,0.000902414,0.19832228,0.00023496489,0.00007822642,0.00023295013,0.002604576,0.00017956378,0.0013144931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99633396,0.0014174115,0.00027275956,0.0008206943,0.00089655584,0.00025865293],"domain_scores_gemma":[0.9932615,0.004344741,0.00043153163,0.0011144318,0.0005724683,0.00027520562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029632472,0.0011618768,0.0015099957,0.0011585889,0.0012704561,0.0026759275,0.0025663967,0.001759184,0.0020429844],"category_scores_gemma":[0.008222364,0.0006368567,0.0006827807,0.0045420416,0.0008652061,0.005762126,0.0019359696,0.0012420092,0.00065082987],"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":[0.0013209746,0.0006685601,0.008317386,0.0008425446,0.00026054357,0.0007504417,0.0013509401,0.7203297,0.014651596,0.030853195,0.018822955,0.20183124],"study_design_scores_gemma":[0.00009070087,0.00022235443,0.001001342,0.000019760728,0.00005739991,0.00029187076,0.0010139652,0.9608315,0.004709302,0.025426548,0.006302635,0.000032543816],"about_ca_topic_score_codex":0.013471849,"about_ca_topic_score_gemma":0.009874851,"teacher_disagreement_score":0.013471849,"about_ca_system_score_codex":0.0014643534,"about_ca_system_score_gemma":0.001148912,"threshold_uncertainty_score":0.026786864},"labels":[],"label_agreement":null},{"id":"W2996459922","doi":"","title":"Data Structures for Range-Aggregate Extent Queries.","year":2008,"lang":"en","type":"article","venue":"","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":"Carleton University","funders":"","keywords":"Aggregate (composite); Range (aeronautics); Computer science; Engineering; Materials science","score_opus":0.07607027244695423,"score_gpt":0.2844721136917034,"score_spread":0.20840184124474914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996459922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021046313,0.0023270233,0.91054016,0.0018382568,0.00034982944,0.0004959037,0.014216324,0.026202627,0.022983536],"genre_scores_gemma":[0.32514873,0.0027911286,0.58456916,0.0015396482,0.0006508459,0.0014124288,0.045484625,0.0055291126,0.03287422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969703,0.00054218416,0.00047318335,0.00034943165,0.0014001988,0.00026477978],"domain_scores_gemma":[0.990824,0.0027582992,0.0005326934,0.004562093,0.0010239878,0.00029895816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002951507,0.0007251308,0.0011500824,0.002219146,0.001469093,0.004171783,0.0020887803,0.0013784013,0.0189903],"category_scores_gemma":[0.017058233,0.0007917563,0.000919216,0.0037838537,0.0012449482,0.010827031,0.0054417853,0.0021814664,0.0076301876],"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":[0.0016730336,0.00036070554,0.007583898,0.0010413852,0.00018769513,0.00040552238,0.0019233649,0.014064539,0.017265934,0.42226055,0.17036138,0.36287194],"study_design_scores_gemma":[0.0003165625,0.00033045007,0.0026437645,0.00039845827,0.00014124793,0.0010759897,0.00090966816,0.08598351,0.031947438,0.543976,0.33213338,0.00014357411],"about_ca_topic_score_codex":0.0016699432,"about_ca_topic_score_gemma":0.0028444026,"teacher_disagreement_score":0.0189903,"about_ca_system_score_codex":0.00093649334,"about_ca_system_score_gemma":0.0014389375,"threshold_uncertainty_score":0.063528895},"labels":[],"label_agreement":null},{"id":"W2999873292","doi":"10.1145/376284.375664","title":"Efficient computation of Iceberg cubes with complex measures","year":2001,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Pruning; Computer science; Online analytical processing; Data cube; Computation; Scalability; Cube (algebra); Data mining; Measure (data warehouse); Tree (set theory); Theoretical computer science; Algorithm; Data warehouse; Database; Mathematics; Combinatorics","score_opus":0.042388281774761956,"score_gpt":0.2641887323800171,"score_spread":0.22180045060525516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999873292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032441907,0.0001995195,0.9645909,0.00015084211,0.000021937845,0.000068453366,0.0002186759,0.00087729545,0.0014304298],"genre_scores_gemma":[0.20555419,0.00023087565,0.79214716,0.00007059908,0.000030492374,0.00016534593,0.0008316444,0.00016410615,0.0008055709],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830735,0.00032810675,0.00017797563,0.00021924409,0.0008098092,0.00015749341],"domain_scores_gemma":[0.9943363,0.0027579335,0.0005731922,0.00094285974,0.0012015008,0.000188189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015574953,0.00079946936,0.0013231976,0.0017752809,0.00087126356,0.0024585684,0.0017908665,0.00066212803,0.001469706],"category_scores_gemma":[0.010469437,0.0006402285,0.001005185,0.0028727655,0.0010443392,0.0047466233,0.0024789541,0.0012854047,0.00046108582],"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":[0.00030719495,0.00009294182,0.0053642727,0.00035351884,0.00016014706,0.00030978033,0.0005536324,0.51284295,0.009890629,0.13347912,0.007221444,0.32942435],"study_design_scores_gemma":[0.000012321984,0.00002829124,0.00030991694,0.000012209676,0.000011104576,0.000064310385,0.00008765049,0.93634814,0.0045898557,0.0564762,0.0020475413,0.000012488533],"about_ca_topic_score_codex":0.004800087,"about_ca_topic_score_gemma":0.0064613954,"teacher_disagreement_score":0.004800087,"about_ca_system_score_codex":0.0011290105,"about_ca_system_score_gemma":0.0019358221,"threshold_uncertainty_score":0.009544313},"labels":[],"label_agreement":null},{"id":"W3003200529","doi":"10.1109/icdm.2019.00160","title":"Efficient Mining and Exploration of Multiple Axis-Aligned Intersecting Objects","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"York University","funders":"","keywords":"Computer science; Intersection (aeronautics); Scalability; Key (lock); Computation; Graph; Data structure; Theoretical computer science; Algorithm; Database","score_opus":0.023639313196268677,"score_gpt":0.23551365302381427,"score_spread":0.2118743398275456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003200529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16348293,0.0021746024,0.81875247,0.00083488284,0.000086875494,0.00042197018,0.0048712874,0.005020498,0.0043545235],"genre_scores_gemma":[0.29784033,0.00067347306,0.6904558,0.00012288903,0.000054780936,0.00030004312,0.008892698,0.00045504977,0.0012048847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707806,0.00033957156,0.00021918159,0.00082518766,0.001222889,0.00031520633],"domain_scores_gemma":[0.99472696,0.00243427,0.00092579174,0.0006629033,0.0009371889,0.00031302273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016664182,0.0019037277,0.0027712474,0.008543539,0.0017705061,0.0041111903,0.00406344,0.0017385589,0.0019939458],"category_scores_gemma":[0.008241291,0.0013574258,0.002321968,0.008949062,0.0013949436,0.0046497,0.005110715,0.0015415297,0.0013466893],"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":[0.0009373453,0.00069189403,0.09756962,0.0016892212,0.0006253732,0.003493392,0.0028699827,0.24668737,0.023315862,0.031448636,0.027352512,0.5633188],"study_design_scores_gemma":[0.00006503617,0.00016592813,0.008760913,0.00016926725,0.00011403442,0.0013565699,0.002168961,0.9072953,0.015213778,0.05141329,0.0131926155,0.00008433805],"about_ca_topic_score_codex":0.0047203247,"about_ca_topic_score_gemma":0.009560424,"teacher_disagreement_score":0.008543539,"about_ca_system_score_codex":0.00081701676,"about_ca_system_score_gemma":0.0026224174,"threshold_uncertainty_score":0.009385705},"labels":[],"label_agreement":null},{"id":"W3004091940","doi":"10.3390/ijgi9020088","title":"A Survey on Big Data for Trajectory Analytics","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Trajectory; Big data; Computer science; Data warehouse; Data science; Field (mathematics); Global Positioning System; Online analytical processing; Analytics; Data management; Urban computing; Volume (thermodynamics); Data analysis; Data mining; Search engine indexing; Information retrieval; Human–computer interaction; Telecommunications","score_opus":0.10564709724002141,"score_gpt":0.3069206289142962,"score_spread":0.2012735316742748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004091940","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006109923,0.7859399,0.12057584,0.020031212,0.003986943,0.0006135995,0.013224824,0.0018082847,0.04770946],"genre_scores_gemma":[0.030857597,0.8410011,0.09151015,0.005141742,0.0040377653,0.0005235754,0.021341939,0.0005054608,0.005080761],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939468,0.0013436245,0.00070472056,0.000742056,0.0029817787,0.00028094125],"domain_scores_gemma":[0.98371464,0.0096422685,0.0007493146,0.0015749807,0.0038174028,0.00050137966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049024853,0.001749369,0.0018317787,0.010546318,0.0015023134,0.006390727,0.0027059421,0.0018763383,0.0073478604],"category_scores_gemma":[0.015293989,0.0008622943,0.0014832034,0.033125166,0.0013371004,0.014097791,0.0033535827,0.0028800936,0.003514229],"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":[0.0001581671,0.000103041006,0.0050308635,0.013752052,0.00017775048,0.00032514718,0.0005613535,0.004811616,0.0014173053,0.11232896,0.14875649,0.7125773],"study_design_scores_gemma":[0.0000084139,0.0000476191,0.0022164767,0.0041266647,0.000058969985,0.0004255118,0.0006435526,0.0052850153,0.0009809876,0.041634187,0.9445081,0.0000644764],"about_ca_topic_score_codex":0.003788226,"about_ca_topic_score_gemma":0.0034364033,"teacher_disagreement_score":0.010546318,"about_ca_system_score_codex":0.002178221,"about_ca_system_score_gemma":0.0034205597,"threshold_uncertainty_score":0.025927126},"labels":[],"label_agreement":null},{"id":"W3007997785","doi":"10.1109/bigdata47090.2019.9006496","title":"Modeling Dynamic Spatial-Temporal Cluster Relationships","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster (spacecraft); Computer science; Computer network","score_opus":0.021943881116822106,"score_gpt":0.23497029543021014,"score_spread":0.21302641431338804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007997785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20402385,0.0006761798,0.77875537,0.0022815152,0.00010054734,0.00018089548,0.0022116578,0.00058373366,0.011186266],"genre_scores_gemma":[0.89542055,0.0006602041,0.09426472,0.00019696704,0.00009368134,0.00031072402,0.0016956932,0.000178338,0.0071790344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982545,0.0005154097,0.00007391616,0.00059822184,0.0002687325,0.00028925142],"domain_scores_gemma":[0.99207544,0.0046898574,0.0011351556,0.0006521504,0.00097310997,0.00047440926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030895525,0.00058832805,0.00065046956,0.0019863104,0.0013516974,0.0031975475,0.0028007487,0.0015833947,0.003598277],"category_scores_gemma":[0.01674435,0.00068883155,0.0011295322,0.0034916522,0.001594067,0.004515617,0.0027936846,0.001704526,0.0005681561],"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":[0.00011302861,0.000057100617,0.013550743,0.000071466435,0.0000781532,0.0002619364,0.0007551279,0.78406894,0.0005429468,0.18306263,0.0032621277,0.014175741],"study_design_scores_gemma":[0.00000744924,0.000008074822,0.00085663237,0.000007889319,0.000014166323,0.000032950255,0.000119247416,0.95879066,0.000089756104,0.03838295,0.0016826447,0.0000076269694],"about_ca_topic_score_codex":0.032725364,"about_ca_topic_score_gemma":0.027438244,"teacher_disagreement_score":0.032725364,"about_ca_system_score_codex":0.003260346,"about_ca_system_score_gemma":0.0022174087,"threshold_uncertainty_score":0.065069735},"labels":[],"label_agreement":null},{"id":"W3011151727","doi":"10.11606/d.55.2020.tde-16032020-150627","title":"Construção Automática de Funções de Proximidade para Redes de Termos usando Evolução Gramatical","year":2020,"lang":"pt","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Network for Business Sustainability","funders":"","keywords":"Humanities; Philosophy","score_opus":0.031613580692406565,"score_gpt":0.29580180325658634,"score_spread":0.2641882225641798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011151727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010795739,0.00038234214,0.97709113,0.00028309296,0.00011061526,0.00007352911,0.00042711495,0.0084635,0.002372827],"genre_scores_gemma":[0.12725928,0.0005523322,0.86153865,0.00020415585,0.00010148713,0.00020627458,0.0017225562,0.0030383053,0.0053769983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99546534,0.0011006648,0.0004153515,0.0014353029,0.0013614723,0.00022193546],"domain_scores_gemma":[0.99058867,0.0050716237,0.0005195549,0.0016054712,0.0020011952,0.00021343009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046717743,0.0023266354,0.0013587206,0.0043230294,0.0012748592,0.0060746283,0.0022097593,0.0016943426,0.011566051],"category_scores_gemma":[0.026298491,0.0013875741,0.0026042245,0.0026292745,0.0018464645,0.008886033,0.0031234955,0.0024195793,0.0066496152],"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":[0.0003955168,0.00014261069,0.004597166,0.0010977059,0.00020212741,0.00038735746,0.002171798,0.058511913,0.039843947,0.069320075,0.011902969,0.8114269],"study_design_scores_gemma":[0.000039118022,0.00019911728,0.0023505008,0.00039754948,0.00016236624,0.0006131036,0.0011028143,0.77789855,0.030493062,0.13829546,0.04834379,0.00010459235],"about_ca_topic_score_codex":0.0037986757,"about_ca_topic_score_gemma":0.005152865,"teacher_disagreement_score":0.011566051,"about_ca_system_score_codex":0.0019474197,"about_ca_system_score_gemma":0.0015795353,"threshold_uncertainty_score":0.038692296},"labels":[],"label_agreement":null},{"id":"W3011231967","doi":"10.1007/s00453-010-9440-y","title":"Biased Range Trees","year":2010,"lang":"en","type":"article","venue":"Algorithmica","topic":"Data Management and Algorithms","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Data structure; Range query (database); Range (aeronautics); Range tree; Theory of computation; Computer science; Mathematics; Tree (set theory); Preprocessor; Tree structure; Set (abstract data type); Query optimization; Combinatorics; Binary tree; Discrete mathematics; Algorithm; Interval tree; Data mining; Sargable; Web search query; Information retrieval; Search engine; Artificial intelligence","score_opus":0.010256384292339894,"score_gpt":0.22795302363897205,"score_spread":0.21769663934663216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011231967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092300475,0.006229557,0.8263948,0.003641889,0.00039459008,0.00014800389,0.0015990277,0.0014770926,0.06781456],"genre_scores_gemma":[0.66939133,0.0034289812,0.27154613,0.0019574768,0.0008694998,0.00026665028,0.0031233008,0.0008137724,0.048602954],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983315,0.00045369953,0.00007633826,0.00036756007,0.00058089296,0.00019008848],"domain_scores_gemma":[0.9936022,0.0033967416,0.00042067686,0.0014013872,0.00083848485,0.0003404069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013411887,0.00048066294,0.0010631992,0.0018379706,0.0013284404,0.0026076995,0.0013262812,0.0015170332,0.015907265],"category_scores_gemma":[0.012804049,0.00058011105,0.0006393946,0.0024423318,0.0013351586,0.004934343,0.0028395862,0.0024135262,0.0040334947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002177336,0.00006236876,0.0022518262,0.00020327856,0.00006242517,0.0001948575,0.00024071809,0.011566641,0.004648961,0.8172335,0.018812058,0.14450565],"study_design_scores_gemma":[0.000032693908,0.000042049407,0.00061252783,0.000053779906,0.000044003024,0.0005504809,0.00007198139,0.052689653,0.0020317,0.9203402,0.023510251,0.00002082488],"about_ca_topic_score_codex":0.00055355474,"about_ca_topic_score_gemma":0.0010646548,"teacher_disagreement_score":0.015907265,"about_ca_system_score_codex":0.0007819958,"about_ca_system_score_gemma":0.0006886136,"threshold_uncertainty_score":0.053215086},"labels":[],"label_agreement":null},{"id":"W3011592252","doi":"10.14778/3236187.3236199","title":"Efficient construction of approximate ad-hoc ML models through materialization and reuse","year":2018,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Online analytical processing; Reuse; Dimension (graph theory); Cluster analysis; Variety (cybernetics); Data warehouse; Data mining; Construct (python library); Mixture model; Machine learning; Artificial intelligence; Mathematics; Programming language","score_opus":0.0167162247919843,"score_gpt":0.22005666246918465,"score_spread":0.20334043767720034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011592252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014802088,0.00014003973,0.9811152,0.00027888792,0.000013750535,0.000090259135,0.00014772874,0.0026009036,0.0008110786],"genre_scores_gemma":[0.23310429,0.00020913318,0.7622724,0.00023556163,0.00006519612,0.0002665839,0.0013107461,0.00087935943,0.0016567322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99415344,0.0019901572,0.00044691676,0.0010427558,0.0018483808,0.00051832374],"domain_scores_gemma":[0.98116106,0.010481738,0.0010443123,0.005667471,0.0012619224,0.00038350967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058200895,0.002239894,0.0026905995,0.0020721988,0.0013524585,0.004736695,0.004772128,0.0020105545,0.0034789182],"category_scores_gemma":[0.028147131,0.0019003085,0.0033305017,0.003361834,0.0023380548,0.00886617,0.0070944736,0.0038882683,0.0017005646],"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":[0.00018448447,0.00020675806,0.0025815852,0.00013380825,0.00011871364,0.00021941777,0.0003984245,0.8080758,0.0027494635,0.03882231,0.004327176,0.1421821],"study_design_scores_gemma":[0.000013230606,0.00001956592,0.0000602112,0.000004352838,0.000011157769,0.00002889935,0.00005403761,0.9783036,0.001054648,0.019617595,0.0008241465,0.000008479864],"about_ca_topic_score_codex":0.009648849,"about_ca_topic_score_gemma":0.012222269,"teacher_disagreement_score":0.009648849,"about_ca_system_score_codex":0.00276346,"about_ca_system_score_gemma":0.0038679263,"threshold_uncertainty_score":0.030779958},"labels":[],"label_agreement":null},{"id":"W3014823298","doi":"10.1111/tgis.12619","title":"Sketch maps for searching in spatial data","year":2020,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sketch; Computer science; Matching (statistics); Redundancy (engineering); Data mining; Abstraction; Data redundancy; Process (computing); Implementation; Spatial analysis; Information retrieval; Algorithm; Database; Mathematics","score_opus":0.09702760982318206,"score_gpt":0.3116397447962315,"score_spread":0.21461213497304946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014823298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029397141,0.00069488666,0.9635451,0.000111069516,0.00005538806,0.00018322119,0.00041485383,0.003122274,0.0024760505],"genre_scores_gemma":[0.34619984,0.0005947199,0.6502315,0.000032453867,0.000025924377,0.00021788842,0.0007083925,0.0002577824,0.0017315501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975043,0.0007396882,0.00017767007,0.0003120295,0.0011363399,0.0001300172],"domain_scores_gemma":[0.9958864,0.0021057904,0.00024918996,0.00090612454,0.00065973407,0.00019274317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001599845,0.0009691936,0.0010766914,0.005167378,0.0006931571,0.0028269014,0.0014300845,0.0010340913,0.008680908],"category_scores_gemma":[0.011233761,0.00040346308,0.0008588466,0.0043602493,0.0010363167,0.004343681,0.003001993,0.0006993885,0.0010784622],"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":[0.0007210245,0.0001522114,0.0040187957,0.000978981,0.0001666197,0.00036492213,0.0008276476,0.22666383,0.016911518,0.08557093,0.0050113094,0.6586122],"study_design_scores_gemma":[0.000047844846,0.00014406806,0.0009182592,0.00007783543,0.000041865067,0.00031810944,0.00037634588,0.929747,0.013803543,0.039254848,0.015212914,0.000057395468],"about_ca_topic_score_codex":0.003150365,"about_ca_topic_score_gemma":0.0016890744,"teacher_disagreement_score":0.008680908,"about_ca_system_score_codex":0.0006401196,"about_ca_system_score_gemma":0.0006826395,"threshold_uncertainty_score":0.029040515},"labels":[],"label_agreement":null},{"id":"W3015182559","doi":"10.3390/s20072057","title":"A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Global Positioning System; Map matching; Trajectory; Computer science; Matching (statistics); Sampling (signal processing); Assisted GPS; Computer vision; Mathematics; Statistics; Telecommunications","score_opus":0.03374187162097791,"score_gpt":0.254841267221003,"score_spread":0.22109939560002506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015182559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016115999,0.00036125557,0.97968274,0.00010856235,0.00010719906,0.00015381968,0.0005114786,0.0019150062,0.0010438806],"genre_scores_gemma":[0.28887713,0.00045060163,0.70330584,0.0001013946,0.00014343536,0.00029595595,0.0028427069,0.00023047841,0.0037524414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977235,0.00025420234,0.00014589401,0.0009451747,0.0007253018,0.00020588645],"domain_scores_gemma":[0.9981008,0.00033355958,0.00025365542,0.0005574981,0.0006247637,0.00012970547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011699933,0.0012678016,0.0017931481,0.0032239857,0.001168113,0.0012585975,0.003316909,0.0013210406,0.0020045245],"category_scores_gemma":[0.0053787357,0.00047540726,0.0014984272,0.00576538,0.00043708205,0.0027235097,0.002800221,0.0012569304,0.0017144148],"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":[0.00052872446,0.00039586113,0.00867106,0.0003787744,0.00034667287,0.00048422147,0.0005354137,0.12549667,0.018840918,0.009809938,0.011402241,0.8231095],"study_design_scores_gemma":[0.000042219745,0.00017336561,0.0026036035,0.000023689772,0.0000818174,0.00047057396,0.00033169158,0.9675744,0.010333265,0.00746762,0.010843079,0.000054613178],"about_ca_topic_score_codex":0.012819296,"about_ca_topic_score_gemma":0.011420826,"teacher_disagreement_score":0.012819296,"about_ca_system_score_codex":0.0007343755,"about_ca_system_score_gemma":0.0023711966,"threshold_uncertainty_score":0.02548933},"labels":[],"label_agreement":null},{"id":"W3020899460","doi":"","title":"Watchtower for k-crossing Visibility.","year":2019,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Data Management and Algorithms","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":"Carleton University; University of Manitoba","funders":"","keywords":"Visibility; Computer science; Telecommunications; Environmental science; Physics; Optics","score_opus":0.027419268007542713,"score_gpt":0.26531223596246645,"score_spread":0.23789296795492373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020899460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026789444,0.0015225122,0.83581173,0.0019072238,0.0015510715,0.0003725441,0.005176993,0.042731434,0.08413694],"genre_scores_gemma":[0.372569,0.0017991748,0.5013256,0.0008374931,0.0005720501,0.00061231764,0.0165141,0.009194695,0.09657555],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99931455,0.000074562915,0.000043652977,0.00019826827,0.00020632375,0.00016270064],"domain_scores_gemma":[0.99837554,0.0004993272,0.000081805185,0.0006337855,0.00023551391,0.00017400089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007603068,0.000926587,0.0010753189,0.0012455033,0.0014899061,0.0024101313,0.0019690513,0.0012975043,0.08350827],"category_scores_gemma":[0.005916339,0.0006416222,0.0012840322,0.0013932149,0.00083217694,0.0051974906,0.0053467667,0.0024193781,0.018485593],"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":[0.0008544866,0.00022971827,0.001963343,0.0007149396,0.00010012847,0.0005203782,0.0007975725,0.015948573,0.0077402326,0.29836628,0.27996618,0.39279822],"study_design_scores_gemma":[0.00017447815,0.00015683942,0.0007670386,0.00022719125,0.00007473964,0.00048523734,0.00038876428,0.11656046,0.008483605,0.6045466,0.26806718,0.00006781391],"about_ca_topic_score_codex":0.0036584556,"about_ca_topic_score_gemma":0.007226634,"teacher_disagreement_score":0.08350827,"about_ca_system_score_codex":0.00069708214,"about_ca_system_score_gemma":0.0010143651,"threshold_uncertainty_score":0.27936298},"labels":[],"label_agreement":null},{"id":"W3021319303","doi":"10.1007/978-3-540-27801-6_4","title":"Optimizing Multiple Spaced Seeds for Homology Search","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":37,"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; University of Waterloo","funders":"","keywords":"Computer science; Homology (biology); Biology; Genetics; Gene","score_opus":0.028448366131551064,"score_gpt":0.2648656440587714,"score_spread":0.23641727792722034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021319303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052648272,0.00040791745,0.93864435,0.00016491434,0.00010787646,0.00012603884,0.00011500333,0.0037461377,0.0040394594],"genre_scores_gemma":[0.22864906,0.00016474223,0.7651975,0.00010344672,0.0000556227,0.0001810988,0.0005055551,0.00082574587,0.0043172017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906355,0.00024164138,0.00004800173,0.00025197736,0.0003053445,0.000089516696],"domain_scores_gemma":[0.9978557,0.001112726,0.00015128663,0.000493299,0.00024607798,0.00014083699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012872148,0.0009938282,0.0017982526,0.0014582722,0.0007385482,0.00086703995,0.0023975768,0.0022254454,0.009995058],"category_scores_gemma":[0.006103038,0.00077670475,0.00073762046,0.00196711,0.0009956828,0.0026390133,0.0020732246,0.0012371971,0.0028392742],"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":[0.0011875338,0.00046540433,0.0014485306,0.0003083604,0.00008551214,0.00027588787,0.0002077942,0.3565788,0.041169476,0.0539791,0.012479121,0.5318145],"study_design_scores_gemma":[0.0001128256,0.00015728455,0.00016026945,0.000013120647,0.00002138057,0.00011890365,0.000041339274,0.95411664,0.009198744,0.03391529,0.0021299147,0.00001424677],"about_ca_topic_score_codex":0.0009980011,"about_ca_topic_score_gemma":0.0025409998,"teacher_disagreement_score":0.009995058,"about_ca_system_score_codex":0.0009844806,"about_ca_system_score_gemma":0.0010596965,"threshold_uncertainty_score":0.033436835},"labels":[],"label_agreement":null},{"id":"W3021556530","doi":"10.3390/ijgi9050315","title":"Disdyakis Triacontahedron DGGS","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Computer science; Grid; Rendering (computer graphics); Modular design; Projection (relational algebra); Atlas (anatomy); Visualization; Polyhedron; Computer graphics (images); Algorithm; Theoretical computer science; Data mining; Mathematics; Geometry","score_opus":0.009389327279779803,"score_gpt":0.23364112496632264,"score_spread":0.22425179768654283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021556530","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020903314,0.000061706836,0.95759743,0.0002138995,0.00012071962,0.00011035087,0.0010048716,0.0030539325,0.016933678],"genre_scores_gemma":[0.23309089,0.00018740019,0.7507831,0.00015112349,0.000032826756,0.0003281251,0.0031765422,0.0007983713,0.011451601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995259,0.0000743713,0.000032636,0.00007928782,0.00023588852,0.000051902454],"domain_scores_gemma":[0.999526,0.00007999509,0.000027818209,0.00017249252,0.0001504402,0.000043215976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002931732,0.0004910275,0.00048183344,0.0005900762,0.0004736764,0.0012617871,0.00089930714,0.00039125155,0.011605039],"category_scores_gemma":[0.0012412356,0.0002410992,0.00071143475,0.0010563435,0.0005149225,0.0009062414,0.0018964857,0.0009720701,0.003662412],"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":[0.00050822354,0.00011878321,0.0035226317,0.00039643122,0.00006182564,0.00060139876,0.00077554537,0.21508232,0.04910867,0.26131174,0.041883927,0.42662856],"study_design_scores_gemma":[0.000088857174,0.00021826709,0.0010029367,0.00003971499,0.000017271988,0.0007145552,0.0005370469,0.74599105,0.03529245,0.08044012,0.13560228,0.00005540224],"about_ca_topic_score_codex":0.003176159,"about_ca_topic_score_gemma":0.004291069,"teacher_disagreement_score":0.011605039,"about_ca_system_score_codex":0.0006397076,"about_ca_system_score_gemma":0.0007434503,"threshold_uncertainty_score":0.03882277},"labels":[],"label_agreement":null},{"id":"W3022249602","doi":"","title":"BR 2 : A Travel Behavioral Approach to Personalized Route Recommendation Based on GPS Trajectories.","year":2020,"lang":"en","type":"article","venue":"EDBT/ICDT Workshops","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Global Positioning System; Computer science; Transport engineering; Simulation; Engineering; Telecommunications","score_opus":0.05535183574913088,"score_gpt":0.27549356419122584,"score_spread":0.22014172844209495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022249602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032051,0.001588112,0.9329716,0.0010004861,0.00036886838,0.0006377868,0.009996005,0.013046216,0.008339996],"genre_scores_gemma":[0.3222294,0.0012704525,0.63874495,0.0004279046,0.00035618886,0.00053637597,0.01858495,0.00055637636,0.017293466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999106,0.00021719742,0.00004424609,0.00025577814,0.0002955849,0.00008124307],"domain_scores_gemma":[0.9991709,0.00025032795,0.000056101766,0.0002245111,0.00021889365,0.0000792125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010403318,0.0009925166,0.0014600591,0.0026424846,0.0004483968,0.0012676277,0.0023165916,0.0011664334,0.0043041487],"category_scores_gemma":[0.0039623794,0.00050989696,0.0010159372,0.0028927648,0.00020202038,0.001375238,0.0012281612,0.0011063374,0.0033511356],"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":[0.0010010652,0.0014441939,0.020246794,0.00081651204,0.0008783797,0.00020195718,0.00041236295,0.15864058,0.0076071057,0.012641872,0.082692206,0.71341693],"study_design_scores_gemma":[0.000070064234,0.00019722884,0.0038390006,0.00003384481,0.00011480047,0.00011138967,0.00012592995,0.9777198,0.0011114954,0.0055776513,0.011064396,0.000034394514],"about_ca_topic_score_codex":0.034976,"about_ca_topic_score_gemma":0.065601885,"teacher_disagreement_score":0.034976,"about_ca_system_score_codex":0.00069060136,"about_ca_system_score_gemma":0.0013903122,"threshold_uncertainty_score":0.06954485},"labels":[],"label_agreement":null},{"id":"W3027467598","doi":"10.3390/ijgi9050335","title":"Modelling Offset Regions around Static and Mobile Locations on a Discrete Global Grid System: An IoT Case Study","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Computer science; Offset (computer science); Grid; Internet of Things; Mobile device; UTC offset; Real-time computing; Digital Earth; Distributed computing; Data mining; Global Positioning System; Remote sensing; Telecommunications; Geography; Embedded system; Geodesy","score_opus":0.02485233786019062,"score_gpt":0.2892804052185582,"score_spread":0.26442806735836755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027467598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6539532,0.0005881939,0.33426622,0.00067528046,0.0000713862,0.000118032265,0.0014327688,0.00042329205,0.008471628],"genre_scores_gemma":[0.9651441,0.00011919505,0.0333982,0.000020301213,0.000011544471,0.000022136977,0.0003452309,0.000026744217,0.0009126368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997352,0.000091981135,0.000017212657,0.00005971535,0.000051648185,0.000044335564],"domain_scores_gemma":[0.9988254,0.00077735004,0.00012066161,0.0001127894,0.00010357038,0.000060254068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005360314,0.00051910616,0.00055924756,0.00061682134,0.0005239622,0.0013484206,0.0008756875,0.0011406069,0.0016082902],"category_scores_gemma":[0.0023504784,0.00019376553,0.00060061394,0.0016345544,0.0009471086,0.00096746976,0.0010854846,0.000665402,0.00016544551],"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":[0.00004560661,0.000012887941,0.0035675678,0.000027250278,0.000008070553,0.00030795898,0.000090500056,0.9857721,0.0003259191,0.00535774,0.0002901927,0.0041941437],"study_design_scores_gemma":[0.0000061144356,0.000008873192,0.0007848086,0.000004842788,0.000004306747,0.000048513604,0.0001148805,0.9955753,0.00019631922,0.0027218547,0.0005275789,0.000006509864],"about_ca_topic_score_codex":0.03721937,"about_ca_topic_score_gemma":0.025489831,"teacher_disagreement_score":0.03721937,"about_ca_system_score_codex":0.0011982642,"about_ca_system_score_gemma":0.00055726385,"threshold_uncertainty_score":0.074005485},"labels":[],"label_agreement":null},{"id":"W3032597430","doi":"10.4018/ijdwm.2020070106","title":"Conceptual Model and Design of Semantic Trajectory Data Warehouse","year":2020,"lang":"en","type":"article","venue":"International Journal of Data Warehousing and Mining","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data warehouse; Trajectory; Data mining; Geospatial analysis; Context (archaeology); Ontology; Inference; Object (grammar); Semantic data model; Information retrieval; Data modeling; Data science; Database; Artificial intelligence","score_opus":0.22540462770170883,"score_gpt":0.32539442731228885,"score_spread":0.09998979961058002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032597430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004026837,0.00017362018,0.9886395,0.0008751649,0.000055563938,0.00023088537,0.0005890183,0.00055392546,0.00485545],"genre_scores_gemma":[0.09182772,0.0010003134,0.8978035,0.0003622988,0.00006354399,0.00075261167,0.0034282813,0.00020189713,0.0045598214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968166,0.0007334676,0.0005865835,0.0006079442,0.0009927611,0.00026278495],"domain_scores_gemma":[0.99827385,0.00029018705,0.00020127391,0.00042138717,0.0006150241,0.00019834212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041376385,0.00072098884,0.000751893,0.0029657942,0.0016474004,0.008265514,0.0037402571,0.0020417084,0.0021101085],"category_scores_gemma":[0.0039305952,0.00089295243,0.0022959034,0.004759428,0.0021501102,0.009164949,0.004027123,0.0021734505,0.0011693215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054338463,0.000089678826,0.0010626564,0.00029930737,0.00006569709,0.0007158054,0.0016900524,0.035131816,0.0027485853,0.92655975,0.004092056,0.02749016],"study_design_scores_gemma":[0.0000597835,0.000105984094,0.00055332656,0.00035247448,0.00013257061,0.0011588593,0.0021340842,0.34117103,0.006265269,0.43295777,0.21501149,0.00009738167],"about_ca_topic_score_codex":0.009800623,"about_ca_topic_score_gemma":0.007444267,"teacher_disagreement_score":0.009800623,"about_ca_system_score_codex":0.0027187816,"about_ca_system_score_gemma":0.005455113,"threshold_uncertainty_score":0.021882176},"labels":[],"label_agreement":null},{"id":"W3033337213","doi":"10.1145/3401071.3401657","title":"Research challenges in deep reinforcement learning-based join query optimization","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Query optimization; Computer science; Sargable; Join (topology); Reinforcement learning; Query plan; Query expansion; Web query classification; Web search query; Set (abstract data type); Theoretical computer science; Data mining; Information retrieval; Artificial intelligence; Search engine; Programming language; Mathematics","score_opus":0.12405690345947706,"score_gpt":0.3115986065888434,"score_spread":0.18754170312936636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033337213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038278442,0.009713983,0.9308334,0.013622419,0.0002662568,0.00013752677,0.00032931392,0.0013349272,0.005483726],"genre_scores_gemma":[0.5727627,0.004117401,0.41507205,0.0020126838,0.00055042846,0.00023784516,0.0006762784,0.00036578596,0.0042047105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99611926,0.0016221446,0.00028477912,0.0007411715,0.0008800266,0.00035265606],"domain_scores_gemma":[0.9854323,0.011074271,0.00048565812,0.0011179659,0.0014395285,0.00045031065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007213202,0.00087421946,0.0022066832,0.00051236094,0.0006906488,0.0031729906,0.0031663373,0.0021628765,0.0034473683],"category_scores_gemma":[0.018232532,0.00074777636,0.00089277717,0.0014655015,0.0017653031,0.00593849,0.0019863453,0.004724011,0.00072090985],"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":[0.00026357424,0.00047604193,0.0024251156,0.00049999554,0.00012697956,0.00007865617,0.0002968031,0.698448,0.0019612277,0.054435004,0.010232591,0.23075593],"study_design_scores_gemma":[0.000018018265,0.00003502926,0.000119690165,0.000015460935,0.00000735162,0.000012905484,0.00007010969,0.98029286,0.00039844852,0.01716196,0.0018601684,0.000008012243],"about_ca_topic_score_codex":0.0128507605,"about_ca_topic_score_gemma":0.0103091225,"teacher_disagreement_score":0.0128507605,"about_ca_system_score_codex":0.0024389834,"about_ca_system_score_gemma":0.003802904,"threshold_uncertainty_score":0.03814751},"labels":[],"label_agreement":null},{"id":"W3034188862","doi":"10.1007/s10707-020-00408-9","title":"SWS: an unsupervised trajectory segmentation algorithm based on change detection with interpolation kernels","year":2020,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Dalhousie University","funders":"","keywords":"Trajectory; Segmentation; Computer science; Interpolation (computer graphics); Artificial intelligence; Sliding window protocol; Window (computing); Algorithm; Kernel (algebra); Pattern recognition (psychology); Computer vision; Mathematics; Motion (physics)","score_opus":0.03146745409551396,"score_gpt":0.2314241320843101,"score_spread":0.19995667798879613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034188862","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008762467,0.00007641027,0.9829229,0.000046491612,0.000053038155,0.000057901972,0.00031371025,0.0072360565,0.00053104834],"genre_scores_gemma":[0.078495085,0.00010241038,0.9155179,0.000049768714,0.00003590875,0.00011294445,0.0017980534,0.0009230574,0.0029647958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996264,0.000032434982,0.00001821745,0.00011788746,0.00015950248,0.000045455905],"domain_scores_gemma":[0.999595,0.000065695844,0.000050273855,0.000091097245,0.00016381608,0.000034052795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004599877,0.00092931144,0.0010911999,0.0022635176,0.0006112383,0.00094557856,0.0016493449,0.00082520803,0.003376372],"category_scores_gemma":[0.0011356194,0.0006925169,0.0010253438,0.002251209,0.00046931976,0.0013219633,0.0014635266,0.0009485313,0.0022550786],"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":[0.00030363552,0.00015450212,0.0023592454,0.00013709399,0.00013982458,0.000083970925,0.00013816448,0.069651894,0.022870649,0.0053159953,0.013045894,0.8857991],"study_design_scores_gemma":[0.00002013907,0.000047169222,0.0012287052,0.000009317191,0.000022221959,0.0000789,0.000025175928,0.97764426,0.011642797,0.0022007169,0.007056395,0.000024210436],"about_ca_topic_score_codex":0.01308669,"about_ca_topic_score_gemma":0.019411363,"teacher_disagreement_score":0.01308669,"about_ca_system_score_codex":0.00060849776,"about_ca_system_score_gemma":0.0015605259,"threshold_uncertainty_score":0.026021004},"labels":[],"label_agreement":null},{"id":"W3035899174","doi":"10.22215/etd/2015-10682","title":"Biased Nearest Neighbour Search","year":2015,"lang":"en","type":"dissertation","venue":"","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":"Carleton University","funders":"","keywords":"Nearest neighbour; Nearest neighbor search; Point (geometry); Set (abstract data type); k-nearest neighbors algorithm; Computer science; Data mining; Algorithm; Mathematics; Artificial intelligence","score_opus":0.04134118352643582,"score_gpt":0.3122846023598404,"score_spread":0.27094341883340456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035899174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021975027,0.0024903074,0.96318483,0.00040466635,0.00025318854,0.00021222694,0.00058492605,0.0012717828,0.009623027],"genre_scores_gemma":[0.24854015,0.0025895692,0.7327649,0.0007530253,0.00023359727,0.00046852173,0.0029860951,0.00042514494,0.011239118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950197,0.0012758168,0.00032063125,0.00088603795,0.0021056228,0.00039223206],"domain_scores_gemma":[0.9942788,0.0020567528,0.00036870624,0.0017247153,0.0014030524,0.00016793187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002609074,0.0007099535,0.002205326,0.0014632711,0.001250693,0.0023871309,0.0028143907,0.0021611627,0.007446067],"category_scores_gemma":[0.018307121,0.00068774365,0.0012419411,0.0036003462,0.0011078126,0.0040364633,0.0033464024,0.0015315323,0.0055313287],"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":[0.0011460296,0.00027795808,0.0059204493,0.0007857039,0.00041317128,0.00041389416,0.0005640794,0.15441865,0.018607397,0.09502695,0.030987583,0.6914381],"study_design_scores_gemma":[0.00029811243,0.00038153696,0.0024378484,0.00026929745,0.00016846549,0.0013857706,0.0004094752,0.72174466,0.017234528,0.20204273,0.053459346,0.00016820124],"about_ca_topic_score_codex":0.003946936,"about_ca_topic_score_gemma":0.00502902,"teacher_disagreement_score":0.007446067,"about_ca_system_score_codex":0.00096407806,"about_ca_system_score_gemma":0.0021514918,"threshold_uncertainty_score":0.024909616},"labels":[],"label_agreement":null},{"id":"W3037488610","doi":"10.4230/lipics.swat.2020.22","title":"Clustering Moving Entities in Euclidean Space","year":2020,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","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 Manitoba","funders":"","keywords":"Cluster analysis; k-medians clustering; Correlation clustering; Disjoint sets; CURE data clustering algorithm; Combinatorics; Canopy clustering algorithm; Mathematics; Single-linkage clustering; Center (category theory); Algorithm; Sequence (biology); Data stream clustering; Euclidean space; Computer science; Statistics","score_opus":0.022118242785892406,"score_gpt":0.23660286487675364,"score_spread":0.21448462209086122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037488610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01981987,0.0012525643,0.9735883,0.00048493873,0.000101699894,0.00013573874,0.0018059136,0.0011504819,0.0016604529],"genre_scores_gemma":[0.26461318,0.0018595689,0.7208226,0.00018809327,0.00015176447,0.00031498526,0.007575516,0.00023926643,0.0042350623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971675,0.0007210469,0.0002171611,0.0010510196,0.00066962006,0.00017359424],"domain_scores_gemma":[0.9983517,0.000703498,0.0002138244,0.00038634942,0.00026878505,0.00007593102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135358,0.0013685126,0.0016148038,0.0024669725,0.001019717,0.001744829,0.0024479423,0.0019648978,0.0020277316],"category_scores_gemma":[0.006059765,0.0007509317,0.0011178917,0.006307842,0.0010163728,0.0035570187,0.0026862859,0.0015869531,0.0013851185],"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":[0.00036038595,0.00008323906,0.0027256766,0.00052914885,0.00020398054,0.0002538867,0.00040246733,0.74473554,0.00520641,0.06113617,0.015284672,0.16907845],"study_design_scores_gemma":[0.000023979108,0.00004523932,0.0010569494,0.00003372932,0.000022984952,0.00012036364,0.00014350549,0.9381033,0.0023598175,0.046560418,0.011493629,0.000036148755],"about_ca_topic_score_codex":0.008931326,"about_ca_topic_score_gemma":0.008133117,"teacher_disagreement_score":0.008931326,"about_ca_system_score_codex":0.0016022391,"about_ca_system_score_gemma":0.0009217124,"threshold_uncertainty_score":0.017758727},"labels":[],"label_agreement":null},{"id":"W3037925904","doi":"10.20382/jocg.v8i1a14","title":"Central Trajectories","year":2015,"lang":"en","type":"preprint","venue":"Journal of Computational Geometry (Carleton University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Danmarks Grundforskningsfond; National Research Foundation","keywords":"Trajectory; Centrality; Combinatorics; RADIUS; Cluster (spacecraft); Cluster analysis; Set (abstract data type); Physics; Mathematics; Computer science","score_opus":0.021899771686030044,"score_gpt":0.22805388347335698,"score_spread":0.20615411178732693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037925904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06365273,0.0008794587,0.8784262,0.00107769,0.00034885583,0.00048379877,0.0059983255,0.006632131,0.042500794],"genre_scores_gemma":[0.38531926,0.00081642607,0.54398525,0.0004735169,0.0001485716,0.0006238278,0.016397597,0.0028866737,0.04934888],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983234,0.0001849399,0.00010118159,0.00077834027,0.00034834453,0.00026383757],"domain_scores_gemma":[0.9972452,0.0004985393,0.0002736179,0.00108554,0.0006486112,0.0002484936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082643755,0.0012967279,0.001199104,0.0014269209,0.002269135,0.0033572628,0.0024360814,0.0018079791,0.04518461],"category_scores_gemma":[0.005439025,0.00068035943,0.0014400759,0.0018094471,0.0011722099,0.0045066644,0.004078055,0.0017938825,0.011341427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014647268,0.00025115008,0.0066240006,0.0008789488,0.00016293378,0.0008084713,0.0009871192,0.17933042,0.02237947,0.4365925,0.075555906,0.27496433],"study_design_scores_gemma":[0.00013018347,0.00021813693,0.0021308511,0.00017782894,0.000083200066,0.0009213322,0.0008058692,0.5452171,0.021061527,0.3160244,0.11312423,0.00010528552],"about_ca_topic_score_codex":0.0046465145,"about_ca_topic_score_gemma":0.006685174,"teacher_disagreement_score":0.04518461,"about_ca_system_score_codex":0.0022871231,"about_ca_system_score_gemma":0.0024906998,"threshold_uncertainty_score":0.15115756},"labels":[],"label_agreement":null},{"id":"W3039098376","doi":"10.1177/0361198120931843","title":"Process for the Encapsulation and Visualization of Dominant Demand and Supply Corridors","year":2020,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Data Management and Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Visualization; Cluster analysis; Process (computing); Public transport; Supply and demand; Smoothing; Quality (philosophy); Relevance (law); Operations research; Transport engineering; Data science; Data mining; Engineering; Machine learning","score_opus":0.09678668199962037,"score_gpt":0.39080848396784457,"score_spread":0.2940218019682242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039098376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005999524,0.00004917407,0.98104745,0.00019368816,0.00004135824,0.0002895959,0.000749691,0.00846315,0.003166494],"genre_scores_gemma":[0.076617904,0.000097858334,0.91560537,0.00007837295,0.000021485554,0.00045086254,0.0016602958,0.0014424074,0.0040255617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982003,0.0004932602,0.00017108333,0.00035275257,0.00063051376,0.00015194889],"domain_scores_gemma":[0.9941461,0.0021652821,0.0003184062,0.0015656818,0.0016309412,0.00017356194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031750633,0.000959671,0.0006635421,0.0027327545,0.0011125612,0.0031008495,0.0011717795,0.0007911119,0.012162669],"category_scores_gemma":[0.011045637,0.0005848006,0.0013781256,0.0020025698,0.000882497,0.0018811774,0.0029893506,0.0018436515,0.0043066754],"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":[0.0006404359,0.00033775598,0.005491654,0.0007911666,0.00012367267,0.00048823477,0.0061693406,0.043619704,0.04758648,0.10060025,0.028436625,0.7657147],"study_design_scores_gemma":[0.00016782821,0.00026643506,0.007852975,0.00033254968,0.00010732394,0.0006034062,0.0024240029,0.4838674,0.12376521,0.056116026,0.32418007,0.0003167651],"about_ca_topic_score_codex":0.0064283065,"about_ca_topic_score_gemma":0.005223849,"teacher_disagreement_score":0.012162669,"about_ca_system_score_codex":0.00094908284,"about_ca_system_score_gemma":0.0027435108,"threshold_uncertainty_score":0.040688157},"labels":[],"label_agreement":null},{"id":"W3041888157","doi":"10.1145/376284.375685","title":"Independence is good","year":2001,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; Curse of dimensionality; Data mining; Histogram; Independence (probability theory); Exploit; Set (abstract data type); Algorithm; Theoretical computer science; Artificial intelligence; Mathematics; Image (mathematics)","score_opus":0.025710358788209507,"score_gpt":0.2582570222251933,"score_spread":0.2325466634369838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041888157","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032417987,0.0032310933,0.8589856,0.009185302,0.0007949766,0.00033392545,0.0048040967,0.003306734,0.08694024],"genre_scores_gemma":[0.7009514,0.006058804,0.21639119,0.00663498,0.0021116873,0.0009393914,0.013439586,0.0020486596,0.05142418],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9886979,0.0022245646,0.0008166891,0.003751446,0.0036419085,0.000867553],"domain_scores_gemma":[0.97638,0.008175113,0.0012838108,0.010256324,0.0030233741,0.000881431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006546968,0.0013303618,0.0022482579,0.0027397885,0.002525625,0.0059406552,0.0021121425,0.0019672092,0.031556774],"category_scores_gemma":[0.049819168,0.0012149063,0.0016528536,0.0050619864,0.0034067316,0.017440377,0.006522203,0.004075538,0.014375335],"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":[0.000878419,0.00023059499,0.015591348,0.00082245487,0.00047503988,0.0007181868,0.0017374853,0.03195253,0.0047108536,0.44283807,0.11889157,0.3811535],"study_design_scores_gemma":[0.000096996424,0.00018144421,0.006437684,0.0002255331,0.00016665841,0.0024717946,0.000992563,0.08963518,0.0036626637,0.74958473,0.14639276,0.00015188854],"about_ca_topic_score_codex":0.0042968797,"about_ca_topic_score_gemma":0.0038271276,"teacher_disagreement_score":0.031556774,"about_ca_system_score_codex":0.0017451787,"about_ca_system_score_gemma":0.0028238855,"threshold_uncertainty_score":0.10556799},"labels":[],"label_agreement":null},{"id":"W3046659318","doi":"10.20382/jocg.v10i1a17","title":"Interesting paths in the mapper complex","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Geometry (Carleton University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Mathematics; Combinatorics; Path (computing); Graph; Enhanced Data Rates for GSM Evolution; Data point; Binary number; Function (biology); Variable (mathematics); Algorithm; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.028764422130967734,"score_gpt":0.24245818655587084,"score_spread":0.21369376442490312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046659318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32668382,0.00041444416,0.6643578,0.0007746545,0.000039987237,0.00013913169,0.0017393859,0.0013365734,0.0045141308],"genre_scores_gemma":[0.63051826,0.00036439113,0.36113015,0.0001962316,0.000037196205,0.00027687213,0.0024197884,0.0004953916,0.0045616664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99891126,0.00022429976,0.000059796585,0.00034517955,0.0003578917,0.00010169288],"domain_scores_gemma":[0.9959306,0.0021858346,0.0005594666,0.00047864649,0.0005385708,0.00030695926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012548801,0.00066248555,0.0008004405,0.0032566213,0.0009472598,0.0024293198,0.0008248888,0.0010543775,0.003932001],"category_scores_gemma":[0.010187442,0.00061157777,0.0008205496,0.0016807042,0.0013024316,0.003642294,0.002246684,0.001052762,0.0005736601],"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":[0.00036724994,0.0001559822,0.030836845,0.00051897834,0.00015829282,0.0015589856,0.0020982015,0.40567628,0.022677407,0.37978506,0.007903479,0.14826325],"study_design_scores_gemma":[0.000025648185,0.00007172387,0.005179251,0.000048522932,0.00003065871,0.00047997612,0.00036883054,0.670081,0.004572223,0.30775648,0.011343551,0.000042052812],"about_ca_topic_score_codex":0.0021637978,"about_ca_topic_score_gemma":0.0026142334,"teacher_disagreement_score":0.003932001,"about_ca_system_score_codex":0.0010615106,"about_ca_system_score_gemma":0.0006725095,"threshold_uncertainty_score":0.013153851},"labels":[],"label_agreement":null},{"id":"W3046886793","doi":"","title":"Materialized views for count aggregates of spatial data","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","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":"Science North","funders":"","keywords":"Aggregate (composite); Online analytical processing; Computer science; Materialized view; Quadtree; Space (punctuation); Spatial analysis; Index (typography); Data mining; Spatial database; Data warehouse; Database; Theoretical computer science; Algorithm; Mathematics; World Wide Web; Statistics","score_opus":0.05278441042897332,"score_gpt":0.3073932149966844,"score_spread":0.2546088045677111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046886793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052654937,0.0012102056,0.936939,0.0003332749,0.00008238923,0.00021711514,0.0013687804,0.0051933005,0.0020009072],"genre_scores_gemma":[0.24755546,0.000664991,0.7458383,0.000116663985,0.00013285532,0.00021736127,0.0037052268,0.0005128696,0.0012561509],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99799037,0.00034921188,0.00018175523,0.0003122518,0.0010127772,0.00015356498],"domain_scores_gemma":[0.99479115,0.0013349704,0.00071420934,0.0023188498,0.0006046587,0.00023606385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012112124,0.0007809261,0.0011357627,0.0018720663,0.0006588318,0.0035848867,0.0019414121,0.0007706948,0.002178303],"category_scores_gemma":[0.0065582446,0.0006728038,0.0010066599,0.0038916217,0.00075078965,0.0059798053,0.0029519896,0.0013352355,0.0009170478],"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":[0.0011101532,0.00047770003,0.012311877,0.0008276925,0.00036130357,0.00060471543,0.0022025625,0.08901602,0.07191754,0.08659291,0.020127373,0.71445024],"study_design_scores_gemma":[0.00013679397,0.0007986575,0.0036172324,0.000105254214,0.00014486251,0.00090798823,0.0010764133,0.8087566,0.064133,0.07232215,0.047855232,0.0001456963],"about_ca_topic_score_codex":0.0020170799,"about_ca_topic_score_gemma":0.0028631466,"teacher_disagreement_score":0.0035848867,"about_ca_system_score_codex":0.0006885372,"about_ca_system_score_gemma":0.0010962475,"threshold_uncertainty_score":0.0072872043},"labels":[],"label_agreement":null},{"id":"W3047749157","doi":"10.1007/978-3-030-57602-8_12","title":"The Spherical k-means++ Algorithm via Local Search","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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 New Brunswick","funders":"","keywords":"Unit sphere; Partition (number theory); Combinatorics; Generalization; Cluster analysis; Integer (computer science); Set (abstract data type); Algorithm; Mathematics; Point (geometry); Approximation algorithm; k-means clustering; Computer science; Discrete mathematics; Artificial intelligence; Geometry; Mathematical analysis","score_opus":0.016940786532604123,"score_gpt":0.2413999742474191,"score_spread":0.224459187714815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047749157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012697345,0.00023319351,0.9947836,0.00005814391,0.00007326452,0.000039026705,0.0000636018,0.0017391582,0.0017404141],"genre_scores_gemma":[0.02383734,0.00023508117,0.9694447,0.00009829434,0.00006922805,0.00016758774,0.00038439367,0.0005698073,0.0051935753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904495,0.000257416,0.000054576485,0.0001999888,0.00037172151,0.00007142548],"domain_scores_gemma":[0.9993315,0.00020193555,0.000039875038,0.00014565443,0.00024250944,0.0000384968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008368025,0.001264172,0.0019905544,0.0010793112,0.00088165695,0.0014075374,0.002765625,0.0015565353,0.009308029],"category_scores_gemma":[0.002455594,0.0007621777,0.0014977362,0.0021360167,0.0006633695,0.0014404586,0.0025187237,0.0016348346,0.009257411],"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":[0.00023039347,0.00008571834,0.00027568048,0.00033074955,0.00014666995,0.000053994176,0.00008339303,0.12772828,0.008524031,0.016688539,0.024973024,0.82087946],"study_design_scores_gemma":[0.000047000158,0.000065930384,0.00018294132,0.00002067095,0.000040294217,0.00014901334,0.000029432093,0.97416615,0.0046582753,0.009826913,0.010778907,0.00003451329],"about_ca_topic_score_codex":0.01088701,"about_ca_topic_score_gemma":0.0154608665,"teacher_disagreement_score":0.01088701,"about_ca_system_score_codex":0.0006465218,"about_ca_system_score_gemma":0.002447486,"threshold_uncertainty_score":0.03113848},"labels":[],"label_agreement":null},{"id":"W3048110077","doi":"10.1109/mdm48529.2020.00027","title":"Trade-off Aware Sequenced Routing Queries (or OSR Queries when POIs are not Free)","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Pruning; Skyline; Overhead (engineering); Point of interest; Constraint (computer-aided design); Routing (electronic design automation); Query optimization; Data mining; Sequence (biology); Computer network; Artificial intelligence; Mathematics","score_opus":0.04575828859600797,"score_gpt":0.24511440296721812,"score_spread":0.19935611437121015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048110077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17716543,0.00083220744,0.7969396,0.0013692781,0.00016734951,0.000709609,0.002845548,0.004234884,0.015736053],"genre_scores_gemma":[0.5704189,0.00034252196,0.42024633,0.00037939518,0.00010530229,0.00023594772,0.0036275077,0.0005552421,0.004088767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786925,0.00055991905,0.0002000129,0.00046534784,0.0006725446,0.00023293511],"domain_scores_gemma":[0.996867,0.0016438644,0.00027299553,0.000785068,0.00029799427,0.00013309943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022165626,0.00094448315,0.001453707,0.0007426101,0.0008093602,0.0016113028,0.0017515303,0.0015801716,0.0040452257],"category_scores_gemma":[0.0058972407,0.0004920686,0.0009575297,0.0017119469,0.0007847687,0.004349414,0.0017567539,0.0011821993,0.00085383735],"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":[0.0015522903,0.00079456484,0.009912961,0.0013530615,0.00038802,0.0016852972,0.0013748273,0.36856437,0.07119352,0.100468315,0.043186266,0.39952648],"study_design_scores_gemma":[0.00008995926,0.0003757447,0.0014235916,0.000035543544,0.00006703988,0.0009858196,0.0008501429,0.90292084,0.0116574485,0.061951414,0.019589454,0.000052886542],"about_ca_topic_score_codex":0.0020498761,"about_ca_topic_score_gemma":0.004441222,"teacher_disagreement_score":0.0040452257,"about_ca_system_score_codex":0.0007897147,"about_ca_system_score_gemma":0.001073064,"threshold_uncertainty_score":0.013532579},"labels":[],"label_agreement":null},{"id":"W30607122","doi":"10.3747/co.25.4117","title":"Personalized Keyword Search with Partial-Order Preferences.","year":2002,"lang":"en","type":"article","venue":"Brazilian Symposium on Databases","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Order (exchange); Information retrieval; Keyword search","score_opus":0.045043638085699285,"score_gpt":0.2636945420906061,"score_spread":0.21865090400490683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30607122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28332922,0.013767111,0.52237874,0.0077296603,0.00042108097,0.0022536025,0.09068899,0.01833679,0.061094828],"genre_scores_gemma":[0.66570276,0.0028219684,0.2781681,0.00088674686,0.00019723458,0.00039730172,0.031616095,0.0005003404,0.019709462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978078,0.00062756863,0.0002662272,0.0003871971,0.0006251882,0.00028605407],"domain_scores_gemma":[0.9950723,0.00315233,0.00028077458,0.0008499983,0.00037532538,0.00026925944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020761539,0.00078521756,0.0020795036,0.002586558,0.0010509478,0.0020148666,0.0014840792,0.001422794,0.015560935],"category_scores_gemma":[0.011374061,0.0005850111,0.0011566635,0.005516102,0.0005907208,0.0056152525,0.0018853166,0.0006772875,0.004241438],"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":[0.003864278,0.00069647183,0.011817052,0.0029207931,0.00058259413,0.0012977057,0.0011033806,0.08863694,0.0073374445,0.059590884,0.1329765,0.68917584],"study_design_scores_gemma":[0.00070693914,0.0006518217,0.0054655485,0.00019347321,0.0004468534,0.0027055573,0.0013680169,0.62588894,0.0074569806,0.28050226,0.07443476,0.00017880845],"about_ca_topic_score_codex":0.02133489,"about_ca_topic_score_gemma":0.059941277,"teacher_disagreement_score":0.02133489,"about_ca_system_score_codex":0.0017885937,"about_ca_system_score_gemma":0.0036651168,"threshold_uncertainty_score":0.05205649},"labels":[],"label_agreement":null},{"id":"W3061738491","doi":"10.1007/978-3-030-57811-4_7","title":"Strategies for Alternate Group Trip Planning Queries in Location-Based Services","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Sequence (biology); Computer science; Set (abstract data type); Group (periodic table); Point of interest; Permutation (music); Information retrieval; Artificial intelligence","score_opus":0.02530748379290816,"score_gpt":0.2798398126520164,"score_spread":0.25453232885910826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3061738491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0660715,0.00060177094,0.9012377,0.0007884666,0.00014095192,0.0004994312,0.0003762835,0.001288588,0.02899532],"genre_scores_gemma":[0.6455487,0.0003082671,0.34518734,0.0002111676,0.00007494796,0.00024736943,0.0005379718,0.0001878468,0.0076964065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998767,0.0003576256,0.000078569916,0.00018492366,0.00038680367,0.00022499014],"domain_scores_gemma":[0.99750453,0.0016824566,0.00009852289,0.00029792506,0.00029314536,0.00012342593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013616846,0.00084112457,0.0012180094,0.0009904933,0.0010443343,0.0026847827,0.002481828,0.0015160093,0.010944087],"category_scores_gemma":[0.0058608525,0.000457285,0.00061507337,0.0014679227,0.000617894,0.0026892063,0.0021905182,0.001143437,0.0016964702],"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":[0.0017714311,0.0007640819,0.0041659907,0.00040804895,0.00015796062,0.0007030555,0.0016190168,0.228622,0.018312993,0.21851853,0.026104735,0.49885216],"study_design_scores_gemma":[0.00008190836,0.00014173512,0.00039109046,0.000030346557,0.000066880224,0.00024873437,0.00072755857,0.9181986,0.004926609,0.06787733,0.0072788037,0.000030436959],"about_ca_topic_score_codex":0.004733412,"about_ca_topic_score_gemma":0.005895703,"teacher_disagreement_score":0.010944087,"about_ca_system_score_codex":0.0008325565,"about_ca_system_score_gemma":0.0014503276,"threshold_uncertainty_score":0.036611676},"labels":[],"label_agreement":null},{"id":"W3081095766","doi":"10.48550/arxiv.2008.11705","title":"Towards A Personal Shopper's Dilemma: Time vs Cost","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Skyline; Order (exchange); Profit (economics); Set (abstract data type); Heuristic; Baseline (sea); Dilemma; Measure (data warehouse); Operations research; Data mining; Business; Microeconomics; Mathematics; Economics; Artificial intelligence","score_opus":0.07958839085075976,"score_gpt":0.18880816698924355,"score_spread":0.10921977613848378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081095766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22869346,0.002858338,0.7382156,0.010056347,0.00024825663,0.00059712055,0.0015271672,0.0013045087,0.016499132],"genre_scores_gemma":[0.50625473,0.001053311,0.48505977,0.0009993198,0.0002202094,0.00034250965,0.0013305376,0.00045344012,0.0042861425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99671423,0.0015921188,0.00015014809,0.0006715544,0.00058029825,0.00029157256],"domain_scores_gemma":[0.9877957,0.009216223,0.00063659746,0.0009724112,0.0007661812,0.0006129038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004192906,0.0013699913,0.00266366,0.0015068315,0.0018630613,0.003974553,0.0029457284,0.00337986,0.005011951],"category_scores_gemma":[0.018376742,0.0009795481,0.0011756484,0.0027878932,0.0025904053,0.008838194,0.0028709823,0.002788832,0.00093820505],"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":[0.0011570902,0.00058836606,0.004217411,0.0008327693,0.00029636399,0.000531695,0.0013938337,0.7306818,0.0058354945,0.086671785,0.03283636,0.13495712],"study_design_scores_gemma":[0.0001601109,0.00029437867,0.00078289316,0.00004561981,0.000051038878,0.0005729015,0.00096707925,0.85922575,0.0020029328,0.12857595,0.0072718267,0.000049558766],"about_ca_topic_score_codex":0.004950407,"about_ca_topic_score_gemma":0.005533782,"teacher_disagreement_score":0.005011951,"about_ca_system_score_codex":0.0024015743,"about_ca_system_score_gemma":0.0022421447,"threshold_uncertainty_score":0.022174478},"labels":[],"label_agreement":null},{"id":"W3081170401","doi":"10.1109/cjece.2020.2966733","title":"UFleSe: User-Friendly Parametric Framework for Expressive Flexible Searches","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Syntax; Scalability; SQL; JSON; Natural language; Interface (matter); Similarity (geometry); Information retrieval; Fuzzy logic; Programming language; Artificial intelligence; Natural language processing; Data mining; Database","score_opus":0.02210366387799143,"score_gpt":0.2237459265400002,"score_spread":0.20164226266200877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081170401","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014125607,0.0001957819,0.9615083,0.00017342222,0.000037669935,0.0001789361,0.0011968078,0.03192432,0.0033721672],"genre_scores_gemma":[0.06018581,0.0005615262,0.9187513,0.00042475775,0.00007765451,0.00083486317,0.00493253,0.0077999537,0.006431628],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99616146,0.0008915709,0.00063279324,0.00048369318,0.0014453948,0.00038504694],"domain_scores_gemma":[0.9955096,0.0023360965,0.00021325352,0.0011551332,0.000544764,0.00024120537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005707163,0.00184814,0.0020214836,0.00391689,0.0014218633,0.0067043966,0.0049266396,0.0022946466,0.020408107],"category_scores_gemma":[0.01495696,0.0015381478,0.002777172,0.0027077508,0.0021823645,0.010851094,0.0071184994,0.0025341108,0.0078926375],"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":[0.0013402865,0.00033693865,0.0024290998,0.0018942065,0.0002519204,0.0013323113,0.0035434822,0.024537386,0.0180851,0.4263346,0.08875603,0.43115857],"study_design_scores_gemma":[0.00021906228,0.00021750267,0.0006759889,0.00059819344,0.00009646322,0.0019886817,0.0005765828,0.22483605,0.027666636,0.27233693,0.47034585,0.0004421084],"about_ca_topic_score_codex":0.0037525531,"about_ca_topic_score_gemma":0.0044789156,"teacher_disagreement_score":0.020408107,"about_ca_system_score_codex":0.001174491,"about_ca_system_score_gemma":0.001653273,"threshold_uncertainty_score":0.068271875},"labels":[],"label_agreement":null},{"id":"W3081818585","doi":"10.1002/net.21987","title":"Plowing with precedence in polynomial time","year":2020,"lang":"en","type":"article","venue":"Networks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Consejo Nacional de Ciencia y Tecnología; Bundesministerium für Bildung und Forschung; Deutscher Akademischer Austauschdienst","keywords":"Heuristics; Traverse; Tree traversal; Time complexity; Computer science; State (computer science); Mathematical optimization; Polynomial; Mathematics; Algorithm; Geography; Cartography","score_opus":0.008302955797465502,"score_gpt":0.17694568193358984,"score_spread":0.16864272613612433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081818585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27456826,0.00088326057,0.6799242,0.0037981302,0.00028017917,0.0008670156,0.0027711552,0.0035857614,0.033322144],"genre_scores_gemma":[0.5353542,0.0006519631,0.44319105,0.0004636788,0.00015510926,0.00025971653,0.0032520639,0.0005897126,0.016082523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99877125,0.00020148118,0.00007974722,0.0002884516,0.00021895472,0.00044009258],"domain_scores_gemma":[0.9975236,0.0013346308,0.00028748254,0.0004193807,0.00016035828,0.00027439077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082150754,0.00112566,0.0011207868,0.00064605,0.0012236492,0.0024241772,0.0016057808,0.001068323,0.017953452],"category_scores_gemma":[0.0038766982,0.0005172291,0.0011717645,0.0018890081,0.00088828563,0.0048158984,0.001645177,0.0016957476,0.0012861204],"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":[0.0010904236,0.0009975556,0.0035608467,0.0014983455,0.00016346342,0.0007856166,0.0004775874,0.42822403,0.024675375,0.16940561,0.044250473,0.32487065],"study_design_scores_gemma":[0.00033135197,0.00032844912,0.0011220861,0.00006543307,0.00009492912,0.0004842311,0.00054785964,0.58552456,0.0140251275,0.3715507,0.025877524,0.000047778263],"about_ca_topic_score_codex":0.0068240357,"about_ca_topic_score_gemma":0.010336369,"teacher_disagreement_score":0.017953452,"about_ca_system_score_codex":0.0013694337,"about_ca_system_score_gemma":0.002071486,"threshold_uncertainty_score":0.060060263},"labels":[],"label_agreement":null},{"id":"W3083067449","doi":"10.1109/cec48606.2020.9185609","title":"Discovering Numerous Strassen’s Equivalent Equations Using a Simple Micro Multimodal GA: Evolution in Action","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Strassen algorithm; Simple (philosophy); Action (physics); Computer science; Statistical physics; Physics; Quantum mechanics","score_opus":0.08043503582654463,"score_gpt":0.3102707707902285,"score_spread":0.2298357349636839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083067449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078506775,0.0002480644,0.91226673,0.00036869466,0.00006475172,0.00008594186,0.0000633742,0.00027903885,0.0081166765],"genre_scores_gemma":[0.44627565,0.0002687053,0.5480481,0.00017698207,0.000023636281,0.00022395387,0.0001380461,0.000051906107,0.004793149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998311,0.000038486232,0.0000099213285,0.00003806002,0.00006184932,0.000020595127],"domain_scores_gemma":[0.9998246,0.000077213735,0.000029426757,0.000023921846,0.000033909062,0.0000109896755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030877138,0.00046878812,0.00048818745,0.00054362224,0.00041341598,0.00060812896,0.00084380014,0.00082474184,0.0019687347],"category_scores_gemma":[0.00090282987,0.00026533654,0.0008211296,0.0004759271,0.00055961276,0.0006981748,0.000565843,0.0006274918,0.00023148459],"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":[0.000048418413,0.00007310899,0.0026207718,0.00013764633,0.00009077855,0.00024411974,0.0001336633,0.80558693,0.012540267,0.084611356,0.0018281477,0.0920848],"study_design_scores_gemma":[0.000009991964,0.00003653609,0.00018214725,0.00000778414,0.000013044314,0.00006960322,0.000021899223,0.9888115,0.0010408596,0.008092293,0.0017066823,0.000007604408],"about_ca_topic_score_codex":0.002965388,"about_ca_topic_score_gemma":0.003936602,"teacher_disagreement_score":0.002965388,"about_ca_system_score_codex":0.00042796644,"about_ca_system_score_gemma":0.00092417334,"threshold_uncertainty_score":0.006586075},"labels":[],"label_agreement":null},{"id":"W3086389904","doi":"10.1109/access.2020.3023376","title":"A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"Computer science; Data mining; Cluster analysis; Merge (version control); Spatial analysis; Trajectory; Crime analysis; Cluster (spacecraft); Identification (biology); Temporal database; Artificial intelligence; Information retrieval; Geography","score_opus":0.07179478281687209,"score_gpt":0.32325340033716315,"score_spread":0.2514586175202911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086389904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005971644,0.00014928875,0.99828714,0.00010569261,0.00002045567,0.00006169512,0.00012677381,0.00018253617,0.00046927703],"genre_scores_gemma":[0.052935358,0.0005392621,0.9434607,0.00011328103,0.000106663865,0.00054009096,0.00088558905,0.0001658505,0.0012532086],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959948,0.0014951229,0.00029202082,0.0009896127,0.0009610586,0.0002675005],"domain_scores_gemma":[0.9958972,0.0016934293,0.0004846253,0.00064848916,0.0010364735,0.00023986484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048162597,0.0015638794,0.0014384388,0.0063287956,0.0024019082,0.00403248,0.0038891512,0.0015775664,0.0031540003],"category_scores_gemma":[0.011529041,0.0008378257,0.0027768775,0.0098430645,0.002518695,0.0052497345,0.004662329,0.0026709198,0.0014354173],"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":[0.00011307306,0.0001309708,0.0044659423,0.00032742356,0.00034237123,0.000469958,0.0016270608,0.28460607,0.0024460356,0.55735105,0.009805692,0.13831435],"study_design_scores_gemma":[0.000018915369,0.000056979916,0.0009955112,0.00006091137,0.000067978566,0.00022093237,0.0003904299,0.8099809,0.0007200536,0.16799098,0.019427637,0.00006876276],"about_ca_topic_score_codex":0.022650234,"about_ca_topic_score_gemma":0.016286787,"teacher_disagreement_score":0.022650234,"about_ca_system_score_codex":0.002784287,"about_ca_system_score_gemma":0.0033048668,"threshold_uncertainty_score":0.045036793},"labels":[],"label_agreement":null},{"id":"W3089750468","doi":"10.1007/978-3-030-58942-4_17","title":"An Exact CP Approach for the Cardinality-Constrained Euclidean Minimum Sum-of-Squares Clustering Problem","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Cluster analysis; Cardinality (data modeling); Computer science; Euclidean geometry; Mathematical optimization; Constrained clustering; Algorithm; Correlation clustering; Mathematics; Canopy clustering algorithm; Artificial intelligence; Data mining","score_opus":0.032993069767874895,"score_gpt":0.2621210442020565,"score_spread":0.22912797443418162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089750468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011784744,0.000246745,0.9952559,0.00012859216,0.000080448364,0.000042930267,0.00012309519,0.00022427726,0.0027194512],"genre_scores_gemma":[0.054893862,0.0005646318,0.93716896,0.0002444575,0.00027006256,0.000297464,0.000802385,0.0003874955,0.00537071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99836224,0.00038376058,0.000080606915,0.00036033062,0.0006713065,0.00014178033],"domain_scores_gemma":[0.9976355,0.0012663205,0.00009726217,0.00033781797,0.00055481907,0.00010825057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015867491,0.0015042673,0.0022571685,0.0012492411,0.0009801581,0.0023243134,0.004410949,0.0024852715,0.012710938],"category_scores_gemma":[0.008441484,0.0009942742,0.0017498054,0.004165099,0.0008295307,0.0032723339,0.0031048139,0.003942818,0.0027950678],"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":[0.00016733712,0.00016669999,0.00024978304,0.00039774718,0.000093591756,0.00011530509,0.00008991538,0.6328873,0.002404337,0.065822914,0.01998108,0.27762398],"study_design_scores_gemma":[0.000014030279,0.000020097064,0.000059791633,0.000013038307,0.000009510343,0.000045362918,0.000019097619,0.96904,0.00032347598,0.028473414,0.0019731729,0.0000090240355],"about_ca_topic_score_codex":0.011312841,"about_ca_topic_score_gemma":0.0115793655,"teacher_disagreement_score":0.012710938,"about_ca_system_score_codex":0.0016039571,"about_ca_system_score_gemma":0.0033347812,"threshold_uncertainty_score":0.04252231},"labels":[],"label_agreement":null},{"id":"W3090242834","doi":"10.1155/2020/2098495","title":"Advancing Transportation Routing Decisions Using Riemannian Manifold Surfaces","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Routing (electronic design automation); Flexibility (engineering); Set (abstract data type); Curvature; Mathematical optimization; Computer science; Riemannian manifold; Manifold (fluid mechanics); Event (particle physics); Algorithm; Topology (electrical circuits); Mathematics; Simulation; Engineering; Statistics","score_opus":0.024305085659637984,"score_gpt":0.26928555048469055,"score_spread":0.24498046482505256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090242834","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045840558,0.00029211052,0.9468143,0.0006263227,0.00006179237,0.00007094598,0.000097792436,0.00040476242,0.0057914746],"genre_scores_gemma":[0.6898508,0.00033172412,0.30501917,0.0001974143,0.0000689435,0.00021324352,0.00025087452,0.00021646473,0.0038512684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995894,0.00017491698,0.000019625535,0.0000780779,0.000078186844,0.00005979405],"domain_scores_gemma":[0.9979249,0.0013814071,0.0001888246,0.000100281315,0.00028557435,0.000118942626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013119471,0.0012837438,0.0012550355,0.0012057676,0.00065203087,0.0015502755,0.0012264339,0.0017742055,0.0028875403],"category_scores_gemma":[0.0046838317,0.0006922253,0.0010871155,0.0007202327,0.0010513915,0.0017431122,0.001656466,0.0012265688,0.00044464227],"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":[0.000009296184,0.000010650536,0.00018562214,0.000014064381,0.000007423582,0.000016619988,0.000022334289,0.98950535,0.00018127948,0.0054513197,0.00029685284,0.004299178],"study_design_scores_gemma":[0.0000019200809,0.000007270571,0.000015926433,0.0000017224431,0.0000010175478,0.0000020015668,0.000004502379,0.9974511,0.000043418768,0.0023124698,0.00015712934,0.0000015127476],"about_ca_topic_score_codex":0.010999313,"about_ca_topic_score_gemma":0.006548233,"teacher_disagreement_score":0.010999313,"about_ca_system_score_codex":0.0020284788,"about_ca_system_score_gemma":0.001721169,"threshold_uncertainty_score":0.021870613},"labels":[],"label_agreement":null},{"id":"W3090906250","doi":"10.1145/3406096","title":"Vehicle Trajectory Similarity","year":2020,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Trajectory; Computer science; Robustness (evolution); Data mining; Similarity (geometry); Preprocessor; Field (mathematics); Artificial intelligence; Mathematics","score_opus":0.09361225653001852,"score_gpt":0.33177787279066506,"score_spread":0.23816561626064653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090906250","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063423276,0.22344232,0.6297802,0.002900647,0.0027229625,0.00084370666,0.012438413,0.0015936551,0.062854946],"genre_scores_gemma":[0.62746114,0.14515506,0.17343776,0.0007937818,0.0026946554,0.0008468604,0.027799742,0.00040454016,0.02140651],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99728835,0.00038757556,0.00025286325,0.0007285139,0.0012042706,0.00013838332],"domain_scores_gemma":[0.9962064,0.001269164,0.00047471846,0.00052921614,0.001397102,0.00012345318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013202463,0.0007739872,0.0013471909,0.0071040126,0.00074190495,0.00278669,0.00179269,0.0012115863,0.0038643123],"category_scores_gemma":[0.010011198,0.00028318702,0.0009841636,0.011137426,0.00086382387,0.0050013117,0.0017475514,0.00088762306,0.0027462363],"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":[0.000102466794,0.000088252666,0.015820054,0.0020118575,0.00028181716,0.00012720533,0.00024401455,0.022304658,0.0012681186,0.08425907,0.02230159,0.8511908],"study_design_scores_gemma":[0.000041202304,0.0005514406,0.05994593,0.0020113394,0.00046241662,0.004644807,0.0020096952,0.16882841,0.006532467,0.2277671,0.526864,0.00034127352],"about_ca_topic_score_codex":0.003924251,"about_ca_topic_score_gemma":0.0025026249,"teacher_disagreement_score":0.0071040126,"about_ca_system_score_codex":0.0016598271,"about_ca_system_score_gemma":0.0011730576,"threshold_uncertainty_score":0.012927413},"labels":[],"label_agreement":null},{"id":"W3096006257","doi":"","title":"5th Workshop on GRAph Searching, Theory and Applications (GRASTA 2012)","year":2012,"lang":"en","type":"book","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Statistics Canada","funders":"","keywords":"Computer science; Graph theory; Theoretical computer science; Mathematics; Combinatorics","score_opus":0.015219171007238035,"score_gpt":0.24061292781547708,"score_spread":0.22539375680823903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096006257","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008052806,0.09376351,0.5687057,0.023299359,0.03908131,0.0005381678,0.005565216,0.009706093,0.25128782],"genre_scores_gemma":[0.046051446,0.067252696,0.41097474,0.00789673,0.016409121,0.0011010123,0.019780684,0.00589391,0.4246397],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99796546,0.0006032369,0.00014006939,0.00049182225,0.00063280907,0.00016673052],"domain_scores_gemma":[0.9970612,0.0011539953,0.00009118474,0.00074418454,0.00057397096,0.00037548892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023986574,0.0017158696,0.002173761,0.002511731,0.0010079325,0.0055968473,0.0031716966,0.0025788099,0.108437456],"category_scores_gemma":[0.0061519747,0.00077989016,0.0023454733,0.0032111928,0.0008745011,0.0054310784,0.0039078402,0.0042623654,0.056298282],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013972912,0.00018987799,0.0002917036,0.0007950406,0.00010962487,0.00018685106,0.00022356052,0.003918664,0.0016989731,0.08278007,0.48652062,0.42314532],"study_design_scores_gemma":[0.000031552536,0.0000653871,0.0005936085,0.0004038775,0.000045675293,0.000574507,0.00011608023,0.009036597,0.0009873253,0.11435536,0.87374973,0.000040430503],"about_ca_topic_score_codex":0.0016188325,"about_ca_topic_score_gemma":0.0029604197,"teacher_disagreement_score":0.108437456,"about_ca_system_score_codex":0.001897172,"about_ca_system_score_gemma":0.0015841292,"threshold_uncertainty_score":0.3627594},"labels":[],"label_agreement":null},{"id":"W3097495674","doi":"10.1016/j.ejtl.2020.100023","title":"Learning to handle parameter perturbations in Combinatorial Optimization: An application to facility location","year":2020,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Exploit; A priori and a posteriori; Computer science; Optimization problem; Mathematical optimization; Classifier (UML); Combinatorial optimization; Context (archaeology); Variation (astronomy); Constraint (computer-aided design); Perspective (graphical); Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.017901540150668076,"score_gpt":0.2270287146354921,"score_spread":0.209127174484824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097495674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003367016,0.00014520893,0.99535465,0.00043414638,0.000018144288,0.000034248544,0.000030838466,0.00013973545,0.00047597647],"genre_scores_gemma":[0.18776116,0.0004252002,0.8090524,0.00038942465,0.00022491105,0.0002701554,0.00017718847,0.00020818852,0.0014914391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966419,0.0019927702,0.00014003123,0.00057761953,0.0005161028,0.00013161293],"domain_scores_gemma":[0.9868976,0.010015716,0.001067384,0.0010892245,0.0006269857,0.0003030702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060376446,0.0013568352,0.0018451307,0.0014626915,0.00068027485,0.0019480231,0.002823433,0.002923372,0.0025452625],"category_scores_gemma":[0.02720713,0.0008078684,0.0015208812,0.0023763392,0.0025815142,0.003111485,0.0036962652,0.0051351413,0.00050127297],"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":[0.000046112862,0.00009135721,0.0012994327,0.00012628293,0.0000734641,0.00012977583,0.00013704809,0.8990562,0.00072699226,0.04489591,0.0015701535,0.051847227],"study_design_scores_gemma":[0.0000060400125,0.000023918165,0.00010898905,0.000010291747,0.0000049580317,0.000024265066,0.0000172282,0.96730953,0.0002587632,0.031369198,0.00085787213,0.000008818777],"about_ca_topic_score_codex":0.0034274142,"about_ca_topic_score_gemma":0.0030264147,"teacher_disagreement_score":0.0060376446,"about_ca_system_score_codex":0.001666066,"about_ca_system_score_gemma":0.0017132857,"threshold_uncertainty_score":0.031930447},"labels":[],"label_agreement":null},{"id":"W3098157538","doi":"10.1007/s41651-020-00066-3","title":"Geospatial Operations of Discrete Global Grid Systems—a Comparison with Traditional GIS","year":2020,"lang":"en","type":"article","venue":"Journal of Geovisualization and Spatial Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Computer science; Implementation; Grid; Visualization; Geographic information system; Computation; Development (topology); Data mining; Database; Systems engineering; Software engineering; Algorithm; Engineering; Geography; Mathematics; Remote sensing","score_opus":0.028628589295871098,"score_gpt":0.2733972601617692,"score_spread":0.2447686708658981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098157538","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49831447,0.0013712549,0.46576086,0.0013859787,0.00012939029,0.0001764828,0.0014460055,0.0009463206,0.030469308],"genre_scores_gemma":[0.93425196,0.0007330156,0.063247986,0.000029939238,0.000038831662,0.000032284475,0.00037563033,0.000095844116,0.001194446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849594,0.00057782023,0.000103527935,0.00014228144,0.0006017475,0.00007865166],"domain_scores_gemma":[0.9947577,0.0032714661,0.0003197091,0.0008748604,0.00061742694,0.00015881329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017240486,0.00025788049,0.00048082357,0.0016662817,0.00038336343,0.002723524,0.0011677742,0.00028011136,0.0035351086],"category_scores_gemma":[0.007638634,0.00015835224,0.00024668637,0.004482825,0.0010675768,0.0037468532,0.0008887923,0.0003894455,0.0003418299],"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":[0.0011394084,0.00034108124,0.03352044,0.0004470283,0.000085467334,0.0001627719,0.0013788876,0.37731278,0.0032519381,0.22650121,0.004652073,0.35120693],"study_design_scores_gemma":[0.00009312124,0.00024157752,0.009554713,0.00002844027,0.000034897163,0.00009845811,0.0019776651,0.9264912,0.0018263796,0.047734376,0.011898133,0.000021049646],"about_ca_topic_score_codex":0.012367204,"about_ca_topic_score_gemma":0.009375099,"teacher_disagreement_score":0.012367204,"about_ca_system_score_codex":0.00144138,"about_ca_system_score_gemma":0.0013595684,"threshold_uncertainty_score":0.024590433},"labels":[],"label_agreement":null},{"id":"W31006655","doi":"10.3233/bd-180350","title":"Storage and Indexing of Relational OLAP Views with Mixed Categorical and Continuous Dimensions","year":2007,"lang":"en","type":"article","venue":"Journal of Digital Information Management","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":"Dalhousie University","funders":"","keywords":"Categorical variable; Online analytical processing; Computer science; Search engine indexing; Data mining; Dimension (graph theory); Feature (linguistics); Cardinality (data modeling); Data warehouse; Relational database; Data cube; Spatial analysis; Information retrieval; Database; Machine learning; Mathematics; Statistics","score_opus":0.016233007136505807,"score_gpt":0.22655437867754596,"score_spread":0.21032137154104016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W31006655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31496647,0.004487745,0.61482203,0.0018108385,0.0007519932,0.0005342274,0.02245336,0.027894886,0.012278457],"genre_scores_gemma":[0.71791697,0.0011159949,0.25968146,0.00025247235,0.00019548232,0.00023996219,0.016528878,0.0007228985,0.0033458686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977824,0.00025510092,0.00035694963,0.0003348352,0.0009961093,0.0002745803],"domain_scores_gemma":[0.99103546,0.0014126543,0.00051225437,0.0051920423,0.0014282773,0.00041923212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017809778,0.00066534523,0.0015181055,0.0035441923,0.0010012395,0.0067290803,0.0022395775,0.00080805126,0.0041889576],"category_scores_gemma":[0.009238061,0.000566323,0.00097500713,0.009225596,0.0007543241,0.008371827,0.0034900252,0.0012248865,0.0013265073],"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":[0.0037690161,0.0008163995,0.033559076,0.0011011974,0.00056437147,0.0013143333,0.0026804728,0.039649557,0.044718143,0.09665272,0.04433349,0.7308412],"study_design_scores_gemma":[0.0003577232,0.0011861579,0.012864202,0.00038366462,0.000496775,0.0016746902,0.0043165237,0.6024923,0.1110767,0.17738464,0.08737529,0.00039134515],"about_ca_topic_score_codex":0.004284435,"about_ca_topic_score_gemma":0.0049087633,"teacher_disagreement_score":0.0067290803,"about_ca_system_score_codex":0.0010622022,"about_ca_system_score_gemma":0.0015601454,"threshold_uncertainty_score":0.014013469},"labels":[],"label_agreement":null},{"id":"W3104298487","doi":"","title":"Towards Knowledge-Enriched Path Computation","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Bundesministerium für Wirtschaft und Technologie; European Commission","keywords":"Computer science; Closeness; Probabilistic logic; Data mining; Point of interest; Geospatial analysis; Measure (data warehouse); Path (computing); Inference; Ground truth; Node (physics); Shortest path problem; Graph; Function (biology); Theoretical computer science; Artificial intelligence; Geography; Mathematics","score_opus":0.02729310518709432,"score_gpt":0.28839647628930437,"score_spread":0.26110337110221005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104298487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012321774,0.00026556844,0.9829549,0.00026935182,0.000035352405,0.00008865296,0.0009906731,0.0016923134,0.0013814789],"genre_scores_gemma":[0.17563102,0.00039385905,0.81529653,0.00020769227,0.00008470912,0.0002736241,0.005198352,0.00043793468,0.0024762384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99835557,0.00036472554,0.00010257275,0.0005492861,0.00046183827,0.00016597407],"domain_scores_gemma":[0.9962658,0.0020138202,0.00025591036,0.000776628,0.00055270264,0.00013509484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010613261,0.00192684,0.0018551764,0.0043303506,0.001046595,0.0027775206,0.0032054451,0.0028929175,0.004753345],"category_scores_gemma":[0.011678185,0.00091980735,0.001786459,0.0057213837,0.0011438151,0.004863677,0.0040288195,0.002141496,0.0016853815],"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":[0.00023133586,0.00020153163,0.0026386764,0.00040691608,0.00014892557,0.00024068913,0.0003789386,0.6941434,0.0020844764,0.04774825,0.011072572,0.24070425],"study_design_scores_gemma":[0.000019022378,0.00001852219,0.0002157518,0.000025374602,0.000021061838,0.000049002523,0.00008607522,0.932256,0.00083052827,0.06317576,0.0032906411,0.0000122222855],"about_ca_topic_score_codex":0.013695202,"about_ca_topic_score_gemma":0.018978609,"teacher_disagreement_score":0.013695202,"about_ca_system_score_codex":0.0019223271,"about_ca_system_score_gemma":0.0026070324,"threshold_uncertainty_score":0.027230978},"labels":[],"label_agreement":null},{"id":"W3107439925","doi":"10.2200/s01048ed1v01y202009dtm063","title":"Skylines and Other Dominance-Based Queries","year":2020,"lang":"en","type":"article","venue":"Synthesis lectures on data management","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Dominance (genetics); Computer science; Information retrieval; Theoretical computer science; Chemistry","score_opus":0.05543170520960824,"score_gpt":0.2604830773332338,"score_spread":0.20505137212362554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107439925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04190424,0.016582396,0.78070104,0.017538253,0.003194265,0.00019270893,0.0022965034,0.0022385411,0.13535199],"genre_scores_gemma":[0.56451046,0.013278852,0.29341248,0.006279993,0.0076991646,0.00046863666,0.0039225896,0.0015706451,0.10885724],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99638426,0.0009330902,0.00017389531,0.00048233592,0.0017186371,0.00030783948],"domain_scores_gemma":[0.9936273,0.0038966779,0.0003463528,0.0009474622,0.0008042061,0.00037810652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034318748,0.0008691516,0.0016725479,0.0018239417,0.001828411,0.0051535885,0.0015440844,0.0017450284,0.015391811],"category_scores_gemma":[0.015014394,0.00054405036,0.001025828,0.00499672,0.0024007645,0.014219666,0.0023863635,0.0036094876,0.0021933164],"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":[0.00020184519,0.000057698675,0.0003961718,0.00020648507,0.000029316298,0.00006128201,0.00020892243,0.0053900485,0.0014173354,0.87283087,0.053282246,0.06591776],"study_design_scores_gemma":[0.000052364005,0.000048482485,0.00026163433,0.000041960575,0.000020550871,0.00021735409,0.00012804534,0.032751314,0.001082687,0.90570664,0.059662767,0.000026195854],"about_ca_topic_score_codex":0.0022140015,"about_ca_topic_score_gemma":0.0023646804,"teacher_disagreement_score":0.015391811,"about_ca_system_score_codex":0.0023659507,"about_ca_system_score_gemma":0.0011073533,"threshold_uncertainty_score":0.051490664},"labels":[],"label_agreement":null},{"id":"W3108171104","doi":"10.1145/3416010.3423239","title":"Calibrating Bus Mobility Data for Bus-based Urban Vehicular Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Global Positioning System; Public transport; Asynchronous communication; Vehicular ad hoc network; Intelligent transportation system; State (computer science); Sample (material); Computer network; Distributed computing; Transport engineering; Wireless ad hoc network; Telecommunications; Engineering; Wireless","score_opus":0.062225196439499385,"score_gpt":0.26521486839021197,"score_spread":0.20298967195071257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108171104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5590346,0.0011643798,0.42266935,0.0006593409,0.00031299447,0.00027612643,0.009054722,0.0039985366,0.0028299869],"genre_scores_gemma":[0.9057005,0.00037619294,0.07659458,0.00003469268,0.000060784158,0.00012417909,0.016362542,0.0000885721,0.00065795437],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911183,0.00027157218,0.00007495886,0.00025939956,0.00019865387,0.00008366061],"domain_scores_gemma":[0.9980665,0.00045388762,0.0002569398,0.00048546147,0.000640803,0.00009631384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013028174,0.00087606185,0.00062103523,0.0021683471,0.0005217544,0.0006745543,0.0014256048,0.00088308123,0.0006112634],"category_scores_gemma":[0.006938629,0.00029008783,0.00041277707,0.002348021,0.0002831484,0.0013667522,0.0008546583,0.0008234469,0.00057139655],"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":[0.00020319717,0.00021444773,0.053467415,0.00022610636,0.0001803783,0.00022926043,0.0002088151,0.7903047,0.0040323297,0.003469738,0.004887273,0.14257635],"study_design_scores_gemma":[0.000009480898,0.00005497801,0.010165809,0.000023824045,0.000016620306,0.00007518539,0.00012136221,0.98236406,0.0027103224,0.0012903039,0.0031456891,0.00002235532],"about_ca_topic_score_codex":0.011752413,"about_ca_topic_score_gemma":0.013191507,"teacher_disagreement_score":0.011752413,"about_ca_system_score_codex":0.0008186213,"about_ca_system_score_gemma":0.0007814543,"threshold_uncertainty_score":0.02336806},"labels":[],"label_agreement":null},{"id":"W3110157014","doi":"10.1109/dsaa49011.2020.00046","title":"MRSweep: Distributed In-Memory Sweep-line for Scalable Object Intersection Problems","year":2020,"lang":"en","type":"article","venue":"","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":"York University","funders":"","keywords":"Computer science; Scalability; Intersection (aeronautics); Line (geometry); Line segment; Computational geometry; Source lines of code; Algorithm; SPARK (programming language); Theoretical computer science; Artificial intelligence; Mathematics; Programming language; Database; Software","score_opus":0.030422264418453967,"score_gpt":0.24350670728128523,"score_spread":0.21308444286283126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110157014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02295274,0.0005941185,0.9499961,0.0005128733,0.00018376063,0.00042511005,0.0013363754,0.018328445,0.005670478],"genre_scores_gemma":[0.11419968,0.00025498672,0.8767595,0.0002015561,0.00008012831,0.0005317718,0.004113949,0.0009277976,0.0029306405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833626,0.0002338108,0.00012688262,0.00044793167,0.00057081523,0.00028441657],"domain_scores_gemma":[0.9977464,0.00070988195,0.00016951075,0.0007657442,0.00041039393,0.000198085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016658225,0.0018669679,0.0019637481,0.0013555973,0.0015006062,0.002344032,0.0060369745,0.0013887811,0.0063905544],"category_scores_gemma":[0.0053383773,0.0008445269,0.0018280047,0.002966639,0.0009803986,0.004041195,0.004584569,0.0025673527,0.002550746],"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":[0.0010283956,0.0008307855,0.0034259367,0.00082714413,0.00028135628,0.000505084,0.000617289,0.41006848,0.009742488,0.045039315,0.07777833,0.44985533],"study_design_scores_gemma":[0.00023806735,0.00013670967,0.00037994797,0.000020979107,0.000024826404,0.00017410089,0.00018112332,0.9455184,0.005316453,0.035617214,0.01235928,0.00003293889],"about_ca_topic_score_codex":0.0052552084,"about_ca_topic_score_gemma":0.0070855077,"teacher_disagreement_score":0.0063905544,"about_ca_system_score_codex":0.0010106922,"about_ca_system_score_gemma":0.0024336583,"threshold_uncertainty_score":0.021378517},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16906837,0.005695209,0.7946385,0.0012094263,0.0004897703,0.0004128425,0.011663531,0.014670994,0.002151354],"genre_scores_gemma":[0.42092803,0.0014257684,0.55316484,0.00024594116,0.00035265178,0.00018252549,0.019810276,0.0005091312,0.0033807335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704283,0.000261481,0.0005071144,0.00054866483,0.0013370054,0.00030278543],"domain_scores_gemma":[0.9947239,0.0024795888,0.0004668138,0.0009783914,0.0010475937,0.00030378802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014253767,0.0012325775,0.002845907,0.004682842,0.0012957742,0.0038489907,0.0033584214,0.0017721765,0.0024958919],"category_scores_gemma":[0.010548558,0.0008081599,0.0015048283,0.0077216127,0.0004750313,0.0051386016,0.0024857682,0.001050637,0.002210282],"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":[0.0026097372,0.0006413876,0.013887471,0.0014187474,0.00035959473,0.0009362742,0.00073703454,0.045128793,0.09054016,0.0061575994,0.025615035,0.8119681],"study_design_scores_gemma":[0.000113654256,0.00017404737,0.0027639144,0.000046332738,0.00017840535,0.0009848824,0.0005407218,0.9563679,0.02068529,0.011993737,0.006093496,0.00005767385],"about_ca_topic_score_codex":0.007799348,"about_ca_topic_score_gemma":0.015633078,"teacher_disagreement_score":0.007799348,"about_ca_system_score_codex":0.0008022858,"about_ca_system_score_gemma":0.0026308645,"threshold_uncertainty_score":0.0155079365},"labels":[],"label_agreement":null},{"id":"W3111359078","doi":"10.1609/aaai.v35i5.16483","title":"A SAT-based Resolution of Lam's Problem","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Windsor; University of Waterloo; Carleton University","funders":"","keywords":"Satisfiability; Consistency (knowledge bases); Projective test; Projective plane; Resolution (logic); Computer science; Code (set theory); Plane (geometry); Boolean satisfiability problem; Theoretical computer science; Algorithm; Mathematics; Discrete mathematics; Programming language; Algebra over a field; Pure mathematics; Geometry","score_opus":0.0691774485863179,"score_gpt":0.2791805394646435,"score_spread":0.21000309087832558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111359078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04150371,0.0009058472,0.863188,0.012627466,0.0011505254,0.0007522453,0.0015784423,0.0064153774,0.07187833],"genre_scores_gemma":[0.25179058,0.00053047156,0.7170798,0.003446586,0.00035618132,0.0006054381,0.0019562296,0.0008181333,0.023416542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99239963,0.003022178,0.00038232593,0.0013463997,0.0018912443,0.0009582248],"domain_scores_gemma":[0.9866671,0.007927891,0.00058542966,0.0023441878,0.002234526,0.00024093741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005654557,0.0009359202,0.00095641916,0.0017707631,0.003677328,0.0030290226,0.004363047,0.002423889,0.014416442],"category_scores_gemma":[0.031680454,0.0011812779,0.0033614857,0.0023321752,0.0035976223,0.005482144,0.0063147023,0.006519323,0.00395348],"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":[0.0005028477,0.00030665883,0.0024891985,0.00084562233,0.00022426323,0.0014344032,0.0016996616,0.027781522,0.010409813,0.69148904,0.07771645,0.1851005],"study_design_scores_gemma":[0.0004720423,0.0003798554,0.0011161701,0.00044415673,0.00038285257,0.0015521662,0.0015118555,0.2295531,0.041675214,0.5355406,0.18710703,0.00026497993],"about_ca_topic_score_codex":0.0071188663,"about_ca_topic_score_gemma":0.007091935,"teacher_disagreement_score":0.014416442,"about_ca_system_score_codex":0.00250428,"about_ca_system_score_gemma":0.005753268,"threshold_uncertainty_score":0.048227847},"labels":[],"label_agreement":null},{"id":"W3112799508","doi":"10.22215/etd/2008-08635","title":"Two results on distribution-sensitive data structures","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Library and Archives Canada","funders":"","keywords":"Computer science; Mathematics; Humanities; Art","score_opus":0.03487575735801498,"score_gpt":0.30342258647528164,"score_spread":0.2685468291172667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112799508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029368162,0.0043407325,0.88172257,0.010823869,0.0015373685,0.00050310994,0.0013407255,0.0030825804,0.06728083],"genre_scores_gemma":[0.4821421,0.0074742907,0.3655166,0.010460109,0.005736291,0.0010744431,0.004326312,0.0034104954,0.11985934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9819972,0.0020434111,0.0013081841,0.0028159544,0.009790632,0.0020446198],"domain_scores_gemma":[0.9317591,0.03126865,0.0031963033,0.025377,0.006339463,0.0020595128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007643305,0.0016673529,0.0019832256,0.0051923944,0.0037010482,0.007806757,0.0055650068,0.003052839,0.017905198],"category_scores_gemma":[0.04172184,0.001610348,0.0025751316,0.009679888,0.006211412,0.027817376,0.010902307,0.007286752,0.004296215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069618394,0.00053115864,0.0028102444,0.000624352,0.000103629005,0.0004135167,0.0010303915,0.015109618,0.00737986,0.7523959,0.040600616,0.17830452],"study_design_scores_gemma":[0.0001732388,0.00036578003,0.0021574183,0.00024523822,0.00032427988,0.0020932541,0.0004913263,0.07693472,0.021709912,0.7761239,0.119199045,0.000181942],"about_ca_topic_score_codex":0.0028991797,"about_ca_topic_score_gemma":0.0025858248,"teacher_disagreement_score":0.017905198,"about_ca_system_score_codex":0.0057976544,"about_ca_system_score_gemma":0.004820668,"threshold_uncertainty_score":0.059898853},"labels":[],"label_agreement":null},{"id":"W3113489201","doi":"10.1109/cloud49709.2020.00040","title":"PONCHE: Personalized and Context-Aware Vehicle Rerouting Service","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Context (archaeology); Contextual design; Perception; Service (business); Point (geometry); Data type; Advanced driver assistance systems; Quality (philosophy); Data mining; Human–computer interaction; Data science; Machine learning; Artificial intelligence","score_opus":0.033905940625647925,"score_gpt":0.2307741497907715,"score_spread":0.19686820916512357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113489201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28385434,0.0014398092,0.5808282,0.0010125325,0.00040741544,0.0010115042,0.011385667,0.106517754,0.013542785],"genre_scores_gemma":[0.832,0.00050092244,0.15154171,0.00027335878,0.00007070033,0.00023606655,0.009605246,0.0003987708,0.0053731836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995852,0.000058999423,0.000027691427,0.00013668076,0.00012241963,0.00006913267],"domain_scores_gemma":[0.99956936,0.00008247613,0.00004334783,0.00015530059,0.0000981025,0.000051414165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003378475,0.0007507636,0.00062877923,0.0011203106,0.00044483494,0.00079230167,0.0011626064,0.00064184476,0.0020601097],"category_scores_gemma":[0.0013594384,0.00023965945,0.0004080091,0.0010402423,0.00017884796,0.0013143364,0.001509614,0.00081352826,0.00086764985],"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":[0.0016653779,0.00076561037,0.029343084,0.00066954875,0.00033475764,0.0012471587,0.00093840517,0.09632073,0.049890336,0.006107912,0.06837426,0.74434274],"study_design_scores_gemma":[0.00013583465,0.00037539683,0.02033264,0.000063712825,0.00016026097,0.0010773917,0.0013488026,0.87843424,0.02993831,0.008062844,0.059917625,0.00015288971],"about_ca_topic_score_codex":0.009289676,"about_ca_topic_score_gemma":0.01568826,"teacher_disagreement_score":0.009289676,"about_ca_system_score_codex":0.00046866367,"about_ca_system_score_gemma":0.00084645726,"threshold_uncertainty_score":0.018471241},"labels":[],"label_agreement":null},{"id":"W3116728211","doi":"10.1109/iemcon51383.2020.9284839","title":"Approximate Continuous Nearest Neighbour Query Processing in Clustered Point Sets","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Nearest neighbour; Computer science; k-nearest neighbors algorithm; Set (abstract data type); Point (geometry); Cluster analysis; Data mining; Nearest-neighbor chain algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematics; Fuzzy clustering; Canopy clustering algorithm","score_opus":0.024479007906169137,"score_gpt":0.24103186278618963,"score_spread":0.2165528548800205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116728211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026442375,0.000523852,0.9696871,0.00013830575,0.0000705432,0.000119990465,0.00029481685,0.0015030598,0.0012200023],"genre_scores_gemma":[0.38799658,0.00030554805,0.60782456,0.0001657933,0.000084865605,0.00017443458,0.0015731043,0.00020335904,0.0016717669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99400616,0.001007266,0.00052456115,0.0012404971,0.0027801243,0.00044134184],"domain_scores_gemma":[0.9947056,0.0019540025,0.00035503338,0.0014867238,0.0013471957,0.00015151456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002412157,0.0010549045,0.0024760433,0.0022752741,0.0013604797,0.0027447257,0.004187848,0.0021549002,0.0023780717],"category_scores_gemma":[0.012680349,0.0008098535,0.0012963895,0.0051308996,0.0010114609,0.004295071,0.0030969398,0.0014924485,0.0018785748],"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":[0.0014854162,0.00043856713,0.005111529,0.0007146587,0.00034200365,0.00078608264,0.0012935073,0.55485237,0.030313507,0.027262893,0.009475603,0.36792386],"study_design_scores_gemma":[0.00003083716,0.000100445315,0.00064677635,0.000018706824,0.00001784803,0.00026950327,0.0002695053,0.97591066,0.005542157,0.014625366,0.0025300824,0.000038046433],"about_ca_topic_score_codex":0.010601744,"about_ca_topic_score_gemma":0.010194508,"teacher_disagreement_score":0.010601744,"about_ca_system_score_codex":0.001438595,"about_ca_system_score_gemma":0.001214896,"threshold_uncertainty_score":0.021080077},"labels":[],"label_agreement":null},{"id":"W3120844761","doi":"10.1007/s10707-020-00429-4","title":"Hidden Markov map matching based on trajectory segmentation with heading homogeneity","year":2021,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Map matching; Global Positioning System; Hidden Markov model; Computer science; Segmentation; Matching (statistics); Artificial intelligence; Trajectory; Markov chain; Markov model; Heading (navigation); Pattern recognition (psychology); Geography; Mathematics; Machine learning; Statistics; Geodesy","score_opus":0.01061293708920493,"score_gpt":0.22345521429437873,"score_spread":0.2128422772051738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120844761","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04367737,0.00015720936,0.9531532,0.000096515214,0.00008535661,0.00005365859,0.00036907036,0.0014590948,0.000948461],"genre_scores_gemma":[0.70409787,0.00020705204,0.28824726,0.00008336446,0.00009740271,0.00011397288,0.0027810996,0.0002529561,0.0041190195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992887,0.000091648195,0.000036494614,0.00028110808,0.0001618632,0.00014018062],"domain_scores_gemma":[0.99925345,0.00029236134,0.00008312416,0.00014579286,0.00017243711,0.000052742183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005694094,0.0005327384,0.0010493161,0.0015823555,0.0007183596,0.0007508845,0.0011344979,0.0008171892,0.0019433942],"category_scores_gemma":[0.0020140584,0.00052329164,0.0010372392,0.0025555233,0.00040310403,0.0011396371,0.0011521823,0.0008476158,0.0013370941],"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":[0.0012162516,0.00029989288,0.009190364,0.00018298844,0.00029370145,0.00035223307,0.00024114289,0.26644713,0.026818799,0.0113462135,0.007091512,0.6765198],"study_design_scores_gemma":[0.000017484317,0.000039528804,0.0019081724,0.000007346287,0.000041090047,0.00007025935,0.000027693362,0.98667175,0.005679303,0.0045602894,0.00096122216,0.00001585969],"about_ca_topic_score_codex":0.01369257,"about_ca_topic_score_gemma":0.011795054,"teacher_disagreement_score":0.01369257,"about_ca_system_score_codex":0.0005194794,"about_ca_system_score_gemma":0.0017951962,"threshold_uncertainty_score":0.027225733},"labels":[],"label_agreement":null},{"id":"W3129019482","doi":"10.48550/arxiv.2102.03954","title":"Large-data determinantal clustering","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cluster analysis; Determinantal point process; Point process; Mathematics; Correlation clustering; Single-linkage clustering; Clustering high-dimensional data; Data point; Algorithm; Eigenvalues and eigenvectors; Computer science; CURE data clustering algorithm; Random matrix; Statistics","score_opus":0.112099694896418,"score_gpt":0.20601875196460612,"score_spread":0.09391905706818812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129019482","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010339133,0.00016397354,0.9876747,0.00017526418,0.000030117342,0.00005256948,0.0001971285,0.00069483963,0.00067228125],"genre_scores_gemma":[0.4223484,0.00027637702,0.5707118,0.00022570664,0.000113718,0.00029077067,0.0030805927,0.0004522999,0.0025003732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99497813,0.002058812,0.00024702481,0.0011862755,0.0012657302,0.00026404308],"domain_scores_gemma":[0.9898699,0.0036358624,0.0005411362,0.0032114293,0.0024354206,0.00030616764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038825932,0.000838837,0.0014612596,0.0019276995,0.0013299919,0.0023073612,0.0028010441,0.0016092331,0.001879155],"category_scores_gemma":[0.020807996,0.00055967306,0.0011267291,0.0027906904,0.0014196403,0.0025413167,0.003642858,0.0019444516,0.0015039064],"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":[0.00041854274,0.00027515102,0.008276823,0.000516077,0.00046011547,0.00041840246,0.00090780534,0.5148582,0.013568243,0.12329527,0.02084841,0.31615698],"study_design_scores_gemma":[0.000015304859,0.000023587188,0.00092425686,0.000011126253,0.000013553201,0.00007211343,0.00010071543,0.9322797,0.0025753744,0.060859073,0.003100806,0.000024388388],"about_ca_topic_score_codex":0.002893048,"about_ca_topic_score_gemma":0.004781154,"teacher_disagreement_score":0.0038825932,"about_ca_system_score_codex":0.0010516028,"about_ca_system_score_gemma":0.0016199203,"threshold_uncertainty_score":0.020533323},"labels":[],"label_agreement":null},{"id":"W3132888396","doi":"10.48550/arxiv.2102.12072","title":"Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Instant; Timestamp; Computer science; Ranking (information retrieval); Function (biology); Window (computing); Sequence (biology); Rank (graph theory); Data mining; Information retrieval; Algorithm; Mathematics; World Wide Web; Combinatorics; Real-time computing","score_opus":0.07096059361997485,"score_gpt":0.17750443204715888,"score_spread":0.10654383842718403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132888396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23775265,0.0034597984,0.7037758,0.003085831,0.0004674159,0.0014102897,0.013208678,0.026299689,0.010539892],"genre_scores_gemma":[0.3642379,0.0005795584,0.6045459,0.00055427826,0.00029556587,0.0004207305,0.020459224,0.00090291776,0.008003921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99367577,0.00126465,0.00087164785,0.0017026474,0.0016516955,0.00083362096],"domain_scores_gemma":[0.9838239,0.0078959,0.0013105733,0.004394443,0.0017799928,0.00079521374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005217977,0.0027983803,0.003636448,0.00460651,0.0020591747,0.0055202497,0.006075656,0.003862826,0.009342831],"category_scores_gemma":[0.022293376,0.00091071666,0.0018942433,0.008435885,0.001003015,0.008150487,0.0034483252,0.002780941,0.0049091517],"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":[0.0019769622,0.0020063643,0.020432135,0.0014121372,0.00036674904,0.00037130577,0.00096462254,0.20233531,0.012703696,0.02378738,0.077674896,0.6559684],"study_design_scores_gemma":[0.00019978652,0.00029337718,0.002175861,0.000042775555,0.00007783424,0.00055366475,0.000644754,0.9526672,0.0045174523,0.03215246,0.006605836,0.00006892654],"about_ca_topic_score_codex":0.006459024,"about_ca_topic_score_gemma":0.014139937,"teacher_disagreement_score":0.009342831,"about_ca_system_score_codex":0.002205729,"about_ca_system_score_gemma":0.0035578553,"threshold_uncertainty_score":0.031254828},"labels":[],"label_agreement":null},{"id":"W3134583825","doi":"10.1109/bigcomp51126.2021.00055","title":"Conceptual Modeling and Smart Computing for Big Transportation Data","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Big data; Computer science; Data science; Popularity; Data modeling; Abstraction; Variety (cybernetics); Smart city; Conceptual model; Intelligent transportation system; Software; World Wide Web; Database; Data mining; Transport engineering; Internet of Things; Engineering; Artificial intelligence","score_opus":0.10966903387442663,"score_gpt":0.288619403781572,"score_spread":0.1789503699071454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134583825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038980383,0.000580785,0.9842815,0.0037408555,0.000116335366,0.0001245257,0.00047057492,0.0002874214,0.0064998916],"genre_scores_gemma":[0.15536101,0.001484172,0.83684325,0.0008127923,0.00020199419,0.0005412002,0.0016142145,0.0001583041,0.002982994],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99729365,0.0011345462,0.00024177293,0.00043691666,0.0007004464,0.00019262054],"domain_scores_gemma":[0.9966702,0.0011816885,0.00022368474,0.0011390208,0.00051365397,0.00027175364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038499688,0.0009467436,0.00075313065,0.0023409608,0.0019037131,0.0058455276,0.003150813,0.0017503559,0.003160898],"category_scores_gemma":[0.0081737805,0.0007487672,0.0029818462,0.0038202584,0.0038706241,0.008758821,0.0042895377,0.0036069173,0.0007458573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000078595,0.000013885769,0.00036907772,0.000060079015,0.000017547565,0.000067218854,0.00032631404,0.017175116,0.00016820084,0.9737458,0.0020400037,0.006008859],"study_design_scores_gemma":[0.000010141633,0.00001249073,0.0002255482,0.00006276174,0.000021410133,0.000097088705,0.0003405668,0.19350657,0.00029058955,0.76320434,0.042207092,0.000021439739],"about_ca_topic_score_codex":0.0198061,"about_ca_topic_score_gemma":0.021262703,"teacher_disagreement_score":0.0198061,"about_ca_system_score_codex":0.004232576,"about_ca_system_score_gemma":0.004015685,"threshold_uncertainty_score":0.039381623},"labels":[],"label_agreement":null},{"id":"W3138059096","doi":"10.3390/ijgi10030182","title":"A Decentralized Semantic Reasoning Approach for the Detection and Representation of Continuous Spatial Dynamic Phenomena in Wireless Sensor Networks","year":2021,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Data mining; Object (grammar); Representation (politics); Fuzzy logic; Artificial intelligence; Kernel (algebra); Theoretical computer science; Mathematics","score_opus":0.007102453161193325,"score_gpt":0.246544079804734,"score_spread":0.23944162664354068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138059096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027208913,0.000047287907,0.99638957,0.000098942386,0.0000099396875,0.000030401405,0.000046762314,0.00014156445,0.00051461044],"genre_scores_gemma":[0.30191243,0.00024822418,0.6961173,0.000109752975,0.00005507643,0.00020168573,0.00037427817,0.000056693156,0.0009246139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976417,0.00064843305,0.00022362215,0.00058040133,0.00077913323,0.00012682675],"domain_scores_gemma":[0.9981845,0.0008154689,0.00023238201,0.00042054817,0.00028236193,0.00006472108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024120111,0.00060899265,0.0010819043,0.0017890041,0.001126259,0.0024000828,0.002285658,0.0011056004,0.0011456907],"category_scores_gemma":[0.0050364337,0.0005960927,0.0020287847,0.0018021256,0.0017342072,0.004170207,0.0020499523,0.0016174889,0.0002373966],"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":[0.00016717982,0.00015606612,0.0015306028,0.00037970408,0.00016437432,0.00059739227,0.00086267124,0.555684,0.010603638,0.32329935,0.0021400284,0.10441509],"study_design_scores_gemma":[0.000017169426,0.000027122838,0.00025220783,0.000023615054,0.000046869376,0.00010574907,0.00010763145,0.88027567,0.0023584233,0.11345655,0.0033100618,0.000018990655],"about_ca_topic_score_codex":0.005394866,"about_ca_topic_score_gemma":0.007466263,"teacher_disagreement_score":0.005394866,"about_ca_system_score_codex":0.0017092917,"about_ca_system_score_gemma":0.0018069502,"threshold_uncertainty_score":0.01275605},"labels":[],"label_agreement":null},{"id":"W3138447845","doi":"10.1016/s0925-7721(02)00093-7","title":"Computing contour trees in all dimensions","year":2002,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":460,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of North Carolina at Chapel Hill; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Simple (philosophy); Computer science; Contour line; Algorithm; Mathematics; Artificial intelligence; Cartography; Geography","score_opus":0.04481388461551654,"score_gpt":0.2730690683067274,"score_spread":0.22825518369121084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138447845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08574105,0.00087026885,0.9049456,0.00035256764,0.00009984215,0.00005441053,0.000554489,0.0017011823,0.0056806025],"genre_scores_gemma":[0.37076816,0.0009743756,0.6217643,0.00009092058,0.00008809056,0.0000799154,0.001396578,0.00048719914,0.0043503917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993332,0.00010155107,0.00007052905,0.00014464198,0.00025758258,0.00009243809],"domain_scores_gemma":[0.9970059,0.001330577,0.00022566864,0.0008000748,0.00045569194,0.00018211266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008292071,0.0006596583,0.0012794056,0.0019324474,0.0010049159,0.003467733,0.0014554766,0.00097609044,0.0053854487],"category_scores_gemma":[0.007557273,0.00076324464,0.0006203008,0.003191063,0.0010609501,0.0054003657,0.002495886,0.0013860374,0.0013884513],"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":[0.00046669046,0.00012248344,0.0053571016,0.00045089892,0.00009372753,0.00024780518,0.000632905,0.21212307,0.010736398,0.29511347,0.012230165,0.4624252],"study_design_scores_gemma":[0.00003364111,0.00006022469,0.0007096301,0.0000402727,0.00003600415,0.00012242257,0.0001247135,0.63500136,0.005699695,0.3487515,0.009400516,0.000020073376],"about_ca_topic_score_codex":0.001860953,"about_ca_topic_score_gemma":0.0030600675,"teacher_disagreement_score":0.0053854487,"about_ca_system_score_codex":0.00061560684,"about_ca_system_score_gemma":0.0007756648,"threshold_uncertainty_score":0.01801616},"labels":[],"label_agreement":null},{"id":"W3138836727","doi":"10.5383/juspn.15.02.005","title":"Unbounded Spatial Data Stream Query Processing using Spatial Semijoins","year":2021,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spatial query; Computer science; Spatial analysis; Spatial database; Object-based spatial database; Data stream mining; Data mining; Focus (optics); Query optimization; Object (grammar); Web search query; Sargable; Information retrieval; Geography; Artificial intelligence; Remote sensing","score_opus":0.04769116251962889,"score_gpt":0.27910525443777806,"score_spread":0.23141409191814916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138836727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019167589,0.00043694163,0.9782495,0.00013200291,0.000055788314,0.0001241547,0.00009192852,0.000979432,0.00076265517],"genre_scores_gemma":[0.46616986,0.00046332413,0.5302217,0.00019333058,0.000134744,0.00028704622,0.0005755707,0.00020897949,0.0017454206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956097,0.0010636153,0.0004555071,0.0007555008,0.0018723395,0.00024328259],"domain_scores_gemma":[0.9933891,0.0033321742,0.00052531785,0.0014411764,0.0009954283,0.00031670675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039480985,0.0007769713,0.0018878173,0.0010647973,0.0012693747,0.00242144,0.0028095518,0.00097608624,0.0022158688],"category_scores_gemma":[0.00819028,0.0005309542,0.0011988418,0.0017914481,0.0011195596,0.004028431,0.0042945836,0.0014115033,0.000751614],"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":[0.0036554665,0.0006074658,0.006845416,0.000828227,0.00041188658,0.0009991755,0.0026557867,0.3155047,0.09857146,0.086741604,0.0076679206,0.47551087],"study_design_scores_gemma":[0.000058615256,0.00023306035,0.00037999914,0.000018178658,0.00003485155,0.0002399662,0.00027070046,0.9551654,0.013572114,0.026039964,0.003957066,0.000030140973],"about_ca_topic_score_codex":0.0020810775,"about_ca_topic_score_gemma":0.0018587152,"teacher_disagreement_score":0.0039480985,"about_ca_system_score_codex":0.00068419677,"about_ca_system_score_gemma":0.0014418344,"threshold_uncertainty_score":0.020879745},"labels":[],"label_agreement":null},{"id":"W3139152607","doi":"10.1145/3507904","title":"A Survey on Spatio-temporal Data Analytics Systems","year":2022,"lang":"en","type":"preprint","venue":"ACM Computing Surveys","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Analytics; Big data; Data science; NoSQL; Spatial analysis; Temporal database; Popularity; Data mining; Geography; Remote sensing","score_opus":0.1648382059673039,"score_gpt":0.3339813399706263,"score_spread":0.1691431340033224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139152607","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013445425,0.5727818,0.30798247,0.014158979,0.0025641387,0.0006330693,0.0061014,0.00490189,0.077430785],"genre_scores_gemma":[0.07540851,0.7576205,0.13488227,0.0032617163,0.0029630186,0.0004958889,0.011369488,0.00067353534,0.013324985],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9957808,0.0008502937,0.0005760743,0.00059029594,0.001930026,0.000272474],"domain_scores_gemma":[0.99416673,0.0028205912,0.0002999167,0.0007413352,0.0017168543,0.00025451777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030800207,0.0009980211,0.0013234434,0.005891588,0.0010251348,0.005327329,0.0023896089,0.00135768,0.008285046],"category_scores_gemma":[0.008556215,0.00081415515,0.00092664524,0.019180313,0.0008218341,0.010099741,0.002265361,0.0018413741,0.0048023653],"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":[0.00014464457,0.00010994768,0.00471958,0.0057065194,0.00009521724,0.0002643017,0.00065984094,0.0046194363,0.0017529932,0.10319011,0.103701636,0.7750357],"study_design_scores_gemma":[0.000011192195,0.00007713171,0.0024557076,0.0014181472,0.000044219283,0.0009022347,0.0005076203,0.013906197,0.001130444,0.03776259,0.9417422,0.000042304247],"about_ca_topic_score_codex":0.0034443997,"about_ca_topic_score_gemma":0.0018362576,"teacher_disagreement_score":0.008285046,"about_ca_system_score_codex":0.0014137734,"about_ca_system_score_gemma":0.0024813404,"threshold_uncertainty_score":0.02771622},"labels":[],"label_agreement":null},{"id":"W3139941699","doi":"10.15353/acmla.n167.3645","title":"Spatial Analytics Dashboards: Assisting Researchers to Select the Appropriate Tool","year":2021,"lang":"en","type":"article","venue":"Bulletin - Association of Canadian Map Libraries and Archives (ACMLA)","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":true,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Dashboard; Analytics; Computer science; Session (web analytics); Context (archaeology); Learning analytics; Visualization; Visual analytics; Curriculum; Data science; Data visualization; Software; World Wide Web; Database; Data mining; Geography","score_opus":0.02007211748605098,"score_gpt":0.21526745532543387,"score_spread":0.1951953378393829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139941699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031351186,0.016192988,0.66094524,0.03251742,0.004612165,0.006925668,0.04269314,0.116018444,0.088743754],"genre_scores_gemma":[0.04415253,0.010624475,0.9039025,0.002346148,0.00066644175,0.006646883,0.016420275,0.0055852635,0.009655528],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9726289,0.012978429,0.0051158997,0.0017624162,0.0069115716,0.0006027986],"domain_scores_gemma":[0.81087554,0.12574472,0.00957094,0.013632873,0.036971547,0.0032043122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06775972,0.0026131272,0.0023591318,0.014430943,0.0017951466,0.012317274,0.0034638448,0.0018101275,0.025521263],"category_scores_gemma":[0.19244911,0.0016049547,0.0017275811,0.0154404845,0.0013940278,0.01362932,0.0075702663,0.0031585146,0.012634149],"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":[0.00029849418,0.0002145696,0.0060898853,0.0062723923,0.00014874528,0.00018828342,0.006702948,0.001229793,0.0024303559,0.010129106,0.1947732,0.77152234],"study_design_scores_gemma":[0.0003567068,0.00038138765,0.008887526,0.009481858,0.00035560867,0.0002913592,0.01352806,0.007585964,0.00749217,0.03457827,0.9166067,0.00045435352],"about_ca_topic_score_codex":0.0025650496,"about_ca_topic_score_gemma":0.0061510126,"teacher_disagreement_score":0.06775972,"about_ca_system_score_codex":0.0014645506,"about_ca_system_score_gemma":0.005560968,"threshold_uncertainty_score":0.35835195},"labels":[],"label_agreement":null},{"id":"W3144812761","doi":"","title":"A privacy preserving framework for efficient computation of trajectory similarity","year":2015,"lang":"en","type":"article","venue":"","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":"Novelis (Canada)","funders":"","keywords":"Homomorphic encryption; Computation; Trajectory; Similarity (geometry); Computer science; Encryption; Protocol (science); Theoretical computer science; Secure multi-party computation; Algorithm; Image (mathematics); Artificial intelligence; Computer security","score_opus":0.07535874184465109,"score_gpt":0.3173116365152341,"score_spread":0.241952894670583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144812761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028907042,0.00009697621,0.99604464,0.00009160193,0.00002587531,0.00005341549,0.00005205451,0.00025601243,0.0004887621],"genre_scores_gemma":[0.43217227,0.00059843756,0.5630293,0.00018638237,0.00019463939,0.0003491418,0.0005096173,0.000116930125,0.002843263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99510115,0.0011150041,0.00041422524,0.00090192154,0.0020281926,0.00043956083],"domain_scores_gemma":[0.99634534,0.0008573503,0.0003785795,0.0017333956,0.00053463085,0.00015079195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029680948,0.00066458853,0.0012368095,0.0015217287,0.001604297,0.0024513286,0.0025117812,0.0015061084,0.0022689956],"category_scores_gemma":[0.006897388,0.00055941514,0.0015169076,0.0023234687,0.0020810855,0.008542756,0.004734187,0.0030593406,0.0006830456],"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":[0.00040406693,0.00014618377,0.00086950546,0.00021659643,0.000111984366,0.0005399943,0.0004637552,0.12557045,0.016820073,0.71108264,0.002790461,0.1409842],"study_design_scores_gemma":[0.000100765654,0.00034513508,0.00043943638,0.0000532519,0.000086814325,0.0007910262,0.00019122107,0.63251305,0.0299381,0.31767306,0.017744739,0.00012343394],"about_ca_topic_score_codex":0.0013052791,"about_ca_topic_score_gemma":0.0006782725,"teacher_disagreement_score":0.0029680948,"about_ca_system_score_codex":0.0012204988,"about_ca_system_score_gemma":0.002473883,"threshold_uncertainty_score":0.015696943},"labels":[],"label_agreement":null},{"id":"W3147597394","doi":"10.5441/002/edbt.2021.42","title":"Efficient Contact Similarity Query over Uncertain Trajectories","year":2021,"lang":"en","type":"article","venue":"Movebank","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Similarity (geometry); Query optimization; Information retrieval; Artificial intelligence","score_opus":0.017696849139547718,"score_gpt":0.2506765952562189,"score_spread":0.23297974611667116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147597394","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16134197,0.001419723,0.81657684,0.0011096168,0.00023199341,0.00036573573,0.007625587,0.0063047106,0.0050239153],"genre_scores_gemma":[0.7921221,0.00044329706,0.18782885,0.00016913142,0.0001571011,0.00020582735,0.013505325,0.00042713297,0.005141186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997384,0.00034280526,0.00025075182,0.0007841819,0.0010115586,0.00022666916],"domain_scores_gemma":[0.99706334,0.0012278651,0.00032226596,0.0008110199,0.00043243504,0.00014312867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011626995,0.0008841205,0.0024656455,0.003226012,0.0013406626,0.0026330762,0.002729261,0.0021910165,0.0045550545],"category_scores_gemma":[0.009478251,0.000566927,0.000981813,0.0059823175,0.00061887765,0.00561026,0.0036307715,0.0012461839,0.0017611102],"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":[0.0035750696,0.0007735753,0.014117724,0.001251819,0.00039591076,0.002034173,0.0011213627,0.2970558,0.023852084,0.06148954,0.061350808,0.5329822],"study_design_scores_gemma":[0.000046241144,0.00013471353,0.0013229246,0.000021786036,0.00003983697,0.00041208975,0.00039682115,0.9607732,0.006148792,0.025109652,0.0055695716,0.000024385781],"about_ca_topic_score_codex":0.008582904,"about_ca_topic_score_gemma":0.0077076694,"teacher_disagreement_score":0.008582904,"about_ca_system_score_codex":0.001200667,"about_ca_system_score_gemma":0.0015379288,"threshold_uncertainty_score":0.017065883},"labels":[],"label_agreement":null},{"id":"W3149546047","doi":"10.1109/ideas.2007.4318110","title":"The LBF R-tree: Efficient Multidimensional Indexing with Graceful Degradation","year":2007,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Search engine indexing; Computer science; Exploit; Range query (database); Tree (set theory); Curse of dimensionality; Data mining; R-tree; Data structure; Range (aeronautics); Theoretical computer science; Web search query; Information retrieval; Search engine; Artificial intelligence; Sargable; Mathematics; Spatial analysis; Spatial database","score_opus":0.009359691985560319,"score_gpt":0.22604001249685227,"score_spread":0.21668032051129194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149546047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030337574,0.0036236332,0.9512881,0.00066313264,0.00016197885,0.00017250142,0.00032270336,0.01108522,0.0023450162],"genre_scores_gemma":[0.30020702,0.0021714615,0.68916965,0.0005925212,0.00028557194,0.00032591014,0.0015392329,0.0009667234,0.004741919],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738234,0.0005432899,0.00018698009,0.00025177342,0.0013195731,0.00031599432],"domain_scores_gemma":[0.9926884,0.0017716802,0.00061419985,0.0033851103,0.0012607947,0.0002799043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003274218,0.0009916084,0.0019475418,0.0020302865,0.0011836246,0.002209032,0.0042428905,0.0017908309,0.0015511623],"category_scores_gemma":[0.01169474,0.0006383768,0.00073768024,0.0051698177,0.0014092127,0.006511376,0.0030770442,0.0014840977,0.0022703789],"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":[0.0011362275,0.00041863183,0.0036128387,0.0005821705,0.00013490312,0.00053530245,0.0006491501,0.07676063,0.048836205,0.023917172,0.04245873,0.8009581],"study_design_scores_gemma":[0.00017901447,0.0007089531,0.001084118,0.0000660047,0.00008376976,0.0015184991,0.00019324191,0.9100098,0.02817165,0.034312263,0.023514757,0.00015785472],"about_ca_topic_score_codex":0.0028817207,"about_ca_topic_score_gemma":0.0027810507,"teacher_disagreement_score":0.0042428905,"about_ca_system_score_codex":0.00092287856,"about_ca_system_score_gemma":0.001477955,"threshold_uncertainty_score":0.017315924},"labels":[],"label_agreement":null},{"id":"W3151440080","doi":"10.1109/tfuzz.2021.3069332","title":"High-Efficient Fuzzy Querying With HiveQL for Big Data Warehousing","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Qinglan Project of Jiangsu Province of China; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Silesian University of Technology; Amazon Web Services","keywords":"Computer science; Data warehouse; Scalability; Big data; Fuzzy logic; Flexibility (engineering); Database; Data mining; Data transformation; Dimensional modeling; Information retrieval; Artificial intelligence","score_opus":0.06450566782920338,"score_gpt":0.2554359208388662,"score_spread":0.19093025300966282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151440080","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018389964,0.00023718824,0.92448676,0.00047978156,0.00008008428,0.00024963258,0.0018749556,0.049498245,0.0047034244],"genre_scores_gemma":[0.15692195,0.0001739804,0.833318,0.00053347397,0.0000455111,0.0002606987,0.0040109344,0.0013270817,0.003408392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973846,0.00033877435,0.00029369435,0.00035513306,0.0014429298,0.00018480812],"domain_scores_gemma":[0.99754214,0.0007610457,0.0001150911,0.0009431734,0.0005381953,0.00010031161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022387602,0.00073507533,0.0007895529,0.0013840267,0.0011236881,0.0039645233,0.0030883234,0.0010726659,0.0064478065],"category_scores_gemma":[0.0056211287,0.0007818606,0.0014862728,0.0017010827,0.0010016637,0.004787091,0.0036651238,0.0015217041,0.0020812775],"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":[0.0013864264,0.0008643694,0.0083700055,0.0012478773,0.00033151938,0.001112093,0.0021324886,0.07678636,0.07749255,0.11202903,0.08112147,0.63712585],"study_design_scores_gemma":[0.00020126799,0.00016934876,0.0017810767,0.00011180233,0.000047688998,0.0005818153,0.00052612013,0.8164944,0.072719164,0.049529612,0.057608474,0.00022921541],"about_ca_topic_score_codex":0.006208799,"about_ca_topic_score_gemma":0.006924233,"teacher_disagreement_score":0.0064478065,"about_ca_system_score_codex":0.0013138583,"about_ca_system_score_gemma":0.0016344432,"threshold_uncertainty_score":0.021570027},"labels":[],"label_agreement":null},{"id":"W3165145967","doi":"10.1007/978-3-030-75075-6_13","title":"Triangle Enumeration for Billion-Scale Graphs in RDBMS","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Relational database management system; Computer science; Enumeration; Graph; Analytics; Relational database; Graph database; Theoretical computer science; Database; Discrete mathematics; Mathematics","score_opus":0.01804466850158521,"score_gpt":0.22600514477976677,"score_spread":0.20796047627818157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165145967","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032833476,0.0018331446,0.9084228,0.0014502888,0.00034678838,0.00024189214,0.0022143386,0.006189185,0.046468023],"genre_scores_gemma":[0.20611876,0.001794606,0.7488003,0.0004593208,0.00024364676,0.00032902276,0.00558046,0.0021913033,0.034482606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889874,0.00023996909,0.00008694884,0.00021853017,0.00035567253,0.00020007209],"domain_scores_gemma":[0.9970995,0.0010250761,0.00011558604,0.0012944548,0.00029143333,0.00017400288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000857381,0.00068851886,0.0012468185,0.0011815386,0.001191171,0.0031903822,0.0025706138,0.0011534271,0.019224145],"category_scores_gemma":[0.005951278,0.00070906297,0.0010526062,0.0040650247,0.00075080286,0.0057739555,0.0032867298,0.0023145073,0.0046197767],"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":[0.00029407814,0.00021569042,0.0014214359,0.0007417538,0.00006691336,0.00019165689,0.00048489103,0.114581294,0.005855734,0.39031816,0.078137994,0.40769047],"study_design_scores_gemma":[0.000047657704,0.000042459895,0.0004356219,0.00013583242,0.00003326438,0.00023046946,0.00023763631,0.3783248,0.003865629,0.56244284,0.054175306,0.000028594035],"about_ca_topic_score_codex":0.0042228363,"about_ca_topic_score_gemma":0.008451323,"teacher_disagreement_score":0.019224145,"about_ca_system_score_codex":0.0014968814,"about_ca_system_score_gemma":0.0014005289,"threshold_uncertainty_score":0.06431115},"labels":[],"label_agreement":null},{"id":"W3167470018","doi":"10.1145/3448611","title":"Random Graph-based Multiple Instance Learning for Structured IoT Smart City Applications","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Internet Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; University of Guelph","funders":"","keywords":"Computer science; Probabilistic logic; Theoretical computer science; Core (optical fiber); Graph; Substructure; Random graph; Clustering coefficient; Artificial intelligence; Data mining; Cluster analysis","score_opus":0.01569730639367877,"score_gpt":0.24729888884886145,"score_spread":0.23160158245518267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167470018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030004881,0.00083513855,0.9633509,0.0010289479,0.000084921565,0.00015196389,0.0010350874,0.002198209,0.0013099113],"genre_scores_gemma":[0.5963009,0.0005983376,0.39367822,0.0008480406,0.0001993309,0.00046827245,0.004530317,0.00042545545,0.0029511375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984592,0.00055652304,0.0000833986,0.00054629525,0.0002080011,0.00014642802],"domain_scores_gemma":[0.99610656,0.0026234875,0.0003780289,0.00037866842,0.00031153017,0.00020160587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002255864,0.001612966,0.0022018254,0.0019198669,0.00055757444,0.0017853082,0.0038463457,0.0023136684,0.0032625797],"category_scores_gemma":[0.008066317,0.0008838974,0.002220842,0.0024189178,0.0009741714,0.0036646693,0.0017935938,0.0034953405,0.0008131043],"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":[0.00016156542,0.00023130955,0.0029428669,0.00016973738,0.00016007057,0.00017824357,0.000096742406,0.89047855,0.0006575008,0.013061053,0.0050744177,0.086787865],"study_design_scores_gemma":[0.000004965097,0.0000071281916,0.00007606233,0.0000046427176,0.000006023809,0.000007984468,0.000009150336,0.98946244,0.00009845422,0.01009406,0.00022615431,0.0000029025084],"about_ca_topic_score_codex":0.0093425885,"about_ca_topic_score_gemma":0.012378484,"teacher_disagreement_score":0.0093425885,"about_ca_system_score_codex":0.002166965,"about_ca_system_score_gemma":0.0012499939,"threshold_uncertainty_score":0.018576384},"labels":[],"label_agreement":null},{"id":"W3167861891","doi":"10.1145/3447548.3467359","title":"Graph Summarization with Controlled Utility Loss","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Automatic summarization; Computer science; Scalability; Theoretical computer science; Graph; Lossless compression; PageRank; Algorithm; Artificial intelligence; Data compression","score_opus":0.010159320837887691,"score_gpt":0.21190662867919735,"score_spread":0.20174730784130965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167861891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014837866,0.00039571262,0.97820675,0.00022870355,0.00006178963,0.00013276776,0.00047749947,0.0038078998,0.001850988],"genre_scores_gemma":[0.20973365,0.00045795058,0.7795778,0.00022569015,0.00013926277,0.00026965144,0.0032425208,0.0008692601,0.0054842294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917334,0.00018936182,0.00006095603,0.00017642014,0.00033603248,0.00006385999],"domain_scores_gemma":[0.9978993,0.000762078,0.00021008553,0.0006522107,0.00039911544,0.0000772057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066279754,0.0010520096,0.0008396395,0.0017878456,0.0006193506,0.0012939748,0.0012579185,0.0007253658,0.003265017],"category_scores_gemma":[0.0046854587,0.0003683448,0.0006273833,0.0022067912,0.0005698873,0.0023759007,0.0014136188,0.0012200339,0.0014996242],"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":[0.00051748357,0.00017212279,0.0010042147,0.00058050774,0.00013189325,0.00032560783,0.000415128,0.15699378,0.05769663,0.032123044,0.026339319,0.7237004],"study_design_scores_gemma":[0.00008609896,0.00021359567,0.00056317507,0.000038188406,0.00005857055,0.00029732438,0.00016066348,0.8840948,0.04101921,0.05626717,0.017171543,0.00002967543],"about_ca_topic_score_codex":0.0011594903,"about_ca_topic_score_gemma":0.0024303764,"teacher_disagreement_score":0.003265017,"about_ca_system_score_codex":0.0007263574,"about_ca_system_score_gemma":0.00071886886,"threshold_uncertainty_score":0.010922611},"labels":[],"label_agreement":null},{"id":"W3169048261","doi":"10.21428/594757db.efb4fcff","title":"Driving Scene Understanding: How much temporal context and spatial resolution is necessary?","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Variety (cybernetics); Context (archaeology); Metadata; Field (mathematics); Modalities; Spatial contextual awareness; Modality (human–computer interaction); Human–computer interaction; Action (physics); Artificial intelligence; Data science; Computer vision; World Wide Web; Geography","score_opus":0.04808237679590738,"score_gpt":0.24879006744626672,"score_spread":0.20070769065035934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169048261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3228376,0.015820961,0.6345157,0.007879049,0.00036898875,0.00023388863,0.002834193,0.0011132341,0.01439637],"genre_scores_gemma":[0.86286384,0.0045802537,0.12976852,0.00030621586,0.0001935594,0.000060538325,0.0010828108,0.00013146174,0.0010127849],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948645,0.00009944404,0.00004352828,0.00016510296,0.0001253303,0.00008016459],"domain_scores_gemma":[0.9977247,0.000911665,0.0003031743,0.00045092136,0.00042845862,0.00018109566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008502736,0.00047740698,0.0008129702,0.0012284331,0.00050726003,0.0025799738,0.0011563391,0.0008749169,0.0015563804],"category_scores_gemma":[0.007119864,0.0005599481,0.0005758788,0.0011789955,0.0008547503,0.008022908,0.0015000757,0.0012940352,0.0005293866],"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":[0.00073826284,0.00029011266,0.03150317,0.0013768882,0.00025756666,0.00052247284,0.0014671129,0.029398795,0.07683029,0.027761225,0.006942537,0.82291156],"study_design_scores_gemma":[0.00011318221,0.0005864023,0.12416109,0.0009809972,0.0007416639,0.003128283,0.008014948,0.5714661,0.06545064,0.17768428,0.047396757,0.0002756479],"about_ca_topic_score_codex":0.008987493,"about_ca_topic_score_gemma":0.0123637505,"teacher_disagreement_score":0.008987493,"about_ca_system_score_codex":0.0005716978,"about_ca_system_score_gemma":0.0010739439,"threshold_uncertainty_score":0.017870367},"labels":[],"label_agreement":null},{"id":"W3172623769","doi":"10.1145/3451159","title":"Graph Indexing for Efficient Evaluation of Label-constrained Reachability Queries","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reachability; Computer science; Vertex (graph theory); Search engine indexing; Transitive closure; Combinatorics; Graph; Theoretical computer science; Discrete mathematics; Mathematics; Information retrieval","score_opus":0.06869570056074788,"score_gpt":0.31231993113975526,"score_spread":0.2436242305790074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172623769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087747864,0.0017570045,0.8453371,0.00072369754,0.0001937649,0.00065179233,0.007715393,0.04781965,0.008053695],"genre_scores_gemma":[0.40250847,0.00050333457,0.5793366,0.00029210013,0.00011839129,0.00045174506,0.012925195,0.0016625843,0.0022016568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698883,0.00044592153,0.00034020565,0.0005408675,0.0013392831,0.0003449882],"domain_scores_gemma":[0.99347275,0.002859826,0.0004901552,0.0020871775,0.0008483431,0.00024172726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014288954,0.0012762643,0.0016634952,0.0031921414,0.0011049433,0.002818245,0.0029651327,0.0011315014,0.005496247],"category_scores_gemma":[0.010603084,0.00064741616,0.001119495,0.0055700727,0.00088560104,0.0077147945,0.0023321572,0.0014977056,0.0016579096],"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":[0.0028150508,0.00073580653,0.006244778,0.0013918682,0.0002516806,0.00059817167,0.00081667246,0.088189244,0.08228969,0.060102653,0.054070648,0.7024937],"study_design_scores_gemma":[0.00023402795,0.000283384,0.0014444944,0.00006239052,0.0000863442,0.00050467567,0.00037068152,0.86473596,0.042480756,0.07646699,0.013249776,0.00008046638],"about_ca_topic_score_codex":0.0073620123,"about_ca_topic_score_gemma":0.010884592,"teacher_disagreement_score":0.0073620123,"about_ca_system_score_codex":0.002178643,"about_ca_system_score_gemma":0.0032189987,"threshold_uncertainty_score":0.018386722},"labels":[],"label_agreement":null},{"id":"W3174896615","doi":"10.1145/3448016.3457269","title":"Distributed Stream KNN Join","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Join (topology); Computer science; Data stream mining; Operator (biology); Streaming data; Distributed computing; Data stream; Data mining","score_opus":0.015247584946159742,"score_gpt":0.23047074184687047,"score_spread":0.21522315690071073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174896615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03119847,0.0011384104,0.9452271,0.00061240455,0.0008368123,0.0006168149,0.0025562036,0.0062586456,0.011554983],"genre_scores_gemma":[0.45227417,0.0009886204,0.50794816,0.0008603318,0.00087257515,0.0007888169,0.011766819,0.0010985284,0.023402017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99642134,0.00038433322,0.0003278363,0.001046226,0.0014729542,0.0003473314],"domain_scores_gemma":[0.99615014,0.000841702,0.00024817957,0.0013017838,0.0011040322,0.00035423005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023837152,0.00090957317,0.0017019673,0.0011936426,0.0018404094,0.0026724278,0.00245288,0.001210743,0.010389442],"category_scores_gemma":[0.007111379,0.00041186283,0.0007820808,0.0022815708,0.00077913544,0.0031006988,0.005036914,0.0013093437,0.0040459074],"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":[0.004877867,0.0008082136,0.008652672,0.00091086456,0.00036289403,0.00097381184,0.0008958142,0.17029363,0.032559797,0.06055313,0.09601344,0.62309796],"study_design_scores_gemma":[0.00037324915,0.00035820928,0.0012741609,0.000060754184,0.000078632256,0.00065522286,0.0005034485,0.8276332,0.019390883,0.07496491,0.0746312,0.000076110584],"about_ca_topic_score_codex":0.0021365962,"about_ca_topic_score_gemma":0.0031768552,"teacher_disagreement_score":0.010389442,"about_ca_system_score_codex":0.0009254831,"about_ca_system_score_gemma":0.001837926,"threshold_uncertainty_score":0.034756184},"labels":[],"label_agreement":null},{"id":"W3179597376","doi":"10.1111/tgis.12787","title":"Building <i>k</i>‐partite association graphs for finding recommendation patterns from questionnaire data","year":2021,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Cisco Systems","keywords":"Association rule learning; Recommender system; Computer science; Centrality; Data mining; Apriori algorithm; Graph; Association (psychology); Information retrieval; Theoretical computer science; Data science; Mathematics; Combinatorics; Psychology","score_opus":0.04648396684555205,"score_gpt":0.30733263696219226,"score_spread":0.2608486701166402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179597376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11367375,0.0005095479,0.8810487,0.00049239316,0.00004655684,0.00023713404,0.0016242847,0.00079147983,0.0015761674],"genre_scores_gemma":[0.5918265,0.0005975474,0.39976802,0.00026742223,0.00010752452,0.00040229113,0.004981787,0.00013322382,0.0019156979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99683183,0.0011414022,0.00025804064,0.000933164,0.0005493788,0.00028631548],"domain_scores_gemma":[0.97627187,0.01634419,0.002198048,0.0025773696,0.0021027413,0.0005057222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029028265,0.00096572423,0.0012357121,0.009993491,0.0016665387,0.0023575467,0.0016400995,0.0017768363,0.0015751504],"category_scores_gemma":[0.021138063,0.0009755497,0.0024112046,0.0076637277,0.0010963269,0.002676975,0.0014133885,0.0015165053,0.0010380647],"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":[0.00057424686,0.00081575074,0.1713701,0.0009259376,0.0015060457,0.001441364,0.0029473167,0.38762596,0.0077261114,0.037143916,0.012306948,0.3756162],"study_design_scores_gemma":[0.000014353671,0.000086846754,0.007408245,0.00005934828,0.000111767586,0.0003286511,0.0005003704,0.9551451,0.0009377582,0.032521788,0.0028477926,0.000038022037],"about_ca_topic_score_codex":0.0132901855,"about_ca_topic_score_gemma":0.019225797,"teacher_disagreement_score":0.0132901855,"about_ca_system_score_codex":0.00094178336,"about_ca_system_score_gemma":0.0011446658,"threshold_uncertainty_score":0.02642566},"labels":[],"label_agreement":null},{"id":"W3181666189","doi":"10.1109/mdm52706.2021.00022","title":"Efficient Spatio-Textual Similarity Join Processing on NUMA Systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Computer science; Exploit; Similarity (geometry); Join (topology); Context (archaeology); Architecture; Theoretical computer science; Distributed computing; Parallel computing; Artificial intelligence; Image (mathematics)","score_opus":0.02463191388345858,"score_gpt":0.24975612677665152,"score_spread":0.22512421289319293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181666189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30107754,0.0017202966,0.65798306,0.00088179397,0.00044202426,0.00055512524,0.0011943104,0.0218072,0.014338648],"genre_scores_gemma":[0.6291224,0.0002863938,0.36295328,0.00019824483,0.00011423155,0.00022233569,0.0021319753,0.0003478897,0.0046233214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763286,0.0002736616,0.0002799107,0.00066855166,0.00090821326,0.00023667453],"domain_scores_gemma":[0.9967173,0.0007018009,0.00040410645,0.0011849788,0.000688697,0.00030321238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013592779,0.00095575873,0.0011884628,0.0013816061,0.0024700004,0.0030210959,0.0028714084,0.00080128456,0.0035899524],"category_scores_gemma":[0.005607542,0.00052104,0.0006398765,0.00290583,0.00086714077,0.0035280823,0.003498355,0.00086216035,0.0021031878],"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":[0.0038815194,0.0010835048,0.014196028,0.000762376,0.0002831798,0.0009029051,0.0016917665,0.20376171,0.1165417,0.023298612,0.029609032,0.6039877],"study_design_scores_gemma":[0.00013014903,0.00027516705,0.0021198038,0.000019509373,0.000043499986,0.0003597806,0.0006066977,0.9432836,0.027894918,0.012358204,0.012864582,0.000043988406],"about_ca_topic_score_codex":0.0035760098,"about_ca_topic_score_gemma":0.0067797746,"teacher_disagreement_score":0.0035899524,"about_ca_system_score_codex":0.001306297,"about_ca_system_score_gemma":0.002017507,"threshold_uncertainty_score":0.012009621},"labels":[],"label_agreement":null},{"id":"W3184011463","doi":"10.14778/3476249.3476263","title":"LES <sup>3</sup>","year":2021,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Search engine indexing; Computer science; Set (abstract data type); Pruning; Similarity (geometry); Bitmap; Nearest neighbor search; Representation (politics); Data mining; Encoding (memory); Theoretical computer science; Artificial intelligence","score_opus":0.01984668302522739,"score_gpt":0.22997774859488967,"score_spread":0.2101310655696623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184011463","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038447995,0.0035028988,0.57583904,0.013732589,0.0059359586,0.0017225042,0.14951576,0.034780324,0.17652287],"genre_scores_gemma":[0.23565386,0.002734763,0.40790936,0.005795896,0.0025187836,0.0015388501,0.24167362,0.0054789153,0.09669588],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99806684,0.00026302878,0.00024810922,0.00026698087,0.0010224823,0.00013260765],"domain_scores_gemma":[0.9917476,0.002996085,0.0007039544,0.0024174114,0.0018562372,0.00027865617],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015501657,0.0008745698,0.0011566316,0.001830933,0.0009556618,0.0038348415,0.0018592292,0.0016618052,0.0846277],"category_scores_gemma":[0.011188397,0.00044039317,0.00087334187,0.0032588325,0.0009019816,0.0032751537,0.002385258,0.001466443,0.03946854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077040715,0.00015217613,0.0049218573,0.00081247685,0.000056787216,0.0006236722,0.00012498986,0.012523568,0.01023033,0.037915066,0.5812503,0.35061833],"study_design_scores_gemma":[0.00013568664,0.00029541127,0.0053920615,0.000263026,0.000041026233,0.0016435389,0.00042495533,0.17787586,0.036324147,0.07426441,0.70325416,0.000085691725],"about_ca_topic_score_codex":0.002071415,"about_ca_topic_score_gemma":0.005814899,"teacher_disagreement_score":0.9153723,"about_ca_system_score_codex":0.0012454435,"about_ca_system_score_gemma":0.0012969268,"threshold_uncertainty_score":0.28310788},"labels":[],"label_agreement":null},{"id":"W3185489179","doi":"10.1016/j.comnet.2021.108337","title":"On the prediction of large-scale road-network constrained trajectories","year":2021,"lang":"en","type":"article","venue":"Computer Networks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"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":"Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Research Chairs","keywords":"Computer science; Trajectory; Cluster analysis; Data mining; Scale (ratio); Train; Hierarchical clustering; Machine learning; Artificial intelligence","score_opus":0.012205791133143457,"score_gpt":0.2113775098579765,"score_spread":0.19917171872483302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185489179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43348137,0.002481645,0.5546559,0.0028019624,0.0002614152,0.00013715836,0.002105682,0.001010919,0.0030639914],"genre_scores_gemma":[0.9755942,0.0005794291,0.020424057,0.00011325424,0.00011525142,0.000056290464,0.0012608599,0.00005840613,0.0017981797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931407,0.00022843304,0.000037707294,0.00020527873,0.00009798349,0.000116368596],"domain_scores_gemma":[0.98650604,0.011380365,0.0006247,0.00042397904,0.0006376705,0.00042724912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008446,0.0010654554,0.0021093998,0.0014914222,0.00082146097,0.0013853606,0.0024261314,0.0021939457,0.0019852216],"category_scores_gemma":[0.015398084,0.001014528,0.00063998386,0.0024645715,0.0013916814,0.0027227004,0.0018528557,0.0016887906,0.00034606122],"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":[0.00005546661,0.000019478095,0.0013457291,0.000015802269,0.000015741009,0.000018611494,0.000013305564,0.9924745,0.000044177494,0.0013049238,0.0005216612,0.004170632],"study_design_scores_gemma":[0.000002056945,0.0000030592005,0.000098620105,0.0000012143676,8.72475e-7,0.0000013445591,0.000003117539,0.9988727,0.00001379698,0.0009778218,0.000024412402,9.3141205e-7],"about_ca_topic_score_codex":0.06520441,"about_ca_topic_score_gemma":0.03534632,"teacher_disagreement_score":0.06520441,"about_ca_system_score_codex":0.0018680922,"about_ca_system_score_gemma":0.0019498179,"threshold_uncertainty_score":0.12964976},"labels":[],"label_agreement":null},{"id":"W318551555","doi":"10.1007/978-3-662-43984-5_26","title":"A Hybrid Scale-Out Cloud-Based Data Service for Worldwide Sensors","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Cloud computing; Scalability; Geospatial analysis; Computer science; Construct (python library); Search engine indexing; Distributed computing; Scale (ratio); Service (business); Architecture; Tree (set theory); Volume (thermodynamics); Data science; Database; Geography; Remote sensing; Computer network; World Wide Web; Cartography; Operating system","score_opus":0.031971041188222526,"score_gpt":0.2619694287126282,"score_spread":0.22999838752440566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W318551555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105459146,0.0014766967,0.8459711,0.0014818402,0.0009642955,0.00070704386,0.0010749171,0.018143278,0.024721585],"genre_scores_gemma":[0.82523113,0.000424354,0.15788822,0.0007525269,0.00027957433,0.00016421931,0.0013701614,0.0005131137,0.01337679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992582,0.00007078499,0.00003913061,0.00013496281,0.00031009363,0.00018692327],"domain_scores_gemma":[0.99940956,0.000062629064,0.000031582036,0.00017367901,0.00019853804,0.00012403002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065606483,0.00078169006,0.001009059,0.00068656163,0.0011331474,0.0016764953,0.0029312703,0.0009579253,0.007588774],"category_scores_gemma":[0.000991811,0.00032294443,0.00063025847,0.0017318199,0.00041771875,0.0021054298,0.0029145705,0.0008438211,0.0021805419],"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":[0.0023387256,0.0013072253,0.006280489,0.00037067322,0.00024432247,0.0016662298,0.00046966583,0.0654676,0.14391299,0.027548056,0.107455075,0.64293903],"study_design_scores_gemma":[0.00016826249,0.00033997186,0.0028025343,0.000029385223,0.000076231976,0.0006697055,0.00036460004,0.92357785,0.02205896,0.011777429,0.03804855,0.000086586435],"about_ca_topic_score_codex":0.007239594,"about_ca_topic_score_gemma":0.0076868543,"teacher_disagreement_score":0.007588774,"about_ca_system_score_codex":0.0009855152,"about_ca_system_score_gemma":0.0013231031,"threshold_uncertainty_score":0.02538699},"labels":[],"label_agreement":null},{"id":"W31871017","doi":"10.1136/jech-2019-213357","title":"Heuristics to Site Observers in a Terrain Represented by a Digital Elevation Matrix.","year":2010,"lang":"en","type":"article","venue":"Brazilian Symposium on GeoInformatics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Canadian Institutes of Health Research; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canada Research Chairs","keywords":"Digital elevation model; Elevation (ballistics); Terrain; Heuristics; Computer science; Matrix (chemical analysis); Remote sensing; Geography; Mathematics; Cartography","score_opus":0.005176499113223808,"score_gpt":0.2362771326221698,"score_spread":0.23110063350894597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W31871017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38335875,0.0012233859,0.5700242,0.0030336466,0.00030943548,0.0010255772,0.0036284789,0.002875592,0.03452098],"genre_scores_gemma":[0.7573845,0.00029696687,0.23540004,0.00022924483,0.000045827983,0.00019889443,0.002288128,0.0001595626,0.003996864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988226,0.0004942076,0.000080312,0.00031946044,0.00012655376,0.000156787],"domain_scores_gemma":[0.995076,0.0030006692,0.0005130745,0.00061367784,0.0005138909,0.0002827215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017621784,0.0006554317,0.0006748018,0.0016215167,0.0008777935,0.002568298,0.0015491032,0.0013185443,0.010017395],"category_scores_gemma":[0.017076632,0.00038775158,0.00069643836,0.001881371,0.0008060886,0.002519916,0.0016219984,0.0009428859,0.0016988777],"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":[0.0021385776,0.00077477866,0.088517,0.00082913396,0.00037730683,0.00042533755,0.002335852,0.19896546,0.0041617365,0.091735214,0.04028087,0.56945866],"study_design_scores_gemma":[0.00038774745,0.0004726489,0.01645701,0.00027274145,0.0002024356,0.0003791157,0.0036942796,0.83461004,0.0038892927,0.11981967,0.019710544,0.00010451495],"about_ca_topic_score_codex":0.014829121,"about_ca_topic_score_gemma":0.03385224,"teacher_disagreement_score":0.014829121,"about_ca_system_score_codex":0.0012564678,"about_ca_system_score_gemma":0.001978532,"threshold_uncertainty_score":0.03351152},"labels":[],"label_agreement":null},{"id":"W3188162669","doi":"10.30564/aia.v3i2.3219","title":"A New Approach of Intelligent Data Retrieval Paradigm","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Institute of Standards and Technology; University of Ottawa","keywords":"Computer science; Ranking (information retrieval); Rank (graph theory); Information retrieval; Focus (optics); Learning to rank; Scheme (mathematics); Raw data; Resource (disambiguation); Data mining","score_opus":0.12242496522354752,"score_gpt":0.3434368944768644,"score_spread":0.22101192925331686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188162669","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002631637,0.0026794947,0.9758646,0.0030996304,0.00039540228,0.00021668943,0.00017678882,0.0006270589,0.014308652],"genre_scores_gemma":[0.07939687,0.00397773,0.89330953,0.0026619083,0.0015089037,0.0006523815,0.0005794151,0.00018316902,0.017730072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946996,0.0012682962,0.0004717483,0.0014461208,0.0019143241,0.00019994348],"domain_scores_gemma":[0.9977703,0.00070342596,0.00014658901,0.0006144103,0.00063894293,0.00012639764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033175445,0.00081633776,0.0013713189,0.0045955232,0.001457841,0.0073911697,0.0032034898,0.0022516672,0.0035244918],"category_scores_gemma":[0.005574176,0.00050326,0.0017326626,0.004419574,0.0024533055,0.010477749,0.0038015477,0.0027613752,0.003047396],"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":[0.00016214984,0.00021278531,0.0012702193,0.00080004684,0.00023374976,0.00043108035,0.0014632862,0.007624173,0.009558939,0.5779471,0.026002401,0.37429428],"study_design_scores_gemma":[0.00008554555,0.00019382789,0.0006120126,0.00017662492,0.00014155214,0.0011891337,0.00059059483,0.15794697,0.006067645,0.58419406,0.24868257,0.00011948806],"about_ca_topic_score_codex":0.0016798377,"about_ca_topic_score_gemma":0.001329336,"teacher_disagreement_score":0.0073911697,"about_ca_system_score_codex":0.0018053211,"about_ca_system_score_gemma":0.0020031112,"threshold_uncertainty_score":0.017545104},"labels":[],"label_agreement":null},{"id":"W3193719172","doi":"10.1145/3468791.3468818","title":"NIR-Tree: A Non-Intersecting R-Tree","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bounding overwatch; Computer science; Search engine indexing; Tree (set theory); Range query (database); R-tree; B-tree; Range (aeronautics); Point (geometry); Interval tree; K-ary tree; Database index; Segment tree; Range tree; Data mining; Data structure; Access method; Binary tree; Tree structure; Algorithm; Spatial database; Mathematics; Information retrieval; Database; Artificial intelligence; Combinatorics; Spatial analysis; Search engine; Statistics; Web search query; Sargable","score_opus":0.013261753570778207,"score_gpt":0.2352353544712552,"score_spread":0.221973600900477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193719172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026216272,0.0036783107,0.8894525,0.0007350663,0.0004325937,0.000573933,0.009182031,0.0553362,0.01439306],"genre_scores_gemma":[0.094953835,0.001940004,0.8690881,0.00048050916,0.00017445486,0.00047973928,0.021756269,0.004187185,0.006939925],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99713635,0.00036964827,0.0003457306,0.00038297658,0.0015491438,0.00021614131],"domain_scores_gemma":[0.99404645,0.0013624572,0.00064446917,0.0021069974,0.0014602931,0.00037930658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016137563,0.0009924453,0.0015516209,0.002822916,0.0012840899,0.0031989247,0.003792906,0.0010167123,0.006203211],"category_scores_gemma":[0.01265934,0.0007874457,0.0011549251,0.006255397,0.00084685936,0.0065257563,0.0037148886,0.0012469984,0.007583357],"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":[0.0010988633,0.00036367602,0.009013288,0.0014159736,0.0002634542,0.0007126188,0.00094252336,0.032505743,0.02586425,0.052846182,0.20004195,0.6749315],"study_design_scores_gemma":[0.0004069878,0.0009047904,0.0030382879,0.0003437292,0.00021524365,0.0022549503,0.00067014864,0.46653935,0.042887285,0.083525084,0.39887476,0.00033938038],"about_ca_topic_score_codex":0.0033660447,"about_ca_topic_score_gemma":0.005067232,"teacher_disagreement_score":0.006203211,"about_ca_system_score_codex":0.0007207144,"about_ca_system_score_gemma":0.002296029,"threshold_uncertainty_score":0.020751774},"labels":[],"label_agreement":null},{"id":"W3194145758","doi":"10.1145/3469830.3470892","title":"SPRIG: A Learned Spatial Index for Range and kNN Queries","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Spatial query; Data mining; Spatial database; Overhead (engineering); Index (typography); Spatial analysis; Interpolation (computer graphics); Range (aeronautics); Grid; Range query (database); Exploit; Function (biology); Process (computing); Multivariate interpolation; Information retrieval; Artificial intelligence; Sargable; Web search query; Geography; Remote sensing","score_opus":0.023078580823026443,"score_gpt":0.2549051005605764,"score_spread":0.23182651973754997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194145758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035246935,0.001454402,0.927353,0.00038176647,0.0001648066,0.00034507064,0.003625002,0.027147766,0.0042812536],"genre_scores_gemma":[0.28719923,0.00093516544,0.69394547,0.0003245152,0.00014746746,0.00038767615,0.012397778,0.0009865271,0.0036761297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867815,0.00013426843,0.00014060506,0.00025100802,0.00066397403,0.00013196007],"domain_scores_gemma":[0.9980825,0.00043876315,0.00017621358,0.0007359159,0.0004460868,0.000120602235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009020886,0.0011104774,0.0013115411,0.0021875363,0.0006398597,0.0016944234,0.0026381349,0.00088355714,0.0034696765],"category_scores_gemma":[0.006734456,0.00038735036,0.000794381,0.0040540355,0.0006187954,0.004973604,0.0024305254,0.0012090154,0.002427773],"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":[0.00067701103,0.0005291321,0.009856946,0.00053128286,0.00015807131,0.0003103194,0.00033728025,0.17045236,0.013966256,0.015723312,0.053036336,0.73442173],"study_design_scores_gemma":[0.00005102584,0.0001487947,0.00081412296,0.000016456637,0.000019608475,0.00019816685,0.000110239256,0.9744916,0.0059571853,0.008777046,0.009383022,0.00003271417],"about_ca_topic_score_codex":0.009837493,"about_ca_topic_score_gemma":0.013231561,"teacher_disagreement_score":0.009837493,"about_ca_system_score_codex":0.0010965436,"about_ca_system_score_gemma":0.0025370123,"threshold_uncertainty_score":0.019560516},"labels":[],"label_agreement":null},{"id":"W3204354173","doi":"","title":"Enabling NUMA-aware Main Memory Spatial Join Processing: An Experimental Study.","year":2020,"lang":"en","type":"article","venue":"Very Large Data Bases","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Join (topology); Parallel computing; Computer architecture","score_opus":0.08098202661924112,"score_gpt":0.3075131336643204,"score_spread":0.2265311070450793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204354173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92650664,0.0014961886,0.049681313,0.0005412762,0.00039384916,0.00023653034,0.0007536702,0.0068169613,0.013573582],"genre_scores_gemma":[0.9594269,0.00024072807,0.03630263,0.000118102806,0.000046158908,0.000097944,0.0004826852,0.00026441074,0.0030203394],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904364,0.00019061324,0.000044017754,0.0001789809,0.00037111886,0.00017151977],"domain_scores_gemma":[0.9967919,0.0013797256,0.00018143139,0.0008735945,0.00049667014,0.00027660918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095951464,0.0005570193,0.00058331934,0.00038037467,0.0009271509,0.0014601592,0.002264723,0.0007407466,0.005949197],"category_scores_gemma":[0.004254949,0.00029083053,0.00021958291,0.0008764104,0.00065119716,0.002041416,0.0010636532,0.00089733215,0.0012236299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015300618,0.0070385775,0.00943735,0.0015852869,0.00041026573,0.0007484042,0.0008917885,0.07948508,0.6688377,0.01288812,0.018690204,0.18468662],"study_design_scores_gemma":[0.00085425767,0.0043096496,0.007810457,0.00004895022,0.00025285437,0.0004154318,0.0007222652,0.5452675,0.4193237,0.0061873193,0.014713295,0.00009423065],"about_ca_topic_score_codex":0.0025578325,"about_ca_topic_score_gemma":0.0023929693,"teacher_disagreement_score":0.005949197,"about_ca_system_score_codex":0.0005484652,"about_ca_system_score_gemma":0.00095204834,"threshold_uncertainty_score":0.01990205},"labels":[],"label_agreement":null},{"id":"W3207367190","doi":"10.20382/jocg.v12i1a4","title":"Walking the dog fast in practice: Algorithm engineering of the Fréchet distance","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Geometry (Carleton University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algorithm; Mathematics; Similarity (geometry); Bottleneck; Distance measures; Data structure; Measure (data warehouse); Benchmark (surveying); Computer science; Quadratic equation; Data mining; Artificial intelligence; Image (mathematics)","score_opus":0.0057959754496999015,"score_gpt":0.20680898383789056,"score_spread":0.20101300838819067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207367190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011055463,0.00054580305,0.9816828,0.0006746269,0.00010689061,0.00006740999,0.00008826852,0.002285677,0.0034931675],"genre_scores_gemma":[0.15800469,0.00051460136,0.83664346,0.00029354155,0.00015126179,0.00025040805,0.00055750454,0.00091991585,0.0026646117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993036,0.0019673973,0.0005085859,0.0019082732,0.0020549977,0.00052478665],"domain_scores_gemma":[0.9877659,0.0066089337,0.00047712034,0.0033466758,0.0015207006,0.0002806269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039545037,0.0013088264,0.0013873251,0.0014898991,0.0012126068,0.004231401,0.0033379951,0.002355745,0.007356411],"category_scores_gemma":[0.035423446,0.0010467811,0.0012812781,0.002155169,0.0025698377,0.008975867,0.0047950805,0.0048526577,0.0036963022],"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":[0.00038333642,0.00018686496,0.0029021492,0.00048441422,0.00009575681,0.00020640357,0.00063249544,0.1779849,0.008073574,0.24891423,0.020125264,0.54001063],"study_design_scores_gemma":[0.000082781684,0.0001171111,0.0003205038,0.000092666625,0.00002102251,0.00023308877,0.00014761653,0.6752516,0.005991024,0.30322155,0.014473391,0.00004762855],"about_ca_topic_score_codex":0.0045054215,"about_ca_topic_score_gemma":0.0035823376,"teacher_disagreement_score":0.007356411,"about_ca_system_score_codex":0.0022852067,"about_ca_system_score_gemma":0.003102604,"threshold_uncertainty_score":0.024609625},"labels":[],"label_agreement":null},{"id":"W3209525680","doi":"10.1016/j.knosys.2021.107623","title":"Enabling the interpretability of pretrained venue representations using semantic categories","year":2021,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Shandong University","keywords":"Interpretability; Computer science; Representation (politics); Semantics (computer science); Embedding; Feature (linguistics); Similarity (geometry); Focus (optics); Artificial intelligence; Popularity; Dimension (graph theory); Natural language processing; Information retrieval; Linguistics; Mathematics","score_opus":0.03900939636961332,"score_gpt":0.29606021368801183,"score_spread":0.2570508173183985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209525680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08004997,0.0006471831,0.89103526,0.0009142438,0.00029729816,0.00019732858,0.006167536,0.009822647,0.010868511],"genre_scores_gemma":[0.66006154,0.0006027388,0.32650906,0.00023602476,0.00007082343,0.00016064911,0.008527398,0.00059342734,0.0032383983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905294,0.00020236519,0.000082731065,0.0002920377,0.0002496259,0.00012023314],"domain_scores_gemma":[0.99689484,0.001342539,0.00014437907,0.0009165078,0.0005791897,0.0001224313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011326926,0.00072710565,0.0007568965,0.0030302973,0.000596457,0.0038593195,0.0020593738,0.001220103,0.0052204975],"category_scores_gemma":[0.009970533,0.00038882357,0.0010554182,0.0028850085,0.0007163672,0.0061186887,0.0027822675,0.001707266,0.0013290434],"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":[0.00085725886,0.0004756703,0.012250875,0.00091535744,0.0002569705,0.000588902,0.0021437963,0.07212029,0.01987022,0.10699277,0.026035314,0.7574926],"study_design_scores_gemma":[0.00003497929,0.00009168066,0.0026610182,0.00029073533,0.000098377095,0.00020831317,0.0012484144,0.71959686,0.0146098435,0.2358643,0.025226885,0.00006858743],"about_ca_topic_score_codex":0.013723669,"about_ca_topic_score_gemma":0.019725733,"teacher_disagreement_score":0.013723669,"about_ca_system_score_codex":0.0010359007,"about_ca_system_score_gemma":0.0013750853,"threshold_uncertainty_score":0.027287602},"labels":[],"label_agreement":null},{"id":"W3210470410","doi":"","title":"CEONet: A Distributed Resource Discovery Service for the Canadian Geospatial Data Infrastructure","year":2000,"lang":"en","type":"article","venue":"Data Systems in Aerospace","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Resource (disambiguation); Web Coverage Service; Data science; Service (business); Computer science; Spatial data infrastructure; Business; World Wide Web; Geography; Web service; Remote sensing; Spatial analysis; Computer network","score_opus":0.03643665144842727,"score_gpt":0.2604137054933642,"score_spread":0.2239770540449369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210470410","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060364712,0.0030269928,0.21744764,0.013192305,0.0019566338,0.0019144124,0.095808595,0.26656333,0.33972535],"genre_scores_gemma":[0.41857052,0.003064866,0.21207832,0.0028308877,0.0004964547,0.00080037164,0.15070221,0.0137229245,0.19773345],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849594,0.0001071583,0.00006658866,0.00016809082,0.00075299543,0.0004092903],"domain_scores_gemma":[0.99414057,0.0005597643,0.0002165622,0.0008634704,0.0027248254,0.0014948328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022110266,0.00096047967,0.0008460746,0.0039749346,0.0044150297,0.0047738673,0.002830194,0.0010396165,0.023232482],"category_scores_gemma":[0.00841149,0.0005803502,0.00040661573,0.0051762126,0.0009695177,0.0031123029,0.002912076,0.0012917934,0.0073502017],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056973746,0.00011663899,0.0052101794,0.00020176527,0.00005768578,0.00018676152,0.00056990894,0.006265418,0.0054582297,0.032562334,0.82459784,0.124203466],"study_design_scores_gemma":[0.00017771643,0.00003203987,0.0050958535,0.00007279035,0.000051210205,0.00008912695,0.0005964057,0.071846575,0.006155537,0.009024488,0.90668243,0.00017575621],"about_ca_topic_score_codex":0.86967945,"about_ca_topic_score_gemma":0.8925372,"teacher_disagreement_score":0.9823326,"about_ca_system_score_codex":0.017667415,"about_ca_system_score_gemma":0.04026643,"threshold_uncertainty_score":0.26217604},"labels":[],"label_agreement":null},{"id":"W3211131339","doi":"","title":"The Counting Problem of Slot Mereology","year":2021,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Université de Sherbrooke","funders":"","keywords":"Mereology; Computer science; Artificial intelligence; Philosophy; Epistemology","score_opus":0.018608784687835216,"score_gpt":0.2266465671324772,"score_spread":0.20803778244464197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211131339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16793686,0.0029448979,0.6497008,0.022631887,0.0013125191,0.00014043026,0.0014702894,0.0013693757,0.15249293],"genre_scores_gemma":[0.8683951,0.0010852333,0.0996434,0.001536385,0.0013852337,0.00021933744,0.0010541691,0.0008570072,0.025824202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99324447,0.002558166,0.00042999646,0.0015866233,0.0011066681,0.0010739783],"domain_scores_gemma":[0.9757066,0.016308047,0.0011823461,0.0042007174,0.0015086699,0.0010936025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058247377,0.00087606645,0.0033481917,0.0030634517,0.0074368888,0.011240856,0.0042424854,0.0049264105,0.020624738],"category_scores_gemma":[0.033349663,0.0022795962,0.0022449624,0.0071579446,0.010313283,0.041474823,0.0063461727,0.0077546053,0.0019773948],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027648395,0.00001017143,0.00013741165,0.000027186226,0.000004644535,0.000023662553,0.0001509775,0.00035923693,0.00007460782,0.99409735,0.001739192,0.0033478595],"study_design_scores_gemma":[0.00000797867,0.0000025210798,0.00004591731,0.000009782889,0.000005369242,0.000043225507,0.000068217814,0.0023077272,0.000080083046,0.99534756,0.00207385,0.000007870072],"about_ca_topic_score_codex":0.0025132129,"about_ca_topic_score_gemma":0.0018136133,"teacher_disagreement_score":0.020624738,"about_ca_system_score_codex":0.0035552075,"about_ca_system_score_gemma":0.0023665528,"threshold_uncertainty_score":0.06899661},"labels":[],"label_agreement":null},{"id":"W3214334206","doi":"10.1007/s10707-021-00453-y","title":"Personalized route recommendation through historical travel behavior analysis","year":2021,"lang":"en","type":"article","venue":"GeoInformatica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Global Positioning System; Histogram; Recommender system; Process (computing); Visualization; Shortest path problem; Dependency (UML); Data mining; Graph; Information retrieval; Artificial intelligence; Theoretical computer science","score_opus":0.028794651535476053,"score_gpt":0.2604650121550624,"score_spread":0.23167036061958635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214334206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.674719,0.0029537687,0.29231843,0.0008733489,0.00039171812,0.00020628268,0.0135665955,0.004664713,0.010306279],"genre_scores_gemma":[0.9437163,0.00063423306,0.042635705,0.000065919645,0.00012710212,0.000046800054,0.007938293,0.00010043382,0.0047352174],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996877,0.000043321303,0.000019866156,0.00012701743,0.00008223801,0.00003985997],"domain_scores_gemma":[0.99939644,0.00017000885,0.000068925234,0.00010860683,0.00021201331,0.000043913442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000273404,0.00069055066,0.00066529255,0.0040439502,0.00029061627,0.0007330788,0.00073502696,0.00060830027,0.0022166092],"category_scores_gemma":[0.0013303218,0.00028577232,0.0007133948,0.0032432817,0.00013104879,0.00091017835,0.00029182265,0.00049952575,0.001742787],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096453866,0.0013509347,0.17398101,0.00036817466,0.00086618395,0.0005113301,0.00022909013,0.13599925,0.011838959,0.0018598493,0.033170734,0.63885987],"study_design_scores_gemma":[0.000030189203,0.00013028801,0.039362624,0.000032029035,0.0003278481,0.00027894674,0.00018753603,0.9486262,0.003114814,0.0019494963,0.0059185694,0.000041342293],"about_ca_topic_score_codex":0.024775274,"about_ca_topic_score_gemma":0.05820036,"teacher_disagreement_score":0.024775274,"about_ca_system_score_codex":0.00044340608,"about_ca_system_score_gemma":0.0005170908,"threshold_uncertainty_score":0.049262106},"labels":[],"label_agreement":null},{"id":"W3214905160","doi":"10.1145/3462675","title":"Origin-Aware Location Prediction Based on Historical Vehicle Trajectories","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Baseline (sea); Exploit; Markov chain; Data mining; Machine learning; Predictive modelling; Temporal difference learning; Travel time; Artificial intelligence","score_opus":0.02315088312893285,"score_gpt":0.2409021286629922,"score_spread":0.21775124553405936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214905160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39139497,0.0014324296,0.59006,0.0006937269,0.00020227184,0.00009946238,0.009252069,0.0034670737,0.0033980887],"genre_scores_gemma":[0.9404292,0.00058568607,0.048498824,0.000037141996,0.00006023374,0.000034391116,0.008818276,0.00006925988,0.001467029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970454,0.00003289138,0.000020111966,0.00013095516,0.0000683029,0.00004310164],"domain_scores_gemma":[0.99899405,0.00024975312,0.00016023392,0.00020172996,0.00032260804,0.00007170424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035479607,0.00078464014,0.00061407936,0.001373314,0.00035239558,0.00061195326,0.0013923667,0.0005016051,0.0010370421],"category_scores_gemma":[0.002196263,0.00031326863,0.00047846208,0.0020614895,0.00023282954,0.0020245807,0.0007625271,0.0010350414,0.0010733856],"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":[0.0003882329,0.00017225585,0.10251975,0.00020404025,0.0001396171,0.00040542273,0.00019254135,0.71112233,0.004262479,0.0036692815,0.0089945635,0.16792956],"study_design_scores_gemma":[0.0000046670893,0.00002040296,0.0035229481,0.000010235486,0.000017174936,0.00005956563,0.0000498256,0.9927527,0.0011507731,0.001504811,0.00089840643,0.0000086369],"about_ca_topic_score_codex":0.022788435,"about_ca_topic_score_gemma":0.039864328,"teacher_disagreement_score":0.022788435,"about_ca_system_score_codex":0.0005236472,"about_ca_system_score_gemma":0.0007760585,"threshold_uncertainty_score":0.04531157},"labels":[],"label_agreement":null},{"id":"W3215823109","doi":"10.5281/zenodo.4665255","title":"A new semantic trajectory ontology model for modelling and reasoning on movement data","year":2021,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Ontology; Trajectory; Movement (music); Data modeling; Semantic data model; Artificial intelligence; Natural language processing; Data model (GIS); Information retrieval; Database","score_opus":0.09626397838339557,"score_gpt":0.27991979541369705,"score_spread":0.18365581703030148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215823109","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014796915,0.0001352957,0.9926836,0.00038889967,0.00007237873,0.00013915596,0.0024912031,0.0011408605,0.0014689544],"genre_scores_gemma":[0.03993269,0.00068867963,0.9451091,0.0003048708,0.00007153869,0.0005111762,0.010583659,0.00034922193,0.0024490205],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966563,0.00053366774,0.0006302659,0.00076735596,0.0012202305,0.00019219349],"domain_scores_gemma":[0.9977761,0.00059926306,0.00022938113,0.00064895384,0.00059879344,0.00014761354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026168085,0.0011501587,0.0012113381,0.0047506387,0.0013564664,0.004181422,0.0031560443,0.0017947339,0.0028706482],"category_scores_gemma":[0.0060091843,0.00081671897,0.0051116166,0.0062049576,0.0013856879,0.010342004,0.0034004205,0.0029733954,0.0015621091],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000250523,0.00038417394,0.00514829,0.00110436,0.00048139493,0.0012241879,0.002082791,0.13045165,0.008497126,0.59224546,0.0283132,0.22981685],"study_design_scores_gemma":[0.000049791004,0.000054181808,0.0012103289,0.00027190192,0.00020912738,0.0006069206,0.0005901359,0.61182994,0.0039348886,0.22204813,0.1590965,0.000098140146],"about_ca_topic_score_codex":0.032497518,"about_ca_topic_score_gemma":0.044209972,"teacher_disagreement_score":0.032497518,"about_ca_system_score_codex":0.0025860597,"about_ca_system_score_gemma":0.0048991228,"threshold_uncertainty_score":0.06461674},"labels":[],"label_agreement":null},{"id":"W3216398592","doi":"10.20382/jocg.v14i1a1","title":"Approximating Longest Spanning Tree with Neighborhoods","year":2021,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Spanning tree; Computer science; Minimum spanning tree; Tree (set theory); Combinatorics; Mathematics; Algorithm","score_opus":0.01329564247200219,"score_gpt":0.23161460095429337,"score_spread":0.2183189584822912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216398592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1864522,0.0024678279,0.8050176,0.00061644975,0.00009468793,0.00006842997,0.00055352657,0.0008352946,0.0038938962],"genre_scores_gemma":[0.53696537,0.00092485413,0.45737302,0.000097403914,0.00008887168,0.000114494695,0.0014529967,0.0002560675,0.002726936],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992761,0.00027070142,0.00004109551,0.00018395139,0.00013582106,0.000092413946],"domain_scores_gemma":[0.99844414,0.0009250015,0.00019777921,0.00017765745,0.00014007911,0.00011529672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008781909,0.0007553473,0.0015274036,0.0010818945,0.00053842424,0.0008672407,0.0013508031,0.0010181319,0.001796415],"category_scores_gemma":[0.005080521,0.0005329479,0.0007202726,0.0022365905,0.00045938694,0.0024039207,0.0011936841,0.00075172965,0.00053304236],"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":[0.00031295736,0.00011078897,0.0017695106,0.00025391212,0.00008663378,0.00022003955,0.00015025827,0.89203835,0.004071833,0.025575062,0.006084814,0.06932587],"study_design_scores_gemma":[0.000014670148,0.00003675154,0.00016533755,0.0000088617,0.000008321892,0.000072054216,0.00002272081,0.9829317,0.0006268645,0.015142724,0.00096535875,0.0000045697548],"about_ca_topic_score_codex":0.0024527386,"about_ca_topic_score_gemma":0.0023810272,"teacher_disagreement_score":0.0024527386,"about_ca_system_score_codex":0.0007775034,"about_ca_system_score_gemma":0.00050677935,"threshold_uncertainty_score":0.0060096383},"labels":[],"label_agreement":null},{"id":"W3217002698","doi":"10.1109/swc50871.2021.00068","title":"Smart City Transportation Data Analytics with Conceptual Models and Knowledge Graphs","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Big data; Computer science; Data science; Smart city; Analytics; Timestamp; Conceptual model; Data modeling; Data analysis; Knowledge graph; Data mining; World Wide Web; Computer security; Database; Information retrieval; Internet of Things","score_opus":0.1004552496346959,"score_gpt":0.27714318164638047,"score_spread":0.17668793201168456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217002698","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005297197,0.00049780076,0.98652923,0.0016313123,0.000050533457,0.000113083675,0.0018669853,0.00072584196,0.0032880397],"genre_scores_gemma":[0.22576089,0.002034573,0.7623478,0.00075497985,0.00011682872,0.0004142945,0.0064958753,0.00019162787,0.0018831209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963031,0.0015099199,0.00031346458,0.0007244115,0.000990343,0.00015885991],"domain_scores_gemma":[0.99111485,0.004981081,0.00088598125,0.0017567826,0.0009913907,0.00026985336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030412192,0.0011737187,0.00081780186,0.005865516,0.0009189087,0.0060579125,0.0023076357,0.0014715329,0.002587978],"category_scores_gemma":[0.017376715,0.0009924802,0.0026429056,0.0075106947,0.0018630099,0.010231639,0.0042582024,0.0027250487,0.00095487863],"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":[0.00007619755,0.0001877656,0.0038078749,0.0003929497,0.0003144638,0.00035931153,0.0007272469,0.42677346,0.0008796693,0.45555094,0.009624356,0.10130574],"study_design_scores_gemma":[0.0000124281805,0.000019171119,0.0003729162,0.00009810843,0.00004649809,0.000080834514,0.0002529245,0.5139954,0.0003792073,0.4686587,0.016058052,0.000025744594],"about_ca_topic_score_codex":0.019748133,"about_ca_topic_score_gemma":0.022351263,"teacher_disagreement_score":0.019748133,"about_ca_system_score_codex":0.0025733502,"about_ca_system_score_gemma":0.002487779,"threshold_uncertainty_score":0.039266348},"labels":[],"label_agreement":null},{"id":"W38130022","doi":"10.1111/jcpp.13940","title":"Sphere-of-Influence Graphs on a Sphere.","year":2004,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Combinatorics","score_opus":0.009295260172094756,"score_gpt":0.22710268250822138,"score_spread":0.21780742233612663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W38130022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028895067,0.0015125095,0.93205917,0.0027957798,0.00027197835,0.00040300292,0.0050838296,0.0023220682,0.026656568],"genre_scores_gemma":[0.5309493,0.002991956,0.4106035,0.0011393934,0.00052764517,0.0011030146,0.0110218115,0.0013791837,0.040284157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973592,0.00092389947,0.00023289333,0.00065353996,0.0005890929,0.00024128646],"domain_scores_gemma":[0.98929673,0.0061708125,0.00084506074,0.0021273564,0.0009100734,0.00064984924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023217394,0.0012389561,0.0018638266,0.003223037,0.001675668,0.0035776307,0.001961627,0.0017555361,0.017405268],"category_scores_gemma":[0.018689377,0.00079080666,0.0021550357,0.0030204295,0.0023444223,0.004936404,0.0033162078,0.0021526376,0.0036022987],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036205043,0.00009761058,0.0040361555,0.0005309868,0.00025303676,0.0004680306,0.00055882294,0.121505454,0.0013933425,0.706806,0.037066948,0.12692167],"study_design_scores_gemma":[0.000054502372,0.000049863498,0.00093741337,0.00006437929,0.00008300117,0.00029592228,0.00012655671,0.2585493,0.001158144,0.6879216,0.05072912,0.000030156101],"about_ca_topic_score_codex":0.0062690675,"about_ca_topic_score_gemma":0.010306972,"teacher_disagreement_score":0.017405268,"about_ca_system_score_codex":0.0018160925,"about_ca_system_score_gemma":0.0016969822,"threshold_uncertainty_score":0.058226407},"labels":[],"label_agreement":null},{"id":"W4205828231","doi":"10.4018/ijsi.292022","title":"A Scalable Real-Time Agent-Based Information Retrieval Engine","year":2022,"lang":"en","type":"article","venue":"International Journal of Software Innovation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Scalability; Computer science; Rank (graph theory); Information retrieval; Search engine; Scheme (mathematics); Resource (disambiguation); Data mining; Simplicity; Database","score_opus":0.011175712506767103,"score_gpt":0.2419523240304863,"score_spread":0.2307766115237192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205828231","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027627988,0.001659725,0.8036132,0.00053536275,0.0004901758,0.0009549309,0.0013068574,0.15360121,0.010210531],"genre_scores_gemma":[0.22388883,0.0006015235,0.75074273,0.0005757798,0.0002531795,0.000589695,0.0034288554,0.0012104331,0.01870895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992099,0.00009536835,0.00008906092,0.00018417687,0.0003383931,0.00008307972],"domain_scores_gemma":[0.9992318,0.00014867976,0.00006032155,0.00019562364,0.00026953843,0.00009405317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012447082,0.00082341715,0.0015291156,0.0010176043,0.0006800435,0.0018437919,0.0028483125,0.0010206491,0.0063167047],"category_scores_gemma":[0.0019119385,0.000491585,0.0005412945,0.0011727308,0.00032354073,0.0026670634,0.0013585032,0.0012299259,0.0077135433],"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":[0.0020471136,0.0012743624,0.0021722026,0.0007761383,0.00038691415,0.0008394142,0.0002780175,0.046292618,0.08774794,0.011784009,0.10022577,0.7461755],"study_design_scores_gemma":[0.00046798974,0.00028550974,0.0009733273,0.00003050274,0.00014811147,0.00029341655,0.0000668922,0.9067398,0.025655614,0.006157729,0.059057023,0.00012413652],"about_ca_topic_score_codex":0.004818276,"about_ca_topic_score_gemma":0.0059237136,"teacher_disagreement_score":0.0063167047,"about_ca_system_score_codex":0.0006368634,"about_ca_system_score_gemma":0.0014315936,"threshold_uncertainty_score":0.021131456},"labels":[],"label_agreement":null},{"id":"W4206612087","doi":"10.1109/ickg52313.2021.00042","title":"UFreS: A New Technique for Discovering Frequent Subgraph Patterns in Uncertain Graph Databases","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Subgraph isomorphism problem; Scalability; Graph database; Data mining; Graph; Embedding; Theoretical computer science; Database; Artificial intelligence","score_opus":0.043619745974592325,"score_gpt":0.2913923747721751,"score_spread":0.24777262879758277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206612087","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018343663,0.00065434724,0.96763057,0.00040433006,0.000070933915,0.00027173138,0.0032591207,0.008107025,0.0012582885],"genre_scores_gemma":[0.11483364,0.0005101942,0.8725267,0.00020173522,0.00010151413,0.00031405519,0.009429579,0.00041398592,0.001668586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780744,0.00034817,0.0002385209,0.0005427057,0.00090939936,0.00015385343],"domain_scores_gemma":[0.99447787,0.002437012,0.00065744785,0.0014681184,0.0008130112,0.00014645376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016688219,0.0013955927,0.0013507162,0.00849205,0.0010646903,0.0017233645,0.002623137,0.0014510992,0.0024429406],"category_scores_gemma":[0.013539522,0.0007256825,0.0017232562,0.007259506,0.00073975447,0.0051500965,0.002180357,0.0013120195,0.0012740145],"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":[0.0004843714,0.00032051106,0.012692051,0.00082694006,0.0004756139,0.0010231344,0.00075024425,0.096997924,0.0124490345,0.03366976,0.03900509,0.80130535],"study_design_scores_gemma":[0.000054400836,0.00012556283,0.0018152707,0.00007176954,0.000063749445,0.001372294,0.0003022648,0.9257692,0.006309979,0.044830713,0.019232156,0.00005262894],"about_ca_topic_score_codex":0.0057020755,"about_ca_topic_score_gemma":0.011812775,"teacher_disagreement_score":0.00849205,"about_ca_system_score_codex":0.0007134414,"about_ca_system_score_gemma":0.0014823201,"threshold_uncertainty_score":0.011337757},"labels":[],"label_agreement":null},{"id":"W4206852126","doi":"","title":"Proceedings of the 15th International Symposium on Temporal Representation and Reasoning (TIME 2008), Montréal, Canada","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Representation (politics); Operations research; History; Political science; Mathematics; Law; Politics","score_opus":0.008780881741554574,"score_gpt":0.2016540108123043,"score_spread":0.19287312907074972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206852126","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0139842,0.06821153,0.63830775,0.027233645,0.021550672,0.0006887578,0.016057372,0.016516194,0.1974499],"genre_scores_gemma":[0.05968524,0.051159717,0.29427296,0.0022936568,0.004090966,0.0004561066,0.029217143,0.0049520032,0.55387217],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983057,0.00034528412,0.000119906836,0.0003911716,0.0005976196,0.00024022924],"domain_scores_gemma":[0.99521,0.0011787858,0.00009675017,0.00084735744,0.002062391,0.0006046171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004696923,0.0019650562,0.0025680831,0.0022360543,0.0024648774,0.0100021325,0.0034150747,0.0014117957,0.11572716],"category_scores_gemma":[0.006299754,0.0013617144,0.0017875453,0.0036425863,0.0026226132,0.005019849,0.002726302,0.0033283227,0.02524297],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037199777,0.00016454946,0.0010662653,0.00037939448,0.00012792266,0.00013855581,0.0006562586,0.002623384,0.0028484522,0.04280942,0.68825877,0.26055503],"study_design_scores_gemma":[0.00008463986,0.000061601764,0.002205387,0.00037913962,0.00016656701,0.0002065087,0.00053080707,0.019356653,0.0026753333,0.04370643,0.9305398,0.000087119486],"about_ca_topic_score_codex":0.53334993,"about_ca_topic_score_gemma":0.5944739,"teacher_disagreement_score":0.53334993,"about_ca_system_score_codex":0.011521457,"about_ca_system_score_gemma":0.022712465,"threshold_uncertainty_score":0.9387965},"labels":[],"label_agreement":null},{"id":"W4210981376","doi":"10.1007/978-1-4471-7452-3_30","title":"Data Mining","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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":"Concordia University","funders":"","keywords":"Web mining; Computer science; Data mining; Knowledge extraction; Data science; Information retrieval; World Wide Web; Web page","score_opus":0.08571840790610807,"score_gpt":0.2643398679505388,"score_spread":0.17862146004443075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210981376","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021808306,0.016738536,0.41606814,0.007063055,0.0029020533,0.00060506945,0.006217071,0.007469427,0.54075575],"genre_scores_gemma":[0.013991402,0.019476641,0.26016423,0.0035846264,0.001923426,0.00042126913,0.01219671,0.0012233924,0.6870184],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918944,0.00007187973,0.000056483972,0.00021895242,0.00043409216,0.000029199222],"domain_scores_gemma":[0.9989882,0.0003498846,0.000044054323,0.00028433482,0.000282775,0.00005073432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010537921,0.0012128656,0.0008559206,0.0031782235,0.0008232861,0.003573693,0.0019273235,0.0008850376,0.078336254],"category_scores_gemma":[0.0029985027,0.00061591814,0.0008874214,0.004413942,0.00057587115,0.003641967,0.0019774588,0.0015933051,0.08982015],"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":[0.000017717108,0.00004277565,0.00021144476,0.000457354,0.000022300232,0.000056168305,0.00008154394,0.00054810196,0.0015768432,0.039901175,0.25332934,0.70375514],"study_design_scores_gemma":[0.0000044568624,0.000018139164,0.00036640425,0.00019988554,0.000013654761,0.00040461752,0.00005226992,0.0027742586,0.0021304071,0.050272036,0.9437497,0.000014177596],"about_ca_topic_score_codex":0.0005844922,"about_ca_topic_score_gemma":0.0009565372,"teacher_disagreement_score":0.078336254,"about_ca_system_score_codex":0.0006558088,"about_ca_system_score_gemma":0.0010889522,"threshold_uncertainty_score":0.26206088},"labels":[],"label_agreement":null},{"id":"W4214724944","doi":"10.1007/s00357-021-09407-3","title":"On Assessments of Agreement Between Fuzzy Partitions","year":2022,"lang":"en","type":"article","venue":"Journal of Classification","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"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; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Interpretability; Fuzzy logic; Probabilistic logic; Property (philosophy); Monte Carlo method; Mathematics; Measure (data warehouse); Cluster (spacecraft); Computer science; Data mining; Artificial intelligence; Statistics","score_opus":0.07695818300159624,"score_gpt":0.33464940112893354,"score_spread":0.2576912181273373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214724944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45167705,0.0023372641,0.51955754,0.00201464,0.00029493604,0.0005061861,0.0010900876,0.00034919794,0.022173034],"genre_scores_gemma":[0.93111366,0.00026142228,0.066503696,0.00014565946,0.00014859639,0.0001913619,0.0006799476,0.00006078433,0.00089481863],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.93958664,0.029081535,0.005412976,0.0072750514,0.016951546,0.0016922551],"domain_scores_gemma":[0.5263161,0.40825772,0.015434688,0.016734445,0.029350666,0.003906382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0718672,0.0013779209,0.0020626178,0.012501561,0.0022834113,0.0077305436,0.0035217113,0.003598412,0.0043588565],"category_scores_gemma":[0.2875439,0.00087379804,0.0019568866,0.0049467087,0.0074235997,0.015462201,0.0076728933,0.0040376117,0.0005379146],"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":[0.007880915,0.00063770165,0.17381331,0.001704189,0.0030505005,0.0007225145,0.012025488,0.12947437,0.006677094,0.31668591,0.006485012,0.34084305],"study_design_scores_gemma":[0.0002258688,0.0015887092,0.04503184,0.0007867861,0.00063537905,0.00067361136,0.0055230395,0.35274997,0.004283902,0.5845307,0.0036802723,0.00028996376],"about_ca_topic_score_codex":0.0028114978,"about_ca_topic_score_gemma":0.0023195976,"teacher_disagreement_score":0.0718672,"about_ca_system_score_codex":0.0033050333,"about_ca_system_score_gemma":0.0014294554,"threshold_uncertainty_score":0.38007462},"labels":[],"label_agreement":null},{"id":"W4214752466","doi":"10.1007/978-0-387-39940-9_2261","title":"Conceptual Modeling for Spatio-Temporal Applications","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Cognitive science; Psychology","score_opus":0.032059078610176046,"score_gpt":0.2537990918078484,"score_spread":0.22174001319767234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214752466","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010678956,0.0024740004,0.98758984,0.00061894767,0.00013312773,0.000067775196,0.00054406084,0.0007781964,0.0067261755],"genre_scores_gemma":[0.075744964,0.0072452324,0.89787567,0.000353208,0.00027529174,0.00046310507,0.0032925087,0.0005157656,0.0142342895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998792,0.0003640473,0.00018664818,0.00021727852,0.00037099002,0.00006901526],"domain_scores_gemma":[0.9987399,0.0005399216,0.0000747913,0.00031009922,0.00026810117,0.000067262285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017509309,0.0015317219,0.0011054866,0.0014963811,0.0008237915,0.00508409,0.0040159957,0.0016862707,0.010174752],"category_scores_gemma":[0.0040654666,0.0009424624,0.0021982316,0.003431086,0.001458832,0.00665193,0.0021830664,0.0026822125,0.003465371],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020085854,0.000039244405,0.00022097486,0.00034574012,0.00004820391,0.00016584221,0.00045365884,0.028145831,0.00081893645,0.89375985,0.011839422,0.06414229],"study_design_scores_gemma":[0.000013030734,0.00001639056,0.00012446975,0.00017728076,0.000053067346,0.00025550803,0.00020751636,0.21301351,0.0008380634,0.6396642,0.14561065,0.000026298618],"about_ca_topic_score_codex":0.009941599,"about_ca_topic_score_gemma":0.007700291,"teacher_disagreement_score":0.010174752,"about_ca_system_score_codex":0.0018418481,"about_ca_system_score_gemma":0.0017195637,"threshold_uncertainty_score":0.034037948},"labels":[],"label_agreement":null},{"id":"W4214771680","doi":"10.1109/tdsc.2022.3153759","title":"Towards Efficient and Privacy-Preserving Interval Skyline Queries Over Time Series Data","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Skyline; Encryption; Homomorphic encryption; Security analysis; Information privacy; Data mining; Database; Computer security","score_opus":0.019673830660524024,"score_gpt":0.25014859585316107,"score_spread":0.23047476519263704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214771680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066802084,0.00034573092,0.929607,0.00047538505,0.000035241792,0.0001570874,0.0004936212,0.000887706,0.0011961029],"genre_scores_gemma":[0.728152,0.0004926062,0.26747808,0.00027429144,0.00014867306,0.00022882213,0.0012292403,0.00009649692,0.001899754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964644,0.0008251667,0.00040376067,0.0005187018,0.0014304591,0.00035750036],"domain_scores_gemma":[0.9942667,0.0016966524,0.000835439,0.0023039114,0.00072971836,0.00016756316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031475662,0.00047729208,0.000998653,0.00084213226,0.00084937754,0.002024919,0.0014503346,0.0010233193,0.0012521603],"category_scores_gemma":[0.0069658672,0.00038662672,0.0008781248,0.0021755788,0.0010921515,0.006520624,0.0034424034,0.00149231,0.00063297764],"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":[0.0029752897,0.00048631447,0.0077988133,0.00059211085,0.00033995774,0.0012729944,0.00249249,0.21285377,0.17818482,0.26772746,0.013380558,0.31189546],"study_design_scores_gemma":[0.00019684716,0.00042953077,0.0012456558,0.000024223596,0.00005534006,0.00067643286,0.0006189087,0.85552,0.04866065,0.08363967,0.0088643255,0.000068526555],"about_ca_topic_score_codex":0.0013126977,"about_ca_topic_score_gemma":0.0007952925,"teacher_disagreement_score":0.0031475662,"about_ca_system_score_codex":0.00073322305,"about_ca_system_score_gemma":0.0015199265,"threshold_uncertainty_score":0.016646147},"labels":[],"label_agreement":null},{"id":"W4226482498","doi":"10.1016/j.tcs.2022.04.002","title":"An efficient oracle for counting shortest paths in planar graphs","year":2022,"lang":"en","type":"article","venue":"Theoretical Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Oracle; Combinatorics; Planar graph; Shortest path problem; Voronoi diagram; Vertex (graph theory); Mathematics; Discrete mathematics; Data structure; Computer science; Graph; Geometry","score_opus":0.010823555602555292,"score_gpt":0.2515598887280446,"score_spread":0.24073633312548928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226482498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08945737,0.0016344007,0.87531626,0.0028874855,0.0004106397,0.00043297934,0.005145281,0.012113653,0.01260204],"genre_scores_gemma":[0.43270382,0.00088424236,0.54740876,0.00068636495,0.00044282613,0.0005642464,0.009056839,0.0012246198,0.0070283166],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99418783,0.0012044233,0.00067155843,0.0013174998,0.0018799474,0.0007387847],"domain_scores_gemma":[0.9745364,0.016018962,0.0011414174,0.005526724,0.0016538597,0.0011226299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029340268,0.0019169452,0.003119596,0.0024771437,0.0012088576,0.0058699446,0.0064362935,0.0037412238,0.013326359],"category_scores_gemma":[0.023867024,0.0013127007,0.0014502883,0.006797458,0.0018894982,0.015122631,0.0059724296,0.004827236,0.0031242694],"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":[0.0042238683,0.0013373496,0.006456416,0.0023231285,0.00024725802,0.0004554848,0.0006709159,0.09100002,0.036486078,0.12965338,0.051661786,0.67548424],"study_design_scores_gemma":[0.00090977544,0.00050169055,0.0025287222,0.00016237117,0.00030648985,0.00082847016,0.0005319267,0.6545379,0.020189198,0.30318677,0.016145471,0.00017119093],"about_ca_topic_score_codex":0.0033094988,"about_ca_topic_score_gemma":0.0048240987,"teacher_disagreement_score":0.013326359,"about_ca_system_score_codex":0.002546975,"about_ca_system_score_gemma":0.004513843,"threshold_uncertainty_score":0.044581056},"labels":[],"label_agreement":null},{"id":"W4226501514","doi":"10.1007/978-3-030-98974-3_3","title":"Fuzzy Relations","year":2022,"lang":"en","type":"book-chapter","venue":"Big and integrated artificial intelligence","topic":"Data Management and Algorithms","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 Alberta","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.07253563816006178,"score_gpt":0.25900066062391675,"score_spread":0.18646502246385496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226501514","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004569726,0.0037426285,0.18427043,0.0021833763,0.0012899797,0.00012608623,0.0009600885,0.0005565075,0.80230117],"genre_scores_gemma":[0.19042306,0.0044765286,0.15356706,0.0015432251,0.001099864,0.00026427567,0.0019471968,0.00027119325,0.6464076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99946827,0.00007468029,0.000031569554,0.00015756623,0.0002287685,0.000039139133],"domain_scores_gemma":[0.99965644,0.00010826729,0.000022097429,0.00009138922,0.000100536105,0.000021234257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048207393,0.0005616358,0.0004240143,0.0016274629,0.0014476238,0.0025180876,0.00074399775,0.00088231167,0.05795848],"category_scores_gemma":[0.0020726812,0.00025678135,0.0005484471,0.0015533617,0.0014376978,0.0030266864,0.00083801453,0.0013436134,0.014828583],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021384536,0.000013610376,0.00007056699,0.000081467784,0.000009811941,0.00004264117,0.00015857311,0.0005849464,0.00096292386,0.88121647,0.021243077,0.09559451],"study_design_scores_gemma":[0.000009033968,0.00001875704,0.0002397693,0.00007191193,0.00001624098,0.00018232982,0.000112578426,0.0035792876,0.0018105626,0.64042634,0.3535185,0.000014696631],"about_ca_topic_score_codex":0.002269348,"about_ca_topic_score_gemma":0.0023241541,"teacher_disagreement_score":0.05795848,"about_ca_system_score_codex":0.0009844106,"about_ca_system_score_gemma":0.0006250726,"threshold_uncertainty_score":0.19389045},"labels":[],"label_agreement":null},{"id":"W4229516452","doi":"10.4018/978-1-60960-818-7.ch804","title":"Pattern Discovery as Event Association","year":2011,"lang":"en","type":"book-chapter","venue":"Machine Learning","topic":"Data Management and Algorithms","field":"Computer Science","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":"Pattern Discovery Technologies (Canada); University of Waterloo","funders":"","keywords":"Event (particle physics); Association (psychology); Task (project management); Binary number; Mathematics; Computer science; Artificial intelligence; Algorithm; Combinatorics; Discrete mathematics; Arithmetic; Psychology; Physics","score_opus":0.012529066127516131,"score_gpt":0.2193140326999921,"score_spread":0.20678496657247597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229516452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010571811,0.0077003934,0.9542015,0.0069310777,0.00087978365,0.00058628526,0.00510888,0.0021156834,0.011904659],"genre_scores_gemma":[0.20104288,0.0077219573,0.752078,0.003627794,0.0023382916,0.0017944057,0.017092573,0.00040725278,0.013896925],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98498166,0.0042217546,0.0010810042,0.0054480312,0.003790215,0.00047739188],"domain_scores_gemma":[0.96723425,0.020851143,0.0036589608,0.0049012876,0.0026361707,0.0007182131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008186597,0.0023971254,0.002476479,0.0072279647,0.0017163255,0.0069727106,0.004639714,0.0034467673,0.011310608],"category_scores_gemma":[0.042632543,0.0015366137,0.0031462498,0.01115354,0.0040505715,0.0138826575,0.0068087666,0.004956485,0.0043874034],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006091633,0.00031704752,0.011441121,0.002115439,0.0006058344,0.0017171734,0.0011202956,0.036358956,0.0018629557,0.56341445,0.04783433,0.33260328],"study_design_scores_gemma":[0.000071198214,0.000075284835,0.0011787586,0.0002410121,0.00011958258,0.0010188335,0.00021160819,0.13474952,0.0015360384,0.8149632,0.045770373,0.00006464965],"about_ca_topic_score_codex":0.0029618305,"about_ca_topic_score_gemma":0.001987393,"teacher_disagreement_score":0.011310608,"about_ca_system_score_codex":0.0022828204,"about_ca_system_score_gemma":0.002353832,"threshold_uncertainty_score":0.043295383},"labels":[],"label_agreement":null},{"id":"W4230187284","doi":"10.1177/0361198105190200113","title":"Spatiotemporal Data Model and Query Language for Tracking Land Use Change","year":2005,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","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":"University of Calgary","funders":"","keywords":"Object Query Language; Computer science; Query language; Data model (GIS); Object (grammar); Context (archaeology); Data modeling; Spatial analysis; Representation (politics); Geographic information system; Database; Data mining; Programming language; Sargable; Information retrieval; Artificial intelligence; Geography; Web search query; Remote sensing","score_opus":0.32818616778389326,"score_gpt":0.43141805838158304,"score_spread":0.10323189059768978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230187284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026462483,0.000141975,0.9873312,0.00084477523,0.00006925719,0.00022605469,0.003063886,0.0034531148,0.0022234384],"genre_scores_gemma":[0.084709756,0.00059632055,0.8949661,0.0009648815,0.00012806305,0.0011549838,0.012792897,0.0006092966,0.004077636],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966467,0.00086889556,0.0008868006,0.0004569898,0.001009416,0.00013119688],"domain_scores_gemma":[0.99537176,0.0016659205,0.000434016,0.0012101132,0.0010961003,0.00022201112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053717936,0.0006482679,0.00076329376,0.0019604194,0.00093490025,0.0038034373,0.0027059086,0.0012239977,0.0035564473],"category_scores_gemma":[0.0111978715,0.00047166774,0.0014321957,0.0035339007,0.001053196,0.00611292,0.0020326413,0.001482273,0.0018245773],"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":[0.0004267861,0.00014721502,0.003903486,0.00066879555,0.00011220654,0.001179544,0.0014318789,0.052427076,0.012173003,0.7508791,0.070877776,0.10577307],"study_design_scores_gemma":[0.00014059762,0.00018539303,0.00093487906,0.00020305246,0.0000985157,0.001138672,0.00081081444,0.49130976,0.010802792,0.15940313,0.3348534,0.000119020435],"about_ca_topic_score_codex":0.0073505808,"about_ca_topic_score_gemma":0.0067929714,"teacher_disagreement_score":0.0073505808,"about_ca_system_score_codex":0.0015226767,"about_ca_system_score_gemma":0.0024224252,"threshold_uncertainty_score":0.028409123},"labels":[],"label_agreement":null},{"id":"W4230523959","doi":"10.22215/etd/2008-08073","title":"Algorithms for static and dynamic path problems in trees","year":2008,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Path (computing); Computer science; Humanities; Art; Operating system","score_opus":0.01727759919918152,"score_gpt":0.2691569636644638,"score_spread":0.25187936446528225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230523959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025134725,0.0018885458,0.9435808,0.0022224332,0.00039769668,0.0004590731,0.0007096366,0.0022949914,0.023312008],"genre_scores_gemma":[0.12753728,0.0012013827,0.851394,0.00039523756,0.00049230957,0.0006816852,0.0021943795,0.00082319527,0.01528057],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976624,0.0006197707,0.00017348389,0.00059043727,0.00055955694,0.00039433254],"domain_scores_gemma":[0.98861223,0.00898794,0.00040808046,0.00094070565,0.0006196617,0.0004313431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038764386,0.0017355748,0.0013572004,0.0028399888,0.0020339224,0.004326004,0.0041874363,0.0035730342,0.020064691],"category_scores_gemma":[0.018261777,0.0010870451,0.0019947288,0.003900723,0.0020112118,0.011139474,0.003959127,0.004624636,0.0034091969],"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":[0.00056618673,0.0005309735,0.0019167857,0.00093703804,0.00015940906,0.00014450702,0.0007407284,0.25275004,0.001419611,0.30389792,0.047213446,0.38972336],"study_design_scores_gemma":[0.00020821262,0.000131206,0.00033101026,0.000106246356,0.00006643822,0.00020250004,0.0002882759,0.59997904,0.0009839728,0.38306206,0.014611772,0.00002913386],"about_ca_topic_score_codex":0.00415121,"about_ca_topic_score_gemma":0.008120179,"teacher_disagreement_score":0.020064691,"about_ca_system_score_codex":0.0025429016,"about_ca_system_score_gemma":0.0032436855,"threshold_uncertainty_score":0.067123055},"labels":[],"label_agreement":null},{"id":"W4231860724","doi":"10.4018/9781591403845.ch009.ch000","title":"An Object-Oriented Approach to Managing Fuzziness in Spatially Explicit Models Coupled to a Geographic Database","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; University of Toronto","funders":"","keywords":"Computer science; Fuzzy logic; Data mining; Schema (genetic algorithms); Component (thermodynamics); Spatial database; Database; Geographic information system; Spatial analysis; Database schema; Information retrieval; Database design; Artificial intelligence; Geography; Cartography","score_opus":0.030830684915060545,"score_gpt":0.24227002449647236,"score_spread":0.2114393395814118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231860724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035582115,0.0005618559,0.991045,0.00066842185,0.000037050584,0.00004647499,0.00006171191,0.00023005682,0.0037912796],"genre_scores_gemma":[0.064615756,0.0015671868,0.9280061,0.00023890611,0.000060689596,0.00017507306,0.00028572112,0.00011404504,0.0049365475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792224,0.0006439718,0.0002020028,0.00031608486,0.00083579536,0.00007995666],"domain_scores_gemma":[0.99713576,0.0016743201,0.00015923608,0.00071796565,0.00023049462,0.00008221533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044922787,0.00066508306,0.0010287552,0.0014940078,0.001389063,0.007591217,0.003599089,0.0015138052,0.0027424162],"category_scores_gemma":[0.007126366,0.0010025153,0.0017954457,0.0042791837,0.0022775407,0.009142922,0.0043883263,0.003345628,0.0004902439],"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":[0.000029385295,0.000053118132,0.0008930485,0.00020650163,0.00009070674,0.00025643894,0.0012584158,0.055128656,0.0018319361,0.8411579,0.0030287066,0.09606516],"study_design_scores_gemma":[0.000018781424,0.00003125305,0.00036431404,0.00013345544,0.00008440658,0.00032081135,0.00044957956,0.31867567,0.002414014,0.5963548,0.08111187,0.000041085244],"about_ca_topic_score_codex":0.004794892,"about_ca_topic_score_gemma":0.0057609924,"teacher_disagreement_score":0.007591217,"about_ca_system_score_codex":0.00234197,"about_ca_system_score_gemma":0.00199652,"threshold_uncertainty_score":0.023757756},"labels":[],"label_agreement":null},{"id":"W4232351833","doi":"10.1109/icra.2011.5980094","title":"Offline navigation summaries","year":2011,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Surprise; Computer science; Artificial intelligence; Context (archaeology); Sampling (signal processing); Trajectory; Task (project management); Set (abstract data type); Robotics; Noise (video); Robot; Sample (material); Focus (optics); Machine learning; Computer vision; Geography","score_opus":0.04136571761821523,"score_gpt":0.221183433797658,"score_spread":0.17981771617944278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232351833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056612357,0.003490918,0.8793754,0.00090812804,0.0006514114,0.000587728,0.028140325,0.02507059,0.0051631704],"genre_scores_gemma":[0.30257586,0.0013788706,0.599997,0.00021795128,0.00057523587,0.0005337317,0.08352156,0.0020937114,0.009106007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998539,0.00025377187,0.00016358795,0.00048767103,0.00047534288,0.000080777805],"domain_scores_gemma":[0.99286824,0.0027363803,0.0006718129,0.0018182993,0.0016364639,0.00026884346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013622722,0.0014473854,0.0012635184,0.0024741853,0.0007012412,0.0018283058,0.0011883231,0.00086277176,0.008712645],"category_scores_gemma":[0.014276203,0.00043409286,0.0006688537,0.0022661833,0.00026057643,0.0024823262,0.0014462989,0.0009581736,0.0054142335],"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":[0.0012260178,0.00014177563,0.006186653,0.0010016778,0.00014703146,0.00034539067,0.0006811425,0.03334732,0.011967101,0.0066580214,0.060185358,0.8781125],"study_design_scores_gemma":[0.0002564397,0.0013413395,0.012204695,0.00042153904,0.00037917623,0.0016116565,0.0017317053,0.61261004,0.053329322,0.059016302,0.25689894,0.00019878056],"about_ca_topic_score_codex":0.0018638542,"about_ca_topic_score_gemma":0.003686516,"teacher_disagreement_score":0.008712645,"about_ca_system_score_codex":0.00057763874,"about_ca_system_score_gemma":0.0014581999,"threshold_uncertainty_score":0.02914673},"labels":[],"label_agreement":null},{"id":"W4232957048","doi":"10.1007/978-1-4939-7131-2_101350","title":"Time-Aware Social Search","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Computer science","score_opus":0.03418376257417852,"score_gpt":0.25580859171379383,"score_spread":0.22162482913961531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232957048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02342397,0.011032916,0.8110003,0.004878458,0.0015161711,0.00019728148,0.0008608458,0.0024025668,0.14468761],"genre_scores_gemma":[0.5304996,0.009907001,0.26413026,0.000978045,0.0021184029,0.00027246395,0.0015081958,0.0010502103,0.18953589],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989999,0.00021393548,0.00003901468,0.00019202015,0.00045375698,0.00010139694],"domain_scores_gemma":[0.9977956,0.0011496115,0.00011929322,0.0005139562,0.00030071073,0.000120896635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000890069,0.000846375,0.00097188534,0.0011613496,0.0008724066,0.0024459136,0.0016215993,0.0010797463,0.0119001325],"category_scores_gemma":[0.0055477466,0.0003689491,0.00056597276,0.0023312334,0.0007185353,0.005064674,0.0015520494,0.0014013922,0.0037590116],"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":[0.00020642189,0.00021261846,0.0005501086,0.0005082203,0.00008011704,0.00007909995,0.0003051822,0.038128465,0.005091544,0.3959691,0.073314466,0.48555464],"study_design_scores_gemma":[0.00003826597,0.00010034978,0.0007089823,0.00010930046,0.000094324656,0.0004138811,0.0002133228,0.33874118,0.005121242,0.5272739,0.1271303,0.000054888744],"about_ca_topic_score_codex":0.001320569,"about_ca_topic_score_gemma":0.0015272406,"teacher_disagreement_score":0.0119001325,"about_ca_system_score_codex":0.0012248089,"about_ca_system_score_gemma":0.00093318534,"threshold_uncertainty_score":0.039809883},"labels":[],"label_agreement":null},{"id":"W4233297996","doi":"10.1007/978-3-030-24124-7_13","title":"Towards Dynamic Data Placement for Polystore Ingestion","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Data Management and Algorithms","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":"Workload; Computer science; Latency (audio); Data warehouse; Benchmark (surveying); Context (archaeology); Database; Distributed computing; Operating system","score_opus":0.025926823530976573,"score_gpt":0.26740734649487397,"score_spread":0.24148052296389738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233297996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04300858,0.0014399115,0.9022953,0.0007198449,0.0007060396,0.00026245127,0.0010340076,0.031313088,0.019220745],"genre_scores_gemma":[0.17851342,0.0006910391,0.7924973,0.0004960722,0.00016828346,0.00023378954,0.0036308707,0.0041315868,0.01963772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891305,0.00013501351,0.00010578712,0.00019450774,0.00045237193,0.00019929076],"domain_scores_gemma":[0.9980033,0.00032891904,0.00010224342,0.001006843,0.000438107,0.00012063936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074353314,0.0011470049,0.0010535558,0.0013154973,0.001450923,0.0035246408,0.0030914815,0.0011716015,0.013309598],"category_scores_gemma":[0.003369646,0.0010280195,0.00093406154,0.0026013697,0.00084416324,0.004319082,0.0046337433,0.001858235,0.00554615],"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":[0.0011572231,0.00045218132,0.0031919996,0.00076484063,0.00012743114,0.0004701413,0.0006373964,0.04396883,0.080553755,0.046778113,0.06851027,0.7533878],"study_design_scores_gemma":[0.00020943758,0.00040822692,0.0014381186,0.00021285833,0.0001170045,0.00061376585,0.0008008574,0.6073705,0.14230905,0.11398591,0.13239986,0.00013428673],"about_ca_topic_score_codex":0.0026227916,"about_ca_topic_score_gemma":0.007214161,"teacher_disagreement_score":0.013309598,"about_ca_system_score_codex":0.0012841038,"about_ca_system_score_gemma":0.0017614177,"threshold_uncertainty_score":0.044525027},"labels":[],"label_agreement":null},{"id":"W4233755731","doi":"10.1007/978-1-4419-9380-9_5","title":"Probabilistic Ranking Queries on Uncertain Data","year":2011,"lang":"en","type":"book-chapter","venue":"˜The œKluwer international series on advances in database systems","topic":"Data Management and Algorithms","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":"Simon Fraser University","funders":"","keywords":"Probabilistic logic; Probabilistic database; Ranking (information retrieval); Computer science; Probabilistic relevance model; Uncertain data; Section (typography); Statistical model; Data mining; Information retrieval; Probabilistic analysis of algorithms; Machine learning; Artificial intelligence; Database theory; Database design","score_opus":0.055439776905385395,"score_gpt":0.2888677037111156,"score_spread":0.23342792680573018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233755731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009231359,0.0049510906,0.9760418,0.0014189624,0.0003021,0.00007555079,0.0009830093,0.0012321455,0.0057639508],"genre_scores_gemma":[0.40395853,0.011479239,0.55034024,0.00079126825,0.002822425,0.00037155172,0.0065320767,0.00077704934,0.022927571],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948515,0.001378263,0.00041330914,0.0005542108,0.0025889017,0.00021375962],"domain_scores_gemma":[0.9942821,0.0038370986,0.00026466814,0.000900109,0.0005960011,0.00012001636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030943705,0.000982627,0.0020336602,0.0020543262,0.0005055777,0.0034729938,0.0023467499,0.0013319236,0.0047300546],"category_scores_gemma":[0.011970828,0.00072877057,0.0009619632,0.005243165,0.00095381576,0.006678111,0.0017688796,0.0021513577,0.0020279544],"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":[0.00032499572,0.00014641274,0.0010946223,0.0010202175,0.00016614521,0.00037907774,0.00025149854,0.18146487,0.0056870827,0.22928782,0.07064355,0.50953364],"study_design_scores_gemma":[0.000033718326,0.000087733024,0.00037869322,0.000060812974,0.00004145938,0.00052503595,0.00006586057,0.64319956,0.002615813,0.3276875,0.02525112,0.00005265726],"about_ca_topic_score_codex":0.0008238641,"about_ca_topic_score_gemma":0.0009998026,"teacher_disagreement_score":0.0047300546,"about_ca_system_score_codex":0.00093774335,"about_ca_system_score_gemma":0.0006140741,"threshold_uncertainty_score":0.016364813},"labels":[],"label_agreement":null},{"id":"W4233789597","doi":"10.1007/978-1-4939-7131-2_100901","title":"Preference Propagation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Preference; Mathematics; Statistics","score_opus":0.05099018862218241,"score_gpt":0.22651968490105398,"score_spread":0.17552949627887157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233789597","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024323005,0.0023461736,0.82160103,0.0015984004,0.0008891697,0.00015444454,0.00078626786,0.0012024053,0.16898978],"genre_scores_gemma":[0.11906496,0.006771799,0.5136883,0.001231181,0.00114385,0.00040519622,0.003160489,0.0011294935,0.35340476],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988426,0.00028021325,0.00006265492,0.00031237394,0.00042213238,0.00008007636],"domain_scores_gemma":[0.9982893,0.00064926926,0.00005482582,0.0004870242,0.000449502,0.00007008331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014707344,0.0011211156,0.00072892755,0.0013868681,0.00095861737,0.0026345842,0.0017018158,0.00086600083,0.04586863],"category_scores_gemma":[0.0061282706,0.00048382624,0.001003381,0.002778477,0.0009178656,0.0044512018,0.0014421908,0.0027399086,0.017149916],"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":[0.00006472887,0.00008074166,0.00020503005,0.00023362448,0.00004285994,0.000036066915,0.000084741456,0.007897684,0.0011960694,0.33453903,0.08081844,0.57480097],"study_design_scores_gemma":[0.000024534009,0.00006904792,0.00040503268,0.00015059764,0.000070440525,0.0002918398,0.00008497463,0.09029207,0.0047716545,0.6419706,0.26181227,0.000056973473],"about_ca_topic_score_codex":0.0025631934,"about_ca_topic_score_gemma":0.0030559856,"teacher_disagreement_score":0.04586863,"about_ca_system_score_codex":0.0012891736,"about_ca_system_score_gemma":0.0013264826,"threshold_uncertainty_score":0.1534459},"labels":[],"label_agreement":null},{"id":"W4234739062","doi":"10.22215/etd/2005-07989","title":"Parallel implementation of bisector epsilon-approximation shortest path algorithm for concurrent queries","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Ottawa; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shortest path problem; Computer science; Path (computing); Combinatorics; Mathematics; Algorithm; Theoretical computer science; Operating system","score_opus":0.02094093202601259,"score_gpt":0.3157385165097247,"score_spread":0.29479758448371207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234739062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11522944,0.0006592061,0.85520756,0.00033883093,0.00016624478,0.00037071857,0.0005149324,0.011527351,0.015985735],"genre_scores_gemma":[0.41145423,0.00019387405,0.5787865,0.000093184804,0.000033193413,0.0002221318,0.0013698827,0.00042799307,0.007419046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902534,0.00011078689,0.00007919341,0.00022760128,0.00037255426,0.00018454579],"domain_scores_gemma":[0.9991302,0.00019239956,0.00004758178,0.00021800831,0.00032335805,0.00008853591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006497242,0.0006936081,0.0008146298,0.0011213638,0.0009810756,0.0015793925,0.0023358003,0.0005694743,0.007731751],"category_scores_gemma":[0.0016778448,0.00034853513,0.00047669056,0.0015736776,0.00034424174,0.0014468817,0.0012677641,0.0006498586,0.0015282669],"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":[0.0028168685,0.00087509403,0.00444111,0.0004662937,0.00012493158,0.0003155933,0.0005521392,0.09947905,0.057724934,0.025412558,0.018022753,0.78976864],"study_design_scores_gemma":[0.00032619087,0.00023869255,0.0009517965,0.000019532368,0.000044322012,0.00022132685,0.00018495989,0.951808,0.026433773,0.009820074,0.009921802,0.000029648552],"about_ca_topic_score_codex":0.011308795,"about_ca_topic_score_gemma":0.01330025,"teacher_disagreement_score":0.011308795,"about_ca_system_score_codex":0.0014013348,"about_ca_system_score_gemma":0.0024247032,"threshold_uncertainty_score":0.025865316},"labels":[],"label_agreement":null},{"id":"W4235981791","doi":"10.1007/978-1-4939-7131-2_101245","title":"Spatiotemporal Context-Aware Recommendation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Computer science; Context (archaeology); Geography; Archaeology","score_opus":0.03487420251450731,"score_gpt":0.2421463464023778,"score_spread":0.2072721438878705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235981791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008253784,0.025416784,0.91945964,0.0013499251,0.0016665328,0.00009343709,0.0016589737,0.003599577,0.038501393],"genre_scores_gemma":[0.22984175,0.040661436,0.64156514,0.0006861936,0.0018934014,0.00014854527,0.0070307883,0.00063542626,0.07753735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967587,0.000034624656,0.000022932294,0.00010785851,0.00013219046,0.000026566611],"domain_scores_gemma":[0.99959785,0.00010228399,0.000014855057,0.00016135233,0.000102770144,0.00002091984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004135946,0.0009132614,0.0011053086,0.000835966,0.00037535376,0.0013410769,0.001404419,0.00068146456,0.005984402],"category_scores_gemma":[0.0016353187,0.00043148178,0.0006002737,0.0033865517,0.00025048916,0.0024861714,0.00076048216,0.0013080849,0.003855014],"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":[0.000098641845,0.00009840367,0.0007517707,0.00037136942,0.00013204334,0.00008682238,0.00006344904,0.03533636,0.005450389,0.043909926,0.07494701,0.83875376],"study_design_scores_gemma":[0.00002370717,0.0000794479,0.0018829312,0.00019168312,0.00017009738,0.00069978426,0.00009646856,0.6830855,0.010616628,0.11570127,0.18736854,0.000083980405],"about_ca_topic_score_codex":0.0066784746,"about_ca_topic_score_gemma":0.0074064173,"teacher_disagreement_score":0.0066784746,"about_ca_system_score_codex":0.00058398885,"about_ca_system_score_gemma":0.000552697,"threshold_uncertainty_score":0.02001977},"labels":[],"label_agreement":null},{"id":"W4237406792","doi":"10.1007/978-1-4939-7131-2_101314","title":"Task-Based Search","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Task (project management); Computer science; Engineering; Systems engineering","score_opus":0.03424247913284766,"score_gpt":0.2480588993063463,"score_spread":0.21381642017349864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237406792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00467273,0.008143967,0.7779515,0.001700198,0.0010097044,0.00017591713,0.0013710138,0.0030905486,0.20188446],"genre_scores_gemma":[0.14092313,0.012097264,0.5092323,0.0011981174,0.0010015105,0.00043662306,0.007665027,0.0022478385,0.32519826],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99934953,0.00011580191,0.000044996374,0.00014877712,0.00027172023,0.00006909706],"domain_scores_gemma":[0.99915576,0.0003053584,0.00003100347,0.0002861781,0.0001693085,0.000052523763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072029245,0.00092034653,0.00088129117,0.0013320943,0.00072735065,0.002415804,0.0018368248,0.0009096551,0.028710091],"category_scores_gemma":[0.0030117936,0.00033759122,0.0007506297,0.0030861867,0.00065663405,0.0040939115,0.0017695416,0.0013864355,0.016730877],"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":[0.00012683678,0.00009435845,0.00021706385,0.0005338032,0.000048169226,0.00004234682,0.00013899588,0.0129957795,0.0035776931,0.22827357,0.13763528,0.6163161],"study_design_scores_gemma":[0.000040786963,0.000113279835,0.00053506467,0.00021841998,0.000059880505,0.00044010617,0.00012481894,0.12674096,0.005595184,0.4495403,0.41653514,0.000055997105],"about_ca_topic_score_codex":0.0020566364,"about_ca_topic_score_gemma":0.0022839839,"teacher_disagreement_score":0.028710091,"about_ca_system_score_codex":0.0007903554,"about_ca_system_score_gemma":0.0013577465,"threshold_uncertainty_score":0.09604484},"labels":[],"label_agreement":null},{"id":"W4239171964","doi":"10.1007/s00454-011-9347-7","title":"Cache-Oblivious Range Reporting with Optimal Queries Requires Superlinear Space","year":2011,"lang":"en","type":"article","venue":"Discrete & Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Killam Trusts; Dalhousie University; Canada Research Chairs","keywords":"Computer science; Cache; Range (aeronautics); Space (punctuation); Information retrieval; Parallel computing; Materials science; Operating system","score_opus":0.03748508906001073,"score_gpt":0.2570956064961202,"score_spread":0.21961051743610943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239171964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11911189,0.0074109677,0.76529735,0.016962575,0.0015168566,0.0008264619,0.0059889546,0.043510143,0.03937488],"genre_scores_gemma":[0.72850543,0.0016128214,0.24198167,0.0027596701,0.0009828192,0.00065525534,0.0040702634,0.0027632385,0.016668832],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97686654,0.0046322932,0.001752404,0.0034370925,0.009214704,0.0040969593],"domain_scores_gemma":[0.9204253,0.036559094,0.0034732325,0.034750283,0.003415404,0.0013767623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063548684,0.0030751256,0.005232658,0.002183962,0.0025221533,0.008222489,0.007276564,0.0039535123,0.024048101],"category_scores_gemma":[0.04149289,0.0018330493,0.0025752406,0.009408274,0.0035061731,0.026077682,0.009014489,0.005931755,0.008216751],"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":[0.010679143,0.0021829843,0.008699586,0.0022993127,0.00077810197,0.00073961075,0.0013999039,0.15950069,0.038561907,0.0968738,0.14427903,0.5340058],"study_design_scores_gemma":[0.0009550135,0.00046353074,0.0016680289,0.00015083958,0.00046528864,0.00094584445,0.0008514289,0.76244634,0.023061125,0.19656852,0.012279099,0.00014489482],"about_ca_topic_score_codex":0.0072537246,"about_ca_topic_score_gemma":0.011165311,"teacher_disagreement_score":0.024048101,"about_ca_system_score_codex":0.0051015727,"about_ca_system_score_gemma":0.010945594,"threshold_uncertainty_score":0.080448866},"labels":[],"label_agreement":null},{"id":"W4239565922","doi":"10.5220/0008124300002366","title":"A New Data Structure for Processing Natural Language Database Queries","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Database; Information retrieval; Database design; Data structure; Database theory; Data definition language; Natural language processing; Programming language","score_opus":0.017211160907278516,"score_gpt":0.28174308514309687,"score_spread":0.26453192423581834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239565922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054367883,0.0006339686,0.9562701,0.0008070564,0.0005465412,0.00042301466,0.0041172025,0.029760044,0.0020052653],"genre_scores_gemma":[0.039358597,0.0004764762,0.9289149,0.0012368308,0.0004694051,0.00092227105,0.013190743,0.004217967,0.011212771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949713,0.00053191226,0.0011246675,0.0008108281,0.0022630885,0.00029823536],"domain_scores_gemma":[0.98819435,0.0029239156,0.00069412263,0.0047570853,0.0027616892,0.0006688877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041932184,0.001320372,0.002696566,0.0055004037,0.0021379488,0.0057312385,0.003586626,0.0016930414,0.012229715],"category_scores_gemma":[0.013206059,0.0019309674,0.0024489008,0.0060928967,0.002016121,0.012647819,0.0072756405,0.004112182,0.006714544],"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":[0.0014462806,0.00044501087,0.004991294,0.0010538256,0.0002430699,0.00037093685,0.0011309047,0.0054439832,0.027264707,0.117382735,0.1349221,0.7053052],"study_design_scores_gemma":[0.0006123561,0.00085369113,0.0029951762,0.0004349517,0.00053984765,0.0014600345,0.0006544054,0.2041021,0.06579931,0.22855462,0.4934747,0.0005187866],"about_ca_topic_score_codex":0.005208192,"about_ca_topic_score_gemma":0.00825453,"teacher_disagreement_score":0.012229715,"about_ca_system_score_codex":0.0020635524,"about_ca_system_score_gemma":0.0038534063,"threshold_uncertainty_score":0.04091245},"labels":[],"label_agreement":null},{"id":"W4240507166","doi":"10.1145/584865.584867","title":"Discovering approximate keys in XML data","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Computer science; XML; Document Structure Description; Information retrieval; Key (lock); XML validation; XML database; Streaming XML; Well-formed document; Set (abstract data type); XML Encryption; Representation (politics); Search engine indexing; XML Schema Editor; Data mining; Database; World Wide Web; Programming language; Computer security","score_opus":0.08292524527261654,"score_gpt":0.25329512341279864,"score_spread":0.17036987814018212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240507166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055645443,0.0014442493,0.9400022,0.0006052309,0.000036602723,0.00013817042,0.0010402696,0.00062376814,0.0004641033],"genre_scores_gemma":[0.28264377,0.001587858,0.7114523,0.0001982805,0.00009359303,0.00029532862,0.002656014,0.0001212684,0.0009515771],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99406415,0.0014201046,0.0010102282,0.00090161856,0.0023009707,0.00030301846],"domain_scores_gemma":[0.98093843,0.011887842,0.002525003,0.0030735305,0.0013747036,0.00020047145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037605409,0.0007052103,0.0014385771,0.004134993,0.00097135716,0.003061572,0.0015831136,0.0014499968,0.00074660865],"category_scores_gemma":[0.030792238,0.00086600124,0.0011186231,0.0063486695,0.0015144733,0.0120371925,0.003271945,0.0015308165,0.000520682],"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":[0.0010197399,0.00017377744,0.020625124,0.0015224904,0.0002506489,0.0020006213,0.0031089354,0.187805,0.028418459,0.1853377,0.007581877,0.56215566],"study_design_scores_gemma":[0.00006684147,0.00019989941,0.0022543438,0.00017939863,0.000121249504,0.0019751145,0.00108377,0.6136177,0.026686357,0.33293542,0.020786533,0.000093346964],"about_ca_topic_score_codex":0.0013975751,"about_ca_topic_score_gemma":0.0014594739,"teacher_disagreement_score":0.004134993,"about_ca_system_score_codex":0.0009059596,"about_ca_system_score_gemma":0.0011435623,"threshold_uncertainty_score":0.019887924},"labels":[],"label_agreement":null},{"id":"W4240799125","doi":"10.22215/etd/2006-07651","title":"Generating longitudinally correlated binary data","year":2006,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage","funders":"","keywords":"Binary number; Physics; Humanities; Arithmetic; Computer science; Mathematics; Art","score_opus":0.03229451580461643,"score_gpt":0.2786084530962388,"score_spread":0.24631393729162235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240799125","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26513624,0.0003307876,0.72417223,0.00091421825,0.00020531451,0.00025630242,0.004377635,0.002532233,0.002075051],"genre_scores_gemma":[0.6931346,0.00022958528,0.2891511,0.0001613002,0.00020295814,0.00038784053,0.012230806,0.00022938677,0.0042723734],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978902,0.00091057346,0.000103306884,0.00048093344,0.0004476004,0.00016747107],"domain_scores_gemma":[0.9832585,0.009511126,0.0011150428,0.0036822692,0.002022005,0.00041102467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037674622,0.00038392076,0.00060248014,0.0013024185,0.00045290953,0.0011864732,0.0011252617,0.0008356276,0.0031642036],"category_scores_gemma":[0.025189659,0.000544213,0.0008445185,0.0013667373,0.0004884772,0.000860225,0.0016318167,0.0009295434,0.0014014903],"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":[0.0016731601,0.00075615256,0.15571626,0.00038123076,0.00029604556,0.0010448917,0.0010321945,0.21407048,0.013803187,0.06017919,0.025865374,0.52518195],"study_design_scores_gemma":[0.00008144622,0.00017988257,0.011950159,0.000048709237,0.000052062984,0.00026083004,0.00018288911,0.9383564,0.0072034597,0.03654425,0.005100069,0.00003972008],"about_ca_topic_score_codex":0.0024178664,"about_ca_topic_score_gemma":0.004042178,"teacher_disagreement_score":0.0037674622,"about_ca_system_score_codex":0.0007977977,"about_ca_system_score_gemma":0.0012920382,"threshold_uncertainty_score":0.019924462},"labels":[],"label_agreement":null},{"id":"W4241057454","doi":"10.1007/978-1-4939-7131-2_100407","title":"Geodesic Distance: Graph Theoretic Distance","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Geodesic; Mathematics; Graph; Combinatorics; Computer science; Geometry","score_opus":0.012596886361347901,"score_gpt":0.21546677841841333,"score_spread":0.20286989205706543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241057454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002603857,0.051261116,0.7459227,0.005923865,0.007856521,0.00011262944,0.0016954936,0.0024378356,0.18218605],"genre_scores_gemma":[0.077304736,0.086130925,0.5075936,0.002780261,0.007652404,0.00047357718,0.0052772183,0.0047040917,0.30808327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99907434,0.0001417427,0.00005539471,0.00019306352,0.00048432816,0.00005102348],"domain_scores_gemma":[0.99932337,0.00022319221,0.000036516598,0.00014510866,0.00021327903,0.000058557114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005747793,0.0011792745,0.0010692644,0.0023656117,0.00077338784,0.0033500383,0.0015566858,0.0015432737,0.015539112],"category_scores_gemma":[0.0026389481,0.00055640645,0.0005469207,0.006273534,0.0017946494,0.0065449984,0.0021126976,0.002709084,0.013446999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017081886,0.000019997506,0.0000919545,0.0003825552,0.000017757493,0.000047379006,0.00015578598,0.0045140525,0.000904704,0.5777098,0.13917932,0.27695966],"study_design_scores_gemma":[0.000003849883,0.000011218461,0.000115556635,0.00013911966,0.000008787149,0.00017780613,0.000054761425,0.0063782274,0.00067195797,0.48894012,0.5034748,0.000023890118],"about_ca_topic_score_codex":0.0016505638,"about_ca_topic_score_gemma":0.0014744336,"teacher_disagreement_score":0.015539112,"about_ca_system_score_codex":0.0017233323,"about_ca_system_score_gemma":0.00125257,"threshold_uncertainty_score":0.051983476},"labels":[],"label_agreement":null},{"id":"W4241572164","doi":"10.32920/14639637","title":"Personalized Multi-Criteria Decision Strategies in Location-based Decision Support","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decision support system; Space (punctuation); Outcome (game theory); Decision analysis; Decision model; Operator (biology); Data mining; Machine learning; Artificial intelligence; Operations research; Mathematics","score_opus":0.040734581604727885,"score_gpt":0.3292917507183061,"score_spread":0.2885571691135782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241572164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052767366,0.0011230438,0.9423295,0.00062827376,0.000053122676,0.00032731378,0.000088383786,0.0001595346,0.0025234795],"genre_scores_gemma":[0.572576,0.0004464457,0.4250556,0.00015849662,0.00005936928,0.00045828853,0.00012910878,0.000057127116,0.0010595908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9834557,0.011605174,0.00083052774,0.0012004678,0.0023580906,0.0005500523],"domain_scores_gemma":[0.9821468,0.014448762,0.00089313136,0.0005104168,0.0014214597,0.0005794033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014385661,0.0019878186,0.002038395,0.0022134085,0.001104179,0.0034949936,0.0017684123,0.0019608308,0.0018097466],"category_scores_gemma":[0.024234239,0.0009308147,0.0012647224,0.0025708778,0.0017067232,0.0029322824,0.0021232904,0.0020261286,0.00034344976],"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":[0.00038643825,0.00044657473,0.0027005859,0.00050117966,0.00038995524,0.00035091603,0.00091915193,0.83801484,0.0027343878,0.04451913,0.001210849,0.10782599],"study_design_scores_gemma":[0.00004707318,0.00014392818,0.00035699457,0.000046587367,0.00004411337,0.00004556897,0.00014814489,0.96004033,0.0010704385,0.037102573,0.00092165516,0.000032631713],"about_ca_topic_score_codex":0.0046607316,"about_ca_topic_score_gemma":0.0040290114,"teacher_disagreement_score":0.014385661,"about_ca_system_score_codex":0.002507596,"about_ca_system_score_gemma":0.0017108113,"threshold_uncertainty_score":0.07607955},"labels":[],"label_agreement":null},{"id":"W4242283844","doi":"10.1007/978-1-4939-7131-2_101248","title":"Spatio-temporal Querying of Big Data and Results Summarization","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Automatic summarization; Computer science; Big data; Information retrieval; Data mining","score_opus":0.08041800393860918,"score_gpt":0.258592237879088,"score_spread":0.17817423394047885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242283844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025742804,0.025618937,0.9128542,0.0025334035,0.0018486036,0.0001610531,0.0038564228,0.009851573,0.040701613],"genre_scores_gemma":[0.05370451,0.043615825,0.78410566,0.0015121751,0.002548308,0.00029793818,0.017492995,0.004561833,0.092160664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892235,0.00011458926,0.00009888693,0.00019886486,0.0006164381,0.000048941285],"domain_scores_gemma":[0.99865085,0.0005655969,0.00005825366,0.0004151591,0.00024974445,0.000060481063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013196882,0.0011518094,0.0012670796,0.0022530947,0.00050833746,0.004486951,0.002401595,0.000891734,0.013870593],"category_scores_gemma":[0.003329188,0.00061489345,0.001140369,0.006496971,0.00082387665,0.0058988035,0.0021203929,0.0017125101,0.009338798],"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":[0.000092552305,0.000058884216,0.00033868113,0.0014548452,0.000081141035,0.0001492409,0.0003232217,0.008364846,0.008560918,0.12750593,0.20478347,0.6482864],"study_design_scores_gemma":[0.000021479249,0.000048033282,0.00079602114,0.00041563294,0.00007061222,0.0008708601,0.000292529,0.07818655,0.012156152,0.27855936,0.62850803,0.000074814896],"about_ca_topic_score_codex":0.0010298741,"about_ca_topic_score_gemma":0.0011945171,"teacher_disagreement_score":0.013870593,"about_ca_system_score_codex":0.0008065677,"about_ca_system_score_gemma":0.00073597394,"threshold_uncertainty_score":0.0464018},"labels":[],"label_agreement":null},{"id":"W4242613513","doi":"10.1007/978-0-387-39940-9_3665","title":"Spatio-Temporal Stream Processing","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Stream processing; Parallel computing","score_opus":0.017612522034230034,"score_gpt":0.23660218689026782,"score_spread":0.2189896648560378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242613513","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020571065,0.008381109,0.92480904,0.0004773212,0.0011546884,0.00012585764,0.001063762,0.005971343,0.055959802],"genre_scores_gemma":[0.060203873,0.026980707,0.6486801,0.00065293396,0.0014909158,0.00021751475,0.0067838402,0.0013327812,0.25365737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998274,0.000010495916,0.000014407832,0.000039700764,0.00009714975,0.000010862988],"domain_scores_gemma":[0.99975556,0.00004981661,0.000010329967,0.0000634467,0.00010493726,0.00001598256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002858576,0.0008839866,0.0005938703,0.0010094215,0.00030541734,0.0019249436,0.0010460429,0.0005713835,0.025992464],"category_scores_gemma":[0.00064828363,0.00033374233,0.0004646555,0.002136764,0.00037781993,0.0012834918,0.0008501521,0.00080104783,0.018949214],"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":[0.000097994016,0.000048066344,0.00020782779,0.00038461422,0.00005106243,0.00016478535,0.00008038597,0.007859247,0.019918617,0.04320961,0.10565732,0.8223204],"study_design_scores_gemma":[0.00003281401,0.00010301333,0.001342828,0.00017298636,0.0000923823,0.001347743,0.00008874022,0.19410664,0.041112464,0.1040219,0.65751976,0.00005880213],"about_ca_topic_score_codex":0.001031479,"about_ca_topic_score_gemma":0.0013338748,"teacher_disagreement_score":0.025992464,"about_ca_system_score_codex":0.00025972867,"about_ca_system_score_gemma":0.0004996436,"threshold_uncertainty_score":0.0869534},"labels":[],"label_agreement":null},{"id":"W4242716906","doi":"10.1007/978-1-4939-7131-2_100943","title":"Querying and Summarizing Social Networks Posts with Respect to Spatial and Temporal Dimensions","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Computer science; Data science; Geography; Cartography","score_opus":0.020847042173267383,"score_gpt":0.23417018421835104,"score_spread":0.21332314204508365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242716906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086414784,0.0030455887,0.8769687,0.0013412487,0.0003388492,0.00035557113,0.017597072,0.0070269913,0.0069111562],"genre_scores_gemma":[0.2707565,0.0033150488,0.6830531,0.0002146635,0.00051795563,0.00030571522,0.031618383,0.000581689,0.009636997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991524,0.00011432374,0.000096514945,0.00023106234,0.00034009796,0.00006569087],"domain_scores_gemma":[0.998555,0.0007427649,0.00011996653,0.00031914253,0.00019739752,0.00006569431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088741677,0.0010015578,0.00091634126,0.003108649,0.000613255,0.002773494,0.0010696795,0.00077903306,0.0029607506],"category_scores_gemma":[0.0049791764,0.0004185682,0.0011452372,0.0056690853,0.0004460457,0.00447774,0.0014631128,0.00077455054,0.0022520197],"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":[0.00033361022,0.00023023246,0.012892099,0.0011412642,0.00028143212,0.00033554866,0.001165213,0.031963058,0.046071365,0.02914382,0.038881566,0.83756083],"study_design_scores_gemma":[0.00003141057,0.00021489256,0.013140769,0.00019159696,0.0003033993,0.0009782237,0.002193004,0.6809655,0.04449735,0.17392561,0.08345278,0.000105460516],"about_ca_topic_score_codex":0.0025985923,"about_ca_topic_score_gemma":0.006745405,"teacher_disagreement_score":0.003108649,"about_ca_system_score_codex":0.00059330004,"about_ca_system_score_gemma":0.0007453955,"threshold_uncertainty_score":0.009904683},"labels":[],"label_agreement":null},{"id":"W4242803917","doi":"10.1002/9781118786352.wbieg0454","title":"Data Structure: Spatial Data on the Web","year":2017,"lang":"en","type":"other","venue":"International Encyclopedia of Geography","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; The Internet; Spatial analysis; Web mapping; GRASP; Heading (navigation); Service (business); Geographic information system; World Wide Web; Database; Geography; Cartography; Web navigation; Remote sensing","score_opus":0.02569035306180688,"score_gpt":0.27781368929499384,"score_spread":0.25212333623318695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242803917","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012794881,0.000874336,0.14348662,0.0032653841,0.000947652,0.00094134535,0.6774531,0.11557149,0.0561806],"genre_scores_gemma":[0.014129802,0.0013170983,0.07381515,0.0015298654,0.00036145563,0.0014082951,0.87136334,0.01978643,0.01628858],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99472886,0.0008608015,0.0011667477,0.0007496582,0.0021829605,0.00031104797],"domain_scores_gemma":[0.98211664,0.0037116313,0.00076797616,0.0069504944,0.0053813206,0.0010717973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041041896,0.0017550456,0.0017066192,0.0072506065,0.001425241,0.00904342,0.004246619,0.0025042885,0.10507814],"category_scores_gemma":[0.033785895,0.0011191549,0.001290405,0.013858452,0.00095140416,0.010546199,0.0058138464,0.003413786,0.16765107],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111802095,0.000052958436,0.0008932796,0.0006128701,0.00004200659,0.00011537176,0.00015524581,0.0015615018,0.0007319358,0.017595774,0.9154755,0.06265179],"study_design_scores_gemma":[0.00004633678,0.000017807948,0.0008713701,0.00024296783,0.000019077865,0.00016811666,0.00011574583,0.003935501,0.0018948753,0.02702186,0.9656109,0.00005533963],"about_ca_topic_score_codex":0.009302939,"about_ca_topic_score_gemma":0.0044928845,"teacher_disagreement_score":0.10507814,"about_ca_system_score_codex":0.0016253996,"about_ca_system_score_gemma":0.0038435494,"threshold_uncertainty_score":0.35152137},"labels":[],"label_agreement":null},{"id":"W4243973573","doi":"10.1007/s41019-017-0053-1","title":"Sliding Window Top-K Monitoring over Distributed Data Streams","year":2017,"lang":"en","type":"article","venue":"Data Science and Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Scalability; Sliding window protocol; Distributed computing; Overhead (engineering); Data stream mining; Distributed Computing Environment; Distributed algorithm; Aggregate (composite); Window (computing); Data stream; Real-time computing; Data mining; Database; Operating system","score_opus":0.06100806266574163,"score_gpt":0.2964756951502873,"score_spread":0.2354676324845457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243973573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09881251,0.00057500444,0.8962637,0.00014122113,0.000059332913,0.0001098127,0.00012379565,0.0033097586,0.0006048605],"genre_scores_gemma":[0.7732824,0.00021864836,0.2254398,0.000045409954,0.00004597057,0.00007658989,0.00020964316,0.000112694244,0.00056875014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788314,0.00032634588,0.00020346639,0.00072302506,0.00063095277,0.0002331495],"domain_scores_gemma":[0.9954364,0.0017340536,0.0009170209,0.0009432589,0.00064413506,0.000325169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021727206,0.0010718242,0.0019589802,0.0013900369,0.0011685381,0.0019220245,0.001943457,0.0006219027,0.00055904186],"category_scores_gemma":[0.007877362,0.0005326995,0.0005850716,0.0020967626,0.0008598796,0.0031113606,0.0016742336,0.00095898454,0.00020892113],"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":[0.0021268958,0.00034625948,0.020748958,0.0002820256,0.000259211,0.00051632844,0.00082104775,0.3916993,0.04859462,0.007049248,0.00322003,0.52433604],"study_design_scores_gemma":[0.000028201059,0.00007500013,0.0010281974,0.000004944734,0.000025888916,0.00008770394,0.00006926727,0.9873713,0.007629842,0.0030673256,0.00060011004,0.000012258576],"about_ca_topic_score_codex":0.0050561493,"about_ca_topic_score_gemma":0.00408344,"teacher_disagreement_score":0.0050561493,"about_ca_system_score_codex":0.00095830596,"about_ca_system_score_gemma":0.0013831807,"threshold_uncertainty_score":0.011490583},"labels":[],"label_agreement":null},{"id":"W4243988023","doi":"10.1007/978-1-4939-7131-2_100606","title":"Link-Based Ranking","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Ranking (information retrieval); Computer science; Information retrieval","score_opus":0.02552812000136311,"score_gpt":0.2264357580925119,"score_spread":0.20090763809114878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243988023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004231045,0.01097517,0.8024161,0.0018001114,0.0023171592,0.00021922914,0.0030263532,0.008553682,0.16646114],"genre_scores_gemma":[0.09183685,0.012527636,0.5585392,0.0011065188,0.0022986496,0.00027394947,0.012024329,0.0029141603,0.31847876],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99827445,0.00027796775,0.00008420835,0.00031539742,0.00093362766,0.00011440556],"domain_scores_gemma":[0.998431,0.00045184087,0.00005873862,0.000522539,0.00046389012,0.000071851064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013555444,0.0013254926,0.001364525,0.0038115038,0.0010860437,0.0034306229,0.0021363392,0.00090841175,0.038129397],"category_scores_gemma":[0.0044721044,0.0005094774,0.0008745326,0.006547811,0.0007090049,0.004372951,0.0017392964,0.0018898498,0.03523429],"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":[0.000046145593,0.00009449067,0.00021581144,0.00030160765,0.000051103325,0.000023180393,0.00003736293,0.007183904,0.001414049,0.07989058,0.17437574,0.73636603],"study_design_scores_gemma":[0.000030705607,0.00013596473,0.0011339904,0.00019486045,0.00009812609,0.00050518475,0.00009134973,0.09459121,0.0073053758,0.33931136,0.55648744,0.00011445416],"about_ca_topic_score_codex":0.0016410775,"about_ca_topic_score_gemma":0.0033300063,"teacher_disagreement_score":0.038129397,"about_ca_system_score_codex":0.0011803394,"about_ca_system_score_gemma":0.0012154799,"threshold_uncertainty_score":0.12755549},"labels":[],"label_agreement":null},{"id":"W4245107420","doi":"10.14778/3236187.3269462","title":"Efficient construction of approximate ad-hoc ML models through materialization and reuse","year":2018,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Online analytical processing; Reuse; Dimension (graph theory); Cluster analysis; Data mining; Variety (cybernetics); Construct (python library); Data warehouse; Mixture model; Machine learning; Artificial intelligence; Mathematics","score_opus":0.0167162247919843,"score_gpt":0.22005666246918465,"score_spread":0.20334043767720034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245107420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014802088,0.00014003973,0.9811152,0.00027888792,0.000013750535,0.000090259135,0.00014772874,0.0026009036,0.0008110786],"genre_scores_gemma":[0.23310429,0.00020913318,0.7622724,0.00023556163,0.00006519612,0.0002665839,0.0013107461,0.00087935943,0.0016567322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99415344,0.0019901572,0.00044691676,0.0010427558,0.0018483808,0.00051832374],"domain_scores_gemma":[0.98116106,0.010481738,0.0010443123,0.005667471,0.0012619224,0.00038350967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058200895,0.002239894,0.0026905995,0.0020721988,0.0013524585,0.004736695,0.004772128,0.0020105545,0.0034789182],"category_scores_gemma":[0.028147131,0.0019003085,0.0033305017,0.003361834,0.0023380548,0.00886617,0.0070944736,0.0038882683,0.0017005646],"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":[0.00018448447,0.00020675806,0.0025815852,0.00013380825,0.00011871364,0.00021941777,0.0003984245,0.8080758,0.0027494635,0.03882231,0.004327176,0.1421821],"study_design_scores_gemma":[0.000013230606,0.00001956592,0.0000602112,0.000004352838,0.000011157769,0.00002889935,0.00005403761,0.9783036,0.001054648,0.019617595,0.0008241465,0.000008479864],"about_ca_topic_score_codex":0.009648849,"about_ca_topic_score_gemma":0.012222269,"teacher_disagreement_score":0.009648849,"about_ca_system_score_codex":0.00276346,"about_ca_system_score_gemma":0.0038679263,"threshold_uncertainty_score":0.030779958},"labels":[],"label_agreement":null},{"id":"W4245210711","doi":"10.1145/376284.375666","title":"Iceberg-cube computation with PC clusters","year":2001,"lang":"en","type":"article","venue":"ACM SIGMOD Record","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia Hospital","funders":"","keywords":"Computer science; Computation; Cube (algebra); Cuboid; Parallel computing; Algorithm; Data cube; Data mining; Mathematics","score_opus":0.020947862199152976,"score_gpt":0.246467051847856,"score_spread":0.22551918964870302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245210711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12862606,0.0007934883,0.83054835,0.0011080959,0.00026132702,0.00026585805,0.0008908684,0.0071731615,0.030332778],"genre_scores_gemma":[0.4207923,0.0005673945,0.5675953,0.0002394524,0.000075744734,0.00029346746,0.0015545599,0.00040050625,0.008481237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874985,0.00024914474,0.00007345533,0.00027401192,0.00044116855,0.00021242505],"domain_scores_gemma":[0.9975884,0.00082607626,0.00009971041,0.000873143,0.00047612723,0.0001365337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009944371,0.00080232363,0.0012103089,0.0007164747,0.0012758401,0.0024842657,0.0032085427,0.00077384675,0.006522969],"category_scores_gemma":[0.0047108554,0.0005301471,0.000704361,0.0031945824,0.0008903866,0.0044657756,0.002447761,0.0011067105,0.0019961302],"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":[0.0015996727,0.00034672668,0.0058821877,0.00041362824,0.00020101057,0.00040660318,0.0006149264,0.5958847,0.00792232,0.09634133,0.038787514,0.2515994],"study_design_scores_gemma":[0.00006893655,0.00007254632,0.00031999263,0.000010560748,0.000017422677,0.00007250053,0.0001398966,0.95666873,0.0047247508,0.030622495,0.007266431,0.000015790927],"about_ca_topic_score_codex":0.016354175,"about_ca_topic_score_gemma":0.017040685,"teacher_disagreement_score":0.016354175,"about_ca_system_score_codex":0.0014783272,"about_ca_system_score_gemma":0.0021207056,"threshold_uncertainty_score":0.03251797},"labels":[],"label_agreement":null},{"id":"W4245730866","doi":"10.1137/1.9781611973105.18","title":"Adaptive and Approximate Orthogonal Range Counting","year":2013,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Center for Massive Data Algorithmics","keywords":"Range (aeronautics); Computer science; Algorithm; Mathematics; Engineering","score_opus":0.015671469470899358,"score_gpt":0.20491975323049333,"score_spread":0.18924828375959396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245730866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03099042,0.0010769751,0.948368,0.0005707555,0.00017924611,0.00015399989,0.00088408095,0.003142804,0.014633724],"genre_scores_gemma":[0.3242937,0.0011121498,0.65942276,0.00073358463,0.00044246975,0.00063874526,0.002708245,0.0006721064,0.0099762045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99417603,0.0006120785,0.00047235104,0.0012582726,0.0027535078,0.0007277792],"domain_scores_gemma":[0.9927133,0.0017648425,0.0006390495,0.0038012941,0.00085010385,0.00023142237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012784591,0.001333434,0.001858234,0.002229762,0.0010859469,0.0031652057,0.0049518375,0.0014153881,0.010260149],"category_scores_gemma":[0.010398196,0.00071162794,0.0016153019,0.0058271596,0.0019457411,0.010334506,0.007580641,0.0028640195,0.0028770987],"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":[0.0008628928,0.00031138703,0.003670921,0.0006498104,0.000120526245,0.00030077776,0.00042390096,0.11807811,0.024640845,0.3377081,0.023504086,0.48972866],"study_design_scores_gemma":[0.0001202391,0.00026903593,0.001171037,0.00009000924,0.000091272406,0.00083834224,0.00019508153,0.6295878,0.02189396,0.29894105,0.046673845,0.00012837269],"about_ca_topic_score_codex":0.0017485338,"about_ca_topic_score_gemma":0.0016602197,"teacher_disagreement_score":0.010260149,"about_ca_system_score_codex":0.0015519114,"about_ca_system_score_gemma":0.0014382045,"threshold_uncertainty_score":0.034323573},"labels":[],"label_agreement":null},{"id":"W4245795676","doi":"10.32920/14639637.v1","title":"Personalized Multi-Criteria Decision Strategies in Location-based Decision Support","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decision support system; Space (punctuation); Outcome (game theory); Decision analysis; Decision model; Operator (biology); Data mining; Machine learning; Operations research; Mathematics","score_opus":0.040734581604727885,"score_gpt":0.3292917507183061,"score_spread":0.2885571691135782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245795676","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052767366,0.0011230438,0.9423295,0.00062827376,0.000053122676,0.00032731378,0.000088383786,0.0001595346,0.0025234795],"genre_scores_gemma":[0.572576,0.0004464457,0.4250556,0.00015849662,0.00005936928,0.00045828853,0.00012910878,0.000057127116,0.0010595908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9834557,0.011605174,0.00083052774,0.0012004678,0.0023580906,0.0005500523],"domain_scores_gemma":[0.9821468,0.014448762,0.00089313136,0.0005104168,0.0014214597,0.0005794033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014385661,0.0019878186,0.002038395,0.0022134085,0.001104179,0.0034949936,0.0017684123,0.0019608308,0.0018097466],"category_scores_gemma":[0.024234239,0.0009308147,0.0012647224,0.0025708778,0.0017067232,0.0029322824,0.0021232904,0.0020261286,0.00034344976],"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":[0.00038643825,0.00044657473,0.0027005859,0.00050117966,0.00038995524,0.00035091603,0.00091915193,0.83801484,0.0027343878,0.04451913,0.001210849,0.10782599],"study_design_scores_gemma":[0.00004707318,0.00014392818,0.00035699457,0.000046587367,0.00004411337,0.00004556897,0.00014814489,0.96004033,0.0010704385,0.037102573,0.00092165516,0.000032631713],"about_ca_topic_score_codex":0.0046607316,"about_ca_topic_score_gemma":0.0040290114,"teacher_disagreement_score":0.014385661,"about_ca_system_score_codex":0.002507596,"about_ca_system_score_gemma":0.0017108113,"threshold_uncertainty_score":0.07607955},"labels":[],"label_agreement":null},{"id":"W4245831121","doi":"10.4018/978-1-4666-0333-2.ch012","title":"Describing Spatio-Temporal Phenomena for Environmental System Development","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Temporalities; Temporality; Rotation formalisms in three dimensions; Computer science; Simple (philosophy); Focus (optics); Extension (predicate logic); Field (mathematics); Cognitive science; Presentation (obstetrics); Epistemology; Programming language; Psychology; Mathematics; Philosophy","score_opus":0.03622930235892268,"score_gpt":0.2134441766372524,"score_spread":0.17721487427832971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245831121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018631719,0.0027278964,0.97236437,0.00075104844,0.00014225826,0.00006971942,0.00045423312,0.00035440596,0.021272847],"genre_scores_gemma":[0.07451819,0.008450262,0.88774425,0.00033948163,0.00023024177,0.0005617528,0.0022402543,0.0003717718,0.02554373],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993876,0.00025184284,0.00006621984,0.00009257854,0.00016678113,0.00003494196],"domain_scores_gemma":[0.999188,0.00049278536,0.000072589995,0.00013944732,0.000077530676,0.00002962929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012571979,0.0011147058,0.0005015623,0.0011789758,0.0006084624,0.0033066093,0.0014718391,0.0010651996,0.011276416],"category_scores_gemma":[0.002458524,0.00038374076,0.001236177,0.0027842913,0.0012620775,0.0048422697,0.002058774,0.0018908223,0.0023580224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008086446,0.000017169757,0.0003033479,0.00035586863,0.000023760702,0.00023256073,0.00060680136,0.043601584,0.0009077298,0.9005702,0.005916388,0.047456425],"study_design_scores_gemma":[0.000008298303,0.000013769867,0.0002576526,0.00022831264,0.000022673832,0.0002725767,0.00031513063,0.18614882,0.0009868281,0.57269883,0.23902445,0.000022709406],"about_ca_topic_score_codex":0.0028869004,"about_ca_topic_score_gemma":0.004744853,"teacher_disagreement_score":0.011276416,"about_ca_system_score_codex":0.001585411,"about_ca_system_score_gemma":0.0009707295,"threshold_uncertainty_score":0.037723422},"labels":[],"label_agreement":null},{"id":"W4246148897","doi":"10.1007/978-1-4614-8265-9_80680","title":"Rank-Aware Query Processing","year":2018,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Rank (graph theory); Information retrieval; Web search query; Search engine; Mathematics; Combinatorics","score_opus":0.01851590366942882,"score_gpt":0.23699367722465758,"score_spread":0.21847777355522877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246148897","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005205548,0.010222851,0.9340497,0.0009823922,0.000728341,0.00022261144,0.0020540548,0.014433542,0.03210091],"genre_scores_gemma":[0.11145894,0.010868328,0.7975625,0.00087567605,0.0012442063,0.000193705,0.009713445,0.0017876531,0.0662956],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822253,0.00017747714,0.00011645897,0.00031792192,0.0009970807,0.00016850665],"domain_scores_gemma":[0.9986339,0.00030748235,0.000050872986,0.0005486125,0.00040928755,0.000049854487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091768935,0.0012391448,0.0015583668,0.0015469593,0.00073484774,0.0033580225,0.0025250823,0.00081472826,0.017717302],"category_scores_gemma":[0.002745676,0.0006102156,0.0008162537,0.0034639519,0.00047009654,0.0029972186,0.001862578,0.0019333664,0.015763232],"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":[0.00023027623,0.00018153917,0.0002747957,0.0006950564,0.00008500532,0.00007502909,0.000078945464,0.012507074,0.021675726,0.034431428,0.12931389,0.8004513],"study_design_scores_gemma":[0.0000869095,0.0002688176,0.0010581558,0.00018457045,0.0001853439,0.0011870259,0.00017442153,0.44880173,0.06433597,0.15375072,0.32982296,0.00014333807],"about_ca_topic_score_codex":0.0016789681,"about_ca_topic_score_gemma":0.00275738,"teacher_disagreement_score":0.017717302,"about_ca_system_score_codex":0.0006892597,"about_ca_system_score_gemma":0.0012916372,"threshold_uncertainty_score":0.059270263},"labels":[],"label_agreement":null},{"id":"W4246166785","doi":"10.1109/wi.2004.10005","title":"Clustering Web Surfers with Probabilistic Models in a Real Application","year":2005,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Web Intelligence (WI'04)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Open Text (Canada); York University","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Probabilistic logic; Machine learning; Artificial intelligence","score_opus":0.07176416831107739,"score_gpt":0.30816714513295246,"score_spread":0.23640297682187505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246166785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6434892,0.00031415885,0.3524152,0.00045429083,0.000028717033,0.00011725352,0.000293987,0.002144535,0.00074272556],"genre_scores_gemma":[0.85879207,0.00015089237,0.13887711,0.00006175414,0.00003593512,0.00007418459,0.0007342646,0.000103708495,0.0011700478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979748,0.00094316056,0.00010819067,0.00048215335,0.000377503,0.00011428138],"domain_scores_gemma":[0.99180835,0.0059628473,0.00046498215,0.00087849185,0.00066908303,0.00021626813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004689573,0.00077230524,0.0010460673,0.0024061596,0.0006810419,0.0013915236,0.0018118935,0.0020577495,0.0006973858],"category_scores_gemma":[0.01180706,0.0007531468,0.0007933578,0.0023823709,0.00061447045,0.0021595766,0.000877763,0.0010838081,0.00058250845],"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":[0.0010332613,0.0007473604,0.04451797,0.0001949303,0.00028304948,0.00037860713,0.0009676927,0.8056198,0.008865206,0.0024659073,0.0022652575,0.13266096],"study_design_scores_gemma":[0.000011856716,0.00006148852,0.0026192165,0.0000037993395,0.000011593857,0.000043755637,0.00009189589,0.994576,0.0009108229,0.0014205397,0.00023717909,0.00001185511],"about_ca_topic_score_codex":0.009481924,"about_ca_topic_score_gemma":0.011042815,"teacher_disagreement_score":0.009481924,"about_ca_system_score_codex":0.00082270853,"about_ca_system_score_gemma":0.00056261686,"threshold_uncertainty_score":0.024801135},"labels":[],"label_agreement":null},{"id":"W4246220482","doi":"10.1007/978-3-319-24612-3_1278","title":"Aggregating Data","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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":"Western University","funders":"","keywords":"Computer science","score_opus":0.09370094832609663,"score_gpt":0.25740525504357503,"score_spread":0.1637043067174784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246220482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008608401,0.0014661395,0.8691302,0.0011112463,0.0009116387,0.000627979,0.0027134838,0.012899906,0.10253095],"genre_scores_gemma":[0.089571804,0.0032594246,0.7442812,0.0013708516,0.00086496916,0.0005787772,0.014920606,0.0031986255,0.14195383],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755365,0.00036650707,0.00018877164,0.00048304017,0.0012612741,0.00014682215],"domain_scores_gemma":[0.9955154,0.0007701025,0.000121849145,0.0028766808,0.0006081062,0.00010784451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002682211,0.0017400217,0.0013431591,0.003660643,0.0016497856,0.005605141,0.0021928072,0.0008411686,0.026529996],"category_scores_gemma":[0.005740835,0.00088643684,0.0014797152,0.0072001284,0.0009036724,0.0056475396,0.005258774,0.0020106875,0.017818395],"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":[0.00018827441,0.00014539577,0.0019156046,0.00051665993,0.00014421793,0.00026783466,0.0007507305,0.007300661,0.012410312,0.10491319,0.08168828,0.7897588],"study_design_scores_gemma":[0.000036773836,0.00014212306,0.0022212514,0.00034271576,0.00025836786,0.0010082192,0.000716226,0.07387839,0.052574836,0.20619635,0.6625203,0.00010434841],"about_ca_topic_score_codex":0.0018844303,"about_ca_topic_score_gemma":0.0019842724,"teacher_disagreement_score":0.026529996,"about_ca_system_score_codex":0.0007639854,"about_ca_system_score_gemma":0.0012524781,"threshold_uncertainty_score":0.08875167},"labels":[],"label_agreement":null},{"id":"W4246292764","doi":"10.1504/ijipt.2016.10002231","title":"K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns","year":2016,"lang":"en","type":"article","venue":"International Journal of Internet Protocol Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Complex event processing; Leverage (statistics); SQL; Data mining; Event (particle physics); Data science; Database; Machine learning; Process (computing)","score_opus":0.040949103624080774,"score_gpt":0.406556510535927,"score_spread":0.36560740691184623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246292764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010648649,0.00011505444,0.99561656,0.00015578151,0.00003128224,0.00007441107,0.0002509466,0.0018314343,0.0008597065],"genre_scores_gemma":[0.104149505,0.0005370447,0.89186275,0.00023914529,0.00008330041,0.00026598282,0.001144631,0.00023665078,0.0014809667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989636,0.0001906216,0.00014486175,0.0002275553,0.00038243196,0.0000909976],"domain_scores_gemma":[0.99838567,0.0006122786,0.00018932733,0.00032204774,0.00036955858,0.00012112928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018506012,0.00084115146,0.00083852455,0.0015283732,0.0006436832,0.0027273449,0.0026419982,0.00091790676,0.002254436],"category_scores_gemma":[0.0039806627,0.0004568136,0.0011758937,0.0016251698,0.00095345685,0.003946563,0.001947402,0.0017193519,0.0009278105],"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":[0.000551207,0.00036003403,0.003753831,0.001132953,0.00033833424,0.00165561,0.0014169364,0.23155408,0.016798725,0.29454982,0.032771993,0.4151164],"study_design_scores_gemma":[0.00004860223,0.000065956214,0.0005229295,0.00009888442,0.00008177247,0.00035845465,0.00020091765,0.8375687,0.006047212,0.11039062,0.04455032,0.00006562919],"about_ca_topic_score_codex":0.005488476,"about_ca_topic_score_gemma":0.005427193,"teacher_disagreement_score":0.005488476,"about_ca_system_score_codex":0.0008031471,"about_ca_system_score_gemma":0.0014483326,"threshold_uncertainty_score":0.010913074},"labels":[],"label_agreement":null},{"id":"W4246628364","doi":"10.32920/ryerson.14662971","title":"Implementation of meta search engine with different rank aggregation algorithms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fuse (electrical); Rank (graph theory); Ranking (information retrieval); Computer science; Condorcet method; Search engine; Metasearch engine; Algorithm; Learning to rank; Data mining; Information retrieval; Mathematics; Voting; Web search query; Engineering","score_opus":0.04202704840148233,"score_gpt":0.30186209072411707,"score_spread":0.25983504232263477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246628364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0407356,0.0015101539,0.9114745,0.00028834655,0.00016224569,0.00040525504,0.0014861394,0.03634462,0.007593154],"genre_scores_gemma":[0.2067878,0.0005160191,0.78171086,0.00014102296,0.00006628297,0.00032527326,0.003999646,0.00076178653,0.005691304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799097,0.00042416385,0.00027607437,0.00026171273,0.0008140778,0.00023293687],"domain_scores_gemma":[0.9980584,0.00038196793,0.00010110988,0.0005605873,0.00079507695,0.00010276856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021948833,0.00077357836,0.0013687283,0.002793699,0.0005526419,0.0027109203,0.0024999445,0.00121664,0.00391614],"category_scores_gemma":[0.0038544112,0.0005236481,0.0012989616,0.0030910494,0.0002902743,0.002152828,0.0011184178,0.0010114291,0.0021355564],"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":[0.0027424516,0.0008837912,0.0079458,0.001144754,0.0007941963,0.00062438357,0.0003995447,0.09136499,0.058875635,0.041954007,0.024077982,0.7691925],"study_design_scores_gemma":[0.00029836557,0.0005287113,0.0021027734,0.00008328646,0.0002622624,0.0008128029,0.00013369668,0.844288,0.10094514,0.012007391,0.038399164,0.0001384511],"about_ca_topic_score_codex":0.0029800409,"about_ca_topic_score_gemma":0.0019355789,"teacher_disagreement_score":0.00391614,"about_ca_system_score_codex":0.0009192073,"about_ca_system_score_gemma":0.0018284348,"threshold_uncertainty_score":0.013100803},"labels":[],"label_agreement":null},{"id":"W4247346550","doi":"10.22215/etd/2011-07299","title":"Road distance estimation from co-ordinates using Adaptive Tertiary Search schemes","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Library and Archives Canada","funders":"","keywords":"Ordinate; Computer science; Mathematics; Information retrieval; Geometry","score_opus":0.03837977242108923,"score_gpt":0.30081913381743036,"score_spread":0.2624393613963411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247346550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041321058,0.00027006274,0.95558524,0.00006146089,0.000054945747,0.000042125914,0.00009320505,0.00063341187,0.001938586],"genre_scores_gemma":[0.4393117,0.00022868332,0.55636024,0.000037597852,0.000047158806,0.00008072112,0.00037992751,0.00011184745,0.0034422523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995604,0.000097510725,0.000032281696,0.00008175066,0.00017212002,0.000055922534],"domain_scores_gemma":[0.9987583,0.00040246808,0.000109426524,0.00033985023,0.00033464242,0.000055214554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005404936,0.00038698147,0.00076663715,0.0016661389,0.00047262688,0.0008734147,0.0012581234,0.0008266282,0.0031386705],"category_scores_gemma":[0.0035493304,0.0003010087,0.0004471871,0.0020663117,0.00033671982,0.0014332319,0.0011736702,0.00062387926,0.001370221],"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":[0.00041410737,0.00016802896,0.0029961602,0.00016389068,0.000087197885,0.00006996478,0.00019492369,0.30359066,0.023929229,0.011501886,0.0034317484,0.65345216],"study_design_scores_gemma":[0.000018728748,0.000038486847,0.00056195736,0.00000806598,0.000008861747,0.000035264507,0.00002444344,0.9921854,0.004399773,0.0017361186,0.00097423093,0.000008586086],"about_ca_topic_score_codex":0.006940715,"about_ca_topic_score_gemma":0.0096634785,"teacher_disagreement_score":0.006940715,"about_ca_system_score_codex":0.0005042266,"about_ca_system_score_gemma":0.0012147733,"threshold_uncertainty_score":0.013800621},"labels":[],"label_agreement":null},{"id":"W4247603740","doi":"10.32920/14638896","title":"A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Search engine indexing; Tree (set theory); Data mining; Hash table; Database; Hash function; Trajectory; Access method; NoSQL; R-tree; Temporal database; Data structure; Database index; Table (database); Field (mathematics); Spatial database; Big data; Information retrieval; Spatial analysis; Mathematics","score_opus":0.13078521545752836,"score_gpt":0.35878698639044077,"score_spread":0.2280017709329124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247603740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008045151,0.0008508157,0.9845293,0.00016895568,0.000093330935,0.00016960953,0.0012499484,0.0033068266,0.0015860827],"genre_scores_gemma":[0.12362025,0.0009179756,0.86640304,0.00014098625,0.00012615467,0.00029276242,0.00506494,0.00032809636,0.0031058209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982937,0.00018036946,0.00024637414,0.0003779529,0.00079931406,0.00010223241],"domain_scores_gemma":[0.99826354,0.00025919836,0.00014184488,0.0006291847,0.00058760704,0.00011855858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010297524,0.00062195124,0.0010050711,0.0035240264,0.0011237068,0.0020671948,0.002143495,0.00069727184,0.0027869167],"category_scores_gemma":[0.0029766494,0.0003987234,0.000960428,0.008155928,0.00045077168,0.00487372,0.0020459956,0.0007644766,0.0016087336],"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":[0.0005638626,0.0002917214,0.0041280817,0.0005225481,0.00017222532,0.0002850727,0.0005063968,0.018785797,0.036397465,0.045569185,0.021396771,0.8713809],"study_design_scores_gemma":[0.00020526405,0.000425908,0.0030315833,0.000104761464,0.00017081537,0.0020920746,0.00064534525,0.80689794,0.049705166,0.042641398,0.09386809,0.00021173622],"about_ca_topic_score_codex":0.0062570185,"about_ca_topic_score_gemma":0.0045542005,"teacher_disagreement_score":0.0062570185,"about_ca_system_score_codex":0.0009292663,"about_ca_system_score_gemma":0.0017932593,"threshold_uncertainty_score":0.012441218},"labels":[],"label_agreement":null},{"id":"W4248071932","doi":"10.22215/etd/2014-10204","title":"Parallel Olap on Multi/Many-Core and Cloud Platforms","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Online analytical processing; Computer science; Data cube; Cloud computing; Parallel computing; Multi-core processor; Cube (algebra); Distributed computing; Data warehouse; Database; Data mining; Operating system","score_opus":0.02843647196615171,"score_gpt":0.2734157038382292,"score_spread":0.2449792318720775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248071932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09268375,0.015387142,0.7438331,0.0063328296,0.0020940474,0.0006066968,0.0018047418,0.007882195,0.12937546],"genre_scores_gemma":[0.37578696,0.011561552,0.5545403,0.0008667563,0.0008622371,0.00045029848,0.0040907445,0.0011668459,0.050674327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989766,0.000094100746,0.000044526238,0.00017544022,0.0005340493,0.00017519644],"domain_scores_gemma":[0.99878734,0.00018661811,0.00006191145,0.000337857,0.00050107954,0.00012526641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009364439,0.0006434344,0.0004626038,0.00064222625,0.00090119557,0.0019717107,0.0011244179,0.00040794004,0.007420249],"category_scores_gemma":[0.001831831,0.000351,0.0005921007,0.0016483237,0.00046132432,0.002277944,0.0013916402,0.0012669602,0.0030183326],"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":[0.0007543629,0.00045149,0.0022722734,0.0009180929,0.0002219758,0.00049590634,0.0003872283,0.12441387,0.046973944,0.1589616,0.117161974,0.54698724],"study_design_scores_gemma":[0.00016254095,0.00023852415,0.0028493206,0.0002025057,0.00007943854,0.0003694116,0.0003881963,0.51214707,0.050733656,0.20267299,0.2300689,0.000087543056],"about_ca_topic_score_codex":0.0028675513,"about_ca_topic_score_gemma":0.003554554,"teacher_disagreement_score":0.007420249,"about_ca_system_score_codex":0.00076297123,"about_ca_system_score_gemma":0.0017723553,"threshold_uncertainty_score":0.024823189},"labels":[],"label_agreement":null},{"id":"W4248175911","doi":"10.32920/ryerson.14662971.v1","title":"Implementation of meta search engine with different rank aggregation algorithms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Fuse (electrical); Rank (graph theory); Ranking (information retrieval); Condorcet method; Computer science; Metasearch engine; Search engine; Algorithm; Learning to rank; Data mining; Information retrieval; Mathematics; Voting; Engineering; Web search query; Combinatorics","score_opus":0.04202704840148233,"score_gpt":0.30186209072411707,"score_spread":0.25983504232263477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248175911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0407356,0.0015101539,0.9114745,0.00028834655,0.00016224569,0.00040525504,0.0014861394,0.03634462,0.007593154],"genre_scores_gemma":[0.2067878,0.0005160191,0.78171086,0.00014102296,0.00006628297,0.00032527326,0.003999646,0.00076178653,0.005691304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799097,0.00042416385,0.00027607437,0.00026171273,0.0008140778,0.00023293687],"domain_scores_gemma":[0.9980584,0.00038196793,0.00010110988,0.0005605873,0.00079507695,0.00010276856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021948833,0.00077357836,0.0013687283,0.002793699,0.0005526419,0.0027109203,0.0024999445,0.00121664,0.00391614],"category_scores_gemma":[0.0038544112,0.0005236481,0.0012989616,0.0030910494,0.0002902743,0.002152828,0.0011184178,0.0010114291,0.0021355564],"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":[0.0027424516,0.0008837912,0.0079458,0.001144754,0.0007941963,0.00062438357,0.0003995447,0.09136499,0.058875635,0.041954007,0.024077982,0.7691925],"study_design_scores_gemma":[0.00029836557,0.0005287113,0.0021027734,0.00008328646,0.0002622624,0.0008128029,0.00013369668,0.844288,0.10094514,0.012007391,0.038399164,0.0001384511],"about_ca_topic_score_codex":0.0029800409,"about_ca_topic_score_gemma":0.0019355789,"teacher_disagreement_score":0.00391614,"about_ca_system_score_codex":0.0009192073,"about_ca_system_score_gemma":0.0018284348,"threshold_uncertainty_score":0.013100803},"labels":[],"label_agreement":null},{"id":"W4248395555","doi":"10.1080/15427951.2012.678140","title":"Special Issue on Algorithms and Models for the Web Graph","year":2013,"lang":"en","type":"article","venue":"Internet Mathematics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Frieze; Mathematics; Graph; Algorithm; French horn; Theoretical computer science; Discrete mathematics; Computer science; Art history","score_opus":0.029427968311985117,"score_gpt":0.2512374160329353,"score_spread":0.22180944772095018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248395555","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073437924,0.109826155,0.14161006,0.082584195,0.1853304,0.00054002873,0.013436214,0.0036426347,0.4556865],"genre_scores_gemma":[0.047981527,0.088903524,0.04219265,0.010336771,0.20414141,0.00061466155,0.024522018,0.0034296117,0.5778779],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979054,0.0003128094,0.0001472141,0.0006521979,0.00083633617,0.0001460306],"domain_scores_gemma":[0.9950218,0.0019906582,0.00027056702,0.0011701897,0.0009961305,0.0005505869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022635132,0.0020493204,0.0024069909,0.004300684,0.0017232489,0.006846086,0.0020201134,0.0024392584,0.14661181],"category_scores_gemma":[0.0067270696,0.0008413153,0.002128595,0.005238374,0.001465669,0.0073079336,0.0023901851,0.005444263,0.040775772],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004801688,0.00009380767,0.00046639462,0.00042312135,0.000057032008,0.000086548585,0.0000369368,0.0019786935,0.00035008212,0.0844992,0.8448374,0.06712274],"study_design_scores_gemma":[0.000043397686,0.00005619024,0.001590136,0.00034497268,0.00005464885,0.00044267075,0.000060159568,0.009622012,0.00033570413,0.1840936,0.8033208,0.00003572439],"about_ca_topic_score_codex":0.0032593978,"about_ca_topic_score_gemma":0.0046602213,"teacher_disagreement_score":0.14661181,"about_ca_system_score_codex":0.0050115488,"about_ca_system_score_gemma":0.0024599172,"threshold_uncertainty_score":0.49046534},"labels":[],"label_agreement":null},{"id":"W4250011572","doi":"10.1007/s00454-009-9177-z","title":"On Approximate Range Counting and Depth","year":2009,"lang":"en","type":"article","venue":"Discrete & Computational Geometry","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Las vegas; Range (aeronautics); Combinatorics; Tree (set theory); Expected value; Monte Carlo method; Value (mathematics); Binary logarithm; Discrete mathematics; Statistics","score_opus":0.012770673390030974,"score_gpt":0.2538683223519223,"score_spread":0.24109764896189134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250011572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021622093,0.0048562656,0.9452041,0.002982729,0.0003415249,0.000109288114,0.0005643465,0.0009859584,0.023333628],"genre_scores_gemma":[0.42996907,0.006641904,0.5331175,0.0020179902,0.0016117592,0.00037519814,0.0016983735,0.0014061923,0.023162015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9921135,0.0020920425,0.0005131739,0.0013916373,0.0028428533,0.0010469022],"domain_scores_gemma":[0.9732425,0.017984333,0.0011190276,0.0051391097,0.0017780801,0.0007369565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059721298,0.0022530144,0.0042021,0.005736433,0.0025776215,0.0060543073,0.007189512,0.0030832933,0.011994618],"category_scores_gemma":[0.048726298,0.0019249977,0.0029210448,0.011772483,0.006294592,0.031613912,0.0137494,0.007003354,0.001824546],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038253298,0.000083871295,0.0017371562,0.00029773594,0.00006833766,0.000080462785,0.0004513803,0.044806525,0.0010315654,0.8194235,0.00999478,0.12164213],"study_design_scores_gemma":[0.000025919648,0.000036749767,0.00034033533,0.00007856079,0.00006425148,0.00014649106,0.00012399085,0.14248943,0.0011337973,0.8476839,0.007844625,0.00003180776],"about_ca_topic_score_codex":0.0057274867,"about_ca_topic_score_gemma":0.004806987,"teacher_disagreement_score":0.011994618,"about_ca_system_score_codex":0.0040131053,"about_ca_system_score_gemma":0.0020923165,"threshold_uncertainty_score":0.040126026},"labels":[],"label_agreement":null},{"id":"W4251527353","doi":"10.1007/978-94-017-1506-5_1","title":"Random Media","year":2003,"lang":"en","type":"book-chapter","venue":"Mathematical modelling: theory and applications","topic":"Data Management and Algorithms","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":"York University","funders":"","keywords":"Markov chain; Ergodic theory; Martingale (probability theory); Markov property; Mathematics; Markov renewal process; Markov process; Statistical physics; Variable-order Markov model; Computer science; Discrete mathematics; Pure mathematics; Markov model; Applied mathematics; Statistics; Physics","score_opus":0.02993747984002548,"score_gpt":0.2327226450670196,"score_spread":0.2027851652269941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251527353","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004771743,0.009865425,0.16965552,0.0047658104,0.0039993417,0.00009822831,0.0010797816,0.0021387811,0.80362535],"genre_scores_gemma":[0.08251563,0.008607543,0.028865857,0.0015654839,0.0026010976,0.00019129952,0.0012896977,0.00070081913,0.87366253],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996731,0.00004981022,0.000013417047,0.00008816405,0.00014988818,0.000025506553],"domain_scores_gemma":[0.9994617,0.0001819548,0.000038335038,0.00014045181,0.00013165768,0.000045898625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029581663,0.00074858847,0.00063674746,0.0016458875,0.0007262951,0.0028619487,0.00073702,0.0010364564,0.061698012],"category_scores_gemma":[0.0019045586,0.000322589,0.00034740774,0.0011658164,0.0009716956,0.0027730153,0.0011159383,0.0012346643,0.02423389],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030669686,0.000024756077,0.00011562173,0.00017272418,0.00001660669,0.00012898377,0.00008885467,0.0017605554,0.0028291503,0.78293085,0.10513769,0.10676345],"study_design_scores_gemma":[0.000012845917,0.000024020765,0.00026030676,0.00008296989,0.000019569348,0.00041837562,0.00007054974,0.0095382035,0.0031389187,0.4159915,0.570413,0.000029650624],"about_ca_topic_score_codex":0.00056745764,"about_ca_topic_score_gemma":0.0005780306,"teacher_disagreement_score":0.061698012,"about_ca_system_score_codex":0.0005376731,"about_ca_system_score_gemma":0.00042730343,"threshold_uncertainty_score":0.20640045},"labels":[],"label_agreement":null},{"id":"W4251529305","doi":"10.1007/978-3-319-17885-1_1625","title":"Clustering of Geospatial Big Data in a Distributed Environment","year":2017,"lang":"en","type":"book-chapter","venue":"Encyclopedia of GIS","topic":"Data Management and Algorithms","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":"Computer Research Institute of Montréal","funders":"","keywords":"Geospatial analysis; Cluster analysis; Computer science; Big data; Data science; Data mining; Geography; Cartography; Artificial intelligence","score_opus":0.03924912840173062,"score_gpt":0.23981079524989043,"score_spread":0.20056166684815982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251529305","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020649448,0.0032770939,0.96397084,0.00066195196,0.00037298683,0.0001391605,0.00052945776,0.001588306,0.008810779],"genre_scores_gemma":[0.2501167,0.0046115043,0.7149015,0.0002831088,0.00054840057,0.0002837685,0.0036511205,0.0010785988,0.024525287],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99922895,0.00014404052,0.00004620543,0.00018509796,0.0003316717,0.00006404304],"domain_scores_gemma":[0.9986934,0.00044688568,0.00009017554,0.00039936334,0.00027370156,0.000096593234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065628055,0.00068777316,0.0009314287,0.0017593916,0.0011144854,0.0019869902,0.0019205933,0.0006337382,0.0022978648],"category_scores_gemma":[0.0026318266,0.0005558014,0.0008676556,0.005415751,0.000678399,0.002128994,0.001936549,0.0009696545,0.0012483633],"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":[0.00017657822,0.000149567,0.0038639854,0.0007982706,0.0002883312,0.000346703,0.00086546526,0.2717338,0.011701882,0.08076874,0.06702601,0.56228065],"study_design_scores_gemma":[0.000027240178,0.00007591581,0.004103823,0.00011912304,0.00006885922,0.00049943716,0.00058011163,0.676732,0.0076736114,0.23096104,0.07910698,0.000051852105],"about_ca_topic_score_codex":0.0043466208,"about_ca_topic_score_gemma":0.0064681456,"teacher_disagreement_score":0.0043466208,"about_ca_system_score_codex":0.0011638108,"about_ca_system_score_gemma":0.0011078148,"threshold_uncertainty_score":0.008642614},"labels":[],"label_agreement":null},{"id":"W4252131373","doi":"10.1155/2013/284904","title":"Modelling Spatio-Temporal Relevancy in Urban Context-Aware Pervasive Systems Using Voronoi Continuous Range Query and Multi-Interval Algebra","year":2013,"lang":"en","type":"article","venue":"Mobile Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Voronoi diagram; Context (archaeology); Interval (graph theory); Range (aeronautics); Process (computing); Space (punctuation); Data mining; Theoretical computer science; Mathematics; Geography","score_opus":0.02270075350401192,"score_gpt":0.22803275788379027,"score_spread":0.20533200437977833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252131373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039602872,0.00023892449,0.95860857,0.00009678753,0.000016067628,0.000049782102,0.00010749571,0.00023253089,0.0010469835],"genre_scores_gemma":[0.8109531,0.00033439338,0.18735994,0.00003887271,0.000034458757,0.000106249325,0.00021140862,0.000042559815,0.0009192022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998453,0.000570513,0.00011679967,0.00029344537,0.00044362075,0.00012259226],"domain_scores_gemma":[0.9980952,0.0011550594,0.0002344563,0.00020815125,0.00022423283,0.00008273504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013683204,0.00049465464,0.00081978866,0.0010115231,0.0004696632,0.002192781,0.0016479847,0.0007103579,0.001025762],"category_scores_gemma":[0.0052302233,0.00031400597,0.0009577729,0.0015625093,0.0008153364,0.003066001,0.0012902858,0.0008573564,0.0002444257],"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":[0.00013863499,0.00006208602,0.0026716073,0.00009172867,0.000049868017,0.00023804179,0.0003128514,0.8931031,0.0038821865,0.06854496,0.00041834248,0.03048649],"study_design_scores_gemma":[0.0000038579924,0.000017016564,0.0001309445,0.0000027033252,0.0000059297568,0.000039247927,0.00003985862,0.9912881,0.00044667875,0.007653252,0.00036655014,0.0000059304352],"about_ca_topic_score_codex":0.0115435375,"about_ca_topic_score_gemma":0.008283431,"teacher_disagreement_score":0.0115435375,"about_ca_system_score_codex":0.0010742847,"about_ca_system_score_gemma":0.0010277075,"threshold_uncertainty_score":0.022952676},"labels":[],"label_agreement":null},{"id":"W4253829582","doi":"10.32920/14638896.v1","title":"A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Search engine indexing; Data mining; Tree (set theory); Hash table; Database; Temporal database; Trajectory; Hash function; R-tree; NoSQL; Access method; Data structure; Database index; Table (database); Field (mathematics); Spatial database; Big data; Information retrieval; Spatial analysis; Mathematics","score_opus":0.13078521545752836,"score_gpt":0.35878698639044077,"score_spread":0.2280017709329124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253829582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008045151,0.0008508157,0.9845293,0.00016895568,0.000093330935,0.00016960953,0.0012499484,0.0033068266,0.0015860827],"genre_scores_gemma":[0.12362025,0.0009179756,0.86640304,0.00014098625,0.00012615467,0.00029276242,0.00506494,0.00032809636,0.0031058209],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982937,0.00018036946,0.00024637414,0.0003779529,0.00079931406,0.00010223241],"domain_scores_gemma":[0.99826354,0.00025919836,0.00014184488,0.0006291847,0.00058760704,0.00011855858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010297524,0.00062195124,0.0010050711,0.0035240264,0.0011237068,0.0020671948,0.002143495,0.00069727184,0.0027869167],"category_scores_gemma":[0.0029766494,0.0003987234,0.000960428,0.008155928,0.00045077168,0.00487372,0.0020459956,0.0007644766,0.0016087336],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005638626,0.0002917214,0.0041280817,0.0005225481,0.00017222532,0.0002850727,0.0005063968,0.018785797,0.036397465,0.045569185,0.021396771,0.8713809],"study_design_scores_gemma":[0.00020526405,0.000425908,0.0030315833,0.000104761464,0.00017081537,0.0020920746,0.00064534525,0.80689794,0.049705166,0.042641398,0.09386809,0.00021173622],"about_ca_topic_score_codex":0.0062570185,"about_ca_topic_score_gemma":0.0045542005,"teacher_disagreement_score":0.0062570185,"about_ca_system_score_codex":0.0009292663,"about_ca_system_score_gemma":0.0017932593,"threshold_uncertainty_score":0.012441218},"labels":[],"label_agreement":null},{"id":"W4254895427","doi":"10.22215/etd/2010-09029","title":"OLAP for trajectories","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Online analytical processing; Computer science; Information retrieval; Data mining; Data warehouse","score_opus":0.01249896302712996,"score_gpt":0.2690094075350616,"score_spread":0.25651044450793165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254895427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042384346,0.0019986692,0.9669162,0.0020426097,0.0007076261,0.000075921394,0.003903536,0.0065464634,0.013570571],"genre_scores_gemma":[0.11588196,0.005503611,0.83072346,0.00045067436,0.0006415253,0.00023919801,0.013194854,0.0022936906,0.031071082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986265,0.00015243782,0.00011212448,0.00033166716,0.00065188453,0.00012541955],"domain_scores_gemma":[0.9982495,0.00037277222,0.00008028812,0.00060466933,0.0005658116,0.00012682803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008753242,0.0010780195,0.0009446001,0.0022267257,0.0013373406,0.0047798543,0.0014934046,0.0009735085,0.016551135],"category_scores_gemma":[0.004709919,0.0005207545,0.0011889056,0.0043681143,0.00061102776,0.0060491846,0.0031367303,0.0022716615,0.009459151],"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":[0.00019486921,0.000046405556,0.0013467541,0.0004732938,0.0001148231,0.00030323723,0.00034914142,0.039426975,0.005145168,0.30321297,0.074677184,0.5747092],"study_design_scores_gemma":[0.0000223322,0.00004320384,0.0008737313,0.00020175299,0.00003460875,0.0004135234,0.00039629044,0.30424398,0.009518067,0.41963753,0.26453173,0.000083336054],"about_ca_topic_score_codex":0.006585029,"about_ca_topic_score_gemma":0.0041185217,"teacher_disagreement_score":0.016551135,"about_ca_system_score_codex":0.0013756781,"about_ca_system_score_gemma":0.0016419189,"threshold_uncertainty_score":0.05536908},"labels":[],"label_agreement":null},{"id":"W4254983785","doi":"10.1109/tsmcb.2008.925728","title":"A Multifaceted Perspective at Data Analysis: A Study in Collaborative Intelligent Agents","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Context (archaeology); Perspective (graphical); Process (computing); Exploit; Consistency (knowledge bases); Heuristics; Focus (optics); Function (biology); Fuzzy logic; Cluster analysis; Data science; Human–computer interaction; Artificial intelligence","score_opus":0.06271541074539363,"score_gpt":0.29962480710807243,"score_spread":0.2369093963626788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254983785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008397599,0.007668468,0.9583677,0.016072743,0.00014897858,0.00008819268,0.000034019275,0.00008078988,0.009141494],"genre_scores_gemma":[0.2118094,0.005182712,0.77825665,0.0012107872,0.00064049487,0.00031885927,0.000059711754,0.00008740932,0.0024339173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97678035,0.015175486,0.0011403867,0.002831656,0.0036005876,0.0004715261],"domain_scores_gemma":[0.9544012,0.038089816,0.0015598857,0.003376729,0.0017169873,0.0008555171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02360547,0.0011814827,0.0015605252,0.0048592044,0.0038947393,0.0111731095,0.0037423917,0.006165613,0.0017192307],"category_scores_gemma":[0.03466119,0.0010698503,0.0024761716,0.0058016544,0.020632451,0.016989497,0.005892381,0.006435218,0.00040803765],"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":[0.000013850218,0.00003528934,0.00064794347,0.00018175246,0.00006433022,0.00017325566,0.002972855,0.009222335,0.0003543038,0.96641505,0.00067388377,0.019245308],"study_design_scores_gemma":[0.00001952595,0.000045333563,0.00039127804,0.00016249891,0.00003679075,0.00019757557,0.0010033697,0.07015552,0.00047300867,0.91314703,0.014326796,0.000041377927],"about_ca_topic_score_codex":0.0038665398,"about_ca_topic_score_gemma":0.002604773,"teacher_disagreement_score":0.02360547,"about_ca_system_score_codex":0.003798257,"about_ca_system_score_gemma":0.0026038897,"threshold_uncertainty_score":0.12483913},"labels":[],"label_agreement":null},{"id":"W4255315553","doi":"10.1017/s0001867800011642","title":"Density approximation and exact simulation of random variables that are solutions of fixed-point equations","year":2002,"lang":"en","type":"article","venue":"Advances in Applied Probability","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Fixed point; Random variable; Probabilistic logic; Applied mathematics; Class (philosophy); Limit (mathematics); Sampling (signal processing); Mathematical analysis; Statistics","score_opus":0.03694009101673632,"score_gpt":0.2498786806779737,"score_spread":0.21293858966123738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255315553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027378043,0.00005890728,0.9965252,0.000045258654,0.000016389162,0.000022863378,0.0000136293465,0.00009898021,0.00048078928],"genre_scores_gemma":[0.2701196,0.00049544824,0.7240732,0.00014643425,0.000101996884,0.000625685,0.00032022333,0.0002549493,0.0038623968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976954,0.0010745687,0.00009866304,0.00027075046,0.00068242574,0.0001780736],"domain_scores_gemma":[0.98843354,0.0087387515,0.0006751167,0.0009533828,0.0009325615,0.0002666727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042557125,0.0011842264,0.0015513181,0.0017414114,0.0008568619,0.0016570158,0.0027905672,0.0019481621,0.0031986178],"category_scores_gemma":[0.028945263,0.0008826228,0.0016187755,0.0015916463,0.0025696065,0.0027874785,0.0026088036,0.002632966,0.0007660529],"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":[0.000105016436,0.000051078085,0.0010662347,0.00008392074,0.000055182772,0.00012786478,0.00013522289,0.57519853,0.0011779765,0.39995858,0.00070465606,0.021335833],"study_design_scores_gemma":[0.000011130141,0.000011954491,0.00004532857,0.000010350002,0.000004724588,0.000034132525,0.000006418219,0.9453334,0.00042734298,0.053577516,0.00052813516,0.000009475016],"about_ca_topic_score_codex":0.0040265587,"about_ca_topic_score_gemma":0.0020137813,"teacher_disagreement_score":0.0042557125,"about_ca_system_score_codex":0.0017308779,"about_ca_system_score_gemma":0.0013466239,"threshold_uncertainty_score":0.022506654},"labels":[],"label_agreement":null},{"id":"W4255361341","doi":"10.1007/978-1-4614-6170-8_100779","title":"Preference Propagation","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Psychology","score_opus":0.04001202717700208,"score_gpt":0.21315088920023195,"score_spread":0.17313886202322987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255361341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024092046,0.0023557555,0.81762064,0.0014581647,0.00077863765,0.00014818924,0.00069712015,0.0010665985,0.17346576],"genre_scores_gemma":[0.116799966,0.006740168,0.50790566,0.0011234432,0.001004488,0.00039063636,0.0027595314,0.001018721,0.36225745],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988972,0.00027124392,0.000059495742,0.000287952,0.00040969226,0.000074258554],"domain_scores_gemma":[0.9985227,0.00056752114,0.000049210976,0.00040821213,0.00039270896,0.00005967917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001396768,0.0010776124,0.00071340194,0.0013639742,0.0009445633,0.0024830005,0.0016380847,0.00080621574,0.04389571],"category_scores_gemma":[0.005497825,0.00047590764,0.00094493997,0.0027006585,0.0009316577,0.0042582345,0.0013668928,0.0025669876,0.015401497],"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":[0.000058915713,0.00007111087,0.00019598665,0.00021431784,0.000039841107,0.000034075307,0.00008812925,0.0075805895,0.0011438492,0.33207452,0.07552625,0.5829724],"study_design_scores_gemma":[0.000023257471,0.000064194996,0.00040239093,0.0001482786,0.00006491826,0.0002904201,0.000090624126,0.08545563,0.004638634,0.648057,0.26070738,0.000057425277],"about_ca_topic_score_codex":0.002673067,"about_ca_topic_score_gemma":0.0032661061,"teacher_disagreement_score":0.04389571,"about_ca_system_score_codex":0.0012701028,"about_ca_system_score_gemma":0.0012595496,"threshold_uncertainty_score":0.14684582},"labels":[],"label_agreement":null},{"id":"W4256193303","doi":"10.1016/b978-012088469-8/50128-5","title":"Efficient Constraint Processing for Highly Personalized Location Based Services","year":2004,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Data Management and Algorithms","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; Tracking (education); Location-based service; Wireless; Constraint (computer-aided design); Positioning technology; Video tracking; Distributed computing; Object (grammar); Human–computer interaction; Real-time computing; Artificial intelligence; Computer network; Telecommunications; Engineering","score_opus":0.015810502446586456,"score_gpt":0.2379889543604717,"score_spread":0.22217845191388524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256193303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008323347,0.0004153135,0.98045564,0.00035241587,0.00006742242,0.00012903265,0.0007983596,0.0020592427,0.007399222],"genre_scores_gemma":[0.16498601,0.0006258267,0.8195123,0.00017579344,0.00013078804,0.0002452531,0.002010566,0.00054635835,0.011767142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897265,0.0002329302,0.00006285844,0.00014973915,0.0004470718,0.0001347783],"domain_scores_gemma":[0.99818987,0.00105424,0.00008602816,0.00037430527,0.00023548875,0.00006005046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068629434,0.0010976237,0.0013788175,0.00081249664,0.0009996626,0.0023728758,0.0021471628,0.0011040458,0.018426577],"category_scores_gemma":[0.0039312113,0.00070414727,0.0008582899,0.0033539361,0.0006681478,0.0022991989,0.001744296,0.0018313035,0.002800678],"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":[0.00034216663,0.00020603205,0.00062012195,0.0005061812,0.00009713664,0.00033576397,0.00024052948,0.3692324,0.011483393,0.08822124,0.053506643,0.4752084],"study_design_scores_gemma":[0.000033566543,0.000028516555,0.00014417189,0.000015520647,0.000016890748,0.00009709161,0.00007146051,0.9353886,0.003146838,0.05272112,0.00831943,0.000016860491],"about_ca_topic_score_codex":0.018696355,"about_ca_topic_score_gemma":0.025180953,"teacher_disagreement_score":0.018696355,"about_ca_system_score_codex":0.0009947713,"about_ca_system_score_gemma":0.0016410094,"threshold_uncertainty_score":0.061643004},"labels":[],"label_agreement":null},{"id":"W4256228093","doi":"10.1007/3-540-36175-8_56","title":"DBRS: A Density-Based Spatial Clustering Method with Random Sampling","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":63,"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 Regina","funders":"","keywords":"DBSCAN; Cluster analysis; Computer science; Sampling (signal processing); Point (geometry); Data mining; Pattern recognition (psychology); Artificial intelligence; Algorithm; Correlation clustering; CURE data clustering algorithm; Mathematics; Computer vision","score_opus":0.02417341461940737,"score_gpt":0.2638808334063544,"score_spread":0.239707418786947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256228093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00112495,0.00008988822,0.9970048,0.000036418147,0.000044482964,0.000060313,0.00008075452,0.0011772125,0.00038121262],"genre_scores_gemma":[0.02658744,0.0001259776,0.9701635,0.000084264975,0.00005710589,0.00024064271,0.00048326975,0.00037963438,0.0018781773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973347,0.0009863664,0.00010833761,0.00039692284,0.0010607204,0.00011293502],"domain_scores_gemma":[0.9974503,0.0009435224,0.00012558165,0.0005627478,0.00080272707,0.000115174706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026195056,0.0011181888,0.002346719,0.0023673342,0.0013513371,0.0014443878,0.0046623843,0.0015550051,0.005354723],"category_scores_gemma":[0.0073974133,0.0012450472,0.0019183462,0.003005876,0.00093335385,0.0018968713,0.003080876,0.0016524106,0.003704723],"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":[0.0004401336,0.00020184922,0.001260842,0.0003874047,0.0003471435,0.00011373602,0.00029594914,0.2939896,0.007511781,0.037563033,0.01834347,0.6395451],"study_design_scores_gemma":[0.000053612177,0.000035772417,0.00021078359,0.000015040862,0.00003088045,0.00009420696,0.000024524992,0.9825439,0.0021201672,0.009316698,0.005518916,0.000035611512],"about_ca_topic_score_codex":0.011555055,"about_ca_topic_score_gemma":0.013622194,"teacher_disagreement_score":0.011555055,"about_ca_system_score_codex":0.0010836233,"about_ca_system_score_gemma":0.0019800202,"threshold_uncertainty_score":0.022975564},"labels":[],"label_agreement":null},{"id":"W4281929615","doi":"10.3390/ijgi11060322","title":"Efficient Calculation of Distance Transform on Discrete Global Grid Systems","year":2022,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Distance transform; Computer science; Distortion (music); Discretization; Grid; Polygon mesh; Algorithm; Boundary (topology); Artificial intelligence; Mathematics; Image (mathematics); Geography; Remote sensing; Geometry; Computer graphics (images)","score_opus":0.005550111020099068,"score_gpt":0.24163025902686566,"score_spread":0.2360801480067666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281929615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0128700845,0.00007398893,0.98379993,0.000080034995,0.000044511533,0.000032864064,0.00013156411,0.00080126297,0.0021657625],"genre_scores_gemma":[0.2574163,0.00017843705,0.7384478,0.00004786044,0.000032260083,0.000108946435,0.00071517564,0.00034570295,0.0027076027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994179,0.000102152706,0.0000426282,0.00008560334,0.00030661,0.000045075347],"domain_scores_gemma":[0.9989297,0.0004506753,0.000074115436,0.00021245783,0.00028923844,0.000043853182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048132663,0.0004923393,0.0006163589,0.0010818791,0.00045062584,0.0012559772,0.0009052373,0.00055653666,0.0043801647],"category_scores_gemma":[0.0036138233,0.00023896535,0.0005094621,0.0012775814,0.00047937993,0.0015909411,0.0015112371,0.0008750153,0.001470453],"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":[0.00012035031,0.00005774907,0.0023170062,0.00018572279,0.000037645143,0.00030271336,0.00026755052,0.5534915,0.01076535,0.06826002,0.008199001,0.35599533],"study_design_scores_gemma":[0.00001080442,0.000015165813,0.00020553569,0.0000048212046,0.000002766397,0.000056614943,0.00004711011,0.9796602,0.002348224,0.013677926,0.0039641587,0.0000066307184],"about_ca_topic_score_codex":0.0041159745,"about_ca_topic_score_gemma":0.0038606322,"teacher_disagreement_score":0.0043801647,"about_ca_system_score_codex":0.0006142348,"about_ca_system_score_gemma":0.0007086631,"threshold_uncertainty_score":0.014653146},"labels":[],"label_agreement":null},{"id":"W4282821942","doi":"10.3390/a15060206","title":"Correction: Filion, G.J. Analytic Combinatorics for Computing Seeding Probabilities. Algorithms 2018, 11, 3","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Seeding; Computer science; Algorithm; Combinatorics; Mathematics; Physics","score_opus":0.024576397843192536,"score_gpt":0.2545848241560057,"score_spread":0.23000842631281315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282821942","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019691841,0.0014083973,0.0050408784,0.075891145,0.9091755,0.000026964974,0.0036770948,0.0013595341,0.0032236648],"genre_scores_gemma":[0.04131973,0.012307737,0.025661632,0.15073179,0.48772728,0.0004704179,0.011511588,0.010623145,0.25964662],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98974633,0.0019002255,0.001444933,0.0019186354,0.0042238794,0.00076583505],"domain_scores_gemma":[0.8820069,0.027834553,0.004142814,0.007370619,0.07548056,0.003164467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007903802,0.0034277774,0.003154747,0.005605941,0.0057251304,0.008553355,0.005458889,0.0073010013,0.083185926],"category_scores_gemma":[0.17925957,0.0015853099,0.0030026995,0.006042047,0.004790091,0.006555666,0.003629497,0.015918354,0.054636948],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018674418,0.0000027463325,0.000039891944,0.00009993756,0.000015362419,0.00006347888,0.000051301417,0.00006768508,0.000039284605,0.0024423755,0.9944141,0.0027450535],"study_design_scores_gemma":[0.000048065358,0.000012387214,0.00043367172,0.00029473272,0.000059721573,0.0003756414,0.000082151164,0.000539925,0.00041239653,0.007491186,0.9901786,0.00007149993],"about_ca_topic_score_codex":0.019803612,"about_ca_topic_score_gemma":0.019023547,"teacher_disagreement_score":0.083185926,"about_ca_system_score_codex":0.0070555997,"about_ca_system_score_gemma":0.0091659585,"threshold_uncertainty_score":0.27828467},"labels":[],"label_agreement":null},{"id":"W4283587045","doi":"10.1613/jair.1.13544","title":"Path Counting for Grid-Based Navigation","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Grid; Computer science; Vertex (graph theory); Traverse; Dijkstra's algorithm; Visibility; Any-angle path planning; Algorithm; Shortest path problem; Grid method multiplication; Theoretical computer science; Motion planning; Mathematics; Artificial intelligence; Graph; Geometry; Geography","score_opus":0.19304573332997296,"score_gpt":0.4236703514921283,"score_spread":0.23062461816215532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283587045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00501192,0.00019243413,0.99230886,0.000089558576,0.000047663827,0.000029546693,0.00010743206,0.00040001064,0.0018125627],"genre_scores_gemma":[0.22307032,0.00054554886,0.772406,0.00012722971,0.00008886962,0.00032083705,0.000568601,0.00024948263,0.0026232009],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828714,0.0005166724,0.0001226093,0.00045697368,0.0004831594,0.00013340743],"domain_scores_gemma":[0.9894188,0.005602315,0.0008617925,0.0020085506,0.0017694102,0.00033909973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018442736,0.0005828086,0.0008944085,0.0023246815,0.0011438897,0.0020449054,0.002038947,0.00090607314,0.0037578244],"category_scores_gemma":[0.023989573,0.0004824729,0.0007331923,0.0035644406,0.0016803098,0.0036624903,0.002474773,0.0018861747,0.0008491506],"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":[0.00013508832,0.000045451678,0.0024075864,0.00020461869,0.00005252382,0.00010141925,0.00020982977,0.37812963,0.0022174653,0.46160975,0.005462361,0.14942428],"study_design_scores_gemma":[0.000014093119,0.00003018397,0.00024976933,0.000025751939,0.000010557634,0.00007440252,0.00002918909,0.8003203,0.00082718197,0.19382098,0.0045791664,0.000018382416],"about_ca_topic_score_codex":0.008857652,"about_ca_topic_score_gemma":0.006271236,"teacher_disagreement_score":0.008857652,"about_ca_system_score_codex":0.0015137497,"about_ca_system_score_gemma":0.0023685687,"threshold_uncertainty_score":0.01761216},"labels":[],"label_agreement":null},{"id":"W4285043084","doi":"10.1007/s12652-022-04306-w","title":"Correction to: E-ware: a big data system for the incremental discovery of spatio-temporal events from microblogs","year":2022,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Data Management and Algorithms","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":"Dalhousie University","funders":"","keywords":"Microblogging; Computer science; Big data; Social media; Computational intelligence; Data science; Information retrieval; Data mining; World Wide Web; Artificial intelligence","score_opus":0.07142351939846545,"score_gpt":0.28310853218444604,"score_spread":0.2116850127859806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285043084","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016351638,0.00043615865,0.0010261979,0.073935285,0.9200606,0.000028808756,0.0025827652,0.0007788323,0.0009877593],"genre_scores_gemma":[0.022842588,0.006113537,0.009256131,0.20519264,0.5637977,0.0004773719,0.007647885,0.0035040407,0.18116811],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9924568,0.0010573451,0.0014919331,0.0013813366,0.0027990253,0.00081367],"domain_scores_gemma":[0.89979,0.02175049,0.0044617825,0.007371721,0.06119029,0.0054357112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006035113,0.0024805996,0.0030242614,0.0052771573,0.004394912,0.0065505477,0.004921156,0.010181872,0.0772805],"category_scores_gemma":[0.12953494,0.001489555,0.0019794812,0.004324571,0.003818876,0.003482805,0.0038037172,0.015381438,0.055392634],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005904812,0.000008442871,0.00010566597,0.00012697438,0.000020813135,0.00019175139,0.000057572666,0.0000629027,0.00007483313,0.00075765117,0.9950191,0.0035152426],"study_design_scores_gemma":[0.000098205644,0.000027865535,0.0012781526,0.00029961645,0.00005179572,0.00045415226,0.00017886923,0.00055274565,0.0005957074,0.002074142,0.99430877,0.00008013645],"about_ca_topic_score_codex":0.028523777,"about_ca_topic_score_gemma":0.02885251,"teacher_disagreement_score":0.0772805,"about_ca_system_score_codex":0.004913512,"about_ca_system_score_gemma":0.009391814,"threshold_uncertainty_score":0.258529},"labels":[],"label_agreement":null},{"id":"W4285223139","doi":"10.1109/jiot.2022.3177474","title":"EPGQ: Efficient and Private Feature-Based Group Nearest Neighbor Query Over Road Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Information retrieval; Location-based service; Encryption; Feature (linguistics); k-nearest neighbors algorithm; Data mining; Similarity (geometry); Set (abstract data type); Theoretical computer science; Point of interest; Artificial intelligence; Computer security; Computer network; Image (mathematics)","score_opus":0.00861508327016352,"score_gpt":0.22031883312549314,"score_spread":0.2117037498553296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285223139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08435533,0.0017736744,0.89731765,0.0010413543,0.00014453995,0.0005388875,0.00301449,0.00601321,0.0058008814],"genre_scores_gemma":[0.7449048,0.0005976998,0.2436544,0.00034675444,0.000090643436,0.00029018486,0.0048126276,0.00021742341,0.0050853975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975696,0.00047768393,0.00019225317,0.00046143556,0.00098701,0.0003119705],"domain_scores_gemma":[0.9980008,0.0006151803,0.00017105957,0.0008465274,0.00026863036,0.00009779324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014564055,0.00079084205,0.0018020254,0.0009292291,0.0010673262,0.0015538506,0.002976122,0.0017940523,0.0028485751],"category_scores_gemma":[0.005440751,0.00029939276,0.00074501336,0.0028128428,0.00079301995,0.0053927894,0.004339958,0.0010448915,0.001310959],"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":[0.0024635217,0.0006263686,0.0077015716,0.0008033311,0.00025734326,0.0014711925,0.0012880913,0.30373365,0.030234348,0.0916845,0.067540385,0.49219579],"study_design_scores_gemma":[0.00010458095,0.00014880601,0.00081405987,0.000011933301,0.00002560294,0.00039785047,0.0002974609,0.95341367,0.0045808493,0.030109828,0.010052945,0.000042369164],"about_ca_topic_score_codex":0.009456041,"about_ca_topic_score_gemma":0.0080465665,"teacher_disagreement_score":0.009456041,"about_ca_system_score_codex":0.0012222275,"about_ca_system_score_gemma":0.0011716215,"threshold_uncertainty_score":0.018801987},"labels":[],"label_agreement":null},{"id":"W4286656147","doi":"10.1109/tbdata.2022.3186857","title":"Efficient Learned Spatial Index With Interpolation Function Based Learned Model","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Big Data","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Leverage (statistics); Spatial database; Data mining; Spatial analysis; Spatial query; Index (typography); Overhead (engineering); Interpolation (computer graphics); Multivariate interpolation; Function (biology); Machine learning; Artificial intelligence; Information retrieval; Sargable; Web search query; Bilinear interpolation; Mathematics","score_opus":0.08700016958677916,"score_gpt":0.2643861053845783,"score_spread":0.17738593579779915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286656147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047436558,0.00081880635,0.94240725,0.00034375268,0.00007100628,0.00010137102,0.0011246451,0.00436884,0.0033277355],"genre_scores_gemma":[0.67958385,0.0010351791,0.30721098,0.0002595745,0.00010199863,0.00025556676,0.0047150804,0.0003460663,0.0064916653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992341,0.0000807385,0.000057316855,0.00018547563,0.00034335058,0.000099128],"domain_scores_gemma":[0.9988945,0.0003197611,0.000088194996,0.00031851936,0.0003196297,0.0000593547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005431248,0.0008841226,0.0012721283,0.00087588455,0.00043879746,0.0015455832,0.0021384393,0.0008407664,0.0020238948],"category_scores_gemma":[0.0034898724,0.00037210787,0.00090842525,0.0022065411,0.00049054285,0.0044937227,0.0016383382,0.001298862,0.0009831383],"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":[0.0002786351,0.00021093566,0.0058471393,0.00018085232,0.000069278234,0.00024626424,0.00017760127,0.7080814,0.009042935,0.018486306,0.010061802,0.24731684],"study_design_scores_gemma":[0.000005001153,0.000020117393,0.00015053824,0.0000022901752,0.0000054826596,0.000037775517,0.000013037326,0.9959694,0.0011812521,0.0019181056,0.0006903001,0.000006692008],"about_ca_topic_score_codex":0.015455055,"about_ca_topic_score_gemma":0.011722804,"teacher_disagreement_score":0.015455055,"about_ca_system_score_codex":0.0012003062,"about_ca_system_score_gemma":0.0022026522,"threshold_uncertainty_score":0.030730188},"labels":[],"label_agreement":null},{"id":"W4288420235","doi":"10.48550/arxiv.1903.02076","title":"Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal\\n Joins","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Joins; Computer science; Intersection (aeronautics); Binary number; Query optimization; Query plan; Matching (statistics); Vertex (graph theory); Partition (number theory); Theoretical computer science; Graph; Mathematics; Sargable; Data mining; Search engine; Combinatorics; Information retrieval","score_opus":0.054827228183465124,"score_gpt":0.18231339072625027,"score_spread":0.12748616254278516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288420235","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17284442,0.0005584922,0.8132547,0.0006945118,0.000044431672,0.00023580017,0.00041091934,0.003117296,0.008839349],"genre_scores_gemma":[0.5873644,0.00016946955,0.40904412,0.00015463124,0.00004010258,0.00012645755,0.0007328555,0.00060996565,0.0017579662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996851,0.0007791181,0.00018073029,0.00048180635,0.0012534132,0.00045382715],"domain_scores_gemma":[0.9979686,0.0009946106,0.00022854247,0.00047006572,0.00021319799,0.00012497717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023009968,0.0009351671,0.0009754958,0.0011448693,0.0007314274,0.0018332807,0.0015712033,0.00075659936,0.0018277481],"category_scores_gemma":[0.004954575,0.0004915335,0.0008217147,0.0019307799,0.0013558329,0.003277832,0.0020274296,0.0010081275,0.0003225124],"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":[0.0006000188,0.000416191,0.004963552,0.000166057,0.00010764291,0.00013825367,0.00021115293,0.7809182,0.013294073,0.03398964,0.0050354223,0.16015986],"study_design_scores_gemma":[0.00003301459,0.00013972186,0.00046067758,0.000007061362,0.00002656047,0.000052305586,0.00007850628,0.97113985,0.006215981,0.020409843,0.0014233877,0.000013128076],"about_ca_topic_score_codex":0.005989115,"about_ca_topic_score_gemma":0.011553209,"teacher_disagreement_score":0.005989115,"about_ca_system_score_codex":0.001917647,"about_ca_system_score_gemma":0.0019080163,"threshold_uncertainty_score":0.013913572},"labels":[],"label_agreement":null},{"id":"W4288754847","doi":"10.5311/josis.2022.24.199","title":"Temporally relevant parallel top-k spatial keyword search","year":2022,"lang":"en","type":"article","venue":"Journal of Spatial Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Computer science; Exploit; Search engine indexing; Information retrieval; Ranking (information retrieval); Data mining; Relevance (law); Visitor pattern","score_opus":0.019932350311511565,"score_gpt":0.26737424443389285,"score_spread":0.2474418941223813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288754847","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23743632,0.0059474073,0.6878909,0.00083922537,0.00040253266,0.0005903139,0.009933487,0.042583566,0.014376293],"genre_scores_gemma":[0.5458932,0.001740362,0.43356803,0.00018240584,0.00018776274,0.00024213939,0.010378234,0.0007291184,0.00707878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894947,0.00008872501,0.00014470397,0.00027557553,0.00044992196,0.00009168273],"domain_scores_gemma":[0.9981554,0.0003825497,0.000182506,0.0005974613,0.00054654066,0.00013555105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075173,0.0007878446,0.0012547707,0.0021477728,0.0010210878,0.0022560626,0.0016633217,0.00057418906,0.002438204],"category_scores_gemma":[0.0038132877,0.00048673933,0.0005962569,0.004924896,0.00035596287,0.003670468,0.001473287,0.0005830089,0.0019391258],"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":[0.0018913175,0.00056010555,0.010709618,0.001329678,0.00032610542,0.0007560535,0.0009963473,0.06234986,0.13095555,0.010197674,0.04080707,0.7391206],"study_design_scores_gemma":[0.00030135078,0.00040626488,0.0067262445,0.000054922726,0.00024052557,0.0014237784,0.0008255058,0.8436015,0.083198555,0.020298092,0.0427936,0.00012962517],"about_ca_topic_score_codex":0.010734869,"about_ca_topic_score_gemma":0.018084604,"teacher_disagreement_score":0.010734869,"about_ca_system_score_codex":0.00081419083,"about_ca_system_score_gemma":0.0020961042,"threshold_uncertainty_score":0.021344721},"labels":[],"label_agreement":null},{"id":"W4289977807","doi":"","title":"Logical consistency for vague spatiotemporal objects and relations","year":2007,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Consistency (knowledge bases); Theoretical computer science; Artificial intelligence","score_opus":0.02550412377743246,"score_gpt":0.24695914231199764,"score_spread":0.22145501853456517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289977807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07095941,0.00092695694,0.91723084,0.0015402413,0.000084772226,0.000117256524,0.00043167686,0.00024031685,0.00846854],"genre_scores_gemma":[0.59095126,0.0008151094,0.40305713,0.0005879715,0.00026985328,0.00035635327,0.0009911815,0.00010786851,0.0028631773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9862839,0.003626424,0.001756368,0.002099723,0.0057213996,0.00051226147],"domain_scores_gemma":[0.97249275,0.014489185,0.0032069525,0.005254676,0.0039244397,0.00063189963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012794716,0.00040787202,0.0011214195,0.0029911378,0.0016867644,0.0054471716,0.002600254,0.0017093022,0.0019544666],"category_scores_gemma":[0.04717209,0.00061733625,0.0018657241,0.0027199064,0.0054559154,0.009588476,0.004543085,0.0022136485,0.0003127792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011871578,0.000044633965,0.002027976,0.00023680551,0.000095273266,0.0004965501,0.0013232556,0.022509268,0.0038824256,0.94545656,0.0013723053,0.02243627],"study_design_scores_gemma":[0.000043690903,0.00004326433,0.0011148924,0.00009126362,0.00007101398,0.00039911573,0.0005193136,0.11871109,0.004655625,0.86398107,0.010308699,0.00006103087],"about_ca_topic_score_codex":0.0021208094,"about_ca_topic_score_gemma":0.0014623104,"teacher_disagreement_score":0.012794716,"about_ca_system_score_codex":0.002021402,"about_ca_system_score_gemma":0.0016768131,"threshold_uncertainty_score":0.067665756},"labels":[],"label_agreement":null},{"id":"W4292904533","doi":"10.1007/978-3-319-07153-4_19-1","title":"Variable Neighborhood Search","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Variable (mathematics); Computer science; Mathematics; Mathematical analysis","score_opus":0.0215776124563754,"score_gpt":0.22285010164376273,"score_spread":0.20127248918738733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292904533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042124535,0.017923137,0.75453156,0.0011773572,0.0015724053,0.00009316668,0.00065305794,0.0015518322,0.21828505],"genre_scores_gemma":[0.08905959,0.01485146,0.4800627,0.000850769,0.00090642605,0.00023909961,0.0033214868,0.0015240966,0.40918443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996401,0.000053105923,0.000014375861,0.00009052837,0.00017515972,0.000026659947],"domain_scores_gemma":[0.9998011,0.00006364935,0.0000101091255,0.000053302527,0.000059133457,0.000012735358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032691477,0.0006836604,0.0009062773,0.0008779742,0.00057344657,0.0012643804,0.0011034916,0.0006763338,0.021520216],"category_scores_gemma":[0.0012670308,0.00030100637,0.0004334538,0.0021281445,0.00060328515,0.0015054567,0.0010807805,0.0013039091,0.008473645],"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":[0.000041988147,0.000037063706,0.00018484548,0.00019610139,0.000029609313,0.00003760015,0.00005818535,0.022461116,0.0013155277,0.17500044,0.121519335,0.6791182],"study_design_scores_gemma":[0.0000225905,0.0000573254,0.0004774036,0.0001903553,0.00003818014,0.00033077577,0.000060618757,0.14728256,0.0032628176,0.27356339,0.57467675,0.000037203336],"about_ca_topic_score_codex":0.0021785642,"about_ca_topic_score_gemma":0.003845686,"teacher_disagreement_score":0.021520216,"about_ca_system_score_codex":0.0006654169,"about_ca_system_score_gemma":0.0007827932,"threshold_uncertainty_score":0.07199234},"labels":[],"label_agreement":null},{"id":"W4295767229","doi":"10.1109/fuzz-ieee55066.2022.9882645","title":"Transportation Analytics with Fuzzy Logic and Regression","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Schedule; Public transport; Fuzzy logic; Computer science; Transport engineering; Transit (satellite); Analytics; Real-time computing; Regression analysis; Operations research; Engineering; Data mining; Artificial intelligence; Machine learning","score_opus":0.05806613571502524,"score_gpt":0.2879523163196346,"score_spread":0.22988618060460936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295767229","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017640788,0.000519871,0.97810787,0.00044780882,0.00003831249,0.000047720056,0.00032090434,0.00093490485,0.0019417807],"genre_scores_gemma":[0.6278742,0.0010484952,0.3671804,0.00021983952,0.00014632268,0.00014104927,0.0009120554,0.00010032242,0.0023773224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850345,0.0004760663,0.000117147596,0.00037859532,0.00043786093,0.00008692737],"domain_scores_gemma":[0.9966884,0.0021502122,0.00037115978,0.00020122332,0.00053983065,0.000049175887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020920706,0.0012282974,0.0011100417,0.0022778907,0.00045937576,0.0018610272,0.0009925815,0.0008452325,0.0015894807],"category_scores_gemma":[0.008537842,0.0004354477,0.001259134,0.0021535668,0.00053611153,0.0019283934,0.0008184985,0.0015773838,0.00055715523],"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":[0.00006622834,0.000084377105,0.004655085,0.0000847931,0.0001169473,0.000098186734,0.0000879828,0.87850684,0.0009206061,0.01218698,0.0012652088,0.10192677],"study_design_scores_gemma":[0.000002584563,0.000010097717,0.0002574531,0.0000082287925,0.000006533942,0.000010304234,0.000015238795,0.99177647,0.00024030932,0.007348621,0.0003181026,0.0000060816205],"about_ca_topic_score_codex":0.025386019,"about_ca_topic_score_gemma":0.011593138,"teacher_disagreement_score":0.025386019,"about_ca_system_score_codex":0.0014680149,"about_ca_system_score_gemma":0.0011010042,"threshold_uncertainty_score":0.05047649},"labels":[],"label_agreement":null},{"id":"W4296289779","doi":"10.48550/arxiv.0812.0146","title":"Lower Bounds on Performance of Metric Tree Indexing Schemes for Exact\\n Similarity Search in High Dimensions","year":2008,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Mathematics; Omega; Combinatorics; Nearest neighbor search; Tree (set theory); Search engine indexing; Dimension (graph theory); Context (archaeology); Metric (unit); Metric space; Intrinsic dimension; Discrete mathematics; Upper and lower bounds; Curse of dimensionality; Computer science; Artificial intelligence; Statistics; Mathematical analysis","score_opus":0.09612717945864159,"score_gpt":0.2153419356837935,"score_spread":0.11921475622515192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296289779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20010397,0.018802615,0.7268177,0.010969628,0.000889993,0.0007434652,0.002784656,0.004796761,0.03409118],"genre_scores_gemma":[0.8392391,0.004462477,0.14432973,0.0014412781,0.00080598256,0.0006940037,0.0024973734,0.0007684082,0.0057617156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9808091,0.0061015263,0.0016851586,0.002937555,0.005901768,0.002564906],"domain_scores_gemma":[0.89198124,0.07488582,0.0054293456,0.02137086,0.0040470753,0.0022856423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017912537,0.002381648,0.003863714,0.0028260157,0.0025875631,0.0073783174,0.006137359,0.0047441707,0.008052928],"category_scores_gemma":[0.11092165,0.0011487795,0.0019495015,0.0058223275,0.004788495,0.018591296,0.009951455,0.004371116,0.0027633864],"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":[0.0036214124,0.0007260736,0.00817458,0.0017391774,0.00033085328,0.00021998736,0.0005862353,0.5131612,0.013307797,0.25772324,0.019927897,0.18048163],"study_design_scores_gemma":[0.000080386366,0.00035817028,0.0008692503,0.00010563072,0.000055263863,0.0001991427,0.00010429192,0.90734583,0.0027866522,0.08591783,0.0021147865,0.00006283346],"about_ca_topic_score_codex":0.0036557731,"about_ca_topic_score_gemma":0.004478643,"teacher_disagreement_score":0.017912537,"about_ca_system_score_codex":0.007749323,"about_ca_system_score_gemma":0.006718721,"threshold_uncertainty_score":0.09473169},"labels":[],"label_agreement":null},{"id":"W4297970676","doi":"10.1561/1100000046","title":"Supporting and Exploiting Spatial Memory in User Interfaces","year":2013,"lang":"en","type":"article","venue":"Foundations and Trends® in Human–Computer Interaction","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Human–computer interaction; Cognitive psychology; Psychology","score_opus":0.028109450966757737,"score_gpt":0.3175107030948831,"score_spread":0.2894012521281254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297970676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16791804,0.07409545,0.66685075,0.0038054734,0.0004472549,0.0008796634,0.00035466344,0.0048478385,0.08080088],"genre_scores_gemma":[0.5985534,0.026179165,0.36648458,0.0010393081,0.00024268542,0.000830618,0.00024525568,0.0002616259,0.0061634025],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977324,0.000947564,0.00028938008,0.00022698958,0.00066029676,0.00014332256],"domain_scores_gemma":[0.99051976,0.00709224,0.0005399553,0.0009388462,0.00078645,0.0001227967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023174083,0.0010264937,0.0005280874,0.0016356222,0.0006828376,0.005148174,0.0016271864,0.0017979874,0.0041728015],"category_scores_gemma":[0.017287737,0.0005993639,0.00080268143,0.0013730165,0.0018408203,0.008765911,0.0030850559,0.0009445718,0.0011997431],"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":[0.00035232806,0.00019069765,0.0042467294,0.013627288,0.000180176,0.0007028417,0.021436866,0.004559466,0.04880743,0.08678494,0.00500912,0.8141022],"study_design_scores_gemma":[0.00035093218,0.0027114928,0.0372805,0.017803257,0.001715092,0.009991564,0.01607779,0.051706556,0.114204176,0.21579802,0.5316141,0.0007464737],"about_ca_topic_score_codex":0.0010192285,"about_ca_topic_score_gemma":0.0009726245,"teacher_disagreement_score":0.005148174,"about_ca_system_score_codex":0.00052331534,"about_ca_system_score_gemma":0.0008128134,"threshold_uncertainty_score":0.013959408},"labels":[],"label_agreement":null},{"id":"W4298176586","doi":"10.1007/978-1-4939-7131-2_100611","title":"Location-Based Recommendation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Business","score_opus":0.02729451194850085,"score_gpt":0.23875779239850933,"score_spread":0.21146328045000848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298176586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007086205,0.034537613,0.7390664,0.0029794825,0.003681699,0.00022383562,0.0023745075,0.0058217198,0.20422855],"genre_scores_gemma":[0.12542632,0.041162364,0.35454613,0.0013000448,0.0028716163,0.00021270144,0.006460641,0.0007849502,0.46723515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946934,0.000063917054,0.000025526851,0.00016635125,0.00023385663,0.00004101203],"domain_scores_gemma":[0.9994062,0.00014289899,0.000019751013,0.00024255489,0.00016199992,0.000026541344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005703997,0.0012716437,0.0013919246,0.0013653805,0.00063742144,0.0018420097,0.0019574026,0.001335708,0.030034604],"category_scores_gemma":[0.0017703715,0.00051256805,0.0007744181,0.004086544,0.0003548972,0.0029706117,0.0008764977,0.001445711,0.028425371],"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":[0.00007778748,0.00012675341,0.00039370978,0.00031227613,0.00010100884,0.000057668152,0.000034496494,0.011225388,0.0028096854,0.029412022,0.15290548,0.80254364],"study_design_scores_gemma":[0.00006349564,0.00021292797,0.002216409,0.00037086033,0.00021346516,0.0013054414,0.00012062656,0.3240903,0.01125273,0.105402514,0.55459476,0.00015644827],"about_ca_topic_score_codex":0.0046939794,"about_ca_topic_score_gemma":0.006462801,"teacher_disagreement_score":0.030034604,"about_ca_system_score_codex":0.0008641898,"about_ca_system_score_gemma":0.0005936649,"threshold_uncertainty_score":0.10047573},"labels":[],"label_agreement":null},{"id":"W4298780211","doi":"","title":"Development of business spatial analysis tools: methodology and framework","year":2015,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Data science; Systems engineering; Software engineering; Engineering","score_opus":0.06654460554948696,"score_gpt":0.27478097921575,"score_spread":0.20823637366626305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298780211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020593058,0.000097697666,0.9910711,0.0005635748,0.000020294932,0.00024417095,0.00048254553,0.0023782288,0.0030831003],"genre_scores_gemma":[0.026771031,0.00023848689,0.96999145,0.00007289939,0.000022761169,0.00036096593,0.001065733,0.00046512688,0.0010115139],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.993562,0.0028043801,0.00089416554,0.0006321998,0.0017934131,0.00031386848],"domain_scores_gemma":[0.98235476,0.008862841,0.0011370139,0.0025890272,0.004273211,0.0007832167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013073523,0.001257355,0.00097517995,0.008839205,0.0014455238,0.008895836,0.0029059958,0.0013315724,0.0064991536],"category_scores_gemma":[0.030995874,0.0010636167,0.0020557665,0.0069388454,0.0019991524,0.0069133393,0.004097858,0.0021973837,0.002917267],"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":[0.00008315442,0.000294995,0.008120097,0.0012812827,0.00021026706,0.0002854441,0.0028770114,0.030020365,0.0056582685,0.49294066,0.01639259,0.44183582],"study_design_scores_gemma":[0.00006970032,0.00015639722,0.003959375,0.0016901729,0.0002055628,0.0005947437,0.003784546,0.33014572,0.021905445,0.37949046,0.2578525,0.00014535968],"about_ca_topic_score_codex":0.0060982616,"about_ca_topic_score_gemma":0.005376833,"teacher_disagreement_score":0.013073523,"about_ca_system_score_codex":0.0027586718,"about_ca_system_score_gemma":0.008930216,"threshold_uncertainty_score":0.069140196},"labels":[],"label_agreement":null},{"id":"W4299716523","doi":"","title":"Extraction et gestion de connaissances dans un contexte spatio-temporel : applications à la géomatique pour la gestion des territoires","year":2008,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Political science; Humanities; Philosophy","score_opus":0.02885394583863831,"score_gpt":0.2566578654040829,"score_spread":0.2278039195654446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299716523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023903256,0.0045146155,0.9468377,0.002523919,0.0005020444,0.0002173103,0.0018116231,0.0057295724,0.013959949],"genre_scores_gemma":[0.18211792,0.007044975,0.78094685,0.0004720931,0.00027263127,0.00030825555,0.0032783805,0.001717751,0.023841059],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99841523,0.00034346894,0.00014482561,0.00044091413,0.0005518177,0.00010376757],"domain_scores_gemma":[0.9975623,0.0010333003,0.00024258508,0.0004851859,0.0005410088,0.00013565278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018587348,0.0011847261,0.001127371,0.0039455006,0.001318462,0.0073328717,0.0011221403,0.0020847705,0.0122225555],"category_scores_gemma":[0.0051492606,0.00070855033,0.0019336683,0.004939386,0.001874181,0.0043765027,0.003943959,0.0017297084,0.0052680215],"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":[0.0003635766,0.00013343123,0.011057682,0.002123571,0.00025663266,0.0013468465,0.008680194,0.025526868,0.047097377,0.07781161,0.024261845,0.80134034],"study_design_scores_gemma":[0.00007372148,0.00021079853,0.03681705,0.0014752919,0.00035232678,0.0028993208,0.01230968,0.22582763,0.05866899,0.12685905,0.53408235,0.00042384738],"about_ca_topic_score_codex":0.011657449,"about_ca_topic_score_gemma":0.016013255,"teacher_disagreement_score":0.0122225555,"about_ca_system_score_codex":0.001180765,"about_ca_system_score_gemma":0.0019413049,"threshold_uncertainty_score":0.040888548},"labels":[],"label_agreement":null},{"id":"W4300934746","doi":"10.1007/978-1-4614-6170-8_100572","title":"Location-Based Recommendation","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Computer science; Geography","score_opus":0.021004065057828605,"score_gpt":0.22433946377126268,"score_spread":0.2033353987134341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300934746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007334195,0.03494601,0.7300491,0.0026580459,0.0030563069,0.00021124263,0.0021283946,0.005303222,0.21431354],"genre_scores_gemma":[0.117793836,0.03943962,0.34102982,0.0010914336,0.0022109416,0.0001861107,0.0054950016,0.00067406055,0.49207914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995272,0.000058089106,0.000023249351,0.00014085646,0.00021495669,0.000035725257],"domain_scores_gemma":[0.9995204,0.000119885015,0.000016706223,0.00018529945,0.0001366362,0.000021046766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005081122,0.0012069546,0.0013003163,0.0013735717,0.00061310775,0.0016524132,0.0018337467,0.0012248048,0.028249536],"category_scores_gemma":[0.00147157,0.00049009576,0.00070487236,0.003895772,0.0003439378,0.0026711153,0.00078625017,0.0012731701,0.025451565],"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":[0.00006549432,0.00011019084,0.0003750601,0.0002723826,0.00008914366,0.000058984453,0.000036303176,0.01042197,0.002769935,0.027107792,0.13938545,0.8193072],"study_design_scores_gemma":[0.000060263454,0.0001992393,0.0023331943,0.00037990237,0.00019914356,0.0014241381,0.00012906016,0.31206363,0.0115461,0.09768928,0.5738142,0.00016180286],"about_ca_topic_score_codex":0.005175096,"about_ca_topic_score_gemma":0.0075718467,"teacher_disagreement_score":0.028249536,"about_ca_system_score_codex":0.000806658,"about_ca_system_score_gemma":0.0005326503,"threshold_uncertainty_score":0.09450412},"labels":[],"label_agreement":null},{"id":"W4302611064","doi":"10.1007/978-0-387-39940-9_849","title":"Answering Queries Using Views","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database 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":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval","score_opus":0.04029706804401342,"score_gpt":0.2609331415122563,"score_spread":0.22063607346824285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302611064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00849932,0.0043911054,0.9186229,0.001981792,0.00055991556,0.00022962141,0.0037765622,0.013102783,0.048835963],"genre_scores_gemma":[0.13033032,0.007513436,0.76558673,0.0016445932,0.0007328662,0.00025654494,0.02306949,0.0027401224,0.06812589],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975357,0.00047213558,0.00019081678,0.0005090345,0.0011359694,0.0001561984],"domain_scores_gemma":[0.99802077,0.00084999984,0.000049669972,0.0007403589,0.00027831006,0.00006101894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001448676,0.0012923441,0.001496912,0.0021382764,0.00075658713,0.005626059,0.002555753,0.0016242802,0.017126935],"category_scores_gemma":[0.0060609654,0.0007898114,0.0016772219,0.0029121037,0.0008754151,0.008794691,0.0029600568,0.0024369378,0.009952465],"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":[0.00019887465,0.00016280326,0.00095386896,0.00059273327,0.00014281792,0.00020045222,0.0007845792,0.0053695804,0.013326592,0.15834513,0.14136814,0.6785544],"study_design_scores_gemma":[0.00006495037,0.00011038772,0.00073439046,0.0003216686,0.00018452469,0.0011119978,0.0006174579,0.11170269,0.025910193,0.42768005,0.43146294,0.00009876198],"about_ca_topic_score_codex":0.002653819,"about_ca_topic_score_gemma":0.0032264306,"teacher_disagreement_score":0.017126935,"about_ca_system_score_codex":0.00074429775,"about_ca_system_score_gemma":0.0007650327,"threshold_uncertainty_score":0.057295322},"labels":[],"label_agreement":null},{"id":"W4307918117","doi":"10.1007/s00453-022-01054-3","title":"Selected Papers of the 32nd International Workshop on Combinatorial Algorithms, IWOCA 2021","year":2022,"lang":"en","type":"editorial","venue":"Algorithmica","topic":"Data Management and Algorithms","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 Ottawa","funders":"","keywords":"Theory of computation; Computer science; Algorithm; Library science","score_opus":0.007573675400381433,"score_gpt":0.23803725725127073,"score_spread":0.2304635818508893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307918117","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015019109,0.026592156,0.0013897527,0.018084269,0.9288159,0.00006416023,0.00035354658,0.00012373862,0.024426378],"genre_scores_gemma":[0.0024259314,0.03476736,0.0017935698,0.0075084614,0.7712125,0.00013222841,0.00076644466,0.0004728833,0.18092063],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965023,0.00047695308,0.00025458029,0.0005191145,0.0020079,0.00023910133],"domain_scores_gemma":[0.9918698,0.0022126709,0.00051461736,0.0003310176,0.003759277,0.0013126241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004555774,0.00357826,0.0031554608,0.006574285,0.00214073,0.00904498,0.0022899944,0.0037608,0.03858496],"category_scores_gemma":[0.008157568,0.0008462556,0.002176345,0.0036426191,0.0013002564,0.0035977436,0.0015657627,0.0074945395,0.027289497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005280832,0.00001661605,0.00001834648,0.00021991383,0.000014252339,0.00003392481,0.000006914931,0.0000916544,0.00012810565,0.000981185,0.9869113,0.011524887],"study_design_scores_gemma":[0.000027934395,0.00001926197,0.00013946448,0.00020449825,0.000028110428,0.000054368593,0.00001425638,0.00025499222,0.0001646314,0.0016599237,0.99742293,0.000009571273],"about_ca_topic_score_codex":0.0016606854,"about_ca_topic_score_gemma":0.004823272,"teacher_disagreement_score":0.03858496,"about_ca_system_score_codex":0.0037166697,"about_ca_system_score_gemma":0.0027334238,"threshold_uncertainty_score":0.12907952},"labels":[],"label_agreement":null},{"id":"W4312241835","doi":"10.1007/978-3-030-95995-1_6","title":"Distance-Based Classification","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point (geometry); Class (philosophy); Feature (linguistics); Space (punctuation); Mathematics; Pattern recognition (psychology); Combinatorics; Artificial intelligence; Computer science; Geometry; Philosophy; Linguistics","score_opus":0.039564482123288044,"score_gpt":0.23114231360837664,"score_spread":0.1915778314850886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312241835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004360353,0.0077707646,0.8466841,0.0010874883,0.0017309363,0.00018286787,0.0012692794,0.0034990292,0.1334151],"genre_scores_gemma":[0.06490984,0.008022634,0.59882367,0.00089972967,0.00084875576,0.00022520458,0.0056318417,0.0009557212,0.31968257],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99911004,0.00009327251,0.00005176526,0.00022658079,0.0004623857,0.000055929388],"domain_scores_gemma":[0.999203,0.00021436154,0.00003461488,0.00022092262,0.00029409264,0.000032971995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076771626,0.0010680234,0.0010179568,0.0021632873,0.00078468764,0.0024725772,0.0023125482,0.0009953907,0.030244647],"category_scores_gemma":[0.0022724138,0.00036321642,0.0007588193,0.003623632,0.0006853254,0.0027646571,0.0014227553,0.001518495,0.030513708],"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":[0.00003611325,0.000045230423,0.00035793436,0.00014367703,0.00002520515,0.000023565142,0.000043313343,0.005271045,0.0022957968,0.041228,0.06838427,0.8821458],"study_design_scores_gemma":[0.00001591201,0.00014435194,0.0021193053,0.00023013716,0.000063371495,0.00097504637,0.00017546424,0.18629366,0.013319264,0.1643546,0.63222283,0.000086108164],"about_ca_topic_score_codex":0.0026356445,"about_ca_topic_score_gemma":0.0033116953,"teacher_disagreement_score":0.030244647,"about_ca_system_score_codex":0.000904639,"about_ca_system_score_gemma":0.0008052388,"threshold_uncertainty_score":0.10117841},"labels":[],"label_agreement":null},{"id":"W4312505544","doi":"10.1109/tsc.2022.3219099","title":"Efficient and Privacy-Preserving Spatial-Feature-Based Reverse kNN Query","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Services Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Homomorphic encryption; Data mining; Encryption; Location-based service; Feature (linguistics); Leverage (statistics); Artificial intelligence; Computer security; Computer network","score_opus":0.01077334907041782,"score_gpt":0.22364905085688136,"score_spread":0.21287570178646353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312505544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048710253,0.00072573835,0.9416188,0.00065212697,0.000113429334,0.0004356297,0.0010178926,0.0013331934,0.0053929463],"genre_scores_gemma":[0.72145694,0.0005611865,0.2689863,0.0004756645,0.000101226826,0.00038691744,0.0018740116,0.00011444781,0.0060432623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99441177,0.0009703391,0.0006499429,0.0009864214,0.002292086,0.00068950444],"domain_scores_gemma":[0.994696,0.0012814148,0.00054540427,0.0023816759,0.00093688275,0.00015858159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023500244,0.0005918513,0.00180806,0.000824433,0.0017258173,0.0015981643,0.0027706902,0.0016047416,0.0021907976],"category_scores_gemma":[0.008138908,0.0003943,0.0010421214,0.002127854,0.0010965525,0.0066474546,0.005794431,0.001461693,0.0011139754],"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":[0.002392906,0.0006221236,0.007992175,0.0009867115,0.00027584092,0.0013011878,0.002159099,0.18806194,0.06389622,0.22023933,0.026057705,0.4860147],"study_design_scores_gemma":[0.00017448525,0.00039753484,0.0011330397,0.000044493165,0.000094471536,0.0018327946,0.00082431565,0.8575678,0.03187637,0.08704909,0.018836353,0.00016926114],"about_ca_topic_score_codex":0.003081894,"about_ca_topic_score_gemma":0.0030877378,"teacher_disagreement_score":0.003081894,"about_ca_system_score_codex":0.0014665709,"about_ca_system_score_gemma":0.0025448436,"threshold_uncertainty_score":0.012428224},"labels":[],"label_agreement":null},{"id":"W4312828852","doi":"10.1609/socs.v4i1.18276","title":"Invited Speakers","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Graph; Linguistics; Theoretical computer science; Philosophy","score_opus":0.015686973692458896,"score_gpt":0.24841830991334163,"score_spread":0.23273133622088274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312828852","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016672312,0.015945632,0.004253666,0.11187763,0.7236621,0.00071838516,0.0037814996,0.001037124,0.13705683],"genre_scores_gemma":[0.011808938,0.010341801,0.0027935994,0.03513759,0.24250735,0.0010957915,0.004218151,0.0010099926,0.6910867],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99744725,0.0004341957,0.00014230466,0.00046815365,0.0010507948,0.000457286],"domain_scores_gemma":[0.9864913,0.0012091602,0.00042228878,0.00033544505,0.0060200775,0.0055217026],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0038502885,0.001868253,0.0013098489,0.0020498342,0.002838138,0.0066129714,0.0020119606,0.005418752,0.3715533],"category_scores_gemma":[0.014439395,0.0005152193,0.0014748704,0.0011122169,0.00057657296,0.0031263817,0.0046276925,0.0055061714,0.20603524],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008575195,0.000023540151,0.00008399197,0.00012444286,0.00000574779,0.00009485559,0.000066079134,0.00004517414,0.00032487966,0.0011782312,0.9803704,0.017596837],"study_design_scores_gemma":[0.000020271083,0.000030117075,0.00027862017,0.00011343584,0.000008067656,0.00010999246,0.00018247768,0.00006309051,0.0001319654,0.00160439,0.9974458,0.00001174799],"about_ca_topic_score_codex":0.0011201202,"about_ca_topic_score_gemma":0.0022974515,"teacher_disagreement_score":0.6284467,"about_ca_system_score_codex":0.002069555,"about_ca_system_score_gemma":0.0032021606,"threshold_uncertainty_score":0.89640296},"labels":[],"label_agreement":null},{"id":"W4312981500","doi":"10.1609/socs.v3i1.18231","title":"Preface","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Philosophy","score_opus":0.015604662883654495,"score_gpt":0.25272188448375094,"score_spread":0.23711722160009643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312981500","genre_codex":"other","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012540184,0.013401488,0.0074428017,0.06428845,0.41161823,0.00056832266,0.0127090365,0.001659485,0.4870582],"genre_scores_gemma":[0.007694827,0.008808435,0.0032260546,0.014182909,0.096932806,0.00035683397,0.009559905,0.00114878,0.85808945],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992895,0.00009511254,0.00005650617,0.00013080507,0.00036481163,0.0000632952],"domain_scores_gemma":[0.9898831,0.0014232044,0.0003379638,0.0004991229,0.0066930302,0.0011635246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001257743,0.0010448616,0.00082421815,0.0022880896,0.0021220108,0.003058516,0.001294254,0.001235505,0.42980415],"category_scores_gemma":[0.01318511,0.00031111581,0.0005730546,0.001598111,0.0005439698,0.0026018436,0.001451742,0.0033270356,0.26861206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012092153,0.000010338697,0.000040486942,0.000051407264,9.978102e-7,0.000023872379,0.00003150499,0.000035913632,0.000068541354,0.0013031224,0.98368984,0.014731928],"study_design_scores_gemma":[0.0000039634992,0.000015128364,0.00021852583,0.00010958387,0.0000013312039,0.000054349905,0.000048841197,0.000033121152,0.00005463886,0.001963059,0.9974929,0.000004547556],"about_ca_topic_score_codex":0.004061661,"about_ca_topic_score_gemma":0.004039585,"teacher_disagreement_score":0.42980415,"about_ca_system_score_codex":0.0018884535,"about_ca_system_score_gemma":0.0015385154,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4313125234","doi":"10.1109/igarss46834.2022.9883531","title":"An Improved Progressive Transmission Method for the Vector Map Data","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Natural Resources","keywords":"Voronoi diagram; Monotone polygon; Intersection (aeronautics); Chain (unit); Computer science; Algorithm; Diagram; Transmission (telecommunications); Topology (electrical circuits); Mathematics; Combinatorics; Engineering; Physics","score_opus":0.02628385807562654,"score_gpt":0.3188998251329902,"score_spread":0.29261596705736365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313125234","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003038559,0.00011235246,0.9954733,0.000044916967,0.0000410599,0.00005146393,0.00007228462,0.0005951346,0.0005709144],"genre_scores_gemma":[0.03335249,0.00022986741,0.962892,0.00004197618,0.000062301915,0.00010594114,0.00052369497,0.00024223122,0.0025495547],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986929,0.0001835835,0.000077811215,0.00025386884,0.0007007873,0.000091031005],"domain_scores_gemma":[0.9987839,0.00030035162,0.00008137007,0.00034373262,0.0004407332,0.00004992687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009591623,0.00089575123,0.0008725289,0.00256452,0.000890393,0.0012389094,0.0022079863,0.00073165464,0.0042077545],"category_scores_gemma":[0.0033732604,0.000566557,0.0011153723,0.0028325366,0.00052191876,0.0027488023,0.0018599223,0.0013816113,0.0022006922],"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":[0.00014667315,0.00009320384,0.0007087089,0.00022196294,0.00006519124,0.000234542,0.00034855976,0.062997326,0.033749964,0.0172813,0.006613934,0.8775387],"study_design_scores_gemma":[0.000053803768,0.00008791432,0.0005316525,0.000022031192,0.000040636187,0.00056001067,0.00010343479,0.9498594,0.020666583,0.007947774,0.020064102,0.00006263295],"about_ca_topic_score_codex":0.007344206,"about_ca_topic_score_gemma":0.0052573476,"teacher_disagreement_score":0.007344206,"about_ca_system_score_codex":0.00053842575,"about_ca_system_score_gemma":0.0014477265,"threshold_uncertainty_score":0.014602959},"labels":[],"label_agreement":null},{"id":"W4313400513","doi":"10.1007/s10878-022-00976-6","title":"Approximation algorithms for the lower bounded correlation clustering problem","year":2022,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Data Management and Algorithms","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":"University of New Brunswick","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Postdoctoral Research Foundation of China; National Natural Science Foundation of China","keywords":"Rounding; Mathematics; Cluster analysis; Bounded function; Combinatorics; Theory of computation; Approximation algorithm; Algorithm; Correlation clustering; Generalization; Discrete mathematics; Time complexity; Computer science","score_opus":0.01637880291401984,"score_gpt":0.24217942203516082,"score_spread":0.225800619121141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313400513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014189407,0.00090387306,0.9781569,0.0012659587,0.000112760405,0.00011567876,0.00026072288,0.00073644496,0.004258241],"genre_scores_gemma":[0.31671247,0.001400269,0.66617656,0.0010005363,0.0004674929,0.0007777955,0.0025493826,0.0007728364,0.010142654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965366,0.001429938,0.00013271201,0.0006064762,0.000839755,0.00045450768],"domain_scores_gemma":[0.9843709,0.010942487,0.0009065754,0.001783879,0.0013428634,0.00065316615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048007523,0.0021004565,0.003468012,0.0025711067,0.0017432766,0.003888403,0.0071840105,0.0040278975,0.007332577],"category_scores_gemma":[0.027746944,0.0015327572,0.0018952723,0.0064537013,0.0020176817,0.005133334,0.004578902,0.006237108,0.002199325],"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":[0.0005081952,0.00046812894,0.001240833,0.00028046727,0.00014881043,0.00008574453,0.00019594464,0.77589726,0.00094730814,0.09314338,0.017373944,0.10970996],"study_design_scores_gemma":[0.00004494335,0.000019165424,0.00009162306,0.000013802796,0.000013532006,0.000022578963,0.000018513123,0.9652532,0.00015510462,0.03371687,0.0006436465,0.0000070066076],"about_ca_topic_score_codex":0.011175838,"about_ca_topic_score_gemma":0.011806715,"teacher_disagreement_score":0.011175838,"about_ca_system_score_codex":0.0050963066,"about_ca_system_score_gemma":0.0049331705,"threshold_uncertainty_score":0.036976457},"labels":[],"label_agreement":null},{"id":"W4315629677","doi":"10.1109/globecom48099.2022.10000905","title":"PPsky: Privacy-Preserving Skyline Queries with Secret Sharing in eHealthcare","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skyline; Computer science; Secret sharing; Scheme (mathematics); Information privacy; Security analysis; Computer security; Cryptography; Data mining","score_opus":0.04884211409982258,"score_gpt":0.3001426850261781,"score_spread":0.2513005709263555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315629677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074335456,0.0010938119,0.9146144,0.0014969675,0.000118835575,0.00058566994,0.000471892,0.0021774487,0.0051054917],"genre_scores_gemma":[0.9033137,0.00042800436,0.09246266,0.00040452695,0.00008205779,0.0003178775,0.00044984612,0.00007863692,0.0024626192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99384165,0.0026401822,0.0004792797,0.00063355034,0.0016820265,0.0007234175],"domain_scores_gemma":[0.99391323,0.0019375301,0.0006992061,0.002533317,0.00062781246,0.00028885386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058584246,0.00051880296,0.00088257773,0.00077454315,0.0013542037,0.0019858046,0.0017730176,0.0015789542,0.002473223],"category_scores_gemma":[0.007798414,0.0003666883,0.00073263387,0.0016963094,0.0023349305,0.008493872,0.007501136,0.0017099589,0.0007468937],"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":[0.005049639,0.00051722943,0.00804975,0.0010616336,0.0003867432,0.0021241473,0.0038861658,0.11744895,0.09001067,0.4146507,0.022240665,0.3345736],"study_design_scores_gemma":[0.0006445423,0.0013442815,0.0030485392,0.00011812749,0.00016322984,0.002455265,0.001766301,0.62248,0.08404163,0.23655836,0.047111645,0.0002681327],"about_ca_topic_score_codex":0.0012606664,"about_ca_topic_score_gemma":0.0005294051,"teacher_disagreement_score":0.0058584246,"about_ca_system_score_codex":0.0011953196,"about_ca_system_score_gemma":0.0017913661,"threshold_uncertainty_score":0.030982673},"labels":[],"label_agreement":null},{"id":"W4318147483","doi":"10.1109/bigdata55660.2022.10020802","title":"A Graph-based Analysis Approach to Cluster Lifetime Dynamics","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Computer science; Cluster analysis; Cluster (spacecraft); Graph; Data mining; Outlier; Power graph analysis; Theoretical computer science; Visualization; Data visualization; Artificial intelligence","score_opus":0.21032828965218048,"score_gpt":0.3246473363864822,"score_spread":0.11431904673430171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318147483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062007196,0.0004198831,0.9896271,0.00050509366,0.000044727294,0.00008770117,0.00056516693,0.00057454564,0.00197499],"genre_scores_gemma":[0.22752726,0.0017449899,0.7622585,0.00034072023,0.00018187556,0.00030238045,0.0021700084,0.00042574923,0.0050485083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910897,0.00026299647,0.000053900654,0.00027084566,0.00024260995,0.000060569313],"domain_scores_gemma":[0.9966828,0.0016306903,0.00041206798,0.00044284353,0.000713564,0.000117974254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011655294,0.0011531201,0.0007435842,0.006532225,0.0012116249,0.00272646,0.0014935242,0.00097558653,0.0027127166],"category_scores_gemma":[0.0057714377,0.00054498384,0.0013151497,0.006080729,0.0011047239,0.0038901896,0.001173108,0.0017632935,0.00065727456],"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":[0.00012811864,0.0001775587,0.008137002,0.0004987851,0.00034105073,0.0005536452,0.0016789035,0.41504097,0.010463484,0.34373567,0.013247969,0.20599686],"study_design_scores_gemma":[0.00001007027,0.0000391394,0.0017482233,0.00005193561,0.000059807742,0.00024835073,0.0002989972,0.8276446,0.0017794648,0.15192658,0.016146235,0.000046582667],"about_ca_topic_score_codex":0.012756425,"about_ca_topic_score_gemma":0.009784157,"teacher_disagreement_score":0.012756425,"about_ca_system_score_codex":0.00224258,"about_ca_system_score_gemma":0.0012404633,"threshold_uncertainty_score":0.025364399},"labels":[],"label_agreement":null},{"id":"W4318978541","doi":"10.5206/mt.v3i1.15951","title":"About this issue","year":2023,"lang":"en","type":"article","venue":"Maple Transactions","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Presentation (obstetrics); Function (biology); Constant (computer programming); Variety (cybernetics); Point (geometry); State (computer science); Software; Computation; Programming language; Mathematics; Artificial intelligence","score_opus":0.018005705759383678,"score_gpt":0.2493585582749102,"score_spread":0.23135285251552654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318978541","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042527614,0.00493333,0.0024999324,0.116573304,0.5291374,0.0003295041,0.00441047,0.004988796,0.33670202],"genre_scores_gemma":[0.0021715874,0.0030810384,0.0011064785,0.037262917,0.10362629,0.00019893673,0.003294254,0.002251929,0.84700656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973597,0.00031556192,0.00020689728,0.00043703645,0.0013731393,0.00030775493],"domain_scores_gemma":[0.98541325,0.0017794016,0.0006532734,0.0013704055,0.0066754706,0.0041082283],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00238827,0.0015158433,0.0014214199,0.002509899,0.0030365272,0.013014658,0.0025007997,0.0054202587,0.63401634],"category_scores_gemma":[0.020610452,0.0005753952,0.0012347478,0.00218872,0.0009900645,0.008900373,0.0036852644,0.00598453,0.5743001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053585727,0.0000066132066,0.000025521644,0.000034774836,0.0000010367332,0.000013472594,0.000007641837,0.000006069789,0.000025748563,0.00046229377,0.99103713,0.008374261],"study_design_scores_gemma":[0.0000031567179,0.0000052471687,0.000058768055,0.000033578148,0.0000010366457,0.000021145646,0.00002471394,0.000015966169,0.000026007729,0.00040806207,0.9993992,0.0000033179085],"about_ca_topic_score_codex":0.0010663596,"about_ca_topic_score_gemma":0.001753535,"teacher_disagreement_score":0.36598366,"about_ca_system_score_codex":0.0015652559,"about_ca_system_score_gemma":0.0035043561,"threshold_uncertainty_score":0.52203125},"labels":[],"label_agreement":null},{"id":"W4319159023","doi":"10.3390/su15032717","title":"A Knowledge-Based AI Framework for Mobility as a Service","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"","keywords":"Computer science; Knowledge management; Data science; Knowledge base; Process (computing); Service (business); Knowledge integration; Knowledge extraction; Artificial intelligence","score_opus":0.021114956024867734,"score_gpt":0.3372967855999437,"score_spread":0.31618182957507596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319159023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003314285,0.0003933685,0.98683935,0.0014408197,0.00004186246,0.00021560345,0.00035065785,0.00031773548,0.0070862942],"genre_scores_gemma":[0.13542117,0.00059737876,0.8597815,0.00031751278,0.00006484847,0.00043261988,0.0009758225,0.000038421666,0.0023707163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979672,0.00071825326,0.00020991277,0.00037634102,0.00061684445,0.0001115521],"domain_scores_gemma":[0.9973035,0.0015682962,0.00017540515,0.00031074986,0.00050279417,0.00013926916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002651643,0.0007171486,0.0006096904,0.0030534253,0.0013407953,0.0037918824,0.0029093593,0.0015510538,0.0032352929],"category_scores_gemma":[0.0071534966,0.00037647493,0.0012490222,0.0037569322,0.001866663,0.0044899043,0.0029820662,0.0021683655,0.0006511469],"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":[0.00006367534,0.00027988548,0.0018635754,0.00039007995,0.00014866563,0.00048869534,0.00086096633,0.24694788,0.0018560046,0.55869216,0.0074528838,0.18095557],"study_design_scores_gemma":[0.000019349236,0.000038462535,0.00032888446,0.00010356421,0.00004530672,0.00013600918,0.00031266155,0.757656,0.0010019654,0.2118087,0.028521292,0.000027822087],"about_ca_topic_score_codex":0.025073715,"about_ca_topic_score_gemma":0.022192273,"teacher_disagreement_score":0.025073715,"about_ca_system_score_codex":0.0026049812,"about_ca_system_score_gemma":0.003634087,"threshold_uncertainty_score":0.04985553},"labels":[],"label_agreement":null},{"id":"W4319719500","doi":"10.11606/d.55.2022.tde-09022023-114906","title":"ORTree: Aumentando a eficiência de buscas por similaridade diversificadas por meio de particionamento de dados","year":2022,"lang":"pt","type":"dissertation","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Adidas (Canada)","funders":"","keywords":"Humanities; Physics; Philosophy","score_opus":0.023288165020737186,"score_gpt":0.2872527515110828,"score_spread":0.26396458649034565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319719500","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.601842,0.0057367724,0.32834724,0.0022070608,0.00068707764,0.00094918394,0.0027812582,0.017641814,0.039807566],"genre_scores_gemma":[0.8234659,0.0014525501,0.15559858,0.00041534813,0.00008763098,0.0003858432,0.0025403663,0.0006462609,0.015407598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977149,0.00056275405,0.00013698301,0.0004413347,0.00094465015,0.00019948404],"domain_scores_gemma":[0.99250185,0.0037388045,0.00060591573,0.0014421286,0.0012874922,0.00042377942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030449878,0.0011606391,0.00087908964,0.0017867398,0.0009281328,0.0028618579,0.0015973572,0.0011050517,0.010027629],"category_scores_gemma":[0.016748896,0.0006538433,0.0008698269,0.0018663167,0.00070398283,0.0033579075,0.0029582137,0.0013078325,0.0020474342],"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":[0.0033228002,0.0008474178,0.03414859,0.0021459083,0.0005715725,0.00084797165,0.0033782988,0.056325495,0.088295184,0.014968443,0.01648234,0.778666],"study_design_scores_gemma":[0.0005435228,0.004931005,0.09700243,0.00065005146,0.001288002,0.002472498,0.0055712922,0.59873366,0.10276568,0.034137137,0.15137684,0.0005277709],"about_ca_topic_score_codex":0.009548624,"about_ca_topic_score_gemma":0.009214291,"teacher_disagreement_score":0.010027629,"about_ca_system_score_codex":0.0010388122,"about_ca_system_score_gemma":0.0014200156,"threshold_uncertainty_score":0.033545732},"labels":[],"label_agreement":null},{"id":"W4322747644","doi":"10.1007/978-3-031-23876-5_14","title":"Locating Billboards with the Jefferson-d’Hondt Method","year":2023,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Data Management and Algorithms","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 New Brunswick","funders":"","keywords":"Computer science; Art","score_opus":0.08032925403072175,"score_gpt":0.41906517235668495,"score_spread":0.33873591832596317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322747644","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047931555,0.00023352989,0.9764191,0.0002503437,0.0001253541,0.00010159116,0.00018093751,0.0009140467,0.01698196],"genre_scores_gemma":[0.08154202,0.0003304511,0.8898952,0.00011254629,0.00008367272,0.00016013405,0.0006391102,0.0008294512,0.026407337],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980994,0.00056217314,0.00010611546,0.00041599348,0.0005843875,0.00023193579],"domain_scores_gemma":[0.99752563,0.00094908033,0.00011833013,0.0008111952,0.00051541737,0.000080384445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020890783,0.0012194139,0.0015833142,0.003850861,0.0017841375,0.0031164319,0.0026572745,0.0022669532,0.03745113],"category_scores_gemma":[0.011837649,0.0012422138,0.0017811846,0.004404895,0.0017771891,0.005112206,0.003410574,0.0021286712,0.010629604],"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":[0.00037409662,0.00012623145,0.0026986331,0.00023571895,0.000079926365,0.0002294536,0.00048834697,0.058234457,0.003914341,0.30773297,0.038536284,0.5873495],"study_design_scores_gemma":[0.00010920739,0.000091275964,0.0012083991,0.000108348446,0.00008017231,0.00041013336,0.00050903985,0.70302534,0.0073047406,0.19850111,0.08855347,0.00009877137],"about_ca_topic_score_codex":0.012347969,"about_ca_topic_score_gemma":0.012703945,"teacher_disagreement_score":0.03745113,"about_ca_system_score_codex":0.001395927,"about_ca_system_score_gemma":0.0021562942,"threshold_uncertainty_score":0.12528646},"labels":[],"label_agreement":null},{"id":"W4323076526","doi":"10.48550/arxiv.2303.01415","title":"Local data structures","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Data structure; Metric space; Context (archaeology); Theoretical computer science; Metric (unit); Set (abstract data type); Cluster analysis; Mathematics; Algorithm; Data mining; Discrete mathematics; Geography; Artificial intelligence","score_opus":0.19860409782974342,"score_gpt":0.21640594140014635,"score_spread":0.01780184357040293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323076526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068322625,0.0025979872,0.9606346,0.0027176682,0.00034914605,0.00042286058,0.011801562,0.0046880986,0.009955797],"genre_scores_gemma":[0.18640645,0.0047470247,0.74139744,0.002972813,0.001044699,0.0021914074,0.038348544,0.0024024239,0.020489192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9832343,0.002960033,0.0031894192,0.004911477,0.0050709667,0.00063374784],"domain_scores_gemma":[0.9582198,0.012125815,0.0025112324,0.02080195,0.005203181,0.0011379542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008881613,0.0010748877,0.0025417022,0.0062423325,0.002540311,0.01304993,0.005303502,0.0025346351,0.013532181],"category_scores_gemma":[0.05253058,0.0013972484,0.00292136,0.009794757,0.0046416675,0.027513528,0.009826445,0.0042105187,0.00901788],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016111726,0.00007612709,0.0043003014,0.00096975174,0.00018847352,0.00039430315,0.0013829337,0.007033368,0.001994608,0.82468456,0.0316553,0.12715921],"study_design_scores_gemma":[0.000028917517,0.00008960011,0.0009187291,0.00029202984,0.00010222276,0.00080166,0.000493928,0.020698452,0.0038007807,0.7526947,0.21999474,0.00008421286],"about_ca_topic_score_codex":0.002242888,"about_ca_topic_score_gemma":0.0020078125,"teacher_disagreement_score":0.013532181,"about_ca_system_score_codex":0.0029259638,"about_ca_system_score_gemma":0.0036596258,"threshold_uncertainty_score":0.046971023},"labels":[],"label_agreement":null},{"id":"W4324118149","doi":"10.1109/bdcat56447.2022.00012","title":"Fast Exploratory Analysis with Spatio-temporal Aggregation over Polygonal Regions","year":2022,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Computer science; Exploratory data analysis; Data mining; Polygon (computer graphics); Exploratory analysis; Set (abstract data type); A priori and a posteriori; Grid; Computation; Workflow; Theoretical computer science; Algorithm; Database; Data science; Mathematics","score_opus":0.016754648369545558,"score_gpt":0.22427603746533048,"score_spread":0.20752138909578494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324118149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012568959,0.0001376132,0.9794068,0.000102264756,0.000023302446,0.000077316676,0.00034991428,0.0064728744,0.0008609762],"genre_scores_gemma":[0.1874094,0.00026293145,0.8082453,0.000094100804,0.000044983855,0.00024856086,0.001660901,0.00079123507,0.0012426425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978709,0.00035460514,0.00019121577,0.00042209352,0.0010208559,0.00014034279],"domain_scores_gemma":[0.9923889,0.0035230124,0.00062721915,0.002007111,0.0011663594,0.0002873739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018328086,0.0012211645,0.001116923,0.0018058325,0.000898128,0.0023382842,0.001851788,0.00063437753,0.0025575114],"category_scores_gemma":[0.009376804,0.0006953176,0.0015664301,0.0028954162,0.0009486434,0.002840787,0.0037822884,0.0011994062,0.0013223088],"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":[0.0015478188,0.0002957823,0.012760909,0.00069961837,0.00033742786,0.0010463017,0.0028006358,0.255577,0.07953229,0.037367254,0.02090208,0.58713293],"study_design_scores_gemma":[0.000059291797,0.00011483299,0.0015251308,0.000028021967,0.000037844035,0.00025009864,0.000358561,0.94224507,0.022924596,0.01957104,0.0128334565,0.00005202667],"about_ca_topic_score_codex":0.0043974295,"about_ca_topic_score_gemma":0.005403972,"teacher_disagreement_score":0.0043974295,"about_ca_system_score_codex":0.0006939347,"about_ca_system_score_gemma":0.0013576315,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W43621356","doi":"10.1007/978-3-319-08783-2_39","title":"Improved Approximation for Time-Dependent Shortest Paths","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","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":"Carleton University","funders":"","keywords":"Lambda; Combinatorics; Shortest path problem; Approximation algorithm; Binary logarithm; Time complexity; Computation; Dijkstra's algorithm; Mathematics; Path (computing); Piecewise linear function; Node (physics); Function (biology); Algorithm; Discrete mathematics; Computer science; Physics; Mathematical analysis; Graph","score_opus":0.012753644988605888,"score_gpt":0.2283138038738385,"score_spread":0.2155601588852326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W43621356","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021225076,0.0024854753,0.95249313,0.00097590726,0.00079901156,0.00023732329,0.00093275844,0.0023688262,0.018482411],"genre_scores_gemma":[0.16842557,0.0021349224,0.8004078,0.00047028146,0.0007019745,0.0007081761,0.0029971197,0.001051239,0.023102866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99605197,0.00082721136,0.00018407866,0.0007048682,0.0017123747,0.0005196584],"domain_scores_gemma":[0.99148047,0.005037345,0.00029391778,0.0018533707,0.00094050996,0.00039440714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029095972,0.0030684758,0.00304103,0.002613392,0.0012085595,0.0028345843,0.0069463965,0.0024596036,0.018447055],"category_scores_gemma":[0.017568149,0.0011564883,0.0025158452,0.005599797,0.0014504761,0.005751424,0.004449182,0.006597624,0.0040969476],"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":[0.0013920022,0.00068307744,0.00086740794,0.00093374786,0.0001690607,0.00016851728,0.00033719596,0.4916939,0.0047572027,0.11392754,0.045318037,0.33975226],"study_design_scores_gemma":[0.00013391767,0.00010303126,0.00023236751,0.00005818657,0.00006921943,0.00011848606,0.00005049016,0.92501384,0.0011548781,0.066482686,0.0065615065,0.00002142613],"about_ca_topic_score_codex":0.0081044175,"about_ca_topic_score_gemma":0.010021326,"teacher_disagreement_score":0.018447055,"about_ca_system_score_codex":0.0048712827,"about_ca_system_score_gemma":0.003571549,"threshold_uncertainty_score":0.06171149},"labels":[],"label_agreement":null},{"id":"W4362606319","doi":"10.1007/s10723-023-09653-2","title":"Mining Skyline Patterns from Big Data Environments based on a Spark Framework","year":2023,"lang":"en","type":"article","venue":"Journal of Grid Computing","topic":"Data Management and Algorithms","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":"Brandon University","funders":"","keywords":"Skyline; Computer science; SPARK (programming language); Big data; Cloud computing; Data mining; Edge computing; Distributed Computing Environment; Enhanced Data Rates for GSM Evolution; Distributed computing; Artificial intelligence; Operating system","score_opus":0.07696542449973474,"score_gpt":0.2824288848869958,"score_spread":0.20546346038726104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362606319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39772084,0.0019266653,0.55484474,0.0011079169,0.00026315133,0.0006621361,0.015562821,0.024307305,0.0036044002],"genre_scores_gemma":[0.5409867,0.00055452104,0.42286953,0.00010462315,0.00011034,0.00023051375,0.033338774,0.00047867364,0.0013263354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986253,0.00012886617,0.00014970893,0.00042676565,0.00047515277,0.00019426596],"domain_scores_gemma":[0.99804485,0.0005049172,0.0002458562,0.0004514366,0.0004361226,0.00031686953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009594096,0.0010264736,0.0012056574,0.0046251058,0.0012184567,0.0018827261,0.0017345053,0.00059907034,0.0009588918],"category_scores_gemma":[0.0037449934,0.00054050004,0.0015401307,0.0061463993,0.0005118311,0.002694842,0.0015242972,0.0007963891,0.00060175906],"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":[0.002554774,0.0018624873,0.17137894,0.0016335646,0.0014435975,0.0040730457,0.0022655372,0.16064335,0.04605372,0.024645051,0.0704731,0.51297283],"study_design_scores_gemma":[0.00011021235,0.00020933207,0.0152514335,0.000033456734,0.00013037003,0.0009571274,0.0011996708,0.93971634,0.0051936903,0.02703063,0.010127171,0.00004059252],"about_ca_topic_score_codex":0.005815194,"about_ca_topic_score_gemma":0.015110995,"teacher_disagreement_score":0.005815194,"about_ca_system_score_codex":0.00039335748,"about_ca_system_score_gemma":0.0017678577,"threshold_uncertainty_score":0.011562705},"labels":[],"label_agreement":null},{"id":"W4378603520","doi":"10.1007/978-3-031-34500-5","title":"Variable Neighborhood Search","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Variable (mathematics); Variable neighborhood search; Algorithm; Metaheuristic; Mathematics","score_opus":0.0202929384344353,"score_gpt":0.2562958123320804,"score_spread":0.23600287389764507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378603520","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01138934,0.005493708,0.84395754,0.0004884779,0.00063635083,0.000097060096,0.00062018924,0.0016083885,0.13570899],"genre_scores_gemma":[0.1863513,0.0033104867,0.5526771,0.00042816432,0.00032465399,0.00022952314,0.0026946939,0.0011214254,0.25286263],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977356,0.000043971508,0.0000072469975,0.000056526424,0.000092912785,0.000025694626],"domain_scores_gemma":[0.99981004,0.00006800739,0.0000106479,0.000048551592,0.00004756428,0.000015302314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026175947,0.00048694093,0.00086503755,0.0008247882,0.0005846254,0.00085456873,0.0009468402,0.0006114123,0.02072874],"category_scores_gemma":[0.0011013331,0.00027542273,0.00042261643,0.0014922183,0.00039272947,0.0008787045,0.0010124809,0.000939707,0.0049272426],"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":[0.00012780441,0.00007352724,0.000358582,0.0001396761,0.00004803473,0.000051728144,0.00004741186,0.07142488,0.0025305552,0.14546427,0.0765239,0.7032097],"study_design_scores_gemma":[0.000063208,0.00014715907,0.0008287133,0.000100965735,0.00005939564,0.00037925955,0.00006594693,0.6274149,0.004480706,0.19428422,0.17214392,0.000031633837],"about_ca_topic_score_codex":0.0021153472,"about_ca_topic_score_gemma":0.0040959693,"teacher_disagreement_score":0.02072874,"about_ca_system_score_codex":0.0004675,"about_ca_system_score_gemma":0.0005592699,"threshold_uncertainty_score":0.06934452},"labels":[],"label_agreement":null},{"id":"W4379390584","doi":"10.1145/3555041.3589408","title":"An Overview of Reachability Indexes on Graphs","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reachability; Computer science; Transitive closure; Transitive reduction; Theoretical computer science; Vertex (graph theory); Graph; Search engine indexing; Modular decomposition; Pathwidth; Mathematics; Discrete mathematics; Information retrieval; Voltage graph; Line graph","score_opus":0.09244482801013996,"score_gpt":0.3401269591744718,"score_spread":0.2476821311643318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379390584","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010514205,0.0565874,0.8617756,0.0021151206,0.0007596476,0.0006639724,0.008142996,0.015488012,0.043953072],"genre_scores_gemma":[0.0810168,0.067018144,0.82098967,0.0013743003,0.0016096116,0.00081689627,0.012863684,0.0031209441,0.011190012],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962883,0.00055524387,0.00058364763,0.00059992913,0.0016424236,0.0003304671],"domain_scores_gemma":[0.9971317,0.0013320481,0.00022648716,0.0007421661,0.00045630263,0.0001113134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020551914,0.0013910917,0.0017767984,0.008366695,0.0013881373,0.007144179,0.0028001934,0.0013595083,0.0082080085],"category_scores_gemma":[0.008648727,0.0011855344,0.0018679295,0.016966006,0.0016838851,0.017432895,0.003470077,0.0025728436,0.0046776184],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013721919,0.00010546618,0.0007994389,0.002189713,0.00007579742,0.00015012741,0.0004983844,0.020252325,0.004294382,0.48447478,0.04235276,0.44466963],"study_design_scores_gemma":[0.000033464315,0.000090597234,0.0008098709,0.0008783368,0.00008194736,0.0007398881,0.00023036117,0.06616227,0.0072625717,0.49796307,0.42563453,0.000113151546],"about_ca_topic_score_codex":0.0071226764,"about_ca_topic_score_gemma":0.0045848168,"teacher_disagreement_score":0.008366695,"about_ca_system_score_codex":0.0038076832,"about_ca_system_score_gemma":0.0030695728,"threshold_uncertainty_score":0.027626753},"labels":[],"label_agreement":null},{"id":"W4379931434","doi":"10.1109/tits.2023.3280114","title":"A Novel Scalable Framework to Reconstruct Vehicular Trajectories From Unreliable GPS Datasets","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Research Chairs","keywords":"Global Positioning System; Scalability; Computer science; Artificial intelligence; Telecommunications","score_opus":0.03441121252716296,"score_gpt":0.2679881560483463,"score_spread":0.23357694352118336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379931434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005351067,0.00027920285,0.9883817,0.00010286592,0.000070338305,0.00012459568,0.0012072884,0.0039938926,0.00048895756],"genre_scores_gemma":[0.11728187,0.0004930294,0.86388004,0.00011481849,0.00015498213,0.00037354985,0.01523792,0.00039904352,0.0020646744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989722,0.00011521455,0.00006588184,0.0003437859,0.00037926878,0.00012368416],"domain_scores_gemma":[0.9990804,0.00016137975,0.00010018013,0.00032090032,0.00027024862,0.00006688957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009868386,0.0018065725,0.0015883173,0.002512852,0.0008423951,0.0015184626,0.0033567986,0.0013183502,0.002069884],"category_scores_gemma":[0.0036154068,0.00060878514,0.0014471813,0.003770562,0.0005170652,0.0027281425,0.0027123115,0.0019698143,0.0018804319],"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":[0.00037584562,0.00029822224,0.0044064494,0.00029583517,0.00028165325,0.00048697353,0.00019960933,0.40710658,0.011954516,0.014929568,0.02859126,0.53107345],"study_design_scores_gemma":[0.00001856426,0.00002579121,0.00037886968,0.0000082279785,0.000014090173,0.00010711089,0.000038658123,0.9910204,0.001395196,0.0039115194,0.0030681875,0.000013561182],"about_ca_topic_score_codex":0.028830284,"about_ca_topic_score_gemma":0.0355934,"teacher_disagreement_score":0.028830284,"about_ca_system_score_codex":0.00088190485,"about_ca_system_score_gemma":0.0030790707,"threshold_uncertainty_score":0.057324946},"labels":[],"label_agreement":null},{"id":"W4379933229","doi":"10.48550/arxiv.2306.04005","title":"Edge Addition and the Change in Kemeny's Constant","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Combinatorics; Vertex (graph theory); Mathematics; Graph; Constant (computer programming); Enhanced Data Rates for GSM Evolution; Random walk; Distance matrix; Discrete mathematics; Statistics; Computer science","score_opus":0.1375193075662365,"score_gpt":0.19693387827846523,"score_spread":0.05941457071222872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379933229","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6319101,0.0053104656,0.26928616,0.004737741,0.00037352284,0.00018340998,0.001828432,0.0012062611,0.085163794],"genre_scores_gemma":[0.9439056,0.0014300424,0.04599732,0.0003287454,0.0001562231,0.00014473553,0.00028798214,0.00021554202,0.0075338962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972389,0.000558604,0.00012588041,0.0008212588,0.0006204933,0.00063491287],"domain_scores_gemma":[0.9455378,0.042706765,0.0030746597,0.004715038,0.0020799884,0.0018857929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037392408,0.000714949,0.0008852678,0.0032176848,0.0016227036,0.0029288493,0.002949267,0.0015031355,0.00756864],"category_scores_gemma":[0.044835996,0.0006295914,0.00091909815,0.0028274069,0.0042420947,0.008652841,0.0022802902,0.0024008695,0.0009233082],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012293773,0.00019615954,0.010648098,0.0005634493,0.00017513872,0.0006508865,0.0009404099,0.17599264,0.02327619,0.71285015,0.008854433,0.064623185],"study_design_scores_gemma":[0.00010167716,0.00033237675,0.011135818,0.00019230875,0.00022934355,0.0014434896,0.00052893214,0.37060785,0.023505574,0.5730305,0.018670274,0.00022186678],"about_ca_topic_score_codex":0.002462727,"about_ca_topic_score_gemma":0.0020227376,"teacher_disagreement_score":0.00756864,"about_ca_system_score_codex":0.0026252614,"about_ca_system_score_gemma":0.0013821193,"threshold_uncertainty_score":0.025319576},"labels":[],"label_agreement":null},{"id":"W4380343454","doi":"10.1007/978-981-99-2295-6_8","title":"Geometry of Space: A New Look","year":2023,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Data Management and Algorithms","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":"Contingency table; Row; Table (database); Space (punctuation); Geometry; Row and column spaces; Computer science; Graphical display; Mathematics; Computer graphics (images); Data mining; Statistics; Programming language","score_opus":0.05719337939217942,"score_gpt":0.2660201728423437,"score_spread":0.20882679345016428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380343454","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080666,0.2691197,0.18859576,0.05240027,0.011454446,0.000041847135,0.000614116,0.0006538375,0.46905345],"genre_scores_gemma":[0.32274288,0.20558791,0.14041385,0.01330983,0.03512319,0.00019622002,0.0010041427,0.0017156434,0.2799064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994103,0.00018891261,0.000023044462,0.00011322132,0.00023436746,0.000030296316],"domain_scores_gemma":[0.9992822,0.00033481157,0.00003453594,0.00009868171,0.00017189565,0.0000779323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072067697,0.0009984823,0.0010911453,0.002451367,0.0011986307,0.006200211,0.0011797424,0.0012491471,0.010866141],"category_scores_gemma":[0.002183139,0.00039842818,0.00055403484,0.0033478786,0.0071231043,0.011595416,0.0019661037,0.0039271913,0.0023786544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005389142,0.00000478557,0.00009717823,0.00006628509,0.0000045029947,0.0000118870275,0.0001586953,0.0002987662,0.0000886016,0.9451105,0.027675286,0.02647816],"study_design_scores_gemma":[0.000002415488,0.000011707912,0.00018132142,0.00006553282,0.0000040032924,0.00009664114,0.00028285416,0.0013092135,0.00006531058,0.76852745,0.22944273,0.000010793448],"about_ca_topic_score_codex":0.0025559913,"about_ca_topic_score_gemma":0.0033733535,"teacher_disagreement_score":0.010866141,"about_ca_system_score_codex":0.0025455798,"about_ca_system_score_gemma":0.00085812266,"threshold_uncertainty_score":0.036350846},"labels":[],"label_agreement":null},{"id":"W4380352252","doi":"10.1007/978-3-031-42935-4_2","title":"Diversifying Top-k Answers in a Query by Example Setting","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Tuple; Query optimization; Information retrieval; Diversification (marketing strategy); Set (abstract data type); Constraint (computer-aided design); Measure (data warehouse); Web query classification; Result set; Query plan; Query language; Theoretical computer science; Data mining; Web search query; Sargable; Search engine; Mathematics; Discrete mathematics; Programming language","score_opus":0.024240341476337985,"score_gpt":0.23897511389018275,"score_spread":0.21473477241384475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380352252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21598516,0.0034100243,0.7489713,0.0051625785,0.00021197474,0.00046704814,0.0022076336,0.008204177,0.0153801115],"genre_scores_gemma":[0.5693526,0.00094614766,0.4158963,0.0009651428,0.00039348577,0.00020067037,0.003994832,0.0007612609,0.0074894708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9888784,0.0036378277,0.00096130103,0.0021709686,0.003179646,0.0011718356],"domain_scores_gemma":[0.9712361,0.018692149,0.00075471355,0.0061276588,0.002036501,0.001153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007823276,0.0011882302,0.0051103025,0.0030549474,0.0026685847,0.005499762,0.0054365285,0.0043380116,0.009824237],"category_scores_gemma":[0.03291247,0.0011699889,0.0019211702,0.005898429,0.0025562905,0.012887783,0.009555924,0.0040995926,0.0034111254],"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":[0.008397421,0.0018209403,0.013780138,0.0017063381,0.0005334962,0.0015516264,0.0016969454,0.15776433,0.021390412,0.09592351,0.052788496,0.6426464],"study_design_scores_gemma":[0.0003080881,0.00047356894,0.0009562972,0.00008873598,0.00020415123,0.0015987497,0.00086260005,0.7752501,0.009562653,0.20078346,0.009826638,0.00008505413],"about_ca_topic_score_codex":0.00251257,"about_ca_topic_score_gemma":0.0038913852,"teacher_disagreement_score":0.009824237,"about_ca_system_score_codex":0.0014379437,"about_ca_system_score_gemma":0.0019015761,"threshold_uncertainty_score":0.04137391},"labels":[],"label_agreement":null},{"id":"W4382866761","doi":"10.1609/socs.v16i1.27299","title":"Hybrid Search with Graph Neural Networks for Constraint-Based Navigation Planning [Extended Abstract]","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","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":"Safran Electronics (Canada)","funders":"","keywords":"Computer science; Artificial neural network; Constraint (computer-aided design); Graph; Overhead (engineering); Vehicle routing problem; Routing (electronic design automation); Inference; Artificial intelligence; Task (project management); Constraint programming; Route planning; Mathematical optimization; Machine learning; Theoretical computer science; Engineering; Computer network; Mathematics","score_opus":0.023424752202367352,"score_gpt":0.2782809439319044,"score_spread":0.25485619172953705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382866761","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010703705,0.00019859358,0.9853361,0.00020795941,0.000045147084,0.00005155546,0.00010045314,0.0007525469,0.0026039304],"genre_scores_gemma":[0.45968068,0.00023529369,0.53303444,0.00030373424,0.000075805925,0.00037577597,0.00041823884,0.0002825484,0.0055934824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999666,0.00012487163,0.000017249027,0.00007603844,0.00007910231,0.000036745816],"domain_scores_gemma":[0.9989229,0.000741448,0.000070432085,0.00007584343,0.00015134174,0.000038037095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069017964,0.0008812551,0.00072151533,0.000680881,0.00038495593,0.0008261782,0.0014385236,0.0011506754,0.004433544],"category_scores_gemma":[0.00259262,0.00045348602,0.00053689553,0.0013182485,0.00073045184,0.0012788299,0.0011227893,0.0012990215,0.0005443147],"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":[0.000026153008,0.000020123509,0.00014688211,0.000025253972,0.000019106998,0.00002319429,0.000011506298,0.9655704,0.0004891132,0.0044254567,0.0008250584,0.02841784],"study_design_scores_gemma":[0.000002159286,0.000003652756,0.000011991635,9.919155e-7,0.0000010785516,0.0000016223344,9.4511773e-7,0.9987111,0.000090450354,0.0010590568,0.00011574658,0.0000011107189],"about_ca_topic_score_codex":0.020688836,"about_ca_topic_score_gemma":0.021069847,"teacher_disagreement_score":0.020688836,"about_ca_system_score_codex":0.00093757367,"about_ca_system_score_gemma":0.0011109319,"threshold_uncertainty_score":0.0411368},"labels":[],"label_agreement":null},{"id":"W4384521155","doi":"10.2139/ssrn.4504635","title":"A Fast Algorithm for Computing Product Moments of Multivariate Normal Random Variables","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data Management and Algorithms","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":"Multivariate statistics; Algorithm; Computer science; Product (mathematics); Mathematics; Statistics","score_opus":0.011536197865815537,"score_gpt":0.2543669894587774,"score_spread":0.24283079159296186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384521155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015462039,0.0001091942,0.9965314,0.000035497796,0.000043320193,0.000041416995,0.00006747389,0.0013390566,0.00028633818],"genre_scores_gemma":[0.027926853,0.00016019096,0.9695952,0.000040008763,0.00007901801,0.00019376048,0.00037570298,0.0003066527,0.0013227456],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99840516,0.00035845258,0.00015423713,0.0002513957,0.00068916683,0.00014163142],"domain_scores_gemma":[0.9948488,0.0026657905,0.0002984129,0.0008800612,0.0011070094,0.00019997462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023931297,0.0017088563,0.0014357372,0.0030017004,0.00093943987,0.0022828032,0.0022056322,0.0011128713,0.009407314],"category_scores_gemma":[0.013610103,0.0009049178,0.001479318,0.0029908859,0.00085336686,0.0028301482,0.002938934,0.0025289885,0.0054613333],"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":[0.00055350806,0.00010704429,0.0016395639,0.00025194752,0.00016820674,0.0002576629,0.00017481876,0.064013325,0.01422816,0.05406745,0.010268758,0.8542696],"study_design_scores_gemma":[0.0001657469,0.00013913699,0.0011128916,0.000047138186,0.00007645972,0.00069599546,0.00006714661,0.87110245,0.011662753,0.1035996,0.011237147,0.000093530885],"about_ca_topic_score_codex":0.0025320312,"about_ca_topic_score_gemma":0.0037293278,"teacher_disagreement_score":0.009407314,"about_ca_system_score_codex":0.0008613454,"about_ca_system_score_gemma":0.0019967356,"threshold_uncertainty_score":0.031470597},"labels":[],"label_agreement":null},{"id":"W4385317406","doi":"10.1007/978-3-031-38906-1_21","title":"General Space-Time Tradeoffs via Relational Queries","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Conjunctive query; Relational database; Theoretical computer science; Data structure; Relational algebra; Query language; Data mining; Programming language","score_opus":0.020505409110681057,"score_gpt":0.22861862985513853,"score_spread":0.20811322074445748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385317406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1284103,0.028911151,0.66398776,0.012342041,0.0010074638,0.00033160872,0.0029998051,0.004142878,0.15786694],"genre_scores_gemma":[0.8222962,0.010676769,0.12761702,0.0017386876,0.0023445967,0.00038755097,0.0015193623,0.0032177235,0.030202083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9876952,0.0037886563,0.00050800643,0.0016355348,0.003535986,0.002836466],"domain_scores_gemma":[0.9497289,0.037523832,0.0012808298,0.008843915,0.001614612,0.0010079317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009228542,0.003277846,0.0040010666,0.0031300264,0.0020389133,0.011014101,0.0058305915,0.004502836,0.04106142],"category_scores_gemma":[0.049209252,0.0016040563,0.0021221107,0.008291457,0.003453153,0.031555843,0.005437106,0.0049523474,0.005902632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002556474,0.0004246221,0.00097198726,0.0011568974,0.00017844001,0.00017916881,0.0005304401,0.15603706,0.012883308,0.67282194,0.03255432,0.11970533],"study_design_scores_gemma":[0.00013690648,0.00014802162,0.0004154821,0.00012765081,0.00015522329,0.00032438402,0.00023517435,0.26067254,0.004087078,0.72637594,0.0072535863,0.00006813102],"about_ca_topic_score_codex":0.001649919,"about_ca_topic_score_gemma":0.0016767117,"teacher_disagreement_score":0.04106142,"about_ca_system_score_codex":0.0053882077,"about_ca_system_score_gemma":0.0023433997,"threshold_uncertainty_score":0.13736415},"labels":[],"label_agreement":null},{"id":"W4385724181","doi":"10.1061/9780784485019.025","title":"But Why There? A Data-Driven Approach to Route Selection","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Stantec (Canada)","funders":"","keywords":"Computer science; Selection (genetic algorithm); Artificial intelligence","score_opus":0.06794077054296215,"score_gpt":0.27541543638705523,"score_spread":0.20747466584409308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385724181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006580676,0.0007439831,0.9071916,0.040574126,0.00062089175,0.0026451836,0.0050468445,0.0033490513,0.033247687],"genre_scores_gemma":[0.07600732,0.00064827694,0.90583146,0.0035792051,0.0002323251,0.0024018604,0.0037937008,0.0014322023,0.0060736737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.926688,0.044146784,0.0045048716,0.007608448,0.015308801,0.001743108],"domain_scores_gemma":[0.86479175,0.05285008,0.005185131,0.02500752,0.046797276,0.0053682765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07513275,0.0021542984,0.0025752038,0.009229463,0.004784191,0.021103393,0.008182483,0.0041080327,0.013646716],"category_scores_gemma":[0.123410575,0.0018759515,0.003140254,0.011230362,0.0065545957,0.022022799,0.008830624,0.0094898,0.008043229],"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":[0.00043545308,0.0007042983,0.014349239,0.002913992,0.0005803929,0.0005851285,0.01419561,0.035526395,0.005943971,0.26691714,0.12836064,0.5294877],"study_design_scores_gemma":[0.00015170335,0.00033618204,0.004125363,0.0022816078,0.0001727829,0.00032838972,0.022049503,0.055093672,0.005068671,0.41982633,0.49008226,0.00048356506],"about_ca_topic_score_codex":0.014995732,"about_ca_topic_score_gemma":0.021495169,"teacher_disagreement_score":0.07513275,"about_ca_system_score_codex":0.008650808,"about_ca_system_score_gemma":0.014414844,"threshold_uncertainty_score":0.3973447},"labels":[],"label_agreement":null},{"id":"W4386128170","doi":"10.14778/3611479.3611493","title":"Semi-Oblivious Chase Termination for Linear Existential Rules: An Experimental Study","year":2023,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Chase; Set (abstract data type); Class (philosophy); Focus (optics); Existential quantification; Existentialism; Theoretical computer science; Implementation; Key (lock); Algorithm; Programming language; Database; Artificial intelligence; Mathematics; Computer security","score_opus":0.0371439558842468,"score_gpt":0.3083710072932785,"score_spread":0.2712270514090317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386128170","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81725764,0.0024542569,0.13639645,0.0020198624,0.000573833,0.0011311778,0.0018467783,0.0069176564,0.031402305],"genre_scores_gemma":[0.8870313,0.000586687,0.102992356,0.00051745574,0.00013162073,0.00067446096,0.0022343716,0.0011646285,0.004667012],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98861593,0.0042502605,0.0010172743,0.0017230875,0.003072477,0.0013209129],"domain_scores_gemma":[0.88000536,0.08790834,0.0032926807,0.0217573,0.005120391,0.0019159376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009835738,0.0010469257,0.0015096702,0.0008746893,0.0017294695,0.0023146635,0.0032579605,0.0021574055,0.010541339],"category_scores_gemma":[0.061588284,0.0005583208,0.0010080665,0.0013823793,0.0026327844,0.0067519415,0.0032751192,0.0052437144,0.0021240604],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02057628,0.018929454,0.023455346,0.0048454357,0.00087517337,0.0015058967,0.0039244494,0.20894453,0.09081427,0.16079956,0.054522093,0.41080746],"study_design_scores_gemma":[0.0017977697,0.004595242,0.0036427355,0.00027228985,0.00023303337,0.0006556158,0.0012084472,0.81693745,0.061174665,0.09219864,0.017131655,0.00015240959],"about_ca_topic_score_codex":0.002349041,"about_ca_topic_score_gemma":0.0024575547,"teacher_disagreement_score":0.010541339,"about_ca_system_score_codex":0.001847658,"about_ca_system_score_gemma":0.003383743,"threshold_uncertainty_score":0.052016973},"labels":[],"label_agreement":null},{"id":"W4386204732","doi":"10.1145/3603719.3603732","title":"Indexing Temporal Relations for Range-Duration Queries","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Search engine indexing; Computer science; Duration (music); Range (aeronautics); Range query (database); Information retrieval; Search engine; Web search query; Query by Example","score_opus":0.03437978022151286,"score_gpt":0.27408667090911526,"score_spread":0.2397068906876024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386204732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041806858,0.003442385,0.9161416,0.0010062358,0.00040996828,0.0006476598,0.008188473,0.01730855,0.011048257],"genre_scores_gemma":[0.2632152,0.0018392818,0.7104544,0.0005488837,0.00058711757,0.00062192697,0.01681592,0.0017654847,0.0041517434],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99373573,0.0007062622,0.0012878783,0.0010358404,0.002772009,0.00046222997],"domain_scores_gemma":[0.9848872,0.004641537,0.0012833184,0.0064892857,0.0021614393,0.0005372989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003381425,0.001269324,0.0018423699,0.0032612262,0.0015292576,0.0048274393,0.0029505445,0.0013192502,0.0062048608],"category_scores_gemma":[0.023773814,0.0006966654,0.0013681967,0.007372498,0.0011267182,0.010895085,0.004091624,0.0018890081,0.0032724317],"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":[0.0018174197,0.0007882816,0.015297811,0.0017465807,0.00028203387,0.00075951585,0.0023083137,0.057760287,0.06048968,0.14881073,0.09004967,0.61988974],"study_design_scores_gemma":[0.00037828388,0.00058774604,0.003948421,0.00027408922,0.00020207142,0.0021273072,0.0011618277,0.63746905,0.038587563,0.16553123,0.14950633,0.00022609073],"about_ca_topic_score_codex":0.0042149634,"about_ca_topic_score_gemma":0.005373883,"teacher_disagreement_score":0.0062048608,"about_ca_system_score_codex":0.001561842,"about_ca_system_score_gemma":0.0029590153,"threshold_uncertainty_score":0.020757377},"labels":[],"label_agreement":null},{"id":"W4386282413","doi":"10.1109/tvcg.2023.3309941","title":"A Grid-Based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Government of Canada","keywords":"Dimensionality reduction; Computer science; Reduction (mathematics); Grid; Visualization; Curse of dimensionality; Data visualization; Computational science; Artificial intelligence; Algorithm; Data mining; Computer graphics (images); Mathematics","score_opus":0.032083599165866415,"score_gpt":0.318647892528477,"score_spread":0.2865642933626106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386282413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017048769,0.00035050354,0.9737795,0.0001602283,0.00012207676,0.00012920584,0.00042674242,0.0060452376,0.001937782],"genre_scores_gemma":[0.09973283,0.00030048567,0.89451367,0.00010321584,0.000051822535,0.0002907919,0.0011692776,0.0014705998,0.0023673016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830437,0.00029234367,0.00013593557,0.00030990073,0.0008133089,0.00014413014],"domain_scores_gemma":[0.99690336,0.0010191143,0.00024412289,0.00067328225,0.0010135677,0.00014650161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012877561,0.001353191,0.001200735,0.0022892242,0.0008840165,0.001973436,0.0011653845,0.00063695473,0.0057379673],"category_scores_gemma":[0.0067772414,0.00068602304,0.0012975743,0.0025493284,0.0007357681,0.0015849763,0.0026072406,0.0011443796,0.0026787592],"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":[0.00059645297,0.00017091678,0.004342941,0.000932056,0.00014247019,0.00048001725,0.0023854685,0.034651686,0.059947535,0.018081745,0.025401378,0.8528674],"study_design_scores_gemma":[0.00027527622,0.00053862354,0.0065530576,0.0002086928,0.0001867475,0.0016502663,0.0017544534,0.729834,0.10197085,0.037334614,0.11941797,0.0002753776],"about_ca_topic_score_codex":0.0021032928,"about_ca_topic_score_gemma":0.0031035051,"teacher_disagreement_score":0.0057379673,"about_ca_system_score_codex":0.0004244122,"about_ca_system_score_gemma":0.0012851103,"threshold_uncertainty_score":0.019195378},"labels":[],"label_agreement":null},{"id":"W4387074962","doi":"10.48550/arxiv.2309.13287","title":"Approximating Queries on Probabilistic Graphs","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","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":"University of Toronto","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Ministry of Education, India; Agence Nationale de la Recherche; National Research Foundation; Deutsche Forschungsgemeinschaft; National Research Foundation Singapore","keywords":"Probabilistic logic; Computer science; Combinatorics; Chordal graph; Conjunctive query; Mathematics; Theoretical computer science; Discrete mathematics; Graph; Artificial intelligence; Information retrieval","score_opus":0.1214280963833,"score_gpt":0.19408442997314262,"score_spread":0.07265633358984262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387074962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10316687,0.00072687713,0.8835396,0.002048612,0.000056767243,0.00018653263,0.0013653176,0.00255592,0.0063534086],"genre_scores_gemma":[0.66487205,0.0006060911,0.32630572,0.0006624712,0.00019023946,0.00031101666,0.0021034537,0.0005337423,0.0044152853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891677,0.0028250697,0.0008068566,0.002901042,0.0033909099,0.0009083351],"domain_scores_gemma":[0.96127516,0.028696675,0.0018000458,0.0059566535,0.0018123991,0.00045907995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004984409,0.0012028413,0.0015648172,0.0017811873,0.0007894954,0.0044463677,0.003770755,0.002027118,0.004656683],"category_scores_gemma":[0.041642774,0.0010005516,0.0029129288,0.003257321,0.0020441115,0.011505464,0.0035903975,0.0034131324,0.000773106],"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":[0.0006637249,0.00017374786,0.0044361753,0.00063829304,0.00023385248,0.00025129993,0.0007112597,0.5771458,0.006095926,0.30955172,0.005298122,0.09480001],"study_design_scores_gemma":[0.000035421912,0.000036496658,0.00031101427,0.000023374392,0.000049348197,0.000117254116,0.000093658025,0.7304218,0.0019925102,0.2651776,0.0017267347,0.000014805349],"about_ca_topic_score_codex":0.004991746,"about_ca_topic_score_gemma":0.0053836205,"teacher_disagreement_score":0.004991746,"about_ca_system_score_codex":0.0047184546,"about_ca_system_score_gemma":0.0015770235,"threshold_uncertainty_score":0.03423494},"labels":[],"label_agreement":null},{"id":"W4387378868","doi":"10.1007/s11222-023-10300-9","title":"Optimization via Rejection-Free Partial Neighbor Search","year":2023,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Knapsack problem; Simulated annealing; Mathematics; Mathematical optimization; Local search (optimization); Combinatorial optimization; Computer science; Quadratic unconstrained binary optimization; Algorithm","score_opus":0.018477776299225347,"score_gpt":0.26422557706139665,"score_spread":0.24574780076217131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387378868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004976524,0.0002123338,0.9923096,0.00015805333,0.00005376627,0.000040155446,0.00003317278,0.0003172581,0.0018991858],"genre_scores_gemma":[0.2843012,0.00024167102,0.70495147,0.00035099903,0.00016471889,0.0004898407,0.00037548618,0.00039892536,0.008725758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979772,0.0010262746,0.0000834764,0.0002477089,0.0005076085,0.00015766612],"domain_scores_gemma":[0.9943944,0.0042652814,0.00025613827,0.0003920383,0.0005367003,0.00015534002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029862882,0.001063366,0.002472857,0.0013596908,0.00079631404,0.0013051174,0.0028203106,0.0023797024,0.0057576993],"category_scores_gemma":[0.011641467,0.0010000741,0.00095412746,0.0016417453,0.0013257886,0.00200555,0.0018142418,0.0018225828,0.0016091702],"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":[0.0005671499,0.0002053969,0.00057934946,0.00019756012,0.00011477799,0.00009371571,0.000082908744,0.83128786,0.0013277549,0.030687097,0.0069368947,0.12791951],"study_design_scores_gemma":[0.000032782722,0.000028569775,0.000029092535,0.0000044666826,0.000005645361,0.0000124945855,0.000005048957,0.9950649,0.00017473091,0.004254638,0.00038327326,0.0000042817715],"about_ca_topic_score_codex":0.004486135,"about_ca_topic_score_gemma":0.004938412,"teacher_disagreement_score":0.0057576993,"about_ca_system_score_codex":0.0007706448,"about_ca_system_score_gemma":0.0016249826,"threshold_uncertainty_score":0.01926142},"labels":[],"label_agreement":null},{"id":"W4387398448","doi":"10.1145/3624575","title":"Authenticated Range Querying of Historical Blockchain Healthcare Data Using Authenticated Multi-Version Index","year":2023,"lang":"en","type":"article","venue":"Distributed Ledger Technologies Research and Practice","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":"University of Saskatchewan; University of New Brunswick","funders":"","keywords":"Computer science; Blockchain; Scalability; Node (physics); Index (typography); Computer security; Data mining; Database; World Wide Web","score_opus":0.31173369032416065,"score_gpt":0.43695296066040984,"score_spread":0.1252192703362492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387398448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2922188,0.001462146,0.67824686,0.0012518934,0.00017524781,0.00058209454,0.002886675,0.014574071,0.008602172],"genre_scores_gemma":[0.7913825,0.0003862733,0.20218952,0.00020392946,0.00008127914,0.00018041614,0.003170714,0.00035706183,0.0020483083],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99435836,0.0013824222,0.0008037463,0.00070403004,0.0024046032,0.00034679213],"domain_scores_gemma":[0.9833467,0.0054116594,0.0012430309,0.007418309,0.0019986357,0.00058166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045193397,0.0004950145,0.0011271547,0.0017617062,0.0011079715,0.0038111666,0.0019814624,0.0011126943,0.002484846],"category_scores_gemma":[0.018994458,0.0003942485,0.0005554514,0.0031186473,0.0011450977,0.0077547226,0.004255276,0.0010790686,0.0009632004],"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":[0.0040018624,0.00068992336,0.027545163,0.00089364673,0.00027420864,0.0017769388,0.0043222485,0.15795112,0.096791886,0.12235702,0.023381269,0.5600147],"study_design_scores_gemma":[0.00024327972,0.0005257203,0.0022876314,0.00007143937,0.00004678233,0.00086128013,0.0007067462,0.87168336,0.043769643,0.058942463,0.020740952,0.000120727265],"about_ca_topic_score_codex":0.002379502,"about_ca_topic_score_gemma":0.001964522,"teacher_disagreement_score":0.0045193397,"about_ca_system_score_codex":0.0010493831,"about_ca_system_score_gemma":0.0017945896,"threshold_uncertainty_score":0.023900867},"labels":[],"label_agreement":null},{"id":"W4387928372","doi":"10.48550/arxiv.2310.13098","title":"SRAI: Towards Standardization of Geospatial AI","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Geospatial analysis; Python (programming language); Computer science; Standardization; Geospatial PDF; Embedding; Data science; Database; Software engineering; Artificial intelligence; Geography; Programming language; Cartography; Operating system","score_opus":0.0898239813681688,"score_gpt":0.20472702499769493,"score_spread":0.11490304362952614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387928372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088388904,0.00036991283,0.9685952,0.0010186151,0.0004054148,0.000120387704,0.0025799326,0.01648123,0.009545406],"genre_scores_gemma":[0.022167524,0.0013647989,0.93404794,0.0009760552,0.0003685377,0.0007589863,0.018976795,0.011594796,0.009744643],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9915884,0.0025148864,0.0010654775,0.0013637792,0.0030621,0.00040522113],"domain_scores_gemma":[0.98634005,0.0021699942,0.00048619867,0.0066184313,0.0034555404,0.0009297047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008925701,0.0015073753,0.0010841877,0.0049997293,0.0011728666,0.0076504336,0.005499371,0.0015932203,0.016375376],"category_scores_gemma":[0.023489129,0.0010874227,0.001920545,0.007102667,0.0029685237,0.009528208,0.011418964,0.008530083,0.015196047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008946575,0.0001153433,0.0009081603,0.00077095005,0.00011790013,0.000089623056,0.00077550724,0.00834456,0.0035720922,0.6211586,0.11266266,0.2513951],"study_design_scores_gemma":[0.000024728275,0.00002770439,0.0007041384,0.0002782069,0.00003118217,0.00018706698,0.00016579319,0.03870395,0.0042069666,0.31238765,0.6432282,0.00005447128],"about_ca_topic_score_codex":0.0036124159,"about_ca_topic_score_gemma":0.0032205577,"teacher_disagreement_score":0.016375376,"about_ca_system_score_codex":0.0016810689,"about_ca_system_score_gemma":0.0064836633,"threshold_uncertainty_score":0.05478102},"labels":[],"label_agreement":null},{"id":"W4388190725","doi":"10.18280/mmep.100542","title":"Employing the Bellman-Ford Algorithm with Score Functions to Address the Linear Diophantine Fuzzy Shortest Path Problem in Network Analysis","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shortest path problem; Diophantine equation; Path (computing); K shortest path routing; Algorithm; Mathematical optimization; Mathematics; Computer science; Fuzzy logic; Discrete mathematics; Artificial intelligence; Graph","score_opus":0.028542255779392418,"score_gpt":0.22211575283799054,"score_spread":0.1935734970585981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388190725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018253978,0.00021555353,0.9969506,0.000087678185,0.000027141705,0.0000431497,0.000014624268,0.00005788524,0.00077788625],"genre_scores_gemma":[0.08908677,0.00073740893,0.90766764,0.00007106483,0.00004216411,0.00016078075,0.0000937479,0.00004676542,0.0020937014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.00029865542,0.000054077365,0.00017391615,0.00020574636,0.000061332765],"domain_scores_gemma":[0.99830794,0.0010920723,0.00012492636,0.00009193281,0.0003309627,0.00005212719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028413262,0.0011238995,0.0013556934,0.0015835916,0.0011162641,0.0015343487,0.0023622506,0.0018552894,0.0022879457],"category_scores_gemma":[0.0048889862,0.00059721014,0.00076499226,0.0029153521,0.0012033846,0.0020722433,0.0011875164,0.0023451573,0.00047442358],"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":[0.00003539702,0.00005032198,0.0005095575,0.00016310341,0.000044508502,0.00007186409,0.000118928874,0.80287266,0.0010980828,0.066596456,0.0014031533,0.12703589],"study_design_scores_gemma":[0.000006025006,0.000023368191,0.00003895681,0.000008828113,0.0000062937593,0.000020508563,0.000011122,0.9845793,0.00041981353,0.01360857,0.0012689403,0.000008197665],"about_ca_topic_score_codex":0.01617538,"about_ca_topic_score_gemma":0.016781658,"teacher_disagreement_score":0.01617538,"about_ca_system_score_codex":0.0027120886,"about_ca_system_score_gemma":0.0042499253,"threshold_uncertainty_score":0.032162488},"labels":[],"label_agreement":null},{"id":"W4388860803","doi":"10.1145/3615885.3628008","title":"A Data Augmentation Algorithm for Trajectory Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Canada First Research Excellence Fund; Ocean Frontier Institute; Dalhousie University","keywords":"Trajectory; Computer science; Data mining; Perturbation (astronomy); Raw data; Machine learning; Field (mathematics); Data point; Artificial intelligence; Mathematics","score_opus":0.17815192730234253,"score_gpt":0.3579250594167012,"score_spread":0.1797731321143587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388860803","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007383029,0.00023000194,0.9882079,0.00023676475,0.00016264862,0.00012322416,0.0005952711,0.0024297563,0.0006314262],"genre_scores_gemma":[0.11735871,0.00036176218,0.87310094,0.00027937014,0.00020838107,0.000586287,0.005024571,0.0003322773,0.0027477397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986533,0.0002725656,0.00012022483,0.00045013748,0.00039496517,0.00010871325],"domain_scores_gemma":[0.9981122,0.000646258,0.00015114814,0.0005211645,0.00049013813,0.000079131896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016165167,0.0012199979,0.0015424775,0.0016428158,0.0009590203,0.001190707,0.0025265804,0.0017272523,0.0030808467],"category_scores_gemma":[0.005730849,0.00073094136,0.0017018846,0.00296049,0.0012032213,0.0027859597,0.0028293193,0.003516344,0.0024838015],"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":[0.00059411605,0.00034723888,0.003368191,0.00029218395,0.0001535235,0.00026855402,0.0003279353,0.27225134,0.020544825,0.015648881,0.017794402,0.66840875],"study_design_scores_gemma":[0.000023086823,0.000092913455,0.00039592086,0.00002433729,0.000015236845,0.000121020654,0.000052540778,0.97867674,0.0058715357,0.0078356955,0.0068701534,0.000020959918],"about_ca_topic_score_codex":0.004337087,"about_ca_topic_score_gemma":0.0047652074,"teacher_disagreement_score":0.004337087,"about_ca_system_score_codex":0.0006958396,"about_ca_system_score_gemma":0.0015812869,"threshold_uncertainty_score":0.010306478},"labels":[],"label_agreement":null},{"id":"W4389519537","doi":"10.18653/v1/2023.emnlp-industry.71","title":"An Integrated Search System for Korea Weather Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Institute of Meteorological Sciences","keywords":"Computer science; Weather forecasting; Simple (philosophy); Data science; Data mining; Meteorology; Geography","score_opus":0.09566007743853336,"score_gpt":0.3289321525492549,"score_spread":0.23327207511072154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389519537","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12174243,0.0038145352,0.5077017,0.0017417974,0.00050540763,0.0013339436,0.09707932,0.2491286,0.016952213],"genre_scores_gemma":[0.23510613,0.000748359,0.5929159,0.0007658728,0.00010940975,0.00063972373,0.1567042,0.004156813,0.008853549],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897087,0.00016556511,0.00021176664,0.0003082289,0.00027092808,0.0000726637],"domain_scores_gemma":[0.99790287,0.0004852421,0.00013892559,0.0007611997,0.0005724164,0.00013930876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017535733,0.000629147,0.0007652766,0.0031905095,0.00065583223,0.0015863691,0.0016106188,0.00065219216,0.008852583],"category_scores_gemma":[0.0066720718,0.00046976775,0.0006415078,0.004760472,0.00018496084,0.0043842825,0.0024113767,0.00067118893,0.004992447],"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":[0.001442357,0.0003198058,0.015090988,0.0014047654,0.0004510509,0.000601487,0.0009789445,0.008516958,0.0430218,0.0072592455,0.24296774,0.67794484],"study_design_scores_gemma":[0.0004463184,0.00070368045,0.02265947,0.00024914742,0.00043655068,0.0016221202,0.0015529627,0.41692325,0.08144225,0.01565045,0.45797426,0.000339591],"about_ca_topic_score_codex":0.008950843,"about_ca_topic_score_gemma":0.015245923,"teacher_disagreement_score":0.008950843,"about_ca_system_score_codex":0.0006775784,"about_ca_system_score_gemma":0.001412271,"threshold_uncertainty_score":0.029614806},"labels":[],"label_agreement":null},{"id":"W4389763747","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-861-2023","title":"AN APPROACH OF HIGH DEFINITION MAP INFORMATION INTERACTION","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Focus (optics); Interaction information; Realization (probability); Information system; Cloud computing; Interaction design; Human–computer interaction; Real-time computing; Engineering","score_opus":0.025963783411451623,"score_gpt":0.2612936949151712,"score_spread":0.23532991150371957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389763747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025968034,0.0002650897,0.95663404,0.00037254806,0.00005852717,0.0002437786,0.00012148515,0.002443344,0.013893279],"genre_scores_gemma":[0.5572289,0.00029639446,0.4341157,0.00021765039,0.00005076254,0.00026677144,0.0005809692,0.00020407693,0.0070387674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765503,0.0006262703,0.00014275679,0.00045452293,0.0009154266,0.00020605774],"domain_scores_gemma":[0.998926,0.00025986155,0.00006996942,0.0003591189,0.00028073764,0.000104233644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011685977,0.0007051835,0.00040443853,0.0014066349,0.0010970973,0.0026001032,0.0020447,0.0009738058,0.0038298683],"category_scores_gemma":[0.0019850056,0.00040163798,0.00079869875,0.001288088,0.0008534636,0.00418551,0.0043906705,0.0010976122,0.0007521961],"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":[0.0008455264,0.0008315886,0.018108923,0.0008268978,0.00038291086,0.002888346,0.0072612143,0.05477627,0.087400265,0.22192654,0.0172131,0.5875384],"study_design_scores_gemma":[0.00010595902,0.0005270019,0.005621721,0.00012516891,0.00036366147,0.0015978011,0.0026083076,0.65535533,0.07938868,0.052570086,0.20157488,0.00016139097],"about_ca_topic_score_codex":0.0030147186,"about_ca_topic_score_gemma":0.0018037055,"teacher_disagreement_score":0.0038298683,"about_ca_system_score_codex":0.00077777536,"about_ca_system_score_gemma":0.0010890379,"threshold_uncertainty_score":0.012812197},"labels":[],"label_agreement":null},{"id":"W4390100413","doi":"10.1145/3589132.3625622","title":"PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"York University","funders":"","keywords":"Computer science; Merge (version control); Trajectory; Reinforcement learning; Dictionary learning; Range (aeronautics); Artificial intelligence; Set (abstract data type); Speedup; Parallel computing","score_opus":0.02792953563845239,"score_gpt":0.24590444093413888,"score_spread":0.2179749052956865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390100413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008772545,0.000154216,0.9871313,0.000098400684,0.00006631824,0.00007532477,0.00028801837,0.0024505327,0.0009633823],"genre_scores_gemma":[0.2592259,0.0003661555,0.7283894,0.00033814937,0.00007701979,0.00033220596,0.0040747044,0.00067031017,0.0065261545],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996437,0.000052117255,0.000021766931,0.00012870782,0.000104474595,0.000049194343],"domain_scores_gemma":[0.99922717,0.00024305275,0.00008654618,0.00017083636,0.00020475958,0.00006761263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005211281,0.0010684248,0.0010523193,0.00072115683,0.00039860656,0.00060578034,0.0017045446,0.0010037281,0.004061592],"category_scores_gemma":[0.002280326,0.0005337407,0.00074383506,0.00089221,0.0005503718,0.0015702628,0.001474185,0.002195793,0.0018246286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002558625,0.00017127839,0.001940619,0.00020196543,0.0000807487,0.00026791423,0.00013182925,0.42343086,0.012181779,0.0091404505,0.019295014,0.53290164],"study_design_scores_gemma":[0.000015522808,0.000034476678,0.00012311451,0.000007217654,0.000005173926,0.00005611541,0.000011325159,0.9935694,0.002334594,0.0020720311,0.0017622826,0.000008717152],"about_ca_topic_score_codex":0.0075415396,"about_ca_topic_score_gemma":0.008272262,"teacher_disagreement_score":0.0075415396,"about_ca_system_score_codex":0.00065359095,"about_ca_system_score_gemma":0.0013146688,"threshold_uncertainty_score":0.014995277},"labels":[],"label_agreement":null},{"id":"W4390120254","doi":"10.1109/tkde.2023.3344662","title":"ODIN: Object Density Aware Index for C$k$k NN Queries Over Moving Objects on Road Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","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":"Wilfrid Laurier University; York University","funders":"National Natural Science Foundation of China","keywords":"Notation; Object (grammar); Computer science; Index (typography); Identification (biology); Theoretical computer science; Algorithm; Data mining; Artificial intelligence; Information retrieval; Mathematics; Programming language; Arithmetic","score_opus":0.026107012340016018,"score_gpt":0.2666511080454351,"score_spread":0.2405440957054191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390120254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13098884,0.001614597,0.844403,0.00094147003,0.00018115866,0.0004934578,0.003958097,0.0086673945,0.008751938],"genre_scores_gemma":[0.4626136,0.0005905506,0.5240151,0.0003158374,0.00011194787,0.00029530955,0.006722515,0.0006218024,0.0047132866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989661,0.00008911667,0.00008151098,0.00026901384,0.00043021757,0.00016405153],"domain_scores_gemma":[0.9985656,0.000513502,0.00016905172,0.00042249452,0.00020870643,0.000120757766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077591575,0.0010472859,0.0016542492,0.0012930558,0.0010639034,0.0024001547,0.0025380647,0.001328736,0.003106227],"category_scores_gemma":[0.0045341337,0.0004739575,0.000703334,0.0026351467,0.00060385553,0.004053822,0.0029331981,0.0010165975,0.0013227648],"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":[0.0008502309,0.00043465305,0.0094751865,0.0009174157,0.00013896101,0.00043699052,0.0009415956,0.45473778,0.02612313,0.03142813,0.03064584,0.44387007],"study_design_scores_gemma":[0.00002269009,0.000071042676,0.00081465096,0.000016361864,0.000015069692,0.00016549249,0.00021404497,0.977758,0.0048604384,0.010727463,0.0053161574,0.000018640807],"about_ca_topic_score_codex":0.009407345,"about_ca_topic_score_gemma":0.01423769,"teacher_disagreement_score":0.009407345,"about_ca_system_score_codex":0.001476537,"about_ca_system_score_gemma":0.0016275126,"threshold_uncertainty_score":0.01870519},"labels":[],"label_agreement":null},{"id":"W4390480779","doi":"10.1109/tkde.2023.3346377","title":"A Distributed Solution for Efficient K Shortest Paths Computation Over Dynamic Road Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Toronto; York University","funders":"National Natural Science Foundation of China","keywords":"Shortest path problem; Graph; Computer science; Centrality; Computation; Notation; Algorithm; Scalability; Theoretical computer science; Combinatorics; Mathematics; Database","score_opus":0.016598783254482878,"score_gpt":0.26794138589068084,"score_spread":0.25134260263619795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390480779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019574532,0.00034731894,0.9718436,0.00047949443,0.00012753728,0.00012334017,0.0005568735,0.0019895392,0.0049577532],"genre_scores_gemma":[0.26525342,0.0003136322,0.7251087,0.00015381542,0.0000999858,0.00033827533,0.0014809469,0.00032613537,0.006925157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931955,0.000098488716,0.000043308657,0.00028228943,0.00012859004,0.00012782733],"domain_scores_gemma":[0.9990876,0.0004700671,0.00006940847,0.00013602561,0.00016217917,0.00007465291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000701281,0.0012085111,0.0016856189,0.0008395324,0.0012216187,0.0017567197,0.0024647252,0.0018659816,0.0073963036],"category_scores_gemma":[0.0029848705,0.0006063701,0.0011325388,0.0017791647,0.0006822031,0.0021392626,0.0021946589,0.0013047582,0.0018834028],"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":[0.00035915466,0.00013032142,0.00077188347,0.0002883171,0.00007318992,0.00020042156,0.00022704976,0.8399811,0.003162248,0.022561716,0.013612715,0.11863185],"study_design_scores_gemma":[0.00004413597,0.000019819296,0.00006018093,0.000006482146,0.00000781958,0.000025280771,0.00006132507,0.98816586,0.00032367627,0.0099186115,0.0013603867,0.0000063856496],"about_ca_topic_score_codex":0.011704025,"about_ca_topic_score_gemma":0.015968036,"teacher_disagreement_score":0.011704025,"about_ca_system_score_codex":0.0012408491,"about_ca_system_score_gemma":0.0025800513,"threshold_uncertainty_score":0.02474314},"labels":[],"label_agreement":null},{"id":"W4390599221","doi":"10.1111/tgis.13126","title":"<scp>QuadGridSIM</scp>: A quadrilateral grid‐based method for high‐performance and robust trajectory similarity analysis","year":2024,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Trajectory; Robustness (evolution); Computer science; Quadrilateral; Similarity (geometry); Similarity measure; Grid; Data mining; Cluster analysis; Algorithm; Mathematics; Artificial intelligence; Engineering","score_opus":0.023582689494635357,"score_gpt":0.27273237979237264,"score_spread":0.2491496902977373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390599221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005345021,0.00006332767,0.99006337,0.000096803175,0.000059526938,0.000036434754,0.00017861428,0.0028152235,0.0013416957],"genre_scores_gemma":[0.24752249,0.00017469678,0.74474704,0.00017724557,0.000058154703,0.00019579241,0.0014707661,0.0014165671,0.0042373054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999723,0.00004838347,0.000019380474,0.000043906057,0.0001418791,0.00002339848],"domain_scores_gemma":[0.99936885,0.00014186265,0.000067952686,0.0001215053,0.00024611852,0.000053732656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004731094,0.00053445366,0.00051024696,0.00096238306,0.0004483406,0.00084485486,0.001137677,0.00056057796,0.007613795],"category_scores_gemma":[0.0017859379,0.00027138297,0.00058375933,0.0013719635,0.00049643515,0.0009284366,0.001081555,0.0007020697,0.0020025775],"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":[0.00023764484,0.0000846466,0.0025530278,0.00032220664,0.000095776486,0.00044130333,0.00029571628,0.4893274,0.024117041,0.04041972,0.037375834,0.40472978],"study_design_scores_gemma":[0.0000071421023,0.000010984941,0.00016329714,0.0000058122387,0.0000022411723,0.000029157693,0.000016500811,0.9917612,0.0021411774,0.0024847118,0.0033694557,0.000008303597],"about_ca_topic_score_codex":0.008103283,"about_ca_topic_score_gemma":0.0060782162,"teacher_disagreement_score":0.008103283,"about_ca_system_score_codex":0.00051399815,"about_ca_system_score_gemma":0.0009678104,"threshold_uncertainty_score":0.025470734},"labels":[],"label_agreement":null},{"id":"W4391093905","doi":"10.1109/bigdata59044.2023.10386216","title":"Knowledge Graphs in Spatial-Temporal Cluster Evolution Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Computer science; Cluster (spacecraft); Knowledge graph; Data mining; Theoretical computer science; Data science; Artificial intelligence","score_opus":0.01909388420110801,"score_gpt":0.2670023461446523,"score_spread":0.24790846194354427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391093905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030123642,0.0013316034,0.9644835,0.0010348875,0.000049547543,0.00009282664,0.0005699954,0.00033795356,0.001976039],"genre_scores_gemma":[0.57655257,0.0020676842,0.41664857,0.00042611657,0.00013442642,0.0001840497,0.0014028167,0.00019923162,0.0023845779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968335,0.0014173554,0.00017201345,0.00069091545,0.0006919368,0.00019418426],"domain_scores_gemma":[0.97750115,0.016063897,0.0022458648,0.002044552,0.0014754835,0.00066915667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040328936,0.00067434536,0.00089302775,0.00706953,0.001288069,0.003027868,0.0017629091,0.0014762094,0.0019322167],"category_scores_gemma":[0.022274256,0.0006447432,0.0012514335,0.008014325,0.002727835,0.0060890424,0.0021891627,0.0017530333,0.00032133237],"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":[0.00014792077,0.00011748863,0.014622833,0.00035417674,0.0003632476,0.0005125133,0.0011359139,0.44333133,0.0019823748,0.41456723,0.0034948494,0.119370185],"study_design_scores_gemma":[0.000008900474,0.000026957605,0.0030097286,0.000067640336,0.00007271991,0.0001236696,0.00042655325,0.6761894,0.000774384,0.31297213,0.006291007,0.000036890702],"about_ca_topic_score_codex":0.022341387,"about_ca_topic_score_gemma":0.019719759,"teacher_disagreement_score":0.022341387,"about_ca_system_score_codex":0.00352517,"about_ca_system_score_gemma":0.0016241056,"threshold_uncertainty_score":0.044422746},"labels":[],"label_agreement":null},{"id":"W4391419540","doi":"","title":"DynaClose : Une approche de fouille de données pour la sélection des index de jointure binaires dans les entrepôts de données","year":2007,"lang":"fr","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Humanities; Art","score_opus":0.02375445345232931,"score_gpt":0.23755264733906403,"score_spread":0.2137981938867347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391419540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010614985,0.00090931763,0.9549716,0.0007457656,0.00015243817,0.0002859391,0.0021433462,0.028663822,0.0015129107],"genre_scores_gemma":[0.08313007,0.00053028,0.901971,0.00048700688,0.00014205712,0.00045609503,0.0041805366,0.0037691272,0.0053339032],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9905827,0.0016901853,0.0011161912,0.0017668393,0.004293614,0.0005505326],"domain_scores_gemma":[0.9843784,0.0075079408,0.00064456364,0.0041346834,0.0027301158,0.0006042828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0080108605,0.0025032535,0.0033157994,0.0066251485,0.0021395443,0.009185744,0.0043097087,0.0033441668,0.010502611],"category_scores_gemma":[0.031174129,0.0019718148,0.0031299952,0.005885885,0.0017236894,0.007818944,0.007688597,0.004131162,0.0038108267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003175721,0.00040758928,0.012471369,0.0016218479,0.0011478818,0.00047160583,0.0018888552,0.042870652,0.028166948,0.040319376,0.044661306,0.82279694],"study_design_scores_gemma":[0.00042557582,0.00025685696,0.0040684263,0.00035993403,0.00044780248,0.00078225567,0.00086827157,0.8102623,0.031016832,0.07734176,0.07394078,0.00022913188],"about_ca_topic_score_codex":0.01435994,"about_ca_topic_score_gemma":0.01716656,"teacher_disagreement_score":0.01435994,"about_ca_system_score_codex":0.0019458188,"about_ca_system_score_gemma":0.0040216967,"threshold_uncertainty_score":0.042366028},"labels":[],"label_agreement":null},{"id":"W4391786199","doi":"10.1080/10095020.2024.2310590","title":"Stronger compact representations of object trajectories","year":2024,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Xunta de Galicia; Ministerio de Ciencia e Innovación","keywords":"Object (grammar); Computer science; Artificial intelligence","score_opus":0.01905193954378181,"score_gpt":0.2940601720607943,"score_spread":0.2750082325170125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391786199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11114355,0.0009628763,0.8757664,0.00082727853,0.00015330766,0.00014871295,0.004335248,0.0021158648,0.0045466535],"genre_scores_gemma":[0.6638428,0.0010980393,0.32025436,0.00032197684,0.000259131,0.0002927319,0.009880969,0.0003385344,0.0037115547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982322,0.00029231474,0.00016805733,0.0004079346,0.0007463123,0.00015326198],"domain_scores_gemma":[0.99329126,0.002167906,0.00090034126,0.0025507465,0.00091039523,0.0001792284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011962405,0.00066830445,0.0011890472,0.002652594,0.0005607888,0.0032225472,0.0013691358,0.0012062835,0.0039909566],"category_scores_gemma":[0.014602683,0.0004242138,0.0006001364,0.0051764925,0.0010349004,0.0084507475,0.0027293414,0.001957721,0.0011443867],"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":[0.00095720566,0.00028607325,0.006861615,0.0005324878,0.000090642,0.00033838372,0.0011706785,0.28528568,0.020304246,0.33577073,0.012585093,0.3358172],"study_design_scores_gemma":[0.00006718513,0.00024473306,0.0017052208,0.000064347325,0.000027930431,0.00031908494,0.00034088382,0.8668738,0.008022923,0.105552666,0.01672606,0.000055256234],"about_ca_topic_score_codex":0.0043919757,"about_ca_topic_score_gemma":0.0037956075,"teacher_disagreement_score":0.0043919757,"about_ca_system_score_codex":0.0011761722,"about_ca_system_score_gemma":0.0010807059,"threshold_uncertainty_score":0.013351023},"labels":[],"label_agreement":null},{"id":"W4392172125","doi":"10.2139/ssrn.4737334","title":"Spatial Search","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.010141526569555722,"score_gpt":0.2533841095656469,"score_spread":0.24324258299609117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392172125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049149275,0.0044181375,0.68323886,0.0022335816,0.00096885196,0.00091988186,0.010770664,0.009212932,0.23908779],"genre_scores_gemma":[0.4352314,0.0024685238,0.36721566,0.0013332793,0.00039448947,0.0004998756,0.013829868,0.0011223805,0.17790462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990927,0.0001278695,0.00006219026,0.00026663893,0.0003260016,0.00012464063],"domain_scores_gemma":[0.99856746,0.00030522284,0.00007393215,0.0005593477,0.0004103112,0.0000836996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006023134,0.0007303962,0.0010259403,0.0028935845,0.0011859862,0.0024391168,0.0014499672,0.0013050366,0.0594032],"category_scores_gemma":[0.0045656045,0.00034022218,0.0009422603,0.0042509744,0.0006779152,0.0028334644,0.0021039415,0.00058650144,0.020083675],"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":[0.00070376793,0.00023569004,0.0034936243,0.00071951945,0.0001497073,0.000285676,0.00015223243,0.025370473,0.012650434,0.15244111,0.09830439,0.70549345],"study_design_scores_gemma":[0.0002702628,0.000573145,0.0038090893,0.00024210164,0.00026261894,0.002477178,0.0007823924,0.3684953,0.03827556,0.23942836,0.3452673,0.00011674825],"about_ca_topic_score_codex":0.0057493253,"about_ca_topic_score_gemma":0.01081706,"teacher_disagreement_score":0.0594032,"about_ca_system_score_codex":0.0008455266,"about_ca_system_score_gemma":0.0026053789,"threshold_uncertainty_score":0.1987235},"labels":[],"label_agreement":null},{"id":"W4392213001","doi":"","title":"An algebra for the spatio-temporal representation and manipulation of the semantics of mobile object trajectories","year":2015,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Representation (politics); Object (grammar); Semantics (computer science); Computer science; Algebra over a field; Theoretical computer science; Programming language; Artificial intelligence; Mathematics; Pure mathematics","score_opus":0.03766401892173175,"score_gpt":0.2719344270683672,"score_spread":0.23427040814663544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392213001","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006001968,0.0007758976,0.9843834,0.00042945522,0.00017253197,0.000103473045,0.0010022239,0.0008881444,0.0062428187],"genre_scores_gemma":[0.20590518,0.0026885953,0.770559,0.00049206446,0.0005290393,0.0005410002,0.0038962811,0.0004477991,0.014941165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973521,0.0006755024,0.0004978451,0.00051663566,0.00077275647,0.00018518035],"domain_scores_gemma":[0.99820685,0.00045952198,0.00025759154,0.00049716653,0.00045064953,0.00012815968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003262397,0.0007535323,0.0007569066,0.0024310169,0.0020860706,0.005142755,0.0018352713,0.0009706277,0.006366387],"category_scores_gemma":[0.0039583202,0.00062348094,0.0026066354,0.00355958,0.0028444808,0.008843956,0.0031050765,0.0022400294,0.002198939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056978275,0.00002967405,0.00029837203,0.00013787868,0.00002500627,0.00017217029,0.0005418966,0.0043047788,0.0020852324,0.965952,0.0025124955,0.023883518],"study_design_scores_gemma":[0.000044549324,0.00008793678,0.00037476225,0.00013196042,0.0000698521,0.00045483795,0.00036052155,0.053812854,0.0034005742,0.8267771,0.11441516,0.00006979976],"about_ca_topic_score_codex":0.008917206,"about_ca_topic_score_gemma":0.006717171,"teacher_disagreement_score":0.008917206,"about_ca_system_score_codex":0.0018520417,"about_ca_system_score_gemma":0.002200229,"threshold_uncertainty_score":0.021297753},"labels":[],"label_agreement":null},{"id":"W4392452257","doi":"10.1007/978-3-031-55598-5_14","title":"On Approximate Colored Path Counting","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Dalhousie University; Université du Québec en Outaouais","funders":"","keywords":"Computer science; Colored; Path (computing); Artificial intelligence; Algorithm; Computer network","score_opus":0.014044541462618677,"score_gpt":0.2327552379191174,"score_spread":0.2187106964564987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392452257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01848191,0.0018759096,0.88592064,0.0016555617,0.00087926973,0.0001769601,0.00083925953,0.002239678,0.08793088],"genre_scores_gemma":[0.28444764,0.0029836223,0.6262187,0.0019840961,0.0011833627,0.00074834516,0.0030938059,0.0026389777,0.07670147],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968976,0.00082796736,0.00012476188,0.0005735794,0.0011691637,0.00040700397],"domain_scores_gemma":[0.9930831,0.0034546645,0.00023793508,0.0021929466,0.00068760023,0.00034376746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002368659,0.0021338814,0.002886608,0.0035294606,0.002374059,0.004208166,0.0053589,0.0019701584,0.022973388],"category_scores_gemma":[0.015053455,0.0010741384,0.0019744742,0.008648265,0.0027186584,0.009028048,0.0063421694,0.005667983,0.004427592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001739518,0.00009202291,0.0004714573,0.00018911035,0.000045335473,0.00005553077,0.000113308495,0.03815714,0.0010818903,0.80961263,0.025020747,0.12498692],"study_design_scores_gemma":[0.000014904363,0.00001818554,0.00016168454,0.00004309518,0.000026695701,0.00006458398,0.000029175288,0.12715383,0.0005862006,0.86078537,0.011101137,0.000015221433],"about_ca_topic_score_codex":0.0054642763,"about_ca_topic_score_gemma":0.0067276363,"teacher_disagreement_score":0.022973388,"about_ca_system_score_codex":0.004118972,"about_ca_system_score_gemma":0.0024954835,"threshold_uncertainty_score":0.07685363},"labels":[],"label_agreement":null},{"id":"W4392452947","doi":"10.2139/ssrn.4748027","title":"Spatial Search","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data Management and Algorithms","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":"Geography; Computer science","score_opus":0.010141526569555722,"score_gpt":0.2533841095656469,"score_spread":0.24324258299609117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392452947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049149275,0.0044181375,0.68323886,0.0022335816,0.00096885196,0.00091988186,0.010770664,0.009212932,0.23908779],"genre_scores_gemma":[0.4352314,0.0024685238,0.36721566,0.0013332793,0.00039448947,0.0004998756,0.013829868,0.0011223805,0.17790462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990927,0.0001278695,0.00006219026,0.00026663893,0.0003260016,0.00012464063],"domain_scores_gemma":[0.99856746,0.00030522284,0.00007393215,0.0005593477,0.0004103112,0.0000836996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006023134,0.0007303962,0.0010259403,0.0028935845,0.0011859862,0.0024391168,0.0014499672,0.0013050366,0.0594032],"category_scores_gemma":[0.0045656045,0.00034022218,0.0009422603,0.0042509744,0.0006779152,0.0028334644,0.0021039415,0.00058650144,0.020083675],"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":[0.00070376793,0.00023569004,0.0034936243,0.00071951945,0.0001497073,0.000285676,0.00015223243,0.025370473,0.012650434,0.15244111,0.09830439,0.70549345],"study_design_scores_gemma":[0.0002702628,0.000573145,0.0038090893,0.00024210164,0.00026261894,0.002477178,0.0007823924,0.3684953,0.03827556,0.23942836,0.3452673,0.00011674825],"about_ca_topic_score_codex":0.0057493253,"about_ca_topic_score_gemma":0.01081706,"teacher_disagreement_score":0.0594032,"about_ca_system_score_codex":0.0008455266,"about_ca_system_score_gemma":0.0026053789,"threshold_uncertainty_score":0.1987235},"labels":[],"label_agreement":null},{"id":"W4392453829","doi":"10.1007/978-3-031-55598-5_18","title":"Minimizing the Size of the Uncertainty Regions for Centers of Moving Entities","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science","score_opus":0.01916650100272064,"score_gpt":0.2392212954782524,"score_spread":0.22005479447553178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392453829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019694686,0.0003937736,0.9769652,0.00025982084,0.000042204443,0.00008532234,0.00017626237,0.0003897985,0.0019929677],"genre_scores_gemma":[0.39128712,0.0006164642,0.6019546,0.00013508167,0.0001580235,0.00030246863,0.0005853604,0.0007578064,0.004203091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851733,0.00058111636,0.00006821332,0.00028772338,0.00037400835,0.00017165238],"domain_scores_gemma":[0.992615,0.0055274474,0.00037997944,0.0005369916,0.0007453498,0.00019529171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002623016,0.001557548,0.0017423226,0.0011857721,0.0005775556,0.0017367797,0.0024675087,0.0018587188,0.0033560265],"category_scores_gemma":[0.018286834,0.00095437287,0.0011519366,0.00127895,0.00088326086,0.0026614,0.0026025095,0.0017438718,0.0006301023],"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":[0.0004943768,0.0000679805,0.0007858905,0.00032030774,0.000088750596,0.00008478904,0.0001676516,0.8414255,0.007692145,0.024621343,0.004049583,0.12020177],"study_design_scores_gemma":[0.000020348474,0.00008173795,0.0003031522,0.000020959094,0.00002488121,0.000057036657,0.000044838616,0.9807751,0.003363429,0.014334271,0.00095940987,0.000014711378],"about_ca_topic_score_codex":0.003725857,"about_ca_topic_score_gemma":0.0031414304,"teacher_disagreement_score":0.003725857,"about_ca_system_score_codex":0.0012617487,"about_ca_system_score_gemma":0.001590455,"threshold_uncertainty_score":0.013872027},"labels":[],"label_agreement":null},{"id":"W4392966781","doi":"10.20944/preprints202403.1007.v1","title":"DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Global Water Futures","keywords":"Search engine indexing; Computer science; Data mining; Information retrieval","score_opus":0.1377305705771439,"score_gpt":0.35062159400363224,"score_spread":0.21289102342648833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392966781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034705028,0.0008999383,0.96738476,0.00078087917,0.0002967407,0.00043021262,0.01039497,0.012166832,0.0041752853],"genre_scores_gemma":[0.084656395,0.0019739014,0.86313224,0.0006240796,0.00029837296,0.001576617,0.039700557,0.0021146256,0.0059231636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738616,0.00046385167,0.0004887765,0.0003478264,0.0011519967,0.00016136165],"domain_scores_gemma":[0.9941117,0.0011525436,0.00054703915,0.0025555426,0.0011929548,0.00044016587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025447782,0.0009891117,0.0013955255,0.0034373344,0.0019468886,0.0040333956,0.004128632,0.0013151322,0.005793967],"category_scores_gemma":[0.010226029,0.0007973277,0.0011284819,0.006617117,0.0012109563,0.0070870738,0.0044225794,0.002095797,0.004137458],"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":[0.0007112315,0.00022916429,0.0040485426,0.0014319518,0.0002110954,0.0004532434,0.0011736334,0.058976524,0.013529818,0.24876581,0.17828211,0.49218684],"study_design_scores_gemma":[0.0001974977,0.0002581325,0.0013243508,0.0002462972,0.00006305643,0.000790085,0.0004021019,0.27162436,0.012908678,0.21750343,0.49449173,0.00019033186],"about_ca_topic_score_codex":0.005250845,"about_ca_topic_score_gemma":0.006674999,"teacher_disagreement_score":0.005793967,"about_ca_system_score_codex":0.0017526395,"about_ca_system_score_gemma":0.002922091,"threshold_uncertainty_score":0.019382715},"labels":[],"label_agreement":null},{"id":"W4393089540","doi":"10.5267/j.dsl.2024.1.002","title":"A novel scenario planning approach considering criteria interaction in multi-criteria evaluation: An application to urban mobility","year":2024,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Management science; Transport engineering; Operations research; Risk analysis (engineering); Mathematical optimization; Process management; Engineering; Business; Mathematics","score_opus":0.12490406144744177,"score_gpt":0.40931124396273105,"score_spread":0.2844071825152893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393089540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027167075,0.0002026022,0.9945643,0.00023381677,0.000025850903,0.000092041635,0.00003998028,0.00004213286,0.002082553],"genre_scores_gemma":[0.1599549,0.00061812595,0.83680844,0.00012267014,0.00008137004,0.00048237815,0.00012399889,0.00006727189,0.0017408245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964412,0.0022862977,0.00014424953,0.00034582152,0.0006331684,0.00014935601],"domain_scores_gemma":[0.9975546,0.0015991891,0.0002195232,0.00010475878,0.00037520356,0.00014674837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040116324,0.0015021288,0.0012428465,0.002577374,0.0009559906,0.0021778583,0.0019112091,0.001618875,0.00357122],"category_scores_gemma":[0.004348373,0.0006603329,0.0019657079,0.002926164,0.0011476869,0.0024339352,0.0024196783,0.0019209298,0.00035429953],"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":[0.000043752352,0.00011473874,0.0007501217,0.00033336913,0.00015568138,0.00034610782,0.000246082,0.83379704,0.0014466505,0.087937556,0.0016944635,0.073134385],"study_design_scores_gemma":[0.000020462892,0.00008429453,0.00019390082,0.000050762395,0.00003672945,0.00015733806,0.00011315896,0.95367235,0.00043483527,0.040592816,0.0046087224,0.00003463471],"about_ca_topic_score_codex":0.0027265316,"about_ca_topic_score_gemma":0.0032848231,"teacher_disagreement_score":0.0040116324,"about_ca_system_score_codex":0.0017127204,"about_ca_system_score_gemma":0.0021395253,"threshold_uncertainty_score":0.021215796},"labels":[],"label_agreement":null},{"id":"W4393394735","doi":"10.1109/tsp.2024.3383284","title":"Spatial Registration of Heterogeneous Sensors on Mobile Platforms","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.020629043937227504,"score_gpt":0.2605317007188656,"score_spread":0.2399026567816381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393394735","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10630246,0.00023180633,0.89076024,0.00012301764,0.00008241987,0.00003891517,0.00006999008,0.0003970017,0.0019941407],"genre_scores_gemma":[0.8866706,0.00016987571,0.11129363,0.00005882725,0.00004424117,0.00006947447,0.00015504638,0.00003696576,0.0015012404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906045,0.00023029094,0.000046772522,0.00027966744,0.00029591582,0.00008686188],"domain_scores_gemma":[0.9992842,0.00013430188,0.0001678767,0.00024276685,0.00014599299,0.000024830915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076167233,0.000569978,0.0005519957,0.0005719188,0.00038200137,0.0006490252,0.0007849771,0.00058382045,0.00051926327],"category_scores_gemma":[0.0031084511,0.0002925065,0.0005439148,0.0010122248,0.0005204302,0.0013071166,0.0019023826,0.0005438518,0.0003768868],"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":[0.0004366641,0.00005807813,0.0095502725,0.00013885436,0.00011200502,0.0006499194,0.0003588316,0.61801404,0.07131368,0.023397293,0.0013881465,0.27458227],"study_design_scores_gemma":[0.000022982991,0.00020168953,0.003888207,0.000017173867,0.00003480749,0.00018848863,0.00017689215,0.95151556,0.030286528,0.008656693,0.0049751825,0.000035699188],"about_ca_topic_score_codex":0.0017048288,"about_ca_topic_score_gemma":0.0014192746,"teacher_disagreement_score":0.0017048288,"about_ca_system_score_codex":0.00038342204,"about_ca_system_score_gemma":0.00049440004,"threshold_uncertainty_score":0.0040281415},"labels":[],"label_agreement":null},{"id":"W4393764489","doi":"10.5281/zenodo.3269890","title":"Models and Data for Simple Applications of BERT for Ad Hoc Document Retrieval","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","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":"Computer science; Simple (philosophy); Information retrieval","score_opus":0.11441677004267531,"score_gpt":0.3490369527446988,"score_spread":0.2346201827020235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393764489","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009711821,0.0016468697,0.015698468,0.0016217936,0.002143593,0.0007464733,0.8947479,0.058560617,0.015122417],"genre_scores_gemma":[0.01491262,0.0003592466,0.018850366,0.00033409166,0.00019664505,0.0005265539,0.9550624,0.0020403028,0.007717829],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974903,0.00057690585,0.00025125203,0.0006750753,0.00067585567,0.00033053747],"domain_scores_gemma":[0.99400276,0.0013936127,0.0001767183,0.0028876988,0.001255031,0.00028433508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041091684,0.004079988,0.0013864349,0.0035841968,0.0014002162,0.0032056482,0.0046081743,0.002780313,0.059684884],"category_scores_gemma":[0.018003913,0.0010850421,0.0026340145,0.003974863,0.0008179285,0.0031270224,0.0022557105,0.0032602376,0.11634065],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002132765,0.00015677938,0.00093080924,0.0004257426,0.00007450107,0.000040641928,0.000024293502,0.005682025,0.00036590607,0.00094071263,0.96906763,0.022077693],"study_design_scores_gemma":[0.0012561764,0.0004580541,0.008406469,0.00043575704,0.00021204508,0.0005341158,0.00034568927,0.15431182,0.011775083,0.022487031,0.7995348,0.0002429241],"about_ca_topic_score_codex":0.023851342,"about_ca_topic_score_gemma":0.052821886,"teacher_disagreement_score":0.059684884,"about_ca_system_score_codex":0.0023231802,"about_ca_system_score_gemma":0.0028057164,"threshold_uncertainty_score":0.19966584},"labels":[],"label_agreement":null},{"id":"W4393771746","doi":"10.5281/zenodo.7386967","title":"Three-way model differencing and conflict detection","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Trent University; Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.04718132228837098,"score_gpt":0.23891334886151788,"score_spread":0.19173202657314692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393771746","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081680775,0.003274233,0.099341236,0.0012290459,0.00078566046,0.0013152625,0.7203273,0.0809643,0.011082177],"genre_scores_gemma":[0.055051066,0.00026988686,0.109377615,0.00025016337,0.00003253829,0.0009178844,0.83051103,0.0016258217,0.0019639602],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98839146,0.0022208805,0.0016759305,0.0038174223,0.0031928201,0.000701427],"domain_scores_gemma":[0.9857487,0.00440626,0.0009317286,0.006142683,0.0024381066,0.00033253946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009689963,0.00341832,0.0013853922,0.009594878,0.0018487164,0.0036210197,0.006490963,0.0025950454,0.0057555023],"category_scores_gemma":[0.027487727,0.00091396685,0.0033680624,0.007022704,0.001177644,0.004068955,0.00502996,0.0029423484,0.0053982777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013245875,0.0007114828,0.067458495,0.004647326,0.0010204148,0.0008464644,0.0010304753,0.027281942,0.006349592,0.01669429,0.69196445,0.18067048],"study_design_scores_gemma":[0.00089626014,0.00040150568,0.028244108,0.0007329608,0.0006058243,0.0016691459,0.0016692398,0.25250545,0.033914585,0.034542065,0.6445128,0.00030603548],"about_ca_topic_score_codex":0.020736277,"about_ca_topic_score_gemma":0.043993626,"teacher_disagreement_score":0.020736277,"about_ca_system_score_codex":0.002031797,"about_ca_system_score_gemma":0.0037979682,"threshold_uncertainty_score":0.051245987},"labels":[],"label_agreement":null},{"id":"W4393832861","doi":"10.5281/zenodo.3544492","title":"Three-dimensional Stable Matching with Cyclic Preferences: Computational proofs log files","year":2018,"lang":"en","type":"dataset","venue":"Figshare","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":"University of Waterloo; University of Ottawa","funders":"","keywords":"Mathematical proof; Matching (statistics); Computer science; Mathematics; Combinatorics; Theoretical computer science; Statistics; Geometry","score_opus":0.03992379600801263,"score_gpt":0.25368434339321644,"score_spread":0.2137605473852038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393832861","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006907723,0.00009651646,0.00037506208,0.00020808465,0.000043232892,0.00004027267,0.99428034,0.0026861762,0.0015794846],"genre_scores_gemma":[0.0021567552,0.00006140138,0.0012921867,0.00011513691,0.000012376955,0.00022236418,0.99513274,0.00025849117,0.00074846135],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980545,0.00028515176,0.00025752888,0.00050397846,0.000641444,0.0002574086],"domain_scores_gemma":[0.9912975,0.0034873455,0.00054209545,0.0028464838,0.0014232766,0.00040333418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015487294,0.0027381775,0.0011241281,0.0032342072,0.0010961111,0.0028097136,0.0038950485,0.0028783684,0.11949377],"category_scores_gemma":[0.017368078,0.00085866,0.0018521987,0.004708502,0.0005612591,0.002242102,0.0023512947,0.0028028642,0.08989412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011371884,0.00005010641,0.0008740714,0.0005283043,0.000030059588,0.000028846556,0.000016871745,0.00073745835,0.00012372737,0.00085981755,0.9931892,0.0034477084],"study_design_scores_gemma":[0.0021440615,0.00009116966,0.0069520213,0.00058516295,0.000096578915,0.00027398532,0.00017285476,0.008430788,0.002782245,0.0130256275,0.9653805,0.00006505296],"about_ca_topic_score_codex":0.009666016,"about_ca_topic_score_gemma":0.023273584,"teacher_disagreement_score":0.11949377,"about_ca_system_score_codex":0.0021144433,"about_ca_system_score_gemma":0.0036065113,"threshold_uncertainty_score":0.39974648},"labels":[],"label_agreement":null},{"id":"W4393838383","doi":"10.5281/zenodo.7386968","title":"Three-way model differencing and conflict detection","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Trent University; Université de Montréal","funders":"","keywords":"Computer science","score_opus":0.04718132228837098,"score_gpt":0.23891334886151788,"score_spread":0.19173202657314692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393838383","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081680775,0.003274233,0.099341236,0.0012290459,0.00078566046,0.0013152625,0.7203273,0.0809643,0.011082177],"genre_scores_gemma":[0.055051066,0.00026988686,0.109377615,0.00025016337,0.00003253829,0.0009178844,0.83051103,0.0016258217,0.0019639602],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98839146,0.0022208805,0.0016759305,0.0038174223,0.0031928201,0.000701427],"domain_scores_gemma":[0.9857487,0.00440626,0.0009317286,0.006142683,0.0024381066,0.00033253946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009689963,0.00341832,0.0013853922,0.009594878,0.0018487164,0.0036210197,0.006490963,0.0025950454,0.0057555023],"category_scores_gemma":[0.027487727,0.00091396685,0.0033680624,0.007022704,0.001177644,0.004068955,0.00502996,0.0029423484,0.0053982777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013245875,0.0007114828,0.067458495,0.004647326,0.0010204148,0.0008464644,0.0010304753,0.027281942,0.006349592,0.01669429,0.69196445,0.18067048],"study_design_scores_gemma":[0.00089626014,0.00040150568,0.028244108,0.0007329608,0.0006058243,0.0016691459,0.0016692398,0.25250545,0.033914585,0.034542065,0.6445128,0.00030603548],"about_ca_topic_score_codex":0.020736277,"about_ca_topic_score_gemma":0.043993626,"teacher_disagreement_score":0.020736277,"about_ca_system_score_codex":0.002031797,"about_ca_system_score_gemma":0.0037979682,"threshold_uncertainty_score":0.051245987},"labels":[],"label_agreement":null},{"id":"W4394078363","doi":"10.6084/m9.figshare.19929380.v2","title":"FUZZY LOGIC APPLIED IN THE PROSPECTING OF SUITABLE AREAS FOR THE ESTABLISHMENT OF COMMERCIAL FOREST PLANTATIONS","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prospecting; Fuzzy logic; Computer science; Agroforestry; Forestry; Agricultural engineering; Artificial intelligence; Environmental science; Geography; Mining engineering; Engineering","score_opus":0.058060798596516854,"score_gpt":0.284850170661525,"score_spread":0.22678937206500815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394078363","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059960433,0.00007719062,0.00031972965,0.00013429405,0.00004735997,0.000026235424,0.9973295,0.00027051877,0.0011956007],"genre_scores_gemma":[0.002462778,0.0000987191,0.002049364,0.000063685955,0.000011668823,0.00017008059,0.99415046,0.00004857871,0.0009447737],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913436,0.00018220216,0.000118766155,0.00023863933,0.00021131872,0.00011476769],"domain_scores_gemma":[0.9970554,0.0013527272,0.00023486068,0.0005317432,0.0006739099,0.00015132368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014259239,0.0013182473,0.0009423125,0.0036161628,0.0007384658,0.0019291835,0.0027222433,0.0017858281,0.044876374],"category_scores_gemma":[0.008376923,0.00043332714,0.0014096636,0.0052113575,0.00033604348,0.0010643956,0.001401134,0.0014932163,0.036005277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012747706,0.00007103501,0.004170459,0.0011381791,0.000057694793,0.000039683466,0.000047450027,0.0029071474,0.00010906706,0.0015847107,0.9811999,0.008547052],"study_design_scores_gemma":[0.0005164625,0.000057950852,0.026264904,0.0010679493,0.00012039837,0.0001368868,0.00038053913,0.010314574,0.00081281655,0.010266505,0.9499623,0.00009881736],"about_ca_topic_score_codex":0.037388276,"about_ca_topic_score_gemma":0.09454395,"teacher_disagreement_score":0.044876374,"about_ca_system_score_codex":0.0020064819,"about_ca_system_score_gemma":0.0019961703,"threshold_uncertainty_score":0.1501264},"labels":[],"label_agreement":null},{"id":"W4394322198","doi":"10.6084/m9.figshare.19929380","title":"FUZZY LOGIC APPLIED IN THE PROSPECTING OF SUITABLE AREAS FOR THE ESTABLISHMENT OF COMMERCIAL FOREST PLANTATIONS","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prospecting; Fuzzy logic; Agroforestry; Forestry; Business; Agricultural engineering; Computer science; Environmental science; Geography; Artificial intelligence; Mining engineering; Engineering","score_opus":0.058060798596516854,"score_gpt":0.284850170661525,"score_spread":0.22678937206500815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394322198","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059960433,0.00007719062,0.00031972965,0.00013429405,0.00004735997,0.000026235424,0.9973295,0.00027051877,0.0011956007],"genre_scores_gemma":[0.002462778,0.0000987191,0.002049364,0.000063685955,0.000011668823,0.00017008059,0.99415046,0.00004857871,0.0009447737],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913436,0.00018220216,0.000118766155,0.00023863933,0.00021131872,0.00011476769],"domain_scores_gemma":[0.9970554,0.0013527272,0.00023486068,0.0005317432,0.0006739099,0.00015132368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014259239,0.0013182473,0.0009423125,0.0036161628,0.0007384658,0.0019291835,0.0027222433,0.0017858281,0.044876374],"category_scores_gemma":[0.008376923,0.00043332714,0.0014096636,0.0052113575,0.00033604348,0.0010643956,0.001401134,0.0014932163,0.036005277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012747706,0.00007103501,0.004170459,0.0011381791,0.000057694793,0.000039683466,0.000047450027,0.0029071474,0.00010906706,0.0015847107,0.9811999,0.008547052],"study_design_scores_gemma":[0.0005164625,0.000057950852,0.026264904,0.0010679493,0.00012039837,0.0001368868,0.00038053913,0.010314574,0.00081281655,0.010266505,0.9499623,0.00009881736],"about_ca_topic_score_codex":0.037388276,"about_ca_topic_score_gemma":0.09454395,"teacher_disagreement_score":0.044876374,"about_ca_system_score_codex":0.0020064819,"about_ca_system_score_gemma":0.0019961703,"threshold_uncertainty_score":0.1501264},"labels":[],"label_agreement":null},{"id":"W4394335133","doi":"10.6084/m9.figshare.19929380.v1","title":"FUZZY LOGIC APPLIED IN THE PROSPECTING OF SUITABLE AREAS FOR THE ESTABLISHMENT OF COMMERCIAL FOREST PLANTATIONS","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prospecting; Fuzzy logic; Agricultural engineering; Forestry; Computer science; Agroforestry; Geography; Environmental science; Mining engineering; Geology; Artificial intelligence; Engineering","score_opus":0.058060798596516854,"score_gpt":0.284850170661525,"score_spread":0.22678937206500815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394335133","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059960433,0.00007719062,0.00031972965,0.00013429405,0.00004735997,0.000026235424,0.9973295,0.00027051877,0.0011956007],"genre_scores_gemma":[0.002462778,0.0000987191,0.002049364,0.000063685955,0.000011668823,0.00017008059,0.99415046,0.00004857871,0.0009447737],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913436,0.00018220216,0.000118766155,0.00023863933,0.00021131872,0.00011476769],"domain_scores_gemma":[0.9970554,0.0013527272,0.00023486068,0.0005317432,0.0006739099,0.00015132368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014259239,0.0013182473,0.0009423125,0.0036161628,0.0007384658,0.0019291835,0.0027222433,0.0017858281,0.044876374],"category_scores_gemma":[0.008376923,0.00043332714,0.0014096636,0.0052113575,0.00033604348,0.0010643956,0.001401134,0.0014932163,0.036005277],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012747706,0.00007103501,0.004170459,0.0011381791,0.000057694793,0.000039683466,0.000047450027,0.0029071474,0.00010906706,0.0015847107,0.9811999,0.008547052],"study_design_scores_gemma":[0.0005164625,0.000057950852,0.026264904,0.0010679493,0.00012039837,0.0001368868,0.00038053913,0.010314574,0.00081281655,0.010266505,0.9499623,0.00009881736],"about_ca_topic_score_codex":0.037388276,"about_ca_topic_score_gemma":0.09454395,"teacher_disagreement_score":0.044876374,"about_ca_system_score_codex":0.0020064819,"about_ca_system_score_gemma":0.0019961703,"threshold_uncertainty_score":0.1501264},"labels":[],"label_agreement":null},{"id":"W4394747849","doi":"10.1016/j.engappai.2024.108365","title":"TL-TSD: A two-layer traffic sub-area division framework based on trajectory clustering","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Data Management and Algorithms","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":"Université de Montréal","funders":"","keywords":"Computer science; Cluster analysis; Trajectory; Data mining; Division (mathematics); Dynamic time warping; Metric (unit); Matching (statistics); Euclidean distance; Artificial intelligence","score_opus":0.02751727852393089,"score_gpt":0.28014337161276803,"score_spread":0.25262609308883716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394747849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011341297,0.00020314619,0.9798752,0.00009458083,0.000075693315,0.0001413649,0.00044610677,0.0054282704,0.0023943526],"genre_scores_gemma":[0.37827358,0.00028286196,0.6140765,0.00009701105,0.000061008544,0.00023434253,0.0019967442,0.00030547942,0.0046723755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952316,0.000071750575,0.000025291476,0.000104153725,0.00018917356,0.00008652064],"domain_scores_gemma":[0.9995937,0.000054565695,0.000034996196,0.00011382472,0.00012566824,0.00007716116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054755755,0.0006626782,0.00086057716,0.001275896,0.00091891864,0.0014082273,0.0024691971,0.00060231594,0.0030781548],"category_scores_gemma":[0.0011253308,0.00031236318,0.0006269765,0.0014387792,0.00037967617,0.0014021484,0.0021599734,0.00081519317,0.001106984],"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":[0.0004719633,0.00037788658,0.0036545862,0.00024839788,0.00019854816,0.0001772347,0.00030774623,0.42404768,0.028882336,0.045284048,0.023032913,0.47331676],"study_design_scores_gemma":[0.0000129022355,0.000037640522,0.0003151464,0.0000064920764,0.000015761243,0.000035640114,0.000042586864,0.98526865,0.003123214,0.0047407453,0.0063817394,0.000019412633],"about_ca_topic_score_codex":0.018587494,"about_ca_topic_score_gemma":0.021333562,"teacher_disagreement_score":0.018587494,"about_ca_system_score_codex":0.0012376992,"about_ca_system_score_gemma":0.0024990886,"threshold_uncertainty_score":0.036958635},"labels":[],"label_agreement":null},{"id":"W4398169855","doi":"10.5753/ercemapi.2023.236434","title":"Uma análise comparativa de técnicas de detecção de pontos de parada em ambientes urbanos","year":2023,"lang":"pt","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Humanities; Computer science; Philosophy","score_opus":0.042620707764202605,"score_gpt":0.300246464850381,"score_spread":0.25762575708617835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398169855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43199256,0.0031739944,0.5365754,0.00076885964,0.00028863462,0.00081890286,0.0048313425,0.0054896125,0.016060688],"genre_scores_gemma":[0.69749755,0.0015605176,0.29378995,0.00008450391,0.000037011072,0.00043609348,0.0027392104,0.00061437016,0.00324075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99330926,0.0018831546,0.00051005057,0.0012604623,0.002695616,0.00034142804],"domain_scores_gemma":[0.98062164,0.009905796,0.0012057836,0.0035700013,0.0043329317,0.00036377774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005649722,0.0010835005,0.00095080247,0.0037021015,0.0012853104,0.00374871,0.0013591846,0.0011563816,0.003290412],"category_scores_gemma":[0.023106124,0.000702325,0.001517169,0.0044653453,0.00097098196,0.0033443784,0.0024453853,0.0010701546,0.0008163308],"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":[0.0014355968,0.0003873964,0.11512349,0.00612883,0.0009425165,0.00084473175,0.012543563,0.08542535,0.05210295,0.012442268,0.0076639024,0.7049594],"study_design_scores_gemma":[0.00017606583,0.0017217025,0.2514897,0.0025023047,0.0014586331,0.0025584067,0.039266754,0.3663816,0.111137256,0.026249409,0.19623376,0.00082443753],"about_ca_topic_score_codex":0.012607033,"about_ca_topic_score_gemma":0.01733619,"teacher_disagreement_score":0.012607033,"about_ca_system_score_codex":0.0010591949,"about_ca_system_score_gemma":0.0016495357,"threshold_uncertainty_score":0.029878914},"labels":[],"label_agreement":null},{"id":"W4398624482","doi":"10.7910/dvn/omdh4h","title":"rail data (railway lines, stations)","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","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":"Canadian Nautical Research Society","funders":"","keywords":"Transport engineering; Geography; Computer science; Engineering","score_opus":0.04873948775276314,"score_gpt":0.2802284006062698,"score_spread":0.23148891285350665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398624482","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008011928,0.000030340327,0.00005468195,0.000051813473,0.000023158169,0.000006841456,0.99900633,0.00033592875,0.00041086043],"genre_scores_gemma":[0.00029366114,0.000054114153,0.00020587882,0.000036174806,0.0000064733154,0.000036114445,0.9988348,0.000077148856,0.0004556584],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980134,0.00023011102,0.000358129,0.00049704453,0.00058367715,0.0003176126],"domain_scores_gemma":[0.99556834,0.0007990236,0.0005084719,0.0012057839,0.0014328902,0.0004855898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014837282,0.002580455,0.0012059342,0.008072225,0.000846354,0.0031633512,0.0024502992,0.0022015036,0.08221296],"category_scores_gemma":[0.013030213,0.0009442515,0.0014923427,0.013894314,0.0005867083,0.0017844731,0.0025881135,0.002088157,0.12933975],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003301852,0.000013567712,0.0006474439,0.000371197,0.000021306305,0.000010335955,0.00001892694,0.00024204428,0.00009055279,0.0006142305,0.99565446,0.0022829291],"study_design_scores_gemma":[0.00015383965,0.000014503943,0.0036392424,0.000310016,0.00003523593,0.00004185667,0.00007316451,0.00044916448,0.0003269719,0.0015562405,0.9933735,0.00002619079],"about_ca_topic_score_codex":0.037028708,"about_ca_topic_score_gemma":0.04001778,"teacher_disagreement_score":0.917787,"about_ca_system_score_codex":0.0018722988,"about_ca_system_score_gemma":0.0049092616,"threshold_uncertainty_score":0.27502972},"labels":[],"label_agreement":null},{"id":"W4398864","doi":"10.1016/0006-2952(71)90175-4","title":"Searching through spatial relationships using the 2DR-tree.","year":2006,"lang":"en","type":"article","venue":"IMSA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Optimal binary search tree; Tree (set theory); Computer science; Binary search tree; Ternary search tree; Binary tree; Data mining; Random binary tree; Spatial analysis; Curse of dimensionality; Binary data; Tree traversal; Space partitioning; Binary number; Artificial intelligence; Interval tree; Mathematics; Tree structure; Algorithm; Statistics","score_opus":0.064313225270257,"score_gpt":0.28421519169776815,"score_spread":0.21990196642751114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009018634,0.0008302724,0.9852084,0.00018291426,0.000036251182,0.00015442754,0.00052177487,0.0018300752,0.0022171973],"genre_scores_gemma":[0.061459884,0.00044524056,0.9351826,0.000116385665,0.000026978238,0.00015592952,0.0010605203,0.00012003801,0.0014325235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977356,0.00069516356,0.00023443799,0.0003803287,0.00082385977,0.00013054401],"domain_scores_gemma":[0.995103,0.0022801529,0.00048961525,0.0011584524,0.0007962329,0.00017258924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017406266,0.00055431266,0.0010945768,0.0033542311,0.0009724286,0.0026738592,0.001822442,0.00112383,0.004984225],"category_scores_gemma":[0.013037893,0.00048246715,0.00085497514,0.00529877,0.0008014441,0.0055299597,0.0030342499,0.0006979084,0.0026067472],"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":[0.00041662602,0.00026343306,0.0050068703,0.001001952,0.00021562917,0.00041718764,0.0011857852,0.033124287,0.01870268,0.07063041,0.020435644,0.8485995],"study_design_scores_gemma":[0.00016100159,0.00042154727,0.0023664697,0.00027827348,0.0001639974,0.0027180372,0.0007274913,0.7585548,0.024989648,0.13084573,0.07861699,0.00015599675],"about_ca_topic_score_codex":0.0031222636,"about_ca_topic_score_gemma":0.0058698473,"teacher_disagreement_score":0.004984225,"about_ca_system_score_codex":0.0005283542,"about_ca_system_score_gemma":0.0018366177,"threshold_uncertainty_score":0.016673923},"labels":[],"label_agreement":null},{"id":"W4399058103","doi":"10.1007/978-3-031-60599-4_6","title":"LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Binary decision diagram; Computer science; Binary number; Influence diagram; Artificial intelligence; Operations research; Theoretical computer science; Decision tree; Mathematics; Arithmetic","score_opus":0.013037306246668113,"score_gpt":0.254650362214452,"score_spread":0.24161305596778387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399058103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006618558,0.00024395136,0.9874084,0.0001502058,0.000067782916,0.00011183126,0.0008214473,0.002233364,0.002344523],"genre_scores_gemma":[0.102690436,0.000266822,0.88831204,0.0001608531,0.00005626962,0.0004947594,0.0027746593,0.00063709286,0.0046070605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909544,0.00032551755,0.00006214181,0.0001683625,0.00027283223,0.00007561299],"domain_scores_gemma":[0.9962792,0.002809834,0.00013411361,0.00028622526,0.0003460104,0.00014465475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001922341,0.001189447,0.0012298973,0.0016416126,0.00048518865,0.001318271,0.0012909631,0.00089251774,0.013463177],"category_scores_gemma":[0.008254178,0.0008433283,0.0015465904,0.0014956809,0.0005188746,0.002220845,0.0019146112,0.0027407086,0.00225183],"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":[0.00025808142,0.0002362581,0.0011296425,0.0004852595,0.00006747735,0.000058336107,0.00013041614,0.21224743,0.0020148032,0.030437581,0.017854165,0.73508054],"study_design_scores_gemma":[0.000074773336,0.000095258256,0.00023307237,0.00008674587,0.000017498807,0.000023545334,0.000034367655,0.9182355,0.0012795061,0.0719783,0.007926237,0.000015284042],"about_ca_topic_score_codex":0.003400435,"about_ca_topic_score_gemma":0.0060101924,"teacher_disagreement_score":0.013463177,"about_ca_system_score_codex":0.001289146,"about_ca_system_score_gemma":0.0017728606,"threshold_uncertainty_score":0.04503876},"labels":[],"label_agreement":null},{"id":"W4399207140","doi":"10.3390/mca29030042","title":"DSTree: A Spatio-Temporal Indexing Data Structure for Distributed Networks","year":2024,"lang":"en","type":"article","venue":"Mathematical and Computational Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Global Water Futures","keywords":"Search engine indexing; Computer science; Information retrieval","score_opus":0.027176071559373224,"score_gpt":0.285693085630551,"score_spread":0.2585170140711778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399207140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00480454,0.0014095722,0.9281454,0.0010284174,0.00047854052,0.00074775587,0.02473558,0.030938694,0.0077114245],"genre_scores_gemma":[0.10362234,0.0025915198,0.7915744,0.0009895859,0.00033764803,0.0022812367,0.08556415,0.0040624877,0.008976622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974486,0.00041197188,0.0005206476,0.00035699498,0.00109143,0.00017031693],"domain_scores_gemma":[0.9949909,0.0010094453,0.0004978798,0.0019513076,0.0011210076,0.00042936092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002468591,0.0011561232,0.0013661719,0.003192636,0.0017769684,0.00399671,0.0041660583,0.0013175318,0.0071186917],"category_scores_gemma":[0.008759611,0.0008528816,0.0012584967,0.005574803,0.0010854603,0.007515111,0.0045212237,0.0021308174,0.005052418],"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":[0.00095538335,0.00025494484,0.0048561166,0.002091087,0.00026428385,0.0006225582,0.0016562002,0.049333706,0.016357252,0.19472463,0.26591974,0.46296406],"study_design_scores_gemma":[0.00022601748,0.00027880186,0.0015336699,0.0002883382,0.00006905626,0.0007380972,0.00047845577,0.18851073,0.014607115,0.13387193,0.6591526,0.00024511936],"about_ca_topic_score_codex":0.007272407,"about_ca_topic_score_gemma":0.008806717,"teacher_disagreement_score":0.007272407,"about_ca_system_score_codex":0.0018596345,"about_ca_system_score_gemma":0.0028686435,"threshold_uncertainty_score":0.02381444},"labels":[],"label_agreement":null},{"id":"W4399246642","doi":"10.1007/s10796-024-10495-w","title":"Efficiently Labeling and Retrieving Temporal Anomalies in Relational Databases","year":2024,"lang":"en","type":"article","venue":"Information Systems Frontiers","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Provincia autonoma di Bolzano - Alto Adige; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Computer science; Database; Relational database; Information retrieval; Temporal database","score_opus":0.01802214673386673,"score_gpt":0.23028306140069993,"score_spread":0.2122609146668332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399246642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20076491,0.0008183934,0.7912253,0.00072604965,0.000072899486,0.00011538087,0.0011462659,0.00422747,0.0009032814],"genre_scores_gemma":[0.5143665,0.00034278684,0.48308828,0.00015263548,0.00005537224,0.00006928531,0.0013875582,0.00021128033,0.000326342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99351186,0.001669803,0.0008905781,0.0010872545,0.0025103313,0.00033015103],"domain_scores_gemma":[0.976278,0.011807579,0.0033364668,0.0044824206,0.0036048912,0.00049064733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052657328,0.0006595857,0.0012067937,0.0029318251,0.0009687463,0.0042664595,0.0023386066,0.0010508742,0.0005214854],"category_scores_gemma":[0.024567155,0.00042876112,0.0006734867,0.0045313393,0.001139528,0.005195267,0.0025701087,0.0014668914,0.00030185847],"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":[0.0017874043,0.0004596455,0.062116403,0.00069137505,0.00030188708,0.0014232339,0.00358508,0.21159406,0.05678577,0.09018706,0.011124656,0.55994344],"study_design_scores_gemma":[0.000040411418,0.000108667205,0.0024016132,0.000048991922,0.0000689454,0.0005082821,0.0007346098,0.89282775,0.034402557,0.06287789,0.0059253187,0.00005491682],"about_ca_topic_score_codex":0.0054496545,"about_ca_topic_score_gemma":0.0043600923,"teacher_disagreement_score":0.0054496545,"about_ca_system_score_codex":0.0010544489,"about_ca_system_score_gemma":0.0016697914,"threshold_uncertainty_score":0.027848184},"labels":[],"label_agreement":null},{"id":"W4399261742","doi":"10.1609/socs.v17i1.31553","title":"Clique Analysis and Bypassing in Continuous-Time Conflict-Based Search","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Denver; Canadian Institute for Advanced Research; University of Alberta; National Science Foundation","keywords":"Clique; Computer science; Mathematics; Combinatorics","score_opus":0.012409085446213029,"score_gpt":0.26638053095995334,"score_spread":0.2539714455137403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399261742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08125255,0.00036481483,0.9096615,0.00056584727,0.000072091876,0.00020606477,0.00019900383,0.00094561256,0.006732464],"genre_scores_gemma":[0.4693113,0.00016265009,0.5265988,0.00019769999,0.000043341497,0.0002607049,0.00039060562,0.0002441581,0.0027907395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982829,0.0007628677,0.000068333116,0.00028596193,0.00039503974,0.00020494197],"domain_scores_gemma":[0.99154377,0.0061646453,0.000434927,0.0010438513,0.00048204692,0.00033067373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024231465,0.0006932043,0.00097382895,0.0008701514,0.0008053876,0.0012280915,0.0027604878,0.0012503482,0.0058115097],"category_scores_gemma":[0.012305931,0.0005944499,0.0010302204,0.0015103373,0.0014848263,0.0029355518,0.0020815548,0.0019610175,0.0005225948],"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":[0.00035933446,0.0003601419,0.0020042907,0.0002654759,0.0001035202,0.00016367789,0.00037791152,0.7456012,0.0039944733,0.09718533,0.0042610657,0.14532357],"study_design_scores_gemma":[0.00007424486,0.000064757456,0.00016058193,0.000011781945,0.000018531255,0.00004531651,0.000051045758,0.9625766,0.0012133945,0.033901885,0.001869817,0.000012063693],"about_ca_topic_score_codex":0.0056976,"about_ca_topic_score_gemma":0.006350092,"teacher_disagreement_score":0.0058115097,"about_ca_system_score_codex":0.0010192848,"about_ca_system_score_gemma":0.0020309214,"threshold_uncertainty_score":0.019441426},"labels":[],"label_agreement":null},{"id":"W4399328367","doi":"10.1080/03610918.2024.2360136","title":"A fast algorithm for computing product moments of multivariate normal random variables","year":2024,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Multivariate statistics; Random variable; Product (mathematics); Algorithm; Computer science; Mathematics; Statistics; Geometry","score_opus":0.07866160872159249,"score_gpt":0.4008946615529053,"score_spread":0.3222330528313128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399328367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008263557,0.000058323654,0.99755716,0.000029834437,0.000033429365,0.000035916608,0.000051332056,0.001073557,0.0003340832],"genre_scores_gemma":[0.023504965,0.0001413156,0.97373736,0.00005094003,0.000068924615,0.00022571381,0.00042109517,0.00046605364,0.0013835478],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99849975,0.00028732212,0.00014333474,0.00021630517,0.0007079696,0.0001452847],"domain_scores_gemma":[0.9961479,0.0017663565,0.00024074552,0.0006521023,0.0010345897,0.0001584536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002214543,0.0016094092,0.0012921251,0.0026410196,0.000841691,0.0019379643,0.0020360607,0.0009573454,0.010599173],"category_scores_gemma":[0.012023862,0.00081557577,0.0013895757,0.0024362633,0.00081195374,0.0025844688,0.003044021,0.0024595393,0.006309989],"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":[0.00037915388,0.0001308545,0.0018380508,0.00026788304,0.00017318863,0.0003703065,0.00019315872,0.09941008,0.012915591,0.084225126,0.01535683,0.78473973],"study_design_scores_gemma":[0.000114833594,0.000090103924,0.0005949469,0.000039778035,0.000048847724,0.0005030585,0.00005214791,0.87600756,0.009186342,0.100348316,0.0129339015,0.000080187645],"about_ca_topic_score_codex":0.002581405,"about_ca_topic_score_gemma":0.0033415598,"teacher_disagreement_score":0.010599173,"about_ca_system_score_codex":0.0008003928,"about_ca_system_score_gemma":0.0022492143,"threshold_uncertainty_score":0.03545773},"labels":[],"label_agreement":null},{"id":"W4399386969","doi":"10.3390/a17060246","title":"Minimizing Query Frequency to Bound Congestion Potential for Moving Entities at a Fixed Target Time","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Upper and lower bounds; Mathematical optimization; Mathematics; Mathematical analysis","score_opus":0.011892679398638221,"score_gpt":0.24317464642662545,"score_spread":0.23128196702798723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399386969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21909271,0.00084017956,0.77470624,0.0008982323,0.00005658403,0.00024270933,0.00021404402,0.0004867632,0.00346253],"genre_scores_gemma":[0.92865354,0.00030407234,0.0694521,0.00009164216,0.000062292995,0.000134044,0.00013562279,0.000110361216,0.0010563028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968945,0.0008973683,0.00016693244,0.00067872636,0.00068021356,0.0006822367],"domain_scores_gemma":[0.9880679,0.008450327,0.0011181127,0.0009758047,0.00080964615,0.0005780663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032306106,0.0011351812,0.001963251,0.0009806808,0.0009946915,0.0018265628,0.0031012178,0.0017282737,0.0016537745],"category_scores_gemma":[0.01984547,0.0005725299,0.00053860515,0.0016710686,0.0014505528,0.004645414,0.0021801519,0.0012376728,0.00024108932],"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":[0.00042657863,0.00018694956,0.0023200277,0.00026274004,0.00008955339,0.00019084092,0.00020901267,0.92777586,0.015921105,0.021490468,0.001420698,0.029706238],"study_design_scores_gemma":[0.000018334265,0.00022864752,0.00077981595,0.000009292045,0.00003864872,0.0001639507,0.00013295517,0.985488,0.0029118173,0.009503927,0.0007084146,0.000016058184],"about_ca_topic_score_codex":0.0032752776,"about_ca_topic_score_gemma":0.002439855,"teacher_disagreement_score":0.0032752776,"about_ca_system_score_codex":0.0018175868,"about_ca_system_score_gemma":0.0015756434,"threshold_uncertainty_score":0.017085314},"labels":[],"label_agreement":null},{"id":"W4399543318","doi":"10.2139/ssrn.4861223","title":"Optimizing Emission-Efficient Long-Distance Travel Using Modal Substitutions","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Modal; Computer science; Transport engineering; Engineering; Materials science","score_opus":0.02021537679567413,"score_gpt":0.271511751814411,"score_spread":0.25129637501873686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399543318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07773262,0.00021217806,0.9130219,0.00025785068,0.00007521293,0.000049940605,0.00017102937,0.00086629385,0.0076129567],"genre_scores_gemma":[0.7615815,0.00014119157,0.2265791,0.00011081904,0.000043812335,0.00011091157,0.0003302248,0.00067593093,0.010426498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936074,0.00020681083,0.000025339994,0.00011897333,0.000121673846,0.00016648285],"domain_scores_gemma":[0.99876237,0.0007369447,0.00006634846,0.00019228396,0.00014513276,0.0000969081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084660767,0.0010655654,0.0013779085,0.00054451317,0.0007610973,0.0010936126,0.0020353205,0.0017038123,0.008789264],"category_scores_gemma":[0.003150361,0.0005716899,0.0008862739,0.0010973451,0.000952311,0.0020595647,0.002089113,0.0014254703,0.0012422274],"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":[0.0004098521,0.00016508228,0.0006088972,0.00012208233,0.000046385336,0.00007401787,0.00010387628,0.90053785,0.005785046,0.035263546,0.0032144987,0.053668957],"study_design_scores_gemma":[0.000020085217,0.000036356243,0.0000694209,0.000006235035,0.000008854837,0.00001772526,0.000031462805,0.9848585,0.0010501998,0.013397855,0.0004967502,0.0000065700096],"about_ca_topic_score_codex":0.003273877,"about_ca_topic_score_gemma":0.0042161667,"teacher_disagreement_score":0.008789264,"about_ca_system_score_codex":0.0010660737,"about_ca_system_score_gemma":0.0012340735,"threshold_uncertainty_score":0.02940303},"labels":[],"label_agreement":null},{"id":"W4400909560","doi":"10.1109/icde60146.2024.00084","title":"Learning k-Determinantal Point Processes for Personalized Ranking","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada)","funders":"","keywords":"Determinantal point process; Ranking (information retrieval); Computer science; Point process; Point (geometry); Artificial intelligence; Mathematics; Statistics; Physics; Geometry","score_opus":0.017101827042279753,"score_gpt":0.2700053144373386,"score_spread":0.25290348739505886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400909560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013310244,0.00015416884,0.9856264,0.00012250476,0.0000145551885,0.000032308977,0.000046909507,0.0002440635,0.00044892373],"genre_scores_gemma":[0.64208317,0.0005081797,0.3524253,0.00023143542,0.00011213932,0.00032760308,0.00058952864,0.00017949678,0.0035431562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978504,0.0010063745,0.00011407681,0.00042570048,0.0004405334,0.00016301587],"domain_scores_gemma":[0.99162185,0.0060766554,0.00057823514,0.0006142354,0.0009252901,0.0001837363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044758273,0.0010993504,0.0019887062,0.001392451,0.00070971774,0.001689174,0.0019039206,0.0017520649,0.0021519063],"category_scores_gemma":[0.015672725,0.0008371749,0.0014430885,0.0015334666,0.0013526677,0.002796937,0.0018277473,0.0030393098,0.0010053829],"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":[0.00009266055,0.00011052787,0.0015308184,0.00009750617,0.00008011633,0.00006305963,0.000112014306,0.89026195,0.001297318,0.0439065,0.0014151108,0.061032437],"study_design_scores_gemma":[0.0000035952705,0.000010660759,0.00008384736,0.0000023414693,0.000002944351,0.0000043309456,0.0000034881368,0.9882981,0.00013909429,0.011334373,0.00011205164,0.0000050655963],"about_ca_topic_score_codex":0.0050159926,"about_ca_topic_score_gemma":0.0052359477,"teacher_disagreement_score":0.0050159926,"about_ca_system_score_codex":0.0014769287,"about_ca_system_score_gemma":0.0013432762,"threshold_uncertainty_score":0.023670733},"labels":[],"label_agreement":null},{"id":"W4400909730","doi":"10.1109/icde60146.2024.00383","title":"GaussDB-Global: A Geographically Distributed Database System","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Distributed database; Database; Spatiotemporal database; Database design; Database tuning; View","score_opus":0.009484433151845805,"score_gpt":0.23913363774244187,"score_spread":0.22964920459059607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400909730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087960534,0.0039373324,0.49899542,0.0012305607,0.000464157,0.0012345985,0.019052329,0.34281287,0.0443121],"genre_scores_gemma":[0.5847694,0.0015434922,0.34481287,0.0013997258,0.0002288794,0.000681186,0.040595226,0.0047651576,0.021204032],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988747,0.00019428226,0.00010884993,0.00030757234,0.00041688693,0.000097720884],"domain_scores_gemma":[0.9982009,0.00015152268,0.00009515528,0.0009740555,0.00032444514,0.00025384978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012777902,0.0006502746,0.0008510801,0.0012208917,0.0007153988,0.0021690622,0.0029172227,0.0006848994,0.0075766607],"category_scores_gemma":[0.0025361883,0.0004964898,0.00032340133,0.0020920408,0.0005637183,0.0022332603,0.0033635597,0.0009260755,0.00470341],"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":[0.003493091,0.00093314826,0.0136260325,0.0016712878,0.00049645314,0.0010074297,0.0007742337,0.03763057,0.06171351,0.038446695,0.35162127,0.48858616],"study_design_scores_gemma":[0.0018535103,0.0010936471,0.009050702,0.00011996717,0.00029840195,0.002170663,0.00044435868,0.35682842,0.05187513,0.026308699,0.5496769,0.0002795842],"about_ca_topic_score_codex":0.0033230374,"about_ca_topic_score_gemma":0.0025349197,"teacher_disagreement_score":0.0075766607,"about_ca_system_score_codex":0.0005872071,"about_ca_system_score_gemma":0.0016420011,"threshold_uncertainty_score":0.025346518},"labels":[],"label_agreement":null},{"id":"W4400909774","doi":"10.1109/icde60146.2024.00408","title":"Robust Query Optimization in the Era of Machine Learning: State-of-the-Art and Future Directions","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"IBM (Canada); University of Ottawa","funders":"","keywords":"Computer science; Query optimization; State (computer science); Artificial intelligence; Machine learning; Information retrieval; Programming language","score_opus":0.011671235054987046,"score_gpt":0.21825128872434152,"score_spread":0.20658005366935447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400909774","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005765401,0.3322615,0.6359424,0.017525379,0.0007294004,0.00010670067,0.00019130149,0.0010522123,0.0064257914],"genre_scores_gemma":[0.17978725,0.32875568,0.4727177,0.0054251896,0.00816339,0.00026155188,0.0007817214,0.00089909503,0.0032084002],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99043196,0.0038069284,0.00063645403,0.0015881419,0.0030180982,0.00051839167],"domain_scores_gemma":[0.93784416,0.050798405,0.0020707613,0.0044727596,0.0041423663,0.0006715313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01737963,0.0019627167,0.00297076,0.0023678117,0.0008680517,0.007927288,0.005196714,0.0042389096,0.004216879],"category_scores_gemma":[0.04487592,0.0013987215,0.0025407595,0.0048925276,0.003746184,0.01839518,0.0038630937,0.0073106918,0.0016663261],"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":[0.00033222302,0.00027351073,0.002694287,0.0036258239,0.0003731033,0.00013984904,0.0004337313,0.114778444,0.0019959642,0.17935221,0.016896341,0.6791044],"study_design_scores_gemma":[0.00004977218,0.00027678823,0.0015161097,0.0015916179,0.00019268444,0.0003290404,0.0005598703,0.58316696,0.00323628,0.3258831,0.08300467,0.00019307256],"about_ca_topic_score_codex":0.0038788568,"about_ca_topic_score_gemma":0.001739318,"teacher_disagreement_score":0.01737963,"about_ca_system_score_codex":0.0026868775,"about_ca_system_score_gemma":0.0026462914,"threshold_uncertainty_score":0.09191334},"labels":[],"label_agreement":null},{"id":"W4401353058","doi":"10.14778/3675034.3675040","title":"Incremental Sliding Window Connectivity over Streaming Graphs","year":2024,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Search engine indexing; Sliding window protocol; Computation; Leverage (statistics); Latency (audio); Window (computing); Stream processing; Throughput; Graph; Theoretical computer science; Data mining; Parallel computing; Algorithm; Artificial intelligence","score_opus":0.012101642950259544,"score_gpt":0.2308971289352785,"score_spread":0.21879548598501897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401353058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32322684,0.0011937782,0.6634788,0.00049291604,0.00008711869,0.0002193847,0.0011805715,0.0057401247,0.0043804506],"genre_scores_gemma":[0.8412493,0.00044173113,0.1550122,0.00006485287,0.000092366005,0.0001215347,0.0013569843,0.00022225767,0.0014387438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916613,0.00011981921,0.000054626275,0.00020033932,0.0003184732,0.00014056233],"domain_scores_gemma":[0.9967542,0.0016434401,0.000398868,0.00064591353,0.00037977347,0.00017777029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006495852,0.0005765972,0.0007281264,0.0015236093,0.00056549313,0.0013399522,0.0016402011,0.00047338323,0.0014216942],"category_scores_gemma":[0.0061121644,0.00034466997,0.00040306462,0.0028108815,0.00064165756,0.004054977,0.0012560982,0.0005781664,0.00030648874],"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":[0.0011951225,0.0002736353,0.010858359,0.0004699334,0.0001309005,0.00094506587,0.0009596936,0.53214216,0.06272421,0.07174402,0.011480427,0.30707648],"study_design_scores_gemma":[0.000025802014,0.000105107545,0.0009405907,0.000008316797,0.000019654182,0.00013992925,0.0001122151,0.96759105,0.00933685,0.01950081,0.0022064277,0.000013253801],"about_ca_topic_score_codex":0.007530583,"about_ca_topic_score_gemma":0.0056459415,"teacher_disagreement_score":0.007530583,"about_ca_system_score_codex":0.0009417891,"about_ca_system_score_gemma":0.0007323533,"threshold_uncertainty_score":0.014973462},"labels":[],"label_agreement":null},{"id":"W4401468895","doi":"10.1016/j.geomat.2024.100004","title":"Understanding the impact of geotagging on location inference models for accurate generalization to non-geotagged datasets","year":2024,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Geotagging; Inference; Generalization; Computer science; Geography; Data mining; Artificial intelligence; Information retrieval; Mathematics","score_opus":0.11570116180420643,"score_gpt":0.34759595285791156,"score_spread":0.23189479105370514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401468895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5104802,0.0025157633,0.4616811,0.008639154,0.0007066801,0.00064089464,0.0056083323,0.0031760219,0.0065517537],"genre_scores_gemma":[0.92155874,0.00045245772,0.07140688,0.00071837025,0.0002011468,0.00027381047,0.0045895553,0.00016246283,0.00063666515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989099,0.0068924897,0.0008421881,0.001999084,0.0007318753,0.00043528152],"domain_scores_gemma":[0.8442211,0.13127561,0.004197785,0.015474567,0.003933229,0.00089773606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036831353,0.0013824123,0.0011182022,0.001210458,0.0009569391,0.0036582767,0.0021504753,0.002032124,0.002087865],"category_scores_gemma":[0.18209001,0.0009261024,0.0017854271,0.0018309862,0.001579391,0.010932258,0.0026318554,0.0042126165,0.0011888252],"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":[0.0013355671,0.00065440417,0.24603967,0.000915442,0.0016671853,0.00047492032,0.0014374852,0.528285,0.00709964,0.010232114,0.0110676065,0.19079094],"study_design_scores_gemma":[0.0000476845,0.000263925,0.028209098,0.00013752092,0.00020353954,0.0001206797,0.0005457049,0.9443722,0.0034670709,0.020091934,0.0024815935,0.000059007747],"about_ca_topic_score_codex":0.016995639,"about_ca_topic_score_gemma":0.018960262,"teacher_disagreement_score":0.036831353,"about_ca_system_score_codex":0.0016465144,"about_ca_system_score_gemma":0.0022013634,"threshold_uncertainty_score":0.19478512},"labels":[],"label_agreement":null},{"id":"W4401608703","doi":"10.1109/tvt.2024.3443742","title":"Dynamic Virtual Network Embedding Leveraging Neighborhood and Preceding Mappings Information","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedding; Computer science; Computer network; Distributed computing; Artificial intelligence","score_opus":0.005898160685220277,"score_gpt":0.22235520348653262,"score_spread":0.21645704280131234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401608703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15652387,0.0005558484,0.83884215,0.00017843641,0.00008165707,0.00007605101,0.00011404757,0.000667021,0.002960818],"genre_scores_gemma":[0.8930801,0.00018623397,0.10514713,0.00004322476,0.000023510422,0.00004640481,0.00027614724,0.00003973812,0.0011574735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994203,0.00014969743,0.000032578257,0.00014521313,0.00014634452,0.00010594819],"domain_scores_gemma":[0.9987515,0.0005027435,0.00017941026,0.00025373997,0.00019670397,0.00011587265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005506783,0.0008505142,0.0009885719,0.0009162893,0.0005772827,0.0009245279,0.0012984,0.00047582074,0.00081850745],"category_scores_gemma":[0.003247661,0.0002978565,0.0003707402,0.00091289287,0.00046179598,0.002377523,0.0015793354,0.00051567523,0.00017785515],"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":[0.00015142378,0.00011281614,0.0031658616,0.000070706075,0.00004968194,0.00020181337,0.0001675073,0.7724625,0.0067304047,0.011215047,0.0018373154,0.2038349],"study_design_scores_gemma":[0.0000040722134,0.000033765966,0.00021334481,0.000003846181,0.0000083257955,0.00006646983,0.000054405806,0.994313,0.0012197817,0.0034192093,0.0006569152,0.0000069819307],"about_ca_topic_score_codex":0.0028882131,"about_ca_topic_score_gemma":0.0040877475,"teacher_disagreement_score":0.0028882131,"about_ca_system_score_codex":0.00054406904,"about_ca_system_score_gemma":0.00087549945,"threshold_uncertainty_score":0.0057427883},"labels":[],"label_agreement":null},{"id":"W4402265611","doi":"10.1109/tqcebt59414.2024.10545107","title":"Complex Event Processing in Web Streams with Ontology-Based Abstraction Layers for Smart City Frameworks","year":2024,"lang":"en","type":"article","venue":"","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Ontology; Abstraction; Complex event processing; Event (particle physics); STREAMS; World Wide Web; Programming language; Process (computing); Computer network","score_opus":0.02955860682144413,"score_gpt":0.29752522578589885,"score_spread":0.26796661896445473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402265611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004056233,0.00017539904,0.9928343,0.00041326074,0.000045659457,0.00014370243,0.0001265823,0.00094266946,0.0012620508],"genre_scores_gemma":[0.13045083,0.0007059077,0.8652936,0.00026488106,0.0000766979,0.00038728167,0.0009173783,0.00023382495,0.0016695224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795496,0.0005363758,0.00034856616,0.0002563142,0.0006814887,0.00022229625],"domain_scores_gemma":[0.998211,0.00056253717,0.00019790868,0.0004935476,0.00038101902,0.00015404355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004005959,0.00073759584,0.00079181476,0.0015854441,0.0011645445,0.0055945204,0.0019306063,0.0009967991,0.0011264848],"category_scores_gemma":[0.0050838585,0.00067043136,0.0026051407,0.0017755639,0.0020398798,0.0057937913,0.0044099498,0.003183759,0.00045452145],"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":[0.00016549298,0.00026912705,0.0030142167,0.000419452,0.0002614867,0.00083806796,0.0018021973,0.14740105,0.0074786004,0.74104875,0.006569997,0.09073152],"study_design_scores_gemma":[0.00003315728,0.00003237554,0.00059724244,0.00013462435,0.000108994056,0.00017018017,0.00035699352,0.74314463,0.0046558823,0.20389804,0.04679841,0.00006948291],"about_ca_topic_score_codex":0.01426785,"about_ca_topic_score_gemma":0.01806432,"teacher_disagreement_score":0.01426785,"about_ca_system_score_codex":0.0019556745,"about_ca_system_score_gemma":0.0035280255,"threshold_uncertainty_score":0.028369606},"labels":[],"label_agreement":null},{"id":"W4402351867","doi":"10.1109/ijcnn60899.2024.10651125","title":"GS-SQL: Modeling Spatial Semantics in Spatial Text-to-SQL","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission; Ministry of Natural Resources","keywords":"Computer science; SQL; SQL/PSM; Programming language; Data definition language; Semantics (computer science); PL/SQL; Database; Query by Example; Information retrieval","score_opus":0.01877647370813558,"score_gpt":0.2581648012388266,"score_spread":0.23938832753069106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402351867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013003492,0.00022542998,0.9390146,0.0006138591,0.000059930713,0.00037877107,0.012435614,0.032662906,0.0016053204],"genre_scores_gemma":[0.17230327,0.00040729457,0.7835488,0.000711644,0.00008491432,0.0005706968,0.03704521,0.0028034812,0.0025247603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99732804,0.00066634105,0.00045665068,0.0006461581,0.000775504,0.00012722534],"domain_scores_gemma":[0.9958988,0.0016974614,0.00038284506,0.0012152643,0.00067788817,0.00012769151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002744767,0.0014175809,0.00081599323,0.002404964,0.0006211222,0.0033387074,0.0025742243,0.0013574834,0.004367913],"category_scores_gemma":[0.008548929,0.0006688811,0.002247935,0.0024502967,0.001521844,0.008403293,0.00281636,0.0017513869,0.0018570723],"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":[0.0008936951,0.00060649676,0.023429489,0.0026236943,0.00033194447,0.0012366682,0.0035161313,0.21016178,0.023106208,0.35138407,0.09388513,0.2888247],"study_design_scores_gemma":[0.00008450944,0.00012244831,0.0016433689,0.00013436047,0.00008135266,0.0003541555,0.00055704283,0.7194221,0.018412402,0.17338173,0.085721284,0.00008531704],"about_ca_topic_score_codex":0.007921009,"about_ca_topic_score_gemma":0.010308028,"teacher_disagreement_score":0.007921009,"about_ca_system_score_codex":0.0014758236,"about_ca_system_score_gemma":0.0022716583,"threshold_uncertainty_score":0.015749812},"labels":[],"label_agreement":null},{"id":"W4402564288","doi":"10.48550/arxiv.2408.07650","title":"Exact Trajectory Similarity Search With N-tree: An Efficient Metric Index for kNN and Range Queries","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Nearest neighbor search; Similarity (geometry); Index (typography); Metric (unit); Range (aeronautics); Trajectory; Tree (set theory); Mathematics; Computer science; Range query (database); Data mining; Algorithm; Artificial intelligence; Combinatorics; Information retrieval; Search engine; Web search query; Engineering; Physics; World Wide Web; Sargable","score_opus":0.06774126754605381,"score_gpt":0.21009785073267936,"score_spread":0.14235658318662556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402564288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036903065,0.0051995264,0.93938935,0.0006794414,0.00030339186,0.00044846404,0.0032110505,0.005858785,0.008006967],"genre_scores_gemma":[0.25491703,0.0016403128,0.72970104,0.00029361102,0.00024378355,0.00052382326,0.00822538,0.0005062506,0.0039487397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967483,0.0005145478,0.00041705812,0.00044248422,0.0016337753,0.00024373694],"domain_scores_gemma":[0.9963834,0.00090500334,0.0003789641,0.0013442428,0.0007560627,0.00023226997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015510884,0.00091278594,0.0027332106,0.0048283627,0.0013708188,0.002425677,0.0025852115,0.0014313762,0.0036061634],"category_scores_gemma":[0.013108953,0.0003839526,0.0006438571,0.010444889,0.0008954437,0.008329892,0.00379628,0.0010241185,0.0028383662],"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":[0.0008484546,0.00046797123,0.0040089265,0.0007428513,0.00012754746,0.0003149292,0.0005814492,0.07383368,0.011770227,0.066413246,0.04175297,0.79913765],"study_design_scores_gemma":[0.00016548698,0.0005897883,0.0015391499,0.00012479539,0.000079403944,0.0015105789,0.00047085568,0.80435395,0.009501958,0.12595497,0.055589925,0.00011910557],"about_ca_topic_score_codex":0.0044336263,"about_ca_topic_score_gemma":0.0055159847,"teacher_disagreement_score":0.0048283627,"about_ca_system_score_codex":0.001660027,"about_ca_system_score_gemma":0.0027923323,"threshold_uncertainty_score":0.012063861},"labels":[],"label_agreement":null},{"id":"W4402626604","doi":"10.1109/tfuzz.2024.3464848","title":"A Granular Aggregation of Multifaceted Gaussian Process Models","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Gaussian process; Process (computing); Computer science; Statistical physics; Biological system; Gaussian; Physics; Chemistry; Computational chemistry","score_opus":0.023399261755128183,"score_gpt":0.2563447556756685,"score_spread":0.23294549392054031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402626604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017241683,0.00022715285,0.9807513,0.00013636783,0.000029194149,0.00005527231,0.00010852168,0.00028219313,0.0011683018],"genre_scores_gemma":[0.65301484,0.00058074103,0.34385398,0.00011623917,0.00008199228,0.0002141071,0.0003965922,0.00007855046,0.0016628983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728274,0.0008145939,0.00022272095,0.0006332558,0.0008009299,0.0002458337],"domain_scores_gemma":[0.9960264,0.0021266204,0.00055683777,0.0006252348,0.0005279355,0.00013692841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036532294,0.0011844633,0.0014554191,0.002048661,0.00075984176,0.0035798587,0.0014375672,0.0012554611,0.0015265283],"category_scores_gemma":[0.009690695,0.000724084,0.0024485039,0.0022872656,0.0011902382,0.0030206235,0.0024804862,0.0019103106,0.0003330215],"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":[0.00015253597,0.00006740386,0.0035452908,0.00018258655,0.00015054566,0.00028802003,0.00043236848,0.83513033,0.003257673,0.10360587,0.00073097437,0.052456338],"study_design_scores_gemma":[0.0000062646145,0.000028273573,0.00033507965,0.000018718601,0.000023540848,0.000031135798,0.000026892545,0.9761806,0.00044132172,0.022242945,0.00064987305,0.000015258951],"about_ca_topic_score_codex":0.0064709256,"about_ca_topic_score_gemma":0.0036011038,"teacher_disagreement_score":0.0064709256,"about_ca_system_score_codex":0.0015110587,"about_ca_system_score_gemma":0.0011926668,"threshold_uncertainty_score":0.019320369},"labels":[],"label_agreement":null},{"id":"W4402628937","doi":"10.1109/icee63041.2024.10667760","title":"Enhancing Kriging with Inductive Spatio-Temporal GraphODE","year":2024,"lang":"en","type":"article","venue":"","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":"York University","funders":"","keywords":"Computer science; Kriging; Machine learning","score_opus":0.01307233720259102,"score_gpt":0.24014385444668745,"score_spread":0.22707151724409644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402628937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008074279,0.0000829339,0.9907845,0.000069260306,0.000017739034,0.00001151952,0.00004280686,0.0005517486,0.0003651755],"genre_scores_gemma":[0.40750992,0.00028480793,0.5886152,0.00016139615,0.000043333177,0.00008483974,0.00042447326,0.000314465,0.0025615843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956673,0.000118762604,0.000023327873,0.0001028698,0.00014903928,0.00003921533],"domain_scores_gemma":[0.99875355,0.00073469034,0.00010327711,0.00014712567,0.00023062057,0.000030726515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005645776,0.0007452009,0.0006614739,0.00078392605,0.00031014474,0.0005652775,0.0014477045,0.00076076447,0.0009098739],"category_scores_gemma":[0.003135733,0.00048372408,0.000820769,0.0011106048,0.00048402854,0.0013005987,0.00087603345,0.0011096426,0.00043103943],"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":[0.000040926985,0.00003646438,0.0006932002,0.000059913775,0.000041874533,0.000038041337,0.00007336926,0.89758223,0.005385598,0.0045435135,0.00067579723,0.09082901],"study_design_scores_gemma":[0.0000014491457,0.0000032363303,0.00005080411,0.0000010097197,0.0000020804487,0.0000047034196,0.0000033036029,0.9982029,0.0006160353,0.0008801005,0.0002317593,0.000002543383],"about_ca_topic_score_codex":0.020694163,"about_ca_topic_score_gemma":0.032995682,"teacher_disagreement_score":0.020694163,"about_ca_system_score_codex":0.000714812,"about_ca_system_score_gemma":0.0011052081,"threshold_uncertainty_score":0.04114741},"labels":[],"label_agreement":null},{"id":"W4402671649","doi":"10.18653/v1/2024.acl-long.311","title":"A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Preference; Artificial intelligence; Economics; Microeconomics","score_opus":0.025607229488819706,"score_gpt":0.2552919323939405,"score_spread":0.22968470290512077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402671649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04635719,0.023166716,0.90100425,0.008233519,0.0005416134,0.00014659844,0.0003068851,0.0017974625,0.018445838],"genre_scores_gemma":[0.3199367,0.007898743,0.66117805,0.0019307706,0.00087981357,0.00019971289,0.0007734761,0.0024426926,0.0047600386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9809296,0.013280046,0.0012131897,0.0014797975,0.0025475316,0.00054970215],"domain_scores_gemma":[0.90993834,0.06578104,0.0013458111,0.019814262,0.0023817476,0.0007388146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018198784,0.0018315285,0.0024818268,0.0027072385,0.0017067605,0.0054093935,0.0040786243,0.0035208229,0.007960534],"category_scores_gemma":[0.08616239,0.0012569358,0.0014150013,0.005317814,0.002225494,0.024420623,0.006045702,0.006055541,0.0058922083],"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":[0.0009814309,0.00048609145,0.002511258,0.00051730743,0.0003297945,0.00013632946,0.0008588018,0.03492728,0.005837146,0.1023845,0.015208241,0.83582175],"study_design_scores_gemma":[0.00018816504,0.00067208736,0.0015854003,0.00036584397,0.00024523403,0.000860342,0.0012980058,0.43254322,0.0071327575,0.5284757,0.026487153,0.00014614644],"about_ca_topic_score_codex":0.0016489822,"about_ca_topic_score_gemma":0.0046274797,"teacher_disagreement_score":0.018198784,"about_ca_system_score_codex":0.0012173089,"about_ca_system_score_gemma":0.0016781619,"threshold_uncertainty_score":0.09624553},"labels":[],"label_agreement":null},{"id":"W4402922096","doi":"10.1038/s41598-024-72504-x","title":"A framework for spatial-temporal cluster evolution representation and analysis based on graphs","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cluster (spacecraft); Representation (politics); Theoretical computer science; Computational biology; Data mining; Biology; Computer network","score_opus":0.020341029762146592,"score_gpt":0.29174854833627367,"score_spread":0.2714075185741271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402922096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007339517,0.00010005941,0.9978175,0.000121722194,0.000014345614,0.000043622935,0.00023428781,0.00044756016,0.0004868544],"genre_scores_gemma":[0.03912452,0.00038797877,0.9576272,0.00009411301,0.000051866053,0.0002563713,0.0013180865,0.00019927887,0.0009407384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99767596,0.00084688887,0.00020779354,0.00057681574,0.000554958,0.0001376866],"domain_scores_gemma":[0.99682474,0.0013931826,0.00042386886,0.0005251812,0.00065955357,0.00017347746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028463397,0.0012139439,0.0009983348,0.0053766514,0.0013522364,0.0036378165,0.0031631074,0.0012284712,0.0021696764],"category_scores_gemma":[0.0073078442,0.0007022781,0.0028380288,0.006110348,0.001786311,0.004315233,0.0026283956,0.0022270097,0.0007699148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000698965,0.00007112964,0.001970127,0.0003027786,0.00016826046,0.0003444908,0.00073844433,0.38708478,0.0028566278,0.48391253,0.0077892803,0.11469163],"study_design_scores_gemma":[0.000015370639,0.000030504916,0.00048643502,0.000059982474,0.00004882041,0.00013963724,0.0001855655,0.7294075,0.0009830678,0.2497001,0.018899268,0.000043758617],"about_ca_topic_score_codex":0.023929564,"about_ca_topic_score_gemma":0.018223647,"teacher_disagreement_score":0.023929564,"about_ca_system_score_codex":0.0026515408,"about_ca_system_score_gemma":0.0026069677,"threshold_uncertainty_score":0.04758054},"labels":[],"label_agreement":null},{"id":"W4403095001","doi":"10.1109/ms.2024.3472016","title":"On Build Hermeticity in Bazel-Based Build Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Software","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":"Queen's University","funders":"","keywords":"Computer science; Systems engineering; Software engineering; Engineering","score_opus":0.014388998615915189,"score_gpt":0.2513422562157066,"score_spread":0.2369532575997914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403095001","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61544997,0.00023548333,0.36913112,0.00039624397,0.000041906682,0.0001928954,0.0002281485,0.0037045917,0.010619628],"genre_scores_gemma":[0.89423054,0.00008151352,0.10072832,0.00009764249,0.000027410837,0.00017990283,0.000468848,0.000910689,0.0032751446],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9871983,0.003700876,0.00065844215,0.002017597,0.004712674,0.001712154],"domain_scores_gemma":[0.950616,0.025674243,0.0044137267,0.014968613,0.003391711,0.00093578413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008378738,0.00051288935,0.00050728366,0.0024943443,0.001988867,0.0038626627,0.0016986075,0.0010557817,0.0035849751],"category_scores_gemma":[0.048982028,0.0011415216,0.00083186745,0.002048124,0.004728885,0.008153125,0.005038515,0.002423338,0.0006473424],"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":[0.001020779,0.0006690054,0.08919983,0.0004391195,0.00016837705,0.0012990917,0.0098298,0.17329986,0.024602622,0.5810716,0.0025401795,0.11585978],"study_design_scores_gemma":[0.00017560301,0.0005133774,0.021223504,0.00015050248,0.00017845922,0.0010576687,0.0018152117,0.5803368,0.036976006,0.31810343,0.03927365,0.00019587597],"about_ca_topic_score_codex":0.003151758,"about_ca_topic_score_gemma":0.0025848094,"teacher_disagreement_score":0.008378738,"about_ca_system_score_codex":0.0014861221,"about_ca_system_score_gemma":0.002645632,"threshold_uncertainty_score":0.044311583},"labels":[],"label_agreement":null},{"id":"W4403165798","doi":"10.61091/jcmcc122-06","title":"Neighbor Locating Coloring on Graphs: Three Products","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Graph coloring; Complete coloring; Computer science; Combinatorics; Greedy coloring; Business; Mathematics; Graph; Line graph","score_opus":0.019782868263130166,"score_gpt":0.25251457148166506,"score_spread":0.2327317032185349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403165798","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74817073,0.0008478712,0.19809887,0.00024670555,0.00006876188,0.00008977687,0.000292014,0.0003218058,0.051863458],"genre_scores_gemma":[0.9167892,0.00045812465,0.07574637,0.000065590226,0.000039310038,0.000052444113,0.00025161242,0.00007672173,0.0065205204],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995852,0.000087389635,0.000024061252,0.000111782,0.00013332523,0.000058187514],"domain_scores_gemma":[0.99878734,0.00044116893,0.00019060868,0.00025784192,0.00018294768,0.00014000597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040156062,0.00034582854,0.00024513845,0.000646744,0.0006852875,0.0014430137,0.0005071817,0.00042987324,0.002539903],"category_scores_gemma":[0.0015420846,0.0003274107,0.00035902436,0.001081928,0.0014190888,0.0016549132,0.0008721527,0.00038974558,0.00027605207],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002092074,0.000071112736,0.0061206133,0.00020056538,0.000026214784,0.000546244,0.0012821851,0.018496642,0.015873142,0.9058963,0.0022639446,0.049013764],"study_design_scores_gemma":[0.000058638605,0.00043305013,0.0055651395,0.00007631443,0.00008191419,0.0024959545,0.0011420945,0.12375085,0.015090695,0.8028221,0.048398487,0.00008469468],"about_ca_topic_score_codex":0.0013771076,"about_ca_topic_score_gemma":0.0013290638,"teacher_disagreement_score":0.002539903,"about_ca_system_score_codex":0.00075869315,"about_ca_system_score_gemma":0.00039325585,"threshold_uncertainty_score":0.008496821},"labels":[],"label_agreement":null},{"id":"W4403450086","doi":"10.2139/ssrn.4989700","title":"On Approximate Colored Path Counting","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data Management and Algorithms","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":"Dalhousie University; Université du Québec en Outaouais","funders":"","keywords":"Colored; Path (computing); Mathematics; Combinatorics; Computer science; Materials science; Computer network","score_opus":0.009498252363250003,"score_gpt":0.23812494107412205,"score_spread":0.22862668871087205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403450086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06637396,0.0017384901,0.87590665,0.0038880517,0.00087357365,0.00023658047,0.0011863549,0.0022358876,0.04756034],"genre_scores_gemma":[0.541448,0.0019417094,0.40816766,0.0029510309,0.0013585433,0.0007239378,0.0030524095,0.002198761,0.038157962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99250925,0.0026126832,0.00031006723,0.0014078765,0.0021690207,0.0009911101],"domain_scores_gemma":[0.97383434,0.01578545,0.000932325,0.0064726924,0.0017277402,0.0012474126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054905396,0.0025628265,0.0041906466,0.0046846676,0.0037553653,0.0068582157,0.007943478,0.0035828534,0.020600954],"category_scores_gemma":[0.042297192,0.0015151608,0.0026008082,0.010398368,0.004648643,0.014870597,0.009957818,0.0070581017,0.0030463487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050740823,0.00017747152,0.0016933887,0.00026152484,0.000110980065,0.00011061511,0.00024799784,0.07151927,0.0012580026,0.8309449,0.018488677,0.07467966],"study_design_scores_gemma":[0.000030276493,0.000025347916,0.00021664055,0.000039803854,0.000045687815,0.00006171271,0.000044176737,0.21059723,0.0005343992,0.7845252,0.0038605789,0.000018885608],"about_ca_topic_score_codex":0.0067818076,"about_ca_topic_score_gemma":0.0074368645,"teacher_disagreement_score":0.020600954,"about_ca_system_score_codex":0.005297888,"about_ca_system_score_gemma":0.0037885427,"threshold_uncertainty_score":0.068917036},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024642171,0.00083787733,0.96085894,0.0016867771,0.00016076263,0.00029545405,0.0007643226,0.005605658,0.00514792],"genre_scores_gemma":[0.33734977,0.00084419467,0.6485621,0.0011134087,0.0003206528,0.0006106692,0.0026991877,0.0045890715,0.00391099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9728175,0.007940743,0.0027008338,0.0035427185,0.010817629,0.0021807132],"domain_scores_gemma":[0.92740273,0.050207183,0.0028866224,0.012352974,0.006096511,0.0010539605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015444594,0.0025059066,0.0026860111,0.004193726,0.0026038191,0.009304522,0.003985394,0.003979808,0.009127742],"category_scores_gemma":[0.07115862,0.0014734567,0.004158119,0.005384861,0.0063707624,0.032870848,0.010081017,0.006178158,0.0028523607],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009957505,0.00038193047,0.0048374883,0.001468016,0.00022916246,0.0014576743,0.0053053237,0.08268508,0.013335695,0.7149393,0.015024835,0.1593398],"study_design_scores_gemma":[0.00009708264,0.00018401607,0.00044136684,0.00019618153,0.00013744994,0.0005900834,0.00093607797,0.21719109,0.015886433,0.7429671,0.021241358,0.00013165052],"about_ca_topic_score_codex":0.0050832797,"about_ca_topic_score_gemma":0.0033687477,"teacher_disagreement_score":0.015444594,"about_ca_system_score_codex":0.0038415964,"about_ca_system_score_gemma":0.0034690155,"threshold_uncertainty_score":0.08167976},"labels":[],"label_agreement":null},{"id":"W4403486757","doi":"10.3233/faia240991","title":"On Parallel External-Memory Bidirectional Search","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Israel Science Foundation; United States-Israel Binational Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Parallel computing","score_opus":0.04542716143602398,"score_gpt":0.28629372500191125,"score_spread":0.24086656356588726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403486757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014018531,0.0064961137,0.89224017,0.0005775023,0.00024887407,0.00011867201,0.00023604011,0.0021293752,0.08393475],"genre_scores_gemma":[0.24251622,0.007189026,0.68142086,0.00067430874,0.00022529183,0.00042267688,0.0010472293,0.0011393555,0.06536498],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996221,0.00010489003,0.000020141804,0.000064302185,0.00013765016,0.000050908082],"domain_scores_gemma":[0.9994137,0.0002781258,0.000030996278,0.00014348893,0.0001069768,0.000026691761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005341776,0.0008255552,0.0005503957,0.0007258211,0.00049114146,0.0012146698,0.0012287239,0.00065111584,0.009283184],"category_scores_gemma":[0.002014925,0.0002753156,0.00040621395,0.0022305993,0.0006511121,0.0019301617,0.0018054148,0.0012868525,0.0031211255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019060445,0.00011480791,0.00050378084,0.0004545908,0.00004263299,0.00010735599,0.000124258,0.1605291,0.0049124327,0.21115226,0.019612972,0.60225517],"study_design_scores_gemma":[0.000081796126,0.00013449731,0.0003865712,0.00018107225,0.00003969028,0.00034076907,0.00008712094,0.62110716,0.008553729,0.24746415,0.12158669,0.00003680478],"about_ca_topic_score_codex":0.0016525696,"about_ca_topic_score_gemma":0.0018143713,"teacher_disagreement_score":0.009283184,"about_ca_system_score_codex":0.00052740227,"about_ca_system_score_gemma":0.0011152246,"threshold_uncertainty_score":0.031055331},"labels":[],"label_agreement":null},{"id":"W4403513002","doi":"10.1145/3681770.3698572","title":"Realistic Trajectory Generation using Simple Probabilistic Language Models","year":2024,"lang":"en","type":"article","venue":"","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":"University of Alberta","funders":"","keywords":"Trajectory; Probabilistic logic; Computer science; Simple (philosophy); Algorithm; Artificial intelligence","score_opus":0.06612016735095674,"score_gpt":0.2932081473075745,"score_spread":0.22708797995661775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403513002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06362445,0.000151672,0.93029493,0.00052338047,0.00007387484,0.00016494546,0.0017752066,0.0019103077,0.0014812242],"genre_scores_gemma":[0.7177166,0.0002455427,0.27087972,0.0001899442,0.000058949023,0.00052850856,0.0076906383,0.0003027436,0.0023873763],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990382,0.00032530865,0.000066239765,0.00030219276,0.0001965086,0.00007159918],"domain_scores_gemma":[0.99579716,0.002861763,0.00024430917,0.0004986823,0.0005010943,0.00009702519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012557834,0.00080915005,0.00065953255,0.00086435047,0.00047405963,0.00093627704,0.0018124337,0.001150471,0.0022694233],"category_scores_gemma":[0.008572649,0.0005480353,0.0009734134,0.0014814035,0.0006627356,0.0018496774,0.0010944491,0.0011380396,0.0008320993],"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":[0.00012621781,0.000051346655,0.0014836473,0.00007510971,0.00003134008,0.00019605062,0.00013442134,0.9584663,0.001330699,0.011039202,0.0023431852,0.024722397],"study_design_scores_gemma":[0.000013800441,0.000010834022,0.000058483834,0.0000031504876,0.0000030349706,0.000021541033,0.000013917103,0.9949699,0.00032153842,0.0041678133,0.00041104294,0.000004876166],"about_ca_topic_score_codex":0.010107519,"about_ca_topic_score_gemma":0.010784394,"teacher_disagreement_score":0.010107519,"about_ca_system_score_codex":0.0010738402,"about_ca_system_score_gemma":0.0013308998,"threshold_uncertainty_score":0.020097375},"labels":[],"label_agreement":null},{"id":"W4403532159","doi":"10.5194/isprs-annals-x-4-2024-51-2024","title":"Geometry, Topology and p-Adic Numbers in Geospatial Data Modelling, Management and Processing: Review and Future Approaches","year":2024,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Deutsche Forschungsgemeinschaft","keywords":"Geospatial analysis; Topology (electrical circuits); Geometry; Computer science; Mathematics; Geography; Cartography; Combinatorics","score_opus":0.10059758468005499,"score_gpt":0.313896075041476,"score_spread":0.213298490361421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403532159","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023190083,0.9907911,0.005486601,0.0009261077,0.0004888065,0.0000066645066,0.000016575914,0.000014720937,0.0020375857],"genre_scores_gemma":[0.0041591325,0.987983,0.005311217,0.00036900147,0.0015347337,0.000020847936,0.00004150653,0.000012324094,0.0005682351],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990081,0.00032206724,0.00011840657,0.0001706701,0.0003296404,0.000051117895],"domain_scores_gemma":[0.9959466,0.002785472,0.00023689789,0.00016171912,0.00075389596,0.0001154285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028085248,0.0011477426,0.0019844077,0.0046678646,0.0006089584,0.003395686,0.0015441998,0.0020127976,0.0022643306],"category_scores_gemma":[0.0037893492,0.00067975186,0.0007296375,0.010522793,0.0029880549,0.0059470423,0.0017294935,0.002926242,0.0012929604],"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":[0.00005893439,0.000092393144,0.0006308496,0.0124119595,0.00012262721,0.00016417711,0.00037899468,0.0045376318,0.0006204603,0.18831438,0.02693063,0.7657369],"study_design_scores_gemma":[0.000015005997,0.00013122991,0.0009399329,0.0056092814,0.000092826456,0.0008393785,0.0004290007,0.0043171505,0.00047035547,0.11818351,0.8688713,0.00010106088],"about_ca_topic_score_codex":0.0018346144,"about_ca_topic_score_gemma":0.0012172236,"teacher_disagreement_score":0.0046678646,"about_ca_system_score_codex":0.0014763788,"about_ca_system_score_gemma":0.002089635,"threshold_uncertainty_score":0.01485306},"labels":[],"label_agreement":null},{"id":"W4403636704","doi":"10.1145/3681769.3698586","title":"Machine Learning Model Specification for Cataloging Spatio-Temporal Models (Demo Paper)","year":2024,"lang":"en","type":"article","venue":"","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":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Programming language; Cataloging; Software engineering; Artificial intelligence; Human–computer interaction; World Wide Web","score_opus":0.04760925276183622,"score_gpt":0.26199836097217066,"score_spread":0.21438910821033444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403636704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015854657,0.00021690503,0.9866851,0.0009842969,0.00012884184,0.00011365818,0.003162549,0.0015243582,0.005598927],"genre_scores_gemma":[0.09581687,0.00082231034,0.87479043,0.0012056727,0.00020469139,0.0009736684,0.014726421,0.0011545994,0.010305352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99601656,0.0012348676,0.00082592387,0.00054672844,0.0011830305,0.0001928342],"domain_scores_gemma":[0.9937012,0.002237728,0.00047752424,0.0020703624,0.0013558683,0.0001573368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072743283,0.00085191824,0.000667242,0.0018380056,0.00071962556,0.0033877431,0.0027577877,0.0017912323,0.0065861614],"category_scores_gemma":[0.0146539705,0.0007769372,0.0021554283,0.0024805735,0.0011610327,0.0041614296,0.00228764,0.0030037232,0.002981913],"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":[0.00006177757,0.000046352827,0.0016421747,0.000233669,0.000051314946,0.00031203605,0.00029463874,0.04780194,0.0010290107,0.87588716,0.02696058,0.045679383],"study_design_scores_gemma":[0.000038242804,0.000050906663,0.00049964414,0.00029324435,0.00005708583,0.0004666714,0.00013839595,0.32891965,0.0046995045,0.39201537,0.27276024,0.00006118636],"about_ca_topic_score_codex":0.009030208,"about_ca_topic_score_gemma":0.00968608,"teacher_disagreement_score":0.009030208,"about_ca_system_score_codex":0.0017762022,"about_ca_system_score_gemma":0.003794796,"threshold_uncertainty_score":0.038470805},"labels":[],"label_agreement":null},{"id":"W4403741421","doi":"10.1016/j.omega.2024.103219","title":"Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples","year":2024,"lang":"en","type":"article","venue":"Omega","topic":"Data Management and Algorithms","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 Alberta","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; Scientific Research Fund of Liaoning Provincial Education Department; Ministry of Education of the People's Republic of China; Humanities and Social Science Fund of Ministry of Education of China; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Sorting; Computer science; Monotonic function; Scale (ratio); Machine learning; Artificial intelligence; Mathematics; Algorithm","score_opus":0.06573342038570237,"score_gpt":0.295418315511876,"score_spread":0.2296848951261736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403741421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05688461,0.00025697539,0.94008774,0.00059591024,0.000039698898,0.00007072439,0.00017236525,0.00044459922,0.0014473937],"genre_scores_gemma":[0.69437534,0.0001970595,0.30025983,0.0003300645,0.00008194882,0.00020802756,0.00082265836,0.0001232243,0.0036018046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825436,0.000921268,0.00009952028,0.00032723442,0.00027235123,0.00012519336],"domain_scores_gemma":[0.9851172,0.011918905,0.0006542629,0.0008730929,0.001028797,0.0004076749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005177283,0.00082797464,0.0014170111,0.001541333,0.0006115729,0.0022637686,0.0025902227,0.0015549421,0.003820605],"category_scores_gemma":[0.026897304,0.00081654394,0.0011338281,0.0017870391,0.00089714915,0.0038253977,0.0019740362,0.0036173759,0.0007695547],"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":[0.00026676734,0.00039011586,0.0041098036,0.00018044909,0.00013313757,0.00009872483,0.00023652542,0.83001804,0.00072952016,0.027914666,0.0032467986,0.13267548],"study_design_scores_gemma":[0.000005831569,0.000011491799,0.00008983407,0.0000066620078,0.0000037160369,0.000005663033,0.000012144846,0.9847995,0.00008834219,0.014860684,0.00011255334,0.0000035576413],"about_ca_topic_score_codex":0.0058295196,"about_ca_topic_score_gemma":0.011952315,"teacher_disagreement_score":0.0058295196,"about_ca_system_score_codex":0.0014270438,"about_ca_system_score_gemma":0.0018108824,"threshold_uncertainty_score":0.027380466},"labels":[],"label_agreement":null},{"id":"W4403963877","doi":"10.48550/arxiv.2410.04350","title":"TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Preference; Security token; Sampling (signal processing); Statistics; Computer science; Mathematics; Computer network; Telecommunications","score_opus":0.2090452637902154,"score_gpt":0.22210805677221626,"score_spread":0.01306279298200086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403963877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018655268,0.0002157526,0.9784681,0.00020170686,0.000067194174,0.00019434602,0.00017489516,0.0011356549,0.0008870871],"genre_scores_gemma":[0.455054,0.00017343643,0.5374133,0.0005850617,0.00015969563,0.0009516555,0.00146922,0.00049929327,0.0036943231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978696,0.0010168512,0.00014160595,0.0004393893,0.00031912947,0.00021340098],"domain_scores_gemma":[0.9953241,0.0032184236,0.00025436416,0.00047457853,0.000502761,0.0002258533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034551814,0.0016144479,0.001838486,0.0009329871,0.0006125514,0.0010393456,0.002183722,0.0014548586,0.004658149],"category_scores_gemma":[0.013318204,0.0007262546,0.0011617064,0.0012931759,0.000899689,0.002172577,0.0018487364,0.0027612953,0.0011154376],"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":[0.0007979455,0.0005188924,0.004012502,0.00037355928,0.00015769854,0.00016268875,0.00018443122,0.58825797,0.005404628,0.017950026,0.0079493625,0.3742303],"study_design_scores_gemma":[0.000031211704,0.000068074536,0.00013410124,0.0000064546066,0.000009034951,0.000015249485,0.000014287553,0.99197054,0.0006928821,0.0066328323,0.00041856416,0.0000067326787],"about_ca_topic_score_codex":0.0036164753,"about_ca_topic_score_gemma":0.0059076096,"teacher_disagreement_score":0.004658149,"about_ca_system_score_codex":0.001186892,"about_ca_system_score_gemma":0.0025053609,"threshold_uncertainty_score":0.018272936},"labels":[],"label_agreement":null},{"id":"W4404612136","doi":"10.1145/3678717.3691289","title":"Query Compilation based Distributed Morsel-driven Parallel Spatial Query Processing","year":2024,"lang":"en","type":"article","venue":"","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":"University of New Brunswick","funders":"","keywords":"Computer science; Query optimization; Sargable; Query expansion; Spatial query; RDF query language; Web search query; Query by Example; Parallel processing; Parallel computing; Web query classification; Information retrieval; Search engine","score_opus":0.017979051525039914,"score_gpt":0.25114758667003334,"score_spread":0.23316853514499342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404612136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118249714,0.00065925234,0.81876105,0.0005567882,0.00031705142,0.00043093442,0.00079789775,0.048514932,0.011712415],"genre_scores_gemma":[0.6803508,0.00024894477,0.3066936,0.00037532116,0.00015998147,0.00028493628,0.0025819726,0.0020556068,0.0072488734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979962,0.00026980607,0.00013634979,0.00049088104,0.00082225923,0.00028454402],"domain_scores_gemma":[0.99701667,0.0006564007,0.00011264261,0.0010045186,0.0010174399,0.00019236623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015772344,0.00081332703,0.0011379361,0.0007457607,0.0011766528,0.0019286823,0.002617818,0.0005972386,0.0037465417],"category_scores_gemma":[0.0031141883,0.0004899862,0.00085572863,0.000976662,0.0010751504,0.0016950971,0.0033746231,0.0010403631,0.0016054133],"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":[0.003224036,0.0014071915,0.010729457,0.0009627116,0.00046774003,0.0022604123,0.0019450277,0.2836103,0.18094318,0.040733583,0.071963884,0.40175244],"study_design_scores_gemma":[0.00020821388,0.00019315106,0.0010300605,0.000014501051,0.000056617217,0.00024777895,0.00026636274,0.9360151,0.03912101,0.011354598,0.011446666,0.000045858065],"about_ca_topic_score_codex":0.005596449,"about_ca_topic_score_gemma":0.005962815,"teacher_disagreement_score":0.005596449,"about_ca_system_score_codex":0.00091665296,"about_ca_system_score_gemma":0.0017364491,"threshold_uncertainty_score":0.012533426},"labels":[],"label_agreement":null},{"id":"W4404848238","doi":"10.1109/focs61266.2024.00098","title":"Sparse Graph Counting and Kelley-Meka Bounds for Binary Systems","year":2024,"lang":"en","type":"article","venue":"","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":"McGill University","funders":"National Science Foundation","keywords":"Computer science; Binary number; Graph; Graph theory; Theoretical computer science; Algorithm; Combinatorics; Mathematics; Arithmetic","score_opus":0.021176371067237423,"score_gpt":0.24705088714302964,"score_spread":0.22587451607579223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404848238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26694477,0.0069801705,0.61543864,0.012159779,0.0005887559,0.00025511748,0.0023238051,0.0014321955,0.09387681],"genre_scores_gemma":[0.9200373,0.0024137017,0.059943374,0.0020756118,0.00083227217,0.0005373799,0.0012408976,0.00042180036,0.01249758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959365,0.00088518974,0.00015337658,0.0009697202,0.0012825956,0.0007725782],"domain_scores_gemma":[0.9659484,0.02572213,0.0016433519,0.0032020959,0.0019070999,0.0015769362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025067315,0.0012829468,0.002270392,0.0040035946,0.0027922543,0.0038733794,0.0034548203,0.0022135654,0.010161564],"category_scores_gemma":[0.034760028,0.0011419941,0.0015958422,0.0038208554,0.0053375117,0.012134268,0.0049605574,0.006181836,0.0014386538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001088869,0.00006378645,0.0011058123,0.00012252471,0.000027654627,0.000067220964,0.0002749069,0.007977497,0.0009554171,0.9727517,0.0049897535,0.011554823],"study_design_scores_gemma":[0.000024774707,0.00002355926,0.00046899138,0.000043585464,0.000021936765,0.000073943025,0.000049839862,0.06984067,0.0008050849,0.9256544,0.0029582472,0.000034847326],"about_ca_topic_score_codex":0.003295569,"about_ca_topic_score_gemma":0.0031711406,"teacher_disagreement_score":0.010161564,"about_ca_system_score_codex":0.004337556,"about_ca_system_score_gemma":0.0016064913,"threshold_uncertainty_score":0.03399378},"labels":[],"label_agreement":null},{"id":"W4404848320","doi":"10.1109/focs61266.2024.00120","title":"Almost-Linear Time Algorithms for Decremental Graphs: Min-Cost Flow and More via Duality","year":2024,"lang":"en","type":"article","venue":"","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":"University of Toronto","funders":"National Science Foundation","keywords":"Duality (order theory); Flow (mathematics); Computer science; Algorithm; Mathematics; Mathematical optimization; Discrete mathematics; Geometry","score_opus":0.02647731292047804,"score_gpt":0.29523271375545673,"score_spread":0.2687554008349787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404848320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038947277,0.0006217569,0.9467178,0.0014756527,0.000088023065,0.00019267599,0.0006086339,0.0033662673,0.007981983],"genre_scores_gemma":[0.30363172,0.00042646742,0.6859822,0.0007329958,0.00015089742,0.000317266,0.0019380492,0.0010320757,0.00578827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975574,0.00053996546,0.00012399418,0.0007586547,0.0005324134,0.0004875535],"domain_scores_gemma":[0.9947655,0.002666746,0.00055862113,0.0012984751,0.0004346527,0.00027603345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021394815,0.0018972324,0.001703016,0.0017175587,0.0009753086,0.0035600539,0.0038376607,0.0018314517,0.007149711],"category_scores_gemma":[0.008859305,0.0010572094,0.002306796,0.0022943518,0.0016593286,0.0065418677,0.0030160376,0.0039480873,0.0016076042],"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":[0.0011382918,0.00068224565,0.0024821544,0.00074073765,0.00015581447,0.00023240803,0.00052803935,0.38871467,0.010074781,0.28692296,0.020531742,0.2877961],"study_design_scores_gemma":[0.00011302209,0.0000957653,0.00033324957,0.00004229853,0.00004679052,0.00018300457,0.000085948646,0.7206575,0.0055974093,0.2678184,0.004991239,0.00003531112],"about_ca_topic_score_codex":0.0041014072,"about_ca_topic_score_gemma":0.004489586,"teacher_disagreement_score":0.007149711,"about_ca_system_score_codex":0.0034752258,"about_ca_system_score_gemma":0.0026527832,"threshold_uncertainty_score":0.025214612},"labels":[],"label_agreement":null},{"id":"W4404914989","doi":"10.1109/ismar-adjunct64951.2024.00059","title":"Gander: the Preliminary Design and Evaluation of an AR+Tablet System for Geospatial Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Mitacs","keywords":"Geospatial analysis; Computer science; Remote sensing; Geography","score_opus":0.0439134113893441,"score_gpt":0.29712840775197247,"score_spread":0.25321499636262834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404914989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50624937,0.00089716516,0.44996595,0.0007781022,0.00024301713,0.0063114455,0.0023782046,0.020312073,0.012864695],"genre_scores_gemma":[0.3824403,0.0004564165,0.60531294,0.0004897106,0.00003515394,0.0012773476,0.0020672134,0.0006674472,0.00725346],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978771,0.0009326453,0.00014783192,0.00033526577,0.0005747823,0.0001322952],"domain_scores_gemma":[0.9968862,0.0015392019,0.00010774796,0.00048352327,0.00076563976,0.00021764629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002874897,0.0010346171,0.0005952627,0.0005269824,0.00030797906,0.001719978,0.0019580417,0.0009966984,0.0059874463],"category_scores_gemma":[0.0068646846,0.00047809852,0.000560958,0.00034264376,0.00050045055,0.0016576154,0.00092439307,0.00065314793,0.001234144],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043504713,0.0022695847,0.011784083,0.002597132,0.0004127395,0.0021740096,0.0056941784,0.022088928,0.37248337,0.004195766,0.019323656,0.55262613],"study_design_scores_gemma":[0.0029930586,0.028239476,0.055416055,0.00062894844,0.00084537535,0.004370489,0.002866547,0.4504468,0.29638737,0.0023466598,0.15482555,0.00063376705],"about_ca_topic_score_codex":0.003995628,"about_ca_topic_score_gemma":0.004225759,"teacher_disagreement_score":0.0059874463,"about_ca_system_score_codex":0.00044005262,"about_ca_system_score_gemma":0.000599146,"threshold_uncertainty_score":0.020029962},"labels":[],"label_agreement":null},{"id":"W4405112553","doi":"10.1016/j.procs.2024.11.088","title":"Evaluating Safe Region Sizes for Accuracy in Approximate Continuous Nearest Neighbour Queries","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","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":"University of Lethbridge","funders":"","keywords":"Computer science; Nearest neighbour; k-nearest neighbors algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology); Information retrieval","score_opus":0.049995626381539336,"score_gpt":0.33551698660068136,"score_spread":0.28552136021914204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405112553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48791617,0.00677283,0.49344614,0.0007019395,0.0002741865,0.0005944576,0.0010437883,0.002882235,0.006368362],"genre_scores_gemma":[0.9132884,0.0006006098,0.084845155,0.000062323095,0.000046403675,0.00010845287,0.00056715007,0.00013071699,0.00035078175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98202366,0.0050482573,0.0013905321,0.0017427079,0.00888845,0.0009063945],"domain_scores_gemma":[0.87441623,0.09615579,0.006685711,0.009373214,0.012261377,0.0011076876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009403248,0.0010374695,0.001700499,0.0024806028,0.0010344199,0.0027813218,0.002730346,0.0018024859,0.0011660652],"category_scores_gemma":[0.112280704,0.0004197623,0.00063117937,0.002770768,0.0013450306,0.005134637,0.0020937857,0.0010787083,0.0004302553],"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":[0.0034331544,0.00037282694,0.02382765,0.00051275396,0.000272691,0.00028744558,0.0007374132,0.7649071,0.012264427,0.008003461,0.0026685698,0.18271247],"study_design_scores_gemma":[0.000046182035,0.00065814157,0.004401048,0.00003759517,0.0000525285,0.00030747367,0.000432976,0.9784387,0.011884821,0.0027124896,0.000968082,0.000059947564],"about_ca_topic_score_codex":0.010789055,"about_ca_topic_score_gemma":0.006233548,"teacher_disagreement_score":0.010789055,"about_ca_system_score_codex":0.0023999722,"about_ca_system_score_gemma":0.0021134939,"threshold_uncertainty_score":0.049729705},"labels":[],"label_agreement":null},{"id":"W4405173271","doi":"10.1109/tvcg.2024.3513275","title":"The Census-Stub Graph Invariant Descriptor","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Invariant (physics); Census; Graph; Theoretical computer science; Artificial intelligence; Mathematics; Population; Demography","score_opus":0.02331838280266489,"score_gpt":0.2638073537679133,"score_spread":0.2404889709652484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405173271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050610095,0.00040453242,0.8991844,0.00069237174,0.00023126691,0.00027826393,0.02020774,0.013714284,0.014677065],"genre_scores_gemma":[0.55231595,0.000745748,0.39545327,0.00033396157,0.00014550556,0.00066674605,0.036991045,0.002676179,0.010671645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996151,0.000051521954,0.000025374595,0.00008366412,0.00017640962,0.00004791998],"domain_scores_gemma":[0.99836713,0.000419449,0.00017231127,0.00040988627,0.00050410425,0.00012712175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043201164,0.0006020568,0.00055331923,0.0024476175,0.00056992605,0.0017640028,0.0009127529,0.0005163306,0.012155445],"category_scores_gemma":[0.005520195,0.00018398884,0.00044728132,0.0028889887,0.00060778763,0.002587342,0.001318813,0.0007173267,0.002469298],"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":[0.00057955546,0.00017109375,0.012511955,0.0006202724,0.00007501611,0.0002929001,0.0006417641,0.08706129,0.019771848,0.1870474,0.16434592,0.52688104],"study_design_scores_gemma":[0.000065554326,0.00016574247,0.007992861,0.00010144116,0.00003553687,0.0006718181,0.0006653984,0.71919674,0.021393968,0.13824211,0.11132936,0.00013943705],"about_ca_topic_score_codex":0.00835598,"about_ca_topic_score_gemma":0.009954002,"teacher_disagreement_score":0.012155445,"about_ca_system_score_codex":0.000860334,"about_ca_system_score_gemma":0.0010121998,"threshold_uncertainty_score":0.040664017},"labels":[],"label_agreement":null},{"id":"W4405692022","doi":"10.1007/s10489-024-05927-8","title":"Efficient strategies for spatial data clustering using topological relations","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Foundation for Science and Technology Development","keywords":"Computer science; Cluster analysis; Topology (electrical circuits); Spatial analysis; Data mining; Artificial intelligence; Remote sensing; Mathematics","score_opus":0.11480727300849575,"score_gpt":0.3420600388496763,"score_spread":0.22725276584118054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405692022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009432797,0.00018703006,0.98891515,0.000113633934,0.000018169192,0.0000780492,0.00006772779,0.00029071566,0.0008966978],"genre_scores_gemma":[0.1608026,0.0002851924,0.83622235,0.000064240114,0.000033720633,0.00020629774,0.0004637324,0.00013436154,0.0017874656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851984,0.00047620808,0.00011863279,0.0002690953,0.0004716115,0.00014460835],"domain_scores_gemma":[0.99675375,0.0016903144,0.00021362635,0.000628886,0.0005758744,0.00013746136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001588998,0.0010023129,0.0013126582,0.0032525454,0.0015515612,0.0026208463,0.0025603008,0.001080834,0.003016449],"category_scores_gemma":[0.00684571,0.0007500441,0.0011684707,0.0035542466,0.0011459652,0.003411475,0.003237397,0.0010300345,0.0010017868],"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":[0.00028463497,0.00023612536,0.0015463822,0.00040884098,0.0001525766,0.00013828716,0.00082070974,0.26005647,0.013325916,0.13633046,0.0057425923,0.58095706],"study_design_scores_gemma":[0.000041123745,0.00011170174,0.0003638461,0.00003077965,0.000058689813,0.00015341263,0.0004275604,0.89427996,0.0064781937,0.0932939,0.0047270283,0.000033774475],"about_ca_topic_score_codex":0.004973342,"about_ca_topic_score_gemma":0.007119345,"teacher_disagreement_score":0.004973342,"about_ca_system_score_codex":0.0012822914,"about_ca_system_score_gemma":0.0019831841,"threshold_uncertainty_score":0.010091007},"labels":[],"label_agreement":null},{"id":"W4405908075","doi":"10.1109/tpami.2024.3524180","title":"Efficient Signed Graph Sampling via Balancing &amp; Gershgorin Disc Perfect Alignment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; York University; College of Veterinarians of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Graph; Graph theory; Algorithm; Computer vision; Combinatorics; Pattern recognition (psychology); Mathematics; Theoretical computer science","score_opus":0.022572477060658368,"score_gpt":0.27460941314012693,"score_spread":0.2520369360794686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024966551,0.00010650032,0.97290355,0.00013415674,0.000041924817,0.00004404507,0.00009527582,0.0009091365,0.00079891353],"genre_scores_gemma":[0.5131655,0.0001428798,0.4818623,0.00026668256,0.00008911633,0.00016062662,0.0008600183,0.00028759943,0.0031653473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910104,0.00027300735,0.00003991727,0.00021262949,0.00029070268,0.00008273844],"domain_scores_gemma":[0.9984503,0.0006005585,0.00019091433,0.00043636095,0.00022505871,0.00009682036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010353216,0.0009909123,0.00083261647,0.0010199983,0.0006080832,0.00080348045,0.0013159271,0.00093523564,0.002424522],"category_scores_gemma":[0.0056912685,0.00034663014,0.0005670616,0.0013450871,0.00078895426,0.0017531693,0.0013992192,0.0009030019,0.0010732015],"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":[0.00064077065,0.00021403501,0.0029213286,0.0001858246,0.00007434483,0.00028821235,0.00032808425,0.3514254,0.031851918,0.048129052,0.009159689,0.55478126],"study_design_scores_gemma":[0.000017969593,0.000044345656,0.0002454589,0.0000039914703,0.000005177696,0.000059823684,0.00002956388,0.98008406,0.0042247456,0.014115631,0.0011602119,0.000009109768],"about_ca_topic_score_codex":0.003327532,"about_ca_topic_score_gemma":0.0048184246,"teacher_disagreement_score":0.003327532,"about_ca_system_score_codex":0.00066844426,"about_ca_system_score_gemma":0.000993309,"threshold_uncertainty_score":0.008110762},"labels":[],"label_agreement":null},{"id":"W4406162498","doi":"10.1007/s10619-024-07452-6","title":"Indexing temporal relations for range-duration queries","year":2025,"lang":"en","type":"article","venue":"Distributed and Parallel Databases","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Università degli Studi di Padova","keywords":"Search engine indexing; Computer science; Data structure; Range (aeronautics); Duration (music); Information retrieval; Data mining; Programming language","score_opus":0.02640828025020287,"score_gpt":0.2848948208440601,"score_spread":0.25848654059385723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406162498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086216345,0.003514922,0.8790755,0.000987962,0.00030921004,0.0004990244,0.0052321707,0.01514415,0.009020805],"genre_scores_gemma":[0.40816247,0.0013671694,0.57499754,0.00039100606,0.00045030063,0.0004453617,0.010063549,0.0013005134,0.0028220634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99399924,0.0007638817,0.0010406843,0.0008517045,0.0029226865,0.00042179017],"domain_scores_gemma":[0.9857918,0.0051540215,0.0014843871,0.0050045443,0.0019455869,0.0006197222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029160744,0.0010077587,0.0016233888,0.0029134192,0.0011840701,0.004017375,0.0024341538,0.0010374428,0.003914461],"category_scores_gemma":[0.01822824,0.00054214464,0.0009431298,0.006167846,0.0010926516,0.007940743,0.0034152549,0.0016164145,0.001588207],"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":[0.0024987801,0.001032905,0.022997243,0.0017001067,0.00028278475,0.00093219656,0.002383182,0.103198245,0.07632829,0.16739516,0.06934532,0.55190575],"study_design_scores_gemma":[0.00024494974,0.000403966,0.0031826126,0.00016708711,0.00010503838,0.0011305999,0.00072633865,0.7711482,0.031794455,0.11376171,0.07720368,0.0001312678],"about_ca_topic_score_codex":0.003733999,"about_ca_topic_score_gemma":0.003590401,"teacher_disagreement_score":0.004017375,"about_ca_system_score_codex":0.0012684303,"about_ca_system_score_gemma":0.0021219896,"threshold_uncertainty_score":0.015421808},"labels":[],"label_agreement":null},{"id":"W4406195461","doi":"10.1016/j.trpro.2024.12.227","title":"Understanding Traffic Patterns using Clustered Semantic Trajectories and Local Geographic Units","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Mitacs","keywords":"Computer science; Transport engineering; Geography; Engineering","score_opus":0.17699123757716012,"score_gpt":0.35227255125766077,"score_spread":0.17528131368050065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406195461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20702755,0.00045108626,0.7789656,0.00024438655,0.000047092704,0.00019330485,0.007724912,0.0017265605,0.0036195319],"genre_scores_gemma":[0.70515007,0.0004806238,0.27974924,0.000035437217,0.00003624944,0.00015950756,0.012965471,0.00018463067,0.0012388697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994355,0.00011587296,0.000042161726,0.00023889983,0.00010911438,0.00005839971],"domain_scores_gemma":[0.9989114,0.00035199017,0.00017255325,0.00024445952,0.0002631188,0.000056448007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047153406,0.0007924587,0.000427576,0.0060591036,0.0004804882,0.0018315895,0.0006448578,0.0007053797,0.0012130627],"category_scores_gemma":[0.002947268,0.00027612262,0.0008621242,0.00577016,0.0004909341,0.0021977518,0.0010725218,0.0004574175,0.00075743004],"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":[0.0005277524,0.0003603184,0.1082795,0.0005839027,0.00034832652,0.0006227701,0.0020110272,0.5047913,0.013449862,0.048465967,0.0052506556,0.31530854],"study_design_scores_gemma":[0.000012133876,0.000074348565,0.022633629,0.00007711514,0.00008154557,0.00020622149,0.0014805627,0.9205355,0.003992616,0.04114583,0.009710856,0.000049764876],"about_ca_topic_score_codex":0.017336613,"about_ca_topic_score_gemma":0.02158127,"teacher_disagreement_score":0.017336613,"about_ca_system_score_codex":0.0008963669,"about_ca_system_score_gemma":0.00075748237,"threshold_uncertainty_score":0.034471393},"labels":[],"label_agreement":null},{"id":"W4406206083","doi":"10.4230/lipics.icdt.2024.15","title":"Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability","year":2024,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Ministry of Education, India; Agence Nationale de la Recherche; National Research Foundation; Deutsche Forschungsgemeinschaft; National Research Foundation Singapore","keywords":"Probabilistic logic; Computer science; Conjunctive query; Theoretical computer science; Artificial intelligence; Information retrieval; Relational database","score_opus":0.02464285751339443,"score_gpt":0.2679624310476123,"score_spread":0.24331957353421788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406206083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09549912,0.0035768151,0.8737667,0.011158307,0.00015824735,0.00021755048,0.0010679802,0.0023023384,0.012252946],"genre_scores_gemma":[0.69030416,0.0021168103,0.29703468,0.0020301794,0.0008913961,0.00046632133,0.0012170428,0.0011230581,0.0048163235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9749619,0.0071935398,0.0016438412,0.0059199845,0.007872631,0.0024081522],"domain_scores_gemma":[0.8290335,0.1424006,0.0044579194,0.018908584,0.0036130506,0.0015862591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017576002,0.0019244023,0.0036264982,0.0019951,0.001998225,0.01128444,0.006988209,0.0041977167,0.004812851],"category_scores_gemma":[0.105461664,0.0019479295,0.005994954,0.0034629316,0.0063954163,0.033543695,0.007347955,0.0120806685,0.0010144251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009982216,0.00031767983,0.0044643534,0.0008882212,0.00040598944,0.00039924085,0.0017675898,0.20384696,0.0031093962,0.7110716,0.0065651066,0.06616555],"study_design_scores_gemma":[0.00006914724,0.000045371395,0.00033783674,0.00006005119,0.00011610891,0.00019853107,0.00016134985,0.4146361,0.0014018564,0.58078086,0.0021636991,0.000029114446],"about_ca_topic_score_codex":0.0060778335,"about_ca_topic_score_gemma":0.00405736,"teacher_disagreement_score":0.017576002,"about_ca_system_score_codex":0.006948785,"about_ca_system_score_gemma":0.0029514467,"threshold_uncertainty_score":0.092951834},"labels":[],"label_agreement":null},{"id":"W4406460507","doi":"10.1109/bigdata62323.2024.10825523","title":"Scalable Big Spatial Data Processing with SQL Query Compilation and Distributed Morsel-driven Parallelism","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"","keywords":"Computer science; Scalability; SQL; Parallel computing; Parallelism (grammar); Spatial query; Query by Example; Query optimization; Sargable; Big data; Query language; Database; Programming language; Web search query; Information retrieval; Operating system; Search engine","score_opus":0.03632082855626306,"score_gpt":0.25692752069968505,"score_spread":0.220606692143422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406460507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14340799,0.00034355442,0.7959247,0.00035890582,0.000098819095,0.00034593372,0.0003376116,0.05394337,0.005239086],"genre_scores_gemma":[0.55371296,0.00018542187,0.4389633,0.00030931373,0.00009445328,0.00025399597,0.0015117499,0.0020343033,0.0029345136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853575,0.00024837352,0.00011350685,0.00034828242,0.0006048894,0.00014903897],"domain_scores_gemma":[0.9976338,0.0005543974,0.00018565374,0.0009584469,0.0005048262,0.00016282404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011666735,0.00089207094,0.00065379776,0.0005959674,0.00063278875,0.0014446141,0.0020288068,0.00037161636,0.0011105239],"category_scores_gemma":[0.002559685,0.00050251366,0.0008714329,0.00086862716,0.0010797457,0.0015575961,0.0020256408,0.00085165724,0.0005871671],"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":[0.002064208,0.0017023782,0.025394073,0.00078847224,0.0005026824,0.0018316286,0.0021686833,0.23892069,0.18107906,0.022374758,0.035398193,0.4877752],"study_design_scores_gemma":[0.00024453705,0.00036609895,0.0033075812,0.000022976259,0.00007598145,0.00041315207,0.00025302838,0.90501,0.07038513,0.010100968,0.009742656,0.00007790824],"about_ca_topic_score_codex":0.0033033201,"about_ca_topic_score_gemma":0.004177645,"teacher_disagreement_score":0.0033033201,"about_ca_system_score_codex":0.00057718746,"about_ca_system_score_gemma":0.0014096072,"threshold_uncertainty_score":0.0065681934},"labels":[],"label_agreement":null},{"id":"W4406523973","doi":"10.1109/tits.2024.3516956","title":"Elevation-Aware Map Matching Model Leveraging Transfer Learning in Sparse Data Conditions","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Data Management and Algorithms","field":"Computer Science","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transfer of learning; Elevation (ballistics); Artificial intelligence; Map matching; Matching (statistics); Computer vision; Engineering; Mathematics; Global Positioning System; Telecommunications; Statistics","score_opus":0.055004626816275916,"score_gpt":0.29345577014528507,"score_spread":0.23845114332900916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406523973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061085787,0.0001567406,0.93592584,0.0002989346,0.000038574733,0.000058953603,0.00017593682,0.0007423036,0.0015168445],"genre_scores_gemma":[0.9158023,0.00016592936,0.08038584,0.00013991601,0.000059847644,0.00012596663,0.00047773783,0.000085003565,0.0027574296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950016,0.000091386086,0.000025436995,0.000186335,0.000113516406,0.000083174105],"domain_scores_gemma":[0.99882275,0.00048491114,0.00014595187,0.00020758211,0.00027005092,0.00006874457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011458625,0.00060826895,0.00095235417,0.00083954073,0.00042139489,0.0012838196,0.0025766778,0.0013546493,0.001483031],"category_scores_gemma":[0.004299314,0.00037295595,0.0007438344,0.001279709,0.0008276646,0.0025618286,0.0018816843,0.0015713689,0.00054826075],"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":[0.00005150057,0.000050477254,0.0010010862,0.000020588079,0.000020152665,0.000047933496,0.000036688674,0.95840186,0.0008951199,0.0032622674,0.00047855897,0.0357337],"study_design_scores_gemma":[0.0000013652946,0.0000069548487,0.000069660775,9.724175e-7,0.000002214803,0.000005722938,0.0000032807452,0.9986511,0.00016741634,0.0010171678,0.00007241267,0.0000018180443],"about_ca_topic_score_codex":0.009406233,"about_ca_topic_score_gemma":0.005528098,"teacher_disagreement_score":0.009406233,"about_ca_system_score_codex":0.00092005194,"about_ca_system_score_gemma":0.0012495803,"threshold_uncertainty_score":0.018702984},"labels":[],"label_agreement":null},{"id":"W4406785293","doi":"10.48550/arxiv.2501.13274","title":"T-Graphormer: Using Transformers for Spatiotemporal Forecasting","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Alliance de recherche numérique du Canada","keywords":"Transformer; Computer science; Engineering; Electrical engineering; Voltage","score_opus":0.14765918188500718,"score_gpt":0.3131391285356081,"score_spread":0.16547994665060092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406785293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0104387915,0.00023790965,0.9763674,0.00025970317,0.000082991486,0.0000562495,0.0014546852,0.010040712,0.0010615765],"genre_scores_gemma":[0.47505048,0.00074795657,0.51215833,0.00031381348,0.0001008721,0.00017802074,0.006642592,0.0017056749,0.003102196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951446,0.00014327884,0.000038917096,0.00015823611,0.00010410049,0.000040890856],"domain_scores_gemma":[0.99805236,0.001015669,0.00014952106,0.00045929404,0.000250543,0.00007259939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014492149,0.0011984269,0.00073658506,0.0018746332,0.00036688408,0.0012260095,0.0018009637,0.00082579366,0.0046171243],"category_scores_gemma":[0.009014127,0.0004655276,0.0011304079,0.0018899594,0.0006301014,0.003663724,0.0013426631,0.001553258,0.0020330292],"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":[0.00035237442,0.00014461044,0.0053486894,0.00024367943,0.00015410976,0.00019141655,0.000171224,0.5451209,0.0058898567,0.035763416,0.019025413,0.3875943],"study_design_scores_gemma":[0.000010528368,0.000017783854,0.000105722014,0.000007003553,0.00000912175,0.000022322547,0.000014820414,0.979391,0.0013067663,0.01758984,0.0015183799,0.00000670169],"about_ca_topic_score_codex":0.010733806,"about_ca_topic_score_gemma":0.017820891,"teacher_disagreement_score":0.010733806,"about_ca_system_score_codex":0.0009180921,"about_ca_system_score_gemma":0.001358293,"threshold_uncertainty_score":0.021342695},"labels":[],"label_agreement":null},{"id":"W4407086046","doi":"10.3390/ijgi14020058","title":"A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Trajectory; Computer science; Scalability; Context (archaeology); Piecewise; Robustness (evolution); Segmentation; Artificial intelligence; Algorithm; Mathematics","score_opus":0.009724316283204451,"score_gpt":0.29348982257214606,"score_spread":0.2837655062889416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407086046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067869755,0.00025446352,0.991394,0.00010313033,0.000069197245,0.000045347733,0.00010001017,0.0005611636,0.00068567466],"genre_scores_gemma":[0.20860545,0.00061007304,0.7844963,0.00019546908,0.00013173884,0.00020979995,0.0008519585,0.0002892885,0.004609962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996629,0.000043312586,0.000026768934,0.00010158419,0.00011933081,0.00004609528],"domain_scores_gemma":[0.9996092,0.00010942547,0.000039344286,0.00007561927,0.00013895571,0.000027519622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031562583,0.0009275958,0.00060966075,0.0010101964,0.0005057385,0.00075921824,0.0013746232,0.0009609267,0.0025887818],"category_scores_gemma":[0.002058252,0.00043098762,0.0006981309,0.0014127197,0.00046763435,0.0018285243,0.0013151116,0.0017034904,0.0007977289],"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":[0.00020467557,0.00006408671,0.0010219432,0.00014541684,0.000043381642,0.00024934718,0.00017717415,0.21150725,0.022176376,0.013883893,0.004898588,0.7456279],"study_design_scores_gemma":[0.000014871961,0.00005266934,0.00029658634,0.000013602774,0.000013375457,0.00017474331,0.000037906902,0.9842812,0.0075991955,0.0032409583,0.0042569833,0.000017927005],"about_ca_topic_score_codex":0.00763082,"about_ca_topic_score_gemma":0.0061618215,"teacher_disagreement_score":0.00763082,"about_ca_system_score_codex":0.0006346451,"about_ca_system_score_gemma":0.0014431587,"threshold_uncertainty_score":0.015172839},"labels":[],"label_agreement":null},{"id":"W4407189050","doi":"10.1007/978-3-031-82670-2_18","title":"Representing Hypergraphs by Point-Line Incidences","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Point (geometry); Line (geometry); Theoretical computer science; Mathematics; Geometry","score_opus":0.013225429642853382,"score_gpt":0.24809922291847442,"score_spread":0.23487379327562102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407189050","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015718756,0.0002891585,0.9495155,0.00019470826,0.0001377612,0.00006683083,0.00068332057,0.0026271057,0.030766748],"genre_scores_gemma":[0.31908622,0.0016359179,0.6257321,0.00021694125,0.00018401498,0.00019444773,0.0025304991,0.0014093627,0.04901048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99949574,0.00015495719,0.000040713123,0.000103200015,0.00014196296,0.00006345442],"domain_scores_gemma":[0.9989907,0.0004064345,0.000080164806,0.00025554627,0.00020278514,0.00006439654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052671396,0.0008004176,0.0005407888,0.002079939,0.00070587936,0.0034509106,0.0013962446,0.00082180003,0.019665066],"category_scores_gemma":[0.002423692,0.0006359078,0.00080137805,0.003765988,0.00084769627,0.0057495134,0.0018984175,0.0016984312,0.0055583976],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009597826,0.00007037037,0.00040786504,0.00016596561,0.000026246857,0.00016619591,0.0004408815,0.034658406,0.0040065404,0.77540344,0.012085829,0.17247227],"study_design_scores_gemma":[0.000021604033,0.000027090087,0.000119723074,0.000074027594,0.00003538852,0.00017403183,0.00016580707,0.115974076,0.005541562,0.8218418,0.05599566,0.000029225605],"about_ca_topic_score_codex":0.0013039353,"about_ca_topic_score_gemma":0.0017223408,"teacher_disagreement_score":0.019665066,"about_ca_system_score_codex":0.00071968796,"about_ca_system_score_gemma":0.00032152268,"threshold_uncertainty_score":0.06578618},"labels":[],"label_agreement":null},{"id":"W4407277142","doi":"10.1145/3716825","title":"Exact Trajectory Similarity Search With N-tree: An Efficient Metric Index for kNN and Range Queries","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Spatial Algorithms and Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Fredericton; University of New Brunswick","funders":"","keywords":"Nearest neighbor search; Metric (unit); Similarity (geometry); Range (aeronautics); Trajectory; Index (typography); Tree (set theory); Computer science; Metric space; Range query (database); Cover tree; Mathematics; Algorithm; Theoretical computer science; Data mining; Combinatorics; Discrete mathematics; Artificial intelligence; Information retrieval; Search engine; Physics; Web search query; World Wide Web; Materials science","score_opus":0.02446961917894443,"score_gpt":0.27160186745058723,"score_spread":0.2471322482716428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407277142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03240972,0.005587798,0.9426197,0.00059796177,0.00031372055,0.00047737142,0.0031597295,0.0066084526,0.008225646],"genre_scores_gemma":[0.24031825,0.0018983185,0.74393386,0.00028682087,0.00024059588,0.0005382877,0.008246384,0.0005165019,0.004020991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969187,0.00044732974,0.0003928435,0.00041645704,0.0015956423,0.00022892538],"domain_scores_gemma":[0.99684036,0.00074138975,0.00034050032,0.0011210089,0.00074176886,0.00021500354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013930007,0.0009703217,0.0026721698,0.0048346356,0.0013506075,0.0024084225,0.0026090182,0.0013783934,0.0037332966],"category_scores_gemma":[0.012057737,0.00038710286,0.0006476219,0.010226193,0.0008563453,0.008546201,0.0037131521,0.0009845091,0.0030774712],"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":[0.0007811936,0.00044025405,0.0035550953,0.0007489523,0.00011666439,0.0003356306,0.00056043867,0.062948644,0.012557277,0.060526796,0.044438362,0.8129907],"study_design_scores_gemma":[0.00016090824,0.00061358395,0.0014663254,0.00012938037,0.00008002616,0.0017130432,0.00046645408,0.81673384,0.010140137,0.10331399,0.065052725,0.00012962206],"about_ca_topic_score_codex":0.0048136706,"about_ca_topic_score_gemma":0.006008409,"teacher_disagreement_score":0.0048346356,"about_ca_system_score_codex":0.0015464136,"about_ca_system_score_gemma":0.0027518703,"threshold_uncertainty_score":0.01248914},"labels":[],"label_agreement":null},{"id":"W4407694635","doi":"10.1016/j.comgeo.2025.102172","title":"Approximating average bounded-angle minimum spanning trees","year":2025,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Data Management and Algorithms","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":"University of British Columbia; Carleton University; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bounded function; Minimum spanning tree; Mathematics; Spanning tree; Combinatorics; Mathematical analysis","score_opus":0.014400568408841604,"score_gpt":0.2617562545055416,"score_spread":0.24735568609669997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407694635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18221006,0.0016174286,0.8029538,0.00085144315,0.00027222413,0.00012476518,0.0009298689,0.0011716452,0.009868754],"genre_scores_gemma":[0.6603886,0.0007908426,0.33312535,0.00016442391,0.000115887415,0.00012493868,0.0016180923,0.00030013162,0.003371669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987571,0.00037662958,0.000086138476,0.00023724469,0.00037971075,0.000163239],"domain_scores_gemma":[0.9950151,0.0032268674,0.00031065868,0.00059392845,0.0006276363,0.00022589519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012388545,0.0010656918,0.0015481721,0.0014344068,0.00059707667,0.0016799562,0.0022407626,0.0013661893,0.0049135173],"category_scores_gemma":[0.015644949,0.00067846006,0.00068488927,0.0029315741,0.0005797874,0.003042421,0.0019184421,0.0014996828,0.0009606374],"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":[0.00067659485,0.00019565491,0.0027618867,0.0002742565,0.00006437337,0.00011230223,0.00013247716,0.8254158,0.0038865248,0.029510254,0.0074906796,0.1294792],"study_design_scores_gemma":[0.000016959928,0.000037042897,0.00023482267,0.000011315279,0.000008252139,0.000044081076,0.0000328355,0.9853795,0.0006177766,0.012973238,0.0006409286,0.0000031647912],"about_ca_topic_score_codex":0.0026682257,"about_ca_topic_score_gemma":0.0045642634,"teacher_disagreement_score":0.0049135173,"about_ca_system_score_codex":0.0013426137,"about_ca_system_score_gemma":0.0008097674,"threshold_uncertainty_score":0.016437352},"labels":[],"label_agreement":null},{"id":"W4407718984","doi":"10.1016/j.jag.2025.104400","title":"Efficient management of ubiquitous location information using geospatial grid region name","year":2025,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","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":"3v Geomatics (Canada)","funders":"National Key Research and Development Program of China; Fuzhou University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Geospatial analysis; Geography; Grid; Geospatial PDF; Cartography; Data science; Computer science","score_opus":0.012664202562075426,"score_gpt":0.23579968046636537,"score_spread":0.22313547790428995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407718984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11211328,0.0024342905,0.839436,0.00091305305,0.00024309137,0.00043550003,0.002187226,0.0328405,0.009397108],"genre_scores_gemma":[0.6499236,0.00095001434,0.339317,0.00023052767,0.00009477159,0.00018366626,0.0039656786,0.00033400473,0.005000651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879277,0.0002193256,0.00012845804,0.00026021898,0.00047536392,0.00012388505],"domain_scores_gemma":[0.99815816,0.00020563306,0.00019104441,0.00083152746,0.00049149775,0.00012207919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010663372,0.00052260305,0.00083779974,0.0017246298,0.00078926934,0.0022359735,0.0018687956,0.0005342135,0.0016309229],"category_scores_gemma":[0.0033095651,0.0002410022,0.0004346423,0.0030031877,0.00038799306,0.004075594,0.002352775,0.0005579224,0.0014570936],"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":[0.00085382623,0.0003456659,0.01989876,0.0004971787,0.00017002813,0.0008615891,0.0011549052,0.01808589,0.04281352,0.022769215,0.03568986,0.8568596],"study_design_scores_gemma":[0.00022328757,0.0005483446,0.019718127,0.000101295795,0.00023549113,0.002147597,0.002133962,0.6644472,0.10850385,0.023479473,0.17818289,0.0002784953],"about_ca_topic_score_codex":0.0076354574,"about_ca_topic_score_gemma":0.0060700923,"teacher_disagreement_score":0.0076354574,"about_ca_system_score_codex":0.00074578094,"about_ca_system_score_gemma":0.0009949395,"threshold_uncertainty_score":0.015182018},"labels":[],"label_agreement":null},{"id":"W4407924522","doi":"10.1016/j.cstp.2025.101399","title":"Unveiling Route Choice Preferences and Classifying Travelers Based on Distinct Travel Patterns Using Trajectory Data","year":2025,"lang":"en","type":"article","venue":"Case Studies on Transport Policy","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"Tongji University; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Operations research; Transport engineering; Data mining; Engineering","score_opus":0.12727698713095956,"score_gpt":0.3623982295108463,"score_spread":0.23512124237988677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407924522","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97578365,0.00010913456,0.022046342,0.00031071668,0.0000096137055,0.000042672993,0.001053804,0.000065667125,0.0005783369],"genre_scores_gemma":[0.97378,0.00008223401,0.024456466,0.000017681008,0.0000073906217,0.000016085434,0.0013244806,0.000013758601,0.0003019503],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99861443,0.00065447134,0.00011332802,0.0002700211,0.00018482894,0.00016287796],"domain_scores_gemma":[0.9937219,0.004572953,0.0005201062,0.00046349154,0.00052279053,0.00019873497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002941453,0.00048659113,0.00049891026,0.0031007447,0.0004739921,0.0021719341,0.00069630734,0.000972493,0.0010464545],"category_scores_gemma":[0.00905716,0.00025113914,0.0007999856,0.004905375,0.0004963474,0.0024708079,0.0006673294,0.0010067088,0.00022926832],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013566733,0.0005869202,0.78438574,0.00027475937,0.0003563441,0.00030594264,0.0014960427,0.07213551,0.005671192,0.0046654535,0.0014905557,0.12727495],"study_design_scores_gemma":[0.00006177567,0.00037089162,0.17327783,0.000071751565,0.00024332505,0.00032167404,0.0093286475,0.79774594,0.004505137,0.010856126,0.0031378558,0.00007906648],"about_ca_topic_score_codex":0.022199227,"about_ca_topic_score_gemma":0.029414989,"teacher_disagreement_score":0.022199227,"about_ca_system_score_codex":0.0008029956,"about_ca_system_score_gemma":0.00093703193,"threshold_uncertainty_score":0.04413998},"labels":[],"label_agreement":null},{"id":"W4407953359","doi":"10.1145/3701551.3703480","title":"Query Performance Prediction: Theory, Techniques and Applications","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Data mining","score_opus":0.004753132183198136,"score_gpt":0.22032447132221142,"score_spread":0.21557133913901327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407953359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071320934,0.02997131,0.95573777,0.0014740593,0.00020790017,0.00010202203,0.0004119948,0.0008433989,0.004119524],"genre_scores_gemma":[0.58627284,0.054991912,0.34698156,0.0009971228,0.003791613,0.0005813302,0.0017063397,0.00031642985,0.004360933],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957741,0.0011395909,0.00027558356,0.00096320576,0.0015905829,0.00025693435],"domain_scores_gemma":[0.98569393,0.0098716505,0.0013353782,0.0011704158,0.0017102013,0.00021849184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004746571,0.0016122606,0.0020300567,0.0034831779,0.00062399317,0.0027922024,0.002422106,0.0018232532,0.0018987645],"category_scores_gemma":[0.019448493,0.0006819148,0.00093624846,0.0075300587,0.0016585704,0.0035088847,0.0016762583,0.0025369278,0.0012240455],"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":[0.00019774816,0.00030050648,0.011980197,0.0010957789,0.00023987069,0.00021305791,0.0003157756,0.27009436,0.004376034,0.10984044,0.017385947,0.5839603],"study_design_scores_gemma":[0.000016774447,0.0001099738,0.002228497,0.00014926896,0.0000714685,0.00022966698,0.000069950154,0.8914254,0.001427639,0.09521624,0.008989203,0.00006605597],"about_ca_topic_score_codex":0.005093614,"about_ca_topic_score_gemma":0.0020920767,"teacher_disagreement_score":0.005093614,"about_ca_system_score_codex":0.00166815,"about_ca_system_score_gemma":0.0012606358,"threshold_uncertainty_score":0.025102556},"labels":[],"label_agreement":null},{"id":"W4407973770","doi":"10.2139/ssrn.5081944","title":"Learning and Uncertainty in Spatial Resource Management &lt;br&gt;","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Data Management and Algorithms","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 Alberta","funders":"","keywords":"Resource (disambiguation); Environmental resource management; Computer science; Environmental science","score_opus":0.0038257941207572163,"score_gpt":0.2227181784823419,"score_spread":0.2188923843615847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407973770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31671163,0.004270216,0.64523065,0.014827775,0.00023074112,0.000068644746,0.00052292144,0.00024129677,0.017896153],"genre_scores_gemma":[0.9794678,0.0008542951,0.015256189,0.00019548454,0.00014800511,0.0000409354,0.000119359705,0.00003355419,0.0038842536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969311,0.0017629227,0.00017354114,0.0004638545,0.0003590031,0.0003096155],"domain_scores_gemma":[0.9144751,0.0784701,0.0030509415,0.0011965686,0.0018248607,0.0009823669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008028356,0.0004521934,0.001508825,0.0015483814,0.0010241673,0.0041798158,0.0016610111,0.002192127,0.005474156],"category_scores_gemma":[0.06525945,0.000622477,0.0007507431,0.0023533783,0.0038722649,0.00801828,0.002530501,0.0023412025,0.0002468779],"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":[0.0001409772,0.00006418313,0.0066474825,0.00009817037,0.0000823138,0.00008684963,0.00030672026,0.7260942,0.000083515544,0.23054636,0.0017774645,0.034071807],"study_design_scores_gemma":[0.0000079480415,0.000017898756,0.00061219145,0.000019482091,0.000012574698,0.000014744838,0.00013699007,0.7268069,0.000073992625,0.27168253,0.00059876445,0.000016065194],"about_ca_topic_score_codex":0.030020721,"about_ca_topic_score_gemma":0.01985166,"teacher_disagreement_score":0.030020721,"about_ca_system_score_codex":0.005124551,"about_ca_system_score_gemma":0.0021582032,"threshold_uncertainty_score":0.059691966},"labels":[],"label_agreement":null},{"id":"W4408061162","doi":"10.14778/3705829.3705830","title":"RED: Effective Trajectory Representation Learning with Comprehensive Information","year":2024,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Trajectory; Representation (politics); Computer science; Artificial intelligence; Human–computer interaction; Political science; Physics; Politics","score_opus":0.00837476942388125,"score_gpt":0.22392301270177595,"score_spread":0.2155482432778947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408061162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01829392,0.00042676297,0.97269404,0.00021496268,0.0000711058,0.000079365636,0.0007094361,0.0063918736,0.0011184961],"genre_scores_gemma":[0.3952116,0.00052220625,0.5858401,0.0005150308,0.00014774693,0.00028238105,0.008052205,0.00060849526,0.008820211],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992385,0.00017251792,0.000040782063,0.00031126136,0.00015358937,0.00008341483],"domain_scores_gemma":[0.9988966,0.00034529544,0.00012628076,0.00036131893,0.00020222453,0.0000683046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010718345,0.0014545122,0.0010368549,0.001101882,0.00040122066,0.00088960945,0.0026163468,0.001373565,0.0027596767],"category_scores_gemma":[0.0032245037,0.00057033694,0.0011618636,0.0012478068,0.0005636004,0.0028650232,0.0020221833,0.0021564818,0.0015542575],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028191396,0.0003037952,0.0023371533,0.00020210604,0.00016686149,0.00014033,0.00014521822,0.36384773,0.009310083,0.010468743,0.017701352,0.59509474],"study_design_scores_gemma":[0.000011628548,0.00005247787,0.00021710874,0.000008486413,0.000009483869,0.000031006442,0.000013052004,0.9931664,0.0018812963,0.0033767666,0.0012228381,0.000009482051],"about_ca_topic_score_codex":0.0072143804,"about_ca_topic_score_gemma":0.008909013,"teacher_disagreement_score":0.0072143804,"about_ca_system_score_codex":0.0009063856,"about_ca_system_score_gemma":0.0015155156,"threshold_uncertainty_score":0.014344752},"labels":[],"label_agreement":null},{"id":"W4408401921","doi":"10.1109/icftic64248.2024.10913131","title":"A Modular Agent-Based Approach to Complex Data Question-Answering with SQL Generation and Parameter-Efficient Fine-Tuning","year":2024,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Modular design; Computer science; SQL; Question answering; Stored procedure; Programming language; Artificial intelligence; Database; Information retrieval; Query by Example; Search engine","score_opus":0.09667619869056282,"score_gpt":0.288333229888341,"score_spread":0.19165703119777816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408401921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0080057075,0.00013171749,0.98180515,0.00030300728,0.00006020261,0.00020555554,0.0001449242,0.007922639,0.0014211056],"genre_scores_gemma":[0.21020025,0.0001459962,0.78252196,0.0004084962,0.00010162652,0.0004082968,0.0008508054,0.00042750547,0.0049349926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895203,0.0002844647,0.000101907804,0.00031755012,0.0002695167,0.000074513264],"domain_scores_gemma":[0.99849296,0.0004939733,0.00011492887,0.00044441683,0.00034351827,0.00011015835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001728004,0.0008076563,0.0008616134,0.00067449996,0.0006288373,0.0015442263,0.002581765,0.0010115438,0.0036401714],"category_scores_gemma":[0.004470493,0.0004830062,0.00088723755,0.0005962882,0.0006401025,0.0021219966,0.0021045534,0.0020521083,0.0023434332],"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":[0.0006006025,0.0011548576,0.004467557,0.00039541576,0.00030570925,0.0005506502,0.0011828409,0.18108061,0.06652626,0.032285184,0.02933349,0.68211675],"study_design_scores_gemma":[0.00004033727,0.000071871174,0.00030503084,0.0000091882775,0.00003221222,0.00008773744,0.000045504126,0.9706373,0.008597999,0.010486387,0.009657255,0.000029144507],"about_ca_topic_score_codex":0.004128134,"about_ca_topic_score_gemma":0.005266242,"teacher_disagreement_score":0.004128134,"about_ca_system_score_codex":0.0006022812,"about_ca_system_score_gemma":0.0014704571,"threshold_uncertainty_score":0.012177587},"labels":[],"label_agreement":null},{"id":"W4408429872","doi":"10.5194/egusphere-egu25-14940","title":"Evaluation the suitability of contrasting performance metrics and signature measures with the identifiability quote index","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Identifiability; Signature (topology); Index (typography); Statistics; Mathematics; Computer science; Econometrics; World Wide Web","score_opus":0.03165842985264215,"score_gpt":0.27016410818721676,"score_spread":0.2385056783345746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408429872","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77953106,0.00067350344,0.21249084,0.00028468325,0.00008329651,0.0002905355,0.0016545256,0.0013950679,0.003596512],"genre_scores_gemma":[0.9225402,0.000100046076,0.07491071,0.000041450014,0.000021559037,0.00022445415,0.0016907591,0.00017536075,0.00029555336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99067676,0.004030774,0.0011909207,0.000997265,0.0026324624,0.00047178683],"domain_scores_gemma":[0.9163752,0.06657528,0.0059015495,0.0051444247,0.005069051,0.0009344908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019899532,0.0020493125,0.0012207725,0.0064923214,0.00045948965,0.0022097847,0.0013130084,0.0021682861,0.00088970736],"category_scores_gemma":[0.0631445,0.0003133487,0.0016321026,0.0036454136,0.0013319871,0.0032320681,0.0022916573,0.0010875211,0.00027387726],"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":[0.0020287484,0.0010943379,0.19355218,0.00084241346,0.0015207641,0.00044500007,0.0006222996,0.5687916,0.01779845,0.008486705,0.0028841947,0.20193326],"study_design_scores_gemma":[0.00010071868,0.0017055364,0.05096103,0.00008256103,0.00020267397,0.00025707769,0.00035404743,0.9184554,0.020343512,0.0059033376,0.0014399909,0.00019404296],"about_ca_topic_score_codex":0.002355696,"about_ca_topic_score_gemma":0.0012804467,"teacher_disagreement_score":0.019899532,"about_ca_system_score_codex":0.0011043188,"about_ca_system_score_gemma":0.0010512007,"threshold_uncertainty_score":0.10524005},"labels":[],"label_agreement":null},{"id":"W4408439263","doi":"10.5194/egusphere-egu25-5476","title":"Tracking Air Pollution: Global Near Real-Time Fire PM2.5 Retrievals from Multisource Data Fusion","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Tracking (education); Air pollution; Fusion; Remote sensing; Sensor fusion; Meteorology; Pollution; Computer science; Geography; Artificial intelligence","score_opus":0.0393983557257031,"score_gpt":0.29781036345746886,"score_spread":0.25841200773176576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408439263","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86902374,0.0012875173,0.11723716,0.00060829753,0.00039483028,0.00007342064,0.0046948846,0.0028374153,0.0038427482],"genre_scores_gemma":[0.9558418,0.00027312723,0.03785678,0.00012761563,0.00009218866,0.000039040235,0.0051432066,0.00006338523,0.00056287413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996921,0.000037822705,0.000014867711,0.00012923064,0.00007987298,0.00004613841],"domain_scores_gemma":[0.99983656,0.000024808416,0.00002804085,0.000034250992,0.000060567716,0.000015813433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062248023,0.0006819743,0.0005093123,0.001373711,0.000247022,0.00061106094,0.00062923844,0.00070301443,0.000587734],"category_scores_gemma":[0.0009557519,0.00025628094,0.001063979,0.0012784834,0.00021552629,0.0010025206,0.00076083915,0.0005375986,0.0003266817],"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":[0.0007140818,0.0007259037,0.13661554,0.00040640656,0.0006721654,0.0007856518,0.00031708498,0.4430511,0.08347314,0.0017440176,0.01030263,0.3211923],"study_design_scores_gemma":[0.000036823873,0.00008563358,0.06369354,0.000024387888,0.000103038605,0.00013667253,0.00009916924,0.9217703,0.010608376,0.0012190525,0.0021734757,0.0000495251],"about_ca_topic_score_codex":0.009823267,"about_ca_topic_score_gemma":0.00872002,"teacher_disagreement_score":0.009823267,"about_ca_system_score_codex":0.00040013742,"about_ca_system_score_gemma":0.00044094873,"threshold_uncertainty_score":0.019532144},"labels":[],"label_agreement":null},{"id":"W4408892882","doi":"10.1061/jhend8.hyeng-14163","title":"Simulation of Water Quality in Pipeline Networks: A Graph Theory Approach","year":2025,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; BC Hydro (Canada)","funders":"","keywords":"Water quality; Graph theory; Pipeline (software); Computer science; Petroleum engineering; Pipeline transport; Environmental science; Geology; Environmental engineering; Mathematics; Programming language","score_opus":0.011218728333639584,"score_gpt":0.25179111741739885,"score_spread":0.24057238908375927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408892882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081097946,0.00009796246,0.911635,0.00038336078,0.000029090175,0.00006096925,0.00028365495,0.00045303514,0.0059590116],"genre_scores_gemma":[0.8718804,0.00038191964,0.123945154,0.00010314786,0.000031602292,0.00016684596,0.000358412,0.00011950217,0.0030129896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999806,0.00007642499,0.000009246052,0.000032739863,0.000048212034,0.000027359352],"domain_scores_gemma":[0.99940145,0.00040213676,0.000054733115,0.000045651665,0.00006367453,0.000032448777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043408375,0.00047571142,0.00041401314,0.0008801071,0.0004215932,0.00069768925,0.0008664672,0.0008112477,0.001737223],"category_scores_gemma":[0.0014982519,0.00034744947,0.0006911495,0.00075629266,0.0009854157,0.0011659265,0.0007732899,0.0005806117,0.00018743266],"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":[0.00000411164,0.0000050033814,0.00018428561,0.000004755933,0.0000036623783,0.000007061163,0.0000075936123,0.99329364,0.00022153113,0.005001948,0.00006816806,0.0011981573],"study_design_scores_gemma":[0.0000016354875,0.000002680904,0.0000252805,7.0005757e-7,9.807821e-7,0.0000012296426,0.0000021759645,0.9971704,0.000082685736,0.0026061134,0.00010506635,0.0000010992727],"about_ca_topic_score_codex":0.016129483,"about_ca_topic_score_gemma":0.011523689,"teacher_disagreement_score":0.016129483,"about_ca_system_score_codex":0.001090564,"about_ca_system_score_gemma":0.0012380518,"threshold_uncertainty_score":0.032071233},"labels":[],"label_agreement":null},{"id":"W4408968267","doi":"10.61091/ars162-11","title":"Counting nodes in Smolyak grids","year":2025,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics","score_opus":0.008651257426982471,"score_gpt":0.24075948327289914,"score_spread":0.23210822584591667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408968267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12087166,0.0002591408,0.86653006,0.00024371177,0.00008594176,0.00004220987,0.0002197364,0.00033805644,0.011409488],"genre_scores_gemma":[0.72353274,0.00040530914,0.26571012,0.00015028288,0.00011819509,0.0001902203,0.00042485417,0.00033736762,0.009130966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990528,0.00024699426,0.000074896976,0.00014637623,0.0003408056,0.00013807333],"domain_scores_gemma":[0.9968323,0.0017294451,0.00024823516,0.00052647095,0.0005023358,0.00016122973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014202528,0.00037305904,0.0006969091,0.0018114055,0.0007050709,0.0015194939,0.0016030105,0.0007516125,0.0038083491],"category_scores_gemma":[0.007260413,0.00035153827,0.00060200517,0.0010997117,0.001998016,0.0042256685,0.001648319,0.0015604345,0.0008666261],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042700634,0.000013207379,0.00076174294,0.00004935795,0.0000062542545,0.000050477145,0.00011221809,0.010864121,0.0021852646,0.973459,0.0007207987,0.0117347445],"study_design_scores_gemma":[0.0000135646005,0.000025098509,0.00031951044,0.000025267464,0.000006609389,0.000057651385,0.000045189256,0.16094872,0.0032242772,0.83295184,0.0023645207,0.00001776717],"about_ca_topic_score_codex":0.00089197664,"about_ca_topic_score_gemma":0.0011979726,"teacher_disagreement_score":0.0038083491,"about_ca_system_score_codex":0.0007857467,"about_ca_system_score_gemma":0.00048314044,"threshold_uncertainty_score":0.012740195},"labels":[],"label_agreement":null},{"id":"W4409363673","doi":"10.1609/aaai.v39i18.34151","title":"OmniCount: Multi-label Object Counting with Semantic-Geometric Priors","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Prior probability; Object (grammar); Computer science; Artificial intelligence; Mathematics; Pattern recognition (psychology); Bayesian probability","score_opus":0.06264939534395512,"score_gpt":0.29685181977362796,"score_spread":0.23420242442967282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05918763,0.0054241884,0.5033812,0.0013467921,0.0010432224,0.0015744497,0.07619249,0.32900855,0.022841442],"genre_scores_gemma":[0.11869384,0.0008039332,0.67094475,0.0011355453,0.00020479182,0.0009197733,0.18810157,0.011236948,0.007958889],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950612,0.0006328169,0.00027415156,0.0018609655,0.0016613501,0.0005094608],"domain_scores_gemma":[0.99364024,0.0019485378,0.00042461423,0.0024649377,0.0011580272,0.00036365743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003315787,0.0053212307,0.0027705545,0.0059695966,0.002177273,0.0054630535,0.009993491,0.0045515224,0.008236581],"category_scores_gemma":[0.01368552,0.001633051,0.0028047706,0.0054582236,0.0017632489,0.006451815,0.006620713,0.0043643294,0.0076968158],"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":[0.0013183418,0.00074449956,0.012749476,0.0027114647,0.0005404026,0.00041032166,0.00047230028,0.059944496,0.017052973,0.016335772,0.41240862,0.47531134],"study_design_scores_gemma":[0.00034351228,0.00029620592,0.005606516,0.0003197761,0.00012602883,0.00086752063,0.0004015242,0.7913219,0.02595071,0.02760039,0.1469981,0.00016787625],"about_ca_topic_score_codex":0.043468602,"about_ca_topic_score_gemma":0.090151,"teacher_disagreement_score":0.043468602,"about_ca_system_score_codex":0.0040340675,"about_ca_system_score_gemma":0.0034840698,"threshold_uncertainty_score":0.086431146},"labels":[],"label_agreement":null},{"id":"W4409579938","doi":"10.61091/jcmcc127a-070","title":"Combinatorial Optimization Algorithms in Tourism Planning and Emergency Management and Their Application to Resource Allocation","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Resource allocation; Computer science; Tourism; Mathematical optimization; Resource planning; Operations research; Algorithm; Engineering; Environmental resource management; Mathematics; Geography; Environmental science","score_opus":0.00958854311294038,"score_gpt":0.2522237586805554,"score_spread":0.24263521556761503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409579938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009237037,0.0030975225,0.9801922,0.000771582,0.000154872,0.00007188404,0.00006694548,0.000112096655,0.006295833],"genre_scores_gemma":[0.4133237,0.010418109,0.5672629,0.0005002824,0.00053847244,0.00068367453,0.00031391898,0.00017156468,0.006787301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988182,0.0007427801,0.000052297473,0.00013650948,0.00016964998,0.00008051901],"domain_scores_gemma":[0.9974583,0.0020923484,0.00017526248,0.000052248684,0.00016586366,0.000056030767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019137064,0.0016271578,0.0015358106,0.0013344715,0.0007639662,0.0020590913,0.0011460251,0.0015299312,0.0021195565],"category_scores_gemma":[0.0044031614,0.00081382244,0.0013518165,0.0032845656,0.0012167953,0.0015967946,0.001090338,0.0021685679,0.00028531603],"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":[0.000012390379,0.000039317405,0.00026094986,0.00008505349,0.000042067724,0.000045529414,0.000025540492,0.964968,0.000112661124,0.019117428,0.00079600816,0.01449508],"study_design_scores_gemma":[0.0000076028614,0.0000118285325,0.00006496976,0.000010203505,0.000008054982,0.000011123403,0.000013715071,0.9878235,0.000051440813,0.011316759,0.00067579444,0.0000050100157],"about_ca_topic_score_codex":0.011358314,"about_ca_topic_score_gemma":0.007762166,"teacher_disagreement_score":0.011358314,"about_ca_system_score_codex":0.00149345,"about_ca_system_score_gemma":0.0018290317,"threshold_uncertainty_score":0.022584438},"labels":[],"label_agreement":null},{"id":"W4410235836","doi":"10.1016/j.oceaneng.2025.121404","title":"TrajReducer: a cross-dimension indexer-based reducer for vessel destination prediction","year":2025,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"","keywords":"Reducer; Dimension (graph theory); Marine engineering; Computer science; Engineering; Mathematics; Mechanical engineering; Combinatorics","score_opus":0.009081502940013645,"score_gpt":0.2459964538396883,"score_spread":0.23691495089967465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410235836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038840104,0.0012250592,0.92897975,0.0004721022,0.0003877066,0.0002547705,0.0032882993,0.02203951,0.0045127273],"genre_scores_gemma":[0.28966856,0.00074153015,0.6857914,0.00029657516,0.00018635837,0.0003576039,0.011947768,0.00083465164,0.010175564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924797,0.00007994722,0.000046796118,0.00021261166,0.0003237047,0.00008902186],"domain_scores_gemma":[0.9994375,0.00009249421,0.000053204232,0.0001890385,0.00018972029,0.000037974103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060651894,0.0016738407,0.0018926734,0.0015435221,0.0008718918,0.0011539405,0.0024658064,0.0007336172,0.0036960929],"category_scores_gemma":[0.0019137721,0.000547669,0.0015016848,0.0018546416,0.0003824809,0.0016884868,0.0017052378,0.0016953109,0.002366186],"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":[0.00042757255,0.0004791141,0.00670343,0.0003155231,0.0003115879,0.00027462497,0.00018150738,0.30433518,0.010494418,0.008734733,0.04760949,0.6201328],"study_design_scores_gemma":[0.00002110248,0.00006763389,0.00080498896,0.00000889422,0.000025258336,0.000055308778,0.0000421207,0.9870263,0.0029970729,0.0030783515,0.0058491076,0.000023985238],"about_ca_topic_score_codex":0.032330446,"about_ca_topic_score_gemma":0.044766463,"teacher_disagreement_score":0.032330446,"about_ca_system_score_codex":0.0008878342,"about_ca_system_score_gemma":0.0023226265,"threshold_uncertainty_score":0.06428456},"labels":[],"label_agreement":null},{"id":"W4410416203","doi":"10.1007/978-981-97-9855-1_33","title":"Analyze User Navigational Patterns from Log Data Using Hadoop Techniques","year":2025,"lang":"en","type":"book-chapter","venue":"Learning and analytics in intelligent systems","topic":"Data Management and Algorithms","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Database","score_opus":0.060646770989884614,"score_gpt":0.3123121297863696,"score_spread":0.25166535879648494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410416203","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6283943,0.0011116841,0.3178075,0.0007812293,0.00034235392,0.00039009977,0.021826329,0.02346969,0.0058768224],"genre_scores_gemma":[0.7830697,0.0005057087,0.19084507,0.00008908171,0.00007066056,0.00016133061,0.0194976,0.00052671187,0.0052341404],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994543,0.00005426672,0.000047358957,0.00011831879,0.0002407636,0.00008505764],"domain_scores_gemma":[0.9983297,0.0006719566,0.00010707427,0.00034561593,0.00043543841,0.000110207584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046879996,0.0007121816,0.0005611323,0.0034485038,0.0006397418,0.0012811128,0.00070287875,0.00030293787,0.0013094759],"category_scores_gemma":[0.0025393388,0.00028319214,0.0007982327,0.0055242563,0.00024136968,0.0014949954,0.0005832429,0.0007128073,0.0011130961],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059336115,0.00094990496,0.16256146,0.0004999445,0.0004148743,0.00056960766,0.00085286185,0.043798093,0.020934094,0.003344123,0.031314768,0.734167],"study_design_scores_gemma":[0.000038943585,0.00023723749,0.073933646,0.000059993738,0.00012931289,0.0008481101,0.0016364893,0.8747589,0.019775815,0.014962596,0.0135300895,0.00008882376],"about_ca_topic_score_codex":0.012898695,"about_ca_topic_score_gemma":0.02250629,"teacher_disagreement_score":0.012898695,"about_ca_system_score_codex":0.00040577757,"about_ca_system_score_gemma":0.0010421721,"threshold_uncertainty_score":0.025647223},"labels":[],"label_agreement":null},{"id":"W4410461442","doi":"10.1007/s43069-025-00467-4","title":"Time-Dependent Routing and Road Network Precision","year":2025,"lang":"en","type":"article","venue":"Operations Research Forum","topic":"Data Management and Algorithms","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":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Routing (electronic design automation); Computer network","score_opus":0.029420819693897735,"score_gpt":0.34907756549632113,"score_spread":0.31965674580242337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410461442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1452234,0.00270352,0.8324404,0.0024151206,0.0004593922,0.00004674512,0.00044608905,0.00037245988,0.015892917],"genre_scores_gemma":[0.9328557,0.0015709065,0.057454072,0.00017272632,0.0002181338,0.00003516236,0.0002035718,0.00011164116,0.007378021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977489,0.0004887076,0.000116020026,0.00065356126,0.0006936324,0.00029912073],"domain_scores_gemma":[0.9676481,0.021765405,0.0037321453,0.004235294,0.0022248176,0.00039432486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004723457,0.00058712653,0.0011789376,0.0019712988,0.0010902736,0.0031771786,0.0024449911,0.0016204849,0.0044908053],"category_scores_gemma":[0.049464893,0.0008197122,0.0009071749,0.00328106,0.0021867808,0.007824169,0.0018066027,0.0024284986,0.00037064726],"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":[0.0001636457,0.000027327686,0.0017769098,0.00008926508,0.0000575179,0.000095345895,0.00016135233,0.54153174,0.0012311528,0.42344463,0.0017139311,0.029707238],"study_design_scores_gemma":[0.00002579544,0.000056202996,0.001143837,0.000027578091,0.000043665408,0.00012811611,0.000060335547,0.6094068,0.0012841459,0.38605443,0.0017319422,0.000037127935],"about_ca_topic_score_codex":0.005717552,"about_ca_topic_score_gemma":0.0040462073,"teacher_disagreement_score":0.005717552,"about_ca_system_score_codex":0.0026678261,"about_ca_system_score_gemma":0.0022686752,"threshold_uncertainty_score":0.024980366},"labels":[],"label_agreement":null},{"id":"W4410475241","doi":"10.56553/popets-2025-0093","title":"A non comparison oblivious sort and its application to k-NN","year":2025,"lang":"en","type":"article","venue":"Proceedings on Privacy Enhancing Technologies","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":"Université du Québec à Montréal","funders":"","keywords":"sort; Computer science; Parallel computing; Information retrieval","score_opus":0.01208223661284677,"score_gpt":0.27345112539574384,"score_spread":0.2613688887828971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410475241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007502668,0.00044956652,0.98595667,0.0002552014,0.00016242066,0.00006869786,0.00017162276,0.0019774758,0.0034557758],"genre_scores_gemma":[0.1734344,0.00061521004,0.8165307,0.00030095756,0.00016498318,0.00015189502,0.0004490205,0.00042351836,0.007929222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797314,0.00033605835,0.00018816034,0.00031707378,0.0010185101,0.00016703374],"domain_scores_gemma":[0.99754924,0.000757139,0.00017414248,0.00092569756,0.00052505016,0.00006882698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014300225,0.00047622772,0.00084894587,0.0018123491,0.0012571316,0.002062445,0.0017556904,0.0009687052,0.005284263],"category_scores_gemma":[0.0065478887,0.00032113024,0.0007355399,0.0033634235,0.0011848977,0.0040217447,0.0017862057,0.0013745334,0.0016593877],"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":[0.00064268586,0.00012535442,0.0013171573,0.0003786874,0.000111582194,0.00019793921,0.00021567865,0.07141343,0.012416298,0.19997571,0.0102493875,0.70295614],"study_design_scores_gemma":[0.00009484936,0.00028629045,0.00090586074,0.0000897194,0.000058066915,0.0008964597,0.00015143405,0.6646456,0.037476536,0.24039966,0.05488822,0.000107275555],"about_ca_topic_score_codex":0.0032100836,"about_ca_topic_score_gemma":0.0038882832,"teacher_disagreement_score":0.005284263,"about_ca_system_score_codex":0.0012502916,"about_ca_system_score_gemma":0.0025923538,"threshold_uncertainty_score":0.017677605},"labels":[],"label_agreement":null},{"id":"W4410527555","doi":"10.46298/dmtcs.12743","title":"On Separating Path and Tree Systems in Graphs","year":2025,"lang":"en","type":"article","venue":"Discrete Mathematics & Theoretical Computer Science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds De La Recherche Scientifique - FNRS; National Science Foundation","keywords":"Mathematics; Combinatorics; Path (computing); Tree (set theory); Computer science","score_opus":0.010106605733822108,"score_gpt":0.26072299739890475,"score_spread":0.25061639166508265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410527555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12572889,0.01893556,0.6140739,0.04300125,0.0046297098,0.00030905538,0.0018531127,0.00097583927,0.19049275],"genre_scores_gemma":[0.7011444,0.020527361,0.22357579,0.013751344,0.008453649,0.0007794698,0.0016527142,0.00069729367,0.029418014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981571,0.00048942637,0.00009859335,0.0005161453,0.0004556281,0.00028313263],"domain_scores_gemma":[0.99011445,0.0073191887,0.00054854143,0.0006946276,0.0009190134,0.0004043123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025280013,0.00074949715,0.0007885337,0.0025936463,0.0022443123,0.0024456694,0.0014935439,0.0019767475,0.009455269],"category_scores_gemma":[0.013252097,0.000411541,0.0009007694,0.0045954227,0.0060830377,0.02005987,0.0040620645,0.00403538,0.0010665536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024829249,0.0000067433284,0.00021788845,0.00008690202,0.0000059817376,0.00006181164,0.00028746523,0.0020623263,0.0004563015,0.9811866,0.0075485557,0.008054626],"study_design_scores_gemma":[0.000009885411,0.000012986818,0.00023886951,0.000026242451,0.0000044973362,0.000086933185,0.000104280836,0.004224488,0.0002989724,0.9739828,0.02099688,0.00001315175],"about_ca_topic_score_codex":0.0034642697,"about_ca_topic_score_gemma":0.0018822209,"teacher_disagreement_score":0.009455269,"about_ca_system_score_codex":0.0026303977,"about_ca_system_score_gemma":0.00078127824,"threshold_uncertainty_score":0.031630993},"labels":[],"label_agreement":null},{"id":"W4410887374","doi":"10.1109/syscon64521.2025.11014824","title":"A Fuzzy Set-Theoretic Approach to Occupancy Grid Mapping","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Occupancy grid mapping; Occupancy; Computer science; Fuzzy set; Grid; Fuzzy logic; Set (abstract data type); Data mining; Artificial intelligence; Mathematics; Engineering; Programming language","score_opus":0.02098207250555116,"score_gpt":0.2523479548286725,"score_spread":0.23136588232312133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410887374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010004868,0.00013682102,0.9973023,0.00007668627,0.000030397348,0.000016993008,0.000039570634,0.000051278872,0.001345467],"genre_scores_gemma":[0.29112643,0.0008564987,0.70472795,0.00018165247,0.0002005977,0.00025569316,0.0002186242,0.00005473857,0.002377835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987897,0.0003742135,0.00007855807,0.00021056981,0.00047243704,0.00007451651],"domain_scores_gemma":[0.99901354,0.0005401957,0.000096015465,0.00010720649,0.00020935775,0.000033656383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412448,0.0008160632,0.0010140998,0.0016259149,0.0007144967,0.0017153993,0.0021064568,0.0008852534,0.0019644932],"category_scores_gemma":[0.0034049125,0.0005071007,0.0014829104,0.0015863524,0.0013159084,0.0021427067,0.0012184399,0.0015918828,0.00036086587],"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":[0.000043936736,0.00004848174,0.0007976294,0.00017790256,0.00008863267,0.00017188743,0.0002447437,0.70555055,0.002245314,0.22117856,0.0016147747,0.06783763],"study_design_scores_gemma":[0.000007007584,0.000030745694,0.0001911247,0.000029903886,0.000015439262,0.00008811195,0.00004877173,0.9114551,0.0008729572,0.083170496,0.0040624575,0.000027941434],"about_ca_topic_score_codex":0.00578872,"about_ca_topic_score_gemma":0.004094061,"teacher_disagreement_score":0.00578872,"about_ca_system_score_codex":0.0016159597,"about_ca_system_score_gemma":0.0011635625,"threshold_uncertainty_score":0.011724651},"labels":[],"label_agreement":null},{"id":"W4410996627","doi":"10.1175/mwr-d-24-0090.1","title":"A Feature-Based Framework to Investigate Atmospheric Predictability","year":2025,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Data Management and Algorithms","field":"Computer Science","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":"Environment and Climate Change Canada","funders":"Deutsche Forschungsgemeinschaft","keywords":"Predictability; Feature (linguistics); Environmental science; Climatology; Computer science; Atmospheric models; Meteorology; Geology; Mathematics; Atmosphere (unit); Statistics; Geography","score_opus":0.011039534345529475,"score_gpt":0.2658798652350494,"score_spread":0.25484033088951996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410996627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07441344,0.00042105195,0.92295045,0.0002842887,0.000031295785,0.00003405332,0.0003578885,0.0003288642,0.0011786873],"genre_scores_gemma":[0.8470069,0.00021021516,0.15168977,0.000053331576,0.00009356699,0.00006910378,0.0003697309,0.000050356746,0.00045702982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951756,0.00021236522,0.000025033656,0.00009365808,0.00010161864,0.000049783284],"domain_scores_gemma":[0.99727243,0.0017390822,0.0002986684,0.0002778739,0.00032120245,0.00009075851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013397295,0.0003880526,0.0005799925,0.0021729893,0.0004885824,0.0011704246,0.0008192897,0.00066171226,0.0010964391],"category_scores_gemma":[0.0047110165,0.0001963477,0.00078080635,0.0017756571,0.00078823586,0.0012832016,0.0009690218,0.0008494051,0.000111346766],"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":[0.000226503,0.00022992163,0.021927489,0.0001352751,0.0003558923,0.00045712205,0.00024253826,0.7308188,0.0134263,0.1104041,0.0021912185,0.11958484],"study_design_scores_gemma":[0.000004009063,0.00002205704,0.0018740969,0.0000048464626,0.000012685901,0.000022737813,0.00001614993,0.97772175,0.0003660347,0.01955716,0.0003907404,0.0000077801],"about_ca_topic_score_codex":0.004995975,"about_ca_topic_score_gemma":0.003061622,"teacher_disagreement_score":0.004995975,"about_ca_system_score_codex":0.00062289985,"about_ca_system_score_gemma":0.0005105413,"threshold_uncertainty_score":0.00993377},"labels":[],"label_agreement":null},{"id":"W4411174390","doi":"10.1007/978-3-031-90200-0_14","title":"Towards Using Partitioned GPU Virtual Functions for Mixture of Experts","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Computer graphics (images); Computational science; Parallel computing","score_opus":0.02732884269420938,"score_gpt":0.2680875058637235,"score_spread":0.2407586631695141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411174390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071483795,0.00011681139,0.9880884,0.00007090931,0.000067916786,0.000026381038,0.000032875723,0.0015132563,0.0029350352],"genre_scores_gemma":[0.108728185,0.00014702172,0.88187104,0.00017411158,0.00007795928,0.00009483008,0.00024102975,0.0007603685,0.007905311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989536,0.00032996113,0.000049856506,0.00019675033,0.00030178527,0.00016802935],"domain_scores_gemma":[0.99833494,0.0004860725,0.000052521566,0.00055675843,0.00036318036,0.00020654002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013508603,0.0011478967,0.0012001747,0.0007806044,0.00081778516,0.0022474087,0.00328303,0.0014934522,0.009255037],"category_scores_gemma":[0.004697875,0.0007858254,0.0012325065,0.00086985005,0.00077125273,0.0024241162,0.0046910215,0.0022074936,0.0043480755],"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":[0.0011939354,0.00039462105,0.0015635692,0.00022404837,0.00022523083,0.00026929198,0.0004984219,0.26176792,0.046640173,0.110856876,0.018829666,0.5575363],"study_design_scores_gemma":[0.00004710172,0.00006593796,0.00014580505,0.000020389065,0.00002556718,0.0001178665,0.000058149777,0.9464908,0.009218808,0.031544477,0.01224617,0.000018890209],"about_ca_topic_score_codex":0.0031123606,"about_ca_topic_score_gemma":0.004257527,"teacher_disagreement_score":0.009255037,"about_ca_system_score_codex":0.00087806676,"about_ca_system_score_gemma":0.0011863998,"threshold_uncertainty_score":0.030961156},"labels":[],"label_agreement":null},{"id":"W4411259610","doi":"10.1145/3735950.3735953","title":"Arborescent Garbage Collection: A Dynamic Graph Approach to Immediate Cycle Collection","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Garbage collection; Computer science; Manual memory management; Graph; Parallel computing; Database; Garbage; Programming language; Theoretical computer science","score_opus":0.00653791225284416,"score_gpt":0.22886111602886475,"score_spread":0.22232320377602058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411259610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005125107,0.0005553605,0.9877537,0.00026339412,0.00007396253,0.00010170169,0.00012579853,0.0022380839,0.0037630112],"genre_scores_gemma":[0.2127193,0.0017122484,0.7683733,0.0004812189,0.00022713619,0.00035894683,0.0006244885,0.002490397,0.013012952],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987733,0.00032863297,0.000063610845,0.0003200295,0.00035213956,0.00016222977],"domain_scores_gemma":[0.9966372,0.0010960227,0.00027870166,0.00127179,0.0005340307,0.00018220946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013898821,0.0010080568,0.0009613242,0.0028862928,0.0014964488,0.0020804796,0.004182333,0.00081146526,0.0038659335],"category_scores_gemma":[0.0043218313,0.0007684749,0.0012131291,0.0032031988,0.002703559,0.0053656036,0.0034711096,0.0017627882,0.0011555644],"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":[0.00034549178,0.00020504625,0.0011311074,0.0006002368,0.000118188385,0.00035394993,0.0011782326,0.16776861,0.015990958,0.52566266,0.011685616,0.27496004],"study_design_scores_gemma":[0.00004990095,0.00013918905,0.00033006162,0.00011577394,0.000117176074,0.0003128903,0.00020864386,0.48310167,0.012799808,0.45646656,0.04624975,0.000108648215],"about_ca_topic_score_codex":0.0068382705,"about_ca_topic_score_gemma":0.012622993,"teacher_disagreement_score":0.0068382705,"about_ca_system_score_codex":0.0014662672,"about_ca_system_score_gemma":0.0023723058,"threshold_uncertainty_score":0.013596892},"labels":[],"label_agreement":null},{"id":"W4411319974","doi":"10.1145/3717823.3718194","title":"Correlation Clustering and (De)Sparsification: Graph Sketches Can Match Classical Algorithms","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","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 Waterloo","funders":"","keywords":"Computer science; Cluster analysis; Correlation; Graph; Algorithm; Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.015816673114113618,"score_gpt":0.24884199260412274,"score_spread":0.23302531949000913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411319974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010160618,0.000545149,0.98362064,0.00074777356,0.00015347186,0.00008320196,0.00023874696,0.0006509228,0.003799531],"genre_scores_gemma":[0.3673747,0.0021333192,0.6170546,0.00080583175,0.0005325796,0.00040005057,0.0015217485,0.00073121325,0.009445895],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99642724,0.0011191699,0.00018313678,0.0010041209,0.0010415537,0.00022466225],"domain_scores_gemma":[0.97062165,0.013970213,0.0012046493,0.011624111,0.0019735852,0.00060576544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004314465,0.001386295,0.0019200186,0.0018967882,0.0010524151,0.0031740358,0.0030956164,0.0030737533,0.0072692577],"category_scores_gemma":[0.044793256,0.0011575134,0.0011262923,0.0031513807,0.0031182885,0.008934914,0.0044573187,0.004769316,0.0020639452],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048618307,0.0001770605,0.0021245223,0.0006080802,0.00015835768,0.0001652863,0.0003768504,0.30748332,0.005641264,0.4628054,0.013738203,0.20623548],"study_design_scores_gemma":[0.00004077827,0.000087063854,0.0003981175,0.00007208787,0.000031168223,0.00019183857,0.00010106116,0.595491,0.0028643569,0.39179298,0.008894358,0.000035198318],"about_ca_topic_score_codex":0.0022055097,"about_ca_topic_score_gemma":0.0029554863,"teacher_disagreement_score":0.0072692577,"about_ca_system_score_codex":0.0011224225,"about_ca_system_score_gemma":0.0019015498,"threshold_uncertainty_score":0.024318099},"labels":[],"label_agreement":null},{"id":"W4411374364","doi":"10.1145/3722212.3724440","title":"Including Bloom Filters in Bottom-up Optimization","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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":"Huawei Technologies (Canada)","funders":"","keywords":"Bloom filter; Bloom; Computer science; Geology; Algorithm; Oceanography","score_opus":0.03709317963472425,"score_gpt":0.2897979060623975,"score_spread":0.2527047264276733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411374364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025410399,0.000634914,0.9581012,0.00053990463,0.000085058404,0.00038361613,0.00046477062,0.00869638,0.005683779],"genre_scores_gemma":[0.21238884,0.00031970942,0.7812055,0.00040723418,0.000053033298,0.00021048135,0.000633265,0.0008346051,0.003947286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954059,0.0009635344,0.00040787467,0.00053075724,0.0020352942,0.00065673044],"domain_scores_gemma":[0.99564976,0.0018512731,0.00021912406,0.0013686977,0.0008027511,0.00010844098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028471898,0.0018607493,0.0015267484,0.0014843309,0.0014004776,0.00325751,0.0019052367,0.0011160254,0.0038283858],"category_scores_gemma":[0.0072169416,0.00091430824,0.0017448191,0.0026849143,0.0011271446,0.004771675,0.0024030427,0.0019517748,0.0014735674],"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":[0.00090214703,0.00047243462,0.006427234,0.00050590956,0.00025919714,0.00043720778,0.000528969,0.25001588,0.0837015,0.043206804,0.025697848,0.58784485],"study_design_scores_gemma":[0.000076799784,0.0001543069,0.0007864718,0.000037741065,0.000098302735,0.00015643655,0.0001567518,0.8873845,0.053826496,0.037998218,0.019266622,0.000057358513],"about_ca_topic_score_codex":0.013245289,"about_ca_topic_score_gemma":0.022067457,"teacher_disagreement_score":0.013245289,"about_ca_system_score_codex":0.0026626862,"about_ca_system_score_gemma":0.004238209,"threshold_uncertainty_score":0.026336372},"labels":[],"label_agreement":null},{"id":"W4411687193","doi":"10.61091/um123-15","title":"Alpha-trees over paths and caterpillars","year":2025,"lang":"en","type":"article","venue":"Utilitas Mathematica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Alpha (finance); Statistics","score_opus":0.009181463388676548,"score_gpt":0.25228080326702074,"score_spread":0.2430993398783442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411687193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25107396,0.0016795681,0.43552017,0.001812869,0.0004834903,0.00015353,0.0036242704,0.00307948,0.30257267],"genre_scores_gemma":[0.7882662,0.0013625012,0.10218983,0.0005347378,0.00023880038,0.00014839378,0.0022786483,0.0005087324,0.104472175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99960583,0.000055424967,0.00002024962,0.000116430994,0.00008920588,0.00011277864],"domain_scores_gemma":[0.99928445,0.00022168229,0.00009672601,0.00011863354,0.00012775934,0.00015080873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031004747,0.00043488268,0.0003855849,0.0009549964,0.0011983886,0.0018311553,0.0006665171,0.000625796,0.018355755],"category_scores_gemma":[0.0012343046,0.00042047,0.0005847664,0.0018031338,0.0010378126,0.0029291653,0.0012848164,0.0013550266,0.0033227894],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088688044,0.000018611629,0.00044081293,0.0000716107,0.000008627909,0.0001725901,0.00029904308,0.003950551,0.0018277505,0.95705533,0.0063052075,0.02976126],"study_design_scores_gemma":[0.000020704592,0.00003579009,0.0006470938,0.000041251227,0.000013818813,0.00029249617,0.00021231527,0.009275937,0.0014416896,0.9186054,0.069397606,0.000015911379],"about_ca_topic_score_codex":0.0022626875,"about_ca_topic_score_gemma":0.0028271757,"teacher_disagreement_score":0.018355755,"about_ca_system_score_codex":0.0013549648,"about_ca_system_score_gemma":0.00046478442,"threshold_uncertainty_score":0.061406136},"labels":[],"label_agreement":null},{"id":"W4411687194","doi":"10.61091/um123-03","title":"Maps of combinatorial trees with weakly connected Markov graphs","year":2025,"lang":"en","type":"article","venue":"Utilitas Mathematica","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Markov chain; Combinatorics; Discrete mathematics; Statistics","score_opus":0.008671139799528452,"score_gpt":0.23015525979199436,"score_spread":0.22148411999246592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411687194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66332847,0.00020262277,0.3237751,0.0002966841,0.000035016572,0.0000574042,0.00031776878,0.00027050907,0.011716438],"genre_scores_gemma":[0.96305645,0.00017249967,0.031140924,0.00006797045,0.000048541104,0.000088435714,0.0002838094,0.000080020676,0.0050613442],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996613,0.00009712889,0.000020283525,0.000084324456,0.000088084686,0.000048863047],"domain_scores_gemma":[0.9976174,0.0012450592,0.00034643206,0.00023364203,0.00024193333,0.00031554536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003811242,0.00023883232,0.00029687866,0.0013677237,0.0007990165,0.0013220764,0.0005524784,0.0005068125,0.002590818],"category_scores_gemma":[0.0032433541,0.00035659733,0.0006340276,0.0006877692,0.0011888365,0.0021858478,0.0011909719,0.000706884,0.000261422],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006495204,0.000030654086,0.0017903661,0.00004040085,0.000016470032,0.0002734121,0.0004914641,0.020699602,0.0049201464,0.9624241,0.00049346546,0.008754946],"study_design_scores_gemma":[0.000012295808,0.000025204734,0.0009735639,0.000014872524,0.000011753763,0.00019326071,0.00014776776,0.12846366,0.0021717306,0.86556935,0.0023999054,0.000016750835],"about_ca_topic_score_codex":0.00111668,"about_ca_topic_score_gemma":0.0012760989,"teacher_disagreement_score":0.002590818,"about_ca_system_score_codex":0.0007777932,"about_ca_system_score_gemma":0.00041960488,"threshold_uncertainty_score":0.008667111},"labels":[],"label_agreement":null},{"id":"W4412202024","doi":"10.1145/3747591","title":"Minimum Plane Bichromatic Spanning Trees","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Spanning tree; Combinatorics; Plane (geometry); Mathematics; Minimum degree spanning tree; Computer science; Discrete mathematics; Physics; Geometry","score_opus":0.01779323630805467,"score_gpt":0.265220523802979,"score_spread":0.24742728749492432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412202024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17729656,0.0020962693,0.80045253,0.00065153965,0.00008180244,0.00024233032,0.0026188078,0.001614512,0.014945663],"genre_scores_gemma":[0.4093286,0.001443079,0.57768166,0.00017618686,0.000047168465,0.00017505151,0.004719209,0.00021655746,0.006212442],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99949837,0.000089877016,0.00003293268,0.00014515207,0.00012556952,0.00010809976],"domain_scores_gemma":[0.9994692,0.00014692434,0.000102727136,0.000112910115,0.00011392294,0.00005436828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002240151,0.0006161923,0.00091429957,0.0007770672,0.000576866,0.0010623068,0.0009400101,0.0008821775,0.0031395077],"category_scores_gemma":[0.001739342,0.00044194792,0.0004899344,0.0021935774,0.00026792486,0.0016733484,0.0011314992,0.0006796777,0.0009680091],"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":[0.000837817,0.00025339847,0.0024347058,0.0010424118,0.00018080736,0.0006600684,0.00066093134,0.38842756,0.05318039,0.09402784,0.029446686,0.42884743],"study_design_scores_gemma":[0.00008009701,0.0002154108,0.0020532787,0.0000876773,0.00006584115,0.0009725177,0.0003198344,0.8887723,0.01281474,0.0708175,0.023760246,0.000040541592],"about_ca_topic_score_codex":0.0024810585,"about_ca_topic_score_gemma":0.0029827256,"teacher_disagreement_score":0.0031395077,"about_ca_system_score_codex":0.00061955454,"about_ca_system_score_gemma":0.00047516558,"threshold_uncertainty_score":0.010502636},"labels":[],"label_agreement":null},{"id":"W4412376451","doi":"10.55016/ojs/cdm.v10i1.62293","title":"Chasing robbers on percolated random geometric graphs","year":2015,"lang":"en","type":"article","venue":"Contributions to Discrete Mathematics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Combinatorics; Mathematics; Random graph; Discrete mathematics; Graph","score_opus":0.025639752924634742,"score_gpt":0.2881849538665438,"score_spread":0.2625452009419091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412376451","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5315549,0.0008709475,0.44494754,0.0019044217,0.00009837938,0.00028081646,0.0005125828,0.0006579841,0.01917246],"genre_scores_gemma":[0.95454365,0.0005653792,0.035597127,0.00043911845,0.000104071245,0.00032814418,0.00048479368,0.00023551572,0.0077021327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996252,0.0017609257,0.0001220049,0.0006018742,0.0005718972,0.0006914573],"domain_scores_gemma":[0.978123,0.01524349,0.0026726471,0.0013517258,0.00068263727,0.0019265305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028042693,0.0017702661,0.002231185,0.0019991496,0.002126609,0.00234312,0.0032259475,0.0028542248,0.0052607004],"category_scores_gemma":[0.024330392,0.0010675798,0.0013836595,0.0013017172,0.00494424,0.0067082336,0.0041680587,0.0027064288,0.0008039652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058856123,0.00024716867,0.002730606,0.00029756827,0.00020278685,0.00080380676,0.0006841159,0.38799202,0.0053563435,0.5855155,0.0048003327,0.010781253],"study_design_scores_gemma":[0.0000881061,0.00011837453,0.00043427496,0.00003485413,0.000037267422,0.00018236798,0.00019418134,0.76002795,0.00094494346,0.23642196,0.0014750148,0.00004061429],"about_ca_topic_score_codex":0.0036316412,"about_ca_topic_score_gemma":0.003784102,"teacher_disagreement_score":0.0052607004,"about_ca_system_score_codex":0.0025119926,"about_ca_system_score_gemma":0.0013392058,"threshold_uncertainty_score":0.018225849},"labels":[],"label_agreement":null},{"id":"W4412514797","doi":"10.1007/978-3-031-98679-6_11","title":"Assessing the Quality of Binomial Samplers: A Statistical Distance Framework","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; University of Toronto; Innovation, Science and Economic Development Canada","keywords":"Computer science; Binomial (polynomial); Quality (philosophy); Negative binomial distribution; Statistics; Mathematics; Poisson distribution","score_opus":0.04402618707810627,"score_gpt":0.3454310784813723,"score_spread":0.301404891403266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412514797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009874979,0.0004181722,0.9860963,0.0003012425,0.000042990578,0.00005135146,0.00006696113,0.0004338074,0.0027141473],"genre_scores_gemma":[0.3542378,0.0006082536,0.64016545,0.00041963952,0.00017355024,0.00031021258,0.00032424444,0.0006368652,0.003123918],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9796071,0.008574597,0.00089963933,0.0023384874,0.007810666,0.0007693816],"domain_scores_gemma":[0.9165124,0.06334268,0.005480511,0.007851489,0.0053103566,0.0015025461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0200271,0.0011852534,0.0017565599,0.0034025493,0.0010937424,0.005860233,0.0042542964,0.002378668,0.00395104],"category_scores_gemma":[0.09427395,0.00096697634,0.00100486,0.0026171594,0.0054906206,0.0074236495,0.0061397273,0.004003509,0.0010563673],"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":[0.0003720996,0.00015086033,0.008731151,0.0003176189,0.000118582255,0.00016172916,0.00037671564,0.26463905,0.004937087,0.58333606,0.004452354,0.13240674],"study_design_scores_gemma":[0.000023597597,0.0001325554,0.0007559081,0.0000899886,0.000022546681,0.00018228768,0.00006950405,0.75878173,0.0035510354,0.23309703,0.0032502396,0.00004364028],"about_ca_topic_score_codex":0.0014522051,"about_ca_topic_score_gemma":0.0012758025,"teacher_disagreement_score":0.0200271,"about_ca_system_score_codex":0.003097159,"about_ca_system_score_gemma":0.002548801,"threshold_uncertainty_score":0.10591471},"labels":[],"label_agreement":null},{"id":"W4413232535","doi":"10.1007/978-3-032-00281-5_9","title":"A Subquadratic Time Algorithm for the Weighted k-Center Problem on Cactus Graphs","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"Simon Fraser University","funders":"","keywords":"Computer science; Center (category theory); Algorithm; Cactus","score_opus":0.010870916836559892,"score_gpt":0.23094171266794614,"score_spread":0.22007079583138625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413232535","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08828671,0.0012830849,0.8081335,0.0027891768,0.0006665143,0.0009598903,0.0026462455,0.0185485,0.07668643],"genre_scores_gemma":[0.14285378,0.00036872004,0.827355,0.00053091405,0.0002530337,0.0005519593,0.0043984344,0.001971529,0.021716649],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99869776,0.00018533984,0.000055735953,0.00042795966,0.00033129758,0.00030182884],"domain_scores_gemma":[0.9976259,0.0010754595,0.00013463701,0.0006104139,0.0003067007,0.00024695593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008921946,0.0021417537,0.0022902817,0.0020663263,0.002152871,0.003193105,0.0053176302,0.002492939,0.037246414],"category_scores_gemma":[0.00434962,0.0010019329,0.0016711918,0.004608975,0.0011395286,0.0066069877,0.0037212083,0.0032879112,0.008262087],"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":[0.0012551277,0.00092933374,0.0010047492,0.0009567007,0.00012411989,0.0001690302,0.00031306426,0.08140088,0.0115440795,0.089788646,0.13404249,0.67847174],"study_design_scores_gemma":[0.00073179987,0.00023701631,0.00086324714,0.000079810015,0.00011161892,0.00030316398,0.00033063034,0.74316394,0.0069211354,0.2240092,0.023182204,0.00006616906],"about_ca_topic_score_codex":0.010248727,"about_ca_topic_score_gemma":0.0145194745,"teacher_disagreement_score":0.037246414,"about_ca_system_score_codex":0.0035842718,"about_ca_system_score_gemma":0.0034530032,"threshold_uncertainty_score":0.12460166},"labels":[],"label_agreement":null},{"id":"W4413360655","doi":"10.1109/icde65448.2025.00101","title":"A Length Enhanced B<sup>+</sup>-Tree Based Index for Efficient Set Similarity Query","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","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 New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Computer science; Set (abstract data type); Index (typography); Similarity (geometry); Tree (set theory); Algorithm; Mathematics; Combinatorics; Artificial intelligence","score_opus":0.016089858702479858,"score_gpt":0.26462293099022155,"score_spread":0.24853307228774169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413360655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027790392,0.0010843449,0.9518438,0.0007039443,0.0001691487,0.0005920122,0.0026897443,0.008367223,0.006759312],"genre_scores_gemma":[0.13828723,0.00048000613,0.85007626,0.0004851183,0.00013078099,0.00046104236,0.005100931,0.00044308632,0.0045355144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795914,0.0002433342,0.00030802094,0.00030969048,0.0009845368,0.00019530703],"domain_scores_gemma":[0.99687696,0.00077641354,0.00025547997,0.0011005168,0.0007588096,0.00023179603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001569572,0.00062418764,0.001444299,0.0028297228,0.0011118995,0.0021784035,0.0025960712,0.0011184531,0.0050708675],"category_scores_gemma":[0.007698735,0.0004939321,0.00071232347,0.004902532,0.0006918692,0.0066600894,0.004520524,0.0012905385,0.0036130352],"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":[0.0014142353,0.0006190008,0.0036972344,0.0006858977,0.00010439364,0.00031556186,0.0006416781,0.026158735,0.06670946,0.091529496,0.06898539,0.7391389],"study_design_scores_gemma":[0.00033461573,0.0007388804,0.0017573263,0.00011055111,0.00008950662,0.0012043439,0.00036318938,0.771655,0.046526697,0.09028463,0.08676544,0.00016971656],"about_ca_topic_score_codex":0.0034207304,"about_ca_topic_score_gemma":0.004839458,"teacher_disagreement_score":0.0050708675,"about_ca_system_score_codex":0.0017364183,"about_ca_system_score_gemma":0.0031541742,"threshold_uncertainty_score":0.01696372},"labels":[],"label_agreement":null},{"id":"W4413827533","doi":"10.14778/3725688.3725702","title":"G-View: View Management for Graph Databases","year":2025,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph database; Database; Graph; Information retrieval; Theoretical computer science","score_opus":0.019736695528153752,"score_gpt":0.26537238348359177,"score_spread":0.24563568795543803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413827533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006827001,0.00086167443,0.8948014,0.00063178915,0.00015241881,0.0003469056,0.005846601,0.08622141,0.004310789],"genre_scores_gemma":[0.14266354,0.0016977894,0.8086345,0.001150756,0.00017461488,0.00091704284,0.02659115,0.01345263,0.00471796],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99665713,0.00079924933,0.00044444198,0.0006033123,0.0012223576,0.000273498],"domain_scores_gemma":[0.9933344,0.0017040499,0.00033135686,0.003426098,0.0008339308,0.00037023317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036425018,0.0014405916,0.0012890889,0.0027176284,0.0009117641,0.006097507,0.0051711495,0.0016217143,0.0065693264],"category_scores_gemma":[0.013348754,0.0011842512,0.0019633202,0.003485774,0.0011323323,0.008940582,0.0056981593,0.0026392192,0.0026705144],"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":[0.0018147187,0.00031592953,0.008628903,0.0014919868,0.0005388198,0.0005597016,0.0014012979,0.03415604,0.025077496,0.24489208,0.20332867,0.47779432],"study_design_scores_gemma":[0.0004705528,0.0004364266,0.0020532808,0.00035403224,0.00028867862,0.0011567933,0.00062412414,0.3929759,0.055340067,0.23099525,0.3149927,0.00031215686],"about_ca_topic_score_codex":0.005855026,"about_ca_topic_score_gemma":0.006012671,"teacher_disagreement_score":0.0065693264,"about_ca_system_score_codex":0.0013601844,"about_ca_system_score_gemma":0.0021688628,"threshold_uncertainty_score":0.02197659},"labels":[],"label_agreement":null},{"id":"W4414116777","doi":"10.1109/tkde.2025.3609302","title":"Flexible Keyword-Aware Top-$k$k Route Search","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Process (computing); Sequence (biology); Route planning; Semantics (computer science); Point of interest","score_opus":0.028948581485631814,"score_gpt":0.29190815446982205,"score_spread":0.26295957298419026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414116777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0825481,0.0013655962,0.887858,0.00058160623,0.00009547752,0.0003675386,0.0036061353,0.01733654,0.0062411586],"genre_scores_gemma":[0.47933358,0.00035787938,0.50814044,0.0002888191,0.000053233554,0.00018040679,0.0062864446,0.0012664322,0.0040927944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998259,0.00035399888,0.00019330853,0.0005046014,0.0004718151,0.00021734118],"domain_scores_gemma":[0.9975932,0.0009357628,0.00019700588,0.0007205647,0.00041320975,0.00014011157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091934775,0.0014972272,0.002297961,0.0016587906,0.0010514861,0.00211139,0.002989142,0.0016532133,0.005234547],"category_scores_gemma":[0.005349002,0.0007628963,0.0015707166,0.0028411774,0.0006554436,0.004431242,0.003248558,0.0011435356,0.0030520845],"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":[0.0017830962,0.00064096064,0.008869327,0.0014403767,0.00041227654,0.0012335366,0.0014376506,0.33428714,0.051759124,0.027504994,0.048078734,0.5225528],"study_design_scores_gemma":[0.00008142105,0.0001942849,0.00074937125,0.000023037439,0.00007671417,0.000661186,0.00056121463,0.9505915,0.011404437,0.0277558,0.007831033,0.00006987583],"about_ca_topic_score_codex":0.007964812,"about_ca_topic_score_gemma":0.019373769,"teacher_disagreement_score":0.007964812,"about_ca_system_score_codex":0.0008993243,"about_ca_system_score_gemma":0.0016501381,"threshold_uncertainty_score":0.017511308},"labels":[],"label_agreement":null},{"id":"W4414149036","doi":"10.21203/rs.3.rs-7172530/v1","title":"Group Querying in Tridimensional Social Networks","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Data Management and Algorithms","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":"Université du Québec en Outaouais","funders":"","keywords":"Metric (unit); Similarity (geometry); Scalability; Group (periodic table); Process (computing); Meaning (existential); Empirical research","score_opus":0.07863004852963112,"score_gpt":0.39979602427041877,"score_spread":0.32116597574078765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414149036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13788436,0.0021564476,0.8451165,0.005368419,0.00023366789,0.00037460186,0.0030647898,0.002742892,0.003058219],"genre_scores_gemma":[0.6160014,0.0013698778,0.3707197,0.00070258137,0.00046145453,0.000395738,0.0058261794,0.00035394344,0.0041691726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98495275,0.005446536,0.0016449774,0.003250407,0.0037899248,0.0009153722],"domain_scores_gemma":[0.94103223,0.03724711,0.0040580463,0.01251881,0.0028126277,0.002331239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010918022,0.0010731192,0.0041304026,0.0060163005,0.0034863006,0.01128453,0.0044884873,0.0041974736,0.0043402878],"category_scores_gemma":[0.05395808,0.0013787334,0.0020265009,0.01209287,0.0026026133,0.024390707,0.007502964,0.0031981755,0.0013565161],"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":[0.0022747861,0.0013537233,0.03333493,0.0016296031,0.00077967695,0.00081338704,0.005282969,0.29110157,0.009125324,0.34501255,0.033344317,0.27594715],"study_design_scores_gemma":[0.00007031694,0.00007266011,0.00087482063,0.000051349143,0.00006146784,0.00022251363,0.0010432774,0.76782155,0.002108016,0.22442722,0.003215998,0.00003086073],"about_ca_topic_score_codex":0.0052271034,"about_ca_topic_score_gemma":0.004543167,"teacher_disagreement_score":0.01128453,"about_ca_system_score_codex":0.0024190857,"about_ca_system_score_gemma":0.0017433855,"threshold_uncertainty_score":0.05774069},"labels":[],"label_agreement":null},{"id":"W4414266787","doi":"10.14778/3750601.3750638","title":"SQL:Trek Automated Index Design at Airbnb","year":2025,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","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":"Air Canada","funders":"","keywords":"Index (typography); Scalability; Index selection; False positive paradox; Selection (genetic algorithm); Database index; Compiler; Relational database","score_opus":0.011183793592322706,"score_gpt":0.22989208488466692,"score_spread":0.2187082912923442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414266787","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046322364,0.001137004,0.38391688,0.00058698544,0.00036399218,0.00052466366,0.0049405643,0.5482848,0.0139226485],"genre_scores_gemma":[0.36195397,0.0006819779,0.54159665,0.00097522576,0.00019384103,0.0006050255,0.027363054,0.052173797,0.014456521],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940971,0.0010720184,0.00048511455,0.0011195025,0.0027132737,0.0005128666],"domain_scores_gemma":[0.9927532,0.0020706027,0.00039718527,0.0024915466,0.0019195465,0.00036792888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005140516,0.0017124043,0.0012883162,0.0013989854,0.0007947845,0.004414225,0.0040711467,0.0010332183,0.015791016],"category_scores_gemma":[0.0140424995,0.001362544,0.001225554,0.0015495443,0.0011439616,0.00534754,0.0026248854,0.0022679176,0.011149406],"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":[0.005073494,0.00091845234,0.011456672,0.0013882045,0.00039930674,0.00075364596,0.00085049385,0.051671475,0.09659583,0.02289318,0.33268306,0.47531617],"study_design_scores_gemma":[0.00083133334,0.00069378776,0.003599538,0.00011628235,0.00008495302,0.0005101602,0.00020340356,0.7817953,0.09665499,0.01593696,0.099366456,0.00020684328],"about_ca_topic_score_codex":0.0046679066,"about_ca_topic_score_gemma":0.004255686,"teacher_disagreement_score":0.015791016,"about_ca_system_score_codex":0.0016543919,"about_ca_system_score_gemma":0.003537254,"threshold_uncertainty_score":0.052826226},"labels":[],"label_agreement":null},{"id":"W4414360375","doi":"10.24963/ijcai.2025/999","title":"Bidirectional Search while Ensuring Meet-In-The-Middle via Effective and Efficient-to-Compute Termination Conditions","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Property (philosophy); Heuristic; Range (aeronautics); Inefficiency; Search algorithm; Quality (philosophy)","score_opus":0.015822225543947615,"score_gpt":0.26390766193258963,"score_spread":0.24808543638864203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414360375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018052429,0.00018986982,0.97428507,0.0003377431,0.000030897212,0.00015386968,0.000088223016,0.0010831719,0.005778702],"genre_scores_gemma":[0.26392782,0.00019423505,0.73116195,0.00029499334,0.00006466995,0.0006009835,0.00039223873,0.00062485033,0.0027382718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99596065,0.0016625674,0.00029041944,0.00043260487,0.0011163896,0.0005373675],"domain_scores_gemma":[0.98654836,0.009197736,0.0008560394,0.0016566768,0.0012696108,0.00047155187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005028037,0.00129265,0.0017234776,0.0014732665,0.0013483019,0.0026070175,0.002124854,0.002189201,0.006102569],"category_scores_gemma":[0.026814632,0.0007468189,0.0011006843,0.0014818707,0.001523013,0.0037903807,0.0043964963,0.0026736327,0.002820163],"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":[0.0011641586,0.0005244074,0.0030080734,0.0006937157,0.00010184953,0.0004173728,0.0008541083,0.39883867,0.0151109565,0.23858456,0.01129749,0.32940462],"study_design_scores_gemma":[0.00012043844,0.00018737347,0.00015302372,0.00009071779,0.000032570195,0.00014566687,0.00018184552,0.85533583,0.007816709,0.13078536,0.0051234467,0.000026992933],"about_ca_topic_score_codex":0.0019226795,"about_ca_topic_score_gemma":0.0027885523,"teacher_disagreement_score":0.006102569,"about_ca_system_score_codex":0.0008119453,"about_ca_system_score_gemma":0.004270257,"threshold_uncertainty_score":0.026591122},"labels":[],"label_agreement":null},{"id":"W4414638560","doi":"10.1007/978-981-96-9248-4_45","title":"Exploring Complex Fuzzy Sets for Enhanced Rank Data Generation Using Visualization Ranking Comparison","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Data Management and Algorithms","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Ranking (information retrieval); Rank (graph theory); Visualization; Fuzzy logic; Key (lock); Data visualization","score_opus":0.2603575309839385,"score_gpt":0.3457335903140132,"score_spread":0.0853760593300747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414638560","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032979313,0.00028956015,0.9606069,0.00017593036,0.000052456195,0.000089713096,0.00059259933,0.0022028228,0.003010702],"genre_scores_gemma":[0.24014537,0.00021044194,0.75629175,0.000048666257,0.00004036064,0.000098562945,0.0011136524,0.00027723343,0.0017740839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936026,0.00016452653,0.00004172521,0.00010936233,0.0002754665,0.00004877743],"domain_scores_gemma":[0.99823606,0.0010220219,0.0000912908,0.00018492232,0.0004124607,0.000053232492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013535854,0.00073377,0.0007432516,0.0026530644,0.0005196302,0.0021522227,0.0010836862,0.0005090136,0.0067124167],"category_scores_gemma":[0.0044650636,0.00031762474,0.0009227694,0.002500255,0.00029582676,0.0015715371,0.0009758747,0.0008011914,0.0009955387],"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":[0.00044603008,0.0002150959,0.0027750174,0.00044192048,0.00010820891,0.0002422386,0.0005993614,0.10713628,0.029580103,0.039017834,0.011436084,0.8080018],"study_design_scores_gemma":[0.000032271757,0.000104279985,0.00111935,0.00004473283,0.000036335052,0.00011100578,0.00022348527,0.9501306,0.012144556,0.029867793,0.006146825,0.000038730763],"about_ca_topic_score_codex":0.0026235427,"about_ca_topic_score_gemma":0.0029982433,"teacher_disagreement_score":0.0067124167,"about_ca_system_score_codex":0.00052813266,"about_ca_system_score_gemma":0.0006409129,"threshold_uncertainty_score":0.022455215},"labels":[],"label_agreement":null},{"id":"W4414874910","doi":"10.1007/s10115-025-02552-w","title":"LINDAS: a learned approach to index algorithm selection","year":2025,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Data Management and Algorithms","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":"York University","funders":"National Natural Science Foundation of China","keywords":"Search engine indexing; Index (typography); Selection (genetic algorithm); Hash function; Index selection; Range (aeronautics)","score_opus":0.012550522390299163,"score_gpt":0.2511002347204886,"score_spread":0.23854971233018943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414874910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017816199,0.00022593993,0.9875897,0.00028280416,0.00012492198,0.00010137833,0.00035544357,0.007880293,0.0016578018],"genre_scores_gemma":[0.0567,0.00027872648,0.9315127,0.00046542834,0.00028962796,0.00035894246,0.0015765207,0.0015895925,0.00722845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99390596,0.0020644378,0.0004585481,0.00081069645,0.0024495448,0.00031088258],"domain_scores_gemma":[0.9916781,0.0039876658,0.00032507494,0.0017877871,0.0018759116,0.00034542521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004811449,0.0014720255,0.0017403814,0.0037861518,0.0015787015,0.0039827013,0.0060766926,0.0024419308,0.01629964],"category_scores_gemma":[0.028140556,0.0011551275,0.0018109856,0.0030072036,0.0012211122,0.005321223,0.0045650713,0.004039236,0.007183031],"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":[0.00066339143,0.0004009618,0.0026738117,0.00037211858,0.00035008817,0.00017037935,0.00021712855,0.060399126,0.0030520954,0.044137973,0.047823884,0.83973914],"study_design_scores_gemma":[0.000115525385,0.00010476259,0.00026002436,0.00006237984,0.0000717003,0.00016254616,0.00005573384,0.90885067,0.0040465286,0.06895257,0.01727892,0.000038755767],"about_ca_topic_score_codex":0.0034688425,"about_ca_topic_score_gemma":0.00963736,"teacher_disagreement_score":0.01629964,"about_ca_system_score_codex":0.0014749538,"about_ca_system_score_gemma":0.0034539865,"threshold_uncertainty_score":0.0545277},"labels":[],"label_agreement":null},{"id":"W4414898614","doi":"10.1109/cluster59342.2025.11186497","title":"Cascade: a Collaborative Algorithm for Scalable and Efficient Neighborhood Allgather","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Alliance de recherche numérique du Canada","keywords":"Speedup; Scalability; Cascade; Multiplication (music); Sparse matrix; Feature (linguistics); Message passing","score_opus":0.006544639729131289,"score_gpt":0.24746304096440166,"score_spread":0.24091840123527036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414898614","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024135258,0.00025582223,0.9652815,0.0002432266,0.00012224476,0.00021238993,0.000110746696,0.004643168,0.0049957167],"genre_scores_gemma":[0.2622078,0.00019811564,0.72683203,0.00019715384,0.00008620007,0.0006492277,0.0005960939,0.00058670685,0.008646662],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992417,0.00012231665,0.00004084813,0.00016742798,0.0003259234,0.00010179649],"domain_scores_gemma":[0.99900514,0.0002962758,0.000085648,0.00029824153,0.0002109157,0.00010378888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007743421,0.00088783586,0.0011589554,0.0009471076,0.0018584012,0.0009935086,0.0032225025,0.0012082349,0.0053328807],"category_scores_gemma":[0.0027910674,0.0005263185,0.00081445504,0.00096656545,0.00079876115,0.0022124883,0.0027699736,0.00095814065,0.0013627711],"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":[0.0005800542,0.00032322307,0.0030371512,0.00025230006,0.00016171629,0.00037510376,0.00067628676,0.42556694,0.021174464,0.060221728,0.03571091,0.45192015],"study_design_scores_gemma":[0.00006321641,0.000077007506,0.0001512922,0.000006853871,0.000016013531,0.00006238739,0.000054750843,0.9788052,0.003871897,0.010949418,0.005923796,0.000018272145],"about_ca_topic_score_codex":0.007515118,"about_ca_topic_score_gemma":0.012374928,"teacher_disagreement_score":0.007515118,"about_ca_system_score_codex":0.0009869848,"about_ca_system_score_gemma":0.0017755724,"threshold_uncertainty_score":0.017840266},"labels":[],"label_agreement":null},{"id":"W4415048008","doi":"10.1109/tits.2025.3615448","title":"RouteKG: A Knowledge Graph-Based Framework for Route Prediction on Road Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation 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":"McGill University","funders":"University Research Committee, University of Hong Kong; National Natural Science Foundation of China","keywords":"Intelligent transportation system; ENCODE; Construct (python library); Variety (cybernetics); Graph; Baseline (sea); Focus (optics); Traffic congestion; Trajectory; Flow network","score_opus":0.026743408871309295,"score_gpt":0.28493064231384835,"score_spread":0.2581872334425391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415048008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041477233,0.00034860813,0.9863507,0.0001781141,0.00005049069,0.00007575468,0.0028080177,0.005072192,0.00096851285],"genre_scores_gemma":[0.221972,0.0011119526,0.7551809,0.00022390741,0.00012591238,0.000420104,0.017075121,0.00077708426,0.0031130477],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947935,0.000085925145,0.000026391786,0.0002341866,0.00012200447,0.000052148454],"domain_scores_gemma":[0.9992661,0.00028683682,0.00008704405,0.00013451067,0.00016812168,0.000057411802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005997773,0.0017135957,0.0015029623,0.00288004,0.0006235704,0.001559852,0.0035014118,0.001576483,0.0036075003],"category_scores_gemma":[0.0026896158,0.0008577511,0.0020603538,0.0028270576,0.0005410768,0.0022981644,0.0013771455,0.0022977719,0.0015988065],"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":[0.000054896318,0.00007070587,0.0014553583,0.0001411527,0.000105093444,0.00011351959,0.00006209295,0.8861386,0.00080934167,0.010653425,0.009225171,0.09117066],"study_design_scores_gemma":[0.0000049294486,0.000008383484,0.0001270758,0.000007849954,0.00000932045,0.000015084385,0.000009439477,0.992273,0.00018623612,0.005982428,0.0013691974,0.0000071393983],"about_ca_topic_score_codex":0.0663289,"about_ca_topic_score_gemma":0.085096896,"teacher_disagreement_score":0.0663289,"about_ca_system_score_codex":0.0015870951,"about_ca_system_score_gemma":0.0022016347,"threshold_uncertainty_score":0.13188559},"labels":[],"label_agreement":null},{"id":"W4415147752","doi":"10.1145/3748777.3748782","title":"The Batch Insertion Operator for Shared Mobility Route Planning on Time-Dependent Road Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","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":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Route planning; Operator (biology); Routing (electronic design automation); Key (lock); Sequence (biology)","score_opus":0.013986780086820957,"score_gpt":0.2688720991100636,"score_spread":0.25488531902324263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415147752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023409357,0.00009703068,0.97384834,0.00011291364,0.00007976276,0.00009522911,0.00014344335,0.0011065818,0.001107251],"genre_scores_gemma":[0.42941505,0.00013042855,0.56498015,0.00009633694,0.00008622224,0.00020427114,0.000459682,0.00025596822,0.0043719313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991554,0.0002013149,0.000055478507,0.0002021323,0.00021124861,0.0001742998],"domain_scores_gemma":[0.9974807,0.0014465231,0.00013965783,0.00039862972,0.00032708244,0.00020747368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023943947,0.00093808793,0.0017852462,0.0006824928,0.0009086195,0.001269954,0.0033071234,0.001094387,0.0047625033],"category_scores_gemma":[0.004547765,0.00071191427,0.000807197,0.0012531809,0.0010986396,0.0025475589,0.0018448479,0.0017935649,0.0006096047],"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":[0.0014554729,0.00024206025,0.0012803827,0.00013667838,0.00008210418,0.00017809638,0.000227346,0.7090404,0.006798055,0.03723549,0.008927152,0.23439677],"study_design_scores_gemma":[0.000019728435,0.00005713857,0.000059757884,0.000002478826,0.0000071613404,0.000010872594,0.000016435535,0.9933483,0.0007450371,0.005362617,0.00036348714,0.000006964827],"about_ca_topic_score_codex":0.020142114,"about_ca_topic_score_gemma":0.021640189,"teacher_disagreement_score":0.020142114,"about_ca_system_score_codex":0.0012140301,"about_ca_system_score_gemma":0.0035103224,"threshold_uncertainty_score":0.04004973},"labels":[],"label_agreement":null},{"id":"W4415180125","doi":"10.23919/ecc65951.2025.11187259","title":"Robust Shortest Path with Incremental Information Revelation","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Shortest path problem; Pruning; Partially observable Markov decision process; Markov decision process; Graph; Path (computing); Dynamic programming; Markov process; State space","score_opus":0.011217913153176613,"score_gpt":0.19917325262242164,"score_spread":0.18795533946924503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415180125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018298825,0.00022185288,0.97944325,0.000214544,0.000020092524,0.000049091366,0.00008527226,0.00032153132,0.001345573],"genre_scores_gemma":[0.65613407,0.00021316293,0.3403648,0.00013376914,0.00004185567,0.0002198831,0.00035412787,0.00013626083,0.0024020614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99869245,0.00043549217,0.00005587557,0.0003469592,0.00032541325,0.00014391841],"domain_scores_gemma":[0.99634355,0.002477336,0.00046200442,0.00029790626,0.00030986508,0.000109326764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018759784,0.0009025042,0.0015300486,0.0010738279,0.000588503,0.0010455402,0.0021075227,0.0014376512,0.0020297735],"category_scores_gemma":[0.008693129,0.00072463654,0.00081709266,0.0013418007,0.0012567599,0.0031877777,0.0020321393,0.0016297526,0.0002867518],"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":[0.00008722218,0.000032606415,0.00028398962,0.00006004127,0.000035923196,0.00006271872,0.000058679514,0.95791465,0.00065100327,0.017666701,0.0006104771,0.022536006],"study_design_scores_gemma":[0.000008258647,0.000022452861,0.00003778912,0.000003413818,0.0000049405157,0.000013117864,0.000007488746,0.98746747,0.00027480337,0.011893859,0.00026203645,0.0000045017296],"about_ca_topic_score_codex":0.0054049776,"about_ca_topic_score_gemma":0.0045104586,"teacher_disagreement_score":0.0054049776,"about_ca_system_score_codex":0.0014644084,"about_ca_system_score_gemma":0.001942376,"threshold_uncertainty_score":0.0107470155},"labels":[],"label_agreement":null},{"id":"W4415250303","doi":"10.1007/978-3-032-00385-0_19","title":"Variable Neighborhood Search","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Variable neighborhood search; Metaheuristic; Local search (optimization); Variable (mathematics); Mathematical proof; Linear programming; Graph; Nonlinear programming","score_opus":0.016921106003505958,"score_gpt":0.22826170864385356,"score_spread":0.2113406026403476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415250303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00413635,0.011051759,0.6965317,0.000862346,0.0011458233,0.00009979343,0.0006404476,0.0018014095,0.28373036],"genre_scores_gemma":[0.076956816,0.008029599,0.42711955,0.0007124931,0.00054232316,0.0002015386,0.00269716,0.0013925208,0.4823479],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997242,0.00004338035,0.000008601176,0.00006578126,0.00013467646,0.000023365705],"domain_scores_gemma":[0.99981755,0.000060428727,0.000008472446,0.0000492136,0.000052039395,0.000012284899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026533756,0.00061035954,0.0008403787,0.0008413811,0.00060332223,0.0011707982,0.0010900736,0.0006740052,0.032513928],"category_scores_gemma":[0.0010803605,0.00030757295,0.00041031506,0.0020289188,0.00051827333,0.0013185511,0.0009658575,0.0011879202,0.011667881],"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":[0.000048189966,0.00004458216,0.00018303996,0.00016146043,0.000028390652,0.000037884012,0.00005043863,0.02638912,0.0015981627,0.1640102,0.14186156,0.66558695],"study_design_scores_gemma":[0.000025185189,0.000078648765,0.00055974803,0.00014957842,0.00003962247,0.0003537376,0.0000692751,0.20472503,0.0038423713,0.22664551,0.5634767,0.00003463258],"about_ca_topic_score_codex":0.0023322916,"about_ca_topic_score_gemma":0.004716907,"teacher_disagreement_score":0.032513928,"about_ca_system_score_codex":0.00058310066,"about_ca_system_score_gemma":0.0006308763,"threshold_uncertainty_score":0.10876989},"labels":[],"label_agreement":null},{"id":"W4415303479","doi":"10.1002/pra2.1451","title":"A <scp>GenAI</scp> Approach to Suggesting Broad and Narrow Queries for Exploratory Search","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Association for Information Science and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Exploratory search; Timeline; Web search query; Semantic search; Interface (matter); Filter (signal processing); Search engine; Search engine indexing; Control (management); Relevance (law)","score_opus":0.012309618062336146,"score_gpt":0.24383383150836554,"score_spread":0.2315242134460294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415303479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012452887,0.00036678347,0.9564667,0.00367541,0.000039030343,0.00035861833,0.0002141514,0.004232756,0.022193708],"genre_scores_gemma":[0.24052097,0.00030942183,0.74935037,0.0010363498,0.000056904297,0.00030127433,0.0004846682,0.0006094449,0.0073306113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913208,0.0054145074,0.00038541036,0.00081405113,0.0017135665,0.0003517291],"domain_scores_gemma":[0.9698299,0.020384638,0.0008999306,0.0056336657,0.0022976347,0.0009541081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009336478,0.0009105656,0.00083406956,0.003448286,0.0018066921,0.005828521,0.0033346084,0.0023995726,0.0075360984],"category_scores_gemma":[0.026949367,0.0012227381,0.0017846624,0.0032020542,0.009527897,0.008075509,0.0063434197,0.004201671,0.002235846],"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":[0.00057212566,0.0003472309,0.004620148,0.0006365625,0.00009738197,0.0009070374,0.017413618,0.02384753,0.01807789,0.6763593,0.020631194,0.23648989],"study_design_scores_gemma":[0.00021048392,0.0002295008,0.0016717165,0.00035305068,0.00015947774,0.0011344544,0.003980248,0.44959012,0.013254195,0.43335533,0.09585985,0.0002015533],"about_ca_topic_score_codex":0.010811022,"about_ca_topic_score_gemma":0.016279712,"teacher_disagreement_score":0.010811022,"about_ca_system_score_codex":0.002552761,"about_ca_system_score_gemma":0.003462382,"threshold_uncertainty_score":0.049376547},"labels":[],"label_agreement":null},{"id":"W4415439859","doi":"10.1177/23998083251387986","title":"Route network simplification for transport planning","year":2025,"lang":"en","type":"article","venue":"Environment and Planning B Urban Analytics and City Science","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":"Esri (Canada)","funders":"Economic and Social Research Council","keywords":"Visualization; Transport network; Representation (politics); Data visualization; Voronoi diagram; Graph drawing; Resource (disambiguation); Flow network","score_opus":0.024271326318475516,"score_gpt":0.2510681349085155,"score_spread":0.22679680859003998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415439859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040842765,0.00035970073,0.9728909,0.0005058554,0.00015099584,0.00013501094,0.005193642,0.0073699383,0.009309764],"genre_scores_gemma":[0.08196407,0.0009837827,0.8884229,0.00022882204,0.00010985098,0.00047280666,0.017225858,0.0045559853,0.0060359067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987018,0.00033538003,0.000087415545,0.00027752694,0.00048907666,0.00010880015],"domain_scores_gemma":[0.9974849,0.0012553864,0.00019850562,0.00058296317,0.00038456006,0.00009374935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012705547,0.0014799646,0.0011362231,0.0023156693,0.0010493941,0.0034529378,0.0022499347,0.0012241366,0.02590529],"category_scores_gemma":[0.011023927,0.0008478925,0.0022890803,0.0025293722,0.000679228,0.0030477392,0.0031091208,0.0026666932,0.006525806],"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":[0.000089384695,0.000056870907,0.0023374334,0.0007462295,0.00016277084,0.00026670215,0.0004161455,0.66739523,0.0029068962,0.103672944,0.05112727,0.17082216],"study_design_scores_gemma":[0.000017709954,0.000013817581,0.0005290196,0.00014203644,0.000029333696,0.00014811219,0.00012237245,0.84552836,0.0021279303,0.07543911,0.075868055,0.00003405093],"about_ca_topic_score_codex":0.017507786,"about_ca_topic_score_gemma":0.024487277,"teacher_disagreement_score":0.02590529,"about_ca_system_score_codex":0.0018142996,"about_ca_system_score_gemma":0.0026847967,"threshold_uncertainty_score":0.086661816},"labels":[],"label_agreement":null},{"id":"W4416039378","doi":"10.1007/978-3-032-06161-4_26","title":"Evaluating the Accuracy of Random User Trajectories in Cluster-Based Continuous Query Processing","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Data Management and Algorithms","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 Lethbridge","funders":"","keywords":"Focus (optics); Path (computing); Trajectory; Nearest neighbour; k-nearest neighbors algorithm; Online aggregation","score_opus":0.05138139314045795,"score_gpt":0.31106526572596693,"score_spread":0.25968387258550896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416039378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7508783,0.0030553415,0.24150395,0.00049639854,0.00016732488,0.00012257147,0.0006972409,0.0016304962,0.0014484187],"genre_scores_gemma":[0.9663115,0.00035130046,0.031834904,0.000040020932,0.000066809625,0.000025705453,0.0007960931,0.00005082574,0.0005228465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966642,0.0009770193,0.00025994837,0.0006140782,0.0011739816,0.00031066264],"domain_scores_gemma":[0.97808516,0.017816754,0.0006775452,0.0013647128,0.0016111407,0.00044471317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043531135,0.00088115135,0.0017651039,0.0015827745,0.00074726634,0.0016173836,0.002172757,0.002378453,0.00072748103],"category_scores_gemma":[0.024711177,0.00041221044,0.00049820193,0.0025828455,0.00082821643,0.0021114494,0.0013418895,0.0009224737,0.00030626732],"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":[0.0031208643,0.00028082126,0.015877543,0.00015920027,0.00020788932,0.00009713599,0.00013583736,0.86762726,0.0044542984,0.0013301255,0.0017526398,0.10495634],"study_design_scores_gemma":[0.000008842133,0.000083299004,0.00094966067,0.0000026655298,0.000009887733,0.000024267276,0.000028294231,0.99781007,0.00069308287,0.000341592,0.000043504493,0.0000048582156],"about_ca_topic_score_codex":0.026976796,"about_ca_topic_score_gemma":0.013726593,"teacher_disagreement_score":0.026976796,"about_ca_system_score_codex":0.0014130083,"about_ca_system_score_gemma":0.0014420789,"threshold_uncertainty_score":0.05363953},"labels":[],"label_agreement":null},{"id":"W4416371852","doi":"10.2316/j.2026.206-1247","title":"GRASPING TASK PLANNING ALGORITHM FOR DEXTEROUS HAND BASED ON SCENE UNDERSTANDING AND SEMANTIC INFORMATION. 295-306","year":2025,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Task (project management); Feature (linguistics); Key (lock); Complete information; Semantics (computer science)","score_opus":0.019458827815113863,"score_gpt":0.27649595280646144,"score_spread":0.25703712499134757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416371852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018627483,0.00027938583,0.97478753,0.000076687844,0.000026994312,0.00009880552,0.00023075339,0.0026704003,0.0032019035],"genre_scores_gemma":[0.33121324,0.00041884056,0.6589845,0.00007422725,0.000020236728,0.00023133322,0.0013199146,0.00027931842,0.007458446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999833,0.0000116037445,0.000014199279,0.000068908405,0.000046462723,0.00002582486],"domain_scores_gemma":[0.99991643,0.000020420448,0.000007738667,0.000014992857,0.000029037818,0.000011297039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017632365,0.00072424405,0.00074633333,0.000894132,0.0005551552,0.00063857855,0.00094069197,0.0007662205,0.0069550793],"category_scores_gemma":[0.0003445635,0.00050563534,0.0008728049,0.00053493754,0.00027218004,0.0007506544,0.00058046146,0.0005951126,0.0016353694],"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":[0.00039102827,0.00020771354,0.0016607586,0.00031440717,0.0000883656,0.0003924142,0.00018496533,0.16624682,0.08537677,0.0063731363,0.008051376,0.73071235],"study_design_scores_gemma":[0.000052379975,0.000104886756,0.0016032407,0.000026863238,0.000051513925,0.00022683796,0.00007175707,0.9639576,0.022979017,0.00725969,0.0036393548,0.000026899052],"about_ca_topic_score_codex":0.013012808,"about_ca_topic_score_gemma":0.01380658,"teacher_disagreement_score":0.013012808,"about_ca_system_score_codex":0.0004932781,"about_ca_system_score_gemma":0.0014867405,"threshold_uncertainty_score":0.025874078},"labels":[],"label_agreement":null},{"id":"W4416956238","doi":"10.1145/3769102.3774388","title":"Poster: Leveraging Geo-Spatiality in Geo-Distributed Vector Databases","year":2025,"lang":"","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Embedding; Replication (statistics); k-nearest neighbors algorithm; Routing (electronic design automation); Throughput; Web query classification; Query expansion; Nearest neighbor search","score_opus":0.035788439689870825,"score_gpt":0.28698101447256547,"score_spread":0.25119257478269463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416956238","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066419095,0.004605306,0.9005847,0.003452942,0.0012143974,0.00025654628,0.0020860517,0.010820524,0.010560416],"genre_scores_gemma":[0.6432356,0.0019914177,0.332355,0.00073900056,0.0010585603,0.00012212705,0.0061699804,0.00050932396,0.013819007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989279,0.0002976062,0.00008765466,0.00027764164,0.00031882356,0.00009044341],"domain_scores_gemma":[0.9977367,0.00048156412,0.00007909254,0.0009685498,0.000605808,0.00012834693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001887465,0.00081694906,0.00092077494,0.001147489,0.00061328337,0.0017669651,0.001332944,0.0007608515,0.0059598368],"category_scores_gemma":[0.004542995,0.00027926313,0.00046984924,0.0020211039,0.0005000086,0.003923686,0.0018957983,0.0011038659,0.004064465],"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":[0.00089827226,0.00035176074,0.005437448,0.00022060603,0.00018562439,0.00019965628,0.0002638207,0.070624806,0.0179052,0.011846907,0.08973454,0.80233127],"study_design_scores_gemma":[0.00009052902,0.0002893679,0.002012021,0.000028018198,0.00006561664,0.00045124305,0.00025681182,0.9260511,0.013250976,0.024311222,0.033132903,0.000060180944],"about_ca_topic_score_codex":0.0041704304,"about_ca_topic_score_gemma":0.0049210554,"teacher_disagreement_score":0.0059598368,"about_ca_system_score_codex":0.00047864893,"about_ca_system_score_gemma":0.00069427106,"threshold_uncertainty_score":0.019937634},"labels":[],"label_agreement":null},{"id":"W4417496854","doi":"10.1145/3785662","title":"Semi-Oblivious Chase Termination for Linear Existential Rules and Beyond: An Experimental Analysis","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Existentialism; Chase; Set (abstract data type); Generalization; Linearization; Class (philosophy); Focus (optics)","score_opus":0.023561886560447394,"score_gpt":0.30921964395633783,"score_spread":0.28565775739589044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417496854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7630474,0.003720193,0.17480701,0.0019415416,0.0005833557,0.0011677969,0.0028749479,0.0076343957,0.044223417],"genre_scores_gemma":[0.8824702,0.0008589999,0.10427738,0.00050121074,0.0001377792,0.0007728923,0.0034994523,0.0013819742,0.0061000227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99028695,0.002798481,0.00069724466,0.0015013992,0.0034896361,0.0012262996],"domain_scores_gemma":[0.9106302,0.06457847,0.0026589553,0.015800431,0.0050548604,0.0012770223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007777838,0.0010739425,0.0014745754,0.000977125,0.0015424136,0.0024966886,0.0030834663,0.0020103345,0.012126594],"category_scores_gemma":[0.05304022,0.0005569426,0.0011919258,0.0014902991,0.0022667074,0.0059846262,0.0026697982,0.0047636745,0.0025403048],"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":[0.010777949,0.012893413,0.017913822,0.004457474,0.00078371423,0.0014008033,0.002700428,0.27429795,0.09989347,0.20839877,0.05543042,0.31105182],"study_design_scores_gemma":[0.0007167508,0.0019419752,0.0023630064,0.00019447766,0.00016341277,0.00045326384,0.0006705613,0.8380319,0.053997185,0.0893058,0.012053585,0.000108084474],"about_ca_topic_score_codex":0.0020856436,"about_ca_topic_score_gemma":0.0022089395,"teacher_disagreement_score":0.012126594,"about_ca_system_score_codex":0.0020013133,"about_ca_system_score_gemma":0.0026971276,"threshold_uncertainty_score":0.041133642},"labels":[],"label_agreement":null},{"id":"W49657471","doi":"10.1609/aiide.v6i1.12400","title":"A Comparison of High-Level Approaches for Speeding Up Pathfinding","year":2010,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Heuristics; Abstraction; Waypoint; Graph; Theoretical computer science; Variety (cybernetics); Artificial intelligence; Shortest path problem","score_opus":0.16633179665656853,"score_gpt":0.3307709812536276,"score_spread":0.16443918459705906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W49657471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19169803,0.010288179,0.72361475,0.001001548,0.0004778253,0.0010219434,0.0013398655,0.013827388,0.05673046],"genre_scores_gemma":[0.35818833,0.004672971,0.62791973,0.00021104875,0.000042213815,0.00032802997,0.002117753,0.00073439424,0.0057854247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759966,0.00055112905,0.00017745269,0.00025968615,0.0010542343,0.00035789862],"domain_scores_gemma":[0.9946392,0.0030851471,0.0001685988,0.0010035752,0.0008239641,0.0002795313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018271854,0.0013200081,0.0010571848,0.0019702476,0.0008118942,0.0017888467,0.0026739505,0.001611355,0.0076849996],"category_scores_gemma":[0.006738785,0.00067963207,0.0014019147,0.0021037303,0.0006137339,0.003538957,0.0019171004,0.0020731813,0.0016106954],"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":[0.0017329991,0.0005407159,0.0023842833,0.0021387266,0.0003556014,0.00009648338,0.00061858667,0.23960184,0.007279667,0.02392466,0.009458656,0.71186787],"study_design_scores_gemma":[0.000531996,0.0017294287,0.0068246713,0.0003248456,0.0004022912,0.0003305427,0.0009917615,0.91267616,0.013230552,0.020343905,0.042462744,0.00015116725],"about_ca_topic_score_codex":0.014712313,"about_ca_topic_score_gemma":0.021068776,"teacher_disagreement_score":0.014712313,"about_ca_system_score_codex":0.002265015,"about_ca_system_score_gemma":0.0028070617,"threshold_uncertainty_score":0.029253364},"labels":[],"label_agreement":null},{"id":"W5261904","doi":"10.1016/0030-4220(70)90406-8","title":"Recent developments in the von Mises transformation and its applications in the computational sciences","year":2009,"lang":"en","type":"article","venue":"International Conference on Mathematical methods, Computational techniques and Intelligent systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"von Mises yield criterion; Computer science; Transformation (genetics); Applied mathematics; Jacobian matrix and determinant; Mathematical economics; Mathematics; Engineering; Finite element method; Structural engineering","score_opus":0.14571765047391075,"score_gpt":0.4207321048796926,"score_spread":0.27501445440578187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5261904","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014474417,0.08356506,0.8525976,0.0050935345,0.0015957484,0.000082048246,0.00016751527,0.00030392368,0.042120233],"genre_scores_gemma":[0.2774935,0.16769114,0.5317257,0.0009419024,0.0083335,0.0002731368,0.00042179128,0.00045630615,0.012663094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984194,0.00056977285,0.0001302651,0.00021786195,0.00060423836,0.000058496946],"domain_scores_gemma":[0.992042,0.005266884,0.000420554,0.00065263396,0.0014161828,0.0002017183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024811092,0.0013506867,0.0010595457,0.0025165994,0.00073683605,0.0021926058,0.0010846944,0.0010214769,0.0035571472],"category_scores_gemma":[0.010044467,0.00051045883,0.0009420118,0.004040652,0.002775944,0.004147748,0.0017869431,0.0032885864,0.0013684148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029319464,0.00002698157,0.0006498461,0.000595397,0.00004118328,0.00012910743,0.00023156838,0.03046839,0.0019312417,0.8706239,0.0027878725,0.092485294],"study_design_scores_gemma":[0.0000084409,0.00004862812,0.0007267808,0.00016492228,0.000027946277,0.0003193092,0.00010856956,0.15875745,0.0026691651,0.7592604,0.077841155,0.000067281544],"about_ca_topic_score_codex":0.001272884,"about_ca_topic_score_gemma":0.00088496256,"teacher_disagreement_score":0.0035571472,"about_ca_system_score_codex":0.0010761382,"about_ca_system_score_gemma":0.0011203234,"threshold_uncertainty_score":0.013121486},"labels":[],"label_agreement":null},{"id":"W571841459","doi":"","title":"Innover dans la collecte de données transport pour des données actuelles et de meilleure qualité","year":2014,"lang":"fr","type":"article","venue":"Transportation 2014: Past, Present, Future - 2014 Conference and Exhibition of the Transportation Association of Canada // Transport 2014 : Du passé vers l'avenir - 2014 Congrès et Exposition de 'Association des transports du Canada","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Art","score_opus":0.011495645274408113,"score_gpt":0.2132189711707632,"score_spread":0.20172332589635508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W571841459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13135317,0.008422151,0.66363347,0.055063512,0.0012169031,0.0054437164,0.015167514,0.010470097,0.10922943],"genre_scores_gemma":[0.29868868,0.006123273,0.59671617,0.004223694,0.0004840412,0.0022036696,0.016751591,0.0018147061,0.07299425],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9604341,0.014088756,0.0034777115,0.0061799926,0.013987545,0.0018319087],"domain_scores_gemma":[0.88234866,0.035848435,0.0074029737,0.023594558,0.04610922,0.0046961117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051920082,0.0016165621,0.0013848817,0.006777521,0.0055763945,0.016301416,0.0043618665,0.0024415546,0.014070916],"category_scores_gemma":[0.09119394,0.0016252642,0.0024208217,0.0077048396,0.0041399538,0.013200902,0.009886803,0.00370521,0.0073963557],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046764335,0.00056430354,0.10538165,0.005412116,0.00094412337,0.0011876014,0.05468587,0.0074887727,0.023312574,0.07056735,0.060954504,0.6690336],"study_design_scores_gemma":[0.000086303575,0.0004028343,0.07492982,0.0027207306,0.00036231565,0.00084947667,0.031812806,0.012770843,0.011623566,0.01944613,0.84457755,0.0004176189],"about_ca_topic_score_codex":0.22484675,"about_ca_topic_score_gemma":0.24992087,"teacher_disagreement_score":0.7751533,"about_ca_system_score_codex":0.012564452,"about_ca_system_score_gemma":0.04761337,"threshold_uncertainty_score":0.44707602},"labels":[],"label_agreement":null},{"id":"W58252060","doi":"10.1007/978-3-642-24873-3_8","title":"Efficiently Eliciting Preferences from a Group of Users","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Group (periodic table); Human–computer interaction","score_opus":0.027811000839785306,"score_gpt":0.2309118307609883,"score_spread":0.203100829921203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W58252060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3239814,0.00024821516,0.6662787,0.0006643798,0.00003869006,0.0005184222,0.0007672818,0.0009837906,0.0065191435],"genre_scores_gemma":[0.750466,0.00015963771,0.24377525,0.00019872881,0.00005751185,0.00062533544,0.0015480014,0.00009199706,0.0030775098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930976,0.00433587,0.00028285693,0.0005668456,0.0012682524,0.00044855993],"domain_scores_gemma":[0.98577255,0.010994096,0.000481805,0.0013594702,0.0010158189,0.00037626387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005334406,0.0010181254,0.0016023185,0.00077576307,0.0005011129,0.0017079031,0.0011085607,0.0012882653,0.0034572869],"category_scores_gemma":[0.016564414,0.00045577274,0.0010353773,0.001342365,0.0003656379,0.0024863132,0.0021488236,0.0012468154,0.0016080047],"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":[0.008938744,0.001970122,0.0263455,0.00094799284,0.00057183247,0.0005088365,0.0021430147,0.086834304,0.091126464,0.016598014,0.011439464,0.75257576],"study_design_scores_gemma":[0.00056542654,0.0020804005,0.009100384,0.00007247557,0.00023782597,0.00047369613,0.0026481606,0.87924755,0.03946944,0.059999492,0.0059833964,0.00012176725],"about_ca_topic_score_codex":0.0011866762,"about_ca_topic_score_gemma":0.0020624588,"teacher_disagreement_score":0.005334406,"about_ca_system_score_codex":0.00048166834,"about_ca_system_score_gemma":0.0011237843,"threshold_uncertainty_score":0.028211355},"labels":[],"label_agreement":null},{"id":"W585824775","doi":"","title":"Geometric ordering of concepts, logical disjunction, and learning by induction","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mereology; Computer science; Distributive property; Theoretical computer science; Convexity; Distributive lattice; Representation (politics); Boolean algebra; Closure (psychology); Artificial intelligence; Algorithm; Mathematics; Pure mathematics","score_opus":0.10093473530874908,"score_gpt":0.32903458272633357,"score_spread":0.2280998474175845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585824775","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03444896,0.0014199323,0.93487215,0.002548596,0.0001499977,0.000083257466,0.00021674139,0.00036748315,0.025892815],"genre_scores_gemma":[0.5928354,0.0016458762,0.39468458,0.00083437626,0.00045462436,0.00029584888,0.0007623435,0.00018760444,0.008299396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99691147,0.0010300766,0.00019219192,0.00078654644,0.00088500994,0.00019471314],"domain_scores_gemma":[0.9943341,0.003930067,0.0004014318,0.0006533578,0.0004886914,0.00019226884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003343754,0.000555283,0.0007181991,0.0013846542,0.0012198375,0.0022590023,0.0015400802,0.00070614985,0.0057018264],"category_scores_gemma":[0.00985588,0.00048899685,0.0014925381,0.0019674262,0.00785558,0.009161996,0.0037516174,0.0030595833,0.0006568281],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000432721,0.000029473567,0.0004604145,0.00011321089,0.000015860634,0.00006833245,0.00031785408,0.0060671237,0.00050919666,0.93927133,0.0014490247,0.051654957],"study_design_scores_gemma":[0.000009802054,0.000013293507,0.00010207568,0.000010628448,0.0000048028173,0.00003903842,0.000033842785,0.014467923,0.0004017229,0.98246163,0.0024481511,0.0000070966007],"about_ca_topic_score_codex":0.0026001278,"about_ca_topic_score_gemma":0.002805543,"teacher_disagreement_score":0.0057018264,"about_ca_system_score_codex":0.0022820255,"about_ca_system_score_gemma":0.0013497119,"threshold_uncertainty_score":0.01907456},"labels":[],"label_agreement":null},{"id":"W62729806","doi":"10.1007/978-3-642-38457-8_17","title":"Construction of Privacy Preserving Hypertree Agent Organization as Distributed Maximum Spanning Tree","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Soundness; Computer science; Hyperbolic tree; Probabilistic logic; Spanning tree; Tree (set theory); Theoretical computer science; Constraint (computer-aided design); Multi-agent system; Distributed computing; Artificial intelligence; Mathematics; Programming language; Discrete mathematics","score_opus":0.015507017129936787,"score_gpt":0.22268750221011288,"score_spread":0.20718048508017609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W62729806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05951766,0.00020528177,0.93362206,0.00029936078,0.000057145557,0.00012880677,0.00020227945,0.0005394108,0.0054281033],"genre_scores_gemma":[0.5610709,0.00035389094,0.43304878,0.00009978207,0.000034834324,0.00018629411,0.00062192447,0.00009584525,0.0044878046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948835,0.00013440351,0.000031123927,0.00011818297,0.00015861618,0.00006932225],"domain_scores_gemma":[0.99938035,0.00011893131,0.000062381165,0.00022571457,0.00013248969,0.0000800599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004493482,0.00024319944,0.0006260575,0.0005335082,0.0010341918,0.001168827,0.0011546253,0.00073876785,0.0016107698],"category_scores_gemma":[0.001288296,0.0003093524,0.0005229107,0.001281039,0.0005082597,0.001580365,0.0018519511,0.000737604,0.00044584463],"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":[0.00038371576,0.00030007013,0.0038216533,0.00041246586,0.0001265691,0.0009837114,0.0009739302,0.28856528,0.05060642,0.32992098,0.014608145,0.30929708],"study_design_scores_gemma":[0.000035002497,0.00011651187,0.00063156243,0.000028641452,0.00005007946,0.00052866887,0.0002775322,0.85747844,0.009945732,0.117905155,0.012978765,0.000024009396],"about_ca_topic_score_codex":0.000631146,"about_ca_topic_score_gemma":0.0008170654,"teacher_disagreement_score":0.0016107698,"about_ca_system_score_codex":0.000451036,"about_ca_system_score_gemma":0.0010296048,"threshold_uncertainty_score":0.0053884983},"labels":[],"label_agreement":null},{"id":"W62987670","doi":"10.1007/978-3-642-41218-9_5","title":"Some New Progress in Analyzing and Mining Uncertain and Probabilistic Data for Big Data Analytics","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Big data; Probabilistic logic; Computer science; Data science; Analytics; Probabilistic database; Data analysis; Uncertain data; Data mining; Artificial intelligence","score_opus":0.10132424590722992,"score_gpt":0.29906607045976674,"score_spread":0.1977418245525368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W62987670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004872307,0.36630142,0.5773334,0.015907904,0.0042159003,0.00008510555,0.00086014735,0.001020793,0.029403057],"genre_scores_gemma":[0.061397854,0.35158357,0.54147214,0.0038597086,0.013908805,0.00013067864,0.0019467745,0.0006292313,0.025071243],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99793434,0.00030955995,0.00018687031,0.0004390833,0.0010572531,0.0000729089],"domain_scores_gemma":[0.9899601,0.007084218,0.00020597423,0.0011044985,0.0014551765,0.00019000932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005407677,0.001212783,0.0012322624,0.0031958323,0.0007881367,0.005471545,0.0025683346,0.0014441224,0.004879345],"category_scores_gemma":[0.010978205,0.0009933771,0.0016039205,0.008498264,0.003251108,0.015910879,0.002279249,0.005502454,0.0021137865],"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":[0.00009105279,0.00012969268,0.0016290775,0.0024683168,0.00014340853,0.00022510132,0.0005419722,0.011198029,0.0019527231,0.28645658,0.058675326,0.63648874],"study_design_scores_gemma":[0.000012868457,0.00007536507,0.0010242539,0.0005709301,0.00008300877,0.00061452214,0.0002543099,0.056238465,0.0034538158,0.5406922,0.39689207,0.00008827974],"about_ca_topic_score_codex":0.0015299654,"about_ca_topic_score_gemma":0.0014583193,"teacher_disagreement_score":0.005471545,"about_ca_system_score_codex":0.0012025486,"about_ca_system_score_gemma":0.0011018703,"threshold_uncertainty_score":0.028598845},"labels":[],"label_agreement":null},{"id":"W64917140","doi":"10.1137/1.9781611972757.64","title":"Building Decision Trees on Records Linked through Key References","year":2005,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Key (lock); XML; Table (database); Decision tree; Table of contents; Information retrieval; Classifier (UML); Relational database; Tree (set theory); Data mining; Artificial intelligence; World Wide Web; Mathematics; Combinatorics","score_opus":0.04288259887349749,"score_gpt":0.30237248232717645,"score_spread":0.25948988345367896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W64917140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09824183,0.0011169068,0.8936827,0.00077740324,0.00016285859,0.0003003082,0.0018713499,0.0024751243,0.0013714964],"genre_scores_gemma":[0.34397414,0.0010959108,0.64229554,0.0003426809,0.00035017342,0.00036435184,0.0084962975,0.00014882065,0.0029320277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981451,0.00037729891,0.00021616938,0.0005205543,0.00052873424,0.0002121167],"domain_scores_gemma":[0.9934569,0.004338595,0.00045583703,0.0005429391,0.0010061009,0.00019971273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024869023,0.0009770637,0.0016441997,0.0028841402,0.0010804783,0.0016472756,0.002246698,0.001607312,0.0016712124],"category_scores_gemma":[0.012479823,0.0006419735,0.0012831399,0.0035272308,0.0005305315,0.004335944,0.001461501,0.001320594,0.0013170526],"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":[0.0006148624,0.00039043228,0.015598406,0.0004977591,0.00024789377,0.00093782553,0.00065308885,0.2550835,0.0064356206,0.010772653,0.012142481,0.69662553],"study_design_scores_gemma":[0.000032699576,0.00017108045,0.0014525448,0.00006972624,0.00010182726,0.00015114436,0.0001886791,0.9552205,0.005895812,0.032397386,0.004290575,0.000027990083],"about_ca_topic_score_codex":0.0036303408,"about_ca_topic_score_gemma":0.004287294,"teacher_disagreement_score":0.0036303408,"about_ca_system_score_codex":0.00057607394,"about_ca_system_score_gemma":0.0009929328,"threshold_uncertainty_score":0.0131521225},"labels":[],"label_agreement":null},{"id":"W649264798","doi":"10.1007/s13042-015-0386-x","title":"Analysis of spatiotemporal data relationship using information granules","year":2015,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Communication University of China; National Natural Science Foundation of China","keywords":"Computer science; Data mining; Granular computing; Granulation; Representation (politics); Computational intelligence; Fuzzy logic; Process (computing); Artificial intelligence; Pattern recognition (psychology); Rough set","score_opus":0.0590416853413767,"score_gpt":0.3212517047273116,"score_spread":0.2622100193859349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W649264798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3047125,0.001689277,0.68805444,0.00048727792,0.0001311886,0.00019081877,0.0016744884,0.0010847457,0.0019751843],"genre_scores_gemma":[0.9067945,0.00069485465,0.09032802,0.000035895224,0.000085717424,0.000099945246,0.0013933528,0.000046486657,0.0005212625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919885,0.000112767986,0.00011853924,0.00021311501,0.00027714734,0.000079552374],"domain_scores_gemma":[0.997341,0.0012709451,0.0004976352,0.00036968378,0.0004201529,0.000100573256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001055493,0.0005416023,0.0009210592,0.0041502225,0.00054791756,0.002372974,0.0006876565,0.0005065215,0.000939417],"category_scores_gemma":[0.0062201307,0.00031672788,0.00078992726,0.0051196543,0.00046289826,0.0025642994,0.0010820146,0.00066957984,0.00017120402],"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":[0.0016190775,0.0005980079,0.12785856,0.00078633195,0.000763927,0.0037568111,0.0017506352,0.1917933,0.037521485,0.08042689,0.008432784,0.54469216],"study_design_scores_gemma":[0.00002104928,0.00011270247,0.020219622,0.000044737782,0.0001795431,0.00041726234,0.00046418,0.93463063,0.0046712346,0.035878476,0.0033246125,0.000036061367],"about_ca_topic_score_codex":0.002889037,"about_ca_topic_score_gemma":0.0019125637,"teacher_disagreement_score":0.0041502225,"about_ca_system_score_codex":0.0004488621,"about_ca_system_score_gemma":0.00065801386,"threshold_uncertainty_score":0.0057444572},"labels":[],"label_agreement":null},{"id":"W68466192","doi":"","title":"Geometrically Distributed Random Variables And Permutations Avoiding Consecutive 3-Letter Patterns.","year":2003,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Combinatorics; Discrete mathematics","score_opus":0.010723194242734557,"score_gpt":0.22109577845445608,"score_spread":0.21037258421172153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68466192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20651868,0.0022084904,0.7297071,0.0052571506,0.00085807696,0.0002504714,0.0011337424,0.0011137642,0.052952565],"genre_scores_gemma":[0.85153145,0.0013996018,0.105567925,0.0013225685,0.0007603195,0.0005973126,0.0011725748,0.00025559159,0.037392683],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959038,0.0020227511,0.00018845189,0.00059234415,0.0007067286,0.0005859712],"domain_scores_gemma":[0.98799324,0.0070825676,0.0016983602,0.0018882928,0.00075944245,0.0005780557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024258338,0.0007302326,0.0011212864,0.0023604736,0.002139575,0.0043964484,0.001908186,0.0022331306,0.008245626],"category_scores_gemma":[0.020235334,0.0006335057,0.0009830545,0.002849514,0.003954258,0.005298559,0.0029142727,0.0028673303,0.003455948],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009677301,0.000027507858,0.000537682,0.000028649345,0.000017147651,0.00019308337,0.000081956794,0.0056761834,0.00051195384,0.9796214,0.0034874785,0.0097201625],"study_design_scores_gemma":[0.00004276259,0.000029057117,0.00024626483,0.000026034115,0.00001608339,0.00028825426,0.00007199748,0.031955216,0.00069107994,0.96022433,0.0063804826,0.000028429033],"about_ca_topic_score_codex":0.0007419869,"about_ca_topic_score_gemma":0.0012754344,"teacher_disagreement_score":0.008245626,"about_ca_system_score_codex":0.001729999,"about_ca_system_score_gemma":0.0015829002,"threshold_uncertainty_score":0.027584314},"labels":[],"label_agreement":null},{"id":"W68506346","doi":"10.1007/1-4020-5392-4_4","title":"Spatial Data and Spatial Database Systems","year":2007,"lang":"en","type":"book-chapter","venue":"˜The œGeojournal library","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spatial database; Spatial analysis; Database; Object-based spatial database; Computer science; Spatiotemporal database; Geography; Database design; Remote sensing; View; Database tuning","score_opus":0.04561211621966977,"score_gpt":0.24163762913345407,"score_spread":0.1960255129137843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68506346","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017497872,0.06766787,0.52865344,0.006514434,0.0052579115,0.00024123034,0.002729248,0.0081345355,0.3790515],"genre_scores_gemma":[0.020116009,0.091344945,0.26846078,0.003519418,0.004085315,0.00039643026,0.007805717,0.0030750835,0.6011964],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99885714,0.00015472133,0.000086988766,0.00014756546,0.00070600305,0.000047599315],"domain_scores_gemma":[0.9987424,0.0004620646,0.000038359212,0.0003974793,0.0003049696,0.00005486969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009076003,0.0012759338,0.0013507445,0.0028432938,0.0009161103,0.006652482,0.0019400571,0.0010565666,0.03742837],"category_scores_gemma":[0.003314139,0.0009030982,0.00062921684,0.010509546,0.0014169567,0.008957829,0.0021961639,0.002627623,0.028158832],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014578711,0.000030830182,0.0001636304,0.00046593335,0.000020892958,0.000061115,0.00022096573,0.0013696196,0.00063688884,0.1981888,0.42043132,0.37839538],"study_design_scores_gemma":[0.000004851005,0.00000758648,0.000153565,0.00011956643,0.000013191217,0.0001974817,0.000066351204,0.0033229608,0.00042162265,0.088759296,0.90692234,0.000011150392],"about_ca_topic_score_codex":0.0025287913,"about_ca_topic_score_gemma":0.0027085724,"teacher_disagreement_score":0.03742837,"about_ca_system_score_codex":0.0011597599,"about_ca_system_score_gemma":0.0015208515,"threshold_uncertainty_score":0.1252104},"labels":[],"label_agreement":null},{"id":"W6888440185","doi":"10.20382/v16i1a5","title":"Density approximation for moving groups","year":2023,"lang":"en","type":"article","venue":"Journal of Computational Geometry (Carleton University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kernel density estimation; Track (disk drive); Group (periodic table); Kernel (algebra); Tracking (education); Density estimation; Maxima","score_opus":0.01758062281630666,"score_gpt":0.2240018861337144,"score_spread":0.20642126331740776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888440185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0144692045,0.0002819122,0.9834567,0.00026615133,0.000036902275,0.000036220183,0.00019986353,0.00025009568,0.0010029128],"genre_scores_gemma":[0.5774754,0.0012255065,0.4080693,0.0003084752,0.00021802752,0.00040550396,0.0027351123,0.0003347563,0.009227919],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987804,0.00035463873,0.00008068877,0.00028841657,0.00032113792,0.0001746846],"domain_scores_gemma":[0.99180675,0.00491622,0.000700029,0.0012488171,0.0010243937,0.0003037806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028124847,0.00072629406,0.0014917807,0.0025619185,0.00086520985,0.0018134295,0.0033404836,0.0017261393,0.0030870314],"category_scores_gemma":[0.022008665,0.0006599095,0.0014383984,0.0025782483,0.0017433033,0.004680554,0.0024994118,0.0033078825,0.0010554609],"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":[0.00018128913,0.00008475282,0.004368216,0.00015786538,0.000067642795,0.00014342928,0.0004150521,0.6593317,0.0012770188,0.27331147,0.005890044,0.05477154],"study_design_scores_gemma":[0.0000043878817,0.000009853452,0.00020511588,0.000008470181,0.0000046670557,0.000024835183,0.00002718973,0.9560579,0.00015752086,0.04254431,0.0009498303,0.0000059000035],"about_ca_topic_score_codex":0.010918449,"about_ca_topic_score_gemma":0.005554174,"teacher_disagreement_score":0.010918449,"about_ca_system_score_codex":0.002245049,"about_ca_system_score_gemma":0.00102931,"threshold_uncertainty_score":0.0217098},"labels":[],"label_agreement":null},{"id":"W6890023240","doi":"10.3389/fonc.2023.1086118.s002","title":"Image_2_The clinical and neurocognitive functional changes with awake brain mapping for gliomas invading eloquent areas: Institutional experience and the utility of The Montreal Cognitive Assessment.tif","year":2023,"lang":"en","type":"other","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neurocognitive; Glioma; Perioperative; Craniotomy; Awake craniotomy; Temporal lobe; Frontal lobe; Brain mapping","score_opus":0.08587390184835457,"score_gpt":0.313848822907975,"score_spread":0.22797492105962042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890023240","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28950936,0.010940387,0.01557995,0.014459446,0.0023777608,0.0010505349,0.062064536,0.004403874,0.59961414],"genre_scores_gemma":[0.814376,0.008553033,0.01797555,0.0026881439,0.0023295062,0.0003291049,0.027402077,0.0011110913,0.12523563],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9999517,0.0000100166235,0.0000037181237,0.0000057787015,0.000019267814,0.000009446701],"domain_scores_gemma":[0.99980444,0.000055194057,0.000028810347,0.000014428017,0.000069781745,0.00002739832],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024696617,0.0001955969,0.00011762183,0.0014910259,0.00014258201,0.00042501243,0.0003041191,0.00041878328,0.053591337],"category_scores_gemma":[0.00073387177,0.000080972815,0.0002715481,0.00064349425,0.00017539904,0.00034442998,0.00024814208,0.00029540402,0.006981585],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046392213,0.00017847292,0.074473515,0.0004919196,0.00007388154,0.004245364,0.00026729904,0.0012205817,0.006419302,0.00096360536,0.5239437,0.38725838],"study_design_scores_gemma":[0.00017711213,0.00051054114,0.37686354,0.0005896926,0.00010737589,0.024990406,0.00064450636,0.008623149,0.007028351,0.0014858961,0.5788785,0.00010091246],"about_ca_topic_score_codex":0.009140145,"about_ca_topic_score_gemma":0.011894499,"teacher_disagreement_score":0.9464087,"about_ca_system_score_codex":0.00023457823,"about_ca_system_score_gemma":0.00021719707,"threshold_uncertainty_score":0.17928088},"labels":[],"label_agreement":null},{"id":"W6893899281","doi":"10.5281/zenodo.4665254","title":"A new semantic trajectory ontology model for modelling and reasoning on movement data","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Trajectory; Ontology; Inference; Raw data; Semantic data model; Movement (music); Semantics (computer science)","score_opus":0.0930868861795873,"score_gpt":0.2686038527552753,"score_spread":0.17551696657568802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893899281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014885517,0.00013447096,0.9933262,0.00036723053,0.0000689072,0.0001275963,0.0021902632,0.0010017079,0.0012949576],"genre_scores_gemma":[0.042265248,0.0006971518,0.94454545,0.00030597489,0.000070950664,0.0004971943,0.009138792,0.000304791,0.002174448],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99668056,0.0005181206,0.0006369736,0.0007679764,0.0012005802,0.00019575524],"domain_scores_gemma":[0.99774265,0.00060386054,0.00024396823,0.00064663176,0.0006130743,0.00014988462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026210332,0.0011085025,0.0012104638,0.0046613337,0.0013188029,0.003970712,0.0031269048,0.0017293436,0.002625536],"category_scores_gemma":[0.006043416,0.00080716395,0.00529052,0.0059423735,0.0013315026,0.009753129,0.0033228828,0.0029014675,0.0013808432],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025387725,0.00037616468,0.005614654,0.0011207649,0.0005183671,0.0012212093,0.0020240194,0.13989988,0.008798146,0.579393,0.025315885,0.23546404],"study_design_scores_gemma":[0.000048403406,0.000057042234,0.0012735213,0.0002696766,0.00021822107,0.0006045059,0.00056812476,0.627843,0.0038614988,0.223285,0.14187077,0.00010022132],"about_ca_topic_score_codex":0.03167652,"about_ca_topic_score_gemma":0.04279983,"teacher_disagreement_score":0.03167652,"about_ca_system_score_codex":0.0024661792,"about_ca_system_score_gemma":0.0047791917,"threshold_uncertainty_score":0.06298429},"labels":[],"label_agreement":null},{"id":"W6901554265","doi":"10.60692/dbmpb-4q392","title":"A Semi-Supervised Approach for the Semantic Segmentation of Trajectories","year":2018,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Segmentation; Trajectory; Pattern recognition (psychology); Set (abstract data type); Process (computing); Image segmentation; Object (grammar); Scale-space segmentation","score_opus":0.03965301900595358,"score_gpt":0.22388507271090113,"score_spread":0.18423205370494755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901554265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004464177,0.00009255561,0.9932904,0.000053003016,0.000016367227,0.00010576041,0.00017665685,0.0012810711,0.00051996636],"genre_scores_gemma":[0.14584449,0.00014513986,0.84925467,0.000108514665,0.00005802679,0.0003799447,0.0020830652,0.0003577736,0.001768344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976852,0.00059824035,0.0001651314,0.00096802105,0.00045269117,0.00013081558],"domain_scores_gemma":[0.99739933,0.0009974601,0.0003638203,0.00053214177,0.00058945775,0.00011775637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001600494,0.0015539859,0.0013287005,0.0028696381,0.0010596315,0.00131664,0.0025670389,0.0015841398,0.002309783],"category_scores_gemma":[0.0041539925,0.0006678295,0.0018244375,0.002264376,0.0013516125,0.0021594595,0.0016307855,0.0017175146,0.0013113937],"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":[0.0005712391,0.00040498204,0.003594981,0.0007710294,0.00037300333,0.00037377785,0.0011446222,0.25806203,0.042399593,0.027765173,0.011605011,0.65293455],"study_design_scores_gemma":[0.000018728457,0.0000935375,0.00066837244,0.000040411396,0.00003346193,0.00015760786,0.00012996681,0.9703627,0.008345439,0.015824035,0.0042949254,0.000030899508],"about_ca_topic_score_codex":0.00468666,"about_ca_topic_score_gemma":0.009141501,"teacher_disagreement_score":0.00468666,"about_ca_system_score_codex":0.0010514957,"about_ca_system_score_gemma":0.0024302814,"threshold_uncertainty_score":0.009318769},"labels":[],"label_agreement":null},{"id":"W6944799364","doi":"10.18910/84851","title":"Estimating gaits of an ancient crocodile-line archosaur","year":2021,"lang":"en","type":"article","venue":"OUKA (Osaka University Knowledge Archive) (Osaka University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Motion (physics); Gait; Adaptive control; Adaptive filter; Motion analysis","score_opus":0.015170702497595315,"score_gpt":0.21936717270503844,"score_spread":0.20419647020744314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6944799364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882631,0.00018439091,0.009482137,0.000052720417,0.00004634086,0.000034874127,0.00085857307,0.00019446961,0.00088360894],"genre_scores_gemma":[0.98218507,0.00009448498,0.014804584,0.000013034768,0.000010525117,0.000014816446,0.001285513,0.000036060752,0.0015558898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990416,0.00001224876,0.0000060312273,0.00004315521,0.00001697778,0.000017480768],"domain_scores_gemma":[0.9997634,0.000025941865,0.000018271783,0.000028764196,0.00011925328,0.00004430638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027265665,0.00053521374,0.00038021585,0.0020313535,0.00045631026,0.00028854207,0.0004813993,0.0004990282,0.0012794666],"category_scores_gemma":[0.0005139844,0.00021350491,0.0003251285,0.00094529707,0.00021263193,0.00018591013,0.00040554997,0.000300547,0.00067768653],"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":[0.0017471622,0.00035905655,0.29828894,0.00043711014,0.00040381806,0.002890512,0.0018281303,0.02914725,0.17004701,0.00058843166,0.0063953563,0.4878673],"study_design_scores_gemma":[0.00005958887,0.0003972654,0.8392656,0.00009121737,0.00015910155,0.0016272045,0.0012213109,0.1470266,0.007094534,0.00040327522,0.0025899082,0.00006443788],"about_ca_topic_score_codex":0.018520296,"about_ca_topic_score_gemma":0.051557865,"teacher_disagreement_score":0.018520296,"about_ca_system_score_codex":0.00019812149,"about_ca_system_score_gemma":0.00032518615,"threshold_uncertainty_score":0.036825},"labels":[],"label_agreement":null},{"id":"W6945021117","doi":"10.25316/ir-11323","title":"Nanaimo Free Press [Monday, October 7, 1895]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.009823111398815568,"score_gpt":0.19343307518437616,"score_spread":0.1836099637855606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945021117","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005857872,0.010153645,0.00014095339,0.004631742,0.0063059423,0.0000237111,0.00317124,0.00024059507,0.9747463],"genre_scores_gemma":[0.00086551707,0.0012157002,0.00004483942,0.00018063808,0.00015996123,0.0000056664576,0.00024369391,0.000072677656,0.9972113],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996511,0.000024923675,0.00001403297,0.000060271916,0.00017249126,0.00007720394],"domain_scores_gemma":[0.99969506,0.000035291276,0.000019092615,0.000025581658,0.00014376658,0.000081261285],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003845939,0.0010410978,0.000663587,0.0014727316,0.0039442414,0.005910755,0.0007542378,0.0018186071,0.48370224],"category_scores_gemma":[0.0014347408,0.0004351456,0.00041132138,0.0025094908,0.00064571906,0.002664637,0.0016562948,0.0026499222,0.23532665],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027596858,0.000007089008,0.000108433785,0.000053796233,0.000003158492,0.00005558644,0.00005484414,0.000036280402,0.000058389858,0.0059674256,0.97123647,0.022390887],"study_design_scores_gemma":[0.0000016532277,0.0000018050387,0.00024755413,0.000034869114,7.8983567e-7,0.000009781726,0.00002660257,0.0000109629345,0.000019641731,0.00025879891,0.9993855,0.0000020968607],"about_ca_topic_score_codex":0.064729184,"about_ca_topic_score_gemma":0.2966684,"teacher_disagreement_score":0.9352708,"about_ca_system_score_codex":0.0029874681,"about_ca_system_score_gemma":0.002047896,"threshold_uncertainty_score":0.73643607},"labels":[],"label_agreement":null},{"id":"W6945236346","doi":"10.24385/lincoln.25162595","title":"Small Machine Talks","year":2024,"lang":"en","type":"other","venue":"Lincoln Repository (University of Lincoln)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Key (lock); Government (linguistics)","score_opus":0.011089437830360257,"score_gpt":0.1810510820432166,"score_spread":0.16996164421285634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945236346","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009301319,0.0021224192,0.033967648,0.07476793,0.03012116,0.00032472346,0.0021865387,0.004227108,0.8429811],"genre_scores_gemma":[0.078720324,0.0010139943,0.005846436,0.01100829,0.006084268,0.00052908016,0.001116861,0.005130953,0.89054984],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945532,0.0028331503,0.00018715249,0.0005520762,0.0013488062,0.00052546413],"domain_scores_gemma":[0.970788,0.016100058,0.00067098223,0.0062407656,0.0033448767,0.0028553987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007838518,0.0008331204,0.0007052893,0.0037173617,0.009010413,0.01157218,0.0016505726,0.0035430002,0.29047325],"category_scores_gemma":[0.039537877,0.0006283131,0.0007371047,0.0034471045,0.0037709037,0.016059153,0.008987998,0.005609342,0.076990314],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026888336,0.00007302287,0.00036134347,0.00015348972,0.000008574,0.00018251323,0.0071522444,0.00018259836,0.0011386927,0.20364164,0.72660273,0.06023439],"study_design_scores_gemma":[0.000013365966,0.000020241896,0.00024077274,0.00008086371,0.0000026081127,0.000048000125,0.0017868148,0.00014112973,0.0002639025,0.014993047,0.982396,0.000013236982],"about_ca_topic_score_codex":0.0020533712,"about_ca_topic_score_gemma":0.0035883423,"teacher_disagreement_score":0.29047325,"about_ca_system_score_codex":0.0032230709,"about_ca_system_score_gemma":0.0016952107,"threshold_uncertainty_score":0.9717298},"labels":[],"label_agreement":null},{"id":"W6945284300","doi":"10.25316/ir-10040","title":"Nanaimo Free Press [Wednesday, July 28, 1897]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.010652567463136036,"score_gpt":0.19596767729494124,"score_spread":0.1853151098318052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945284300","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079139293,0.00845885,0.00019218765,0.002082078,0.0029511717,0.000023907318,0.003562632,0.00022026125,0.9817176],"genre_scores_gemma":[0.0013374506,0.0014028463,0.000070806076,0.00010308374,0.0001031576,0.0000073593487,0.0003638774,0.00008537015,0.9965262],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965286,0.000020952039,0.00001387685,0.0000687592,0.00017310228,0.0000704486],"domain_scores_gemma":[0.999724,0.000034829805,0.000017183498,0.00002596178,0.00013870157,0.00005930108],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031597688,0.0008947542,0.0006388534,0.0018284348,0.0035218934,0.005306315,0.00075758743,0.00148441,0.39308107],"category_scores_gemma":[0.0012988038,0.00043595937,0.00032325336,0.0032090568,0.00061505724,0.0021552704,0.001360845,0.0021216474,0.16295853],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040158608,0.000010353298,0.0002361752,0.000089067136,0.0000047290996,0.00008759835,0.00010496054,0.000085298714,0.00012616809,0.013425312,0.93519694,0.05059339],"study_design_scores_gemma":[0.00000167914,0.0000025095867,0.00036484236,0.00005011411,8.805715e-7,0.000015826852,0.00003238296,0.000019580026,0.000044934768,0.00041668848,0.9990478,0.000002746963],"about_ca_topic_score_codex":0.09612987,"about_ca_topic_score_gemma":0.40956992,"teacher_disagreement_score":0.9038701,"about_ca_system_score_codex":0.003363122,"about_ca_system_score_gemma":0.00217002,"threshold_uncertainty_score":0.8656962},"labels":[],"label_agreement":null},{"id":"W6945287142","doi":"10.25318/3410025401-eng","title":"Average expected useful life of new publicly owned public transit assets, Infrastructure Canada","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Public transport; Transit (satellite); Transit system; Value (mathematics); Government (linguistics)","score_opus":0.01099130798205808,"score_gpt":0.22897916843077304,"score_spread":0.21798786044871496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945287142","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010618232,0.00008998433,0.000038452967,0.00008285343,0.000011371467,0.000005441706,0.997929,0.00005067908,0.0007303548],"genre_scores_gemma":[0.0073248553,0.00020951047,0.00015922976,0.00004659819,0.0000087111985,0.000029550545,0.9907644,0.000024571733,0.0014326075],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991341,0.00004539489,0.00009255189,0.00018963701,0.00033504842,0.0002032317],"domain_scores_gemma":[0.9945417,0.0006881102,0.0004855528,0.00034907096,0.0035540897,0.000381607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007015333,0.0009795154,0.0010247144,0.0045833155,0.00082642335,0.0022125398,0.0023635286,0.00073754665,0.01461026],"category_scores_gemma":[0.0094760405,0.000447231,0.0010189028,0.01313116,0.00035651747,0.0010675631,0.00089793594,0.001637302,0.0055010067],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102788385,0.000019480087,0.018266417,0.00042804203,0.00010136847,0.000027400001,0.000028959994,0.002436779,0.000031863892,0.0011124238,0.97281826,0.0046262653],"study_design_scores_gemma":[0.00044729633,0.000039836912,0.23918967,0.00092626957,0.00021518838,0.00022669628,0.0005831283,0.00886502,0.0006428126,0.0023201061,0.74641466,0.00012919611],"about_ca_topic_score_codex":0.9424595,"about_ca_topic_score_gemma":0.9499139,"teacher_disagreement_score":0.057540476,"about_ca_system_score_codex":0.0137938205,"about_ca_system_score_gemma":0.019015184,"threshold_uncertainty_score":0.11575866},"labels":[],"label_agreement":null},{"id":"W6945504519","doi":"10.25384/sage.c.6161234","title":"The Games We Play: Prosocial Choices Under Time Pressure Reflect Context-Sensitive Information Priorities","year":2022,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prosocial behavior; Deliberation; Altruism (biology); Information processing; Field (mathematics); Social pressure; Negative information","score_opus":0.03149210806396833,"score_gpt":0.2927622431464673,"score_spread":0.26127013508249897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945504519","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9885592,0.000061762345,0.0043740883,0.00028988355,0.000014598777,0.000058235662,0.00011082638,0.000018787223,0.006512542],"genre_scores_gemma":[0.99699366,0.000027307435,0.0023149077,0.00008497593,0.0000055112446,0.000068805704,0.00004336183,0.000007010796,0.0004545414],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99863213,0.0007764921,0.000051150775,0.00024147553,0.00019409377,0.000104614315],"domain_scores_gemma":[0.99496245,0.0028133227,0.0010923824,0.00041874257,0.00023217242,0.000481033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001661806,0.00032987335,0.00029441543,0.00041228882,0.0005535764,0.0018077269,0.00035914365,0.0004269455,0.0031048185],"category_scores_gemma":[0.013951249,0.00023672935,0.00019082971,0.0003512497,0.0013404551,0.0010476445,0.000989281,0.0008484324,0.00016933712],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009568449,0.0031607912,0.54157084,0.00093658426,0.0006962778,0.0010024995,0.021994822,0.01094137,0.1829155,0.0854321,0.005192335,0.13658847],"study_design_scores_gemma":[0.0004347065,0.0019995938,0.87697196,0.000060460778,0.0002470509,0.00029891505,0.0045761075,0.035532095,0.015976323,0.058222253,0.005516763,0.0001637432],"about_ca_topic_score_codex":0.005872071,"about_ca_topic_score_gemma":0.008028178,"teacher_disagreement_score":0.005872071,"about_ca_system_score_codex":0.0008096672,"about_ca_system_score_gemma":0.00068082224,"threshold_uncertainty_score":0.011675775},"labels":[],"label_agreement":null},{"id":"W6945539858","doi":"10.25549/wpacards-m24784","title":"WPA block face card for household census of Hoefner, Eagle, Eastmont Streets, in Los Angeles County","year":2012,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Block (permutation group theory); Face (sociological concept); Quarter (Canadian coin)","score_opus":0.013540571051100425,"score_gpt":0.17088302897468505,"score_spread":0.15734245792358462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945539858","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002766446,0.000011866362,0.000045032997,0.000019476085,0.000010978297,0.00001663444,0.99884653,0.00008808149,0.0006846941],"genre_scores_gemma":[0.00068015215,0.00002287679,0.00015298369,0.00001758731,0.0000056811878,0.00010396279,0.997357,0.00002342631,0.0016363133],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995041,0.00004293994,0.0000642338,0.00014140422,0.00014429716,0.00010307281],"domain_scores_gemma":[0.9980075,0.00020075499,0.00021131644,0.00032634515,0.0010325091,0.00022157222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004868516,0.0013881248,0.0010816158,0.0037556563,0.0006157221,0.0013540973,0.0018581996,0.0008228253,0.08728725],"category_scores_gemma":[0.003539233,0.00059362303,0.0005941545,0.006648217,0.00019683319,0.0008353569,0.0009818703,0.0010702575,0.094663434],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038597624,0.000018469129,0.0015284836,0.000112259906,0.000008778724,0.000011821842,0.000012706207,0.00010881433,0.00003976406,0.0001042128,0.99544084,0.0025752385],"study_design_scores_gemma":[0.00022672222,0.000024689698,0.043259528,0.00026625968,0.00002821083,0.000078760204,0.00029797954,0.00085808936,0.00048369524,0.00052132655,0.9539144,0.00004036593],"about_ca_topic_score_codex":0.09480514,"about_ca_topic_score_gemma":0.16672419,"teacher_disagreement_score":0.09480514,"about_ca_system_score_codex":0.0012499404,"about_ca_system_score_gemma":0.002546569,"threshold_uncertainty_score":0.29200494},"labels":[],"label_agreement":null},{"id":"W6945585410","doi":"10.25446/oxford.25920505","title":"Lucky escape of Helen Meyer and her dinner guests","year":2024,"lang":"en","type":"other","venue":"University of Oxford","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"nobody; Nazism; Charge (physics); Judaism; Antique; Quarter (Canadian coin); Nazi Germany","score_opus":0.007468774890618561,"score_gpt":0.18923047327029585,"score_spread":0.1817616983796773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945585410","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07339785,0.04470692,0.0015940232,0.44068262,0.11934677,0.0001710926,0.00045501228,0.00078847626,0.3188572],"genre_scores_gemma":[0.20348242,0.01463484,0.0019782544,0.08927108,0.010109781,0.00014930675,0.00032657094,0.0009082846,0.6791395],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828196,0.00045965146,0.000041154624,0.00026697043,0.00040357566,0.0005467637],"domain_scores_gemma":[0.9965578,0.00014463201,0.00009163276,0.000069870264,0.0002849447,0.0028510848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020778833,0.0013500038,0.00085370435,0.0005804833,0.012543272,0.0070724557,0.0012702438,0.002627978,0.038781356],"category_scores_gemma":[0.004472667,0.0005338648,0.0004606922,0.00029541415,0.0035742112,0.004478067,0.009999948,0.009887975,0.012737624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011630447,0.00012788066,0.0007791826,0.00012578236,0.000012560859,0.002313752,0.017882224,0.000048405076,0.0008772223,0.008477251,0.94412977,0.025109667],"study_design_scores_gemma":[0.0000067769915,0.00008421552,0.0009136249,0.00032780741,0.0000054509837,0.0016432605,0.05278141,0.000032335105,0.00022831497,0.0010631748,0.94287795,0.000035768702],"about_ca_topic_score_codex":0.005794667,"about_ca_topic_score_gemma":0.019674957,"teacher_disagreement_score":0.038781356,"about_ca_system_score_codex":0.0023998008,"about_ca_system_score_gemma":0.0027009938,"threshold_uncertainty_score":0.12973654},"labels":[],"label_agreement":null},{"id":"W6945648459","doi":"10.25384/sage.24492828.v1","title":"sj-docx-1-cjk-10.1177_20543581231209233 – Supplemental material for Associations of Estradiol With Mortality and Health Outcomes in Patients Undergoing Hemodialysis: A Prospective Cohort Study","year":2023,"lang":"en","type":"article","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prospective cohort study; Cohort study; Disease; Cohort; Kidney disease","score_opus":0.05473571344020473,"score_gpt":0.3219925450276592,"score_spread":0.2672568315874545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6945648459","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027499505,0.000043381555,0.0006725087,0.00033102854,0.00022092639,0.0001162393,0.9932724,0.0020672246,0.003001201],"genre_scores_gemma":[0.005394171,0.00022241918,0.005933088,0.0014469967,0.00045311372,0.0016905856,0.9538075,0.004687679,0.02636444],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9980861,0.00024400401,0.00039605724,0.00046467813,0.0005213999,0.00028779832],"domain_scores_gemma":[0.973383,0.015380503,0.0017039542,0.0024073597,0.005300891,0.0018242359],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022909446,0.001575842,0.0020069021,0.0044882013,0.0015597704,0.0043871077,0.0035717543,0.0027012895,0.9005514],"category_scores_gemma":[0.030726328,0.0018840231,0.0014575183,0.0052912654,0.00056805374,0.002749542,0.002277816,0.002177936,0.5185864],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013536068,0.000056834157,0.0010776618,0.00064484787,0.000026825772,0.000030925134,0.000027377986,0.00012378042,0.0001626717,0.00035204808,0.9925061,0.004855626],"study_design_scores_gemma":[0.0028153963,0.00020276681,0.025915926,0.0016432386,0.00015680458,0.00049809396,0.0003802442,0.001716729,0.0022747184,0.0075611095,0.95661974,0.00021529525],"about_ca_topic_score_codex":0.013705015,"about_ca_topic_score_gemma":0.02104188,"teacher_disagreement_score":0.9005514,"about_ca_system_score_codex":0.0017693898,"about_ca_system_score_gemma":0.0032804876,"threshold_uncertainty_score":0.14185137},"labels":[],"label_agreement":null},{"id":"W6946069757","doi":"10.25549/webster-c100-17635","title":"Conference on Civil Disorders, 1980-06-30/1980-07-01","year":2021,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Commission; Newspaper; Royal Commission; Economic Justice; Variety (cybernetics)","score_opus":0.00938042102451549,"score_gpt":0.16744342395800213,"score_spread":0.15806300293348663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6946069757","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038478794,0.00014421005,0.00010391578,0.00031446802,0.0004492363,0.000063485495,0.99242485,0.00035505064,0.005760038],"genre_scores_gemma":[0.0006131226,0.00016042456,0.00026986207,0.000109736815,0.000127886,0.0002052194,0.9896998,0.00009699269,0.008717089],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871063,0.00015131824,0.00013561815,0.0002666338,0.0004894918,0.00024636654],"domain_scores_gemma":[0.996114,0.00046566298,0.00028511215,0.0007203548,0.001667772,0.00074701913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014060015,0.0015494816,0.0011862044,0.0060218177,0.0014448796,0.0038702695,0.0014226936,0.00086824695,0.118660256],"category_scores_gemma":[0.007814521,0.00048261252,0.0005539078,0.008135887,0.00040817348,0.0012618966,0.0019207415,0.001986661,0.1621653],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013495975,0.000006021371,0.00025678248,0.00003115075,0.0000013457989,0.0000064459387,0.0000043116356,0.000021863074,0.000012468091,0.000091745846,0.9979741,0.0015803257],"study_design_scores_gemma":[0.000060758597,0.000011569217,0.009589357,0.0001482646,0.0000052406685,0.000029094968,0.00009454347,0.0001487413,0.00014821823,0.00043902668,0.98931354,0.0000116588],"about_ca_topic_score_codex":0.036964234,"about_ca_topic_score_gemma":0.076553516,"teacher_disagreement_score":0.118660256,"about_ca_system_score_codex":0.0023127364,"about_ca_system_score_gemma":0.0038305267,"threshold_uncertainty_score":0.3969581},"labels":[],"label_agreement":null},{"id":"W6946423915","doi":"10.3389/fpsyg.2018.01306.s003","title":"Table_3_Predictors of Language Dominance: An Integrated Analysis of First Language Attrition and Second Language Acquisition in Late Bilinguals.pdf","year":2018,"lang":"en","type":"dataset","venue":"Figshare","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Second-language acquisition; First language; Second-language attrition; Focus (optics); Comprehension approach; Language acquisition; Dominance (genetics); Second language; Process (computing)","score_opus":0.013834964133683196,"score_gpt":0.2664173534764833,"score_spread":0.25258238934280014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6946423915","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9248462,0.0009806076,0.00077669864,0.00048810453,0.00008377731,0.000059507212,0.0653975,0.00022357861,0.007144108],"genre_scores_gemma":[0.9673665,0.0004312668,0.00080360216,0.000078578705,0.00004797816,0.00012181178,0.019201213,0.00009730716,0.011851675],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996413,0.00007849094,0.000041230633,0.00008014788,0.00009916084,0.000059641447],"domain_scores_gemma":[0.99536514,0.00286891,0.0006354305,0.00014677299,0.0005406998,0.00044298664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010804315,0.0004494568,0.0006115708,0.0019221305,0.0006694407,0.00090135867,0.0006375071,0.00044412693,0.04748921],"category_scores_gemma":[0.0043663066,0.00019129683,0.0010338505,0.0019655938,0.00021689408,0.00069689675,0.0005903074,0.00068574946,0.003408557],"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":[0.00018753365,0.000026120417,0.98916084,0.000073807634,0.00015581046,0.00014190491,0.00019149484,0.00012921057,0.00025676895,0.00007442958,0.0043610907,0.00524108],"study_design_scores_gemma":[0.00002041007,0.0001305854,0.99340844,0.000073905685,0.00014862762,0.0003552321,0.0008859046,0.0009307059,0.00018721807,0.00008373892,0.00376171,0.000013562161],"about_ca_topic_score_codex":0.050265513,"about_ca_topic_score_gemma":0.051438764,"teacher_disagreement_score":0.050265513,"about_ca_system_score_codex":0.00051214243,"about_ca_system_score_gemma":0.0014155828,"threshold_uncertainty_score":0.15886718},"labels":[],"label_agreement":null},{"id":"W6946459488","doi":"10.34943/d9e96ae3-679c-47a0-8c7f-4b54d58103d2","title":"China Creek Power Supply Deployed 2019-11-04","year":2019,"lang":"en","type":"dataset","venue":"Ocean Networks Canada Society","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Software deployment; Power (physics); Quality (philosophy); Quality assurance","score_opus":0.0051470943270911845,"score_gpt":0.19304031323791027,"score_spread":0.1878932189108191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6946459488","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020496985,0.00005820703,0.00032259664,0.0002361174,0.00007968457,0.000048262227,0.99076617,0.0013351298,0.0051042233],"genre_scores_gemma":[0.002016348,0.00003436472,0.00032743436,0.000050371098,0.0000083235855,0.000048083733,0.9956554,0.000085757856,0.0017738068],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999226,0.000069750684,0.000064322456,0.00018178945,0.0003073221,0.00015089053],"domain_scores_gemma":[0.9984774,0.00009742162,0.00005905652,0.00033944353,0.000863211,0.00016346526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007468246,0.0011445704,0.0006376019,0.001889454,0.0009860843,0.0011940354,0.002056797,0.0008944222,0.01567805],"category_scores_gemma":[0.0028661774,0.0003141006,0.00035852124,0.004464078,0.00046873494,0.0012336223,0.0011607021,0.0011758797,0.03242283],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006010169,0.000023927809,0.0013351525,0.000083039115,0.000008654523,0.00002982801,0.000025601432,0.0004240444,0.00023816995,0.0005655678,0.99433833,0.0028675855],"study_design_scores_gemma":[0.00012036637,0.00002484782,0.010808342,0.00008450179,0.000011595757,0.000063097395,0.00019699414,0.0022016922,0.0010759272,0.0011066322,0.98427564,0.00003045156],"about_ca_topic_score_codex":0.13008688,"about_ca_topic_score_gemma":0.23670293,"teacher_disagreement_score":0.13008688,"about_ca_system_score_codex":0.002348194,"about_ca_system_score_gemma":0.0032013115,"threshold_uncertainty_score":0.25865942},"labels":[],"label_agreement":null},{"id":"W6946681999","doi":"10.34989/swp-2023-10","title":"Exporting and Investment Under Credit Constraints","year":2023,"lang":"en","type":"article","venue":"Econstor (Econstor)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Lakehead University; Bank of Canada","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Leverage (statistics); Productivity; Credit crunch; Investment (military); Trade credit; Financial market; Bond market; Empirical research","score_opus":0.027265859200013814,"score_gpt":0.24945479664906123,"score_spread":0.2221889374490474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6946681999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99087244,0.00034698652,0.00038710877,0.0005279788,0.000004886029,0.000017646957,0.00080510456,0.000010897406,0.0070270104],"genre_scores_gemma":[0.9978517,0.00014333968,0.00006385117,0.00003183421,0.000008055528,0.000003933589,0.00061310973,0.0000020117152,0.0012820435],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99937147,0.00006832404,0.000034658373,0.000091222304,0.00012209314,0.00031219854],"domain_scores_gemma":[0.9910715,0.001774661,0.0049137496,0.00027524642,0.00042794197,0.0015368205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008052967,0.0002798057,0.00040880017,0.0010637576,0.00066177465,0.0022227045,0.00050655194,0.0006399561,0.0062162443],"category_scores_gemma":[0.006195931,0.000115818904,0.0004322071,0.0021498606,0.0011742102,0.0008266506,0.00095539825,0.0010823455,0.0003592772],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066054105,0.00005465158,0.9882808,0.000021522177,0.000041918047,0.00020661416,0.00016584776,0.0023362294,0.00019046736,0.0021574383,0.0004901002,0.005988443],"study_design_scores_gemma":[0.0000069655807,0.000040549843,0.9930883,0.000029917492,0.0000355006,0.000085119376,0.00043916272,0.0031145504,0.00020591298,0.0013088777,0.0016364949,0.000008611177],"about_ca_topic_score_codex":0.27300447,"about_ca_topic_score_gemma":0.29213092,"teacher_disagreement_score":0.27300447,"about_ca_system_score_codex":0.0042658034,"about_ca_system_score_gemma":0.0023375824,"threshold_uncertainty_score":0.5428308},"labels":[],"label_agreement":null},{"id":"W6946785650","doi":"10.34990/fk2/4nifdr","title":"Spread of honey bee (Apis mellifera L.) viruses to wax by worker traffic and aerosolization","year":2023,"lang":"en","type":"dataset","venue":"Borealis","topic":"Data Management and Algorithms","field":"Computer Science","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 Manitoba","funders":"","keywords":"Honey bee; Wax; Honey Bees; Cage; Aerosolization; Drone; Apoidea","score_opus":0.02032566374458735,"score_gpt":0.2623920251206981,"score_spread":0.24206636137611073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6946785650","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00941652,0.00009873177,0.00026165845,0.000112839334,0.00007118296,0.000050959476,0.9888708,0.0004292966,0.0006878949],"genre_scores_gemma":[0.006276761,0.00004306223,0.00077981985,0.000051512747,0.0000131907145,0.00015098522,0.99203146,0.000042749165,0.00061043585],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991837,0.000113607806,0.00010864607,0.00027290755,0.000184397,0.00013676952],"domain_scores_gemma":[0.99834955,0.00033271138,0.00020661476,0.0005078568,0.00045991913,0.0001434833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014502006,0.0016156158,0.0009583822,0.00173227,0.0007138093,0.0011514775,0.0020536224,0.001405903,0.0056962254],"category_scores_gemma":[0.0032501346,0.00038906737,0.0011950459,0.0020110924,0.0004272183,0.0005950922,0.0010898364,0.0011497665,0.008958849],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007571124,0.0003573637,0.040847573,0.0020730158,0.0002826973,0.00016901083,0.00017754496,0.0025933487,0.0028756005,0.00071182486,0.9343463,0.014808458],"study_design_scores_gemma":[0.0014130244,0.0003681194,0.24330877,0.0008448211,0.00033915092,0.000636164,0.00079085934,0.009195904,0.007993744,0.0018039976,0.7331109,0.00019467145],"about_ca_topic_score_codex":0.022955837,"about_ca_topic_score_gemma":0.042618863,"teacher_disagreement_score":0.022955837,"about_ca_system_score_codex":0.0010209623,"about_ca_system_score_gemma":0.0011802365,"threshold_uncertainty_score":0.045644462},"labels":[],"label_agreement":null},{"id":"W6947358344","doi":"10.3897/zookeys.332.4753.habitus13","title":"Habitus 13 from: Barney R, LeSage L, Savard K (2013) Pachybrachis (Coleoptera, Chrysomelidae, Cryptocephalinae) of Eastern Canada. ZooKeys 332: 95-176. https://doi.org/10.3897/zookeys.332.4753","year":2013,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Habitus; Field (mathematics); Scale (ratio); Identity (music)","score_opus":0.02138287649124515,"score_gpt":0.21220651111963842,"score_spread":0.19082363462839327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6947358344","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036819344,0.0051389076,0.0020811623,0.0017118864,0.0010929878,0.00024442922,0.18568659,0.0076152184,0.7927469],"genre_scores_gemma":[0.016219603,0.004992522,0.0028342414,0.0008046234,0.00022559195,0.00021541494,0.10051431,0.0034349314,0.8707588],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951947,0.000025291045,0.000034646848,0.00008482535,0.00024539107,0.0000903952],"domain_scores_gemma":[0.99866354,0.00018004199,0.000095785224,0.00020849037,0.0005730869,0.00027893638],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037269952,0.0014116471,0.0009748506,0.0032622556,0.00186069,0.0024031526,0.001493588,0.0008895268,0.76368445],"category_scores_gemma":[0.0023370597,0.00056252704,0.00045053646,0.0039355005,0.0012715542,0.0024143092,0.0030832319,0.0015601803,0.6162029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051994313,0.000020462958,0.0009287212,0.0005594302,0.000012190789,0.00012173948,0.00025559595,0.000053879303,0.00036457973,0.0008630581,0.9131025,0.08366584],"study_design_scores_gemma":[0.0000086564305,0.0000071478685,0.0034898098,0.00023278636,0.0000071305612,0.00025974074,0.00012450035,0.000045360433,0.00027945233,0.00051885337,0.9950147,0.000011855014],"about_ca_topic_score_codex":0.1741002,"about_ca_topic_score_gemma":0.3772602,"teacher_disagreement_score":0.8258998,"about_ca_system_score_codex":0.0025886528,"about_ca_system_score_gemma":0.0046479465,"threshold_uncertainty_score":0.3461737},"labels":[],"label_agreement":null},{"id":"W6947673242","doi":"10.4224/23000148","title":"A greenhouse on Venus","year":2014,"lang":"en","type":"article","venue":"NPARC","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Herzberg Institute of Astrophysics","funders":"","keywords":"","score_opus":0.012983332569192242,"score_gpt":0.21914950595384028,"score_spread":0.20616617338464804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6947673242","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20036218,0.0025023045,0.0040425532,0.023675658,0.019031469,0.00041694954,0.0018499747,0.002029541,0.74608946],"genre_scores_gemma":[0.5071923,0.0008166198,0.003461166,0.0017447998,0.001129264,0.000059700495,0.0016180348,0.0004865056,0.4834917],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994369,0.00014592591,0.000010340866,0.0001092324,0.00016466615,0.000132899],"domain_scores_gemma":[0.9986909,0.00015692816,0.000040054038,0.00023026286,0.00038835927,0.000493508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065545796,0.0002671416,0.00035386306,0.0006215732,0.0037060112,0.0015999783,0.00071503135,0.0010412411,0.048568368],"category_scores_gemma":[0.0016924702,0.00013715622,0.0003104847,0.00049267896,0.0011335167,0.0010460757,0.001670448,0.000979221,0.009281667],"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":[0.00092405325,0.00012926193,0.015828663,0.0004474803,0.00009240092,0.0071240123,0.0024633294,0.0031690944,0.020571858,0.05212431,0.73559356,0.16153203],"study_design_scores_gemma":[0.000035170833,0.000107264015,0.010439246,0.00011277746,0.000015604941,0.00028373455,0.0027603472,0.0008553673,0.0022581522,0.0063043856,0.97680295,0.000025068217],"about_ca_topic_score_codex":0.016428925,"about_ca_topic_score_gemma":0.05169542,"teacher_disagreement_score":0.048568368,"about_ca_system_score_codex":0.0010849263,"about_ca_system_score_gemma":0.003086992,"threshold_uncertainty_score":0.16247737},"labels":[],"label_agreement":null},{"id":"W6947713066","doi":"10.3897/zookeys.179.2598.map18","title":"Map 18 from: Webster R, Sweeney J, DeMerchant I (2012) New Coleoptera records from New Brunswick, Canada: Mycetophagidae, Tetratomidae, and Melandryidae. ZooKeys 179: 215-242. https://doi.org/10.3897/zookeys.179.2598","year":2012,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Liquation; Emperipolesis; Triacetin; Fusible alloy","score_opus":0.03091813504345756,"score_gpt":0.22178097596731206,"score_spread":0.1908628409238545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6947713066","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005035714,0.00085797446,0.0017367275,0.0007020457,0.0004785276,0.00013489038,0.8192299,0.011027374,0.16532904],"genre_scores_gemma":[0.0047817333,0.002676824,0.005567338,0.0002638349,0.00026110254,0.00032660144,0.8048775,0.0083528515,0.17289227],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995041,0.000027856944,0.000029753812,0.00009921495,0.00022500829,0.0001140825],"domain_scores_gemma":[0.9980756,0.00029217915,0.00011360948,0.0002377802,0.0010030024,0.00027775526],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004530773,0.0020599745,0.0014279364,0.0053374707,0.0009162967,0.0048043462,0.0021603936,0.0011591209,0.7446756],"category_scores_gemma":[0.0048170644,0.00080505694,0.0010644287,0.009484481,0.0006887889,0.0025510823,0.0022741843,0.0015646107,0.6864974],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022511447,0.0000069368807,0.00018610046,0.00040103446,0.000008804426,0.000030946303,0.00004582037,0.00005382507,0.000070763876,0.00021065654,0.98657924,0.012383363],"study_design_scores_gemma":[0.000029531735,0.0000053433796,0.0015519352,0.00027613595,0.000015214731,0.000054946075,0.000076085045,0.00010063054,0.00020783315,0.00045681276,0.9972088,0.000016777483],"about_ca_topic_score_codex":0.120986424,"about_ca_topic_score_gemma":0.16945592,"teacher_disagreement_score":0.8790136,"about_ca_system_score_codex":0.0015223367,"about_ca_system_score_gemma":0.0043094,"threshold_uncertainty_score":0.36418927},"labels":[],"label_agreement":null},{"id":"W6947772399","doi":"10.3897/zookeys.179.2625.map2","title":"Map 2 from: Webster R, LeSage L, DeMerchant I (2012) New Coleoptera records from New Brunswick, Canada: Megalopodidae and Chrysomelidae. ZooKeys 179: 312-348. https://doi.org/10.3897/zookeys.179.2625","year":2012,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data collection; Historical record; Topographic map (neuroanatomy); Field (mathematics)","score_opus":0.02718877606141553,"score_gpt":0.21503767500928217,"score_spread":0.18784889894786663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6947772399","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004585218,0.00093829486,0.002456959,0.00087598676,0.00061471213,0.00013573204,0.8627236,0.0134463515,0.1183499],"genre_scores_gemma":[0.005342689,0.0028954947,0.007971823,0.0004608471,0.00030312402,0.00043989776,0.8508832,0.010996453,0.1207065],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994498,0.00003360801,0.000034088036,0.00010715087,0.00024836417,0.00012700548],"domain_scores_gemma":[0.9974873,0.000515159,0.00014034075,0.00029602434,0.0012336642,0.00032754036],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004835171,0.00217698,0.0016190136,0.007015435,0.00097162515,0.0049185436,0.0023716886,0.0014661023,0.73465437],"category_scores_gemma":[0.006106081,0.0009587493,0.001117263,0.011131395,0.00071568415,0.0030900592,0.0027301852,0.0019813767,0.6086194],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021362386,0.0000071985146,0.00014097859,0.0004839175,0.000008732477,0.000031234587,0.00004093628,0.000065887005,0.00006936255,0.00024208201,0.9873368,0.011551428],"study_design_scores_gemma":[0.000033734927,0.0000062296845,0.0012803242,0.0003119618,0.00001731858,0.00007216598,0.00008903783,0.00017661398,0.00027607597,0.0009956441,0.99671644,0.000024473055],"about_ca_topic_score_codex":0.095052645,"about_ca_topic_score_gemma":0.14240563,"teacher_disagreement_score":0.90494734,"about_ca_system_score_codex":0.0014028731,"about_ca_system_score_gemma":0.003981264,"threshold_uncertainty_score":0.3784833},"labels":[],"label_agreement":null},{"id":"W6947785541","doi":"10.3897/zookeys.327.5908.figures11-13","title":"Figures 11-13 from: Klimaszewski J, Webster R, Langor D, Bourdon C, Jacobs J (2013) Review of Canadian species of the genus Dinaraea Thomson, with descriptions of six new species (Coleoptera, Staphylinidae, Aleocharinae, Athetini). ZooKeys 327: 65-101. https://doi.org/10.3897/zookeys.327.5908","year":2013,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aedeagus; Spermatheca; Genus; Male genitalia; Taxonomy (biology); Type species; Type (biology)","score_opus":0.03857428474321903,"score_gpt":0.21891260112260855,"score_spread":0.18033831637938952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6947785541","genre_codex":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014371984,0.005768422,0.0035290245,0.0009973518,0.0036198748,0.00039462623,0.09662085,0.002952684,0.88468],"genre_scores_gemma":[0.014219513,0.008265634,0.0107481135,0.00064028637,0.0007295673,0.0002755177,0.086280994,0.0031344548,0.87570596],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996842,0.00001456409,0.000019594443,0.000055369455,0.00018941183,0.00003695774],"domain_scores_gemma":[0.999464,0.000101837584,0.00005325443,0.000063346226,0.0002428814,0.00007459903],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021727475,0.0014516742,0.0007511625,0.0038601684,0.00130033,0.0015236123,0.0012128258,0.0007294557,0.7708884],"category_scores_gemma":[0.0017848315,0.00047794494,0.0006540343,0.00508922,0.0008867609,0.0023937603,0.0012362801,0.0013890484,0.47816563],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037605034,0.000010114629,0.00026673762,0.00046864204,0.0000070750957,0.000100874495,0.000111994894,0.00007952002,0.00040598493,0.001339397,0.9457812,0.051390905],"study_design_scores_gemma":[0.0000025694815,0.000002610129,0.0012486312,0.000087381784,0.0000036101183,0.00012814034,0.000035582576,0.000023139233,0.00010124861,0.00030753989,0.9980562,0.0000034050067],"about_ca_topic_score_codex":0.04571563,"about_ca_topic_score_gemma":0.12853225,"teacher_disagreement_score":0.95428437,"about_ca_system_score_codex":0.0017707438,"about_ca_system_score_gemma":0.0018882026,"threshold_uncertainty_score":0.32679987},"labels":[],"label_agreement":null},{"id":"W6948018149","doi":"10.5061/dryad.sf7m0cg2k","title":"Behavioural plasticity is associated with reduced extinction risk in birds","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Diafiltration; Dysgeusia; Liquation; Hyporeflexia; Fusible alloy; Triacetin","score_opus":0.039270869105776096,"score_gpt":0.23838697925788185,"score_spread":0.19911611015210576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948018149","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066505425,0.0012992267,0.0008338711,0.0004574442,0.00008068632,0.000019688752,0.92861784,0.0004657035,0.0017200338],"genre_scores_gemma":[0.053303704,0.0002686946,0.0012268602,0.00014706072,0.00001958451,0.000062243125,0.9441708,0.00007206722,0.00072900276],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99943644,0.000108579065,0.000057552075,0.00025795854,0.00007857618,0.000060853923],"domain_scores_gemma":[0.9989899,0.0003484787,0.0002714707,0.00018984974,0.00011693794,0.00008325811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006470935,0.0005544231,0.0005857568,0.0012593475,0.00031312468,0.00097393856,0.0007862801,0.00073581684,0.0047174334],"category_scores_gemma":[0.0034233192,0.0001995521,0.00063322525,0.0020426754,0.00030358002,0.00055217935,0.0007976062,0.00049706205,0.0037247825],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011989081,0.00022176278,0.30618972,0.0031536785,0.00074521586,0.00027147323,0.0002499517,0.008522458,0.0028421066,0.0015939063,0.6309478,0.04406305],"study_design_scores_gemma":[0.00027947623,0.00012348022,0.75180656,0.00043098006,0.00027220097,0.00076888123,0.00031167362,0.01092738,0.0011375032,0.003130748,0.23072945,0.000081576996],"about_ca_topic_score_codex":0.012677984,"about_ca_topic_score_gemma":0.028884485,"teacher_disagreement_score":0.012677984,"about_ca_system_score_codex":0.00051030854,"about_ca_system_score_gemma":0.00029059107,"threshold_uncertainty_score":0.025208414},"labels":[],"label_agreement":null},{"id":"W6948169205","doi":"10.48550/arxiv.astro-ph/0506288","title":"A Deep Infrared Search for AXP 1E 1841-045","year":2005,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pulsar; Infrared; Photometry (optics); Limiting; Position (finance); Limiting magnitude","score_opus":0.08690087589726818,"score_gpt":0.20388470585486668,"score_spread":0.1169838299575985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948169205","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9936434,0.00032235577,0.0024955114,0.00027319053,0.00001385094,0.000021025888,0.0005562106,0.0000949751,0.0025795188],"genre_scores_gemma":[0.99038094,0.00008770405,0.005928663,0.000074350464,0.00002838238,0.00000898044,0.0024489225,0.000010245524,0.0010318975],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996724,0.00004817935,0.000014449988,0.00011179217,0.00009632209,0.00005691502],"domain_scores_gemma":[0.99939346,0.00012430939,0.00014650522,0.00007640591,0.000058871494,0.00020039466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062448846,0.00030123434,0.00030812618,0.0016253979,0.0006899044,0.0005832179,0.00039822218,0.0004395353,0.0021247752],"category_scores_gemma":[0.00083185156,0.00018296628,0.000174775,0.0011429715,0.0002677908,0.0004043288,0.0009503641,0.00034883566,0.00068548485],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010617566,0.00041669572,0.7593818,0.00018330205,0.00018312594,0.0024281018,0.00094988494,0.0018779995,0.075018935,0.0038363894,0.0078632245,0.14679882],"study_design_scores_gemma":[0.000053549757,0.00029919142,0.9703363,0.000024795596,0.00006888605,0.0017457699,0.00051725673,0.008037598,0.0061161434,0.0021458075,0.010634271,0.000020388057],"about_ca_topic_score_codex":0.001065185,"about_ca_topic_score_gemma":0.0025132033,"teacher_disagreement_score":0.0021247752,"about_ca_system_score_codex":0.00028784116,"about_ca_system_score_gemma":0.00028883657,"threshold_uncertainty_score":0.0071080923},"labels":[],"label_agreement":null},{"id":"W6948185420","doi":"10.48448/4gcb-xp21","title":"HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding","year":2024,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Decoding methods; Object (grammar); Sentence; Image (mathematics); Association (psychology); Encoding (memory); Modality (human–computer interaction); Generative grammar; Channel (broadcasting)","score_opus":0.008228171800360307,"score_gpt":0.26922926896420385,"score_spread":0.26100109716384357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948185420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0363133,0.00087346067,0.9515455,0.0006126598,0.00017492117,0.00010638239,0.0002352821,0.007876118,0.002262415],"genre_scores_gemma":[0.6700521,0.0003315684,0.31740075,0.0011508549,0.00016450965,0.00022366358,0.0013792444,0.00089812244,0.008399191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992174,0.0002552092,0.00004953834,0.00017433273,0.00021814671,0.00008540858],"domain_scores_gemma":[0.9981286,0.0010113027,0.00014490061,0.000257095,0.00032044086,0.00013760726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013694116,0.0013222005,0.0008257783,0.00060346146,0.00039042573,0.0009199165,0.0019770477,0.0013486537,0.0036687371],"category_scores_gemma":[0.0068639694,0.00035598644,0.0006068098,0.00045689524,0.00077626563,0.0015421538,0.0027170263,0.0020407748,0.0013633111],"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":[0.0005167814,0.00031041517,0.0017537186,0.00029387765,0.00010325441,0.00035942005,0.0002327107,0.271367,0.025685333,0.006097684,0.015403519,0.6778763],"study_design_scores_gemma":[0.000030501133,0.000081986254,0.00012901211,0.000009253329,0.000009849818,0.00004918572,0.000021812775,0.9898197,0.005724098,0.0031798605,0.0009332408,0.000011514735],"about_ca_topic_score_codex":0.0046382826,"about_ca_topic_score_gemma":0.007959769,"teacher_disagreement_score":0.0046382826,"about_ca_system_score_codex":0.0007252072,"about_ca_system_score_gemma":0.0011962326,"threshold_uncertainty_score":0.012273133},"labels":[],"label_agreement":null},{"id":"W6948210343","doi":"10.5061/dryad.51c59zw62","title":"Defining Relictual Biodiversity: Conservation Units in Speckled Dace (Leuciscidae: Rhinichthys osculus) of the Greater Death Valley Ecosystem","year":2020,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coalescent theory; Population; Threatened species; Biodiversity; Metapopulation; Habitat; Population fragmentation; Wildlife conservation; Ecosystem; Conservation biology","score_opus":0.06258344746041572,"score_gpt":0.26535257073492635,"score_spread":0.20276912327451063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948210343","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99910057,0.00008388504,0.00032246127,0.000005413704,5.1262737e-7,0.0000051401007,0.00008064762,0.0000033286005,0.00039804567],"genre_scores_gemma":[0.9987587,0.000018021845,0.0009845672,0.0000053336785,5.831309e-7,0.0000067928263,0.00015316684,0.0000016793522,0.000071231094],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995222,0.0001069424,0.000060557042,0.00019367834,0.00006985572,0.00004682298],"domain_scores_gemma":[0.99880755,0.000323368,0.0004999541,0.000120010256,0.00013965728,0.000109497894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010061384,0.00016569054,0.00017179894,0.0032733,0.0007346947,0.0006917498,0.0003762046,0.00025208673,0.0007265824],"category_scores_gemma":[0.0018195638,0.000106852814,0.00017399681,0.0015788015,0.0010938086,0.0005479969,0.0010844516,0.00018796977,0.000091524016],"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":[0.00009849214,0.000019161942,0.9747791,0.000054864795,0.00003569688,0.00008574349,0.0025206774,0.00018027342,0.00992652,0.00038614796,0.00004375208,0.011869443],"study_design_scores_gemma":[0.0000017675765,0.000033200442,0.9970004,0.000009634781,0.000011362858,0.00013059171,0.0015826746,0.000454203,0.00042144526,0.000120128214,0.00023060887,0.0000041209705],"about_ca_topic_score_codex":0.006969793,"about_ca_topic_score_gemma":0.038575865,"teacher_disagreement_score":0.006969793,"about_ca_system_score_codex":0.0006460512,"about_ca_system_score_gemma":0.00036739704,"threshold_uncertainty_score":0.013858438},"labels":[],"label_agreement":null},{"id":"W6948267428","doi":"10.48597/pc2e-sua8","title":"Measurement of Particle_number_size_distribution at London - North Kensington","year":2023,"lang":"en","type":"dataset","venue":"EBAS","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Sample (material); Period (music)","score_opus":0.029605780682171606,"score_gpt":0.24437867206919503,"score_spread":0.21477289138702343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948267428","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013672324,0.00007502057,0.00023107015,0.000085656335,0.00004427413,0.000021043943,0.9955309,0.0010373084,0.0016074523],"genre_scores_gemma":[0.002217134,0.000073354306,0.00093814917,0.000042890464,0.00001336271,0.000093246235,0.9947619,0.00016109816,0.0016989089],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915504,0.000062564155,0.000074404415,0.00025870322,0.00029930755,0.00014989711],"domain_scores_gemma":[0.9979074,0.00026904055,0.00016123394,0.0005159058,0.000928502,0.00021799476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008335114,0.0022327758,0.0013835382,0.0029535927,0.00081748085,0.0017371749,0.0021392961,0.0018638648,0.023155887],"category_scores_gemma":[0.003391849,0.00065015163,0.0012010764,0.0045794058,0.00041630256,0.000977416,0.0014803439,0.00119615,0.050948035],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009097689,0.00003514892,0.0031069221,0.00033695486,0.000042546428,0.000040916344,0.00003747047,0.0007489197,0.00043471664,0.00033302145,0.990333,0.0044595166],"study_design_scores_gemma":[0.00029563816,0.00003104125,0.04485959,0.00024418233,0.00006944875,0.00009913732,0.00024929518,0.003111514,0.0037529338,0.0016900125,0.9454926,0.00010448036],"about_ca_topic_score_codex":0.07291991,"about_ca_topic_score_gemma":0.12899785,"teacher_disagreement_score":0.07291991,"about_ca_system_score_codex":0.0013605225,"about_ca_system_score_gemma":0.0020168659,"threshold_uncertainty_score":0.14499092},"labels":[],"label_agreement":null},{"id":"W6948291645","doi":"10.5061/dryad.pg4f4qrpf","title":"Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université Laval; Université du Québec à Rimouski","funders":"","keywords":"Predation; Trophic level; Habitat; Food web; Vertebrate; Herbivore; Predator; Competition (biology); Arctic","score_opus":0.040493019700205975,"score_gpt":0.2524051740059981,"score_spread":0.21191215430579213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948291645","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012019505,0.00025956286,0.00019701789,0.000115878735,0.000026318274,0.000014500272,0.9866217,0.0001962612,0.0005492737],"genre_scores_gemma":[0.0094950525,0.00009164979,0.0005951127,0.000034645396,0.000006619843,0.000068315014,0.9893852,0.000029948342,0.0002934802],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991171,0.00019501238,0.00012368051,0.00030646188,0.00014113246,0.00011665641],"domain_scores_gemma":[0.998492,0.000659496,0.00022891589,0.00024424298,0.00025025234,0.00012512872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013451959,0.0009067727,0.0010890788,0.0023705517,0.00051869487,0.0013487738,0.0013346773,0.0011396165,0.0071204714],"category_scores_gemma":[0.0046317885,0.00033808773,0.00089412986,0.0037508407,0.0003652418,0.00068518234,0.001160513,0.0007604861,0.0055898214],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094163977,0.00026079407,0.08959956,0.0047513708,0.0007047372,0.00044479148,0.00033487327,0.0056012487,0.0019525059,0.0017862751,0.8723172,0.021304926],"study_design_scores_gemma":[0.0010440458,0.00016740964,0.37164727,0.0010093985,0.0004840349,0.0010729877,0.00072462845,0.011959722,0.0017685196,0.0030445056,0.60690314,0.00017426685],"about_ca_topic_score_codex":0.029753346,"about_ca_topic_score_gemma":0.051784933,"teacher_disagreement_score":0.029753346,"about_ca_system_score_codex":0.0012000598,"about_ca_system_score_gemma":0.000993635,"threshold_uncertainty_score":0.05916035},"labels":[],"label_agreement":null},{"id":"W6948325603","doi":"10.48550/arxiv.astro-ph/0009306","title":"Multiresolution Analysis of Substructure in Dark Matter Halos","year":2000,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Halo; Substructure; Dark matter; Population; Cluster analysis; Wavelet; Wavelet transform; Multiresolution analysis","score_opus":0.03962676767940601,"score_gpt":0.17718666999983115,"score_spread":0.13755990232042514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948325603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6463633,0.00076918636,0.3504719,0.00027113303,0.000023142242,0.000021086069,0.0001292325,0.00034711216,0.0016038377],"genre_scores_gemma":[0.95893425,0.00031558584,0.0401533,0.000016101656,0.000019360537,0.00000848857,0.00009911701,0.000052579537,0.0004012603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998518,0.00003846516,0.000006215527,0.000020341677,0.000052197796,0.000030981755],"domain_scores_gemma":[0.99940944,0.00027902602,0.00010860792,0.000084330466,0.000064728,0.00005389825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050892093,0.00026048528,0.00035861525,0.001397681,0.00034540298,0.00086418,0.00026328675,0.00037140137,0.0006226879],"category_scores_gemma":[0.0024133194,0.00025107808,0.00038706244,0.0010230688,0.00035831347,0.0005667625,0.0008399404,0.00043485512,0.00014214747],"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":[0.00038850086,0.00009461401,0.025070501,0.00016091684,0.00016795775,0.00095160626,0.0006616499,0.71609515,0.077878594,0.0361337,0.0016275616,0.14076917],"study_design_scores_gemma":[0.000008122899,0.000014027123,0.006383991,0.000007012247,0.000013220639,0.00009646803,0.00005748778,0.97813725,0.004465934,0.010248443,0.000555446,0.00001256707],"about_ca_topic_score_codex":0.0011643847,"about_ca_topic_score_gemma":0.00060425396,"teacher_disagreement_score":0.001397681,"about_ca_system_score_codex":0.00025269866,"about_ca_system_score_gemma":0.00016955055,"threshold_uncertainty_score":0.0026914477},"labels":[],"label_agreement":null},{"id":"W6948376403","doi":"10.5061/dryad.p8cz8w9vz","title":"Density and genetic diversity of grizzly bears at the northern edge of their distribution","year":2023,"lang":"en","type":"dataset","venue":"DRYAD","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria; University of Calgary","funders":"","keywords":"Genetic diversity; Tundra; Range (aeronautics); Ursus; Population; Population density; Species richness; Grizzly Bears; Habitat","score_opus":0.01653240462580413,"score_gpt":0.21302272866317334,"score_spread":0.1964903240373692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948376403","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99969757,0.00003914423,0.000031271364,0.000008657316,0.0000010499011,0.0000013142879,0.000043151438,6.649132e-7,0.00017720202],"genre_scores_gemma":[0.9996462,0.00004380127,0.00006461122,0.0000077424875,0.0000056302447,0.0000023528305,0.00011852843,6.744394e-7,0.00011046958],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99975353,0.000054614302,0.000015595386,0.000092491166,0.000046840607,0.000036957063],"domain_scores_gemma":[0.9994691,0.000076783945,0.00023915518,0.00004973484,0.00007764477,0.00008757887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046591734,0.00012347802,0.00014704035,0.00066600787,0.00033228798,0.0002942844,0.00021781944,0.00018127578,0.0008403555],"category_scores_gemma":[0.00085162645,0.000098256685,0.00018516606,0.00036236044,0.00038561615,0.00028302587,0.00032877887,0.00019809842,0.00015947597],"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":[0.00003151461,0.000022303217,0.99753076,0.0000043218374,0.000025509142,0.000049357237,0.00047422617,0.000056235596,0.0004988128,0.000036532874,0.000050028946,0.0012204663],"study_design_scores_gemma":[9.1250314e-7,0.000019261595,0.99950206,0.0000023066953,0.000005321054,0.00006334067,0.00019650947,0.00006928618,0.0000475314,0.00001198607,0.00007993041,0.0000016043209],"about_ca_topic_score_codex":0.017359097,"about_ca_topic_score_gemma":0.029234365,"teacher_disagreement_score":0.017359097,"about_ca_system_score_codex":0.00023599717,"about_ca_system_score_gemma":0.0001356925,"threshold_uncertainty_score":0.034516156},"labels":[],"label_agreement":null},{"id":"W6948431325","doi":"10.48550/arxiv.astro-ph/0011041","title":"Tidal stirring and the origin of dwarf spheroidals in the Local Group","year":2000,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Dwarf galaxy; Dark matter; Cuspy halo problem; Local Group; Milky Way; Dark matter halo; Halo; Galaxy","score_opus":0.049562823294764385,"score_gpt":0.18430613141371718,"score_spread":0.1347433081189528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948431325","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9941497,0.000059184295,0.0009299186,0.00010089329,0.000016730914,0.000014544361,0.00029486872,0.00011307576,0.00432105],"genre_scores_gemma":[0.99854904,0.000028725779,0.0006910726,0.000023839524,0.00000556899,0.000009056648,0.00025274116,0.000029837141,0.0004100249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999362,0.000015395148,0.0000031550485,0.000010741327,0.0000104085375,0.0000241973],"domain_scores_gemma":[0.9992878,0.0002370486,0.00012374153,0.000048636408,0.00007264767,0.00023011412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003222162,0.00036983847,0.00035685598,0.00051399006,0.0006321465,0.0008103627,0.00069178734,0.000707614,0.002343322],"category_scores_gemma":[0.0014709213,0.00023293433,0.00044352276,0.000388449,0.0006141713,0.00040962876,0.0005944986,0.0004977372,0.00029560743],"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":[0.00024141786,0.00019986874,0.06090867,0.000052662286,0.0000928737,0.00050645944,0.0001764293,0.9267305,0.003327964,0.0033828549,0.0012003223,0.0031799385],"study_design_scores_gemma":[0.000060557384,0.000046069672,0.008133928,0.000006409369,0.000015771338,0.000027776003,0.00007994078,0.9903251,0.0003467487,0.0006192403,0.00033020493,0.000008269973],"about_ca_topic_score_codex":0.025480121,"about_ca_topic_score_gemma":0.01498062,"teacher_disagreement_score":0.025480121,"about_ca_system_score_codex":0.00093890954,"about_ca_system_score_gemma":0.0006267124,"threshold_uncertainty_score":0.05066365},"labels":[],"label_agreement":null},{"id":"W6948616277","doi":"10.5061/dryad.t4b8gtj19","title":"Divergence in life history and behaviour between hybridizing Phymata","year":2021,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Data Management and Algorithms","field":"Computer Science","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 Victoria","funders":"","keywords":"Parapatric speciation; Divergence (linguistics); Life history theory; Life history; Gene flow; Natural selection; Juvenile; Vertebrate","score_opus":0.08198395331410434,"score_gpt":0.29664740360567093,"score_spread":0.21466345029156658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948616277","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014542193,0.00010960354,0.00023988659,0.00011903003,0.00002928552,0.000043619195,0.983878,0.00010364405,0.00093477615],"genre_scores_gemma":[0.011090059,0.000055932247,0.0008165872,0.00006984067,0.000007655841,0.00023660461,0.98703194,0.000027410608,0.0006639326],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99937505,0.00009624652,0.00010480092,0.00018460334,0.00009651399,0.00014280449],"domain_scores_gemma":[0.9983852,0.0005255743,0.00030456152,0.00031674522,0.0003115441,0.00015632022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013178318,0.0007999109,0.0009341425,0.0020306294,0.0006909898,0.0010168164,0.001744198,0.001104069,0.01328341],"category_scores_gemma":[0.0042957845,0.0003316022,0.00076788705,0.0030381298,0.00034246856,0.0005999468,0.0009953276,0.0012022222,0.009744282],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015229346,0.00038691875,0.16520475,0.0033901748,0.00045352144,0.00053315435,0.00059709884,0.0020711464,0.0040098354,0.0023560985,0.7960074,0.023466926],"study_design_scores_gemma":[0.00083611906,0.00022451566,0.46947557,0.0007643768,0.00021286322,0.0011057284,0.0010056525,0.0026685554,0.0022828989,0.0027402872,0.5185617,0.00012162079],"about_ca_topic_score_codex":0.015397405,"about_ca_topic_score_gemma":0.034780324,"teacher_disagreement_score":0.015397405,"about_ca_system_score_codex":0.00076924707,"about_ca_system_score_gemma":0.00073448365,"threshold_uncertainty_score":0.04443741},"labels":[],"label_agreement":null},{"id":"W6948637795","doi":"10.5281/zenodo.11691086","title":"Qss facts mcdonalds pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Quality (philosophy); Work (physics); Population; TSG101; Limiting","score_opus":0.029635298586989937,"score_gpt":0.24202891009161756,"score_spread":0.21239361150462763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948637795","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001960397,0.00073985104,0.0006694836,0.0032336453,0.00325709,0.00022522539,0.013196915,0.0035806268,0.97490114],"genre_scores_gemma":[0.0008088662,0.00057101157,0.00047799156,0.00097023795,0.00047657097,0.000050340703,0.004761669,0.0014809826,0.99040234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976526,0.00011602299,0.00013822186,0.00021497585,0.0016715574,0.00020665115],"domain_scores_gemma":[0.99335384,0.00071637,0.00022891526,0.0005539325,0.004421202,0.00072578096],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012497933,0.0013208374,0.0012718012,0.0038681414,0.0023854976,0.009080858,0.0028843568,0.0039563673,0.9107604],"category_scores_gemma":[0.009520125,0.0011415298,0.0012166458,0.004085048,0.0012350507,0.0072314288,0.003030682,0.0031077422,0.84127206],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012662597,0.000010245989,0.000021062277,0.00009652798,8.544315e-7,0.00003692324,0.000020259315,0.000025719924,0.00008727482,0.0017964976,0.98419833,0.013693492],"study_design_scores_gemma":[0.000004955913,0.0000062130616,0.000082293256,0.000047128455,8.4109826e-7,0.000030690302,0.000028748254,0.000014416721,0.000066944594,0.00034031624,0.99937326,0.000004157666],"about_ca_topic_score_codex":0.02447395,"about_ca_topic_score_gemma":0.038502283,"teacher_disagreement_score":0.0892396,"about_ca_system_score_codex":0.0044017113,"about_ca_system_score_gemma":0.005471856,"threshold_uncertainty_score":0.12728935},"labels":[],"label_agreement":null},{"id":"W6948663810","doi":"10.5281/zenodo.11333709","title":"Sylvilagus (Sylvilagus) nuttallii","year":2005,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subspecies; IUCN Red List; Allopatric speciation; Fauna; Endangered species","score_opus":0.027875214653893397,"score_gpt":0.23386471504264264,"score_spread":0.20598950038874925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948663810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7385577,0.0113038,0.0059254314,0.0004980124,0.00043657087,0.00040333587,0.0076958183,0.00054834684,0.234631],"genre_scores_gemma":[0.9501072,0.0031432856,0.008541384,0.0003242767,0.00009246064,0.00010737023,0.0052511976,0.000042494776,0.03239033],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9999281,0.000005984093,0.000006719626,0.000020713405,0.000029240266,0.000009287837],"domain_scores_gemma":[0.9999095,0.000009207673,0.000041657942,0.000007714841,0.00002222774,0.000009785229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009714848,0.00044039023,0.00015852129,0.0008109543,0.0005137493,0.00018812396,0.00033932104,0.00010882238,0.004285513],"category_scores_gemma":[0.00018426517,0.000115277915,0.000110670684,0.00041561658,0.0001907657,0.00039200924,0.00043895433,0.00017368949,0.002140742],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036280337,0.00018497238,0.24062777,0.00063521677,0.00011317707,0.001325103,0.0025423507,0.00061598216,0.09890439,0.0065383413,0.04213584,0.6060141],"study_design_scores_gemma":[0.00002980845,0.00016709557,0.7490944,0.00012358346,0.0000596873,0.0021882923,0.00047072917,0.0006447738,0.0042841295,0.0007279166,0.24219161,0.000018064435],"about_ca_topic_score_codex":0.013369407,"about_ca_topic_score_gemma":0.054467507,"teacher_disagreement_score":0.013369407,"about_ca_system_score_codex":0.0003952823,"about_ca_system_score_gemma":0.00015876697,"threshold_uncertainty_score":0.026583195},"labels":[],"label_agreement":null},{"id":"W6948728287","doi":"10.5255/ukda-sn-8809-1","title":"Quarterly Labour Force Survey Household Dataset, January - March, 2021","year":2021,"lang":"en","type":"other","venue":"UK Data Archive","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Unemployment; Work (physics); Sample (material); Survey data collection; Northern ireland; Work force; Data collection; Data source","score_opus":0.04867880581222507,"score_gpt":0.2716636286344817,"score_spread":0.22298482282225662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948728287","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041764017,0.00014197979,0.000092078124,0.00018941879,0.00007329075,0.00008416364,0.9968695,0.000048378202,0.0020834887],"genre_scores_gemma":[0.0021027832,0.00030011605,0.0002808592,0.00024622376,0.00005487795,0.0007590369,0.98981094,0.00003366839,0.006411641],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9984433,0.0002591741,0.00025965925,0.00028315576,0.00050290383,0.00025179976],"domain_scores_gemma":[0.99625576,0.00042227525,0.000344489,0.00025090808,0.0024825644,0.00024385497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017904778,0.00097126793,0.0013068925,0.0027037545,0.0006684284,0.0014202888,0.0015469571,0.0011421992,0.06683608],"category_scores_gemma":[0.010090125,0.0005592776,0.00070471247,0.0071105063,0.00016105887,0.0011691271,0.0010502442,0.0015204065,0.074532814],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049518017,0.00001886053,0.0029828819,0.00034991035,0.000023862369,0.000017171953,0.000033603745,0.00009163586,0.00001872309,0.00032955062,0.99071056,0.005373557],"study_design_scores_gemma":[0.0004429544,0.00008000137,0.12564793,0.0007874812,0.00007580564,0.00008736292,0.00050786056,0.00044350687,0.000089541005,0.0007812472,0.8710105,0.0000458767],"about_ca_topic_score_codex":0.11635217,"about_ca_topic_score_gemma":0.115180165,"teacher_disagreement_score":0.11635217,"about_ca_system_score_codex":0.00188235,"about_ca_system_score_gemma":0.0036053942,"threshold_uncertainty_score":0.23134983},"labels":[],"label_agreement":null},{"id":"W6948853753","doi":"10.5281/zenodo.10817998","title":"Probabilités et statistique : sciences de la nature","year":2023,"lang":"fr","type":"book","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Cegep Edouard Montpetit","funders":"","keywords":"Statistical analysis; Context (archaeology); Field (mathematics); Natural (archaeology)","score_opus":0.0730909250878944,"score_gpt":0.3304073033254954,"score_spread":0.257316378237601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948853753","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009138619,0.51493186,0.12594096,0.032848757,0.014524332,0.00005585098,0.0016243265,0.0016549795,0.30750504],"genre_scores_gemma":[0.03489701,0.2356836,0.080245845,0.010774592,0.03183475,0.00036279278,0.0022279434,0.0029207082,0.6010528],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983758,0.00032557975,0.00006818739,0.00029157574,0.00087563414,0.00006327991],"domain_scores_gemma":[0.99559504,0.00312978,0.00015128631,0.0003677799,0.00058737665,0.00016876403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002231633,0.0017312536,0.0016218617,0.0030660464,0.0010885949,0.006431551,0.0014113272,0.0022525718,0.028901404],"category_scores_gemma":[0.006308091,0.0010903619,0.0010085563,0.005112325,0.004107204,0.0080795055,0.0016030403,0.0076121166,0.022129172],"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":[0.000027522296,0.000018545668,0.00012031234,0.00038960535,0.000028129647,0.000031745833,0.000122018784,0.0009783433,0.00036197578,0.46368495,0.44803512,0.08620174],"study_design_scores_gemma":[0.000009293873,0.000011643506,0.0003010807,0.0001900016,0.000009070003,0.00012855466,0.000024191311,0.0015191702,0.00015232721,0.34747022,0.65016884,0.000015652546],"about_ca_topic_score_codex":0.003412907,"about_ca_topic_score_gemma":0.0039646477,"teacher_disagreement_score":0.028901404,"about_ca_system_score_codex":0.003561695,"about_ca_system_score_gemma":0.0025163025,"threshold_uncertainty_score":0.09668481},"labels":[],"label_agreement":null},{"id":"W6948974085","doi":"10.5281/zenodo.12020037","title":"les voyelles orales et nasales pdf","year":2024,"lang":"fr","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Work (physics); Context (archaeology)","score_opus":0.0665771695601961,"score_gpt":0.27745813939132624,"score_spread":0.21088096983113014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6948974085","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027933815,0.011272744,0.0012058647,0.0028336542,0.008342452,0.00014793436,0.006036732,0.0025505745,0.96481663],"genre_scores_gemma":[0.0063820216,0.0058133993,0.0011356641,0.0011413943,0.0013255364,0.000062722465,0.0014508821,0.0007297974,0.98195857],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996351,0.000031653395,0.00001548639,0.000038665814,0.00022050468,0.00005861002],"domain_scores_gemma":[0.99931395,0.00013012618,0.00003728494,0.00005365156,0.00025084894,0.00021411359],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00042242452,0.00070065534,0.000651357,0.00094504893,0.0008329997,0.003498225,0.00048491402,0.001235997,0.73610276],"category_scores_gemma":[0.002018723,0.0002557181,0.00055161153,0.00066354236,0.0003988928,0.0024268276,0.0016490592,0.0016239989,0.49461913],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009537279,0.000031502746,0.00014863152,0.00031689374,0.0000045840793,0.00021356797,0.00010848995,0.000051846262,0.0014744758,0.0014410437,0.7988007,0.19731289],"study_design_scores_gemma":[0.000013823097,0.00002683324,0.0005710719,0.00014420698,0.0000022284044,0.00036936687,0.00010185997,0.000021934764,0.0003482377,0.00023601015,0.99815756,0.000006847335],"about_ca_topic_score_codex":0.0022943332,"about_ca_topic_score_gemma":0.0044497335,"teacher_disagreement_score":0.26389724,"about_ca_system_score_codex":0.00055348035,"about_ca_system_score_gemma":0.0008004824,"threshold_uncertainty_score":0.37641734},"labels":[],"label_agreement":null},{"id":"W6949009258","doi":"10.5281/zenodo.11843940","title":"Geographie gebhardt pdf","year":2024,"lang":"de","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Plan (archaeology); Statistical analysis; Quarter (Canadian coin)","score_opus":0.02640309473926354,"score_gpt":0.23485727150310476,"score_spread":0.2084541767638412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949009258","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001034589,0.0037199522,0.0014083132,0.0015778346,0.0013159969,0.000091612106,0.024627501,0.004350311,0.9618739],"genre_scores_gemma":[0.004034198,0.0030648513,0.0008412249,0.00043434484,0.00019625273,0.000055634377,0.011728978,0.001772176,0.9778723],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996674,0.000027311922,0.000020149193,0.000065515334,0.00016735752,0.0000523335],"domain_scores_gemma":[0.99963355,0.00007019341,0.00002461627,0.00007714717,0.00012723386,0.000067178225],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00026313696,0.00083880546,0.00065291545,0.0027078409,0.0009050235,0.004042141,0.0006519563,0.0011822077,0.72086936],"category_scores_gemma":[0.0009469399,0.0006010661,0.0004045021,0.004913701,0.0002823467,0.0029983674,0.0019354102,0.0011946412,0.63801795],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021145073,0.00001095779,0.00018167739,0.00013335084,0.0000033482604,0.00004328913,0.000053737647,0.00009758241,0.00032248354,0.0024485525,0.9089153,0.087768555],"study_design_scores_gemma":[0.0000025641207,0.0000037267946,0.0005464523,0.000038899445,0.0000014249872,0.000043821074,0.000032104206,0.000028488956,0.00018026984,0.00025779795,0.99886,0.000004510562],"about_ca_topic_score_codex":0.010335378,"about_ca_topic_score_gemma":0.015633233,"teacher_disagreement_score":0.27913064,"about_ca_system_score_codex":0.0008967411,"about_ca_system_score_gemma":0.0008834145,"threshold_uncertainty_score":0.39814597},"labels":[],"label_agreement":null},{"id":"W6949152593","doi":"10.5281/zenodo.14981045","title":"Fig. 80 in A revision of the genus Sclerocoelus Marshall (Diptera: Sphaeroceridae)","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Genus; Dorsum; Scale (ratio); Taxonomy (biology)","score_opus":0.023500312835162185,"score_gpt":0.2352306461710854,"score_spread":0.21173033333592323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949152593","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049438618,0.05607449,0.04514467,0.0075477296,0.032237798,0.0018537997,0.04375842,0.0098340735,0.7541104],"genre_scores_gemma":[0.21971919,0.046716608,0.08251697,0.005319097,0.008323982,0.0017496805,0.067635596,0.003510142,0.5645088],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998109,0.000039412393,0.000022260676,0.00004458715,0.00005692508,0.000025879492],"domain_scores_gemma":[0.99974316,0.000053566913,0.000038225055,0.000040527935,0.00008269244,0.000041733634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033951292,0.0010387573,0.0003532589,0.0035152996,0.0009911545,0.00073720305,0.0008660152,0.0007762941,0.051245425],"category_scores_gemma":[0.0011200871,0.00025899473,0.00034157693,0.0018966926,0.001197169,0.0013051417,0.0008822616,0.001012862,0.023671623],"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":[0.0001767085,0.000031832613,0.0030190481,0.0010683502,0.000039903323,0.0004841093,0.00065110583,0.00025845633,0.0059380676,0.0070434297,0.4480938,0.5331952],"study_design_scores_gemma":[0.00001127151,0.000026824871,0.009158763,0.0002174331,0.000027247068,0.0006009515,0.00014686467,0.00011790523,0.0004541884,0.00069937867,0.9885252,0.00001402294],"about_ca_topic_score_codex":0.009126547,"about_ca_topic_score_gemma":0.017907288,"teacher_disagreement_score":0.051245425,"about_ca_system_score_codex":0.0006939264,"about_ca_system_score_gemma":0.00075651467,"threshold_uncertainty_score":0.17143303},"labels":[],"label_agreement":null},{"id":"W6990512358","doi":"","title":"Disk-based Indexing for NIR-Trees using Polygon Overlays","year":2024,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Data Management and Algorithms","field":"Computer Science","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":"Blackberry (Canada)","funders":"","keywords":"Search engine indexing; Polygon (computer graphics); Set (abstract data type); Bounding overwatch; Point (geometry); Extension (predicate logic); Construct (python library); Spurious relationship; Range (aeronautics); Data structure","score_opus":0.017077268630315043,"score_gpt":0.22746914627542125,"score_spread":0.2103918776451062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990512358","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06190837,0.0009658697,0.9132293,0.0001912485,0.00009807913,0.00022828713,0.0011596878,0.01124602,0.010973271],"genre_scores_gemma":[0.21020877,0.0008254111,0.7767006,0.000096237716,0.000092252965,0.00021062267,0.004751954,0.0012727376,0.0058414396],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986688,0.00015591197,0.00013202002,0.00021883457,0.00068290817,0.0001415655],"domain_scores_gemma":[0.9975661,0.0005790129,0.00024462646,0.0010714185,0.00043368008,0.00010511806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006630983,0.0005495229,0.0008741977,0.0015185472,0.0010591263,0.0020879614,0.002247056,0.00045075902,0.0032734878],"category_scores_gemma":[0.004097718,0.00039105507,0.0006205077,0.0034773399,0.00071230484,0.003920385,0.002846704,0.0006560073,0.0023732428],"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":[0.00073648925,0.00030755723,0.0046968674,0.0005818499,0.0000852903,0.00043939566,0.0011823281,0.06796395,0.08045588,0.07344398,0.028282946,0.7418234],"study_design_scores_gemma":[0.00013458519,0.00050639175,0.00248723,0.00010741585,0.000067792156,0.0010146572,0.00080622843,0.7533573,0.088713564,0.048979335,0.10370526,0.000120272525],"about_ca_topic_score_codex":0.0030414837,"about_ca_topic_score_gemma":0.004144978,"teacher_disagreement_score":0.0032734878,"about_ca_system_score_codex":0.000833876,"about_ca_system_score_gemma":0.0010681867,"threshold_uncertainty_score":0.010950923},"labels":[],"label_agreement":null},{"id":"W6990527104","doi":"","title":"Distributed Spatial Data Sharing: a new era in sharing spatial data","year":2023,"lang":"en","type":"article","venue":"Scholars Commons (Wilfrid Laurier University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spatial analysis; Variety (cybernetics); Data sharing; Tree (set theory); Process (computing); Geographic information system; Set (abstract data type); Distributed database; Information sharing","score_opus":0.07518964620542996,"score_gpt":0.2697038739696385,"score_spread":0.19451422776420857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990527104","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010853824,0.018546723,0.93625677,0.013155654,0.0013802715,0.00020331473,0.00037060806,0.0013227097,0.017910136],"genre_scores_gemma":[0.31033456,0.03742542,0.62981886,0.0037263038,0.0032884704,0.00049061986,0.0015031699,0.0005488159,0.012863686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99492526,0.0014846361,0.00037045815,0.0008908093,0.0019714965,0.0003574283],"domain_scores_gemma":[0.9890715,0.0036185125,0.00058458844,0.003886928,0.0020511288,0.0007873593],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0060143243,0.00069995824,0.0012364073,0.0016832711,0.0022193245,0.0064708674,0.0046168985,0.0025838753,0.0035062279],"category_scores_gemma":[0.0091581885,0.0006694793,0.0011521204,0.0072905133,0.004065042,0.021260273,0.0074325115,0.0038314878,0.0009870887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014406847,0.000090140355,0.0018037547,0.0011403587,0.00011427174,0.00029034022,0.0031740167,0.016743504,0.0046851835,0.626031,0.030566314,0.31521705],"study_design_scores_gemma":[0.000041720126,0.00018681906,0.0010237758,0.0004914695,0.00007655276,0.0009766944,0.002146527,0.07075743,0.0044442774,0.36698696,0.5527447,0.00012305667],"about_ca_topic_score_codex":0.0035445036,"about_ca_topic_score_gemma":0.0024387247,"teacher_disagreement_score":0.9953831,"about_ca_system_score_codex":0.0027795553,"about_ca_system_score_gemma":0.0039219814,"threshold_uncertainty_score":0.031807125},"labels":[],"label_agreement":null},{"id":"W6996646632","doi":"","title":"Succinct and Compact Data Structures for Intersection Graphs","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Chordal graph; Tree traversal; Interval graph; Intersection (aeronautics); Data structure; Interval (graph theory); Indifference graph; Graph; Space (punctuation); Intersection graph","score_opus":0.02664222795657068,"score_gpt":0.24213324484190177,"score_spread":0.2154910168853311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996646632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020786498,0.00056790205,0.9606597,0.00090592296,0.00013518431,0.0002681333,0.004855926,0.0076822066,0.0041385437],"genre_scores_gemma":[0.20628957,0.0008398799,0.77023304,0.0005924026,0.00016329484,0.0009360244,0.012797154,0.0019071603,0.006241535],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9924595,0.0012771321,0.0015645885,0.0012954662,0.002749131,0.00065421406],"domain_scores_gemma":[0.9749234,0.006799217,0.0023564517,0.012504731,0.0028208198,0.00059549016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036038342,0.0013912845,0.0018485122,0.0034148148,0.00199214,0.0069191386,0.0057308227,0.0015675073,0.010437416],"category_scores_gemma":[0.021114117,0.0016222972,0.0023841916,0.0075347545,0.002467138,0.023633175,0.0076369066,0.004654686,0.0032999702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086970703,0.00032899823,0.0036638493,0.0007035858,0.00009574421,0.00021005602,0.001562132,0.0578752,0.0081122415,0.6176232,0.022228263,0.28672704],"study_design_scores_gemma":[0.00017947855,0.0004107729,0.00065637636,0.00046997407,0.00011694707,0.00044255998,0.0010006233,0.20174271,0.02903088,0.65415645,0.11156349,0.0002297],"about_ca_topic_score_codex":0.0032132033,"about_ca_topic_score_gemma":0.0054162755,"teacher_disagreement_score":0.010437416,"about_ca_system_score_codex":0.0036127865,"about_ca_system_score_gemma":0.0032215822,"threshold_uncertainty_score":0.0349167},"labels":[],"label_agreement":null},{"id":"W7015107224","doi":"","title":"Solving Canadian Traveller Problem","year":2019,"lang":"sk","type":"dissertation","venue":"Brno University of Technology Digital Library (Brno University of Technology)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Government (linguistics); Field (mathematics)","score_opus":0.004536303439662006,"score_gpt":0.1519327285313811,"score_spread":0.1473964250917191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015107224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32562208,0.0048805643,0.33629957,0.018433915,0.0017913631,0.0014935173,0.01174084,0.0041467613,0.2955913],"genre_scores_gemma":[0.60938627,0.0023989507,0.24914356,0.001768441,0.00027866825,0.00041360478,0.015622558,0.0006356863,0.12035235],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987772,0.0001703549,0.00003745092,0.00033592858,0.0002890796,0.0003899455],"domain_scores_gemma":[0.9988825,0.00039241492,0.00005776386,0.000095174226,0.00039536224,0.00017680816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055189495,0.0011251476,0.0010758548,0.0009201623,0.0027872424,0.0022960869,0.0021375604,0.002478671,0.027545422],"category_scores_gemma":[0.0035065683,0.0003276552,0.0010366386,0.0014053294,0.0008508652,0.0024481716,0.0016939879,0.0022301897,0.0017736076],"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":[0.00080081756,0.00060472894,0.0070651984,0.0011032148,0.00021752693,0.0011377191,0.00077517366,0.22994085,0.0031852636,0.17296508,0.3303674,0.25183704],"study_design_scores_gemma":[0.00030674334,0.00018942352,0.0039598593,0.00022802621,0.0001721948,0.0008428587,0.0029163929,0.72931147,0.004611067,0.103631176,0.15367585,0.0001549488],"about_ca_topic_score_codex":0.3551009,"about_ca_topic_score_gemma":0.34579656,"teacher_disagreement_score":0.3551009,"about_ca_system_score_codex":0.00517747,"about_ca_system_score_gemma":0.010260125,"threshold_uncertainty_score":0.706068},"labels":[],"label_agreement":null},{"id":"W70182391","doi":"10.1007/978-3-642-31603-6_10","title":"Fuzzy Logic Ranking for Personalized Geographic Information Retrieval","year":2012,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Information retrieval; Ranking (information retrieval); Computer science; Fuzzy logic; Artificial intelligence","score_opus":0.02428969269139836,"score_gpt":0.2655734433281391,"score_spread":0.24128375063674073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W70182391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014108612,0.004262185,0.963992,0.00049483514,0.00034318748,0.00008848069,0.0004389602,0.001364324,0.014907413],"genre_scores_gemma":[0.355178,0.0027129685,0.6117097,0.00026203608,0.00051812263,0.0001489435,0.0010863331,0.00017366094,0.02821035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926823,0.00016920698,0.000039111488,0.00007448783,0.0003910107,0.000058003872],"domain_scores_gemma":[0.9996594,0.00013497156,0.000019597544,0.000054628596,0.00011631371,0.000015091661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069972215,0.00042123004,0.0009156265,0.0012313806,0.00048204977,0.0011718252,0.0011767313,0.0006284503,0.007843188],"category_scores_gemma":[0.0019133923,0.00018958704,0.0005022048,0.0015401146,0.0002694645,0.0014115854,0.00044258244,0.0007822403,0.0019719256],"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":[0.00017632716,0.00013884166,0.00032695272,0.00027372508,0.00006723453,0.000080284684,0.000078256795,0.069065295,0.0068805814,0.07436512,0.028456701,0.82009065],"study_design_scores_gemma":[0.000040439245,0.00013484969,0.00075671636,0.000052993357,0.000060260216,0.00019328858,0.000058391946,0.8604133,0.0056075817,0.11001817,0.022608502,0.000055549688],"about_ca_topic_score_codex":0.003910843,"about_ca_topic_score_gemma":0.0057555377,"teacher_disagreement_score":0.007843188,"about_ca_system_score_codex":0.0009823757,"about_ca_system_score_gemma":0.00070763694,"threshold_uncertainty_score":0.026238084},"labels":[],"label_agreement":null},{"id":"W7022295737","doi":"","title":"Editorial","year":2025,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Data Management and Algorithms","field":"Computer Science","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":"The Journal of Student Science and Technology","funders":"","keywords":"Commit; Pleasure; Directory; Mistake; Publishing; Ridiculous","score_opus":0.16447767475935532,"score_gpt":0.5401351807391032,"score_spread":0.37565750597974784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7022295737","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013497181,0.0041605555,0.000343074,0.040600374,0.9432199,0.00004425789,0.00031312226,0.00019833744,0.010985423],"genre_scores_gemma":[0.0026208784,0.007859831,0.00046806003,0.042027734,0.86762697,0.00007169266,0.00055793015,0.00026261774,0.07850419],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99579597,0.0004723036,0.00045708288,0.0008264547,0.0020621605,0.00038603388],"domain_scores_gemma":[0.97302794,0.004078203,0.0013698859,0.0016624056,0.015710223,0.0041513117],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0038915442,0.0014712695,0.0013043607,0.0019891178,0.0019487288,0.006641411,0.0023811585,0.004549462,0.09794026],"category_scores_gemma":[0.03165808,0.00049066066,0.0012975743,0.000856984,0.0016120694,0.003815469,0.0014958262,0.00816179,0.06999329],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002042144,0.0000062307304,0.000036662525,0.00010114809,0.000004685773,0.00007126349,0.0000114086515,0.000011031932,0.000047070585,0.00039325835,0.9906137,0.008683149],"study_design_scores_gemma":[0.000012605451,0.000013655751,0.00013030079,0.00022383877,0.00000765553,0.0002927147,0.00003994765,0.000031683572,0.00012722073,0.00064524607,0.9984681,0.0000069136036],"about_ca_topic_score_codex":0.00073642255,"about_ca_topic_score_gemma":0.0013432757,"teacher_disagreement_score":0.90205973,"about_ca_system_score_codex":0.0019163205,"about_ca_system_score_gemma":0.0030711752,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7026718464","doi":"","title":"ARCHIVEIT-3490-PATCH-ERMBEN-20130129204122-00000-wbgrp-crawl065.us.archive.org-6681.warc.gz","year":2013,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","score_opus":0.005793147156845324,"score_gpt":0.17965805814514155,"score_spread":0.17386491098829623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026718464","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005238519,0.00023046405,0.0052179117,0.00054825726,0.00029147055,0.00016535552,0.5474493,0.23205066,0.21352285],"genre_scores_gemma":[0.0028048097,0.00042215106,0.006500181,0.0004210809,0.00012264817,0.0002623325,0.6064211,0.1448168,0.23822889],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99879813,0.00011071311,0.00007926183,0.00028613763,0.0005206902,0.00020498752],"domain_scores_gemma":[0.9915883,0.0011449985,0.00025624747,0.003324938,0.0027254533,0.00096002716],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014646016,0.0029130203,0.001820812,0.0079528745,0.002054332,0.0061039715,0.0033287196,0.0019109752,0.79870445],"category_scores_gemma":[0.011806004,0.0023602904,0.0011166868,0.010835112,0.00076915073,0.0049161757,0.005049212,0.0018479906,0.92603743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029813718,0.000012246009,0.0001372805,0.00007729425,0.0000040473346,0.000005902325,0.000030697305,0.00003866226,0.00010170803,0.0003126208,0.98651516,0.012734583],"study_design_scores_gemma":[0.000037761067,0.0000081454955,0.00083811226,0.0000620918,0.000005009945,0.000027446124,0.000036467824,0.00022993116,0.0009115444,0.00061796024,0.9972047,0.000020741025],"about_ca_topic_score_codex":0.032128073,"about_ca_topic_score_gemma":0.027904732,"teacher_disagreement_score":0.20129555,"about_ca_system_score_codex":0.0025457104,"about_ca_system_score_gemma":0.002174247,"threshold_uncertainty_score":0.28712362},"labels":[],"label_agreement":null},{"id":"W7028363456","doi":"","title":"Enchansing the Ionic Purity of Hydrophilic Channels by Blending Fully Sulfonated Graft Copolymers with PVDF Homopolymer.","year":2013,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Data Management and Algorithms","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":"Simon Fraser University","funders":"","keywords":"Copolymer; Ionic bonding; Polymer; Ionic liquid; Polymerization","score_opus":0.006322794460151059,"score_gpt":0.17143946225631151,"score_spread":0.16511666779616047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028363456","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9777118,0.0011347554,0.016751092,0.00015227594,0.00006135155,0.000029854626,0.00022311592,0.00039311923,0.0035426228],"genre_scores_gemma":[0.98804605,0.000701412,0.008612537,0.000044118584,0.000013935745,0.000029942965,0.00021829714,0.00008870606,0.0022450031],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990606,0.000014841354,0.00000700562,0.00002005283,0.000026017842,0.000026046568],"domain_scores_gemma":[0.999746,0.00009950242,0.00007517938,0.00002802636,0.000024333984,0.00002692851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024708794,0.0002192557,0.000111423666,0.0001649984,0.0000999348,0.00027275024,0.00011804104,0.00018253292,0.001720657],"category_scores_gemma":[0.00045408538,0.00010209384,0.00009780872,0.00019742282,0.00014501896,0.0005943103,0.00026975086,0.00037953828,0.0003569834],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000889726,0.000014882234,0.000112499794,0.000028954455,0.0000030716262,0.000016160366,0.000013014429,0.00013980291,0.99464214,0.00028857353,0.00009770684,0.0045541655],"study_design_scores_gemma":[0.0000062881863,0.00003356846,0.0002412869,0.0000023577977,0.0000034739414,0.000018386054,0.0000048603706,0.0011348994,0.99739254,0.000052986263,0.0011070628,0.0000023334153],"about_ca_topic_score_codex":0.00029462774,"about_ca_topic_score_gemma":0.0005088043,"teacher_disagreement_score":0.001720657,"about_ca_system_score_codex":0.0001718286,"about_ca_system_score_gemma":0.00019425353,"threshold_uncertainty_score":0.0057561994},"labels":[],"label_agreement":null},{"id":"W7036937941","doi":"","title":"Chas. Nelson Making Canoe Mile Record on Moosehead Lake, Maine","year":2013,"lang":"en","type":"article","venue":"DigitalCommons (California Polytechnic State University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mile; Historical record; Quarter (Canadian coin); Plank","score_opus":0.018556149998232144,"score_gpt":0.2114615323804029,"score_spread":0.19290538238217075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036937941","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007654369,0.008534926,0.002305807,0.01762675,0.004391619,0.000066767905,0.009316702,0.0023424155,0.9477606],"genre_scores_gemma":[0.013119626,0.0042787204,0.0015032438,0.0005713846,0.00035778063,0.000017089804,0.002746157,0.00045234806,0.9769537],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99981207,0.000012574166,0.000004617755,0.00003088677,0.000115728624,0.000024140154],"domain_scores_gemma":[0.99938107,0.000051742772,0.000030219888,0.00007497447,0.0003097061,0.00015217203],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030623024,0.00029662915,0.00018986671,0.0011211856,0.0016570733,0.0018222063,0.00038405694,0.00045999163,0.21621029],"category_scores_gemma":[0.001151936,0.00024596232,0.000108652646,0.0019530384,0.00039466083,0.0015810708,0.001224281,0.00087943353,0.08662188],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023539253,0.000006475907,0.000876675,0.000022654973,0.000001492238,0.000026425641,0.00007809733,0.000038798637,0.00021475794,0.0027025468,0.93296176,0.06304677],"study_design_scores_gemma":[0.0000010938302,0.000002000949,0.00094830507,0.000019755873,9.15434e-7,0.000009030358,0.0000523731,0.000024406092,0.00007760472,0.00017928903,0.99868315,0.000002092434],"about_ca_topic_score_codex":0.088619806,"about_ca_topic_score_gemma":0.26272774,"teacher_disagreement_score":0.7837897,"about_ca_system_score_codex":0.001131916,"about_ca_system_score_gemma":0.00095389615,"threshold_uncertainty_score":0.72329545},"labels":[],"label_agreement":null},{"id":"W7039283916","doi":"","title":"Letter from Charles Maltby to Lewis V. Bogy, 1866","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Whiting; Quarter (Canadian coin); Mercantilism; Period (music)","score_opus":0.02558401916080072,"score_gpt":0.2239214044289957,"score_spread":0.19833738526819497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039283916","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028211412,0.016285187,0.00067007565,0.66705,0.03292306,0.00007581305,0.0010539964,0.00030039967,0.27882028],"genre_scores_gemma":[0.01055826,0.0022382243,0.00017242832,0.16603966,0.0027002466,0.00003615238,0.00018776432,0.000109408276,0.8179578],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991192,0.0001367247,0.000040628176,0.0001739534,0.0003091119,0.00022042409],"domain_scores_gemma":[0.9985098,0.00050808897,0.00006569428,0.00006184893,0.0005453896,0.00030907986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010193998,0.00043185294,0.00048178356,0.0006726563,0.0059615206,0.0031779595,0.00063122774,0.005132475,0.086264044],"category_scores_gemma":[0.008303436,0.00048495768,0.00032793553,0.0005685542,0.0013753873,0.002060638,0.0016437662,0.008261594,0.033869967],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001116356,0.000002011639,0.00011298778,0.000006051316,9.917856e-7,0.00003429802,0.000060563016,0.0000051902107,0.000023937255,0.0025957725,0.9946806,0.0024663978],"study_design_scores_gemma":[0.000004900793,0.0000073985166,0.00044450304,0.00005547348,0.0000016015589,0.000083812876,0.00017964926,0.000021312271,0.000088396824,0.0013804774,0.99772424,0.000008259461],"about_ca_topic_score_codex":0.045813814,"about_ca_topic_score_gemma":0.09556212,"teacher_disagreement_score":0.086264044,"about_ca_system_score_codex":0.0028793951,"about_ca_system_score_gemma":0.0038764698,"threshold_uncertainty_score":0.28858197},"labels":[],"label_agreement":null},{"id":"W7042704397","doi":"","title":"IN QUEBEC &amp;amp;\\\\-⓶௵(+27`72`994`9256)&amp;amp;\\\\-⓶௵BLACK MAGIC *MARRIAGE SPELL* QUEBEC, QUEBEC","year":2021,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thriving; Happiness; Spell; MAGIC (telescope); Happening; Appeasement","score_opus":0.023296864008367328,"score_gpt":0.26901574355412117,"score_spread":0.24571887954575383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7042704397","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013351226,0.00091835874,0.00029196334,0.0023258764,0.0006830849,0.000072757735,0.008065327,0.0007435787,0.985564],"genre_scores_gemma":[0.0021053466,0.0002389287,0.00012274247,0.00029169483,0.000012843584,0.00000965878,0.00072312204,0.00020559948,0.9962901],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996475,0.000020522377,0.0000075923895,0.000069749636,0.0001323464,0.0001222202],"domain_scores_gemma":[0.9986872,0.000067172965,0.000027067852,0.00006905956,0.0006396646,0.0005098433],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00030729274,0.00082250463,0.0005869209,0.00051640737,0.007085776,0.0044565825,0.0010092142,0.0018233419,0.88549525],"category_scores_gemma":[0.0010757046,0.00051528093,0.0005493802,0.0013105919,0.0009432511,0.0012622012,0.0015600296,0.0016838346,0.6153519],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042513173,0.000031756346,0.00060738384,0.00007173083,0.0000033645365,0.00009262046,0.000107924076,0.00005883442,0.00034646626,0.0014087716,0.9634681,0.03376057],"study_design_scores_gemma":[0.000009170035,0.0000065288878,0.0013658487,0.00005463798,0.0000016458756,0.000025435133,0.00020749032,0.00003761461,0.00013513773,0.00007727952,0.9980716,0.0000075587495],"about_ca_topic_score_codex":0.8540465,"about_ca_topic_score_gemma":0.96071565,"teacher_disagreement_score":0.8540465,"about_ca_system_score_codex":0.01048445,"about_ca_system_score_gemma":0.013267451,"threshold_uncertainty_score":0.293626},"labels":[],"label_agreement":null},{"id":"W7043032612","doi":"","title":"Similarity search and applications 5th international conference, SISAP 2012, Toronto, ON, Canada, August 9 - 10, 2012 ; proceedings","year":2012,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Similarity (geometry); Similitude; Feature (linguistics); Nearest neighbor search; Search engine indexing","score_opus":0.033676597567305545,"score_gpt":0.28072333174730646,"score_spread":0.24704673418000092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043032612","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041129302,0.104312405,0.7042744,0.016127003,0.028176751,0.0014409258,0.010096676,0.019420747,0.07502184],"genre_scores_gemma":[0.09403466,0.042767137,0.48219058,0.0018542771,0.003251274,0.0006500499,0.037648976,0.0025526832,0.33505037],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976267,0.00043914723,0.00021737155,0.00029449468,0.0011690068,0.00025320172],"domain_scores_gemma":[0.9947812,0.0004456633,0.00007805758,0.0007773588,0.0033330333,0.0005847171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045740837,0.0013533082,0.0029001778,0.0038913058,0.002106495,0.005792179,0.0030604738,0.0014945974,0.038468257],"category_scores_gemma":[0.0054918285,0.00063774013,0.0015515959,0.0055617807,0.0016307995,0.0031342353,0.0034040082,0.0022048317,0.016116124],"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":[0.00033406477,0.0003269851,0.0014638063,0.0004872263,0.00015520646,0.00012145685,0.00024575726,0.00213881,0.0050851507,0.009682458,0.4563612,0.52359796],"study_design_scores_gemma":[0.000100429446,0.0003558347,0.0074109086,0.00043606592,0.00021596838,0.00073836703,0.0009190266,0.06523145,0.013020821,0.027336504,0.8841123,0.00012230803],"about_ca_topic_score_codex":0.064614244,"about_ca_topic_score_gemma":0.09162735,"teacher_disagreement_score":0.064614244,"about_ca_system_score_codex":0.004039048,"about_ca_system_score_gemma":0.01018563,"threshold_uncertainty_score":0.12868917},"labels":[],"label_agreement":null},{"id":"W7097865146","doi":"","title":"Abstract","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Online analytical processing; Data cube; Cube (algebra); Visualization; Row; Search engine indexing; Terabyte; Data visualization","score_opus":0.0302815369291705,"score_gpt":0.2266563152090262,"score_spread":0.19637477827985572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097865146","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001485199,0.00093985925,0.010033688,0.0024986246,0.002086404,0.00013659146,0.0069486173,0.0058909897,0.96998],"genre_scores_gemma":[0.0073281694,0.0007093787,0.003741703,0.0009825024,0.0002727054,0.00006645556,0.007245447,0.001232157,0.97842145],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996195,0.000048931553,0.00002194026,0.00009131197,0.00015933886,0.000059036232],"domain_scores_gemma":[0.9992799,0.00007373921,0.000022254457,0.00019218978,0.00021763775,0.00021425274],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046356348,0.0007476599,0.00040614154,0.001157859,0.00084538676,0.0028734596,0.0011225529,0.0012503898,0.74648535],"category_scores_gemma":[0.0011104478,0.0002693574,0.0003059883,0.00095840136,0.00028507784,0.0027781585,0.0024015605,0.0010453725,0.6388733],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104764586,0.000077113786,0.00048279852,0.00015957029,0.000007505278,0.00015205452,0.00007220595,0.00026013746,0.0016084567,0.014073329,0.68485653,0.29814544],"study_design_scores_gemma":[0.0000067535607,0.000008269522,0.00016775516,0.00003088626,0.0000017755096,0.00007685403,0.00002938939,0.00013556042,0.00037697173,0.0014809567,0.9976802,0.0000046226232],"about_ca_topic_score_codex":0.001391853,"about_ca_topic_score_gemma":0.0020413296,"teacher_disagreement_score":0.25351465,"about_ca_system_score_codex":0.0006751733,"about_ca_system_score_gemma":0.00065567443,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W7099915757","doi":"","title":"Diverse methodological approaches and considerations for SOTL in higher education. The Canadian Journal for the Scholarship of Teaching and","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scholarship of Teaching and Learning; Scholarship; Higher education; Educational research; Openness to experience; Transformational leadership","score_opus":0.42885223037755493,"score_gpt":0.37356781407432754,"score_spread":0.05528441630322739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099915757","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009738571,0.18823476,0.08095971,0.6068316,0.011317839,0.00072021014,0.0002709145,0.00017431329,0.10175214],"genre_scores_gemma":[0.6448444,0.0930167,0.15001024,0.06823085,0.005861357,0.004034894,0.00031978948,0.00048054048,0.03320121],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.75169355,0.19100443,0.008624004,0.009669831,0.03417769,0.004830496],"domain_scores_gemma":[0.7993436,0.14463891,0.0070482,0.01206659,0.029874861,0.0070279064],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1625629,0.0014774025,0.0016998767,0.018758776,0.028393596,0.03628994,0.008327206,0.009264621,0.0044945697],"category_scores_gemma":[0.15903659,0.0011183945,0.0014407542,0.02480527,0.12354771,0.019129116,0.020390134,0.01301186,0.0007225881],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016751304,0.000016364478,0.0006896063,0.00061813876,0.000019412997,0.00010957487,0.041609075,0.00016939998,0.00008128737,0.9214644,0.010666825,0.024539132],"study_design_scores_gemma":[0.000028245386,0.000027823426,0.0016489441,0.004435084,0.000034745834,0.00015768125,0.08447816,0.0010006566,0.00018901803,0.655348,0.25258186,0.00006968621],"about_ca_topic_score_codex":0.26013133,"about_ca_topic_score_gemma":0.4390471,"teacher_disagreement_score":0.90939826,"about_ca_system_score_codex":0.09060174,"about_ca_system_score_gemma":0.11557315,"threshold_uncertainty_score":0.8597251},"labels":[],"label_agreement":null},{"id":"W7100737388","doi":"","title":"BAKILLAH MOHAMED REAL TIME SEMANTIC INTEROPERABILITY IN AD HOC NETWORKS OF GEOSPATIAL DATABASES: DISASTER MANAGEMENT CONTEXT","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interoperability; Context (archaeology); Geospatial analysis; Semantics (computer science); Emergency management; Semantic interoperability","score_opus":0.009913733190089067,"score_gpt":0.22818062014972287,"score_spread":0.2182668869596338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100737388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27534312,0.045702104,0.4764096,0.10002795,0.004981498,0.00044989062,0.0008457758,0.0009340771,0.095305994],"genre_scores_gemma":[0.8667777,0.019917095,0.078349054,0.0027569986,0.0011953549,0.0001264854,0.0010515978,0.00008719627,0.029738493],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779415,0.00066000206,0.00020708337,0.00032191098,0.0008099574,0.00020697773],"domain_scores_gemma":[0.99485683,0.002340683,0.00036259947,0.00067308726,0.0015573415,0.00020957169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003502094,0.000320587,0.0005821424,0.0011880237,0.0016477989,0.0038483012,0.0011427554,0.0015589857,0.0030187673],"category_scores_gemma":[0.007702208,0.00031047076,0.00035466958,0.0026983004,0.001677462,0.0069953008,0.0023807406,0.0013023082,0.00075059396],"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":[0.0010569452,0.00031192793,0.010923595,0.0009915569,0.00015243665,0.0032152622,0.0057978104,0.028900031,0.013753496,0.598132,0.045609422,0.29115555],"study_design_scores_gemma":[0.0001596431,0.00027540827,0.010001879,0.0004675497,0.0001660919,0.0042726886,0.012451079,0.14699651,0.023606565,0.26337644,0.53795487,0.00027136222],"about_ca_topic_score_codex":0.015039232,"about_ca_topic_score_gemma":0.006719322,"teacher_disagreement_score":0.015039232,"about_ca_system_score_codex":0.0016397795,"about_ca_system_score_gemma":0.002235749,"threshold_uncertainty_score":0.029903412},"labels":[],"label_agreement":null},{"id":"W7116947360","doi":"10.1016/j.eswa.2025.130889","title":"Skyline operators in multi-criteria decision making: A review of characterization, comparison, and perspectives","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Saint Mary's University","funders":"","keywords":"Skyline; Robustness (evolution); Preference; Operator (biology); Decision support system","score_opus":0.027474986559130412,"score_gpt":0.34044039518064284,"score_spread":0.31296540862151245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116947360","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028733606,0.93006206,0.06053158,0.0021828262,0.00026755055,0.000045183355,0.00008298912,0.00003167595,0.0039228103],"genre_scores_gemma":[0.06966092,0.840705,0.08625048,0.00092706986,0.0014122706,0.00012941081,0.00017416968,0.000040437448,0.0007002975],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99456304,0.0023992418,0.0005815783,0.0007036393,0.0015935557,0.00015900756],"domain_scores_gemma":[0.9767553,0.019398406,0.0010360747,0.0004035039,0.0021838297,0.000222907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013895314,0.0016272569,0.0036111614,0.005045536,0.0006291047,0.004786087,0.0028404116,0.00209697,0.00196171],"category_scores_gemma":[0.015132561,0.000667007,0.001982764,0.010639397,0.0024904595,0.0062898872,0.0015408928,0.0031791723,0.00039214402],"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":[0.0002801734,0.00024290601,0.0017504435,0.018956432,0.0006301128,0.00012371737,0.00045687833,0.0279597,0.00063033996,0.15421318,0.0073067704,0.78744924],"study_design_scores_gemma":[0.00022013634,0.00142294,0.005391397,0.025773508,0.0013436557,0.0012367746,0.0018069394,0.13695359,0.0027831953,0.5404886,0.28212744,0.00045179512],"about_ca_topic_score_codex":0.0034946585,"about_ca_topic_score_gemma":0.003760339,"teacher_disagreement_score":0.013895314,"about_ca_system_score_codex":0.0023510756,"about_ca_system_score_gemma":0.0033551066,"threshold_uncertainty_score":0.07348633},"labels":[],"label_agreement":null},{"id":"W7118015757","doi":"10.1145/3712285.3787168","title":"10.1145/3712285.3787168","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); k-nearest neighbors algorithm; Nearest neighbor search; Key (lock)","score_opus":0.005783491473290162,"score_gpt":0.1698792218710571,"score_spread":0.16409573039776695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118015757","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024454533,0.006670213,0.013633876,0.0018108847,0.002284277,0.000513838,0.02023345,0.023380289,0.9290278],"genre_scores_gemma":[0.0040411875,0.0027430847,0.0024684973,0.00092197815,0.00018393215,0.00022880576,0.010891542,0.0027491893,0.9757717],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99910116,0.000060761613,0.000071850074,0.0002650038,0.000308799,0.00019239707],"domain_scores_gemma":[0.99801433,0.00042271012,0.00010208788,0.0007192479,0.00040294873,0.00033864347],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002000422,0.0039858483,0.0029650398,0.0034576156,0.0027838144,0.005326447,0.0034050213,0.005737519,0.9421247],"category_scores_gemma":[0.0036968235,0.0022046554,0.0014436343,0.011192814,0.0015999592,0.011711297,0.0062673385,0.0031798345,0.9674453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002642651,0.00018980048,0.0004856606,0.0005527169,0.00004592454,0.00018782537,0.00008846506,0.00068099744,0.0011179865,0.006423502,0.69465786,0.295305],"study_design_scores_gemma":[0.0000437156,0.000036422585,0.00079710834,0.00027405503,0.00004731501,0.00015932326,0.0000708835,0.0008912584,0.0005494886,0.0016960754,0.9953969,0.000037418442],"about_ca_topic_score_codex":0.01991703,"about_ca_topic_score_gemma":0.014778379,"teacher_disagreement_score":0.057875276,"about_ca_system_score_codex":0.0030742267,"about_ca_system_score_gemma":0.0012324209,"threshold_uncertainty_score":0.082551956},"labels":[],"label_agreement":null},{"id":"W7125181930","doi":"10.1109/sensors59705.2025.11331069","title":"Personalized POI Prediction from GPS Data Using Graph-Enhanced Transformers: Toward an Intelligent Navigation System","year":2025,"lang":"","type":"article","venue":"","topic":"Data Management and Algorithms","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 Calgary","funders":"","keywords":"Global Positioning System; Navigation system; Assisted GPS; Navigational aid; Satellite navigation; Field (mathematics)","score_opus":0.07706063031217932,"score_gpt":0.3135691226733787,"score_spread":0.23650849236119942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125181930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060485754,0.00021477816,0.9311767,0.0001791227,0.000077245924,0.00004177773,0.0009079025,0.0051018037,0.0018148709],"genre_scores_gemma":[0.8340761,0.00023690562,0.1623415,0.00006136182,0.000056324883,0.000031373984,0.001323029,0.00016801798,0.0017054178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999877,0.000016919199,0.000006283808,0.000042784493,0.00004099965,0.00001600897],"domain_scores_gemma":[0.9997229,0.00008155826,0.000026420506,0.00005725795,0.0000907184,0.000021177304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016821382,0.00046167406,0.00061622023,0.0010952235,0.000269257,0.00062215194,0.00079403626,0.00034629126,0.0010158898],"category_scores_gemma":[0.0010913054,0.00022180201,0.0003043407,0.0015907609,0.00019964555,0.0011122995,0.0006102939,0.00051335426,0.0007611354],"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":[0.0004948222,0.00028031302,0.013941403,0.00010651107,0.0000924511,0.00018925263,0.0001438616,0.32608595,0.018470475,0.008283265,0.00913247,0.62277925],"study_design_scores_gemma":[0.000008720342,0.000020752734,0.00095416483,0.0000029407263,0.000014456993,0.000032901848,0.000019995641,0.9923379,0.001928332,0.003889762,0.0007820507,0.0000080044565],"about_ca_topic_score_codex":0.011146495,"about_ca_topic_score_gemma":0.017297348,"teacher_disagreement_score":0.011146495,"about_ca_system_score_codex":0.0002718157,"about_ca_system_score_gemma":0.00054516434,"threshold_uncertainty_score":0.022163212},"labels":[],"label_agreement":null},{"id":"W7125586383","doi":"10.1109/cascon66301.2025.00049","title":"Jackpine3D: A Benchmark for Evaluating 3D Spatial Database Features","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Queen's University; University of New Brunswick","funders":"","keywords":"Benchmark (surveying); Spatial database; Suite; Spatial analysis; Spatiotemporal database; Spatial query","score_opus":0.024203318033109655,"score_gpt":0.331581221013276,"score_spread":0.3073779029801663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125586383","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46276444,0.014484327,0.2092582,0.0027979028,0.002041969,0.0021175626,0.09825598,0.13304463,0.075234964],"genre_scores_gemma":[0.48348728,0.0027039333,0.33422643,0.00071176566,0.00014121919,0.001214712,0.16455884,0.0064978465,0.0064579505],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.990635,0.0018809104,0.0014162317,0.0009890427,0.004432303,0.0006465562],"domain_scores_gemma":[0.9865855,0.005484028,0.0007754524,0.002537107,0.0038730614,0.0007449619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056984317,0.0026830428,0.0010701749,0.0038528682,0.0010996751,0.002991421,0.0062223626,0.0014121037,0.0035863821],"category_scores_gemma":[0.018642588,0.00073567935,0.0013367127,0.007120387,0.0011937921,0.0035609538,0.0028754196,0.0016532943,0.0013764073],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003143061,0.0018622484,0.033034477,0.005748944,0.0010916436,0.0008715988,0.00074744254,0.30178463,0.022737155,0.02864928,0.34704396,0.25328553],"study_design_scores_gemma":[0.00080131105,0.0012410338,0.014445031,0.0003262526,0.0002392186,0.00061467744,0.00062562496,0.7910969,0.048978385,0.011739159,0.12968232,0.00021009802],"about_ca_topic_score_codex":0.023828479,"about_ca_topic_score_gemma":0.019136854,"teacher_disagreement_score":0.023828479,"about_ca_system_score_codex":0.0021203111,"about_ca_system_score_gemma":0.0026411894,"threshold_uncertainty_score":0.047379553},"labels":[],"label_agreement":null},{"id":"W7140790900","doi":"10.1109/fpl68686.2025.00066","title":"Versatile Place and Route with Continuous Routing Runtime Prediction and Smart Route Termination","year":2025,"lang":"","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Routing (electronic design automation); Process (computing); Key (lock); Sequence (biology)","score_opus":0.00466020797550005,"score_gpt":0.2026068102082478,"score_spread":0.19794660223274776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7140790900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022839516,0.00025186499,0.94289017,0.0003913849,0.0001814098,0.000058005236,0.0006383525,0.025858419,0.0068909694],"genre_scores_gemma":[0.44284147,0.00028944196,0.5467118,0.00022831712,0.000084251464,0.00014167184,0.0015087408,0.0013479732,0.0068463557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994784,0.000071229355,0.000026391208,0.0001362952,0.0002332142,0.0000545361],"domain_scores_gemma":[0.99858165,0.00040787997,0.00016066083,0.00058263046,0.00019610097,0.00007113329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068052515,0.0009506869,0.0006388553,0.00065160595,0.00040990746,0.0013901779,0.0017780369,0.00068209,0.005294454],"category_scores_gemma":[0.0026556903,0.00041599252,0.0005602158,0.00060282246,0.00066691457,0.0017482851,0.0011761681,0.0014513762,0.0017853541],"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":[0.0004924377,0.00015468706,0.003597534,0.00015886694,0.00007601277,0.00020377735,0.00011650053,0.56209123,0.013091888,0.023375588,0.02299687,0.37364468],"study_design_scores_gemma":[0.000017478795,0.00004559327,0.0002513482,0.000010682373,0.000010446008,0.00005688169,0.000014421665,0.9768361,0.006784056,0.011364297,0.0045897607,0.000019013463],"about_ca_topic_score_codex":0.003276187,"about_ca_topic_score_gemma":0.00838454,"teacher_disagreement_score":0.005294454,"about_ca_system_score_codex":0.0006234795,"about_ca_system_score_gemma":0.001212474,"threshold_uncertainty_score":0.017711699},"labels":[],"label_agreement":null},{"id":"W75042331","doi":"10.1007/978-3-642-22922-0_39","title":"Efficient Spatio-temporal Sensor Data Loading for a Sensor Web Browser","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Sensor web; Computer science; Mashup; Wireless sensor network; Cache; Web server; Web API; Computer network; Web service; Web navigation; Distributed computing; The Internet; World Wide Web; Key distribution in wireless sensor networks; Operating system","score_opus":0.049109525857391284,"score_gpt":0.26437931111356133,"score_spread":0.21526978525617005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W75042331","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028941732,0.00043683514,0.8209722,0.00024556863,0.0001580224,0.00027470247,0.0014725368,0.1386357,0.008862677],"genre_scores_gemma":[0.33883363,0.00068073825,0.6159133,0.0003633718,0.000096952564,0.0004920109,0.005581614,0.012254286,0.025784288],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951744,0.00004280057,0.000046723995,0.00009576524,0.0002421247,0.000055128614],"domain_scores_gemma":[0.9985259,0.00039117935,0.000049632967,0.000542882,0.0003443999,0.00014610188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005319476,0.0013376838,0.0009684067,0.0010104938,0.00068898813,0.002065075,0.0018107212,0.00070152496,0.014320976],"category_scores_gemma":[0.0023652515,0.00089274574,0.0007219603,0.001231003,0.0004036939,0.002035463,0.0021052444,0.0009289255,0.0060895886],"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":[0.001376113,0.00043846059,0.005952271,0.0005725649,0.00021261633,0.00094791746,0.00088336784,0.016059227,0.16273683,0.011749405,0.08492053,0.7141507],"study_design_scores_gemma":[0.00021951714,0.00013164768,0.0053145294,0.00009818714,0.00015281625,0.00091740873,0.00042936802,0.6822125,0.219578,0.0155852465,0.075214,0.00014688056],"about_ca_topic_score_codex":0.004045967,"about_ca_topic_score_gemma":0.006973913,"teacher_disagreement_score":0.014320976,"about_ca_system_score_codex":0.0007225977,"about_ca_system_score_gemma":0.0010126551,"threshold_uncertainty_score":0.047908425},"labels":[],"label_agreement":null},{"id":"W79208629","doi":"10.14778/2732269.2732275","title":"Computing k-regret minimizing sets","year":2014,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Regret; Dimension (graph theory); Relaxation (psychology); Set (abstract data type); Greedy algorithm; Linear programming relaxation; Dynamic programming; Linear programming; Duality (order theory); Mathematics; Combinatorics; Computer science; Mathematical optimization; Algorithm; Statistics","score_opus":0.011085699081155623,"score_gpt":0.21721503497013323,"score_spread":0.2061293358889776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W79208629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11136877,0.0010736702,0.87516546,0.0016216907,0.000108389075,0.00030686203,0.0016570407,0.002060795,0.0066373283],"genre_scores_gemma":[0.45373535,0.00051808846,0.53486,0.00077576726,0.00022168714,0.0006087493,0.004102672,0.0007210879,0.004456599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643505,0.0014025902,0.00021974534,0.00085058867,0.0006826647,0.00040939002],"domain_scores_gemma":[0.98898727,0.007886954,0.0008022841,0.0012414826,0.0006963024,0.00038564045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039756577,0.0017362292,0.003138189,0.0019181216,0.001086796,0.0032677318,0.0026688501,0.0020753082,0.0041907504],"category_scores_gemma":[0.021193119,0.0011706755,0.0017472368,0.0028300998,0.0017332474,0.0037163552,0.0022444702,0.0025355022,0.0010292243],"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":[0.0005436638,0.00027807776,0.0022509035,0.0003601787,0.00019812428,0.00012787474,0.000323603,0.8541072,0.002071622,0.039221726,0.011694138,0.08882293],"study_design_scores_gemma":[0.000037623682,0.000083495506,0.00032851045,0.00002984834,0.000022813172,0.000046376666,0.00007783878,0.93958235,0.0014273288,0.05702335,0.0013212548,0.00001924856],"about_ca_topic_score_codex":0.0023215548,"about_ca_topic_score_gemma":0.002960521,"teacher_disagreement_score":0.0041907504,"about_ca_system_score_codex":0.002865898,"about_ca_system_score_gemma":0.0019207625,"threshold_uncertainty_score":0.021025538},"labels":[],"label_agreement":null},{"id":"W81184795","doi":"10.1007/978-3-642-22351-8_5","title":"Using Medians to Generate Consensus Rankings for Biological Data","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Ranking (information retrieval); Computer science; Heuristic; Set (abstract data type); Permutation (music); Information retrieval; Data mining; Theoretical computer science; Artificial intelligence","score_opus":0.17465872594774828,"score_gpt":0.31319250443270624,"score_spread":0.13853377848495796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W81184795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022934157,0.00049452484,0.96909976,0.0002823447,0.00021560493,0.00020992145,0.0010823339,0.0041190735,0.001562184],"genre_scores_gemma":[0.18646942,0.00022831078,0.8027865,0.00018535706,0.00020514693,0.0004926851,0.0065021794,0.00091609015,0.0022142644],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992733,0.0025849068,0.00058247463,0.0013367073,0.0022939255,0.00046890514],"domain_scores_gemma":[0.980372,0.011467296,0.00084929477,0.0023571292,0.0043380754,0.00061619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01292947,0.0017435523,0.0028608306,0.0075416504,0.0022291427,0.0032955785,0.0034081002,0.0020359743,0.005333326],"category_scores_gemma":[0.035763714,0.0011854114,0.002742277,0.004759607,0.0010794932,0.0034431084,0.0035334157,0.0026847275,0.0025832609],"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":[0.0008586707,0.00033301534,0.007226454,0.000650281,0.00089795626,0.0002167307,0.00061030604,0.18176201,0.008575353,0.025429808,0.028786248,0.7446532],"study_design_scores_gemma":[0.0001441811,0.00020777706,0.001449954,0.00006339357,0.00010027044,0.00010564583,0.0002902991,0.8773212,0.0057152673,0.10957907,0.0049526775,0.00007028179],"about_ca_topic_score_codex":0.0036648752,"about_ca_topic_score_gemma":0.00660557,"teacher_disagreement_score":0.01292947,"about_ca_system_score_codex":0.0019371131,"about_ca_system_score_gemma":0.0025058447,"threshold_uncertainty_score":0.06837839},"labels":[],"label_agreement":null},{"id":"W83853472","doi":"10.1007/978-3-642-29063-3_13","title":"Automated Traffic Route Identification Through the Shared Nearest Neighbour Algorithm","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Identification (biology); Cluster analysis; Position (finance); Computer science; Data mining; Noise (video); k-nearest neighbors algorithm; Range (aeronautics); Key (lock); Algorithm; Artificial intelligence; Engineering","score_opus":0.011443266391156106,"score_gpt":0.22529386623457487,"score_spread":0.21385059984341875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W83853472","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049714126,0.00040156048,0.94317925,0.000057009984,0.000109759116,0.00006453956,0.00019094013,0.0016865865,0.004596269],"genre_scores_gemma":[0.51686066,0.00024144768,0.47679448,0.00003575333,0.000067212,0.000071787385,0.00067035743,0.00013699458,0.005121345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988856,0.00024974218,0.000050353872,0.0002604825,0.00042999786,0.0001238402],"domain_scores_gemma":[0.999292,0.00018170856,0.000055945708,0.00019512896,0.0002532005,0.00002183621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075347454,0.00052558235,0.0012562805,0.0014831532,0.00090334745,0.0010799628,0.0015320926,0.0008955,0.0026531138],"category_scores_gemma":[0.0024701299,0.00039734112,0.00076793873,0.0020500694,0.00047149402,0.0017857595,0.0013967628,0.00059069524,0.0016283247],"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":[0.00029360934,0.00011239604,0.0023623186,0.00007676221,0.00012693531,0.00012832897,0.00009413924,0.26294836,0.007878282,0.00847109,0.0046850834,0.7128227],"study_design_scores_gemma":[0.000012970897,0.000037274567,0.000928418,0.000008804003,0.000023430839,0.00015597002,0.000040308067,0.98724335,0.0026312992,0.006573457,0.0023248433,0.00001976159],"about_ca_topic_score_codex":0.0075934087,"about_ca_topic_score_gemma":0.010206554,"teacher_disagreement_score":0.0075934087,"about_ca_system_score_codex":0.0004085717,"about_ca_system_score_gemma":0.0011951204,"threshold_uncertainty_score":0.015098453},"labels":[],"label_agreement":null},{"id":"W85822258","doi":"10.1007/978-3-319-06483-3_10","title":"Analyzing User Trajectories from Mobile Device Data with Hierarchical Dirichlet Processes","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"McGill University","funders":"","keywords":"Computer science; Trajectory; Latent Dirichlet allocation; Hierarchical Dirichlet process; Variety (cybernetics); Cluster analysis; Hierarchical clustering; Domain (mathematical analysis); Artificial intelligence; Dirichlet distribution; Human–computer interaction; Data mining; Machine learning; Topic model","score_opus":0.022440963865522252,"score_gpt":0.25676246133965075,"score_spread":0.2343214974741285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W85822258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070758685,0.0015272518,0.92446476,0.0005621706,0.00015323183,0.00010046803,0.001078184,0.00075642555,0.00059890136],"genre_scores_gemma":[0.748701,0.0022434082,0.23522276,0.00023274758,0.00073358795,0.00045944995,0.0062723323,0.00029117457,0.0058436333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961443,0.0017301824,0.00022580975,0.0009988138,0.00046151906,0.00043942092],"domain_scores_gemma":[0.985827,0.012032568,0.00044647107,0.0010493803,0.00042152213,0.00022303815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006042577,0.0014225128,0.0025731202,0.0030104348,0.0009927924,0.0021265573,0.002966767,0.0023423021,0.0014316501],"category_scores_gemma":[0.016404249,0.0018308416,0.0039717415,0.0044518844,0.0015848208,0.0034591407,0.0029139568,0.0039725057,0.0013835292],"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":[0.00081836677,0.00040252123,0.022748172,0.0004153733,0.0007489215,0.00031873616,0.0012904016,0.73372245,0.0032903403,0.028551068,0.0049883886,0.20270522],"study_design_scores_gemma":[0.000008999579,0.000030046214,0.0015089266,0.00001989592,0.00003530814,0.0000403089,0.000093850605,0.9801676,0.00040516944,0.01713309,0.00053630414,0.000020378464],"about_ca_topic_score_codex":0.017329434,"about_ca_topic_score_gemma":0.020606153,"teacher_disagreement_score":0.017329434,"about_ca_system_score_codex":0.0014072473,"about_ca_system_score_gemma":0.0014633351,"threshold_uncertainty_score":0.034457088},"labels":[],"label_agreement":null},{"id":"W954180850","doi":"10.1007/978-3-642-33398-9_2","title":"Basic Structure of High-Dimensional Spaces","year":2012,"lang":"en","type":"book-chapter","venue":"SpringerBriefs in computer science","topic":"Data Management and Algorithms","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":"Queen's University","funders":"","keywords":"Simple (philosophy); Space (punctuation); Point (geometry); Computer science; Data structure; High dimensional; Theoretical computer science; Mathematics; Epistemology; Artificial intelligence; Geometry; Philosophy; Programming language","score_opus":0.013379262102407011,"score_gpt":0.218162839136037,"score_spread":0.20478357703363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W954180850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042402234,0.035526663,0.66413933,0.007299981,0.0022347656,0.00015210999,0.002300202,0.0011870522,0.24475768],"genre_scores_gemma":[0.5253661,0.025656603,0.33424038,0.0019432697,0.005205225,0.00071046746,0.0033783494,0.000651106,0.10284842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930406,0.00015504645,0.000060090548,0.00014441763,0.00027592795,0.00006058934],"domain_scores_gemma":[0.9992106,0.00027822758,0.00006675877,0.00015487871,0.00018031558,0.00010925506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074745825,0.0010135045,0.00093321595,0.0024248715,0.0015939994,0.0053934054,0.0017238347,0.001173261,0.009325317],"category_scores_gemma":[0.0020416433,0.0005550464,0.00071855314,0.002939398,0.004974279,0.006365835,0.0024665887,0.0037567522,0.0031024786],"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":[0.000005478198,0.000005915475,0.000053223386,0.000051424373,0.0000029332666,0.00002071925,0.00018058188,0.00028067318,0.00025003977,0.9877055,0.003021109,0.008422388],"study_design_scores_gemma":[0.0000030067192,0.0000062404793,0.00010472831,0.000013611767,0.000002060132,0.00007033265,0.000031517196,0.0010935715,0.00011185192,0.9806804,0.017876973,0.000005613146],"about_ca_topic_score_codex":0.0007147609,"about_ca_topic_score_gemma":0.000755239,"teacher_disagreement_score":0.009325317,"about_ca_system_score_codex":0.0009792378,"about_ca_system_score_gemma":0.0005867805,"threshold_uncertainty_score":0.031196296},"labels":[],"label_agreement":null},{"id":"W95632006","doi":"10.1007/978-3-642-40131-2_8","title":"Discovering Co-location Patterns in Datasets with Extended Spatial Objects","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","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":"University of Alberta","funders":"","keywords":"Data mining; Computer science; Grid; A priori and a posteriori; Database transaction; Spatial analysis; Geography; Pattern recognition (psychology); Artificial intelligence; Database; Remote sensing","score_opus":0.012426441170611957,"score_gpt":0.23879380284367144,"score_spread":0.2263673616730595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W95632006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44263363,0.009109242,0.45391858,0.0020458617,0.00038365705,0.0003867008,0.08090188,0.004637702,0.0059827454],"genre_scores_gemma":[0.5205763,0.0028354013,0.37373295,0.00026409698,0.00030165655,0.00030249576,0.098997615,0.0002322848,0.002757217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983633,0.00020189484,0.00019746205,0.0006554613,0.00044862527,0.00013325548],"domain_scores_gemma":[0.9963337,0.0017393094,0.00045867282,0.00096174364,0.00034629574,0.00016015049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010931898,0.0009229141,0.0011241469,0.006086115,0.0008547548,0.002347516,0.0018575311,0.001233241,0.0014942737],"category_scores_gemma":[0.0066696703,0.00055259187,0.0018887259,0.014153624,0.000565256,0.0042114425,0.0024706975,0.001223162,0.0016499347],"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":[0.0010568562,0.000681164,0.28125718,0.002416658,0.0016510679,0.0024593966,0.0018886417,0.033341564,0.02299286,0.011026013,0.043592,0.59763664],"study_design_scores_gemma":[0.00013949405,0.0004551321,0.19713764,0.0006668415,0.0011573146,0.00705376,0.0061890497,0.57079804,0.019141559,0.10461268,0.09244088,0.00020763079],"about_ca_topic_score_codex":0.005459834,"about_ca_topic_score_gemma":0.015622832,"teacher_disagreement_score":0.006086115,"about_ca_system_score_codex":0.00064438843,"about_ca_system_score_gemma":0.0010213698,"threshold_uncertainty_score":0.010856152},"labels":[],"label_agreement":null},{"id":"W966483212","doi":"10.1007/978-1-4020-6264-3_37","title":"Implementation of a Spatial Data Structure on a FPGA","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Implementation; Field-programmable gate array; Spatial analysis; Software; Software implementation; Data structure; Computer architecture; Data mining; Software engineering; Embedded system; Operating system; Geography; Remote sensing","score_opus":0.06790560313765769,"score_gpt":0.3248515901510272,"score_spread":0.2569459870133695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W966483212","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017848887,0.00086506625,0.9091758,0.00036122045,0.0004494291,0.00014490173,0.00073549565,0.012977206,0.057441957],"genre_scores_gemma":[0.14249285,0.0009504944,0.7933402,0.00039748457,0.000115517956,0.00019803629,0.0020572967,0.0011810963,0.05926701],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975663,0.000029263958,0.00002667323,0.000037603157,0.000108602566,0.00004120406],"domain_scores_gemma":[0.99958545,0.00008541983,0.000021334257,0.0001664528,0.00012319075,0.000018162753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024397238,0.0006349915,0.00037469933,0.000743271,0.000502915,0.001373206,0.0014136557,0.00046616545,0.020361828],"category_scores_gemma":[0.0006828643,0.00040258194,0.0003997392,0.0010582338,0.00031002946,0.001349895,0.0006379414,0.0006198369,0.0059748194],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033950855,0.0000884756,0.0010304812,0.00063365675,0.000059147143,0.00039172007,0.00025071471,0.013840824,0.051944602,0.12751329,0.052361947,0.75154555],"study_design_scores_gemma":[0.00018728258,0.00057940633,0.0014058925,0.00024619425,0.00016400701,0.001884287,0.00024062447,0.11435081,0.2658272,0.062407356,0.552621,0.00008584452],"about_ca_topic_score_codex":0.0015673864,"about_ca_topic_score_gemma":0.0023801597,"teacher_disagreement_score":0.020361828,"about_ca_system_score_codex":0.0006654974,"about_ca_system_score_gemma":0.0008431122,"threshold_uncertainty_score":0.06811714},"labels":[],"label_agreement":null},{"id":"W99206240","doi":"10.4018/978-1-59140-041-7.ch013","title":"Entity Identification of Fuzzy Multidatabase Systems with Incompatible Keys","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","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 Saskatchewan","funders":"","keywords":"Computer science; Schema (genetic algorithms); Fuzzy logic; Identification (biology); Data mining; Data integration; Relational database; Focus (optics); Database; Theoretical computer science; Information retrieval; Artificial intelligence","score_opus":0.0238865712327487,"score_gpt":0.2306072496955817,"score_spread":0.20672067846283299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W99206240","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015424636,0.0024408214,0.9687928,0.0002519465,0.00013121933,0.00011062133,0.00012428348,0.000563383,0.012160383],"genre_scores_gemma":[0.11425914,0.0021452077,0.8688139,0.00013325246,0.000055469227,0.00011588829,0.0006057144,0.00016118813,0.01371037],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987411,0.00028138063,0.00013432966,0.00026399683,0.0004876719,0.00009157168],"domain_scores_gemma":[0.9990815,0.00038543975,0.00006715873,0.00025718097,0.00017561432,0.000033011813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015578119,0.00046598355,0.0006191004,0.0015713118,0.0008454484,0.003156358,0.0012965817,0.00089365896,0.0028033864],"category_scores_gemma":[0.0029994096,0.00048689658,0.00091164984,0.0024940534,0.000697224,0.0050084353,0.0020181704,0.0014175558,0.0012832022],"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":[0.00011051436,0.00007125857,0.002511545,0.0005978157,0.00007761394,0.0009278297,0.0021327266,0.015809968,0.017504182,0.36119583,0.009359022,0.58970183],"study_design_scores_gemma":[0.000029684998,0.00010680898,0.0028762207,0.000529374,0.00018445119,0.0033053062,0.0012266756,0.32545283,0.067974254,0.321031,0.2771664,0.00011701041],"about_ca_topic_score_codex":0.0009104416,"about_ca_topic_score_gemma":0.00076289196,"teacher_disagreement_score":0.003156358,"about_ca_system_score_codex":0.00086050376,"about_ca_system_score_gemma":0.00064299064,"threshold_uncertainty_score":0.009378254},"labels":[],"label_agreement":null},{"id":"W99233158","doi":"10.4018/978-1-59140-560-3.ch105","title":"Signature Files and Signature File Construction","year":2005,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Winnipeg","funders":"","keywords":"Computer science; Signature (topology); Inverted index; Information retrieval; Keyword search; File format; Search engine indexing; Database; Mathematics","score_opus":0.00858681020888056,"score_gpt":0.2090336344345606,"score_spread":0.20044682422568005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W99233158","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041428865,0.0021045643,0.9630829,0.0013660943,0.0005040801,0.00025942497,0.00097186916,0.0032759148,0.02429238],"genre_scores_gemma":[0.045096282,0.0035300332,0.91886854,0.00054909254,0.00049570313,0.00044946192,0.0031592625,0.0016553075,0.02619626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967818,0.0008960355,0.0002809532,0.00042475923,0.0013664565,0.0002499991],"domain_scores_gemma":[0.9916146,0.0035300232,0.00042673276,0.0027433336,0.0014549517,0.00023046954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028533875,0.00096036884,0.00091300596,0.0038965344,0.0018693035,0.0048410133,0.0034015374,0.0024411306,0.016234118],"category_scores_gemma":[0.014597236,0.0007105075,0.0010226595,0.008715467,0.0029157654,0.014647348,0.0032521498,0.002897766,0.0109404335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010569747,0.000046219335,0.00028622788,0.0003606195,0.000010845221,0.00020661192,0.00042463126,0.0027167436,0.0034498912,0.5574139,0.038363494,0.39661515],"study_design_scores_gemma":[0.000045551307,0.00010500822,0.00023889609,0.00023560945,0.000021107038,0.0019910808,0.000361129,0.01902217,0.018911194,0.4578709,0.5010903,0.00010703603],"about_ca_topic_score_codex":0.0012404757,"about_ca_topic_score_gemma":0.0007226666,"teacher_disagreement_score":0.016234118,"about_ca_system_score_codex":0.0018178929,"about_ca_system_score_gemma":0.0026464574,"threshold_uncertainty_score":0.054308593},"labels":[],"label_agreement":null},{"id":"W995150301","doi":"10.1007/978-3-319-06483-3_11","title":"Use of Ontology and Cluster Ensembles for Geospatial Clustering Analysis","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Husky Energy (Canada); University of Calgary; Petro-Canada","funders":"","keywords":"Cluster analysis; Geospatial analysis; Computer science; Ontology; Data mining; Consensus clustering; Correlation clustering; Clustering high-dimensional data; Fuzzy clustering; Information retrieval; CURE data clustering algorithm; Artificial intelligence; Geography; Remote sensing","score_opus":0.02788286127351197,"score_gpt":0.25147343388740095,"score_spread":0.22359057261388898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W995150301","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005959773,0.00019448792,0.99066144,0.00012337812,0.00004389489,0.00002995176,0.0002419968,0.00064527005,0.0020998488],"genre_scores_gemma":[0.08314718,0.0003503944,0.913324,0.00005994581,0.000034990433,0.00010418525,0.0010839035,0.00037852288,0.001516922],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974848,0.0007732964,0.00022157026,0.00036639493,0.0010628599,0.00009113062],"domain_scores_gemma":[0.99681014,0.0014488299,0.00022423347,0.00073597895,0.0006682505,0.000112463975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034893868,0.00057436695,0.00073644053,0.0029832416,0.0013955309,0.0032017773,0.0013378692,0.0006690287,0.0016910189],"category_scores_gemma":[0.0103310235,0.00048871705,0.001823295,0.0043392167,0.000914557,0.0033558735,0.0025648873,0.0015821778,0.00069151644],"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":[0.00012112889,0.00019040772,0.008092645,0.00041302235,0.0006423412,0.00032037296,0.0017278219,0.1143607,0.011458978,0.3052403,0.009204861,0.5482274],"study_design_scores_gemma":[0.000012616835,0.00002879832,0.002916524,0.00012215562,0.00015044984,0.00043467124,0.00054387876,0.6710124,0.007502797,0.28358993,0.033618525,0.000067251516],"about_ca_topic_score_codex":0.0069370093,"about_ca_topic_score_gemma":0.012698724,"teacher_disagreement_score":0.0069370093,"about_ca_system_score_codex":0.0013320919,"about_ca_system_score_gemma":0.0015108428,"threshold_uncertainty_score":0.018453896},"labels":[],"label_agreement":null}]}