{"meta":{"query_hash":"0ac3632425aa","filters":{"venue":"Computing"},"cohort_total":45,"direct_labels_cover":0,"predictions_cover":45,"exported":45,"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/0ac3632425aa","api":"https://metacan.xera.ac/api/v1/cohort?venue=Computing"},"results":[{"id":"W1977066728","doi":"10.1007/s00607-013-0334-0","title":"Energy efficiency on location based applications in mobile cloud computing: a survey","year":2013,"lang":"en","type":"article","venue":"Computing","topic":"IoT and Edge/Fog Computing","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 Ottawa","funders":"","keywords":"Cloud computing; Computer science; Mobile cloud computing; Efficient energy use; Mobile device; Systems engineering; Energy (signal processing); Global Positioning System; Distributed computing; Data science; Real-time computing; Telecommunications; Engineering; World Wide Web; Electrical engineering; Operating system","score_opus":0.02022659973194987,"score_gpt":0.25448156252793486,"score_spread":0.23425496279598498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977066728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18654345,0.00012972673,0.80946314,0.000084619125,0.0020260722,0.00039686044,1.5972263e-7,0.00031743938,0.0010385016],"genre_scores_gemma":[0.98867434,9.942565e-7,0.00988277,0.0004719935,0.00086285255,0.000029926749,0.000023367395,0.000022163893,0.00003156354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736935,0.00030462968,0.0005727222,0.00076264,0.00033332733,0.0006573297],"domain_scores_gemma":[0.9974312,0.0011422292,0.00023455113,0.000774122,0.00027833122,0.00013953012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009989863,0.00027565385,0.00029562457,0.00035290752,0.00040518888,0.0003240841,0.0012229981,0.00010531065,0.0000033647061],"category_scores_gemma":[0.00008612902,0.00027841487,0.00007165662,0.0019906065,0.000055177148,0.00018549272,0.000404394,0.00026656868,0.00014901884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008533792,0.0011944717,0.05126907,0.0001026493,0.000019593956,0.000008517566,0.0022935998,0.30630076,0.00033559185,0.009662589,0.0072486172,0.621556],"study_design_scores_gemma":[0.00033218414,0.00008646031,0.05147226,0.000085910346,0.0000014854631,0.000002307087,0.000020146857,0.9449982,0.0004109819,0.00035672373,0.0019221879,0.0003111219],"about_ca_topic_score_codex":0.00064188987,"about_ca_topic_score_gemma":0.000013008399,"teacher_disagreement_score":0.80213094,"about_ca_system_score_codex":0.00015043982,"about_ca_system_score_gemma":0.0001498835,"threshold_uncertainty_score":0.9999668},"labels":[],"label_agreement":null},{"id":"W1977292054","doi":"10.1007/s00607-008-0001-z","title":"A new local stabilized nonconforming finite element method for the Stokes equations","year":2008,"lang":"en","type":"article","venue":"Computing","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":60,"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":"CMG Reservoir Simulation Foundation; National Science Foundation","keywords":"Finite element method; Mathematics; Mixed finite element method; Gauss; Stability (learning theory); Extended finite element method; Mathematical analysis; Applied mathematics; Order (exchange); Finite element limit analysis; Physics; Computer science","score_opus":0.0670170312128948,"score_gpt":0.35834435376723295,"score_spread":0.29132732255433813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977292054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019070892,0.00026185997,0.99817705,0.00010642902,0.00035497555,0.00037569884,0.0000031953668,0.0002541508,0.00027595615],"genre_scores_gemma":[0.084909216,0.0000068468717,0.9146987,0.00008734737,0.00020962548,0.000019788613,0.000003000461,0.000030718522,0.00003475944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905413,0.000036098452,0.00036344433,0.00013089886,0.0001712132,0.0002442105],"domain_scores_gemma":[0.98755527,0.012066848,0.00006976993,0.00017551864,0.00006983674,0.000062757965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042423262,0.00013650964,0.000191341,0.000043395074,0.00029248514,0.000017577013,0.0001903127,0.000032221087,0.000017575147],"category_scores_gemma":[0.00073676446,0.00011029555,0.0000842523,0.0002103858,0.0000335632,0.000065070206,0.000060391343,0.00014387968,0.00000834294],"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":[0.000002316554,0.0000042624774,0.0000035638207,0.000026541662,0.000021599113,4.1496563e-7,0.0006652298,0.71566856,0.000056978675,0.003095224,0.000109926004,0.28034538],"study_design_scores_gemma":[0.00032260086,0.000022109902,0.000017145338,0.000026460271,0.000017314404,0.0000074387176,0.0002187207,0.97976553,0.0007046913,0.014351859,0.004413155,0.0001329798],"about_ca_topic_score_codex":0.000004008857,"about_ca_topic_score_gemma":9.903507e-7,"teacher_disagreement_score":0.2802124,"about_ca_system_score_codex":0.00008427611,"about_ca_system_score_gemma":0.000044610264,"threshold_uncertainty_score":0.4497722},"labels":[],"label_agreement":null},{"id":"W1979595178","doi":"10.1007/s00607-009-0032-0","title":"Discrete logarithms for finite groups","year":2009,"lang":"en","type":"article","venue":"Computing","topic":"Coding theory and cryptography","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":"Discrete logarithm; Multiset; Mathematics; Generalization; Cryptography; Logarithm; Abelian group; Prime (order theory); Cryptographic primitive; Group (periodic table); Finite group; Discrete mathematics; Cryptographic protocol; Finite field; Algebra over a field; Pure mathematics; Combinatorics; Computer science; Algorithm; Public-key cryptography; Encryption; Mathematical analysis","score_opus":0.016501538301748064,"score_gpt":0.25995526874435926,"score_spread":0.2434537304426112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979595178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011898313,0.00008514019,0.98441964,0.00068401307,0.00033798016,0.00010783594,8.0191603e-7,0.00029867026,0.0021676216],"genre_scores_gemma":[0.89700204,0.000001053058,0.10200823,0.0007821572,0.00017205306,0.0000013464102,0.0000017535002,0.0000039120678,0.00002744452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917626,0.000032249616,0.00014631868,0.0002801872,0.00009039466,0.00027457927],"domain_scores_gemma":[0.99922913,0.0002942983,0.000058753387,0.00032273523,0.000036698835,0.000058368314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036688885,0.00010007453,0.00011397288,0.00007857776,0.00025188565,0.0001513687,0.0006279328,0.00003388354,0.000002086855],"category_scores_gemma":[0.000053564534,0.000094840805,0.00011383638,0.0002876063,0.000021759148,0.00016315308,0.00011770028,0.0000839207,0.000009538913],"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":[0.0000065372815,0.000024098923,0.00014741883,0.0000062009585,0.000006784535,0.0000037496181,0.00051276054,0.00065067003,0.00016975365,0.6963557,0.00033759893,0.3017787],"study_design_scores_gemma":[0.00048803742,0.00031206207,0.0038397438,0.000052414794,0.0000068075524,0.000010796369,0.0000227273,0.607985,0.0005192721,0.37932235,0.0071417084,0.00029906444],"about_ca_topic_score_codex":5.143453e-7,"about_ca_topic_score_gemma":2.3761534e-7,"teacher_disagreement_score":0.88510376,"about_ca_system_score_codex":0.00000852339,"about_ca_system_score_gemma":0.000010642051,"threshold_uncertainty_score":0.3867496},"labels":[],"label_agreement":null},{"id":"W1984058114","doi":"10.1007/s00607-013-0329-x","title":"An integrated fine-grain runtime system for MPI","year":2013,"lang":"en","type":"article","venue":"Computing","topic":"Parallel Computing and Optimization Techniques","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 British Columbia","funders":"Vice Chancellor for Research and Technology, Kerman University of Medical Sciences","keywords":"Computer science; Parallel computing; Computational science","score_opus":0.015965434546085056,"score_gpt":0.26291138819052345,"score_spread":0.2469459536444384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984058114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0108293975,0.000045058925,0.9846877,0.000199739,0.0003967057,0.00044854282,0.0000013081534,0.0024378377,0.0009537185],"genre_scores_gemma":[0.55352914,2.5384645e-7,0.4461549,0.000102572725,0.000089336274,0.000015527323,0.000009687772,0.000011281967,0.00008730618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859005,0.000104733524,0.0003440672,0.00044997284,0.00014312011,0.0003680329],"domain_scores_gemma":[0.99870753,0.00014130677,0.00015784273,0.0005596492,0.00031781898,0.00011586156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049362326,0.00017859194,0.00022194818,0.00014117414,0.00030357152,0.00047087978,0.0010830566,0.00007941215,0.0000063014068],"category_scores_gemma":[0.000061000956,0.00016375884,0.00007653714,0.00039482515,0.000025120178,0.00037025881,0.00018356756,0.00011920268,0.000079721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010220145,0.00027357167,0.0013634419,0.00026517286,0.00007893127,0.000011350772,0.0024240494,0.10490845,0.0036573727,0.10968343,0.051892415,0.72543156],"study_design_scores_gemma":[0.00021569412,0.000092022456,0.0002880587,0.0000802989,0.0000027030746,0.000011790374,0.000043535998,0.99500775,0.0020325717,0.000557518,0.0014564785,0.00021157792],"about_ca_topic_score_codex":0.0000549907,"about_ca_topic_score_gemma":9.001962e-7,"teacher_disagreement_score":0.8900993,"about_ca_system_score_codex":0.000060863535,"about_ca_system_score_gemma":0.000051867395,"threshold_uncertainty_score":0.66778916},"labels":[],"label_agreement":null},{"id":"W1988420580","doi":"10.1007/s006070070013","title":"Output-Sensitive Algorithm for Computing β-Skeletons","year":2000,"lang":"en","type":"article","venue":"Computing","topic":"Digital Image Processing Techniques","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 Windsor","funders":"","keywords":"Intersection (aeronautics); Mathematics; Combinatorics; Planar; Algorithm; Graph; Measure (data warehouse); Skeleton (computer programming); RADIUS; Discrete mathematics; Computer science","score_opus":0.020301539706283823,"score_gpt":0.28330157766687813,"score_spread":0.2630000379605943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988420580","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035092179,0.00008118839,0.9805065,0.00032775186,0.0002121918,0.00028756636,0.000004501646,0.0017866452,0.013284445],"genre_scores_gemma":[0.2865224,0.0000015105221,0.71216846,0.00059990893,0.0001967667,0.0000031658765,0.000005440045,0.000022922737,0.00047945607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981313,0.000044321598,0.00036508258,0.0006375267,0.00022783154,0.0005939379],"domain_scores_gemma":[0.99871534,0.00034794875,0.00012871469,0.00047281958,0.00021627378,0.00011887345],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045319056,0.00023869275,0.00027559948,0.00011792088,0.0004117708,0.0007108615,0.00097100344,0.0000728005,0.0000035279108],"category_scores_gemma":[0.000072043804,0.00025352748,0.0001319705,0.00044177036,0.000085274856,0.0006515121,0.00039318902,0.00017724327,0.00006307564],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.871791e-7,0.000039413062,0.000010314628,0.0000124689295,0.000010856237,0.000013454514,0.0004032385,0.00012785174,0.000065042215,0.0014205362,0.0013652022,0.99653065],"study_design_scores_gemma":[0.00024791693,0.00007616133,0.00013770392,0.00012313531,0.0000057077086,0.000067149085,0.000019652982,0.9819975,0.004630409,0.0050562904,0.0072999583,0.00033844434],"about_ca_topic_score_codex":0.00000999049,"about_ca_topic_score_gemma":3.791785e-7,"teacher_disagreement_score":0.9961922,"about_ca_system_score_codex":0.00006314634,"about_ca_system_score_gemma":0.00007218698,"threshold_uncertainty_score":0.9999917},"labels":[],"label_agreement":null},{"id":"W1997660235","doi":"10.1007/s00607-013-0372-7","title":"System on chip failure rate assessment using the executable model of a system","year":2013,"lang":"en","type":"article","venue":"Computing","topic":"Radiation Effects in Electronics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Executable; Workload; Computer science; Reliability engineering; Reliability (semiconductor); Transient (computer programming); Failure rate; Embedded system; Fault (geology); Fault injection; System on a chip; Real-time computing; Engineering; Operating system; Software","score_opus":0.011072706899412777,"score_gpt":0.22652878767592977,"score_spread":0.21545608077651698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997660235","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6760709,0.00008861376,0.32067877,0.000011035123,0.00024563,0.00033366395,0.000001325816,0.0002862108,0.0022838125],"genre_scores_gemma":[0.9941844,4.6330064e-7,0.0056535853,0.00001058702,0.00009458214,0.000009513999,0.00000139368,0.00003566417,0.000009805802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910253,0.00008402823,0.00025841847,0.00012659864,0.00014962515,0.00027877782],"domain_scores_gemma":[0.9993792,0.00015804847,0.00009358937,0.00028695137,0.000049169477,0.000033013206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039676443,0.00013409642,0.00019498674,0.00005545416,0.00012249485,0.000057697405,0.0001882348,0.000053168522,0.0000012186363],"category_scores_gemma":[0.0000078937865,0.00010573251,0.00004723617,0.00015842944,0.000012037142,0.00007320594,0.000032327105,0.00020381412,0.000010532841],"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":[4.287805e-7,0.0000036170245,0.000025881911,0.0004166197,0.000029118863,3.5651067e-7,0.00012669763,0.98438716,0.008108059,0.006309179,0.00009260406,0.00050026446],"study_design_scores_gemma":[0.00012420549,0.000014122248,0.00008351047,0.00041586522,0.000014568267,0.0000063996677,0.00028334503,0.9972136,0.0017192956,0.000016676408,0.0000102757795,0.00009815097],"about_ca_topic_score_codex":0.00002463903,"about_ca_topic_score_gemma":7.3433336e-7,"teacher_disagreement_score":0.31811348,"about_ca_system_score_codex":0.00047268873,"about_ca_system_score_gemma":0.000036958394,"threshold_uncertainty_score":0.43116465},"labels":[],"label_agreement":null},{"id":"W1999144075","doi":"10.1007/s00607-012-0196-x","title":"Load balancing in peer-to-peer systems using a diffusive approach","year":2012,"lang":"en","type":"article","venue":"Computing","topic":"Peer-to-Peer Network Technologies","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 Ottawa","funders":"","keywords":"Workload; Convergence (economics); Computer science; Upper and lower bounds; Overlay; Overlay network; Load balancing (electrical power); Distributed computing; Node (physics); Peer-to-peer; Mathematical optimization; Mathematics; Engineering","score_opus":0.03449987351668637,"score_gpt":0.28006286485362714,"score_spread":0.24556299133694076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999144075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39260298,0.00031404902,0.60289294,0.0005244977,0.0011452809,0.00037409348,9.710938e-7,0.00058526633,0.0015598938],"genre_scores_gemma":[0.78120613,3.507158e-7,0.21795972,0.0002592472,0.00038052187,0.000012695734,0.0000013978083,0.00002412342,0.00015583185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962717,0.00012219539,0.00051089603,0.0006962006,0.0010616435,0.0013373167],"domain_scores_gemma":[0.9980228,0.00020194141,0.0001623396,0.0009591268,0.00037513548,0.0002786907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022061698,0.00032427761,0.00046293877,0.00045160588,0.00021146136,0.00036524504,0.0017955184,0.00016640904,7.6040214e-7],"category_scores_gemma":[0.000702061,0.00032861263,0.00006936763,0.00195897,0.000036395082,0.0004612294,0.0022544223,0.00044029334,0.000084175976],"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":[0.000020043775,0.0005723103,0.17439346,0.0002784277,0.000096747266,0.00011746209,0.0366669,0.63225,0.008172822,0.031913765,0.0129945455,0.10252352],"study_design_scores_gemma":[0.00031466305,0.00003562329,0.012100347,0.0002535184,0.0000065605814,0.000117061165,0.0007887226,0.9817113,0.00031285023,0.00010258249,0.0036937331,0.0005630055],"about_ca_topic_score_codex":0.00024530973,"about_ca_topic_score_gemma":0.0000058988826,"teacher_disagreement_score":0.38860312,"about_ca_system_score_codex":0.0006506713,"about_ca_system_score_gemma":0.00009995449,"threshold_uncertainty_score":0.9999166},"labels":[],"label_agreement":null},{"id":"W2004663077","doi":"10.1007/s00607-009-0064-5","title":"Performance of several stabilized finite element methods for the Stokes equations based on the lowest equal-order pairs","year":2009,"lang":"en","type":"article","venue":"Computing","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","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":"University of Calgary","funders":"","keywords":"Gauss; Finite element method; Mathematics; Convergence (economics); Order (exchange); Element (criminal law); Mathematical analysis; Gauss–Seidel method; Polynomial; Applied mathematics; Mixed finite element method; Mathematical optimization; Physics; Iterative method; Law","score_opus":0.0601805366785815,"score_gpt":0.3685665198685561,"score_spread":0.30838598318997457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004663077","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004197645,0.000063284286,0.99423486,0.00045490955,0.00020363333,0.00045979983,0.000004229482,0.00010960234,0.00027206575],"genre_scores_gemma":[0.4681179,0.0000019801557,0.53163743,0.00017251648,0.00004028386,0.000013708278,0.0000019423392,0.000011135342,0.0000030922197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899817,0.00014324777,0.00037081153,0.00011909378,0.00016903585,0.00019963182],"domain_scores_gemma":[0.97830415,0.021183593,0.00010713646,0.00025849446,0.00012280727,0.0000238154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013022069,0.00013700809,0.00017842668,0.000041730233,0.00021035419,0.000018484836,0.00022937737,0.000027599948,0.000016839116],"category_scores_gemma":[0.0017151397,0.00008781072,0.00007324186,0.00029720226,0.000043726483,0.00003918725,0.000024175397,0.00015522691,0.0000018686435],"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":[0.000008998182,0.000023580462,0.00001023304,0.000043394106,0.000012940122,3.1663212e-8,0.00020346652,0.8711997,0.00013061462,0.00336539,0.000016742655,0.1249849],"study_design_scores_gemma":[0.00022565482,0.00011481542,0.00020721275,0.00006243179,0.000017477252,2.0228147e-7,0.000069572096,0.99103564,0.000871104,0.006945331,0.0003441989,0.000106362975],"about_ca_topic_score_codex":3.3120995e-7,"about_ca_topic_score_gemma":9.902986e-8,"teacher_disagreement_score":0.46392024,"about_ca_system_score_codex":0.00005101861,"about_ca_system_score_gemma":0.000021200643,"threshold_uncertainty_score":0.3580817},"labels":[],"label_agreement":null},{"id":"W2011727948","doi":"10.1007/s00607-013-0361-x","title":"Comparing redundancy models for high availability middleware","year":2013,"lang":"en","type":"article","venue":"Computing","topic":"Software System Performance and Reliability","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":"Concordia University; Ericsson (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Redundancy (engineering); Computer science; Provisioning; High availability; Fault tolerance; Distributed computing; Component (thermodynamics); Middleware (distributed applications); Single point of failure; Reliability engineering; Context (archaeology); Operating system; Engineering","score_opus":0.038419832611046874,"score_gpt":0.24587086504130357,"score_spread":0.2074510324302567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011727948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42588452,0.00009914073,0.5718047,0.00018673365,0.00069144054,0.00049224315,6.580279e-7,0.00038105072,0.00045951785],"genre_scores_gemma":[0.8938574,0.0000012957317,0.10571077,0.000113865906,0.00018339703,0.000042141375,0.0000037519337,0.000011873943,0.00007548832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813974,0.00006187322,0.00049220485,0.00060301804,0.00021599978,0.0004871859],"domain_scores_gemma":[0.9982281,0.00029297962,0.0001551885,0.0008874858,0.00031864186,0.00011764947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006321806,0.00018937257,0.0003289695,0.000061797335,0.00038873946,0.00025424207,0.0009392156,0.00008838188,0.0000108418935],"category_scores_gemma":[0.00009218732,0.00016510392,0.000113461625,0.00026426662,0.000056529676,0.0008515844,0.00044672037,0.00014818517,0.00012921215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000314698,0.0006829312,0.2759888,0.0026804418,0.00016928893,0.0000053218187,0.010397843,0.11662192,0.00076555234,0.100714296,0.01631041,0.47563174],"study_design_scores_gemma":[0.0003738157,0.00004758941,0.021324987,0.00009524186,0.0000036726415,0.000006006342,0.000040655592,0.94927955,0.00039354025,0.028019436,0.00017493348,0.00024056259],"about_ca_topic_score_codex":0.00023945625,"about_ca_topic_score_gemma":0.0000040947516,"teacher_disagreement_score":0.83265764,"about_ca_system_score_codex":0.00009802919,"about_ca_system_score_gemma":0.00006681169,"threshold_uncertainty_score":0.6732742},"labels":[],"label_agreement":null},{"id":"W2017161972","doi":"10.1007/s00607-013-0332-2","title":"Privacy preserving and transactional advertising for mobile services","year":2013,"lang":"en","type":"article","venue":"Computing","topic":"Cryptography and Data Security","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Encryption; Upload; Computer security; Scalability; Cryptography; Cloud computing; Context (archaeology); Server; Internet privacy; Homomorphic encryption; Client-side encryption; Transaction data; World Wide Web; Database transaction; Database; On-the-fly encryption","score_opus":0.008173638724672866,"score_gpt":0.24075835658924624,"score_spread":0.23258471786457338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017161972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33449626,0.00032924995,0.6645187,0.00010296403,0.00011689381,0.00022537455,0.0000022376537,0.00010274638,0.000105569496],"genre_scores_gemma":[0.83156383,0.000005243771,0.16819625,0.00013695081,0.00007026875,0.000016131089,0.0000044891544,0.000004515535,0.0000023244431],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992354,0.000019225552,0.00014744827,0.0002861721,0.00010084977,0.00021089698],"domain_scores_gemma":[0.99939656,0.00018227653,0.000050609167,0.00024116169,0.00006144462,0.00006796408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014429906,0.00008696593,0.00009465588,0.000054593973,0.00029905696,0.0003050631,0.00045088457,0.000032478692,0.00001187668],"category_scores_gemma":[0.000007036614,0.0000854814,0.000043745127,0.00014646507,0.000019082216,0.0008964757,0.00022696049,0.00006892606,0.000004013885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011132503,0.00016643113,0.013396768,0.0008936801,0.00007236513,0.000002540608,0.014603656,0.0013735753,0.0056605428,0.04701039,0.0010575312,0.9157514],"study_design_scores_gemma":[0.00038727577,0.00005475452,0.023139922,0.00009031327,0.000005461297,0.0000148450135,0.00016539574,0.95004606,0.0007277173,0.019322937,0.005838908,0.00020642718],"about_ca_topic_score_codex":0.00007586714,"about_ca_topic_score_gemma":0.00000402389,"teacher_disagreement_score":0.9486725,"about_ca_system_score_codex":0.000006790215,"about_ca_system_score_gemma":0.000011620213,"threshold_uncertainty_score":0.34858304},"labels":[],"label_agreement":null},{"id":"W2025042589","doi":"10.1007/s00607-013-0375-4","title":"TurboLock: increasing associativity of lock table in transactional memory","year":2013,"lang":"en","type":"article","venue":"Computing","topic":"Distributed systems and fault tolerance","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":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transactional memory; Software transactional memory; Lock (firearm); Parallel computing; Overhead (engineering); Concurrency; Exploit; Table (database); Distributed computing; Operating system; Programming language; Computer security; Database transaction; Data mining","score_opus":0.008302495638667196,"score_gpt":0.2172443057043419,"score_spread":0.2089418100656747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025042589","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64209336,0.00010328619,0.35518926,0.00012655211,0.00022278687,0.00011114531,0.0000025382658,0.000050498234,0.0021005527],"genre_scores_gemma":[0.9909037,7.361716e-7,0.008943437,0.00004774609,0.000048072598,0.000002798821,0.0000016456889,0.0000042815395,0.000047577414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988458,0.00012935726,0.0003233607,0.00023182698,0.00021443109,0.0002551948],"domain_scores_gemma":[0.99925333,0.00022493895,0.00016352323,0.00020300089,0.00010851117,0.000046680627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006278389,0.000093364586,0.00022789171,0.00006405902,0.000073562514,0.000072145325,0.00036656758,0.000057580495,0.000012699965],"category_scores_gemma":[0.000066205204,0.000094269606,0.000045200046,0.00044147414,0.000021696282,0.00037169826,0.00007469604,0.00014892567,0.00001735117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023147146,0.0011081708,0.2806352,0.00045387115,0.00014796814,0.000044738415,0.0074968194,0.08306669,0.03588647,0.016805204,0.004798607,0.5695331],"study_design_scores_gemma":[0.0004866782,0.000020491862,0.2542009,0.00016352664,0.0000019269394,0.00001528685,0.00008176293,0.74217564,0.0017518582,0.0006884955,0.00022512591,0.00018828109],"about_ca_topic_score_codex":0.0012711951,"about_ca_topic_score_gemma":0.000016620419,"teacher_disagreement_score":0.659109,"about_ca_system_score_codex":0.000070017064,"about_ca_system_score_gemma":0.000062920255,"threshold_uncertainty_score":0.38442028},"labels":[],"label_agreement":null},{"id":"W2025944808","doi":"10.1007/s006070050007","title":"Finite Volume Element Approximations of Nonlocal in Time One-Dimensional Flows in Porous Media","year":2000,"lang":"en","type":"article","venue":"Computing","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Texas A and M University; Lawrence Livermore National Laboratory; U.S. Department of Energy; National Science Foundation","keywords":"Finite element method; Finite volume method; Mathematics; Norm (philosophy); Volume (thermodynamics); Approximations of π; Mathematical analysis; Applied mathematics; Porous medium; Mixed finite element method; hp-FEM; Partial differential equation; Representative elementary volume; Finite element limit analysis; Mechanics; Porosity; Physics; Materials science","score_opus":0.020461392545174515,"score_gpt":0.26807967768122914,"score_spread":0.24761828513605463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025944808","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36029533,0.0000723722,0.6385393,0.000026575057,0.000076325465,0.00014793294,0.000005141599,0.00008825576,0.00074876676],"genre_scores_gemma":[0.42918733,0.0000017104242,0.57071567,0.000017965436,0.000036728823,0.000003827737,0.000009449156,0.000016528878,0.0000107791575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988145,0.00005168368,0.0005431304,0.0001404311,0.0002419507,0.00020827292],"domain_scores_gemma":[0.9987537,0.0010104558,0.000045132627,0.00012436708,0.00002697364,0.0000393663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003294906,0.00011494809,0.0002568885,0.0001304711,0.00001966426,0.000006887333,0.00014314693,0.00004816706,0.00019518631],"category_scores_gemma":[0.00018469436,0.00013384841,0.000029986624,0.00038774902,0.000025909687,0.000054881002,0.000045502868,0.00018815724,0.00007093255],"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":[0.0000032884282,0.00007037718,0.00021837733,0.00004233011,0.0000053684316,0.0000035844962,0.00032416984,0.91944474,0.0001503686,0.00011796752,0.000012340302,0.07960711],"study_design_scores_gemma":[0.000278091,0.000012909777,0.0018422873,0.00015244966,0.0000028585885,0.0000028238912,0.000011539072,0.99030674,0.00013742004,0.0070831743,0.000046955895,0.00012275887],"about_ca_topic_score_codex":0.0000032436687,"about_ca_topic_score_gemma":0.0000023035334,"teacher_disagreement_score":0.07948434,"about_ca_system_score_codex":0.00008997006,"about_ca_system_score_gemma":0.0000142567915,"threshold_uncertainty_score":0.545818},"labels":[],"label_agreement":null},{"id":"W2038514592","doi":"10.1007/s00607-015-0445-x","title":"Specification, verification, and quantification of security in model-based systems","year":2015,"lang":"en","type":"article","venue":"Computing","topic":"Formal Methods in Verification","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":"Concordia University","funders":"","keywords":"Computer science; Systems Modeling Language; Correctness; Unified Modeling Language; Software security assurance; Software engineering; Modeling language; Computer security model; Computer security; Security testing; Software; Systems engineering; Security information and event management; Information security; Programming language; Security service; Cloud computing security; Engineering; Operating system","score_opus":0.0877375456571489,"score_gpt":0.32004104581729464,"score_spread":0.23230350016014573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038514592","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10349501,0.00025888934,0.895138,0.00011372942,0.00024260723,0.00020715794,0.0000010150029,0.00007676456,0.00046679634],"genre_scores_gemma":[0.73849833,0.0000033832932,0.2614463,0.0000127515,0.000020337924,0.0000059441354,0.000004422706,0.0000050363337,0.000003478411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986018,0.00019039598,0.00049181207,0.00033866492,0.00023280377,0.00014454877],"domain_scores_gemma":[0.9985642,0.0000845636,0.00030703022,0.0006459234,0.00032608837,0.00007221951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019351911,0.00009424755,0.00016108176,0.00018094729,0.00004910775,0.000075439464,0.0004725301,0.00006234999,1.8281357e-7],"category_scores_gemma":[0.00029508545,0.000103262406,0.000016880793,0.00056247145,0.00006500102,0.00031741924,0.00007344808,0.00009715164,0.000004830921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015677275,0.00014967243,0.007941957,0.0001565133,0.0000047861913,6.276078e-7,0.005235096,0.3419218,0.004779843,0.6263225,0.00013078866,0.01334075],"study_design_scores_gemma":[0.0002308857,0.00001809672,0.0064197863,0.000040303366,0.0000016914075,0.000002367951,0.00013556902,0.984936,0.0050822217,0.0029184134,0.00011404611,0.00010063095],"about_ca_topic_score_codex":0.000033145076,"about_ca_topic_score_gemma":0.000001386135,"teacher_disagreement_score":0.6430142,"about_ca_system_score_codex":0.00007876236,"about_ca_system_score_gemma":0.00012098334,"threshold_uncertainty_score":0.42109188},"labels":[],"label_agreement":null},{"id":"W2039888183","doi":"10.1007/s00607-011-0165-9","title":"Towards a better way of computing: a return from bits to atoms via physics and biology","year":2011,"lang":"en","type":"article","venue":"Computing","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","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":"Impact","funders":"","keywords":"Computer science; Parallels; Human–computer interaction; Data science; Task (project management); Metaverse; Software; Virtual reality; Systems engineering; Engineering","score_opus":0.04812683119579398,"score_gpt":0.25089175365588795,"score_spread":0.20276492246009398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039888183","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52914596,0.00009929726,0.47000307,0.000008341636,0.00017693696,0.000048260412,0.0000024527553,0.00006168965,0.00045398946],"genre_scores_gemma":[0.96575475,0.000003408122,0.03394213,0.00009569276,0.0001762334,5.0629234e-7,0.000003908168,0.00002115019,0.0000022341856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992739,0.000020259953,0.00023295525,0.00020085652,0.000051722185,0.00022033286],"domain_scores_gemma":[0.99962103,0.000042144795,0.000040662337,0.00018628531,0.000037746384,0.00007215381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000096561336,0.00013549002,0.00023319255,0.000039946302,0.00003737682,0.000010562878,0.00015858513,0.00006565204,0.00001919445],"category_scores_gemma":[0.0000120302,0.00012755989,0.000037777452,0.00011935459,0.000036629077,0.000025599036,0.00014579835,0.00012353579,0.000013151118],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000083289915,0.000038288294,0.026156383,0.00009744823,0.00012505011,0.000008502503,0.015772289,0.005082999,0.033987075,0.0014542271,0.00019342307,0.917076],"study_design_scores_gemma":[0.00020385797,0.00010802671,0.029673789,0.0001709133,0.000022631839,0.000007649382,0.000069379756,0.8632112,0.10086847,0.004835945,0.000406312,0.00042178328],"about_ca_topic_score_codex":0.00017204354,"about_ca_topic_score_gemma":0.0000046342375,"teacher_disagreement_score":0.9166542,"about_ca_system_score_codex":0.0000121805015,"about_ca_system_score_gemma":0.000004290111,"threshold_uncertainty_score":0.52017415},"labels":[],"label_agreement":null},{"id":"W2043734742","doi":"10.1007/s00607-003-0022-6","title":"Stability of solutions of delay functional integro-differential equations and their discretizations","year":2003,"lang":"en","type":"article","venue":"Computing","topic":"Numerical methods for differential equations","field":"Mathematics","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":"Memorial University of Newfoundland","funders":"","keywords":"Collocation (remote sensing); Mathematics; Runge–Kutta methods; Stability (learning theory); Basis (linear algebra); Collocation method; Differential equation; Applied mathematics; Delay differential equation; Numerical stability; Mathematical analysis; Class (philosophy); Numerical analysis; Ordinary differential equation; Computer science; Geometry","score_opus":0.1409085499038111,"score_gpt":0.33158253388014447,"score_spread":0.19067398397633337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043734742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2374814,0.0000390403,0.7617083,0.00003070879,0.00014266015,0.00015297471,0.00002974537,0.000031284155,0.0003838718],"genre_scores_gemma":[0.8930347,0.000001239588,0.10689191,0.000003296389,0.000023253653,0.000005042849,0.000015137983,0.0000127453695,0.000012692124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99860203,0.0003348297,0.00054567395,0.00018644863,0.00015264678,0.0001783762],"domain_scores_gemma":[0.99668443,0.0024874927,0.00026798106,0.00025194816,0.00024475058,0.00006337948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046204275,0.00012679056,0.00028754462,0.000093919356,0.00021421892,0.0000122821375,0.000085408436,0.00005870298,0.00021754568],"category_scores_gemma":[0.0034893982,0.000106637635,0.000110048924,0.00031866552,0.00022115858,0.000062708685,0.00008825298,0.00013737964,9.2276747e-7],"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":[0.0000069444186,0.00039485967,0.0020600087,0.00010242519,0.000087721375,5.570808e-8,0.0013595513,0.00013405371,0.019141264,0.9707788,0.000018350085,0.005915943],"study_design_scores_gemma":[0.0005834651,0.000090942594,0.006173286,0.0001092735,0.0001433817,0.0000054092316,0.0013572372,0.07154423,0.016568286,0.90314066,0.000036243124,0.00024757886],"about_ca_topic_score_codex":0.000015280168,"about_ca_topic_score_gemma":0.000015711255,"teacher_disagreement_score":0.6555533,"about_ca_system_score_codex":0.000029607207,"about_ca_system_score_gemma":0.000067263914,"threshold_uncertainty_score":0.43485567},"labels":[],"label_agreement":null},{"id":"W2067222153","doi":"10.1007/s006070070001","title":"Simulated Annealing for Fitting Linear Combinations of Gaussians to Data","year":2000,"lang":"en","type":"article","venue":"Computing","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","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":"Maxima and minima; Gaussian; Simulated annealing; Algorithm; Computer science; Gradient descent; Gaussian function; Convergence (economics); Mixture model; Mathematical optimization; Applied mathematics; Mathematics; Artificial intelligence; Artificial neural network; Physics","score_opus":0.06808277752787258,"score_gpt":0.38755941518681464,"score_spread":0.3194766376589421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067222153","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8601848,0.000017846285,0.13223115,0.00018036661,0.00008334577,0.00025008927,0.00021103563,0.000023664297,0.0068177325],"genre_scores_gemma":[0.9915672,1.4219596e-7,0.007553791,0.000017871762,0.00013883814,9.850235e-7,0.00024758367,0.0000067765995,0.00046680166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993047,0.000016425036,0.00017768882,0.00019464389,0.000103309314,0.00020320721],"domain_scores_gemma":[0.9993435,0.00018423343,0.000039166447,0.00030179587,0.00006865228,0.000062658786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029800402,0.000053807747,0.00009692454,0.000038140533,0.00020487182,0.000056954163,0.00029515964,0.00000968567,0.00019947659],"category_scores_gemma":[0.000052797917,0.000051873893,0.000031567364,0.00020715526,0.000025538295,0.00010354319,0.00011377337,0.000043688415,0.00001589256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000721088,0.0005303129,0.02145631,0.0000880774,0.00013016009,0.0000011860421,0.0035110144,0.3039083,0.0023061344,0.012119819,0.016076244,0.63980037],"study_design_scores_gemma":[0.0004809768,0.00002956664,0.00052675884,0.00005092563,0.000005832111,8.162063e-8,0.0004372874,0.9904834,0.0018798865,0.0008474885,0.005176754,0.000081015925],"about_ca_topic_score_codex":0.00009223476,"about_ca_topic_score_gemma":0.0000011709334,"teacher_disagreement_score":0.6865752,"about_ca_system_score_codex":0.0000041492926,"about_ca_system_score_gemma":0.00003910352,"threshold_uncertainty_score":0.21841289},"labels":[],"label_agreement":null},{"id":"W2084095496","doi":"10.1007/s00607-008-0009-4","title":"On the numerical solution of nonlinear systems of Volterra integro-differential equations with delay arguments","year":2008,"lang":"en","type":"article","venue":"Computing","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":60,"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":"Memorial University of Newfoundland","keywords":"Superconvergence; Piecewise; Nonlinear system; Mathematics; Collocation method; Convergence (economics); Polynomial; Differential equation; Applied mathematics; Collocation (remote sensing); Volterra integral equation; Numerical analysis; Mathematical analysis; Computer science; Ordinary differential equation; Finite element method; Integral equation","score_opus":0.05106779509966347,"score_gpt":0.29030405803498355,"score_spread":0.23923626293532008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084095496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48995113,0.000012434903,0.5090332,0.00015603055,0.00004279374,0.000095402414,8.5683024e-7,0.000007793863,0.00070034864],"genre_scores_gemma":[0.99881756,0.0000013794285,0.000980172,0.00009488006,0.00004862569,0.000003030626,0.0000054038037,0.0000045884135,0.000044367196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992427,0.00010166214,0.00032135897,0.000102034086,0.00010085872,0.00013133946],"domain_scores_gemma":[0.99850416,0.0011278922,0.00012864523,0.0001312942,0.00006659086,0.00004140371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002685331,0.00007154623,0.00031166774,0.000026875307,0.00009354924,8.4656745e-7,0.000058684825,0.00006724591,0.0000885541],"category_scores_gemma":[0.0004838391,0.000036174468,0.000056807392,0.00006580018,0.00028692486,0.00000905962,0.000030406269,0.00017084308,0.000013037352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034689056,0.00071159407,0.0035574916,0.0001715442,0.00021057451,0.000010476532,0.00065943756,0.0029995812,0.0005251493,0.99031794,0.00019851659,0.000290806],"study_design_scores_gemma":[0.000398703,0.00046098357,0.0015186645,0.00023664297,0.00005158578,0.000047313544,0.000035468995,0.9949434,0.00029375858,0.0019690627,0.000003609802,0.00004082212],"about_ca_topic_score_codex":0.000011631819,"about_ca_topic_score_gemma":1.797053e-7,"teacher_disagreement_score":0.9919438,"about_ca_system_score_codex":0.000010135994,"about_ca_system_score_gemma":0.000022784112,"threshold_uncertainty_score":0.1475152},"labels":[],"label_agreement":null},{"id":"W2098548175","doi":"10.1007/s00607-005-0137-z","title":"A Numerical Study of the Xu Polynomial Interpolation Formula in Two Variables","year":2005,"lang":"en","type":"article","venue":"Computing","topic":"Mathematical functions and polynomials","field":"Mathematics","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 Calgary","funders":"Università degli Studi di Padova","keywords":"Interpolation (computer graphics); Mathematics; Lagrange polynomial; Polynomial interpolation; Chebyshev nodes; Trigonometric interpolation; Birkhoff interpolation; Applied mathematics; Polynomial; Linear interpolation; Chebyshev polynomials; Chebyshev filter; Variable (mathematics); Stability (learning theory); Mathematical analysis; Computer science","score_opus":0.042951203238913896,"score_gpt":0.34581327033634857,"score_spread":0.3028620670974347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098548175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9691434,0.0000093964945,0.025861694,0.00012101477,0.0001634438,0.00035543644,7.095409e-7,0.000027784132,0.0043171556],"genre_scores_gemma":[0.9893931,7.573182e-8,0.0102734985,0.00003263134,0.00020297938,0.0000038822604,2.4585313e-7,0.000010579982,0.000082986066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900067,0.00010915192,0.00048662524,0.00012065112,0.00013184118,0.00015107951],"domain_scores_gemma":[0.9988326,0.0007094228,0.0001600614,0.00024742284,0.00002600839,0.000024476562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004899861,0.000089993875,0.0002249819,0.000055877517,0.00007624862,0.000018694602,0.00016614597,0.000026585682,0.000084938365],"category_scores_gemma":[0.00038848448,0.00006179525,0.000055791083,0.00020892452,0.00001570705,0.000048914702,0.00011350504,0.00013105702,0.0000071574586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004836813,0.019984528,0.21896511,0.0010898947,0.00054537575,0.000009448139,0.12175636,0.041387167,0.009302903,0.39292285,0.01972717,0.1738255],"study_design_scores_gemma":[0.0029414769,0.0001924032,0.006489581,0.0003382462,0.000073863965,0.000015852962,0.0015873262,0.9659252,0.0010206602,0.02002302,0.0010877158,0.0003046348],"about_ca_topic_score_codex":0.00013538451,"about_ca_topic_score_gemma":0.00007403369,"teacher_disagreement_score":0.9245381,"about_ca_system_score_codex":0.000038250844,"about_ca_system_score_gemma":0.000020624137,"threshold_uncertainty_score":0.25199372},"labels":[],"label_agreement":null},{"id":"W2105143665","doi":"10.1007/s00607-014-0409-6","title":"ACS: an effective admission control scheme with deadlock resolutions for workflow scheduling in clouds","year":2014,"lang":"en","type":"article","venue":"Computing","topic":"Distributed and Parallel Computing Systems","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 New Brunswick","funders":"","keywords":"Computer science; Distributed computing; Cloud computing; Provisioning; Workflow; Scheduling (production processes); Workflow management system; Virtual machine; Benchmark (surveying); Workflow technology; Deadlock prevention algorithms; Deadlock; Computer network; Database; Operating system; Engineering","score_opus":0.012774922078309913,"score_gpt":0.26326635956201466,"score_spread":0.2504914374837047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105143665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24536358,0.00011257945,0.7530035,0.00014588505,0.00028850016,0.00052211253,0.0000019382774,0.00026625043,0.00029566634],"genre_scores_gemma":[0.8126916,2.9127395e-7,0.18679355,0.00012515498,0.00033022405,0.00002361708,0.000009917245,0.000016369439,0.00000927292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780464,0.00033619985,0.00037653916,0.0006514459,0.00022809667,0.000603047],"domain_scores_gemma":[0.998065,0.000808268,0.0001963649,0.00055810466,0.00017754422,0.00019469463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014548174,0.00023819227,0.00036616804,0.00013248589,0.00040782557,0.00024113087,0.00081041944,0.00011122413,5.9157355e-7],"category_scores_gemma":[0.00027911068,0.00021037557,0.000069805006,0.00057357544,0.000045252782,0.00025716846,0.00015371673,0.00026030035,0.000009666268],"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":[0.00016062097,0.0002843474,0.023689296,0.00015897489,0.00006572582,0.000013303263,0.0018744213,0.77706,0.0009783178,0.07126007,0.00024453923,0.12421042],"study_design_scores_gemma":[0.0020588937,0.00037820535,0.0056742085,0.0005330659,0.000006902108,0.000018525168,0.000037309517,0.9883996,0.000092698334,0.0011614132,0.0013453778,0.00029375713],"about_ca_topic_score_codex":0.000026750638,"about_ca_topic_score_gemma":0.000014861357,"teacher_disagreement_score":0.56732804,"about_ca_system_score_codex":0.00009330957,"about_ca_system_score_gemma":0.000079080004,"threshold_uncertainty_score":0.8578867},"labels":[],"label_agreement":null},{"id":"W2115333632","doi":"10.1007/s00607-012-0215-y","title":"Monitoring and recovery for web service applications","year":2012,"lang":"en","type":"article","venue":"Computing","topic":"Service-Oriented Architecture and Web Services","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":"Correctness; Business Process Execution Language; Computer science; Web service; Automaton; Finite-state machine; Service (business); Distributed computing; Software engineering; Web application; Adaptation (eye); Service-oriented architecture; Programming language; World Wide Web; Theoretical computer science","score_opus":0.017972692974113077,"score_gpt":0.2640509283156918,"score_spread":0.24607823534157874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115333632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22631495,0.0014684707,0.7692096,0.0007056335,0.0006716428,0.0003159968,0.0000018545446,0.0002558233,0.0010560276],"genre_scores_gemma":[0.8725261,0.00000904456,0.12581478,0.0006997719,0.0009001795,0.00002875543,0.0000020363934,0.00001004555,0.000009269418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916935,0.000020288193,0.0001461088,0.0002391209,0.00009335471,0.00033179225],"domain_scores_gemma":[0.99917394,0.0002604852,0.000072334784,0.0003048946,0.00007535474,0.000112984424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022714914,0.000107552245,0.00010474346,0.00006603916,0.00027696593,0.00010709502,0.00043435104,0.000040229937,6.589858e-7],"category_scores_gemma":[0.000003944038,0.00010475157,0.000030461919,0.00034121136,0.0000069391067,0.00034332104,0.00034227484,0.00007895689,0.000013777365],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000082297165,0.00009620033,0.032606393,0.00039107824,0.00005113619,4.919327e-7,0.006747998,0.00047349292,0.008851747,0.024238655,0.000081390106,0.9264532],"study_design_scores_gemma":[0.002684192,0.00024348061,0.14567478,0.000701704,0.000120959136,0.0001832873,0.002178915,0.44832247,0.032556288,0.01757638,0.34750587,0.0022516847],"about_ca_topic_score_codex":0.00001699425,"about_ca_topic_score_gemma":0.000004048086,"teacher_disagreement_score":0.9242015,"about_ca_system_score_codex":0.000013661011,"about_ca_system_score_gemma":0.000019066829,"threshold_uncertainty_score":0.42716452},"labels":[],"label_agreement":null},{"id":"W2131771497","doi":"10.1007/s00607-004-0056-9","title":"Robust Spherical Parameterization of Triangular Meshes","year":2004,"lang":"en","type":"article","venue":"Computing","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":77,"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":"Polygon mesh; Morphing; Triangle mesh; Volume mesh; Triangulation; Mathematics; Unit sphere; Computer science; Spherical trigonometry; Position (finance); Computer graphics; Domain (mathematical analysis); Mesh generation; Geometry; Computer graphics (images); Combinatorics; Finite element method; Mathematical analysis; Physics","score_opus":0.02487536545239894,"score_gpt":0.20891234347659537,"score_spread":0.18403697802419644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131771497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42255226,0.00012210534,0.57696503,0.000008743557,0.00004303263,0.000012560533,2.7224382e-7,0.00008507624,0.00021095644],"genre_scores_gemma":[0.9577845,0.0000059656386,0.042127203,0.000008287221,0.000054392796,2.6884345e-7,0.0000039292822,0.000010750675,0.0000047361323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996028,0.0000072438493,0.00015819521,0.00007257358,0.00006655086,0.00009266836],"domain_scores_gemma":[0.99983627,0.000017044633,0.000021386084,0.00008461731,0.00001806501,0.00002262105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006823485,0.000056684134,0.00012509972,0.000027643111,0.000023942517,0.0000109265675,0.000057177673,0.000029493203,0.00000799588],"category_scores_gemma":[0.00002328293,0.00005688586,0.000053462758,0.00016709827,0.000009965793,0.000023617056,0.000012293918,0.00004739698,0.000005626854],"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":[5.22611e-7,0.0000061036117,0.00010521245,0.000018683684,0.00001741201,0.0000010543507,0.00012337354,0.98941404,0.0029115917,0.000039309853,0.0000068885743,0.007355803],"study_design_scores_gemma":[0.000158717,0.000008104234,0.00013374204,0.0000390435,0.000016031714,0.0000010601384,0.000031619238,0.99301565,0.0063303565,0.00018367682,0.000016694501,0.00006532043],"about_ca_topic_score_codex":0.000008747506,"about_ca_topic_score_gemma":6.4006525e-7,"teacher_disagreement_score":0.53523225,"about_ca_system_score_codex":0.000018647184,"about_ca_system_score_gemma":0.0000058925043,"threshold_uncertainty_score":0.2319738},"labels":[],"label_agreement":null},{"id":"W2146228093","doi":"10.1007/s00607-005-0141-3","title":"Adaptive Techniques for Spline Collocation","year":2005,"lang":"en","type":"article","venue":"Computing","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Piecewise; Mathematics; Collocation method; Estimator; Hermite spline; Collocation (remote sensing); Spline (mechanical); Applied mathematics; Grid; Boundary value problem; Quadratic equation; Mathematical optimization; Smoothing spline; Algorithm; Mathematical analysis; Spline interpolation; Computer science; Geometry; Differential equation; Bilinear interpolation; Ordinary differential equation","score_opus":0.011937958552365622,"score_gpt":0.26677117930548816,"score_spread":0.25483322075312254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146228093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003667513,0.0001369067,0.9929517,0.000076664466,0.000022438908,0.00018613202,0.0000014938457,0.001523241,0.001433943],"genre_scores_gemma":[0.5803094,0.000005629839,0.41941175,0.000042723965,0.00017167412,0.000014459699,0.000004276368,0.000014123211,0.000025985997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953175,0.000004704268,0.00016158976,0.000109857334,0.000051800525,0.00014028147],"domain_scores_gemma":[0.99972576,0.00006355395,0.000028267697,0.00010280624,0.00005272206,0.000026915168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008323036,0.000084593725,0.00012107222,0.000055536108,0.000050461527,0.000009797527,0.000084595646,0.000036721016,0.0000039551264],"category_scores_gemma":[0.000023195627,0.00008736932,0.000046397618,0.00016283916,0.000013359276,0.00007180166,0.000021080985,0.00006428529,0.0000066190464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043040495,0.000016600412,0.000034987064,0.000019245928,0.00002151095,2.3745103e-7,0.00006611581,0.065527506,0.008539497,0.0012173338,0.0018577111,0.9226949],"study_design_scores_gemma":[0.000049718536,0.000026428635,0.00003910975,0.000021383126,0.0000088344805,9.709528e-7,0.000012102013,0.8739491,0.10298624,0.0009117757,0.021877835,0.00011651269],"about_ca_topic_score_codex":0.0000011597833,"about_ca_topic_score_gemma":0.0000019754507,"teacher_disagreement_score":0.92257845,"about_ca_system_score_codex":0.000086390944,"about_ca_system_score_gemma":0.0000032081605,"threshold_uncertainty_score":0.35628176},"labels":[],"label_agreement":null},{"id":"W2165372370","doi":"10.1007/s00607-005-0140-4","title":"Optimal Quadratic and Cubic Spline Collocation on Nonuniform Partitions","year":2005,"lang":"en","type":"article","venue":"Computing","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Mathematics; Collocation method; Collocation (remote sensing); Orthogonal collocation; Spline (mechanical); Applied mathematics; Boundary value problem; Quadratic equation; Uniqueness; Mathematical analysis; Nonlinear system; Ordinary differential equation; Geometry; Differential equation; Computer science","score_opus":0.007540692905724686,"score_gpt":0.24516778160595393,"score_spread":0.23762708870022925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165372370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47612357,0.00009514396,0.52101195,0.00014026852,0.000020741196,0.00006962967,8.010017e-7,0.0004748674,0.0020630255],"genre_scores_gemma":[0.9176408,0.000014872774,0.082143195,0.00005170267,0.000099878635,0.0000040443683,0.000006178939,0.000011662035,0.000027659044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999569,0.000005966541,0.00014614074,0.00009725114,0.00005957652,0.000122063546],"domain_scores_gemma":[0.9997742,0.000049165577,0.00002094412,0.000097264514,0.000017750684,0.000040634695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004441864,0.000077519486,0.00010017508,0.00005360331,0.00007554327,0.000017996377,0.000044339773,0.000024833418,0.000011805373],"category_scores_gemma":[0.00001697902,0.00007770446,0.000021068665,0.00015952154,0.000018196084,0.000071918505,0.000019299665,0.000087727625,0.000029624909],"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":[0.0000012939714,0.000020297015,0.000081106686,0.000011825431,0.000011373354,7.3867466e-7,0.000107801534,0.9325872,0.0021703017,0.0006912913,0.00022862885,0.06408812],"study_design_scores_gemma":[0.00007006426,0.000025303927,0.00053028326,0.000029275101,0.000009421256,0.0000024598553,0.000029207018,0.99102026,0.006597022,0.00014959548,0.0014377421,0.00009938325],"about_ca_topic_score_codex":0.0000013849749,"about_ca_topic_score_gemma":0.0000040142404,"teacher_disagreement_score":0.44151723,"about_ca_system_score_codex":0.00005120324,"about_ca_system_score_gemma":0.000002557755,"threshold_uncertainty_score":0.31686962},"labels":[],"label_agreement":null},{"id":"W2623963253","doi":"10.1007/s00607-017-0560-y","title":"Performance impacts of hybrid cloud storage","year":2017,"lang":"en","type":"article","venue":"Computing","topic":"Advanced Data Storage Technologies","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 Ottawa","funders":"","keywords":"Cloud computing; Computer science; Cloud storage; Operating system","score_opus":0.023440478562033733,"score_gpt":0.2770958936438562,"score_spread":0.25365541508182243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623963253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58062464,0.00007652217,0.41774392,0.0001100999,0.00045579858,0.000047456695,0.0000018853048,0.00025212308,0.00068755244],"genre_scores_gemma":[0.89141244,0.0000149683565,0.10845321,0.000027107448,0.00006502125,5.614225e-7,9.3927633e-7,0.000006742442,0.000018995042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902743,0.000013577098,0.00020366143,0.0002877831,0.0001759147,0.000291658],"domain_scores_gemma":[0.9974794,0.00006385468,0.00042284376,0.0019325158,0.000064861546,0.00003654323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027283354,0.00011554905,0.00018624881,0.00007148271,0.00043606095,0.00012814587,0.0028695518,0.000026186162,0.0000015993013],"category_scores_gemma":[0.00038185483,0.00011341346,0.000038230693,0.00007829184,0.00017302224,0.0008861119,0.0021399623,0.00015956131,0.000020305357],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008442244,0.00006666113,0.029232569,0.00014319803,0.000027862734,0.00013333428,0.0005016832,0.0035583605,0.0057731634,0.018067596,0.0022057786,0.94028133],"study_design_scores_gemma":[0.00055484404,0.00018149229,0.064613596,0.0002952256,0.0000058997134,0.00011026437,0.000040560408,0.82680464,0.10229706,0.002414085,0.002221832,0.00046049614],"about_ca_topic_score_codex":0.000009520991,"about_ca_topic_score_gemma":7.9790397e-7,"teacher_disagreement_score":0.9398208,"about_ca_system_score_codex":0.00003693518,"about_ca_system_score_gemma":0.000030280096,"threshold_uncertainty_score":0.5332389},"labels":[],"label_agreement":null},{"id":"W2807971989","doi":"10.1007/s00607-018-0631-8","title":"On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition","year":2018,"lang":"en","type":"article","venue":"Computing","topic":"Context-Aware Activity Recognition Systems","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":"McGill University","funders":"","keywords":"Computer science; Provisioning; Personalization; Cloud computing; Context (archaeology); World Wide Web; Service provider; Service (business); Service delivery framework; Mobile computing; Mobile QoS; Computer network; Business","score_opus":0.055359060092074815,"score_gpt":0.30339243517674586,"score_spread":0.24803337508467105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807971989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4733688,0.000058202317,0.52487427,0.00022743871,0.0005024208,0.0006990316,0.000009830741,0.0001953215,0.000064707325],"genre_scores_gemma":[0.9803963,6.0707765e-7,0.016522724,0.0025539836,0.0004445282,0.000029769864,0.000010376156,0.000030391846,0.000011362247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979647,0.00021136503,0.00036540406,0.0007463039,0.0002965977,0.0004156211],"domain_scores_gemma":[0.99730784,0.00093938835,0.0003262808,0.00036401252,0.00092136743,0.000141088],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005551847,0.0002761185,0.00036916937,0.00014988089,0.00083799724,0.00035099773,0.00045291494,0.000109627246,0.000007249365],"category_scores_gemma":[0.000036701305,0.00029087334,0.00007780462,0.0005287128,0.00006916512,0.0005709131,0.0003744403,0.00017889537,0.00003539028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014592325,0.00082054356,0.0020813604,0.001411393,0.0006786009,0.00004867861,0.099571586,0.0055206507,0.041547254,0.027750598,0.0016166912,0.8174934],"study_design_scores_gemma":[0.0015531894,0.0003967172,0.00021071789,0.0007587915,0.000021889782,0.000078380814,0.0015042212,0.99201375,0.0020598934,0.0003686732,0.0006515848,0.00038219918],"about_ca_topic_score_codex":0.00023930584,"about_ca_topic_score_gemma":0.00008453157,"teacher_disagreement_score":0.9864931,"about_ca_system_score_codex":0.00015401644,"about_ca_system_score_gemma":0.00010577981,"threshold_uncertainty_score":0.99995434},"labels":[],"label_agreement":null},{"id":"W2888674064","doi":"10.1007/s00607-018-0655-0","title":"Policy expressions and the bottom-up design of computing policies","year":2018,"lang":"en","type":"article","venue":"Computing","topic":"Network Packet Processing and Optimization","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":"National Science Foundation","keywords":"Equivalence (formal languages); Computer science; Set (abstract data type); Generalization; Predicate (mathematical logic); Policy analysis; Expression (computer science); Representation (politics); Theoretical computer science; Mathematics; Discrete mathematics; Law; Programming language; Political science","score_opus":0.026249889761590837,"score_gpt":0.28960667376180615,"score_spread":0.26335678400021534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888674064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017827384,0.00018793788,0.9795576,0.00084006146,0.00025741916,0.00010649605,1.6749959e-7,0.00012889835,0.00109406],"genre_scores_gemma":[0.835442,0.000011525186,0.16371092,0.0003413182,0.00043748802,5.506005e-7,2.4005396e-7,0.000006987241,0.000048940692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884725,0.00020371558,0.00025812723,0.00022787372,0.0001981954,0.00026485807],"domain_scores_gemma":[0.998622,0.00057925197,0.0002148519,0.00031649068,0.00021552274,0.000051899733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009828607,0.00011166255,0.00017745586,0.00010116217,0.0006246621,0.00017169976,0.0005440134,0.000042354786,7.571011e-7],"category_scores_gemma":[0.00029016912,0.00007739743,0.000030645828,0.00060361746,0.0004010486,0.00014629682,0.00052617525,0.00011659963,0.0000027097303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006953918,0.00008255465,0.0014668126,0.000112166585,0.000076477576,0.0000026572516,0.089294605,0.112402685,0.0022322708,0.3528989,0.0041335556,0.4372278],"study_design_scores_gemma":[0.00048513518,0.000032533575,0.00048268132,0.00014284966,0.0000048973798,0.0000142961935,0.00010786452,0.9928507,0.0010708176,0.0046673617,0.000045885638,0.000094932075],"about_ca_topic_score_codex":0.000054867432,"about_ca_topic_score_gemma":6.317337e-7,"teacher_disagreement_score":0.88044804,"about_ca_system_score_codex":0.000015873198,"about_ca_system_score_gemma":0.00011122197,"threshold_uncertainty_score":0.4804459},"labels":[],"label_agreement":null},{"id":"W2900493992","doi":"10.1007/s00607-018-0678-6","title":"An end-to-end joint learning framework of artery-specific coronary calcium scoring in non-contrast cardiac CT","year":2018,"lang":"en","type":"article","venue":"Computing","topic":"Advanced X-ray and CT Imaging","field":"Engineering","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":"Western University","funders":"","keywords":"Calcification; Medicine; Coronary artery disease; Radiology; Artery; Contrast (vision); Internal medicine; Cardiology; Artificial intelligence; Computer science","score_opus":0.014306880084942249,"score_gpt":0.25176855067769227,"score_spread":0.23746167059275003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900493992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6487007,0.00032524514,0.34902176,0.000007825061,0.0007475888,0.00011016677,0.0000017208478,0.00016127236,0.00092373043],"genre_scores_gemma":[0.98091507,0.00001414497,0.018195242,0.000027145348,0.00077405735,0.0000038351436,0.000004823619,0.000059695463,0.0000059735908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852496,0.000048756967,0.00041729934,0.00031422128,0.00016823005,0.0005265208],"domain_scores_gemma":[0.9993208,0.00013894669,0.000064520296,0.0002995403,0.00005062757,0.0001255762],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035061894,0.00021970249,0.0003889205,0.00018436329,0.0001371645,0.000041526957,0.00018885691,0.00005231661,0.000031951553],"category_scores_gemma":[0.000039550006,0.0002602824,0.00007327897,0.00034918188,0.000067614325,0.00019456433,0.00008670503,0.00056983274,0.000038348684],"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":[0.000020543739,0.000041700936,0.057629786,0.00013422218,0.000048817983,0.00007611124,0.0060901428,0.5408743,0.12446822,0.00025935314,0.000042986103,0.2703138],"study_design_scores_gemma":[0.00073507125,0.0002790941,0.3187874,0.002131737,0.000024654326,0.000051892785,0.003291094,0.59329605,0.07735457,0.00043566505,0.002482348,0.0011304332],"about_ca_topic_score_codex":0.000014900315,"about_ca_topic_score_gemma":0.000002849707,"teacher_disagreement_score":0.3322144,"about_ca_system_score_codex":0.0001015851,"about_ca_system_score_gemma":0.00001289362,"threshold_uncertainty_score":0.9999849},"labels":[],"label_agreement":null},{"id":"W2945051179","doi":"10.1007/s00607-019-00771-y","title":"On cycling risk and discomfort: urban safety mapping and bike route recommendations","year":2019,"lang":"en","type":"preprint","venue":"Computing","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Leeds","keywords":"Cycling; Transport engineering; Bike sharing; Artifact (error); Business; Computer science; Engineering; Geography","score_opus":0.03172444649453365,"score_gpt":0.31484882983828555,"score_spread":0.2831243833437519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945051179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9670776,0.0005130083,0.020934952,0.00062559656,0.0012640108,0.00047773414,0.000072878756,0.00013611972,0.008898086],"genre_scores_gemma":[0.99667585,0.00027908897,0.0021411842,0.00008712548,0.00044320908,0.000002260533,0.00009633389,0.00001623298,0.0002587443],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982551,0.00019033815,0.00041925625,0.00062106195,0.0001930654,0.00032122261],"domain_scores_gemma":[0.9986783,0.00052744005,0.000359836,0.0002884335,0.00002813535,0.00011782132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013112525,0.00020730987,0.0003399475,0.000098360826,0.0011841321,0.00029399336,0.0002529863,0.0002035509,0.000024927125],"category_scores_gemma":[0.00011706075,0.00020780283,0.00008425192,0.00012537335,0.00017790006,0.00012341831,0.0003803025,0.00075119094,0.0000025973654],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074249015,0.0000257264,0.9356333,0.000099996054,0.000033396704,0.0000012228206,0.013191122,0.00054638716,0.0000016602944,0.0013608424,0.00016109661,0.048937835],"study_design_scores_gemma":[0.00064181315,0.000029171317,0.9315746,0.0018440171,0.00012773203,4.0838242e-7,0.0040386724,0.024173606,0.000007020615,0.017557038,0.019060122,0.0009458373],"about_ca_topic_score_codex":0.0018175667,"about_ca_topic_score_gemma":0.00070452737,"teacher_disagreement_score":0.047991995,"about_ca_system_score_codex":0.00009822036,"about_ca_system_score_gemma":0.0001114063,"threshold_uncertainty_score":0.9107506},"labels":[],"label_agreement":null},{"id":"W2998920459","doi":"10.1007/s00607-019-00788-3","title":"Special issue on deep learning for natural language processing","year":2020,"lang":"en","type":"article","venue":"Computing","topic":"Educational Technology and Pedagogy","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 British Columbia","funders":"","keywords":"Computer science; Natural (archaeology); Artificial intelligence; Natural language; Natural language processing; Deep learning; Cognitive science; Psychology; History; Archaeology","score_opus":0.027636928266905607,"score_gpt":0.3278781083299954,"score_spread":0.3002411800630898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998920459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097319506,0.0013263446,0.8562438,0.025674483,0.0030308678,0.0003311799,4.6466718e-7,0.0011466695,0.014926673],"genre_scores_gemma":[0.95129246,6.1975175e-7,0.03713909,0.0017110992,0.009655609,0.000002107318,0.000003734813,0.0000065897584,0.0001886954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993774,0.000021314327,0.000102792495,0.00024410797,0.000079507925,0.00017486661],"domain_scores_gemma":[0.99962574,0.0001504188,0.0000624147,0.00008495546,0.000039241877,0.000037212674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000100400015,0.00007315412,0.00008636333,0.000037065623,0.00024624603,0.00006310862,0.00040629364,0.000045706034,0.000013664165],"category_scores_gemma":[0.00021321293,0.00007281959,0.000031151812,0.00017873757,0.000020373069,0.00008941685,0.000110815985,0.00025131297,0.00005884619],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005304949,0.000011435457,0.00030955012,0.000024624702,0.0000037866118,0.0000036850752,0.006917482,0.0008623738,0.0001614172,0.0066281715,0.001795463,0.9832767],"study_design_scores_gemma":[0.0002419,0.000121808436,0.0015488453,0.000033329437,0.000002919682,0.000009544784,0.0007314267,0.8965296,0.0011559821,0.0003978692,0.09904919,0.00017757379],"about_ca_topic_score_codex":9.0119386e-7,"about_ca_topic_score_gemma":6.900974e-7,"teacher_disagreement_score":0.9830991,"about_ca_system_score_codex":0.000014574602,"about_ca_system_score_gemma":0.00003595011,"threshold_uncertainty_score":0.29694965},"labels":[],"label_agreement":null},{"id":"W2999331497","doi":"10.1007/s00607-019-00777-6","title":"Micro-journal mining to understand mood triggers","year":2020,"lang":"en","type":"article","venue":"Computing","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multinomial logistic regression; Mood; Trigram; Sentiment analysis; Computer science; Word (group theory); Logistic regression; Psychology; Multinomial distribution; Natural language processing; Cognitive psychology; Artificial intelligence; Machine learning; Linguistics; Data science; Social psychology; Econometrics; Mathematics","score_opus":0.05212175688882666,"score_gpt":0.27568205735296525,"score_spread":0.22356030046413858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999331497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1880763,0.0001298578,0.8049204,0.005155601,0.00034508278,0.00004525262,1.4980692e-7,0.000083445986,0.0012439145],"genre_scores_gemma":[0.83105147,0.0000027569852,0.16632067,0.0022159459,0.00036922094,1.1257006e-7,5.5689964e-7,0.000007866861,0.000031419833],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878955,0.000050583483,0.00029962658,0.00031706755,0.00024636163,0.000296829],"domain_scores_gemma":[0.99928284,0.00009482468,0.00012715088,0.0001648961,0.00005701938,0.00027326398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003312027,0.0001167719,0.00018993711,0.0001088046,0.00027748258,0.00049719313,0.0006825856,0.000027665867,0.000022346203],"category_scores_gemma":[0.00006941488,0.000112533984,0.00011112664,0.00067070645,0.000011795307,0.00018356969,0.00032133074,0.00013697818,0.00006635097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006036403,0.00016805563,0.02162255,0.000081426566,0.0006804306,0.00029401988,0.17601623,0.09837157,0.0309909,0.0122191245,0.09650334,0.562992],"study_design_scores_gemma":[0.00089538516,0.00018608315,0.00071717886,0.00009702802,0.00002231153,0.00004676457,0.004313066,0.98375404,0.0035152007,0.00015825733,0.005875713,0.00041900153],"about_ca_topic_score_codex":9.559014e-7,"about_ca_topic_score_gemma":2.1112832e-7,"teacher_disagreement_score":0.8853824,"about_ca_system_score_codex":0.00003811749,"about_ca_system_score_gemma":0.000045378474,"threshold_uncertainty_score":0.47944447},"labels":[],"label_agreement":null},{"id":"W3005539492","doi":"10.1007/s00607-020-00793-x","title":"Editorial for the special issue on big time series data","year":2020,"lang":"en","type":"article","venue":"Computing","topic":"Time Series Analysis and Forecasting","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":"St. Francis Xavier University","funders":"","keywords":"Series (stratigraphy); Big data; Computer science; Time series; Data science; Data mining; Geology; Machine learning","score_opus":0.045722221486386876,"score_gpt":0.25664936828695556,"score_spread":0.21092714680056868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005539492","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020343413,0.000095971955,0.87672144,0.01984573,0.096733786,0.00036827792,0.00003465479,0.00031584062,0.0056808586],"genre_scores_gemma":[0.008049074,0.0000029452424,0.020089801,0.0008621857,0.9706724,0.0000015786497,0.00002392793,0.000015529555,0.00028254878],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988726,0.000028016815,0.00020688114,0.00041952415,0.00023532996,0.0002376526],"domain_scores_gemma":[0.99877536,0.0003555748,0.00010994855,0.0006304275,0.000064222564,0.000064487234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037105122,0.00011496159,0.00016537642,0.000018141634,0.00046180218,0.00036422763,0.0018540483,0.000036647092,0.000035358236],"category_scores_gemma":[0.00031054535,0.00008194528,0.00006679455,0.0002850878,0.000034843306,0.00022830014,0.001184978,0.00012891555,0.00015085709],"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":[0.000019640038,0.0000072506705,0.0000064556934,0.0000063485513,0.000026545442,0.000001055494,0.00056909636,0.00144875,0.000043420157,0.0017840982,0.6541005,0.34198684],"study_design_scores_gemma":[0.000097770884,0.000066800516,0.000009239766,0.0000057897696,0.000007663321,6.9873363e-7,0.000027609809,0.4414707,0.00004981567,0.00006419265,0.55813336,0.00006634676],"about_ca_topic_score_codex":0.0000060160137,"about_ca_topic_score_gemma":0.0000019179668,"teacher_disagreement_score":0.8739386,"about_ca_system_score_codex":0.000009923039,"about_ca_system_score_gemma":0.000037109254,"threshold_uncertainty_score":0.35518557},"labels":[],"label_agreement":null},{"id":"W3131825464","doi":"10.1007/s00607-021-00912-2","title":"Adaptive ensembles of autoencoders for unsupervised IoT network intrusion detection","year":2021,"lang":"en","type":"article","venue":"Computing","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intrusion detection system; Anomaly detection; Scalability; Artificial intelligence; Machine learning; Inference; Cloud computing; Enhanced Data Rates for GSM Evolution; Artificial neural network; Internet of Things; Data mining","score_opus":0.021956507265652916,"score_gpt":0.23997028373647636,"score_spread":0.21801377647082343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131825464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09769058,0.00035962992,0.89996475,0.00011571243,0.0012918328,0.00015916611,6.7661705e-7,0.00014693878,0.0002706913],"genre_scores_gemma":[0.8591123,0.000015952297,0.14028499,0.0001506059,0.00040219649,0.000004000934,0.0000020107939,0.000009130575,0.000018811517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987019,0.00012510871,0.0003263034,0.00037533557,0.00017891199,0.00029244547],"domain_scores_gemma":[0.9988579,0.00032541557,0.00016948236,0.00030725746,0.0002827483,0.000057183493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040875986,0.00012415546,0.00021075676,0.00006407787,0.00035561234,0.000062947554,0.00028247933,0.00009549524,0.000008231225],"category_scores_gemma":[0.00009370778,0.00013273436,0.00012156542,0.0006588763,0.000031325144,0.00013470137,0.00029852512,0.00014210293,0.0000036289716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005418494,0.00006535088,0.00008498667,0.00006148069,0.000040840478,0.000006642027,0.0011411104,0.15368392,0.0120153725,0.014421924,0.00050594786,0.81791824],"study_design_scores_gemma":[0.00033835284,0.00016600372,0.00041554536,0.00010796345,0.000008436365,0.000018332661,0.00006797291,0.9511769,0.038575735,0.007507508,0.0014770207,0.00014022192],"about_ca_topic_score_codex":0.00001923692,"about_ca_topic_score_gemma":0.000041797997,"teacher_disagreement_score":0.81777805,"about_ca_system_score_codex":0.000044728145,"about_ca_system_score_gemma":0.00007149784,"threshold_uncertainty_score":0.541275},"labels":[],"label_agreement":null},{"id":"W3137125111","doi":"10.1007/s00607-020-00899-2","title":"Novel evolutionary-EAC instance-learning-based algorithm for fast data stream mining in assisted living with extreme connectivity","year":2021,"lang":"en","type":"article","venue":"Computing","topic":"Data Stream Mining Techniques","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":"Lakehead University","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Cloud computing; Feature (linguistics); Feature selection; Evolutionary algorithm; Data stream; Extreme learning machine; Genetic algorithm; Process (computing); Data mining; Algorithm","score_opus":0.06716225142728892,"score_gpt":0.28660397415038863,"score_spread":0.2194417227230997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137125111","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014543373,0.00016029771,0.98360217,0.0001544164,0.00016707467,0.0002384092,0.00007259933,0.0006673898,0.00039424794],"genre_scores_gemma":[0.3880184,0.0000011923933,0.6116462,0.00006174264,0.000063664884,0.000011317477,0.00015706326,0.000021285918,0.000019139463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972502,0.00017542127,0.00040955664,0.0012357798,0.00035919537,0.0005698279],"domain_scores_gemma":[0.99612963,0.0017721182,0.00030240408,0.0014313937,0.00026785114,0.00009658984],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010023444,0.00028142487,0.0003784214,0.0002033896,0.00032481726,0.00030053683,0.0016087398,0.00009877778,0.000003829946],"category_scores_gemma":[0.0009341967,0.0003025861,0.000048051086,0.0010013925,0.00007488831,0.0008031162,0.0016886882,0.0003459324,0.0000015196046],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006413137,0.00049625005,0.017556287,0.00005611035,0.000036329988,0.00007273581,0.00045642862,0.0023281537,0.0005502292,0.00055348873,0.0005617354,0.97732586],"study_design_scores_gemma":[0.0006997453,0.00013071594,0.03206242,0.0009167652,0.000010660432,0.00007522102,0.0002483551,0.96420664,0.0004040874,0.000029735891,0.00084528455,0.00037037698],"about_ca_topic_score_codex":0.0000725012,"about_ca_topic_score_gemma":0.00026909876,"teacher_disagreement_score":0.9769555,"about_ca_system_score_codex":0.00019937319,"about_ca_system_score_gemma":0.0005444102,"threshold_uncertainty_score":0.9999426},"labels":[],"label_agreement":null},{"id":"W3154426907","doi":"10.1007/s00607-021-00940-y","title":"Fast-MICDTN: a new decentralized control mechanism for content-centric disruption tolerant networks","year":2021,"lang":"en","type":"article","venue":"Computing","topic":"Opportunistic and Delay-Tolerant Networks","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":"HEC Montréal","funders":"","keywords":"Decentralised system; Computer science; Distributed computing; Mechanism (biology); Control (management); Computer network; Artificial intelligence","score_opus":0.039549783023194986,"score_gpt":0.255667243213561,"score_spread":0.216117460190366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154426907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035263319,0.0010762704,0.990625,0.0011931566,0.0025396592,0.0005161797,0.0000050054773,0.00029511988,0.00022328006],"genre_scores_gemma":[0.9262051,0.00006588425,0.06991845,0.0026779727,0.0007135665,0.000009553827,0.000036496855,0.000026667232,0.00034628424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973904,0.00012900778,0.00061011803,0.00071473955,0.00029207647,0.0008636853],"domain_scores_gemma":[0.9980042,0.000447041,0.00026820583,0.00053825683,0.0003614153,0.00038090846],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037000913,0.00029100126,0.0005031389,0.00006486565,0.00034719112,0.00041783263,0.0005795566,0.000156452,0.000023916049],"category_scores_gemma":[0.000041896885,0.00028882723,0.00025754297,0.0004230272,0.000026005293,0.00022603893,0.00023962457,0.000229706,0.000013948992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097319804,0.00017587129,0.00046768255,0.00003982318,0.00015003949,0.00025259034,0.00048635932,0.015338374,0.0004636391,0.22650798,0.004801878,0.75121844],"study_design_scores_gemma":[0.0036815167,0.00006465694,0.00011570516,0.000105919986,0.000049780945,0.00011811358,0.000054553522,0.98976004,0.00010471349,0.004200991,0.001407029,0.0003369924],"about_ca_topic_score_codex":0.000018171277,"about_ca_topic_score_gemma":0.0000060106113,"teacher_disagreement_score":0.9744217,"about_ca_system_score_codex":0.000077891564,"about_ca_system_score_gemma":0.0002628461,"threshold_uncertainty_score":0.99995637},"labels":[],"label_agreement":null},{"id":"W3190810079","doi":"10.1007/s00607-021-00985-z","title":"Special issue on ‘‘artificial intelligence in cloud computing’’","year":2021,"lang":"en","type":"article","venue":"Computing","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","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":"Lakehead University","funders":"","keywords":"Cloud computing; Computer science; Artificial intelligence; Data science; Operating system","score_opus":0.0738313035886359,"score_gpt":0.31623604499376945,"score_spread":0.24240474140513354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190810079","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81679887,0.0000185824,0.041461404,0.0033951304,0.014028121,0.00030651366,0.0000031907266,0.0003680801,0.12362012],"genre_scores_gemma":[0.9882277,0.0000026764499,0.0003556509,0.001344554,0.009791321,7.096735e-7,0.0000014122137,0.000013485114,0.00026245273],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99842024,0.00022351698,0.0003492324,0.0005041342,0.00023067764,0.00027217952],"domain_scores_gemma":[0.99915946,0.00040912136,0.0001012377,0.0002333213,0.000038571277,0.000058311707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027947628,0.00011719534,0.00014112864,0.00009764952,0.00021052227,0.0001076126,0.00019563008,0.000055653723,0.00031054716],"category_scores_gemma":[0.0009369925,0.00013239104,0.000050682516,0.00073895,0.00006816244,0.000044287503,0.00010095429,0.00032213065,0.00064273627],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030161302,0.00023026753,0.0001296688,0.000015096974,0.0000012899003,0.00014102334,0.0012774518,0.0048395735,0.048051514,0.039768305,0.0019273013,0.90358835],"study_design_scores_gemma":[0.00015441203,0.00007744673,0.0029605264,0.0001051564,0.0000028438476,0.00008053802,0.00089262525,0.21789949,0.7304649,0.003366427,0.04365811,0.00033754116],"about_ca_topic_score_codex":0.0000041773533,"about_ca_topic_score_gemma":0.000013461795,"teacher_disagreement_score":0.9032508,"about_ca_system_score_codex":0.000084684805,"about_ca_system_score_gemma":0.00005076229,"threshold_uncertainty_score":0.8261289},"labels":[],"label_agreement":null},{"id":"W4317211950","doi":"10.1007/s00607-023-01149-x","title":"Correction to: Special issue on “artificial intelligence in cloud computing”","year":2023,"lang":"en","type":"article","venue":"Computing","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","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":"Lakehead University","funders":"","keywords":"Cloud computing; Computer science; Data science; Artificial intelligence; Operating system","score_opus":0.07439000865109235,"score_gpt":0.3270407649448769,"score_spread":0.25265075629378453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317211950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8901091,0.000001491399,0.03469276,0.0023154279,0.037409812,0.0005132186,0.0000020861999,0.0009615068,0.033994604],"genre_scores_gemma":[0.9921019,0.0000012840679,0.00007925709,0.0008958257,0.006265737,0.0000024421345,0.0000016958877,0.000017211141,0.00063465803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984373,0.00015903068,0.0003370697,0.00049761083,0.0002462118,0.00032279888],"domain_scores_gemma":[0.9989754,0.0006417825,0.00008986734,0.00019156496,0.000025085814,0.00007629155],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045757918,0.00012207867,0.00012824955,0.00033281013,0.0002696663,0.00009417516,0.00022050698,0.000045046305,0.000093365976],"category_scores_gemma":[0.0014431857,0.00013701609,0.00004266015,0.0019126893,0.0000430809,0.00004490399,0.00009955325,0.0003025123,0.0035082756],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051152452,0.000077012584,0.00012009048,0.000007461479,7.401487e-7,0.000023460838,0.0018083762,0.037044477,0.027695809,0.0045880363,0.018505828,0.9100776],"study_design_scores_gemma":[0.00008011982,0.00015006783,0.008482399,0.00010756684,0.0000015216783,0.000016941243,0.0007484988,0.7933808,0.17806694,0.0010341301,0.017671822,0.00025918518],"about_ca_topic_score_codex":0.000016192611,"about_ca_topic_score_gemma":0.00002810487,"teacher_disagreement_score":0.90981835,"about_ca_system_score_codex":0.000114055634,"about_ca_system_score_gemma":0.000023006267,"threshold_uncertainty_score":0.9972676},"labels":[],"label_agreement":null},{"id":"W4322768055","doi":"10.1007/s00607-023-01163-z","title":"Advances of machine learning in IoT-cloud for healthcare","year":2023,"lang":"en","type":"article","venue":"Computing","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","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 Ottawa","funders":"","keywords":"Cloud computing; Internet of Things; Computer science; Health care; Artificial intelligence; Embedded system; Operating system","score_opus":0.2072215111091151,"score_gpt":0.5270424325630477,"score_spread":0.3198209214539326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322768055","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98624593,0.0016921633,0.0034575008,0.003762067,0.0022792232,0.0013901541,0.000021398107,0.0004057462,0.000745813],"genre_scores_gemma":[0.9976747,0.00011157463,0.0010977685,0.00027138437,0.0005232074,0.00004279031,0.000028507211,0.000031597778,0.00021845724],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997655,0.00041277468,0.00085830124,0.00026921055,0.00015387399,0.00065085624],"domain_scores_gemma":[0.9964516,0.0027423427,0.00032746224,0.0001693389,0.00022603862,0.00008320089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001562726,0.000110832276,0.00031732922,0.00020420988,0.00062965736,0.0000026866774,0.00017912922,0.00013805981,0.00002510042],"category_scores_gemma":[0.0011556575,0.00011207092,0.000054869233,0.0006908519,0.000039306466,0.000037035126,0.00014867031,0.0006631739,0.00009462705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047149857,0.000013186124,0.8125279,0.0017689635,0.0000029677853,0.0000036082076,0.010618275,0.007864487,0.000101791265,0.0027936732,0.00026013976,0.16399784],"study_design_scores_gemma":[0.00043974453,0.00028966085,0.026919775,0.0024194121,0.000005241425,8.6055485e-7,0.024646236,0.9016506,0.00036827158,0.009027897,0.033931002,0.00030128355],"about_ca_topic_score_codex":0.0019814363,"about_ca_topic_score_gemma":0.004192884,"teacher_disagreement_score":0.89378613,"about_ca_system_score_codex":0.0001176187,"about_ca_system_score_gemma":0.00021437812,"threshold_uncertainty_score":0.4842879},"labels":[],"label_agreement":null},{"id":"W4387609398","doi":"10.1007/s00607-023-01225-2","title":"A graphical deep learning technique-based VNF dependencies for multi resource requirements prediction in virtualized environments","year":2023,"lang":"en","type":"article","venue":"Computing","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Cloud computing; Distributed computing; Workload; Exploit; Resource (disambiguation); Virtual network; Graph; Data mining; Artificial intelligence; Machine learning; Computer network; Theoretical computer science","score_opus":0.03934790051608235,"score_gpt":0.2820110362366872,"score_spread":0.24266313572060488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387609398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15505803,0.000043198077,0.8428685,0.00018007279,0.00018464477,0.0006791248,7.848449e-7,0.00082295306,0.00016267413],"genre_scores_gemma":[0.9440335,0.0000017823355,0.055243503,0.00015330614,0.00008745533,0.00008922391,0.00001627322,0.000029891848,0.0003450422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973735,0.0002591171,0.00051205646,0.000743769,0.00047835725,0.0006331597],"domain_scores_gemma":[0.9989107,0.00034342526,0.00019185043,0.00043869892,0.000021538617,0.000093751936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019181637,0.00023033384,0.00025516143,0.0005453097,0.0004742639,0.00013735406,0.0008109655,0.000118251824,0.0000020315013],"category_scores_gemma":[0.00021274667,0.00023985737,0.00013848182,0.0010469619,0.000057505364,0.00003991597,0.0007822128,0.00032370587,0.000024975892],"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":[0.00005963321,0.00035788177,0.022907648,0.0001733919,0.00006844626,0.00008338971,0.0028027487,0.7959488,0.008099338,0.001840469,0.00038692984,0.16727132],"study_design_scores_gemma":[0.0014057424,0.00013753191,0.012062946,0.00016798798,0.000007705059,0.0000031384218,0.00017742654,0.9798082,0.00064752647,0.00019369753,0.005158928,0.0002291609],"about_ca_topic_score_codex":0.000014891234,"about_ca_topic_score_gemma":0.0000040715267,"teacher_disagreement_score":0.7889755,"about_ca_system_score_codex":0.00013754689,"about_ca_system_score_gemma":0.000018672761,"threshold_uncertainty_score":0.97810996},"labels":[],"label_agreement":null},{"id":"W4404507082","doi":"10.1007/s00607-024-01356-0","title":"Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches","year":2024,"lang":"en","type":"article","venue":"Computing","topic":"Privacy-Preserving Technologies in Data","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 Waterloo","funders":"","keywords":"Computer science; Distributed computing; Multi-agent system; Artificial intelligence; Machine learning; Data science","score_opus":0.14156027526005646,"score_gpt":0.31401811547275466,"score_spread":0.1724578402126982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404507082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10359244,0.0050921533,0.8885643,0.0004909999,0.00065342337,0.0002730754,0.000041942116,0.0012677212,0.000023977524],"genre_scores_gemma":[0.8662534,0.000030280007,0.13353764,0.0000064254114,0.000014533469,0.0000025754944,0.00013064739,0.000020992735,0.0000035563821],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682343,0.0006312954,0.0007871433,0.0008012061,0.00034076575,0.00061617285],"domain_scores_gemma":[0.99598515,0.0012106776,0.0002335072,0.0024180778,0.00008234609,0.000070254224],"candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0030379663,0.000263251,0.0004829178,0.00033282232,0.00013210486,0.00036941946,0.009983741,0.00013830961,0.0000017824025],"category_scores_gemma":[0.012736525,0.00025656444,0.000072461145,0.0019147537,0.00009312485,0.00028021802,0.03528328,0.0006791875,0.000010860087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030489387,0.0005493203,0.57108045,0.0023367372,0.0003331597,0.00043316153,0.0023134206,0.24088217,0.00061336264,0.007533498,0.005216079,0.16867815],"study_design_scores_gemma":[0.0003623098,0.000024339233,0.035441965,0.00064765743,0.0000050096996,0.000023096009,0.00005497036,0.9625445,0.00018550058,0.00029594486,0.00017806873,0.00023666231],"about_ca_topic_score_codex":0.0012668715,"about_ca_topic_score_gemma":0.000074971256,"teacher_disagreement_score":0.7626609,"about_ca_system_score_codex":0.00020011085,"about_ca_system_score_gemma":0.00009114665,"threshold_uncertainty_score":0.9999887},"labels":[],"label_agreement":null},{"id":"W4405143323","doi":"10.1007/s00607-024-01385-9","title":"Enhancing POI recommendations on social media: a sequential approach incorporating LSTM and user feedback","year":2024,"lang":"en","type":"article","venue":"Computing","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Social media; Computer science; Artificial intelligence; Multimedia; Machine learning; Human–computer interaction; World Wide Web","score_opus":0.0515753249354012,"score_gpt":0.29681454505019295,"score_spread":0.24523922011479174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405143323","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010460409,0.00022850007,0.9810667,0.0017855989,0.0012358291,0.00016279348,0.0000020377943,0.00088441465,0.004173684],"genre_scores_gemma":[0.8371553,0.000004011165,0.16196482,0.0001450913,0.0006672866,0.000008992869,0.0000072132207,0.000017876686,0.000029425291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848616,0.00015166863,0.00036773543,0.0005266462,0.00019643742,0.00027132168],"domain_scores_gemma":[0.9990841,0.0004822874,0.000109554705,0.00021227192,0.000043634245,0.00006813767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084110734,0.00017777961,0.00021098822,0.00018396146,0.0004809682,0.00084648613,0.0003529084,0.00009190805,0.0000037237842],"category_scores_gemma":[0.000061595456,0.00016719096,0.000067064706,0.0004189342,0.0000378944,0.0004205734,0.00046124996,0.00032468306,0.000013216532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028596603,0.00008316564,0.00024014628,0.00034600278,0.00007640717,0.000037621405,0.016167266,0.000038219165,0.0018649624,0.47211772,0.018486772,0.49053887],"study_design_scores_gemma":[0.0008319455,0.00022227316,0.0016887218,0.002321022,0.000056487246,0.00043335513,0.0024516918,0.91927487,0.011555252,0.029537907,0.029686334,0.0019401424],"about_ca_topic_score_codex":0.000026404558,"about_ca_topic_score_gemma":0.000011126872,"teacher_disagreement_score":0.91923666,"about_ca_system_score_codex":0.00007420761,"about_ca_system_score_gemma":0.0000589809,"threshold_uncertainty_score":0.8162685},"labels":[],"label_agreement":null},{"id":"W4405453640","doi":"10.1007/s00607-024-01391-x","title":"Efficient exploration of indoor localization using genetic algorithm and signal propagation model","year":2024,"lang":"en","type":"article","venue":"Computing","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","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":"Ontario Tech University","funders":"Fundação de Amparo à Pesquisa do Estado do Amazonas; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Genetic algorithm; Computer science; Algorithm; Radio propagation; SIGNAL (programming language); Telecommunications; Machine learning","score_opus":0.021570172785926375,"score_gpt":0.23830924293507522,"score_spread":0.21673907014914884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405453640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13574474,0.00061787444,0.8629468,0.000004186182,0.000115004266,0.00011335248,0.0000019568586,0.00041027393,0.000045792447],"genre_scores_gemma":[0.9477699,0.000012623475,0.052148204,0.000004065459,0.000036960606,0.0000017614456,0.0000061035507,0.000018628867,0.0000017876047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946034,0.000010102857,0.00020723986,0.00011819468,0.00010235533,0.00010176442],"domain_scores_gemma":[0.9998342,0.000018014242,0.000023587922,0.000060707145,0.000048470985,0.000015060191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008371942,0.00008252653,0.00008658091,0.00014888994,0.000055680084,0.000042109903,0.00003933208,0.00006169731,0.0000014520405],"category_scores_gemma":[0.000009839121,0.00008342679,0.000016914697,0.00027224212,0.000032005373,0.000073592215,0.000028481945,0.00006319724,0.0000013004067],"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":[4.2330694e-7,0.0000031049403,0.000027104743,0.00013289815,0.0000058017304,9.570764e-7,0.00051976054,0.90913075,0.002170606,0.00047902993,0.0000074038126,0.08752215],"study_design_scores_gemma":[0.000065816086,0.000010410199,0.000023401357,0.00012431863,0.000011870792,0.0000040640552,0.00010377188,0.9794129,0.019351091,0.0008017509,0.0000050040453,0.000085601234],"about_ca_topic_score_codex":0.0000022754264,"about_ca_topic_score_gemma":1.2693788e-7,"teacher_disagreement_score":0.8120251,"about_ca_system_score_codex":0.000041836698,"about_ca_system_score_gemma":0.000016909553,"threshold_uncertainty_score":0.3402046},"labels":[],"label_agreement":null},{"id":"W4411326007","doi":"10.1007/s00607-025-01505-z","title":"Predictive container orchestration in the cloud using artificial intelligence techniques","year":2025,"lang":"en","type":"article","venue":"Computing","topic":"Cloud Computing and Resource Management","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":"Orchestration; Container (type theory); Cloud computing; Computer science; Artificial intelligence; Engineering; Operating system; Mechanical engineering","score_opus":0.03760798050484105,"score_gpt":0.3057208766827272,"score_spread":0.26811289617788614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411326007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14893872,0.000056778816,0.8455766,0.0009856834,0.00031546556,0.00024701332,1.0380719e-7,0.0001776024,0.0037020412],"genre_scores_gemma":[0.97468394,6.417725e-7,0.024550684,0.00052891707,0.00021204852,0.0000037382783,4.355354e-7,0.0000035422345,0.000016064258],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871296,0.00020074657,0.00032915737,0.0003246849,0.00018669889,0.00024577795],"domain_scores_gemma":[0.99919325,0.00028037303,0.00008960593,0.00037285968,0.000046883662,0.000017029428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011708363,0.00011761119,0.00012305337,0.0001678429,0.00024686364,0.0002341417,0.0008965197,0.000048819005,5.010324e-7],"category_scores_gemma":[0.00007197504,0.000090577094,0.000046992376,0.0008764461,0.000051886844,0.000028055538,0.00043043125,0.0002383531,0.0000020866598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006899051,0.00010044731,0.0005789045,0.000024756817,0.000014823299,0.000021485119,0.0030527175,0.07960088,0.00021325059,0.5077016,0.00009601801,0.40858826],"study_design_scores_gemma":[0.000027727532,0.000036924495,0.000755196,0.00013403209,0.0000051216234,0.0000052114324,0.00063775806,0.9682457,0.0012350153,0.028493417,0.00032889238,0.000095032075],"about_ca_topic_score_codex":0.00007056363,"about_ca_topic_score_gemma":0.000008043209,"teacher_disagreement_score":0.8886448,"about_ca_system_score_codex":0.00008531691,"about_ca_system_score_gemma":0.00005406079,"threshold_uncertainty_score":0.36936265},"labels":[],"label_agreement":null},{"id":"W4414067394","doi":"10.1007/s00607-025-01547-3","title":"An experience-based classification of quantum bugs in quantum software","year":2025,"lang":"en","type":"article","venue":"Computing","topic":"Quantum Computing Algorithms and Architecture","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 British Columbia","funders":"Bundesministerium für Digitalisierung und Wirtschaftsstandort; Technische Universität München; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; European Commission","keywords":"Debugging; Quantum; Software; Quantum computer; Set (abstract data type); Quantum algorithm; Software bug","score_opus":0.018729021149814503,"score_gpt":0.29190807941563596,"score_spread":0.27317905826582145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414067394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47678092,0.000073079515,0.5223232,0.00020470169,0.00032867803,0.00008391612,5.99589e-7,0.00016042216,0.000044476074],"genre_scores_gemma":[0.93873465,9.507918e-7,0.06099826,0.00019686767,0.000046891073,0.0000043219916,0.000004084332,0.000009015378,0.0000049491346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980775,0.000174274,0.0005319359,0.00058915856,0.00025134615,0.00037577524],"domain_scores_gemma":[0.9984844,0.0003320663,0.00020590583,0.0007920818,0.000115790914,0.00006981072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005020895,0.00018938615,0.000289421,0.00040246246,0.00017645306,0.00011247111,0.0012593283,0.000089538495,0.000002023861],"category_scores_gemma":[0.0001375256,0.00018588368,0.000078417994,0.0012497833,0.000092731556,0.00017224559,0.00021930736,0.0002593666,0.0000027806955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026548067,0.00063667144,0.07445324,0.00022612368,0.000017468552,0.000027947623,0.008065212,0.31265715,0.008706081,0.13794242,0.0001395733,0.45710155],"study_design_scores_gemma":[0.00033456527,0.000073595795,0.073425405,0.00024432375,0.000001942086,0.000002091978,0.00012670869,0.92085767,0.0014771438,0.0031736973,0.00011414622,0.0001687367],"about_ca_topic_score_codex":0.00007887041,"about_ca_topic_score_gemma":0.00000540428,"teacher_disagreement_score":0.6082005,"about_ca_system_score_codex":0.000052846506,"about_ca_system_score_gemma":0.00020093308,"threshold_uncertainty_score":0.75801164},"labels":[],"label_agreement":null},{"id":"W4415311529","doi":"10.1007/s00607-025-01564-2","title":"Collaborative filtering in the age of AI: foundations, innovations, and emerging trends","year":2025,"lang":"en","type":"article","venue":"Computing","topic":"Recommender Systems and Techniques","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":"Collaborative filtering; Recommender system; Personalization; Baseline (sea); Benchmark (surveying); Field (mathematics); Big data","score_opus":0.019666113079006602,"score_gpt":0.32284719130338724,"score_spread":0.3031810782243806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415311529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034631178,0.0001976882,0.9540485,0.0033934547,0.00016100938,0.0001004994,5.838358e-7,0.00006046551,0.0074066245],"genre_scores_gemma":[0.9703278,0.000004382113,0.029258106,0.00034989903,0.000014127062,0.0000059329045,0.0000022318657,0.000001700725,0.000035801626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993631,0.00008361299,0.00025455694,0.00013912276,0.00006910454,0.000090449525],"domain_scores_gemma":[0.99952096,0.00013673615,0.00007254074,0.00020251719,0.00006208425,0.0000051386514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005178018,0.000058264486,0.00010731271,0.00028058523,0.00012000171,0.00013149841,0.00030554878,0.000020059158,6.3745756e-7],"category_scores_gemma":[0.000030026962,0.000047033678,0.000012608057,0.0016233581,0.000020237158,0.00015621819,0.00018987113,0.0000926024,1.2538668e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.642075e-7,0.000030478608,0.01739183,0.00005085799,0.000015533788,0.000005392261,0.0068064956,0.00006130182,0.00085148413,0.5960866,0.0025378375,0.37616134],"study_design_scores_gemma":[0.001515888,0.00018401151,0.42422056,0.0016877642,0.000018615372,0.000034735483,0.0046767164,0.356663,0.00774379,0.057201866,0.14527158,0.00078146794],"about_ca_topic_score_codex":0.000047931455,"about_ca_topic_score_gemma":0.000023064953,"teacher_disagreement_score":0.93569666,"about_ca_system_score_codex":0.0000161333,"about_ca_system_score_gemma":0.000030172796,"threshold_uncertainty_score":0.19179778},"labels":[],"label_agreement":null},{"id":"W627525495","doi":"10.1007/s00607-015-0463-8","title":"Special issue on contributions of computational intelligence in designing complex information systems","year":2015,"lang":"en","type":"article","venue":"Computing","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège de Maisonneuve","funders":"","keywords":"Turnkey; Computer science; Computational intelligence; Artificial intelligence; Machine learning; Artificial neural network; Bayesian network; Fuzzy logic; Computational Science and Engineering; Bayesian probability; Data science","score_opus":0.09605182917915579,"score_gpt":0.3161076946213896,"score_spread":0.22005586544223382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W627525495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03393972,0.00005712718,0.8747067,0.0004831917,0.0040362542,0.0007087583,0.00003428776,0.00017024257,0.08586372],"genre_scores_gemma":[0.99353206,7.3408637e-7,0.0011644507,0.00028172426,0.004798429,0.000002582937,0.00020384585,0.0000065268982,0.000009654205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987747,0.000018007657,0.0005417531,0.00013878872,0.00031889367,0.00020787476],"domain_scores_gemma":[0.9988367,0.00012407804,0.00032771513,0.00012714992,0.0005690195,0.000015353668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063902757,0.00012494889,0.00021070175,0.0003543139,0.000095522235,0.00020413898,0.00026025396,0.000054067856,0.000056621273],"category_scores_gemma":[0.00043569983,0.00012485229,0.000028974757,0.0006909561,0.00006308196,0.0010361235,0.00014728932,0.0001285675,0.00043449344],"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":[0.0000625241,0.00010023808,0.004657464,0.00019124226,0.000011081711,0.000003847144,0.00033109216,0.79911095,0.000019688245,0.10537605,0.024916679,0.06521912],"study_design_scores_gemma":[0.00035694463,0.00001883198,0.0056170593,0.00034479753,0.000010221491,0.00000447682,0.0010493879,0.86484563,0.0000956154,0.0024632604,0.12495953,0.00023424631],"about_ca_topic_score_codex":0.00034707834,"about_ca_topic_score_gemma":0.000008262381,"teacher_disagreement_score":0.95959234,"about_ca_system_score_codex":0.00006881654,"about_ca_system_score_gemma":0.00004530303,"threshold_uncertainty_score":0.5584679},"labels":[],"label_agreement":null}]}