{"meta":{"query_hash":"56a0dbac733f","filters":{"topic":"Advanced Graph Neural Networks"},"cohort_total":518,"direct_labels_cover":2,"predictions_cover":518,"exported":518,"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/56a0dbac733f","api":"https://metacan.xera.ac/api/v1/cohort?topic=Advanced+Graph+Neural+Networks"},"results":[{"id":"W1509961009","doi":"10.22061/jecei.2014.42","title":"DALD:-Distributed-Asynchronous-Local-Decontamination Algorithm in Arbitrary Graphs","year":2014,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Distributed computing; Asynchronous communication; Node (physics); Distributed algorithm; Human decontamination; Computation; Algorithm; Computer network; Engineering","score_opus":0.08912439903010141,"score_gpt":0.4720683068139423,"score_spread":0.3829439077838409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509961009","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":[0.0694047,0.0047969776,0.9220484,0.0003100204,0.0009809929,0.0005441803,0.000029176555,0.0001408159,0.0017447632],"genre_scores_gemma":[0.97582203,0.003191822,0.019858398,0.00077830313,0.0001501683,0.00006572596,0.000037090147,0.000051739866,0.00004474448],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9959316,0.00049732084,0.001161051,0.000877008,0.0008167849,0.0007162092],"domain_scores_gemma":[0.99679494,0.00063043024,0.000937574,0.0009966646,0.0002932646,0.00034713442],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0015276032,0.00045221136,0.00084664306,0.0012215355,0.00025997328,0.001224668,0.0060525076,0.00018923478,0.00039445178],"category_scores_gemma":[0.00017641921,0.00044237098,0.00023161943,0.0029548118,0.0002074297,0.0067411778,0.0014949017,0.00079002837,0.000017680124],"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":[0.000057117995,0.00048742045,0.09392336,0.000060472386,0.00009882751,0.00013863877,0.00017528041,0.008124075,0.0042855646,0.008896266,0.011102916,0.8726501],"study_design_scores_gemma":[0.001454229,0.00005300352,0.617073,0.0004985604,0.000030244646,0.000063933476,0.000035571946,0.1415346,0.011074827,0.22058602,0.006535631,0.0010604007],"about_ca_topic_score_codex":0.0002608436,"about_ca_topic_score_gemma":0.000091076094,"teacher_disagreement_score":0.9064173,"about_ca_system_score_codex":0.000178639,"about_ca_system_score_gemma":0.000110255445,"threshold_uncertainty_score":0.9998122},"labels":[],"label_agreement":null},{"id":"W1533179233","doi":"10.1109/saci.2015.7208196","title":"A graph digital signal processing method for semantic analysis","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Sentence; Natural language understanding; Parsing; Graph; Theoretical computer science; Natural language","score_opus":0.03385053670813177,"score_gpt":0.31419562101317583,"score_spread":0.28034508430504407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533179233","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":[0.00047912938,0.00009378154,0.997539,0.00032037465,0.000045035544,0.00014828453,0.0000014530826,0.0002440597,0.0011289081],"genre_scores_gemma":[0.5149268,3.253476e-7,0.48465583,0.00019039495,0.0000294306,0.000014743972,0.0000024015285,0.0000054579727,0.00017461811],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988575,0.000023814448,0.00018773951,0.0004054487,0.00023101506,0.00029447037],"domain_scores_gemma":[0.99917334,0.00013628374,0.00008126704,0.00027555312,0.00016900011,0.00016454128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022919531,0.00012613177,0.00021222021,0.00025188515,0.0000762241,0.00033025813,0.00052375783,0.000038352668,0.00000252109],"category_scores_gemma":[0.000024435762,0.00009987575,0.00018577179,0.0021995592,0.000025466987,0.0011331982,0.0001263992,0.00006667969,0.0000045633333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027093914,0.00009200517,0.0026612931,0.000022133605,0.00028341226,0.00001613589,0.0004935149,0.06319861,0.000143562,0.03221277,0.0015735879,0.8992759],"study_design_scores_gemma":[0.00021045646,0.000064751264,0.000103953156,0.0000037726807,0.000058785456,0.0000072886373,0.000028259417,0.9328029,0.0001574423,0.06601892,0.00038777906,0.00015569478],"about_ca_topic_score_codex":0.0000036046472,"about_ca_topic_score_gemma":0.000007622041,"teacher_disagreement_score":0.8991202,"about_ca_system_score_codex":0.000014314195,"about_ca_system_score_gemma":0.00003908293,"threshold_uncertainty_score":0.4072815},"labels":[],"label_agreement":null},{"id":"W1562235957","doi":"","title":"Proceedings of the sixth workshop on Ph.D. students in information and knowledge management","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Computer science; Viewpoints; Presentation (obstetrics); Point (geometry); Diversity (politics); Work (physics); Library science; Sociology","score_opus":0.006904744267530809,"score_gpt":0.25589811824680037,"score_spread":0.24899337397926954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1562235957","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":[0.9609931,0.000009033796,0.0028681061,0.0003608059,0.00028748074,0.0003744675,1.1882593e-7,0.000043062373,0.035063803],"genre_scores_gemma":[0.99528307,0.000016516584,0.0041630217,0.00021317367,0.000008282081,0.000010685383,8.165087e-8,0.0000017360503,0.00030346317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99948,0.0000033361812,0.00013961698,0.0001042424,0.00016674188,0.00010603937],"domain_scores_gemma":[0.9997236,0.000025688214,0.000057757657,0.00013606698,0.00003431822,0.000022550885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013053576,0.00006208587,0.000057043762,0.000084764884,0.000033363383,0.0000646472,0.00058453105,0.000028472092,0.000001249416],"category_scores_gemma":[0.000009759025,0.00003954691,0.000014088692,0.00043858602,0.00002890861,0.00064035715,0.0004740669,0.00015623766,0.0000040688465],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014750679,0.00014654263,0.038476743,0.000050813356,0.000009972902,4.630867e-7,0.0018936794,0.00010527466,0.00033259107,0.7050711,0.0012321603,0.25266594],"study_design_scores_gemma":[0.00162233,0.00010170231,0.93605494,0.0002431631,0.0000068161166,0.0000051620073,0.0005427892,0.027730463,0.00355931,0.020247977,0.009537112,0.0003482323],"about_ca_topic_score_codex":9.0755697e-7,"about_ca_topic_score_gemma":0.00002282234,"teacher_disagreement_score":0.8975782,"about_ca_system_score_codex":0.000007091344,"about_ca_system_score_gemma":0.0000029816222,"threshold_uncertainty_score":0.16126762},"labels":[],"label_agreement":null},{"id":"W1974873233","doi":"10.1142/s0219720013410059","title":"ENHANCING GENOMICS INFORMATION RETRIEVAL THROUGH DIMENSIONAL ANALYSIS","year":2013,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Université de Neuchâtel; University of Melbourne","keywords":"Computer science; Linear subspace; Information retrieval; Dimension (graph theory); Rank (graph theory); Homogeneity (statistics); Graph; Data mining; Set (abstract data type); Genomics; Theoretical computer science; Machine learning; Mathematics; Genome","score_opus":0.00768950690829641,"score_gpt":0.23559019145499488,"score_spread":0.22790068454669846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974873233","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":[0.12606789,0.00011432504,0.87286973,0.00057177513,0.00019746227,0.00007035125,0.000003364668,0.000012372664,0.000092754824],"genre_scores_gemma":[0.49639407,0.00004798692,0.5023018,0.00118973,0.000042137595,6.677351e-7,0.000019113597,0.0000018567172,0.0000026476089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869615,0.000032225682,0.0008387205,0.000067564404,0.00019724297,0.00016808193],"domain_scores_gemma":[0.9983027,0.0002681934,0.00075450516,0.000103264705,0.00048746253,0.00008386401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023699358,0.00011185291,0.000261013,0.00031196416,0.00010893729,0.00012988882,0.00027882546,0.000074947166,0.00001109042],"category_scores_gemma":[0.000046581805,0.00008552198,0.00011862623,0.0005714645,0.00006708743,0.0024193123,0.00013618269,0.00017120391,0.000016679158],"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":[0.00008432917,0.000093329916,0.0030007542,0.00006646062,0.0014603074,0.0000061570904,0.0043145292,0.70320266,0.0010617904,0.20165873,0.0014088702,0.083642066],"study_design_scores_gemma":[0.00039172138,0.00022687897,0.0056087496,0.00000964079,0.000044596516,0.00014117574,0.000077015626,0.9033213,0.00018168004,0.08898448,0.0008695611,0.00014318847],"about_ca_topic_score_codex":0.0000037467057,"about_ca_topic_score_gemma":5.616838e-7,"teacher_disagreement_score":0.37056792,"about_ca_system_score_codex":0.000032056294,"about_ca_system_score_gemma":0.00007475614,"threshold_uncertainty_score":0.34874848},"labels":[],"label_agreement":null},{"id":"W2089201173","doi":"10.1145/2629530","title":"Trust Prediction via Belief Propagation","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Belief propagation; Graphical model; Factor graph; Probabilistic logic; Inference; Exploit; Machine learning; Expectation propagation; Artificial intelligence; Solver; Propagation of uncertainty; Theoretical computer science; Approximate inference; Data mining; Algorithm","score_opus":0.00904383903233889,"score_gpt":0.21179865925749425,"score_spread":0.20275482022515537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089201173","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":[0.0007944592,0.000007790103,0.9937553,0.00038954846,0.0017985585,0.00049052294,0.000009673161,0.0006374403,0.0021167358],"genre_scores_gemma":[0.9905234,0.000009841814,0.008697668,0.00029639827,0.000121695644,0.00018435883,0.000024980052,0.00000862634,0.00013305547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849135,0.00009147198,0.00055550324,0.00019219599,0.00044180674,0.00022769212],"domain_scores_gemma":[0.99853307,0.00008229535,0.00023747308,0.00086931343,0.000181363,0.000096488184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032247562,0.00016328777,0.00014807277,0.00031366522,0.00035180865,0.00024737883,0.00058084907,0.00011410059,0.000007352907],"category_scores_gemma":[0.000030342697,0.0001514375,0.00007112646,0.00065373024,0.000027876347,0.00463226,0.000009643823,0.0002178332,0.00032875768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027773101,0.000068968286,0.00007760137,0.00009356939,0.000034405835,3.6573562e-7,0.0011489484,0.433894,0.00018717571,0.018171003,0.00061987614,0.5456763],"study_design_scores_gemma":[0.00061403133,0.00027044633,0.0009785829,0.000063120126,0.000008843835,0.000049546805,0.000071403934,0.9511583,0.0010564984,0.0012721742,0.044213466,0.00024359282],"about_ca_topic_score_codex":0.000020397125,"about_ca_topic_score_gemma":0.0000013423356,"teacher_disagreement_score":0.9897289,"about_ca_system_score_codex":0.00008472865,"about_ca_system_score_gemma":0.000019310317,"threshold_uncertainty_score":0.6175442},"labels":[],"label_agreement":null},{"id":"W2156954687","doi":"10.1609/aaai.v25i1.7917","title":"Learning Structured Embeddings of Knowledge Bases","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"WordNet; Computer science; Artificial intelligence; Natural language processing; Process (computing); Knowledge extraction; Natural language; Information retrieval; Information extraction; Word (group theory); Annotation","score_opus":0.08527527762667422,"score_gpt":0.2914836337513474,"score_spread":0.20620835612467317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156954687","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":[0.8428326,0.00013224951,0.09104174,0.00061719696,0.0014330866,0.0009293151,0.0000059129934,0.00040200207,0.06260586],"genre_scores_gemma":[0.9890026,0.000032604858,0.010665024,0.000050713377,0.000038565413,0.0000113736605,1.8271427e-7,0.000014505498,0.00018445471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817437,0.00002533684,0.0005963441,0.0004871307,0.00035321785,0.00036363018],"domain_scores_gemma":[0.99810183,0.000118322176,0.00062024564,0.00031206658,0.000756362,0.00009116604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030854254,0.00024727307,0.00032839607,0.000168415,0.00016357777,0.000057702477,0.0023412576,0.000097669166,0.00007885647],"category_scores_gemma":[0.0004117554,0.0001850229,0.00015853246,0.0009975332,0.00040638976,0.00051525235,0.00054547825,0.00042766205,0.00002317752],"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":[0.00006335795,0.00010813981,0.0008752369,0.000043119075,0.000015270205,4.408329e-7,0.004130788,0.00010020283,0.04865031,0.8481479,0.000050301227,0.0978149],"study_design_scores_gemma":[0.00002223591,0.00031106695,0.00059147645,0.00020062651,0.00001020274,0.0000043383234,0.00042765154,0.032776866,0.7511393,0.21426396,0.000056404213,0.00019585645],"about_ca_topic_score_codex":0.000021875036,"about_ca_topic_score_gemma":0.0000074837317,"teacher_disagreement_score":0.702489,"about_ca_system_score_codex":0.000022323617,"about_ca_system_score_gemma":0.000058688816,"threshold_uncertainty_score":0.75450146},"labels":[],"label_agreement":null},{"id":"W2182562048","doi":"10.1609/aaai.v28i1.8976","title":"Convex Co-embedding","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Embedding; Ranking (information retrieval); Regular polygon; Computer science; Relation (database); Relevance (law); Convex optimization; Learning to rank; Artificial intelligence; Current (fluid); Machine learning; Theoretical computer science; Mathematical optimization; Mathematics; Data mining; Geometry","score_opus":0.056841868913256155,"score_gpt":0.3135525068299718,"score_spread":0.25671063791671567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182562048","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":[0.14820601,0.000041193904,0.75940925,0.008355288,0.001832318,0.000940294,0.0000047915287,0.000588783,0.08062205],"genre_scores_gemma":[0.9910804,0.0000200574,0.00795215,0.0005947776,0.000108092885,0.00001811672,2.6004182e-7,0.000014878622,0.00021123474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978915,0.000025653117,0.00052032026,0.0005769916,0.0005312271,0.0004542972],"domain_scores_gemma":[0.9983685,0.0002026467,0.0004203591,0.0004367582,0.00045019962,0.000121514895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005129433,0.00025538853,0.0003059461,0.00012639214,0.0002469899,0.00023239758,0.0027436055,0.00009841369,0.000043023218],"category_scores_gemma":[0.00042577993,0.0001910258,0.0001413729,0.00072549924,0.00034617053,0.0005575657,0.0003827306,0.0003973932,0.0001393726],"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":[0.00001843418,0.000043469863,0.00016198852,0.00001528506,0.0000064606475,2.141969e-7,0.00029374444,0.00026506258,0.020036997,0.9060219,0.00025276947,0.07288365],"study_design_scores_gemma":[0.000022938686,0.00015231923,0.00010232039,0.0001276262,0.0000052403802,0.0000052826786,0.00009147384,0.2607835,0.39851537,0.33934793,0.0006198311,0.0002261351],"about_ca_topic_score_codex":0.0000071510435,"about_ca_topic_score_gemma":0.000002062635,"teacher_disagreement_score":0.8428744,"about_ca_system_score_codex":0.000029679997,"about_ca_system_score_gemma":0.000036085992,"threshold_uncertainty_score":0.7789806},"labels":[],"label_agreement":null},{"id":"W2387462954","doi":"10.1145/2939672.2939751","title":"Asymmetric Transitivity Preserving Graph Embedding","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1293,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Transitive relation; Embedding; Scalability; Computer science; Theoretical computer science; Transitive reduction; Graph embedding; Directed graph; Graph; Vertex (graph theory); Mathematics; Algorithm; Combinatorics; Artificial intelligence; Voltage graph; Line graph","score_opus":0.014101384185911841,"score_gpt":0.25414677896115334,"score_spread":0.2400453947752415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2387462954","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":[0.0054117395,0.00021778875,0.98656,0.001207244,0.0002378009,0.00009132877,6.4100306e-7,0.00042372124,0.00584974],"genre_scores_gemma":[0.8990439,0.00011466726,0.10011691,0.000287431,0.000047509915,0.000007532952,1.101589e-7,0.000008483535,0.00037346198],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988051,0.00005989656,0.0001452728,0.0004007168,0.00022425938,0.00036477798],"domain_scores_gemma":[0.99888325,0.00038323167,0.000041899384,0.00054045516,0.000043861313,0.00010728632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018551308,0.000121590216,0.00012023819,0.00021051824,0.000097504104,0.000057782257,0.0007687859,0.000044641496,0.00002012008],"category_scores_gemma":[0.000035334833,0.000075325916,0.000085762695,0.0012406154,0.000033610504,0.0012851538,0.00019779918,0.000079073994,0.000033428103],"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":[0.0000055282476,0.00003874509,0.0012050951,0.000005934585,0.000013926513,0.000030908537,0.00006050359,0.00040847962,0.0096363695,0.10406834,0.004030056,0.88049614],"study_design_scores_gemma":[0.004302527,0.00055270124,0.046305023,0.00040280234,0.000021723934,0.00020230576,0.00002763438,0.20271313,0.1425067,0.53836834,0.06157785,0.0030192651],"about_ca_topic_score_codex":0.000004510398,"about_ca_topic_score_gemma":0.00000570543,"teacher_disagreement_score":0.8936322,"about_ca_system_score_codex":0.00001776028,"about_ca_system_score_gemma":0.000007850421,"threshold_uncertainty_score":0.30717018},"labels":[],"label_agreement":null},{"id":"W2546973305","doi":"10.14778/3007263.3007267","title":"GraphJet","year":2016,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Graph; Exploit; Theoretical computer science; Clique-width; Line graph; Voltage graph","score_opus":0.006945037593673362,"score_gpt":0.19420157507786545,"score_spread":0.18725653748419208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546973305","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":[0.7553497,0.00109715,0.12309057,0.06280648,0.0042061163,0.002636927,0.000014381383,0.0013109469,0.04948776],"genre_scores_gemma":[0.9898244,0.00006108803,0.00931329,0.00026523307,0.000042148335,0.000025590756,2.2415202e-8,0.0000072895946,0.00046091407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900234,0.0000036860688,0.00018449422,0.00025888477,0.00030026282,0.00025031768],"domain_scores_gemma":[0.99940956,0.000044188593,0.0001711645,0.00021760612,0.00010201606,0.000055470657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012707924,0.00011171973,0.00011490628,0.000057398294,0.00007183212,0.000026576838,0.0013751762,0.000027891345,0.0000055932137],"category_scores_gemma":[0.000041042942,0.000052800227,0.0001070718,0.00041530002,0.00008620361,0.00042965563,0.00053687685,0.00006220458,0.000009251295],"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":[0.000013329016,0.0000679858,0.008830296,0.000021522503,0.000030339877,5.88333e-7,0.00018880851,0.000007053108,0.2449301,0.6714856,0.008027935,0.06639641],"study_design_scores_gemma":[0.0008428018,0.00014835905,0.0087744165,0.00020951417,0.000012232948,0.000027301494,0.000020314848,0.00026681146,0.6893929,0.28645137,0.0135690905,0.00028488695],"about_ca_topic_score_codex":0.000002165058,"about_ca_topic_score_gemma":4.5766782e-7,"teacher_disagreement_score":0.4444628,"about_ca_system_score_codex":0.00002751165,"about_ca_system_score_gemma":0.000007783081,"threshold_uncertainty_score":0.25554425},"labels":[],"label_agreement":null},{"id":"W2604314403","doi":"10.1007/978-3-319-93417-4_38","title":"Modeling Relational Data with Graph Convolutional Networks","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5060,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Science Foundation","keywords":"Computer science; Inference; Predicate (mathematical logic); Encoder; Relational database; Convolutional neural network; Graph; Information retrieval; Artificial intelligence; Knowledge base; Data mining; Theoretical computer science; Programming language","score_opus":0.03535772873792242,"score_gpt":0.25225338356626403,"score_spread":0.2168956548283416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604314403","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":[0.00002666134,0.0007429385,0.99616355,0.00040680045,0.0014641699,0.00033458645,0.000014326371,0.0002175902,0.00062937255],"genre_scores_gemma":[0.07327548,0.000075535325,0.9234707,0.00145396,0.0014155789,0.0000075551307,0.00010382958,0.000055903747,0.0001414632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99466103,0.00003528019,0.00055620936,0.0025336505,0.0013834904,0.00083035487],"domain_scores_gemma":[0.9956374,0.00045203368,0.0002761165,0.0029683555,0.00044507996,0.00022100612],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007681181,0.00061211194,0.00047422017,0.0006603496,0.0004673292,0.00035395043,0.0060633384,0.00036889926,0.000031996296],"category_scores_gemma":[0.000047098216,0.0005214535,0.00008209667,0.001018824,0.0013604019,0.001782121,0.0031441185,0.001227933,0.000025231828],"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":[0.000011448024,0.000010609565,0.000035414818,0.000005164904,0.000014087333,0.000041181193,0.000052945703,0.9210442,0.0000015536524,0.03621203,0.000051410836,0.042519998],"study_design_scores_gemma":[0.00019539236,0.00010201435,0.000023586726,0.00024304245,0.000007436596,0.00011508357,3.5464048e-8,0.77644134,0.0000029117384,0.2220209,0.0003417445,0.0005065341],"about_ca_topic_score_codex":0.000008044485,"about_ca_topic_score_gemma":0.00012034877,"teacher_disagreement_score":0.18580887,"about_ca_system_score_codex":0.00017196014,"about_ca_system_score_gemma":0.0005449088,"threshold_uncertainty_score":0.99972373},"labels":[],"label_agreement":null},{"id":"W2604942799","doi":"10.1609/aaai.v31i1.10488","title":"Community Preserving Network Embedding","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":929,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Tencent","keywords":"Computer science; Embedding; Correctness; Community structure; Modularity (biology); Theoretical computer science; Exploit; Variety (cybernetics); Representation (politics); Feature (linguistics); Feature learning; Artificial intelligence; Non-negative matrix factorization; Matrix decomposition; Algorithm; Mathematics","score_opus":0.1242917882980847,"score_gpt":0.342916314103725,"score_spread":0.2186245258056403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604942799","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":[0.56271434,0.00012705565,0.2638744,0.020529626,0.004588694,0.0018548681,0.000008494451,0.0007573134,0.14554518],"genre_scores_gemma":[0.988738,0.000031472966,0.01048179,0.00022998196,0.00015831474,0.00002013513,1.7793514e-7,0.000015583151,0.00032452503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980026,0.000048526606,0.0005021336,0.00042279158,0.00047353603,0.00055045215],"domain_scores_gemma":[0.9969682,0.00017992903,0.00086134695,0.0013502191,0.00052517507,0.00011514026],"candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0009223125,0.00026260415,0.00030599968,0.000063042186,0.0022225755,0.000862438,0.008760423,0.000105977015,0.00002512496],"category_scores_gemma":[0.0009485787,0.00020326865,0.00015330511,0.00034576538,0.00050182873,0.0013013349,0.0027729436,0.0009041574,0.000033434415],"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":[0.000034549546,0.00007893779,0.0009864944,0.000030628555,0.000015531421,6.9239104e-7,0.0007696782,0.0018216589,0.0054738563,0.93328565,0.00060028123,0.05690203],"study_design_scores_gemma":[0.000026612426,0.0001305293,0.0017254134,0.0004520589,0.000009113089,0.000004940029,0.0002023415,0.22725996,0.090092905,0.6796217,0.00018740272,0.00028700763],"about_ca_topic_score_codex":0.00008734123,"about_ca_topic_score_gemma":0.000042738382,"teacher_disagreement_score":0.42602366,"about_ca_system_score_codex":0.000034090088,"about_ca_system_score_gemma":0.000043901302,"threshold_uncertainty_score":0.99907637},"labels":[],"label_agreement":null},{"id":"W2766453196","doi":"10.17863/cam.48429","title":"Graph Attention Networks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":947,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Computer science; Graph; Attention network; Theoretical computer science; Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.06497125518221562,"score_gpt":0.19409011292821438,"score_spread":0.12911885774599874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766453196","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":[0.030464686,0.0002026525,0.9639569,0.00012437748,0.0022318896,0.00029724903,0.000004475698,0.00048548268,0.0022322962],"genre_scores_gemma":[0.9946767,0.0007305532,0.0022240714,0.000104101564,0.0002077796,0.0000013619915,0.0000218705,0.000025578336,0.00200796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997492,0.00012826004,0.0002084662,0.001517468,0.000100512385,0.0005532499],"domain_scores_gemma":[0.9960863,0.00007648709,0.0005491457,0.0029089577,0.00015677018,0.00022235991],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019640216,0.0004162483,0.0003835599,0.00026265273,0.00047556774,0.00029989096,0.003951175,0.00045920396,0.000009016185],"category_scores_gemma":[0.00002102762,0.0004894316,0.00042221055,0.0004320547,0.00020477704,0.0008351289,0.0035060835,0.0010764119,0.0000531378],"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":[0.000015930187,0.000042943902,0.0035331512,0.000022722947,0.00007294442,0.00047709388,0.000017452432,0.8202468,0.00000747131,0.17147034,0.0008701301,0.0032229752],"study_design_scores_gemma":[0.000319943,0.000032397646,0.004440421,0.000116052084,0.000046938232,0.000007392797,0.0000045909683,0.8028061,0.0000063599427,0.19118854,0.0004847323,0.00054652564],"about_ca_topic_score_codex":0.000045091398,"about_ca_topic_score_gemma":0.00004192836,"teacher_disagreement_score":0.96421206,"about_ca_system_score_codex":0.00010463818,"about_ca_system_score_gemma":0.000061978244,"threshold_uncertainty_score":0.99975574},"labels":[],"label_agreement":null},{"id":"W2783238014","doi":"10.1145/3178422.3178424","title":"How do ideas flow around SIGMM conferences?","year":2018,"lang":"en","type":"article","venue":"ACM SIGMultimedia Records","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"CITES; Download; Computer science; Task (project management); Library science; World Wide Web; Citation; Quarter (Canadian coin); Academic community; Information retrieval; History; Engineering","score_opus":0.02317305913789772,"score_gpt":0.2652905193800475,"score_spread":0.24211746024214978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783238014","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":[0.03396922,0.00065073505,0.93968314,0.008386472,0.009772434,0.00071331527,0.000014332333,0.00112245,0.005687922],"genre_scores_gemma":[0.72527736,0.00009704314,0.2710145,0.0008714101,0.0015466942,0.00004647486,0.000010441587,0.000030474364,0.0011056033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711686,0.00012462471,0.00034164812,0.0010220712,0.0005605622,0.0008342342],"domain_scores_gemma":[0.99596316,0.0008475507,0.00023096336,0.002259811,0.0003355473,0.00036298356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002894603,0.00039974554,0.0003867905,0.0001846711,0.00027496935,0.0006253159,0.0032774583,0.0002121252,0.000108900735],"category_scores_gemma":[0.0009868239,0.00035271636,0.00014804954,0.0009208736,0.00041531568,0.0014706455,0.0009163526,0.0004234729,0.00031122632],"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":[0.0000385616,0.00007642298,0.0060891453,0.000010176754,0.000053215048,0.00006653135,0.0006304311,0.000082503844,0.00067894463,0.005859979,0.023400074,0.963014],"study_design_scores_gemma":[0.001784809,0.00095434295,0.003956778,0.00009272031,0.000028894847,0.00006732108,0.0001556504,0.6088442,0.003899286,0.16186869,0.21688764,0.0014596244],"about_ca_topic_score_codex":0.00003233038,"about_ca_topic_score_gemma":0.000114348455,"teacher_disagreement_score":0.9615544,"about_ca_system_score_codex":0.00006229345,"about_ca_system_score_gemma":0.0001418969,"threshold_uncertainty_score":0.9998925},"labels":[],"label_agreement":null},{"id":"W2788408359","doi":"10.1609/aaai.v32i1.11548","title":"Embedding of Hierarchically Typed Knowledge Bases","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Beijing Advanced Innovation Center for Big Data and Brain Computing; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Embedding; Computer science; Set (abstract data type); Inference; Theoretical computer science; Knowledge base; Constraint (computer-aided design); Base (topology); Scheme (mathematics); Artificial intelligence; Mathematics; Programming language","score_opus":0.07006822215061718,"score_gpt":0.3312770358805869,"score_spread":0.2612088137299697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788408359","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":[0.5884693,0.00013491319,0.33818334,0.004048571,0.0022438185,0.0010645296,0.000010701934,0.00037524008,0.065469526],"genre_scores_gemma":[0.9862224,0.000023561071,0.013260227,0.00013749802,0.00012861981,0.000009861112,1.5433672e-7,0.000012124559,0.00020559582],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982331,0.000022560538,0.00055985944,0.00046566062,0.00036655564,0.00035230524],"domain_scores_gemma":[0.99785,0.0001958462,0.00041523718,0.00037953662,0.0010673385,0.00009203934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034734985,0.00020741836,0.0002846007,0.00015559673,0.0001726313,0.00008184958,0.0023318047,0.0000834343,0.000056469686],"category_scores_gemma":[0.00048930215,0.00015283309,0.00012846998,0.0010695801,0.0007338247,0.00037891394,0.00059091806,0.0002831628,0.00006483876],"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":[0.000060620237,0.00011806706,0.00011673617,0.000028652901,0.000011114278,2.4166326e-7,0.00092438154,0.00004798103,0.06392356,0.8321697,0.00017735374,0.102421604],"study_design_scores_gemma":[0.000021610722,0.0003640095,0.00011526597,0.0002658602,0.0000075062967,0.0000033061551,0.00009939999,0.13069183,0.64405364,0.22397003,0.00023412911,0.00017344637],"about_ca_topic_score_codex":0.000007968331,"about_ca_topic_score_gemma":0.000008747622,"teacher_disagreement_score":0.60819966,"about_ca_system_score_codex":0.00002186925,"about_ca_system_score_gemma":0.0000883875,"threshold_uncertainty_score":0.6232352},"labels":[],"label_agreement":null},{"id":"W2791941169","doi":"10.5441/002/edbt.2018.52","title":"Point-of-Interest Recommendation Using Heterogeneous Link Prediction","year":2018,"lang":"en","type":"article","venue":"Movebank","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Link (geometry); Point (geometry); Point of interest; Data mining; Artificial intelligence; Computer network; Mathematics","score_opus":0.0692021735810043,"score_gpt":0.2914990192153362,"score_spread":0.2222968456343319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791941169","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":[0.22550139,0.000023650684,0.77239686,0.00030289145,0.001137531,0.00010238059,0.0000041983335,0.00013487862,0.00039620136],"genre_scores_gemma":[0.9569868,0.0000066555017,0.042261884,0.00029143586,0.0003902129,0.000002903995,0.000006260042,0.000011303444,0.00004258418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991153,0.000049835417,0.00025760132,0.00029371044,0.00008492089,0.00019864744],"domain_scores_gemma":[0.9992521,0.000043347525,0.00015021133,0.00038200588,0.00011922011,0.000053131876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012314727,0.000104373175,0.000109271496,0.00009465754,0.00009180058,0.000045914498,0.00034450935,0.00006409058,0.000038110968],"category_scores_gemma":[0.000027152584,0.00010300671,0.000057317364,0.00030158367,0.00007000505,0.00054570707,0.00016936519,0.00009965498,0.000018422155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013406847,0.00020569598,0.009487493,0.000055171422,0.00012060039,0.00003226273,0.00087551883,0.012656701,0.08405515,0.025666513,0.0021021299,0.8646087],"study_design_scores_gemma":[0.00040219704,0.00043611927,0.0021676144,0.000060756556,0.00000879943,0.00008140469,0.000003536214,0.8985052,0.07714809,0.01613734,0.0048221606,0.00022675088],"about_ca_topic_score_codex":0.000011295592,"about_ca_topic_score_gemma":0.000013663428,"teacher_disagreement_score":0.8858485,"about_ca_system_score_codex":0.000040054383,"about_ca_system_score_gemma":0.000012299553,"threshold_uncertainty_score":0.42004916},"labels":[],"label_agreement":null},{"id":"W2808284704","doi":"10.24963/ijcai.2018/611","title":"Bootstrapping Entity Alignment with Knowledge Graph Embedding","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":531,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Bootstrapping (finance); Embedding; Computer science; Knowledge graph; Graph; Artificial intelligence; Process (computing); Graph embedding; Training set; Labeled data; Natural language processing; Theoretical computer science; Machine learning; Data mining; Mathematics; Programming language","score_opus":0.016141416615473612,"score_gpt":0.27651770015883614,"score_spread":0.2603762835433625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808284704","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":[0.017757371,0.00013658764,0.9652267,0.00011925443,0.00034791767,0.0001257015,1.9784515e-7,0.00033947488,0.01594678],"genre_scores_gemma":[0.9195716,0.000021792623,0.07960327,0.0002657497,0.00014156365,0.0000097266575,4.1169577e-7,0.000009061542,0.00037687365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988249,0.00003070416,0.0001471167,0.0004363218,0.00018654004,0.00037440174],"domain_scores_gemma":[0.99919635,0.00004244644,0.000055740176,0.00050723617,0.0000844642,0.00011375632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011145793,0.00014796964,0.000119610806,0.000099453275,0.00021078972,0.0001110188,0.0005979502,0.000035449015,0.000028718112],"category_scores_gemma":[0.000002975131,0.00011255881,0.000046520796,0.00066524255,0.00012806623,0.0005987342,0.00022812716,0.00009829026,0.00007930532],"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":[0.000054285352,0.0004462474,0.0070835506,0.000046140387,0.00018690867,0.00009744034,0.0031129485,0.0034121352,0.010906163,0.8041527,0.027717134,0.1427843],"study_design_scores_gemma":[0.003792512,0.002736947,0.01133228,0.00053910154,0.00004384431,0.00031015874,0.00034493822,0.5469335,0.12543754,0.114098,0.19096702,0.003464171],"about_ca_topic_score_codex":0.0000048979045,"about_ca_topic_score_gemma":0.000035823807,"teacher_disagreement_score":0.90181416,"about_ca_system_score_codex":0.00002540701,"about_ca_system_score_gemma":0.000017226559,"threshold_uncertainty_score":0.4590015},"labels":[],"label_agreement":null},{"id":"W2808466528","doi":"10.24963/ijcai.2018/438","title":"ANRL: Attributed Network Representation Learning via Deep Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":235,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Zhejiang University; National Natural Science Foundation of China","keywords":"Computer science; Autoencoder; Node (physics); Representation (politics); Focus (optics); Artificial intelligence; Feature learning; Encoder; Network structure; Artificial neural network; Data mining; Theoretical computer science; Machine learning","score_opus":0.01612407182908719,"score_gpt":0.2656443981401106,"score_spread":0.2495203263110234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808466528","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":[0.005089123,0.00017164549,0.99049854,0.00047127152,0.0013520853,0.00021487416,8.970723e-8,0.00096666545,0.0012357079],"genre_scores_gemma":[0.93486655,0.0000193712,0.062285416,0.0012494265,0.0011972316,0.000015305432,0.000015180947,0.000025143276,0.00032637623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975877,0.00024110716,0.0003612884,0.00071169867,0.00029840932,0.00079981424],"domain_scores_gemma":[0.99847454,0.00024788937,0.00018268357,0.0007049468,0.00020697944,0.00018297732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028413543,0.00023987069,0.00024093152,0.00007653472,0.00050956185,0.00019417457,0.00084961846,0.00012673067,0.00006784766],"category_scores_gemma":[0.000056844492,0.00022171786,0.00010582004,0.0018071498,0.00013690039,0.0010210038,0.0004840186,0.00043611394,0.00006887647],"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":[0.000022743596,0.000017316039,0.012527444,0.0000013509695,0.000020390431,0.000021977568,0.00007604847,0.80283797,0.00008273436,0.0050852518,0.0025532506,0.1767535],"study_design_scores_gemma":[0.00026494556,0.00018377049,0.007967066,0.0000056255612,0.0000062168638,0.00003842746,0.000007741878,0.9853053,0.00014120573,0.0042794333,0.0015338612,0.00026637944],"about_ca_topic_score_codex":0.000020227904,"about_ca_topic_score_gemma":0.000076876066,"teacher_disagreement_score":0.92977744,"about_ca_system_score_codex":0.000031858584,"about_ca_system_score_gemma":0.0000070263886,"threshold_uncertainty_score":0.90413916},"labels":[],"label_agreement":null},{"id":"W2808483757","doi":"10.24963/ijcai.2018/475","title":"Lightweight Label Propagation for Large-Scale Network Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Overhead (engineering); Scale (ratio); Graph; Theoretical computer science; Stochastic gradient descent; Algorithm; Data mining; Artificial intelligence; Artificial neural network","score_opus":0.03688565519832589,"score_gpt":0.29574545368964944,"score_spread":0.25885979849132357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808483757","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":[0.00030890852,0.000080281374,0.9937125,0.0015384052,0.0008544039,0.00039720605,0.000009144191,0.000325857,0.002773329],"genre_scores_gemma":[0.04286611,0.000013702507,0.95101386,0.0019817061,0.0017455091,0.00003817275,0.00006753931,0.000018569028,0.002254809],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985794,0.00002702139,0.00018899601,0.0005872059,0.00016190646,0.0004555116],"domain_scores_gemma":[0.99818873,0.00009383456,0.00007851898,0.0014236015,0.00013425724,0.00008105123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030410718,0.00012065008,0.000119113836,0.000030922787,0.0002748171,0.000099184595,0.0016309915,0.000058968388,0.000018158511],"category_scores_gemma":[0.000024214662,0.0000950209,0.000024186305,0.0004984131,0.000037810365,0.0010975521,0.00074780406,0.00007465225,0.00007110346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030919295,0.00012594297,0.00056168955,0.000014834832,0.000022808947,0.0000027171882,0.0001840449,0.00014221611,0.000367031,0.601941,0.33718354,0.059423264],"study_design_scores_gemma":[0.00038155852,0.00014188081,0.00014936915,0.000011790416,0.0000046384785,0.000003731806,0.0000030203842,0.79840136,0.0010645877,0.032777447,0.1668946,0.00016600774],"about_ca_topic_score_codex":0.0000012322719,"about_ca_topic_score_gemma":0.00010234198,"teacher_disagreement_score":0.79825914,"about_ca_system_score_codex":0.000010830548,"about_ca_system_score_gemma":0.000022709062,"threshold_uncertainty_score":0.38748398},"labels":[],"label_agreement":null},{"id":"W2809156873","doi":"10.1145/3219819.3219969","title":"Arbitrary-Order Proximity Preserved Network Embedding","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":211,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Embedding; Scalability; Computer science; Node (physics); Order (exchange); Decomposition; Mathematical optimization; Algorithm; Theoretical computer science; Topology (electrical circuits); Mathematics; Artificial intelligence","score_opus":0.018157348298126093,"score_gpt":0.2724486667270325,"score_spread":0.2542913184289064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809156873","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":[0.009019885,0.00010640798,0.96283156,0.00066431327,0.0007722194,0.00025129583,2.429153e-7,0.0006904825,0.025663584],"genre_scores_gemma":[0.3470154,0.000011386116,0.6483314,0.002106842,0.0009057585,0.000021505368,0.0000011720491,0.000017093816,0.0015894196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983571,0.00006608864,0.00021763392,0.000521432,0.00024177873,0.0005959402],"domain_scores_gemma":[0.9987007,0.00010282873,0.00007621677,0.0008311621,0.00015032437,0.0001387614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058798,0.00017086805,0.00015713467,0.000042387528,0.00025540643,0.00013588495,0.001130857,0.00007518391,0.00008649187],"category_scores_gemma":[0.000041944324,0.00014203043,0.00006072486,0.001024483,0.00010925937,0.0012796128,0.0005656839,0.00020786798,0.000109597204],"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":[0.00006408397,0.00019470838,0.01215853,0.000029278921,0.0000960321,0.00006395377,0.00048065576,0.017890764,0.0006341799,0.78932226,0.122401565,0.056664],"study_design_scores_gemma":[0.00031888534,0.00017906923,0.0026271837,0.00002808134,0.000004234845,0.000015998157,0.0000051146576,0.8275427,0.0015912547,0.1461324,0.021153571,0.0004014884],"about_ca_topic_score_codex":0.0000092613855,"about_ca_topic_score_gemma":0.000040553066,"teacher_disagreement_score":0.809652,"about_ca_system_score_codex":0.000015341762,"about_ca_system_score_gemma":0.000030107465,"threshold_uncertainty_score":0.5791833},"labels":[],"label_agreement":null},{"id":"W2811124557","doi":"","title":"Hierarchical graph representation learning with differentiable pooling","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":909,"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":"Pooling; Differentiable function; Computer science; Graph; Artificial intelligence; Theoretical computer science; Benchmark (surveying); Pattern recognition (psychology); Mathematics","score_opus":0.014365399824819249,"score_gpt":0.2542677497504681,"score_spread":0.23990234992564888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811124557","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":[0.09989683,0.00006930816,0.89684165,0.00018278259,0.00038584688,0.0002489224,4.5584082e-7,0.0006126785,0.0017615304],"genre_scores_gemma":[0.9933959,0.000003703287,0.0060017486,0.00019645548,0.00018773104,0.000033944107,0.000015838936,0.000010308953,0.00015432732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983741,0.00008778108,0.00045651715,0.00024891607,0.00047565947,0.0003570017],"domain_scores_gemma":[0.998807,0.000053686876,0.00039937533,0.00027708453,0.00036084463,0.00010199864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016513458,0.00017679384,0.00018129412,0.00024238492,0.00059210556,0.0010185317,0.00042780212,0.00006966793,0.0000024775816],"category_scores_gemma":[0.00004286081,0.00013700115,0.00003596345,0.0010553834,0.000105529034,0.0067051863,0.00009762873,0.00031399704,0.000031313983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027760744,0.00008291481,0.018504923,0.0007891597,0.000071766306,0.00001929322,0.013379885,0.44614044,0.0019433856,0.030354375,0.00157034,0.4868659],"study_design_scores_gemma":[0.00036736773,0.00019369605,0.0013003871,0.00014788486,0.0000055701275,0.000112896705,0.00018887063,0.99468756,0.0006129457,0.00048387272,0.0016768101,0.00022214762],"about_ca_topic_score_codex":0.000018743074,"about_ca_topic_score_gemma":0.0000019619838,"teacher_disagreement_score":0.89349914,"about_ca_system_score_codex":0.000029001945,"about_ca_system_score_gemma":0.00003525498,"threshold_uncertainty_score":0.9821725},"labels":[],"label_agreement":null},{"id":"W2885592141","doi":"","title":"RecService: Distributed Real-Time Graph Processing at Twitter.","year":2018,"lang":"en","type":"article","venue":"USENIX conference on Hot Topics in Cloud Ccomputing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Graph; Theoretical computer science","score_opus":0.043533479712902966,"score_gpt":0.3036088071203162,"score_spread":0.26007532740741324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885592141","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":[0.80281806,0.000044500077,0.18653248,0.0022997742,0.0014696016,0.00041339465,0.0000055391215,0.00075377605,0.005662881],"genre_scores_gemma":[0.9679506,0.00003598683,0.030169208,0.00056727714,0.00090576144,0.000010362853,0.000015770362,0.000029879577,0.00031515618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673593,0.00017473109,0.00063673395,0.0010842585,0.0004706805,0.0008976476],"domain_scores_gemma":[0.99794376,0.00025776113,0.00034006473,0.000989705,0.00028932554,0.00017940899],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041088974,0.00040367915,0.00042922614,0.00021060895,0.00046230474,0.00029523467,0.0015548876,0.00021187148,0.000033462595],"category_scores_gemma":[0.00008969723,0.00039999315,0.000095889925,0.0013894865,0.00019954443,0.00031911908,0.00086580747,0.00052485993,0.00010506864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048953446,0.0009782802,0.04073534,0.00041372495,0.00012222354,0.0008483127,0.015732132,0.0138399415,0.018208262,0.29470602,0.0073681762,0.606558],"study_design_scores_gemma":[0.0013320264,0.00043811044,0.012863407,0.00092113536,0.000012207173,0.00006644704,0.00008755015,0.9138018,0.0053138407,0.06103042,0.0028570495,0.0012760008],"about_ca_topic_score_codex":0.00004449007,"about_ca_topic_score_gemma":0.00008930795,"teacher_disagreement_score":0.8999619,"about_ca_system_score_codex":0.00018359012,"about_ca_system_score_gemma":0.000092807786,"threshold_uncertainty_score":0.9998452},"labels":[],"label_agreement":null},{"id":"W2890848214","doi":"10.1109/icassp.2018.8462291","title":"A Graph-CNN for 3D Point Cloud Classification","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":181,"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; Upsampling; Point cloud; Graph; Pooling; Theoretical computer science; Convolutional neural network; Topological graph theory; Artificial intelligence; Line graph; Voltage graph","score_opus":0.031463371795567595,"score_gpt":0.2832721333078607,"score_spread":0.25180876151229314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890848214","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":[0.0010889485,0.000023385453,0.9897592,0.0018130963,0.00079563604,0.00025562593,7.0147877e-7,0.00029801004,0.005965367],"genre_scores_gemma":[0.40713233,0.000007835086,0.59006155,0.0015778652,0.00042701786,0.00004973548,0.00000227149,0.000009413449,0.0007320063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910074,0.000019526062,0.0001591433,0.00035086687,0.00011762849,0.00025211903],"domain_scores_gemma":[0.99910724,0.000092730086,0.00006680845,0.0005286792,0.00013455449,0.00006998582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013246047,0.000094543946,0.0000865853,0.00006807096,0.00013832263,0.00006206814,0.0005259987,0.00004688062,0.000012830361],"category_scores_gemma":[0.000025576319,0.00007816387,0.00006341984,0.00041304552,0.000081536375,0.0003956301,0.00008827349,0.000057465084,0.000070268965],"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":[0.000015778598,0.000034185872,0.00011102626,0.0000046029095,0.000008444341,7.3070845e-7,0.00013897437,0.000020721329,0.0015156411,0.83633554,0.024510793,0.13730358],"study_design_scores_gemma":[0.0005894491,0.00048742126,0.0022646065,0.000013629691,0.000006432867,0.000012162507,0.000025074543,0.5666329,0.005463091,0.34413287,0.08001437,0.0003579478],"about_ca_topic_score_codex":0.0000024431604,"about_ca_topic_score_gemma":0.00001308833,"teacher_disagreement_score":0.5666122,"about_ca_system_score_codex":0.000014180027,"about_ca_system_score_gemma":0.000013553072,"threshold_uncertainty_score":0.318743},"labels":[],"label_agreement":null},{"id":"W2893944917","doi":"","title":"Deep Graph Infomax.","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Infomax; Computer science; Graph; Artificial intelligence; Node (physics); Unsupervised learning; Machine learning; Competitive learning; Feature learning; Random walk; Theoretical computer science; Mathematics","score_opus":0.05111203280535453,"score_gpt":0.1820539192501569,"score_spread":0.13094188644480237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893944917","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":[0.056770742,0.000113871,0.93635994,0.00007635994,0.0014544587,0.00026802698,0.00000366624,0.00062898535,0.004323926],"genre_scores_gemma":[0.98785704,0.00025045592,0.010559718,0.00031499457,0.00019622452,0.0000011548067,0.000011101458,0.00002676664,0.00078254455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750024,0.00011843181,0.00022794987,0.0014607074,0.00012308368,0.0005695939],"domain_scores_gemma":[0.9969502,0.00010913174,0.00031002218,0.0021539573,0.00021384741,0.00026281946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014248324,0.00043158157,0.00036423822,0.00039183057,0.00021376612,0.00013806322,0.0034392644,0.00039918188,0.00003476044],"category_scores_gemma":[0.00002630271,0.0004991917,0.00032242833,0.0012392261,0.0002819135,0.0006458379,0.003889688,0.0008001484,0.0002321202],"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":[0.000040611714,0.00010205837,0.003985114,0.00006596207,0.00015932249,0.0009110627,0.00024465777,0.57199484,0.000017793605,0.4166835,0.0024682214,0.0033268766],"study_design_scores_gemma":[0.0003240814,0.000073327086,0.0012219329,0.00006436456,0.00003682529,0.0000104371475,0.000012530721,0.5841883,0.00007504043,0.4117925,0.0015430517,0.00065764313],"about_ca_topic_score_codex":0.000029154804,"about_ca_topic_score_gemma":0.00005290884,"teacher_disagreement_score":0.9310863,"about_ca_system_score_codex":0.00011381578,"about_ca_system_score_gemma":0.00008952164,"threshold_uncertainty_score":0.99974597},"labels":[],"label_agreement":null},{"id":"W2896398259","doi":"10.1109/bsc.2018.8494688","title":"A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Graph; Inference; Signal processing; Feature extraction; Embedding; Leverage (statistics); Ranging; Time series","score_opus":0.03275585631511516,"score_gpt":0.28348922980777225,"score_spread":0.25073337349265706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896398259","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":[0.01935121,0.0000475448,0.97331965,0.0005203432,0.000093150826,0.00024595985,0.000002457048,0.00038938675,0.0060303016],"genre_scores_gemma":[0.6106276,9.785063e-7,0.38783336,0.0012450262,0.00019649815,0.000019590218,0.000003529774,0.000008943822,0.00006448493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978737,0.00008621942,0.00030973688,0.0008005312,0.0004422835,0.00048753482],"domain_scores_gemma":[0.9987979,0.00014624553,0.00008874196,0.00049475837,0.00016591085,0.00030647736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015997513,0.00022796672,0.0002287285,0.00017543662,0.00022309044,0.00027317172,0.0012292993,0.00009353372,0.000042638225],"category_scores_gemma":[0.00002835036,0.00018856631,0.00006740522,0.001247062,0.00017703524,0.00079732866,0.0004489156,0.00026006054,0.000079652615],"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":[0.0000790093,0.00034480085,0.0029492914,0.000017167653,0.000032177322,0.000018410867,0.0026252565,0.0034102825,0.009809216,0.061505087,0.002426929,0.9167824],"study_design_scores_gemma":[0.00043473532,0.00047730244,0.0049360595,0.00007264247,0.000007223431,0.000009146877,0.000031359963,0.7967104,0.011578417,0.18330087,0.0016746207,0.00076725107],"about_ca_topic_score_codex":0.00006247267,"about_ca_topic_score_gemma":0.000022649907,"teacher_disagreement_score":0.91601515,"about_ca_system_score_codex":0.00001863232,"about_ca_system_score_gemma":0.00007988301,"threshold_uncertainty_score":0.76895106},"labels":[],"label_agreement":null},{"id":"W2899568612","doi":"10.3233/fi-2018-1746","title":"A Descriptive Tolerance Nearness Measure for Performing Graph Comparison","year":2018,"lang":"en","type":"article","venue":"Fundamenta Informaticae","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Disjoint sets; Bipartite graph; Mathematics; Enumeration; Clique; Combinatorics; Graph; Theoretical computer science; Discrete mathematics; Computer science; Algorithm","score_opus":0.037069702012035785,"score_gpt":0.28832069830113355,"score_spread":0.25125099628909775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899568612","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":[0.07217985,0.000082080216,0.9230404,0.00025349704,0.0009615043,0.0007345591,0.0000065595286,0.00026864576,0.0024729427],"genre_scores_gemma":[0.87037367,0.00000395404,0.12824458,0.0009654629,0.00017466386,0.00012959407,0.00000722367,0.000014320049,0.000086518034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812216,0.00003073837,0.00055490923,0.00028911413,0.0003850496,0.00061801524],"domain_scores_gemma":[0.9985928,0.0001284887,0.0002583841,0.00060796784,0.000249716,0.00016264978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033194956,0.0002485338,0.0003124913,0.00014017007,0.00054780795,0.00031775716,0.0010330265,0.00007988904,0.000020284237],"category_scores_gemma":[0.000053747317,0.00022267636,0.00012590764,0.00061562797,0.00021430038,0.00247931,0.00024933042,0.00020910002,0.00010938297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000549989,0.00041565296,0.015139711,0.00047117274,0.00028110106,0.0000069524126,0.062447555,0.0035342476,0.0010893374,0.24686676,0.029895732,0.6393018],"study_design_scores_gemma":[0.0024460007,0.0014500784,0.0029565394,0.00032022645,0.000034863715,0.000047310317,0.001867408,0.906488,0.00808665,0.017841669,0.057415944,0.0010453311],"about_ca_topic_score_codex":0.0000056728895,"about_ca_topic_score_gemma":0.000017876266,"teacher_disagreement_score":0.90295374,"about_ca_system_score_codex":0.00008445738,"about_ca_system_score_gemma":0.000048849866,"threshold_uncertainty_score":0.90804785},"labels":[],"label_agreement":null},{"id":"W2903329593","doi":"10.1109/bigdata.2018.8621910","title":"dynnode2vec: Scalable Dynamic Network Embedding","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Embedding; Computer science; Graph embedding; Scalability; Random walk; Theoretical computer science; Timestamp; Dynamic network analysis; Graph; Representation (politics); Vector space; Artificial intelligence; Mathematics; Computer network","score_opus":0.008102776568752312,"score_gpt":0.2681077697511505,"score_spread":0.2600049931823982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903329593","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":[0.003294204,0.00014818132,0.96875083,0.00041413135,0.0010659792,0.00010335662,2.2452625e-7,0.00062794867,0.025595164],"genre_scores_gemma":[0.6449018,0.00001819042,0.35121635,0.0013146431,0.0003028837,0.000006788565,9.605775e-7,0.0000145558515,0.0022238502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849933,0.00003587051,0.0001928944,0.000466871,0.00019971358,0.0006052982],"domain_scores_gemma":[0.99894536,0.00008266478,0.00006314339,0.0007058908,0.000083412786,0.00011952452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016304817,0.00015415865,0.00014548918,0.000056059842,0.00027712574,0.00013542255,0.00093898905,0.00006357204,0.00006106209],"category_scores_gemma":[0.000014194636,0.00013379424,0.000057757134,0.00092858955,0.00010531832,0.00070867647,0.00043254162,0.0001443443,0.00040098972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002550868,0.000086218686,0.0016046283,0.000012560642,0.00006231782,0.00006149849,0.00028930005,0.054363254,0.0014615683,0.43021843,0.06606928,0.44574544],"study_design_scores_gemma":[0.0001307808,0.00007940767,0.00035975617,0.000017481056,0.0000021654844,0.00002119911,0.000004052027,0.94433093,0.00021987727,0.041614734,0.013009427,0.0002101668],"about_ca_topic_score_codex":0.0000055723503,"about_ca_topic_score_gemma":0.000042089512,"teacher_disagreement_score":0.8899677,"about_ca_system_score_codex":0.000034281937,"about_ca_system_score_gemma":0.000018031024,"threshold_uncertainty_score":0.545597},"labels":[],"label_agreement":null},{"id":"W2905284239","doi":"10.1609/aaai.v33i01.33012895","title":"LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Beijing Advanced Innovation Center for Big Data and Brain Computing; State Key Laboratory of Software Development Environment; National Natural Science Foundation of China","keywords":"Embedding; Locality; Knowledge base; Neighbourhood (mathematics); Computer science; Theoretical computer science; Graph; Artificial neural network; Artificial intelligence; Mathematics","score_opus":0.08924279712722161,"score_gpt":0.32619592035492556,"score_spread":0.23695312322770395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905284239","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":[0.46698532,0.00007908941,0.5174003,0.0042900993,0.0024745893,0.0023635386,0.000014238,0.00035847063,0.0060343253],"genre_scores_gemma":[0.9927219,0.000012204109,0.0065459223,0.0002750764,0.000101870675,0.00005328134,0.0000012684496,0.00001899115,0.00026948677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775124,0.000027452268,0.00059178926,0.0007034892,0.00037681824,0.0005492104],"domain_scores_gemma":[0.99790996,0.00033794326,0.0004169527,0.00042255808,0.00079608784,0.000116468815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056295947,0.00028756898,0.0003705773,0.00013932717,0.00024319626,0.00021194837,0.002027852,0.00010550171,0.00003278854],"category_scores_gemma":[0.0003182032,0.00022296137,0.00021508402,0.00074411725,0.00020910063,0.00066776917,0.00044324782,0.00035753244,0.000086497574],"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":[0.00011966418,0.00014481768,0.00034729234,0.000096415024,0.00001294722,2.0176267e-7,0.0007304174,0.0016110439,0.04563826,0.8937081,0.0002145856,0.057376243],"study_design_scores_gemma":[0.000063701365,0.00026948197,0.0001020877,0.00017632199,0.000008479627,0.0000042430474,0.00023210194,0.68294716,0.18585253,0.1299115,0.00018550485,0.0002468813],"about_ca_topic_score_codex":0.000008513103,"about_ca_topic_score_gemma":0.00000806906,"teacher_disagreement_score":0.7637966,"about_ca_system_score_codex":0.00006910216,"about_ca_system_score_gemma":0.00006345418,"threshold_uncertainty_score":0.9092101},"labels":[],"label_agreement":null},{"id":"W2905514808","doi":"10.1609/aaai.v33i01.33014360","title":"Learning Multi-Task Communication with Message Passing for Sequence Learning","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"Science and Technology Commission of Shanghai Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Sequence learning; Artificial intelligence; Graph; Multi-task learning; Machine learning; Sequence (biology); Transfer of learning; Message passing; Natural language processing; Theoretical computer science; Distributed computing","score_opus":0.07632874879394373,"score_gpt":0.30763395760968065,"score_spread":0.2313052088157369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905514808","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":[0.3383378,0.00009507726,0.65230244,0.0032416906,0.00033972214,0.0017549741,0.0000026156376,0.0004234654,0.0035022083],"genre_scores_gemma":[0.95671874,0.000055885786,0.042508524,0.00012452745,0.000020864727,0.000055822064,0.0000012704076,0.000023208358,0.0004911727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981298,0.000056021727,0.00044384124,0.0005868403,0.00035405194,0.00042944835],"domain_scores_gemma":[0.99777603,0.0003846444,0.0006520398,0.0004023417,0.00071055285,0.000074416406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069661526,0.00025879644,0.00030152337,0.00011066435,0.00045557838,0.00030504496,0.002110622,0.00009456437,0.000011406952],"category_scores_gemma":[0.0003578583,0.00019123734,0.000105854655,0.00071775774,0.00026586215,0.0008660517,0.0003974428,0.00071931863,0.000030345194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016877685,0.00011195977,0.002449661,0.00009990965,0.000028630207,4.5687455e-7,0.0026556863,0.014326004,0.15985008,0.64075285,0.000016087119,0.1795399],"study_design_scores_gemma":[0.00009944697,0.00059693016,0.00028286345,0.00065954204,0.000014278654,0.000006276796,0.001034025,0.77342856,0.18741563,0.035784043,0.00030488413,0.00037351678],"about_ca_topic_score_codex":0.000015247118,"about_ca_topic_score_gemma":0.000008474228,"teacher_disagreement_score":0.7591026,"about_ca_system_score_codex":0.00005424958,"about_ca_system_score_gemma":0.00006644644,"threshold_uncertainty_score":0.7798433},"labels":[],"label_agreement":null},{"id":"W2907079035","doi":"10.48550/arxiv.1901.01484","title":"LanczosNet: Multi-Scale Deep Graph Convolutional Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Graph; Scale (ratio); Artificial intelligence; Theoretical computer science; Cartography; Geography","score_opus":0.051265114663079224,"score_gpt":0.18529132808432733,"score_spread":0.1340262134212481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907079035","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":[0.019121738,0.0005135802,0.9762119,0.00006568091,0.0022656284,0.00047876508,0.00001583405,0.00050174305,0.00082512986],"genre_scores_gemma":[0.98093176,0.00046230006,0.016402187,0.00032229177,0.00019408154,0.000002411216,0.000057379184,0.000038191098,0.001589406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685264,0.00018007505,0.000293324,0.0017779351,0.00014507819,0.00075093674],"domain_scores_gemma":[0.9970763,0.00019018415,0.00037755517,0.0018670032,0.00020900856,0.00027997556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017858954,0.00053235545,0.00052221614,0.00030951723,0.0002143966,0.0001168342,0.0028312057,0.0005810281,0.000031662235],"category_scores_gemma":[0.000012490658,0.0006237194,0.00043490375,0.00097297406,0.00022653716,0.0005433225,0.0028047482,0.0013444931,0.0001661161],"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":[0.000022476537,0.000075923956,0.009610408,0.000024322248,0.00006972579,0.00013695666,0.000041881645,0.94687927,0.00000824954,0.04227466,0.00043033354,0.00042578348],"study_design_scores_gemma":[0.00072173914,0.000038715934,0.0058764587,0.0000697651,0.000038966642,0.000009982529,0.000014452889,0.9685754,0.00001062229,0.023497757,0.00047372837,0.00067240256],"about_ca_topic_score_codex":0.000045393324,"about_ca_topic_score_gemma":0.00011158556,"teacher_disagreement_score":0.96181,"about_ca_system_score_codex":0.00018180869,"about_ca_system_score_gemma":0.00011311948,"threshold_uncertainty_score":0.9996214},"labels":[],"label_agreement":null},{"id":"W2907596805","doi":"10.1109/tsipn.2018.2890231","title":"Graph-Based Compression for Distributed Particle Filters","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Information Processing over Networks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Overhead (engineering); Particle filter; Laplacian matrix; ENCODE; Graph; Algorithm; Theoretical computer science; Distributed computing; Filter (signal processing)","score_opus":0.008885015688578418,"score_gpt":0.2240665093562346,"score_spread":0.21518149366765618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907596805","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":[0.008232418,0.00005226903,0.9905754,0.000188419,0.00032027156,0.00036589688,0.000016865139,0.00019804928,0.00005045866],"genre_scores_gemma":[0.9930407,0.000013949687,0.0053858426,0.0014272553,0.000020022051,0.000060364302,0.000022892827,0.000007508521,0.000021487751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989586,0.000023700059,0.00031586378,0.00020649063,0.00020591465,0.00028945642],"domain_scores_gemma":[0.99930173,0.00013909013,0.00016052464,0.00018669575,0.000105605104,0.000106329695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011345204,0.0001685125,0.00015592271,0.000095984215,0.00033191778,0.00026624888,0.00021262343,0.00009336605,0.00002059816],"category_scores_gemma":[0.0000013260282,0.0001461887,0.00007973175,0.00048343395,0.000047287973,0.0036914758,0.0000031111013,0.00020289693,0.000007341448],"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":[0.00010995562,0.000032640484,0.00004021014,0.000042468182,0.000007542548,1.702908e-7,0.00007105571,0.8361898,0.000096652155,0.00039525668,0.00022326242,0.16279098],"study_design_scores_gemma":[0.0010480364,0.00017782731,0.00027516412,0.00010738142,0.00000951337,0.0000022954284,0.0000136442995,0.99299014,0.0033233117,0.00041790848,0.001437495,0.00019728529],"about_ca_topic_score_codex":0.0000017467434,"about_ca_topic_score_gemma":6.259377e-7,"teacher_disagreement_score":0.9851895,"about_ca_system_score_codex":0.000023048087,"about_ca_system_score_gemma":0.000028270648,"threshold_uncertainty_score":0.5961402},"labels":[],"label_agreement":null},{"id":"W2916335115","doi":"10.1137/1.9781611975673.40","title":"Edge Replacement Grammars : A Formal Language Approach for Generating Graphs","year":2019,"lang":"en","type":"preprint","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Generative grammar; Rule-based machine translation; Theoretical computer science; Probabilistic logic; Grammar; Artificial intelligence; Graph; Natural language processing; Grammar induction","score_opus":0.05247469205831674,"score_gpt":0.26549739678904605,"score_spread":0.21302270473072932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916335115","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":[0.0075480817,0.00010677389,0.98388034,0.000044758453,0.0005732994,0.0060756016,0.0001113287,0.00023049148,0.0014293059],"genre_scores_gemma":[0.013240695,0.000010690494,0.9820893,0.00024195288,0.00068405084,0.002599807,0.00018436831,0.0000745597,0.00087456696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972695,0.000011830298,0.0006992823,0.0009545838,0.0003244943,0.0007403113],"domain_scores_gemma":[0.9979325,0.00033520424,0.00065064913,0.00086255325,0.00007670441,0.00014237997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009238772,0.0005455662,0.00074512046,0.000057078516,0.0004270482,0.00035675822,0.0008390068,0.0007721074,5.9532624e-7],"category_scores_gemma":[0.000027809378,0.00048704536,0.00079211954,0.000082866136,0.00010682032,0.000086510336,0.001387289,0.0008057928,4.3574826e-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":[0.00013348812,0.00020162367,0.0000025732868,0.0030880538,0.00071088556,4.979285e-7,0.012563824,0.008509192,0.002328483,0.8658638,0.010913546,0.09568401],"study_design_scores_gemma":[0.0050640134,0.00028122708,1.4470073e-7,0.00021528997,0.00025882028,0.000009083681,0.0022899704,0.72902995,0.0037948233,0.2559551,0.0017851834,0.0013163756],"about_ca_topic_score_codex":0.0000022575018,"about_ca_topic_score_gemma":6.9572985e-7,"teacher_disagreement_score":0.7205208,"about_ca_system_score_codex":0.00006144236,"about_ca_system_score_gemma":0.00013243186,"threshold_uncertainty_score":0.9997581},"labels":[],"label_agreement":null},{"id":"W2929664959","doi":"","title":"Cultural Differences and Latent Profiles from the InCLASS Observational Tool","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Observational study; Computer science; Statistics; Mathematics","score_opus":0.034723691066284,"score_gpt":0.23922692782903954,"score_spread":0.20450323676275553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929664959","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":[0.62146795,0.002266294,0.3418264,0.030942269,0.00061061257,0.00077482226,0.000100139405,0.00029636943,0.0017151304],"genre_scores_gemma":[0.9002739,0.0008710544,0.09578728,0.00044379372,0.000053188123,0.00009694985,0.00030425086,0.000021642643,0.0021479358],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99523556,0.0025522113,0.00043733453,0.00093596795,0.0005222488,0.0003166738],"domain_scores_gemma":[0.9933029,0.0029511156,0.00047953028,0.0020811742,0.0010898709,0.00009541063],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014358211,0.00034502853,0.00033126856,0.00005406354,0.0004173918,0.00096054544,0.0027338068,0.00022183292,0.000025706006],"category_scores_gemma":[0.00056912965,0.0002500714,0.00014111985,0.0003001848,0.00028324206,0.00042791426,0.0041403617,0.00077372894,0.000019487243],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019092031,0.00035590248,0.22700886,0.00012314238,0.00023190539,0.000007770449,0.015415203,0.0010328974,0.0016557572,0.62539345,0.004678145,0.124077894],"study_design_scores_gemma":[0.0005044863,7.563052e-7,0.61828303,0.0014835153,0.0000362819,0.000007957827,0.00007665764,0.29687348,0.0028555107,0.073982514,0.0051173135,0.0007784959],"about_ca_topic_score_codex":0.00054218393,"about_ca_topic_score_gemma":0.0004432871,"teacher_disagreement_score":0.5514109,"about_ca_system_score_codex":0.000056206936,"about_ca_system_score_gemma":0.00015055633,"threshold_uncertainty_score":0.9999952},"labels":[],"label_agreement":null},{"id":"W2942315111","doi":"10.1613/jair.1.13225","title":"Graph Kernels: A Survey","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Graph; Theoretical computer science; Data science","score_opus":0.2937579756576601,"score_gpt":0.4530306973098932,"score_spread":0.1592727216522331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942315111","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":[0.059329897,0.0014847753,0.936141,0.0016016816,0.00096452096,0.0000914292,0.0000019643035,0.000024961455,0.00035974357],"genre_scores_gemma":[0.96978205,0.00091896503,0.028642768,0.00011109941,0.0003663133,0.0000024792387,8.875746e-7,0.000015208375,0.00016025337],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954671,0.0012297417,0.00082410686,0.00034506587,0.0014729406,0.0006610096],"domain_scores_gemma":[0.9936244,0.0018259643,0.00023073702,0.0005942302,0.0034167636,0.00030790313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005932336,0.00013130631,0.00029232373,0.0005245474,0.00024718078,0.00040113588,0.0016731912,0.0000953419,0.000072476134],"category_scores_gemma":[0.0024172936,0.00011376804,0.00019528136,0.0038840226,0.0002445156,0.00081787654,0.0004490283,0.0012522106,0.00009820436],"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":[0.00019109604,0.00063070154,0.0021830015,0.00002463211,0.00010331111,0.0027953887,0.001025244,0.008257848,0.023881325,0.17598864,0.003962173,0.7809566],"study_design_scores_gemma":[0.00007061201,0.00073600997,0.0033807813,0.00016380758,0.0000060689545,0.0007620552,0.00070636364,0.030561144,0.28168735,0.6793451,0.0022101146,0.0003706135],"about_ca_topic_score_codex":0.000031824227,"about_ca_topic_score_gemma":0.00014895975,"teacher_disagreement_score":0.9104521,"about_ca_system_score_codex":0.00007678657,"about_ca_system_score_gemma":0.00050692214,"threshold_uncertainty_score":0.54403013},"labels":[],"label_agreement":null},{"id":"W2948598318","doi":"10.48550/arxiv.1906.02174","title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Theoretical computer science; Graph; Deep learning; Expressive power; Convolution (computer science); Artificial intelligence; Artificial neural network","score_opus":0.052558334547358046,"score_gpt":0.18686765448412118,"score_spread":0.13430931993676315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948598318","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":[0.022435406,0.0005441494,0.97322285,0.00016583485,0.0021606595,0.00058403285,0.000013605311,0.00033902217,0.0005344425],"genre_scores_gemma":[0.9911673,0.00032302053,0.006454012,0.0003002855,0.00022456284,0.0000029690439,0.000034059416,0.000034552853,0.0014592466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689394,0.00027017677,0.00029909523,0.0016098174,0.00017231177,0.0007546559],"domain_scores_gemma":[0.99659073,0.0003577372,0.0003989464,0.002199211,0.0002351601,0.00021823891],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000276925,0.00052461756,0.00043368424,0.00020759169,0.00037933618,0.00016001654,0.0036820436,0.0004756776,0.000025344543],"category_scores_gemma":[0.000015038834,0.00047899957,0.00048922235,0.000929295,0.00038668045,0.0004943002,0.0031947214,0.0016467078,0.0000866664],"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":[0.000021615084,0.000065218745,0.0060458416,0.000013564506,0.0000840256,0.000058463735,0.00006559512,0.9542232,0.0000060776756,0.038624983,0.00033432027,0.00045710325],"study_design_scores_gemma":[0.00055650214,0.000031202893,0.0059707076,0.000049896607,0.000056785324,0.000010857565,0.00004246359,0.9769184,0.000009018684,0.0153243095,0.00049337547,0.0005364944],"about_ca_topic_score_codex":0.000053211705,"about_ca_topic_score_gemma":0.00013590943,"teacher_disagreement_score":0.9687319,"about_ca_system_score_codex":0.00017066237,"about_ca_system_score_gemma":0.00013507897,"threshold_uncertainty_score":0.9997662},"labels":[],"label_agreement":null},{"id":"W2949865801","doi":"","title":"LanczosNet: Multi-Scale Deep Graph Convolutional Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Graph; Theoretical computer science; Laplacian matrix; Deep learning; Adjacency matrix; Algorithm; Artificial intelligence","score_opus":0.051265114663079224,"score_gpt":0.18529132808432733,"score_spread":0.1340262134212481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949865801","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":[0.019121738,0.0005135802,0.9762119,0.00006568091,0.0022656284,0.00047876508,0.00001583405,0.00050174305,0.00082512986],"genre_scores_gemma":[0.98093176,0.00046230006,0.016402187,0.00032229177,0.00019408154,0.000002411216,0.000057379184,0.000038191098,0.001589406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685264,0.00018007505,0.000293324,0.0017779351,0.00014507819,0.00075093674],"domain_scores_gemma":[0.9970763,0.00019018415,0.00037755517,0.0018670032,0.00020900856,0.00027997556],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017858954,0.00053235545,0.00052221614,0.00030951723,0.0002143966,0.0001168342,0.0028312057,0.0005810281,0.000031662235],"category_scores_gemma":[0.000012490658,0.0006237194,0.00043490375,0.00097297406,0.00022653716,0.0005433225,0.0028047482,0.0013444931,0.0001661161],"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":[0.000022476537,0.000075923956,0.009610408,0.000024322248,0.00006972579,0.00013695666,0.000041881645,0.94687927,0.00000824954,0.04227466,0.00043033354,0.00042578348],"study_design_scores_gemma":[0.00072173914,0.000038715934,0.0058764587,0.0000697651,0.000038966642,0.000009982529,0.000014452889,0.9685754,0.00001062229,0.023497757,0.00047372837,0.00067240256],"about_ca_topic_score_codex":0.000045393324,"about_ca_topic_score_gemma":0.00011158556,"teacher_disagreement_score":0.96181,"about_ca_system_score_codex":0.00018180869,"about_ca_system_score_gemma":0.00011311948,"threshold_uncertainty_score":0.9996214},"labels":[],"label_agreement":null},{"id":"W2950501737","doi":"10.48550/arxiv.1905.13132","title":"Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Information retrieval; Graph traversal; Cold start (automotive); Tree traversal; Weighting; Relevance (law); Recommender system; Similarity (geometry); Graph; Set (abstract data type); Data mining; Artificial intelligence; Algorithm; Theoretical computer science","score_opus":0.09263336830495084,"score_gpt":0.20885560795810298,"score_spread":0.11622223965315215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950501737","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":[0.062141836,0.00012749272,0.9330086,0.00013626825,0.0022985344,0.00062747864,0.00003251225,0.00035966776,0.0012676211],"genre_scores_gemma":[0.995664,0.00022808551,0.002663604,0.00028670643,0.00006540446,0.0000029793916,0.00011319941,0.00003084561,0.0009451985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970495,0.00024194189,0.00033892476,0.0017313659,0.00011351905,0.00052474043],"domain_scores_gemma":[0.9970381,0.00023331789,0.0004670759,0.0017807824,0.00024972874,0.00023099827],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019763118,0.00050341187,0.00048744894,0.00033818488,0.00018691228,0.00015436028,0.0019827401,0.00033305946,0.00006706181],"category_scores_gemma":[0.000025758234,0.0005841733,0.00040658686,0.0010268097,0.00013128952,0.00085346005,0.0015655629,0.00088109035,0.000114637834],"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":[0.00019757643,0.0008666761,0.11182173,0.0002650721,0.00025669948,0.00024541235,0.00017284753,0.4491975,0.00027446888,0.41762033,0.0036082617,0.015473416],"study_design_scores_gemma":[0.0008285112,0.00007479602,0.008028877,0.00013920634,0.000058883008,0.0000017246568,0.000014369576,0.9497027,0.00013830012,0.03670383,0.0035176463,0.0007911506],"about_ca_topic_score_codex":0.00013316839,"about_ca_topic_score_gemma":0.0005705245,"teacher_disagreement_score":0.93352216,"about_ca_system_score_codex":0.00035037848,"about_ca_system_score_gemma":0.0001423928,"threshold_uncertainty_score":0.99966097},"labels":[],"label_agreement":null},{"id":"W2950887295","doi":"","title":"GMNN: Graph Markov Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Statistical relational learning; Computer science; Artificial intelligence; Conditional random field; Graph; Markov random field; Artificial neural network; Convolutional neural network; Pattern recognition (psychology); Object (grammar); Theoretical computer science; Machine learning; Relational database; Data mining","score_opus":0.0398459198662946,"score_gpt":0.17811349811419547,"score_spread":0.13826757824790087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950887295","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":[0.044802554,0.00036342398,0.94769955,0.00015829699,0.0035323815,0.0005490421,0.000008680853,0.00068203255,0.0022040547],"genre_scores_gemma":[0.9942953,0.0004434842,0.002640682,0.00050452165,0.00023293772,0.000001661892,0.000026760934,0.000047683494,0.00180697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99630076,0.0002435718,0.00032076208,0.0020958793,0.00015141358,0.00088762643],"domain_scores_gemma":[0.9960319,0.00023484629,0.0004355452,0.0028141467,0.0001685926,0.0003150128],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000205796,0.0006492778,0.0006010194,0.000398412,0.00020667212,0.0002087548,0.0041022664,0.0005898942,0.000028456803],"category_scores_gemma":[0.000017484885,0.0007442645,0.0005510569,0.0014291967,0.00018639734,0.0007722315,0.004559487,0.00175371,0.00007808149],"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":[0.00003167048,0.00003942457,0.0032879238,0.000030723986,0.0000652638,0.00042895085,0.000025621643,0.94787073,0.0000026749099,0.04473905,0.0012370761,0.0022408906],"study_design_scores_gemma":[0.00042884116,0.000063047046,0.0014041134,0.000065990374,0.000046668614,0.000014279597,0.000009735409,0.9578275,0.000007373119,0.038970143,0.00039594245,0.00076637673],"about_ca_topic_score_codex":0.000039536964,"about_ca_topic_score_gemma":0.000025836498,"teacher_disagreement_score":0.94949275,"about_ca_system_score_codex":0.00014588091,"about_ca_system_score_gemma":0.00007965995,"threshold_uncertainty_score":0.9995008},"labels":[],"label_agreement":null},{"id":"W2950907416","doi":"10.48550/arxiv.1812.02356","title":"dynnode2vec: Scalable Dynamic Network Embedding","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Embedding; Computer science; Graph embedding; Scalability; Random walk; Theoretical computer science; Timestamp; Dynamic network analysis; Graph; Representation (politics); Vector space; Artificial intelligence; Mathematics","score_opus":0.038995503682803585,"score_gpt":0.2011969557260333,"score_spread":0.16220145204322972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950907416","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":[0.05314774,0.00023076436,0.93879133,0.00008907431,0.002450306,0.00035252923,0.0000079466445,0.00081585854,0.0041144374],"genre_scores_gemma":[0.970898,0.0002953765,0.025710821,0.00023970992,0.00032099176,0.0000016573593,0.000020930343,0.00004931113,0.0024632183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963887,0.0001828087,0.00030721867,0.0019783743,0.00015229851,0.0009906288],"domain_scores_gemma":[0.9965638,0.00016794457,0.00041782367,0.0023387878,0.00021740541,0.00029425733],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002946663,0.00055856985,0.00052492716,0.00025399253,0.00041365574,0.00022599939,0.0035803495,0.00049378606,0.000036972215],"category_scores_gemma":[0.000025946476,0.00066345866,0.00032841958,0.0014902445,0.0002583551,0.0007547816,0.0050831647,0.0010765968,0.00025828058],"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":[0.000019857558,0.000034340093,0.0007055189,0.00003146186,0.00007619826,0.0002958101,0.000041162213,0.9460774,0.0000083319,0.049518894,0.0017471602,0.0014438452],"study_design_scores_gemma":[0.0002357545,0.00004635473,0.0002952194,0.00016325615,0.00003889607,0.000009604643,0.0000075188445,0.8172581,0.000011989276,0.18020947,0.0011385885,0.00058524695],"about_ca_topic_score_codex":0.000028205584,"about_ca_topic_score_gemma":0.000055577002,"teacher_disagreement_score":0.91775024,"about_ca_system_score_codex":0.00032255024,"about_ca_system_score_gemma":0.00013812924,"threshold_uncertainty_score":0.99958163},"labels":[],"label_agreement":null},{"id":"W2951774898","doi":"10.48550/arxiv.1504.01684","title":"Large Margin Nearest Neighbor Embedding for Knowledge Representation","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Key Research and Development Program of China; York University; National Science Foundation","keywords":"Embedding; Margin (machine learning); Benchmark (surveying); Computer science; k-nearest neighbors algorithm; Relation (database); Representation (politics); Simple (philosophy); False positive paradox; Artificial intelligence; Theoretical computer science; Algorithm; Machine learning; Data mining","score_opus":0.12440315866916889,"score_gpt":0.261012721967495,"score_spread":0.13660956329832608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951774898","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":[0.03810664,0.00020537277,0.95672226,0.00009629338,0.0015492504,0.0007491293,0.000043402317,0.00041428732,0.002113368],"genre_scores_gemma":[0.9865754,0.00008527722,0.010130787,0.00007581668,0.00027885652,0.0000073589104,0.00007729254,0.000037918093,0.0027312615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974541,0.00015249435,0.00024734222,0.0014615682,0.000096837386,0.00058764766],"domain_scores_gemma":[0.9972343,0.00032380008,0.0003269387,0.0014323799,0.00042001568,0.00026255104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003258871,0.00036684645,0.00039166838,0.0002942476,0.00022840411,0.00016679287,0.0018696198,0.00032165187,0.000009880523],"category_scores_gemma":[0.00012445396,0.0004377169,0.00027921685,0.00086987595,0.00007067488,0.0007433701,0.0024842464,0.0005475843,0.000058817634],"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":[0.00009811508,0.00016849724,0.0016513586,0.00012333671,0.00009579053,0.00017653462,0.0008120929,0.6392026,0.000029483817,0.34641767,0.009443675,0.0017808526],"study_design_scores_gemma":[0.0008042978,0.000049527614,0.00022976013,0.00007720668,0.000039900893,0.0000031554443,0.00009799273,0.87097454,0.000061908744,0.122661225,0.004560655,0.0004398239],"about_ca_topic_score_codex":0.00001686593,"about_ca_topic_score_gemma":0.00006816579,"teacher_disagreement_score":0.9484688,"about_ca_system_score_codex":0.0002600471,"about_ca_system_score_gemma":0.00020530545,"threshold_uncertainty_score":0.9998075},"labels":[],"label_agreement":null},{"id":"W2962850650","doi":"","title":"SimplE embedding for link prediction in knowledge graphs","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":178,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Simple (philosophy); Embedding; Computer science; Theoretical computer science; Factorization; Link (geometry); Graph embedding; Tying; Artificial intelligence; Algorithm","score_opus":0.02168659529710737,"score_gpt":0.30032031980681734,"score_spread":0.27863372450970997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962850650","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":[0.022266686,0.00038830147,0.9722034,0.00017277357,0.0021346072,0.0008684399,0.000009198498,0.00062277785,0.0013338586],"genre_scores_gemma":[0.99579924,0.0000037387524,0.0034264002,0.00018126235,0.00033346147,0.00015210386,0.000026668693,0.000009648454,0.0000674784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853015,0.00004079078,0.00065322756,0.00021807912,0.00019745427,0.00036028854],"domain_scores_gemma":[0.9988908,0.0000748382,0.00032062022,0.000251087,0.00039079925,0.00007185132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003382379,0.0001626694,0.00017950957,0.00035753546,0.00031095682,0.0005038888,0.00043984238,0.000106482395,0.0000010568687],"category_scores_gemma":[0.0000691809,0.00014985817,0.000049404658,0.0010062545,0.000052248775,0.0066866986,0.00007773079,0.00015047846,0.000026724627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009740683,0.000069523536,0.0035459807,0.0015565356,0.000018747367,0.0000020308623,0.013401231,0.08046452,0.00080122985,0.061246015,0.010229834,0.82856697],"study_design_scores_gemma":[0.0004187043,0.00012252592,0.00053240097,0.0001265729,0.0000022197908,0.000017838445,0.00010172012,0.983013,0.00022232297,0.002052741,0.0132400375,0.00014993329],"about_ca_topic_score_codex":0.000007569638,"about_ca_topic_score_gemma":0.000007739756,"teacher_disagreement_score":0.97353256,"about_ca_system_score_codex":0.00006709254,"about_ca_system_score_gemma":0.000050891776,"threshold_uncertainty_score":0.6111039},"labels":[],"label_agreement":null},{"id":"W2963384510","doi":"10.1109/msp.2018.2887284","title":"Learning Graphs From Data: A Signal Representation Perspective","year":2019,"lang":"en","type":"article","venue":"IEEE Signal Processing Magazine","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":408,"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; Theoretical computer science; Inference; Viewpoints; Graph; Visualization; Topological graph theory; Data visualization; Signal processing; Perspective (graphical); Graph theory; Representation (politics); Artificial intelligence; Machine learning; Voltage graph; Mathematics; Digital signal processing; Line graph","score_opus":0.028404084733004303,"score_gpt":0.2936060278097968,"score_spread":0.26520194307679246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963384510","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":[0.06727533,0.00091221114,0.92557997,0.0005519082,0.00043837904,0.00035456326,0.000011012688,0.0006343292,0.0042422838],"genre_scores_gemma":[0.96130157,0.00001782213,0.037478257,0.00025461198,0.0002596798,0.0000078828425,0.000048590802,0.00003175877,0.0005998176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972859,0.00014247914,0.00032430142,0.0012491173,0.00055873976,0.000439454],"domain_scores_gemma":[0.99829715,0.00023719622,0.00029344708,0.000770549,0.00027981747,0.00012184444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002452544,0.00026714904,0.0002932978,0.00017521571,0.00022556011,0.0003766466,0.0014805293,0.0000925725,0.00007434527],"category_scores_gemma":[0.0000330474,0.00025678365,0.000071465554,0.0011314991,0.000089464476,0.002917942,0.00037147035,0.000592878,0.0002929926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034897716,0.00034997877,0.012832902,0.000108185224,0.00019590459,0.00028623882,0.0051609026,0.10476069,0.31414357,0.010447062,0.0047948193,0.5465708],"study_design_scores_gemma":[0.0009879373,0.0002782687,0.0028928623,0.00017449129,0.0000307355,0.000030809097,0.00027052456,0.90888184,0.0039073313,0.080948435,0.0009751739,0.00062160013],"about_ca_topic_score_codex":0.0000481838,"about_ca_topic_score_gemma":0.000013043457,"teacher_disagreement_score":0.8940263,"about_ca_system_score_codex":0.00006016209,"about_ca_system_score_gemma":0.00012922952,"threshold_uncertainty_score":0.99998844},"labels":[],"label_agreement":null},{"id":"W2963482403","doi":"","title":"Learning Graph Weighted Models on Pictures.","year":2018,"lang":"en","type":"article","venue":"International Colloquium on Grammatical Inference","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Generalization; Computer science; Graph; Theoretical computer science; Automaton; Artificial intelligence; Mathematics; Discrete mathematics; Combinatorics; Algorithm","score_opus":0.0283849953839748,"score_gpt":0.3009206295663356,"score_spread":0.27253563418236076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963482403","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":[0.02762251,0.0000067765495,0.9313788,0.0023915877,0.0015520358,0.00023601673,0.000004387723,0.0004545982,0.036353324],"genre_scores_gemma":[0.9759546,0.000013410865,0.020973142,0.0020419536,0.00030456978,0.000042652984,0.000009177559,0.000017786333,0.0006427205],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747527,0.00011719131,0.00041231178,0.0006311976,0.00091496523,0.00044905464],"domain_scores_gemma":[0.9980078,0.00070015225,0.00015754017,0.00052571524,0.0004005455,0.00020824773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019485733,0.00028678332,0.00022915012,0.00030026873,0.00024097129,0.00030535978,0.0016295037,0.00013134384,0.0001051353],"category_scores_gemma":[0.00029939332,0.00024191993,0.00012286718,0.0006106619,0.0002457959,0.00060762453,0.0003141981,0.0006012476,0.0003845256],"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":[0.000063568914,0.00011176554,0.00014028572,0.0000030355288,0.000031888776,0.000021801967,0.00015328193,0.008263997,0.00017483,0.9767769,0.0006002081,0.013658451],"study_design_scores_gemma":[0.00032467925,0.000714585,0.00050050183,0.00007307062,0.0000032747093,0.0000070054666,0.0000067197147,0.5505441,0.0013671402,0.44371873,0.0024849502,0.00025526452],"about_ca_topic_score_codex":0.0000085469355,"about_ca_topic_score_gemma":0.00001119774,"teacher_disagreement_score":0.9483321,"about_ca_system_score_codex":0.00008941292,"about_ca_system_score_gemma":0.00004559406,"threshold_uncertainty_score":0.9865208},"labels":[],"label_agreement":null},{"id":"W2963702033","doi":"10.1109/tsipn.2017.2742940","title":"Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laplacian matrix; Null graph; Voltage graph; Graph; Computer science; Strength of a graph; Eigenvalues and eigenvectors; Theoretical computer science; Mathematics; Algorithm; Line graph","score_opus":0.019799815997244816,"score_gpt":0.2345992929744548,"score_spread":0.21479947697721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963702033","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":[0.45862672,0.00009367281,0.54020387,0.0007262407,0.000027812286,0.000093029776,0.000023791988,0.000040219555,0.00016462558],"genre_scores_gemma":[0.89863235,0.00031132463,0.10069421,0.00005473085,0.000007443373,0.000010956939,0.00013297037,0.0000073652527,0.00014863978],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982923,0.0006737923,0.00035274655,0.00031554216,0.000238829,0.0001267726],"domain_scores_gemma":[0.9951357,0.0013772497,0.0004343387,0.00064861006,0.002341778,0.0000622877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005728815,0.00011091024,0.00016168994,0.00012081188,0.00016107742,0.000058070418,0.0005877308,0.000054977816,0.000016262531],"category_scores_gemma":[0.0007816778,0.00011128623,0.000030472607,0.00092137535,0.00027211083,0.0006155941,0.00030445188,0.000076193806,0.0000014008286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013164314,0.00036125357,0.038508933,0.0000881269,0.000025455905,5.760376e-7,0.010129022,0.00003607278,0.7608551,0.120595135,0.000032456945,0.069354735],"study_design_scores_gemma":[0.00046422973,0.0000036535857,0.34010047,0.000912073,0.000015301282,0.0000017407477,0.00006071742,0.060964465,0.56510365,0.0315027,0.000606814,0.00026415195],"about_ca_topic_score_codex":0.00013926763,"about_ca_topic_score_gemma":0.00025345676,"teacher_disagreement_score":0.44000563,"about_ca_system_score_codex":0.000007976928,"about_ca_system_score_gemma":0.00008342611,"threshold_uncertainty_score":0.45381206},"labels":[],"label_agreement":null},{"id":"W2963782635","doi":"10.17863/cam.40744","title":"Deep Graph Infomax","year":2018,"lang":"en","type":"article","venue":"Apollo (University of Cambridge)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":332,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"European Commission","keywords":"Infomax; Computer science; Graph; Artificial intelligence; Unsupervised learning; Node (physics); Machine learning; Competitive learning; Feature learning; Theoretical computer science","score_opus":0.007093247053127333,"score_gpt":0.19009235821387466,"score_spread":0.18299911116074732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963782635","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":[0.09580417,0.000072308474,0.8915676,0.00082725595,0.00033319043,0.00009517803,0.0000018470854,0.00017400565,0.011124458],"genre_scores_gemma":[0.9478074,0.000035530786,0.05057031,0.00025905028,0.000058919963,8.4132274e-8,0.0000017101963,0.0000062678187,0.0012607294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907905,0.000034434997,0.00009147411,0.00029644486,0.0002233129,0.00027526138],"domain_scores_gemma":[0.99901193,0.000049468505,0.00011759397,0.00056487165,0.00013679908,0.00011931915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008044065,0.0001136067,0.00016559723,0.00017631802,0.00022231179,0.000017683802,0.0010821783,0.0000649166,0.000019995967],"category_scores_gemma":[0.000009924687,0.00013962775,0.00011795902,0.00083585957,0.0003538836,0.0007690304,0.00037773713,0.00010820043,0.000104952094],"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":[0.00013989916,0.00017946883,0.007804719,0.000043259002,0.00018618988,0.00024367166,0.0033608535,0.00046687626,0.0045030233,0.75130564,0.038429305,0.19333711],"study_design_scores_gemma":[0.008340055,0.0030781538,0.34276196,0.00025569147,0.00019630147,0.00031879678,0.002696672,0.3437509,0.012119739,0.05162951,0.23115274,0.0036994831],"about_ca_topic_score_codex":0.00012345849,"about_ca_topic_score_gemma":0.000103803424,"teacher_disagreement_score":0.8520032,"about_ca_system_score_codex":0.000020300073,"about_ca_system_score_gemma":0.000028763994,"threshold_uncertainty_score":0.5693854},"labels":[],"label_agreement":null},{"id":"W2963885834","doi":"10.1609/aaai.v32i1.11299","title":"TIMERS: Error-Bounded SVD Restart on Dynamic Networks","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Tencent","keywords":"Singular value decomposition; Computer science; Margin (machine learning); Reset (finance); Algorithm; Bounded function; Dynamic network analysis; Singular value; Upper and lower bounds; Process (computing); Mathematical optimization; Mathematics; Machine learning","score_opus":0.06146482267875695,"score_gpt":0.31123523603661013,"score_spread":0.2497704133578532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963885834","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":[0.41134802,0.00012566154,0.5048404,0.01615683,0.006042202,0.0024545742,0.0000117451345,0.0011338714,0.057886694],"genre_scores_gemma":[0.9931258,0.000041124575,0.005236852,0.0009000125,0.00019313398,0.000025342984,6.7303307e-7,0.000026761058,0.00045027776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970735,0.00003327473,0.0006453972,0.0008865638,0.00066787854,0.0006934074],"domain_scores_gemma":[0.99773264,0.00016968325,0.00051189,0.00069184747,0.0007585818,0.00013532896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045295293,0.00038500477,0.0003620001,0.00018874588,0.000443421,0.0003136184,0.0032411828,0.00016702016,0.000071642535],"category_scores_gemma":[0.00026871898,0.00029362913,0.00017651274,0.0013616725,0.00089090347,0.00060270826,0.00056551583,0.0006223216,0.0002938427],"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":[0.00018743798,0.0001490439,0.00006108017,0.000012357246,0.00002127385,0.0000015162984,0.0005158018,0.00090290525,0.0071715848,0.9271912,0.0007101653,0.06307565],"study_design_scores_gemma":[0.000041902465,0.0008457993,0.00019713315,0.00032031216,0.000012122326,0.0000075504545,0.00014622844,0.57313174,0.07987756,0.3447128,0.00031022937,0.00039660468],"about_ca_topic_score_codex":0.000015145694,"about_ca_topic_score_gemma":0.00003436308,"teacher_disagreement_score":0.5824784,"about_ca_system_score_codex":0.00008331666,"about_ca_system_score_gemma":0.00007254721,"threshold_uncertainty_score":0.9999516},"labels":[],"label_agreement":null},{"id":"W2964105372","doi":"10.1109/tsp.2017.2752689","title":"On the Shift Operator, Graph Frequency, and Optimal Filtering in Graph Signal Processing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graph energy; Adjacency matrix; Laplacian matrix; Spectral graph theory; Mathematics; Voltage graph; Line graph; Algorithm; Graph; Discrete mathematics; Computer science","score_opus":0.025219486412952064,"score_gpt":0.2665720012858287,"score_spread":0.24135251487287662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964105372","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":[0.12237931,0.00025202765,0.8757675,0.00080745603,0.00013028643,0.0002656698,0.000003786646,0.00015185199,0.0002421097],"genre_scores_gemma":[0.98700225,0.000023391169,0.012325776,0.00046274424,0.000051769002,0.000080548125,3.6427562e-7,0.00003459322,0.00001857181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976649,0.0001000951,0.00039536288,0.0007979823,0.00045414397,0.0005874761],"domain_scores_gemma":[0.9987877,0.00019445167,0.00023659441,0.0005516753,0.00007964365,0.00014993752],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003917146,0.00038242748,0.00028850808,0.00032283305,0.0024494817,0.0014123882,0.0012394866,0.0001336377,0.000018757599],"category_scores_gemma":[0.0000062283443,0.00029807884,0.00010568251,0.0005606972,0.00036725984,0.0023451038,0.000016871189,0.0009141908,0.0000054015554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001595428,0.0003303059,0.0003142688,0.00015515485,0.000039025235,0.00017673262,0.0026509115,0.19348414,0.015973277,0.00196506,0.000018481103,0.7847331],"study_design_scores_gemma":[0.002470583,0.00095948426,0.0039386493,0.0030417251,0.00006523603,0.00017419235,0.00030387644,0.84356165,0.07815627,0.06519718,0.00004207994,0.0020890892],"about_ca_topic_score_codex":0.000026551334,"about_ca_topic_score_gemma":0.000051344723,"teacher_disagreement_score":0.86462295,"about_ca_system_score_codex":0.000047708963,"about_ca_system_score_gemma":0.0001070586,"threshold_uncertainty_score":0.99994713},"labels":[],"label_agreement":null},{"id":"W2964151357","doi":"","title":"Graph Partition Neural Networks for Semi-Supervised Classification","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Toronto","funders":"","keywords":"Computer science; Graph partition; Artificial neural network; Partition (number theory); Graph; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Machine learning; Mathematics; Combinatorics","score_opus":0.07654905036643554,"score_gpt":0.20110522936097025,"score_spread":0.12455617899453471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964151357","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":[0.117442906,0.000020104852,0.88100606,0.00016990444,0.00047997647,0.0002625686,0.0000022434385,0.00027746128,0.00033876463],"genre_scores_gemma":[0.99627274,0.000031224914,0.0028570124,0.00037013393,0.0002283979,0.0000030465349,0.000012962666,0.000013355664,0.00021114298],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986693,0.00006962432,0.00014780012,0.0006567133,0.000056595836,0.0003999301],"domain_scores_gemma":[0.9987946,0.00012233053,0.00011754537,0.0006308679,0.00019610353,0.00013853522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012326724,0.00016571702,0.00013710863,0.0001264199,0.00033012155,0.00006302915,0.0007344084,0.00010772628,0.000010323972],"category_scores_gemma":[0.000017690334,0.00018376605,0.00012867985,0.0011249031,0.00016749752,0.0009599713,0.00012537817,0.00013298234,0.000020358488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016074374,0.00010987822,0.0057145106,0.0000140357715,0.000042173553,0.000030372506,0.00013283479,0.28517395,0.00086051563,0.6921985,0.0027120067,0.01285049],"study_design_scores_gemma":[0.0004935888,0.00016227242,0.0023927214,0.000008225832,0.000015556456,0.0000031952172,0.00002077806,0.96337646,0.00016817164,0.03255912,0.00059061823,0.0002092834],"about_ca_topic_score_codex":0.0000048973143,"about_ca_topic_score_gemma":0.00002596913,"teacher_disagreement_score":0.87882984,"about_ca_system_score_codex":0.000047822952,"about_ca_system_score_gemma":0.00001476652,"threshold_uncertainty_score":0.7493762},"labels":[],"label_agreement":null},{"id":"W2964626625","doi":"10.24963/ijcai.2019/725","title":"Unsupervised Embedding Enhancements of Knowledge Graphs using Textual Associations","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Embedding; Knowledge graph; Graph; Artificial intelligence; Scope (computer science); Task (project management); Natural language processing; Graph embedding; Information retrieval; Theoretical computer science","score_opus":0.026127218445207878,"score_gpt":0.3061260201468075,"score_spread":0.27999880170159963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964626625","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":[0.53735965,0.00015197687,0.45429164,0.000027857564,0.00044976556,0.00023077014,0.0000028640734,0.00012379771,0.0073616453],"genre_scores_gemma":[0.935451,0.000009948178,0.06398471,0.00007235929,0.00001661837,0.0000025797117,0.0000025099575,0.000007886223,0.00045244483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989223,0.00004859212,0.00026747698,0.00029040818,0.00020126531,0.0002699756],"domain_scores_gemma":[0.9991572,0.00014041849,0.00013066606,0.0003958189,0.000121359466,0.00005453513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013275623,0.000115533425,0.00018803393,0.00014038768,0.00007613567,0.000028522209,0.00054091745,0.000053086056,0.00005429239],"category_scores_gemma":[0.000018317303,0.00010866274,0.00008737805,0.000838752,0.00002347247,0.00057421473,0.00022832051,0.00009877294,0.00004863867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000097868815,0.00041708615,0.06412407,0.00005329701,0.00015769475,0.0000031986287,0.0018451558,0.021893749,0.16930842,0.7167546,0.0005813041,0.024851624],"study_design_scores_gemma":[0.00078143744,0.000109111126,0.0076883524,0.000064778884,0.000012401805,0.0000024682392,0.000087208275,0.9587426,0.02067065,0.0112367375,0.00024340891,0.00036082318],"about_ca_topic_score_codex":0.0000106176385,"about_ca_topic_score_gemma":0.000007229858,"teacher_disagreement_score":0.9368489,"about_ca_system_score_codex":0.00004107958,"about_ca_system_score_gemma":0.000036614103,"threshold_uncertainty_score":0.44311377},"labels":[],"label_agreement":null},{"id":"W2964767742","doi":"10.1145/3292500.3330848","title":"AutoNE","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hyperparameter; Computer science; Machine learning; Artificial intelligence; Node (physics)","score_opus":0.00355252685016396,"score_gpt":0.19966897883946264,"score_spread":0.19611645198929867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964767742","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":[0.02447686,0.00003449521,0.903694,0.00083065644,0.0004644191,0.00007688276,4.4781395e-8,0.00038911635,0.07003358],"genre_scores_gemma":[0.88059455,0.0000026447487,0.10917062,0.001173336,0.000018144021,0.0000014608387,1.4920765e-7,0.0000028309655,0.009036239],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960387,0.0000060839234,0.000047529535,0.00014872402,0.000076128585,0.00011769122],"domain_scores_gemma":[0.9995871,0.00002474624,0.000012461701,0.00033536484,0.000010650324,0.000029640818],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000027035028,0.000037859874,0.000043015156,0.000020885986,0.0000135225755,0.000022785976,0.00036092915,0.000014683588,0.00008588982],"category_scores_gemma":[0.0000017541654,0.000029779418,0.000019567622,0.00018777327,0.000005905715,0.00028848852,0.00011265813,0.000045559285,0.0009705549],"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":[7.376062e-7,0.000009952687,0.0017874602,0.0000015425646,0.0000025025815,0.000004347634,0.000029361885,0.0009808098,0.0016297601,0.9460831,0.002557313,0.04691311],"study_design_scores_gemma":[0.00069001794,0.00019631331,0.021650497,0.000013582543,0.000001495064,0.00004123578,0.000009944408,0.7190513,0.009895403,0.13362688,0.114274345,0.0005490056],"about_ca_topic_score_codex":0.0000012346818,"about_ca_topic_score_gemma":6.180741e-7,"teacher_disagreement_score":0.8561177,"about_ca_system_score_codex":0.000004415147,"about_ca_system_score_gemma":0.000004783434,"threshold_uncertainty_score":0.9998073},"labels":[],"label_agreement":null},{"id":"W2966459188","doi":"10.24963/ijcai.2019/268","title":"TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality","keywords":"Embedding; Computer science; Knowledge graph; Scalability; Theoretical computer science; Graph; Semantics (computer science); Natural language processing; Artificial intelligence; Programming language; Database","score_opus":0.019427653600077865,"score_gpt":0.29096747698190323,"score_spread":0.27153982338182536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966459188","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":[0.0026301078,0.00016931926,0.9921805,0.0006092403,0.0005637279,0.0004006077,0.000009881688,0.00031541154,0.003121188],"genre_scores_gemma":[0.48028326,0.000022771063,0.5152469,0.00021109755,0.000053169995,0.00003557837,0.000027108486,0.000017426735,0.0041026487],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990122,0.000026741873,0.00020126297,0.0003678785,0.00012154832,0.00027042243],"domain_scores_gemma":[0.99909836,0.00039093877,0.000051211195,0.00029011274,0.000097457596,0.000071925126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009854264,0.0001306034,0.00014080042,0.00009478035,0.00018288425,0.000051718245,0.0003697295,0.00006130763,0.000056611596],"category_scores_gemma":[0.000007671435,0.00012222897,0.00012302308,0.0004559511,0.000031631796,0.0004616047,0.000049703824,0.000115236406,0.00008373745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028509365,0.0003865324,0.0037727752,0.00006224909,0.000100505655,0.000001390516,0.0009112876,0.04528391,0.04095864,0.6762626,0.19177231,0.040459245],"study_design_scores_gemma":[0.00059818494,0.00006086098,0.0006685545,0.000014104278,0.000004262198,0.000006312472,0.000017135571,0.8955997,0.0010087909,0.011278712,0.09051019,0.00023319443],"about_ca_topic_score_codex":0.0000020376613,"about_ca_topic_score_gemma":0.000008808145,"teacher_disagreement_score":0.8503158,"about_ca_system_score_codex":0.000022095788,"about_ca_system_score_gemma":0.000015137921,"threshold_uncertainty_score":0.4984353},"labels":[],"label_agreement":null},{"id":"W2967432812","doi":"10.1016/j.eswa.2019.112861","title":"A feature extraction model based on discriminative graph signals","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Classifier (UML); Graph; Machine learning; Data mining; Theoretical computer science","score_opus":0.013403235668727774,"score_gpt":0.27313873927255,"score_spread":0.25973550360382225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967432812","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":[0.00032960996,0.00022082798,0.99311566,0.0014878385,0.00011724223,0.0015745284,0.0000070785436,0.00028390638,0.0028633282],"genre_scores_gemma":[0.94203514,0.00000824863,0.05334757,0.0005642437,0.00008336241,0.0026477224,0.000016191892,0.000025663745,0.0012718318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855554,0.000056133435,0.00015243798,0.0006092102,0.00036676278,0.00025990346],"domain_scores_gemma":[0.99841505,0.00016079603,0.00017363505,0.0010140546,0.00012484635,0.000111604844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008767315,0.00021035722,0.00019572399,0.00015475354,0.00018165338,0.000107953885,0.0005441471,0.00008576915,0.0000026188638],"category_scores_gemma":[0.0000032938867,0.00015729468,0.000062011735,0.00069954124,0.000036720394,0.00044857746,0.000034254408,0.00021363614,0.00006959132],"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":[0.00003465631,0.00017839357,0.00009619055,0.000028747829,0.000020103784,0.0000028382865,0.00046637814,0.8574753,0.00585896,0.12710853,0.005188893,0.0035410284],"study_design_scores_gemma":[0.0003102151,0.00011533604,0.00007944748,0.000083201376,0.0000037006146,0.000010783722,0.000103510785,0.9899997,0.0007268221,0.0011104872,0.0072017205,0.00025506996],"about_ca_topic_score_codex":0.000010360477,"about_ca_topic_score_gemma":0.000003060947,"teacher_disagreement_score":0.9417056,"about_ca_system_score_codex":0.000062861436,"about_ca_system_score_gemma":0.000044817116,"threshold_uncertainty_score":0.64142907},"labels":[],"label_agreement":null},{"id":"W2971074627","doi":"","title":"Graph Normalizing Flows","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Memory footprint; Graph; Theoretical computer science; Autoencoder; Artificial neural network; Algorithm; Artificial intelligence","score_opus":0.03374732179138064,"score_gpt":0.15972172278058633,"score_spread":0.1259744009892057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971074627","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":[0.49217364,0.000035124893,0.50229144,0.000041396284,0.00039538293,0.00011345571,6.138568e-7,0.00024402612,0.0047049043],"genre_scores_gemma":[0.99528867,0.000041766343,0.0032474666,0.00023514043,0.000027407752,1.6438855e-7,0.0000011118425,0.000009303634,0.0011489921],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988726,0.000046594087,0.00009748648,0.00056419254,0.000063730025,0.00035544662],"domain_scores_gemma":[0.9989341,0.00007147324,0.000071646034,0.0007499299,0.000050962695,0.0001219036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000083546845,0.00014599276,0.00013998813,0.00015827394,0.00010702885,0.000043012697,0.0010383563,0.00006456967,0.0000386405],"category_scores_gemma":[0.000005494806,0.00016048945,0.00011471134,0.0012115554,0.0000342901,0.001118625,0.00033388328,0.00018148705,0.00043855474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017430217,0.00003547616,0.019676216,0.000010384636,0.00002438307,0.00021509305,0.00008462059,0.2909373,0.00071596826,0.6863153,0.0002933405,0.0016744857],"study_design_scores_gemma":[0.0007758602,0.00010680522,0.0035093767,0.000026579448,0.000011975011,0.000017263004,0.000044135675,0.93287677,0.00046537875,0.05714932,0.004529956,0.0004865579],"about_ca_topic_score_codex":0.000015300304,"about_ca_topic_score_gemma":0.0000151097365,"teacher_disagreement_score":0.6419395,"about_ca_system_score_codex":0.00003609761,"about_ca_system_score_gemma":0.000018613471,"threshold_uncertainty_score":0.65445703},"labels":[],"label_agreement":null},{"id":"W2972371482","doi":"10.1117/12.2530046","title":"Estimation of time-series on graphs using Bayesian graph convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural 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":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; Series (stratigraphy); Artificial intelligence; Time series; Bayesian probability; Graph; Bayesian network; Pattern recognition (psychology); Machine learning; Data mining; Theoretical computer science","score_opus":0.00927668407840442,"score_gpt":0.23229024524895425,"score_spread":0.22301356117054982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972371482","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":[0.092616975,0.000053296848,0.90559334,0.00013376352,0.00046455674,0.00021429642,0.0000014016551,0.00015434038,0.0007680133],"genre_scores_gemma":[0.9185702,0.0000055717323,0.080984525,0.00024269415,0.000025216337,0.0000030014435,0.0000069270445,0.000011040276,0.00015082445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871933,0.000058700185,0.00028362795,0.00035691302,0.00028851218,0.00029293966],"domain_scores_gemma":[0.9991121,0.00012767609,0.00016027431,0.00046192572,0.000065422944,0.00007256535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010734393,0.00017198706,0.00020850502,0.00017705663,0.000086849286,0.000042269316,0.00044475365,0.00007725559,0.000056567005],"category_scores_gemma":[0.00001023763,0.00015364587,0.000114254115,0.00073317735,0.000097456046,0.00087895314,0.000113668226,0.00015791436,0.000017456672],"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":[0.000020483229,0.000021008522,0.0012416142,0.000004931441,0.000009452206,0.0000017149216,0.000013977217,0.87407726,0.00033116704,0.11907152,0.00011226724,0.0050946036],"study_design_scores_gemma":[0.00020693438,0.0001606969,0.0015079163,0.000023133594,0.0000039582796,0.000016879463,0.0000029737726,0.9751316,0.0004855402,0.022286404,0.000010456545,0.00016352462],"about_ca_topic_score_codex":0.000009541492,"about_ca_topic_score_gemma":0.0000022335405,"teacher_disagreement_score":0.82595325,"about_ca_system_score_codex":0.000019265133,"about_ca_system_score_gemma":0.00001747021,"threshold_uncertainty_score":0.62654966},"labels":[],"label_agreement":null},{"id":"W2973697852","doi":"10.1371/journal.pone.0247936","title":"Learning temporal attention in dynamic graphs with bilinear interactions","year":2021,"lang":"en","type":"preprint","venue":"PLoS ONE","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; University of Saskatchewan; University of Guelph; Vector Institute","funders":"National Institute of Environmental Health Sciences; Defense Advanced Research Projects Agency; Canada Foundation for Innovation; Canada Research Chairs; University of Guelph; University of Saskatchewan; Government of Canada; Defense Sciences Office, DARPA; Vector Institute; Canadian Institute for Advanced Research; U.S. Department of Defense","keywords":"Computer science; Concatenation (mathematics); Node (physics); Term (time); Bilinear interpolation; Task (project management); Artificial intelligence; Feature (linguistics); Flexibility (engineering); Graph; Key (lock); Theoretical computer science; Machine learning; Mathematics","score_opus":0.02905274807875053,"score_gpt":0.2513163439872472,"score_spread":0.2222635959084967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973697852","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":[0.8773687,0.00018562509,0.12089528,0.0005714864,0.00021069785,0.0003552527,0.0000017904454,0.00029237868,0.00011877731],"genre_scores_gemma":[0.8733895,0.0001379755,0.12586066,0.00004560022,0.000031558557,0.000083060135,0.00009785485,0.000026878852,0.0003269204],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979641,0.00016417835,0.00033798243,0.00083284173,0.0003938279,0.00030708863],"domain_scores_gemma":[0.9987037,0.00008957681,0.00026089186,0.00071610743,0.00015470627,0.000075006494],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001300322,0.0002593727,0.00038552276,0.00036603634,0.00008956772,0.00021377527,0.0005929585,0.00013098256,0.000010843074],"category_scores_gemma":[0.000042813695,0.00025674593,0.00010466762,0.0008130591,0.000050226616,0.0004867537,0.0008782272,0.0018720751,0.000010114303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021313342,0.010172051,0.5100506,0.0018807412,0.0018839298,0.0016642489,0.0037660163,0.39759535,0.03432014,0.0026572957,0.00005317381,0.035743315],"study_design_scores_gemma":[0.0004784394,0.00020778172,0.023083583,0.0031481802,0.00006833783,0.00002008947,0.00013997998,0.96815383,0.0005346488,0.0034759413,0.00001969811,0.0006694846],"about_ca_topic_score_codex":0.00005915315,"about_ca_topic_score_gemma":0.00078242953,"teacher_disagreement_score":0.5705585,"about_ca_system_score_codex":0.00010753695,"about_ca_system_score_gemma":0.00007860137,"threshold_uncertainty_score":0.9999885},"labels":[],"label_agreement":null},{"id":"W2975795857","doi":"10.3389/fdata.2019.00006","title":"Attending Over Triads for Learning Signed Network Embedding","year":2019,"lang":"en","type":"article","venue":"Frontiers in Big Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Embedding; Leverage (statistics); Sign (mathematics); Enhanced Data Rates for GSM Evolution; Computer science; Node (physics); Theoretical computer science; Network topology; Topology (electrical circuits); Mathematics; Artificial intelligence; Combinatorics; Computer network; Engineering","score_opus":0.04617225981116451,"score_gpt":0.2949449555788986,"score_spread":0.2487726957677341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975795857","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":[0.003840221,0.00064640783,0.98690605,0.00012926201,0.007611869,0.0004781898,0.000013173154,0.00014696138,0.00022786876],"genre_scores_gemma":[0.22670972,0.00009388884,0.7703555,0.00043716724,0.00108366,0.00003316065,0.00024790395,0.000050470833,0.0009884913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785155,0.00009723629,0.0003109947,0.00085596833,0.00022196161,0.0006623141],"domain_scores_gemma":[0.9980276,0.0002710102,0.00016624675,0.0014356895,0.000021590278,0.00007785811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071307976,0.00019175436,0.00032732927,0.0001634031,0.00013977163,0.00013968864,0.002403982,0.00010351379,0.0000051282773],"category_scores_gemma":[0.00013686878,0.00019437964,0.000058663183,0.0007812623,0.000029756397,0.0011837513,0.0011843691,0.00033005586,0.000010066129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017042641,0.00004145643,0.155925,0.000060223345,0.00007596618,0.00002508375,0.00023087881,0.18343696,0.00029360433,0.004753369,0.24366266,0.41132438],"study_design_scores_gemma":[0.0010463737,0.00006723922,0.0010424564,0.00008447434,0.000006664484,0.0000024429412,0.00003154971,0.919406,0.00001741372,0.005374311,0.0726313,0.00028976574],"about_ca_topic_score_codex":0.000003962135,"about_ca_topic_score_gemma":0.000004223771,"teacher_disagreement_score":0.73596907,"about_ca_system_score_codex":0.00006329122,"about_ca_system_score_gemma":0.000028546898,"threshold_uncertainty_score":0.7926572},"labels":[],"label_agreement":null},{"id":"W2975968959","doi":"10.24963/ijcai.2020/303","title":"Knowledge Hypergraphs: Prediction Beyond Binary Relations","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Embedding; Binary relation; Knowledge graph; Hypergraph; Computer science; Reification (Marxism); Relation (database); Theoretical computer science; Binary number; Graph; Function (biology); Data mining; Artificial intelligence; Mathematics; Discrete mathematics","score_opus":0.02723357558528301,"score_gpt":0.2654968276851004,"score_spread":0.2382632520998174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975968959","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":[0.0011686711,0.001536797,0.93237215,0.0045080977,0.0036355273,0.0005339515,0.000025816958,0.002047201,0.05417176],"genre_scores_gemma":[0.7136543,0.00048162334,0.27880335,0.0010487224,0.0008966054,0.0002208437,0.00019529817,0.00007310595,0.0046261987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978298,0.000102969454,0.0004176179,0.0010792414,0.00025829635,0.00031205988],"domain_scores_gemma":[0.998202,0.00013702939,0.00017177963,0.0011499855,0.00012739241,0.00021180592],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010325065,0.00033866343,0.00030090017,0.0002638903,0.00019340908,0.00014919665,0.0014218867,0.00035433684,0.0000340803],"category_scores_gemma":[0.000036686593,0.00032857354,0.0002454171,0.0009543515,0.00007309637,0.0004933091,0.0027614415,0.001167667,0.0002430313],"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":[0.000020830965,0.00035010785,0.0030136332,0.00017698896,0.00024720063,0.00010273807,0.001774019,0.083820775,0.00088690757,0.60634065,0.25126022,0.052005973],"study_design_scores_gemma":[0.0002231832,0.000118672084,0.0048482036,0.00009096793,0.000033145574,0.000014859169,0.000013092527,0.6518509,0.000107968764,0.32412562,0.018060643,0.0005127764],"about_ca_topic_score_codex":0.0000056566446,"about_ca_topic_score_gemma":0.000009007361,"teacher_disagreement_score":0.7124856,"about_ca_system_score_codex":0.0000652194,"about_ca_system_score_gemma":0.00013467342,"threshold_uncertainty_score":0.9999166},"labels":[],"label_agreement":null},{"id":"W2980593384","doi":"10.1145/3350546.3352511","title":"Deep Dynamic Mixed Membership Stochastic Blockmodel","year":2019,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Web Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Dependency (UML); Feature (linguistics); Data mining; Task (project management); Artificial intelligence; Theoretical computer science","score_opus":0.045211662971375674,"score_gpt":0.3058278037402635,"score_spread":0.26061614076888784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980593384","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":[0.03653338,0.00008015886,0.9427211,0.00241515,0.0062816646,0.00054899236,0.000016601542,0.0004250134,0.010977889],"genre_scores_gemma":[0.9803586,0.00010285228,0.016221043,0.0009268385,0.00013188919,0.00006385194,0.00001812839,0.000041636627,0.002135183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99581224,0.00013056179,0.0007107385,0.0013721813,0.0012130694,0.0007612253],"domain_scores_gemma":[0.99626845,0.0006215845,0.00036672893,0.0018126842,0.0006490904,0.0002814365],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003353085,0.0005528608,0.0004232817,0.0004532522,0.00013862284,0.00037568255,0.005971218,0.00021339727,0.0008225728],"category_scores_gemma":[0.0002661598,0.00054259144,0.0002267227,0.000649003,0.00019649329,0.000932653,0.0006363776,0.000821163,0.002528936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013620722,0.00022521506,0.00020506326,0.000022632847,0.00011901507,0.00005410706,0.00042634006,0.29253826,0.0054066116,0.65360874,0.00030905605,0.046948772],"study_design_scores_gemma":[0.00021603017,0.00020113292,0.00011357741,0.00016632194,0.000008272457,0.000047484988,0.000090326444,0.8952044,0.00237094,0.10080184,0.00021783382,0.0005618281],"about_ca_topic_score_codex":0.000019073399,"about_ca_topic_score_gemma":0.000113074115,"teacher_disagreement_score":0.9438252,"about_ca_system_score_codex":0.0002606071,"about_ca_system_score_gemma":0.00022031601,"threshold_uncertainty_score":0.9997026},"labels":[],"label_agreement":null},{"id":"W2981608876","doi":"10.48550/arxiv.1910.12132","title":"Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Inference; Graph; Artificial intelligence; Random graph; Theoretical computer science; Machine learning","score_opus":0.048245072244459675,"score_gpt":0.19625729861127042,"score_spread":0.14801222636681075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981608876","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":[0.15157019,0.00029683812,0.84482825,0.00002769696,0.0021413749,0.00048110378,0.000005419538,0.00034037713,0.00030876423],"genre_scores_gemma":[0.9927769,0.00024260143,0.006149919,0.00015752162,0.00021144883,0.0000014037637,0.00003757008,0.00005706116,0.00036561594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957014,0.00035227716,0.00043435485,0.002184615,0.0002600817,0.0010672937],"domain_scores_gemma":[0.99665767,0.0003940497,0.00070781214,0.0015930666,0.00027863277,0.0003687578],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00031274758,0.00073561573,0.0007214479,0.0013048638,0.0004972917,0.00024261909,0.00280603,0.0006688525,0.000019828807],"category_scores_gemma":[0.000044112414,0.00089359534,0.0006831173,0.004201906,0.0002868742,0.0009371274,0.0029798257,0.0027075447,0.000026287044],"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":[0.000030655414,0.000046252193,0.01910001,0.000033785185,0.000098848956,0.0002490925,0.000026416079,0.96541727,0.000012000555,0.014552887,0.00008106623,0.0003517212],"study_design_scores_gemma":[0.0005643711,0.00008368537,0.0024314257,0.00009990316,0.00008348924,0.000029121844,0.000020198118,0.97122186,0.000008705042,0.024519738,0.000051038827,0.0008864898],"about_ca_topic_score_codex":0.00011467961,"about_ca_topic_score_gemma":0.000013805987,"teacher_disagreement_score":0.84120667,"about_ca_system_score_codex":0.000278336,"about_ca_system_score_gemma":0.00016570673,"threshold_uncertainty_score":0.99959326},"labels":[],"label_agreement":null},{"id":"W2981898874","doi":"10.1109/tnnls.2020.3044146","title":"Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Pooling; Decimation; Adjacency matrix; Computer science; Preprocessor; Graph; Theoretical computer science; Artificial intelligence; Pattern recognition (psychology); Algorithm","score_opus":0.017642194193401815,"score_gpt":0.24141664207928215,"score_spread":0.22377444788588033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981898874","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":[0.10612726,0.0004156747,0.8910305,0.00083859597,0.0005999289,0.00043917977,4.842901e-7,0.00051937124,0.000028986331],"genre_scores_gemma":[0.99745446,0.0001643054,0.0014888708,0.0004398136,0.0002782732,0.00007034463,0.000007557178,0.000053319945,0.00004303406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99642485,0.00089874933,0.00061557366,0.00095720496,0.0004233405,0.000680265],"domain_scores_gemma":[0.99833703,0.0007062057,0.00028471943,0.00028933588,0.00007707427,0.00030561566],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00032912442,0.00040529942,0.0004839016,0.00025045773,0.0007014863,0.0004384792,0.00037881394,0.00020290368,0.0000029206985],"category_scores_gemma":[0.00002315304,0.0003647168,0.00012247164,0.0017293517,0.000103035825,0.00092043966,0.000012406116,0.002786211,0.0000019600805],"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":[0.00017695712,0.000025561652,0.0023856673,0.000021271122,0.000022537622,0.00006130514,0.00043399754,0.9609151,0.00004232649,0.000097757846,0.000010964304,0.035806537],"study_design_scores_gemma":[0.0008676651,0.0005230828,0.0013012625,0.00012720526,0.000019627261,0.00010892645,0.00016470389,0.9964144,0.000012654865,0.000012522762,0.00006030121,0.00038760513],"about_ca_topic_score_codex":0.00008328637,"about_ca_topic_score_gemma":0.000029882,"teacher_disagreement_score":0.8913272,"about_ca_system_score_codex":0.00004067346,"about_ca_system_score_gemma":0.000013647792,"threshold_uncertainty_score":0.9998805},"labels":[],"label_agreement":null},{"id":"W2985105421","doi":"","title":"Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Euclidean geometry; Scattering; Graph; Computer science; Deep learning; Wavelet; Wavelet transform; Stability (learning theory); Theoretical computer science; Artificial intelligence; Topology (electrical circuits); Mathematics; Machine learning; Combinatorics; Physics; Geometry; Quantum mechanics","score_opus":0.10125462123964325,"score_gpt":0.18597146810336548,"score_spread":0.08471684686372223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985105421","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":[0.061233334,0.00025362324,0.934909,0.00008676287,0.0011583188,0.000631518,0.000005975394,0.00052406517,0.0011974125],"genre_scores_gemma":[0.99688834,0.00045423923,0.002091769,0.00017353473,0.00009935974,0.0000019750826,0.000017660312,0.0000631669,0.00020995963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963168,0.00011825881,0.00033218283,0.0019426291,0.0002549119,0.0010352407],"domain_scores_gemma":[0.99713886,0.0003018031,0.00041335105,0.0017124402,0.000110966066,0.00032260473],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002458805,0.0007076233,0.00066877174,0.0023503106,0.0003062009,0.0003148056,0.0028073748,0.0004448535,0.000007870101],"category_scores_gemma":[0.00001091213,0.0006893236,0.0003911642,0.0088357655,0.00020454153,0.0011524725,0.001360452,0.0016592741,0.000016352256],"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":[0.000050002247,0.000038195034,0.00843319,0.000064934036,0.000115884744,0.00032800157,0.000038679154,0.9663323,0.0000016982207,0.023245325,0.000061125465,0.0012906205],"study_design_scores_gemma":[0.0008631295,0.00019456376,0.0022198067,0.00014192968,0.00009018272,0.000033825865,0.000053293665,0.97973484,0.000015758325,0.015671425,0.000028012977,0.0009532358],"about_ca_topic_score_codex":0.00004261469,"about_ca_topic_score_gemma":0.000034271314,"teacher_disagreement_score":0.935655,"about_ca_system_score_codex":0.0005381943,"about_ca_system_score_gemma":0.00008045274,"threshold_uncertainty_score":0.99955577},"labels":[],"label_agreement":null},{"id":"W2991627008","doi":"10.1007/s00500-019-04451-z","title":"A network representation method based on edge information extraction","year":2019,"lang":"en","type":"article","venue":"Soft Computing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Fundamental Research Funds for the Central Universities; Civil Aviation University of China; National Natural Science Foundation of China","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Node (physics); Representation (politics); Cluster analysis; Field (mathematics); Data mining; Artificial intelligence; Graph; Theoretical computer science; Mathematics","score_opus":0.01626953300654038,"score_gpt":0.304752218662904,"score_spread":0.2884826856563636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991627008","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":[0.006610924,0.000012115741,0.98823446,0.00034178054,0.0013773084,0.00028837885,2.5102088e-7,0.0003983913,0.002736385],"genre_scores_gemma":[0.6428105,4.992855e-7,0.35527557,0.0016717803,0.00019558349,0.0000035306043,0.000010098314,0.0000073223136,0.00002510173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986315,0.00014852852,0.00030034003,0.00030741584,0.00029999536,0.00031221713],"domain_scores_gemma":[0.99835014,0.00073665654,0.00027217716,0.00049769215,0.00008816332,0.000055157067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047968782,0.00013612321,0.00014366166,0.00012322217,0.0001686199,0.000162424,0.00035715484,0.000068089146,0.000007312135],"category_scores_gemma":[0.00007205843,0.00013764985,0.00006819019,0.000796308,0.00000928582,0.0013081393,0.00011602853,0.00025955134,0.0001591648],"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":[0.000011073464,0.000008516338,0.0015616444,0.000007146691,0.0000024611902,0.0000010415984,0.000096456926,0.79464877,0.000055189852,0.0052566445,0.00047987638,0.1978712],"study_design_scores_gemma":[0.00030707548,0.00007010855,0.005694846,0.000051124654,0.0000022806598,0.0000064634064,0.0000113061005,0.9877827,0.00019171239,0.004056207,0.0016767815,0.00014936712],"about_ca_topic_score_codex":0.00000636275,"about_ca_topic_score_gemma":5.301295e-7,"teacher_disagreement_score":0.6361996,"about_ca_system_score_codex":0.00005153519,"about_ca_system_score_gemma":0.000026146212,"threshold_uncertainty_score":0.56131977},"labels":[],"label_agreement":null},{"id":"W2991832894","doi":"10.1007/978-3-030-36802-9_61","title":"Event Prediction in Complex Social Graphs via Feature Learning of Vertex Embeddings","year":2019,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Advanced Graph Neural Networks","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 Windsor","funders":"","keywords":"Vertex (graph theory); Event (particle physics); Computer science; Feature (linguistics); Artificial intelligence; Machine learning; Theoretical computer science; Graph; Physics; Astrophysics; Linguistics; Philosophy","score_opus":0.02425630440471985,"score_gpt":0.2851043032474795,"score_spread":0.2608479988427596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991832894","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":[0.00054498966,0.00034543735,0.92937875,0.0010716435,0.00050765387,0.00079997664,0.000016695556,0.00013697452,0.06719788],"genre_scores_gemma":[0.8935001,0.0015606289,0.102645606,0.0007410945,0.000035793502,0.000025616215,0.00019317186,0.000019798652,0.0012782161],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824774,0.000049194958,0.00069245754,0.00028899626,0.00048042968,0.00024115159],"domain_scores_gemma":[0.99791604,0.00017312418,0.0005434639,0.0010242489,0.00028911539,0.000054002754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071027974,0.0002125394,0.0003226774,0.0011993006,0.00031983844,0.00016492017,0.0024319182,0.00017368663,0.000004784131],"category_scores_gemma":[0.000021361284,0.0002244769,0.000069392714,0.0008475829,0.0006286308,0.0049322783,0.0017831753,0.00082003296,0.000013247639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061377827,0.000022172131,0.00059409253,0.000052241598,0.0000065071827,3.303363e-7,0.0030917497,0.008145055,0.00003159383,0.7510686,0.00048203915,0.23649949],"study_design_scores_gemma":[0.00035473958,0.000075197204,0.01381962,0.00019317126,0.0000033045453,0.000012902632,0.000019452013,0.9428949,0.0000046639466,0.019838005,0.022551391,0.00023266308],"about_ca_topic_score_codex":0.0000070525966,"about_ca_topic_score_gemma":0.000007737981,"teacher_disagreement_score":0.93474984,"about_ca_system_score_codex":0.00013151494,"about_ca_system_score_gemma":0.0001317798,"threshold_uncertainty_score":0.91539025},"labels":[],"label_agreement":null},{"id":"W2995590401","doi":"10.1007/s41109-020-00257-3","title":"Evolving network representation learning based on random walks","year":2020,"lang":"en","type":"article","venue":"Applied Network Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Random walk; Representation (politics); Computer science; Theoretical computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.017249362561230514,"score_gpt":0.24619228134597063,"score_spread":0.2289429187847401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995590401","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":[0.0015861099,0.00013336346,0.981231,0.0017394781,0.0006631151,0.00054237334,1.9876389e-7,0.00076333643,0.013341024],"genre_scores_gemma":[0.9132525,0.000017129381,0.079423934,0.0062652077,0.00093878474,0.0000523103,0.000003267636,0.000023618559,0.000023294671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99574167,0.00012425032,0.00041733115,0.00142922,0.0010914889,0.0011960169],"domain_scores_gemma":[0.99756044,0.000806588,0.00028741593,0.0007980048,0.00011400687,0.00043351724],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011125596,0.00030886757,0.00034320357,0.00009600402,0.0012569581,0.00046940256,0.002155911,0.000082531806,0.000021672477],"category_scores_gemma":[0.00023710146,0.0002947702,0.00010374017,0.0062513216,0.00036651114,0.0008105649,0.0005600569,0.00067064713,0.00009305668],"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":[0.00007271211,0.000010453508,0.0007883995,0.0000034025372,0.0000031106854,0.000011025658,0.00014369615,0.9571252,0.00057605427,0.016725026,0.0032355469,0.021305403],"study_design_scores_gemma":[0.0008641319,0.00012347764,0.0016664702,0.00003457993,0.0000057121374,0.0000021624512,0.000014081592,0.98876756,0.00039337392,0.005727033,0.0020542087,0.0003471828],"about_ca_topic_score_codex":0.0000024253109,"about_ca_topic_score_gemma":0.0000013857765,"teacher_disagreement_score":0.91166633,"about_ca_system_score_codex":0.000069642454,"about_ca_system_score_gemma":0.0001303032,"threshold_uncertainty_score":0.99995047},"labels":[],"label_agreement":null},{"id":"W2997242078","doi":"10.1609/aaai.v34i04.6178","title":"DGE: Deep Generative Network Embedding Based on Commonality and Individuality","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Zhejiang University; China Postdoctoral Science Foundation; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Embedding; Computer science; Node (physics); Generative model; Network topology; Topology (electrical circuits); Artificial intelligence; Generative grammar; Focus (optics); Theoretical computer science; Variety (cybernetics); Machine learning; Mathematics; Computer network","score_opus":0.09933017429236297,"score_gpt":0.3111534523431887,"score_spread":0.21182327805082574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997242078","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":[0.18265764,0.000092994145,0.76306874,0.04481866,0.00073440775,0.0013282538,0.000019516749,0.00039415725,0.0068856217],"genre_scores_gemma":[0.9780357,0.000015493406,0.017399779,0.0043482296,0.00015573086,0.000021197084,8.1265887e-7,0.000012957137,0.000010125078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997659,0.00006718196,0.0005332736,0.0007326664,0.00057660794,0.00043126725],"domain_scores_gemma":[0.99840176,0.00030570448,0.00043356596,0.0003003288,0.00035488597,0.00020377508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052028557,0.00030190876,0.00035410246,0.000051875595,0.00035838547,0.00030135497,0.0016609802,0.00010296263,0.000022361106],"category_scores_gemma":[0.0004268435,0.0002328518,0.00011064001,0.00094222405,0.00037200048,0.00042645133,0.00055731885,0.0005621373,0.000015092387],"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":[0.00013778244,0.0000888348,0.0017315738,0.000038890546,0.00001733725,0.0000012729583,0.0012663152,0.019943275,0.002371599,0.9234718,0.00026037495,0.05067092],"study_design_scores_gemma":[0.000039943672,0.00028865825,0.0007550798,0.00011485831,0.000009639659,0.0000012159317,0.00010134411,0.79250103,0.053754415,0.15210538,0.00007810074,0.00025033497],"about_ca_topic_score_codex":0.000007902811,"about_ca_topic_score_gemma":0.0000068961,"teacher_disagreement_score":0.795378,"about_ca_system_score_codex":0.00003010661,"about_ca_system_score_gemma":0.000051263585,"threshold_uncertainty_score":0.9495421},"labels":[],"label_agreement":null},{"id":"W2997940961","doi":"10.1609/aaai.v34i05.6214","title":"Multi-Agent Actor-Critic with Hierarchical Graph Attention Network","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Reinforcement learning; Transferability; Artificial intelligence; Graph; Attention network; Machine learning; Representation (politics); Theoretical computer science","score_opus":0.08352121217448213,"score_gpt":0.28645645522763924,"score_spread":0.2029352430531571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997940961","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":[0.15719093,0.000067821224,0.8213138,0.018161928,0.0006619019,0.000990135,0.0000048987745,0.00038226935,0.0012263213],"genre_scores_gemma":[0.96812624,0.000039337036,0.030503519,0.0010866601,0.00015658437,0.00002912308,6.268437e-7,0.000019587986,0.0000383004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975576,0.000031811713,0.00051967637,0.00073816907,0.0006049341,0.000547848],"domain_scores_gemma":[0.9985725,0.00010224569,0.00032750375,0.00030580626,0.000454162,0.00023777764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002181491,0.00030641776,0.00031991687,0.00007086475,0.0002692723,0.00022950035,0.0021382247,0.000094243595,0.00002099354],"category_scores_gemma":[0.00018108856,0.00021383139,0.00016115255,0.0014068887,0.00041281257,0.0005429893,0.00045702895,0.00059845415,0.000049116545],"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":[0.00024498842,0.00021438288,0.0020345463,0.00007340454,0.000043436605,0.000005303037,0.0010142623,0.0025278286,0.03741798,0.9134746,0.00032563158,0.042623606],"study_design_scores_gemma":[0.00021073996,0.0016043846,0.0041034,0.00080093415,0.000059280697,0.000024390118,0.0004523215,0.64607286,0.109974295,0.23534933,0.00029861665,0.0010494248],"about_ca_topic_score_codex":0.000005988029,"about_ca_topic_score_gemma":0.0000063696334,"teacher_disagreement_score":0.8109353,"about_ca_system_score_codex":0.000026557871,"about_ca_system_score_gemma":0.000054743185,"threshold_uncertainty_score":0.87197906},"labels":[],"label_agreement":null},{"id":"W2999909502","doi":"10.1145/3341161.3343696","title":"Water governance network analysis using graphlet mining","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Corporate governance; Network analysis; Urbanization; Sophistication; Centrality; Population; Business; Popularity; Environmental economics; Environmental planning; Environmental science; Engineering; Economics; Economic growth; Political science","score_opus":0.011564453424455529,"score_gpt":0.23210036825820984,"score_spread":0.22053591483375432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999909502","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":[0.35710534,0.00011273503,0.6398869,0.00014714032,0.0004523031,0.00009082719,3.608116e-7,0.00015888919,0.0020454994],"genre_scores_gemma":[0.83343613,0.000011478581,0.16492753,0.00070879253,0.000080040554,0.0000019254078,0.000001809832,0.000009056434,0.00082324934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984308,0.000041507443,0.00021743747,0.0005021368,0.00025724003,0.0005509061],"domain_scores_gemma":[0.99899995,0.00006475499,0.00008035401,0.00073886657,0.000040312403,0.00007574589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015787409,0.00015776821,0.00025475028,0.00008359876,0.0000987149,0.000104722145,0.0007249332,0.000053410153,0.0001283422],"category_scores_gemma":[0.0000028763059,0.000111901834,0.00019411383,0.0017915203,0.00002381818,0.00066803646,0.00033251397,0.00011163935,0.000072874915],"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":[0.000006618934,0.000015228617,0.07984894,0.000004733642,0.00024534008,0.000019713812,0.00018252672,0.87920547,0.0013049609,0.03366416,0.00078233855,0.0047199945],"study_design_scores_gemma":[0.00017357685,0.000028731236,0.006713442,0.000012348493,0.000053961976,0.0000069599405,0.000008182666,0.98649925,0.0010161508,0.0030403764,0.0021280453,0.0003189685],"about_ca_topic_score_codex":0.000016494721,"about_ca_topic_score_gemma":0.00002164649,"teacher_disagreement_score":0.47633076,"about_ca_system_score_codex":0.000022847367,"about_ca_system_score_gemma":0.000007035924,"threshold_uncertainty_score":0.45632243},"labels":[],"label_agreement":null},{"id":"W3003775208","doi":"10.1109/tip.2020.2969052","title":"Point Cloud Denoising via Feature Graph Laplacian Regularization","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Laplacian matrix; Artificial intelligence; Computer science; Noise reduction; Fidelity; Algorithm; Regularization (linguistics); Graph; Mathematics; Theoretical computer science","score_opus":0.011336804226354307,"score_gpt":0.234045933363947,"score_spread":0.2227091291375927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003775208","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":[0.00048268394,0.00017032244,0.9922899,0.005506159,0.00042650325,0.0002046202,0.0000029115872,0.0006989701,0.00021793679],"genre_scores_gemma":[0.78198856,0.000019040543,0.21556629,0.0021203388,0.00015112435,0.000016502601,0.0000024079134,0.000039488845,0.00009623646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983272,0.00006954528,0.0002518031,0.00064012303,0.00033315792,0.0003781659],"domain_scores_gemma":[0.99913394,0.0000429228,0.00014384104,0.00033540596,0.00014034733,0.00020356811],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000092681694,0.00026038254,0.00020596612,0.00016048184,0.00070520386,0.0004005485,0.0005319114,0.0001210194,0.000007686279],"category_scores_gemma":[0.0000083539935,0.00026364945,0.00012393476,0.0016535168,0.00008646793,0.001985697,0.0000055991113,0.00057948526,0.000024733352],"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":[0.00012859688,0.00016988599,0.000009348397,0.0002044094,0.00004328658,0.0001124293,0.0028447965,0.06697539,0.118355125,0.00046415883,0.00054169283,0.81015086],"study_design_scores_gemma":[0.0009616503,0.0002322863,0.000041322906,0.0002502589,0.000056717792,0.00012604741,0.00010226137,0.67767626,0.31061706,0.008410759,0.00068301207,0.0008423744],"about_ca_topic_score_codex":0.0000017091121,"about_ca_topic_score_gemma":0.000004774053,"teacher_disagreement_score":0.80930847,"about_ca_system_score_codex":0.00004493123,"about_ca_system_score_gemma":0.000041754847,"threshold_uncertainty_score":0.9999816},"labels":[],"label_agreement":null},{"id":"W3006053005","doi":"10.48550/arxiv.1911.06962","title":"Inductive Relation Prediction by Subgraph Reasoning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Relation (database); Inductive reasoning; Mathematics; Computer science; Artificial intelligence; Data mining","score_opus":0.03719935338676308,"score_gpt":0.17199681477441398,"score_spread":0.1347974613876509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006053005","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":[0.18758853,0.00010476468,0.8085555,0.00005112989,0.0012263685,0.00036353825,0.000021405265,0.0004218027,0.0016669522],"genre_scores_gemma":[0.99424577,0.00023372297,0.004130384,0.00006338395,0.00008202206,0.0000013802735,0.00006785456,0.000023492224,0.0011520099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977604,0.00016785467,0.00020663854,0.0013735064,0.0001293167,0.00036228457],"domain_scores_gemma":[0.9980032,0.000103446255,0.0003993619,0.0012079339,0.00015460564,0.00013148236],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016591667,0.00032726864,0.0002831646,0.00029120682,0.00017074746,0.000091185306,0.001210265,0.00047055908,0.0000070639717],"category_scores_gemma":[0.000027823575,0.0003987704,0.00018807221,0.00096688,0.000089240406,0.0011215694,0.0011924406,0.0011108756,0.000050971812],"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":[0.00003865603,0.00005911248,0.02997504,0.00003098146,0.00010346293,0.00005904136,0.00023688789,0.785209,0.00013091892,0.18095937,0.002082546,0.0011149865],"study_design_scores_gemma":[0.0004713668,0.00008727206,0.009489422,0.00016168004,0.00005676352,0.0000053684926,0.000041431147,0.90219754,0.00014417962,0.08614918,0.0006463373,0.00054946594],"about_ca_topic_score_codex":0.0000615261,"about_ca_topic_score_gemma":0.0000072889607,"teacher_disagreement_score":0.8066572,"about_ca_system_score_codex":0.00026975034,"about_ca_system_score_gemma":0.00008568198,"threshold_uncertainty_score":0.9998464},"labels":[],"label_agreement":null},{"id":"W3006544536","doi":"10.48550/arxiv.2002.05825","title":"An Inductive Bias for Distances: Neural Nets that Respect the Triangle\\n Inequality","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Triangle inequality; Inductive bias; Subadditivity; Artificial neural network; Mathematics; Reinforcement learning; Metric space; Metric (unit); Euclidean space; Computer science; Discrete mathematics; Theoretical computer science; Artificial intelligence; Combinatorics; Multi-task learning; Task (project management)","score_opus":0.3062366027530909,"score_gpt":0.25673988863396807,"score_spread":0.04949671411912282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006544536","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":[0.18812437,0.000114539725,0.80730057,0.001484716,0.0011616511,0.0011052646,0.000073622025,0.00040948592,0.00022578736],"genre_scores_gemma":[0.9960211,0.00007423869,0.002724113,0.0007204479,0.000273394,0.000008664558,0.000034262703,0.000032325417,0.000111477864],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964417,0.00067260826,0.00030243513,0.0018572503,0.00017466054,0.00055130763],"domain_scores_gemma":[0.9960599,0.0007626651,0.00059811125,0.0021312172,0.00017701769,0.00027108655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004559425,0.00049475237,0.00057023426,0.00014198682,0.00039693384,0.000277298,0.004090452,0.00028302908,0.0000051018856],"category_scores_gemma":[0.00014025875,0.0004190579,0.00044379703,0.0010580948,0.00027571077,0.0010067318,0.0016080268,0.0010885496,0.000007016797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009128503,0.00021541686,0.0056034443,0.00012261575,0.00027581604,0.0005149437,0.0023111224,0.40931284,0.00008751904,0.5708265,0.0014652796,0.008351684],"study_design_scores_gemma":[0.0008624085,0.00026277403,0.0028783518,0.000051529583,0.0000730733,0.000003499252,0.0002858502,0.64782023,0.00015339282,0.34515905,0.0017682669,0.0006815462],"about_ca_topic_score_codex":0.00008725569,"about_ca_topic_score_gemma":0.00019449313,"teacher_disagreement_score":0.80789673,"about_ca_system_score_codex":0.00017736535,"about_ca_system_score_gemma":0.00014368244,"threshold_uncertainty_score":0.99982613},"labels":[],"label_agreement":null},{"id":"W3008125782","doi":"10.1088/2632-2153/abf5b8","title":"Neural message passing on high order paths","year":2021,"lang":"en","type":"preprint","venue":"Machine Learning Science and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Office of Naval Research; Tata Steel","keywords":"Computer science; Graph; Artificial neural network; Aggregate (composite); Theoretical computer science; Message passing; Property (philosophy); Order (exchange); Topology (electrical circuits); Artificial intelligence; Mathematics; Distributed computing; Combinatorics","score_opus":0.007794981430785009,"score_gpt":0.2462904889328129,"score_spread":0.23849550750202791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008125782","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":[0.8047325,0.002597845,0.15957034,0.026849693,0.0022907814,0.0004562129,0.000003447076,0.0026411342,0.000858044],"genre_scores_gemma":[0.9676965,0.00019008364,0.031317517,0.0005464812,0.000047424077,0.00002861691,0.0000066673674,0.000022349564,0.00014435258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964415,0.000104554565,0.00030207212,0.0017260246,0.00066620123,0.00075965544],"domain_scores_gemma":[0.9979265,0.00011970219,0.00028571373,0.0011750873,0.00037205202,0.00012097312],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00059796905,0.00038355496,0.00044902743,0.0013272475,0.0010081566,0.0010125283,0.002328638,0.00039810996,0.000008956305],"category_scores_gemma":[0.00078612816,0.00035507826,0.000050475915,0.004105814,0.0012985994,0.00044151666,0.0053336592,0.0031721292,0.00000611658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004661925,0.00007917493,0.0045526787,0.000042983487,0.000018696073,0.00046192552,0.00025327806,0.033992916,0.0052414145,0.053055782,0.000038296104,0.9022582],"study_design_scores_gemma":[0.0004482795,0.00042600473,0.0032484885,0.0002593265,0.000016946344,0.00021479714,0.00008399252,0.9586767,0.00302409,0.029274568,0.0033546085,0.0009722094],"about_ca_topic_score_codex":0.00014087641,"about_ca_topic_score_gemma":0.00005002804,"teacher_disagreement_score":0.92468375,"about_ca_system_score_codex":0.00009911234,"about_ca_system_score_gemma":0.0002870783,"threshold_uncertainty_score":0.99989015},"labels":[],"label_agreement":null},{"id":"W3009604719","doi":"10.1109/tsp.2021.3061575","title":"Graphon Filters: Graph Signal Processing in the Limit","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"King Abdullah University of Science and Technology; Natural Sciences and Engineering Research Council of Canada; Technische Universiteit Delft","keywords":"Adjacency matrix; Graph energy; Integral graph; Strength of a graph; Mathematics; Spectral graph theory; Graph; Voltage graph; Discrete mathematics; Computer science; Line graph; Algorithm","score_opus":0.02793251529424654,"score_gpt":0.26216168202481505,"score_spread":0.2342291667305685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009604719","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":[0.005332269,0.00082304375,0.9917353,0.0010540433,0.00016421007,0.00020672967,0.0000030182603,0.0002346927,0.00044673297],"genre_scores_gemma":[0.9820946,0.000060075356,0.015916273,0.00168643,0.00007396405,0.00007597035,0.0000019302204,0.000029389732,0.00006139687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972039,0.00023432268,0.00046743982,0.00079685915,0.00067519955,0.00062230235],"domain_scores_gemma":[0.9989113,0.0002093861,0.00016182387,0.00042346976,0.00018312156,0.0001108914],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035538245,0.00033963533,0.00027447855,0.0003668455,0.0007280254,0.00061822723,0.0009574781,0.00013565905,0.000025312094],"category_scores_gemma":[0.000002980536,0.00027771154,0.00020195189,0.0034680793,0.00014654714,0.0016345468,0.0000068692043,0.0009417033,0.000012056791],"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":[0.000040582614,0.0003515149,0.000024967872,0.000083919585,0.000011619657,0.00023172937,0.0019602033,0.08057478,0.0072141234,0.00013363769,0.000059479025,0.90931344],"study_design_scores_gemma":[0.0024111432,0.00059614,0.00070507044,0.001843971,0.000115481496,0.00092509255,0.002058325,0.8066639,0.14993982,0.031370156,0.0014081215,0.0019628194],"about_ca_topic_score_codex":0.0000059882764,"about_ca_topic_score_gemma":0.00004905455,"teacher_disagreement_score":0.9767623,"about_ca_system_score_codex":0.000052314,"about_ca_system_score_gemma":0.00020907196,"threshold_uncertainty_score":0.9999675},"labels":[],"label_agreement":null},{"id":"W3009964655","doi":"10.48550/arxiv.2003.03872","title":"Graph Clustering Via QUBO and Digital Annealing","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Computer science; Graph; Data science; Data mining; Theoretical computer science; Artificial intelligence","score_opus":0.05960492448063558,"score_gpt":0.17815480188883287,"score_spread":0.1185498774081973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009964655","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":[0.08095049,0.00013084315,0.91707355,0.00018073096,0.00044661216,0.00019389384,0.00001041123,0.00041821078,0.00059525616],"genre_scores_gemma":[0.99515176,0.0001777473,0.0042640516,0.00017807331,0.00009185902,4.9459067e-7,0.000010822971,0.000022944792,0.000102245205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798435,0.000046402394,0.00018571141,0.0013183393,0.00008392298,0.00038126422],"domain_scores_gemma":[0.9985888,0.00009518158,0.0001985345,0.00077513314,0.00006341365,0.000278963],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000060812392,0.00034844142,0.00033099856,0.00019246669,0.0001650036,0.00029449814,0.001321955,0.00020547103,0.0000023848406],"category_scores_gemma":[0.000017350916,0.00042001213,0.00016899151,0.000674609,0.00012883607,0.0008828818,0.004166734,0.000687343,0.00001563708],"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":[0.00005606032,0.00003885309,0.004953593,0.0001762612,0.00014123475,0.0016197757,0.00043612954,0.93326545,0.000104564104,0.045804556,0.00016631668,0.013237181],"study_design_scores_gemma":[0.00025528256,0.00004402192,0.00053904334,0.000074914315,0.000021445523,0.0000169814,0.000023208297,0.8734101,0.000028500317,0.124922745,0.00019775714,0.00046597156],"about_ca_topic_score_codex":0.000020290992,"about_ca_topic_score_gemma":0.000014017719,"teacher_disagreement_score":0.91420126,"about_ca_system_score_codex":0.000054316544,"about_ca_system_score_gemma":0.000034005916,"threshold_uncertainty_score":0.9998252},"labels":[],"label_agreement":null},{"id":"W3011257201","doi":"","title":"Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks.","year":2020,"lang":"en","type":"preprint","venue":"PubMed","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"National Institutes of Health; Institut de Valorisation des Données","keywords":"Graph; Computer science; Scattering; Artificial intelligence; Convolutional neural network; Residual; Pattern recognition (psychology); Theoretical computer science; Algorithm; Physics","score_opus":0.03333962040462993,"score_gpt":0.2303594570777287,"score_spread":0.19701983667309877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011257201","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":[0.016103432,0.0020344174,0.9698765,0.0032015794,0.0052750604,0.0017412645,0.000013736235,0.0007631643,0.0009908054],"genre_scores_gemma":[0.98776937,0.00013054161,0.00833095,0.0013956577,0.0005507688,0.0017097141,0.000025912894,0.000043314783,0.00004376818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9964964,0.00013348667,0.0005704357,0.001333525,0.00046507223,0.0010011205],"domain_scores_gemma":[0.9982119,0.00020966529,0.00032347802,0.0009098301,0.000052251973,0.00029286035],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036153998,0.0004646409,0.00056040543,0.00030859263,0.00009985511,0.00027881356,0.0020387815,0.00032616756,0.0000041638864],"category_scores_gemma":[0.000067386754,0.0005312307,0.000249832,0.0008053649,0.00012216331,0.00053074455,0.003687088,0.0014813069,0.000005418889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000071752576,0.0001024882,0.018270342,0.0002135646,0.00012436825,0.00037478516,0.00024060303,0.78209865,0.00001931892,0.044079147,0.003422876,0.1509821],"study_design_scores_gemma":[0.0008582623,0.0000100376255,0.53401214,0.0001779009,0.000017013122,0.000024245475,0.0000060972943,0.37233743,0.000047901965,0.089381695,0.0020020113,0.0011252654],"about_ca_topic_score_codex":0.000040993884,"about_ca_topic_score_gemma":0.000026295453,"teacher_disagreement_score":0.9716659,"about_ca_system_score_codex":0.00023922275,"about_ca_system_score_gemma":0.000057986585,"threshold_uncertainty_score":0.9997139},"labels":[],"label_agreement":null},{"id":"W3011643994","doi":"10.1109/camsap45676.2019.9022508","title":"Node Copying for Protection Against Graph Neural Network Topology Attacks","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Copying; Adversarial system; Graph; Artificial intelligence; Theoretical computer science; Network topology; Machine learning; Computation; Topological graph theory; Data mining; Algorithm; Computer network; Line graph","score_opus":0.020075402245770323,"score_gpt":0.2617249710921488,"score_spread":0.2416495688463785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011643994","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":[0.06391401,0.00006412029,0.9293726,0.0011859796,0.0020686397,0.0010076336,4.390842e-7,0.00041568215,0.0019708727],"genre_scores_gemma":[0.89780784,0.000012131134,0.097953886,0.003078951,0.00030392795,0.00013252384,0.0000043491623,0.000021095077,0.0006853211],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850696,0.00005297115,0.00022913989,0.0005208871,0.0001293463,0.00056069833],"domain_scores_gemma":[0.99908775,0.00013542612,0.00010865958,0.0005193452,0.00007191937,0.00007692825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015383711,0.00017226094,0.00020183664,0.00007630302,0.00018506947,0.0000709698,0.00055870466,0.00010675674,0.000015498821],"category_scores_gemma":[0.000014336654,0.00015225583,0.00012535504,0.0005170291,0.00004092848,0.0005031348,0.00017390071,0.0002089207,0.000060053113],"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":[0.00010889845,0.000057108362,0.005310767,0.000058494934,0.00004806475,0.000010118256,0.0001384342,0.71740645,0.0052207876,0.11871196,0.0073549203,0.14557399],"study_design_scores_gemma":[0.0007006838,0.00027997082,0.00077985215,0.000019797411,0.0000041424096,0.000020404244,0.000012707315,0.96652985,0.0010226341,0.023120293,0.0071526617,0.0003569921],"about_ca_topic_score_codex":0.0000071917984,"about_ca_topic_score_gemma":0.000021200609,"teacher_disagreement_score":0.83389384,"about_ca_system_score_codex":0.000023901135,"about_ca_system_score_gemma":0.000013674445,"threshold_uncertainty_score":0.62088126},"labels":[],"label_agreement":null},{"id":"W3012073743","doi":"10.2196/17645","title":"A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Embedding; Probabilistic logic; Representation (politics); Medical knowledge; Knowledge graph; Knowledge representation and reasoning; Graph","score_opus":0.027764589076377275,"score_gpt":0.34304966760358213,"score_spread":0.31528507852720483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012073743","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":[0.001474217,0.00004872009,0.9948377,0.0020030641,0.00016051457,0.00046447385,0.0000014054145,0.00023546902,0.00077441405],"genre_scores_gemma":[0.007737593,0.000010196404,0.9872778,0.004711881,0.000121285724,0.00009736105,0.0000047186372,0.000014374601,0.000024809899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961953,0.00011284158,0.0011892985,0.00024813306,0.0017531473,0.0005012381],"domain_scores_gemma":[0.9973005,0.0004978039,0.000222476,0.00033985317,0.00016770446,0.0014716843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009529377,0.00023725812,0.00046586932,0.00016032546,0.00009067182,0.00004448446,0.0017519067,0.00023305486,0.00008678667],"category_scores_gemma":[0.0007219119,0.0001925183,0.000102610255,0.0015775313,0.00012495041,0.00038958967,0.0011677303,0.0006559003,0.00010943139],"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":[0.0000071621066,0.000091337526,0.000013490524,0.00025314078,0.000028723543,0.000025262785,0.020861452,0.00042258255,0.0000046370715,0.004868911,0.0027667137,0.9706566],"study_design_scores_gemma":[0.0004999027,0.00019574085,0.00004070117,0.00028636106,0.0000052183536,0.00004712322,0.00028311924,0.9567486,0.00024570592,0.001014468,0.040361628,0.00027141426],"about_ca_topic_score_codex":9.4890845e-7,"about_ca_topic_score_gemma":0.0000024895123,"teacher_disagreement_score":0.9703852,"about_ca_system_score_codex":0.000044057713,"about_ca_system_score_gemma":0.000589749,"threshold_uncertainty_score":0.7850669},"labels":[],"label_agreement":null},{"id":"W3012568466","doi":"10.1145/3366423.3380134","title":"Deep Adversarial Completion for Sparse Heterogeneous Information Network Embedding","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Adversarial system; Computer science; Embedding; Artificial intelligence; Theoretical computer science","score_opus":0.024173432701773405,"score_gpt":0.24932868293784682,"score_spread":0.2251552502360734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012568466","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":[0.00065902586,0.000028593819,0.99668646,0.0009900783,0.00060598337,0.00037002793,0.0000012691091,0.00034448682,0.0003141021],"genre_scores_gemma":[0.5754687,0.000008660883,0.41785178,0.0060209576,0.0005778532,0.000028558627,0.0000298221,0.000008136596,0.0000054862426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990379,0.000023704008,0.00026258404,0.00020699603,0.00016208924,0.00030672364],"domain_scores_gemma":[0.99940026,0.00009929163,0.00011119879,0.00019391853,0.00007047266,0.00012485267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007840449,0.00012079139,0.00013871986,0.00002785777,0.0001575695,0.00012559067,0.00041670562,0.000051979412,0.000011550961],"category_scores_gemma":[0.00003917721,0.000116303694,0.00008534154,0.0003127069,0.000016708345,0.0012638656,0.00016044376,0.000083778905,0.000046518886],"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":[0.000040413088,0.000004045689,0.000027768656,0.000008633928,0.000009186716,0.0000014986053,0.00021659984,0.9420438,0.000029576835,0.021987524,0.0034927027,0.032138273],"study_design_scores_gemma":[0.00043348846,0.00010115439,0.000033058117,0.0000041934627,0.0000040574623,0.0000063354737,0.0000070974497,0.9693482,0.00012924115,0.0034495455,0.02634347,0.00014019113],"about_ca_topic_score_codex":0.000001601181,"about_ca_topic_score_gemma":0.0000043262066,"teacher_disagreement_score":0.57883465,"about_ca_system_score_codex":0.00002106322,"about_ca_system_score_gemma":0.000010815042,"threshold_uncertainty_score":0.4742727},"labels":[],"label_agreement":null},{"id":"W3014017986","doi":"10.1137/1.9781611976236.55","title":"A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs","year":2020,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institutes of Health; National Science Foundation","keywords":"Prior probability; Constraint (computer-aided design); Computer science; Node (physics); Key (lock); Encoding (memory); Range (aeronautics); Theoretical computer science; Artificial intelligence; Semi-supervised learning; Machine learning; Mathematics","score_opus":0.0552653009393757,"score_gpt":0.26602612358051253,"score_spread":0.21076082264113682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014017986","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":[0.00006211518,0.000019094094,0.9760809,0.0001963315,0.0003350482,0.0037870964,0.000113189555,0.00030403092,0.01910222],"genre_scores_gemma":[0.008861908,0.000069348025,0.9517561,0.0007181267,0.001771925,0.0008624954,0.00022358217,0.0003383341,0.03539818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977581,0.0000071710483,0.00060198706,0.00083245704,0.00037637693,0.0004238996],"domain_scores_gemma":[0.99697906,0.0016792474,0.00065254123,0.00038065168,0.00011456331,0.00019395941],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022833211,0.0006430047,0.0008213572,0.000069526184,0.00051179033,0.00021265836,0.00054916897,0.0012263947,0.0000031427821],"category_scores_gemma":[0.00007514705,0.0005983777,0.0006991678,0.000083276915,0.00014544271,0.00006858827,0.0002237284,0.0013089188,0.000003613322],"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":[0.00006640142,0.000013972332,1.06957216e-7,0.0002260505,0.00020247661,4.5457858e-7,0.0020332942,0.00018666386,0.0001860221,0.9872134,0.00092377665,0.008947387],"study_design_scores_gemma":[0.0016088996,0.0003794169,4.3441045e-8,0.00040016044,0.00013903112,0.0000010646836,0.0002183514,0.013678602,0.0010993851,0.9733742,0.008455041,0.00064579205],"about_ca_topic_score_codex":3.882767e-7,"about_ca_topic_score_gemma":2.6947058e-7,"teacher_disagreement_score":0.024324775,"about_ca_system_score_codex":0.0000611488,"about_ca_system_score_gemma":0.000095072566,"threshold_uncertainty_score":0.9996468},"labels":[],"label_agreement":null},{"id":"W3016358231","doi":"10.48550/arxiv.2004.08532","title":"DGL-KE: Training Knowledge Graph Embeddings at Scale","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Knowledge graph; Graph; Training (meteorology); Scale (ratio); Computer science; Mathematics; Artificial intelligence; Theoretical computer science; Physics; Geography; Cartography","score_opus":0.1089707518289814,"score_gpt":0.20839990224725885,"score_spread":0.09942915041827745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016358231","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":[0.15448348,0.00026213602,0.8341252,0.00029738178,0.0014457274,0.0004033855,0.000017344033,0.0010413121,0.007924038],"genre_scores_gemma":[0.98993677,0.00021165214,0.006685572,0.00026949108,0.00019514194,0.0000020151228,0.000020102942,0.000047356552,0.0026318699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965072,0.00016178725,0.00030402277,0.002156953,0.00013672939,0.00073325145],"domain_scores_gemma":[0.9973797,0.00018941304,0.0003558466,0.0014351838,0.00014816504,0.0004916427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016874677,0.00057926576,0.0006076798,0.0003449059,0.00036291007,0.00013151237,0.0032356558,0.00041739357,0.000032821514],"category_scores_gemma":[0.000030274214,0.0007016659,0.0005059054,0.0016872606,0.00024694816,0.00058317976,0.005879243,0.0012058532,0.00022957448],"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":[0.00018281669,0.00027305234,0.0048216823,0.00038626904,0.00043406145,0.002383065,0.009151061,0.48571384,0.0009087172,0.47006622,0.010358188,0.015321038],"study_design_scores_gemma":[0.00080817565,0.000105296196,0.000897398,0.00023326628,0.00009949986,0.00002569079,0.00016823626,0.84112185,0.0004010859,0.15002929,0.0047548627,0.0013553598],"about_ca_topic_score_codex":0.000015592865,"about_ca_topic_score_gemma":0.00006773575,"teacher_disagreement_score":0.83545333,"about_ca_system_score_codex":0.00027274434,"about_ca_system_score_gemma":0.00015071037,"threshold_uncertainty_score":0.9995434},"labels":[],"label_agreement":null},{"id":"W3021155905","doi":"10.3760/cma.j.cn121430-20200225-00200","title":"[Visualization analysis on treatment of coronavirus based on knowledge graph].","year":2020,"lang":"en","type":"article","venue":"PubMed","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Visualization; Subject (documents); Web of science; Field (mathematics); Chinese science; Data science; Bibliometrics; Medicine; Library science; Computer science; Data mining; Geography; Meta-analysis","score_opus":0.05919560618358911,"score_gpt":0.2915829110737246,"score_spread":0.23238730489013548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021155905","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":[0.05932196,0.0002810807,0.93453425,0.0012534922,0.00032523467,0.0010986082,0.000016427553,0.00037289987,0.00279605],"genre_scores_gemma":[0.9983128,0.000022084017,0.0007606416,0.0005619952,0.00004123153,0.00024079348,0.000012681111,0.000008276478,0.00003955245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891233,0.00008217265,0.00019300041,0.0003862491,0.00020005416,0.00022616309],"domain_scores_gemma":[0.9991702,0.00014770793,0.00011346349,0.0003745853,0.000043997177,0.00015008701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055203942,0.00014483968,0.00024432733,0.00023219523,0.00004395258,0.000022684537,0.00029258913,0.000038520524,0.0000044301946],"category_scores_gemma":[0.000021810083,0.00011656764,0.0001911779,0.002397681,0.00002881138,0.00009977091,0.000026520416,0.000038207232,0.000008390934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014795619,0.00074981037,0.012436078,0.00001242852,0.00025066873,0.00001434394,0.00041943463,0.14866449,0.000029782204,0.016067801,0.00017512978,0.82103205],"study_design_scores_gemma":[0.0011612993,0.0008294751,0.2718737,0.000005419787,0.00016449274,2.1897179e-7,0.000004595283,0.7162661,0.0060453047,0.0005636033,0.002817897,0.0002679139],"about_ca_topic_score_codex":0.000004367973,"about_ca_topic_score_gemma":0.000008584558,"teacher_disagreement_score":0.9389908,"about_ca_system_score_codex":0.0000554803,"about_ca_system_score_gemma":0.000010800841,"threshold_uncertainty_score":0.475349},"labels":[],"label_agreement":null},{"id":"W3021610249","doi":"10.1007/978-3-030-47426-3_30","title":"Retrofitting Embeddings for Unsupervised User Identity Linkage","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Key Laboratory of Computer Network and Information Integration; China Scholarship Council; Natural Science Foundation of Jiangsu Province; Government of Jiangsu Province; National Social Science Fund of China; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; National Research Foundation","keywords":"Computer science; Discriminative model; Embedding; Retrofitting; Leverage (statistics); Matching (statistics); Identity (music); User modeling; Linkage (software); Machine learning; Data mining; Information retrieval; Artificial intelligence; User interface","score_opus":0.020793468286241075,"score_gpt":0.26716948257962636,"score_spread":0.2463760142933853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021610249","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":[0.000090973284,0.00038712422,0.99351466,0.0016429292,0.002458578,0.00083910616,0.000010017611,0.00041626455,0.0006403211],"genre_scores_gemma":[0.053512763,0.000050765204,0.93882805,0.005910213,0.0012739347,0.000027014496,0.00001136733,0.00008870871,0.00029719365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946508,0.000034196648,0.0007497706,0.0024680942,0.0011280031,0.00096915703],"domain_scores_gemma":[0.9964496,0.00093155843,0.00044983558,0.001495849,0.00035488626,0.00031829363],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008133382,0.00072139216,0.00077610096,0.00059875916,0.00045050864,0.0009232334,0.0055613657,0.00044700134,0.000010158761],"category_scores_gemma":[0.00030763182,0.0007074572,0.0003314742,0.0011959379,0.00054191076,0.0021097772,0.002348592,0.001314141,0.000032225056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033934328,0.00003897664,0.00024581508,0.00028367512,0.000047339265,0.00029307982,0.0012680995,0.06860805,0.0012104656,0.2745558,0.00023869249,0.65317607],"study_design_scores_gemma":[0.0004112966,0.00020469615,0.00006207018,0.00038161202,0.000012179799,0.00003012648,1.497764e-7,0.6384748,0.0011376855,0.35597154,0.0024738875,0.0008399543],"about_ca_topic_score_codex":0.000005064543,"about_ca_topic_score_gemma":0.000027841994,"teacher_disagreement_score":0.6523361,"about_ca_system_score_codex":0.00025410345,"about_ca_system_score_gemma":0.00027955987,"threshold_uncertainty_score":0.99981904},"labels":[],"label_agreement":null},{"id":"W3021719071","doi":"","title":"Image Classification with Hierarchical Multigraph Networks.","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"MNIST database; Computer science; Artificial intelligence; Image translation; Convolutional neural network; Pascal (unit); Multigraph; Contextual image classification; Pattern recognition (psychology); Machine learning; Graph; Image (mathematics); Deep learning; Theoretical computer science","score_opus":0.03022378203750923,"score_gpt":0.16900810425459303,"score_spread":0.1387843222170838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021719071","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":[0.2168133,0.000015179204,0.78028774,0.00010624861,0.00013380023,0.00019004571,6.386626e-7,0.00023248815,0.0022205457],"genre_scores_gemma":[0.98517877,0.000042919404,0.014081183,0.00016445569,0.000032500368,5.9226033e-7,0.000003863076,0.000012994343,0.0004827049],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862784,0.00008033225,0.00010965792,0.0007338256,0.000077201206,0.0003711135],"domain_scores_gemma":[0.99867266,0.00012615827,0.000104363826,0.00085670495,0.00008351208,0.00015657423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008754177,0.00017676783,0.00015856283,0.00014270157,0.00011362042,0.00006019269,0.0008963373,0.00008421678,0.000018534478],"category_scores_gemma":[0.000005798281,0.00016583283,0.00008078695,0.0013450952,0.00013919664,0.0009778015,0.00017412359,0.00030639695,0.0001216398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001300347,0.000109385524,0.042131554,0.000011526062,0.00004350227,0.00021909864,0.00006596592,0.24184087,0.00081953884,0.71075755,0.00028286263,0.0035881428],"study_design_scores_gemma":[0.0006358299,0.00011477472,0.020766873,0.000016381207,0.000008886224,0.0000093633325,0.00002194262,0.973092,0.00003725383,0.0046263677,0.00041991842,0.0002503758],"about_ca_topic_score_codex":0.0000073245583,"about_ca_topic_score_gemma":0.000013057587,"teacher_disagreement_score":0.7683655,"about_ca_system_score_codex":0.000042285446,"about_ca_system_score_gemma":0.000026119833,"threshold_uncertainty_score":0.67624664},"labels":[],"label_agreement":null},{"id":"W3030885106","doi":"10.1109/tkde.2020.2997938","title":"SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cluster analysis; Computer science; Set (abstract data type); Algorithm; Theoretical computer science; Artificial intelligence; Data mining; Information retrieval; Programming language","score_opus":0.019994299086997888,"score_gpt":0.25005122190912826,"score_spread":0.23005692282213036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030885106","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":[0.0030125102,0.00032377988,0.9951977,0.00005041383,0.0004077247,0.0005600492,0.0001321647,0.00031371517,0.0000019138668],"genre_scores_gemma":[0.9190846,0.00010134277,0.080370925,0.00014413949,0.000103175065,0.00009012017,0.000080716694,0.00002359049,0.00000139571],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895513,0.000026724014,0.000214937,0.00043241706,0.000084452186,0.00028635786],"domain_scores_gemma":[0.9990735,0.00018419168,0.00003520736,0.00042245106,0.00005675366,0.00022786415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012939092,0.00021590319,0.00020112519,0.00010505765,0.00019409663,0.00014942265,0.0003847557,0.00008664087,6.365705e-7],"category_scores_gemma":[0.000007754385,0.00022268915,0.000034625817,0.00034810533,0.000020200054,0.001090777,0.000034137214,0.00022399407,0.0000020326045],"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":[0.000010547598,0.000016269869,4.2767635e-7,0.00003472724,0.000018884304,0.0000013531362,0.00024159983,0.6429139,0.00010195491,0.000009726532,0.000006943712,0.35664368],"study_design_scores_gemma":[0.00069473963,0.00021238599,0.000016528787,0.00004178208,0.000021366232,0.000027866254,0.000011391304,0.9970986,0.0010026647,0.00000316919,0.00063450984,0.00023495952],"about_ca_topic_score_codex":0.0000018021342,"about_ca_topic_score_gemma":0.0000027173664,"teacher_disagreement_score":0.9160721,"about_ca_system_score_codex":0.000023841969,"about_ca_system_score_gemma":0.000009308077,"threshold_uncertainty_score":0.9081},"labels":[],"label_agreement":null},{"id":"W3030895774","doi":"10.1145/3391274.3393637","title":"What is the schema of your knowledge graph?","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Knowledge graph; Schema (genetic algorithms); Cluster analysis; Graph; Clustering coefficient; Information retrieval; Theoretical computer science; Artificial intelligence","score_opus":0.04264118021735799,"score_gpt":0.2856134814438224,"score_spread":0.24297230122646443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030895774","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":[0.012226104,0.005090096,0.9232418,0.051190674,0.00085472263,0.00029034802,8.418316e-7,0.00031666047,0.0067887665],"genre_scores_gemma":[0.9637694,0.00046763563,0.025716996,0.0093161855,0.00011399024,0.0000072414714,3.2268764e-7,0.000010320517,0.00059790397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927735,0.000028612178,0.00014819963,0.00024821056,0.00013482591,0.00016281479],"domain_scores_gemma":[0.9992847,0.0000896728,0.000055905835,0.00043190835,0.000061080944,0.00007672126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000061801155,0.00008981174,0.00011136499,0.00002337858,0.000056482935,0.00007475456,0.0010101816,0.000029791872,0.000026872753],"category_scores_gemma":[0.000012730009,0.00005475286,0.00009138667,0.000819465,0.00006757004,0.0007225488,0.00034733047,0.000118629265,0.000054112224],"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":[0.000020987383,0.00012264527,0.0009495698,0.00006183264,0.00008327335,0.0000144313135,0.014403011,0.00060840795,0.006728878,0.5831418,0.07605273,0.3178124],"study_design_scores_gemma":[0.0014566231,0.00067489775,0.0025348389,0.00017323419,0.000033957047,0.000032138418,0.001954116,0.4599162,0.23039995,0.1364754,0.1652153,0.001133339],"about_ca_topic_score_codex":0.0000013046998,"about_ca_topic_score_gemma":0.0000019943168,"teacher_disagreement_score":0.9515433,"about_ca_system_score_codex":0.000002713759,"about_ca_system_score_gemma":0.000013461084,"threshold_uncertainty_score":0.22327569},"labels":[],"label_agreement":null},{"id":"W3034116706","doi":"10.48550/arxiv.1909.03790","title":"Graph Random Neural Features for Distance-Preserving Graph Representations","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Graph embedding; Strength of a graph; Voltage graph; Null graph; Line graph; Embedding; Graph property; Graph isomorphism; Butterfly graph; Random geometric graph; Mathematics; Complement graph; Discrete mathematics; Random graph; Computer science; Theoretical computer science; Graph; Artificial intelligence","score_opus":0.05277552369281031,"score_gpt":0.21709079579755056,"score_spread":0.16431527210474026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034116706","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":[0.027167799,0.00048518923,0.96674186,0.00034499148,0.002108778,0.0014314101,0.00007264664,0.00048222655,0.0011650863],"genre_scores_gemma":[0.98867947,0.00033803523,0.0077744126,0.00020990032,0.00015732854,0.000016091997,0.000082919214,0.00004552961,0.0026963304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966341,0.00020686921,0.00033342568,0.0019545588,0.00017212145,0.00069892505],"domain_scores_gemma":[0.99567604,0.0007462853,0.0004646797,0.002598641,0.00028927243,0.00022507751],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021312753,0.00053026987,0.00059684226,0.00047765765,0.00039513785,0.0002668234,0.003518938,0.0003492322,0.000009888861],"category_scores_gemma":[0.000097396995,0.00058489834,0.0007830978,0.0012826571,0.00018078569,0.0009741498,0.0023754241,0.00090547535,0.000010489264],"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":[0.00019389034,0.000047922214,0.002739516,0.00010598981,0.00011871543,0.00008719931,0.00013205854,0.8689214,0.000027800568,0.12341777,0.0037801678,0.0004275514],"study_design_scores_gemma":[0.0022589648,0.00006454885,0.0028146694,0.00012247745,0.00011080813,0.00000784993,0.000067575944,0.61670685,0.00008698112,0.3763017,0.0006639802,0.00079358305],"about_ca_topic_score_codex":0.00007947648,"about_ca_topic_score_gemma":0.00014119063,"teacher_disagreement_score":0.9615117,"about_ca_system_score_codex":0.00009075853,"about_ca_system_score_gemma":0.000083947605,"threshold_uncertainty_score":0.99966025},"labels":[],"label_agreement":null},{"id":"W3034239155","doi":"10.60692/6b5x9-hbn81","title":"Inductive Relation Prediction by Subgraph Reasoning","year":2019,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Embedding; Inductive bias; Knowledge graph; Artificial intelligence; Relation (database); Theoretical computer science; Holy Grail; Graph; Set (abstract data type); Machine learning; Multi-task learning; Data mining","score_opus":0.008794174846235568,"score_gpt":0.20663636552344866,"score_spread":0.1978421906772131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034239155","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":[0.9750349,0.00016882551,0.0011080648,0.00007041734,0.0017269483,0.0007125403,0.00013337717,0.0009946004,0.020050343],"genre_scores_gemma":[0.982352,0.00006415401,0.016264072,0.0003655552,0.000036397967,0.000046448098,0.000043515713,0.000060689137,0.0007671216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965015,0.0003215329,0.000540253,0.0012228937,0.00070996763,0.0007038585],"domain_scores_gemma":[0.99782664,0.00022736557,0.0003729895,0.0010794258,0.00022084093,0.0002727702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006135638,0.0004133295,0.00034282988,0.00028754293,0.0007234998,0.0001162026,0.0009752965,0.00034801016,0.000036315578],"category_scores_gemma":[0.00019377893,0.0004362447,0.00018441574,0.0015491014,0.00006548164,0.0045257173,0.00039535086,0.0011780143,0.00031636408],"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":[0.000061170336,0.0001364132,0.004783762,0.000024475072,0.0000913534,0.000021572665,0.000014193749,0.0018103418,0.08625355,0.7550784,0.000047180583,0.15167761],"study_design_scores_gemma":[0.0061164056,0.0018940525,0.05662287,0.0008189649,0.00015248387,0.000336825,0.00017095002,0.044162277,0.16633688,0.57372713,0.14526393,0.0043972475],"about_ca_topic_score_codex":0.00004917146,"about_ca_topic_score_gemma":0.000014978715,"teacher_disagreement_score":0.18135126,"about_ca_system_score_codex":0.00038806262,"about_ca_system_score_gemma":0.000016605296,"threshold_uncertainty_score":0.9998089},"labels":[],"label_agreement":null},{"id":"W3034674099","doi":"10.1609/aaai.v35i11.17168","title":"Implicit Kernel Attention","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Theoretical computer science; Transformer; Artificial intelligence; Machine learning","score_opus":0.057908882148459066,"score_gpt":0.29487738774555106,"score_spread":0.23696850559709198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034674099","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":[0.529206,0.000184306,0.4035455,0.021733567,0.0026209843,0.00089664024,0.000010133258,0.0005244241,0.041278444],"genre_scores_gemma":[0.9924958,0.000058825553,0.0063728224,0.00044615412,0.00007398003,0.000017046212,5.3648125e-7,0.000011885587,0.0005229515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99807435,0.000017938393,0.00047531037,0.000593324,0.00046484877,0.00037421254],"domain_scores_gemma":[0.99824595,0.00007668713,0.00032094208,0.00042315075,0.00084240676,0.00009085799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022328664,0.0002070019,0.00023235513,0.00007643337,0.00019562879,0.00023368576,0.001714467,0.00008766354,0.00004617824],"category_scores_gemma":[0.0002464733,0.0001634245,0.00017210795,0.0011004851,0.00017390244,0.000530435,0.00059414795,0.0003312223,0.00008733246],"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":[0.000011997713,0.0000731129,0.00025838686,0.000013774947,0.000008662031,0.0000014773046,0.00013760262,0.000073281735,0.12706457,0.81710744,0.00014571982,0.055103965],"study_design_scores_gemma":[0.000021052098,0.000072967334,0.00089735724,0.00013971837,0.000007899193,0.000018233972,0.00016248065,0.032613575,0.54315084,0.42256945,0.00014691475,0.00019952828],"about_ca_topic_score_codex":0.000007722242,"about_ca_topic_score_gemma":0.000007379945,"teacher_disagreement_score":0.4632898,"about_ca_system_score_codex":0.000034344255,"about_ca_system_score_gemma":0.000072573064,"threshold_uncertainty_score":0.66642576},"labels":[],"label_agreement":null},{"id":"W3035403290","doi":"10.1145/3397271.3401172","title":"DGL-KE","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Speedup; Parallel computing; Overhead (engineering); Locality; Computation; Key (lock); Theoretical computer science; Algorithm; Programming language","score_opus":0.022021934113621775,"score_gpt":0.21377063861309278,"score_spread":0.191748704499471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035403290","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":[0.00085722114,0.000040112183,0.97323006,0.011072444,0.00010451454,0.000037531358,9.4015874e-8,0.00038055636,0.014277475],"genre_scores_gemma":[0.8094523,0.0000069766456,0.17236279,0.017706107,0.00010105771,0.0000023035375,2.5490075e-7,0.000004465406,0.0003637595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995364,0.0000086918835,0.000061503575,0.0001852866,0.00008508192,0.00012306662],"domain_scores_gemma":[0.9996759,0.000023522685,0.000014036501,0.0001776554,0.000012040057,0.00009679147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000014902881,0.000047745394,0.000049905084,0.000009533606,0.000027638102,0.000032234115,0.00047246457,0.000014816233,0.000030055899],"category_scores_gemma":[0.000010414096,0.00003892617,0.00002576371,0.0002998346,0.000010878125,0.00025745833,0.00016549067,0.00006457363,0.00019992859],"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":[0.000004258785,0.000015804075,0.0007189794,0.000005235368,0.000007677682,0.00005932851,0.00034959643,0.0022333905,0.0020816915,0.7800138,0.052691784,0.16181844],"study_design_scores_gemma":[0.00040550577,0.00019439367,0.0015893824,0.0000047790263,0.0000021626029,0.000014728018,0.000015670565,0.7470914,0.007872963,0.034982532,0.20741855,0.00040791644],"about_ca_topic_score_codex":7.09005e-7,"about_ca_topic_score_gemma":5.7096577e-7,"teacher_disagreement_score":0.80859506,"about_ca_system_score_codex":0.0000024070846,"about_ca_system_score_gemma":0.0000050678973,"threshold_uncertainty_score":0.25697443},"labels":[],"label_agreement":null},{"id":"W3035691406","doi":"","title":"Graph Representation Learning by Maximizing Mutual Information Between Spatial and Spectral Views","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mutual information; Graph; Representation (politics); Artificial intelligence; Theoretical computer science","score_opus":0.04797287760010207,"score_gpt":0.29932861233777697,"score_spread":0.2513557347376749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035691406","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":[0.060092863,0.000048768656,0.92590004,0.007916841,0.00022784491,0.00018049616,0.000009006952,0.0003329365,0.0052912203],"genre_scores_gemma":[0.9931079,0.00010161495,0.0056372825,0.0006846034,0.00017818598,0.000008875143,0.00018564996,0.000011180123,0.00008468357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998348,0.00017379082,0.00037437087,0.00039424325,0.000473653,0.000235963],"domain_scores_gemma":[0.9991699,0.00014683258,0.0003084395,0.00011766244,0.00011174208,0.00014546339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016993462,0.00020283338,0.00020005295,0.00015313782,0.0002077191,0.0004123045,0.00053032115,0.00006587706,0.0000659671],"category_scores_gemma":[0.00028231405,0.00020490304,0.00006207254,0.0002670841,0.00004729749,0.0015647163,0.00024486173,0.0008858147,0.000060071605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018046789,0.000029914303,0.17732547,0.000027317017,0.000115758216,0.000023251638,0.004931945,0.03661314,0.004794841,0.067591235,0.000545278,0.70782137],"study_design_scores_gemma":[0.00072183536,0.00036250375,0.009558902,0.000042078078,0.000007868199,0.000009938691,0.000120190045,0.9753623,0.0008078554,0.0030260345,0.009658163,0.00032235717],"about_ca_topic_score_codex":0.000080575286,"about_ca_topic_score_gemma":0.0000074477102,"teacher_disagreement_score":0.93874913,"about_ca_system_score_codex":0.00003058578,"about_ca_system_score_gemma":0.000018433107,"threshold_uncertainty_score":0.83557034},"labels":[],"label_agreement":null},{"id":"W3035707368","doi":"10.18653/v1/2020.acl-main.526","title":"ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Embedding; Relation (database); Computer science; Theoretical computer science; Knowledge graph; Graph; Artificial intelligence; Data mining","score_opus":0.017801494211374645,"score_gpt":0.25499947446314464,"score_spread":0.23719798025177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035707368","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":[0.0039447844,0.00008779384,0.9932275,0.00090712163,0.00032366763,0.00056073803,0.0000059111135,0.0005144813,0.00042804817],"genre_scores_gemma":[0.80760884,0.000013789742,0.19108593,0.00093501376,0.0002336657,0.000042428597,0.00006213765,0.000008449762,0.000009759401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853915,0.000045506273,0.00041451456,0.00036282,0.00024686707,0.00039112207],"domain_scores_gemma":[0.998985,0.000070175105,0.00022648297,0.0002850336,0.00025464446,0.00017867121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107266314,0.00022429478,0.00020205698,0.000050560975,0.0003566454,0.00017015806,0.0003830218,0.000100907455,0.000014912631],"category_scores_gemma":[0.000020427691,0.0001831601,0.00009278322,0.0012619188,0.00007928299,0.0033244903,0.00016333093,0.00017874212,0.000036232006],"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":[0.00007549694,0.0000052445057,0.0009470724,0.000034666282,0.000018757353,0.0000010389989,0.00073419354,0.78487104,0.000014342752,0.12549856,0.0061242715,0.081675306],"study_design_scores_gemma":[0.0007016464,0.00025755388,0.0045203143,0.00004418036,0.000007774646,0.000013420364,0.00012226733,0.97203296,0.0000380645,0.019515218,0.0024397958,0.00030680964],"about_ca_topic_score_codex":0.0000035322016,"about_ca_topic_score_gemma":0.000016346847,"teacher_disagreement_score":0.803664,"about_ca_system_score_codex":0.00010912187,"about_ca_system_score_gemma":0.000045712997,"threshold_uncertainty_score":0.7469052},"labels":[],"label_agreement":null},{"id":"W3036233147","doi":"10.1007/978-3-030-50433-5_15","title":"Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Social network (sociolinguistics); Classifier (UML); Machine learning; Graph; Node (physics); Social network analysis; Embedding; Theoretical computer science; World Wide Web","score_opus":0.032333963428220786,"score_gpt":0.2701564825082692,"score_spread":0.2378225190800484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036233147","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":[0.00034597333,0.00085932144,0.9938489,0.0023486686,0.0011400641,0.0004716712,0.0000063095868,0.00023558894,0.0007434941],"genre_scores_gemma":[0.7709366,0.00007772773,0.22471136,0.0031628672,0.0009585533,0.000023212788,0.000034069555,0.000056608085,0.000038992977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99547315,0.00009046311,0.0007342009,0.0020465131,0.00074888556,0.000906774],"domain_scores_gemma":[0.997981,0.00052418275,0.0004106574,0.00063031126,0.00021216145,0.00024168074],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050055276,0.0006745599,0.0007241546,0.0007698754,0.0005035738,0.00037159384,0.0023309262,0.00040822825,0.000007932888],"category_scores_gemma":[0.000036905367,0.0006946617,0.00017283593,0.0019511495,0.0015577928,0.0007538651,0.0013938494,0.0014318972,0.000014319686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052147923,0.00007718737,0.0015549702,0.00009857755,0.000030768966,0.00013277303,0.0020540429,0.15081263,0.00061802054,0.2914935,0.0005771642,0.5524982],"study_design_scores_gemma":[0.00028951076,0.00006656767,0.007131847,0.0000825868,0.0000052401833,0.00004104353,1.6383424e-7,0.66582847,0.000009588017,0.32551473,0.0005233974,0.0005068695],"about_ca_topic_score_codex":0.0000088200095,"about_ca_topic_score_gemma":0.00011788219,"teacher_disagreement_score":0.77059066,"about_ca_system_score_codex":0.00021746803,"about_ca_system_score_gemma":0.00019820867,"threshold_uncertainty_score":0.99955046},"labels":[],"label_agreement":null},{"id":"W3040712421","doi":"10.24963/ijcai.2020/739","title":"Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Compute Canada; International Business Machines Corporation","keywords":"Computer science; Artificial intelligence; Graph; Machine learning; Social network (sociolinguistics); Convolutional neural network; Statistical relational learning; Social network analysis; Node (physics); Theoretical computer science; Data mining; Social media; Relational database; World Wide Web","score_opus":0.03336783642512357,"score_gpt":0.30602238947383315,"score_spread":0.27265455304870956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040712421","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":[0.050090127,0.0006048373,0.947103,0.0010250364,0.00020676387,0.00016047468,4.4010844e-7,0.00043723462,0.00037208837],"genre_scores_gemma":[0.9316747,0.000026894197,0.06627117,0.00081748405,0.0010878469,0.000006850781,0.000014193881,0.000018469362,0.00008234341],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764407,0.00025031407,0.00040475727,0.0008159302,0.00030170506,0.0005832394],"domain_scores_gemma":[0.99892676,0.00026762928,0.00024571762,0.00019842193,0.00014144287,0.00022005572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022720805,0.00025235573,0.00041065674,0.00012705752,0.00082230236,0.00020628507,0.0004155385,0.00011402012,0.000025188037],"category_scores_gemma":[0.00003797634,0.00025659063,0.0002482983,0.0049380893,0.00013903096,0.00078420824,0.0004899579,0.0004134293,0.0000073238507],"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":[0.000021291247,0.000011481449,0.034896787,0.0000068501736,0.00021802464,0.000009598255,0.00084537355,0.9455034,0.0001824849,0.00877123,0.0019603635,0.00757308],"study_design_scores_gemma":[0.0002497462,0.000043919434,0.011662346,0.00000564976,0.00013436744,0.000008836535,0.000029371013,0.9803949,0.00001649749,0.00661811,0.0005509686,0.00028531186],"about_ca_topic_score_codex":0.000015051839,"about_ca_topic_score_gemma":0.00001873473,"teacher_disagreement_score":0.88158464,"about_ca_system_score_codex":0.00003146885,"about_ca_system_score_gemma":0.000023478842,"threshold_uncertainty_score":0.9999886},"labels":[],"label_agreement":null},{"id":"W3043002554","doi":"10.1109/cbi49978.2020.00009","title":"Competitive Analysis with Graph Embedding on Patent Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Latent Dirichlet allocation; Computer science; Competitor analysis; Embedding; Graph; Topic model; Theoretical computer science; Competitive advantage; Latent semantic analysis; Network analysis; Data science; Data mining; Artificial intelligence; Business; Marketing","score_opus":0.026650532757348272,"score_gpt":0.23543933648317256,"score_spread":0.20878880372582428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043002554","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":[0.0026264281,0.000059979262,0.9908378,0.0013987668,0.00007571112,0.00012035607,5.728253e-7,0.00033666415,0.0045437166],"genre_scores_gemma":[0.9425783,0.00003789482,0.05218848,0.0050918693,0.000059974544,0.000008672409,0.0000045756415,0.000009576523,0.000020665188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986248,0.000053242056,0.00016597024,0.00056498114,0.0002684169,0.00032261753],"domain_scores_gemma":[0.99916,0.0001216733,0.00008178301,0.00036729925,0.00005984509,0.00020938591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051589235,0.00018244112,0.00025175663,0.00012410813,0.00010891399,0.00009943663,0.0005570974,0.000038257567,0.000025712952],"category_scores_gemma":[0.0000041776875,0.00012998223,0.00014021431,0.0030373365,0.00004657572,0.00026564478,0.00014923961,0.0002201203,0.000016907958],"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":[0.000019980554,0.000015914547,0.0010298964,9.75773e-7,0.00014422389,0.000043986147,0.00009845512,0.7040792,0.0000073646756,0.29081568,0.00028471096,0.0034596168],"study_design_scores_gemma":[0.00028813462,0.00036236504,0.0029835226,0.000012340341,0.00004785392,0.0000024742712,0.000043466647,0.99386203,0.00015337409,0.00050927815,0.0014645983,0.00027053352],"about_ca_topic_score_codex":0.0000040496543,"about_ca_topic_score_gemma":0.000016432645,"teacher_disagreement_score":0.93995184,"about_ca_system_score_codex":0.000013640987,"about_ca_system_score_gemma":0.0000060713382,"threshold_uncertainty_score":0.5300522},"labels":[],"label_agreement":null},{"id":"W3043614790","doi":"10.48550/arxiv.2007.06704","title":"Node Copying for Protection Against Graph Neural Network Topology Attacks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Copying; Adversarial system; Graph; Theoretical computer science; Artificial intelligence; Computation; Network topology; Machine learning; Node (physics); Topology (electrical circuits); Algorithm; Computer network; Mathematics","score_opus":0.10061097543101094,"score_gpt":0.21523857364977123,"score_spread":0.11462759821876028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043614790","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":[0.05308226,0.00008394165,0.94152236,0.00082047936,0.0022345972,0.0012026285,0.000008916353,0.00066296355,0.000381879],"genre_scores_gemma":[0.984778,0.00012727994,0.013350286,0.0009955915,0.00050682906,0.000015693951,0.000032562926,0.00004147973,0.0001523072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969561,0.00019042072,0.0003005477,0.0017242612,0.00009178743,0.0007368586],"domain_scores_gemma":[0.9979111,0.0001759052,0.0004351247,0.0010686157,0.00016913036,0.00024010491],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015989058,0.00047211067,0.0005132856,0.00019583569,0.0004214383,0.000107102795,0.0019057521,0.00045426504,0.000004718593],"category_scores_gemma":[0.000043774024,0.00056867074,0.0004368312,0.0010912903,0.0002077491,0.00043979246,0.0019019424,0.0011256031,0.000023124556],"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":[0.00008946279,0.000022742903,0.0003586198,0.000074193966,0.00006779547,0.00014160175,0.00007102599,0.95130813,0.00007487422,0.044934645,0.0007978037,0.002059101],"study_design_scores_gemma":[0.00049969513,0.00011570848,0.00015850834,0.000062805004,0.00004246517,0.000006074331,0.000019246652,0.86666137,0.00007543043,0.13081695,0.0010178372,0.0005239314],"about_ca_topic_score_codex":0.00002511861,"about_ca_topic_score_gemma":0.000043914144,"teacher_disagreement_score":0.9316957,"about_ca_system_score_codex":0.00014236824,"about_ca_system_score_gemma":0.00008561628,"threshold_uncertainty_score":0.99967647},"labels":[],"label_agreement":null},{"id":"W3044604502","doi":"10.1145/3480243","title":"Time-Aware Graph Embedding: A Temporal Smoothness and Task-Oriented Approach","year":2021,"lang":"en","type":"preprint","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Embedding; Computer science; Granularity; Theoretical computer science; Graph embedding; Graph; Smoothing; Artificial intelligence; Machine learning","score_opus":0.038241671914906405,"score_gpt":0.2946700430901839,"score_spread":0.2564283711752775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044604502","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":[0.010730222,0.0031505013,0.9775425,0.00024660275,0.0020844662,0.0006665132,0.00501364,0.00044052352,0.00012501712],"genre_scores_gemma":[0.69744295,0.0019646725,0.27316913,0.0002599502,0.00044520272,0.00037287298,0.025117679,0.00020939327,0.0010181814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994463,0.00033006797,0.00067406445,0.003404497,0.0004937968,0.00063456106],"domain_scores_gemma":[0.9893352,0.0004983218,0.00029893976,0.009411076,0.00016792481,0.00028858124],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0002520662,0.0008635396,0.0008942301,0.00039647182,0.0004419841,0.0010387978,0.0063143503,0.00052772014,0.000029391784],"category_scores_gemma":[0.00004214874,0.0008714191,0.00029027654,0.0011134683,0.00032439342,0.0031876178,0.002556949,0.0017979471,0.000028733706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012224894,0.019981597,0.0017395962,0.002994747,0.008175198,0.0011715969,0.02127901,0.2325454,0.002408377,0.003428422,0.03721682,0.6678367],"study_design_scores_gemma":[0.0032938935,0.00026355745,0.0013308622,0.0023260766,0.00072158047,0.00010797735,0.0010890391,0.9544118,0.0010671631,0.016337479,0.014128042,0.004922511],"about_ca_topic_score_codex":0.00012213326,"about_ca_topic_score_gemma":0.00011030024,"teacher_disagreement_score":0.7218664,"about_ca_system_score_codex":0.000092471455,"about_ca_system_score_gemma":0.00028643783,"threshold_uncertainty_score":0.9999982},"labels":[],"label_agreement":null},{"id":"W3046554155","doi":"10.1145/3400903.3400928","title":"Node Classification and Link Prediction in Social Graphs using RLVECN","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Node (physics); Artificial intelligence; Social network (sociolinguistics); Machine learning; Link (geometry); Context (archaeology); Graph; Enhanced Data Rates for GSM Evolution; Social network analysis; Feature (linguistics); Link analysis; Theoretical computer science; Social media; World Wide Web","score_opus":0.06589696179850675,"score_gpt":0.2793493648002963,"score_spread":0.21345240300178958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046554155","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":[0.10931843,0.00004286307,0.88289857,0.006752791,0.00011127068,0.00011623415,8.744735e-7,0.00018770738,0.00057123555],"genre_scores_gemma":[0.9619123,0.000012351386,0.037071496,0.0008767289,0.00011054604,0.000002913408,0.0000014256107,0.0000043637065,0.000007926604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993583,0.000029655988,0.00013336535,0.0002575486,0.000098024226,0.00012313681],"domain_scores_gemma":[0.9997854,0.000021651174,0.000041866107,0.00008373162,0.00001887125,0.000048465696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000492259,0.00006374496,0.000071858994,0.00005290295,0.00008400383,0.000045023065,0.00014602051,0.000051687457,0.0000022958266],"category_scores_gemma":[0.000009940918,0.00006188927,0.000019388677,0.0005381614,0.00002967291,0.00044019063,0.00007650578,0.00012197078,0.0000027932895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006369364,0.0000882616,0.12349661,0.00006338172,0.00002636741,0.000028642222,0.0052115754,0.011557821,0.084629335,0.5443111,0.0030643602,0.22745885],"study_design_scores_gemma":[0.0001863027,0.000018665995,0.059766162,0.000004013903,0.0000015844552,0.0000020668717,0.000020581221,0.92990357,0.00015652053,0.0097011095,0.00017053385,0.00006890558],"about_ca_topic_score_codex":0.0000048461843,"about_ca_topic_score_gemma":0.000004310392,"teacher_disagreement_score":0.91834575,"about_ca_system_score_codex":0.00001402246,"about_ca_system_score_gemma":0.000009580627,"threshold_uncertainty_score":0.25237712},"labels":[],"label_agreement":null},{"id":"W3047456059","doi":"10.1109/infocom41043.2020.9155370","title":"Guardian: Evaluating Trust in Online Social Networks with Graph Convolutional Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Guardian; Scalability; Social graph; Graph; Social network (sociolinguistics); Viral marketing; Data science; Social media; Artificial intelligence; Theoretical computer science; World Wide Web","score_opus":0.03636358331498197,"score_gpt":0.2838109764218088,"score_spread":0.24744739310682684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047456059","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":[0.018058147,0.0003011658,0.9753787,0.0048473235,0.00022596835,0.000305237,0.0000025559527,0.00032524025,0.0005556638],"genre_scores_gemma":[0.89854044,0.000026522908,0.094394416,0.0060670027,0.0008506277,0.000024676408,0.000029537423,0.000025778907,0.000040980663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976029,0.00015477843,0.00041172298,0.0007076389,0.00045652772,0.0006664527],"domain_scores_gemma":[0.9990953,0.0001780762,0.00015257411,0.00026459488,0.00011146425,0.00019799286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024096825,0.00027799915,0.00034309426,0.00008674374,0.00021633875,0.00010030893,0.0007869367,0.00014319654,0.00003515104],"category_scores_gemma":[0.000029964978,0.00023983173,0.00011157704,0.0019696606,0.00013634475,0.0005530054,0.0002884908,0.0006512267,0.0000059176987],"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":[0.00007293272,0.00005175411,0.008279046,0.0000043336295,0.0000247093,0.000045979807,0.0001803321,0.9443988,0.00001195911,0.021424811,0.0016220538,0.023883255],"study_design_scores_gemma":[0.0009930934,0.000217007,0.01812339,0.000018666555,0.0000075574367,0.000011869612,0.000052198124,0.9788727,0.0000028165166,0.0011822766,0.00019776572,0.00032064898],"about_ca_topic_score_codex":0.00001404671,"about_ca_topic_score_gemma":0.000095160765,"teacher_disagreement_score":0.8809843,"about_ca_system_score_codex":0.000047398524,"about_ca_system_score_gemma":0.00007116775,"threshold_uncertainty_score":0.9780054},"labels":[],"label_agreement":null},{"id":"W3070904253","doi":"10.48550/arxiv.2008.08838","title":"Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Training (meteorology); Perspective (graphical); Expressive power; Convolutional neural network; Artificial intelligence; Machine learning; Operator (biology); Limit (mathematics); Risk analysis (engineering); Theoretical computer science; Mathematics; Business","score_opus":0.10437881252635828,"score_gpt":0.20044822116092553,"score_spread":0.09606940863456725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3070904253","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":[0.040125467,0.00020495051,0.95725095,0.0005077726,0.00116352,0.00029088795,0.000016069007,0.0003020423,0.0001383203],"genre_scores_gemma":[0.99202424,0.00009680479,0.0066956957,0.00088625535,0.0001892348,0.0000012169916,0.000030168994,0.000032296433,0.00004406269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718136,0.00022482644,0.00043706683,0.0013856813,0.00017469176,0.00059637957],"domain_scores_gemma":[0.99778146,0.00021925135,0.00061060104,0.0009413617,0.00018537497,0.00026193244],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018207134,0.00046132118,0.00063364324,0.00030465383,0.00018064895,0.00008683415,0.0022674908,0.00033970308,0.000023072],"category_scores_gemma":[0.000024254841,0.00055564404,0.0005088838,0.0011149652,0.0002851195,0.0005602152,0.0023070525,0.0010394771,0.000007087097],"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":[0.000030965362,0.000025506202,0.0007937977,0.000039765557,0.00011687912,0.0002589738,0.00017596608,0.9124576,0.00006807728,0.08478626,0.00019941403,0.0010467878],"study_design_scores_gemma":[0.00038552124,0.00004727212,0.0009161045,0.00011615023,0.00007269846,0.0000147821875,0.00006385609,0.90690446,0.000036970596,0.09089883,0.0000579647,0.00048538434],"about_ca_topic_score_codex":0.00003713187,"about_ca_topic_score_gemma":0.000010588884,"teacher_disagreement_score":0.9518988,"about_ca_system_score_codex":0.00008363751,"about_ca_system_score_gemma":0.00009545337,"threshold_uncertainty_score":0.9996895},"labels":[],"label_agreement":null},{"id":"W3080134346","doi":"10.1145/3394486.3406469","title":"Recent Advances on Graph Analytics and Its Applications in Healthcare","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Science Foundation; Amazon Web Services; Research Grants Council, University Grants Committee; Office of Academic Research, U.S. Naval Academy; Google","keywords":"Interpretability; Computer science; Data science; Inference; Graph; Analytics; Health care; Data mining; Machine learning; Theoretical computer science; Artificial intelligence","score_opus":0.03631693065387832,"score_gpt":0.30041957086105675,"score_spread":0.26410264020717844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080134346","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":[0.007908216,0.024104923,0.8147264,0.14964011,0.00014801482,0.0010492755,0.000006302956,0.0004912941,0.0019254589],"genre_scores_gemma":[0.9491952,0.02087996,0.017501507,0.012284724,0.000050075832,0.000047699516,0.0000036069357,0.0000091433985,0.0000280902],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992429,0.00002227925,0.00013605686,0.0003266696,0.00011105853,0.00016103144],"domain_scores_gemma":[0.9995376,0.00008525157,0.000037788006,0.00017030882,0.00003423797,0.00013483159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000320472,0.00008225386,0.00009920587,0.00005566187,0.00005184501,0.000022674334,0.00026929443,0.000026228312,0.0000019129711],"category_scores_gemma":[0.00001711649,0.000069753354,0.00001566346,0.0010171678,0.000014500417,0.00022509208,0.00008532488,0.00013380019,0.0000059879044],"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":[0.000009388942,0.000039613762,0.0041272095,0.000029782022,0.0000037540874,0.0000067986075,0.00017974144,0.007925952,0.000040305473,0.5265649,0.00025722795,0.46081534],"study_design_scores_gemma":[0.0010480789,0.00083295145,0.012570663,0.000061795574,0.0000068719232,0.000007652081,0.00016613028,0.5021619,0.0021110245,0.1474842,0.33263728,0.0009114562],"about_ca_topic_score_codex":0.0000012465358,"about_ca_topic_score_gemma":0.000036544727,"teacher_disagreement_score":0.941287,"about_ca_system_score_codex":0.000010463122,"about_ca_system_score_gemma":0.000010161236,"threshold_uncertainty_score":0.2844459},"labels":[],"label_agreement":null},{"id":"W3081520483","doi":"10.1145/3406116","title":"Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","keywords":"Computer science; Pairwise comparison; Discriminative model; Relation (database); Artificial intelligence; Benchmark (surveying); Dependency (UML); Word (group theory); Machine learning; A priori and a posteriori; Representation (politics); Theoretical computer science; Data mining; Mathematics","score_opus":0.041519174015214017,"score_gpt":0.25393608106521703,"score_spread":0.21241690705000302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081520483","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":[0.0002558318,0.00007018983,0.9964858,0.0007068308,0.0010530408,0.0006657242,0.00015485434,0.00031981335,0.00028788732],"genre_scores_gemma":[0.97680426,0.0000406291,0.022359269,0.00028486614,0.00009036133,0.00024342214,0.00003505547,0.000010183029,0.00013198366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998738,0.000038687038,0.00062856224,0.00016460232,0.00023835455,0.00019183234],"domain_scores_gemma":[0.9987736,0.00011931717,0.00022491746,0.00049595715,0.0002674482,0.00011875836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017892336,0.00014592428,0.00019733964,0.0001823381,0.00016396045,0.0000887297,0.00051903806,0.000098362354,0.0000022290787],"category_scores_gemma":[0.000039259758,0.00014373854,0.00013745824,0.0003408453,0.00003170325,0.0028589135,0.000013604568,0.00014937368,0.00003873839],"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":[0.000050090686,0.00003338348,0.000012570718,0.00035724835,0.00003634408,1.6503925e-7,0.008620582,0.94668156,0.00008266128,0.004528742,0.001157501,0.038439132],"study_design_scores_gemma":[0.0005052405,0.00016486901,0.0000101423875,0.00006276891,0.000014001492,0.0000019384786,0.00024361924,0.9920369,0.00066411763,0.00035828468,0.0058184825,0.00011966417],"about_ca_topic_score_codex":0.0000023571988,"about_ca_topic_score_gemma":0.0000020335244,"teacher_disagreement_score":0.9765484,"about_ca_system_score_codex":0.000043546963,"about_ca_system_score_gemma":0.00006817114,"threshold_uncertainty_score":0.58614874},"labels":[],"label_agreement":null},{"id":"W3081963674","doi":"","title":"Contrastive Multi-View Representation Learning on Graphs","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Node (physics); Binary classification; Theoretical computer science; Representation (politics); Machine learning; Binary number; Feature learning; Mathematics; Support vector machine","score_opus":0.0770792012378027,"score_gpt":0.3372805158083014,"score_spread":0.26020131457049867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081963674","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":[0.015599506,0.00012612893,0.94995916,0.017297955,0.0007490451,0.00035387458,0.000006462586,0.00082194596,0.015085914],"genre_scores_gemma":[0.9900829,0.00015426688,0.0068561747,0.0022562004,0.00011072778,0.000020511552,0.00004290639,0.000020309746,0.00045600516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979013,0.00028550753,0.00030623263,0.00069668156,0.00054123544,0.0002690123],"domain_scores_gemma":[0.9988784,0.0002999784,0.00026827006,0.00017906373,0.00021672515,0.00015755968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015221175,0.00024453943,0.00022576418,0.00015354638,0.0002095139,0.00023388806,0.00086050754,0.00006341645,0.00015359839],"category_scores_gemma":[0.0006670758,0.00023268064,0.0001123928,0.0003890457,0.00005172377,0.00047494896,0.0001995629,0.0011980698,0.00021940522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022926113,0.00010844005,0.020340407,0.000010797633,0.00010445551,0.00012444211,0.0014339626,0.24698532,0.0031154086,0.52782255,0.00009991682,0.19962503],"study_design_scores_gemma":[0.0008034695,0.00045788416,0.00434363,0.00008603114,0.000004458516,0.000007104474,0.00006856927,0.98709995,0.00043985713,0.0027575453,0.003670356,0.0002611371],"about_ca_topic_score_codex":0.000023909957,"about_ca_topic_score_gemma":0.000006278411,"teacher_disagreement_score":0.9744834,"about_ca_system_score_codex":0.00004097548,"about_ca_system_score_gemma":0.00002700934,"threshold_uncertainty_score":0.94884413},"labels":[],"label_agreement":null},{"id":"W3088352442","doi":"10.18653/v1/2020.emnlp-main.492","title":"Structure Aware Negative Sampling in Knowledge Graphs","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Adversarial system; Computer science; Embedding; Discriminative model; Scalability; Graph; Sampling (signal processing); Theoretical computer science; Simple (philosophy); Artificial intelligence; Machine learning","score_opus":0.04925334177672447,"score_gpt":0.31414655193891594,"score_spread":0.2648932101621915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088352442","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":[0.008045047,0.00085064676,0.9847656,0.0012954107,0.0019326793,0.00068773783,0.000027917433,0.00068785343,0.0017071116],"genre_scores_gemma":[0.81782365,0.000084312225,0.18128642,0.0005331971,0.000141493,0.000027177022,0.000020356787,0.000028397377,0.00005501437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99753094,0.00011293851,0.00040732542,0.0012839327,0.00022649542,0.00043836437],"domain_scores_gemma":[0.9983586,0.00026181783,0.00018543264,0.0009218581,0.00010448164,0.00016778515],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000672338,0.00044370288,0.00049610843,0.00026633978,0.00007247145,0.00018040687,0.0021367378,0.0003491985,0.000023643326],"category_scores_gemma":[0.0000599849,0.00040515474,0.0001728781,0.001108197,0.00006419606,0.00028231714,0.0037430776,0.0018356835,0.000015625948],"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":[0.000041332118,0.00012411935,0.0032200234,0.00053794106,0.00015809064,0.00022899063,0.008723598,0.18228756,0.00062768825,0.68983454,0.0038574615,0.11035867],"study_design_scores_gemma":[0.00023436554,0.000027786671,0.002885726,0.00017799214,0.000005002645,0.000005002375,0.000037439855,0.18758883,0.00064256217,0.80753577,0.00030455794,0.0005549502],"about_ca_topic_score_codex":0.00003877877,"about_ca_topic_score_gemma":0.00041285003,"teacher_disagreement_score":0.8097786,"about_ca_system_score_codex":0.00007991809,"about_ca_system_score_gemma":0.00013536499,"threshold_uncertainty_score":0.99984},"labels":[],"label_agreement":null},{"id":"W3089842617","doi":"10.1007/978-3-030-58799-4_65","title":"Classification of Actors in Social Networks Using RLVECO","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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":"Computer science; Classifier (UML); Categorization; Artificial intelligence; Social network (sociolinguistics); Graph; Node (physics); Machine learning; Social network analysis; Data science; Theoretical computer science; World Wide Web","score_opus":0.039362634082580325,"score_gpt":0.27704550950975365,"score_spread":0.2376828754271733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089842617","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":[0.00048966706,0.00022120588,0.99681866,0.00043789422,0.0011343358,0.0003164075,0.0000020701414,0.000084579166,0.0004951589],"genre_scores_gemma":[0.80029243,0.000026477572,0.19848247,0.00056675664,0.00057949574,0.0000033841377,0.000004065171,0.00003294858,0.000011975163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966422,0.00005237433,0.0006992595,0.0013297815,0.0007147748,0.00056162087],"domain_scores_gemma":[0.99811184,0.00034533528,0.00055869407,0.000719236,0.00014803928,0.00011683477],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000402599,0.0004410939,0.0006203714,0.00070964213,0.00016364842,0.00015219761,0.0026580484,0.0004064632,0.000004201699],"category_scores_gemma":[0.000050264578,0.00044498994,0.00014804208,0.0015863607,0.00064656,0.0006370889,0.0010023462,0.0011196844,0.0000026906305],"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":[0.000013387286,0.00002732334,0.00058225536,0.00003584988,0.000008833458,0.00005555238,0.00063252286,0.57043517,0.00073267904,0.041794483,0.0000118700045,0.3856701],"study_design_scores_gemma":[0.00016805522,0.00005888268,0.0013146707,0.00017928156,0.0000041737,0.000009722015,1.5776449e-7,0.92492056,0.00018436629,0.07268246,0.00008683585,0.00039082213],"about_ca_topic_score_codex":0.000009695543,"about_ca_topic_score_gemma":0.000050084007,"teacher_disagreement_score":0.7998028,"about_ca_system_score_codex":0.00031690783,"about_ca_system_score_gemma":0.00027953662,"threshold_uncertainty_score":0.9998002},"labels":[],"label_agreement":null},{"id":"W3090786554","doi":"","title":"Non Parametric Graph Learning for Bayesian Graph Neural Networks","year":2020,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Computer science; Adjacency matrix; Inference; Graph; Theoretical computer science; Artificial intelligence; Machine learning; Statistical relational learning; Data mining; Relational database","score_opus":0.04336867985110546,"score_gpt":0.2901535011615251,"score_spread":0.24678482131041965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090786554","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":[0.0106334165,0.0002379478,0.9849193,0.0022589646,0.00072624016,0.00080036133,0.0000018135154,0.00032711047,0.00009486596],"genre_scores_gemma":[0.98393875,0.00008121945,0.013921,0.0015821868,0.00030647693,0.00011288664,0.0000122155525,0.00003418943,0.000011065288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646944,0.00014479541,0.0008725834,0.0011143332,0.000369234,0.0010296338],"domain_scores_gemma":[0.99783915,0.000882119,0.00026547533,0.00050006656,0.00016288724,0.00035029498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044359703,0.00040940367,0.00047578482,0.00044228273,0.00030891143,0.0002621942,0.0015835775,0.00019808377,0.000015893072],"category_scores_gemma":[0.0005406115,0.00041446075,0.00029502166,0.0051453756,0.00019101851,0.0005782825,0.00026721784,0.0009109877,0.00001847114],"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":[0.00008500252,0.000042807118,0.00058262737,0.000012682224,0.000008623659,0.000023985494,0.0004291863,0.81509036,0.00009443361,0.020580912,0.00007759252,0.16297182],"study_design_scores_gemma":[0.00007803739,0.00040942925,0.00008410281,0.000025942776,0.0000071192467,0.000004406406,0.00017191848,0.93551326,0.00088082516,0.06214985,0.00023922019,0.0004358636],"about_ca_topic_score_codex":0.00008285142,"about_ca_topic_score_gemma":0.00013641687,"teacher_disagreement_score":0.97330534,"about_ca_system_score_codex":0.00006188462,"about_ca_system_score_gemma":0.00003783708,"threshold_uncertainty_score":0.9998307},"labels":[],"label_agreement":null},{"id":"W3091261939","doi":"10.48550/arxiv.2010.02089","title":"CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"ENCODE; Computer science; Leverage (statistics); Theoretical computer science; Graph; Correlation; Artificial neural network; Copula (linguistics); Node (physics); Artificial intelligence; Machine learning; Data mining; Mathematics; Econometrics","score_opus":0.05592077022521464,"score_gpt":0.21002633409231508,"score_spread":0.15410556386710045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091261939","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":[0.32174742,0.00035556918,0.67652774,0.00020607092,0.00049011275,0.00028894836,0.000014531887,0.00010883374,0.00026079113],"genre_scores_gemma":[0.9945067,0.00026461697,0.0049663493,0.00011919705,0.00004758575,0.0000017892868,0.000051030784,0.000015360676,0.000027333954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784523,0.00019502954,0.00040298371,0.0011069764,0.00015190236,0.00029785704],"domain_scores_gemma":[0.99844384,0.00029071057,0.00043595265,0.00052706676,0.00014715132,0.00015527869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015857068,0.0003239518,0.00043007685,0.00038995492,0.000092222945,0.000063627675,0.0010190719,0.0002541072,0.0000067287633],"category_scores_gemma":[0.000085594256,0.00037272632,0.0001972223,0.0013307272,0.00023893449,0.00050914724,0.0015984979,0.0010046302,7.174671e-7],"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":[0.00004348249,0.000027416245,0.050532788,0.000028808532,0.000032103257,0.00015655905,0.00016704955,0.7446545,0.000013651531,0.20232671,0.00004744168,0.001969494],"study_design_scores_gemma":[0.00036780807,0.000044291737,0.023735251,0.000086089356,0.000017613418,0.000008181199,0.000067526584,0.79471856,0.000009524872,0.18069239,0.0000037538437,0.00024899314],"about_ca_topic_score_codex":0.00011775075,"about_ca_topic_score_gemma":0.00016309353,"teacher_disagreement_score":0.67275935,"about_ca_system_score_codex":0.00005190398,"about_ca_system_score_gemma":0.00007761245,"threshold_uncertainty_score":0.99987245},"labels":[],"label_agreement":null},{"id":"W3091984691","doi":"10.48550/arxiv.2010.04029","title":"RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Probabilistic logic network; Computer science; Artificial intelligence; Rule of inference; Probabilistic logic; Inference; Machine learning; Set (abstract data type); Logic programming; Autoepistemic logic; Multimodal logic; Description logic; Programming language","score_opus":0.10621017339645404,"score_gpt":0.22409572232527014,"score_spread":0.1178855489288161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091984691","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":[0.037833,0.00040903085,0.954908,0.00025060002,0.0009721085,0.00064307643,0.000012716749,0.0010220684,0.003949385],"genre_scores_gemma":[0.9802961,0.00044649062,0.017962812,0.00026657677,0.00015038816,0.00000534903,0.000038723894,0.000038616083,0.0007949699],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970106,0.00022138392,0.00023521917,0.0018509257,0.00009140091,0.0005904682],"domain_scores_gemma":[0.99777824,0.00050147343,0.00037529803,0.00091608847,0.00017107665,0.00025783223],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020615612,0.00050357677,0.0005051721,0.00024488653,0.00040699527,0.00014240331,0.0022752935,0.0003707924,0.000007454995],"category_scores_gemma":[0.00013947468,0.00054729235,0.00046995355,0.0008134867,0.00012585605,0.0003088112,0.002088592,0.0013657063,0.00009929163],"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":[0.000046302044,0.000042222408,0.00035269244,0.00005309208,0.000045146604,0.00012893479,0.000103710125,0.30790123,0.00001925994,0.68952584,0.00031421613,0.0014673535],"study_design_scores_gemma":[0.0003661999,0.0002315745,0.00028992735,0.0001457914,0.000035993842,0.000002921106,0.00003024602,0.54559296,0.000049772407,0.45127565,0.0014831844,0.0004957818],"about_ca_topic_score_codex":0.0000066991547,"about_ca_topic_score_gemma":0.00000691349,"teacher_disagreement_score":0.9424631,"about_ca_system_score_codex":0.00014002797,"about_ca_system_score_gemma":0.00009332145,"threshold_uncertainty_score":0.99969786},"labels":[],"label_agreement":null},{"id":"W3092206109","doi":"10.1609/aaai.v35i11.17203","title":"GraphMix: Improved Training of GNNs for Semi-Supervised Learning","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Graph; Convolutional neural network; Theoretical computer science; Artificial intelligence; Machine learning; Regularization (linguistics); Artificial neural network","score_opus":0.0913165739348398,"score_gpt":0.29778026928480694,"score_spread":0.20646369534996714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092206109","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":[0.31771746,0.00017163901,0.66964996,0.004306404,0.0011868393,0.0011818967,0.000014704743,0.00027727793,0.0054937955],"genre_scores_gemma":[0.976676,0.000056416502,0.02280969,0.00015614257,0.00006044444,0.000040421295,0.0000010447968,0.000018572622,0.00018129412],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978869,0.000025013249,0.00067106506,0.000601068,0.0003605714,0.00045539316],"domain_scores_gemma":[0.99753636,0.00029466982,0.0005201087,0.00032217818,0.0012299517,0.00009674401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004273824,0.00024518656,0.00041249904,0.00012263953,0.00022439127,0.00012711814,0.0015871756,0.00011024882,0.00001999958],"category_scores_gemma":[0.0008843141,0.00020308357,0.00027682236,0.0011229285,0.0002497361,0.00035715755,0.0003607503,0.0003930716,0.0000033267781],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005126948,0.00005989534,0.00011893822,0.00006257864,0.000021363463,4.4123365e-7,0.001789458,0.00051526516,0.35249233,0.5363854,0.00002248447,0.10848057],"study_design_scores_gemma":[0.00004864394,0.00021016572,0.00004593157,0.00019647401,0.00001280261,0.000004765016,0.001052029,0.22038768,0.6262359,0.15154931,0.000073301075,0.00018299883],"about_ca_topic_score_codex":0.0000067825426,"about_ca_topic_score_gemma":0.0000078967705,"teacher_disagreement_score":0.6589585,"about_ca_system_score_codex":0.000021122454,"about_ca_system_score_gemma":0.0001424169,"threshold_uncertainty_score":0.82815075},"labels":[],"label_agreement":null},{"id":"W3093694263","doi":"","title":"FLAG: Adversarial Data Augmentation for Graph Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Graph; Adversarial system; Flag (linear algebra); Node (physics); Code (set theory); Artificial neural network; Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.11529997396812945,"score_gpt":0.22300162707284968,"score_spread":0.10770165310472023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093694263","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":[0.017987398,0.00019903324,0.9746874,0.000138319,0.0057939338,0.00070765155,0.00005820067,0.00033145503,0.0000966056],"genre_scores_gemma":[0.9823207,0.0002770975,0.015204453,0.00029150993,0.0004721562,0.0000041118224,0.001055108,0.000036442038,0.00033841026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99656177,0.00018467495,0.00030926554,0.002234203,0.00012457007,0.00058551284],"domain_scores_gemma":[0.9957509,0.00029029255,0.0004018392,0.0031306616,0.00021783749,0.00020843833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025142657,0.0004413393,0.000434371,0.00022034993,0.00028013645,0.00029283302,0.004356369,0.00038729305,0.000011844144],"category_scores_gemma":[0.000048121765,0.00054581824,0.00030911434,0.0009500299,0.00011944881,0.0015701902,0.005759543,0.0007478122,0.0000033302017],"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":[0.00007571671,0.00005072591,0.00030386145,0.000036014862,0.00010768802,0.00018531695,0.000054238906,0.9761512,0.000011571649,0.018901814,0.0014720598,0.0026497704],"study_design_scores_gemma":[0.0009620178,0.00005277412,0.00019428728,0.00005021267,0.000118129006,0.000005945754,0.000050454455,0.9816434,0.000021106796,0.016079862,0.0002929144,0.00052890653],"about_ca_topic_score_codex":0.000056459587,"about_ca_topic_score_gemma":0.00013528918,"teacher_disagreement_score":0.9643333,"about_ca_system_score_codex":0.00012170384,"about_ca_system_score_gemma":0.00012533,"threshold_uncertainty_score":0.99969935},"labels":[],"label_agreement":null},{"id":"W3093959361","doi":"10.1109/tmm.2020.3034530","title":"Anisotropic Graph Convolutional Network for Semi-Supervised Learning","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Multimedia","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Smoothing; Node (physics); Artificial intelligence; Theoretical computer science; Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.031109731398920697,"score_gpt":0.26490500993720517,"score_spread":0.23379527853828447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093959361","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":[0.00064750784,0.00027945,0.98988,0.0012132657,0.0056738956,0.0011644093,0.00011463224,0.000984582,0.000042244672],"genre_scores_gemma":[0.4491241,0.00036779937,0.54726654,0.00089873985,0.0010242691,0.0008022287,0.00011769222,0.00010501102,0.0002936421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664193,0.00018407225,0.0005618859,0.0013593805,0.0004988708,0.0007538543],"domain_scores_gemma":[0.99757755,0.0008359312,0.00025960756,0.00077170227,0.00019947386,0.00035573446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015579387,0.0005950445,0.0006014436,0.00020908218,0.00054886437,0.0001570944,0.0013072139,0.0005040176,0.000036864323],"category_scores_gemma":[0.000022703196,0.0006470763,0.0006479785,0.0006203852,0.00014554564,0.00027921612,0.000031497846,0.0021954891,0.00006082164],"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":[0.00007638789,0.000085182226,0.000032234588,0.00007121787,0.00014573443,0.000012489725,0.00026295544,0.9598563,0.0003686751,0.00040711308,0.0017378202,0.036943946],"study_design_scores_gemma":[0.0011236405,0.00023072393,0.00013158398,0.000142133,0.00007311762,0.000009070878,0.00001178032,0.9838064,0.00073410233,0.010860729,0.0022222733,0.0006544685],"about_ca_topic_score_codex":0.000017270282,"about_ca_topic_score_gemma":0.000023612496,"teacher_disagreement_score":0.44847658,"about_ca_system_score_codex":0.000121509416,"about_ca_system_score_gemma":0.00018932913,"threshold_uncertainty_score":0.999598},"labels":[],"label_agreement":null},{"id":"W3094859785","doi":"10.1016/j.patcog.2022.108973","title":"GripNet: Graph information propagation on supergraph for heterogeneous graphs","year":2022,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute; University of Waterloo; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; University of Sheffield; Compute Canada","keywords":"Epigraph; Computer science; Graph; Combinatorics; Mathematics; Theoretical computer science; Mathematical optimization","score_opus":0.01986458204256742,"score_gpt":0.2278329279900972,"score_spread":0.20796834594752978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094859785","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":[0.16343027,0.000030750023,0.83271736,0.0005313579,0.001482535,0.0011365763,0.00014189805,0.00036360684,0.00016564915],"genre_scores_gemma":[0.9920961,0.000022248401,0.003133636,0.0027212498,0.000087511435,0.0012104949,0.000703908,0.000017367562,0.0000074492627],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984983,0.000114357,0.00033174345,0.0003443402,0.00039015204,0.00032108964],"domain_scores_gemma":[0.9991849,0.00010005467,0.00020108499,0.00032220845,0.00012211605,0.00006961368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022367713,0.00018280055,0.0001327949,0.00043015555,0.00049778406,0.0001134142,0.00041107085,0.00004858227,0.00003384508],"category_scores_gemma":[0.000019002002,0.00019333318,0.00015967141,0.0006385892,0.000022951794,0.0010952567,0.00011351679,0.00022130823,0.000031116437],"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":[0.00007220401,0.00011247441,0.00045759472,0.000038858372,0.00002586832,0.000005494204,0.00035157823,0.005535013,0.00043430223,0.0009225839,0.0011385258,0.9909055],"study_design_scores_gemma":[0.008076444,0.008667555,0.0073446105,0.00020117058,0.000115192925,0.0005464725,0.00032606575,0.3280576,0.036017157,0.57550603,0.031609956,0.0035317144],"about_ca_topic_score_codex":0.000009127089,"about_ca_topic_score_gemma":0.000007891964,"teacher_disagreement_score":0.98737377,"about_ca_system_score_codex":0.000057704292,"about_ca_system_score_gemma":0.000013722337,"threshold_uncertainty_score":0.7883898},"labels":[],"label_agreement":null},{"id":"W3096565974","doi":"10.1007/s10115-020-01521-9","title":"CANE: community-aware network embedding via adversarial training","year":2020,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Advanced Graph Neural Networks","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":"Novelis (Canada)","funders":"","keywords":"Computer science; Node (physics); Discriminative model; Pairwise comparison; Embedding; Adversarial system; Machine learning; Representation (politics); Feature learning; Data mining; Artificial intelligence; Community structure; Theoretical computer science; Mathematics","score_opus":0.0314213780843983,"score_gpt":0.2559193188299602,"score_spread":0.22449794074556192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096565974","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":[0.0020054453,0.0004720958,0.98532814,0.00020804716,0.0014470192,0.00029105935,0.0000037490558,0.00033090348,0.009913518],"genre_scores_gemma":[0.99745417,0.00002290612,0.0012728056,0.00065176195,0.00053227844,0.000017290185,0.000022015292,0.0000061668834,0.000020593603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988734,0.00017751515,0.00042074782,0.00010973663,0.00014481107,0.00027380127],"domain_scores_gemma":[0.9991271,0.00015021373,0.00018389191,0.00023957856,0.000117256444,0.00018194775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034043205,0.0001462736,0.00022154809,0.00006032041,0.00047414863,0.00025309672,0.000438285,0.00008170644,0.0000019563365],"category_scores_gemma":[0.00003310089,0.0001385212,0.000040725197,0.000541997,0.000029972178,0.003942407,0.00024758413,0.00034286352,0.000071418384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073452415,0.00003456961,0.0009194394,0.00090274273,0.00012640994,0.0000093276085,0.2576198,0.18341272,0.00007237815,0.103893444,0.042087015,0.4108487],"study_design_scores_gemma":[0.0004069096,0.00007666868,0.0000970982,0.00007345512,0.0000041655726,0.000024325906,0.0013243249,0.8611942,0.000007638696,0.00010765567,0.13650347,0.00018007326],"about_ca_topic_score_codex":0.000021783706,"about_ca_topic_score_gemma":0.000006810562,"teacher_disagreement_score":0.9954487,"about_ca_system_score_codex":0.000026375983,"about_ca_system_score_gemma":0.000039282928,"threshold_uncertainty_score":0.56487304},"labels":[],"label_agreement":null},{"id":"W3096909414","doi":"10.1109/icassp39728.2021.9414557","title":"Geometric Scattering Attention Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"National Institute of General Medical Sciences","keywords":"Scattering; Computer science; Node (physics); Graph; Artificial intelligence; Representation (politics); Convolution (computer science); Feature learning; Wavelet; Computation; Cascade; Theoretical computer science; Pattern recognition (psychology); Algorithm; Artificial neural network; Physics; Optics; Engineering","score_opus":0.018667417344397298,"score_gpt":0.24933033677326974,"score_spread":0.23066291942887243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096909414","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":[0.012549732,0.0014910435,0.9799245,0.0004748687,0.003508548,0.00022043356,5.001697e-7,0.00059929816,0.0012310861],"genre_scores_gemma":[0.8461846,0.00048335994,0.1514198,0.0006913359,0.00037978066,0.00004876081,0.000042979733,0.00003068898,0.00071866036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975415,0.000081228296,0.00039033074,0.0011259838,0.00035400043,0.0005069895],"domain_scores_gemma":[0.99785346,0.00009648499,0.00022291577,0.0015483602,0.00014100477,0.0001377789],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000195938,0.00033766567,0.000363695,0.0004150569,0.00010991035,0.00071247184,0.0016553173,0.00031570793,0.00003956502],"category_scores_gemma":[0.000023399693,0.00034076834,0.00028325056,0.0016355328,0.000036021785,0.00047494355,0.005311689,0.000976913,0.000019363657],"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":[0.0000024932606,0.00007872999,0.0028411965,0.000100081714,0.00009861357,0.00018725476,0.00003812369,0.7288198,0.00021048925,0.004524751,0.0021675634,0.26093093],"study_design_scores_gemma":[0.00016585058,0.000023449003,0.016533546,0.00022542868,0.00001736114,0.00004848575,0.000009687407,0.97904474,0.00014620546,0.0025835468,0.00045715232,0.00074454775],"about_ca_topic_score_codex":0.00002300792,"about_ca_topic_score_gemma":0.000012335636,"teacher_disagreement_score":0.8336349,"about_ca_system_score_codex":0.00007778995,"about_ca_system_score_gemma":0.000045074554,"threshold_uncertainty_score":0.99990445},"labels":[],"label_agreement":null},{"id":"W3097986917","doi":"10.18653/v1/2020.emnlp-main.541","title":"Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":358,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"NIH Office of the Director; Office of Naval Research; National Science Foundation; Office of the Director of National Intelligence; Advanced Research Projects Agency; Defense Advanced Research Projects Agency; Intelligence Advanced Research Projects Activity","keywords":"Timestamp; Computer science; ENCODE; Inference; Autoregressive model; Event (particle physics); Graph; Artificial intelligence; Task (project management); Machine learning; Data mining; Theoretical computer science; Real-time computing; Mathematics","score_opus":0.02230392439341087,"score_gpt":0.2709334823829315,"score_spread":0.24862955798952066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097986917","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":[0.011660584,0.0032945601,0.9736903,0.0038701312,0.0028382065,0.0006279959,0.000010407727,0.001145962,0.0028618725],"genre_scores_gemma":[0.96071994,0.00005158392,0.036991864,0.001644178,0.00040291672,0.000011813654,0.000012033195,0.000017531367,0.00014815679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979629,0.00011120996,0.000345701,0.00070742576,0.00029624457,0.00057652756],"domain_scores_gemma":[0.9987471,0.000086425694,0.0001734302,0.0005074951,0.000116437994,0.00036908436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066014596,0.00031042477,0.00029140004,0.000052901803,0.00015946415,0.00012319449,0.0011901587,0.00011558595,0.00011386067],"category_scores_gemma":[0.000033368793,0.00024060055,0.00014752043,0.00093385874,0.000074003125,0.00058119325,0.00068508566,0.00045496383,0.000053210642],"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":[0.00004860102,0.00009364819,0.008628541,0.000041162442,0.000077691264,0.00007606022,0.0019856982,0.023706201,0.000118729484,0.68693185,0.118571565,0.15972027],"study_design_scores_gemma":[0.0010716484,0.0005958984,0.009423153,0.0001092324,0.000019170711,0.000023166225,0.000029313846,0.74042755,0.0005168811,0.18938148,0.057309907,0.0010926274],"about_ca_topic_score_codex":0.000003378883,"about_ca_topic_score_gemma":0.000044375316,"teacher_disagreement_score":0.94905937,"about_ca_system_score_codex":0.000031691,"about_ca_system_score_gemma":0.0000825923,"threshold_uncertainty_score":0.98114055},"labels":[],"label_agreement":null},{"id":"W3098770144","doi":"","title":"Graph Policy Network for Transferable Active Learning on Graphs","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Computer science; Reinforcement learning; Machine learning; Artificial intelligence; Graph; Theoretical computer science","score_opus":0.0221627674460945,"score_gpt":0.25844239892404175,"score_spread":0.23627963147794726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098770144","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":[0.004814213,0.0001772694,0.9872771,0.00366144,0.00059446594,0.00095413113,0.000008514693,0.00093918346,0.0015736704],"genre_scores_gemma":[0.99256295,0.000015200074,0.002951238,0.0038412057,0.00039028475,0.00015114245,0.000031540312,0.000017537477,0.000038924947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982492,0.0000751961,0.00053339696,0.0002824496,0.0003501019,0.0005096761],"domain_scores_gemma":[0.9989576,0.00012602343,0.00032861068,0.00018064163,0.0002221307,0.00018502127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016007356,0.00024518368,0.00027338925,0.00018666587,0.00060129334,0.0006248471,0.0005589314,0.00010264559,7.3178734e-7],"category_scores_gemma":[0.00009395261,0.0002200578,0.00012160948,0.0016236525,0.00003607421,0.0046960423,0.00004151621,0.00037423166,0.0000150781325],"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":[0.00010193824,0.000008877867,0.00006196824,0.0002802168,0.000013732559,0.0000010256713,0.0026271269,0.8031144,0.00008002591,0.05263317,0.0014204478,0.13965704],"study_design_scores_gemma":[0.0006785188,0.00044595372,0.00009089391,0.00012432027,0.000006222735,0.000013655599,0.0001902798,0.9758629,0.00030927305,0.0025528364,0.01941018,0.00031494326],"about_ca_topic_score_codex":0.0000110396095,"about_ca_topic_score_gemma":6.246267e-7,"teacher_disagreement_score":0.98774874,"about_ca_system_score_codex":0.000040573068,"about_ca_system_score_gemma":0.000072807416,"threshold_uncertainty_score":0.8973697},"labels":[],"label_agreement":null},{"id":"W3099845049","doi":"10.18653/v1/2020.emnlp-main.462","title":"TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Samsung; Compute Canada; Canadian Institute for Advanced Research","keywords":"Leverage (statistics); Computer science; Knowledge graph; Message passing; Graph; Temporal database; Machine learning; Artificial intelligence; Theoretical computer science; Data mining; Distributed computing","score_opus":0.05205755584159759,"score_gpt":0.2873606080580615,"score_spread":0.23530305221646391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099845049","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":[0.002394414,0.00020022671,0.9882629,0.005413607,0.00040034976,0.0004822804,0.0000055390847,0.00074566423,0.0020950069],"genre_scores_gemma":[0.8022373,0.000004144016,0.19566312,0.0016061137,0.00023058706,0.000039782346,0.000022238077,0.000022303075,0.0001744413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982554,0.00007415263,0.00036525313,0.00065119914,0.00021035223,0.00044366106],"domain_scores_gemma":[0.99894065,0.00016303545,0.00013934384,0.00038884263,0.00011573145,0.00025239348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017450654,0.00024284494,0.00029953322,0.000091593334,0.00027573254,0.0001753278,0.0008264858,0.000083767205,0.000015980155],"category_scores_gemma":[0.000037428934,0.00021452001,0.00018882788,0.0008593232,0.000078442135,0.00078671385,0.0002735007,0.00018908572,0.000035296052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023711048,0.0005362382,0.026339523,0.00041924772,0.00016044787,0.000090696034,0.00319468,0.0028397616,0.012698977,0.5665865,0.22494033,0.16195652],"study_design_scores_gemma":[0.0023034872,0.00074125687,0.0029822171,0.00007250892,0.000022548558,0.000020674013,0.00011956918,0.77048695,0.0039748526,0.04271487,0.17537817,0.0011828755],"about_ca_topic_score_codex":0.000010027756,"about_ca_topic_score_gemma":0.00002767255,"teacher_disagreement_score":0.79984283,"about_ca_system_score_codex":0.000025493291,"about_ca_system_score_gemma":0.000042351465,"threshold_uncertainty_score":0.8747872},"labels":[],"label_agreement":null},{"id":"W3101708158","doi":"10.18653/v1/2020.findings-emnlp.241","title":"Out-of-Sample Representation Learning for Knowledge Graphs","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Boréal","funders":"","keywords":"Computer science; Representation (politics); Benchmark (surveying); Artificial intelligence; Machine learning; Feature learning; Knowledge graph; Sample (material); Graph; Task (project management); Theoretical computer science","score_opus":0.07396768505577844,"score_gpt":0.3258759174721643,"score_spread":0.2519082324163859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101708158","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":[0.0021116184,0.000093245195,0.99550694,0.00075353927,0.00022484208,0.0001721972,5.746841e-7,0.00018785584,0.00094919954],"genre_scores_gemma":[0.7641365,0.000017214987,0.23532274,0.00027223415,0.00006280289,0.000018254404,0.0000051519887,0.000008275224,0.00015683929],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931914,0.00003160427,0.00015869326,0.00026824212,0.00007934957,0.00014298176],"domain_scores_gemma":[0.99908537,0.0005318759,0.00007356911,0.00016450093,0.000078264035,0.00006641984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056399007,0.00006800935,0.000110507404,0.000035835266,0.00006139906,0.000023192593,0.00033386183,0.000025947505,0.0000066034668],"category_scores_gemma":[0.00027261098,0.0000624312,0.00007985257,0.0004162267,0.000021507238,0.0002868136,0.00011851343,0.00007835034,0.000008167517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044704575,0.000055860783,0.007011655,0.0000672443,0.000035245208,0.000002028753,0.006064823,0.024564356,0.0062434217,0.74919623,0.0068568476,0.1998576],"study_design_scores_gemma":[0.00045344673,0.0002702384,0.0004164736,0.000008379729,0.0000054746083,4.5022114e-7,0.00010404211,0.91277504,0.012935988,0.058926318,0.013928963,0.00017516926],"about_ca_topic_score_codex":0.0000032875014,"about_ca_topic_score_gemma":0.000005443651,"teacher_disagreement_score":0.8882107,"about_ca_system_score_codex":0.0000036686472,"about_ca_system_score_gemma":0.000010281441,"threshold_uncertainty_score":0.25458705},"labels":[],"label_agreement":null},{"id":"W3104591796","doi":"","title":"Principal Neighbourhood Aggregation for Graph Nets","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Exploit; Theoretical computer science; Graph; Neighbourhood (mathematics); News aggregator; Graph isomorphism; Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.026325718818596754,"score_gpt":0.25203489105682375,"score_spread":0.225709172238227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104591796","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":[0.0048575113,0.00024796338,0.98959696,0.0026037877,0.00070729014,0.0007620985,0.0000061295314,0.00066360034,0.0005546545],"genre_scores_gemma":[0.9886283,0.000004654992,0.008236647,0.0027401012,0.00020544288,0.00012430342,0.0000315886,0.00001115079,0.000017798684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984388,0.000034677083,0.00060451217,0.00023971335,0.00034974745,0.00033257212],"domain_scores_gemma":[0.99875563,0.000053583673,0.0005080537,0.00021619389,0.00029704225,0.00016948197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014024197,0.00019191254,0.00020170394,0.00011219492,0.00029101892,0.0008025306,0.00059154717,0.000088029585,0.000001191182],"category_scores_gemma":[0.00009568107,0.00017287323,0.00007860142,0.0007520104,0.000027331416,0.0073684184,0.000086030406,0.00016011902,0.0000219962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084569954,0.000026791946,0.00050570624,0.001177536,0.00002283234,0.0000035229668,0.0060449075,0.22787754,0.0005124125,0.05465707,0.0038996506,0.70518744],"study_design_scores_gemma":[0.00048895704,0.00012662256,0.00021776148,0.00006059597,0.000004506861,0.00002269547,0.000076552904,0.98542744,0.00033761087,0.00054920145,0.012471456,0.00021661725],"about_ca_topic_score_codex":0.0000038973,"about_ca_topic_score_gemma":4.998125e-7,"teacher_disagreement_score":0.9837708,"about_ca_system_score_codex":0.000029013014,"about_ca_system_score_gemma":0.00005182268,"threshold_uncertainty_score":0.7738821},"labels":[],"label_agreement":null},{"id":"W3110887761","doi":"10.1155/2020/6831603","title":"CAREA: Cotraining Attribute and Relation Embeddings for Cross-Lingual Entity Alignment in Knowledge Graphs","year":2020,"lang":"en","type":"article","venue":"Discrete Dynamics in Nature and Society","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Chengdu Science and Technology Bureau; Xihua University; Jiangsu Development and Reform Commission; National Natural Science Foundation of China","keywords":"Computer science; Relation (database); Code (set theory); Set (abstract data type); Knowledge graph; Value (mathematics); Data mining; Artificial intelligence; Information retrieval; Natural language processing; Machine learning","score_opus":0.012213204037411373,"score_gpt":0.30391747024477406,"score_spread":0.2917042662073627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110887761","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":[0.87336224,0.0026216942,0.121911295,0.0010362858,0.00030015945,0.00047727625,0.00006589952,0.00007599569,0.0001491348],"genre_scores_gemma":[0.98606616,0.00041904757,0.012957368,0.00042137763,0.00003774917,0.000015504582,0.00006250913,0.000010390245,0.000009897257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884874,0.000029533338,0.00022926906,0.0004928112,0.000120525016,0.0002791318],"domain_scores_gemma":[0.9994845,0.00017393593,0.000079509526,0.00012761923,0.00004986692,0.00008458025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023497839,0.0001589283,0.00020580975,0.00003407299,0.00014399343,0.000114048395,0.00021216644,0.0002938079,1.7372345e-7],"category_scores_gemma":[0.000060118495,0.00015800881,0.000096860436,0.00046409667,0.00010235298,0.0003961184,0.00021343901,0.0006318021,8.114375e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047147165,0.000042189196,0.22290687,0.0002313776,0.000052233365,0.000016966671,0.034917388,0.0026697258,0.00016664717,0.73180765,0.00020834248,0.0069334903],"study_design_scores_gemma":[0.0009771402,0.00006606953,0.025139637,0.000046042776,0.000006472751,0.0000031398667,0.0008338674,0.95360076,0.00002792737,0.018929787,0.00014275215,0.00022642565],"about_ca_topic_score_codex":0.000005770953,"about_ca_topic_score_gemma":0.000089775254,"teacher_disagreement_score":0.950931,"about_ca_system_score_codex":0.0000987949,"about_ca_system_score_gemma":0.000021069218,"threshold_uncertainty_score":0.64434123},"labels":[],"label_agreement":null},{"id":"W3112618978","doi":"10.1109/smc42975.2020.9283008","title":"Link Prediction in Social Graphs using Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN)","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Compute Canada","keywords":"Computer science; Artificial intelligence; Social network (sociolinguistics); Machine learning; Classifier (UML); Feature learning; Graph; Social network analysis; Theoretical computer science; Social media","score_opus":0.03694742911233086,"score_gpt":0.2989916323043575,"score_spread":0.26204420319202665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112618978","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":[0.20938721,0.00015842,0.7879701,0.00096263277,0.00019109639,0.00020118758,5.444789e-7,0.0003433193,0.00078543014],"genre_scores_gemma":[0.9824569,0.0000324059,0.016982066,0.00028415292,0.00018310946,0.000006394372,0.000004720845,0.000012441439,0.000037780374],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869835,0.000119215256,0.00027109028,0.0005060013,0.00015443555,0.00025089853],"domain_scores_gemma":[0.999542,0.0000900729,0.00010734478,0.00010430071,0.000057982714,0.00009828127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013067121,0.00013968594,0.00017556723,0.00016364918,0.00021838621,0.00009807154,0.00020708753,0.000093637,0.000007964214],"category_scores_gemma":[0.000039909624,0.00014509258,0.000054616696,0.0013453364,0.00007004075,0.0008564653,0.00019960893,0.00034321749,0.000004887527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019533632,0.00015300782,0.24683586,0.00016826653,0.00011542189,0.00009804791,0.042131312,0.09601999,0.06787707,0.06891079,0.0026815033,0.47481337],"study_design_scores_gemma":[0.0005175668,0.00008598034,0.0112215495,0.000015935306,0.0000065912322,0.000010476019,0.0001083838,0.9751401,0.00068557315,0.011718384,0.0003164205,0.0001730599],"about_ca_topic_score_codex":0.000020653106,"about_ca_topic_score_gemma":0.000011199094,"teacher_disagreement_score":0.8791201,"about_ca_system_score_codex":0.00002054332,"about_ca_system_score_gemma":0.000014536998,"threshold_uncertainty_score":0.59167033},"labels":[],"label_agreement":null},{"id":"W3115586753","doi":"10.18653/v1/2020.coling-main.520","title":"A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Research Foundation Singapore; National Research Foundation","keywords":"Computer science; Focus (optics); Benchmark (surveying); Graph; Artificial intelligence; Attention network; Task (project management); Natural language processing; Theoretical computer science","score_opus":0.026341944748326615,"score_gpt":0.30509485062306096,"score_spread":0.27875290587473434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115586753","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":[0.13932331,0.0001434249,0.8569396,0.00082138757,0.0010854366,0.00088692986,0.000013454599,0.000427137,0.0003592952],"genre_scores_gemma":[0.9371261,0.000028411341,0.05763234,0.0038635205,0.0006154499,0.00016617813,0.000026582506,0.000027330268,0.00051406113],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996841,0.00007285601,0.00062911405,0.0010636562,0.00052346586,0.00086991605],"domain_scores_gemma":[0.9984646,0.00018109502,0.0002552284,0.00055582385,0.00019691352,0.00034636704],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003295553,0.0003449252,0.00036075167,0.000040096766,0.00044348586,0.0005046917,0.0010443671,0.00012749882,0.00003777548],"category_scores_gemma":[0.00006146672,0.000324953,0.00034654076,0.0005719481,0.00015897006,0.0008110724,0.00047434532,0.00018372694,0.000045903027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013278219,0.0008718107,0.027736986,0.000293313,0.00071151624,0.00010603827,0.0034568657,0.27472222,0.052466772,0.49788564,0.04190525,0.09851576],"study_design_scores_gemma":[0.022858234,0.0059483573,0.03842436,0.000310306,0.00017374942,0.00005046988,0.00039155767,0.44078618,0.24456747,0.1803389,0.06024978,0.0059006275],"about_ca_topic_score_codex":0.000010963625,"about_ca_topic_score_gemma":0.00002171534,"teacher_disagreement_score":0.7993073,"about_ca_system_score_codex":0.00006839332,"about_ca_system_score_gemma":0.000038418206,"threshold_uncertainty_score":0.99992025},"labels":[],"label_agreement":null},{"id":"W3117983049","doi":"10.1109/vcip49819.2020.9301851","title":"GRNet: Deep Convolutional Neural Networks based on Graph Reasoning for Semantic Segmentation","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Research and Development; Fundamental Research Funds for the Central Universities","keywords":"Computer science; Artificial intelligence; Graph; Convolutional neural network; Segmentation; Deep learning; Benchmark (surveying); Feature (linguistics); Pattern recognition (psychology); Machine learning; Theoretical computer science","score_opus":0.017913141524335336,"score_gpt":0.24681229688382822,"score_spread":0.22889915535949287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117983049","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":[0.0016824763,0.00007487058,0.9939093,0.003067476,0.0003405819,0.00042393463,0.0000016778323,0.00033085555,0.00016884031],"genre_scores_gemma":[0.8468739,0.000003712268,0.14231741,0.010492845,0.00019267564,0.000053419106,0.000031136024,0.000015885913,0.00001905163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985816,0.000054366275,0.00022759041,0.00051546254,0.0002518128,0.00036918244],"domain_scores_gemma":[0.99911046,0.00030803293,0.00010349173,0.00023808787,0.000071692004,0.0001682551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009333326,0.00018238046,0.00015651525,0.000064506006,0.00019138318,0.000093688475,0.0004320842,0.00006042204,0.000016442375],"category_scores_gemma":[0.00004195735,0.00016900423,0.00012559678,0.00057830533,0.00004103859,0.00037064584,0.000066470726,0.00016541418,0.0000063278885],"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":[0.000054281,0.000020356323,0.0009604426,0.000008964832,0.000008635678,0.0000054855577,0.000043562955,0.95740896,0.000105632134,0.029558916,0.0011023709,0.010722403],"study_design_scores_gemma":[0.0007024406,0.0002716024,0.0012833576,0.000011279159,0.0000069974585,0.0000023211549,0.00000994891,0.9956163,0.00018949635,0.0016108484,0.00009847452,0.0001968913],"about_ca_topic_score_codex":0.000002738906,"about_ca_topic_score_gemma":0.0000060248317,"teacher_disagreement_score":0.8515919,"about_ca_system_score_codex":0.000024392053,"about_ca_system_score_gemma":0.000015575753,"threshold_uncertainty_score":0.6891792},"labels":[],"label_agreement":null},{"id":"W3118485733","doi":"10.1007/s10898-020-00973-1","title":"Learning chordal extensions","year":2021,"lang":"en","type":"article","venue":"Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Heuristics; Chordal graph; Generalization; Mathematics; Extension (predicate logic); Interval graph; Graph; Mathematical optimization; Computer science; Theoretical computer science; Artificial intelligence; Machine learning","score_opus":0.015406499583471449,"score_gpt":0.22881925229561526,"score_spread":0.2134127527121438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118485733","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":[0.32627988,0.0013226137,0.46121952,0.00837374,0.0023664432,0.0005598135,0.00005054678,0.0013897029,0.19843775],"genre_scores_gemma":[0.89853275,0.00073599117,0.031797647,0.0010030841,0.0001919315,0.000013243378,0.00008255448,0.000048484875,0.06759434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99670196,0.00030938606,0.0003309439,0.0011755235,0.0005435729,0.000938615],"domain_scores_gemma":[0.9980428,0.0003586068,0.00016821543,0.0008558017,0.00028099297,0.00029356597],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016687,0.0004208939,0.00047408443,0.00026868595,0.0010450168,0.00013114356,0.0011941685,0.00011692654,0.00008112893],"category_scores_gemma":[0.00006486356,0.00043934013,0.00025386276,0.0010171602,0.00027377874,0.0007241221,0.0022759691,0.0006682512,0.00027959136],"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":[0.00010030871,0.00050903123,0.018737571,0.000060613296,0.00040494904,0.007827154,0.0014953354,0.025213476,0.009640162,0.9127797,0.0077120573,0.0155196665],"study_design_scores_gemma":[0.0052439515,0.001007819,0.2171228,0.00041324127,0.00037659498,0.003219198,0.0022218227,0.058473937,0.0063661374,0.024103558,0.6771583,0.0042926446],"about_ca_topic_score_codex":0.000006577918,"about_ca_topic_score_gemma":0.00003710429,"teacher_disagreement_score":0.8886761,"about_ca_system_score_codex":0.000119956254,"about_ca_system_score_gemma":0.00016941206,"threshold_uncertainty_score":0.9998058},"labels":[],"label_agreement":null},{"id":"W3120491054","doi":"10.1145/3424672","title":"Knowledge Graph Embedding for Link Prediction","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":476,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Variety (cybernetics); Embedding; Link (geometry); Graph; Scale (ratio); Knowledge graph; Data science; Machine learning; Data mining; Artificial intelligence; Theoretical computer science","score_opus":0.06056504407758513,"score_gpt":0.32793144493453946,"score_spread":0.26736640085695434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120491054","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":[0.0010274919,0.0030198605,0.9866519,0.0007681875,0.0038325158,0.0003725753,0.0035940055,0.0003885637,0.00034487553],"genre_scores_gemma":[0.5396911,0.00286968,0.4436336,0.00052758574,0.0019532922,0.00045993575,0.006690711,0.0001821905,0.0039919023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971006,0.0001379858,0.0004704845,0.0015653883,0.00021430443,0.00051124225],"domain_scores_gemma":[0.99330395,0.00121803,0.00010631437,0.004986697,0.00021514272,0.00016983417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021335336,0.0003577867,0.00034772686,0.00021170963,0.00053947576,0.00041484495,0.0032765584,0.00018488147,0.000031350457],"category_scores_gemma":[0.00010684334,0.00037020253,0.00023996177,0.0012114805,0.00008668724,0.0040898737,0.00029353795,0.00046002638,0.000092098286],"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":[0.00015083805,0.0017347051,0.000121366575,0.00010724586,0.00050922995,0.00003732799,0.0012344314,0.008586855,0.005688496,0.009830159,0.019951787,0.9520476],"study_design_scores_gemma":[0.0050814697,0.0005496453,0.0014967568,0.00086570194,0.0004141126,0.00007229965,0.00033568352,0.48921257,0.043782406,0.1320297,0.3239738,0.0021858765],"about_ca_topic_score_codex":0.0000074234904,"about_ca_topic_score_gemma":0.00016949401,"teacher_disagreement_score":0.9498617,"about_ca_system_score_codex":0.00007979282,"about_ca_system_score_gemma":0.00019610135,"threshold_uncertainty_score":0.999875},"labels":[],"label_agreement":null},{"id":"W3121199044","doi":"10.48550/arxiv.2102.05034","title":"SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Graph; Adjacency list; Task (project management); Artificial intelligence; Theoretical computer science; Machine learning; Algorithm; Engineering","score_opus":0.027290511455729756,"score_gpt":0.18261588190147818,"score_spread":0.15532537044574843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121199044","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":[0.25947276,0.00042961136,0.7372357,0.00006440759,0.0016149668,0.00051577616,0.0000056425283,0.0006183762,0.000042708227],"genre_scores_gemma":[0.9820875,0.0004375268,0.016632952,0.00020141722,0.0002798891,0.0000030227607,0.000098659264,0.000054120344,0.00020491848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964409,0.00024069143,0.00032119593,0.0020721564,0.00013254116,0.0007925219],"domain_scores_gemma":[0.99726707,0.00030017737,0.000396297,0.0014467031,0.0003268944,0.00026285046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001555336,0.00062124705,0.00058736216,0.00028666103,0.000474713,0.00037960315,0.0023311898,0.000650273,0.0000110873225],"category_scores_gemma":[0.000044800378,0.0006936572,0.0005789027,0.0011814204,0.00009398781,0.0009135924,0.002986948,0.0018225237,0.0000015506188],"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":[0.000027988315,0.000034222492,0.00080054294,0.00007592596,0.00010408896,0.00017651614,0.00018860151,0.9849259,0.00016352326,0.008707727,0.0000940658,0.00470089],"study_design_scores_gemma":[0.0005539825,0.00012690485,0.00055856974,0.000082172664,0.00008748202,0.000013189331,0.00008140422,0.9781374,0.00009122391,0.019318877,0.00023844006,0.00071038207],"about_ca_topic_score_codex":0.000026899132,"about_ca_topic_score_gemma":0.000048499398,"teacher_disagreement_score":0.7226147,"about_ca_system_score_codex":0.00012599389,"about_ca_system_score_gemma":0.00009594395,"threshold_uncertainty_score":0.9995515},"labels":[],"label_agreement":null},{"id":"W3122308828","doi":"10.1109/icip42928.2021.9506042","title":"Improving Classification Accuracy With Graph Filtering","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Graph; Pattern recognition (psychology); Machine learning; Class (philosophy); Noise (video); Training set; Data mining; Theoretical computer science; Image (mathematics)","score_opus":0.029982215343688577,"score_gpt":0.2616561434561371,"score_spread":0.2316739281124485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122308828","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":[0.014134063,0.00020185424,0.9825594,0.0004742803,0.0006380779,0.00028558206,0.000001074402,0.0005789707,0.0011266941],"genre_scores_gemma":[0.5503436,0.00007255206,0.44900504,0.00025400493,0.00008790987,0.00007209693,0.000022293654,0.00002217049,0.00012033063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978681,0.000058707497,0.00029914096,0.0010969483,0.000320625,0.00035645606],"domain_scores_gemma":[0.9975528,0.00014061322,0.00033476032,0.00169371,0.00017038203,0.00010774069],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011144295,0.0003152753,0.00027139063,0.00014737499,0.00011815729,0.00070558593,0.0014224359,0.00017741472,0.000013823013],"category_scores_gemma":[0.00004318018,0.00026352657,0.00011702892,0.0004955647,0.00004511211,0.00079861976,0.002094524,0.0007336331,0.000004687517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028631479,0.00015996945,0.0013048124,0.0005187601,0.00018073541,0.00032591698,0.00093842734,0.07012503,0.028542379,0.049904782,0.00055406947,0.84741646],"study_design_scores_gemma":[0.0004262467,0.000098482546,0.007242993,0.0005340526,0.00003374484,0.00010553038,0.0001576963,0.9689703,0.010866766,0.009626615,0.00043893146,0.001498621],"about_ca_topic_score_codex":0.00004666573,"about_ca_topic_score_gemma":0.000049212882,"teacher_disagreement_score":0.8988453,"about_ca_system_score_codex":0.000050971677,"about_ca_system_score_gemma":0.0001281788,"threshold_uncertainty_score":0.9999817},"labels":[],"label_agreement":null},{"id":"W3124885591","doi":"10.48550/arxiv.2101.09951","title":"Fast &amp; Robust Image Interpolation using Gradient Graph Laplacian Regularizer","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Piecewise; Mathematics; Algorithm; Laplacian matrix; Pixel; Quadratic equation; Interpolation (computer graphics); Conjugate gradient method; Graph; Planar; Computer science; Artificial intelligence; Image (mathematics); Combinatorics; Geometry; Mathematical analysis","score_opus":0.07831453599808524,"score_gpt":0.1994874090030349,"score_spread":0.12117287300494967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124885591","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":[0.25368434,0.00007250115,0.74420154,0.00005298789,0.0010403084,0.00023848886,0.000007700731,0.00025558064,0.00044653707],"genre_scores_gemma":[0.8871651,0.00010186149,0.11186804,0.00011531854,0.0001021366,8.8965646e-7,0.0000653182,0.000037386606,0.00054390426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969571,0.00025463692,0.00032325805,0.0017289753,0.00016063605,0.000575393],"domain_scores_gemma":[0.9970418,0.00007536066,0.00043743168,0.0019034165,0.00029329615,0.0002486672],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019037607,0.0004968372,0.0004604786,0.0005021907,0.00028712832,0.00036278463,0.0017450936,0.00037083816,0.00003143496],"category_scores_gemma":[0.000029376453,0.0006113287,0.00043672198,0.0014907971,0.0002029476,0.0011611381,0.0029849645,0.001005201,0.000021263188],"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":[0.000035163477,0.00011041838,0.001946905,0.000071228846,0.00013206466,0.0006785154,0.00046932805,0.95841193,0.0014229824,0.03596016,0.00016549614,0.0005957818],"study_design_scores_gemma":[0.0004334467,0.000026481284,0.0009734789,0.00029053102,0.00008883301,0.000036320147,0.0001142379,0.9662666,0.00019014417,0.030617833,0.0001413218,0.00082079804],"about_ca_topic_score_codex":0.000109081266,"about_ca_topic_score_gemma":0.00025812542,"teacher_disagreement_score":0.6334808,"about_ca_system_score_codex":0.00026887425,"about_ca_system_score_gemma":0.00012662167,"threshold_uncertainty_score":0.9996338},"labels":[],"label_agreement":null},{"id":"W3125117642","doi":"","title":"On Batch-size Selection for Stochastic Training for Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Complement (music); Metric (unit); Artificial neural network; Randomness; Selection (genetic algorithm); Stochastic gradient descent; Graph; Batch processing; Contrast (vision); Artificial intelligence; Estimator; Machine learning; Mathematics; Statistics; Theoretical computer science; Engineering","score_opus":0.028568199177916274,"score_gpt":0.272625345351232,"score_spread":0.24405714617331573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125117642","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":[0.0026925236,0.000062407016,0.9943201,0.00085823453,0.0011279257,0.00050131616,0.000002257557,0.0003142963,0.00012095644],"genre_scores_gemma":[0.8346206,0.0000026734608,0.16220154,0.0021298162,0.00030361165,0.00021305183,0.000008297926,0.000026220128,0.00049422204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843276,0.00003637556,0.00023143958,0.0006034165,0.00014697536,0.0005490259],"domain_scores_gemma":[0.9975555,0.0017885556,0.00008153789,0.000296852,0.00016596721,0.00011161562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014219843,0.00019337524,0.00021354134,0.000061215986,0.0002766883,0.0001256767,0.0003345648,0.000089945584,0.000009267993],"category_scores_gemma":[0.00022108047,0.00018264224,0.00020093222,0.0006954226,0.000025418693,0.000329463,0.000060498794,0.0001770223,9.46092e-7],"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":[0.00006119625,0.000034503042,0.000008725056,0.000008047712,0.000021378708,0.0000021909023,0.00009894963,0.8037306,0.0002521311,0.12537648,0.002226983,0.068178795],"study_design_scores_gemma":[0.0006567001,0.00031764494,0.00006772432,0.000013858845,0.000009308103,0.000024702269,0.000021666747,0.9330687,0.00025396503,0.06516846,0.0001866603,0.0002106047],"about_ca_topic_score_codex":0.0000014670974,"about_ca_topic_score_gemma":0.00003744649,"teacher_disagreement_score":0.8321186,"about_ca_system_score_codex":0.000026247073,"about_ca_system_score_gemma":0.00003451051,"threshold_uncertainty_score":0.7447935},"labels":[],"label_agreement":null},{"id":"W3126979872","doi":"10.1109/epec48502.2020.9320066","title":"GridKG: Knowledge Graph Representation of Distribution Grid Data","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Electric Power and Energy Conference (EPEC)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Grid; Lattice graph; Theoretical computer science; Graph; Power grid; Data mining; Distributed computing; Power (physics); Line graph; Voltage graph; Mathematics","score_opus":0.034920864294094744,"score_gpt":0.26577146043577365,"score_spread":0.2308505961416789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126979872","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":[0.010412481,0.0013798956,0.98467094,0.0011916317,0.00067194935,0.00008264394,0.00005308226,0.00017439344,0.0013629716],"genre_scores_gemma":[0.9970015,0.0013064535,0.0009759386,0.00026203718,0.00018882628,0.000009436837,0.00018027199,0.000010977553,0.00006451729],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980519,0.00011506309,0.00036694884,0.00082809496,0.00026429532,0.0003737099],"domain_scores_gemma":[0.9985274,0.00013583177,0.0001850316,0.0007584568,0.00016791277,0.00022534523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009809197,0.00022326055,0.0003231527,0.00007707223,0.00011138756,0.000089076304,0.0012071709,0.00009527061,0.000013747952],"category_scores_gemma":[0.00008964364,0.00021322584,0.000064482025,0.0020280136,0.00006329595,0.0008273131,0.00036365,0.00021852712,0.000005377655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029393873,0.00042772284,0.0021765607,0.0001081431,0.00029463903,0.00013412882,0.0015868909,0.0016825349,0.061965495,0.3716527,0.12769702,0.43198022],"study_design_scores_gemma":[0.0018602755,0.0013106643,0.008382196,0.00008726469,0.00010321719,0.000090324385,0.000078549594,0.8589769,0.06449159,0.025212493,0.037995446,0.0014110887],"about_ca_topic_score_codex":0.00004434456,"about_ca_topic_score_gemma":0.000020049532,"teacher_disagreement_score":0.9865891,"about_ca_system_score_codex":0.00001620259,"about_ca_system_score_gemma":0.000109945795,"threshold_uncertainty_score":0.8695097},"labels":[],"label_agreement":null},{"id":"W3127274234","doi":"10.18653/v1/2021.eacl-main.245","title":"Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Samsung; Canadian Institute for Advanced Research; Microsoft Research","keywords":"Shot (pellet); Computer science; Relation (database); Link (geometry); Simple (philosophy); Set (abstract data type); Artificial intelligence; One shot; Machine learning; Training set; Baseline (sea); Algorithm; Data mining","score_opus":0.16332852999336606,"score_gpt":0.30437624473467373,"score_spread":0.14104771474130767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127274234","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":[0.47852102,0.006202517,0.4872158,0.0021438568,0.0117628025,0.0013468117,0.000008300767,0.0007199484,0.0120789595],"genre_scores_gemma":[0.98441076,0.0017261737,0.013193604,0.00007488595,0.00017873234,0.00018723903,0.000010170646,0.000019481708,0.00019897068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793583,0.0001848214,0.00053429976,0.0007473524,0.00027951127,0.00031819136],"domain_scores_gemma":[0.9978977,0.0003010577,0.00019467446,0.0013723737,0.00016951023,0.00006472578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034207484,0.0002677374,0.00035404784,0.00026882053,0.00006651679,0.00010966145,0.0015669594,0.00015700137,0.00000785972],"category_scores_gemma":[0.000057328903,0.00020307148,0.00021075913,0.0011488936,0.000073031304,0.00054950686,0.001936962,0.0010807283,0.000004540515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003494556,0.0005100353,0.007911792,0.0006401491,0.0002152799,0.000101072634,0.013507482,0.2120131,0.0021098773,0.19714497,0.0009806757,0.5648306],"study_design_scores_gemma":[0.0011261728,0.00028059093,0.14790161,0.0031802088,0.00006637008,0.000047853493,0.00064391555,0.71853083,0.027691267,0.0968018,0.0021036218,0.001625757],"about_ca_topic_score_codex":0.000033419943,"about_ca_topic_score_gemma":0.00023936412,"teacher_disagreement_score":0.5632049,"about_ca_system_score_codex":0.000046939826,"about_ca_system_score_gemma":0.00010005172,"threshold_uncertainty_score":0.8281015},"labels":[],"label_agreement":null},{"id":"W3127848318","doi":"10.48550/arxiv.2102.02307","title":"Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Benchmark (surveying); Code (set theory); Typing; Artificial intelligence; Word error rate; Quality (philosophy); Natural language processing; Machine learning; Noisy data; Programming language; Speech recognition","score_opus":0.08504675677406587,"score_gpt":0.2124408258249891,"score_spread":0.12739406905092324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127848318","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":[0.7235738,0.00067329616,0.27333122,0.000041468546,0.000778227,0.00022880912,0.0000056707668,0.0002054459,0.0011620271],"genre_scores_gemma":[0.9953831,0.00075401337,0.0033946466,0.00007157993,0.00002973756,0.0000010084958,0.000010607459,0.000018867377,0.00033647267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755585,0.00015910238,0.00023430529,0.0014784689,0.00006692734,0.0005053448],"domain_scores_gemma":[0.9983679,0.00014932905,0.00017519208,0.0009885757,0.000114188835,0.00020481744],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018040964,0.0003879091,0.00044751252,0.00042818207,0.00015760165,0.00015439084,0.0012616708,0.00035312303,0.000009951752],"category_scores_gemma":[0.000035518566,0.00047150938,0.00017420958,0.0010143473,0.00016700599,0.0007377583,0.0033673644,0.0010657536,0.000013806291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001366439,0.00073669717,0.0911154,0.0006950666,0.00034701373,0.004018938,0.012781253,0.3915947,0.00025014,0.48069438,0.0005073017,0.017122436],"study_design_scores_gemma":[0.0010328076,0.000067684385,0.03055795,0.0005521705,0.000052357787,0.000024213765,0.0005793606,0.8020823,0.0002452365,0.16305554,0.0003021654,0.0014482059],"about_ca_topic_score_codex":0.00007866004,"about_ca_topic_score_gemma":0.0009159367,"teacher_disagreement_score":0.4104876,"about_ca_system_score_codex":0.0001539062,"about_ca_system_score_gemma":0.00016317709,"threshold_uncertainty_score":0.9997737},"labels":[],"label_agreement":null},{"id":"W3131239930","doi":"10.48550/arxiv.2102.09557","title":"Knowledge Hypergraph Embedding Meets Relational Algebra","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hypergraph; Embedding; Relational algebra; Theoretical computer science; Computer science; Relational database; Set (abstract data type); Mathematical proof; Relational model; Relation (database); Relational calculus; Projection (relational algebra); Relation algebra; Knowledge representation and reasoning; Graph; Representation (politics); Algebra over a field; Mathematics; Discrete mathematics; Artificial intelligence; Algorithm; Algebra representation; Data mining; Two-element Boolean algebra; Programming language; Pure mathematics","score_opus":0.07236993584907335,"score_gpt":0.20921599886342948,"score_spread":0.13684606301435615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131239930","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":[0.15477155,0.0008351488,0.83737564,0.00016290984,0.0015940065,0.00021512611,0.0000061102305,0.00046960104,0.004569927],"genre_scores_gemma":[0.9780111,0.00034615112,0.019960815,0.00010916882,0.00014284831,0.0000014446581,0.00003753432,0.000029203979,0.00136177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972153,0.00021169499,0.00026849302,0.0016751927,0.00013452876,0.0004948008],"domain_scores_gemma":[0.9974743,0.00026403155,0.0002715978,0.0014692241,0.0002774467,0.00024336796],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017613864,0.0004261638,0.00040732027,0.0003887317,0.00027891702,0.00016450653,0.001884319,0.00041259185,0.000044920343],"category_scores_gemma":[0.00004513384,0.00052768,0.000411789,0.0014676607,0.0001345338,0.000861512,0.0034309442,0.0010395458,0.00006735243],"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":[0.000008343024,0.00008077644,0.0014139161,0.000038501745,0.00010076674,0.0005311495,0.00019497407,0.49919683,0.00004861245,0.49692875,0.00039643588,0.0010609233],"study_design_scores_gemma":[0.00041579935,0.000027354592,0.0020456917,0.00022660948,0.00005954684,0.00002415226,0.000046767782,0.905597,0.000112137845,0.08870047,0.0019637477,0.000780754],"about_ca_topic_score_codex":0.000013286075,"about_ca_topic_score_gemma":0.000035713576,"teacher_disagreement_score":0.8232395,"about_ca_system_score_codex":0.00018426417,"about_ca_system_score_gemma":0.00023773391,"threshold_uncertainty_score":0.9997175},"labels":[],"label_agreement":null},{"id":"W3131544549","doi":"10.1007/s13278-020-00714-y","title":"ShortWalk: an approach to network embedding on directed graphs","year":2021,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Random walk; Embedding; Computer science; Node (physics); Theoretical computer science; Graph; Random graph; Directed graph; Graph embedding; Algorithm; Artificial intelligence; Mathematics","score_opus":0.017996602462022376,"score_gpt":0.2767699594428392,"score_spread":0.2587733569808168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131544549","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":[0.19104932,0.0010064543,0.8003185,0.00033343825,0.0006994222,0.00021392718,0.0000032096866,0.0006121608,0.005763612],"genre_scores_gemma":[0.7769804,0.00007124234,0.22007622,0.0014816066,0.0011290032,0.000026619651,0.00006175968,0.000026746364,0.00014639254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997063,0.0003094548,0.00038213196,0.0010367589,0.00036944658,0.00083921134],"domain_scores_gemma":[0.99873567,0.00018590172,0.00015081011,0.00049971946,0.00012931926,0.00029859436],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047633154,0.0002994566,0.0006323985,0.00019538468,0.0010482381,0.0003456099,0.00046152918,0.00014485723,0.000007222702],"category_scores_gemma":[0.000029251245,0.00030381413,0.0003205894,0.007668957,0.000052370317,0.00030172968,0.00033560072,0.00027739353,0.000001771207],"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":[0.000039326198,0.00013363731,0.02786937,0.0000074841655,0.0011471448,0.00006341361,0.0028139155,0.7461368,0.000030787854,0.08581671,0.0042194007,0.13172202],"study_design_scores_gemma":[0.00034823144,0.00016205167,0.047913957,0.0000519941,0.00067411864,0.0000096394115,0.000537106,0.9344607,0.000012333709,0.011849191,0.002968701,0.001011966],"about_ca_topic_score_codex":0.000011738495,"about_ca_topic_score_gemma":0.00012714553,"teacher_disagreement_score":0.5859311,"about_ca_system_score_codex":0.000028963917,"about_ca_system_score_gemma":0.000030226218,"threshold_uncertainty_score":0.9999414},"labels":[],"label_agreement":null},{"id":"W3132397809","doi":"10.1007/s12652-020-02821-2","title":"Entity alignment via knowledge embedding and type matching constraints for knowledge graph inference","year":2021,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Development and Reform Commission; Department of Science and Technology of Sichuan Province","keywords":"Computer science; Embedding; Knowledge graph; Inference; Matching (statistics); Domain knowledge; Vector space; Artificial intelligence; Graph; Similarity (geometry); Representation (politics); Graph embedding; Pattern recognition (psychology); Data mining; Theoretical computer science; Natural language processing; Mathematics; Image (mathematics)","score_opus":0.046644985972358145,"score_gpt":0.3376758821140567,"score_spread":0.2910308961416985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132397809","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":[0.16612867,0.0030344776,0.82959133,0.00005409152,0.00093650096,0.00012164057,6.372079e-7,0.000027795997,0.00010486707],"genre_scores_gemma":[0.9029876,0.00047127323,0.09623389,0.00009940571,0.00016689321,0.0000010040252,8.6857307e-7,0.000011225571,0.000027839342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982387,0.00010401398,0.0007106048,0.0003828754,0.00019700076,0.00036680058],"domain_scores_gemma":[0.9976675,0.00077367766,0.0004702169,0.00020703781,0.00070110516,0.00018049059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070809474,0.00021835034,0.00040811702,0.00019644327,0.00041824247,0.00032217434,0.00043775528,0.00006745026,0.000008392516],"category_scores_gemma":[0.00014327772,0.0002051868,0.00012488413,0.0003960891,0.00015426321,0.0004964169,0.0004880107,0.0003349984,0.0000024496549],"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":[0.000072574,0.0004662844,0.0013528009,0.0003267543,0.00025003916,0.00014475333,0.017432328,0.0076907095,0.01289149,0.19217972,0.0001768249,0.7670157],"study_design_scores_gemma":[0.0018123083,0.0014838332,0.0015074533,0.0025406654,0.00016317537,0.001946924,0.00312406,0.6005381,0.0369657,0.34627545,0.0023146982,0.0013276874],"about_ca_topic_score_codex":0.0000019077456,"about_ca_topic_score_gemma":0.000009339452,"teacher_disagreement_score":0.76568806,"about_ca_system_score_codex":0.000048742368,"about_ca_system_score_gemma":0.0001020723,"threshold_uncertainty_score":0.8367275},"labels":[],"label_agreement":null},{"id":"W3151536792","doi":"10.1088/2632-2153/abf5b7","title":"MPGVAE: improved generation of small organic molecules using message passing neural nets","year":2021,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"","keywords":"Message passing; Computer science; Artificial neural network; Autoencoder; Matching (statistics); Theoretical computer science; Perceptron; Graph; Encoder; Artificial intelligence; Algorithm; Parallel computing; Mathematics","score_opus":0.01654811459114791,"score_gpt":0.24548077609478475,"score_spread":0.22893266150363684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151536792","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":[0.66999406,0.0007364192,0.32744303,0.001447333,0.00013081451,0.000053689193,2.4672534e-7,0.000164169,0.000030262772],"genre_scores_gemma":[0.9190995,0.000030965577,0.0806755,0.00014168878,0.000016013866,0.0000019312824,0.0000012496588,0.0000083595105,0.000024761739],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985954,0.000057727717,0.00021182644,0.00057111366,0.00020292134,0.00036097193],"domain_scores_gemma":[0.9990928,0.000036674515,0.00016596363,0.00037648148,0.00027421562,0.000053850887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030971147,0.00013357696,0.00018548447,0.0003487627,0.00046497906,0.00012670882,0.00058196386,0.000092870214,0.0000031582038],"category_scores_gemma":[0.0004233579,0.00012576823,0.000022301962,0.0030086273,0.0005086157,0.00040505463,0.0006755311,0.00039130772,4.8935044e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.3372314e-7,0.000011055766,0.0034248596,0.0000045264796,0.000002723398,0.0000240914,0.00005146069,0.0029212276,0.9103854,0.004913082,0.0000010499073,0.07825983],"study_design_scores_gemma":[0.00013398191,0.00007118912,0.00039791915,0.0000110093615,0.0000042957067,0.00015749231,0.000021921867,0.8488579,0.14894095,0.0011978793,0.0000891361,0.000116368166],"about_ca_topic_score_codex":0.000015633703,"about_ca_topic_score_gemma":0.000035106754,"teacher_disagreement_score":0.84593666,"about_ca_system_score_codex":0.00002952873,"about_ca_system_score_gemma":0.00013777516,"threshold_uncertainty_score":0.512868},"labels":[],"label_agreement":null},{"id":"W3152688462","doi":"10.1145/3404835.3462961","title":"TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); Université de Montréal; McGill University","funders":"","keywords":"Computer science; Forgetting; Embedding; Inference; Regularization (linguistics); Encoder; Artificial intelligence; Graph; Knowledge graph; Machine learning; Set (abstract data type); Theoretical computer science","score_opus":0.03242645237418972,"score_gpt":0.3244876535897444,"score_spread":0.2920612012155547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152688462","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":[0.0035845132,0.0002075284,0.99306303,0.0013759177,0.0006338277,0.00017885774,0.000005267012,0.0003263688,0.00062470743],"genre_scores_gemma":[0.4148648,0.0000025632544,0.5833339,0.0014913635,0.00009322341,0.000044603607,0.000030917377,0.000012187448,0.00012641498],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998681,0.00006452055,0.0002327528,0.0004909024,0.00018423688,0.0003465774],"domain_scores_gemma":[0.9988272,0.0003361385,0.000076986806,0.00049062155,0.00016027088,0.000108786946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014288195,0.0001668033,0.00019006849,0.000090981455,0.00019469394,0.000129196,0.00045514497,0.00009128654,0.000038908114],"category_scores_gemma":[0.000060562168,0.00016098883,0.00015877631,0.00076027104,0.000049138966,0.00031488657,0.00018156804,0.00017459803,0.0000177916],"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":[0.000059520156,0.00055731705,0.004297099,0.00009243479,0.000057901627,0.00004721011,0.00027035468,0.004638298,0.002973529,0.9228877,0.02584068,0.038277954],"study_design_scores_gemma":[0.0013380315,0.00027302926,0.0009081326,0.0001259818,0.000013893363,0.00002139053,0.000060097464,0.754228,0.016472831,0.18807605,0.037861243,0.0006213313],"about_ca_topic_score_codex":0.000004489812,"about_ca_topic_score_gemma":0.000046581685,"teacher_disagreement_score":0.7495897,"about_ca_system_score_codex":0.00004280322,"about_ca_system_score_gemma":0.00005360008,"threshold_uncertainty_score":0.65649337},"labels":[],"label_agreement":null},{"id":"W3155334815","doi":"10.1109/infocom42981.2021.9488814","title":"Medley: Predicting Social Trust in Time-Varying Online Social Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Benchmarking; Social media; Pairwise comparison; Social trust; Social network (sociolinguistics); Artificial intelligence; World Wide Web; Social capital; Sociology","score_opus":0.01729186267383214,"score_gpt":0.26417232237769983,"score_spread":0.24688045970386768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155334815","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":[0.21400093,0.0004207004,0.7671181,0.006942347,0.0012078251,0.0003047409,0.00000765227,0.0010812242,0.0089165],"genre_scores_gemma":[0.95048493,0.000027097132,0.044131864,0.0024275198,0.001708183,0.000012724454,0.000041979038,0.000033950535,0.0011317596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980053,0.000132196,0.0003720442,0.0005523742,0.0003518458,0.00058624876],"domain_scores_gemma":[0.999349,0.00016142435,0.000105869585,0.00023077543,0.0000770473,0.00007587806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020810972,0.00019175136,0.00028304546,0.00008108284,0.00032837986,0.00015244061,0.0007075317,0.00016812661,0.00006745633],"category_scores_gemma":[0.00004379401,0.00019951114,0.00011802799,0.0012983142,0.000055108318,0.000541801,0.000654943,0.0005290446,0.000011773704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011482522,0.0014476795,0.08570947,0.00007194071,0.00020795825,0.0020572287,0.0072066872,0.10670954,0.003925684,0.069855064,0.01889847,0.70379543],"study_design_scores_gemma":[0.00079269765,0.000022006372,0.015279391,0.000028227558,0.000006749467,0.00002793723,0.000073723866,0.9788413,0.00024937058,0.0037293097,0.00059305667,0.00035621392],"about_ca_topic_score_codex":0.000009009559,"about_ca_topic_score_gemma":0.000065124405,"teacher_disagreement_score":0.87213176,"about_ca_system_score_codex":0.00007043509,"about_ca_system_score_gemma":0.00006515439,"threshold_uncertainty_score":0.81358284},"labels":[],"label_agreement":null},{"id":"W3156639479","doi":"10.5539/cis.v14n2p63","title":"Research on Entity Label Value Assignment Method in Knowledge Graph","year":2021,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education of Guangdong Province","keywords":"Computer science; Graph; WordNet; Knowledge graph; Information retrieval; Entropy (arrow of time); Data mining; Artificial intelligence; Natural language processing; Theoretical computer science","score_opus":0.06700817207039453,"score_gpt":0.40448584316243874,"score_spread":0.3374776710920442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156639479","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":[0.009387239,0.00007461056,0.98271465,0.00046986793,0.0005447519,0.00012716441,7.347662e-7,0.00004970257,0.0066312524],"genre_scores_gemma":[0.6001607,0.00015148865,0.39778674,0.0017664458,0.000062969455,0.00002017341,0.0000026615867,0.0000034304335,0.00004537866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980851,0.00019444022,0.0002857358,0.00035421678,0.0006799331,0.00040056754],"domain_scores_gemma":[0.9986552,0.000292723,0.000057097244,0.0004575921,0.0003950443,0.00014232937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024952325,0.00009849415,0.00011943346,0.0007078513,0.00036349572,0.00061004976,0.0008176566,0.000039155904,0.0000027420836],"category_scores_gemma":[0.00005925688,0.000088328125,0.000022248552,0.0043657175,0.0002190167,0.0069173295,0.00093537953,0.000302608,0.000043847565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030909941,0.00006949274,0.00021463193,0.000014741074,0.0000016252352,0.0000054836114,0.0014344888,0.008418295,0.00019203965,0.6169411,0.0005609605,0.37214404],"study_design_scores_gemma":[0.000469519,0.00013118987,0.018825445,0.00006274849,5.6860057e-7,0.000024494899,0.00006343755,0.95405716,0.0035219926,0.015077925,0.0075913407,0.00017420024],"about_ca_topic_score_codex":0.0000041858825,"about_ca_topic_score_gemma":0.0000025221966,"teacher_disagreement_score":0.94563884,"about_ca_system_score_codex":0.00008315612,"about_ca_system_score_gemma":0.0001855613,"threshold_uncertainty_score":0.5882724},"labels":[],"label_agreement":null},{"id":"W3158277099","doi":"10.1109/icpr48806.2021.9412799","title":"Social Network Analysis using Knowledge-Graph Embeddings and Convolution Operations","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Compute Canada; International Business Machines Corporation","keywords":"Computer science; Graph; Artificial intelligence; Convolution (computer science); Node (physics); Theoretical computer science; Social network analysis; Feature learning; Social network (sociolinguistics); Machine learning; Artificial neural network; Social media; World Wide Web","score_opus":0.02370498762856411,"score_gpt":0.306408046269753,"score_spread":0.2827030586411889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158277099","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":[0.06415288,0.00063991273,0.9339592,0.0003130514,0.00014893868,0.000049986626,5.602392e-7,0.00011343604,0.000622078],"genre_scores_gemma":[0.83557475,0.000035898873,0.16361248,0.0002738213,0.00017527353,0.0000036148533,0.0000064143896,0.00000557286,0.00031216437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900734,0.000081949176,0.00017474339,0.00037772098,0.00010277596,0.00025545538],"domain_scores_gemma":[0.99947125,0.000052627158,0.00003336821,0.00020808318,0.00016613325,0.00006855212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010627773,0.000106497675,0.00018373223,0.00010989811,0.00052568805,0.00019328862,0.00016868506,0.000060634848,0.000015769876],"category_scores_gemma":[0.000011993681,0.000106400075,0.00011087815,0.0031294774,0.00006122096,0.00048783963,0.00023496142,0.00010652742,0.000003076267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032545358,0.00006764652,0.005425808,0.000007734063,0.00043251028,0.000022096017,0.0013130069,0.48679543,0.0018140115,0.49165133,0.0016576475,0.010809522],"study_design_scores_gemma":[0.00012332278,0.0000075485486,0.0028543842,0.0000042104543,0.00010119552,0.000011348862,0.000027510227,0.9880873,0.00016382472,0.007995978,0.00046441538,0.00015898552],"about_ca_topic_score_codex":0.000010881021,"about_ca_topic_score_gemma":0.00026845874,"teacher_disagreement_score":0.77142185,"about_ca_system_score_codex":0.000023878725,"about_ca_system_score_gemma":0.000042579617,"threshold_uncertainty_score":0.43388692},"labels":[],"label_agreement":null},{"id":"W3158338018","doi":"","title":"Detection and Defense of Topological Adversarial Attacks on Graphs","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Advanced Graph Neural 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":"McGill University","funders":"","keywords":"Adversarial system; Computer science; Computer security; Topology (electrical circuits); Mathematics; Artificial intelligence; Combinatorics","score_opus":0.09422810246771066,"score_gpt":0.33925757296241404,"score_spread":0.2450294704947034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158338018","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":[0.025589982,0.000021904993,0.9709618,0.0006501557,0.0007731209,0.0000679698,0.00004953415,0.000027399881,0.0018581066],"genre_scores_gemma":[0.9774288,0.00029245653,0.021918133,0.00025815386,0.000046501238,0.0000041443113,0.0000073556967,0.0000039285574,0.000040506842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988712,0.00007667414,0.00028823662,0.00035537995,0.00026661457,0.00014188736],"domain_scores_gemma":[0.9988974,0.00045847587,0.000106636486,0.00016248821,0.00030262663,0.00007240912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011143831,0.00012308668,0.00014365872,0.00009373807,0.000084953375,0.00010287416,0.0002108987,0.0000688406,0.000059062266],"category_scores_gemma":[0.00033506707,0.00011510682,0.000028857077,0.00014827153,0.00020433805,0.00012741148,0.000106494976,0.00018009129,0.000009344441],"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":[0.0000771873,0.000049778937,0.00007707339,0.0000027171423,0.0000110386,0.000036078793,0.000103493985,0.0002452097,0.0014449361,0.82987714,0.000012422077,0.1680629],"study_design_scores_gemma":[0.000054266548,0.00041693868,0.0007010786,0.000033882334,0.0000057707466,0.000031422984,0.00017452284,0.17070872,0.01925171,0.80837375,0.000097476026,0.00015049717],"about_ca_topic_score_codex":0.000011647017,"about_ca_topic_score_gemma":0.000085080384,"teacher_disagreement_score":0.95183885,"about_ca_system_score_codex":0.000014327143,"about_ca_system_score_gemma":0.000032597105,"threshold_uncertainty_score":0.46939197},"labels":[],"label_agreement":null},{"id":"W3158399077","doi":"10.1109/ispass51385.2021.00013","title":"GNNMark: A Benchmark Suite to Characterize Graph Neural Network Training on GPUs","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Suite; Benchmark (surveying); Scalability; Machine learning; Software; Graph; Artificial intelligence; Variety (cybernetics); Parallel computing; Theoretical computer science; Database; Programming language","score_opus":0.031334094380693976,"score_gpt":0.25171992454709047,"score_spread":0.22038583016639648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158399077","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":[0.4390336,0.0003546271,0.50683206,0.020934172,0.006942622,0.0008638533,0.000011819397,0.0014901004,0.02353716],"genre_scores_gemma":[0.88171744,0.000039813487,0.087504566,0.028290065,0.0007012226,0.000054079806,0.000020773361,0.000038132686,0.0016338755],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99724203,0.0001375352,0.00035611115,0.0009140704,0.00039444264,0.0009558356],"domain_scores_gemma":[0.99813795,0.00032793448,0.00009135725,0.00096655055,0.00009937034,0.00037686306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021943828,0.00030896426,0.00034727482,0.0001208312,0.0002450628,0.00026841182,0.0008965583,0.00012264324,0.00012018925],"category_scores_gemma":[0.00006506801,0.0002870033,0.00018886964,0.002055397,0.000035257235,0.0005330212,0.00046985538,0.00048566732,0.000059109174],"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":[0.00013189221,0.0002013065,0.0022141817,0.000022355496,0.0001088276,0.001970543,0.0019025013,0.071030624,0.009266838,0.21522483,0.028952854,0.66897327],"study_design_scores_gemma":[0.004024781,0.0022562519,0.36974594,0.0007113468,0.00006831426,0.0014910261,0.00036446465,0.3124966,0.007961136,0.08165626,0.2130306,0.006193262],"about_ca_topic_score_codex":0.000004647033,"about_ca_topic_score_gemma":0.000041948708,"teacher_disagreement_score":0.66278,"about_ca_system_score_codex":0.00002846709,"about_ca_system_score_gemma":0.000047695914,"threshold_uncertainty_score":0.9999582},"labels":[],"label_agreement":null},{"id":"W3159726507","doi":"10.2196/26714","title":"A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-World Environmental Health Observations: Cohort Study and Informatics Application","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institutes of Health","keywords":"Computer science; Informatics; Data science; Biomedicine; Health informatics; Context (archaeology); Graph; Information retrieval; Bioinformatics; Medicine; Theoretical computer science; Public health; Biology; Engineering; Pathology","score_opus":0.024184441161883178,"score_gpt":0.30747199383974194,"score_spread":0.28328755267785877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159726507","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":[0.077097215,0.000066526954,0.9172998,0.00061289425,0.00026967586,0.0041695624,0.000033116303,0.00030080197,0.00015041674],"genre_scores_gemma":[0.7580067,0.00015080503,0.236038,0.0024048497,0.00016185886,0.0029105155,0.00018574757,0.00003416568,0.000107386266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968207,0.000091551905,0.00139343,0.00024660034,0.0009681474,0.00047957984],"domain_scores_gemma":[0.99765325,0.00043067362,0.00038410258,0.0006904274,0.00009601841,0.00074553513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092894066,0.0002584073,0.00047155356,0.00025612419,0.00033383034,0.00014241888,0.0006651727,0.00011972831,0.0000032843855],"category_scores_gemma":[0.00014359818,0.0002231155,0.00006688918,0.0011139134,0.00012132477,0.00060355524,0.0005669693,0.00027803448,0.00002514735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009722791,0.004657125,0.026871793,0.006092455,0.00047404057,0.000048941827,0.0723427,0.0002835237,0.000099473225,0.29677162,0.011284178,0.5809769],"study_design_scores_gemma":[0.0020138843,0.0011928432,0.015607644,0.0005324457,0.0000496789,0.00012925193,0.013584574,0.9490873,0.000055027234,0.0027792344,0.014266862,0.00070125214],"about_ca_topic_score_codex":0.000008057632,"about_ca_topic_score_gemma":0.00007359178,"teacher_disagreement_score":0.9488038,"about_ca_system_score_codex":0.00022205412,"about_ca_system_score_gemma":0.00031603844,"threshold_uncertainty_score":0.90983856},"labels":[],"label_agreement":null},{"id":"W3161822059","doi":"10.24963/ijcai.2021/618","title":"The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Vienna Science and Technology Fund; Deutsche Forschungsgemeinschaft","keywords":"Computer science; Artificial intelligence; Machine learning; Graph isomorphism; Heuristic; Graph; Artificial neural network; Algorithm; Theoretical computer science; Line graph","score_opus":0.009319551344724326,"score_gpt":0.23158749665133258,"score_spread":0.22226794530660826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161822059","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":[0.0022246218,0.0010463885,0.9926514,0.0014817353,0.001051753,0.0007366371,0.00000677894,0.0001346233,0.0006660781],"genre_scores_gemma":[0.4135135,0.00042414176,0.581095,0.0006868673,0.00010499845,0.00028391735,0.000021929773,0.000080067824,0.0037895832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980155,0.00015674722,0.00034314167,0.0006647632,0.00043114857,0.00038870546],"domain_scores_gemma":[0.99719876,0.0005064641,0.00039429264,0.0015796508,0.00026028513,0.000060566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033067976,0.0003096759,0.0003292602,0.00005286544,0.00042762497,0.00023198724,0.0023883989,0.0001348533,0.000008254027],"category_scores_gemma":[0.00004273188,0.00014578566,0.00034004156,0.00050274975,0.00019055005,0.00013445062,0.0024171558,0.0009948896,5.181858e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058133908,0.00021895069,0.003510896,0.0001735668,0.0008529445,0.00003505489,0.0024534718,0.11029235,0.0002006295,0.13749687,0.002130411,0.7425767],"study_design_scores_gemma":[0.00072057283,0.00030409978,0.0022330633,0.00035393657,0.000055441917,0.000051277842,0.0002516681,0.93595845,0.002219297,0.0455503,0.0115438765,0.0007579913],"about_ca_topic_score_codex":0.000037812322,"about_ca_topic_score_gemma":0.00014350773,"teacher_disagreement_score":0.8256661,"about_ca_system_score_codex":0.00001939057,"about_ca_system_score_gemma":0.00010230211,"threshold_uncertainty_score":0.59449667},"labels":[],"label_agreement":null},{"id":"W3162288172","doi":"","title":"Incorporating background knowledge in tucker","year":2021,"lang":"en","type":"article","venue":"International journal of advance research, ideas and innovations in technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Focus (optics); Task (project management); Knowledge graph; Link (geometry); State (computer science); Artificial intelligence; Algorithm","score_opus":0.043889393328746404,"score_gpt":0.40486715102471105,"score_spread":0.36097775769596463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162288172","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":[0.7505772,0.005149187,0.21819687,0.023336144,0.00092294806,0.00013789737,0.0000024026997,0.000042522595,0.0016348],"genre_scores_gemma":[0.8975915,0.00075588125,0.101372,0.00012775627,0.00007731539,0.000009153196,0.0000013786909,0.000007356249,0.000057656856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826777,0.00010295258,0.000685725,0.00025720018,0.00041234415,0.00027400051],"domain_scores_gemma":[0.99616516,0.00030080872,0.00023690744,0.00023459013,0.00302594,0.000036576734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009812665,0.000093601586,0.00019842443,0.0025810369,0.00006438469,0.00009091437,0.0009652987,0.00010703077,0.000004981145],"category_scores_gemma":[0.0010010722,0.00009334255,0.00002633063,0.0048760204,0.00025907287,0.0011156383,0.00064669596,0.0010920236,0.0000027171918],"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":[0.000013064029,0.00012851748,0.014461017,0.000004797332,0.000013799581,0.0005500961,0.000060022645,0.0005036142,0.0033667202,0.83322346,0.0001185162,0.14755635],"study_design_scores_gemma":[0.0010000595,0.000118699325,0.011210619,0.0003079437,7.0156926e-7,0.0008190414,0.00062328205,0.0055953017,0.0030950208,0.9680966,0.008989974,0.00014271367],"about_ca_topic_score_codex":0.000005221145,"about_ca_topic_score_gemma":0.00016816123,"teacher_disagreement_score":0.14741363,"about_ca_system_score_codex":0.00021466703,"about_ca_system_score_gemma":0.00025393814,"threshold_uncertainty_score":0.47443599},"labels":[],"label_agreement":null},{"id":"W3163256474","doi":"10.1109/icassp39728.2021.9414015","title":"Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Id, ego and super-ego; Computer science; Artificial intelligence; Graph; Artificial neural network; Cognitive science; Theoretical computer science; Psychology; Social psychology","score_opus":0.015409898376336214,"score_gpt":0.24471318361061167,"score_spread":0.22930328523427546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163256474","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":[0.09135702,0.0013043751,0.9026186,0.0009518005,0.0011482623,0.00015704327,5.980765e-7,0.00043931868,0.0020230082],"genre_scores_gemma":[0.94771576,0.00007414284,0.04974459,0.0021316377,0.00017417638,0.000014218354,0.000004882531,0.00001849562,0.000122066325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978584,0.00013255107,0.00037815617,0.00070110767,0.00027050008,0.00065928005],"domain_scores_gemma":[0.99876714,0.00022102759,0.00009264008,0.00070617214,0.000078992096,0.00013402505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120703844,0.0002390382,0.00025848052,0.00014344344,0.00012744052,0.00020744206,0.0008001134,0.000105241146,0.000045123226],"category_scores_gemma":[0.000053729822,0.00021955896,0.00012116115,0.0016460485,0.000054678545,0.00068847527,0.00052520965,0.00047266754,0.000004133824],"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":[0.00001136414,0.00004718765,0.011725692,0.000014073,0.000021887436,0.0009983443,0.000330923,0.54204094,0.0007057963,0.28318283,0.0014116605,0.1595093],"study_design_scores_gemma":[0.00051656197,0.0000403191,0.010663252,0.00002997573,0.0000036303907,0.00014452677,0.00014064174,0.8988437,0.0015511122,0.087262124,0.0003192399,0.00048491984],"about_ca_topic_score_codex":0.000019574478,"about_ca_topic_score_gemma":0.00019350837,"teacher_disagreement_score":0.85635877,"about_ca_system_score_codex":0.000024918996,"about_ca_system_score_gemma":0.000024124956,"threshold_uncertainty_score":0.89533544},"labels":[],"label_agreement":null},{"id":"W3164865299","doi":"10.48550/arxiv.2105.11118","title":"Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Server; Scalability; Parallel computing; Queue; Web server; Distributed computing; Operating system; Computer network; The Internet","score_opus":0.06297088811227082,"score_gpt":0.18424410990230114,"score_spread":0.12127322179003032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164865299","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":[0.5629665,0.0002697006,0.4358111,0.00010890248,0.00017848896,0.00024988613,0.00003306086,0.00021701178,0.00016532045],"genre_scores_gemma":[0.9927255,0.0008232061,0.0059173875,0.00012804903,0.000036834383,0.0000022247639,0.00008023861,0.000033030203,0.0002535138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970023,0.00016053775,0.00023953535,0.0017903008,0.00015106633,0.0006562774],"domain_scores_gemma":[0.99779254,0.00018315339,0.0003251783,0.0011028126,0.0002181823,0.00037814054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020960928,0.0005658497,0.0006180666,0.00019888772,0.000347045,0.00047191873,0.0011190963,0.0003339207,0.000007697951],"category_scores_gemma":[0.00001909944,0.0005795097,0.00011000572,0.0013336209,0.00032202512,0.0013773399,0.0026003395,0.0008827184,0.000001844569],"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":[0.00019195261,0.0001260211,0.027949832,0.00052304973,0.0005167003,0.0040344223,0.0018575881,0.89852387,0.0000524932,0.06130946,0.0001302239,0.004784411],"study_design_scores_gemma":[0.0027056145,0.00020894651,0.021652825,0.0010575564,0.0002670248,0.00018933286,0.0022894228,0.94298005,0.00017142166,0.0261511,0.00029337368,0.0020333072],"about_ca_topic_score_codex":0.00012295519,"about_ca_topic_score_gemma":0.00034795306,"teacher_disagreement_score":0.42989373,"about_ca_system_score_codex":0.00012395396,"about_ca_system_score_gemma":0.00020545606,"threshold_uncertainty_score":0.9996656},"labels":[],"label_agreement":null},{"id":"W3164924750","doi":"10.2196/28218","title":"Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chinese Academy of Medical Sciences Initiative for Innovative Medicine; Peking Union Medical College; Chinese Academy of Medical Sciences","keywords":"Computer science; Pituitary adenoma; Artificial intelligence; Graph; Economic shortage; Machine learning; Information retrieval; Natural language processing; Adenoma; Medicine; Pathology","score_opus":0.02464694647546247,"score_gpt":0.33066999365689026,"score_spread":0.3060230471814278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164924750","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":[0.6170879,0.00025167168,0.38082927,0.0006178449,0.0003963073,0.00051731605,0.0000036976885,0.00009081998,0.00020514318],"genre_scores_gemma":[0.9306678,0.00016173448,0.06770487,0.001123758,0.00012642435,0.00015976533,0.0000108955055,0.000011639429,0.00003308623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977044,0.000082833154,0.0007954334,0.0002512273,0.00078857155,0.00037754423],"domain_scores_gemma":[0.99847674,0.00031095682,0.00011749374,0.00041943407,0.00014757275,0.00052778627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058039965,0.00018980358,0.0003298629,0.00015921262,0.00018539741,0.00007405078,0.00046338263,0.00024816868,0.000027750088],"category_scores_gemma":[0.00021542309,0.00016804636,0.00007061992,0.00070665125,0.0002462139,0.00079762505,0.00073724403,0.00058243267,0.0000041376234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006500895,0.0024109278,0.010053255,0.0007674139,0.00009344781,0.010549944,0.039504062,0.0007716658,0.000006517572,0.031401634,0.0048492136,0.8995269],"study_design_scores_gemma":[0.0024496259,0.00020090466,0.00029564573,0.00014346682,0.000008939807,0.006255581,0.00400193,0.9798772,0.000010606173,0.0059643285,0.00055349665,0.00023829704],"about_ca_topic_score_codex":0.00000974876,"about_ca_topic_score_gemma":0.0005545022,"teacher_disagreement_score":0.97910553,"about_ca_system_score_codex":0.000037605678,"about_ca_system_score_gemma":0.0003886338,"threshold_uncertainty_score":0.6852732},"labels":[],"label_agreement":null},{"id":"W3165146808","doi":"10.1016/j.ailsci.2022.100036","title":"Understanding the performance of knowledge graph embeddings in drug discovery","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence in the Life Sciences","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Ranking (information retrieval); Identification (biology); Context (archaeology); Domain knowledge; Machine learning; Hyperparameter; Graph; Drug discovery; Knowledge graph; Embedding; Process (computing); Data science; Artificial intelligence; Data mining; Theoretical computer science; Bioinformatics","score_opus":0.11443593632942002,"score_gpt":0.31981953615235975,"score_spread":0.20538359982293974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165146808","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":[0.8731111,0.00070752256,0.119500056,0.0037184297,0.0011050618,0.00041651164,0.0000019733304,0.000037780403,0.0014015388],"genre_scores_gemma":[0.9988279,0.000082429615,0.0005462537,0.0004329401,0.000039141385,0.0000478261,2.3932395e-7,0.0000042736983,0.00001904729],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974962,0.0004287092,0.00053170434,0.000439749,0.00064253854,0.00046110028],"domain_scores_gemma":[0.9980692,0.0012366037,0.00019568615,0.00044412483,0.000021722051,0.00003264945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037337055,0.00014149393,0.00017087629,0.00034673873,0.0008747517,0.00015505344,0.003950549,0.000019429304,0.000009535069],"category_scores_gemma":[0.00012426132,0.00008729938,0.00007511507,0.005748517,0.0009749414,0.0011787994,0.00065662357,0.00046267125,0.000004890317],"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":[0.000017098811,0.00011436356,0.0044841683,0.0000070041297,0.0000026814225,0.0000050429635,0.017877555,0.30201226,0.00018631737,0.67042327,0.00011696141,0.004753282],"study_design_scores_gemma":[0.000024858286,0.00019529079,0.0008506808,0.000041142073,0.000002890542,0.0000120650175,0.025318986,0.5643408,0.00230529,0.40662265,0.00007813484,0.00020723093],"about_ca_topic_score_codex":0.00011158724,"about_ca_topic_score_gemma":0.00042708873,"teacher_disagreement_score":0.26380062,"about_ca_system_score_codex":0.00006851977,"about_ca_system_score_gemma":0.00012687949,"threshold_uncertainty_score":0.73411685},"labels":[],"label_agreement":null},{"id":"W3173856251","doi":"10.1109/tkde.2021.3090664","title":"Hierarchical Multi-View Graph Pooling with Structure Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pooling; Theoretical computer science; Graph; Upsampling; Artificial intelligence","score_opus":0.019042807564391837,"score_gpt":0.25704086656540814,"score_spread":0.2379980590010163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173856251","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":[0.004413978,0.0016859199,0.99318004,0.000056160112,0.00032902218,0.00006331563,0.000021412467,0.00023520565,0.000014975863],"genre_scores_gemma":[0.84243816,0.0005158379,0.15683888,0.00003615573,0.000053130138,0.000005902796,0.000018786199,0.0000240918,0.00006908045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989044,0.00003303893,0.00014315064,0.0005460316,0.00011133073,0.00026203363],"domain_scores_gemma":[0.9989986,0.0001429391,0.000021646827,0.00066046236,0.000047855876,0.00012854135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006299529,0.00018441628,0.0001786393,0.0001191377,0.00018835856,0.00009779711,0.00041882048,0.00006308717,0.0000060905963],"category_scores_gemma":[0.000007490387,0.00016472119,0.00003089637,0.0006317936,0.000025725369,0.00059296185,0.00002067054,0.00063662126,0.000003522664],"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":[0.000012166338,0.00010800781,0.000045479475,0.00013750928,0.00010101787,0.00012893419,0.0005057104,0.7960659,0.005525045,0.0010568778,0.000025372186,0.19628796],"study_design_scores_gemma":[0.0005480915,0.00006780569,0.00023310403,0.00023411436,0.000028871747,0.00021987167,0.000014879883,0.9883364,0.006607865,0.0000669059,0.003286783,0.00035531961],"about_ca_topic_score_codex":0.000001359171,"about_ca_topic_score_gemma":0.000036126832,"teacher_disagreement_score":0.83802414,"about_ca_system_score_codex":0.000011888044,"about_ca_system_score_gemma":0.000032498694,"threshold_uncertainty_score":0.6717135},"labels":[],"label_agreement":null},{"id":"W3174867666","doi":"10.48550/arxiv.2106.15755","title":"Dual GNNs: Graph Neural Network Learning with Limited Supervision","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Dual (grammatical number); Computer science; Graph; Artificial neural network; Artificial intelligence; Machine learning; Theoretical computer science","score_opus":0.04431417247660114,"score_gpt":0.17546804195336424,"score_spread":0.1311538694767631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174867666","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":[0.60572225,0.00035096952,0.39194933,0.00010286387,0.00071051053,0.0002517912,0.0000016962338,0.0005853158,0.0003252794],"genre_scores_gemma":[0.9904104,0.00053127285,0.008071748,0.00023448828,0.00022404706,0.0000016138007,0.00006132075,0.000049153532,0.00041594787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623746,0.0004248382,0.0002815157,0.001989103,0.00022003577,0.00084706565],"domain_scores_gemma":[0.9972325,0.00025621982,0.00033828104,0.0015697656,0.00029842235,0.00030483608],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002009745,0.000599425,0.000569081,0.0002884513,0.00044777698,0.00033947497,0.0017017452,0.000398871,0.000022276283],"category_scores_gemma":[0.00003013729,0.0006236618,0.00033073747,0.002505241,0.00017903399,0.000766432,0.0033948452,0.0020532936,0.000014216314],"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":[0.00005691882,0.000039734638,0.007722629,0.00003071909,0.000089767775,0.002276216,0.0002080062,0.97991794,0.000020003741,0.008182201,0.000098429286,0.0013574408],"study_design_scores_gemma":[0.00071861723,0.00024608208,0.0047691274,0.0003323255,0.000089521214,0.0000630578,0.00012827091,0.986271,0.000030872994,0.0060045924,0.00038417155,0.0009623162],"about_ca_topic_score_codex":0.000055423207,"about_ca_topic_score_gemma":0.00010471244,"teacher_disagreement_score":0.38468817,"about_ca_system_score_codex":0.00010249213,"about_ca_system_score_gemma":0.00012723531,"threshold_uncertainty_score":0.99962145},"labels":[],"label_agreement":null},{"id":"W3185513611","doi":"10.1109/tkde.2022.3220948","title":"Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Randomness; Computer science; Adjacency matrix; Correlation clustering; Feature (linguistics); Graph; Clustering coefficient; Encoder; Data mining; Pattern recognition (psychology); Theoretical computer science; Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.05141376820259933,"score_gpt":0.27064618378567723,"score_spread":0.2192324155830779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185513611","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":[0.0004127463,0.0006584151,0.99638647,0.00009538344,0.001355463,0.00031419616,0.0002332014,0.00051184866,0.00003226931],"genre_scores_gemma":[0.88582605,0.0001517802,0.1134845,0.00009370599,0.0000544348,0.00021746643,0.000054883592,0.00004628066,0.00007092352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984136,0.000038067432,0.0002618097,0.0007018227,0.00018046677,0.00040422822],"domain_scores_gemma":[0.9985359,0.00026824782,0.000047022924,0.0009876703,0.00004439318,0.00011682222],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035285344,0.00023331102,0.00022319556,0.00035065308,0.00065290486,0.000095009775,0.0010913453,0.00005885577,0.0000046166547],"category_scores_gemma":[0.000004540154,0.00026190034,0.000086190914,0.00083213596,0.000021865411,0.0011765526,0.000079432626,0.00048627233,0.0000012397127],"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":[0.000013596589,0.000052543135,6.1501925e-7,0.00004043485,0.000040124807,0.000005441871,0.00049264333,0.9754489,0.00054756674,0.001219682,0.00019788388,0.021940561],"study_design_scores_gemma":[0.00044719115,0.00009033839,0.000004075422,0.00003327492,0.000023528331,0.000048218582,0.000014628697,0.9925967,0.00043269113,0.00399357,0.0020204075,0.0002953717],"about_ca_topic_score_codex":0.000005519607,"about_ca_topic_score_gemma":0.00001865789,"teacher_disagreement_score":0.8854133,"about_ca_system_score_codex":0.000047728445,"about_ca_system_score_gemma":0.00002582185,"threshold_uncertainty_score":0.9999833},"labels":[],"label_agreement":null},{"id":"W3191294170","doi":"","title":"HNHN: Hypergraph Networks With Hyperedge Neurons","year":2020,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hypergraph; Normalization (sociology); Cardinality (data modeling); Computer science; Representation (politics); Convolution (computer science); Theoretical computer science; Nonlinear system; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Data mining; Mathematics; Discrete mathematics","score_opus":0.034173267919214864,"score_gpt":0.25821426540436776,"score_spread":0.22404099748515288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191294170","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":[0.0131653,0.00009219014,0.93634754,0.026743628,0.00052198826,0.00017863176,0.000004269757,0.00067587144,0.02227056],"genre_scores_gemma":[0.9871283,0.000081586484,0.0082748225,0.003981827,0.00022180921,0.000013322791,0.000021524223,0.000024785844,0.00025197657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983052,0.00011328718,0.0002181403,0.00061115396,0.0004375318,0.00031468217],"domain_scores_gemma":[0.9991099,0.00012932233,0.00015416676,0.00025214697,0.00015076452,0.00020366629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007882753,0.00024710727,0.0001933124,0.00010606618,0.00016827375,0.00023868414,0.0012655661,0.00005710889,0.00011245991],"category_scores_gemma":[0.00008261451,0.00020774602,0.00007463751,0.0004351144,0.000066099485,0.0004348983,0.00026446415,0.00096708117,0.000068021785],"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":[0.00019602042,0.00006321266,0.019368846,0.0000060654197,0.00008009731,0.00022765,0.00031674805,0.5355178,0.0007909982,0.37949675,0.0003718057,0.063563965],"study_design_scores_gemma":[0.0004546835,0.00044846963,0.0021290726,0.00003215859,0.0000044693224,0.0000340046,0.000015687066,0.9869022,0.000053438507,0.0007784621,0.008890376,0.00025702573],"about_ca_topic_score_codex":0.000013255712,"about_ca_topic_score_gemma":0.000011014551,"teacher_disagreement_score":0.973963,"about_ca_system_score_codex":0.00002042009,"about_ca_system_score_gemma":0.000033947923,"threshold_uncertainty_score":0.84716374},"labels":[],"label_agreement":null},{"id":"W3193696571","doi":"10.1145/3447548.3469470","title":"The Sixth International Workshop on Deep Learning on Graphs - Methods and Applications (DLG-KDD'21)","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Intersection (aeronautics); Data science; Deep neural networks; Machine learning; Engineering","score_opus":0.01897514661675541,"score_gpt":0.3376686163242991,"score_spread":0.3186934697075437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193696571","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":[0.00043750546,0.00036715207,0.9800046,0.0037679384,0.00029389732,0.0001154554,2.4372002e-7,0.00014948347,0.014863701],"genre_scores_gemma":[0.24981909,0.0018927361,0.73310024,0.0051582935,0.00032927122,0.0002418468,0.000013361343,0.000037399077,0.009407792],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988149,0.00017451412,0.00015575571,0.00043541504,0.00021323854,0.00020619911],"domain_scores_gemma":[0.9977894,0.0015348949,0.00006522247,0.00045399152,0.000082059494,0.00007446997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026551558,0.00011895466,0.00009012053,0.00006261188,0.00040737068,0.00025794582,0.00057552505,0.000046441906,0.000014316054],"category_scores_gemma":[0.00012400176,0.000084297324,0.00005634818,0.0005505507,0.00006658024,0.0001747747,0.00028317867,0.00036832946,0.000015187152],"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":[0.0000037233203,0.00001624972,0.000083819155,5.4613395e-7,0.000012045868,0.0000028459372,0.000034978177,0.004327353,0.00014044336,0.44246373,0.00011232448,0.55280197],"study_design_scores_gemma":[0.00051612384,0.00012024613,0.004950849,0.00004885818,0.0000118816015,0.00005548331,0.00030456367,0.32175183,0.0042592413,0.3173167,0.35014987,0.00051432446],"about_ca_topic_score_codex":9.536947e-7,"about_ca_topic_score_gemma":0.000012079734,"teacher_disagreement_score":0.55228764,"about_ca_system_score_codex":0.000018531768,"about_ca_system_score_gemma":0.000011543428,"threshold_uncertainty_score":0.3437545},"labels":[],"label_agreement":null},{"id":"W3195085629","doi":"10.1145/3447548.3470820","title":"Deep Learning on Graphs for Natural Language Processing","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Graph; Expressive power; Theoretical computer science; Natural language processing","score_opus":0.009230094958074402,"score_gpt":0.2662218188347842,"score_spread":0.25699172387670977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195085629","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":[0.00985403,0.0020358253,0.9846622,0.00050979067,0.0002968041,0.000101922895,1.1293018e-7,0.00036463124,0.0021747109],"genre_scores_gemma":[0.8621888,0.0000073525107,0.1351948,0.0009800758,0.000047315574,0.00001185936,0.0000039280226,0.0000090755575,0.0015567904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991741,0.000025682572,0.00009678398,0.0003252627,0.00012938117,0.00024875623],"domain_scores_gemma":[0.99951977,0.00012388258,0.00004128148,0.0001974048,0.00007314048,0.000044527595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056350815,0.000094288545,0.00009217872,0.00004745131,0.00016310676,0.000115646726,0.00026595942,0.00003128157,0.0000057850566],"category_scores_gemma":[0.00006500008,0.000079256264,0.0000683358,0.00043296622,0.000015229727,0.0003097893,0.00008824484,0.00019426721,0.000006168008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007689523,0.000023548268,0.00013517521,0.000018785844,0.000006461612,0.000052747666,0.0004942396,0.0054153535,0.002439204,0.04659182,0.00015577266,0.9446592],"study_design_scores_gemma":[0.0003658931,0.00008241742,0.00045897585,0.000036319223,0.0000033135016,0.000032189266,0.00021008667,0.9777392,0.011384734,0.007322572,0.0021187542,0.0002455491],"about_ca_topic_score_codex":7.2166137e-7,"about_ca_topic_score_gemma":0.0000121198045,"teacher_disagreement_score":0.97232383,"about_ca_system_score_codex":0.000010763004,"about_ca_system_score_gemma":0.000015012932,"threshold_uncertainty_score":0.32319766},"labels":[],"label_agreement":null},{"id":"W3198596759","doi":"10.1109/icpads53394.2021.00086","title":"Efficient Asynchronous GCN Training on a GPU Cluster","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; GPU cluster; Asynchronous communication; Cluster (spacecraft); Parallel computing; Training (meteorology); CUDA; Operating system; Computer network","score_opus":0.021106579383279703,"score_gpt":0.2511754930666025,"score_spread":0.23006891368332277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198596759","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":[0.03498145,0.00007374598,0.9389613,0.0027855954,0.0005731782,0.000088678935,4.5017205e-7,0.00027316142,0.022262411],"genre_scores_gemma":[0.8935857,0.0000040383716,0.09978361,0.0057437425,0.00009022532,0.000008375937,0.0000011229824,0.000010105224,0.0007731182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987414,0.000052798678,0.00015503351,0.00046299698,0.0002348686,0.00035293377],"domain_scores_gemma":[0.99909985,0.00016506384,0.00003727894,0.00054682774,0.000049201582,0.0001017636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009354243,0.00012701233,0.0001360617,0.000052449344,0.000103389335,0.00009168141,0.00038568611,0.00006672643,0.000054539683],"category_scores_gemma":[0.000029668243,0.00010815892,0.00007734284,0.00047409855,0.000026826832,0.0000737089,0.00022208807,0.00023788621,0.00011410437],"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":[0.000011950922,0.00017991418,0.000041565174,0.000007956329,0.000022402593,0.0003840309,0.0016004777,0.45202008,0.0011553932,0.24558668,0.002325135,0.29666442],"study_design_scores_gemma":[0.0005506714,0.00011657385,0.00071504666,0.00003822923,0.0000031809645,0.00012247715,0.00008412502,0.9854982,0.0031252287,0.002967599,0.0064609996,0.00031766391],"about_ca_topic_score_codex":0.0000011787441,"about_ca_topic_score_gemma":0.000007678783,"teacher_disagreement_score":0.8586042,"about_ca_system_score_codex":0.000033711876,"about_ca_system_score_gemma":0.00005046476,"threshold_uncertainty_score":0.4410593},"labels":[],"label_agreement":null},{"id":"W3201040314","doi":"10.1109/ijcnn52387.2021.9533416","title":"HRotatE: Hybrid Relational Rotation Embedding for Knowledge Graph","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Embedding; Computer science; Graph embedding; Theoretical computer science; Inverse; Simple (philosophy); Vector space; Graph; Artificial intelligence; Algorithm; Mathematics; Pure mathematics","score_opus":0.029366799314035682,"score_gpt":0.2961394055398517,"score_spread":0.266772606225816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201040314","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":[0.0044387165,0.00023797175,0.9908625,0.0005990919,0.0005448343,0.00016720996,0.0000021792255,0.0001897344,0.0029578023],"genre_scores_gemma":[0.3275872,0.000017424165,0.66934395,0.00038041527,0.00013634347,0.0000694446,0.000035083856,0.000013043709,0.0024171204],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990817,0.000034639812,0.000184094,0.00036131736,0.00012310449,0.00021520231],"domain_scores_gemma":[0.99903125,0.00036030484,0.0000551572,0.00026134765,0.00023027354,0.00006164204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010613337,0.000096373995,0.0000952249,0.00006895414,0.00016703448,0.00006984598,0.0002146541,0.000027568787,0.000024233857],"category_scores_gemma":[0.000083607214,0.00009518134,0.00009165693,0.000472428,0.000020037269,0.0006289136,0.00010198895,0.00008454958,0.00002760409],"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":[0.0000071632153,0.00005996104,0.00025600856,0.000016048376,0.000020702177,0.000017012051,0.00019174193,0.020217072,0.0015393354,0.90350556,0.008655244,0.06551412],"study_design_scores_gemma":[0.0004807994,0.000038075945,0.0008151936,0.000019691222,0.0000052543087,0.000050521052,0.000023196644,0.76902133,0.011579415,0.20721911,0.010519094,0.00022832573],"about_ca_topic_score_codex":4.7151866e-7,"about_ca_topic_score_gemma":0.0000064219776,"teacher_disagreement_score":0.7488043,"about_ca_system_score_codex":0.00002285069,"about_ca_system_score_gemma":0.00005640693,"threshold_uncertainty_score":0.38813826},"labels":[],"label_agreement":null},{"id":"W3201435050","doi":"10.1016/j.simpa.2021.100139","title":"TopoDetect: Framework for topological features detection in graph embeddings","year":2021,"lang":"en","type":"article","venue":"Software Impacts","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Topological data analysis; Cluster analysis; Python (programming language); Visualization; Graph; Topology (electrical circuits); Representation (politics); Data visualization; Clustering coefficient","score_opus":0.015535897344814184,"score_gpt":0.2952982538924351,"score_spread":0.2797623565476209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201435050","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":[0.09251745,0.0006884626,0.9047427,0.00068879355,0.0007451069,0.00021374297,0.000003912546,0.0003740685,0.000025752694],"genre_scores_gemma":[0.7070693,0.000039397062,0.2917259,0.0009795676,0.00010384175,0.000032933058,0.0000024960339,0.000012834577,0.000033730437],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99840426,0.00006468049,0.00021687613,0.0005380765,0.00019726079,0.0005788285],"domain_scores_gemma":[0.9983635,0.00079778227,0.00008709026,0.00048548853,0.00010478126,0.00016134014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015255174,0.00019587035,0.00023410472,0.00013203413,0.00014503716,0.00014572885,0.00043012667,0.00025450438,0.000008936892],"category_scores_gemma":[0.001985428,0.00017835564,0.00015138471,0.0010951355,0.000050528626,0.00046901175,0.00017828806,0.00044731592,0.0000045170386],"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":[0.00026491564,0.00036237412,0.010869793,0.00016402242,0.000078893245,0.00073637493,0.0022495051,0.006292838,0.00860016,0.13206528,0.0014854164,0.83683044],"study_design_scores_gemma":[0.0005089396,0.000289075,0.03511886,0.000116589654,0.000006700851,0.00016467435,0.00004943376,0.0009840312,0.042058945,0.91964096,0.0006439179,0.0004178682],"about_ca_topic_score_codex":0.000009221025,"about_ca_topic_score_gemma":0.00011968528,"teacher_disagreement_score":0.83641255,"about_ca_system_score_codex":0.000067070876,"about_ca_system_score_gemma":0.000048409598,"threshold_uncertainty_score":0.72731316},"labels":[],"label_agreement":null},{"id":"W3203331145","doi":"10.48550/arxiv.2110.00577","title":"Reconstruction for Powerful Graph Representations","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Expressive power; Scalability; Computer science; Leverage (statistics); Theoretical computer science; Graph; Artificial intelligence","score_opus":0.06471616162971469,"score_gpt":0.2085219120755669,"score_spread":0.1438057504458522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203331145","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":[0.08842694,0.00009652981,0.9076244,0.00017012373,0.0019399778,0.00042435818,0.000014482205,0.0002826337,0.0010205468],"genre_scores_gemma":[0.9548218,0.00028643585,0.04356645,0.00010970854,0.00010909583,0.0000037390332,0.000050116865,0.00002060153,0.001032019],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790907,0.0001041189,0.00021804105,0.001361818,0.000069881855,0.00033707454],"domain_scores_gemma":[0.99767834,0.00019756572,0.00028383767,0.0013883499,0.0003124428,0.00013948954],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011400267,0.0002618261,0.000285043,0.00028067172,0.00020123452,0.00016572894,0.0011608629,0.00025829417,0.000017671642],"category_scores_gemma":[0.000053726682,0.00033460237,0.00038305682,0.00094463525,0.00011719484,0.0007192344,0.0010495125,0.0004724798,0.000009236406],"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":[0.000037139613,0.00008783824,0.002579554,0.00007947121,0.00017908608,0.0002299635,0.00021954857,0.7117126,0.00013928862,0.27639732,0.0010385588,0.007299648],"study_design_scores_gemma":[0.00070504,0.00006208509,0.0011761748,0.00014475199,0.00009550737,0.00005103472,0.0002522421,0.57718843,0.00055219466,0.41854835,0.00048277146,0.0007414565],"about_ca_topic_score_codex":0.00003063942,"about_ca_topic_score_gemma":0.00005785288,"teacher_disagreement_score":0.8663949,"about_ca_system_score_codex":0.00009884097,"about_ca_system_score_gemma":0.00012879098,"threshold_uncertainty_score":0.9999106},"labels":[],"label_agreement":null},{"id":"W3208642674","doi":"10.1145/3460210.3493557","title":"User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Computer science; Ground truth; Benchmark (surveying); Link (geometry); Graph; Relevance (law); Machine learning; Task (project management); Artificial intelligence; Data mining; Theoretical computer science","score_opus":0.05815114462211044,"score_gpt":0.31780676461458407,"score_spread":0.2596556199924736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208642674","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":[0.0053009824,0.00074155687,0.9859735,0.0005431798,0.0034498926,0.002065137,0.00011196167,0.00020804712,0.0016057458],"genre_scores_gemma":[0.6041404,0.00022977481,0.3896395,0.00021013043,0.0008576271,0.0016926234,0.0028526427,0.00004889296,0.000328414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962789,0.00036748912,0.0008640839,0.001074464,0.0010336878,0.00038136335],"domain_scores_gemma":[0.99482816,0.0008383632,0.0006027192,0.000977103,0.0026287409,0.00012489478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014360156,0.00038925203,0.00043717556,0.0004911291,0.00029544503,0.00009283518,0.000734022,0.0005411058,0.00004781802],"category_scores_gemma":[0.00015214918,0.00040630123,0.00045314257,0.0010493337,0.000116244984,0.0005164781,0.00050044,0.0006927469,0.0000036580313],"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":[0.000023585933,0.000107902175,0.00066173106,0.000041337957,0.00011669513,4.117027e-7,0.00013967327,0.6330059,0.000029207815,0.3569387,0.005602976,0.0033318994],"study_design_scores_gemma":[0.00082056737,0.0001105879,0.027455488,0.0003777778,0.00009570386,0.000003781327,0.000014635273,0.76310754,0.00013159365,0.20729484,0.00028484972,0.00030264707],"about_ca_topic_score_codex":0.000009000927,"about_ca_topic_score_gemma":0.0000811028,"teacher_disagreement_score":0.5988394,"about_ca_system_score_codex":0.00020399547,"about_ca_system_score_gemma":0.00084856077,"threshold_uncertainty_score":0.9998389},"labels":[],"label_agreement":null},{"id":"W3210430800","doi":"10.1145/3459637.3482277","title":"Modeling Heterogeneous Graph Network on Fraud Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Homogeneous; Revenue; Graph; Aggregate (composite); Reputation; Attention network; Focus (optics); Data mining; Machine learning; Theoretical computer science; Artificial intelligence","score_opus":0.01621942588173742,"score_gpt":0.23099620517143263,"score_spread":0.2147767792896952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210430800","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":[0.033348504,0.0002801241,0.96387714,0.0001820833,0.00082859816,0.00006242946,1.5201641e-7,0.00037001824,0.0010509783],"genre_scores_gemma":[0.9489169,0.00005839181,0.049008146,0.0016745261,0.00019588553,0.000008609008,0.0000010127827,0.000012015684,0.0001245127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987255,0.000057765952,0.00017163977,0.00048067962,0.00020633872,0.00035809877],"domain_scores_gemma":[0.99916947,0.000056051453,0.000029716988,0.00058133446,0.00007205597,0.00009134519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006995951,0.00013739946,0.00012197062,0.000048929483,0.0001841389,0.00009633409,0.00032679574,0.00006874042,0.000013034706],"category_scores_gemma":[0.000012321635,0.00012865527,0.000106290354,0.0007448128,0.000011853234,0.0002306387,0.00016243228,0.00019267955,0.000036233432],"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":[0.0000035878913,0.000013588721,0.000012705529,0.0000011716435,0.0000073319225,0.000052808322,0.000010645692,0.9489246,0.00037896325,0.0072516548,0.000053760064,0.0432892],"study_design_scores_gemma":[0.000100708596,0.000056995832,0.000012919473,0.000011375324,0.000002249478,0.00006191369,0.0000023554298,0.94931716,0.008620263,0.041360073,0.0002987916,0.00015520287],"about_ca_topic_score_codex":0.0000049054406,"about_ca_topic_score_gemma":0.00007650108,"teacher_disagreement_score":0.9155684,"about_ca_system_score_codex":0.000020013636,"about_ca_system_score_gemma":0.0000130451135,"threshold_uncertainty_score":0.524641},"labels":[],"label_agreement":null},{"id":"W3210798976","doi":"10.1109/dsaa53316.2021.9564221","title":"Centrality-based Interpretability Measures for Graph Embeddings","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Interpretability; Centrality; Computer science; Adjacency matrix; Graph embedding; Theoretical computer science; Graph property; Embedding; Graph; Voltage graph; Line graph; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.019197584603886948,"score_gpt":0.2762961420567983,"score_spread":0.2570985574529114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210798976","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":[0.0051283436,0.00013630828,0.9907124,0.0024351813,0.0004413789,0.0001980794,0.0000041184758,0.00030734146,0.0006368686],"genre_scores_gemma":[0.7181631,0.000004193018,0.2793981,0.0022348433,0.000030351115,0.000032186734,0.000004820439,0.000008206713,0.00012420908],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855995,0.00007712093,0.00022706963,0.0005551388,0.00021036748,0.00037033204],"domain_scores_gemma":[0.99859536,0.0002989268,0.00005310369,0.0006953829,0.00023645467,0.00012079959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023798893,0.00014023083,0.00017572344,0.000043106782,0.00010389143,0.00011857256,0.00055756216,0.00005492227,0.000023679722],"category_scores_gemma":[0.00016595991,0.00012463688,0.00021607423,0.0005061429,0.000056545163,0.00035336977,0.00012811611,0.000114819,0.0000040579835],"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":[0.00012606113,0.0005973015,0.014323057,0.00018632336,0.000111733076,0.000055156193,0.0006708618,0.013501953,0.014911004,0.70243,0.010581395,0.24250513],"study_design_scores_gemma":[0.0015247323,0.0001783645,0.0034549274,0.00006754508,0.000024728852,0.000020833992,0.000059883485,0.38678956,0.20738824,0.35948917,0.040115345,0.0008866853],"about_ca_topic_score_codex":0.000003959778,"about_ca_topic_score_gemma":0.000034350593,"teacher_disagreement_score":0.71303475,"about_ca_system_score_codex":0.000028249942,"about_ca_system_score_gemma":0.00006722436,"threshold_uncertainty_score":0.50825447},"labels":[],"label_agreement":null},{"id":"W3211455799","doi":"10.1145/3486622.3493921","title":"Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs","year":2021,"lang":"en","type":"article","venue":"IEEE/WIC/ACM International Conference on Web Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Ground truth; Leverage (statistics); Convolutional neural network; Benchmark (surveying); Graph; Data mining; Machine learning; Metric (unit); Theoretical computer science; Artificial intelligence","score_opus":0.40021149378191556,"score_gpt":0.459318220150168,"score_spread":0.05910672636825243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211455799","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":[0.0067087696,0.00055565214,0.98234135,0.0026038692,0.003530563,0.0011565185,0.0018061922,0.000093986666,0.0012030755],"genre_scores_gemma":[0.876104,0.0004439074,0.120561406,0.0001848206,0.00034794962,0.00043706122,0.0017584843,0.000024918274,0.0001374423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959444,0.0004909588,0.0009071587,0.0012169163,0.0010769474,0.00036365475],"domain_scores_gemma":[0.98750633,0.0062995236,0.000592318,0.0009295635,0.004543105,0.00012913557],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002123497,0.00034155877,0.00036002736,0.00038887336,0.00034570508,0.00014347011,0.0015069718,0.00019007876,0.000051016512],"category_scores_gemma":[0.002666999,0.000356534,0.00016006583,0.0007317518,0.00033087892,0.001090245,0.00027214782,0.0003505098,0.000010757263],"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":[0.000222339,0.0001772567,0.00006699048,0.000023733814,0.00019030849,8.596528e-7,0.001094919,0.020513088,0.000303445,0.9659581,0.0010116161,0.010437302],"study_design_scores_gemma":[0.0005574172,0.00035722816,0.0007484178,0.00020447395,0.00004405274,0.0000060179023,0.00052439165,0.5421897,0.00036524943,0.4543337,0.00045835672,0.00021101876],"about_ca_topic_score_codex":0.0000066149987,"about_ca_topic_score_gemma":0.00008642108,"teacher_disagreement_score":0.86939526,"about_ca_system_score_codex":0.00016081551,"about_ca_system_score_gemma":0.000725741,"threshold_uncertainty_score":0.99988866},"labels":[],"label_agreement":null},{"id":"W3212564326","doi":"","title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction","year":2021,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Computer science; Longest path problem; Artificial neural network; Path (computing); Shortest path problem; Operator (biology); Mathematical optimization; Graph; Representation (politics); Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.018386948677528908,"score_gpt":0.2599354822932214,"score_spread":0.24154853361569248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212564326","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":[0.008216669,0.0030628254,0.97700864,0.0014105544,0.007757714,0.0010969336,0.000023559478,0.0012300094,0.00019310339],"genre_scores_gemma":[0.9221094,0.000119247314,0.067415595,0.00419664,0.0048156567,0.00059471617,0.00042033452,0.00007659774,0.00025178547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954869,0.0002033497,0.0015937125,0.000705365,0.00074643356,0.0012642193],"domain_scores_gemma":[0.99652904,0.00030953417,0.0010395591,0.00088118715,0.00091862166,0.00032204366],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00049810443,0.00058703,0.00061074074,0.00024346609,0.0011716783,0.0020361913,0.0010505838,0.000444849,0.0000037753684],"category_scores_gemma":[0.00016556508,0.00056048454,0.00032237996,0.0023482377,0.00010305848,0.008804605,0.0003200466,0.0008551513,0.00000975743],"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":[0.00004458785,0.000018778646,0.00059892336,0.00023893692,0.000025647618,0.000009701009,0.0003351035,0.86973405,0.00001947029,0.017401287,0.002770592,0.10880289],"study_design_scores_gemma":[0.00066452904,0.00017824624,0.0006796744,0.00026008973,0.000030215451,0.00030122182,0.0000808372,0.983572,0.000048739526,0.0050007883,0.00865174,0.00053194043],"about_ca_topic_score_codex":0.000010305876,"about_ca_topic_score_gemma":0.000005237737,"teacher_disagreement_score":0.91389275,"about_ca_system_score_codex":0.00010814102,"about_ca_system_score_gemma":0.00012461176,"threshold_uncertainty_score":0.9996847},"labels":[],"label_agreement":null},{"id":"W3212747350","doi":"10.1145/3483940","title":"On Directed Densest Subgraph Discovery","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Hong Kong; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Scalability; Induced subgraph isomorphism problem; Enhanced Data Rates for GSM Evolution; Core (optical fiber); Graph; Directed graph; Subgraph isomorphism problem; Efficient algorithm; Theoretical computer science; Algorithm; Artificial intelligence; Database; Line graph","score_opus":0.019904467790991108,"score_gpt":0.25218275451067046,"score_spread":0.23227828671967934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212747350","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":[0.012054928,0.00036479614,0.9830376,0.00046252043,0.0025490555,0.00026155598,0.00031291554,0.00064075907,0.00031589856],"genre_scores_gemma":[0.985059,0.0002131761,0.012486079,0.00045911616,0.000090136346,0.000104633626,0.000116720024,0.000035161018,0.0014359761],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771655,0.00024187792,0.00033843747,0.00082269916,0.00046731028,0.00041313047],"domain_scores_gemma":[0.9959985,0.0007171803,0.000094783005,0.0029226264,0.00010059355,0.00016633327],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013510398,0.00027037284,0.00027302853,0.00021737987,0.000321721,0.00029210778,0.0009613929,0.000080238184,0.000021401558],"category_scores_gemma":[0.000083091385,0.00025273595,0.00015803747,0.0014464317,0.00005215285,0.0010918563,0.000037138812,0.00041123174,0.00015396516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000665136,0.0059878933,0.0008496029,0.0008463941,0.0014392749,0.007877939,0.00096253195,0.3689574,0.05521684,0.3702691,0.033239495,0.15368837],"study_design_scores_gemma":[0.016292524,0.0035501367,0.007492193,0.009009319,0.00074636424,0.006353679,0.0014907301,0.44467002,0.30235082,0.027663868,0.16754675,0.012833593],"about_ca_topic_score_codex":0.000059618516,"about_ca_topic_score_gemma":0.000097662734,"teacher_disagreement_score":0.9730041,"about_ca_system_score_codex":0.000060477203,"about_ca_system_score_gemma":0.00007412403,"threshold_uncertainty_score":0.9999925},"labels":[],"label_agreement":null},{"id":"W3213054824","doi":"","title":"How to transfer algorithmic reasoning knowledge to learn new algorithms","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Dijkstra's algorithm; Graph; Machine learning; Algorithm; Set (abstract data type); Theoretical computer science; Programming language","score_opus":0.06041843821256871,"score_gpt":0.20464863764769903,"score_spread":0.14423019943513032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213054824","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":[0.033708133,0.00028397675,0.9615034,0.0009826702,0.0015443556,0.00064893236,0.00000834395,0.00050860894,0.0008115931],"genre_scores_gemma":[0.85243815,0.00016610416,0.12725872,0.0005453839,0.0005620076,0.0000048844954,0.00001989022,0.00008032641,0.018924553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959999,0.0002011243,0.0002421223,0.0025029574,0.0001776944,0.0008762542],"domain_scores_gemma":[0.9965173,0.00012735903,0.00008302183,0.0019488712,0.00030003107,0.0010233907],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018493486,0.0006306123,0.0006360854,0.0005237761,0.00022939843,0.0005905023,0.0030924135,0.00040108085,0.00001894192],"category_scores_gemma":[0.000065589724,0.0007784546,0.0004055222,0.0025899038,0.000054481832,0.0007601619,0.0031430821,0.0011693247,0.00011194548],"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":[0.000060234506,0.0001898774,0.00027508984,0.000098735596,0.00025576647,0.002760181,0.0034374115,0.7975775,0.0005372802,0.056903075,0.0028690703,0.1350358],"study_design_scores_gemma":[0.0016714762,0.00046313004,0.00095547206,0.0011724943,0.0001933522,0.00009454689,0.00061794877,0.9459679,0.0027702767,0.013966356,0.028411493,0.0037155666],"about_ca_topic_score_codex":0.00012594581,"about_ca_topic_score_gemma":0.00021773421,"teacher_disagreement_score":0.83424467,"about_ca_system_score_codex":0.00032840675,"about_ca_system_score_gemma":0.00040453378,"threshold_uncertainty_score":0.99946666},"labels":[],"label_agreement":null},{"id":"W3213281202","doi":"10.1109/mlsp52302.2021.9596169","title":"Data-Driven Learning of Geometric Scattering Modules for GNNs","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; National Institutes of Health; Institut de Valorisation des Données; National Institute of General Medical Sciences; Canadian Institute for Advanced Research","keywords":"Wavelet; Computer science; Graph; Artificial intelligence; Pattern recognition (psychology); Range (aeronautics); Cascade; Theoretical computer science; Machine learning","score_opus":0.049691956675749806,"score_gpt":0.29246568542372925,"score_spread":0.24277372874797945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213281202","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":[0.014055453,0.00019385191,0.98482585,0.00027780572,0.0001555212,0.00006824172,0.0000063710545,0.000095577365,0.00032135827],"genre_scores_gemma":[0.5801882,0.00003230005,0.41932046,0.000086774,0.00003559552,0.0000042665497,0.000019147385,0.0000066641037,0.0003065541],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990963,0.000022918144,0.00016811857,0.00037951983,0.00013074865,0.00020241892],"domain_scores_gemma":[0.9988336,0.00023632632,0.00007405396,0.000720519,0.000093026014,0.000042494637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009988756,0.00007482748,0.00013913236,0.00012299682,0.000067519584,0.000045043813,0.00087966816,0.000026203816,0.000010446204],"category_scores_gemma":[0.0001233772,0.000070904345,0.000043783373,0.0009647697,0.000022623492,0.00040562588,0.00091050327,0.00008615078,0.0000038382295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007739574,0.00008456847,0.0053493017,0.00012188801,0.00007785785,0.00003170364,0.00012110569,0.3386738,0.023523971,0.078730464,0.0028360165,0.55044156],"study_design_scores_gemma":[0.00020233373,0.000044219112,0.0020291992,0.000019436166,0.0000052446267,0.0000129993705,0.000020750624,0.97990453,0.010413895,0.00196127,0.0052488134,0.00013732923],"about_ca_topic_score_codex":0.000002851818,"about_ca_topic_score_gemma":0.000006953569,"teacher_disagreement_score":0.6412307,"about_ca_system_score_codex":0.000007682424,"about_ca_system_score_gemma":0.000016489359,"threshold_uncertainty_score":0.2891395},"labels":[],"label_agreement":null},{"id":"W3215411791","doi":"10.1145/3478285","title":"Embedding Hierarchical Structures for Venue Category Representation","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Wilfrid Laurier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Embedding; Context (archaeology); Hierarchy; Representation (politics); Semantics (computer science); Information retrieval; Theoretical computer science; Artificial intelligence","score_opus":0.02585947478299782,"score_gpt":0.2997060619374764,"score_spread":0.27384658715447857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215411791","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":[0.0015170716,0.000050965446,0.99573225,0.0004348177,0.0015722436,0.00031651402,0.000028204442,0.00016955569,0.00017840629],"genre_scores_gemma":[0.9514136,0.00001973656,0.047817234,0.00030595652,0.00008580589,0.00019107401,0.00006559638,0.000008081837,0.000092967406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989278,0.000064956614,0.00037698058,0.00018185709,0.00025666825,0.0001917493],"domain_scores_gemma":[0.99866325,0.00027616494,0.0001399943,0.00065203104,0.00020037736,0.00006819061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111391666,0.000110220884,0.00013592666,0.00015876722,0.00029665203,0.00027137983,0.00037889692,0.00008218143,0.0000037896339],"category_scores_gemma":[0.00006919887,0.0001112701,0.00009261555,0.00045546834,0.00002121389,0.0019962098,0.000011068328,0.00017920052,0.000022329345],"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":[0.00003276771,0.000023805589,0.000016900669,0.000099587734,0.00004566948,0.00000384159,0.0021032938,0.7771144,0.0003565975,0.0449947,0.00065250904,0.17455596],"study_design_scores_gemma":[0.0019746607,0.00020002932,0.00072425936,0.000116952346,0.000034622375,0.0003032249,0.0015171795,0.86018777,0.017438175,0.042742457,0.07407431,0.00068637374],"about_ca_topic_score_codex":0.0000081024145,"about_ca_topic_score_gemma":0.0000016754924,"teacher_disagreement_score":0.94989645,"about_ca_system_score_codex":0.00005132575,"about_ca_system_score_gemma":0.000054522887,"threshold_uncertainty_score":0.4537463},"labels":[],"label_agreement":null},{"id":"W3215602957","doi":"10.6339/22-jds1047","title":"A Review on Graph Neural Network Methods in Financial Applications","year":2022,"lang":"en","type":"review","venue":"Journal of Data Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Catastrophic Loss Reduction","keywords":"Computer science; Graph; Categorization; Artificial neural network; Financial modeling; Data mining; Data science; Finance; Artificial intelligence; Theoretical computer science; Business","score_opus":0.19864865051033517,"score_gpt":0.48307997417449006,"score_spread":0.2844313236641549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215602957","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.1535014e-8,0.78103435,0.21731248,0.00014285989,0.0008427524,0.00055868377,0.000020196643,0.000015973186,0.000072693496],"genre_scores_gemma":[4.259521e-8,0.7644638,0.23416394,0.0010173429,0.0002775799,0.000049837887,0.000011313764,0.000011980042,0.000004180936],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99491334,0.0007972986,0.0016112874,0.0010107316,0.001032551,0.0006348083],"domain_scores_gemma":[0.99323416,0.0012003831,0.0022461063,0.0029675548,0.00011172056,0.00024008087],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.008679627,0.00036936134,0.0016223742,0.00077386305,0.0003662109,0.00016832493,0.017546333,0.00007852393,0.000017206845],"category_scores_gemma":[0.000818335,0.00027700153,0.0003575596,0.012282567,0.00037228272,0.002926539,0.003467009,0.0019471283,0.0000054320312],"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":[9.706417e-7,0.00003529955,3.5587598e-7,0.0011483269,0.0000043183254,0.00003809642,0.0000031379336,0.00027883786,8.9073026e-8,0.0027818505,0.0043709483,0.9913378],"study_design_scores_gemma":[0.0000603741,0.00010714198,0.0000026814407,0.011732688,0.00007091598,0.0005201115,5.2828335e-7,0.00076597265,4.3511346e-8,0.002017345,0.9844719,0.00025026314],"about_ca_topic_score_codex":0.0000010980832,"about_ca_topic_score_gemma":0.0000020197917,"teacher_disagreement_score":0.9910875,"about_ca_system_score_codex":0.00019283804,"about_ca_system_score_gemma":0.0014344838,"threshold_uncertainty_score":0.99996823},"labels":[],"label_agreement":null},{"id":"W3217035451","doi":"10.1017/nws.2022.27","title":"A multi-purposed unsupervised framework for comparing embeddings of undirected and directed graphs","year":2022,"lang":"en","type":"article","venue":"Network Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Embedding; Computer science; Scalability; Theoretical computer science; Graph embedding; Graph; Node (physics); Topological graph theory; Set (abstract data type); Representation (politics); Undirected graph; Artificial intelligence; Line graph; Voltage graph","score_opus":0.028061766804376314,"score_gpt":0.283082251646283,"score_spread":0.25502048484190665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217035451","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":[0.27687448,0.00081808324,0.7192971,0.00019214257,0.0012001905,0.00088268984,0.000007122169,0.0006008348,0.00012737335],"genre_scores_gemma":[0.59751517,0.000017921026,0.40214118,0.00018447932,0.000029945324,0.00008247309,0.0000019694544,0.000011799141,0.000015058161],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971008,0.00010975456,0.00040318046,0.00091731723,0.0006402637,0.0008286996],"domain_scores_gemma":[0.99796236,0.00064912316,0.00028612916,0.00069441303,0.0002000087,0.00020795927],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00097406656,0.0002274324,0.00039223564,0.00027479086,0.0013375519,0.00013584355,0.0020203434,0.00005089386,0.0000069831167],"category_scores_gemma":[0.00020515258,0.00023379676,0.00009625195,0.005859909,0.0005926386,0.00058937335,0.0012700544,0.00037301905,4.7489397e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029191098,0.0006227974,0.047220524,0.00011402546,0.00008964725,0.00003272872,0.0041648396,0.28809553,0.030184105,0.57458514,0.0015183747,0.05308035],"study_design_scores_gemma":[0.0006920433,0.00018923255,0.018822419,0.000052112853,0.000010216516,0.000015924601,0.000053911957,0.9301725,0.0004981831,0.048700668,0.0004733296,0.00031945994],"about_ca_topic_score_codex":0.00001351858,"about_ca_topic_score_gemma":0.000010416412,"teacher_disagreement_score":0.64207697,"about_ca_system_score_codex":0.00006658425,"about_ca_system_score_gemma":0.000107531516,"threshold_uncertainty_score":0.99996257},"labels":[],"label_agreement":null},{"id":"W3217040326","doi":"10.1145/3471165","title":"Dual Gated Graph Attention Networks with Dynamic Iterative Training for Cross-Lingual Entity Alignment","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Research Foundation Singapore","keywords":"Computer science; Attention network; Graph; Embedding; Iterative and incremental development; Benchmark (surveying); Dual (grammatical number); Process (computing); Machine learning; Artificial intelligence; Theoretical computer science; Data mining","score_opus":0.0196459319324312,"score_gpt":0.27745233427603794,"score_spread":0.25780640234360674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217040326","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":[0.020110834,0.000042836888,0.97725856,0.00014430945,0.0013561561,0.00067016424,0.000048423255,0.00026486008,0.000103863196],"genre_scores_gemma":[0.98000145,0.000022541275,0.019184962,0.000181481,0.000043134132,0.00022511123,0.0001629016,0.000012899574,0.0001655149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982226,0.00008860636,0.0006194936,0.0003064144,0.00039787785,0.0003649989],"domain_scores_gemma":[0.9982923,0.00019080729,0.00030375796,0.0006178689,0.0004906284,0.00010463412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026922388,0.00022800529,0.00023944327,0.00020869872,0.0005667503,0.00071121915,0.0003264595,0.00012106995,0.000005035118],"category_scores_gemma":[0.0000199894,0.00020997654,0.00013896386,0.0008602783,0.00005499804,0.003287361,0.000012993354,0.00023891895,0.000011441034],"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":[0.00007250231,0.0000508302,0.000053924734,0.00005459003,0.00011695127,0.0000059741496,0.002211098,0.9663814,0.00007418288,0.002628827,0.00003395313,0.028315783],"study_design_scores_gemma":[0.0027856985,0.00046379303,0.0010393596,0.00032260435,0.000048291164,0.00024973505,0.002119405,0.9873609,0.0009477846,0.0005569068,0.0034834868,0.0006220511],"about_ca_topic_score_codex":0.000007755977,"about_ca_topic_score_gemma":0.00002530402,"teacher_disagreement_score":0.9598906,"about_ca_system_score_codex":0.00011889642,"about_ca_system_score_gemma":0.000076072225,"threshold_uncertainty_score":0.8562595},"labels":[],"label_agreement":null},{"id":"W4200115660","doi":"10.1109/pst52912.2021.9647749","title":"User Identification in Online Social Networks using Graph Transformer Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Graph; Social graph; Social network (sociolinguistics); Transformer; Identification (biology); Data mining; Information retrieval; Artificial intelligence; Machine learning; Theoretical computer science; Social media; World Wide Web","score_opus":0.02404116542996576,"score_gpt":0.2838228085807504,"score_spread":0.2597816431507846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200115660","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":[0.04346044,0.00041022358,0.95435363,0.00064714334,0.0006727925,0.00014815642,0.0000012294269,0.00015113527,0.00015523376],"genre_scores_gemma":[0.9620143,0.00018336253,0.036085933,0.0010775253,0.00032733422,0.000009460036,0.000036449102,0.000023694933,0.00024190187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802655,0.00012254101,0.0004884751,0.0005872176,0.00023938372,0.000535844],"domain_scores_gemma":[0.9992158,0.0000775172,0.00010328905,0.00040301227,0.000119183496,0.00008121912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019634177,0.00020056448,0.00023893784,0.00013536535,0.00019099252,0.00016712307,0.00052156294,0.00017713504,0.00002416315],"category_scores_gemma":[0.000009302203,0.00020469721,0.0001445496,0.002212171,0.000058707028,0.0009313128,0.00009104669,0.00041322852,0.0000025727418],"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":[0.000014817678,0.00023025065,0.004174631,0.000008425394,0.000028622158,0.00009756612,0.00020836164,0.8810826,0.00150601,0.040330503,0.0005807967,0.071737416],"study_design_scores_gemma":[0.00040274757,0.00000987395,0.018479925,0.000020132686,0.000008608948,0.000024092395,0.000043265605,0.97603667,0.00035167602,0.0039445506,0.00038741037,0.0002910599],"about_ca_topic_score_codex":0.000023467666,"about_ca_topic_score_gemma":0.0005391442,"teacher_disagreement_score":0.9185539,"about_ca_system_score_codex":0.000054166107,"about_ca_system_score_gemma":0.00004258929,"threshold_uncertainty_score":0.834731},"labels":[],"label_agreement":null},{"id":"W4205160329","doi":"10.1007/s00521-021-06736-7","title":"Improving question answering over incomplete knowledge graphs with relation prediction","year":2022,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Embedding; Computer science; Relation (database); Knowledge graph; Question answering; Graph; Enhanced Data Rates for GSM Evolution; Data mining; Artificial intelligence; Theoretical computer science; Machine learning; Natural language processing","score_opus":0.008986851982562659,"score_gpt":0.2443862057232175,"score_spread":0.23539935374065485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205160329","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":[0.23144591,0.00026051063,0.76695645,0.00015337902,0.00012251936,0.00035947768,0.0000046669047,0.0004955654,0.00020149547],"genre_scores_gemma":[0.985793,0.000005973217,0.013848507,0.00010288521,0.000089795154,0.00010690956,0.000018798082,0.000012853426,0.000021271611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888617,0.00008828285,0.00019760233,0.000454494,0.00016829695,0.00020517912],"domain_scores_gemma":[0.99933165,0.00011128094,0.00014661455,0.0002989077,0.00004516096,0.00006638721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001703464,0.00012789194,0.00010126057,0.00012331306,0.0011841815,0.000086878536,0.00026619015,0.000024712697,0.0000013619983],"category_scores_gemma":[0.0000044913777,0.00012768028,0.000028847884,0.0008028329,0.00004342524,0.00032042412,0.00034722302,0.00033170363,9.764032e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019115738,0.00010182983,0.012302093,0.000038214526,0.000015907688,0.0000035080761,0.0006145514,0.19532606,0.0061633205,0.38302743,0.00008968699,0.40229827],"study_design_scores_gemma":[0.00024802506,0.00012771793,0.027497655,0.000010556723,0.000007805629,0.000066166656,0.000023700451,0.965742,0.0000480225,0.0044246786,0.0016513201,0.0001523516],"about_ca_topic_score_codex":0.000020270489,"about_ca_topic_score_gemma":0.000005307516,"teacher_disagreement_score":0.77041596,"about_ca_system_score_codex":0.00005151062,"about_ca_system_score_gemma":0.000016160375,"threshold_uncertainty_score":0.91078866},"labels":[],"label_agreement":null},{"id":"W4205610464","doi":"10.1007/s11704-021-1057-6","title":"Heterogeneous information network embedding with incomplete multi-view fusion","year":2022,"lang":"en","type":"article","venue":"Frontiers of Computer Science","topic":"Advanced Graph Neural Networks","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":"Novelis (Canada)","funders":"","keywords":"Computer science; Embedding; Fusion; Information fusion; Sensor fusion; Artificial intelligence","score_opus":0.009201404666549198,"score_gpt":0.22456085316221489,"score_spread":0.21535944849566568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205610464","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":[0.012090122,0.00039974818,0.98483455,0.00016128829,0.0020041429,0.00030871207,0.000003044895,0.00016416157,0.00003424909],"genre_scores_gemma":[0.32086703,0.000012716743,0.6784745,0.0005653049,0.000046614212,0.000019065421,0.0000030270926,0.0000061900814,0.0000055885516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739915,0.00009541903,0.00041206297,0.0004884819,0.0010065136,0.00059839815],"domain_scores_gemma":[0.9984229,0.000039439994,0.00040089036,0.0007923611,0.00018681619,0.00015759577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065440254,0.0002044345,0.00028541096,0.00032933053,0.00095694925,0.00018694907,0.0027999347,0.000023427756,0.0000034613104],"category_scores_gemma":[0.000008083434,0.00018492204,0.000072806324,0.0026085882,0.00033785077,0.0026294603,0.0021435402,0.00024736044,0.000003150124],"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":[0.000017194603,0.000028722145,0.0007905005,0.000008157575,0.000008873361,0.000010825092,0.00047851488,0.89222986,0.000053904558,0.00137742,0.0016050106,0.10339104],"study_design_scores_gemma":[0.00038264037,0.00031818773,0.0013546827,0.00002186266,0.000003883789,0.00009317353,0.000014445921,0.98872024,0.00020751961,0.0006967839,0.007936493,0.00025008654],"about_ca_topic_score_codex":0.000006434369,"about_ca_topic_score_gemma":0.0000017586915,"teacher_disagreement_score":0.30877692,"about_ca_system_score_codex":0.00012024128,"about_ca_system_score_gemma":0.00014372553,"threshold_uncertainty_score":0.7540902},"labels":[],"label_agreement":null},{"id":"W4206508642","doi":"10.1007/978-981-16-6054-2_11","title":"Graph Neural Networks: Graph Generation","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Graph Neural Networks","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 Toronto","funders":"","keywords":"Computer science; Graph; Theoretical computer science","score_opus":0.025124001813658012,"score_gpt":0.2279135309878051,"score_spread":0.20278952917414708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206508642","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005767824,0.0021850355,0.7234738,0.00046333962,0.0036185284,0.00046668373,0.000007218513,0.0008231152,0.2689565],"genre_scores_gemma":[0.014998451,0.0051124236,0.06213197,0.013102516,0.0045764586,0.00025986967,0.00072832854,0.0004666002,0.8986234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969954,0.00005567047,0.0005403064,0.0012023341,0.0006563104,0.0005500025],"domain_scores_gemma":[0.99784774,0.000102305865,0.0003447194,0.0014201317,0.000092221,0.00019288991],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016563456,0.0006105879,0.00046088992,0.00043059196,0.00043625885,0.00020590558,0.0016951526,0.0002917021,0.00087310135],"category_scores_gemma":[0.000004923627,0.00059660047,0.0004603042,0.00032014953,0.00010481863,0.0007608001,0.0008919191,0.0011008182,0.000027673324],"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":[0.00000342329,0.000008035818,0.0000034095644,0.0000033458805,0.000036514273,0.000078597994,0.000012676396,0.083712965,0.0000067616447,0.8668936,0.019035952,0.03020471],"study_design_scores_gemma":[0.0002625695,0.00021432011,0.00000815009,0.000014844118,0.000034394707,0.00009969759,0.0000012389373,0.581481,0.000008919917,0.16318403,0.2535581,0.0011327484],"about_ca_topic_score_codex":0.000010831602,"about_ca_topic_score_gemma":0.000049561026,"teacher_disagreement_score":0.7037096,"about_ca_system_score_codex":0.00006662569,"about_ca_system_score_gemma":0.000029251569,"threshold_uncertainty_score":0.9996485},"labels":[],"label_agreement":null},{"id":"W4210797700","doi":"10.14778/3489496.3489504","title":"LargeEA","year":2021,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Scalability; Benchmark (surveying); Exploit; Process (computing); Channel (broadcasting); Partition (number theory); Competitor analysis; Feature (linguistics); Data mining; Artificial intelligence; Database; Programming language","score_opus":0.007208047789126523,"score_gpt":0.2039609482731143,"score_spread":0.1967529004839878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210797700","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":[0.7001984,0.006081498,0.074952126,0.05669829,0.006572707,0.0021463132,0.000014283928,0.0012749392,0.15206142],"genre_scores_gemma":[0.96857095,0.00006409425,0.029670145,0.0007024539,0.000057009234,0.000018530509,2.0033906e-7,0.000008141623,0.0009084475],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990408,0.0000045671427,0.00017162146,0.00026229976,0.00029697875,0.00022372491],"domain_scores_gemma":[0.9994027,0.00002552372,0.0001267309,0.00021310017,0.00018362625,0.000048330807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010343153,0.00009638671,0.00011962219,0.00002710934,0.00008373565,0.0000525752,0.0009073472,0.00002777057,0.000008543218],"category_scores_gemma":[0.000055103195,0.0000671169,0.00010271695,0.00056103966,0.00003701969,0.00028907717,0.0007651329,0.00012112785,0.00000646813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000755201,0.00017758775,0.0058248206,0.00006630682,0.00005163523,0.000006228023,0.00053692196,0.00015931147,0.17577301,0.787488,0.010192885,0.019715738],"study_design_scores_gemma":[0.00039717698,0.000041052295,0.0029313955,0.00007699507,0.0000116321,0.000059356957,0.00006508303,0.0021859335,0.8963573,0.07911799,0.018572854,0.0001832127],"about_ca_topic_score_codex":0.0000017473697,"about_ca_topic_score_gemma":8.0405096e-7,"teacher_disagreement_score":0.7205843,"about_ca_system_score_codex":0.000025347721,"about_ca_system_score_gemma":0.000020538953,"threshold_uncertainty_score":0.27369478},"labels":[],"label_agreement":null},{"id":"W4210799478","doi":"10.52591/lxai2020121213","title":"Graph Neural Networks Learn Twitter Bot Behaviour","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Social media; Graph; Set (abstract data type); Task (project management); Artificial neural network; Contrast (vision); Artificial intelligence; Social network (sociolinguistics); Machine learning; Information retrieval; World Wide Web; Data science; Theoretical computer science","score_opus":0.02686696065914855,"score_gpt":0.24667253382861223,"score_spread":0.2198055731694637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210799478","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":[0.011344375,0.00015728672,0.97503424,0.01060691,0.0005056654,0.00016708873,3.4191964e-7,0.00076871854,0.0014154054],"genre_scores_gemma":[0.9549314,0.000014865173,0.018384214,0.02583949,0.00036804224,0.000010712272,0.0000025107845,0.000021103791,0.00042767153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983331,0.00006136124,0.00024299872,0.00059932296,0.0002520583,0.00051115593],"domain_scores_gemma":[0.9990391,0.000058984177,0.00007261226,0.00047707904,0.000044376266,0.00030782283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060823557,0.00021748531,0.00019440851,0.0000495843,0.00012212234,0.00015587678,0.0010865958,0.00008340954,0.000055634206],"category_scores_gemma":[0.000011714279,0.0001864765,0.00014411529,0.00086022535,0.000058049936,0.0006132873,0.0004405095,0.00044453423,0.00005522648],"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":[0.000080612255,0.00017574968,0.06116878,0.000019792575,0.00007137239,0.00073741126,0.0012747082,0.44046196,0.00057443406,0.046583172,0.2962053,0.15264669],"study_design_scores_gemma":[0.00030502773,0.00016307409,0.00432591,0.0000035739604,0.0000066082584,0.000020064439,0.000012754102,0.99005544,0.00014291033,0.0013486869,0.0032760613,0.00033986245],"about_ca_topic_score_codex":0.000008255949,"about_ca_topic_score_gemma":0.000004606497,"teacher_disagreement_score":0.95665,"about_ca_system_score_codex":0.000008416396,"about_ca_system_score_gemma":0.000007321952,"threshold_uncertainty_score":0.7604291},"labels":[],"label_agreement":null},{"id":"W4223434371","doi":"10.1109/tpami.2022.3166894","title":"Graph Learning on Millions of Data in Seconds: Label Propagation Acceleration on Graph Using Data Distribution","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"National Key Research and Development Program of China; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Margin (machine learning); Graph; Labeled data; Range (aeronautics); Task (project management); Raw data; Key (lock); Data modeling; Pattern recognition (psychology)","score_opus":0.0763549678641931,"score_gpt":0.32202916136743404,"score_spread":0.24567419350324093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223434371","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":[0.040997095,0.00006188872,0.9577491,0.00018945114,0.00013645698,0.00019939247,0.00061736756,0.00004347929,0.000005735216],"genre_scores_gemma":[0.9975513,0.00021763198,0.0012928447,0.00014450237,0.000010546132,0.000018547446,0.0007423013,0.000010510733,0.000011790365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976771,0.00029581637,0.0004949571,0.0008830734,0.0004317091,0.00021735078],"domain_scores_gemma":[0.9981088,0.00020122433,0.00025170017,0.0013269058,0.00005050321,0.000060877886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005413618,0.00020219096,0.00028020778,0.0007972503,0.00045673392,0.00007482609,0.0011604894,0.000043568532,0.000043140142],"category_scores_gemma":[0.000011278052,0.00019804286,0.0000795456,0.0030896056,0.00005482435,0.00076993665,0.000064559616,0.00063871295,0.0000014302138],"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":[0.0000299405,0.00028582138,0.00067658466,0.000009010297,0.00010290114,0.000005031986,0.00009897039,0.6795476,0.00055939064,0.00014610859,0.0000044969324,0.31853417],"study_design_scores_gemma":[0.00014421261,0.00026842608,0.00082596304,0.000029466895,0.00013359626,0.0000052941687,0.000060553055,0.9889178,0.009010401,0.00037986544,0.000025531173,0.00019884227],"about_ca_topic_score_codex":0.00059124903,"about_ca_topic_score_gemma":0.0012690115,"teacher_disagreement_score":0.95655423,"about_ca_system_score_codex":0.00005799029,"about_ca_system_score_gemma":0.000022459484,"threshold_uncertainty_score":0.8075954},"labels":[],"label_agreement":null},{"id":"W4224919236","doi":"10.1109/icassp43922.2022.9747447","title":"Dual Path Graph Convolutional Networks","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Graph; Residual; Theoretical computer science; Deep learning; Convolutional neural network; Exploit; Artificial intelligence; Dual graph; Feature learning; Path (computing); Algorithm; Computer network; Planar graph","score_opus":0.03481613526811564,"score_gpt":0.2810142352713893,"score_spread":0.24619810000327366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224919236","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":[0.019736791,0.0008979608,0.96615446,0.0039875954,0.003936361,0.0005290975,0.00031926265,0.0005161712,0.0039223074],"genre_scores_gemma":[0.9838386,0.00032502686,0.011375713,0.0020656514,0.0007326045,0.0001419041,0.00014116564,0.000050943247,0.0013283478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99475825,0.00025551947,0.0007608079,0.0013477704,0.0020472037,0.00083042495],"domain_scores_gemma":[0.9977417,0.0003430097,0.0005408541,0.00048047377,0.00052454293,0.00036939996],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074471557,0.0005415942,0.00046636743,0.00047580624,0.0012294877,0.0006817831,0.001745909,0.00015821893,0.0010612966],"category_scores_gemma":[0.000078382865,0.0005705258,0.0001863029,0.0008928864,0.00035119295,0.0007569688,0.0009496397,0.0017987499,0.000022453025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010353184,0.001912745,0.0018966922,0.00012655309,0.0005326535,0.0035492904,0.0008850869,0.28346792,0.038789503,0.21553838,0.055715322,0.39655054],"study_design_scores_gemma":[0.00088338106,0.00052284606,0.00052328623,0.000070450464,0.0000315143,0.00049965415,0.00025913015,0.9615326,0.00013948006,0.032081984,0.0027696728,0.000686013],"about_ca_topic_score_codex":0.000021553931,"about_ca_topic_score_gemma":0.0000063570683,"teacher_disagreement_score":0.96410185,"about_ca_system_score_codex":0.0002941296,"about_ca_system_score_gemma":0.0003612545,"threshold_uncertainty_score":0.9998519},"labels":[],"label_agreement":null},{"id":"W4224931240","doi":"10.1109/icassp43922.2022.9746424","title":"Linear-Time Sampling on Signed Graphs Via Gershgorin Disc Perfect Alignment","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Simon Fraser University","funders":"","keywords":"Combinatorics; Laplacian matrix; Eigenvalues and eigenvectors; Mathematics; Graph; Discrete mathematics; Pairwise comparison; Algorithm; Physics","score_opus":0.042813700835788795,"score_gpt":0.30087934845902425,"score_spread":0.2580656476232355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224931240","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":[0.051707502,0.00026381263,0.9353846,0.004357977,0.0026572102,0.0008507692,0.00026591626,0.0005906541,0.0039215754],"genre_scores_gemma":[0.9802346,0.00008219201,0.015757801,0.0015637184,0.0004193392,0.00014910985,0.00009533823,0.00006543903,0.0016324596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944733,0.00027607227,0.0007562323,0.0015041318,0.0021813049,0.000808988],"domain_scores_gemma":[0.9977376,0.00048573216,0.0005236356,0.0005689027,0.00032408896,0.00036007143],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00079716713,0.00061726576,0.00053072436,0.0005484131,0.0011129564,0.0006003486,0.0019126034,0.00013361145,0.0011047146],"category_scores_gemma":[0.00008716261,0.00061720033,0.0002135392,0.0007627405,0.00021978852,0.0005692867,0.00076826283,0.0014273607,0.00006572013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012355584,0.0017734006,0.00030161755,0.00012779245,0.0003972554,0.0013074132,0.0011200181,0.096791945,0.40655813,0.026702844,0.006036577,0.45764744],"study_design_scores_gemma":[0.0009967416,0.0013925852,0.0001175143,0.00013270635,0.00004468183,0.00019489623,0.00022251513,0.96335125,0.0029213673,0.028300889,0.0013901441,0.00093471026],"about_ca_topic_score_codex":0.000019769946,"about_ca_topic_score_gemma":0.0000027191113,"teacher_disagreement_score":0.9285271,"about_ca_system_score_codex":0.00038928768,"about_ca_system_score_gemma":0.0002201174,"threshold_uncertainty_score":0.99980843},"labels":[],"label_agreement":null},{"id":"W4225094274","doi":"10.2196/37215","title":"Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Encyclopedia; Information retrieval; Schema (genetic algorithms); Data science; World Wide Web; Data mining","score_opus":0.046844944348230264,"score_gpt":0.33777219994666857,"score_spread":0.2909272555984383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225094274","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":[0.019499905,0.00028347177,0.97853965,0.0004327591,0.00014779394,0.0005811419,0.00005629706,0.0000954934,0.00036347526],"genre_scores_gemma":[0.32229966,0.00031016604,0.6742763,0.001907955,0.00007647033,0.0006227801,0.00045467133,0.00001574773,0.000036262332],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861485,0.000041623614,0.0006141457,0.00011649034,0.00048236366,0.00013052733],"domain_scores_gemma":[0.9986401,0.00020730366,0.00030783328,0.0005779406,0.0000496996,0.00021711933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045020532,0.000079247846,0.00016851338,0.00013950632,0.0001474661,0.000009960619,0.0011198338,0.00004948182,0.00002127973],"category_scores_gemma":[0.000069372756,0.00007352057,0.000018630146,0.0007086386,0.000253667,0.00030838625,0.001640863,0.00023222953,0.0000013785332],"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":[0.000017446271,0.00017516474,0.0007893793,0.0008962054,0.00006395487,0.0000032000823,0.023293806,0.00033189118,0.00001278289,0.3152572,0.0054750834,0.6536839],"study_design_scores_gemma":[0.0013727213,0.0001507264,0.0003263206,0.000058071797,0.000014934104,0.00022624033,0.00431315,0.39514735,0.000116894655,0.020364229,0.577587,0.00032234372],"about_ca_topic_score_codex":0.0000016254022,"about_ca_topic_score_gemma":0.0000036114336,"teacher_disagreement_score":0.65336156,"about_ca_system_score_codex":0.000028613727,"about_ca_system_score_gemma":0.00043998248,"threshold_uncertainty_score":0.29980817},"labels":[],"label_agreement":null},{"id":"W4225129532","doi":"10.1109/tcyb.2022.3166539","title":"Amer: A New Attribute-Missing Network Embedding Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; National Key Research and Development Program of China; George Washington University","keywords":"Computer science; Embedding; Missing data; Data mining; Representation (politics); Node (physics); Machine learning; Artificial intelligence; Cluster analysis; Process (computing); Constraint (computer-aided design); Theoretical computer science; Mathematics","score_opus":0.02371347438212827,"score_gpt":0.2562391148881683,"score_spread":0.23252564050604005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225129532","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":[0.00076672627,0.00024522084,0.9952153,0.0006964839,0.0016377972,0.00025938195,0.000008982986,0.00045827669,0.0007118532],"genre_scores_gemma":[0.73450917,0.00006024819,0.26204282,0.0010846311,0.00023184843,0.0000661918,0.000004653795,0.000050368573,0.0019500569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976157,0.00016330426,0.00034091625,0.00065044465,0.0005695352,0.0006600885],"domain_scores_gemma":[0.9985746,0.00021024233,0.00013322676,0.0007875388,0.000036325615,0.00025808168],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020556398,0.00027249867,0.00025634078,0.00014746231,0.0009783731,0.0001489422,0.0010331601,0.000070016984,0.00006930649],"category_scores_gemma":[0.0000031935763,0.00030833588,0.00018964209,0.0015537497,0.000057100537,0.00027963534,0.000024086796,0.0009281706,0.00002034183],"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":[0.000012424356,0.00009961681,0.000009343294,0.0000031710863,0.000024367822,0.000013316969,0.00028114044,0.9060406,0.00006835249,0.0014063475,0.0030320191,0.08900926],"study_design_scores_gemma":[0.0006200606,0.00028519964,0.000047047146,0.00001954042,0.00003665898,0.00014376234,0.000066733366,0.96975744,0.0007677085,0.0053111827,0.022395786,0.0005488629],"about_ca_topic_score_codex":0.00002519625,"about_ca_topic_score_gemma":0.0000061581413,"teacher_disagreement_score":0.7337425,"about_ca_system_score_codex":0.00016004403,"about_ca_system_score_gemma":0.00009768038,"threshold_uncertainty_score":0.9999369},"labels":[],"label_agreement":null},{"id":"W4225665835","doi":"10.1109/lcomm.2022.3163468","title":"An Adaptive Rate Allocation Scheme for Time-Varying Graph Signal Quantization","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Quantization (signal processing); Computer science; Telecommunications link; Rate distortion; Graph; Algorithm; Rate–distortion theory; Data compression; Real-time computing; Theoretical computer science; Mathematics; Coding (social sciences); Telecommunications","score_opus":0.039847276622123874,"score_gpt":0.2868055933345587,"score_spread":0.24695831671243482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225665835","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":[0.011601466,0.00011675011,0.9784778,0.008733741,0.00018240676,0.00053726067,0.000013900484,0.00028767792,0.000049046284],"genre_scores_gemma":[0.6785768,0.00001663908,0.31662798,0.004042509,0.000036323672,0.000504869,0.00015438334,0.000021809738,0.00001863529],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983861,0.000508128,0.0002795476,0.00036732724,0.00019877065,0.00026013234],"domain_scores_gemma":[0.99718094,0.00039866284,0.00021843317,0.0020205386,0.000115750794,0.00006565662],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00046772824,0.00014552902,0.00013109088,0.0002181446,0.0013178706,0.00010084526,0.0028171886,0.000028424462,0.000008495873],"category_scores_gemma":[0.000013100805,0.00017719851,0.00008342762,0.00096548954,0.00010545362,0.0009682585,0.00034662784,0.00028789646,0.000010027042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006087676,0.00025541944,0.00010303672,0.0000065428444,0.00005689273,0.0000017743424,0.0009203781,0.37007234,0.5557419,0.05063679,0.004951325,0.017192721],"study_design_scores_gemma":[0.00032576622,0.00017467362,0.000102420345,0.000007744128,0.000009900747,0.0000057326147,0.00003546976,0.9911251,0.0023729887,0.0033760483,0.002216694,0.00024748396],"about_ca_topic_score_codex":0.000011235187,"about_ca_topic_score_gemma":0.0000052247956,"teacher_disagreement_score":0.6669754,"about_ca_system_score_codex":0.00009575591,"about_ca_system_score_gemma":0.000037822876,"threshold_uncertainty_score":0.9999823},"labels":[],"label_agreement":null},{"id":"W4225692987","doi":"10.1609/aaai.v36i9.21224","title":"Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Controllability; Leverage (statistics); Computer science; Graph; Scheduling (production processes); Benchmark (surveying); Search tree; Artificial intelligence; Search algorithm; Theoretical computer science; Mathematical optimization; Algorithm; Mathematics","score_opus":0.04851391012186594,"score_gpt":0.2713783142420653,"score_spread":0.22286440412019937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225692987","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":[0.50209486,0.00008992597,0.49432698,0.0017026912,0.0003112883,0.0010928581,0.000007907663,0.00017403429,0.0001994352],"genre_scores_gemma":[0.9961755,0.000008001589,0.0032640758,0.0003768804,0.00006541656,0.000056768313,0.0000026472678,0.000028205208,0.000022502294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996624,0.00017113553,0.0006329778,0.0009927758,0.00082485663,0.00075428426],"domain_scores_gemma":[0.99772877,0.00046157493,0.0004714426,0.0004812833,0.000671746,0.00018515885],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000714934,0.0004190226,0.0004971829,0.00021467764,0.0010019216,0.00031823697,0.0017613762,0.00010172847,0.00002746659],"category_scores_gemma":[0.00011766492,0.00031357247,0.00014874042,0.0023682746,0.00081319787,0.0005116149,0.00089637126,0.0010913368,7.4360395e-7],"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":[0.000809125,0.00013771642,0.0023231842,0.000011863512,0.00003152676,0.000002759558,0.0001957393,0.93980515,0.0018490993,0.040795457,0.000015484386,0.014022921],"study_design_scores_gemma":[0.00016431896,0.0006564644,0.0009647016,0.000070382266,0.00002776424,0.000012414072,0.00039079462,0.9789599,0.0016891749,0.016710186,0.0000024983053,0.00035138545],"about_ca_topic_score_codex":0.0001754787,"about_ca_topic_score_gemma":0.000056564375,"teacher_disagreement_score":0.4940806,"about_ca_system_score_codex":0.00016143841,"about_ca_system_score_gemma":0.00015642734,"threshold_uncertainty_score":0.99993163},"labels":[],"label_agreement":null},{"id":"W4226101728","doi":"10.1007/s11280-021-00972-6","title":"HOPLoP: multi-hop link prediction over knowledge graph embeddings","year":2021,"lang":"en","type":"article","venue":"World Wide Web","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Traverse; Embedding; Knowledge graph; Theoretical computer science; Graph; Margin (machine learning); Generalization; Machine learning; Artificial intelligence; Mathematics","score_opus":0.015868101327976156,"score_gpt":0.2643288682557699,"score_spread":0.24846076692779373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226101728","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":[0.040551547,0.0064420183,0.9202047,0.00619009,0.008400173,0.0006389827,0.000028053659,0.0027485355,0.014795929],"genre_scores_gemma":[0.87402683,0.00034528534,0.10201154,0.0026612543,0.0007823559,0.000062766674,0.00002615084,0.00006875821,0.02001508],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980286,0.00009932342,0.00034258215,0.0007606125,0.00026809177,0.0005008038],"domain_scores_gemma":[0.9984113,0.00025914973,0.00011768315,0.0008431546,0.00017517422,0.0001935604],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015874254,0.00025140916,0.00024547492,0.00027847115,0.00020790602,0.00015838558,0.00063417054,0.00008770088,0.0000622337],"category_scores_gemma":[0.00009441192,0.0002565257,0.00018427739,0.0023940424,0.00007624952,0.0007829491,0.0004623604,0.00043978263,0.00013499279],"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":[0.00008431669,0.0012786436,0.11539889,0.0002201145,0.00038713834,0.0011934901,0.0035594506,0.014065981,0.0408207,0.13232517,0.366287,0.32437912],"study_design_scores_gemma":[0.002172655,0.0000840804,0.047826722,0.00030156833,0.000039471295,0.00006947674,0.000026439551,0.37965885,0.006955142,0.0129818935,0.5489263,0.00095743954],"about_ca_topic_score_codex":0.0000034876475,"about_ca_topic_score_gemma":0.00037242495,"teacher_disagreement_score":0.8334753,"about_ca_system_score_codex":0.000064088876,"about_ca_system_score_gemma":0.00009152884,"threshold_uncertainty_score":0.9999887},"labels":[],"label_agreement":null},{"id":"W4226139256","doi":"10.3389/fdata.2022.616617","title":"Scaling Graph Propagation Kernels for Predictive Learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Big Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Differentiable function; Inference; Computer science; Graph; Theoretical computer science; Scaling; Robustness (evolution); Exponential function; Artificial intelligence; Mathematics","score_opus":0.05184979099417004,"score_gpt":0.2661570219216134,"score_spread":0.21430723092744336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226139256","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":[0.0015499316,0.0004454842,0.9944061,0.0002405756,0.0026397342,0.00044779657,0.000079567624,0.00013009206,0.000060731294],"genre_scores_gemma":[0.5414527,0.00009596698,0.45650306,0.00028168922,0.00031033138,0.00033838817,0.00072823494,0.00003374086,0.0002558884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849856,0.0001268147,0.00020021693,0.00061981403,0.00026187277,0.00029270857],"domain_scores_gemma":[0.99893373,0.000085902124,0.00012159773,0.0007882662,0.000027173786,0.00004335509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052286213,0.000104999606,0.00013879723,0.00020606625,0.00032955737,0.00005518973,0.0018882977,0.00002966278,0.0000011219425],"category_scores_gemma":[0.00014371607,0.00011459787,0.000030208252,0.00080805965,0.000040351362,0.0007304298,0.0014577867,0.00034809718,4.9597975e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115465526,0.00006914441,0.022417858,0.000028148197,0.00003684403,0.000019833424,0.00067848386,0.30162382,0.000104098726,0.0018074769,0.039374974,0.63372386],"study_design_scores_gemma":[0.00041843764,0.00010225651,0.0009160565,0.000012792364,0.000005590961,0.0000046153555,0.00014402167,0.9582689,0.00005858691,0.016988706,0.022923777,0.00015625771],"about_ca_topic_score_codex":0.0000055286714,"about_ca_topic_score_gemma":0.0000021677574,"teacher_disagreement_score":0.6566451,"about_ca_system_score_codex":0.00007358786,"about_ca_system_score_gemma":0.000036736732,"threshold_uncertainty_score":0.46731654},"labels":[],"label_agreement":null},{"id":"W4226193467","doi":"10.1109/isit50566.2022.9834398","title":"On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Information Theory (ISIT)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Vertex (graph theory); Algorithm; Graph; Theoretical computer science; Time complexity","score_opus":0.019388438974961036,"score_gpt":0.2559160239593848,"score_spread":0.23652758498442375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226193467","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":[0.016816143,0.000016149057,0.96868473,0.005385523,0.0037131584,0.0010880438,0.00018441936,0.0001405701,0.003971271],"genre_scores_gemma":[0.99337,0.000019065516,0.00096387387,0.004358794,0.00008129154,0.000657267,0.00014323328,0.000015014181,0.00039144023],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766636,0.00021332841,0.00056519674,0.00025640905,0.0010464041,0.00025230608],"domain_scores_gemma":[0.997757,0.0008876617,0.00054261944,0.0005409423,0.00021456828,0.00005717312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010795839,0.00019156397,0.00016313186,0.00029239187,0.00048503844,0.000097619355,0.0015236586,0.000041009196,0.00008024294],"category_scores_gemma":[0.000068408575,0.00015353899,0.00020723171,0.00058216014,0.00006158511,0.0005117239,0.00027212262,0.00025580075,0.00002456023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020734119,0.000090035275,0.000005752458,0.000004613428,0.00004427516,0.0000016095253,0.00044260971,0.37804854,0.00021511341,0.6126664,0.005608271,0.0026653972],"study_design_scores_gemma":[0.0021020689,0.0012575772,0.00012460294,0.00005842588,0.00002189267,0.00008961225,0.0005236362,0.7686737,0.017604357,0.18228285,0.026755793,0.0005054588],"about_ca_topic_score_codex":0.000003902341,"about_ca_topic_score_gemma":2.5938039e-7,"teacher_disagreement_score":0.97655386,"about_ca_system_score_codex":0.00029867285,"about_ca_system_score_gemma":0.000032506887,"threshold_uncertainty_score":0.62611383},"labels":[],"label_agreement":null},{"id":"W4226436280","doi":"10.1109/mlbdbi54094.2021.00140","title":"The Application of Graph Neural Network in Natural Language Processing and Computer Vision","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Categorization; Artificial intelligence; Deep learning; Benchmark (surveying); Graph; Artificial neural network; Euclidean geometry; Recurrent neural network; Transformation (genetics); Machine learning; Natural language processing; Theoretical computer science","score_opus":0.03563224655463522,"score_gpt":0.3193743397329246,"score_spread":0.2837420931782894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226436280","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":[0.02875939,0.0073819496,0.95970255,0.0025653832,0.0010264025,0.00021465863,0.00002086331,0.00005657919,0.00027220766],"genre_scores_gemma":[0.99063396,0.0028434286,0.005672002,0.00022437121,0.00024940734,0.000009313683,0.00025027915,0.0000116599,0.00010558314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980653,0.00014920515,0.0004049385,0.00073213794,0.00038190698,0.0002665318],"domain_scores_gemma":[0.9984696,0.00024557172,0.0002806584,0.00056804455,0.00038025944,0.000055880304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036432373,0.0002194912,0.00022581348,0.00012324161,0.00022116669,0.00041379302,0.00126855,0.00006782797,0.000010180239],"category_scores_gemma":[0.00016062515,0.00016716484,0.000027883729,0.0008530786,0.00020129276,0.00068926794,0.001287812,0.00057386624,0.000002805506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046667967,0.000038536917,0.005504234,0.000021598997,0.000012283278,0.000028858647,0.00012068068,0.0075868615,0.00033211248,0.025808437,0.00003325227,0.9604665],"study_design_scores_gemma":[0.00013915206,0.00004982687,0.0071024937,0.00016964243,0.000004835171,0.000053286225,0.000072449344,0.9863015,0.000109852685,0.0030935386,0.0027189488,0.00018451],"about_ca_topic_score_codex":0.00010411939,"about_ca_topic_score_gemma":0.00053537585,"teacher_disagreement_score":0.9787146,"about_ca_system_score_codex":0.000014577961,"about_ca_system_score_gemma":0.00005960954,"threshold_uncertainty_score":0.6816784},"labels":[],"label_agreement":null},{"id":"W4229012760","doi":"10.1007/s10489-022-03235-7","title":"Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"Basic Research Program of Jiangsu Province; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Hash function; Theoretical computer science; Graph; Probabilistic logic; Embedding; Data mining; Information retrieval; Artificial intelligence","score_opus":0.03186132315158871,"score_gpt":0.29859712756404705,"score_spread":0.26673580441245837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229012760","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":[0.00096165936,0.00027895003,0.99522775,0.0005388937,0.0008356482,0.0015183422,0.000009620875,0.00033811817,0.00029103848],"genre_scores_gemma":[0.84441245,0.00004220788,0.15260176,0.0006619109,0.000050767754,0.002058293,0.00009045733,0.000033881177,0.00004826088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972958,0.00010805021,0.00059433974,0.0010197543,0.0003165644,0.0006654941],"domain_scores_gemma":[0.9975982,0.0010841278,0.00031224414,0.0007347852,0.00015387993,0.00011679762],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007662986,0.00031223916,0.00030586548,0.0002098725,0.00084011833,0.00011559368,0.001404639,0.000055733817,0.00008484877],"category_scores_gemma":[0.00010854423,0.00034579303,0.00012832345,0.001545541,0.0000853223,0.00028132024,0.0004932397,0.00040194704,0.00003473144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010336905,0.00037122885,0.000017788287,0.000100437166,0.000029235935,0.0000032445582,0.0006166803,0.42285305,0.003585705,0.14740582,0.0012724934,0.42364094],"study_design_scores_gemma":[0.00026872483,0.0002677597,0.000010299778,0.000026286783,0.0000126977875,0.0000050621966,0.000072726085,0.87874544,0.019660616,0.077408284,0.023049302,0.00047279889],"about_ca_topic_score_codex":0.0000027605327,"about_ca_topic_score_gemma":0.000015352774,"teacher_disagreement_score":0.8434508,"about_ca_system_score_codex":0.00028471992,"about_ca_system_score_gemma":0.00006920251,"threshold_uncertainty_score":0.9998994},"labels":[],"label_agreement":null},{"id":"W4231491089","doi":"10.1007/978-1-4939-7131-2_100618","title":"Longitudinal Network","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.026763764836760004,"score_gpt":0.2418972338864883,"score_spread":0.2151334690497283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231491089","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.883107e-7,0.00039375818,0.40313783,0.00013183968,0.001010977,0.00010450375,7.501139e-7,0.00033654342,0.5948833],"genre_scores_gemma":[0.00015991759,0.00013248411,0.098423496,0.0008718137,0.002072903,0.000004092282,0.000005614065,0.0000454421,0.89828426],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99815255,0.000008290328,0.0002810374,0.00077604514,0.00032980595,0.00045229445],"domain_scores_gemma":[0.9982923,0.00008608784,0.00018066449,0.0011869349,0.000109881265,0.0001440849],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000099561745,0.00037791283,0.00033010435,0.00007707121,0.00013038429,0.00010822915,0.0013916371,0.00028804076,0.0012416677],"category_scores_gemma":[0.0000038010749,0.00033003258,0.00018692511,0.0000801895,0.0001405876,0.0002775285,0.00072153844,0.00040449912,0.0016782057],"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":[0.0000020971386,0.0000018682593,0.0000087862845,0.0000029649943,0.000023531502,0.00004964167,0.0000055387113,0.0001315044,1.90675e-7,0.8327893,0.15692231,0.010062245],"study_design_scores_gemma":[0.00005515748,0.000075018244,0.000022056658,0.000058664034,0.000007927426,0.000036038633,8.142541e-8,0.0019948527,0.0000026430075,0.5032291,0.49417087,0.00034758096],"about_ca_topic_score_codex":9.3306187e-7,"about_ca_topic_score_gemma":0.000020415015,"teacher_disagreement_score":0.33724856,"about_ca_system_score_codex":0.00003333045,"about_ca_system_score_gemma":0.000033516095,"threshold_uncertainty_score":0.9999152},"labels":[],"label_agreement":null},{"id":"W4238595269","doi":"10.1007/978-1-4614-6170-8_100889","title":"Social Graph Dataset","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Graph; Theoretical computer science","score_opus":0.026721443297246468,"score_gpt":0.2621311030729884,"score_spread":0.23540965977574196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238595269","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.608326e-8,0.000075306576,0.5285261,0.00060219766,0.00034894026,0.000108551125,0.00011189634,0.0002582988,0.4699686],"genre_scores_gemma":[0.00034444264,0.000121574376,0.036243793,0.0068645184,0.0012947988,0.000010539235,0.0017304759,0.00008998451,0.9532999],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851,0.0000127876465,0.00023905632,0.0006304474,0.00032199098,0.00028568992],"domain_scores_gemma":[0.9987542,0.00006960942,0.00016747668,0.00087663165,0.00003932289,0.00009275498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000084805295,0.00031615215,0.00030990664,0.00013479478,0.00014594237,0.00009044788,0.0014976171,0.00026854384,0.00014770284],"category_scores_gemma":[0.000003216649,0.00028375516,0.00015372521,0.000052746673,0.00010552071,0.00017996391,0.0005900439,0.0004152565,0.0005044072],"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":[7.597075e-7,0.0000011858793,1.7553761e-7,0.000003224158,0.000010166366,0.00001092045,0.000003849267,0.000004584578,7.4187284e-7,0.6733801,0.30700132,0.019583017],"study_design_scores_gemma":[0.00006571069,0.000019704423,0.0000020802795,0.000008655417,0.0000062293616,0.000007739249,8.486479e-8,0.00032942597,0.0000032330422,0.3228869,0.6764081,0.0002620777],"about_ca_topic_score_codex":0.0000020230766,"about_ca_topic_score_gemma":0.00001320336,"teacher_disagreement_score":0.49228236,"about_ca_system_score_codex":0.000015611797,"about_ca_system_score_gemma":0.000018237763,"threshold_uncertainty_score":0.99996144},"labels":[],"label_agreement":null},{"id":"W4241931197","doi":"10.22215/etd/2020-14292","title":"An Investigation of Attention Mechanisms in Graph Convolution Networks Applied to Link Prediction Problems","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Autoencoder; Theoretical computer science; Link (geometry); Encoder; Deep learning; Machine learning; Data mining","score_opus":0.01189807226327403,"score_gpt":0.22937023244055232,"score_spread":0.2174721601772783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241931197","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":[0.021882696,0.000059790622,0.97443384,0.00018104078,0.0010928382,0.0015767699,0.000004291787,0.0003975252,0.00037120222],"genre_scores_gemma":[0.93775004,0.00006572256,0.05929544,0.00027650272,0.00015740511,0.0003175543,0.0019941456,0.000047889534,0.000095321295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972192,0.00011595708,0.0008523925,0.0009731729,0.00049162854,0.0003476691],"domain_scores_gemma":[0.9984661,0.000039229122,0.00052724505,0.0005497166,0.00019507503,0.00022267537],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003003263,0.00036372562,0.00043632757,0.0005742681,0.000082142025,0.00007818645,0.00071235735,0.0004988183,0.0000042565453],"category_scores_gemma":[0.000011730099,0.00039130085,0.00010234448,0.0021580826,0.000025349997,0.0007169386,0.00005956152,0.0005207094,0.0000070124406],"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":[0.0002111565,0.00011029575,0.0011171367,0.00039853933,0.000059481063,0.000005174758,0.0027367412,0.45734322,0.17208417,0.31827742,0.00038563777,0.047271032],"study_design_scores_gemma":[0.00069393613,0.00073486706,0.036498938,0.00044618172,0.000039300543,0.0000020545262,0.00022529636,0.8302723,0.0076725534,0.1227229,0.000027037726,0.00066464127],"about_ca_topic_score_codex":0.000037735528,"about_ca_topic_score_gemma":0.00038639797,"teacher_disagreement_score":0.9158673,"about_ca_system_score_codex":0.00008234727,"about_ca_system_score_gemma":0.00006481719,"threshold_uncertainty_score":0.9998539},"labels":[],"label_agreement":null},{"id":"W4243610374","doi":"10.1007/978-1-4939-7131-2_100599","title":"Link Prediction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.017377801622239272,"score_gpt":0.22078974242012767,"score_spread":0.20341194079788838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243610374","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.785908e-7,0.00009842803,0.4193792,0.00023463936,0.0008820837,0.0000909621,0.000002549633,0.0004247738,0.5788871],"genre_scores_gemma":[0.00007787299,0.00014238752,0.04257861,0.0006684307,0.0013364328,0.0000037789,0.000010345701,0.00002869241,0.95515347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886405,0.0000048272873,0.00020362716,0.00050139514,0.00024155651,0.00018454585],"domain_scores_gemma":[0.99888706,0.000036760666,0.00011206047,0.0007909902,0.0000901367,0.00008301928],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005357723,0.00021640275,0.00016541743,0.00010299089,0.00006634913,0.00005888669,0.00071421254,0.00027473242,0.0005192991],"category_scores_gemma":[0.0000037911768,0.0001888993,0.00010262061,0.000038818052,0.00007252345,0.0002789039,0.00029883854,0.00029365672,0.00095374556],"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":[0.0000015266944,0.0000017558073,0.0000012909969,0.000004154212,0.000016223523,0.0000124103135,0.000019209601,0.00002036231,0.000004318622,0.8702059,0.05335629,0.076356575],"study_design_scores_gemma":[0.00005383091,0.00008738385,0.000007874929,0.000039555114,0.0000050752396,0.0000157374,1.181619e-7,0.006652579,0.000023095916,0.36358875,0.629351,0.00017496775],"about_ca_topic_score_codex":4.7482925e-7,"about_ca_topic_score_gemma":0.000003396179,"teacher_disagreement_score":0.57599473,"about_ca_system_score_codex":0.00002785992,"about_ca_system_score_gemma":0.000021381042,"threshold_uncertainty_score":0.9998241},"labels":[],"label_agreement":null},{"id":"W4249552842","doi":"10.1017/9781108989817.010","title":"Connected Machine Learning and Networked AI","year":2021,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.013454288004409553,"score_gpt":0.19564263290976525,"score_spread":0.18218834490535568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249552842","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004473589,0.0019085468,0.08623775,0.00007833407,0.00037363576,0.00029289423,0.000028426139,0.00050688145,0.9105288],"genre_scores_gemma":[0.0012498611,0.0006439904,0.0019191758,0.00023589023,0.00011606148,4.595642e-7,0.00005404371,0.000043910124,0.9957366],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820024,0.00009213235,0.00018800116,0.00088739034,0.00024767904,0.00038454746],"domain_scores_gemma":[0.9985789,0.00018131337,0.00023168208,0.0005821957,0.00018907448,0.0002368284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007408605,0.0004337583,0.00048286706,0.00015683369,0.0003300647,0.00013721982,0.00074246625,0.0003430132,0.0000032917087],"category_scores_gemma":[0.000013163417,0.0005230057,0.00015991251,0.0000309156,0.00020358527,0.00022020674,0.0014530787,0.0011710647,0.000003156083],"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":[0.000025547795,0.0000037229645,0.000007637696,0.000029784342,0.00009506236,0.0010396033,0.000020143469,0.00014685481,0.000027156913,0.9873738,0.0039023499,0.007328324],"study_design_scores_gemma":[0.0005677441,0.000062304964,0.000009263611,0.000181799,0.00007158512,0.00008682662,0.000004250856,0.025771322,0.00006442798,0.00006796821,0.9725224,0.00059014215],"about_ca_topic_score_codex":0.000026993777,"about_ca_topic_score_gemma":0.00000453019,"teacher_disagreement_score":0.9873058,"about_ca_system_score_codex":0.00007463217,"about_ca_system_score_gemma":0.00006237933,"threshold_uncertainty_score":0.9997221},"labels":[],"label_agreement":null},{"id":"W4250855575","doi":"10.1007/978-1-4939-7131-2_101110","title":"Social Graph Dataset","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Graph; Theoretical computer science","score_opus":0.03392649535992964,"score_gpt":0.27651844337784204,"score_spread":0.2425919480179124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250855575","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.5135207e-7,0.00012823404,0.2536354,0.0006165651,0.00069781713,0.00018529878,0.0003179196,0.00040645697,0.7440119],"genre_scores_gemma":[0.00009375017,0.00012296687,0.04469598,0.00456415,0.0018793808,0.0000087315475,0.0015136213,0.000081006336,0.94704044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984789,0.00000930964,0.00023070145,0.0006610169,0.00032376478,0.00029630988],"domain_scores_gemma":[0.9987697,0.000044019627,0.00015767216,0.0008785799,0.000060434108,0.00008957952],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007067649,0.0003118308,0.00026689426,0.00013740806,0.00016814588,0.00009696471,0.0015540302,0.00027413425,0.0007069858],"category_scores_gemma":[0.000002747549,0.00027887925,0.00013999426,0.00006634212,0.00019312328,0.00032989876,0.00072373764,0.00033944251,0.0010807059],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010819859,0.0000018822391,1.3006003e-7,0.0000022009226,0.000012480628,0.000016433973,0.000011010452,5.403725e-7,8.287468e-7,0.5138176,0.48093146,0.005204301],"study_design_scores_gemma":[0.00005487798,0.000027930364,0.0000015685622,0.000008577413,0.0000058483215,0.000008489821,2.288822e-7,0.0001203634,0.0000061371034,0.3940071,0.60551983,0.00023905066],"about_ca_topic_score_codex":0.0000016719553,"about_ca_topic_score_gemma":0.000017736764,"teacher_disagreement_score":0.20893943,"about_ca_system_score_codex":0.000018463985,"about_ca_system_score_gemma":0.000025786381,"threshold_uncertainty_score":0.9999663},"labels":[],"label_agreement":null},{"id":"W4280604520","doi":"10.21203/rs.3.rs-1639305/v1","title":"Graph Neural Networks: a bibliometrics overview","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bibliometrics; Computer science; Field (mathematics); Data science; Scopus; Artificial intelligence; Subject (documents); Graph; Convolutional neural network; Science and engineering; Library science; Engineering ethics; Political science; Engineering","score_opus":0.14965168741352466,"score_gpt":0.4316644671126066,"score_spread":0.2820127796990819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280604520","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":[0.009598502,0.19927727,0.764763,0.005449646,0.00850571,0.0058240024,0.00017433184,0.0025607503,0.0038468007],"genre_scores_gemma":[0.8806549,0.08128332,0.029352846,0.0014499506,0.002108765,0.003004806,0.00043029545,0.00035632018,0.0013587946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891484,0.002015166,0.0006744371,0.0020874867,0.0040231235,0.002051382],"domain_scores_gemma":[0.99268013,0.0018316032,0.00028489862,0.003811478,0.0008049129,0.00058696006],"candidate_categories":["metaepi_narrow","bibliometrics","scholarly_communication","open_science","research_integrity"],"consensus_categories":["bibliometrics","open_science"],"category_scores_codex":[0.0035329275,0.0005741825,0.0007035683,0.016290437,0.00092127186,0.0012740742,0.007523279,0.0004240471,0.00026517367],"category_scores_gemma":[0.0005325528,0.0005713935,0.00060883065,0.068859674,0.00025921752,0.0006683217,0.023716057,0.007495738,0.00003241044],"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":[0.000049794173,0.00038134432,0.0049061477,0.0011385245,0.00013542427,0.0012371053,0.00025896507,0.7167008,0.0000060596867,0.049752872,0.04718751,0.17824547],"study_design_scores_gemma":[0.00042331085,0.000514815,0.0076363287,0.00035703686,0.000011956651,0.00004100198,0.000059353042,0.8745436,0.0000070267038,0.07758165,0.03780043,0.0010234985],"about_ca_topic_score_codex":0.00011882424,"about_ca_topic_score_gemma":0.00002172162,"teacher_disagreement_score":0.8710564,"about_ca_system_score_codex":0.00037619044,"about_ca_system_score_gemma":0.00028409212,"threshold_uncertainty_score":0.9997627},"labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4281563651","doi":"10.24963/ijcai.2022/295","title":"TGNN: A Joint Semi-supervised Framework for Graph-level Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Graph; Theoretical computer science; Leverage (statistics); Graph embedding; Semi-supervised learning; Data mining; Machine learning; Artificial intelligence","score_opus":0.16188916389422092,"score_gpt":0.31409551449843304,"score_spread":0.15220635060421212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281563651","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":[0.019568678,0.000035429024,0.9393179,0.0328446,0.0034573164,0.0014157553,0.00010989021,0.0002606363,0.0029898135],"genre_scores_gemma":[0.951121,0.00005222349,0.046849225,0.0010133678,0.00019865355,0.00048458079,0.000007916605,0.000031333944,0.00024175702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99627584,0.00003793397,0.0010311961,0.00091032725,0.0012573435,0.00048736317],"domain_scores_gemma":[0.99696124,0.00030427563,0.00094270834,0.00050989614,0.0011545334,0.00012734519],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008230558,0.0003654676,0.0003773564,0.00038687617,0.0007928049,0.00030294628,0.0036451772,0.00011875446,0.00017244062],"category_scores_gemma":[0.0007761155,0.00031392512,0.00040543932,0.00089482125,0.000265754,0.0006159467,0.0011600617,0.0008390983,0.000026696263],"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":[0.00014169574,0.000248255,0.00010102624,0.000030312767,0.000045285025,7.3575325e-7,0.0010072488,0.0021741963,0.01266786,0.96637297,0.0006541256,0.016556296],"study_design_scores_gemma":[0.00006630474,0.00024895227,0.0004425021,0.0001779913,0.000012524386,0.000014836397,0.00077343825,0.2501132,0.055475824,0.6916936,0.00066899206,0.00031187446],"about_ca_topic_score_codex":0.000019332132,"about_ca_topic_score_gemma":0.00000977102,"teacher_disagreement_score":0.9315523,"about_ca_system_score_codex":0.00023477394,"about_ca_system_score_gemma":0.00010982506,"threshold_uncertainty_score":0.9999313},"labels":[],"label_agreement":null},{"id":"W4281779720","doi":"10.1145/3514221.3517837","title":"A Convex-Programming Approach for Efficient Directed Densest Subgraph Discovery","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 2022 International Conference on Management of Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Hong Kong","keywords":"Scalability; Parameterized complexity; Computer science; Bipartite graph; Approximation algorithm; Linear programming; Matching (statistics); Theoretical computer science; Induced subgraph isomorphism problem; Directed graph; Graph; Mathematical optimization; Algorithm; Mathematics; Line graph","score_opus":0.06422309090837273,"score_gpt":0.2962473222413728,"score_spread":0.23202423133300004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281779720","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":[0.10886915,0.00030405406,0.83398396,0.008358441,0.003830393,0.0065178657,0.0019757326,0.0005720554,0.03558838],"genre_scores_gemma":[0.9272701,0.000037686867,0.0715167,0.00010410518,0.000024277246,0.0002141792,0.00012433551,0.0000128432275,0.0006957473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979525,0.000013769331,0.0003336172,0.0006005149,0.000890944,0.00020862673],"domain_scores_gemma":[0.9986861,0.000053347805,0.00046525817,0.00054569653,0.00022087083,0.00002876585],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00048040875,0.00014920908,0.00017964031,0.0001709663,0.00017867054,0.00012879717,0.006392463,0.000016935164,0.000014064316],"category_scores_gemma":[0.00003509528,0.00012486524,0.000098660916,0.0006215093,0.00009523641,0.0005453807,0.00543437,0.00018120672,2.7524354e-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":[0.00012179789,0.00036793313,0.00051956926,0.00012902467,0.00019771847,7.890182e-7,0.000079993675,0.0017528184,0.0008750056,0.98288846,0.001835809,0.011231068],"study_design_scores_gemma":[0.0010132089,0.00026318536,0.0012315287,0.00013213044,0.000073310526,0.000007701024,0.0011172794,0.9761641,0.0013473628,0.0129002705,0.005416006,0.0003338885],"about_ca_topic_score_codex":0.000004721988,"about_ca_topic_score_gemma":3.4057257e-7,"teacher_disagreement_score":0.9744113,"about_ca_system_score_codex":0.000045152643,"about_ca_system_score_gemma":0.000017740229,"threshold_uncertainty_score":0.99898344},"labels":[],"label_agreement":null},{"id":"W4282913028","doi":"10.1109/tnnls.2022.3177775","title":"CLEAR: Cluster-Enhanced Contrast for Self-Supervised Graph Representation Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Theoretical computer science; Graph; Artificial intelligence; Feature learning","score_opus":0.013269842342648951,"score_gpt":0.23941353429716494,"score_spread":0.226143691954516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282913028","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":[0.047672525,0.00036983046,0.94743377,0.0002415202,0.0023855679,0.001021009,0.0000037298862,0.0008152138,0.000056839515],"genre_scores_gemma":[0.9965162,0.000115781804,0.0016633666,0.00022677852,0.0001920864,0.0006078347,0.000010525819,0.000056360233,0.00061105617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965573,0.0010496293,0.0005129658,0.0008585713,0.0004023304,0.00061917905],"domain_scores_gemma":[0.9980805,0.000994209,0.0002877105,0.00036368083,0.00009928694,0.00017461038],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005744438,0.00032768794,0.00041195797,0.00023569464,0.0026227918,0.00032616028,0.00044651132,0.000107678476,0.000009004866],"category_scores_gemma":[0.000012489828,0.00034104844,0.00022316641,0.00089416653,0.000051548937,0.0005346622,0.000016953802,0.0016306394,0.0000017743365],"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":[0.00018306797,0.00005751156,0.000052248673,0.00002660141,0.000052689884,0.0000060116377,0.00051638385,0.961671,0.00027408704,0.00018425092,0.00008897089,0.0368872],"study_design_scores_gemma":[0.0013745765,0.0011393575,0.000060271464,0.000031847023,0.000033161145,0.00007942789,0.00054850813,0.9950241,0.00006418158,0.00004007241,0.0012406888,0.00036382122],"about_ca_topic_score_codex":0.00004239481,"about_ca_topic_score_gemma":0.000007655632,"teacher_disagreement_score":0.94884366,"about_ca_system_score_codex":0.00006917251,"about_ca_system_score_gemma":0.000015939058,"threshold_uncertainty_score":0.99990416},"labels":[],"label_agreement":null},{"id":"W4283075104","doi":"10.48550/arxiv.2206.08164","title":"LRGB: Long Range Graph Benchmark","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Computer science; Benchmarking; Transformer; Graph; Theoretical computer science; Attention network; Artificial intelligence; Limiting; Machine learning","score_opus":0.05388534934177568,"score_gpt":0.18547651476088653,"score_spread":0.13159116541911084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283075104","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":[0.18854584,0.00044061046,0.80215275,0.00012036511,0.0023567728,0.0005433671,0.000025953648,0.0007280644,0.0050862664],"genre_scores_gemma":[0.9938704,0.0006653013,0.0027883695,0.00030323438,0.00011260402,0.0000043645477,0.000038697224,0.00003555224,0.0021814764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968141,0.00026278693,0.00025804868,0.0018212808,0.0002031677,0.000640616],"domain_scores_gemma":[0.9968606,0.00018587461,0.00034791778,0.0022416103,0.00010353916,0.00026044218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000243877,0.00048090593,0.00044595453,0.0004941939,0.0004013762,0.00012693038,0.0040808273,0.00026705817,0.00022367534],"category_scores_gemma":[0.0000157634,0.0005963446,0.00047264525,0.0017838101,0.00015896739,0.00061568816,0.00464063,0.0015120351,0.00004613394],"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":[0.000045613848,0.00012845731,0.019156655,0.00007477037,0.00014015504,0.0028498783,0.00029088696,0.7690551,0.000007112136,0.2052131,0.0016490861,0.0013891574],"study_design_scores_gemma":[0.0017701973,0.00025314736,0.021808177,0.00017786179,0.0002053663,0.00005128004,0.00013311362,0.50577986,0.000049036873,0.45644072,0.010399758,0.002931508],"about_ca_topic_score_codex":0.000064369,"about_ca_topic_score_gemma":0.00006367433,"teacher_disagreement_score":0.80532455,"about_ca_system_score_codex":0.00023701182,"about_ca_system_score_gemma":0.000103914135,"threshold_uncertainty_score":0.9996488},"labels":[],"label_agreement":null},{"id":"W4283793952","doi":"10.1609/aaai.v36i8.20815","title":"Generalized Equivariance and Preferential Labeling for GNN Node Classification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; Innovation and Technology Commission; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Node (physics); Computer science; Embedding; Property (philosophy); Identity (music); Graph; Theoretical computer science; Data mining; Artificial intelligence","score_opus":0.12324925854859284,"score_gpt":0.3161050023193992,"score_spread":0.19285574377080636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283793952","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":[0.29639605,0.00020486653,0.68324405,0.013387098,0.001977323,0.002186701,0.000044661265,0.0003145751,0.002244649],"genre_scores_gemma":[0.97420067,0.000047454545,0.024927521,0.00033535468,0.000072833594,0.00021619916,0.0000016885675,0.000014883711,0.0001833788],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981536,0.000028018982,0.00046283347,0.0005974339,0.00041528587,0.00034280974],"domain_scores_gemma":[0.99872506,0.00012329407,0.0004328464,0.00028921722,0.00036066675,0.00006893339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004374123,0.00019306393,0.00022336018,0.000102437036,0.00057961466,0.0001804231,0.0016310503,0.000051100364,0.000026062553],"category_scores_gemma":[0.00017900711,0.00016487563,0.00009202306,0.0005790602,0.00015850631,0.00037634218,0.0007198778,0.00030856984,0.0000031810296],"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":[0.000100318386,0.000060059734,0.000056492227,0.000020908923,0.0000091875445,8.328383e-8,0.00032465614,0.0008053779,0.10614888,0.85455775,0.00011658257,0.03779968],"study_design_scores_gemma":[0.00007749534,0.0002018079,0.000098922435,0.000044292865,0.000013543401,0.000004940225,0.00018300355,0.46401173,0.13002661,0.40466902,0.0004428987,0.0002257452],"about_ca_topic_score_codex":0.00000944798,"about_ca_topic_score_gemma":0.0000030574436,"teacher_disagreement_score":0.67780465,"about_ca_system_score_codex":0.000047666323,"about_ca_system_score_gemma":0.000055512057,"threshold_uncertainty_score":0.6723433},"labels":[],"label_agreement":null},{"id":"W4285250766","doi":"10.1007/978-3-031-08757-8_60","title":"Augmenting Graph Inductive Learning Model with Topographical Features","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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":"Embedding; Knowledge graph; Computer science; Graph; Adjacency matrix; Theoretical computer science; Heuristic; Artificial intelligence; Artificial neural network; Machine learning","score_opus":0.012995329388682192,"score_gpt":0.23331388451614446,"score_spread":0.22031855512746226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285250766","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":[0.00031344427,0.0004274627,0.99436456,0.00061774894,0.000747648,0.00042749624,0.0000022679799,0.00035764964,0.002741731],"genre_scores_gemma":[0.21488452,0.000106074156,0.78248,0.0015040603,0.0003284832,0.00004257146,0.00000951484,0.00009166458,0.00055313006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99452025,0.00007350322,0.0004519757,0.002314871,0.0016187682,0.0010206297],"domain_scores_gemma":[0.99747705,0.00044938354,0.00043257952,0.0012199867,0.00019822146,0.00022279895],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006138419,0.00073337357,0.0006178011,0.0014135533,0.00091436773,0.00048242585,0.0035635603,0.0002980307,0.000017881668],"category_scores_gemma":[0.000047626367,0.0006335911,0.00020173981,0.0019808924,0.0010398135,0.0011512698,0.0024913219,0.0036756722,0.000002748302],"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":[0.0000131535835,0.000017713584,0.0001465714,0.0000109007115,0.000017803528,0.00013631037,0.0005059796,0.765316,0.000049236696,0.048497748,0.0000074767786,0.1852811],"study_design_scores_gemma":[0.0003257541,0.0004137834,0.00012376907,0.00020878958,0.000012423015,0.00017696818,6.890363e-7,0.6639096,0.00017417756,0.33308908,0.0005448457,0.0010201155],"about_ca_topic_score_codex":0.000015704203,"about_ca_topic_score_gemma":0.00006348311,"teacher_disagreement_score":0.28459135,"about_ca_system_score_codex":0.00023198694,"about_ca_system_score_gemma":0.00032807232,"threshold_uncertainty_score":0.99961156},"labels":[],"label_agreement":null},{"id":"W4285601870","doi":"10.24963/ijcai.2022/731","title":"Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Bundesministerium für Bildung und Forschung","keywords":"Computer science; Statistical relational learning; Inference; Inductive logic programming; Link (geometry); Metric (unit); Inductive bias; Task (project management); Set (abstract data type); Inductive reasoning; Artificial intelligence; Relational database; Machine learning; Graph; Code (set theory); Training set; Theoretical computer science; Multi-task learning; Information retrieval; Programming language","score_opus":0.09351631533544383,"score_gpt":0.28314915439665816,"score_spread":0.18963283906121434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285601870","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":[0.6145471,0.000051340474,0.3445906,0.019158337,0.009397382,0.0016997094,0.00015146824,0.00052467006,0.009879447],"genre_scores_gemma":[0.98894346,0.000011824195,0.010309176,0.00026756755,0.0002774105,0.000057057427,0.000005111072,0.00002151678,0.000106869586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99661595,0.000027064707,0.00086372916,0.00073327374,0.0013820173,0.0003779528],"domain_scores_gemma":[0.99753976,0.00013957782,0.0009974879,0.00028578276,0.00093530025,0.00010209321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057592854,0.00030017362,0.00026065097,0.00034847175,0.0007863108,0.00021620188,0.002084774,0.000097072574,0.00017885896],"category_scores_gemma":[0.00038886117,0.00026495906,0.00020697303,0.00073506264,0.00023269208,0.0011885678,0.0012602911,0.0010183717,0.000018201421],"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":[0.00013053669,0.0002410283,0.0004864713,0.00001825518,0.00005621496,0.0000022838715,0.0011248146,0.031212255,0.057886258,0.85807586,0.000062650026,0.050703395],"study_design_scores_gemma":[0.00008895725,0.00023077638,0.0030899888,0.00014776905,0.00001769322,0.000053814234,0.0007006322,0.6711453,0.08130223,0.24250555,0.00035307067,0.00036418674],"about_ca_topic_score_codex":0.000081530794,"about_ca_topic_score_gemma":0.000008727624,"teacher_disagreement_score":0.63993305,"about_ca_system_score_codex":0.00039371257,"about_ca_system_score_gemma":0.00015785468,"threshold_uncertainty_score":0.9999803},"labels":[],"label_agreement":null},{"id":"W4285606778","doi":"10.24963/ijcai.2022/465","title":"Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Autoencoder; Flattening; Theoretical computer science; Computer science; Topology (electrical circuits); Graph; Feature (linguistics); Algorithm; Twist; Artificial intelligence; Mathematics; Geometry; Deep learning; Combinatorics; Engineering","score_opus":0.05926104120348049,"score_gpt":0.2824810265796182,"score_spread":0.22321998537613771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285606778","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":[0.0041611474,0.000036105554,0.9584076,0.028673131,0.003408341,0.0009032183,0.00006852354,0.00021523704,0.00412672],"genre_scores_gemma":[0.9508177,0.000022756358,0.047003005,0.0011508138,0.00024235938,0.0002995488,0.000007329377,0.000027724154,0.00042873435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693286,0.000028674529,0.000694854,0.0007677963,0.0011357393,0.00044005885],"domain_scores_gemma":[0.9977078,0.00024569855,0.0007396684,0.00031922702,0.00089479017,0.00009282678],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007169173,0.00031924108,0.00030631718,0.00033626257,0.0008014731,0.00032184695,0.003163429,0.00008501043,0.00010784003],"category_scores_gemma":[0.00038594657,0.00027848364,0.0003215413,0.0007680563,0.00016670655,0.0006675531,0.0014079626,0.00068349735,0.000010401825],"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":[0.00016483928,0.00017084487,0.000111309695,0.000035915367,0.000061614635,0.000001287913,0.0012155378,0.039544176,0.007597846,0.9411551,0.0009171348,0.00902443],"study_design_scores_gemma":[0.00008079018,0.00013751684,0.00023728961,0.00013289887,0.000011006967,0.000028011196,0.00032765078,0.6578476,0.011446864,0.32809186,0.0013782146,0.00028033918],"about_ca_topic_score_codex":0.000018357441,"about_ca_topic_score_gemma":0.000017090946,"teacher_disagreement_score":0.9466566,"about_ca_system_score_codex":0.00022705099,"about_ca_system_score_gemma":0.00010452346,"threshold_uncertainty_score":0.99996674},"labels":[],"label_agreement":null},{"id":"W4285717378","doi":"10.1007/978-3-031-01588-5_10","title":"Conclusion","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on artificial intelligence and machine learning","topic":"Advanced Graph Neural Networks","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":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"","keywords":"Representation (politics); Graph; Computer science; Foundation (evidence); Epistemology; Geography; Political science; Theoretical computer science; Philosophy; Archaeology; Law","score_opus":0.043852146319204574,"score_gpt":0.2636903396629812,"score_spread":0.21983819334377663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285717378","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":[0.000026111036,0.0024396137,0.8053685,0.006614918,0.0006743596,0.0005192418,0.000011175131,0.0008582068,0.1834879],"genre_scores_gemma":[0.9378743,0.00426223,0.012464561,0.0074647283,0.0015021677,0.000048681017,0.00003747902,0.00031176992,0.036034077],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99769527,0.000093097064,0.00045643302,0.0009908694,0.00041403016,0.00035028355],"domain_scores_gemma":[0.99807274,0.00094947807,0.00029816863,0.00042041912,0.00005693961,0.00020223761],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018830305,0.0005369066,0.0005311699,0.00023082006,0.0004844659,0.00021962872,0.0007275397,0.0003172909,0.00024008009],"category_scores_gemma":[0.0004002746,0.00047051557,0.00019536518,0.00011677734,0.00015389339,0.00015005605,0.00047821662,0.0015715951,0.00035424618],"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":[0.000035942478,0.0000067042297,0.0000013360642,0.000015040384,0.00002543296,0.000056796376,0.00013435296,0.004840274,0.00020481984,0.41836485,0.00008244484,0.576232],"study_design_scores_gemma":[0.000024822431,0.000599859,0.0000041450644,0.00045967867,0.00006370648,0.000039328785,0.000014456753,0.14778851,0.019955808,0.65890026,0.17092897,0.0012204631],"about_ca_topic_score_codex":0.000012541171,"about_ca_topic_score_gemma":0.00003286314,"teacher_disagreement_score":0.9378482,"about_ca_system_score_codex":0.00003617,"about_ca_system_score_gemma":0.000034158802,"threshold_uncertainty_score":0.99977463},"labels":[],"label_agreement":null},{"id":"W4285818910","doi":"10.1109/tii.2022.3190548","title":"Multirelational Tensor Graph Attention Networks for Knowledge Fusion in Smart Enterprise Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Relation (database); Domain knowledge; Machine learning; Theoretical computer science; Data mining","score_opus":0.04227203768904665,"score_gpt":0.26138907631917824,"score_spread":0.2191170386301316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285818910","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":[0.015940854,0.000032116786,0.97652024,0.00010327537,0.0058503696,0.0012313209,0.00005709119,0.00016001308,0.00010471754],"genre_scores_gemma":[0.9947039,0.000021789881,0.003974167,0.00011239174,0.00013504812,0.0007063374,0.00003336018,0.000019570069,0.0002934382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979875,0.00014261986,0.0008907083,0.00022168535,0.00036028176,0.0003971995],"domain_scores_gemma":[0.9986301,0.00050401624,0.00029137632,0.00037538563,0.0000946031,0.00010451632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052025076,0.00022085515,0.00025448916,0.0005613081,0.0006487684,0.00011509461,0.000534988,0.00018195454,0.000012761065],"category_scores_gemma":[0.000012062397,0.00023203794,0.00019098115,0.0012291241,0.000039886014,0.0008589768,0.000015938862,0.00091591,0.0000089970035],"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":[0.000116244024,0.00019587096,0.00011757657,0.000011694214,0.000020552188,0.0000012811387,0.00057802175,0.97288305,0.000010832016,0.00081365125,0.0008226432,0.02442859],"study_design_scores_gemma":[0.0023292068,0.00030485497,0.00008038515,0.0000565618,0.000015386624,0.000018067029,0.00032711992,0.9933979,0.000034390378,0.000153985,0.0030347914,0.00024734493],"about_ca_topic_score_codex":0.000018904366,"about_ca_topic_score_gemma":0.000016067968,"teacher_disagreement_score":0.97876304,"about_ca_system_score_codex":0.00027555236,"about_ca_system_score_gemma":0.000076621465,"threshold_uncertainty_score":0.94622326},"labels":[],"label_agreement":null},{"id":"W4287198239","doi":"10.48550/arxiv.2104.13014","title":"Node Embedding using Mutual Information and Self-Supervision based\\n Bi-level Aggregation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Aggregate (composite); Leverage (statistics); Exploit; Node (physics); Embedding; Theoretical computer science; Cluster analysis; Graph; Range (aeronautics); Data mining; Artificial intelligence; Computer security","score_opus":0.06614258378080558,"score_gpt":0.20647641529306676,"score_spread":0.1403338315122612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287198239","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":[0.37823448,0.000043300162,0.6209699,0.00003377506,0.00032246727,0.00014636981,0.0000057151833,0.00018308792,0.000060891343],"genre_scores_gemma":[0.89655995,0.00018650037,0.102944046,0.00018607594,0.000044017284,4.782919e-7,0.000046121426,0.000012364507,0.00002044018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841756,0.00012820736,0.00026041258,0.0007340899,0.00014639729,0.0003133631],"domain_scores_gemma":[0.99840224,0.000114840186,0.00032358072,0.0007672391,0.0002446803,0.00014741649],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001833665,0.0003076764,0.00026437608,0.00039736537,0.0002664098,0.0003463762,0.00071203633,0.00030031046,0.000006506248],"category_scores_gemma":[0.000038306196,0.00033708324,0.00012396718,0.00083903095,0.00005260701,0.0028371362,0.001662692,0.0005122789,0.000008926264],"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":[0.000010592671,0.00003289829,0.0012778793,0.000095443116,0.000027077587,0.00008454457,0.00037420142,0.9884047,0.0000543115,0.0042120256,0.000016893184,0.0054094465],"study_design_scores_gemma":[0.0003885664,0.000021393304,0.00077931694,0.00024810003,0.00003887103,0.000011735045,0.00008827133,0.99546915,0.0002296144,0.0022245136,0.00011226727,0.00038821387],"about_ca_topic_score_codex":0.000060407525,"about_ca_topic_score_gemma":0.000015623147,"teacher_disagreement_score":0.51832545,"about_ca_system_score_codex":0.00021943175,"about_ca_system_score_gemma":0.00018464822,"threshold_uncertainty_score":0.99990815},"labels":[],"label_agreement":null},{"id":"W4287725551","doi":"10.48550/arxiv.2007.06704","title":"Node Copying for Protection Against Graph Neural Network Topology\\n Attacks","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Copying; Adversarial system; Graph; Artificial intelligence; Theoretical computer science; Network topology; Computation; Machine learning; Topology (electrical circuits); Data mining; Algorithm; Computer network; Mathematics","score_opus":0.11305097875407043,"score_gpt":0.21696220832305213,"score_spread":0.1039112295689817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287725551","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":[0.08980651,0.0002079571,0.89851993,0.0013022848,0.0055155708,0.003346365,0.000028308426,0.0007172269,0.00055584],"genre_scores_gemma":[0.984804,0.00071775453,0.010834261,0.0016358667,0.0013331481,0.000031464657,0.00006183809,0.00011611114,0.00046559412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919324,0.0006057467,0.0008857389,0.004359411,0.00023251661,0.0019841753],"domain_scores_gemma":[0.99469215,0.00050478533,0.0013445622,0.0021646884,0.000534108,0.0007597296],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00047726752,0.0013182998,0.0013055782,0.00042198694,0.0016315499,0.0003119861,0.0037420755,0.0011199627,0.000028922434],"category_scores_gemma":[0.00012673905,0.0016839666,0.0012832317,0.0033827259,0.0009135927,0.0012400388,0.003516412,0.002771633,0.00010304183],"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":[0.0005638016,0.00009034323,0.00082793745,0.00022727574,0.00024560024,0.00038075447,0.00024359944,0.9531261,0.0001957084,0.037514146,0.0007797284,0.005805027],"study_design_scores_gemma":[0.0015032332,0.00043992064,0.00033038776,0.00026032358,0.00020816545,0.000020103424,0.00010171207,0.9010222,0.00014000954,0.09243327,0.0021057304,0.0014349468],"about_ca_topic_score_codex":0.00005636451,"about_ca_topic_score_gemma":0.00009121491,"teacher_disagreement_score":0.8949975,"about_ca_system_score_codex":0.00041168946,"about_ca_system_score_gemma":0.00025421797,"threshold_uncertainty_score":0.99995685},"labels":[],"label_agreement":null},{"id":"W4288049515","doi":"10.1109/tnnls.2022.3151046","title":"Status-Aware Signed Heterogeneous Network Embedding With Graph Neural Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Embedding; Benchmarking; Signed graph; Scalability; Theoretical computer science; Robustness (evolution); Exploit; Graph embedding; Artificial intelligence; Graph; Machine learning","score_opus":0.010727710181061906,"score_gpt":0.2262846392412846,"score_spread":0.2155569290602227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288049515","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":[0.03202105,0.0023097426,0.9600285,0.00013059593,0.0037049414,0.0008230586,0.0000071047334,0.00095053704,0.000024426581],"genre_scores_gemma":[0.9973371,0.0001686804,0.0007041693,0.0005076138,0.00047980738,0.00035860884,0.000015235795,0.00012512106,0.00030367202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940069,0.0014241091,0.0007199618,0.0013665652,0.0007546212,0.0017278504],"domain_scores_gemma":[0.99738246,0.00076341844,0.0004639215,0.0007855366,0.00010331481,0.0005013407],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005440114,0.0007349565,0.000732597,0.00028293193,0.0037575832,0.00059671997,0.000830779,0.00018249739,0.000026488651],"category_scores_gemma":[0.0000034009931,0.0006743101,0.0002676941,0.0019822312,0.0001598153,0.0006458329,0.00004769615,0.0030010745,0.0000015925488],"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":[0.00022052017,0.000060382154,0.0003253121,0.000018103428,0.00010151774,0.00020014586,0.00016483472,0.98071915,0.00000850864,0.00009154533,0.00013476363,0.017955216],"study_design_scores_gemma":[0.0008707936,0.0016367261,0.00006116041,0.00006761914,0.000053837175,0.00080281467,0.00019578003,0.99460167,0.000004252487,0.000019657182,0.00091369514,0.000771976],"about_ca_topic_score_codex":0.000101136706,"about_ca_topic_score_gemma":0.000039658764,"teacher_disagreement_score":0.96531606,"about_ca_system_score_codex":0.00012707553,"about_ca_system_score_gemma":0.000027942693,"threshold_uncertainty_score":0.9995708},"labels":[],"label_agreement":null},{"id":"W4288490694","doi":"10.3390/v14081659","title":"Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks","year":2022,"lang":"en","type":"article","venue":"Viruses","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Context (archaeology); Transmission (telecommunications); Coronavirus disease 2019 (COVID-19); Representation (politics); Artificial intelligence; Theoretical computer science; Medicine; Biology; Infectious disease (medical specialty); Disease; Telecommunications","score_opus":0.02794700601417969,"score_gpt":0.28469434709006713,"score_spread":0.2567473410758874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288490694","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":[0.041750047,0.002458574,0.9534953,0.0008331868,0.00059567316,0.00040034857,0.000004014133,0.0004015999,0.00006120351],"genre_scores_gemma":[0.98211384,0.0002832228,0.0089160325,0.008276833,0.00009403335,0.00019474559,0.000024770508,0.00003584817,0.000060693434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976452,0.0003714893,0.00038801992,0.00057269115,0.00040382435,0.00061878376],"domain_scores_gemma":[0.99881595,0.00035889345,0.00016968859,0.00042516034,0.000009774539,0.00022054187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004707622,0.00022176097,0.00023779529,0.00022376684,0.00057933846,0.000111357374,0.00089545426,0.000069011185,0.000062983934],"category_scores_gemma":[0.000023703524,0.00023162931,0.00011296789,0.0018419877,0.000056663306,0.0007361027,0.00028386075,0.00045664964,0.0000050605545],"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":[0.000037799335,0.00008388911,0.00753697,0.00001851397,0.000016074388,0.00005350064,0.00027216945,0.95038164,0.00060924597,0.0033596053,0.014132964,0.023497615],"study_design_scores_gemma":[0.001931727,0.00031729462,0.0033045707,0.00014830264,0.000018960727,0.00007015801,0.00016199374,0.92504126,0.00008578765,0.0362435,0.031745546,0.0009309274],"about_ca_topic_score_codex":0.00005663061,"about_ca_topic_score_gemma":0.000016142696,"teacher_disagreement_score":0.9445793,"about_ca_system_score_codex":0.00015931939,"about_ca_system_score_gemma":0.000047896814,"threshold_uncertainty_score":0.94455695},"labels":[],"label_agreement":null},{"id":"W4290876096","doi":"10.1145/3534678.3542609","title":"Graph Neural Networks: Foundation, Frontiers and Applications","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":199,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Artificial neural network; Graph; Data science; Machine learning; Theoretical computer science","score_opus":0.03770490372849548,"score_gpt":0.27829855517463364,"score_spread":0.24059365144613817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290876096","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":[0.4039814,0.006861685,0.5699207,0.005549035,0.0027422858,0.0028350272,0.00029264353,0.00045902788,0.0073581715],"genre_scores_gemma":[0.99256665,0.00018623333,0.00661786,0.00019799535,0.00008905234,0.000116680116,0.000030539857,0.0000131403995,0.00018187506],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984468,0.00002807363,0.00028219097,0.0007437796,0.00021313205,0.00028599697],"domain_scores_gemma":[0.9985559,0.00016331916,0.00029083926,0.0007919492,0.00011348399,0.00008451331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035552072,0.00020327138,0.00022736564,0.00012075591,0.0007946666,0.00038244147,0.0032449898,0.000036113695,0.0000032221083],"category_scores_gemma":[0.00012061588,0.0001678639,0.000038678318,0.000754197,0.00024800867,0.002169034,0.0055256393,0.00031560534,3.6303064e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014982185,0.00025044254,0.035226736,0.00014099709,0.000104630184,0.0000012334389,0.0021363636,0.00086405023,0.0006801515,0.5683152,0.009062334,0.383068],"study_design_scores_gemma":[0.0011523325,0.0004309291,0.006649479,0.00016976219,0.00010622679,0.00006651128,0.0040522376,0.8650205,0.00026390975,0.114322364,0.0068386868,0.00092704885],"about_ca_topic_score_codex":0.0000027301112,"about_ca_topic_score_gemma":0.0000026618657,"teacher_disagreement_score":0.8641565,"about_ca_system_score_codex":0.000018283845,"about_ca_system_score_gemma":0.000044535765,"threshold_uncertainty_score":0.68873143},"labels":[],"label_agreement":null},{"id":"W4290948538","doi":"10.1145/3534678.3542907","title":"Deep Learning on Graphs","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Data science; Intersection (aeronautics); Deep neural networks; Machine learning; Engineering","score_opus":0.050789844208719355,"score_gpt":0.28848830853912777,"score_spread":0.23769846433040842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290948538","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":[0.9681902,0.00093416165,0.0040797717,0.0015768544,0.00122468,0.00060298765,0.00006250733,0.0002602926,0.023068558],"genre_scores_gemma":[0.9962881,0.000096163596,0.002745111,0.00021415522,0.000047837715,0.000035682893,0.000013945625,0.000018021157,0.00054095953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790424,0.000050475348,0.0003105851,0.00091488205,0.00041891995,0.00040091012],"domain_scores_gemma":[0.9981283,0.00037300843,0.00035016116,0.00094956165,0.00010770721,0.00009127121],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0005463674,0.0002692851,0.000289049,0.00017447352,0.0008377884,0.00032608787,0.0053932727,0.000046463752,0.000011733045],"category_scores_gemma":[0.0005332015,0.00021131472,0.00007194909,0.00082234177,0.0001666027,0.0017770628,0.007914104,0.00069166016,0.0000034103048],"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":[0.00024715072,0.00033164135,0.01362825,0.0001221027,0.00007327202,0.000005195599,0.00437248,0.0006036328,0.003937054,0.80721694,0.0020002525,0.16746204],"study_design_scores_gemma":[0.0040666386,0.0051589347,0.018756185,0.0018653955,0.00020268177,0.00018982546,0.016197518,0.6671642,0.012131055,0.25552773,0.0153732095,0.0033666329],"about_ca_topic_score_codex":0.0000026085877,"about_ca_topic_score_gemma":0.0000025535307,"teacher_disagreement_score":0.6665606,"about_ca_system_score_codex":0.000028666907,"about_ca_system_score_gemma":0.000057019966,"threshold_uncertainty_score":0.999988},"labels":[],"label_agreement":null},{"id":"W4292117613","doi":"10.1155/2022/3915467","title":"Analysis of Traffic Accident Based on Knowledge Graph","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars; Natural Science Foundation of Jiangsu Province; Postdoctoral Science Foundation of Jiangsu Province; Government of Jiangsu Province","keywords":"Accident (philosophy); Computer science; Traffic accident; Visualization; Graph; Knowledge graph; Data visualization; Graph theory; Transport engineering; Data mining; Engineering; Artificial intelligence; Theoretical computer science","score_opus":0.009627605826818776,"score_gpt":0.260424612220158,"score_spread":0.25079700639333924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292117613","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":[0.7259758,0.0002796751,0.27286804,0.00017232262,0.0005391087,0.00010510922,0.000008790029,0.000026470963,0.000024702604],"genre_scores_gemma":[0.98337233,0.000034783017,0.016425183,0.00010618124,0.000020844127,0.0000075223375,0.000015708849,0.000009194517,0.000008234874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99824166,0.00009500849,0.00070810935,0.00020786925,0.00058330613,0.000164025],"domain_scores_gemma":[0.99838483,0.00019330608,0.0008480639,0.0002778784,0.00021433577,0.000081581944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030367353,0.0001299371,0.00037269187,0.001127398,0.00011027731,0.000011082843,0.00058144855,0.000024663932,0.000026534257],"category_scores_gemma":[0.000009435227,0.0001243891,0.00047117678,0.0033375723,0.000024758447,0.00047814703,0.0000066761413,0.0003041769,3.10275e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014970402,0.00021094378,0.0016760668,0.0000059955964,0.0001257993,0.000031874646,0.0007392823,0.9749428,0.0005030155,0.00064370804,0.000018547167,0.020952271],"study_design_scores_gemma":[0.0034702134,0.0028402575,0.64648247,0.00007928157,0.0010241242,0.00001209036,0.0005322892,0.33998376,0.0019569092,0.0018944741,0.0012233888,0.0005007406],"about_ca_topic_score_codex":5.6165453e-7,"about_ca_topic_score_gemma":0.000028330656,"teacher_disagreement_score":0.6448064,"about_ca_system_score_codex":0.000064530126,"about_ca_system_score_gemma":0.000066609035,"threshold_uncertainty_score":0.50724405},"labels":[],"label_agreement":null},{"id":"W4293107948","doi":"10.1007/s10601-022-09327-y","title":"Learning the travelling salesperson problem requires rethinking generalization","year":2022,"lang":"en","type":"article","venue":"Constraints","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Generalization; Computer science; Pipeline (software); Artificial intelligence; Artificial neural network; Machine learning; Deep learning; Graph; Theoretical computer science; Mathematics","score_opus":0.021729185091301555,"score_gpt":0.24121363847145047,"score_spread":0.2194844533801489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293107948","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":[0.040008187,0.0003143734,0.9539479,0.0018252176,0.0005286228,0.00031266478,0.000001359788,0.000373249,0.0026884377],"genre_scores_gemma":[0.9733128,0.000021839023,0.025721628,0.0005948363,0.00007327329,0.000031109725,0.0000058828596,0.000012595463,0.00022605524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859005,0.00027842738,0.0001933396,0.00033438238,0.00032095567,0.00028284558],"domain_scores_gemma":[0.99936324,0.00015029128,0.00013794207,0.00027307452,0.000034460012,0.000040963798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052429095,0.00011087793,0.000094699804,0.00006179079,0.0010477516,0.000117048745,0.00074773206,0.000027909027,0.00005511045],"category_scores_gemma":[0.000034408455,0.00009372955,0.000056214983,0.00051729864,0.00015430851,0.0002495194,0.00028732693,0.0005028095,0.0000052185515],"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":[0.000007471302,0.000022767656,0.0011207702,0.000011199544,0.000023612227,0.00006339452,0.014611891,0.4026646,0.0057818126,0.22496878,0.0004061267,0.35031757],"study_design_scores_gemma":[0.00060568785,0.00023687739,0.0006049322,0.00006157962,0.000015375676,0.00038781884,0.0016360478,0.88320583,0.0013826144,0.098972134,0.012286947,0.00060416234],"about_ca_topic_score_codex":0.000003441044,"about_ca_topic_score_gemma":0.0000022965319,"teacher_disagreement_score":0.9333046,"about_ca_system_score_codex":0.000052106912,"about_ca_system_score_gemma":0.000045999637,"threshold_uncertainty_score":0.80585647},"labels":[],"label_agreement":null},{"id":"W4293118172","doi":"10.1007/978-3-031-01588-5_2","title":"Background and Traditional Approaches","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on artificial intelligence and machine learning","topic":"Advanced Graph Neural 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":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Key (lock); Representation (politics); Graph; Data science; Theoretical computer science","score_opus":0.17223109992347463,"score_gpt":0.2675761701073073,"score_spread":0.09534507018383265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293118172","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":[0.00017869046,0.0045889644,0.83904076,0.008338061,0.00051098224,0.00065927504,0.000034713277,0.00064754457,0.146001],"genre_scores_gemma":[0.9724142,0.0020028327,0.013992096,0.0022635297,0.0010633139,0.000049370257,0.00004748544,0.00016359858,0.008003575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99791735,0.00008521293,0.0003779284,0.00096932595,0.00035413596,0.0002960146],"domain_scores_gemma":[0.9982126,0.0010808082,0.00021636154,0.0002683162,0.000027676633,0.0001942593],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018723964,0.00049131014,0.0004633434,0.00020339203,0.0004102264,0.00030519557,0.0004289479,0.00027015834,0.0001249869],"category_scores_gemma":[0.00015138157,0.00044056392,0.0001324126,0.00008894655,0.00025031748,0.0001996786,0.00019932556,0.0013311297,0.00007214041],"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":[0.000027942642,0.000009639025,0.000002000919,0.000021258222,0.000034884375,0.000023836714,0.000103143524,0.0024517905,0.000028270746,0.5883944,0.00003656003,0.4088663],"study_design_scores_gemma":[0.000024661666,0.0004168971,0.000025610541,0.00023426073,0.00006086082,0.00007473196,0.000030850264,0.13706413,0.0024648607,0.8332179,0.025465403,0.00091979286],"about_ca_topic_score_codex":0.0000067458936,"about_ca_topic_score_gemma":0.000025234938,"teacher_disagreement_score":0.9722355,"about_ca_system_score_codex":0.000025249701,"about_ca_system_score_gemma":0.000028194272,"threshold_uncertainty_score":0.9998046},"labels":[],"label_agreement":null},{"id":"W4293118256","doi":"10.1007/978-3-031-01588-5_4","title":"Multi-Relational Data and Knowledge Graphs","year":2020,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on artificial intelligence and machine learning","topic":"Advanced Graph Neural Networks","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":"Mila - Quebec Artificial Intelligence Institute; McGill University","funders":"","keywords":"Embedding; Computer science; Focus (optics); Knowledge graph; Theoretical computer science; Relational database; Statistical relational learning; Node (physics); Artificial intelligence; Data science; Data mining; Engineering; Physics","score_opus":0.10949688771132361,"score_gpt":0.3035231000219495,"score_spread":0.1940262123106259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293118256","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":[0.000039901533,0.011174341,0.9632114,0.0025736382,0.00050440815,0.0004936819,0.00006687568,0.00048751355,0.021448242],"genre_scores_gemma":[0.85236853,0.01224353,0.10738491,0.0029005199,0.0013138669,0.000055904555,0.00033394908,0.00039372678,0.023005076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760205,0.00010908144,0.00044191937,0.0012721274,0.00027771192,0.00029713995],"domain_scores_gemma":[0.99762213,0.0012407299,0.0002514173,0.0006349907,0.00005638015,0.00019437086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002780197,0.00049322506,0.0004546808,0.00027493166,0.0004584744,0.00020785346,0.00091672817,0.000278144,0.00008146792],"category_scores_gemma":[0.0005347317,0.00044310698,0.00009223818,0.00013184249,0.00023153465,0.00027253552,0.00091973506,0.0013924825,0.00011916292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029069217,0.000017301094,0.00001416499,0.000024447894,0.00005407119,0.000022222424,0.00022498451,0.0020044977,0.00008336402,0.45569494,0.00008813121,0.5417428],"study_design_scores_gemma":[0.000032081614,0.00018316426,0.000041634103,0.00027953018,0.00006770074,0.00002886121,0.000012253771,0.72012794,0.000757907,0.21709694,0.06058729,0.0007846994],"about_ca_topic_score_codex":0.00001170606,"about_ca_topic_score_gemma":0.00013316868,"teacher_disagreement_score":0.8558265,"about_ca_system_score_codex":0.000017410825,"about_ca_system_score_gemma":0.00004780817,"threshold_uncertainty_score":0.99980205},"labels":[],"label_agreement":null},{"id":"W4294959418","doi":"10.1109/wi-iat55865.2022.00049","title":"Impact of Injecting Ground Truth Explanations on Relational Graph Convolutional Networks and their Explanation Methods for Link Prediction on Knowledge Graphs","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Ground truth; Leverage (statistics); Computer science; Knowledge graph; Benchmark (surveying); Theoretical computer science; Graph; Node (physics); Black box; Artificial intelligence; Machine learning","score_opus":0.05978387932113884,"score_gpt":0.3664902829082578,"score_spread":0.306706403587119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294959418","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":[0.01133325,0.000698079,0.98335403,0.00014784091,0.0021571247,0.0010321984,0.00022421741,0.0002521653,0.0008010892],"genre_scores_gemma":[0.87157714,0.0002586434,0.12582576,0.00007118443,0.00036659426,0.0006040295,0.0011834444,0.000045824872,0.00006739607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99703276,0.00052898756,0.000731031,0.0010383535,0.00030623487,0.00036262578],"domain_scores_gemma":[0.9933721,0.004876578,0.00068273465,0.00061256456,0.00032941683,0.000126617],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011887429,0.00046604182,0.00046766663,0.0009956014,0.00056029303,0.00009410101,0.00057564647,0.00034892288,0.000025797339],"category_scores_gemma":[0.00020953319,0.0004034077,0.00047269565,0.00086085644,0.00010443669,0.00042126985,0.0005346353,0.0011048868,3.6661248e-7],"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":[0.00008866496,0.000111039204,0.0007991433,0.0000230602,0.00017726915,2.5728284e-7,0.00031076025,0.5227269,0.000017059763,0.44954595,0.00028798735,0.025911937],"study_design_scores_gemma":[0.0005263876,0.00066321617,0.030852346,0.00008156414,0.000022229513,0.000008635379,0.000043089472,0.74019957,0.000020923571,0.22719735,0.00009612977,0.0002885649],"about_ca_topic_score_codex":0.00002306952,"about_ca_topic_score_gemma":0.000008558142,"teacher_disagreement_score":0.86024386,"about_ca_system_score_codex":0.00030762958,"about_ca_system_score_gemma":0.00017908198,"threshold_uncertainty_score":0.99984175},"labels":[],"label_agreement":null},{"id":"W4295088574","doi":"10.1038/s41598-022-19419-7","title":"Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Classifier (UML); Computer science; Artificial intelligence; Graph; Graph theory; Machine learning; Theoretical computer science; Mathematics; Combinatorics","score_opus":0.02715685552864167,"score_gpt":0.2760701071264671,"score_spread":0.24891325159782546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295088574","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":[0.012438398,0.0005500007,0.97565496,0.0006571224,0.009169624,0.00041907365,0.0000074699897,0.00038887368,0.00071448606],"genre_scores_gemma":[0.9664469,0.000004150527,0.029205237,0.0010043402,0.00026872737,0.00029901176,0.0002183431,0.000037055903,0.0025162457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99380845,0.00070020475,0.0009986971,0.0019560975,0.0014650539,0.001071495],"domain_scores_gemma":[0.9969136,0.00018188285,0.00075907446,0.001800786,0.00018453837,0.00016008379],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0036579175,0.0003365625,0.00048564537,0.00038904956,0.0038117196,0.0007648136,0.0013571258,0.00009035279,0.00037991023],"category_scores_gemma":[0.000060051465,0.000338688,0.0005665102,0.010104305,0.00052819244,0.0007250908,0.00073747954,0.00049670297,0.00001008964],"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":[0.0000697087,0.00031145356,0.0005033109,0.000010484681,0.00024645004,0.0008664825,0.0009928793,0.6240163,0.0022423905,0.24599163,0.11212188,0.012627028],"study_design_scores_gemma":[0.000061794344,0.000051689978,0.000031508032,0.000003872934,0.000099758654,0.000038420414,0.00014813048,0.06714168,0.0033544423,0.8771843,0.051387727,0.00049667654],"about_ca_topic_score_codex":0.000019420635,"about_ca_topic_score_gemma":0.00002089631,"teacher_disagreement_score":0.9540085,"about_ca_system_score_codex":0.00014419506,"about_ca_system_score_gemma":0.00020774624,"threshold_uncertainty_score":0.99990654},"labels":[],"label_agreement":null},{"id":"W4297780663","doi":"10.5281/zenodo.7071856","title":"KBI-Regex Datasets","year":2022,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.03401106583812267,"score_gpt":0.26447791780259905,"score_spread":0.2304668519644764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297780663","genre_codex":"other","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000041367886,0.0018473512,0.27834788,0.0020273607,0.0034709985,0.0015144661,0.007356292,0.0028973792,0.70249695],"genre_scores_gemma":[0.020813758,0.008007162,0.02779256,0.008622528,0.005608404,0.0000020047144,0.4960798,0.031934254,0.40113953],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959933,0.0007199669,0.00041767457,0.001247246,0.000872675,0.0007491407],"domain_scores_gemma":[0.99694985,0.00006083452,0.00031570194,0.0020751464,0.00030080526,0.0002976366],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.00057430344,0.0003742404,0.0003421721,0.0004876294,0.0038761378,0.0016923229,0.0073931324,0.00015447212,0.08845387],"category_scores_gemma":[0.00021738977,0.00041819658,0.0001238184,0.0015996727,0.00017715404,0.000726484,0.01100852,0.0012608807,0.09408079],"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":[0.000013575571,0.00006260803,2.5713204e-8,0.00004388579,0.0000344028,0.000046722773,0.00013449787,0.00077211903,0.00010506452,0.0041649225,0.8975471,0.097075075],"study_design_scores_gemma":[0.00028744165,0.00020692333,0.0000056773906,0.000029035646,0.000009637189,0.00021717245,0.000022776823,0.0017541687,0.000056418507,0.00028805193,0.99669135,0.00043137986],"about_ca_topic_score_codex":0.000009625759,"about_ca_topic_score_gemma":1.469213e-7,"teacher_disagreement_score":0.48872352,"about_ca_system_score_codex":0.00027615725,"about_ca_system_score_gemma":0.000009137661,"threshold_uncertainty_score":0.99982697},"labels":[],"label_agreement":null},{"id":"W4297829713","doi":"10.1016/j.ipm.2022.103076","title":"An efficiency relation-specific graph transformation network for knowledge graph representation learning","year":2022,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Graph; Relation (database); Transformation (genetics); Theoretical computer science; Artificial intelligence; Data mining","score_opus":0.0176962029112127,"score_gpt":0.27233710557709767,"score_spread":0.25464090266588496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297829713","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":[0.002599913,0.00025095165,0.99032366,0.0002391425,0.0005612613,0.0010479611,0.000002593026,0.0006026449,0.0043718545],"genre_scores_gemma":[0.93233144,0.00006742801,0.06598852,0.0003077209,0.00007627005,0.0007691185,0.00034916413,0.00001728689,0.000093081195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997927,0.00012376996,0.00069565536,0.00032567026,0.0005187088,0.0004091598],"domain_scores_gemma":[0.9988256,0.000070677765,0.00047339953,0.0003715234,0.00018461762,0.00007416309],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00084131345,0.00019433929,0.00014983588,0.0005314261,0.0020223616,0.00049775286,0.0007251483,0.00004154975,0.000010681193],"category_scores_gemma":[0.000009261333,0.00021645597,0.00009795312,0.002399244,0.000034162276,0.007397871,0.00014018132,0.0002721983,0.000012185958],"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":[0.000023027975,0.000037198035,0.000046357734,0.0000636953,0.0000073179604,3.8800096e-7,0.0042816973,0.5758125,0.000006631949,0.06397348,0.00058045407,0.35516727],"study_design_scores_gemma":[0.0009753356,0.00020774778,0.001970882,0.000041635412,0.000017499355,0.000008196949,0.001771073,0.8930649,0.00005092973,0.037863817,0.06364369,0.00038429003],"about_ca_topic_score_codex":0.0000014625494,"about_ca_topic_score_gemma":8.052297e-7,"teacher_disagreement_score":0.9297315,"about_ca_system_score_codex":0.000118645905,"about_ca_system_score_gemma":0.000025751571,"threshold_uncertainty_score":0.9992769},"labels":[],"label_agreement":null},{"id":"W4297991630","doi":"10.21428/594757db.06b4cfb6","title":"Deep Learning of Latent Edge Types from Relational Data","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Graph; Encoder; Benchmark (surveying); Enhanced Data Rates for GSM Evolution; Generative grammar; Theoretical computer science; Layer (electronics); Feature (linguistics); Node (physics); Artificial intelligence","score_opus":0.04305249582634049,"score_gpt":0.2554176522886831,"score_spread":0.21236515646234258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297991630","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":[0.00530346,0.00039087635,0.99223,0.0003721607,0.00026019764,0.00004336988,0.000005635772,0.0000858296,0.0013084582],"genre_scores_gemma":[0.8813283,0.000010228071,0.11773888,0.00015190552,0.00003478967,0.0000031032964,0.00009044781,0.000004743364,0.000637613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928683,0.000055136898,0.000116486655,0.00024281607,0.00020820322,0.00009054903],"domain_scores_gemma":[0.9992698,0.00015012555,0.00006404047,0.0004749102,0.000017655242,0.000023503364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009929581,0.000044534143,0.00006410815,0.000032062744,0.00011360085,0.000009620358,0.0008744823,0.000011347276,0.00026863895],"category_scores_gemma":[0.00002104173,0.0000421155,0.000017414648,0.0002442052,0.0000152452485,0.00029543473,0.0016068909,0.00017771703,0.000011703517],"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":[0.000013550272,0.00006109849,0.026394375,0.0000017639434,0.000040527975,0.000014508251,0.00029988543,0.6127891,0.0005147573,0.2629701,0.002328472,0.09457189],"study_design_scores_gemma":[0.00009852598,0.000030593004,0.00986894,0.0000014077829,0.0000026585612,0.0000023862383,0.00000915241,0.9729286,0.00006662881,0.008167263,0.008759584,0.0000642725],"about_ca_topic_score_codex":0.000019844696,"about_ca_topic_score_gemma":0.0000053343406,"teacher_disagreement_score":0.87602484,"about_ca_system_score_codex":0.000010512543,"about_ca_system_score_gemma":0.000012659995,"threshold_uncertainty_score":0.2941408},"labels":[],"label_agreement":null},{"id":"W4299570738","doi":"10.1609/aaai.v28i1.8976","title":"Convex Co-embedding","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Embedding; Ranking (information retrieval); Regular polygon; Relation (database); Computer science; Relevance (law); Convex optimization; Artificial intelligence; Linear scale; Current (fluid); Theoretical computer science; Machine learning; Space (punctuation); Mathematical optimization; Mathematics; Data mining; Geometry; Engineering; Geography","score_opus":0.010130865001321054,"score_gpt":0.2759580041804148,"score_spread":0.26582713917909373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299570738","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":[0.002293818,0.000015051613,0.9568399,0.00036053685,0.00022023077,0.000037780777,6.001037e-8,0.00031450015,0.039918106],"genre_scores_gemma":[0.8708649,0.0000040337923,0.12630822,0.0019506883,0.000071215065,0.0000028759878,3.95844e-7,0.0000047619737,0.0007929064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993746,0.000026175845,0.0000870077,0.0002089517,0.00011195265,0.0001912975],"domain_scores_gemma":[0.9994197,0.00011458474,0.000027697422,0.00035267384,0.000019055273,0.000066271314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009915992,0.00006651426,0.0000786259,0.000034117034,0.00006122795,0.000053945827,0.0004727613,0.000025301846,0.000026286292],"category_scores_gemma":[0.000018420611,0.000054933273,0.000029342507,0.00016496355,0.000024471039,0.00030813896,0.00009073272,0.000074389616,0.00017760607],"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":[0.0000013102135,0.000011781661,0.000892501,0.0000030970323,0.0000043343384,0.0000043990985,0.00006632206,0.0019812388,0.00097406027,0.8723696,0.0125685455,0.111122794],"study_design_scores_gemma":[0.00019162889,0.000047543628,0.00054286665,0.0000055241067,8.599622e-7,0.000012885533,0.0000040372083,0.8742431,0.0046065985,0.034831274,0.085335046,0.00017864494],"about_ca_topic_score_codex":0.0000014805872,"about_ca_topic_score_gemma":8.6720814e-7,"teacher_disagreement_score":0.8722619,"about_ca_system_score_codex":0.0000059276776,"about_ca_system_score_gemma":0.00000397017,"threshold_uncertainty_score":0.2282826},"labels":[],"label_agreement":null},{"id":"W4306317318","doi":"10.1145/3511808.3557688","title":"Sampling Enclosing Subgraphs for Link Prediction","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Scalability; Computer science; Overhead (engineering); Link (geometry); Graph; Theoretical computer science; Representation (politics); Induced subgraph isomorphism problem; Line graph; Computer network","score_opus":0.06510801511284907,"score_gpt":0.30575703596044224,"score_spread":0.24064902084759315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306317318","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":[0.019461725,0.00006166957,0.677238,0.010651505,0.008198593,0.003222151,0.00016266978,0.00060951186,0.2803942],"genre_scores_gemma":[0.9394374,0.00006165777,0.05741103,0.00054235896,0.00016666982,0.00068228063,0.000080512385,0.000015157772,0.0016029184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998243,0.000011826774,0.00057332823,0.00027069225,0.00065782596,0.00024334001],"domain_scores_gemma":[0.9981992,0.00007724102,0.00057989423,0.00034055862,0.00075497414,0.000048125316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053422485,0.00018633022,0.00014556962,0.00047494884,0.0005097811,0.00028294104,0.0029246088,0.00003797958,0.00004740079],"category_scores_gemma":[0.00016054297,0.0001671814,0.00015302245,0.0006312837,0.000052123698,0.0016283283,0.0020040637,0.00026379112,0.000021030306],"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":[0.0000665567,0.0000623679,0.00035902797,0.0001037659,0.000068241774,2.5774227e-8,0.0012110927,0.0028052013,0.00027372915,0.9504565,0.0044970564,0.04009644],"study_design_scores_gemma":[0.0017838978,0.00024021356,0.0025679127,0.00035424117,0.0000545253,0.000015454725,0.0011996112,0.16482447,0.0011255416,0.25450924,0.57272464,0.0006002375],"about_ca_topic_score_codex":0.0000014033285,"about_ca_topic_score_gemma":0.0000014111021,"teacher_disagreement_score":0.9199757,"about_ca_system_score_codex":0.0002187864,"about_ca_system_score_gemma":0.00002651801,"threshold_uncertainty_score":0.68174595},"labels":[],"label_agreement":null},{"id":"W4306317442","doi":"10.1145/3511808.3557181","title":"PyDHNet: A Python Library for Dynamic Heterogeneous Network Representation Learning and Evaluation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Python (programming language); Computer science; Representation (politics); Architecture; Software engineering; Theoretical computer science; Artificial intelligence; Programming language","score_opus":0.042516795527662604,"score_gpt":0.318841784027764,"score_spread":0.27632498850010134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306317442","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2393444,0.0005887916,0.29759496,0.022620117,0.010409219,0.013033116,0.00012300203,0.0016228195,0.41466355],"genre_scores_gemma":[0.9775802,0.00011046428,0.019469526,0.00023643763,0.00006423308,0.0007078343,0.00012079639,0.000015402227,0.0016951398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835694,0.000039277696,0.00046254255,0.00028456174,0.0006487418,0.00020791958],"domain_scores_gemma":[0.9986103,0.00008841533,0.00060011045,0.00022640955,0.0004329501,0.00004177937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005621272,0.00016627963,0.0001379623,0.00026722383,0.00042501473,0.0003136343,0.0014257135,0.00003332518,0.00006376708],"category_scores_gemma":[0.00013540262,0.00015308925,0.00008848741,0.00050585525,0.000044399887,0.001758407,0.0020108856,0.00021568873,0.000011628602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030032685,0.0001158485,0.002256639,0.00020864884,0.00018951506,1.3318667e-7,0.0029951718,0.08174906,0.0001829731,0.698911,0.009302546,0.20378815],"study_design_scores_gemma":[0.0010705135,0.00015081062,0.0018404733,0.0001240547,0.000039654784,0.000014235055,0.00059281127,0.7959221,0.00016372534,0.10356229,0.09622373,0.00029561],"about_ca_topic_score_codex":7.2709616e-7,"about_ca_topic_score_gemma":0.000002210916,"teacher_disagreement_score":0.7382358,"about_ca_system_score_codex":0.00013604251,"about_ca_system_score_gemma":0.000026037633,"threshold_uncertainty_score":0.62427986},"labels":[],"label_agreement":null},{"id":"W4307935090","doi":"10.48550/arxiv.2210.17457","title":"Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Gruppo Nazionale per il Calcolo Scientifico; Ministero dell'Università e della Ricerca; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Computer science; Multigrid method; Grid; Graph partition; Scalability; Graph; Cluster analysis; Theoretical computer science; Inference; Artificial neural network; Algorithm; Artificial intelligence; Mathematics","score_opus":0.05739226460969931,"score_gpt":0.20824898977672382,"score_spread":0.1508567251670245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307935090","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":[0.14950676,0.00006422694,0.8490743,0.000049409482,0.00034230598,0.00068695255,0.000022368999,0.00016357431,0.000090090514],"genre_scores_gemma":[0.97174484,0.00004965425,0.027795192,0.00016154154,0.000098175886,0.000010715697,0.000044135923,0.00002741474,0.00006834322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977675,0.00012924329,0.00027906903,0.001201927,0.00018861929,0.00043363412],"domain_scores_gemma":[0.99783576,0.00010573582,0.0004188545,0.0012334654,0.00018129247,0.00022489451],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012966951,0.0003620679,0.00036831852,0.0004728827,0.00035004687,0.000065007254,0.0017074704,0.00015608128,0.000017772842],"category_scores_gemma":[0.0000053317044,0.0004130708,0.00021154783,0.00221737,0.00014721377,0.00043199924,0.0018580344,0.0007718477,0.0000016221579],"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":[0.00005645613,0.000057315254,0.0026252344,0.00001888595,0.000056615834,0.000045864363,0.00007250795,0.9738486,0.00007849171,0.022446219,0.000036801783,0.0006570492],"study_design_scores_gemma":[0.00033141862,0.00011238298,0.00096157315,0.00003369687,0.00006109192,0.000008793048,0.000050905426,0.99511194,0.000055388144,0.0026817797,0.00014288972,0.0004481527],"about_ca_topic_score_codex":0.00014266028,"about_ca_topic_score_gemma":0.00006821328,"teacher_disagreement_score":0.8222381,"about_ca_system_score_codex":0.000190988,"about_ca_system_score_gemma":0.000115333954,"threshold_uncertainty_score":0.9998321},"labels":[],"label_agreement":null},{"id":"W4311081148","doi":"10.21203/rs.3.rs-2318594/v1","title":"Learning Heterogeneous Subgraph Representations for Team Discovery","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; York University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Overfitting; Ranking (information retrieval); Set (abstract data type); Graph; Machine learning; Task (project management); Artificial intelligence; Baseline (sea); Representation (politics); Data science; Artificial neural network; Theoretical computer science","score_opus":0.06686809311051695,"score_gpt":0.41006030328920096,"score_spread":0.343192210178684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311081148","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":[0.060899444,0.003389926,0.92101395,0.0023407936,0.0021680463,0.006106783,0.0002525567,0.0011383578,0.002690137],"genre_scores_gemma":[0.96730727,0.000942888,0.021120436,0.00008498243,0.0004887659,0.0041832495,0.00046202203,0.000113392955,0.005296985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939343,0.0011791413,0.00044604117,0.0016746017,0.0015996676,0.0011662504],"domain_scores_gemma":[0.9951295,0.0019253901,0.00020205432,0.0020412833,0.0004690172,0.00023271542],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0015815331,0.00033367556,0.00039357838,0.00079746597,0.0014039786,0.0009592289,0.0029737044,0.00021119298,0.000040827188],"category_scores_gemma":[0.00068880396,0.00034941343,0.00051742897,0.0014252535,0.0001895625,0.0005836505,0.007627119,0.0033643593,0.00001290177],"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":[0.0001126555,0.00027062753,0.006372299,0.000717719,0.00016827155,0.00018869892,0.0016654419,0.9286869,0.00031047297,0.023910929,0.00995635,0.027639598],"study_design_scores_gemma":[0.0016209055,0.002687003,0.0065278844,0.00072128314,0.00004215905,0.000099195924,0.0015049535,0.5242821,0.0018005515,0.3407873,0.1175115,0.0024151485],"about_ca_topic_score_codex":0.00009315516,"about_ca_topic_score_gemma":0.000038956627,"teacher_disagreement_score":0.90640783,"about_ca_system_score_codex":0.00028364468,"about_ca_system_score_gemma":0.0003398261,"threshold_uncertainty_score":0.99989605},"labels":[],"label_agreement":null},{"id":"W4311529135","doi":"10.21203/rs.3.rs-2327811/v1","title":"DyHNet: Learning Dynamic Heterogeneous Network Representations","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Computer science; Representation (politics); Learning network; Artificial intelligence; Feature learning; State (computer science); Range (aeronautics); Semantics (computer science); Theoretical computer science; Machine learning; Programming language","score_opus":0.05132208149437733,"score_gpt":0.4066155392973612,"score_spread":0.3552934578029839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311529135","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":[0.048856206,0.022214683,0.8930991,0.0064128605,0.006381217,0.006950664,0.00010988679,0.004009191,0.011966136],"genre_scores_gemma":[0.9628582,0.0021266819,0.027599521,0.00013606228,0.00061332725,0.0014142743,0.0003847985,0.00014532136,0.0047218255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991448,0.0023613009,0.0005317962,0.0018568971,0.0021820357,0.0016199938],"domain_scores_gemma":[0.9951021,0.001246681,0.00024424656,0.0026432013,0.0004222182,0.00034154387],"candidate_categories":["metaepi_narrow","sts","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0018039205,0.0004043774,0.00045134267,0.00058412534,0.0016785145,0.00064978143,0.003809993,0.00028272142,0.00028139626],"category_scores_gemma":[0.00035126423,0.0004467171,0.00035898286,0.002320448,0.00020493133,0.00028948687,0.014213273,0.006528794,0.00008611216],"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":[0.000014969386,0.000050747814,0.0014427884,0.000106415326,0.000053417476,0.00039738193,0.00034884998,0.975958,0.000018759827,0.0040512225,0.0027849476,0.0147725195],"study_design_scores_gemma":[0.00029800893,0.00040099522,0.0035234457,0.00029818382,0.000010690936,0.00007230213,0.00017503121,0.8687058,0.000023679395,0.090654984,0.035033494,0.00080337597],"about_ca_topic_score_codex":0.000099144796,"about_ca_topic_score_gemma":0.000088425135,"teacher_disagreement_score":0.914002,"about_ca_system_score_codex":0.000518675,"about_ca_system_score_gemma":0.0003442469,"threshold_uncertainty_score":0.9997985},"labels":[],"label_agreement":null},{"id":"W4313066861","doi":"10.1109/itc-cscc55581.2022.9895090","title":"Graph Wavelet Convolutional Network with Graph Clustering","year":2022,"lang":"en","type":"article","venue":"2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Computer science; Graph; Voltage graph; Artificial intelligence; Pattern recognition (psychology); Line graph; Theoretical computer science","score_opus":0.04432750973420105,"score_gpt":0.26852405554742304,"score_spread":0.224196545813222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313066861","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":[0.0018838006,0.0017598441,0.9795491,0.005810491,0.0023198694,0.0011055624,0.00016929202,0.0007756047,0.0066264234],"genre_scores_gemma":[0.98160154,0.00065076264,0.015232661,0.0011922799,0.00018381153,0.0005896613,0.00024845396,0.000044421642,0.00025638298],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99544054,0.0006166291,0.00091878715,0.0010811279,0.0012739159,0.0006689966],"domain_scores_gemma":[0.9954767,0.0007725643,0.00059379544,0.0024386772,0.00043907587,0.00027917026],"candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.00082777697,0.0004935682,0.0005530679,0.00050510716,0.001562288,0.0006513301,0.005864613,0.0001366562,0.00005442545],"category_scores_gemma":[0.000031060463,0.0004960741,0.00016884964,0.0013911694,0.00047566567,0.0005921361,0.003869816,0.0014595621,0.0000114780105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041150444,0.00026929515,0.00076745276,0.000017118991,0.00015363964,0.000031117404,0.00008947875,0.06697712,0.000091030444,0.9152578,0.004905627,0.011399163],"study_design_scores_gemma":[0.001445643,0.0007708343,0.0050257114,0.00027161252,0.000036095633,0.00078297895,0.00019347078,0.9177039,0.0000038827748,0.020729274,0.051965993,0.001070586],"about_ca_topic_score_codex":0.00012254363,"about_ca_topic_score_gemma":0.000059478927,"teacher_disagreement_score":0.9797178,"about_ca_system_score_codex":0.00029785186,"about_ca_system_score_gemma":0.00018231307,"threshold_uncertainty_score":0.99974906},"labels":[],"label_agreement":null},{"id":"W4313217515","doi":"10.1016/j.xpro.2022.101966","title":"Implementation of a graph-embedded topic model for analysis of population-level electronic health records","year":2022,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Health records; Population; Data science; Medicine; Theoretical computer science; Environmental health; Health care; Political science","score_opus":0.06876789409001349,"score_gpt":0.4091217982207846,"score_spread":0.3403539041307711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313217515","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":[0.02226943,0.000013119131,0.9337658,0.00018840683,0.000022580494,0.04358379,0.00011921887,0.00003298883,0.000004653865],"genre_scores_gemma":[0.78624135,0.0000020396917,0.103283554,0.0001729516,0.000009664198,0.11013501,0.000100175166,0.000010588325,0.000044660785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985319,0.00010433385,0.0004971638,0.00028728158,0.0002876206,0.00029173872],"domain_scores_gemma":[0.9989052,0.0000528192,0.00050376425,0.00041624936,0.000084875115,0.00003709344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042736315,0.000093572286,0.00030225958,0.00034147935,0.00013980284,0.0000096010235,0.0004283162,0.000016207523,0.000021245796],"category_scores_gemma":[0.0000068747054,0.00009913974,0.00017891577,0.0016142275,0.0000120051955,0.00018626566,0.00012234974,0.00009303584,4.3347136e-8],"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":[0.00018579622,0.0002444153,0.021672782,0.00022501609,0.00055846554,3.7881128e-7,0.0027259495,0.643806,0.0006182145,0.20900898,0.00053776294,0.120416276],"study_design_scores_gemma":[0.00081466924,0.0008521941,0.012128451,0.000007840498,0.00003363275,3.6982388e-7,0.00008793959,0.91427016,0.00037086147,0.071147874,0.00016044166,0.00012557492],"about_ca_topic_score_codex":0.00023502372,"about_ca_topic_score_gemma":0.00077529385,"teacher_disagreement_score":0.83048224,"about_ca_system_score_codex":0.00011668425,"about_ca_system_score_gemma":0.00016695035,"threshold_uncertainty_score":0.40428013},"labels":[],"label_agreement":null},{"id":"W4313288140","doi":"10.3390/systems10060259","title":"Parallel Learning of Dynamics in Complex Systems","year":2022,"lang":"en","type":"article","venue":"Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Huxiang Youth Talent Support Program; National Natural Science Foundation of China","keywords":"Computer science; Theoretical computer science; Metis; Graph partition; Extrapolation; Graph; Artificial intelligence; Mathematics","score_opus":0.021872417516930802,"score_gpt":0.24568439669154787,"score_spread":0.22381197917461707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313288140","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":[0.044821143,0.002946394,0.94454783,0.00018405363,0.0027182277,0.0009541343,0.000013451657,0.00032437217,0.0034903833],"genre_scores_gemma":[0.9981168,0.000008635753,0.001214701,0.000013894474,0.000021982896,0.00008881864,0.000011468346,0.000012056085,0.00051165547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824786,0.00038665137,0.00041838878,0.00029671073,0.00037365453,0.00027675828],"domain_scores_gemma":[0.99915284,0.000110488145,0.00025392667,0.00040154826,0.000036154383,0.000045030512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043263193,0.00011168422,0.0002902188,0.00016400844,0.00013190822,0.000043421565,0.00078366906,0.000029803832,0.0000028731408],"category_scores_gemma":[0.000012849899,0.00011671285,0.000049193706,0.00077403535,0.000026577782,0.00015815363,0.0003967084,0.00031735428,0.0000042322536],"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":[0.0000047322787,0.000021122656,0.0051363953,0.000052340198,0.0000063348193,0.000025755557,0.00019728193,0.87444127,0.000096684314,0.119370505,0.00017017906,0.0004774248],"study_design_scores_gemma":[0.00023825646,0.000086549975,0.00085790304,0.000025510102,0.0000010569864,0.000055724468,0.00087096525,0.99295366,7.832649e-7,0.00022384318,0.0045638895,0.00012185403],"about_ca_topic_score_codex":0.00026093313,"about_ca_topic_score_gemma":0.000023571369,"teacher_disagreement_score":0.95329565,"about_ca_system_score_codex":0.00022737985,"about_ca_system_score_gemma":0.000023454511,"threshold_uncertainty_score":0.47594118},"labels":[],"label_agreement":null},{"id":"W4313426558","doi":"10.1145/3533769","title":"Knowledge Base Embedding for Sampling-Based Prediction","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment","keywords":"Computer science; Latent variable; Protocol (science); Task (project management); Ranking (information retrieval); Machine learning; Sampling (signal processing); Rank (graph theory); Artificial intelligence; Data mining; Embedding; Variable (mathematics); Link (geometry); Mathematics","score_opus":0.038179926302878586,"score_gpt":0.2914206615433705,"score_spread":0.2532407352404919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313426558","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":[0.0005176981,0.000036525744,0.9945502,0.00024465573,0.0029166208,0.00083582674,0.00018269819,0.000490684,0.00022503892],"genre_scores_gemma":[0.9651078,0.0000028637198,0.032566328,0.00033577796,0.000061631894,0.0017266327,0.00008375527,0.000012869768,0.00010236406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861306,0.000082499784,0.0005008321,0.00020181062,0.00032586872,0.00027590955],"domain_scores_gemma":[0.9984706,0.00038545276,0.00020446212,0.0006952111,0.00015553975,0.0000887491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046228408,0.00014660481,0.00014463787,0.00044107845,0.0010408288,0.0001794349,0.0006832456,0.000049896225,0.000014190491],"category_scores_gemma":[0.000031522053,0.00015744167,0.0001243312,0.0007852698,0.000016002585,0.0018814813,0.000018558721,0.00024541092,0.000030352883],"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":[0.00002710137,0.00004098662,0.000005424602,0.000038199294,0.000011463087,1.4424319e-7,0.000514829,0.9669723,0.00001742877,0.0030490914,0.0004989291,0.028824119],"study_design_scores_gemma":[0.00070613873,0.0002249453,0.000020577232,0.00002482131,0.000008047239,0.000013686046,0.0003269172,0.9123734,0.00019682087,0.00028275122,0.0856677,0.00015417283],"about_ca_topic_score_codex":0.000006319509,"about_ca_topic_score_gemma":0.0000015426174,"teacher_disagreement_score":0.9645901,"about_ca_system_score_codex":0.00023651352,"about_ca_system_score_gemma":0.00008909185,"threshold_uncertainty_score":0.80053186},"labels":[],"label_agreement":null},{"id":"W4313575157","doi":"10.3390/make5010004","title":"IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Embedding; Computer science; Benchmarking; Representation (politics); Artificial intelligence; Graph; Machine learning; Feature learning; Knowledge representation and reasoning; Iterative refinement; Task (project management); Transfer of learning; Theoretical computer science","score_opus":0.026325188613280908,"score_gpt":0.34010690381531417,"score_spread":0.31378171520203324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313575157","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":[0.07738749,0.0023461406,0.91433764,0.0007721506,0.0007759383,0.00041155436,0.0000024932108,0.0015282976,0.002438272],"genre_scores_gemma":[0.98816895,0.00022077783,0.003446305,0.000029366412,0.00031048123,0.000083716826,0.000090573216,0.000042325122,0.007607504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819666,0.00029064334,0.00029327854,0.0006566909,0.0001300529,0.00043268985],"domain_scores_gemma":[0.99865466,0.0007224363,0.000097529184,0.0001428999,0.00026781656,0.00011468396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054659636,0.00023980899,0.0002327622,0.00035531557,0.0009746287,0.00026840784,0.00017919704,0.000119808225,0.000007120046],"category_scores_gemma":[0.0003498064,0.00023394306,0.00010461931,0.0010819947,0.000047979924,0.0010553135,0.00009027306,0.0007063609,0.000058581132],"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":[0.00009179237,0.00007878058,0.0032842562,0.0001617267,0.000026892272,0.000008834158,0.010599561,0.008242996,0.014262342,0.0021023029,0.00024124717,0.9608993],"study_design_scores_gemma":[0.0007291352,0.0003115342,0.004170518,0.00009506521,0.000019810273,0.000037153965,0.00045128004,0.95266277,0.0011954476,0.0009180579,0.039083607,0.00032565335],"about_ca_topic_score_codex":0.000011847728,"about_ca_topic_score_gemma":0.000047044203,"teacher_disagreement_score":0.9605736,"about_ca_system_score_codex":0.000043688186,"about_ca_system_score_gemma":0.000042529366,"threshold_uncertainty_score":0.9539921},"labels":[],"label_agreement":null},{"id":"W4317931697","doi":"10.1561/2200000096","title":"Graph Neural Networks for Natural Language Processing: A Survey","year":2023,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":269,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial neural network; Graph; Natural language processing; Artificial intelligence; Theoretical computer science","score_opus":0.02218011106607913,"score_gpt":0.3132030583237363,"score_spread":0.29102294725765715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317931697","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":[0.23458642,0.0043730303,0.75592834,0.0020392197,0.0010012389,0.00040185286,0.000017558177,0.0013463281,0.00030603193],"genre_scores_gemma":[0.9943288,0.000026723108,0.0043218285,0.000078469464,0.000060522892,0.000041685013,0.00041420094,0.00001744899,0.00071032863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998814,0.0001247629,0.00020337505,0.00038334986,0.00010943971,0.0003651143],"domain_scores_gemma":[0.99929756,0.0003446491,0.00009363133,0.00016713761,0.00004186119,0.000055169246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004010958,0.00014940329,0.00015903864,0.0004905671,0.00039831133,0.00023729153,0.00026183846,0.000046149136,0.0000050906488],"category_scores_gemma":[0.00010513265,0.00013737229,0.000052407737,0.00253222,0.000044765595,0.0004846486,0.0001417122,0.00041087065,0.0000018566122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015217864,0.000016343527,0.041420605,0.000009573794,0.000007945108,0.000012036128,0.00048612125,0.1449712,0.000011403118,0.0012825027,0.00011265067,0.8116544],"study_design_scores_gemma":[0.00034877483,0.00003447829,0.14557798,0.000009764044,0.0000027083877,0.000007073322,0.000021164622,0.8530216,5.776513e-7,0.00038514571,0.00045541103,0.00013530227],"about_ca_topic_score_codex":0.00011384488,"about_ca_topic_score_gemma":0.0011708552,"teacher_disagreement_score":0.8115191,"about_ca_system_score_codex":0.000012448483,"about_ca_system_score_gemma":0.0000070055103,"threshold_uncertainty_score":0.56018794},"labels":[],"label_agreement":null},{"id":"W4318147794","doi":"10.1109/bigdata55660.2022.10020299","title":"Temporal Graph Representation Learning via Maximal Cliques","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Clique; Computer science; Graph; Node (physics); Theoretical computer science; Embedding; Representation (politics); Graph embedding; Feature learning; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.304841879149363,"score_gpt":0.3731440942115506,"score_spread":0.06830221506218759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318147794","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":[0.0074849236,0.00009302594,0.9647464,0.0053632827,0.01323283,0.00046658213,0.003843309,0.00054218614,0.0042274185],"genre_scores_gemma":[0.96540105,0.00024959352,0.007581398,0.0010374853,0.0009790782,0.00007939543,0.023824355,0.000034101584,0.0008135544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99531496,0.0004268783,0.0005414111,0.0017674369,0.0015284733,0.00042084788],"domain_scores_gemma":[0.9956364,0.00020366824,0.0004471705,0.0033984785,0.00017510031,0.00013920234],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008250314,0.0002987366,0.0002605509,0.00041044533,0.0005251564,0.0004362739,0.010495506,0.000060900704,0.00026606026],"category_scores_gemma":[0.0001821376,0.00032667114,0.000063434214,0.0007853389,0.0001135658,0.0023371566,0.007543416,0.0010755248,0.00006935865],"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":[0.00036164618,0.00044289415,0.0064173285,0.000014227135,0.00022122699,0.00045062858,0.00025775444,0.005960971,0.0045491974,0.057778582,0.06865,0.85489553],"study_design_scores_gemma":[0.0009909078,0.00044038423,0.0019088855,0.00004522875,0.000022025833,0.00017042401,0.00021668652,0.8289555,0.0006088278,0.025884785,0.1399221,0.0008342757],"about_ca_topic_score_codex":0.00025012527,"about_ca_topic_score_gemma":0.00017696581,"teacher_disagreement_score":0.95791614,"about_ca_system_score_codex":0.00010589236,"about_ca_system_score_gemma":0.00015094374,"threshold_uncertainty_score":0.9999185},"labels":[],"label_agreement":null},{"id":"W4318823726","doi":"10.1109/icdm54844.2022.00138","title":"Augmenting Knowledge Transfer across Graphs","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Data Mining (ICDM)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Theoretical computer science; Knowledge transfer; Domain adaptation; Graph; Generalization; Artificial intelligence; Machine learning; Mathematics","score_opus":0.1724617119252182,"score_gpt":0.37724703656329506,"score_spread":0.20478532463807686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318823726","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":[0.48248,0.00045506397,0.44766277,0.006451412,0.0238656,0.00096526265,0.0031779644,0.0011532451,0.033788685],"genre_scores_gemma":[0.9917869,0.00005967201,0.0049481997,0.0007345853,0.00018389737,0.00010999804,0.00050739455,0.000028761793,0.0016406056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967779,0.00017687191,0.00045681998,0.0011640869,0.0008889678,0.00053535483],"domain_scores_gemma":[0.9979224,0.00023496413,0.00013468674,0.0014501286,0.00013421572,0.00012356744],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007388022,0.00027710674,0.0002329686,0.00022521094,0.00065513665,0.00037473734,0.00674664,0.00004925401,0.0006417844],"category_scores_gemma":[0.000060027694,0.0003033298,0.00009773307,0.0006804098,0.000091631635,0.0012020702,0.0024531079,0.0006757251,0.000061073864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041603434,0.0010541042,0.002499846,0.000035178986,0.00049168203,0.00046672384,0.009527428,0.006390358,0.0129248565,0.4883462,0.0609255,0.4169221],"study_design_scores_gemma":[0.0022554672,0.0004248944,0.0007224905,0.00011557712,0.000021324297,0.00015075244,0.0016713131,0.916895,0.0013658626,0.008474668,0.06679709,0.0011055722],"about_ca_topic_score_codex":0.000016264903,"about_ca_topic_score_gemma":0.000057803434,"teacher_disagreement_score":0.91050464,"about_ca_system_score_codex":0.000114846196,"about_ca_system_score_gemma":0.000109678746,"threshold_uncertainty_score":0.9999419},"labels":[],"label_agreement":null},{"id":"W4319309615","doi":"10.1109/ickg55886.2022.00037","title":"HTransE: Hybrid Translation-based Embedding for Knowledge Graphs","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"International Business Machines Corporation","keywords":"Embedding; Simple (philosophy); Theoretical computer science; Relation (database); Computer science; Knowledge graph; Inverse; Mathematics; Graph; Artificial intelligence; Data mining","score_opus":0.030512807189175047,"score_gpt":0.2897534720164417,"score_spread":0.25924066482726665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319309615","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":[0.001956293,0.0005146733,0.99430025,0.00094767474,0.0005522512,0.00039637974,0.000012227647,0.0003783899,0.00094187935],"genre_scores_gemma":[0.82361186,0.0000031166257,0.17518532,0.00065804593,0.000029138571,0.00024257932,0.000014435573,0.000016180296,0.00023932199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885654,0.000060009865,0.00019403065,0.00039784273,0.00018067137,0.0003108846],"domain_scores_gemma":[0.99899447,0.00049881515,0.000044728455,0.0003480605,0.00004192654,0.00007200649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019685083,0.00012840329,0.00012632516,0.00015230384,0.00047881296,0.000045969307,0.00070743426,0.000014795448,0.00006755853],"category_scores_gemma":[0.0000069736425,0.00013121362,0.00016624886,0.0006291577,0.000025190646,0.0002555205,0.00005009434,0.00014925718,0.0000036995034],"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":[0.00007017534,0.00024882145,0.00012498224,0.00004787601,0.000032148113,0.000017639502,0.00072529895,0.37950188,0.0019827937,0.35535824,0.008702646,0.25318748],"study_design_scores_gemma":[0.0006228397,0.00012221775,0.000017867013,0.0000031553266,0.0000047930653,0.00000678144,0.00001472072,0.93998814,0.0018677127,0.03045158,0.026703393,0.00019682881],"about_ca_topic_score_codex":0.0000018159096,"about_ca_topic_score_gemma":0.000006743934,"teacher_disagreement_score":0.8216556,"about_ca_system_score_codex":0.000027050868,"about_ca_system_score_gemma":0.000052920903,"threshold_uncertainty_score":0.53507364},"labels":[],"label_agreement":null},{"id":"W4319459101","doi":"10.1109/tkde.2023.3243169","title":"Towards Lightweight and Automated Representation Learning System for Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Scalability; Embedding; Singular value decomposition; Parallel computing; Theoretical computer science; Algorithm; Artificial intelligence","score_opus":0.027618364107412027,"score_gpt":0.28861185003460915,"score_spread":0.2609934859271971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319459101","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":[0.0021405183,0.0003400122,0.9942969,0.000048238668,0.0008036941,0.00019290218,0.000016359112,0.002128829,0.00003255999],"genre_scores_gemma":[0.9866048,0.00030886155,0.012782275,0.0000059631625,0.00009101972,0.000057435525,0.000038638125,0.000023751792,0.00008724131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906546,0.000021555039,0.00016329496,0.0004451717,0.00007008792,0.00023441223],"domain_scores_gemma":[0.999199,0.0002343895,0.000029253366,0.00041772384,0.00003346021,0.00008620719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016650427,0.00013651805,0.00014591376,0.00018855036,0.00021106824,0.00010092585,0.00028412454,0.00006696143,3.516908e-7],"category_scores_gemma":[0.0000076877905,0.00013507616,0.000025490106,0.00065586437,0.0000130543785,0.00060215365,0.000019146146,0.00017167113,0.000004980161],"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":[0.000010833907,0.00001510825,0.0000117901045,0.00014499878,0.000043436823,0.00000811538,0.0002534196,0.8830054,0.00062696356,0.00093325553,0.0004859924,0.1144607],"study_design_scores_gemma":[0.00025925852,0.00004300056,0.00015542308,0.00008372428,0.000015096191,0.000016956197,0.000019970934,0.9966074,0.0012121607,0.000012796538,0.0014307171,0.00014347713],"about_ca_topic_score_codex":0.0000027522744,"about_ca_topic_score_gemma":0.000003105867,"teacher_disagreement_score":0.9844643,"about_ca_system_score_codex":0.00001825504,"about_ca_system_score_gemma":0.000009366942,"threshold_uncertainty_score":0.5508246},"labels":[],"label_agreement":null},{"id":"W4321485151","doi":"10.1145/3539597.3570390","title":"Heterogeneous Graph-based Context-aware Document Ranking","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Exploit; Ranking (information retrieval); Information retrieval; Graph; Session (web analytics); Construct (python library); Data mining; Theoretical computer science; World Wide Web","score_opus":0.017248222500324283,"score_gpt":0.2564944381896838,"score_spread":0.23924621568935953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321485151","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":[0.020904426,0.00010756661,0.9740707,0.0017759851,0.00064964895,0.00024730747,0.0000011175626,0.0018597399,0.0003834614],"genre_scores_gemma":[0.990062,0.000015509411,0.006980386,0.002533124,0.000039268118,0.0000340187,0.000005008338,0.000014706279,0.00031597633],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985179,0.00005262086,0.00020985882,0.00045887087,0.00030502613,0.0004557474],"domain_scores_gemma":[0.9990307,0.00016505874,0.00005922449,0.0005880358,0.000045202247,0.00011177969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001406451,0.00016628213,0.00015553473,0.00019131074,0.00016231062,0.000120061806,0.00076670886,0.000050126546,0.000034346016],"category_scores_gemma":[0.000009056516,0.00014355915,0.00013037385,0.0011508577,0.00004452462,0.00028462434,0.00021592368,0.00011920555,0.00020185049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004705655,0.00008750293,0.0025767272,0.000048478138,0.00010043827,0.0011540662,0.00039186853,0.3412746,0.0013339242,0.11449639,0.014965424,0.5235235],"study_design_scores_gemma":[0.0021233757,0.00030881283,0.001133201,0.00009309474,0.000012503269,0.000056333607,0.000051012797,0.8559851,0.026842639,0.09589478,0.016424999,0.0010741396],"about_ca_topic_score_codex":0.000012373651,"about_ca_topic_score_gemma":0.00003353792,"teacher_disagreement_score":0.9691576,"about_ca_system_score_codex":0.000022947956,"about_ca_system_score_gemma":0.00001985534,"threshold_uncertainty_score":0.5854173},"labels":[],"label_agreement":null},{"id":"W4322730971","doi":"10.1109/tkde.2023.3238993","title":"Semi-Supervised Entity Alignment With Global Alignment and Local Information Aggregation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment","keywords":"Computer science; Merge (version control); Knowledge graph; Forcing (mathematics); Data mining; Theoretical computer science; Artificial intelligence; Information retrieval; Mathematics","score_opus":0.014223436611622209,"score_gpt":0.23993458954117783,"score_spread":0.22571115292955563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322730971","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":[0.0085235275,0.000105221334,0.99046916,0.00008220686,0.00028589845,0.00015555746,0.00006305361,0.00027513533,0.000040253923],"genre_scores_gemma":[0.9895592,0.00030810613,0.009979864,0.000037098715,0.0000216504,0.000025117692,0.0000473446,0.000006792363,0.000014856474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922794,0.000012233459,0.00014932202,0.00026756292,0.00014876908,0.0001941901],"domain_scores_gemma":[0.999379,0.00004404913,0.000024154839,0.0004270239,0.000024973438,0.00010080494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000105502426,0.00013874669,0.000101371246,0.00010062514,0.000117894975,0.00009936243,0.00024553118,0.000043682594,0.0000015150358],"category_scores_gemma":[0.000001681721,0.00012756034,0.000013734815,0.0005269676,0.000025497104,0.0016758065,0.000021919775,0.00009449396,0.000019053132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022586897,0.000056372577,0.00006568229,0.00011596291,0.00006150823,0.000008942146,0.000542258,0.30855766,0.00019945033,0.0013152559,0.0004426922,0.6886116],"study_design_scores_gemma":[0.000425333,0.00007447013,0.0003909968,0.00006982615,0.00001443581,0.000019313104,0.00003442697,0.9951574,0.0013983955,0.000066988934,0.0021845608,0.0001638231],"about_ca_topic_score_codex":0.000008179705,"about_ca_topic_score_gemma":0.000024613037,"teacher_disagreement_score":0.98103565,"about_ca_system_score_codex":0.00005022096,"about_ca_system_score_gemma":0.000015304784,"threshold_uncertainty_score":0.520176},"labels":[],"label_agreement":null},{"id":"W4323362462","doi":"10.3390/axioms12030275","title":"Probabilistic Coarsening for Knowledge Graph Embeddings","year":2023,"lang":"en","type":"article","venue":"Axioms","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Embedding; Popularity; Probabilistic logic; Knowledge graph; Computer science; Graph; Theoretical computer science; Graph embedding; Simple (philosophy); Machine learning; Artificial intelligence","score_opus":0.03378476415566694,"score_gpt":0.30073589244610177,"score_spread":0.2669511282904348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323362462","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":[0.02777071,0.00013691303,0.96784717,0.00050164585,0.0011176452,0.000487779,0.0000033003705,0.0012329356,0.0009018787],"genre_scores_gemma":[0.9824973,0.000014385202,0.015476377,0.00017285942,0.00015587013,0.00018561528,0.000009580563,0.000027747801,0.0014602827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998778,0.000022631448,0.00018223812,0.00043629244,0.00012406278,0.00045676678],"domain_scores_gemma":[0.99887186,0.00047649143,0.00006233104,0.00041687794,0.000080301535,0.00009212293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021076272,0.00013819961,0.000149656,0.00017549655,0.00018453674,0.00007937975,0.0006529974,0.000054479282,0.0000025990794],"category_scores_gemma":[0.00013303894,0.00012929662,0.00010676135,0.0015817224,0.0000643975,0.00032419353,0.00023680639,0.0001034019,0.00013081425],"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":[0.000012395019,0.000046215617,0.0000496679,0.00007638021,0.000025041254,0.000026314245,0.0020591987,0.0054638395,0.0014082531,0.8781542,0.013079241,0.09959923],"study_design_scores_gemma":[0.00048693176,0.00015647506,0.00060568494,0.000056459157,0.0000089524065,0.000016313985,0.000054308704,0.7919864,0.00076206547,0.19475886,0.010732638,0.00037490798],"about_ca_topic_score_codex":0.0000018302687,"about_ca_topic_score_gemma":0.000006081369,"teacher_disagreement_score":0.9547266,"about_ca_system_score_codex":0.000018554247,"about_ca_system_score_gemma":0.000020952597,"threshold_uncertainty_score":0.5272563},"labels":[],"label_agreement":null},{"id":"W4324010932","doi":"10.2196/45268","title":"Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Internet Research","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Usability; Computer science; World Wide Web; Human–computer interaction","score_opus":0.04934201919274787,"score_gpt":0.4064725414571596,"score_spread":0.3571305222644117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324010932","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":[0.99277705,0.004460878,0.0016878865,0.0005061916,0.00011668198,0.00036180762,1.9806598e-7,0.000077825076,0.000011480056],"genre_scores_gemma":[0.99672294,0.00017461205,0.002969578,0.000020840283,0.000027104898,0.00001902116,0.0000012949336,0.000011914298,0.000052671305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757177,0.0003981117,0.0005317832,0.0003828182,0.0007236673,0.00039187307],"domain_scores_gemma":[0.9984742,0.0009257906,0.000098129836,0.000102408565,0.00018317357,0.00021631701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005545804,0.0001319719,0.00024151799,0.0006450212,0.00015105933,0.00019865565,0.00049826317,0.00007668079,0.0000014116],"category_scores_gemma":[0.000990964,0.00010494598,0.000020544356,0.0011241183,0.00019023055,0.00037956706,0.00090219313,0.00076532783,5.299184e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012617503,0.0004978711,0.067337036,0.00040668325,0.00004119994,0.00013949833,0.074999675,0.000057984187,0.00050246046,0.00013950959,0.00071906456,0.85503286],"study_design_scores_gemma":[0.0017864467,0.00027886458,0.073330335,0.0006126388,0.0000031996474,0.000076776676,0.005987269,0.91672987,0.00010585695,0.0003793109,0.00055870943,0.00015074373],"about_ca_topic_score_codex":0.000009167275,"about_ca_topic_score_gemma":0.0003122795,"teacher_disagreement_score":0.9166719,"about_ca_system_score_codex":0.00007819053,"about_ca_system_score_gemma":0.00026199894,"threshold_uncertainty_score":0.4279573},"labels":[],"label_agreement":null},{"id":"W4353041063","doi":"10.21203/rs.3.rs-2716622/v1","title":"MSRHNN:Multidimensional Social Relation under Heterogeneous Neural Network for Recommendation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vanier College; University of Lethbridge","funders":"","keywords":"Computer science; Graph; Theoretical computer science; Heterogeneous network; Dynamic network analysis; Popularity; Social network (sociolinguistics); Node (physics); Artificial intelligence; Artificial neural network; Data mining; Machine learning; Social media; Computer network","score_opus":0.1657882665213524,"score_gpt":0.4256941727045555,"score_spread":0.2599059061832031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353041063","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":[0.02029099,0.0003227137,0.95158577,0.018388066,0.004294828,0.0036965297,0.00016877068,0.0011581053,0.00009425078],"genre_scores_gemma":[0.882314,0.0002689919,0.10580743,0.0005612375,0.004933982,0.0017805722,0.0030276019,0.00024965868,0.001056492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99491614,0.00083440455,0.0005470413,0.0013566113,0.0010944206,0.0012513826],"domain_scores_gemma":[0.9960864,0.0019073847,0.00025805703,0.0007583478,0.000797797,0.00019197745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018572479,0.00036150808,0.00038782752,0.00040690586,0.0011817894,0.00031734613,0.0010685067,0.0005625167,0.000019788575],"category_scores_gemma":[0.00022734607,0.00037585554,0.00035477217,0.0010437991,0.00012593395,0.00035765488,0.0028317077,0.0018917511,0.000074101474],"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":[0.00012702012,0.000077701625,0.000665648,0.00023039583,0.00009122458,0.00002597077,0.0002646387,0.8962967,0.000061928156,0.02672679,0.03696713,0.03846485],"study_design_scores_gemma":[0.00045764004,0.00018533523,0.009126204,0.00016812379,0.000008606792,0.0000075804924,0.000022030186,0.7997049,0.00004024199,0.18602726,0.003819876,0.0004321717],"about_ca_topic_score_codex":0.000032738568,"about_ca_topic_score_gemma":0.00008227539,"teacher_disagreement_score":0.86202306,"about_ca_system_score_codex":0.00038450188,"about_ca_system_score_gemma":0.00021197613,"threshold_uncertainty_score":0.99986935},"labels":[],"label_agreement":null},{"id":"W4361192959","doi":"10.48550/arxiv.2303.14241","title":"Data Depth and Core-based Trend Detection on Blockchain Transaction Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Blockchain; Scalability; Computer science; Core (optical fiber); Key (lock); Data science; Data mining; Big data; Computer security; Database; Telecommunications","score_opus":0.16050194673677223,"score_gpt":0.22257738649910785,"score_spread":0.06207543976233562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361192959","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":[0.055139218,0.000041304113,0.9424982,0.00008581043,0.0011391045,0.0003044131,0.000043988555,0.0006707241,0.00007724367],"genre_scores_gemma":[0.9981058,0.00018896801,0.0011018894,0.00011782587,0.00012209658,0.0000016529618,0.00008848165,0.00003520594,0.00023803362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973249,0.00013892121,0.00020279604,0.0018165217,0.00011140263,0.0004054866],"domain_scores_gemma":[0.99716043,0.0002985312,0.00024091692,0.0020745439,0.000042205884,0.00018337362],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024985409,0.00037904526,0.00030602474,0.00038805278,0.00024165577,0.00010502561,0.0016992325,0.0003762093,0.000002703898],"category_scores_gemma":[0.000017763083,0.00044158357,0.00011262654,0.0010173423,0.00011539312,0.00025186152,0.0008912125,0.0009594164,0.0000103593875],"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":[0.00008140383,0.00003645473,0.00035559412,0.000022919307,0.00003952041,0.00017100938,0.000016359976,0.9806512,0.000014443867,0.0020332683,0.000083179046,0.016494643],"study_design_scores_gemma":[0.00051075895,0.000113463226,0.0016974264,0.00009437574,0.000060063263,0.0000042638558,0.000010433838,0.9892182,0.00007507367,0.007661657,0.0001459273,0.00040839642],"about_ca_topic_score_codex":0.000080523576,"about_ca_topic_score_gemma":0.0019655554,"teacher_disagreement_score":0.94296664,"about_ca_system_score_codex":0.00012236564,"about_ca_system_score_gemma":0.000044012875,"threshold_uncertainty_score":0.9998036},"labels":[],"label_agreement":null},{"id":"W4365796043","doi":"10.1007/978-3-031-30678-5_34","title":"GIPA: A General Information Propagation Algorithm for Graph Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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":"Engineering Link (Canada)","funders":"","keywords":"Computer science; Graph; Correlation; Algorithm; Node (physics); Computation; Perceptron; Benchmark (surveying); Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Data mining; Theoretical computer science; Mathematics","score_opus":0.01444329971809373,"score_gpt":0.24278507665615495,"score_spread":0.22834177693806124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365796043","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":[0.000012622765,0.000068511465,0.9955327,0.00037608124,0.0023176055,0.0008652948,0.000006585734,0.0005223941,0.00029821382],"genre_scores_gemma":[0.002021499,0.00006194911,0.99555993,0.0009077543,0.00064726826,0.000076747056,0.00005437732,0.000044601504,0.0006258649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684733,0.00002762746,0.00058040285,0.0009917574,0.0008441304,0.00070875004],"domain_scores_gemma":[0.9978967,0.00042303148,0.0004445421,0.00070591853,0.0003970722,0.00013275936],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007255032,0.0004618797,0.00039982787,0.0011889576,0.00043304922,0.0005973402,0.001915302,0.00030577226,0.0000018529372],"category_scores_gemma":[0.00011285131,0.00043627748,0.00016987008,0.0011895498,0.0003301821,0.0021401693,0.0007660434,0.00084606674,0.0000362972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002076993,0.000003411707,0.000004009253,0.000018514102,0.000004976757,0.0000070542833,0.00022255407,0.18959199,0.000016550244,0.013536433,0.000021603586,0.79657084],"study_design_scores_gemma":[0.0002178965,0.00017022125,0.00003341381,0.00013207264,0.0000043425393,0.000020539926,1.0519099e-7,0.8003513,0.0002479734,0.19692071,0.0014888889,0.00041250724],"about_ca_topic_score_codex":0.000006700994,"about_ca_topic_score_gemma":0.00001659812,"teacher_disagreement_score":0.7961583,"about_ca_system_score_codex":0.00017919065,"about_ca_system_score_gemma":0.00023977464,"threshold_uncertainty_score":0.9998089},"labels":[],"label_agreement":null},{"id":"W4366959011","doi":"10.1109/wi-iat55865.2022.00124","title":"SparseMult: A Tensor Decomposition model based on Sparse Relation Matrix","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Tensor (intrinsic definition); Decomposition; Relation (database); Tensor decomposition; Matrix decomposition; Sparse matrix; Theoretical computer science; Task (project management); Link (geometry); Matrix (chemical analysis); Diagonal; Data mining; Artificial intelligence; Mathematics","score_opus":0.018556525154766346,"score_gpt":0.27584712083430174,"score_spread":0.2572905956795354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366959011","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":[0.016862234,0.000012532208,0.97822285,0.001636809,0.00020937237,0.00023299482,0.0000041273606,0.00037525853,0.0024438396],"genre_scores_gemma":[0.786187,0.0000011846696,0.21143273,0.0016985825,0.000019990634,0.000049793412,0.000012434912,0.000011027568,0.000587295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864435,0.00009178072,0.0001916457,0.00042225403,0.00040607585,0.0002439041],"domain_scores_gemma":[0.9991751,0.00010668868,0.000089374866,0.0005234289,0.000032632175,0.00007280649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015209487,0.00013063566,0.00010756523,0.0001708669,0.0003469717,0.000046416382,0.00047427765,0.00003141224,0.000057872672],"category_scores_gemma":[0.000008983892,0.00012813712,0.00007602446,0.00054300105,0.000016753394,0.0003491101,0.00018890915,0.00025624761,0.00003889655],"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":[0.000028925822,0.00006535238,0.000115518684,0.0000012009012,0.0000017031791,0.0000112371745,0.000031749234,0.94988585,0.0005461511,0.04537757,0.0010750995,0.0028596458],"study_design_scores_gemma":[0.00037210653,0.00014763625,0.00039961573,0.0000042260044,0.0000031024429,0.000009265422,0.000005717457,0.9885599,0.000267987,0.009740471,0.00032857704,0.0001613645],"about_ca_topic_score_codex":0.000004021993,"about_ca_topic_score_gemma":0.0000018978554,"teacher_disagreement_score":0.7693247,"about_ca_system_score_codex":0.00010438571,"about_ca_system_score_gemma":0.000027905124,"threshold_uncertainty_score":0.52252805},"labels":[],"label_agreement":null},{"id":"W4367147569","doi":"10.1109/hipc56025.2022.00018","title":"Joint Partitioning and Sampling Algorithm for Scaling Graph Neural Network","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Joint (building); Scaling; Sampling (signal processing); Artificial neural network; Algorithm; Graph; Artificial intelligence; Theoretical computer science; Mathematics; Computer vision; Engineering","score_opus":0.03739943438473616,"score_gpt":0.2634173471624329,"score_spread":0.22601791277769676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367147569","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":[0.0054307417,0.00041434172,0.9924135,0.00051299244,0.000656782,0.00024026024,0.0000029969603,0.0002574394,0.00007093148],"genre_scores_gemma":[0.20971152,0.000012650569,0.7884779,0.0013672329,0.00021401023,0.00014429631,0.000006217485,0.000014725203,0.000051442632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870515,0.000049193157,0.00021939703,0.00041697317,0.00017583893,0.00043345222],"domain_scores_gemma":[0.9993766,0.00018448586,0.00008026582,0.00023588185,0.00003191804,0.000090878915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003048635,0.00012173459,0.00014636049,0.000065036336,0.0010098893,0.00013152412,0.00028895107,0.000019849273,0.000011871972],"category_scores_gemma":[0.000008007184,0.00012319883,0.00007891032,0.0004938967,0.000032902863,0.00033603507,0.00052004727,0.00019539698,4.7461194e-7],"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":[0.000004300521,0.000013533296,0.00046324666,0.00000513827,0.000012872952,0.0000064656015,0.00010034996,0.57391334,0.000077407334,0.04871613,0.00079386693,0.37589335],"study_design_scores_gemma":[0.0002319428,0.00008045274,0.00053430087,0.00000573013,0.000004060114,0.000042168034,0.00003050258,0.93146664,0.00005216143,0.06582894,0.0015480396,0.00017504422],"about_ca_topic_score_codex":0.000006789737,"about_ca_topic_score_gemma":0.0000026506518,"teacher_disagreement_score":0.3757183,"about_ca_system_score_codex":0.00001958864,"about_ca_system_score_gemma":0.000009439664,"threshold_uncertainty_score":0.77673537},"labels":[],"label_agreement":null},{"id":"W4368370970","doi":"10.20944/preprints202304.1140.v1","title":"Research on Construction and Application of Aircraft Fault Knowledge Graphs","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Fault (geology); Knowledge graph; Computer science; Schema (genetic algorithms); Fault model; Heuristic; Domain knowledge; Knowledge-based systems; Fault management; Engineering; Artificial intelligence; Machine learning","score_opus":0.18414833028510755,"score_gpt":0.42302424386583315,"score_spread":0.2388759135807256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368370970","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":[0.8091101,0.0003926613,0.18198343,0.0009149195,0.0015239279,0.0018210378,0.000017380653,0.00087344105,0.0033631024],"genre_scores_gemma":[0.992094,0.00058571005,0.00660078,0.000020262005,0.00009186489,0.00031649915,0.000014357832,0.00003437494,0.00024217667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99654764,0.0003574716,0.0005198316,0.0015822117,0.0005679517,0.00042486817],"domain_scores_gemma":[0.99636716,0.0005041988,0.0003080248,0.002201005,0.00047134023,0.00014827617],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013410061,0.00028573186,0.0003872146,0.0007206447,0.0001816306,0.000035577574,0.0014910734,0.0003738653,0.0000064253295],"category_scores_gemma":[0.00015455336,0.00030248307,0.0001287828,0.0011628271,0.00048041868,0.00017362532,0.0045954078,0.0015563938,0.00038273388],"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":[0.00011443889,0.00040968426,0.25112557,0.00093635864,0.00019930663,0.000018744262,0.001983317,0.028507588,0.012933356,0.58487314,0.00033615157,0.118562356],"study_design_scores_gemma":[0.00054422097,0.000111957524,0.3153065,0.0005926557,0.000023492628,0.000021760443,0.00014430183,0.056106757,0.040485818,0.58370394,0.0022177387,0.00074083504],"about_ca_topic_score_codex":0.000082255734,"about_ca_topic_score_gemma":0.000026992886,"teacher_disagreement_score":0.18298386,"about_ca_system_score_codex":0.000084293555,"about_ca_system_score_gemma":0.000098502715,"threshold_uncertainty_score":0.9999427},"labels":[],"label_agreement":null},{"id":"W4372267823","doi":"10.1109/icassp49357.2023.10095900","title":"Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Laplacian matrix; Graph; Computer science; Smoothing; Subspace topology; Algorithm; Matrix completion; Pairwise comparison; Graph bandwidth; Artificial intelligence; Theoretical computer science; Voltage graph; Line graph; Gaussian","score_opus":0.014557627235188341,"score_gpt":0.2339888369327207,"score_spread":0.21943120969753238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372267823","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":[0.0058366363,0.00013193012,0.9903895,0.0010424573,0.0004341636,0.00044571154,9.977833e-7,0.0012374073,0.0004811801],"genre_scores_gemma":[0.8445064,0.00013988059,0.15202448,0.00071304524,0.00033601202,0.0001792649,0.00003868902,0.000056232024,0.0020060013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794984,0.000049522278,0.00024148264,0.0006513773,0.00030833035,0.00079943397],"domain_scores_gemma":[0.9988391,0.00037940376,0.00011889901,0.0004131905,0.00008184348,0.00016756637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000251746,0.00024090901,0.0002252406,0.00026543054,0.00044428228,0.00011715803,0.0006632435,0.000087631684,0.000017334269],"category_scores_gemma":[0.000024558243,0.00019634514,0.00014398886,0.0021792322,0.00012805461,0.00048920995,0.00019958954,0.00032402156,0.00003515398],"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":[0.000058504455,0.000026905802,0.004672884,0.000009473236,0.000050142102,0.000035045363,0.00007072757,0.78605855,0.000031224066,0.19702846,0.0024454433,0.009512627],"study_design_scores_gemma":[0.0008043213,0.00032741245,0.008669968,0.000022386961,0.000010885073,0.00003677926,0.000033558914,0.942797,0.00008221387,0.043738827,0.0030872703,0.00038935046],"about_ca_topic_score_codex":0.0000066016282,"about_ca_topic_score_gemma":0.000091317386,"teacher_disagreement_score":0.8386698,"about_ca_system_score_codex":0.000022992685,"about_ca_system_score_gemma":0.00003334745,"threshold_uncertainty_score":0.80067223},"labels":[],"label_agreement":null},{"id":"W4376608267","doi":"10.1007/978-3-031-32296-9_3","title":"Unsupervised Framework for Evaluating Structural Node Embeddings of Graphs","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Embedding; Node (physics); Graph; Theoretical computer science; Set (abstract data type); Data mining; Artificial intelligence","score_opus":0.03948828168851945,"score_gpt":0.3229867891242473,"score_spread":0.2834985074357278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376608267","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":[0.0007491782,0.00025457414,0.99435854,0.00038573844,0.002989065,0.0008191529,0.000017532133,0.00030503582,0.000121157434],"genre_scores_gemma":[0.07145507,0.000029009132,0.92732936,0.00072324835,0.0002711914,0.000026348142,0.000008423428,0.00007444561,0.00008287824],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949438,0.000039197872,0.0008563696,0.0018804929,0.0013179394,0.00096220855],"domain_scores_gemma":[0.99432063,0.0027129163,0.00062298565,0.001639853,0.000524923,0.00017869429],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009913477,0.0006645782,0.00083064736,0.001033004,0.00033461105,0.0002727139,0.0043415655,0.00048659003,0.000007780149],"category_scores_gemma":[0.0005098663,0.00061260856,0.0003423549,0.0016114017,0.0008075609,0.0006326637,0.0014396858,0.0010494259,0.000007081574],"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":[0.000020237265,0.000008009574,0.000085662236,0.00012766285,0.00002476256,0.000023902203,0.0007095131,0.23012134,0.00040909075,0.288005,0.000010307832,0.4804545],"study_design_scores_gemma":[0.00017012077,0.00015452788,0.00007712599,0.0004573191,0.000007774157,0.000009949641,1.627389e-7,0.46746948,0.00061818084,0.5306638,0.00001265542,0.000358878],"about_ca_topic_score_codex":0.0000073497445,"about_ca_topic_score_gemma":0.000015413107,"teacher_disagreement_score":0.48009562,"about_ca_system_score_codex":0.00013311434,"about_ca_system_score_gemma":0.00029742048,"threshold_uncertainty_score":0.99963254},"labels":[],"label_agreement":null},{"id":"W4377093262","doi":"10.3233/shti230243","title":"Building a Disease Knowledge Graph","year":2023,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Knowledge graph; Computer science; Graph; Graph database; Construct (python library); Information retrieval; Knowledge base; Medical knowledge; Disease; Data science; Knowledge management; Theoretical computer science; Artificial intelligence; Medicine; Programming language","score_opus":0.04987050607584835,"score_gpt":0.38300212366031705,"score_spread":0.3331316175844687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377093262","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":[0.43468133,0.09175743,0.40946698,0.049385168,0.004418386,0.0021158084,0.000011758799,0.007146444,0.001016671],"genre_scores_gemma":[0.88988125,0.024508351,0.08409733,0.0012985654,0.000025885058,0.000130998,0.0000012696777,0.000009499102,0.0000468227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988974,0.000020991485,0.00045061045,0.00013003078,0.00007201778,0.00042895883],"domain_scores_gemma":[0.9992942,0.00015877908,0.00012390311,0.00031248498,0.000040438284,0.00007017825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003658914,0.000113265836,0.00022118323,0.00097105745,0.00028277116,0.000010868225,0.00036755454,0.00006413931,1.2005628e-7],"category_scores_gemma":[0.00020296131,0.00009931981,0.000018883267,0.0033812781,0.00032495562,0.00034714528,0.00080667477,0.00027797828,0.000010158349],"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":[0.0000037375444,0.000014402507,0.0057803895,0.0006290082,0.000015437252,0.0000130298085,0.0035445914,0.00046287544,4.175384e-7,0.8082953,0.002125377,0.17911543],"study_design_scores_gemma":[0.0005316949,0.00019310367,0.0063845874,0.0005326171,0.000002800356,0.00001932812,0.0027598126,0.11226122,0.000012005876,0.85580754,0.021195514,0.00029977784],"about_ca_topic_score_codex":5.813173e-7,"about_ca_topic_score_gemma":0.0000054024204,"teacher_disagreement_score":0.45519993,"about_ca_system_score_codex":0.00003657503,"about_ca_system_score_gemma":0.000039411156,"threshold_uncertainty_score":0.40501443},"labels":[],"label_agreement":null},{"id":"W4377099260","doi":"10.1145/3597428","title":"The Evolution of Distributed Systems for Graph Neural Networks and Their Origin in Graph Processing and Deep Learning: A Survey","year":2023,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Deep learning; Categorization; Scalability; Artificial intelligence; Graph; Artificial neural network; Deep neural networks; Machine learning; Theoretical computer science; Data science; Database","score_opus":0.060684143966647945,"score_gpt":0.3193867958571259,"score_spread":0.258702651890478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377099260","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026382093,0.53543997,0.46302712,0.0000063318585,0.0004036177,0.00071113225,0.000016939171,0.00013064689,4.2510402e-7],"genre_scores_gemma":[0.084365554,0.9145521,0.0005867616,0.000003085967,0.00012751862,0.000079240366,0.00019242859,0.00008530406,0.000007991628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99242103,0.004352861,0.001189655,0.0009912105,0.00023413586,0.000811101],"domain_scores_gemma":[0.984891,0.0127757685,0.0011870764,0.0007762028,0.00025643202,0.00011349351],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008779772,0.0005991183,0.001594136,0.00035776343,0.00061590737,0.00040564942,0.0015528197,0.0003085422,2.1683654e-8],"category_scores_gemma":[0.0013869179,0.00041270276,0.00022074007,0.003537067,0.00025882173,0.00022785502,0.0010502121,0.0008863879,1.6794637e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057711877,0.000013837863,0.0056780307,0.002812202,0.000067982175,0.0000036627557,0.00006824991,0.04607738,4.524281e-8,0.00041619097,0.000017980528,0.94483864],"study_design_scores_gemma":[0.00028781765,0.00010199084,0.011691569,0.00443159,0.000038456208,0.000040493087,0.000043176642,0.9793227,2.103602e-8,0.00115432,0.0023769757,0.00051094254],"about_ca_topic_score_codex":0.0004145098,"about_ca_topic_score_gemma":0.00056332967,"teacher_disagreement_score":0.9443277,"about_ca_system_score_codex":0.00009574649,"about_ca_system_score_gemma":0.00009414516,"threshold_uncertainty_score":0.9998325},"labels":[],"label_agreement":null},{"id":"W4377121430","doi":"10.48550/arxiv.2305.10391","title":"Optimality of Message-Passing Architectures for Sparse Graphs","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Classifier (UML); Computer science; Message passing; Algorithm; Node (physics); Mathematics; Theoretical computer science; Artificial intelligence","score_opus":0.12227467462964037,"score_gpt":0.22532956038435745,"score_spread":0.10305488575471708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377121430","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":[0.21317233,0.000056978497,0.78488624,0.00014402096,0.0006585259,0.00047045687,0.00004281644,0.00039348807,0.00017513371],"genre_scores_gemma":[0.96659607,0.00005423128,0.0328084,0.00008329756,0.000048000846,0.0000034741645,0.000013684902,0.000033329125,0.0003595405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976607,0.00013624635,0.00031113002,0.0012880092,0.000120990684,0.00048290304],"domain_scores_gemma":[0.997255,0.00046452446,0.00046765577,0.0014853715,0.0001750009,0.00015241044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029865248,0.0003659284,0.00050670665,0.0004278288,0.00016475352,0.00006305969,0.0022137281,0.0002720183,0.000003541549],"category_scores_gemma":[0.00006784733,0.00041069827,0.00049936,0.0009779667,0.00024790925,0.00012913547,0.0021029562,0.0005751343,0.000004427476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004684551,0.000041677933,0.0011481589,0.00014899243,0.00009265634,0.00008963639,0.000088524444,0.87171483,0.000059310903,0.12527382,0.0003074867,0.0009880728],"study_design_scores_gemma":[0.0003445195,0.000055716246,0.0021207186,0.0001348296,0.000056612553,0.0000023052296,0.0000160543,0.4861437,0.00048903323,0.5101422,0.000114005365,0.00038026116],"about_ca_topic_score_codex":0.00007670037,"about_ca_topic_score_gemma":0.000060097005,"teacher_disagreement_score":0.7534237,"about_ca_system_score_codex":0.00006601596,"about_ca_system_score_gemma":0.00009950917,"threshold_uncertainty_score":0.9998345},"labels":[],"label_agreement":null},{"id":"W4377832615","doi":"10.18280/ts.400238","title":"GCNE: Graph Convolution Networks with Explicitly Influence for Recommendation","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Graph; Convolution (computer science); Theoretical computer science; Mathematics; Artificial intelligence; Artificial neural network","score_opus":0.01668614688481131,"score_gpt":0.24297999574367526,"score_spread":0.22629384885886394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832615","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":[0.05172914,0.000021097618,0.9458008,0.0010353979,0.00019344175,0.0006304783,0.000005820939,0.0005224341,0.00006140859],"genre_scores_gemma":[0.9789124,0.00003997778,0.019530538,0.00085238373,0.00015067389,0.0003536391,0.00009262592,0.000020819823,0.00004692675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985008,0.000050226932,0.00028384553,0.00046825077,0.00022350467,0.00047333352],"domain_scores_gemma":[0.99916613,0.00021395268,0.00014868326,0.00024795003,0.00012326428,0.00010002658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032076077,0.00018928635,0.00015743333,0.00016777567,0.00024645618,0.000099662844,0.00043712227,0.00005842,0.000016750164],"category_scores_gemma":[0.000009060348,0.00016722192,0.00006783803,0.0011270992,0.000052239506,0.00082281855,0.00008165538,0.00012911031,0.00001537944],"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":[0.00020338624,0.00010121741,0.00264911,0.000037573136,0.000078219164,0.000013890378,0.00040074316,0.8048589,0.0023901416,0.040938932,0.010646029,0.13768184],"study_design_scores_gemma":[0.0014281347,0.00062871515,0.013693197,0.000055906425,0.000016475164,0.000009422703,0.000038880222,0.9712037,0.00060006,0.006862378,0.005068864,0.00039427952],"about_ca_topic_score_codex":0.000005312594,"about_ca_topic_score_gemma":0.0000127282065,"teacher_disagreement_score":0.9271833,"about_ca_system_score_codex":0.00004096711,"about_ca_system_score_gemma":0.000022146907,"threshold_uncertainty_score":0.6819112},"labels":[],"label_agreement":null},{"id":"W4379034048","doi":"10.1109/taslp.2023.3282101","title":"TARGAT: A Time-Aware Relational Graph Attention Model for Temporal Knowledge Graph Embedding","year":2023,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Timestamp; Theoretical computer science; Graph; Embedding; Statistical relational learning; Graph embedding; Temporal database; Artificial intelligence; Relational database; Data mining; Machine learning","score_opus":0.02545785647792693,"score_gpt":0.2982908473687284,"score_spread":0.2728329908908015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379034048","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":[0.024528319,0.00074979046,0.97285664,0.0004196337,0.00022928452,0.00036962467,0.000035745306,0.000756313,0.00005462143],"genre_scores_gemma":[0.7842661,0.00007861737,0.21354546,0.00014795674,0.00010270746,0.00012243004,0.00004909179,0.000046898727,0.0016407344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982593,0.00004091624,0.00031313443,0.0006514732,0.00026391752,0.00047126747],"domain_scores_gemma":[0.99905795,0.00015541904,0.00013149563,0.00037818393,0.00012830367,0.00014862957],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002675279,0.00026992528,0.00023429016,0.0005363647,0.0007610455,0.00017247799,0.00038634005,0.00015068612,0.000009294372],"category_scores_gemma":[0.000016026956,0.00026576009,0.00017465619,0.001453483,0.00007645111,0.0009400832,0.00001734404,0.00034419072,0.000029414605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001004164,0.000195643,0.000097429256,0.0002711596,0.00007015391,0.00004628444,0.0041135424,0.15121408,0.01306857,0.0002345953,0.0010841663,0.82950395],"study_design_scores_gemma":[0.0006671588,0.000068518355,0.000099136305,0.00017973919,0.000023849558,0.000037221853,0.0001444888,0.9886162,0.0019776053,0.007732129,0.0001052564,0.00034867247],"about_ca_topic_score_codex":0.0000036903334,"about_ca_topic_score_gemma":0.00002088138,"teacher_disagreement_score":0.83740216,"about_ca_system_score_codex":0.00003986916,"about_ca_system_score_gemma":0.00006540226,"threshold_uncertainty_score":0.99997944},"labels":[],"label_agreement":null},{"id":"W4379258954","doi":"10.48550/arxiv.2306.00788","title":"Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Reproducing kernel Hilbert space; Representation (politics); Feature learning; Generalization; Artificial intelligence; Computer science; Mathematics; Laplace operator; Statistical learning theory; Algorithm; Regression; Machine learning; Applied mathematics; Mathematical optimization; Hilbert space; Statistics; Mathematical analysis","score_opus":0.20315813008108194,"score_gpt":0.2336139396656635,"score_spread":0.03045580958458155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379258954","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":[0.066879734,0.000020586964,0.93094575,0.00021699358,0.00041344354,0.00043040252,0.0000018354547,0.0009455404,0.0001456998],"genre_scores_gemma":[0.981718,0.00015253245,0.017700847,0.000055369346,0.000049665443,0.0000035248015,0.000082381055,0.00003053186,0.00020714157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780923,0.0002922818,0.00023806855,0.0011907283,0.00017028679,0.00029940504],"domain_scores_gemma":[0.9983964,0.00035877153,0.00038790388,0.0006292907,0.00009570439,0.00013195776],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024893624,0.00029804712,0.00025965663,0.0004384801,0.0004142615,0.00018365319,0.000580683,0.00024044084,0.000005011122],"category_scores_gemma":[0.000042852782,0.00034123953,0.00011403085,0.0010412241,0.00007156466,0.0007319775,0.0007828661,0.0006068362,0.000015011139],"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":[0.000035889894,0.000030530657,0.007179569,0.000111638044,0.00004407662,0.00009616465,0.00043400907,0.96882606,0.00018380981,0.022038905,0.00008720342,0.0009321461],"study_design_scores_gemma":[0.00062536524,0.000039314215,0.0008981948,0.00016112695,0.00004052042,0.0000017864368,0.00022537488,0.9184314,0.00018701676,0.079071715,0.0000068559875,0.00031132114],"about_ca_topic_score_codex":0.00003902624,"about_ca_topic_score_gemma":0.000016631062,"teacher_disagreement_score":0.91483825,"about_ca_system_score_codex":0.00038841722,"about_ca_system_score_gemma":0.00006851395,"threshold_uncertainty_score":0.999904},"labels":[],"label_agreement":null},{"id":"W4379521469","doi":"10.21428/594757db.fe7e76c1","title":"Graph learning with programmatic weak supervision","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institute for Advanced Research","keywords":"Graph; Computer science; Theoretical computer science","score_opus":0.012918150130666136,"score_gpt":0.237231945357064,"score_spread":0.22431379522639786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379521469","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":[0.086557895,0.000042892254,0.90504026,0.0011597403,0.00014234227,0.00027244736,6.3009765e-8,0.003023992,0.0037603346],"genre_scores_gemma":[0.88285244,0.000044697594,0.11414886,0.00013378427,0.00003462403,0.000040154613,0.0000045378456,0.000020434965,0.0027204957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892616,0.000040468603,0.000117257434,0.0003118288,0.00025865462,0.00034563703],"domain_scores_gemma":[0.99942225,0.000092709015,0.00003375475,0.00033187636,0.00003782966,0.00008156096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013565253,0.000110625755,0.000103171245,0.0001451257,0.00015142682,0.00011249856,0.00046091253,0.000030061845,0.000009489706],"category_scores_gemma":[0.000013810545,0.000075288386,0.000038603852,0.0021166767,0.00003506296,0.00046087345,0.00019303513,0.00017400173,0.00017375505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017944698,0.00006247242,0.018353624,0.000054763077,0.00003759629,0.00016306725,0.0007960962,0.061515644,0.00090191676,0.15974046,0.0029201973,0.75543624],"study_design_scores_gemma":[0.00075361814,0.0008585295,0.012233717,0.00011272192,0.000008079419,0.00007042987,0.00031372337,0.9329504,0.0008451807,0.03674241,0.014466622,0.0006445732],"about_ca_topic_score_codex":0.000004801201,"about_ca_topic_score_gemma":0.000010763997,"teacher_disagreement_score":0.87143475,"about_ca_system_score_codex":0.0000060508128,"about_ca_system_score_gemma":0.000009877578,"threshold_uncertainty_score":0.30701712},"labels":[],"label_agreement":null},{"id":"W4379780949","doi":"10.1109/tsp.2023.3284364","title":"Joint Sampling and Reconstruction of Time-Varying Signals Over Directed Graphs","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Signal reconstruction; Algorithm; Vertex (graph theory); Frequency domain; Graph; Adjacency matrix; Mathematics; Compressed sensing; Computer science; Signal processing; Theoretical computer science; Digital signal processing; Computer vision","score_opus":0.034453782916287035,"score_gpt":0.27012244615681497,"score_spread":0.23566866324052793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379780949","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":[0.10628585,0.000110729,0.8925084,0.000064941654,0.00016498694,0.00013791119,0.000004215137,0.000639162,0.00008382462],"genre_scores_gemma":[0.97740114,0.00006542534,0.02236386,0.000058694237,0.000022664502,0.000016421844,9.99261e-7,0.000021210259,0.000049610557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852246,0.000063368825,0.00037494604,0.0004539686,0.00028214575,0.00030312434],"domain_scores_gemma":[0.99922323,0.0002099573,0.0001830845,0.0001850831,0.00010655383,0.00009206347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022434653,0.00018781806,0.0002507212,0.000501209,0.0003745576,0.0001034826,0.00018651268,0.00008755494,0.000022482154],"category_scores_gemma":[0.0000046454575,0.00018680716,0.000094808674,0.0017847631,0.000110928224,0.0008984359,0.000005161902,0.00028113832,0.000008986767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027911017,0.000033524015,0.000038918202,0.00006915091,0.000026052801,0.0000053254685,0.00028320338,0.113598295,0.20256935,0.000035271903,0.000010006956,0.683303],"study_design_scores_gemma":[0.0004056146,0.00010820656,0.0004147532,0.0004797,0.0000253913,0.00005998755,0.000027984348,0.84156597,0.1479372,0.008654544,0.000011011368,0.00030962718],"about_ca_topic_score_codex":0.0000069821135,"about_ca_topic_score_gemma":0.0000014828038,"teacher_disagreement_score":0.87111527,"about_ca_system_score_codex":0.000023838906,"about_ca_system_score_gemma":0.000040900562,"threshold_uncertainty_score":0.7617775},"labels":[],"label_agreement":null},{"id":"W4381329270","doi":"10.1145/3589322","title":"Maestro: Automatic Generation of Comprehensive Benchmarks for Question Answering Over Knowledge Graphs","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Management of Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Benchmark (surveying); Question answering; Knowledge base; Vocabulary; Natural language; Information retrieval; Set (abstract data type); Natural language understanding; Knowledge graph; Usability; Artificial intelligence; Natural language processing; Programming language; Human–computer interaction; Linguistics","score_opus":0.10194140780191328,"score_gpt":0.3440120252621289,"score_spread":0.24207061746021558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381329270","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":[0.9802012,0.00021871254,0.015084264,0.0012005747,0.0007021718,0.0017021617,0.00008309697,0.00019445793,0.00061336823],"genre_scores_gemma":[0.8480173,0.0002343549,0.15145014,0.00004335195,0.000039878538,0.00004132933,0.000084568164,0.000015942893,0.000073158786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988128,0.00000867979,0.00034368562,0.00036901564,0.0002909776,0.00017482531],"domain_scores_gemma":[0.9981825,0.00008908675,0.00040375642,0.0011398421,0.0001620451,0.00002277489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032860145,0.00012667074,0.00019256076,0.00018141419,0.000066679546,0.000028753946,0.0039457786,0.000032917265,0.0000014354029],"category_scores_gemma":[0.000075654134,0.000103152604,0.000066367924,0.0008809985,0.00005939749,0.0007582035,0.0036210844,0.00006639669,8.554737e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005910853,0.00029306536,0.0021847463,0.0043589296,0.000354902,9.069694e-7,0.00040499406,0.003033863,0.048578568,0.75344527,0.07105367,0.116231985],"study_design_scores_gemma":[0.0008526017,0.00030815383,0.027237905,0.0011036649,0.000110579385,0.0000010838669,0.00018012988,0.8777387,0.020101933,0.07105391,0.0010479707,0.00026338248],"about_ca_topic_score_codex":0.0000022204908,"about_ca_topic_score_gemma":7.9478787e-7,"teacher_disagreement_score":0.87470484,"about_ca_system_score_codex":0.00001340415,"about_ca_system_score_gemma":0.000005209152,"threshold_uncertainty_score":0.7332304},"labels":[],"label_agreement":null},{"id":"W4381620677","doi":"10.1016/j.knosys.2023.110739","title":"Exploring attention mechanism for graph similarity learning","year":2023,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Graph; Pairwise comparison; Theoretical computer science; Similarity (geometry); Similarity learning; Graph embedding; Null model; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.1280583460162482,"score_gpt":0.29227626186932404,"score_spread":0.16421791585307585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381620677","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":[0.031385407,0.00018138604,0.9609418,0.00019365086,0.004367334,0.00074029143,0.0000042539405,0.0018941055,0.0002917505],"genre_scores_gemma":[0.994367,0.000024107669,0.0035866036,0.000033273856,0.0003616493,0.000895798,0.000027176193,0.000045549583,0.0006588477],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977714,0.00022717926,0.0004056692,0.0006800202,0.00028204577,0.00063370034],"domain_scores_gemma":[0.9982799,0.00050818024,0.00017638858,0.0005674126,0.0003162634,0.00015183103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008182551,0.00025344605,0.00031062696,0.0004561987,0.00050558697,0.00020066829,0.0006814854,0.00010145066,8.6955896e-7],"category_scores_gemma":[0.00013909156,0.00025539842,0.0002406071,0.0017948592,0.000030446234,0.00069278607,0.00014759286,0.00027275798,0.00014985978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007043061,0.00025728953,0.002939994,0.001604564,0.00014967463,0.00007837562,0.0014352845,0.1859011,0.026757607,0.74073595,0.0043668533,0.03570287],"study_design_scores_gemma":[0.00083174114,0.00018477623,0.00050472067,0.00026298495,0.000014309154,0.0000046193104,0.00014084051,0.98254097,0.0026349397,0.007865974,0.0045856033,0.00042851907],"about_ca_topic_score_codex":0.0000101998785,"about_ca_topic_score_gemma":0.000014672482,"teacher_disagreement_score":0.9629816,"about_ca_system_score_codex":0.000075768716,"about_ca_system_score_gemma":0.0000490886,"threshold_uncertainty_score":0.9999898},"labels":[],"label_agreement":null},{"id":"W4382203390","doi":"10.1609/aaai.v37i8.26168","title":"Neighbor Contrastive Learning on Learnable Graph Augmentation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Hainan University; National Natural Science Foundation of China","keywords":"Computer science; Graph; Artificial intelligence; False positive paradox; Theoretical computer science; Machine learning; Pattern recognition (psychology)","score_opus":0.06447864412558238,"score_gpt":0.29985043420944374,"score_spread":0.23537179008386136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382203390","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":[0.7442043,0.000049264498,0.19421105,0.014804922,0.0028495765,0.0021940386,0.000010811579,0.0018361297,0.039839912],"genre_scores_gemma":[0.99794704,0.00007710137,0.00088122045,0.00026070408,0.00006543646,0.000040835283,0.0000011136566,0.000017148783,0.0007094091],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978957,0.000031669457,0.00043713165,0.0005796561,0.00057894795,0.00047689432],"domain_scores_gemma":[0.99858713,0.00026300075,0.00039923744,0.00022551416,0.00043161437,0.000093494375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042029933,0.00024233517,0.00024309094,0.00025550186,0.00037087445,0.00022082313,0.0014117033,0.0000844712,0.00003153111],"category_scores_gemma":[0.00044616376,0.00018839428,0.00013155027,0.0019788938,0.00022380144,0.0005499142,0.00028886626,0.0005382906,0.00026186518],"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":[0.00009572519,0.00006117134,0.0002844968,0.000015797626,0.000015652244,0.0000015509784,0.0007982133,0.008028104,0.02900827,0.8832076,0.00029980246,0.07818359],"study_design_scores_gemma":[0.000065681445,0.00059736974,0.0010342877,0.00026320602,0.00001037699,0.0000027240674,0.00088696636,0.21718973,0.39196354,0.38750672,0.00016153647,0.0003178723],"about_ca_topic_score_codex":0.000013357408,"about_ca_topic_score_gemma":0.0000037433858,"teacher_disagreement_score":0.4957009,"about_ca_system_score_codex":0.000042760952,"about_ca_system_score_gemma":0.00004196818,"threshold_uncertainty_score":0.7682496},"labels":[],"label_agreement":null},{"id":"W4382237408","doi":"10.1609/aaai.v37i7.26019","title":"I’m Me, We’re Us, and I’m Us: Tri-directional Contrastive Learning on Hypergraphs","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Samsung; Ministry of Science and ICT, South Korea; Korea Advanced Institute of Science and Technology","keywords":"Computer science; Contrast (vision); Generalization; Node (physics); Artificial intelligence; Representation (politics); Competitor analysis; Machine learning; Unsupervised learning; Code (set theory); Feature learning; Simple (philosophy); Natural language processing; Mathematics","score_opus":0.059133068894256034,"score_gpt":0.28863630579420063,"score_spread":0.2295032368999446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382237408","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":[0.94575745,0.00016660521,0.019771678,0.012658309,0.0018622815,0.0012044614,0.000015080872,0.001104609,0.017459508],"genre_scores_gemma":[0.9976743,0.00040864345,0.0010171643,0.00028899504,0.00009111889,0.00003819023,8.057995e-7,0.000018483513,0.00046232913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99773335,0.000036550413,0.00045537922,0.0007133953,0.00058382296,0.00047752133],"domain_scores_gemma":[0.99846816,0.00041588847,0.00034217208,0.00020752601,0.00043751244,0.00012871085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043229663,0.00029640645,0.00031828764,0.00028841017,0.0004330738,0.00022531005,0.0011252698,0.00011600359,0.000024671426],"category_scores_gemma":[0.00051081256,0.0002296922,0.00012977183,0.0016985092,0.00038290696,0.00046111713,0.0003715755,0.00063613633,0.000079022655],"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":[0.00020183946,0.000108329776,0.0027807693,0.00003533643,0.000042697473,0.0000034159316,0.0011316382,0.0028133364,0.032347202,0.84562945,0.000491681,0.11441428],"study_design_scores_gemma":[0.00013480203,0.00095447915,0.0056258133,0.00069546455,0.000026259977,0.0000180159,0.0011477841,0.28503165,0.34300154,0.3614338,0.0012425814,0.0006878064],"about_ca_topic_score_codex":0.000016930839,"about_ca_topic_score_gemma":0.000011892113,"teacher_disagreement_score":0.48419565,"about_ca_system_score_codex":0.000031615833,"about_ca_system_score_gemma":0.000046853365,"threshold_uncertainty_score":0.9366576},"labels":[],"label_agreement":null},{"id":"W4382239171","doi":"10.1609/aaai.v37i6.25941","title":"Interpolating Graph Pair to Regularize Graph Classification","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Theoretical computer science; Graph; Leverage (statistics); Algorithm; Pattern recognition (psychology); Machine learning","score_opus":0.10427452839819401,"score_gpt":0.3131700700418429,"score_spread":0.20889554164364887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382239171","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":[0.43924543,0.000028066737,0.5102121,0.03411925,0.002350997,0.001918006,0.000011319227,0.0016704416,0.01044434],"genre_scores_gemma":[0.98994446,0.00002172411,0.009124836,0.00044725812,0.00007308658,0.000069517504,9.259447e-7,0.000021165462,0.00029703663],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99736273,0.00002907092,0.00067429786,0.0007662321,0.0006172718,0.00055036676],"domain_scores_gemma":[0.9981032,0.00017695833,0.00042221582,0.0005477076,0.0005887671,0.00016114335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006416121,0.00028253192,0.00028186262,0.0004551491,0.0003151405,0.00026892932,0.002778788,0.0001111928,0.000016730755],"category_scores_gemma":[0.00055396854,0.00022509335,0.00018962474,0.0042478708,0.00023157867,0.00058275176,0.000713605,0.0004019861,0.00016265793],"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":[0.000033559358,0.000042132604,0.00038618676,0.000019563366,0.000010751917,6.4726345e-7,0.00092089747,0.0003559449,0.12204998,0.7956663,0.00074255955,0.07977149],"study_design_scores_gemma":[0.000023418352,0.00020469037,0.0017010142,0.0003011042,0.000007851492,0.0000037387153,0.00057106314,0.13060969,0.26204556,0.60403025,0.0001759131,0.0003257308],"about_ca_topic_score_codex":0.000015784244,"about_ca_topic_score_gemma":0.000011831991,"teacher_disagreement_score":0.550699,"about_ca_system_score_codex":0.0000403819,"about_ca_system_score_gemma":0.00004197492,"threshold_uncertainty_score":0.917904},"labels":[],"label_agreement":null},{"id":"W4382240030","doi":"10.1609/aaai.v37i4.25538","title":"Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Link (geometry); Computer science; Artificial intelligence; Computer network","score_opus":0.1040917765387271,"score_gpt":0.33803667578493823,"score_spread":0.23394489924621115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382240030","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":[0.40112293,0.00018822632,0.56796646,0.007346532,0.0033876644,0.0031783641,0.00008541118,0.0013487854,0.015375612],"genre_scores_gemma":[0.9896999,0.00012249441,0.009435821,0.000027563487,0.00008456867,0.00008866819,0.0000035149988,0.000017988747,0.00051950495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810946,0.00002412128,0.0006221497,0.0005285372,0.00035151353,0.00036422745],"domain_scores_gemma":[0.99773693,0.0003780305,0.0005307458,0.0002855251,0.0009943269,0.000074442076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059868314,0.00019172505,0.0002585844,0.00032098033,0.00033937974,0.000099519006,0.0013263207,0.00010033098,0.0000062682007],"category_scores_gemma":[0.0010437423,0.00016145065,0.00018447617,0.002254093,0.00025266074,0.0005065713,0.0003672738,0.00033508087,0.00001823478],"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":[0.0000411764,0.000047460588,0.0006669625,0.000054362245,0.00001841881,1.5229953e-7,0.0018699572,0.0025470979,0.03265206,0.86501294,0.0002662898,0.096823104],"study_design_scores_gemma":[0.000038937065,0.000263589,0.0009859727,0.00024942934,0.000014177963,0.0000019277995,0.00056978123,0.4063854,0.21376848,0.3774952,0.00008432985,0.00014276753],"about_ca_topic_score_codex":0.00001046208,"about_ca_topic_score_gemma":0.000006926726,"teacher_disagreement_score":0.588577,"about_ca_system_score_codex":0.000022995526,"about_ca_system_score_gemma":0.000067304965,"threshold_uncertainty_score":0.65837663},"labels":[],"label_agreement":null},{"id":"W4382240086","doi":"10.1609/aaai.v37i4.25565","title":"Signed Laplacian Graph Neural Networks","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; HEC Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Samsung; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; China Scholarship Council; Tencent; Microsoft Research; National Natural Science Foundation of China; Canadian Institute for Advanced Research; People's Government of Jilin Province","keywords":"Computer science; Signed graph; Theoretical computer science; Graph; Spectral graph theory; Laplacian matrix; Laplace operator; Line graph; Voltage graph; Algorithm; Mathematics","score_opus":0.060660420897220214,"score_gpt":0.2864185040554346,"score_spread":0.2257580831582144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382240086","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":[0.41764462,0.00016942121,0.51832837,0.028294476,0.007171876,0.0029085868,0.000017552698,0.0034863753,0.021978738],"genre_scores_gemma":[0.99779,0.00005663144,0.001225075,0.00048252655,0.00012617803,0.000039311584,9.96939e-7,0.000022731294,0.00025655192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973491,0.000028774322,0.00060418696,0.0007073525,0.0005965515,0.0007140253],"domain_scores_gemma":[0.9983167,0.00019760139,0.00039799995,0.0004860668,0.00044858584,0.00015305045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046197063,0.00032448702,0.00032694996,0.0002723792,0.00034639146,0.00027839758,0.0031866978,0.00013698041,0.000025154317],"category_scores_gemma":[0.00023307044,0.00024771798,0.00022185793,0.0033770301,0.00036278955,0.00064839155,0.0007260805,0.0005160658,0.000103614795],"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":[0.00005681051,0.000061068116,0.00027390514,0.00001777403,0.000016088905,0.000003402452,0.00041108436,0.010460701,0.008037398,0.87029684,0.00085419515,0.109510735],"study_design_scores_gemma":[0.000027434584,0.00017125648,0.00031349275,0.00008716222,0.000007579214,0.0000053177937,0.00015243547,0.6896701,0.045238387,0.2639768,0.00006572518,0.00028431372],"about_ca_topic_score_codex":0.000011202537,"about_ca_topic_score_gemma":0.0000106326615,"teacher_disagreement_score":0.6792094,"about_ca_system_score_codex":0.000026590204,"about_ca_system_score_gemma":0.000032907377,"threshold_uncertainty_score":0.9999975},"labels":[],"label_agreement":null},{"id":"W4382317638","doi":"10.1609/aaai.v37i13.26993","title":"Flaky Performances When Pretraining on Relational Databases (Student Abstract)","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; HEC Montréal; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Porting; Leverage (statistics); Relational database; Node (physics); Conjecture; Graph; Artificial intelligence; Natural language processing; Theoretical computer science; Data mining; Programming language; Mathematics","score_opus":0.1693824668639811,"score_gpt":0.3406220593983447,"score_spread":0.17123959253436363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317638","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":[0.9142115,0.000053219752,0.031083627,0.010146331,0.0024412863,0.0011952515,0.0000310192,0.0009367129,0.039901026],"genre_scores_gemma":[0.9947094,0.0000711504,0.004455042,0.00026266114,0.0001255119,0.000034448676,0.0000028274426,0.000014272078,0.00032472174],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756086,0.0000115074545,0.00054083776,0.00059209194,0.00085646834,0.00043824097],"domain_scores_gemma":[0.9985405,0.0002918345,0.0003879939,0.0003630491,0.0003241909,0.00009241193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053117087,0.000246655,0.0002215919,0.0002193302,0.00034283186,0.00017494896,0.0019280928,0.000065333545,0.00006745579],"category_scores_gemma":[0.00031139728,0.0001848204,0.000110680354,0.0009765206,0.00024712618,0.00090580375,0.00051282486,0.0004709821,0.0002698952],"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":[0.000046887464,0.00007703685,0.00092126156,0.000022904407,0.000014505352,0.0000015063116,0.0016454089,0.0026336478,0.004519549,0.9188794,0.00074829155,0.0704896],"study_design_scores_gemma":[0.00008743674,0.0005415901,0.015731141,0.0010598201,0.000017984394,0.00000930155,0.0013233678,0.3883554,0.26466623,0.3263256,0.0011369906,0.00074515014],"about_ca_topic_score_codex":0.000007679001,"about_ca_topic_score_gemma":0.000006663003,"teacher_disagreement_score":0.5925538,"about_ca_system_score_codex":0.000037396094,"about_ca_system_score_gemma":0.00006177611,"threshold_uncertainty_score":0.7536757},"labels":[],"label_agreement":null},{"id":"W4382317874","doi":"10.1609/aaai.v37i8.26102","title":"GENNAPE: Towards Generalized Neural Architecture Performance Estimators","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Architecture; Network architecture; Estimator; Graph; Machine learning; Time delay neural network; Theoretical computer science; Mathematics","score_opus":0.06564785021917369,"score_gpt":0.29487975873710165,"score_spread":0.22923190851792796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317874","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":[0.95570046,0.00003576205,0.031051537,0.0074952557,0.0013501957,0.00065567344,0.0000059637878,0.0007373028,0.0029678766],"genre_scores_gemma":[0.99161047,0.000092125636,0.007408011,0.00040331148,0.00011036446,0.00004795566,0.0000010938676,0.000024847739,0.00030183612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731207,0.000024217785,0.000585478,0.0006959443,0.000708391,0.00067392096],"domain_scores_gemma":[0.9985013,0.00008499994,0.00036394811,0.00049272995,0.0004090393,0.00014798479],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038439536,0.00035303543,0.00034525033,0.00026493674,0.00033083963,0.00023435015,0.0031207814,0.00011794463,0.000029271929],"category_scores_gemma":[0.00023409759,0.00025497057,0.00019237086,0.0024122684,0.00028816983,0.0005429849,0.00079728087,0.0005414669,0.00013250153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090934496,0.000064329295,0.0005137582,0.00007135153,0.000022177523,0.0000032207092,0.0010697257,0.012042658,0.02842407,0.53949726,0.0005073519,0.41769314],"study_design_scores_gemma":[0.000039921597,0.00018775233,0.0011724465,0.00012550197,0.000008987591,0.000015094299,0.00006671184,0.6548672,0.2248497,0.11814783,0.0001815211,0.00033736156],"about_ca_topic_score_codex":0.000013195755,"about_ca_topic_score_gemma":0.0000037179861,"teacher_disagreement_score":0.64282453,"about_ca_system_score_codex":0.000034609788,"about_ca_system_score_gemma":0.00007307958,"threshold_uncertainty_score":0.9999902},"labels":[],"label_agreement":null},{"id":"W4382568066","doi":"10.1109/tkde.2023.3290792","title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"National Natural Science Foundation of China","keywords":"Spurious relationship; Computer science; Graph; Leverage (statistics); Artificial intelligence; Bottleneck; Theoretical computer science; Algorithm; Machine learning","score_opus":0.028363888784600035,"score_gpt":0.2753856377479403,"score_spread":0.24702174896334025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382568066","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":[0.011754152,0.000104189254,0.9864663,0.00002916368,0.0008158382,0.00014976329,0.00051641767,0.00016144762,0.0000027256242],"genre_scores_gemma":[0.9851554,0.00022387916,0.013794596,0.00001259233,0.00004975868,0.00002551296,0.00072197354,0.000008337825,0.000007947127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994364,0.00000781693,0.0001672375,0.00017468064,0.000075041105,0.00013882227],"domain_scores_gemma":[0.9995229,0.00008299194,0.000036909496,0.00026617743,0.000044063076,0.000046920555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010839497,0.00009695376,0.00009317548,0.00016191963,0.000105099854,0.00005533692,0.00021983533,0.00005027212,4.1670083e-7],"category_scores_gemma":[0.000007790597,0.000097776465,0.000019328232,0.00038757897,0.000017081722,0.0013432085,0.000013121393,0.00007368066,0.000001416602],"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":[0.00002838289,0.00002822392,0.000062229796,0.00036274863,0.0001010424,9.2975057e-7,0.0017864377,0.5224536,0.0016988303,0.008798814,0.0019746937,0.46270406],"study_design_scores_gemma":[0.00030203757,0.000051960706,0.0006949366,0.0000260945,0.00001603564,0.0000029323683,0.000010867808,0.9944752,0.0022754963,0.00032737319,0.0016950539,0.00012204409],"about_ca_topic_score_codex":0.0000014160107,"about_ca_topic_score_gemma":0.0000056398167,"teacher_disagreement_score":0.97340125,"about_ca_system_score_codex":0.000007695082,"about_ca_system_score_gemma":0.00000937909,"threshold_uncertainty_score":0.39872086},"labels":[],"label_agreement":null},{"id":"W4383101617","doi":"10.20944/preprints202307.0118.v1","title":"On Addressing the Limitations of Graph Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Graph; Computer science; Smoothing; Convolutional neural network; Data science; Theoretical computer science; Artificial intelligence; Machine learning","score_opus":0.37266419010545576,"score_gpt":0.37494332449801643,"score_spread":0.0022791343925606666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383101617","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":[0.4581289,0.000562025,0.5214339,0.005806574,0.0075484584,0.0018659501,0.000020241754,0.0017176371,0.0029163107],"genre_scores_gemma":[0.99666995,0.00044160464,0.0017317509,0.0004564753,0.00019716877,0.000178733,0.000018242372,0.000055110253,0.00025095305],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965561,0.00036210287,0.0006920586,0.0012532449,0.0005767847,0.0005597032],"domain_scores_gemma":[0.9939226,0.0018324185,0.0006814723,0.0032330242,0.00020193766,0.00012856115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066346815,0.0004350954,0.00045970173,0.00027534936,0.00029982658,0.00008191454,0.0035283437,0.0003034521,0.0000108569],"category_scores_gemma":[0.0005357367,0.00035094685,0.0004252859,0.000984007,0.00026617592,0.00022827857,0.0049878387,0.0019282465,0.000118000076],"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":[0.00001749617,0.0000614028,0.011771581,0.00002765281,0.00008176589,0.000018920304,0.0005056586,0.96017617,0.0001119788,0.01834142,0.0002599947,0.008625969],"study_design_scores_gemma":[0.0002798338,0.000049886305,0.19975814,0.00044051342,0.0000531978,0.00000966993,0.000042472813,0.6366992,0.001098458,0.1606289,0.00029354787,0.00064616103],"about_ca_topic_score_codex":0.00003559083,"about_ca_topic_score_gemma":0.000028892136,"teacher_disagreement_score":0.5385411,"about_ca_system_score_codex":0.000047096855,"about_ca_system_score_gemma":0.00006194668,"threshold_uncertainty_score":0.99989426},"labels":[],"label_agreement":null},{"id":"W4384613985","doi":"10.48550/arxiv.2307.07107","title":"Graph Positional and Structural Encoder","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Encoder; Graph; Identifiability; Artificial intelligence; Theoretical computer science; Machine learning; Pattern recognition (psychology)","score_opus":0.06640210577149619,"score_gpt":0.19323135919575235,"score_spread":0.12682925342425616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384613985","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":[0.3079426,0.00009885279,0.689654,0.00026593645,0.0009814583,0.00020962057,0.000027293509,0.0005417599,0.0002784676],"genre_scores_gemma":[0.99396265,0.00017716813,0.004845049,0.00014122658,0.00008230997,7.671437e-7,0.000024683304,0.000018697612,0.0007474458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99824125,0.00007409385,0.00014104138,0.0011060323,0.00009798243,0.0003395802],"domain_scores_gemma":[0.9987425,0.00012773323,0.00014263712,0.0007299517,0.00008963279,0.00016753227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000076567696,0.00028246368,0.00023373513,0.00027662292,0.00020013093,0.00011222734,0.0011089306,0.00021851635,0.000009402052],"category_scores_gemma":[0.000010440823,0.0003173391,0.00014569046,0.0006344355,0.0001671284,0.0004384129,0.0022945732,0.0005914292,0.000027709046],"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":[0.000020193034,0.000013052158,0.0056339363,0.000042047443,0.00009351556,0.00067034457,0.00011572908,0.50082403,0.000036385518,0.49128863,0.0005599399,0.0007021988],"study_design_scores_gemma":[0.00019987846,0.000023328996,0.015103828,0.00003994187,0.000020511867,0.00001600096,0.000010773621,0.4216691,0.000016227688,0.56252927,0.000038948983,0.0003321917],"about_ca_topic_score_codex":0.000036932255,"about_ca_topic_score_gemma":0.000031572818,"teacher_disagreement_score":0.6860201,"about_ca_system_score_codex":0.000056640085,"about_ca_system_score_gemma":0.00005195595,"threshold_uncertainty_score":0.9999279},"labels":[],"label_agreement":null},{"id":"W4385060307","doi":"10.1007/978-3-031-38333-5_9","title":"Guided Rotational Graph Embeddings for Error Detection in Noisy Knowledge Graphs","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Graph Neural 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":"University of Windsor","funders":"","keywords":"Embedding; Computer science; Knowledge graph; Path (computing); Adversarial system; Ranking (information retrieval); Theoretical computer science; Graph; Artificial intelligence; Benchmark (surveying); Representation (politics); Machine learning; Algorithm","score_opus":0.03185377469005752,"score_gpt":0.2791791002831925,"score_spread":0.24732532559313497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385060307","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":[0.00021123895,0.009399405,0.98314136,0.000096156014,0.0039686463,0.0017107032,0.00001387096,0.00025662532,0.0012019813],"genre_scores_gemma":[0.9812549,0.00179309,0.0055499007,0.00034425055,0.0020043347,0.00095015636,0.00014890963,0.00031200194,0.007642471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711657,0.00007584999,0.000861381,0.0010637441,0.00027737967,0.0006050886],"domain_scores_gemma":[0.99753857,0.001305995,0.00039456994,0.00048402813,0.00016282563,0.00011404368],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005660941,0.000574052,0.0007693722,0.000813832,0.00016735519,0.00019477001,0.00053693535,0.00085189426,0.0000014760166],"category_scores_gemma":[0.00008097244,0.00054318935,0.00022759355,0.00064726616,0.00009342675,0.00022391195,0.00017078611,0.00085958984,0.0000036898057],"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":[0.000050976694,0.00001879398,0.00016240327,0.00026842285,0.00006839677,0.000046803943,0.00036379727,0.7725856,0.00003106969,0.16527137,0.0005054998,0.06062689],"study_design_scores_gemma":[0.00059777615,0.000110424044,0.00015178417,0.0009225579,0.000015359641,0.000043978554,0.0000025290424,0.80009615,0.0000072068847,0.195087,0.0023818628,0.0005833517],"about_ca_topic_score_codex":0.00004049732,"about_ca_topic_score_gemma":0.0016498156,"teacher_disagreement_score":0.98104364,"about_ca_system_score_codex":0.00010609892,"about_ca_system_score_gemma":0.000038595426,"threshold_uncertainty_score":0.999702},"labels":[],"label_agreement":null},{"id":"W4385286458","doi":"10.1109/icde55515.2023.00225","title":"Towards a GML-Enabled Knowledge Graph Platform","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Knowledge graph; Graph; Information retrieval; Theoretical computer science","score_opus":0.031575169062308964,"score_gpt":0.28156985358680353,"score_spread":0.24999468452449458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385286458","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":[0.0316971,0.0003439716,0.8355257,0.0017720967,0.0018622928,0.00038405982,0.0000019994445,0.005269823,0.12314294],"genre_scores_gemma":[0.9351562,0.00021126411,0.050083276,0.0008087421,0.00021453066,0.000072693045,0.0000075820058,0.000034155597,0.013411507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99869066,0.0000174856,0.00018538313,0.00040770616,0.00019644125,0.00050231064],"domain_scores_gemma":[0.999093,0.00010995373,0.000039089995,0.0005638019,0.00006173911,0.0001324162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017668754,0.0001573811,0.00015944005,0.00026598896,0.0001503027,0.00010575627,0.0009618608,0.00006439891,0.00003141567],"category_scores_gemma":[0.000025517065,0.00012517438,0.00010471607,0.0027369147,0.000044424254,0.0007334111,0.00052651943,0.00015145252,0.00072481623],"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":[0.000008153156,0.000055394536,0.00031822946,0.0000220646,0.00003105146,0.00008694116,0.0007460698,0.0011647551,0.00057949044,0.6814357,0.061540898,0.2540112],"study_design_scores_gemma":[0.0010027976,0.00022017496,0.008564241,0.00004728201,0.000008556149,0.000043902397,0.000119808574,0.30701807,0.0063856295,0.6084241,0.06727786,0.00088755053],"about_ca_topic_score_codex":0.00000963881,"about_ca_topic_score_gemma":0.000033640234,"teacher_disagreement_score":0.90345913,"about_ca_system_score_codex":0.000018680448,"about_ca_system_score_gemma":0.000038990747,"threshold_uncertainty_score":0.9316288},"labels":[],"label_agreement":null},{"id":"W4385478332","doi":"10.1109/ijcnn54540.2023.10191717","title":"Transfer Learning with Graph Attention Networks for Team Recommendation","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Machine learning; Graph; Transfer of learning; Autoencoder; Feature learning; Unsupervised learning; Deep learning; Embedding; Theoretical computer science","score_opus":0.01728375462978626,"score_gpt":0.25069845387204703,"score_spread":0.23341469924226077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385478332","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":[0.0084041245,0.0000064382803,0.98829734,0.0013319914,0.00023774768,0.0002964906,5.160792e-7,0.0008608766,0.00056445395],"genre_scores_gemma":[0.95320565,0.00008032349,0.04392331,0.0005843142,0.00013206997,0.00016049227,0.00012988377,0.00003113308,0.0017528334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906504,0.000039768805,0.0001441927,0.00033804984,0.00010160656,0.00031133188],"domain_scores_gemma":[0.9995516,0.00014500553,0.000028404382,0.00016438856,0.000057254536,0.000053359377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021269992,0.00011097162,0.00010054656,0.00012747463,0.00021443507,0.000074583775,0.00022436999,0.0000495711,0.000008083953],"category_scores_gemma":[0.0000066724274,0.00008894498,0.00006550192,0.0010839273,0.00001909858,0.0007537225,0.00003474862,0.0001488839,0.000011924988],"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":[0.000053163647,0.000029129122,0.002408212,0.000014982476,0.000035695717,0.0000026820978,0.000109856104,0.5500995,0.00055258657,0.049124148,0.0066989907,0.39087108],"study_design_scores_gemma":[0.00053329545,0.00026947417,0.0017988939,0.00001700937,0.000006482453,0.0000052234764,0.000043103784,0.98338467,0.00015223892,0.0032333415,0.010363488,0.00019280324],"about_ca_topic_score_codex":0.0000023744624,"about_ca_topic_score_gemma":0.000015276804,"teacher_disagreement_score":0.9448015,"about_ca_system_score_codex":0.000011495628,"about_ca_system_score_gemma":0.000006269231,"threshold_uncertainty_score":0.3627071},"labels":[],"label_agreement":null},{"id":"W4385554858","doi":"10.1016/j.eswa.2023.121086","title":"Incorporating global–local neighbors with Gaussian mixture embedding for few-shot knowledge graph completion","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Encoder; Embedding; Encoding (memory); Relation (database); Graph; Geospatial analysis; k-nearest neighbors algorithm; Metric (unit); Gaussian; Task (project management); ENCODE; Benchmark (surveying); Data mining; Artificial intelligence; Theoretical computer science","score_opus":0.024626177860893484,"score_gpt":0.3069194756039586,"score_spread":0.2822932977430651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385554858","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":[0.00041156594,0.000498854,0.99416417,0.0006800903,0.00020872733,0.0020775176,0.00002811987,0.0009785189,0.00095244957],"genre_scores_gemma":[0.9156563,0.000010956486,0.07762056,0.00013638687,0.00034324362,0.005927902,0.00012683062,0.000044436994,0.0001333796],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979477,0.00006820156,0.0003774438,0.0007891828,0.00031069908,0.00050675956],"domain_scores_gemma":[0.9982083,0.00019080253,0.00028966478,0.00086110947,0.0002386854,0.00021144471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019249812,0.00031073537,0.0003297489,0.00015893861,0.00071553217,0.00020282721,0.0007643768,0.000109938694,8.0516327e-7],"category_scores_gemma":[0.000007229831,0.00023589896,0.00007109489,0.003052304,0.00014668478,0.00041766107,0.00011868091,0.00015364723,0.00003732324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069670234,0.00016599774,0.0014765472,0.00020796963,0.00012458205,0.000015826174,0.0015354726,0.09950681,0.0010841489,0.8659483,0.014941308,0.014923331],"study_design_scores_gemma":[0.0012780736,0.00036004954,0.00089127943,0.000425631,0.00002238897,0.00018237307,0.0014190159,0.9369671,0.0002470873,0.006952266,0.0503005,0.0009542306],"about_ca_topic_score_codex":0.000046680212,"about_ca_topic_score_gemma":0.000106103726,"teacher_disagreement_score":0.9165436,"about_ca_system_score_codex":0.00013527027,"about_ca_system_score_gemma":0.000090146386,"threshold_uncertainty_score":0.96196806},"labels":[],"label_agreement":null},{"id":"W4385570097","doi":"10.18653/v1/2023.findings-acl.882","title":"Learning Query Adaptive Anchor Representation for Inductive Relation Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Central China Normal University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Relation (database); Representation (politics); Task (project management); Relationship extraction; Feature learning; Graph; Feature (linguistics); Artificial intelligence; Machine learning; Data mining; Theoretical computer science","score_opus":0.046350879521938906,"score_gpt":0.29628099727888046,"score_spread":0.24993011775694157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385570097","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":[0.033329364,0.000007766865,0.9635212,0.00040952835,0.00048001672,0.0003851244,0.0000013354698,0.00090828666,0.0009574246],"genre_scores_gemma":[0.9309075,0.00002094685,0.06659171,0.00007122155,0.00020825838,0.00015075882,0.000037028778,0.000013487797,0.0019990772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990659,0.00006333635,0.00015825673,0.000367378,0.00015760599,0.00018755699],"domain_scores_gemma":[0.9993019,0.00027292626,0.00009500497,0.00018211527,0.000110282,0.0000377498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017255885,0.00007917899,0.00007892738,0.00015376012,0.00018876109,0.000040626972,0.00014486689,0.00006359385,0.000002181089],"category_scores_gemma":[0.00012702511,0.000076799304,0.000048508606,0.0010232335,0.000019608917,0.0012645676,0.00007521323,0.0001534996,0.00002544812],"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":[0.000120098564,0.000035825185,0.013693422,0.000011341875,0.00005838115,0.000007720073,0.003363379,0.43670535,0.006057462,0.21249498,0.00927883,0.3181732],"study_design_scores_gemma":[0.00026259644,0.00022254688,0.04075183,0.00001123633,0.0000040367904,0.0000020546688,0.00028433817,0.9133359,0.0012767632,0.04316025,0.0005870295,0.00010137066],"about_ca_topic_score_codex":0.000009334215,"about_ca_topic_score_gemma":0.000004591255,"teacher_disagreement_score":0.8975781,"about_ca_system_score_codex":0.000045230292,"about_ca_system_score_gemma":0.000015025847,"threshold_uncertainty_score":0.31317848},"labels":[],"label_agreement":null},{"id":"W4385570117","doi":"10.18653/v1/2023.findings-acl.458","title":"Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment","keywords":"Timestamp; Embedding; Computer science; Regularization (linguistics); Discrete cosine transform; Theoretical computer science; Graph; Tensor (intrinsic definition); Graph embedding; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics; Image (mathematics)","score_opus":0.024166029997681852,"score_gpt":0.2998599837783786,"score_spread":0.2756939537806967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385570117","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":[0.049637157,0.00003007459,0.9472028,0.00068725325,0.00031897903,0.00043175815,0.000003310999,0.0008477144,0.0008409529],"genre_scores_gemma":[0.83931893,0.000008365681,0.15984075,0.00012934688,0.00008135434,0.000119523014,0.00011982875,0.000016705886,0.0003652096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989018,0.00003900866,0.00018915183,0.00040375837,0.00015543542,0.00031086925],"domain_scores_gemma":[0.9993075,0.00009060654,0.00007636607,0.00030875232,0.00014461888,0.00007216632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001570303,0.00014111,0.00013387683,0.00021379445,0.00021653311,0.000077322344,0.00031914058,0.00004958306,0.0000057697016],"category_scores_gemma":[0.000004777328,0.0001142695,0.00008345204,0.0011883342,0.000043141492,0.0008466165,0.000069432404,0.00007976264,0.00008652898],"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":[0.00009308577,0.0002775033,0.027669603,0.000117194846,0.00008341488,0.000032250427,0.0003415667,0.0028039175,0.021281885,0.8807001,0.021756344,0.044843145],"study_design_scores_gemma":[0.002814403,0.0012869914,0.13689801,0.00016687161,0.00003316175,0.0000763147,0.00006396506,0.50811374,0.0019224721,0.34373125,0.0038173485,0.001075457],"about_ca_topic_score_codex":0.000009753718,"about_ca_topic_score_gemma":0.000093987255,"teacher_disagreement_score":0.7896818,"about_ca_system_score_codex":0.000028090635,"about_ca_system_score_gemma":0.000016168655,"threshold_uncertainty_score":0.4659775},"labels":[],"label_agreement":null},{"id":"W4385571327","doi":"10.18653/v1/2023.repl4nlp-1.21","title":"Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Software Development Environment","keywords":"Computer science; Knowledge graph; Decomposition; Graph; Artificial intelligence; Theoretical computer science","score_opus":0.024166029997681852,"score_gpt":0.2998599837783786,"score_spread":0.2756939537806967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385571327","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":[0.049637157,0.00003007459,0.9472028,0.00068725325,0.00031897903,0.00043175815,0.000003310999,0.0008477144,0.0008409529],"genre_scores_gemma":[0.83931893,0.000008365681,0.15984075,0.00012934688,0.00008135434,0.000119523014,0.00011982875,0.000016705886,0.0003652096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989018,0.00003900866,0.00018915183,0.00040375837,0.00015543542,0.00031086925],"domain_scores_gemma":[0.9993075,0.00009060654,0.00007636607,0.00030875232,0.00014461888,0.00007216632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001570303,0.00014111,0.00013387683,0.00021379445,0.00021653311,0.000077322344,0.00031914058,0.00004958306,0.0000057697016],"category_scores_gemma":[0.000004777328,0.0001142695,0.00008345204,0.0011883342,0.000043141492,0.0008466165,0.000069432404,0.00007976264,0.00008652898],"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":[0.00009308577,0.0002775033,0.027669603,0.000117194846,0.00008341488,0.000032250427,0.0003415667,0.0028039175,0.021281885,0.8807001,0.021756344,0.044843145],"study_design_scores_gemma":[0.002814403,0.0012869914,0.13689801,0.00016687161,0.00003316175,0.0000763147,0.00006396506,0.50811374,0.0019224721,0.34373125,0.0038173485,0.001075457],"about_ca_topic_score_codex":0.000009753718,"about_ca_topic_score_gemma":0.000093987255,"teacher_disagreement_score":0.7896818,"about_ca_system_score_codex":0.000028090635,"about_ca_system_score_gemma":0.000016168655,"threshold_uncertainty_score":0.4659775},"labels":[],"label_agreement":null},{"id":"W4385571859","doi":"10.18653/v1/2023.acl-long.349","title":"To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Zhàng; Chen; Computer science; Memorization; Knowledge graph; Graph; Computational linguistics; Artificial intelligence; Natural language processing; Theoretical computer science; Mathematics education; Philosophy; Cognitive science; Linguistics; Mathematics; Psychology; History; China","score_opus":0.04013096083847964,"score_gpt":0.30810195585665157,"score_spread":0.2679709950181719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385571859","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":[0.02432327,0.000033309345,0.97061944,0.002134806,0.0005296147,0.00042221023,7.806209e-7,0.0009183901,0.0010181924],"genre_scores_gemma":[0.95801127,0.000013638402,0.038897775,0.0005825296,0.00018453973,0.00016719567,0.000010195967,0.00002992388,0.0021029415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867535,0.00006414453,0.00018125503,0.00044889844,0.00016119715,0.00046918067],"domain_scores_gemma":[0.9990012,0.00043298508,0.000020647885,0.00032026588,0.00005479265,0.00017008357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022569037,0.00014512136,0.00018031934,0.00016681259,0.00021131015,0.00008703972,0.0005049165,0.000053025535,0.0000121413705],"category_scores_gemma":[0.00007883528,0.00012652499,0.00010722807,0.001387111,0.000034340534,0.00022241837,0.00021032074,0.00014210424,0.00038453474],"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":[0.00011108287,0.000118497046,0.0027185597,0.000035150577,0.000047198453,0.000020087544,0.0017120909,0.015514627,0.006896642,0.88515496,0.050218984,0.037452098],"study_design_scores_gemma":[0.0011565235,0.0009043006,0.007128198,0.00004620062,0.0000120858585,0.000012448341,0.00007423701,0.8112398,0.00378215,0.08216189,0.09284602,0.0006361561],"about_ca_topic_score_codex":0.0000037699251,"about_ca_topic_score_gemma":0.000021640228,"teacher_disagreement_score":0.933688,"about_ca_system_score_codex":0.000022200418,"about_ca_system_score_gemma":0.00001666642,"threshold_uncertainty_score":0.51595396},"labels":[],"label_agreement":null},{"id":"W4385573095","doi":"10.18653/v1/2022.emnlp-main.278","title":"Mixture of Attention Heads: Selecting Attention Heads Per Token","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"State Key Laboratory of Software Development Environment","keywords":"Computer science; Interpretability; Feed forward; Artificial intelligence; Security token; Computation; Transformer; Set (abstract data type); Machine learning; Computer network; Algorithm","score_opus":0.010614764015150826,"score_gpt":0.2437902451232547,"score_spread":0.23317548110810388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385573095","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":[0.3573837,0.00031832265,0.6381722,0.0016674077,0.0007610937,0.0002648122,0.0000024518808,0.00030027295,0.0011297351],"genre_scores_gemma":[0.9713095,0.000011110128,0.026959784,0.0004288734,0.0000628001,0.000026065367,0.000010804366,0.0000115885405,0.0011794902],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985128,0.00013343935,0.00027456146,0.000390367,0.0003967779,0.0002920503],"domain_scores_gemma":[0.9992905,0.000057068413,0.00014931308,0.00036795984,0.00008237789,0.000052736286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026753428,0.00012346669,0.0001550164,0.00012350318,0.0003109337,0.000037091148,0.00052291615,0.00003693656,0.00011125552],"category_scores_gemma":[0.000011039719,0.00012046277,0.00012585922,0.00080858474,0.000022704773,0.0004904807,0.00042437896,0.00029190682,0.0000098987075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011337285,0.0008473187,0.09368031,0.00015765525,0.00016954633,0.000053345015,0.0018434845,0.05333063,0.41373426,0.11870699,0.027317379,0.29004574],"study_design_scores_gemma":[0.0035432347,0.0023833823,0.19136424,0.0001300688,0.00008182104,0.0006437765,0.0013373372,0.7182934,0.01648445,0.033507403,0.029950999,0.002279855],"about_ca_topic_score_codex":0.000023660403,"about_ca_topic_score_gemma":0.000014078418,"teacher_disagreement_score":0.66496277,"about_ca_system_score_codex":0.0000617976,"about_ca_system_score_gemma":0.000020076106,"threshold_uncertainty_score":0.49123293},"labels":[],"label_agreement":null},{"id":"W4385584355","doi":"10.3233/faia230149","title":"Chapter 17. Approximate Answering of Graph Queries","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Knowledge graph; Computer science; Inference; Graph; Theoretical computer science; Information retrieval; Artificial intelligence","score_opus":0.04169040072157777,"score_gpt":0.264484980040968,"score_spread":0.22279457931939023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385584355","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":[0.0000100978605,0.0007676237,0.9725825,0.00020898588,0.00032315266,0.0005344359,0.000022564585,0.00014537506,0.025405249],"genre_scores_gemma":[0.065320805,0.042361014,0.71877134,0.0008929413,0.0014672397,0.0022917248,0.00022056972,0.0005698093,0.16810457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983271,0.0000075807443,0.0005916023,0.0006117386,0.000195293,0.00026670098],"domain_scores_gemma":[0.9989198,0.0000654326,0.0002514791,0.00061259646,0.00007261307,0.000078065554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013726803,0.00026959393,0.00038182578,0.00043343403,0.00012371686,0.00005658584,0.0006454312,0.00018743167,0.000009448376],"category_scores_gemma":[0.000007060558,0.0002886993,0.000104379484,0.00029411126,0.0004071355,0.00021799702,0.00023632916,0.00032791612,0.0000189568],"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":[0.000004129081,0.000008946597,0.000012443812,0.00002118704,0.000012871549,0.000002782026,0.00012153476,0.00073471025,0.00002883297,0.7705066,0.00018340565,0.22836253],"study_design_scores_gemma":[0.000011185079,0.000028769331,0.000009145782,0.00011073052,0.000010527674,0.000002321393,0.000077341516,0.010966938,0.00093885727,0.9735698,0.013980077,0.00029432148],"about_ca_topic_score_codex":0.000007235971,"about_ca_topic_score_gemma":0.00003874427,"teacher_disagreement_score":0.2538112,"about_ca_system_score_codex":0.000018040799,"about_ca_system_score_gemma":0.00001856744,"threshold_uncertainty_score":0.9999565},"labels":[],"label_agreement":null},{"id":"W4385863304","doi":"10.1109/tkde.2023.3305809","title":"Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Multiplex; Graph; Embedding; Graph theory; Theoretical computer science; Artificial intelligence; Mathematics; Combinatorics; Bioinformatics","score_opus":0.031247219794920875,"score_gpt":0.2927645023608479,"score_spread":0.261517282565927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385863304","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":[0.0051504667,0.00017255385,0.9925979,0.00018767982,0.0009806331,0.00020141146,0.00013650216,0.0005670393,0.0000058637734],"genre_scores_gemma":[0.8678608,0.0001494179,0.13152611,0.000037326874,0.000114265546,0.000094156814,0.0000942881,0.000029075401,0.0000945438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988946,0.000013928234,0.00017876344,0.00050167827,0.000107912376,0.0003031684],"domain_scores_gemma":[0.9986253,0.00056285894,0.000022501363,0.00062826736,0.000038576552,0.00012249134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013859151,0.00015969579,0.00014080314,0.00032966884,0.0002707403,0.0000601921,0.0004950288,0.000062837775,0.000002463558],"category_scores_gemma":[0.000011348848,0.000161367,0.000048077123,0.00078420335,0.000028224436,0.00059648714,0.000022569535,0.00022037941,0.000020559384],"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":[0.000015759082,0.00008247174,0.0000025783136,0.00005139861,0.000054618442,0.000009703672,0.00017637586,0.8245169,0.004527375,0.006757981,0.0020608976,0.16174394],"study_design_scores_gemma":[0.00040376102,0.00004215704,0.00008579343,0.00005260879,0.000008945056,0.000010017818,0.0000025602506,0.9939617,0.0022119163,0.00083255774,0.0021990323,0.00018897775],"about_ca_topic_score_codex":0.000001967842,"about_ca_topic_score_gemma":0.000008210335,"teacher_disagreement_score":0.86271036,"about_ca_system_score_codex":0.000013635141,"about_ca_system_score_gemma":0.000016071763,"threshold_uncertainty_score":0.6580355},"labels":[],"label_agreement":null},{"id":"W4385863765","doi":"10.1109/tkde.2023.3303617","title":"HSMH: A Hierarchical Sequence Multi-Hop Reasoning Model With Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"State Key Laboratory of Integrated Services Networks; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Interpretability; Reinforcement learning; Reasoning system; Information retrieval; Natural language processing","score_opus":0.046428252697422394,"score_gpt":0.289260324197042,"score_spread":0.2428320714996196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385863765","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":[0.0026509438,0.000077135206,0.9961597,0.00005938718,0.00015816497,0.00012298147,0.0000091244565,0.000711831,0.000050782503],"genre_scores_gemma":[0.8903908,0.00023602517,0.10879938,0.000022167222,0.000028193735,0.000035482848,0.00001934995,0.000027364302,0.00044121256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875987,0.00001807369,0.00016598926,0.00053864083,0.00015230665,0.00036513704],"domain_scores_gemma":[0.9989909,0.0001328846,0.000027607117,0.00067074527,0.000032381093,0.00014551793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016474261,0.00019279686,0.00015164936,0.0002221965,0.00023342682,0.00008169112,0.0005674577,0.000054473734,0.0000013915911],"category_scores_gemma":[0.000009866114,0.00017640101,0.000025924624,0.0007810598,0.000032700646,0.00082843076,0.000033138396,0.0005071625,0.000021258018],"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":[0.000007823683,0.000015211363,0.0000032862204,0.000022443544,0.000016849654,0.000016689137,0.00032121307,0.9773555,0.0011475366,0.00048984203,0.000026146581,0.02057748],"study_design_scores_gemma":[0.00032825035,0.00007312102,0.000015850192,0.00012928223,0.0000096171,0.00002388292,0.000011483033,0.997887,0.00083683815,0.000017240201,0.00044815388,0.0002192868],"about_ca_topic_score_codex":0.0000036080344,"about_ca_topic_score_gemma":0.000011210219,"teacher_disagreement_score":0.8877399,"about_ca_system_score_codex":0.000029205878,"about_ca_system_score_gemma":0.00003558516,"threshold_uncertainty_score":0.71934247},"labels":[],"label_agreement":null},{"id":"W4386010134","doi":"10.21203/rs.3.rs-3263186/v1","title":"Uncertainty-Aware Graph Neural Network for Semi-Supervised Diversified Recommendation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Machine learning; Recommender system; Artificial intelligence; Generalization; Graph; Selection (genetic algorithm); Baseline (sea); Set (abstract data type); Supervised learning; Data mining; Artificial neural network; Data science; Theoretical computer science","score_opus":0.14595299060688832,"score_gpt":0.3951745048275275,"score_spread":0.24922151422063915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386010134","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":[0.0064696707,0.0005768539,0.957573,0.017873779,0.006943231,0.0071657356,0.00072380196,0.0023949684,0.00027897808],"genre_scores_gemma":[0.91338015,0.0029495785,0.056688484,0.0012571809,0.006347723,0.005807144,0.009991284,0.00052269566,0.0030557506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932861,0.00095100515,0.00061052246,0.001958301,0.0012529093,0.0019412027],"domain_scores_gemma":[0.99357265,0.0026876195,0.00023765322,0.0018826836,0.00120139,0.00041800586],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002397579,0.00053841807,0.00060967595,0.0007556249,0.0011009049,0.00066831044,0.0033445028,0.0005754897,0.000031697724],"category_scores_gemma":[0.00038070272,0.0005406161,0.0005408115,0.0025897857,0.00019037785,0.0005228093,0.0058164024,0.0025679308,0.00006850541],"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":[0.00023857714,0.00010385993,0.0014215774,0.0010135368,0.0001653105,0.00007655296,0.00058520876,0.7397603,0.00003782658,0.0064250296,0.1641033,0.08606895],"study_design_scores_gemma":[0.00076534407,0.00032128434,0.001094687,0.0004771698,0.000014099391,0.0000031795125,0.0001699495,0.8647683,0.000040369167,0.124116294,0.0075774803,0.00065183593],"about_ca_topic_score_codex":0.00019616199,"about_ca_topic_score_gemma":0.00018059529,"teacher_disagreement_score":0.9069105,"about_ca_system_score_codex":0.0003419648,"about_ca_system_score_gemma":0.0002601962,"threshold_uncertainty_score":0.9997332},"labels":[],"label_agreement":null},{"id":"W4386295558","doi":"10.1098/rspa.2023.0121","title":"Causal lifting and link prediction","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Computer science; Pairwise comparison; Node (physics); Link (geometry); Causal structure; Covariance; Path (computing); Causal model; Graph; Causal inference; Artificial intelligence; Theoretical computer science; Machine learning; Mathematics; Econometrics","score_opus":0.010070560645251893,"score_gpt":0.21437591930160482,"score_spread":0.20430535865635294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386295558","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":[0.9840162,0.000036953003,0.013926994,0.0013207562,0.00007408865,0.00010344372,9.083383e-7,0.0002572152,0.00026344648],"genre_scores_gemma":[0.9855067,0.000009805848,0.014302804,0.000025832533,0.0000980705,0.000008009515,4.4971046e-8,0.0000047234516,0.00004399159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915844,0.0000017100217,0.00012219042,0.00023021849,0.0002629429,0.00022447728],"domain_scores_gemma":[0.99964345,0.00016593462,0.000048171645,0.000050374245,0.000028283868,0.00006379198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025604916,0.000098100805,0.00013749268,0.000017764667,0.00019967929,0.0000963085,0.00034059168,0.000032902335,2.2529062e-7],"category_scores_gemma":[0.00010588701,0.00006127688,0.00007126179,0.0006762593,0.00025148183,0.0002739497,0.00041026296,0.00014132343,0.0000010519009],"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":[0.0000024218368,0.00007120095,0.0020226594,0.00092220056,0.00004833738,3.966157e-7,0.006560844,0.022593508,0.019423628,0.9395612,0.0008175582,0.007976076],"study_design_scores_gemma":[0.00004549943,0.000038519145,0.002751173,0.00008189277,0.000005391396,0.00000206868,0.000055005105,0.92580885,0.0007466331,0.070380196,0.000017552828,0.00006721569],"about_ca_topic_score_codex":8.4048855e-7,"about_ca_topic_score_gemma":1.0426146e-8,"teacher_disagreement_score":0.90321535,"about_ca_system_score_codex":0.0000054352467,"about_ca_system_score_gemma":0.0000046544933,"threshold_uncertainty_score":0.24987987},"labels":[],"label_agreement":null},{"id":"W4386526711","doi":"10.1007/s00521-023-08964-5","title":"A graph encoder–decoder network for unsupervised anomaly detection","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Pooling; Locality; Interpretability; Graph; Laplacian matrix; Theoretical computer science; Pattern recognition (psychology); Decoding methods; Artificial intelligence; Algorithm","score_opus":0.019065453384690352,"score_gpt":0.27337416851622587,"score_spread":0.2543087151315355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386526711","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":[0.050567426,0.00014580417,0.94664687,0.000831485,0.00018824878,0.00059876015,0.0000036016445,0.0009467526,0.00007104616],"genre_scores_gemma":[0.97013783,0.000031857064,0.02862085,0.0004617523,0.00041197418,0.00023085253,0.000015142695,0.000018559236,0.00007117078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986938,0.000035669334,0.00022614692,0.0005215399,0.00010968463,0.00041313973],"domain_scores_gemma":[0.9989686,0.00039840283,0.00008665077,0.00037474578,0.00007043412,0.000101147496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016856969,0.00015196876,0.00014954005,0.00010550012,0.0007547918,0.00012489532,0.00037946514,0.00006092768,4.3252976e-7],"category_scores_gemma":[0.000012203484,0.00015069636,0.000081302634,0.0014642316,0.000048339276,0.00017336989,0.0001770908,0.00014764977,0.000009307558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001309202,0.00003788373,0.0016136932,0.0000472532,0.000025845648,0.000002579336,0.00019670838,0.1943434,0.0025816613,0.03836946,0.0026413065,0.7601271],"study_design_scores_gemma":[0.0002547709,0.000057868445,0.0051464345,0.000009179587,0.0000074485943,0.000014912569,0.000011975558,0.95382446,0.00019778607,0.033495974,0.0067900363,0.00018915911],"about_ca_topic_score_codex":0.000005493998,"about_ca_topic_score_gemma":0.0000135002465,"teacher_disagreement_score":0.91957045,"about_ca_system_score_codex":0.00000922164,"about_ca_system_score_gemma":0.000009069814,"threshold_uncertainty_score":0.6145219},"labels":[],"label_agreement":null},{"id":"W4386609140","doi":"10.1109/tnnls.2023.3309632","title":"Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Graph; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Encoder; Domain adaptation; Domain (mathematical analysis); Theoretical computer science; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.0290642462466722,"score_gpt":0.2674654404000126,"score_spread":0.23840119415334038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386609140","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":[0.027084658,0.0006177637,0.9642201,0.00027880512,0.0053871563,0.0011599758,0.000008128623,0.0011802256,0.00006316662],"genre_scores_gemma":[0.9918828,0.0002922649,0.004682732,0.0001791911,0.0012338038,0.0005768035,0.000028495442,0.00008096513,0.0010429535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610525,0.00055016385,0.00070154556,0.0010909672,0.00038467615,0.0011673977],"domain_scores_gemma":[0.99737936,0.0012188157,0.00033306517,0.0005987809,0.0001987211,0.00027124767],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009802828,0.0004784802,0.00051592005,0.00022390138,0.0023192014,0.00058485713,0.0005816195,0.00030851725,0.0000031190361],"category_scores_gemma":[0.0000070946426,0.0004584531,0.00033666939,0.0022695437,0.00017188286,0.0006823234,0.000013346793,0.0010722744,0.000018679386],"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":[0.00010873924,0.00002657947,0.0006132231,0.000030879142,0.00007113878,0.0000110184765,0.00008977496,0.9752629,0.000043352615,0.0048730695,0.0014522317,0.017417107],"study_design_scores_gemma":[0.0008421501,0.00043263804,0.0041345563,0.00014852302,0.00002985259,0.000039384402,0.00010430076,0.98972666,0.0000022293257,0.00091929466,0.0031352872,0.00048513012],"about_ca_topic_score_codex":0.000018238143,"about_ca_topic_score_gemma":0.000016980632,"teacher_disagreement_score":0.96479815,"about_ca_system_score_codex":0.000055417146,"about_ca_system_score_gemma":0.00002057939,"threshold_uncertainty_score":0.99978673},"labels":[],"label_agreement":null},{"id":"W4386688780","doi":"10.31237/osf.io/fvnh6","title":"Directional Graph Attention Network","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Attention network; Data mining; Margin (machine learning); Theoretical computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.03509300879736632,"score_gpt":0.27251073242100377,"score_spread":0.23741772362363744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386688780","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":[0.00050912384,0.00020338902,0.9829433,0.0014316215,0.009409949,0.00026101418,0.000003058754,0.002349411,0.0028891638],"genre_scores_gemma":[0.09401917,0.0011497138,0.8644262,0.0018886171,0.0050383834,0.0005108093,0.00025226222,0.00016624687,0.032548588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99782044,0.00007832544,0.00031707057,0.00093556783,0.0003993352,0.00044929353],"domain_scores_gemma":[0.9985311,0.00014507181,0.00017777973,0.00094515964,0.00009616451,0.00010471022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024419444,0.00027460087,0.0002508018,0.00016991033,0.00017152108,0.00020284812,0.0012070121,0.0002475703,0.000018382982],"category_scores_gemma":[0.000015783864,0.00026258655,0.0002905542,0.0008479256,0.000044900997,0.00023263619,0.0024939969,0.00070998276,0.00020524474],"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":[0.0000071024856,0.000046147154,0.0045091277,0.000048527167,0.00013766665,0.000058042748,0.000025783409,0.59911484,0.00003734134,0.22374897,0.14692733,0.025339125],"study_design_scores_gemma":[0.000113048256,0.000023278028,0.028159728,0.00011557494,0.000011533852,0.000011850065,0.0000016827861,0.121333815,0.000017386183,0.8457083,0.0039962875,0.0005075095],"about_ca_topic_score_codex":0.000028906068,"about_ca_topic_score_gemma":0.000056309582,"teacher_disagreement_score":0.6219593,"about_ca_system_score_codex":0.000040751413,"about_ca_system_score_gemma":0.000039349543,"threshold_uncertainty_score":0.99998266},"labels":[],"label_agreement":null},{"id":"W4386689587","doi":"10.18280/isi.280428","title":"Temporal Dimensions of Quality in Knowledge Graph Evolution: A Comprehensive Review","year":2023,"lang":"en","type":"review","venue":"Ingénierie des systèmes d information","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Graph; Knowledge graph; Data science; Theoretical computer science; Artificial intelligence","score_opus":0.07815481538933441,"score_gpt":0.35435916248059746,"score_spread":0.27620434709126307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386689587","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003634856,0.9605913,0.036519628,0.000017003978,0.00058542064,0.0015793525,0.000044334334,0.0002952261,0.0003640857],"genre_scores_gemma":[0.0000776748,0.9960079,0.0032658302,0.00007599155,0.000034396842,0.0002821951,0.00021059574,0.000023144583,0.00002223505],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9953011,0.0006284456,0.0028397613,0.0003633575,0.00041398022,0.00045334635],"domain_scores_gemma":[0.9957711,0.00059308973,0.0019851308,0.0010160683,0.00052104285,0.00011356411],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086619257,0.0005014077,0.0021850797,0.0010143466,0.00014555042,0.000071868664,0.0010898055,0.00029984768,0.0000031239304],"category_scores_gemma":[0.00035120884,0.00039599437,0.0005788622,0.00525264,0.00021045266,0.002601663,0.0005813915,0.00052286783,0.00016617507],"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":[0.0000022950192,0.000029458299,0.000012541074,0.1483501,0.00006152487,0.000005077504,0.00041780074,0.00007108898,8.263281e-8,0.017383544,0.0011232869,0.8325432],"study_design_scores_gemma":[0.00028151102,0.00008848796,0.0001766254,0.2060457,0.00012977296,0.00007834123,0.00006216639,0.000955872,4.087735e-7,0.015169337,0.77618474,0.00082703214],"about_ca_topic_score_codex":0.0000699292,"about_ca_topic_score_gemma":0.000033889693,"teacher_disagreement_score":0.8317162,"about_ca_system_score_codex":0.00044789005,"about_ca_system_score_gemma":0.0004096044,"threshold_uncertainty_score":0.9998492},"labels":[],"label_agreement":null},{"id":"W4386691567","doi":"10.52953/afyw5455","title":"Designing graph neural networks training data with limited samples and small network sizes","year":2023,"lang":"en","type":"article","venue":"ITU Journal on Future and Evolving Technologies","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Mitacs","keywords":"Computer science; Artificial neural network; Pipeline (software); Graph; Artificial intelligence; Data mining; Machine learning; Training set; Baseline (sea); Domain (mathematical analysis); Feature (linguistics); Small data; Node (physics); Engineering; Theoretical computer science; Mathematics","score_opus":0.05848140215441747,"score_gpt":0.250777585829844,"score_spread":0.19229618367542653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386691567","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":[0.10925874,0.02365448,0.8451337,0.014750232,0.0015242012,0.00036152411,0.000005646836,0.005252613,0.000058867674],"genre_scores_gemma":[0.7825712,0.0070162374,0.20935264,0.00038050322,0.00061796454,0.000008201286,0.000008363096,0.000034500856,0.000010397358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790484,0.00008224055,0.00030681866,0.00066597824,0.00024528062,0.0007948713],"domain_scores_gemma":[0.9980567,0.0006978244,0.0002507602,0.0008119643,0.00007134007,0.00011139873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058843143,0.0003404975,0.00037851027,0.0003720662,0.0009114759,0.0006693167,0.0015406805,0.00022849644,6.6215944e-7],"category_scores_gemma":[0.00018876478,0.00024001134,0.00004428234,0.0016494668,0.0002400973,0.0009635626,0.0010424464,0.0012036697,5.769783e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111334644,0.000020587058,0.010181207,0.00002531021,0.00017335243,0.00086745917,0.0006102745,0.10724045,0.00016864469,0.0077636056,0.010709327,0.86212844],"study_design_scores_gemma":[0.0013079699,0.0014198838,0.017140426,0.0008924448,0.00008663492,0.0025272472,0.004676727,0.9010422,0.00008749667,0.064601496,0.0049444656,0.0012729659],"about_ca_topic_score_codex":0.0000015950637,"about_ca_topic_score_gemma":0.000024983228,"teacher_disagreement_score":0.86085546,"about_ca_system_score_codex":0.000013452808,"about_ca_system_score_gemma":0.000021461243,"threshold_uncertainty_score":0.9787379},"labels":[],"label_agreement":null},{"id":"W4386727921","doi":"10.20944/preprints202309.0962.v1","title":"Directional Graph Attention Networks","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Attention network; Data mining; Margin (machine learning); Theoretical computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.1264386529009888,"score_gpt":0.3404159394477672,"score_spread":0.21397728654677842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386727921","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":[0.11914688,0.0002853722,0.86134404,0.0014147114,0.011166201,0.0008764455,0.000010716725,0.0034575672,0.0022980445],"genre_scores_gemma":[0.986603,0.00071230135,0.0067168307,0.00028738854,0.00090230204,0.00042283675,0.00010178065,0.00009717261,0.0041563767],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99552023,0.00023773883,0.0006848345,0.002151207,0.00067043485,0.00073553156],"domain_scores_gemma":[0.99626696,0.00021700076,0.000491635,0.0025653045,0.00022113956,0.00023793161],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007371012,0.00054504164,0.0004998597,0.0004032055,0.0002780201,0.000111794,0.0025764492,0.00054410024,0.00007356139],"category_scores_gemma":[0.00011887603,0.00059797196,0.00054219697,0.001003847,0.00013065017,0.00042441685,0.0072672195,0.0018965664,0.0013756901],"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":[0.0000307829,0.00017286601,0.52247065,0.000102126214,0.00030461844,0.00011367968,0.00015323394,0.45351073,0.0009284975,0.014544637,0.0014962037,0.006171978],"study_design_scores_gemma":[0.0002907251,0.00001888218,0.7947738,0.00026771845,0.000038083203,0.00002698516,0.0000066490634,0.09899427,0.0005885614,0.101591304,0.0024707895,0.00093222095],"about_ca_topic_score_codex":0.000069735055,"about_ca_topic_score_gemma":0.00003007374,"teacher_disagreement_score":0.86745614,"about_ca_system_score_codex":0.00013851501,"about_ca_system_score_gemma":0.000079115685,"threshold_uncertainty_score":0.99964714},"labels":[],"label_agreement":null},{"id":"W4386768533","doi":"10.14778/3611540.3611599","title":"Demonstration of SPARQL <sup> <i>ML</i> </sup> : An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs","year":2023,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"SPARQL; Computer science; Named graph; RDF; RDF query language; RDF Schema; Graph; Scripting language; Information retrieval; Query language; Programming language; Web search query; Search engine; Theoretical computer science; Semantic Web; Web query classification","score_opus":0.018095438311099787,"score_gpt":0.27273469682791746,"score_spread":0.2546392585168177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386768533","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":[0.940277,0.00012647767,0.05654213,0.0006691356,0.00020619082,0.001561413,0.000018053584,0.00032162847,0.0002779649],"genre_scores_gemma":[0.9767904,0.000020050622,0.022802442,0.00006249104,0.000046815916,0.00015840791,0.000010106351,0.000025179366,0.000084065156],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982842,0.000012799391,0.000536478,0.00042240563,0.000297817,0.0004462623],"domain_scores_gemma":[0.99873865,0.00017392589,0.00061141065,0.00018562542,0.00021880408,0.0000715793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075471686,0.0001988998,0.00027986124,0.00022774696,0.00018959148,0.00006370569,0.0008520179,0.000057479443,0.0000013014169],"category_scores_gemma":[0.00018495775,0.00015659453,0.00023110854,0.00088382984,0.00006802874,0.0004914816,0.00037536034,0.00018905969,5.116971e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024418285,0.00021949441,0.01896916,0.0011379316,0.0001838032,0.0000015180512,0.01406951,0.055694073,0.69060826,0.11635988,0.0013009057,0.10121126],"study_design_scores_gemma":[0.0010221791,0.00064307643,0.0002911492,0.00021909791,0.000049099504,0.000011669475,0.0019610026,0.47805163,0.4712961,0.04600999,0.0001610975,0.00028389008],"about_ca_topic_score_codex":0.000020530317,"about_ca_topic_score_gemma":0.000006667691,"teacher_disagreement_score":0.42235756,"about_ca_system_score_codex":0.000023797296,"about_ca_system_score_gemma":0.000016949052,"threshold_uncertainty_score":0.63857394},"labels":[],"label_agreement":null},{"id":"W4386804220","doi":"10.1007/978-3-031-43418-1_19","title":"GDM: Dual Mixup for Graph Classification with Limited Supervision","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural 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":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Computer science; Graph; Theoretical computer science","score_opus":0.03549631398267874,"score_gpt":0.2598367178325238,"score_spread":0.22434040384984508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386804220","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":[0.00019151541,0.00015709273,0.99472165,0.0014477748,0.0017299714,0.0009177892,0.000011426665,0.0005305518,0.0002922215],"genre_scores_gemma":[0.0714634,0.00016875251,0.92459863,0.0018790246,0.00079658296,0.000100742596,0.0000642948,0.00015092935,0.00077762856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99537617,0.000032321783,0.0005355503,0.0021382123,0.0010635863,0.000854186],"domain_scores_gemma":[0.9962384,0.0011123843,0.0003298013,0.001671415,0.0004431025,0.00020491598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060980604,0.00062569825,0.0005358827,0.0011970795,0.00039645296,0.00046601932,0.0026359141,0.00038383747,0.0000028739712],"category_scores_gemma":[0.00008321686,0.0005142036,0.0001699788,0.0018129587,0.0006897795,0.00084470713,0.0007858224,0.00077266723,0.00002566841],"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":[0.00005267685,0.000041549014,0.00007875391,0.00007409482,0.000025691916,0.00010473146,0.0006282848,0.08540117,0.000541118,0.062181536,0.00012063897,0.85074973],"study_design_scores_gemma":[0.0005402148,0.00059939234,0.0005184209,0.0005064081,0.000014366809,0.000068410096,2.8166488e-7,0.76504654,0.00054327346,0.22967854,0.0015876238,0.00089650863],"about_ca_topic_score_codex":0.000005135287,"about_ca_topic_score_gemma":0.00010291455,"teacher_disagreement_score":0.8498532,"about_ca_system_score_codex":0.00015246504,"about_ca_system_score_gemma":0.00026012136,"threshold_uncertainty_score":0.99973094},"labels":[],"label_agreement":null},{"id":"W4386942736","doi":"10.48550/arxiv.2309.10890","title":"Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Computer science; Theoretical computer science; Graph; Graph database","score_opus":0.11587906636594186,"score_gpt":0.20816137993638476,"score_spread":0.09228231357044289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386942736","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":[0.03054115,0.00009206939,0.963977,0.00042121066,0.0017498811,0.00083614315,0.00008395381,0.0021972083,0.00010137751],"genre_scores_gemma":[0.97170854,0.0005287433,0.025798455,0.00006461583,0.00039567458,0.000017055821,0.00045364627,0.00010179473,0.0009315032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960289,0.00023019753,0.0004440434,0.0021702899,0.00020873031,0.0009178148],"domain_scores_gemma":[0.9964027,0.0005617005,0.0005726262,0.0018177162,0.00036026837,0.00028499335],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046993035,0.0005950935,0.0005435077,0.0007229599,0.00075388444,0.00030658592,0.0035284034,0.00051695784,0.0000069762104],"category_scores_gemma":[0.00028109798,0.0007396637,0.00058414,0.002284799,0.00013155288,0.0009859358,0.0053227106,0.0016958944,0.000017797645],"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":[0.000038471342,0.00002852436,0.0059092487,0.00013231716,0.00013775247,0.00010961115,0.00015705389,0.98054874,0.000030198344,0.010780286,0.001030008,0.0010977708],"study_design_scores_gemma":[0.0006075926,0.00007434142,0.0027956727,0.000283363,0.000082551756,0.000003822453,0.000061574916,0.8400693,0.00004934776,0.15457745,0.0008144775,0.0005805415],"about_ca_topic_score_codex":0.00007943179,"about_ca_topic_score_gemma":0.00002727403,"teacher_disagreement_score":0.94116735,"about_ca_system_score_codex":0.00024107062,"about_ca_system_score_gemma":0.00011222211,"threshold_uncertainty_score":0.99950546},"labels":[],"label_agreement":null},{"id":"W4387171237","doi":"10.3233/faia230547","title":"An Empirical Study of Retrieval-Enhanced Graph Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Engineering and Physical Sciences Research Council; Samsung","keywords":"Computer science; Graph; Artificial intelligence; Attention network; Machine learning; Artificial neural network; Expressive power; Theoretical computer science","score_opus":0.05082574892373995,"score_gpt":0.3209623049427829,"score_spread":0.27013655601904296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387171237","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":[0.00061181904,0.00035127092,0.99555486,0.00009646414,0.0005798209,0.0012809281,0.000009514942,0.00018883815,0.0013264659],"genre_scores_gemma":[0.96598285,0.00231378,0.023113966,0.00031551757,0.000847261,0.00049465505,0.00008697444,0.00018585425,0.006659126],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99739283,0.00005267013,0.0008707914,0.0009939699,0.00032960725,0.00036011654],"domain_scores_gemma":[0.99815285,0.00014442323,0.00034362756,0.0010704247,0.00013233064,0.00015633633],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024409321,0.00035114912,0.00054367486,0.0004976803,0.00018121823,0.00008862436,0.0011265356,0.00030026538,0.0000043573814],"category_scores_gemma":[0.000013288709,0.00036616434,0.00010297735,0.0008550931,0.00028843383,0.00028053473,0.00022949679,0.0006721464,0.00000769693],"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":[0.00009486893,0.000516881,0.0005334943,0.00002699226,0.00007779271,0.000022462837,0.0012074566,0.113844655,0.000045130255,0.21451712,0.0006617162,0.6684514],"study_design_scores_gemma":[0.000053865304,0.0004743668,0.00014406045,0.0000475717,0.000034955156,0.0000020711561,0.00048593208,0.3702807,0.0001703447,0.62727815,0.0005161821,0.0005117968],"about_ca_topic_score_codex":0.000008993142,"about_ca_topic_score_gemma":0.000088943685,"teacher_disagreement_score":0.9724409,"about_ca_system_score_codex":0.000032687334,"about_ca_system_score_gemma":0.000027682227,"threshold_uncertainty_score":0.999879},"labels":[],"label_agreement":null},{"id":"W4387453959","doi":"10.1007/s10791-023-09421-6","title":"Learning heterogeneous subgraph representations for team discovery","year":2023,"lang":"en","type":"article","venue":"Information Retrieval","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; York University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Overfitting; Ranking (information retrieval); Machine learning; Set (abstract data type); Graph; Task (project management); Artificial intelligence; Baseline (sea); Representation (politics); Data science; Artificial neural network; Theoretical computer science","score_opus":0.014467129190787973,"score_gpt":0.27384187858459824,"score_spread":0.2593747493938103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387453959","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":[0.11781285,0.000014962443,0.878769,0.0004765447,0.00070233323,0.00047144777,0.000013560189,0.0009456194,0.0007936924],"genre_scores_gemma":[0.9928805,0.000037953974,0.0058062314,0.0003788746,0.00008167373,0.000024642792,0.0001629406,0.000009801276,0.00061733375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989251,0.000030572148,0.00032336477,0.00015681272,0.00027812438,0.00028601644],"domain_scores_gemma":[0.9990876,0.00026219827,0.00016135504,0.00030171184,0.00012484184,0.00006233488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023699981,0.000102789934,0.0001011972,0.00027751294,0.00028634863,0.0003397961,0.00038532744,0.000056957808,0.0000021891647],"category_scores_gemma":[0.00027478155,0.00010083798,0.00011058575,0.0014606253,0.00003137578,0.004396531,0.00013819538,0.00013791537,0.000120010525],"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":[0.00015282634,0.000024779501,0.0024931366,0.000059791142,0.00006092834,0.00000802609,0.0035782913,0.8198736,0.0006554859,0.042812742,0.012396891,0.11788352],"study_design_scores_gemma":[0.0008326068,0.00030551577,0.0043461723,0.000019066354,0.0000078233315,0.000030610237,0.00021255646,0.9289084,0.0042346884,0.009526776,0.051218394,0.0003573806],"about_ca_topic_score_codex":0.000002065455,"about_ca_topic_score_gemma":6.8719885e-7,"teacher_disagreement_score":0.8750677,"about_ca_system_score_codex":0.000024828762,"about_ca_system_score_gemma":0.000029263996,"threshold_uncertainty_score":0.41120535},"labels":[],"label_agreement":null},{"id":"W4387559172","doi":"10.48550/arxiv.2310.04562","title":"Towards Foundation Models for Knowledge Graph Reasoning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Samsung; Alliance de recherche numérique du Canada; Tencent; Canadian Institute for Advanced Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Microsoft Research","keywords":"Inference; Vocabulary; Computer science; Knowledge graph; Graph; Artificial intelligence; Natural language processing; Relation (database); Foundation (evidence); Language model; Question answering; Theoretical computer science; Data mining; Linguistics","score_opus":0.14868633609743312,"score_gpt":0.23553853148674814,"score_spread":0.08685219538931502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387559172","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":[0.009277843,0.000079563775,0.98581105,0.00010170358,0.00171606,0.0006069309,0.000013547508,0.0009401182,0.0014531488],"genre_scores_gemma":[0.9659609,0.0003198201,0.030776348,0.000051772993,0.00017471037,0.000010693136,0.00006428,0.00005181376,0.002589664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976142,0.000090497895,0.00023076418,0.0014666541,0.00008854917,0.0005093437],"domain_scores_gemma":[0.9977763,0.00024238105,0.00027745488,0.0012354129,0.00030101312,0.00016738928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002802892,0.00036594793,0.00035670237,0.00044879844,0.0002826348,0.00016163416,0.001988527,0.00031577764,0.0000028760255],"category_scores_gemma":[0.00005833482,0.0004451234,0.00036790268,0.0012214194,0.00009206612,0.00083956216,0.0021022824,0.0005138421,0.000052458734],"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":[0.000013747896,0.000020874753,0.000043597887,0.000044708435,0.00003794494,0.000023810877,0.00013117977,0.5581526,0.0000034369716,0.43837306,0.00030723712,0.002847772],"study_design_scores_gemma":[0.00020824157,0.000025733225,0.000086602486,0.00007962644,0.000025449335,8.067277e-7,0.000015880982,0.5568718,0.00002056951,0.4421761,0.00022495721,0.00026424663],"about_ca_topic_score_codex":0.00004870883,"about_ca_topic_score_gemma":0.00008084513,"teacher_disagreement_score":0.95668304,"about_ca_system_score_codex":0.00019908333,"about_ca_system_score_gemma":0.00018940523,"threshold_uncertainty_score":0.9998},"labels":[],"label_agreement":null},{"id":"W4387848795","doi":"10.1145/3583780.3615227","title":"Stochastic Subgraph Neighborhood Pooling for Subgraph Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Induced subgraph isomorphism problem; Subgraph isomorphism problem; Pooling; Scalability; Computer science; Graph factorization; Graph; Factor-critical graph; Theoretical computer science; Artificial intelligence; Line graph; Database; Voltage graph","score_opus":0.04111999199670234,"score_gpt":0.29030749539578793,"score_spread":0.24918750339908557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387848795","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":[0.0040588505,0.00005142221,0.9914912,0.001398853,0.00062273734,0.00044051808,0.0000034332872,0.001325033,0.00060797244],"genre_scores_gemma":[0.9738339,0.000017653998,0.025181836,0.00036444477,0.00011419557,0.00014382205,0.000020060354,0.000022724958,0.0003013204],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983888,0.000027462445,0.00026704467,0.0005589356,0.00023395357,0.00052375667],"domain_scores_gemma":[0.9986251,0.0004238278,0.00009834971,0.00061357673,0.000114990675,0.00012416426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022577851,0.00017373219,0.00016182447,0.00036191466,0.00022791522,0.00011766979,0.00077105034,0.00007865384,0.000004773238],"category_scores_gemma":[0.00007182256,0.00015935862,0.0001486423,0.00230296,0.00004431565,0.00053325685,0.00012568824,0.00012612951,0.00007046809],"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":[0.000014655187,0.000031865664,0.00063976174,0.000014862386,0.0000224334,0.000003896544,0.00013195534,0.011662998,0.0023271854,0.9369724,0.0032967934,0.044881232],"study_design_scores_gemma":[0.00033052577,0.000070310285,0.0075756456,0.000012575641,0.000006862647,0.000004676165,0.000037837282,0.81194377,0.00025706907,0.17921507,0.00030930474,0.000236356],"about_ca_topic_score_codex":0.0000030965455,"about_ca_topic_score_gemma":0.000009440669,"teacher_disagreement_score":0.9697751,"about_ca_system_score_codex":0.000017901555,"about_ca_system_score_gemma":0.00002568957,"threshold_uncertainty_score":0.6498456},"labels":[],"label_agreement":null},{"id":"W4387940089","doi":"10.1137/21m1465056","title":"Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms","year":2023,"lang":"en","type":"article","venue":"SIAM Journal on Mathematics of Data Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Institut de Valorisation des Données; National Institutes of Health; National Science Foundation","keywords":"Scattering; Graph; Deep learning; Computer science; Theoretical computer science; Wavelet; Stability (learning theory); Graph theory; Wavelet transform; Convolutional neural network; Artificial intelligence; Artificial neural network; Algorithm; Topology (electrical circuits); Mathematics; Machine learning; Combinatorics; Physics; Quantum mechanics","score_opus":0.1583805048980607,"score_gpt":0.31848210176458575,"score_spread":0.16010159686652506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387940089","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":[0.051243976,0.000059313737,0.9473851,0.0005048424,0.00035716238,0.0001626213,0.000007738096,0.00012404118,0.0001552277],"genre_scores_gemma":[0.83880943,0.00030553425,0.16066723,0.00011297633,0.00006617247,0.0000023635132,0.0000046560467,0.000020512502,0.000011137645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967142,0.000034934274,0.0005564761,0.00055363285,0.0013873597,0.000753431],"domain_scores_gemma":[0.99745107,0.00032502197,0.0004476041,0.0013892694,0.00011136482,0.0002756929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021455172,0.00023436478,0.00033873986,0.001419284,0.00058995193,0.00047882667,0.005203897,0.000042823314,0.000004766748],"category_scores_gemma":[0.00009803431,0.00016029144,0.00007266129,0.009709214,0.0005263399,0.004362518,0.00064402143,0.000454862,0.0000050669105],"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":[0.000054196793,0.0002210255,0.00050558004,0.00014793423,0.00008301455,0.0003057376,0.0008870489,0.8485771,0.004124921,0.12033511,0.0009872484,0.023771092],"study_design_scores_gemma":[0.00048935483,0.00027395273,0.00027295758,0.00019999944,0.000012525471,0.00039170828,0.00014477209,0.97253877,0.00040662478,0.02498363,0.000021272766,0.00026444462],"about_ca_topic_score_codex":8.8608857e-7,"about_ca_topic_score_gemma":0.0000021137478,"teacher_disagreement_score":0.78756547,"about_ca_system_score_codex":0.000079167665,"about_ca_system_score_gemma":0.000085699234,"threshold_uncertainty_score":0.9670222},"labels":[],"label_agreement":null},{"id":"W4388097324","doi":"10.15353/joci.v19i1.5191","title":"For whom is data literacy empowering? An awareness-action typology","year":2023,"lang":"en","type":"article","venue":"The Journal of Community Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Typology; Action (physics); Literacy; Psychology; Space (punctuation); Social psychology; Pedagogy; Sociology; Computer science","score_opus":0.16012080403475407,"score_gpt":0.42651723954908527,"score_spread":0.26639643551433123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388097324","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":[0.47917157,0.000022236029,0.5190784,0.0010233916,0.0005000901,0.00008299056,0.000013412418,0.000054239397,0.000053711316],"genre_scores_gemma":[0.9733823,0.000281692,0.024036575,0.0020968036,0.00012206466,0.0000015385367,0.00003870453,0.000010953921,0.000029359946],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987353,0.00027134464,0.0005645396,0.0000160752,0.00020421915,0.0002085304],"domain_scores_gemma":[0.9963521,0.0010233151,0.00060207094,0.0016883581,0.00026689374,0.00006728005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002097381,0.00010082729,0.0001813692,0.00015473041,0.00071562664,0.0001826183,0.0042365477,0.000051116847,0.0000024787166],"category_scores_gemma":[0.00014120298,0.000069678325,0.00004631167,0.00049117935,0.00009324795,0.0038492435,0.0010078502,0.0006708207,0.000009681934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030872063,0.00025700123,0.00087638065,0.0003025248,0.00022976608,0.0000034829118,0.5527964,0.03522068,0.00044040414,0.0057340125,0.06705842,0.3367722],"study_design_scores_gemma":[0.0011286477,0.0012911226,0.0037224584,0.0001205233,0.00006220664,0.0006197243,0.041928247,0.7961354,0.0007217876,0.05747902,0.096438244,0.00035259587],"about_ca_topic_score_codex":0.000014113967,"about_ca_topic_score_gemma":0.000015698239,"teacher_disagreement_score":0.76091474,"about_ca_system_score_codex":0.00001976253,"about_ca_system_score_gemma":0.000078620615,"threshold_uncertainty_score":0.787263},"labels":[],"label_agreement":null},{"id":"W4388144292","doi":"10.1007/978-3-031-47240-4_30","title":"SORBET: A Siamese Network for Ontology Embeddings Using a Distance-Based Regression Loss and BERT","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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":"Polytechnique Montréal","funders":"","keywords":"Ontology; Computer science; Embedding; Benchmark (surveying); Construct (python library); Representation (politics); Artificial intelligence; Focus (optics); Ontology learning; Natural language processing; Theoretical computer science; Upper ontology; Machine learning; Semantic Web; Suggested Upper Merged Ontology; Programming language","score_opus":0.025513991730159537,"score_gpt":0.2850380362882183,"score_spread":0.2595240445580587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388144292","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":[0.00023913833,0.0010045525,0.99458903,0.0009622214,0.0020816626,0.0007496675,0.000010220579,0.00029422416,0.000069274254],"genre_scores_gemma":[0.05174882,0.000070893264,0.94480765,0.0021306404,0.00083994394,0.000036504418,0.000010424302,0.00009960959,0.00025552048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954425,0.00004425138,0.0005705104,0.0021149288,0.0006576673,0.0011701201],"domain_scores_gemma":[0.996384,0.0015138277,0.0004759471,0.0011740901,0.00021806001,0.00023402435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006963266,0.00068799045,0.0007995027,0.0005650941,0.000536541,0.00036965887,0.0021365834,0.0004661168,0.0000018180144],"category_scores_gemma":[0.000118152035,0.0005974697,0.00017972563,0.0010100815,0.001117624,0.00052195083,0.0010773321,0.0007834692,0.000003068788],"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":[0.000081996586,0.000023416513,0.00053555204,0.00014459217,0.000021256044,0.00029222405,0.00045060547,0.481383,0.00009927823,0.043075405,0.00014101104,0.47375166],"study_design_scores_gemma":[0.00035724876,0.0001456904,0.000050020288,0.000894054,0.0000094167335,0.000052575757,7.265527e-8,0.7116182,0.000105716535,0.28552008,0.00073073065,0.0005162009],"about_ca_topic_score_codex":0.000010708343,"about_ca_topic_score_gemma":0.00019588902,"teacher_disagreement_score":0.47323546,"about_ca_system_score_codex":0.00022831316,"about_ca_system_score_gemma":0.00032558298,"threshold_uncertainty_score":0.9996477},"labels":[],"label_agreement":null},{"id":"W4388340544","doi":"10.1007/978-3-031-46674-8_33","title":"Towards Time-Variant-Aware Link Prediction in Dynamic Graph Through Self-supervised Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Interpretability; Theoretical computer science; Graph; Embedding; Graph embedding; Feature learning; Artificial intelligence; Machine learning","score_opus":0.012310496478237,"score_gpt":0.23905250241036094,"score_spread":0.22674200593212393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388340544","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":[0.000101923695,0.00028659252,0.99261415,0.0010981502,0.00258257,0.0006472085,0.000009389855,0.0013304049,0.001329633],"genre_scores_gemma":[0.081973225,0.00088577025,0.9114462,0.0020563523,0.001173403,0.00007846414,0.00009159145,0.00027953627,0.0020154403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99399537,0.00012203413,0.0008564974,0.0025277215,0.0013175216,0.0011808635],"domain_scores_gemma":[0.99716765,0.0006031389,0.00035950434,0.0014548369,0.00023055384,0.00018429036],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009224504,0.000825831,0.0007950174,0.00138802,0.00039560208,0.00048344975,0.0036643837,0.00069892075,0.00002053992],"category_scores_gemma":[0.00009997057,0.00081584766,0.00023889195,0.0028792291,0.00048564846,0.0014804379,0.00188376,0.0025630896,0.00014606882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009281464,0.000028298977,0.00009238189,0.000056428067,0.000021614529,0.00050867285,0.0014153523,0.48040324,0.000064641485,0.0043120137,0.000020766769,0.5130673],"study_design_scores_gemma":[0.0003574425,0.00020418226,0.00032354254,0.00054531894,0.000008512813,0.000083050254,3.0235447e-7,0.8277793,0.000045955258,0.16967298,0.00034093834,0.0006385009],"about_ca_topic_score_codex":0.000031317948,"about_ca_topic_score_gemma":0.00010630512,"teacher_disagreement_score":0.5124288,"about_ca_system_score_codex":0.00046869606,"about_ca_system_score_gemma":0.00045489392,"threshold_uncertainty_score":0.99973804},"labels":[],"label_agreement":null},{"id":"W4388699524","doi":"10.3390/app132212338","title":"Event Knowledge Graph: A Review Based on Scientometric Analysis","year":2023,"lang":"en","type":"review","venue":"Applied Sciences","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing Association for Science and Technology; Ministry of Natural Resources of the People's Republic of China; China Scholarship Council; Strong","keywords":"Data science; Computer science; Citation; Field (mathematics); Event (particle physics); Knowledge graph; Graph; Network analysis; Power graph analysis; Information retrieval; World Wide Web; Engineering","score_opus":0.12785732350427878,"score_gpt":0.4173282751491699,"score_spread":0.2894709516448911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388699524","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.349726e-8,0.9333819,0.059396654,0.00008588192,0.0005639374,0.0013044341,0.000010004188,0.0004799735,0.004777088],"genre_scores_gemma":[0.000025241934,0.9934928,0.0050671035,0.00054898375,0.00007831551,0.00053219794,0.000021531223,0.000030630534,0.00020318873],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.993403,0.0003028082,0.0011087311,0.0024290732,0.0017173509,0.0010390256],"domain_scores_gemma":[0.99502313,0.0018148603,0.0008728885,0.0018624319,0.00009916068,0.00032750066],"candidate_categories":["metaepi_narrow","bibliometrics","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037217473,0.0007503721,0.0025716752,0.010044123,0.00068041676,0.00039674487,0.0064600767,0.00019436007,0.000023456307],"category_scores_gemma":[0.00019248985,0.0005263025,0.0015875963,0.16948825,0.0005427749,0.00025078075,0.00076838594,0.00057789264,0.00081932585],"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":[3.1308932e-7,0.000067977046,0.0000011017713,0.0058428007,0.00012318879,0.000008961293,0.000008091338,0.0011971153,3.716797e-8,0.015394576,0.0019287337,0.9754271],"study_design_scores_gemma":[0.00010511221,0.00013959687,0.000008100581,0.021139542,0.0018162912,0.0000046020964,0.0000024847589,0.017752806,6.6871945e-7,0.0020217123,0.95584077,0.0011683273],"about_ca_topic_score_codex":0.0000027340893,"about_ca_topic_score_gemma":0.000009642504,"teacher_disagreement_score":0.9742588,"about_ca_system_score_codex":0.00014029726,"about_ca_system_score_gemma":0.00058138446,"threshold_uncertainty_score":0.99995863},"labels":[],"label_agreement":null},{"id":"W4388921160","doi":"10.1016/j.camwa.2023.11.015","title":"Agglomeration of polygonal grids using graph neural networks with applications to multigrid solvers","year":2023,"lang":"en","type":"article","venue":"Computers & Mathematics with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Multigrid method; Computer science; Grid; Graph partition; Scalability; Cluster analysis; Graph; Artificial neural network; Theoretical computer science; Inference; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence","score_opus":0.022020946521040655,"score_gpt":0.2690160792643707,"score_spread":0.24699513274333007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388921160","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":[0.0076086456,0.000040800227,0.9894334,0.00037005992,0.0000674484,0.001870917,0.000011931427,0.0005323299,0.00006444199],"genre_scores_gemma":[0.21967225,0.00001592818,0.77892166,0.00021750528,0.000121978585,0.00094303425,0.00004545853,0.000044507127,0.000017656532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997887,0.00003028219,0.00050495344,0.0006379699,0.00044620386,0.0004936031],"domain_scores_gemma":[0.99762106,0.00030742292,0.00036389762,0.0011913156,0.00026572595,0.0002505603],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015700371,0.00031953375,0.00035784364,0.00041598393,0.00038276875,0.00012494704,0.0010817834,0.00007550032,0.000001512703],"category_scores_gemma":[0.000004500351,0.00027246727,0.0000899049,0.004160198,0.000173522,0.0003621116,0.00027583365,0.00021812612,0.000015255565],"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":[0.000011191228,0.00014871232,0.00023579983,0.00006532857,0.00006191244,0.000003255854,0.0004622854,0.92623574,0.00056082546,0.058509804,0.0003178233,0.013387343],"study_design_scores_gemma":[0.0003544363,0.00011690345,0.00039607516,0.00007583079,0.000037174454,0.00004572467,0.000066200424,0.9942341,0.00023015404,0.0036744175,0.00042219384,0.00034679132],"about_ca_topic_score_codex":0.000007820069,"about_ca_topic_score_gemma":0.000013702687,"teacher_disagreement_score":0.21206361,"about_ca_system_score_codex":0.000049025628,"about_ca_system_score_gemma":0.000059652044,"threshold_uncertainty_score":0.99997276},"labels":[],"label_agreement":null},{"id":"W4389098833","doi":"10.1145/3633518","title":"A Survey on Graph Representation Learning Methods","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Graph; Representation (politics); Data science; Theoretical computer science; Artificial intelligence; Information retrieval; Machine learning","score_opus":0.064175238834068,"score_gpt":0.3603229334070372,"score_spread":0.2961476945729692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389098833","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":[0.009868124,0.00021836291,0.9866185,0.00091577356,0.00086210016,0.00026657726,0.000003184044,0.0011903503,0.000057034955],"genre_scores_gemma":[0.9864365,0.0008970474,0.011753071,0.000040161398,0.000016011958,0.00013847409,0.000006754372,0.000020196987,0.0006918065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984047,0.00030645818,0.0002933437,0.00054556876,0.00015570693,0.0002942358],"domain_scores_gemma":[0.9980923,0.0008673823,0.000099411365,0.0008015992,0.00008180087,0.000057506346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053321075,0.00016250389,0.00022643855,0.001051619,0.00029835405,0.000065939195,0.0005572364,0.00019197298,0.0000034988689],"category_scores_gemma":[0.00016713941,0.00014886764,0.00005545084,0.0031880732,0.00008683033,0.00013234414,0.000032348187,0.0004906798,0.00006498137],"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":[0.000020165751,0.000059340928,0.0016417657,0.000017686016,0.00007962138,0.000022873013,0.00017668543,0.0681385,0.00069001276,0.024136068,0.00020759758,0.9048097],"study_design_scores_gemma":[0.0015892566,0.0045913225,0.018595785,0.00066413905,0.00008221978,0.0003476502,0.002682921,0.64213127,0.09022924,0.2047723,0.0319145,0.0023994213],"about_ca_topic_score_codex":0.000116633295,"about_ca_topic_score_gemma":0.000033277935,"teacher_disagreement_score":0.97656834,"about_ca_system_score_codex":0.000024536499,"about_ca_system_score_gemma":0.00000919433,"threshold_uncertainty_score":0.6070646},"labels":[],"label_agreement":null},{"id":"W4389315073","doi":"10.14778/3617838.3617842","title":"FedGTA: Topology-Aware Averaging for Federated Graph Learning","year":2023,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Scalability; Computer science; Graph; Distributed computing; Machine learning; Robustness (evolution); Artificial intelligence; Software deployment; Theoretical computer science; Database","score_opus":0.016568994672844187,"score_gpt":0.2504238959387287,"score_spread":0.23385490126588454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389315073","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":[0.76870424,0.0005074218,0.18970858,0.023419406,0.0042977477,0.0042819353,0.000011184226,0.0034842135,0.0055852556],"genre_scores_gemma":[0.99447817,0.000053230582,0.0041926783,0.0002544957,0.00007134535,0.000135822,0.0000017486719,0.000019498166,0.0007930269],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859077,0.00000939362,0.00026513182,0.00040829947,0.00025814146,0.00046825342],"domain_scores_gemma":[0.99925196,0.000116660674,0.00023424119,0.00013001698,0.00020558645,0.00006153564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030465357,0.0001726713,0.00020759241,0.00014880569,0.00049735047,0.0001120982,0.00090546603,0.00005748729,0.000002099049],"category_scores_gemma":[0.00009993724,0.00012896693,0.00015847875,0.0011331079,0.000072861796,0.000338409,0.00057800155,0.00022663745,0.000006412808],"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":[0.00025036436,0.00032375005,0.044457808,0.0009265199,0.00053616276,0.000013930986,0.008060781,0.029904732,0.21085489,0.5192596,0.05896777,0.12644373],"study_design_scores_gemma":[0.0034355726,0.00079667586,0.007541998,0.0005074869,0.00007599146,0.00007574324,0.001693343,0.33775046,0.35855573,0.26972687,0.018599931,0.0012401739],"about_ca_topic_score_codex":0.0000068543923,"about_ca_topic_score_gemma":0.0000013191944,"teacher_disagreement_score":0.30784574,"about_ca_system_score_codex":0.00004159144,"about_ca_system_score_gemma":0.000017703152,"threshold_uncertainty_score":0.52591187},"labels":[],"label_agreement":null},{"id":"W4389469990","doi":"10.48550/arxiv.2312.02339","title":"Expressive Sign Equivariant Networks for Spectral Geometric Learning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Office of Naval Research; Multidisciplinary University Research Initiative; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Equivariant map; Sign (mathematics); Sign language; Eigenvalues and eigenvectors; Computer science; Node (physics); Homogeneous space; Code (set theory); Link (geometry); Artificial intelligence; Mathematics; Theoretical computer science; Algebra over a field; Topology (electrical circuits); Pure mathematics; Programming language; Combinatorics; Linguistics; Physics; Geometry","score_opus":0.0976965443162144,"score_gpt":0.21045601341647988,"score_spread":0.11275946910026548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389469990","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":[0.010121109,0.00016295201,0.98558146,0.00009520466,0.001858772,0.0007673549,0.00001152609,0.0010722774,0.00032934273],"genre_scores_gemma":[0.98594785,0.00040278595,0.009483915,0.00007438846,0.00043834574,0.000009645189,0.000045318528,0.00007141704,0.0035263465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637616,0.0001880193,0.00032108973,0.00195431,0.00014511506,0.001015316],"domain_scores_gemma":[0.99674,0.0009671826,0.0005268001,0.0012766754,0.00020766756,0.00028169394],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036624953,0.00052607263,0.0005566678,0.00081655075,0.00039002523,0.00023104907,0.0028335757,0.00048981106,0.000011645507],"category_scores_gemma":[0.0001692973,0.0006204745,0.0004934828,0.0026883676,0.000116106494,0.00053644413,0.0032866609,0.0015013556,0.00004865353],"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":[0.000038936843,0.000029299732,0.00045447354,0.000035773694,0.00009027253,0.00043045942,0.0000504101,0.93016595,0.000018816087,0.066708386,0.0008596547,0.0011175437],"study_design_scores_gemma":[0.00052460673,0.00014496868,0.00058098277,0.00010604396,0.00005970807,0.000005055919,0.000034797253,0.9129854,0.00006602121,0.0844053,0.00043256514,0.0006545898],"about_ca_topic_score_codex":0.00003599492,"about_ca_topic_score_gemma":0.000011767566,"teacher_disagreement_score":0.9760975,"about_ca_system_score_codex":0.00023065179,"about_ca_system_score_gemma":0.00011519412,"threshold_uncertainty_score":0.99962467},"labels":[],"label_agreement":null},{"id":"W4389778193","doi":"10.1016/j.patcog.2023.110209","title":"A contrastive variational graph auto-encoder for node clustering","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Randomness; Computer science; Feature (linguistics); Upper and lower bounds; Inference; Algorithm; Encoder; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","score_opus":0.0409770510361664,"score_gpt":0.27279851796812254,"score_spread":0.23182146693195615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389778193","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":[0.0051124217,0.000009676288,0.9922916,0.0008052137,0.0007473577,0.00034944876,0.00006625384,0.0004701064,0.00014788707],"genre_scores_gemma":[0.90834063,0.00003264265,0.087779105,0.0022403197,0.00045135038,0.0006009236,0.00040974666,0.000037797356,0.000107462416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988998,0.000040955674,0.00019568387,0.00037342202,0.00017752296,0.000312574],"domain_scores_gemma":[0.9992004,0.00035446603,0.000097348464,0.00015769294,0.00013003979,0.000060048053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017723956,0.00012520747,0.00011884243,0.00016625442,0.00014938097,0.00008323854,0.00023151324,0.00005944141,0.00001584988],"category_scores_gemma":[0.000049542567,0.00012962664,0.00008568069,0.00046295137,0.000019350522,0.00048659966,0.000093222734,0.000102088445,0.00014211284],"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":[0.00003607802,0.000057471138,0.0014917183,0.000060311606,0.00006488436,0.000023036457,0.0005836857,0.007618644,0.0015513407,0.00049618544,0.0023811907,0.98563546],"study_design_scores_gemma":[0.00066582474,0.00005746776,0.0126716485,0.000057961533,0.000009473746,0.000011563674,0.000014295477,0.9238975,0.00032346105,0.061849568,0.00022408248,0.00021712332],"about_ca_topic_score_codex":0.000006546232,"about_ca_topic_score_gemma":0.000017691362,"teacher_disagreement_score":0.9854183,"about_ca_system_score_codex":0.000024860075,"about_ca_system_score_gemma":0.000014609502,"threshold_uncertainty_score":0.52860206},"labels":[],"label_agreement":null},{"id":"W4389988914","doi":"10.1109/ictai59109.2023.00112","title":"GEDI: A Graph-based End-to-end Data Imputation Framework","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Computer science; Data mining; End-to-end principle; Missing data; Artificial intelligence; Machine learning; Transformer; Graph; Theoretical computer science","score_opus":0.04556739243763991,"score_gpt":0.3195563666805088,"score_spread":0.2739889742428689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389988914","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":[0.0020499167,0.000036016198,0.9908555,0.004532486,0.0006794969,0.00021825892,0.000014076679,0.0012480542,0.00036617613],"genre_scores_gemma":[0.36904696,0.000011061175,0.6263809,0.004155164,0.00012067123,0.00002514279,0.00009623695,0.000020316711,0.00014353509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980664,0.00005522385,0.00022763212,0.00077501877,0.0004206233,0.00045509852],"domain_scores_gemma":[0.9973098,0.0005795742,0.0000619528,0.0018095317,0.00005569696,0.00018343027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031255197,0.00016229057,0.00014439686,0.000313774,0.00014930175,0.000160186,0.0021171852,0.00008458374,0.000043087744],"category_scores_gemma":[0.00016737104,0.00014628175,0.000050657436,0.00347923,0.000042069358,0.00072647573,0.00086543855,0.00023790663,0.00040158565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000362174,0.0000919898,0.0013829595,0.000027903177,0.000043800617,0.00019207802,0.00044928855,0.15245694,0.00076924573,0.34986824,0.07521023,0.4194711],"study_design_scores_gemma":[0.00019244097,0.000100254554,0.003379186,0.00003085995,0.0000049930877,0.000003964379,0.000016486218,0.8735366,0.0004607125,0.11580922,0.0061760177,0.00028925954],"about_ca_topic_score_codex":0.000013952864,"about_ca_topic_score_gemma":0.000021048243,"teacher_disagreement_score":0.72107965,"about_ca_system_score_codex":0.000015361007,"about_ca_system_score_gemma":0.00004548547,"threshold_uncertainty_score":0.59651965},"labels":[],"label_agreement":null},{"id":"W4390033957","doi":"10.3390/fi16010002","title":"Integrating Knowledge Graphs into Distribution Grid Decision Support Systems","year":2023,"lang":"en","type":"article","venue":"Future Internet","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Grid; Theoretical computer science; Graph; Power graph analysis; Representation (politics); Vocabulary; Visualization; Data mining","score_opus":0.010360275336494761,"score_gpt":0.27001397809974376,"score_spread":0.259653702763249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390033957","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":[0.02898174,0.00083982944,0.94217134,0.0007094719,0.024376452,0.00040170774,0.000039530103,0.0016478834,0.00083204784],"genre_scores_gemma":[0.9847921,0.00015935546,0.010823022,0.00022393122,0.0021252476,0.00008639778,0.0005309038,0.00003847574,0.0012205465],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981093,0.000090551235,0.00043216968,0.0006086445,0.00030816885,0.00045120047],"domain_scores_gemma":[0.99869746,0.0001936869,0.00014742842,0.00061547704,0.000152268,0.00019368932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035451862,0.00025885037,0.00025138285,0.00019815969,0.00012049266,0.00025426198,0.0012228878,0.00015562311,0.000014661716],"category_scores_gemma":[0.00010479691,0.00020987767,0.00014564798,0.0016248438,0.000047412974,0.0005810794,0.0005760157,0.00039889477,0.0005665745],"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":[0.000021655898,0.000038427555,0.00072808034,0.000045919776,0.000027883016,0.00010817705,0.0014624111,0.00027694812,0.0002504017,0.24195328,0.6466417,0.10844512],"study_design_scores_gemma":[0.0005179423,0.0003261418,0.0022891546,0.00043970422,0.0000120572095,0.0001275474,0.00033586906,0.30593982,0.00075147644,0.022455432,0.66616803,0.000636789],"about_ca_topic_score_codex":0.00003089607,"about_ca_topic_score_gemma":0.00006373516,"teacher_disagreement_score":0.95581037,"about_ca_system_score_codex":0.00009764881,"about_ca_system_score_gemma":0.000039266586,"threshold_uncertainty_score":0.8558563},"labels":[],"label_agreement":null},{"id":"W4390075187","doi":"10.1109/access.2023.3345795","title":"From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Power electronics; Electrification; Electronics; Workflow; Data science; Bridge (graph theory); Field (mathematics); Graph; Artificial intelligence; Systems engineering; Machine learning; Electrical engineering; Electricity; Engineering; Voltage; Theoretical computer science","score_opus":0.04174997851563435,"score_gpt":0.29958761338982487,"score_spread":0.25783763487419054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390075187","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":[0.6611912,0.0009569351,0.33218578,0.0022143528,0.0021620495,0.00050832034,0.000015270953,0.00037583674,0.00039023094],"genre_scores_gemma":[0.99717,0.00035617614,0.00031124864,0.0018691126,0.00013207931,0.00006317327,0.00000754136,0.000028430433,0.000062247134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976789,0.0002568178,0.0004331173,0.0005185268,0.00037420474,0.00073846465],"domain_scores_gemma":[0.9977927,0.00076684856,0.00017408527,0.0010549627,0.00008370914,0.00012764568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005104948,0.0002578167,0.00030677463,0.000395835,0.00013981515,0.00019250606,0.0033027958,0.00010335026,0.00001339438],"category_scores_gemma":[0.000034791672,0.00019637583,0.00015021667,0.003156889,0.0001565483,0.00063283567,0.00058913004,0.0004641138,0.0000060567654],"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":[0.00022445087,0.00008227684,0.0064800363,0.00001805011,0.00014527827,0.0003112371,0.0034330236,0.68371904,0.0008986762,0.1800746,0.015742509,0.10887082],"study_design_scores_gemma":[0.0008305636,0.0003227007,0.027174825,0.00011414416,0.000029620924,0.00001270608,0.0005400554,0.17563036,0.0029665765,0.7898161,0.0015731801,0.0009892129],"about_ca_topic_score_codex":0.00008581178,"about_ca_topic_score_gemma":0.0001998992,"teacher_disagreement_score":0.60974145,"about_ca_system_score_codex":0.000023020515,"about_ca_system_score_gemma":0.000041403247,"threshold_uncertainty_score":0.8007974},"labels":[],"label_agreement":null},{"id":"W4390504488","doi":"10.31237/osf.io/uhzyd","title":"Addressing the Limitations of Graph Neural Networks on Node-level Tasks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Smoothing; Graph; Theoretical computer science; Artificial intelligence; Homophily; Deep learning; Artificial neural network; Machine learning; Mathematics","score_opus":0.2351599297886663,"score_gpt":0.3315244164259858,"score_spread":0.0963644866373195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390504488","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":[0.0023374916,0.0018706839,0.97799534,0.006121638,0.0044250772,0.00057426,0.00002292537,0.0005196691,0.00613291],"genre_scores_gemma":[0.9585059,0.00031604618,0.038603198,0.0017517467,0.0003037507,0.000081437385,0.000019781817,0.000044348937,0.00037378122],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997441,0.00016752155,0.0005495658,0.00089650665,0.00048620635,0.0004591968],"domain_scores_gemma":[0.99649644,0.0011718455,0.00030905358,0.0017704369,0.0001535415,0.00009866147],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026354194,0.00042408405,0.00037959727,0.0002614479,0.00020606398,0.00036406892,0.002372561,0.00028072065,0.000004816985],"category_scores_gemma":[0.00007327324,0.00027954823,0.00040354763,0.0009444815,0.00021097665,0.00014972227,0.0032410838,0.002099092,0.000012129435],"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":[0.0000060236307,0.00002979435,0.000015494687,0.000028579443,0.000055173692,0.000015835192,0.00021521434,0.8119363,0.000015613448,0.08085698,0.0041681896,0.10265679],"study_design_scores_gemma":[0.00007982408,0.000043034113,0.0006110921,0.00025862848,0.000033080843,0.0000092057035,0.00001947879,0.88686687,0.000088777706,0.111429185,0.00026224938,0.0002985918],"about_ca_topic_score_codex":0.000024984049,"about_ca_topic_score_gemma":0.000047900234,"teacher_disagreement_score":0.9561684,"about_ca_system_score_codex":0.000031386753,"about_ca_system_score_gemma":0.00006564927,"threshold_uncertainty_score":0.99996567},"labels":[],"label_agreement":null},{"id":"W4390541142","doi":"10.1007/s13278-023-01178-6","title":"Review of heterogeneous graph embedding methods based on deep learning techniques and comparing their efficiency in node classification","year":2024,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Embedding; Graph embedding; Computer science; Graph; Cluster analysis; Theoretical computer science; Clustering coefficient; Artificial intelligence; Machine learning; Data mining","score_opus":0.028371005554354838,"score_gpt":0.345334446618343,"score_spread":0.31696344106398816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390541142","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":[0.013714631,0.06875804,0.91691196,0.0001201763,0.00005467742,0.00011505145,2.439031e-7,0.00011964167,0.00020556407],"genre_scores_gemma":[0.8592403,0.011623807,0.128871,0.00015532416,0.00007422924,0.000016949389,0.0000052473288,0.000011105929,0.0000020320956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984017,0.00037856115,0.00038592075,0.00045245918,0.00012808251,0.00025327096],"domain_scores_gemma":[0.99917877,0.0004194684,0.00016638059,0.00015646596,0.00003640358,0.00004249034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001215569,0.00016064137,0.00047863738,0.00035291765,0.00021726545,0.00008743423,0.00018430456,0.00006789504,0.0000012733608],"category_scores_gemma":[0.000022807122,0.00014236305,0.00017408139,0.0029640894,0.00007057049,0.000119012526,0.000097804324,0.00024213703,7.8099426e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008215315,0.000021953478,0.010988692,0.0005379181,0.00014451964,0.000012020873,0.0010452004,0.12866086,0.00017344188,0.002749314,0.00001563006,0.85564226],"study_design_scores_gemma":[0.00005089912,0.000035964793,0.0007614667,0.0017653133,0.0000959936,0.0000020567581,0.00004685307,0.9961183,0.000067828936,0.0006273287,0.00028323667,0.00014477722],"about_ca_topic_score_codex":0.000005250634,"about_ca_topic_score_gemma":0.000011694071,"teacher_disagreement_score":0.86745745,"about_ca_system_score_codex":0.000022645778,"about_ca_system_score_gemma":0.000009340104,"threshold_uncertainty_score":0.58053964},"labels":[],"label_agreement":null},{"id":"W4390628433","doi":"10.1016/j.ipm.2023.103618","title":"Learning dual disentangled representation with self-supervision for temporal knowledge graph reasoning","year":2024,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Dual (grammatical number); Computer science; Graph; Knowledge representation and reasoning; Representation (politics); Knowledge graph; Self representation; Artificial intelligence; Natural language processing; Theoretical computer science","score_opus":0.009978764082536066,"score_gpt":0.27204649820818916,"score_spread":0.2620677341256531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390628433","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":[0.005865911,0.0003037466,0.9873315,0.0003159165,0.0003034587,0.000652806,8.942719e-7,0.0013316609,0.0038941235],"genre_scores_gemma":[0.8065377,0.00004278866,0.19259036,0.00011847506,0.00006772834,0.0002045783,0.00008120011,0.00001800741,0.0003391604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986764,0.000030198686,0.00036882245,0.00031125633,0.00032960088,0.00028371566],"domain_scores_gemma":[0.9993651,0.00005358538,0.00015017473,0.0002238098,0.00014424059,0.0000631083],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00028490752,0.00018061289,0.00012587599,0.0003816127,0.00039114468,0.0010890501,0.0002757263,0.000040883995,0.0000020540172],"category_scores_gemma":[0.000013103369,0.00015090466,0.00006041029,0.0012674393,0.000023759747,0.006065512,0.00017262601,0.0001564966,0.000023532068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000367026,0.000032696174,0.00038688624,0.00091052114,0.000054782784,0.000013224345,0.006380739,0.015000785,0.000006621246,0.02783925,0.0009035282,0.94843423],"study_design_scores_gemma":[0.0006077387,0.00013648413,0.0007866361,0.00061592326,0.00004014597,0.000016642081,0.00085653446,0.96316385,0.00010201615,0.0037535995,0.029628677,0.00029173185],"about_ca_topic_score_codex":0.0000020812865,"about_ca_topic_score_gemma":0.0000018158394,"teacher_disagreement_score":0.9481631,"about_ca_system_score_codex":0.000076713506,"about_ca_system_score_gemma":0.00003483568,"threshold_uncertainty_score":0.9999479},"labels":[],"label_agreement":null},{"id":"W4390659502","doi":"10.1109/tit.2024.3351107","title":"On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Algorithm; Vertex (graph theory); Graph; Time complexity; Theoretical computer science; Efficient algorithm; Connectivity","score_opus":0.02218252933870495,"score_gpt":0.2538713566313062,"score_spread":0.23168882729260126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390659502","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":[0.0007837981,0.000035187622,0.99590683,0.00081526395,0.0012125314,0.0006190661,0.000037143862,0.00023143983,0.00035873885],"genre_scores_gemma":[0.9954788,0.00003295917,0.0034904224,0.00064860494,0.000014967392,0.00020951762,0.00000538571,0.000009028472,0.00011030481],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989546,0.000078765275,0.00033419856,0.0001548883,0.0002828574,0.00019468648],"domain_scores_gemma":[0.998451,0.0008910029,0.00009601231,0.0004258209,0.00009127055,0.00004487762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004914512,0.0001387097,0.00011217378,0.00029953325,0.00022857572,0.000094571675,0.0003761504,0.000059975682,0.000010473458],"category_scores_gemma":[0.000009955905,0.000095610725,0.00017874937,0.00075181416,0.00007072897,0.0005917437,0.0000025471454,0.00017630217,0.00003746146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006186672,0.00004799475,3.5804366e-8,0.00003257251,0.000037566973,7.6028124e-7,0.00066029263,0.3840645,0.000041991767,0.5155399,0.00079498754,0.098717526],"study_design_scores_gemma":[0.0005192567,0.000446928,0.0000057044504,0.00019901116,0.00003117898,0.000030580246,0.00019009557,0.80368066,0.042529944,0.14916052,0.0029665758,0.00023953528],"about_ca_topic_score_codex":0.000001254919,"about_ca_topic_score_gemma":3.001857e-7,"teacher_disagreement_score":0.994695,"about_ca_system_score_codex":0.000060299437,"about_ca_system_score_gemma":0.000030459723,"threshold_uncertainty_score":0.38988924},"labels":[],"label_agreement":null},{"id":"W4390818924","doi":"10.1109/tdsc.2024.3353548","title":"TrustGuard: GNN-Based Robust and Explainable Trust Evaluation With Dynamicity Support","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Dependable and Secure Computing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; Snapshot (computer storage); Aggregate (composite); Graph; Visualization; Layer (electronics); Web of trust; Data mining; Artificial intelligence; Machine learning; Computational trust; Theoretical computer science; Reputation; Database","score_opus":0.01519607042946472,"score_gpt":0.24454544670838999,"score_spread":0.22934937627892527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390818924","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":[0.10193324,0.00030162843,0.89614165,0.00030383642,0.00041283038,0.00033771867,0.0000072481075,0.00033874047,0.00022312505],"genre_scores_gemma":[0.975674,0.00002833177,0.023941673,0.00017730147,0.000041291834,0.000018542762,0.000004740637,0.000025840189,0.0000882821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981153,0.00009841919,0.0002459439,0.0007372328,0.0003965076,0.00040657664],"domain_scores_gemma":[0.9991381,0.0002567316,0.00005809542,0.00030891338,0.00009808861,0.0001400574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004521134,0.00026784613,0.00022931473,0.00025227314,0.00053900684,0.00039996646,0.0002115858,0.00010471125,0.000025078989],"category_scores_gemma":[0.000003475716,0.00023151608,0.00005878095,0.0006709752,0.00007321144,0.00065243384,0.0000065960544,0.00046514452,0.000004986039],"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":[0.00003724574,0.000054091597,0.00005619694,0.000103405444,0.000043776537,0.00010235934,0.00045469872,0.82273394,0.00011778543,0.00085737836,0.000059682716,0.17537944],"study_design_scores_gemma":[0.00057409937,0.00030579764,0.00008266443,0.00015306972,0.00005620613,0.000178672,0.00007723668,0.99589306,0.0016051051,0.0005085482,0.0002790122,0.00028651644],"about_ca_topic_score_codex":0.000030588984,"about_ca_topic_score_gemma":0.00008479079,"teacher_disagreement_score":0.87374073,"about_ca_system_score_codex":0.00007675513,"about_ca_system_score_gemma":0.00012842019,"threshold_uncertainty_score":0.9440952},"labels":[],"label_agreement":null},{"id":"W4391147288","doi":"10.1016/j.inffus.2024.102249","title":"MvTuckER: Multi-view knowledge graphs representation learning based on tensor tucker model","year":2024,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Representation (politics); Tensor (intrinsic definition); Feature learning; Artificial intelligence; Mathematics; Pure mathematics; Political science","score_opus":0.02694871888122955,"score_gpt":0.29835952278918937,"score_spread":0.2714108039079598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391147288","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":[0.009643838,0.00022825436,0.98494345,0.00033892365,0.0005700186,0.00032461475,0.0000015801262,0.00091277756,0.003036547],"genre_scores_gemma":[0.96306115,0.00011944887,0.035600845,0.0006011313,0.000029043907,0.0000521046,0.0000644245,0.0000147977025,0.0004570758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987029,0.00006519727,0.00040025238,0.00026471526,0.00032696672,0.00023996858],"domain_scores_gemma":[0.99916637,0.00012464412,0.00011135087,0.00036605072,0.000145947,0.00008561611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023882938,0.00017892876,0.00013039628,0.00045145585,0.00024239921,0.0002965544,0.0003269307,0.00009216644,0.000015778069],"category_scores_gemma":[0.00006800254,0.0001539361,0.00011417083,0.0010594637,0.000021675802,0.002820799,0.00010331363,0.00034225843,0.000525768],"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":[0.000018022027,0.000028532533,0.00009009591,0.00006515955,0.0000044734434,0.0000033810895,0.0017476693,0.666456,0.00037987492,0.014472594,0.0006984808,0.31603572],"study_design_scores_gemma":[0.00029620962,0.000055753717,0.00047804142,0.00016920957,0.000004545529,0.000004330161,0.000036613736,0.9916712,0.0008293552,0.001304896,0.004968746,0.00018109518],"about_ca_topic_score_codex":0.0000047045733,"about_ca_topic_score_gemma":0.0000033039428,"teacher_disagreement_score":0.9534173,"about_ca_system_score_codex":0.00005077908,"about_ca_system_score_gemma":0.000050975035,"threshold_uncertainty_score":0.6757859},"labels":[],"label_agreement":null},{"id":"W4391423877","doi":"10.1109/ickg59574.2023.00006","title":"Efficient Low-Rank GNN Defense Against Structural Attacks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Carleton University","funders":"","keywords":"Adjacency matrix; Computer science; Rank (graph theory); Singular value decomposition; Graph; Maxima and minima; Sparse matrix; Algorithm; Matrix (chemical analysis); Theoretical computer science; Pattern recognition (psychology); Data mining; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.012937775773211974,"score_gpt":0.25701910574195397,"score_spread":0.244081329968742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391423877","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":[0.840872,0.000055932927,0.15128914,0.0008115076,0.0012535788,0.00022710425,0.0000025862005,0.0016837842,0.0038044085],"genre_scores_gemma":[0.98644906,0.000011354996,0.011629687,0.0010276375,0.00009276744,0.000009630828,0.0000058519345,0.00001618607,0.00075781136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826974,0.000043694643,0.00022877681,0.0005293304,0.0003475118,0.0005809281],"domain_scores_gemma":[0.9988715,0.00015035238,0.000058766174,0.0007035933,0.000059528986,0.00015625248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012391401,0.00019177016,0.00016330135,0.00015433955,0.0001934034,0.000106402505,0.0008463561,0.000064131884,0.000018492368],"category_scores_gemma":[0.000029902207,0.00015470324,0.00011090857,0.0014927303,0.0000652972,0.00015170788,0.00047047372,0.0001915334,0.00052278757],"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":[0.000013651252,0.000022765593,0.0009876692,0.000019734718,0.00002398014,0.00028449323,0.0004809893,0.854062,0.0020680719,0.055348657,0.009974031,0.07671395],"study_design_scores_gemma":[0.00029503182,0.000029834611,0.004715543,0.000014539904,0.0000019197944,0.00001306579,0.000018939127,0.99004924,0.000761116,0.002999328,0.000827828,0.00027362778],"about_ca_topic_score_codex":0.000003197838,"about_ca_topic_score_gemma":0.0000067474,"teacher_disagreement_score":0.1455771,"about_ca_system_score_codex":0.000029538638,"about_ca_system_score_gemma":0.000020283034,"threshold_uncertainty_score":0.67195505},"labels":[],"label_agreement":null},{"id":"W4391557959","doi":"10.1109/icdmw60847.2023.00150","title":"Study of Topology Bias in GNN-based Knowledge Graphs Algorithms","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Topology (electrical circuits); Algorithm; Mathematics; Combinatorics","score_opus":0.08071604601129688,"score_gpt":0.3402569789911048,"score_spread":0.2595409329798079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391557959","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":[0.94464326,0.00010591651,0.051605586,0.0003813826,0.0007562104,0.000476872,8.4854355e-7,0.00050768093,0.0015222348],"genre_scores_gemma":[0.9934212,0.000008287636,0.0062001543,0.00009417013,0.000013519967,0.00002928298,0.0000010672516,0.00000776633,0.00022458042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987274,0.00014321743,0.00028991027,0.00037908967,0.00014859624,0.00031179667],"domain_scores_gemma":[0.99891514,0.0003965228,0.000063209096,0.00052030286,0.00005173981,0.000053060765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026211754,0.00011865364,0.00021882517,0.00051876943,0.000038377166,0.000012558021,0.0007100935,0.00005181065,0.000009068178],"category_scores_gemma":[0.000034379,0.000101831654,0.00005101094,0.0032554006,0.00005776439,0.00013590479,0.00024430535,0.00013754166,0.00003754287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064512395,0.0050714915,0.28480655,0.000070783506,0.00009630141,0.0009797735,0.0106103225,0.07902623,0.0008295501,0.32138252,0.0068236827,0.29023826],"study_design_scores_gemma":[0.003496426,0.001784272,0.14522527,0.000032527238,0.0000075060825,0.000007378576,0.0011756733,0.7997437,0.002483193,0.045033924,0.0004703182,0.00053977733],"about_ca_topic_score_codex":0.000073214906,"about_ca_topic_score_gemma":0.00071559387,"teacher_disagreement_score":0.7207175,"about_ca_system_score_codex":0.000012526261,"about_ca_system_score_gemma":0.000028804638,"threshold_uncertainty_score":0.41525742},"labels":[],"label_agreement":null},{"id":"W4391827219","doi":"10.1109/tcss.2024.3357696","title":"Temporal Interaction Embedding for Link Prediction in Global News Event Graph","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Embedding; Link (geometry); Graph; Event (particle physics); Theoretical computer science; Artificial intelligence; Computer network","score_opus":0.02332129919915053,"score_gpt":0.32320593103412404,"score_spread":0.2998846318349735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391827219","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":[0.0033149256,0.0001228638,0.98635584,0.0009939679,0.007886998,0.0006794854,0.00010229858,0.00045825823,0.00008533767],"genre_scores_gemma":[0.9927393,0.0000056492318,0.006221698,0.0000751097,0.0005629743,0.0002701645,0.000026270569,0.000018013443,0.00008080181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981798,0.000119720426,0.00051756756,0.00051408546,0.00038693144,0.00028191952],"domain_scores_gemma":[0.9993262,0.00028882202,0.00009815088,0.00011180946,0.000103432874,0.000071572525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021456985,0.0001992477,0.0002124208,0.0002580098,0.00033598184,0.00029678433,0.00023276298,0.00013997406,0.00000359936],"category_scores_gemma":[0.0000032168934,0.00021310945,0.00023626478,0.0010805784,0.000032699805,0.0007970873,0.0000028153,0.00028682334,0.000020869937],"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":[0.000030602885,0.000058663303,0.000033914148,0.000051398933,0.0000432542,0.0000061573655,0.00027631992,0.9353371,0.000014372246,0.010303273,0.0005609731,0.05328395],"study_design_scores_gemma":[0.00037304187,0.00011578253,0.00026861433,0.00015655269,0.0000123489535,0.000027605756,0.00009524681,0.9811108,0.000014218446,0.015969627,0.0016763217,0.00017984868],"about_ca_topic_score_codex":0.00015045716,"about_ca_topic_score_gemma":0.000104946994,"teacher_disagreement_score":0.9894244,"about_ca_system_score_codex":0.00052135845,"about_ca_system_score_gemma":0.00009678641,"threshold_uncertainty_score":0.8690351},"labels":[],"label_agreement":null},{"id":"W4391884321","doi":"10.1145/3648358","title":"Distributed Graph Neural Network Training: A Survey","year":2024,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Microsoft Research Asia; Beijing Nova Program; Hong Kong University of Science and Technology; Shanghai Jiao Tong University; National Natural Science Foundation of China; Microsoft Research; National Science and Technology Major Project; National Science Foundation","keywords":"Computer science; Workload; Distributed computing; Partition (number theory); Graph; Artificial neural network; Artificial intelligence; Machine learning; Theoretical computer science","score_opus":0.13086994288069173,"score_gpt":0.35700705758101103,"score_spread":0.2261371147003193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391884321","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000057710054,0.7096337,0.28323108,0.00004243376,0.004946974,0.0005832603,0.0001431665,0.0013764059,0.00003723357],"genre_scores_gemma":[0.0006420878,0.9811278,0.014932885,0.0001226727,0.0012915315,0.000035901227,0.0015201785,0.0002335673,0.00009337796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9859574,0.007213378,0.0017456171,0.0023956664,0.0007218652,0.0019660909],"domain_scores_gemma":[0.9880466,0.0068971883,0.0008986993,0.003535852,0.00020497879,0.0004166715],"candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.008114265,0.0014762379,0.0033446013,0.000435383,0.00046669826,0.00091920083,0.0073751323,0.0006290384,0.000004448195],"category_scores_gemma":[0.0011958065,0.0012379145,0.0013396007,0.007613379,0.00019074458,0.00034943587,0.0044988054,0.0024548273,0.00016582735],"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":[8.7857217e-7,0.000021795066,0.00007715708,0.0013449021,0.00024411143,0.00020487494,0.00005656602,0.002906627,6.5210206e-9,0.0007606849,0.010400277,0.98398215],"study_design_scores_gemma":[0.00038594,0.00023443902,0.002282476,0.01781055,0.0007330959,0.00070407434,0.0000053065246,0.070951276,5.324044e-8,0.01186453,0.89088887,0.0041393726],"about_ca_topic_score_codex":0.00007639192,"about_ca_topic_score_gemma":0.00011827931,"teacher_disagreement_score":0.9798427,"about_ca_system_score_codex":0.0001400429,"about_ca_system_score_gemma":0.0003592945,"threshold_uncertainty_score":0.9998466},"labels":[],"label_agreement":null},{"id":"W4391935658","doi":"10.1007/978-3-031-53468-3_4","title":"When Do We Need Graph Neural Networks for Node Classification?","year":2024,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Graph; Node (physics); Machine learning; Theoretical computer science; Engineering","score_opus":0.10407645892061775,"score_gpt":0.35615108035054255,"score_spread":0.2520746214299248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391935658","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":[0.0000013053306,0.04390124,0.9379436,0.005274942,0.0038101643,0.0008837582,0.000035672852,0.00029579006,0.007853563],"genre_scores_gemma":[0.14791913,0.038594283,0.63941926,0.0045321346,0.0042787865,0.001726026,0.00037736978,0.0006438713,0.16250913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99637634,0.000033650256,0.0010576091,0.0014063155,0.00057726534,0.00054882717],"domain_scores_gemma":[0.995839,0.0023900752,0.0003875244,0.0006475235,0.000629249,0.000106618994],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003158322,0.00067370053,0.0006973067,0.0005904777,0.00024057599,0.00019928193,0.0015324171,0.0002879444,0.000014126142],"category_scores_gemma":[0.000070484464,0.000653608,0.00036905138,0.00040389673,0.00058231043,0.00034995793,0.00079568685,0.0009115674,0.00005332352],"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":[0.000011310847,0.000009820392,0.0000045884062,0.000071263545,0.000108761385,0.000022303619,0.00031772905,0.42294055,1.1145853e-7,0.5459637,0.0031416302,0.027408244],"study_design_scores_gemma":[0.000047448077,0.00005028078,0.0000042366023,0.00036085237,0.000019066749,0.000014077181,0.000055044613,0.46296102,6.8744055e-7,0.5294193,0.006733788,0.00033421064],"about_ca_topic_score_codex":0.0000017079907,"about_ca_topic_score_gemma":0.000018723365,"teacher_disagreement_score":0.2985243,"about_ca_system_score_codex":0.00022903846,"about_ca_system_score_gemma":0.00006623701,"threshold_uncertainty_score":0.9995915},"labels":[],"label_agreement":null},{"id":"W4391945685","doi":"10.1007/978-3-031-53468-3_5","title":"Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks","year":2024,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Advanced Graph Neural Networks","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Convolutional neural network; Computer science; Graph; Artificial intelligence; Machine learning; Theoretical computer science","score_opus":0.11526280053373746,"score_gpt":0.34932592246924044,"score_spread":0.234063121935503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391945685","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":[0.000021109783,0.02219873,0.9681486,0.00081937615,0.0032515633,0.00035059702,0.000024921668,0.00015767459,0.005027429],"genre_scores_gemma":[0.83820546,0.0039398954,0.13856116,0.003137587,0.0012794881,0.000108645414,0.00012986719,0.00022338,0.014414552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99648863,0.000047643058,0.0012306831,0.0009995055,0.0007439007,0.0004896409],"domain_scores_gemma":[0.99708635,0.0014925746,0.00047969734,0.0003817263,0.00047908307,0.000080556434],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037078126,0.00056488276,0.000814685,0.0006253241,0.00014739997,0.00006919928,0.0010744833,0.00022269147,0.000028758019],"category_scores_gemma":[0.00004625561,0.0005670792,0.00034890472,0.0004360013,0.0008799759,0.0003346647,0.0009047166,0.0008782054,0.000023458671],"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":[0.0000059259655,0.000006649205,0.0000064706774,0.000059107006,0.00015490827,0.00007463506,0.00044903654,0.53542805,4.2439004e-7,0.45271713,0.00024084156,0.010856836],"study_design_scores_gemma":[0.00004319792,0.000047931488,0.000014185159,0.00075908744,0.000024288349,0.00006172723,0.000093437666,0.3907113,0.0000033267363,0.6074485,0.00045827965,0.00033473579],"about_ca_topic_score_codex":0.000004667519,"about_ca_topic_score_gemma":0.000016898706,"teacher_disagreement_score":0.8381843,"about_ca_system_score_codex":0.00015458588,"about_ca_system_score_gemma":0.00007534588,"threshold_uncertainty_score":0.9996781},"labels":[],"label_agreement":null},{"id":"W4392015900","doi":"10.21203/rs.3.rs-3960194/v1","title":"Multi-sentence and multi-intent classification using RoBERTa and graph convolutional neural network","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Raytheon Technologies (Canada)","funders":"","keywords":"Sentence; Convolutional neural network; Computer science; Graph; Artificial intelligence; Natural language processing; Theoretical computer science","score_opus":0.1741095780669141,"score_gpt":0.4100080814020322,"score_spread":0.2358985033351181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392015900","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":[0.17963095,0.037412133,0.7734889,0.00372441,0.0023705557,0.0027130079,0.00007437824,0.0005418108,0.000043872602],"genre_scores_gemma":[0.85214716,0.0016221536,0.14540288,0.00008349205,0.0003325196,0.00013677354,0.00003898208,0.000049005695,0.00018705227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953748,0.0005378541,0.00045425072,0.0016919539,0.00094432535,0.0009968431],"domain_scores_gemma":[0.99741143,0.0005010032,0.00014531982,0.00094783946,0.0006147522,0.0003796636],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001030504,0.0004021495,0.00037641992,0.00047374333,0.0005385791,0.00074535713,0.00096651714,0.00033311945,0.0000036029708],"category_scores_gemma":[0.00018721027,0.0003741623,0.00013970195,0.001022578,0.0006652853,0.00031099157,0.006122722,0.0026475948,0.00000972005],"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":[0.00035782842,0.0011102866,0.12233939,0.0074388077,0.00078138453,0.001277812,0.0039256634,0.5189139,0.016694278,0.16785356,0.0072632227,0.15204386],"study_design_scores_gemma":[0.00028331988,0.000057526006,0.03324074,0.0008490535,0.000011660305,0.000063126536,0.00007730761,0.9510056,0.000016509834,0.013943729,0.000118160584,0.00033331444],"about_ca_topic_score_codex":0.00024399113,"about_ca_topic_score_gemma":0.0001683174,"teacher_disagreement_score":0.6725162,"about_ca_system_score_codex":0.00018275628,"about_ca_system_score_gemma":0.00018816287,"threshold_uncertainty_score":0.999871},"labels":[],"label_agreement":null},{"id":"W4392152281","doi":"10.1109/globecom54140.2023.10437361","title":"Topology Design for Robust IoT Data Gathering via Bayesian Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Internet of Things; Network topology; Topology (electrical circuits); Bayesian network; Bayesian probability; Distributed computing; Computer network; Artificial intelligence; Embedded system; Mathematics","score_opus":0.09990636075084362,"score_gpt":0.3019212038571415,"score_spread":0.20201484310629786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392152281","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":[0.000039283903,0.00006443232,0.99613553,0.0015821295,0.0007782758,0.00036346118,0.0000019031507,0.00084974064,0.00018526439],"genre_scores_gemma":[0.04897757,0.0000358623,0.9489025,0.0008989475,0.0003327045,0.000054065513,0.000026993011,0.00003336067,0.00073798356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830705,0.00006358962,0.00021515935,0.0006875786,0.00011296433,0.00061366364],"domain_scores_gemma":[0.9977847,0.00057775073,0.00006203961,0.0014352956,0.000033425837,0.00010679844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042613767,0.00016095614,0.00017985652,0.00009207913,0.00018674052,0.00007581298,0.0022166306,0.00009746717,0.00001641308],"category_scores_gemma":[0.000032350756,0.00014505783,0.000046275276,0.0008240682,0.00005159102,0.00035980772,0.0010162618,0.00014317261,0.000026264628],"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":[0.000009985921,0.00001085123,0.000054682154,0.000004880147,0.000015966778,0.000021079788,0.000032303848,0.8476853,0.00009738109,0.011575045,0.021190235,0.11930233],"study_design_scores_gemma":[0.0001768368,0.00006734482,0.000086417494,0.000005962498,0.0000037223645,0.000014240105,0.0000071725185,0.98360854,0.0000841312,0.012957557,0.002808153,0.00017990184],"about_ca_topic_score_codex":0.000010274958,"about_ca_topic_score_gemma":0.00002976342,"teacher_disagreement_score":0.13592331,"about_ca_system_score_codex":0.000015190848,"about_ca_system_score_gemma":0.000018170216,"threshold_uncertainty_score":0.59152865},"labels":[],"label_agreement":null},{"id":"W4392384600","doi":"10.1145/3616855.3635765","title":"Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Interpretability; Embedding; Theoretical computer science; Artificial intelligence; Graph; Feature learning; Machine learning; Graph embedding; Node (physics)","score_opus":0.007531109664376096,"score_gpt":0.24499426783411815,"score_spread":0.23746315816974206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392384600","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":[0.018419893,0.00095411565,0.9710379,0.0015047486,0.0006261947,0.00023101812,2.2572615e-7,0.0033111793,0.003914681],"genre_scores_gemma":[0.9486733,0.00003232808,0.048517052,0.00012938476,0.00012504896,0.000010731473,0.0000032491987,0.000039553433,0.002469369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977265,0.00014459307,0.00024007328,0.00094329676,0.0004097759,0.0005357651],"domain_scores_gemma":[0.999141,0.00016445159,0.000061759216,0.00036158078,0.00007630024,0.0001949203],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018222338,0.0003084057,0.00021527993,0.0004069975,0.0003020333,0.00029400998,0.00054967136,0.00011876845,0.00002479329],"category_scores_gemma":[0.000039276376,0.00027858678,0.00019547757,0.0013381953,0.00003844447,0.00088375394,0.00021654298,0.0011250098,0.00016799002],"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":[0.00007554218,0.00017315547,0.00485913,0.000093836425,0.0002781358,0.00045753704,0.0122433705,0.55414844,0.004234287,0.35890442,0.0010572107,0.06347492],"study_design_scores_gemma":[0.00026402323,0.00023906842,0.00085547724,0.00008284595,0.000012983381,0.000044962646,0.00036572118,0.9677732,0.00007960256,0.029097442,0.00082054053,0.0003641475],"about_ca_topic_score_codex":0.000354195,"about_ca_topic_score_gemma":0.00006899074,"teacher_disagreement_score":0.9302534,"about_ca_system_score_codex":0.0004981763,"about_ca_system_score_gemma":0.00010444507,"threshold_uncertainty_score":0.9999666},"labels":[],"label_agreement":null},{"id":"W4392772138","doi":"10.21203/rs.3.rs-3992886/v1","title":"Optimizing Recommender Model: Integrating Knowledge Graph Information Fusion and Attention Mechanism","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Recommender system; Mechanism (biology); Computer science; Graph; Fusion; Information retrieval; Artificial intelligence; Theoretical computer science","score_opus":0.06330300034578311,"score_gpt":0.3760085563261915,"score_spread":0.3127055559804084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392772138","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":[0.0075232396,0.0019195398,0.9843134,0.0013938049,0.00081987504,0.0010364268,0.00001385813,0.0005291013,0.0024507502],"genre_scores_gemma":[0.84434015,0.001751009,0.15305892,0.000072401046,0.0001541762,0.0003035763,0.000111119225,0.00004513433,0.00016351498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968851,0.00038017196,0.0004939107,0.00078683865,0.0007887315,0.00066520984],"domain_scores_gemma":[0.99793935,0.00026174102,0.0001421513,0.00081554253,0.00063829706,0.00020290371],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0018291061,0.0003331859,0.00029135172,0.001275251,0.0004608969,0.0011973483,0.00093448104,0.00039195933,0.0000044840353],"category_scores_gemma":[0.00013155317,0.0003004338,0.00016288011,0.0010892365,0.00007665637,0.0011277171,0.008299419,0.0031999187,0.000045359782],"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":[0.000028581675,0.00008050803,0.00002362762,0.002989895,0.00007430988,0.000025777626,0.009330412,0.052813835,0.000843838,0.44661814,0.004089084,0.483082],"study_design_scores_gemma":[0.00008651872,0.000049306906,0.000012082356,0.0010852151,0.000004185283,0.000005600469,0.00020272151,0.6159246,0.000079966565,0.38224956,0.00011809672,0.00018215738],"about_ca_topic_score_codex":0.000026851665,"about_ca_topic_score_gemma":0.000029258988,"teacher_disagreement_score":0.8368169,"about_ca_system_score_codex":0.00022642946,"about_ca_system_score_gemma":0.00017903378,"threshold_uncertainty_score":0.9999448},"labels":[],"label_agreement":null},{"id":"W4392774981","doi":"10.1093/comnet/cnae003","title":"Unsupervised framework for evaluating and explaining structural node embeddings of graphs","year":2024,"lang":"en","type":"article","venue":"Journal of Complex Networks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Embedding; Node (physics); Theoretical computer science; Graph; Set (abstract data type); Data mining; Artificial intelligence","score_opus":0.052181983397978984,"score_gpt":0.3594490573058519,"score_spread":0.30726707390787295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392774981","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":[0.12670901,0.0044884905,0.8670892,0.00035700938,0.0011170179,0.00015966703,0.0000021269223,0.000050890107,0.000026553056],"genre_scores_gemma":[0.62101465,0.00008477577,0.37845787,0.00015465553,0.000264232,0.0000028037928,9.970067e-7,0.000017005412,0.0000030371086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800557,0.00009558638,0.0008074018,0.00029112727,0.0004100817,0.00039024462],"domain_scores_gemma":[0.9970822,0.0017345971,0.0004760911,0.0002646722,0.000292782,0.00014967419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007889205,0.00022097846,0.00047851398,0.00025192383,0.00016060355,0.00022493352,0.00069037714,0.00012009178,0.000010836604],"category_scores_gemma":[0.00014469156,0.00018201805,0.00025762856,0.0007058849,0.00010293854,0.00075384,0.0001778112,0.0006008901,1.8929634e-7],"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":[0.00019298801,0.000020371219,0.0014108602,0.0002032969,0.00027145402,0.000069474205,0.0017904865,0.38941866,0.0017951083,0.34692502,0.0011680175,0.25673425],"study_design_scores_gemma":[0.0003487881,0.0003297858,0.0011889182,0.00056804856,0.00003442279,0.00018426422,0.000050179442,0.80662334,0.000052099003,0.19028617,0.00017939336,0.00015455493],"about_ca_topic_score_codex":9.59616e-7,"about_ca_topic_score_gemma":6.457636e-7,"teacher_disagreement_score":0.4943056,"about_ca_system_score_codex":0.000026776595,"about_ca_system_score_gemma":0.00004827902,"threshold_uncertainty_score":0.74224806},"labels":[],"label_agreement":null},{"id":"W4392903983","doi":"10.1109/icassp48485.2024.10445943","title":"Mixed Graph Signal Analysis of Joint Image Denoising / Interpolation","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noise reduction; Graph; Algorithm; Interpolation (computer graphics); Mathematics; Computer science; Linear interpolation; Separable space; Artificial intelligence; Mathematical optimization; Pattern recognition (psychology); Theoretical computer science; Image (mathematics)","score_opus":0.022041057155287758,"score_gpt":0.2586415680181163,"score_spread":0.23660051086282852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392903983","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":[0.027620675,0.00022214449,0.97069097,0.00018699165,0.0002040311,0.00004882513,0.0000013063087,0.00021439935,0.00081064116],"genre_scores_gemma":[0.8799241,0.0000069033904,0.11991614,0.000048410533,0.000017617533,0.0000018030419,0.0000031281206,0.000005081,0.00007680423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905425,0.000040570227,0.0002622413,0.00030351896,0.00018735122,0.00015206759],"domain_scores_gemma":[0.99945533,0.00010775294,0.000053246098,0.00028249924,0.000056371107,0.000044816803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015277108,0.00009349233,0.00017712722,0.0007178273,0.000034084762,0.00012891211,0.0002763294,0.000030955576,0.000054812623],"category_scores_gemma":[0.00000836797,0.00007714199,0.00023193816,0.003257418,0.00004119469,0.0006565537,0.00013377733,0.00010059702,0.000008523369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013567387,0.000079729754,0.0013620869,0.0000736705,0.0016618384,0.00010067339,0.0012313746,0.029894162,0.3774965,0.41085255,0.002597327,0.1746365],"study_design_scores_gemma":[0.000033095086,0.000028473412,0.0026230824,0.000025981337,0.00010255726,0.0000027954488,0.000016277509,0.97028613,0.013490135,0.013248715,0.000045603298,0.00009716987],"about_ca_topic_score_codex":0.00001614625,"about_ca_topic_score_gemma":0.000028863857,"teacher_disagreement_score":0.94039196,"about_ca_system_score_codex":0.000014843406,"about_ca_system_score_gemma":0.000012031301,"threshold_uncertainty_score":0.31457588},"labels":[],"label_agreement":null},{"id":"W4392931697","doi":"10.1109/tsp.2024.3378001","title":"Learnable Filters for Geometric Scattering Modules","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Chan Zuckerberg Initiative; National Institute of General Medical Sciences; Canadian Institute for Advanced Research; National Institutes of Health; National Science Foundation; Yale University; Fonds de recherche du Québec – Nature et technologies; Alfred P. Sloan Foundation","keywords":"Wavelet; Graph; Computer science; Artificial intelligence; Pattern recognition (psychology); Range (aeronautics); Graph theory; Geometric data analysis; Algorithm; Theoretical computer science; Mathematics","score_opus":0.026235985369268754,"score_gpt":0.2713497393765992,"score_spread":0.24511375400733043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392931697","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":[0.001497158,0.000779125,0.99583334,0.00045651876,0.0005279723,0.00018962797,0.000005191831,0.00060874486,0.00010231872],"genre_scores_gemma":[0.9420366,0.000021661952,0.05707891,0.00019974224,0.00008297193,0.00008013513,6.726808e-7,0.000030381298,0.00046891364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864274,0.000018175057,0.00020933386,0.00052012777,0.00022734782,0.00038225472],"domain_scores_gemma":[0.9994204,0.00020530829,0.000038697373,0.00019144193,0.000057725247,0.000086397515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014513984,0.00017917746,0.00014377077,0.00055635325,0.00039858965,0.00052247,0.00039990275,0.00006201214,0.000014458261],"category_scores_gemma":[0.0000018480375,0.00016988911,0.00013442182,0.0016586377,0.00004714046,0.001296836,0.0000028318107,0.00028818884,0.000025271243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000095760015,0.000023753606,4.8816895e-7,0.00012571218,0.000013641244,0.000008355156,0.00008869839,0.257821,0.004615154,0.0001037641,0.0001109998,0.73707885],"study_design_scores_gemma":[0.00015730721,0.0001236719,0.0000053811823,0.00026151282,0.000016887174,0.00003227311,0.00001781739,0.94537795,0.049260322,0.0027558964,0.0017385638,0.00025242873],"about_ca_topic_score_codex":0.000002510896,"about_ca_topic_score_gemma":0.000001333803,"teacher_disagreement_score":0.9405395,"about_ca_system_score_codex":0.00006384385,"about_ca_system_score_gemma":0.000045873152,"threshold_uncertainty_score":0.6927877},"labels":[],"label_agreement":null},{"id":"W4393147976","doi":"10.1609/aaai.v38i8.28750","title":"Anchoring Path for Inductive Relation Prediction in Knowledge Graphs","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"National Research Foundation Singapore; National Research Foundation","keywords":"Relation (database); Path (computing); Computer science; Knowledge graph; Theoretical computer science; Artificial intelligence; Data mining; Computer network","score_opus":0.07779860327567567,"score_gpt":0.3145675342564972,"score_spread":0.23676893098082152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393147976","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":[0.43502155,0.00070793525,0.54217625,0.0034266615,0.0052570365,0.0026723188,0.00002266238,0.0006792473,0.010036374],"genre_scores_gemma":[0.99525976,0.000060426482,0.0043058926,0.000026733771,0.000113975904,0.00010297013,6.3322346e-7,0.000013435452,0.000116169984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99845296,0.000014231341,0.00045925763,0.00055373146,0.00022879208,0.00029105198],"domain_scores_gemma":[0.99912286,0.0001721042,0.00015054703,0.00017991388,0.0003263765,0.000048197442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000426205,0.00017936436,0.00017632176,0.0002792673,0.000121781275,0.00018320786,0.00084381044,0.000104172635,0.0000049853697],"category_scores_gemma":[0.00020447455,0.00014171764,0.00011111687,0.0014766131,0.00012539294,0.00095879345,0.00017062573,0.00038659322,0.000016042999],"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":[0.000026801907,0.00004769137,0.00023294655,0.00004965771,0.00000684278,2.6768132e-7,0.0015141976,0.00024516392,0.013032774,0.8827807,0.00009298332,0.101969995],"study_design_scores_gemma":[0.00001818197,0.00014300706,0.0004950911,0.0005128479,0.000005706611,0.0000015117474,0.0001583828,0.3459835,0.06920204,0.58325785,0.00010338536,0.000118467244],"about_ca_topic_score_codex":0.000007721899,"about_ca_topic_score_gemma":0.000010040057,"teacher_disagreement_score":0.56023824,"about_ca_system_score_codex":0.000085349726,"about_ca_system_score_gemma":0.0000669532,"threshold_uncertainty_score":0.57790774},"labels":[],"label_agreement":null},{"id":"W4395098143","doi":"10.1007/978-981-97-2266-2_10","title":"Graph Neural Network Approach to Semantic Type Detection in Tables","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Table (database); Graph; Security token; Artificial intelligence; Focus (optics); Key (lock); Artificial neural network; Data mining; Natural language processing; Machine learning; Theoretical computer science","score_opus":0.015491846873628225,"score_gpt":0.23586226254703144,"score_spread":0.2203704156734032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395098143","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":[0.0002738017,0.001482929,0.9900442,0.0003238353,0.004974755,0.0006493491,0.0000015435005,0.00030276392,0.0019468506],"genre_scores_gemma":[0.5957337,0.000099169345,0.39972827,0.0023673282,0.0013729731,0.000035792622,0.0000069612056,0.0001049961,0.00055081554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951936,0.00004193976,0.00058874645,0.0022367649,0.0008290323,0.0011099187],"domain_scores_gemma":[0.9978498,0.00030797496,0.0001599149,0.0013115695,0.00014453965,0.00022620284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006543478,0.00066113786,0.0006103466,0.0014539461,0.00020286124,0.00060886284,0.0029028892,0.00035441265,0.0000032132698],"category_scores_gemma":[0.000044066866,0.0006073964,0.00014307446,0.004338905,0.00034517024,0.00064438075,0.0016743521,0.001485064,0.000046937585],"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":[0.000008060134,0.000013710379,0.000030271132,0.000037151312,0.000005604968,0.00007910959,0.00018894096,0.76495284,0.00006170743,0.011306364,0.000025027544,0.22329119],"study_design_scores_gemma":[0.00009178653,0.00015121998,0.00010124682,0.00032905716,0.000005960627,0.000093330804,1.0826242e-7,0.7315737,0.00014358165,0.2662946,0.00064896944,0.0005664332],"about_ca_topic_score_codex":0.00003171754,"about_ca_topic_score_gemma":0.00040090707,"teacher_disagreement_score":0.5954599,"about_ca_system_score_codex":0.00025120017,"about_ca_system_score_gemma":0.00013938872,"threshold_uncertainty_score":0.9996377},"labels":[],"label_agreement":null},{"id":"W4395664921","doi":"10.3390/math12091324","title":"Revolutionary Strategy for Depicting Knowledge Graphs with Temporal Attributes","year":2024,"lang":"en","type":"article","venue":"Mathematics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Knowledge graph; Computer science; Data science; Artificial intelligence; Knowledge management","score_opus":0.046571408747427226,"score_gpt":0.2993126349266559,"score_spread":0.2527412261792287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395664921","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":[0.0033888738,0.0057636104,0.98887324,0.00023137573,0.00022411562,0.00032693055,0.0000075577077,0.00054336287,0.00064090954],"genre_scores_gemma":[0.40789127,0.0000544801,0.5913023,0.000028289312,0.000101761405,0.00007986586,0.000008965245,0.000030171752,0.00050290395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990219,0.00001568955,0.00023172158,0.00030628612,0.00013673774,0.00028770135],"domain_scores_gemma":[0.9989764,0.0004874062,0.00005907829,0.0003346162,0.00008032792,0.00006214285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018947203,0.00015716642,0.00016694887,0.000097626464,0.00013839235,0.00007461876,0.00039354098,0.00005083348,0.0000026854168],"category_scores_gemma":[0.000028984394,0.00011821964,0.00008025002,0.0006261733,0.000045832818,0.0004061582,0.000095953255,0.000136816,0.00001851986],"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":[0.000003653678,0.00009005718,0.00021184041,0.0006155027,0.000049407558,0.000032871554,0.0006408381,0.0015343298,0.00008175478,0.9750517,0.008162517,0.013525551],"study_design_scores_gemma":[0.0001245918,0.00015199339,0.000056298493,0.00029127696,0.000015321742,0.00008851717,0.000060120135,0.61807525,0.00019683593,0.37762508,0.0031122693,0.00020245797],"about_ca_topic_score_codex":7.4467e-7,"about_ca_topic_score_gemma":0.0000075443954,"teacher_disagreement_score":0.6165409,"about_ca_system_score_codex":0.000028148504,"about_ca_system_score_gemma":0.000056575493,"threshold_uncertainty_score":0.4820857},"labels":[],"label_agreement":null},{"id":"W4396506525","doi":"10.1109/tnnls.2024.3389714","title":"Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Graph; Theoretical computer science; Data mining; Distributed computing; Anomaly detection; Artificial intelligence","score_opus":0.009916444358123496,"score_gpt":0.2235279266217746,"score_spread":0.2136114822636511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396506525","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":[0.037273325,0.0029631683,0.9526713,0.00011690458,0.005163908,0.0007458831,0.0000039240604,0.0010033505,0.000058264217],"genre_scores_gemma":[0.9971959,0.00012990973,0.0012276347,0.000030363824,0.00072399486,0.00018863262,0.000006109197,0.000063012514,0.0004344226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975242,0.00032590426,0.0004509005,0.0008850569,0.00019500457,0.00061890355],"domain_scores_gemma":[0.99837357,0.0008270279,0.00014957675,0.00032266416,0.00014055615,0.00018660711],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029876342,0.00040979692,0.0003774038,0.00025880712,0.0010255785,0.00067584886,0.00024647766,0.00020292909,0.0000023123482],"category_scores_gemma":[0.0000049368386,0.00032913897,0.00015762857,0.0016702475,0.00010488791,0.0007704226,0.0000047969042,0.0009938926,0.0000028201862],"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":[0.00008513292,0.000013736752,0.00007689701,0.000065298445,0.000060183724,0.0000115080975,0.000084295294,0.84880793,0.000051721923,0.00047943092,0.00006672487,0.15019712],"study_design_scores_gemma":[0.00038330993,0.0007612254,0.0002481132,0.00026241635,0.0000592053,0.00031774942,0.000023702882,0.9947713,0.000048548092,0.00008177458,0.002637337,0.0004053042],"about_ca_topic_score_codex":0.000020969992,"about_ca_topic_score_gemma":0.00009227453,"teacher_disagreement_score":0.9599226,"about_ca_system_score_codex":0.00005265055,"about_ca_system_score_gemma":0.000021874102,"threshold_uncertainty_score":0.9999161},"labels":[],"label_agreement":null},{"id":"W4398167754","doi":"10.1145/3605098.3636062","title":"Contextual Embeddings and Graph Convolutional Networks for Concept Prerequisite Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Theoretical computer science; Data science","score_opus":0.010339470846406944,"score_gpt":0.2578149863084936,"score_spread":0.24747551546208665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398167754","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":[0.002591496,0.0046815597,0.9902512,0.0004947307,0.0006144324,0.00026363393,0.000002760561,0.00061515137,0.00048506935],"genre_scores_gemma":[0.9639828,0.00007839993,0.03315745,0.00059642235,0.00023003925,0.000042709387,0.000011061991,0.000017699507,0.0018833999],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872047,0.000033793152,0.0001981096,0.0005328493,0.00014249736,0.00037228316],"domain_scores_gemma":[0.9989855,0.0006388487,0.00003914826,0.00015287094,0.00007019161,0.0001133916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016955574,0.00016330443,0.00015959998,0.00008796524,0.00020019083,0.00029802436,0.00027873795,0.00008238321,0.000019043979],"category_scores_gemma":[0.00003067907,0.00014259697,0.000091204645,0.00035138978,0.0001468213,0.00072555954,0.00018145792,0.0002701148,0.0000051080683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012933304,0.00000750686,0.00027892008,0.000016915308,0.00004265761,0.000011638943,0.00035920183,0.027781334,0.00012856565,0.91489327,0.006093132,0.050373945],"study_design_scores_gemma":[0.00028795886,0.00014721822,0.00023050517,0.00004640828,0.000007774062,0.000042071475,0.000026199079,0.95550954,0.00005807708,0.022134293,0.021301448,0.00020849738],"about_ca_topic_score_codex":0.0000031702739,"about_ca_topic_score_gemma":0.0000027930334,"teacher_disagreement_score":0.9613913,"about_ca_system_score_codex":0.000017109318,"about_ca_system_score_gemma":0.000021379492,"threshold_uncertainty_score":0.5814936},"labels":[],"label_agreement":null},{"id":"W4398760296","doi":"10.1145/3626246.3653399","title":"NPA: Improving Large-scale Graph Neural Networks with Non-parametric Attention","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Computer science; Parametric statistics; Scale (ratio); Artificial neural network; Graph; Artificial intelligence; Theoretical computer science; Mathematics; Statistics; Geography; Cartography","score_opus":0.005637450134911329,"score_gpt":0.22078396532566025,"score_spread":0.21514651519074893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398760296","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":[0.046648398,0.00095006125,0.94924194,0.00033316005,0.0011151605,0.00025000997,9.0339904e-7,0.0009008144,0.0005595625],"genre_scores_gemma":[0.93775016,0.00004817925,0.061111506,0.00037559017,0.00018592144,0.000028989087,0.000005084072,0.000030155721,0.00046440292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978822,0.00004015987,0.00026540805,0.00080369855,0.00033018456,0.0006783761],"domain_scores_gemma":[0.9989746,0.00013932021,0.00006521987,0.0006008245,0.000064677144,0.00015532224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021669696,0.0002589999,0.00020769662,0.0003923076,0.00018844035,0.00049683906,0.0006602391,0.00009671036,0.000012428406],"category_scores_gemma":[0.000006933453,0.00019071103,0.0001439499,0.003890218,0.000051435254,0.0013453129,0.00028796305,0.00046369317,0.000025411655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055506465,0.00023077377,0.012888055,0.00014034202,0.000151896,0.00070024724,0.0002985815,0.32978246,0.0012554545,0.035855357,0.0063653556,0.61227596],"study_design_scores_gemma":[0.00024336223,0.00015471705,0.002532128,0.000042254964,0.000012268167,0.000073602285,0.00001785104,0.9953851,0.000079523285,0.0009300545,0.0002470066,0.00028212753],"about_ca_topic_score_codex":0.000015891916,"about_ca_topic_score_gemma":0.00004649574,"teacher_disagreement_score":0.8911018,"about_ca_system_score_codex":0.000033636978,"about_ca_system_score_gemma":0.000020391031,"threshold_uncertainty_score":0.777697},"labels":[],"label_agreement":null},{"id":"W4398762212","doi":"10.4230/tgdk.1.1.8","title":"Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges","year":2023,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; UC Berkeley College of Chemistry; Agence Nationale de la Recherche; Institute for Catastrophic Loss Reduction","keywords":"Knowledge graph; Data science; Computer science; Knowledge management; Artificial intelligence","score_opus":0.04855171458216422,"score_gpt":0.3062091465260229,"score_spread":0.25765743194385865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398762212","genre_codex":"review","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":[0.2655069,0.3365154,0.075719364,0.1283786,0.0009975116,0.0014630889,0.000018018927,0.0044110413,0.18699007],"genre_scores_gemma":[0.82947934,0.11051905,0.05261192,0.000048495665,0.000084890744,0.000056535784,0.000039626302,0.000053706673,0.0071064522],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945092,0.0038740027,0.00021470507,0.0006565009,0.00029974725,0.00044582505],"domain_scores_gemma":[0.9956439,0.0025698915,0.00010155901,0.00073398906,0.0007703558,0.00018028969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061777295,0.0001644129,0.00017310033,0.00034427436,0.0008676854,0.00028761357,0.0007110597,0.00009600626,0.0000055310643],"category_scores_gemma":[0.0007048665,0.00016279974,0.000041842843,0.0014798669,0.00026266827,0.0003238554,0.0013142197,0.00060777564,0.000018000057],"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":[0.0000025183815,0.000044283446,0.001003418,0.000057482113,0.000009826163,0.000008225504,0.0112058325,0.000005232904,0.0006466569,0.5818055,0.00014949295,0.4050615],"study_design_scores_gemma":[0.001420447,0.0000060899697,0.02322186,0.0015191651,0.000015273465,0.000114308496,0.0022562884,0.3834758,0.0060353447,0.13240488,0.44850376,0.0010267668],"about_ca_topic_score_codex":0.00004444099,"about_ca_topic_score_gemma":0.00043784137,"teacher_disagreement_score":0.5639724,"about_ca_system_score_codex":0.000019422641,"about_ca_system_score_gemma":0.000035344612,"threshold_uncertainty_score":0.6673622},"labels":[],"label_agreement":null},{"id":"W4399092568","doi":"10.1007/978-981-97-2303-4_8","title":"Graph-Enforced Neural Network for Attributed Graph Clustering","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Computer science; Cluster analysis; Graph; Clustering coefficient; Theoretical computer science; Artificial intelligence","score_opus":0.020977182215086345,"score_gpt":0.256839506924215,"score_spread":0.23586232470912863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399092568","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":[0.00002500031,0.0019448409,0.98619235,0.0008306813,0.008297589,0.0011808076,0.000023588816,0.0007481229,0.0007570531],"genre_scores_gemma":[0.06018658,0.0001824349,0.9315608,0.0040017636,0.002527031,0.00011661009,0.0000553274,0.00021012456,0.0011593115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935688,0.00003346465,0.0009081718,0.002754162,0.0010030702,0.0017323575],"domain_scores_gemma":[0.99605364,0.0010237021,0.0003811823,0.0018972824,0.00031089998,0.00033331168],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009065612,0.0010373236,0.00095116766,0.0011903786,0.0005465661,0.0010185818,0.0047157938,0.00053747837,0.0000070706114],"category_scores_gemma":[0.0000609148,0.0009543685,0.000545244,0.0023219495,0.00079425,0.0009779837,0.002401372,0.0015241185,0.000021939633],"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":[0.000016172686,0.0000073766964,0.000011397698,0.00007581602,0.000027672846,0.00011696023,0.00011424509,0.75711495,0.000038648654,0.04558339,0.00016606267,0.1967273],"study_design_scores_gemma":[0.00022651863,0.00017753085,0.000013100179,0.00037242495,0.000016004216,0.00010812974,5.0773373e-8,0.5960579,0.00007705826,0.40054348,0.0017339328,0.0006738598],"about_ca_topic_score_codex":0.000009992617,"about_ca_topic_score_gemma":0.00013777647,"teacher_disagreement_score":0.3549601,"about_ca_system_score_codex":0.00023083847,"about_ca_system_score_gemma":0.00019821752,"threshold_uncertainty_score":0.9992907},"labels":[],"label_agreement":null},{"id":"W4399107553","doi":"10.1016/j.media.2024.103225","title":"MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Graph; Machine learning; Theoretical computer science","score_opus":0.01196817928201128,"score_gpt":0.30379009180225885,"score_spread":0.2918219125202476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399107553","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":[0.0036442578,0.0010028618,0.9883079,0.0055846847,0.00012369227,0.00011068063,0.000010063545,0.0007820937,0.0004337589],"genre_scores_gemma":[0.9115425,0.00043142072,0.085563265,0.0010064377,0.0003439593,0.000036213587,0.0005334373,0.000044766806,0.0004980433],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9928723,0.00037207254,0.0006515278,0.0018908613,0.0034665335,0.0007467118],"domain_scores_gemma":[0.9957803,0.00074817,0.00012114858,0.0022353283,0.00013956235,0.00097550434],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018895041,0.00037535155,0.00087540277,0.0016499723,0.00023967958,0.0005300109,0.0041197366,0.00024660167,0.0014890603],"category_scores_gemma":[0.00073359685,0.00026197982,0.00058872515,0.020624306,0.00048400467,0.0014134282,0.0015297516,0.001418776,0.00009566909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007681838,0.00081232295,0.06329954,0.00021623987,0.07313492,0.02807706,0.0013218403,0.03781097,0.00013062077,0.007156212,0.008755189,0.7792083],"study_design_scores_gemma":[0.0002228331,0.000059215938,0.002032006,0.00005467967,0.004349027,0.00004005335,0.000025958807,0.990177,0.000026610285,0.00027440875,0.0023890138,0.0003492279],"about_ca_topic_score_codex":0.0002125929,"about_ca_topic_score_gemma":0.00085538265,"teacher_disagreement_score":0.952366,"about_ca_system_score_codex":0.000036538822,"about_ca_system_score_gemma":0.00025108244,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W4399477611","doi":"10.1145/3626232.3653257","title":"Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Link (geometry); Graph; Theoretical computer science; Distributed computing; Computer network","score_opus":0.028377776490004118,"score_gpt":0.2552443997413436,"score_spread":0.2268666232513395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399477611","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":[0.0022663216,0.0007789906,0.98985714,0.0024612618,0.0012714652,0.00040773943,0.000011946125,0.0024882234,0.00045690904],"genre_scores_gemma":[0.6412287,0.00018288566,0.35646525,0.00027000744,0.00067967793,0.0001561022,0.0001335236,0.00007121211,0.0008126811],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978047,0.00006993733,0.00036966955,0.0008406342,0.00031085074,0.0006042331],"domain_scores_gemma":[0.998584,0.0004605776,0.000075010576,0.0006139534,0.000119649725,0.00014681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003490544,0.00026113915,0.00020679469,0.00030251723,0.00042601296,0.0005968229,0.0010956401,0.00011910757,0.000019279732],"category_scores_gemma":[0.00015890138,0.00023830964,0.00023122814,0.0016437151,0.00004535127,0.0015412101,0.00062389177,0.0005366733,0.000010117764],"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":[0.000028191005,0.000044541346,0.002743473,0.00026420195,0.00016803718,0.00005322133,0.0008116034,0.723335,0.0017399875,0.06721437,0.021132719,0.18246461],"study_design_scores_gemma":[0.00023521534,0.00009351734,0.0007353425,0.00015206606,0.000016101887,0.000018547937,0.00003032033,0.9337178,0.0005280808,0.04662209,0.017586118,0.00026481028],"about_ca_topic_score_codex":0.000013157649,"about_ca_topic_score_gemma":0.000004932889,"teacher_disagreement_score":0.6389623,"about_ca_system_score_codex":0.000055229862,"about_ca_system_score_gemma":0.000035679073,"threshold_uncertainty_score":0.9717985},"labels":[],"label_agreement":null},{"id":"W4399510106","doi":"10.1016/j.procs.2024.09.567","title":"Link Prediction in Bipartite Networks","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Galatasaray Üniversitesi; Providence Health Care","keywords":"Bipartite graph; Computer science; Recommender system; Benchmark (surveying); Link (geometry); Matching (statistics); Theoretical computer science; Graph; Task (project management); Heuristic; Artificial intelligence; Machine learning; Computer network; Mathematics","score_opus":0.008967276321014407,"score_gpt":0.23833898569584214,"score_spread":0.22937170937482773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399510106","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":[0.0063016866,0.0009895036,0.9859782,0.0011542911,0.004105369,0.00023049503,6.8400055e-7,0.0009010247,0.00033875753],"genre_scores_gemma":[0.8945465,0.00007606058,0.10390475,0.0005504193,0.00083370984,0.000041138035,0.0000011573278,0.0000134087495,0.000032839405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972887,0.000028336559,0.00033377012,0.0011095108,0.0005257712,0.0007138882],"domain_scores_gemma":[0.9989717,0.00013467902,0.000045738372,0.0005503599,0.000100913036,0.0001966124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007368947,0.00019899731,0.0001573205,0.0005497738,0.00016899644,0.0008357151,0.0017540493,0.000070656286,0.000004256709],"category_scores_gemma":[0.000027398703,0.00017917332,0.000055196022,0.0057512047,0.00027497014,0.0031042206,0.0007422406,0.00042453522,0.00006786902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045639076,0.00004333071,0.003776469,0.000051549265,0.000005894837,0.0001513221,0.0010458145,0.11406386,0.0007765364,0.065299764,0.0013404642,0.81344044],"study_design_scores_gemma":[0.00008810045,0.00007326936,0.004257622,0.0001242436,0.00000142755,0.00006022692,0.0000010419774,0.9872361,0.0005106696,0.0057308716,0.0017312927,0.00018514937],"about_ca_topic_score_codex":0.0000028490333,"about_ca_topic_score_gemma":0.0000054094903,"teacher_disagreement_score":0.8882448,"about_ca_system_score_codex":0.00010333253,"about_ca_system_score_gemma":0.00020074543,"threshold_uncertainty_score":0.805882},"labels":[],"label_agreement":null},{"id":"W4399765342","doi":"10.1101/2024.06.17.599219","title":"Path-based reasoning for biomedical knowledge graphs with BioPathNet","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; HEC Montréal; Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Cegep Edouard Montpetit","funders":"Bundesministerium für Bildung und Forschung; National Institutes of Health; Joachim Herz Stiftung","keywords":"Path (computing); Knowledge graph; Computer science; Artificial intelligence; Data science; Programming language","score_opus":0.01147742921684764,"score_gpt":0.2321011121363298,"score_spread":0.22062368291948217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399765342","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":[0.0774421,0.011603566,0.89688724,0.001034114,0.0059889005,0.0025860118,0.000430917,0.004009839,0.00001728237],"genre_scores_gemma":[0.7711248,0.00008064747,0.22701932,0.00025094123,0.0005094795,0.0008036578,0.0000010148677,0.00020513884,0.000005001146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949348,0.00014663704,0.0007088271,0.0024323498,0.0006095914,0.0011678218],"domain_scores_gemma":[0.9956426,0.0002732143,0.00044628332,0.0024393946,0.0005935546,0.00060494995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007628675,0.0009824933,0.0008601916,0.0007763663,0.00027813064,0.000652753,0.002228392,0.0007195375,0.0000044420467],"category_scores_gemma":[0.00014889498,0.0008776074,0.00036387713,0.002309204,0.00034878493,0.00024314265,0.0015035603,0.001498632,0.000051262527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005898733,0.0025245412,0.0037551234,0.013832275,0.0017857996,0.0029422296,0.00017913594,0.0037757177,0.33024237,0.6220024,0.01786818,0.0005023637],"study_design_scores_gemma":[0.004898991,0.002021099,0.007273036,0.015053283,0.00077253237,5.713663e-7,0.0000051351985,0.77705365,0.1332905,0.002202929,0.049137827,0.008290462],"about_ca_topic_score_codex":0.0000084400635,"about_ca_topic_score_gemma":0.0000025495247,"teacher_disagreement_score":0.77327794,"about_ca_system_score_codex":0.00023646926,"about_ca_system_score_gemma":0.0012739764,"threshold_uncertainty_score":0.9993675},"labels":[],"label_agreement":null},{"id":"W4400153839","doi":"10.1007/978-3-031-60916-9_6","title":"Results","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science","score_opus":0.015864227008432284,"score_gpt":0.25743416079707465,"score_spread":0.24156993378864236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400153839","genre_codex":"methods","genre_gemma":"other","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":[8.9039764e-7,0.008075492,0.6940161,0.002764202,0.0035474722,0.00040846312,0.000020542553,0.0006507482,0.2905161],"genre_scores_gemma":[0.38702545,0.0070260894,0.072442755,0.02006712,0.07089062,0.00022157101,0.0008296592,0.0017660616,0.4397307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99677676,0.000039307513,0.0006815857,0.0012842646,0.00046821134,0.0007498788],"domain_scores_gemma":[0.99812424,0.000654289,0.00028141635,0.0007567452,0.000074624506,0.000108712695],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00024807637,0.0006955174,0.0006944979,0.00027413605,0.00020321968,0.0002362128,0.0013571684,0.0015640592,0.000022624243],"category_scores_gemma":[0.00004946884,0.00067518605,0.00039686417,0.0004652745,0.00019422088,0.00017969012,0.0006846112,0.0031146258,0.00009631341],"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":[0.000047501246,0.0000114051345,0.000002091456,0.000031149422,0.000076533375,0.0005453779,0.00045126196,0.06815619,0.0000013395761,0.6317884,0.008084678,0.29080412],"study_design_scores_gemma":[0.0002575714,0.000051983778,0.0000076423075,0.00029437928,0.000025488363,0.000015697156,3.169989e-7,0.048387975,0.0000035363944,0.79994226,0.15030693,0.0007062155],"about_ca_topic_score_codex":0.0000059536396,"about_ca_topic_score_gemma":0.00016866201,"teacher_disagreement_score":0.6215733,"about_ca_system_score_codex":0.0002051781,"about_ca_system_score_gemma":0.000061398816,"threshold_uncertainty_score":0.99973214},"labels":[],"label_agreement":null},{"id":"W4400439379","doi":"10.31219/osf.io/9wk3y","title":"Graph Neural Network, ChebNet, Graph Convolutional Network, and Graph Autoencoder: Tutorial and Survey","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Voltage graph; Autoencoder; Butterfly graph; Null graph; Computer science; Line graph; Graph; Simplex graph; Strength of a graph; Complement graph; Quartic graph; Theoretical computer science; Artificial intelligence; Deep learning","score_opus":0.024025942943237916,"score_gpt":0.2560355513376157,"score_spread":0.23200960839437776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400439379","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":[0.04000909,0.07327596,0.8075264,0.0030839534,0.06400122,0.004026252,0.0003546325,0.0046348963,0.0030876067],"genre_scores_gemma":[0.7545721,0.011593819,0.20897117,0.0040376913,0.016296236,0.00072532735,0.0008825302,0.00047833324,0.0024428144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9921533,0.000763698,0.0011854088,0.0031456733,0.00095375587,0.0017981557],"domain_scores_gemma":[0.99581456,0.0011038611,0.00047416834,0.0015934661,0.0003117138,0.00070221233],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0017338194,0.0013363254,0.0012875688,0.00048235527,0.00051923486,0.0012876935,0.0018308315,0.0009714183,0.000022797098],"category_scores_gemma":[0.0000709597,0.0012411992,0.00047951852,0.0020710432,0.0008629761,0.00056121894,0.008033955,0.0030910464,0.000012718617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002057043,0.000099806064,0.02194659,0.00034769427,0.0007367347,0.0002499621,0.00020212318,0.5087336,0.000012525817,0.2982245,0.15674321,0.012497571],"study_design_scores_gemma":[0.00064688356,0.00014719108,0.031454492,0.00021048565,0.00008747992,0.00014947375,0.0000036163613,0.37990588,0.000002407816,0.5841269,0.001922453,0.0013427582],"about_ca_topic_score_codex":0.0004986287,"about_ca_topic_score_gemma":0.0012907786,"teacher_disagreement_score":0.714563,"about_ca_system_score_codex":0.00006530808,"about_ca_system_score_gemma":0.00024285106,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W4400726464","doi":"10.1093/bioadv/vbae097","title":"Knowledge graph embeddings in the biomedical domain: are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks","year":2024,"lang":"en","type":"review","venue":"Bioinformatics Advances","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Engineering and Physical Sciences Research Council; UK Research and Innovation; Institute for Catastrophic Loss Reduction; Science and Technology Facilities Council; European Commission; Dell EMC; Accenture; Cisco Systems","keywords":"Computer science; Interpretability; Embedding; Machine learning; Artificial intelligence; Knowledge graph; Field (mathematics); Downstream (manufacturing); Graph; Data science; Theoretical computer science","score_opus":0.021973077866220783,"score_gpt":0.3223211017238577,"score_spread":0.3003480238576369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400726464","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016733491,0.9891195,0.0071534025,0.00020778086,0.0010957049,0.0010043929,0.00009886292,0.0003474573,0.0009561108],"genre_scores_gemma":[0.000031568503,0.9894068,0.009514823,0.000112647154,0.0003510161,0.00021826127,0.00011783122,0.00004431693,0.00020272394],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962911,0.00028047324,0.0013834581,0.00067603315,0.0006287269,0.0007401815],"domain_scores_gemma":[0.9974239,0.0007137192,0.0008578463,0.000697356,0.00007001123,0.00023719604],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008858414,0.00079857185,0.0012144024,0.00076386513,0.00040103888,0.00046397216,0.0017340397,0.00034530557,0.0000068663344],"category_scores_gemma":[0.000104879495,0.0004711975,0.0004032169,0.0018479379,0.00041187377,0.0012641248,0.0010937877,0.0015092819,0.00014349396],"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":[0.0000050641584,0.000049188195,0.000069338515,0.008036601,0.000079041674,0.000049590468,0.0023045752,0.000019705592,8.809108e-8,0.0011574134,0.002543922,0.98568547],"study_design_scores_gemma":[0.00027063783,0.000105660736,0.0000071650197,0.006511226,0.00015729791,0.00042814075,0.00024163743,0.005476393,3.145072e-7,0.0030825553,0.9832287,0.0004902993],"about_ca_topic_score_codex":0.0000023211815,"about_ca_topic_score_gemma":0.000021671984,"teacher_disagreement_score":0.98519516,"about_ca_system_score_codex":0.00014593275,"about_ca_system_score_gemma":0.00014724833,"threshold_uncertainty_score":0.999774},"labels":[],"label_agreement":null},{"id":"W4400909954","doi":"10.1109/icde60146.2024.00027","title":"Graph Contrastive Learning for Truth Inference","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Inference; Artificial intelligence; Graph; Natural language processing; Theoretical computer science","score_opus":0.016924933562651567,"score_gpt":0.2879953825098143,"score_spread":0.27107044894716276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400909954","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":[0.00114063,0.00032857098,0.9943678,0.00033804972,0.0006688461,0.00014803949,7.2737106e-7,0.0007006699,0.0023066618],"genre_scores_gemma":[0.95230895,0.000026058822,0.04676087,0.00016106616,0.00006613031,0.000036669702,0.0000015298821,0.000008382081,0.0006303623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991567,0.00002058596,0.00011525322,0.00035816885,0.00009907031,0.0002502186],"domain_scores_gemma":[0.9986749,0.0010525547,0.000019625735,0.00014527905,0.000047444435,0.000060158287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000972437,0.00010163705,0.00009816503,0.00008827094,0.0000881669,0.00018681042,0.00032537893,0.000035576926,0.000009313511],"category_scores_gemma":[0.000099808465,0.00008089688,0.000076191594,0.0004636209,0.000034477758,0.0005081576,0.000081087295,0.00017412857,0.000021776332],"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":[0.0000037179707,0.0000052180526,0.00010146175,0.0000113685755,0.000013437712,0.000008974866,0.000120209115,0.0038637784,0.00020363326,0.89646614,0.00040478815,0.09879729],"study_design_scores_gemma":[0.00016873296,0.00016597274,0.00043277867,0.000046296474,0.00000517235,0.0000074172503,0.000021300808,0.7725297,0.0010842646,0.21685922,0.008479285,0.00019982464],"about_ca_topic_score_codex":0.0000016859815,"about_ca_topic_score_gemma":0.000003232202,"teacher_disagreement_score":0.9511683,"about_ca_system_score_codex":0.000010647772,"about_ca_system_score_gemma":0.000024609513,"threshold_uncertainty_score":0.32988793},"labels":[],"label_agreement":null},{"id":"W4401023415","doi":"10.24963/ijcai.2024/960","title":"GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Graph; Artificial neural network; Natural language processing; Cognitive science; Theoretical computer science; Psychology","score_opus":0.009886258434646859,"score_gpt":0.25274345691565603,"score_spread":0.24285719848100917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401023415","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":[0.009888032,0.004265341,0.98323464,0.00043226694,0.000872712,0.0002955352,8.865709e-7,0.000863755,0.00014684236],"genre_scores_gemma":[0.9772808,0.0003084308,0.021938704,0.00012245857,0.00008704529,0.000029534913,0.000012985227,0.000021389495,0.00019867532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983196,0.000061048195,0.0002673404,0.0006915029,0.00020380905,0.00045668555],"domain_scores_gemma":[0.9992822,0.00011195096,0.000051016104,0.00035620795,0.00006200774,0.00013661658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018401603,0.00023075606,0.00016265328,0.00020472721,0.00019512829,0.0004218247,0.00034148726,0.00014081378,0.000009446554],"category_scores_gemma":[0.0000049690084,0.00020682298,0.00009407434,0.0012280421,0.0000810464,0.0012477328,0.00017303275,0.00038143175,0.000021459926],"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":[0.000010551871,0.000029178238,0.0015347743,0.000056011508,0.00005414364,0.00007467635,0.00003330189,0.5194303,0.00060486526,0.07049707,0.00021370286,0.40746146],"study_design_scores_gemma":[0.000121191944,0.000053721807,0.0039939447,0.00014734287,0.000011322113,0.000050657916,0.000018651328,0.95565456,0.000008240476,0.03963704,0.00006730218,0.00023603166],"about_ca_topic_score_codex":0.0000129398595,"about_ca_topic_score_gemma":0.00006095149,"teacher_disagreement_score":0.96739274,"about_ca_system_score_codex":0.00007543634,"about_ca_system_score_gemma":0.000023726361,"threshold_uncertainty_score":0.84339964},"labels":[],"label_agreement":null},{"id":"W4401025067","doi":"10.24963/ijcai.2024/388","title":"GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Mitacs","keywords":"Embedding; Scalability; Computer science; Constellation; Graph; Theoretical computer science; Artificial intelligence; Database; Physics","score_opus":0.011449786905889476,"score_gpt":0.23726997838516783,"score_spread":0.22582019147927837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401025067","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":[0.030676944,0.0015253299,0.96463555,0.0003087055,0.0006746269,0.00016067966,6.74492e-7,0.0007788309,0.0012386822],"genre_scores_gemma":[0.8924695,0.00001464839,0.10706782,0.00029286832,0.00006175589,0.0000028569236,8.295964e-7,0.00001408463,0.000075631826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844,0.00005109221,0.00021723456,0.0006440523,0.000249365,0.000398245],"domain_scores_gemma":[0.99922556,0.00026818612,0.00003796413,0.00029719365,0.000042033556,0.0001290869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020453858,0.00019750987,0.00017136727,0.0001768185,0.00018738434,0.00025823453,0.00037739123,0.000055286284,0.0000042411352],"category_scores_gemma":[0.0000072320863,0.00016597775,0.000081106904,0.0008589229,0.00009942098,0.00026078292,0.00040165253,0.00027072142,0.000021816724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014289237,0.000052828083,0.0003916457,0.000054500673,0.000058401514,0.0004752651,0.00170299,0.09162449,0.0020281954,0.5397338,0.0008887755,0.36297476],"study_design_scores_gemma":[0.00012299811,0.000071311835,0.0004365794,0.00007792254,0.0000066417424,0.00009744228,0.000026130401,0.98625547,0.00046845173,0.011777289,0.0004380549,0.00022173437],"about_ca_topic_score_codex":0.000015017213,"about_ca_topic_score_gemma":0.000007239633,"teacher_disagreement_score":0.89463097,"about_ca_system_score_codex":0.000020855596,"about_ca_system_score_gemma":0.000014866603,"threshold_uncertainty_score":0.6768376},"labels":[],"label_agreement":null},{"id":"W4401114094","doi":"10.1109/tnse.2024.3435839","title":"GAXG: A Global and Self-Adaptive Optimal Graph Topology Generation Framework for Explaining Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Topology (electrical circuits); Graph; Topological graph theory; Artificial neural network; Mathematics; Theoretical computer science; Voltage graph; Artificial intelligence; Line graph; Combinatorics","score_opus":0.014399628574331453,"score_gpt":0.2477316796725518,"score_spread":0.23333205109822036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401114094","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":[0.012324087,0.001595467,0.9817496,0.00024136298,0.003247706,0.00028374617,0.000003888689,0.0005438224,0.000010340623],"genre_scores_gemma":[0.76512176,0.00032979465,0.23395516,0.00017117972,0.00032432922,0.00008041126,5.1975815e-7,0.000014506789,0.0000023521566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979381,0.000022333317,0.00023735699,0.00079845684,0.00025923026,0.0007445046],"domain_scores_gemma":[0.9990941,0.00030602713,0.000036054105,0.0002638429,0.000075969794,0.00022398225],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004223403,0.0002751615,0.00020982254,0.0002165858,0.00068280776,0.00044990832,0.000360419,0.00013924392,9.568107e-7],"category_scores_gemma":[0.000008983098,0.0002692334,0.00008184688,0.0022788872,0.00019734794,0.0011529753,0.000014217189,0.0004028546,4.7242162e-7],"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":[0.0000091243965,0.000011088056,0.000004443103,0.000008851177,0.000021352584,0.0000092556575,0.00016296901,0.9135336,0.00007392685,0.039911233,0.00004428273,0.04620984],"study_design_scores_gemma":[0.00012475287,0.00025807676,0.00006134886,0.00006994312,0.000022346012,0.000096090276,0.00001901795,0.9962385,0.00013389072,0.002594272,0.000102711165,0.0002790253],"about_ca_topic_score_codex":0.0000037718312,"about_ca_topic_score_gemma":0.0000070922342,"teacher_disagreement_score":0.75279766,"about_ca_system_score_codex":0.00007367016,"about_ca_system_score_gemma":0.000051151197,"threshold_uncertainty_score":0.999976},"labels":[],"label_agreement":null},{"id":"W4401596715","doi":"10.1109/access.2024.3443533","title":"INFLECT-DGNN: Influencer Prediction With Dynamic Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Alliance de recherche numérique du Canada; Canada Research Chairs","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.011190684527242988,"score_gpt":0.28080894994596445,"score_spread":0.26961826541872147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401596715","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":[0.13259082,0.0012207383,0.8614377,0.00039785946,0.0026358357,0.00027936872,0.0000037247664,0.0011818616,0.00025209825],"genre_scores_gemma":[0.9971162,0.000113011316,0.0016395135,0.0006999154,0.00024960432,0.000072739094,0.0000053223685,0.00003317051,0.00007053415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807096,0.000055335066,0.00027342222,0.00072174653,0.000361304,0.0005172203],"domain_scores_gemma":[0.99893796,0.00012765295,0.00007427258,0.00064057065,0.000082112354,0.00013743919],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000108642,0.0002705055,0.00018586892,0.00025711372,0.00019219013,0.0010502418,0.0014155748,0.000113018585,0.000008320418],"category_scores_gemma":[0.0000057234247,0.00021185241,0.00008504157,0.0023746246,0.00012633098,0.004235941,0.00020841697,0.0005392901,0.00001206842],"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":[0.00002892858,0.000021751763,0.004178436,0.000034981218,0.000055055152,0.0002045088,0.00013627061,0.92401814,0.00023218761,0.0015425185,0.0017140874,0.06783311],"study_design_scores_gemma":[0.00017895135,0.00012824291,0.0073621287,0.00009897723,0.000017069879,0.000097407676,0.0000021593282,0.9863213,0.00018369552,0.004620841,0.0007166388,0.00027258915],"about_ca_topic_score_codex":0.000020513879,"about_ca_topic_score_gemma":0.000067954774,"teacher_disagreement_score":0.8645254,"about_ca_system_score_codex":0.00005553823,"about_ca_system_score_gemma":0.000039368908,"threshold_uncertainty_score":0.99998677},"labels":[],"label_agreement":null},{"id":"W4401747735","doi":"10.1109/tsp.2024.3446453","title":"Spectral Graph Learning With Core Eigenvectors Prior via Iterative GLASSO and Projection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Eigenvalues and eigenvectors; Computer science; Graph; Spectral graph theory; Projection (relational algebra); Core (optical fiber); Iterative method; Artificial intelligence; Mathematics; Algorithm; Theoretical computer science; Voltage graph; Telecommunications; Physics; Line graph","score_opus":0.015915706547275296,"score_gpt":0.2551145788556691,"score_spread":0.23919887230839382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401747735","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":[0.061664157,0.0003574626,0.9367283,0.00011386388,0.0002071055,0.0002321016,0.0000011669167,0.0006018938,0.000093930474],"genre_scores_gemma":[0.98418236,0.000024437411,0.015432978,0.000056879297,0.0000669554,0.000036637706,7.6197836e-7,0.000029261888,0.00016974944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985305,0.000051175997,0.00019168819,0.0006203484,0.00028419986,0.00032210775],"domain_scores_gemma":[0.9995395,0.000115359224,0.000063565545,0.00011449736,0.00007005084,0.00009702829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109310655,0.00024530222,0.00016537977,0.00032643462,0.0006276771,0.00047176273,0.00015604327,0.000073436175,0.000008041287],"category_scores_gemma":[0.0000010433921,0.00019687421,0.000063047664,0.0012920266,0.00011556637,0.0013990161,0.0000023174919,0.00071635394,0.0000053265026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114916824,0.000075945754,0.00010277113,0.00015400055,0.00007175518,0.000104473795,0.003384237,0.118313506,0.013032301,0.0002479675,0.000008343532,0.8643898],"study_design_scores_gemma":[0.0003178451,0.0008169691,0.00016922918,0.00050785585,0.000045249686,0.0003285546,0.00011264278,0.96506685,0.03013356,0.0019578796,0.00012324208,0.00042013667],"about_ca_topic_score_codex":0.00000690534,"about_ca_topic_score_gemma":0.00002321534,"teacher_disagreement_score":0.9225182,"about_ca_system_score_codex":0.00007137582,"about_ca_system_score_gemma":0.00008640908,"threshold_uncertainty_score":0.80282974},"labels":[],"label_agreement":null},{"id":"W4402078490","doi":"10.1007/s10489-024-05767-6","title":"Temporal knowledge graph reasoning based on evolutional representation and contrastive learning","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Graph Neural Networks","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 Waterloo","funders":"","keywords":"Computer science; Graph; Representation (politics); Artificial intelligence; Knowledge representation and reasoning; Knowledge graph; Natural language processing; Theoretical computer science","score_opus":0.017138245874816858,"score_gpt":0.28678469990661604,"score_spread":0.26964645403179915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402078490","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":[0.0023550761,0.00073448877,0.9904006,0.00014400932,0.00032579247,0.0001959759,0.0000010709979,0.000380748,0.0054622535],"genre_scores_gemma":[0.97656536,0.000042131112,0.023024593,0.00011178635,0.000079167876,0.000048497284,0.000007464993,0.000013404576,0.00010757872],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861455,0.00005186866,0.00020345607,0.0006537254,0.0002165494,0.0002598522],"domain_scores_gemma":[0.99891716,0.00066584646,0.000052529107,0.00022335743,0.000052470994,0.00008864635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020972185,0.00016714477,0.00013099586,0.00019024823,0.00018733666,0.00016330823,0.00028347914,0.00006089095,0.000011024234],"category_scores_gemma":[0.00006297629,0.00015837484,0.000050329363,0.0009040071,0.00012818041,0.0002649273,0.00010082915,0.00039517228,0.00006704135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003525678,0.0000316942,0.00071680645,0.000021061109,0.00001657983,0.000027257021,0.0005458433,0.0824293,0.00065102713,0.72182286,0.00030355787,0.19339876],"study_design_scores_gemma":[0.000060343827,0.00008960066,0.0010875262,0.00012008124,0.0000052715764,0.000009513898,0.00006779941,0.9569802,0.0032722391,0.037209693,0.0008983117,0.00019937208],"about_ca_topic_score_codex":0.000007310133,"about_ca_topic_score_gemma":0.0000037468508,"teacher_disagreement_score":0.9742103,"about_ca_system_score_codex":0.000043435055,"about_ca_system_score_gemma":0.000048152193,"threshold_uncertainty_score":0.64583385},"labels":[],"label_agreement":null},{"id":"W4403311984","doi":"10.1016/j.neunet.2024.106793","title":"Generalization limits of Graph Neural Networks in identity effects learning","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Gruppo Nazionale per il Calcolo Scientifico; Istituto Nazionale di Alta Matematica \"Francesco Severi\"","keywords":"Generalization; Computer science; Artificial neural network; Graph; Artificial intelligence; Machine learning; Mathematics; Theoretical computer science","score_opus":0.011614713910497529,"score_gpt":0.2606296068351501,"score_spread":0.24901489292465254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403311984","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":[0.15176189,0.008510318,0.83461064,0.0002216765,0.0037340985,0.0004334233,5.419029e-7,0.0006259648,0.00010144532],"genre_scores_gemma":[0.9965623,0.0006288138,0.0016882645,0.0003964471,0.0005408264,0.00003969591,0.000020443087,0.000056738816,0.000066445675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655575,0.000460806,0.000697081,0.00093142915,0.00047627042,0.00087866717],"domain_scores_gemma":[0.99831975,0.0006261866,0.00020350405,0.0005830385,0.00009036321,0.00017715692],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004134693,0.00042736757,0.0004949079,0.00044578643,0.00016009682,0.00034927964,0.0010754995,0.00028432274,0.000006367423],"category_scores_gemma":[0.00007023992,0.00041297873,0.00026866887,0.0038750006,0.000119566765,0.0022951951,0.000374706,0.0012794696,0.0000036801189],"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":[0.00001617218,0.000028460649,0.005747571,0.00006101432,0.00001839684,0.00018009428,0.00008342248,0.9151893,0.00018878246,0.01160714,0.0002884288,0.06659119],"study_design_scores_gemma":[0.00032021373,0.00018139616,0.007883656,0.00018077756,0.000018024306,0.00004095095,0.0000036387396,0.9881892,0.00011949636,0.0025953709,0.00010204703,0.00036521387],"about_ca_topic_score_codex":0.000037111768,"about_ca_topic_score_gemma":0.000115183524,"teacher_disagreement_score":0.8448004,"about_ca_system_score_codex":0.000058542664,"about_ca_system_score_gemma":0.000016732552,"threshold_uncertainty_score":0.9998322},"labels":[],"label_agreement":null},{"id":"W4403577871","doi":"10.1145/3627673.3679993","title":"Scalable Expressiveness through Preprocessed Graph Perturbations","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Scalability; Graph; Theoretical computer science; Operating system","score_opus":0.018489294763549376,"score_gpt":0.2705262755106895,"score_spread":0.2520369807471401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403577871","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":[0.0016325332,0.0017257431,0.97802263,0.0014738138,0.0005805591,0.0001326009,0.0000012162965,0.0011696114,0.015261292],"genre_scores_gemma":[0.8745213,0.00011623151,0.11790374,0.000838437,0.00011312208,0.00009123228,0.000003764687,0.000019883702,0.006392254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99882585,0.000024256082,0.00014931751,0.00053845183,0.0002040499,0.0002580933],"domain_scores_gemma":[0.9991572,0.00017830153,0.000022048313,0.0005233745,0.000063635875,0.000055436743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006327244,0.00012685622,0.00010021861,0.00007201076,0.00013960309,0.0003148342,0.0006537581,0.000049519447,0.00007733072],"category_scores_gemma":[0.000020727028,0.000099365854,0.00007213571,0.0011907057,0.000047495603,0.0019054285,0.00018021403,0.00013641948,0.00009246906],"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":[0.0000033459062,0.00005379783,0.00011166345,0.00008276731,0.000030375839,0.000057357094,0.0009899175,0.009283259,0.0047592283,0.9473893,0.017885838,0.019353155],"study_design_scores_gemma":[0.0001800068,0.000052951273,0.0002934772,0.00016473689,0.00001026388,0.00004644703,0.000042939017,0.29316586,0.025234744,0.62071466,0.05964167,0.00045224265],"about_ca_topic_score_codex":0.000008152787,"about_ca_topic_score_gemma":0.0000030932008,"teacher_disagreement_score":0.8728888,"about_ca_system_score_codex":0.000014266598,"about_ca_system_score_gemma":0.000043255743,"threshold_uncertainty_score":0.40520218},"labels":[],"label_agreement":null},{"id":"W4403582733","doi":"10.1145/3627673.3679541","title":"A Geometric Perspective for High-Dimensional Multiplex Graphs","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Embedding; Theoretical computer science; Computer science; Multiplex; Graph; Rendering (computer graphics); Topological graph theory; Topology (electrical circuits); Mathematics; Artificial intelligence; Voltage graph; Line graph; Combinatorics","score_opus":0.021853325448949405,"score_gpt":0.2857924850907185,"score_spread":0.26393915964176906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403582733","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":[0.0044135363,0.003510694,0.97881365,0.0034261893,0.005510969,0.0015801489,0.00007931065,0.0013413052,0.0013241749],"genre_scores_gemma":[0.58868575,0.00004331357,0.40841606,0.000597799,0.00026625488,0.00037058513,0.000027338532,0.000050465766,0.0015424486],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99686265,0.000041275238,0.00036251947,0.0017205157,0.0004670246,0.00054600916],"domain_scores_gemma":[0.99762076,0.0005447904,0.00015224023,0.0010732401,0.00044417093,0.00016482505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020567515,0.00046937983,0.0004553016,0.0011937593,0.00012577187,0.00027199165,0.0014687633,0.00031958998,0.000021822074],"category_scores_gemma":[0.00011901902,0.00039711388,0.000468875,0.0017926418,0.00008209985,0.00015453277,0.004432699,0.0010171882,0.000068537556],"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":[0.000017115359,0.00006625497,0.000010294971,0.00008262666,0.00015543899,0.000037313952,0.000116081246,0.019925633,0.00005881071,0.9534372,0.013029586,0.013063616],"study_design_scores_gemma":[0.00026756138,0.000084357955,0.00021282374,0.00006713988,0.000026973397,0.000012238566,0.000014176186,0.17955774,0.0003305888,0.8186804,0.00029083292,0.00045517774],"about_ca_topic_score_codex":0.00016309223,"about_ca_topic_score_gemma":0.000034392033,"teacher_disagreement_score":0.5842722,"about_ca_system_score_codex":0.00019091292,"about_ca_system_score_gemma":0.00015333826,"threshold_uncertainty_score":0.99984807},"labels":[],"label_agreement":null},{"id":"W4403722234","doi":"10.1109/tpami.2024.3486319","title":"Ensemble-Enhanced Semi-Supervised Learning With Optimized Graph Construction for High-Dimensional Data","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Guangzhou Municipal Science and Technology Project; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Ensemble learning; Graph; Machine learning; Semi-supervised learning; Pattern recognition (psychology); Theoretical computer science","score_opus":0.021512568547704728,"score_gpt":0.26880601588712194,"score_spread":0.24729344733941722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403722234","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":[0.0044379323,0.000201836,0.99398124,0.000388587,0.00037154573,0.00026202077,0.000074841395,0.00027212632,0.000009848174],"genre_scores_gemma":[0.9273399,0.00026544146,0.071976535,0.00016504325,0.000028928313,0.000050766565,0.00006221241,0.000021078185,0.000090061265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978946,0.00008711952,0.0003670415,0.0010552976,0.0002968807,0.00029908205],"domain_scores_gemma":[0.9985466,0.00042369368,0.000085585954,0.0007180323,0.0001011319,0.0001249425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022759664,0.0002913073,0.00037590993,0.00057458953,0.00032563542,0.00024632266,0.00054527813,0.000075433156,0.000047393325],"category_scores_gemma":[0.000004539164,0.00023005465,0.00017477738,0.0017026657,0.00011397465,0.00064624636,0.0000147340925,0.00042933694,0.0000064809738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054827353,0.000036618083,0.000023863626,0.00002235144,0.0005338182,0.000009283976,0.00006184211,0.46405977,0.00076487917,0.00021121155,0.0000066636717,0.53421485],"study_design_scores_gemma":[0.00021845025,0.00023091445,0.000029988088,0.00007335792,0.0005477232,0.000033167646,0.00002346175,0.9437478,0.053906765,0.00085736427,0.000031945965,0.00029902297],"about_ca_topic_score_codex":0.00018874969,"about_ca_topic_score_gemma":0.00035134543,"teacher_disagreement_score":0.922902,"about_ca_system_score_codex":0.000021876649,"about_ca_system_score_gemma":0.000029534547,"threshold_uncertainty_score":0.9381356},"labels":[],"label_agreement":null},{"id":"W4403944000","doi":"10.1016/j.ipm.2024.103942","title":"Spatial and temporal twin-guided pattern recurrent graph network for implementing reasoning of spatiotemporal knowledge graph","year":2024,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Graph Neural Networks","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":"Novelis (Canada)","funders":"","keywords":"Computer science; Graph; Knowledge graph; Theoretical computer science; Artificial intelligence","score_opus":0.020325392549721537,"score_gpt":0.29244897607838305,"score_spread":0.2721235835286615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403944000","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":[0.0046654083,0.0011078062,0.99136525,0.0002240539,0.0007026645,0.00073070393,0.0000081793005,0.00030938268,0.0008865331],"genre_scores_gemma":[0.91092515,0.0000663953,0.08847259,0.00014737554,0.00012844933,0.00012727983,0.00008291258,0.000015842896,0.0000339811],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980371,0.000033646786,0.000833039,0.00032807206,0.00030380674,0.00046432664],"domain_scores_gemma":[0.9990315,0.000053910193,0.00040905355,0.00026125016,0.00016912613,0.00007518939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007365914,0.00023588822,0.00022207006,0.0004030005,0.0002992195,0.0005725058,0.00039087815,0.000052813055,0.0000029242078],"category_scores_gemma":[0.000012849509,0.00022585323,0.0000997301,0.0009129934,0.000051795476,0.002761087,0.0003906869,0.00013985371,0.0000032459307],"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":[0.000009811242,0.0000149726175,0.001323527,0.0013367283,0.00004243138,0.0000019333763,0.0014191096,0.0007428213,0.0000017094205,0.011592545,0.0024748773,0.9810395],"study_design_scores_gemma":[0.0009654646,0.00016618607,0.004352717,0.0019052554,0.000072882984,0.000012639226,0.00024767552,0.9139822,0.0001468335,0.029245062,0.04833867,0.0005643797],"about_ca_topic_score_codex":0.000025180005,"about_ca_topic_score_gemma":0.00002874596,"teacher_disagreement_score":0.9804751,"about_ca_system_score_codex":0.000041450276,"about_ca_system_score_gemma":0.00004026854,"threshold_uncertainty_score":0.92100275},"labels":[],"label_agreement":null},{"id":"W4404292014","doi":"10.1007/s40747-024-01626-6","title":"Unveiling user identity across social media: a novel unsupervised gradient semantic model for accurate and efficient user alignment","year":2024,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computational intelligence; Identity (music); Computer science; Social media; Artificial intelligence; Human–computer interaction; World Wide Web; Physics","score_opus":0.10032716332197056,"score_gpt":0.3376359452917495,"score_spread":0.23730878196977895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404292014","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":[0.13801208,0.001639883,0.85558707,0.00025709695,0.002873612,0.0011585598,0.00008621492,0.00037360226,0.00001190304],"genre_scores_gemma":[0.993369,0.00006210117,0.005537396,0.00013432468,0.00036361697,0.00023576057,0.000026720665,0.000056971792,0.00021409537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963509,0.00008244995,0.0008510536,0.0011384279,0.00065730145,0.00091987866],"domain_scores_gemma":[0.99830854,0.0005083435,0.00016785991,0.0005736518,0.0001984249,0.0002432032],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00075658486,0.00045673043,0.00055431435,0.00016807794,0.00054772815,0.0011381209,0.0010289579,0.00012059327,0.0000040794207],"category_scores_gemma":[0.00004952835,0.00040860698,0.0002779642,0.0006659669,0.0001535847,0.00060135446,0.0006634875,0.00025786654,0.000037205868],"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":[0.000052553412,0.00029890754,0.00010476915,0.0012000864,0.00029601314,0.000057256664,0.020994892,0.6092895,0.006170973,0.35541072,0.0033392664,0.0027850266],"study_design_scores_gemma":[0.00035217317,0.00004103515,0.00015861812,0.0002535185,0.000033584474,0.000046668312,0.00038961132,0.993187,0.00036170462,0.0019502536,0.002782113,0.00044372244],"about_ca_topic_score_codex":0.000031278014,"about_ca_topic_score_gemma":0.00010962274,"teacher_disagreement_score":0.85535693,"about_ca_system_score_codex":0.00023370111,"about_ca_system_score_gemma":0.000054976186,"threshold_uncertainty_score":0.9998988},"labels":[],"label_agreement":null},{"id":"W4404346969","doi":"10.48550/arxiv.2410.23660","title":"Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Institute for Advanced Research; Nvidia","keywords":"Silo; Computer science; Business; Engineering; Mechanical engineering","score_opus":0.05321270480084002,"score_gpt":0.22365206452061548,"score_spread":0.17043935971977547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404346969","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":[0.31846878,0.00016935807,0.68001235,0.000052161628,0.00035570512,0.00035353444,0.000011006479,0.00038376433,0.00019335675],"genre_scores_gemma":[0.9944995,0.000064137865,0.0035157478,0.00006628553,0.00005030929,0.000008115902,0.0000349402,0.000053027816,0.0017079291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969606,0.000084236075,0.000336915,0.001819918,0.00010976212,0.0006885883],"domain_scores_gemma":[0.99864393,0.00016318903,0.00016447098,0.0006956333,0.0001606667,0.00017209313],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002692303,0.00046599898,0.000489123,0.0004944428,0.00030638932,0.00043913047,0.0014271848,0.0003917723,0.0000029379491],"category_scores_gemma":[0.000041873373,0.0005529139,0.00032119936,0.0011488124,0.00018308115,0.0005096803,0.0030192009,0.0015902817,0.0000307866],"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":[0.000049872127,0.000023327248,0.0007119232,0.00013092389,0.000044269334,0.0003593508,0.00038175628,0.96858436,0.00007161993,0.028759163,0.000033115277,0.00085030956],"study_design_scores_gemma":[0.0004806817,0.00003741227,0.000051000377,0.00019700755,0.000031176056,0.0000058777014,0.00009851433,0.9328723,0.00017116609,0.065439984,0.00008315588,0.00053171406],"about_ca_topic_score_codex":0.00011979323,"about_ca_topic_score_gemma":0.00024102036,"teacher_disagreement_score":0.67649657,"about_ca_system_score_codex":0.0003688944,"about_ca_system_score_gemma":0.00026987272,"threshold_uncertainty_score":0.99969226},"labels":[],"label_agreement":null},{"id":"W4404638618","doi":"10.3390/make6040130","title":"Node-Centric Pruning: A Novel Graph Reduction Approach","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Scalability; Pruning; Graph; Theoretical computer science; Node (physics); Distributed computing; Artificial intelligence; Machine learning; Engineering","score_opus":0.015621257278360324,"score_gpt":0.2815210943851965,"score_spread":0.2658998371068362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404638618","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":[0.0071061025,0.010082453,0.97333544,0.00028696653,0.0014929471,0.00016265736,6.082605e-7,0.0010492094,0.0064835832],"genre_scores_gemma":[0.968661,0.00040513952,0.027134776,0.000011163771,0.00037426545,0.000024936286,0.000014518067,0.000027790176,0.0033464178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865955,0.000104067476,0.00019921445,0.0006109445,0.00014669578,0.0002795146],"domain_scores_gemma":[0.9994996,0.00010699203,0.000070452355,0.00017499753,0.000050080354,0.000097861564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003335608,0.00019447932,0.00014563084,0.000378885,0.00035131953,0.00030934153,0.00016439948,0.000102789345,0.0000054792354],"category_scores_gemma":[0.00004403713,0.00017841771,0.0000761431,0.0012432317,0.000043227577,0.00077081274,0.00010431087,0.0008776614,0.00003403084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000486589,0.0005218824,0.0013372708,0.0004459948,0.00010182582,0.000029879971,0.0033082007,0.017819623,0.028416604,0.046939325,0.0013104054,0.8997203],"study_design_scores_gemma":[0.0002291039,0.00007747332,0.0006946245,0.00007069139,0.000018690453,0.0005067724,0.00004510113,0.94103163,0.00018703207,0.0008923373,0.056009095,0.00023741781],"about_ca_topic_score_codex":0.000016272801,"about_ca_topic_score_gemma":0.0000022181503,"teacher_disagreement_score":0.9615549,"about_ca_system_score_codex":0.000049267754,"about_ca_system_score_gemma":0.000029412684,"threshold_uncertainty_score":0.7275663},"labels":[],"label_agreement":null},{"id":"W4404752338","doi":"10.18653/v1/2022.aacl-main.46","title":"A Decade of Knowledge Graphs in Natural Language Processing: A Survey","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Samsung; Bundesministerium für Bildung und Forschung; Canadian Institute for Advanced Research","keywords":"Computer science; Knowledge graph; Natural language processing; Natural language; Artificial intelligence","score_opus":0.01686055922491003,"score_gpt":0.28621518100072735,"score_spread":0.2693546217758173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404752338","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":[0.94987863,0.015746474,0.030380026,0.00021751772,0.00058089464,0.000381436,0.0000070013034,0.00029740517,0.0025105919],"genre_scores_gemma":[0.9938815,0.0000047340013,0.005619695,0.000120661556,0.0000054710663,0.000022190328,0.0000038007508,0.0000065152603,0.00033540928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989222,0.00018698181,0.00019789023,0.0002794117,0.00018525543,0.00022825082],"domain_scores_gemma":[0.99943817,0.00013882412,0.000073863244,0.00027264174,0.000040315404,0.000036169204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003569803,0.00009102722,0.00014583704,0.00021867132,0.00006905981,0.000016239925,0.00083676324,0.000018480121,0.000014221452],"category_scores_gemma":[0.000036295012,0.00008232332,0.000042089505,0.0021596767,0.000033691165,0.00024002662,0.00053217495,0.00027804697,0.0000015090977],"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":[0.00017969671,0.0012654337,0.1487474,0.00013958894,0.000035252226,0.00024797907,0.023074979,0.007055109,0.007545806,0.09890576,0.004259088,0.7085439],"study_design_scores_gemma":[0.0014525736,0.00026321842,0.33249667,0.00003722011,0.0000034280065,0.0000673425,0.0005649101,0.6529359,0.0028248264,0.008002222,0.0007007366,0.0006509336],"about_ca_topic_score_codex":0.0000917161,"about_ca_topic_score_gemma":0.00074704963,"teacher_disagreement_score":0.70789295,"about_ca_system_score_codex":0.000027273025,"about_ca_system_score_gemma":0.000051925435,"threshold_uncertainty_score":0.33570474},"labels":[],"label_agreement":null},{"id":"W4404804432","doi":"10.1016/j.procs.2024.09.513","title":"Temporal Graph Convolutional Network for Implicit Relation Prediction: Leveraging Timestamps and Confidence","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Timestamp; Graph; Relation (database); Theoretical computer science; Artificial intelligence; Data mining; Real-time computing","score_opus":0.014880243094255117,"score_gpt":0.24786705197039122,"score_spread":0.2329868088761361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404804432","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":[0.0049416353,0.0012513994,0.9890779,0.0010872997,0.0024493185,0.0004766239,0.0000066716793,0.000640733,0.00006841443],"genre_scores_gemma":[0.6229183,0.000040564933,0.3757396,0.0004641894,0.00070782553,0.00006892178,0.0000065083186,0.000012362906,0.000041709896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738187,0.000023479048,0.00033447111,0.0011408136,0.00050174963,0.0006175953],"domain_scores_gemma":[0.9987364,0.00035438314,0.000089320885,0.00035563798,0.00025266732,0.00021158175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000827507,0.0002190102,0.00017516999,0.0002495494,0.0006562672,0.00079587224,0.0008709279,0.000065307766,0.0000017700762],"category_scores_gemma":[0.000036687612,0.00020740439,0.000065364984,0.0020049235,0.0004633281,0.0027858869,0.00043160623,0.00023692948,0.0000072940315],"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":[0.000017224183,0.00002770588,0.00925566,0.0001529339,0.000026159085,0.000021604374,0.0010219932,0.040224917,0.0007198448,0.85459983,0.007679091,0.08625305],"study_design_scores_gemma":[0.00015132493,0.000110654626,0.010144412,0.00012654121,0.000005745727,0.00016950977,0.0000023950436,0.85868925,0.00007847991,0.12827207,0.0020415827,0.00020803844],"about_ca_topic_score_codex":0.0000041705257,"about_ca_topic_score_gemma":0.0000014697429,"teacher_disagreement_score":0.81846434,"about_ca_system_score_codex":0.00007550851,"about_ca_system_score_gemma":0.0003572253,"threshold_uncertainty_score":0.84577054},"labels":[],"label_agreement":null},{"id":"W4404848417","doi":"10.1109/icpads63350.2024.00039","title":"GraphFlow: A Fast and Accurate Distributed Streaming Graph Computation Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computation; Graph; Theoretical computer science; Algorithm","score_opus":0.016043930320370287,"score_gpt":0.26547454072986143,"score_spread":0.24943061040949113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404848417","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":[0.023493873,0.00035592663,0.9744361,0.00046881096,0.0001928546,0.0001162732,0.000012494202,0.0007145316,0.00020914906],"genre_scores_gemma":[0.92644596,0.00005379318,0.073248096,0.00012608588,0.000020221994,0.00000903854,0.000019098308,0.00000999471,0.000067709945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989217,0.000023268549,0.00017747504,0.00046423808,0.00015898202,0.00025435214],"domain_scores_gemma":[0.999527,0.00011289456,0.000029365945,0.0001930209,0.000039766073,0.000097952725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007403269,0.00015493027,0.00011788918,0.00015975257,0.00011408089,0.00036567356,0.0002422113,0.0000464053,0.0000012535232],"category_scores_gemma":[0.0000063176517,0.00013015405,0.00005593528,0.00080123614,0.000051519262,0.0009726777,0.00017151577,0.00015307986,0.000005306516],"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":[0.000004630123,0.000022369071,0.000093107876,0.000039462895,0.000034251614,0.000058168833,0.0002602104,0.45663553,0.00063607044,0.31333482,0.0013021327,0.22757924],"study_design_scores_gemma":[0.00008592261,0.000021692887,0.0002557844,0.000029441411,0.000004841223,0.000018525747,0.000009068318,0.8655738,0.000103883525,0.13372464,0.000033523025,0.00013887064],"about_ca_topic_score_codex":0.0000056740896,"about_ca_topic_score_gemma":0.000008076327,"teacher_disagreement_score":0.9029521,"about_ca_system_score_codex":0.000013191591,"about_ca_system_score_gemma":0.000017911882,"threshold_uncertainty_score":0.53075284},"labels":[],"label_agreement":null},{"id":"W4404851210","doi":"10.1016/j.knosys.2024.112804","title":"Knowledge graph completion with low-dimensional gated hierarchical hyperbolic embedding","year":2024,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Embedding; Graph; Knowledge graph; Computer science; Mathematics; Theoretical computer science; Combinatorics; Artificial intelligence","score_opus":0.017097567532253083,"score_gpt":0.2735283302668388,"score_spread":0.25643076273458576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404851210","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":[0.084109515,0.0122648645,0.8901068,0.00034132865,0.005669733,0.0010966313,0.00002131679,0.0028092968,0.0035805223],"genre_scores_gemma":[0.99330395,0.0000073296933,0.0052231825,0.0000752064,0.0005865613,0.000113797854,0.00004554932,0.00008005744,0.0005643577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632406,0.00048432496,0.0006190968,0.0012169678,0.0005353226,0.000820229],"domain_scores_gemma":[0.99742514,0.00082802284,0.0001148651,0.00089718064,0.00035029926,0.00038448646],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055088825,0.0005278587,0.00057926244,0.00073668204,0.0003777167,0.0005080464,0.0009215507,0.00020255931,0.000014394945],"category_scores_gemma":[0.00002744961,0.0004166982,0.00022599456,0.002866625,0.00024564393,0.0005276586,0.00020189557,0.00070003123,0.00054919784],"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":[0.00055865844,0.002647586,0.0021340768,0.005838585,0.00091168075,0.0017577556,0.005312463,0.42253217,0.03364272,0.40987468,0.049746748,0.065042876],"study_design_scores_gemma":[0.0007977356,0.00025516824,0.00027947102,0.0019347342,0.000027580743,0.0001824827,0.000016105912,0.9761729,0.0008704861,0.00059842237,0.018242668,0.00062223023],"about_ca_topic_score_codex":0.00002180564,"about_ca_topic_score_gemma":0.00005152016,"teacher_disagreement_score":0.90919447,"about_ca_system_score_codex":0.00019465729,"about_ca_system_score_gemma":0.00036830196,"threshold_uncertainty_score":0.99982846},"labels":[],"label_agreement":null},{"id":"W4404884737","doi":"10.1007/978-3-031-78128-5_8","title":"Interpretable Deep Graph-Level Clustering: A Prototype-Based Approach","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Cluster analysis; Graph; Clustering coefficient; Artificial intelligence; Data mining; Theoretical computer science","score_opus":0.023210733518045895,"score_gpt":0.24756078590858913,"score_spread":0.22435005239054323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404884737","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":[0.00000395598,0.0010467973,0.98685634,0.0004056421,0.0023815632,0.001369196,0.000007491784,0.0005745844,0.0073544267],"genre_scores_gemma":[0.07973132,0.000024247705,0.91673356,0.0019168817,0.00043184066,0.00013587032,0.0000110246165,0.0001063004,0.00090894714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941435,0.00003555169,0.0007010624,0.0028013324,0.001188335,0.0011302516],"domain_scores_gemma":[0.99669564,0.00035284096,0.0002784807,0.0021683576,0.00021898797,0.00028569956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066862704,0.0009255853,0.0007736868,0.0015219565,0.00026104343,0.0010153496,0.005186546,0.000474618,0.000014614665],"category_scores_gemma":[0.000043796,0.0008123809,0.00031961227,0.0017335167,0.0008988532,0.0008089312,0.0023465275,0.0018204914,0.000047859827],"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":[0.000027260292,0.000049083115,0.000012058556,0.00021091013,0.00002593601,0.00019536915,0.00041755676,0.6045881,0.00007546662,0.028588988,0.00003143711,0.36577788],"study_design_scores_gemma":[0.00018338641,0.00021399184,0.00000563405,0.0005481695,0.000010477028,0.000085476786,8.3130345e-8,0.7812533,0.00025076256,0.21563491,0.0010929719,0.00072084024],"about_ca_topic_score_codex":0.0000104784995,"about_ca_topic_score_gemma":0.00006648053,"teacher_disagreement_score":0.36505702,"about_ca_system_score_codex":0.0003162613,"about_ca_system_score_gemma":0.00041083957,"threshold_uncertainty_score":0.9994327},"labels":[],"label_agreement":null},{"id":"W4405014026","doi":"10.1007/978-3-031-78122-3_24","title":"Multifaceted Anchor Nodes Attack on Graph Neural Networks: A Budget-Efficient Approach","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial neural network; Graph; Theoretical computer science; Artificial intelligence","score_opus":0.02507208501773168,"score_gpt":0.26559911818943643,"score_spread":0.24052703317170476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405014026","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":[0.00028679147,0.0023317975,0.9874515,0.000592829,0.005135539,0.0010233744,0.000010209121,0.00079461077,0.0023733582],"genre_scores_gemma":[0.54498726,0.00015006388,0.44716972,0.0045521907,0.001874416,0.000087848864,0.000030890693,0.00023294537,0.00091465603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99167365,0.0000698582,0.0008899324,0.0038591283,0.0016388485,0.0018685804],"domain_scores_gemma":[0.9955303,0.00074944564,0.0003863764,0.0026044971,0.00025389757,0.00047550138],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00083630567,0.0013035475,0.0009743982,0.0016232235,0.00045113024,0.0011412501,0.005226148,0.0006143008,0.0000082857505],"category_scores_gemma":[0.000068976326,0.0011017548,0.00044169268,0.002593887,0.0012707508,0.0004983793,0.0022757247,0.0027631912,0.000067282235],"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":[0.000013083472,0.000053361477,0.000008530229,0.000037615206,0.000017114566,0.00014797255,0.00022343805,0.8044629,0.000017155267,0.009205093,0.000058012436,0.18575573],"study_design_scores_gemma":[0.00030940963,0.0002463735,0.000044636803,0.00044996548,0.000016066926,0.00010715826,2.5233348e-7,0.9719439,0.00009224708,0.025092639,0.0005685542,0.0011287603],"about_ca_topic_score_codex":0.00000744493,"about_ca_topic_score_gemma":0.00001327553,"teacher_disagreement_score":0.54470044,"about_ca_system_score_codex":0.00041755207,"about_ca_system_score_gemma":0.00017642629,"threshold_uncertainty_score":0.9999716},"labels":[],"label_agreement":null},{"id":"W4405073635","doi":"10.1145/3706115","title":"Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Topological graph theory; Network topology; Graph; Robustness (evolution); Machine learning; Theoretical computer science; Data mining; Pathwidth; Line graph","score_opus":0.01694117523900615,"score_gpt":0.25195624868701516,"score_spread":0.235015073448009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405073635","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":[0.025537672,0.0057730614,0.96436584,0.00043627646,0.001613349,0.0004229787,0.000002421297,0.0018329852,0.00001542445],"genre_scores_gemma":[0.9890166,0.00079907983,0.009905892,0.000054732336,0.000057545676,0.00008279901,0.0000012664174,0.000034861758,0.00004724183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980639,0.00009652816,0.00041979746,0.0007530831,0.00017255478,0.00049413356],"domain_scores_gemma":[0.99886644,0.00027463323,0.000072373216,0.0005805992,0.000097441196,0.00010848451],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014465752,0.00030499065,0.00032769106,0.00066918053,0.00037998453,0.00023212413,0.00064661936,0.0003283343,0.000004977591],"category_scores_gemma":[0.000016627399,0.00027533353,0.00011629739,0.0014779497,0.00013112245,0.00028785004,0.00004399018,0.0009158291,0.000008060413],"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":[0.000018918907,0.00006523544,0.00011366064,0.00006353799,0.00023875998,0.00020190235,0.00020559815,0.68005335,0.00045267373,0.0104927365,0.000007715063,0.30808592],"study_design_scores_gemma":[0.00011732715,0.0003771301,0.000002949791,0.00015894545,0.00003712137,0.00078506477,0.00010190473,0.993679,0.0008975944,0.002602363,0.0009674681,0.00027312635],"about_ca_topic_score_codex":0.00002701853,"about_ca_topic_score_gemma":0.000010173061,"teacher_disagreement_score":0.9634789,"about_ca_system_score_codex":0.00007308369,"about_ca_system_score_gemma":0.000022888273,"threshold_uncertainty_score":0.9999699},"labels":[],"label_agreement":null},{"id":"W4405112666","doi":"10.1016/j.procs.2024.11.191","title":"BeComE: A Framework for Node Classification in Social Graphs","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Node (physics); Social network (sociolinguistics); Theoretical computer science; Artificial intelligence; World Wide Web; Social media","score_opus":0.032631046847118116,"score_gpt":0.3184168328245523,"score_spread":0.2857857859774342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405112666","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":[0.0048727957,0.00023141051,0.98814994,0.0037279937,0.0020332523,0.00040482407,0.0000020714888,0.00048510765,0.00009263245],"genre_scores_gemma":[0.52864784,0.000011961878,0.47047484,0.00046190215,0.00027856324,0.000107084095,0.0000010316072,0.000010117829,0.0000066914727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974557,0.000021634532,0.000308766,0.0010912814,0.00047439532,0.00064822665],"domain_scores_gemma":[0.99891716,0.0003537382,0.00007076568,0.00039326499,0.00014717526,0.00011789564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006481608,0.00019166146,0.00017931996,0.00059200526,0.0004064376,0.00074677746,0.0019683223,0.00009247196,0.0000011297528],"category_scores_gemma":[0.00007449475,0.00018069515,0.000092626164,0.0048141098,0.00030531327,0.0017921119,0.00039240322,0.0003291711,0.00001802804],"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":[0.0000032423654,0.00002921229,0.00041735906,0.000049132417,0.0000024698631,0.0000063194875,0.00091011263,0.00033276383,0.00027478285,0.8413306,0.00041023348,0.15623379],"study_design_scores_gemma":[0.000081737155,0.00003789289,0.005069883,0.000062858955,0.0000015341825,0.000009435731,0.0000041572466,0.64034265,0.0001822414,0.35327637,0.0007552372,0.00017598769],"about_ca_topic_score_codex":0.0000013968834,"about_ca_topic_score_gemma":0.000003512522,"teacher_disagreement_score":0.6400099,"about_ca_system_score_codex":0.00010661013,"about_ca_system_score_gemma":0.00020836998,"threshold_uncertainty_score":0.7368534},"labels":[],"label_agreement":null},{"id":"W4405143960","doi":"10.1145/3673791.3698434","title":"The First Workshop on Evaluation Methodologies, Testbeds and Community for Information Access Research (EMTCIR 2024)","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data science; World Wide Web; Knowledge management","score_opus":0.44417470971216544,"score_gpt":0.5203434357644982,"score_spread":0.07616872605233271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405143960","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":[0.003774361,0.0008803,0.9725528,0.013159861,0.0011959788,0.0013452908,0.0000029138591,0.0003229651,0.0067655253],"genre_scores_gemma":[0.8808487,0.0013877723,0.11286942,0.0019960233,0.00024328876,0.0013101221,0.000029923991,0.000025290095,0.0012894627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797505,0.0008707061,0.00020887362,0.0001769215,0.0004719327,0.00029649606],"domain_scores_gemma":[0.97747374,0.02149653,0.000036771544,0.0006292829,0.0003210982,0.00004261061],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.010199732,0.000099683355,0.00008394575,0.00014673665,0.0014618377,0.0015708329,0.0011707533,0.00007156822,0.0000039232536],"category_scores_gemma":[0.0027390872,0.000061495804,0.000033080745,0.0011621073,0.00013143435,0.0021887817,0.0007313888,0.0007990387,0.000010406221],"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":[0.000021794767,0.000008513715,0.000032391774,0.00003466039,0.000011249925,2.507359e-7,0.000634174,0.0027791075,0.0000045853308,0.0813438,0.028980494,0.886149],"study_design_scores_gemma":[0.00016138532,0.00016519814,0.0017870523,0.00007716775,0.000005537694,0.0000034877748,0.00032566703,0.6819159,0.00017711203,0.22437613,0.090892,0.00011339865],"about_ca_topic_score_codex":0.00002716568,"about_ca_topic_score_gemma":0.00021223428,"teacher_disagreement_score":0.88603556,"about_ca_system_score_codex":0.0000796786,"about_ca_system_score_gemma":0.000061721104,"threshold_uncertainty_score":0.9998381},"labels":[],"label_agreement":null},{"id":"W4405179881","doi":"10.1109/tmi.2024.3512603","title":"Heterogeneous Graph Representation Learning Framework for Resting-State Functional Connectivity Analysis","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Resting state fMRI; Computer science; Functional connectivity; Representation (politics); Graph; Graph theory; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Neuroscience; Mathematics; Psychology; Combinatorics","score_opus":0.022553580869994636,"score_gpt":0.31230030333307063,"score_spread":0.289746722463076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405179881","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":[0.00592711,0.00020806148,0.9888879,0.0021768282,0.0017613663,0.00018952905,0.000005774153,0.0008155814,0.000027828719],"genre_scores_gemma":[0.9638485,0.00006146288,0.035270397,0.00047046097,0.00012641912,0.00010270575,0.00000569404,0.000024424373,0.00008995662],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766475,0.00016411945,0.0003283596,0.0007865305,0.00066455815,0.00039170965],"domain_scores_gemma":[0.99678755,0.002477105,0.00006702197,0.00034688352,0.00009289411,0.00022852745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004570239,0.00019587541,0.00023512331,0.0005679867,0.00044313126,0.00024793312,0.00031426564,0.00008405039,0.00006472609],"category_scores_gemma":[0.00016268402,0.00018995798,0.00040682996,0.0024256092,0.0001198146,0.00054669805,0.0000057011334,0.00091122417,0.000014754759],"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":[0.000027050608,0.00004788265,0.00016474776,0.000019722556,0.00026465644,0.000095786236,0.00017294614,0.6710356,0.00014573616,0.0012076676,0.000064784916,0.3267534],"study_design_scores_gemma":[0.00016789192,0.000041169034,0.00018029707,0.00009693924,0.00013346701,0.00006379537,0.000017049231,0.9686544,0.0019273349,0.028312493,0.00020700092,0.000198178],"about_ca_topic_score_codex":0.000021031809,"about_ca_topic_score_gemma":0.000026319522,"teacher_disagreement_score":0.9579214,"about_ca_system_score_codex":0.00006227936,"about_ca_system_score_gemma":0.00006339817,"threshold_uncertainty_score":0.77462614},"labels":[],"label_agreement":null},{"id":"W4405364446","doi":"10.1145/3700434","title":"PyGim : An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Measurement and Analysis of Computing Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Bottleneck; Parallel computing; Exploit; Memory model; Computer architecture; Distributed computing; Shared memory; Embedded system","score_opus":0.025345363658966946,"score_gpt":0.25582823251928655,"score_spread":0.2304828688603196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405364446","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":[0.9863439,0.0029229852,0.008770259,0.00055924105,0.00042010527,0.0005826716,0.0000031601082,0.00022728076,0.0001703869],"genre_scores_gemma":[0.9943944,0.000012239627,0.005347416,0.00004049447,0.00016280834,0.00001297738,0.000001325091,0.000019712432,0.000008629735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976303,0.000042471256,0.00063791417,0.0005905054,0.0007368522,0.0003619401],"domain_scores_gemma":[0.9987221,0.0001447511,0.0004218111,0.00042661416,0.00021093385,0.00007379306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013140056,0.00023814965,0.00055504113,0.00058227644,0.00018801712,0.00029337668,0.0017560155,0.000060404305,1.5454758e-7],"category_scores_gemma":[0.00010141778,0.00016255908,0.00029536054,0.0028127022,0.00008898728,0.00020424652,0.0004559327,0.0001992458,5.3124808e-8],"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":[0.000057883964,0.00009388499,0.01567582,0.00087268144,0.00031500126,8.8306297e-7,0.0012583178,0.95392007,0.0021964104,0.007172967,0.00028946993,0.018146588],"study_design_scores_gemma":[0.00018028411,0.00015025292,0.013634942,0.0010225713,0.00023383231,0.000002451451,0.000081785016,0.9816493,0.00067440775,0.0021713001,0.000021690048,0.0001772167],"about_ca_topic_score_codex":0.000011152722,"about_ca_topic_score_gemma":0.0000035643259,"teacher_disagreement_score":0.027729174,"about_ca_system_score_codex":0.000025924868,"about_ca_system_score_gemma":0.00002783164,"threshold_uncertainty_score":0.6628967},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"software","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"split"},{"id":"W4405489164","doi":"10.1109/tnnls.2024.3513546","title":"Exploring Attention and Self-Supervised Learning Mechanism for Graph Similarity Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Mechanism (biology); Similarity (geometry); Computer science; Artificial intelligence; Graph; Psychology; Theoretical computer science; Epistemology","score_opus":0.037559252159681145,"score_gpt":0.2412893141812235,"score_spread":0.20373006202154237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405489164","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":[0.10973338,0.0011335376,0.8844309,0.00020644776,0.0024503237,0.00052009604,0.000001157918,0.001506199,0.000017971008],"genre_scores_gemma":[0.99508864,0.0016380486,0.0022458418,0.00005209511,0.0002808122,0.00026603497,0.000004295197,0.00007021225,0.00035401754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715716,0.00051076146,0.00044050446,0.0009702272,0.00028866218,0.00063266506],"domain_scores_gemma":[0.99848723,0.00086125126,0.00011994322,0.00022071903,0.00009871212,0.00021215821],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0006467748,0.00040605696,0.00039508706,0.0003252295,0.0015696068,0.0008978657,0.00023104828,0.00018137674,0.0000016043615],"category_scores_gemma":[0.000016217202,0.00039050027,0.00020249026,0.00071788783,0.000052044667,0.001335298,0.00001257782,0.0020111136,0.0000023053744],"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":[0.000022983582,0.00002535263,0.00007826819,0.00018017346,0.000076738266,0.000020549402,0.00047619935,0.9330282,0.00034131872,0.0028146827,0.000007705408,0.062927835],"study_design_scores_gemma":[0.00040323794,0.00059159176,0.00008023603,0.0003096087,0.0000626852,0.00011096496,0.00028508902,0.99670386,0.000044798628,0.0001681572,0.00082798064,0.00041181105],"about_ca_topic_score_codex":0.00003483049,"about_ca_topic_score_gemma":0.0000067552946,"teacher_disagreement_score":0.88535523,"about_ca_system_score_codex":0.00004367062,"about_ca_system_score_gemma":0.000012238039,"threshold_uncertainty_score":0.9998547},"labels":[],"label_agreement":null},{"id":"W4406028367","doi":"10.1109/tmc.2025.3525477","title":"Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Correlation; Feature extraction; Graph; Task (project management); Artificial intelligence; Feature (linguistics); Semantic feature; Natural language processing; Pattern recognition (psychology); Theoretical computer science","score_opus":0.009291780807133998,"score_gpt":0.2653748857324918,"score_spread":0.2560831049253578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406028367","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":[0.017821968,0.00020730142,0.9794553,0.0003823813,0.00080547866,0.0006592639,0.0000026321736,0.00060218753,0.00006349924],"genre_scores_gemma":[0.85102963,0.000028532828,0.14838216,0.00024555248,0.000014070611,0.000068966285,0.000009612651,0.000017491093,0.00020394922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983382,0.00019570928,0.0003192707,0.000568082,0.00026640957,0.00031230916],"domain_scores_gemma":[0.9981905,0.00046695757,0.0001856569,0.00089555036,0.0001929388,0.000068386486],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021196206,0.00026344674,0.0002158832,0.0004398909,0.0008754242,0.00015748023,0.0005512143,0.00014763103,0.0000033101257],"category_scores_gemma":[0.0000038713856,0.00025518794,0.00014541962,0.0016367767,0.00008954779,0.00052868586,0.000006073544,0.00072092697,0.000013975682],"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":[0.000027915185,0.00021776819,0.00023183174,0.000022907288,0.00003216578,0.0000033845752,0.0000725069,0.89673805,0.0013676493,0.00016100083,0.000049871232,0.10107496],"study_design_scores_gemma":[0.00093520246,0.00012726025,0.0028155586,0.00035447476,0.00004109069,0.000012944497,0.000047751433,0.99307406,0.0020594343,0.00013915545,0.00014232923,0.00025071396],"about_ca_topic_score_codex":0.00002544283,"about_ca_topic_score_gemma":0.000092218164,"teacher_disagreement_score":0.83320767,"about_ca_system_score_codex":0.00011592798,"about_ca_system_score_gemma":0.000062286585,"threshold_uncertainty_score":0.99999005},"labels":[],"label_agreement":null},{"id":"W4406068137","doi":"10.1007/978-3-031-74003-9_15","title":"Similarity of Concepts in Weighted Knowledge Graphs","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Similarity (geometry); Knowledge graph; Computer science; Mathematics; Artificial intelligence","score_opus":0.015621795690207751,"score_gpt":0.26222298568102254,"score_spread":0.2466011899908148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406068137","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":[0.00033355455,0.44779965,0.4949988,0.00021657739,0.008027084,0.0017720078,0.000029166948,0.00029044566,0.0465327],"genre_scores_gemma":[0.99064237,0.0039715776,0.0007765524,0.00010423248,0.00048849586,0.000033501165,0.000021839272,0.00008040716,0.0038810535],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742144,0.00009017557,0.00085372105,0.0009180726,0.00024993322,0.00046664252],"domain_scores_gemma":[0.99812794,0.000718375,0.0002883782,0.0006756996,0.0000853092,0.000104329745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036696767,0.0005460678,0.0010398207,0.00047808283,0.00004459774,0.000111655725,0.0006483168,0.0009368593,0.00000493176],"category_scores_gemma":[0.000017176484,0.00046460313,0.00017216628,0.00050523307,0.00019491694,0.00013417006,0.00034886866,0.0014701611,0.000003032786],"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":[0.000025044938,0.000030289326,0.0005929426,0.0006237027,0.0000841187,0.00026972656,0.0005769587,0.09653123,0.000007337024,0.8701225,0.00037200688,0.030764163],"study_design_scores_gemma":[0.0003537415,0.00010275411,0.00006949641,0.0031834939,0.000021601338,0.000043670032,0.0000018974279,0.7043995,0.0000055314053,0.28093788,0.0103029115,0.000577503],"about_ca_topic_score_codex":0.000042650092,"about_ca_topic_score_gemma":0.00065424934,"teacher_disagreement_score":0.99030876,"about_ca_system_score_codex":0.00006997453,"about_ca_system_score_gemma":0.00004283109,"threshold_uncertainty_score":0.9997806},"labels":[],"label_agreement":null},{"id":"W4406207049","doi":"10.1109/comst.2025.3527561","title":"Edge Graph Intelligence: Reciprocally Empowering Edge Networks With Graph Intelligence","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Graph; Enhanced Data Rates for GSM Evolution; Computer science; Psychology; Theoretical computer science; Artificial intelligence","score_opus":0.04125900414966655,"score_gpt":0.3267987530736188,"score_spread":0.28553974892395223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406207049","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":[0.0008724887,0.0034968671,0.9841597,0.0008603579,0.0057621817,0.000877638,0.000009341728,0.0007320975,0.0032293017],"genre_scores_gemma":[0.8793741,0.0050029606,0.11365604,0.0005132463,0.00039267662,0.00041106303,0.000042698015,0.00006417927,0.000543029],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99380034,0.002422857,0.0012966851,0.0010724483,0.00050076924,0.0009068805],"domain_scores_gemma":[0.98791546,0.003473993,0.0005080517,0.007000016,0.0008555299,0.00024693614],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.004295839,0.0005929109,0.0007346734,0.00063800777,0.0010672892,0.0005027489,0.008727996,0.00031196358,0.000011976777],"category_scores_gemma":[0.0004120166,0.0005563967,0.0002457715,0.0059693605,0.00094127347,0.0011582732,0.0016129634,0.0011168733,0.000037831436],"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":[0.00012218795,0.00084867177,0.0052752374,0.0000798972,0.00052335334,0.000029996458,0.0017085188,0.16043034,0.0009363647,0.367309,0.0054778038,0.4572586],"study_design_scores_gemma":[0.0024536399,0.0019207736,0.021497706,0.0043756426,0.0005117382,0.00017840604,0.0011857718,0.26509887,0.032402195,0.53525424,0.12581751,0.0093035195],"about_ca_topic_score_codex":0.00015488108,"about_ca_topic_score_gemma":0.00067329244,"teacher_disagreement_score":0.8785016,"about_ca_system_score_codex":0.00015168719,"about_ca_system_score_gemma":0.0003019537,"threshold_uncertainty_score":0.99968874},"labels":[],"label_agreement":null},{"id":"W4406458851","doi":"10.1109/bigdata62323.2024.10825720","title":"The Power of Many: Investigating Defense Mechanisms for Resilient Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Computer science; Artificial neural network; Power (physics); Artificial intelligence","score_opus":0.015350620322290336,"score_gpt":0.2529802248559509,"score_spread":0.23762960453366055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406458851","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":[0.0042501567,0.0013035276,0.99053043,0.0015637156,0.0013464022,0.00036275154,0.0000013132833,0.00030893154,0.00033277745],"genre_scores_gemma":[0.9007349,0.00002982046,0.09818032,0.00065533264,0.00005282467,0.000049305523,0.0000011958587,0.000021220465,0.00027509322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985287,0.000057955083,0.00033426835,0.0004201033,0.00023108334,0.0004278888],"domain_scores_gemma":[0.9983282,0.0008514245,0.00007972077,0.0005660119,0.00007507752,0.000099534336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039400652,0.0001649254,0.0001448687,0.00007452151,0.00024642388,0.0001985122,0.0007841166,0.00006217271,0.0000026564228],"category_scores_gemma":[0.000059350044,0.00010449854,0.00017389713,0.0007397106,0.00011794353,0.00030499703,0.0002637267,0.0002073517,0.0000017775784],"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":[0.000005809578,0.0000056521767,0.000012912645,0.000011624152,0.00001699012,0.0000060138705,0.000120120014,0.028491996,0.00046670437,0.9532837,0.0024403445,0.015138116],"study_design_scores_gemma":[0.0000691947,0.00010625288,0.00003942955,0.000029213517,0.0000052451887,0.000011122268,0.000032752825,0.7305066,0.0011265677,0.2674087,0.00055870204,0.00010623497],"about_ca_topic_score_codex":0.000003805821,"about_ca_topic_score_gemma":0.000011611037,"teacher_disagreement_score":0.89648473,"about_ca_system_score_codex":0.000013037663,"about_ca_system_score_gemma":0.000019206063,"threshold_uncertainty_score":0.42613268},"labels":[],"label_agreement":null},{"id":"W4406609300","doi":"10.1007/s41060-025-00717-y","title":"Supervised graph embedding for classification using discriminating frequent patterns","year":2025,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Advanced Graph Neural Networks","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Dhaka; University Grants Commission of Bangladesh; University of Manitoba","keywords":"Pattern recognition (psychology); Artificial intelligence; Embedding; Computer science; Graph; Machine learning; Theoretical computer science","score_opus":0.12141518112245384,"score_gpt":0.4118745984518881,"score_spread":0.29045941732943426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406609300","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":[0.047608256,0.00010840652,0.94962174,0.0018296384,0.0007160056,0.000053669217,0.000024510142,0.0000073421247,0.000030403891],"genre_scores_gemma":[0.8394482,0.00018795508,0.15991555,0.00033652526,0.000097924494,5.179459e-7,0.00000618243,0.0000025138897,0.0000045850197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855906,0.00001559265,0.00037937268,0.00028016386,0.00060138415,0.0001644189],"domain_scores_gemma":[0.9981257,0.00015465295,0.0002916209,0.00032529834,0.0010369079,0.000065839595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000997276,0.00008011644,0.00011614708,0.0005171829,0.0001786247,0.00044552525,0.0028371753,0.000021343294,7.092131e-7],"category_scores_gemma":[0.00028309316,0.000067186535,0.000040334686,0.000584332,0.00014468495,0.0037311583,0.0005666717,0.00011292793,8.799462e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004572167,0.00020440231,0.022826688,0.000054082433,0.00021492488,0.000053624186,0.00058283476,0.009635101,0.10232366,0.26050925,0.0009598847,0.6025898],"study_design_scores_gemma":[0.0002664328,0.00002455245,0.0020141227,0.00014937918,0.000017259988,0.000034174824,0.00017786262,0.9865249,0.00077886105,0.00949673,0.00043977535,0.000075975644],"about_ca_topic_score_codex":0.000004841449,"about_ca_topic_score_gemma":0.000003121501,"teacher_disagreement_score":0.9768898,"about_ca_system_score_codex":0.00008535914,"about_ca_system_score_gemma":0.00019775284,"threshold_uncertainty_score":0.5272225},"labels":[],"label_agreement":null},{"id":"W4407052498","doi":"10.1007/s10115-025-02346-0","title":"A novel contrastive multi-view framework for heterogeneous graph embedding","year":2025,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Advanced Graph Neural Networks","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":"Mount Royal University","funders":"","keywords":"Embedding; Computer science; Graph; Theoretical computer science; Artificial intelligence; Mathematics; Natural language processing","score_opus":0.0197432400307529,"score_gpt":0.30193892668569033,"score_spread":0.28219568665493744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407052498","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":[0.0002531213,0.0051515726,0.9916275,0.00005459028,0.0013384133,0.0007279244,0.000011772289,0.00014337208,0.0006916898],"genre_scores_gemma":[0.86416775,0.00043286275,0.13391168,0.000715573,0.0001220777,0.00046088532,0.000017576212,0.000009995875,0.0001615923],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906117,0.000025142333,0.00042876953,0.000172337,0.00007896562,0.00023362407],"domain_scores_gemma":[0.998983,0.00029429304,0.00016012327,0.00023730363,0.0002585853,0.0000666575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019683059,0.00015028575,0.00022994798,0.00020626844,0.00021615658,0.00026327776,0.00027986386,0.00010799844,3.2569844e-7],"category_scores_gemma":[0.00006502746,0.00013246223,0.0000692569,0.0004898693,0.00003212758,0.0018871243,0.00010592327,0.000109955734,0.000012865938],"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":[0.000018001432,0.000043046683,0.000083029205,0.00045658599,0.000063851694,4.0808075e-7,0.0023318366,0.004409906,0.00007815482,0.8781603,0.0010222853,0.11333257],"study_design_scores_gemma":[0.0011267189,0.00006602984,0.00016941893,0.0006731154,0.000010095903,0.000026941625,0.00014590951,0.83796906,0.00023105569,0.0026074587,0.15671194,0.00026228212],"about_ca_topic_score_codex":0.0000021567387,"about_ca_topic_score_gemma":0.0000014029523,"teacher_disagreement_score":0.87555283,"about_ca_system_score_codex":0.000028879926,"about_ca_system_score_gemma":0.000027983255,"threshold_uncertainty_score":0.5401653},"labels":[],"label_agreement":null},{"id":"W4407057487","doi":"10.1007/978-3-658-45877-5_3","title":"Preliminaries","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Materials science","score_opus":0.010835367847750027,"score_gpt":0.21918530939675848,"score_spread":0.20834994154900846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407057487","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.0286258e-8,0.00052498456,0.35874617,0.00054111035,0.00040346736,0.00008987979,0.0000011524573,0.00028097106,0.6394122],"genre_scores_gemma":[0.00003385544,0.00019369507,0.052042782,0.0010475079,0.000067325615,0.00000450853,0.000003500589,0.000011166545,0.94659567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900013,0.000004160522,0.00017865255,0.00045857017,0.00017718988,0.00018126536],"domain_scores_gemma":[0.99887353,0.00011760596,0.00007833637,0.00082213554,0.000058361897,0.000050043032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000030516994,0.00023375399,0.00021655786,0.00012425738,0.00006841526,0.000079662466,0.0009993444,0.00019955331,0.00009283094],"category_scores_gemma":[0.000007670887,0.00020558399,0.00011553475,0.000045896395,0.00006121828,0.00019011102,0.00061705924,0.0002977975,0.00012039398],"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":[0.0000011556605,0.0000013522418,3.028814e-7,0.000007939647,0.000011085799,0.000017043592,0.0000065153504,0.000018931802,3.7319919e-7,0.8826436,0.052139256,0.06515248],"study_design_scores_gemma":[0.000034328015,0.000027403843,0.0000018189277,0.00006469052,0.000005895532,0.0000046752803,1.9295086e-7,0.0013665924,0.00001775313,0.42200235,0.57629484,0.00017945573],"about_ca_topic_score_codex":0.0000010013863,"about_ca_topic_score_gemma":0.000005233456,"teacher_disagreement_score":0.5241556,"about_ca_system_score_codex":0.000020729336,"about_ca_system_score_gemma":0.000050600484,"threshold_uncertainty_score":0.8383472},"labels":[],"label_agreement":null},{"id":"W4407702610","doi":"10.1037/xlm0001454","title":"Anchors and ratios to quantify and explain y-axis distortion effects in graphs.","year":2025,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Learning Memory and Cognition","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Psychology; Distortion (music); Cognitive psychology; Physics","score_opus":0.011842650518591616,"score_gpt":0.32061077833882473,"score_spread":0.3087681278202331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407702610","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":[0.9394896,0.0030060539,0.056256566,0.00052114105,0.0003814274,0.00013762443,1.4951857e-7,0.0000141304945,0.00019327426],"genre_scores_gemma":[0.9960309,0.0001961292,0.0028817446,0.0008369197,0.000023384815,0.00001003891,9.0155686e-7,0.0000039663123,0.000015982254],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99910474,0.00020386383,0.00024857098,0.00023427999,0.000079911486,0.00012866127],"domain_scores_gemma":[0.9995626,0.00013362458,0.00013365637,0.00006693999,0.000029785951,0.000073376825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027283453,0.00010394555,0.00017631752,0.00031879792,0.00011904938,0.000041196607,0.00007807253,0.000067874964,0.0000014966413],"category_scores_gemma":[0.000055922257,0.000100059224,0.000025142655,0.00026113965,0.00007963004,0.00041987898,0.000053020885,0.0002982556,4.961907e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013344164,0.00048814557,0.043182455,0.00009050955,0.00009011218,0.00030049405,0.006566697,0.0003675502,0.5646483,0.0064985533,0.0006453879,0.37578744],"study_design_scores_gemma":[0.013292092,0.007900572,0.7690063,0.0017349379,0.00007855373,0.0015307796,0.0034529085,0.003898551,0.14179404,0.05544317,0.000870618,0.0009974393],"about_ca_topic_score_codex":0.0000010545242,"about_ca_topic_score_gemma":0.0000019637155,"teacher_disagreement_score":0.7258239,"about_ca_system_score_codex":0.000013857081,"about_ca_system_score_gemma":0.0000057661186,"threshold_uncertainty_score":0.40802968},"labels":[],"label_agreement":null},{"id":"W4409189376","doi":"10.1016/j.eswa.2025.127356","title":"Unified link prediction modeling for enhanced knowledge graph completion task","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":8,"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":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; National Research Foundation of Korea","keywords":"Computer science; Link (geometry); Knowledge graph; Task (project management); Graph; Machine learning; Artificial intelligence; Theoretical computer science; Computer network","score_opus":0.019156331097123728,"score_gpt":0.2816174827888249,"score_spread":0.2624611516917012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409189376","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":[0.00018726745,0.0014525874,0.9930667,0.0005213125,0.00040132162,0.0023155978,0.0000131205015,0.0005653837,0.0014766713],"genre_scores_gemma":[0.9341649,0.00006361442,0.055206306,0.00013355403,0.00030704087,0.009568072,0.00007150878,0.000019908532,0.00046513084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998575,0.00004614558,0.0003722382,0.00058862806,0.00014125557,0.00027675304],"domain_scores_gemma":[0.9985209,0.00014005035,0.000119740354,0.0007986719,0.00034194352,0.00007865757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013693968,0.00018437752,0.00022266092,0.00020598383,0.00047200808,0.00010729856,0.00056114036,0.00009599162,3.6042087e-7],"category_scores_gemma":[0.000008187011,0.00016357019,0.00006482327,0.0011023831,0.000042548883,0.0002976743,0.00006873845,0.00012207431,0.000008272171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044602188,0.00015496815,0.000031937496,0.00013838749,0.00008554201,2.7301684e-7,0.00086607534,0.22130431,0.018670779,0.73599356,0.0031688288,0.01954074],"study_design_scores_gemma":[0.0005371449,0.000056746092,0.000023480809,0.0001380319,0.000009723904,0.0000035804933,0.00008655368,0.9548103,0.00081276457,0.006230598,0.03710476,0.00018629784],"about_ca_topic_score_codex":0.000021113357,"about_ca_topic_score_gemma":0.000014415475,"teacher_disagreement_score":0.9378604,"about_ca_system_score_codex":0.00007889224,"about_ca_system_score_gemma":0.00006666126,"threshold_uncertainty_score":0.66701984},"labels":[],"label_agreement":null},{"id":"W4409363183","doi":"10.1609/aaai.v39i20.35412","title":"Subgraph Invariant Learning Towards Large-Scale Graph Node Classification","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Graph; Computer science; Invariant (physics); Induced subgraph isomorphism problem; Mathematics; Combinatorics; Theoretical computer science; Artificial intelligence; Line graph; Voltage graph","score_opus":0.05231126368394066,"score_gpt":0.2983596347543106,"score_spread":0.24604837107036995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363183","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":[0.07357778,0.000100220896,0.8632607,0.013973433,0.0015090492,0.00084809534,0.0000058093933,0.00046899312,0.04625596],"genre_scores_gemma":[0.99123925,0.00012084444,0.0074001034,0.0005258651,0.000041379593,0.00004631153,0.0000010067528,0.000012888247,0.00061237416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99753433,0.000042511554,0.00065620284,0.0007431342,0.00050313125,0.0005206794],"domain_scores_gemma":[0.9981456,0.00012722227,0.00045459837,0.00046112214,0.000719044,0.00009242332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063651236,0.000289427,0.00031817862,0.00033477647,0.00044033516,0.00029393184,0.002595316,0.00014973833,0.000022253824],"category_scores_gemma":[0.00042097282,0.0002289865,0.00020157536,0.0024402947,0.0002956446,0.00058618793,0.00058916456,0.00070679263,0.000031505886],"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":[0.000042290176,0.00012175675,0.0010276805,0.00003105398,0.000016388069,3.814381e-7,0.00062529877,0.00038188483,0.026601529,0.92679274,0.00026272138,0.04409627],"study_design_scores_gemma":[0.000051933202,0.000112344576,0.003023034,0.00035143012,0.000019423966,0.0000026392995,0.00074689055,0.267119,0.22869593,0.49915844,0.00041611085,0.000302826],"about_ca_topic_score_codex":0.00002190038,"about_ca_topic_score_gemma":0.000021976355,"teacher_disagreement_score":0.9176614,"about_ca_system_score_codex":0.000048862308,"about_ca_system_score_gemma":0.0001193234,"threshold_uncertainty_score":0.9337798},"labels":[],"label_agreement":null},{"id":"W4409363443","doi":"10.1609/aaai.v39i20.35488","title":"Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Graph; Multiple edges; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Theoretical computer science","score_opus":0.03621241705176568,"score_gpt":0.28505742627367237,"score_spread":0.2488450092219067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409363443","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":[0.51502997,0.00047346583,0.4160715,0.019822303,0.004387294,0.002508254,0.00001028641,0.0008109406,0.04088597],"genre_scores_gemma":[0.99697834,0.00013880766,0.0016686822,0.0007436723,0.00005823693,0.000050507304,4.1824637e-7,0.000014267829,0.0003470367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736744,0.00004022647,0.0007539499,0.0007847088,0.00043771346,0.0006159364],"domain_scores_gemma":[0.9981404,0.00038358037,0.0004429542,0.0002929843,0.000654336,0.00008576097],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055712246,0.00035881152,0.0004594194,0.0004765165,0.00034434555,0.00032651398,0.0023325742,0.00014578878,0.000019291349],"category_scores_gemma":[0.00087446714,0.00025310554,0.0001663994,0.0016330834,0.00044942703,0.00081886706,0.00065552525,0.0008642766,0.000008333774],"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":[0.00010919578,0.00013456144,0.005575524,0.0000681353,0.000031742307,0.000001795065,0.0016895538,0.0011914173,0.020522328,0.8064326,0.00021384683,0.16402929],"study_design_scores_gemma":[0.00009273865,0.0002415073,0.0016100081,0.0015748183,0.000015873642,0.000002897048,0.0018037992,0.07314773,0.5490723,0.371456,0.00049946556,0.00048283522],"about_ca_topic_score_codex":0.00005760627,"about_ca_topic_score_gemma":0.00012033958,"teacher_disagreement_score":0.52855,"about_ca_system_score_codex":0.00008129715,"about_ca_system_score_gemma":0.00009667922,"threshold_uncertainty_score":0.99999213},"labels":[],"label_agreement":null},{"id":"W4409375011","doi":"10.1016/b978-0-443-15888-9.00011-x","title":"Deep learning: fundamentals","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Psychology; Computer science; Artificial intelligence","score_opus":0.012211845552187763,"score_gpt":0.24104598853724732,"score_spread":0.22883414298505955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409375011","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.1936023e-7,0.002800539,0.015533927,0.00013615581,0.00094387366,0.00043753302,0.0000021080534,0.00045563892,0.9796895],"genre_scores_gemma":[0.00017819868,0.00024875576,0.009404086,0.0010214153,0.00020651132,0.000031459924,0.000011094093,0.000054110933,0.9888444],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99759495,0.00004672569,0.00048326934,0.00094710785,0.00044007198,0.00048786198],"domain_scores_gemma":[0.9981308,0.00017214724,0.0003277342,0.0011089304,0.00009264477,0.00016776576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015315523,0.00056313595,0.0005814148,0.00027130108,0.00023866765,0.00017286118,0.0015101499,0.00036426328,0.00008297891],"category_scores_gemma":[0.000017387607,0.0005612235,0.00035362112,0.00004798207,0.00013962663,0.0001470403,0.00095227925,0.0011508252,0.00026462146],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028609268,0.0000030317624,0.0000028142128,0.000027061868,0.000049905535,0.00005748521,0.000055522374,0.000051950705,0.0000051354427,0.12631588,0.0002684313,0.87315995],"study_design_scores_gemma":[0.00015520789,0.000066334826,0.0000038108583,0.0002858071,0.000028858787,0.000018106955,0.0000014395538,0.0012094522,0.000023862805,0.080005325,0.9177133,0.00048854086],"about_ca_topic_score_codex":9.8797315e-8,"about_ca_topic_score_gemma":0.0000060407688,"teacher_disagreement_score":0.9174448,"about_ca_system_score_codex":0.000111935515,"about_ca_system_score_gemma":0.00007624942,"threshold_uncertainty_score":0.9996839},"labels":[],"label_agreement":null},{"id":"W4409634738","doi":"10.3390/make7020038","title":"Knowledge Graphs and Their Reciprocal Relationship with Large Language Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reciprocal; Computer science; Natural language processing; Linguistics; Philosophy","score_opus":0.012439613366926514,"score_gpt":0.2888355388840247,"score_spread":0.2763959255170982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409634738","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":[0.11622123,0.023313884,0.844137,0.00033052138,0.00028373176,0.00021019184,0.0000017796132,0.00055186584,0.014949796],"genre_scores_gemma":[0.99178684,0.0002441003,0.0035160964,0.000031640884,0.0000344485,0.000019203086,0.0000088818015,0.00001387908,0.0043449025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886894,0.00020263735,0.00016626964,0.00045040573,0.000059385817,0.00025237104],"domain_scores_gemma":[0.99913865,0.0004429547,0.00007788842,0.00019623211,0.00006555308,0.00007873522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003450153,0.00019791709,0.00017704516,0.0002546153,0.00053538487,0.00011489369,0.00013488262,0.000102832724,0.000002301112],"category_scores_gemma":[0.000065475884,0.0001515911,0.000035313296,0.0006851629,0.000058006393,0.0005965575,0.00014054203,0.0007242022,0.0000052639734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001391334,0.00031546282,0.06143343,0.00020029787,0.00007429061,0.000014921632,0.010508563,0.0020073294,0.00085412466,0.4176661,0.00034791345,0.50643843],"study_design_scores_gemma":[0.0008534071,0.00015531729,0.013087115,0.00016485057,0.00001830051,0.00006330516,0.00034881048,0.9484797,0.0001678373,0.027034663,0.009338779,0.00028793036],"about_ca_topic_score_codex":0.000013059521,"about_ca_topic_score_gemma":0.00021520728,"teacher_disagreement_score":0.94647235,"about_ca_system_score_codex":0.000023543575,"about_ca_system_score_gemma":0.00003067374,"threshold_uncertainty_score":0.61817056},"labels":[],"label_agreement":null},{"id":"W4409657240","doi":"10.1145/3696410.3714603","title":"SANS: Efficient Densest Subgraph Discovery over Relational Graphs without Materialization","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Theoretical computer science","score_opus":0.008129785943260292,"score_gpt":0.24526579578423013,"score_spread":0.23713600984096983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409657240","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":[0.15887176,0.000083639316,0.83678585,0.00031737878,0.0010965012,0.00018889269,0.000002583956,0.00025205998,0.0024013545],"genre_scores_gemma":[0.97624487,0.000025233787,0.021425977,0.0010538482,0.00002928887,0.000019471083,0.000014249256,0.000008886439,0.0011781796],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986746,0.00006024111,0.0002760142,0.00045454688,0.00027545364,0.00025918754],"domain_scores_gemma":[0.9992389,0.000090680915,0.00008252525,0.00047146398,0.000069101494,0.000047295733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001127102,0.00016417365,0.0001443234,0.0002598067,0.00017355193,0.00023435414,0.0004103852,0.000072814335,0.000024500252],"category_scores_gemma":[0.000024315612,0.00014052674,0.00008400282,0.0012118012,0.00007380264,0.00069692865,0.00018683179,0.00009245417,0.000010554888],"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":[0.000015320105,0.00004109929,0.020814078,0.000007745671,0.00001445454,0.000003256862,0.000031904703,0.018039,0.0011192029,0.95829684,0.0010718455,0.0005452455],"study_design_scores_gemma":[0.0017282497,0.00009690634,0.33133847,0.00021732926,0.00004009072,0.000022356708,0.000025264402,0.4137953,0.008164844,0.23874848,0.0048986073,0.00092411955],"about_ca_topic_score_codex":0.000014336017,"about_ca_topic_score_gemma":0.000020781792,"teacher_disagreement_score":0.8173731,"about_ca_system_score_codex":0.000033557484,"about_ca_system_score_gemma":0.00004329193,"threshold_uncertainty_score":0.5730514},"labels":[],"label_agreement":null},{"id":"W4409671186","doi":"10.1145/3696410.3714938","title":"<scp>Node2binary</scp> : Compact Graph Node Embeddings using Binary Vectors","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Binary number; Node (physics); Theoretical computer science; Graph; Mathematics; Arithmetic; Physics","score_opus":0.0172401512063985,"score_gpt":0.28066084735817914,"score_spread":0.26342069615178065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409671186","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":[0.3872217,0.00048266212,0.59683675,0.0004938895,0.0011161314,0.00023518167,0.000001754792,0.00080223405,0.012809717],"genre_scores_gemma":[0.9281938,0.000039248538,0.06741914,0.0019323645,0.000059332833,0.000005274505,0.0000033865888,0.000025923884,0.0023215115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978109,0.0000854379,0.00035657932,0.0007289106,0.00030688845,0.00071131013],"domain_scores_gemma":[0.99828815,0.0004432172,0.00012628266,0.0008797275,0.00009434496,0.00016829005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017416856,0.00033454978,0.00034099715,0.00050553086,0.00035792467,0.00019202435,0.0013339065,0.00012259914,0.000009204582],"category_scores_gemma":[0.000063233594,0.0003049619,0.00020445009,0.0026181473,0.00012753262,0.0009734502,0.00051511737,0.00036148954,0.000026532058],"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":[0.000030802275,0.0007710151,0.061458576,0.00019459937,0.00038829012,0.000507638,0.0020650998,0.33374295,0.079442084,0.33082005,0.18036392,0.010214982],"study_design_scores_gemma":[0.00083440007,0.00016634332,0.015745407,0.00022093239,0.000037243433,0.000064568834,0.00019638227,0.9236644,0.009127124,0.038060926,0.011522965,0.00035930699],"about_ca_topic_score_codex":0.00007719455,"about_ca_topic_score_gemma":0.0000072774237,"teacher_disagreement_score":0.5899215,"about_ca_system_score_codex":0.00008312021,"about_ca_system_score_gemma":0.00007195619,"threshold_uncertainty_score":0.9999403},"labels":[],"label_agreement":null},{"id":"W4410632617","doi":"10.22215/etd/2025-16414","title":"Learning Robust Graph Neural Networks with Limited Supervision","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Computer science; Graph; Artificial intelligence; Data science; Theoretical computer science","score_opus":0.011786776147170063,"score_gpt":0.23003027629614758,"score_spread":0.21824350014897753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410632617","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":[0.033536837,0.0015249281,0.9463282,0.0002705533,0.0025493884,0.0007234725,0.0000010198496,0.0017595938,0.013305958],"genre_scores_gemma":[0.7781479,0.0016775635,0.13131508,0.0017687168,0.00060918456,0.0002146911,0.0026360245,0.00024524675,0.083385624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723595,0.0001366821,0.00041436567,0.001098377,0.00047227019,0.00064234744],"domain_scores_gemma":[0.9982567,0.00025963405,0.00024628412,0.000808743,0.0002793186,0.0001493422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012036896,0.00057373,0.0004916215,0.00048452613,0.0003817938,0.0003294027,0.0013664999,0.00043062662,0.000023232908],"category_scores_gemma":[0.000030984625,0.0004561681,0.00019414768,0.0023085023,0.00002753641,0.00066108984,0.00017285717,0.0015375522,0.000005134005],"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":[0.00008437406,0.000029023944,0.00031645465,0.00004703986,0.000047220197,0.000054850607,0.00021727887,0.8974279,0.000012376981,0.0021037562,0.0005664657,0.09909326],"study_design_scores_gemma":[0.00037408335,0.0003196658,0.0015504532,0.0003057068,0.000038329345,0.0000119646975,0.00012643987,0.9955871,0.000057118265,0.00026981358,0.00072590745,0.00063338823],"about_ca_topic_score_codex":0.00003408863,"about_ca_topic_score_gemma":0.00045826955,"teacher_disagreement_score":0.8150132,"about_ca_system_score_codex":0.00003243963,"about_ca_system_score_gemma":0.000059366866,"threshold_uncertainty_score":0.999789},"labels":[],"label_agreement":null},{"id":"W4410637878","doi":"10.1145/3701716.3715863","title":"Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLM","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; National Key Research and Development Program of China; Microsoft Research Asia; Weill Cornell Medical College; Tsinghua University; Beijing National Research Center For Information Science And Technology; Microsoft Research; National Natural Science Foundation of China; Universitas Brawijaya; York University; Institute for Catastrophic Loss Reduction","keywords":"Computer science; Extension (predicate logic); Generalization; Graph; Adaptation (eye); Theoretical computer science; Artificial intelligence; Machine learning; Mathematics; Programming language; Psychology","score_opus":0.012898168931688762,"score_gpt":0.2492417734022997,"score_spread":0.23634360447061092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410637878","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":[0.010053183,0.00026089404,0.9854179,0.0035423418,0.00017410165,0.00013542808,0.0000010557267,0.00021352807,0.00020157422],"genre_scores_gemma":[0.9519342,0.00007593335,0.04510025,0.002353888,0.000016831964,0.000009641337,0.000050348342,0.000005100128,0.00045380733],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991616,0.000059883012,0.00015376374,0.000346582,0.00012105245,0.00015712713],"domain_scores_gemma":[0.9995443,0.00005571261,0.000038956056,0.0002013299,0.000098660836,0.00006101199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010889013,0.000099514604,0.00008717205,0.000106541906,0.00020924245,0.00009065344,0.0001512206,0.000040886203,0.000003047589],"category_scores_gemma":[0.000042700824,0.00008896733,0.000021847354,0.0009699477,0.000018723373,0.00036081122,0.00015525676,0.00009166981,0.0000033496558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009373267,0.000021146978,0.001091328,0.0000054352636,0.0000072545677,0.000001605513,0.0000982493,0.23640949,0.0009888877,0.71792525,0.0011173087,0.042324685],"study_design_scores_gemma":[0.00021936612,0.00006553908,0.024865527,0.000026566146,0.0000052957,0.0000023851812,0.000026786114,0.8939131,0.0003975349,0.07671054,0.0036189067,0.0001484661],"about_ca_topic_score_codex":0.00002866575,"about_ca_topic_score_gemma":0.00006432272,"teacher_disagreement_score":0.941881,"about_ca_system_score_codex":0.000024921012,"about_ca_system_score_gemma":0.000011901818,"threshold_uncertainty_score":0.36279824},"labels":[],"label_agreement":null},{"id":"W4410906542","doi":"10.1007/978-3-031-84756-1_13","title":"Open Problems","year":2025,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on games and computational intelligence","topic":"Advanced Graph Neural Networks","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 Sudbury; University of Alberta","funders":"","keywords":"Computer science","score_opus":0.0266379061106281,"score_gpt":0.27515607968098676,"score_spread":0.24851817357035866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410906542","genre_codex":"methods","genre_gemma":"other","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":[0.0000013034062,0.0022071984,0.755436,0.0015808967,0.0002627386,0.0005461155,0.00002163292,0.00012920766,0.23981489],"genre_scores_gemma":[0.098717876,0.0055776215,0.26775128,0.017410459,0.00056037935,0.00050124334,0.00008835401,0.00022419551,0.6091686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978003,0.000043862558,0.00043192916,0.001049376,0.00039192894,0.00028261374],"domain_scores_gemma":[0.9971205,0.0017952838,0.00025915413,0.00054614636,0.0001645013,0.000114416805],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015455557,0.0004787836,0.0004807261,0.00027447144,0.00022016917,0.0005605862,0.0021997017,0.00023481456,0.000097095675],"category_scores_gemma":[0.00008275177,0.00041889885,0.00014236706,0.0001304937,0.00016391683,0.00023319252,0.0009991048,0.00048744475,0.00004867574],"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":[0.000009646107,0.000011336186,6.91504e-7,0.000025388763,0.000047468966,0.000008398028,0.000030679832,0.061475623,5.198443e-7,0.58348936,0.0017668931,0.353134],"study_design_scores_gemma":[0.000031506497,0.00010392768,0.000018425128,0.0007340821,0.000019912128,0.000020565787,0.0000019298188,0.052201476,0.00014761862,0.88193434,0.06432442,0.00046176283],"about_ca_topic_score_codex":0.000008752487,"about_ca_topic_score_gemma":0.000012295299,"teacher_disagreement_score":0.48768473,"about_ca_system_score_codex":0.00005207252,"about_ca_system_score_gemma":0.0001349646,"threshold_uncertainty_score":0.9998263},"labels":[],"label_agreement":null},{"id":"W4411411740","doi":"10.1145/3744970.3727310","title":"PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures","year":2025,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Parallel computing; Bottleneck; Exploit; Computer architecture; Embedded system","score_opus":0.03785837192708655,"score_gpt":0.3364874245987441,"score_spread":0.2986290526716575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411411740","genre_codex":"review","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":[0.2593923,0.503824,0.21166289,0.006174756,0.0032585391,0.011443497,0.000011284048,0.0013328012,0.002899937],"genre_scores_gemma":[0.76018643,0.06292295,0.165307,0.00918285,0.00042808065,0.001612272,0.00014032365,0.00007258989,0.0001474939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966741,0.00031642677,0.00081759936,0.00081924465,0.00074451347,0.00062810676],"domain_scores_gemma":[0.9973808,0.0004874528,0.00036825702,0.0013573484,0.00028991664,0.0001162577],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023786486,0.00032513816,0.00049655413,0.0008080286,0.0003612603,0.00016531363,0.001953308,0.00010258853,0.000012212778],"category_scores_gemma":[0.0007508113,0.00028421797,0.00015650858,0.009516705,0.0000698247,0.0008603233,0.0004097599,0.00036323268,0.00000452559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015345337,0.000053941323,0.0019034472,0.0008207158,0.0000044484636,8.8551565e-7,0.000033384582,0.24797475,0.0000025671861,0.00052035745,0.0014140486,0.7472561],"study_design_scores_gemma":[0.0006380804,0.00019446372,0.021732824,0.0018827878,0.000059931426,0.0000049892237,0.0000030119036,0.96874565,0.000048763326,0.0040454133,0.0023100479,0.00033403328],"about_ca_topic_score_codex":0.0000015864937,"about_ca_topic_score_gemma":0.0000036812662,"teacher_disagreement_score":0.7469221,"about_ca_system_score_codex":0.000074731484,"about_ca_system_score_gemma":0.00026279286,"threshold_uncertainty_score":0.999961},"labels":[],"label_agreement":null},{"id":"W4412133173","doi":"10.1111/coin.70097","title":"SparseMult: A Sparse Tensor Decomposition Model for Knowledge Graph Link Prediction","year":2025,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Barrie Urology Group","funders":"","keywords":"Tensor decomposition; Link (geometry); Tensor (intrinsic definition); Computer science; Decomposition; Graph; Mathematics; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Combinatorics; Pure mathematics; Chemistry","score_opus":0.0470954609821718,"score_gpt":0.33883412109769834,"score_spread":0.2917386601155265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412133173","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":[0.000966277,0.00041766968,0.99542946,0.0009868705,0.0007960637,0.00057105784,0.0000259456,0.00033610544,0.00047053143],"genre_scores_gemma":[0.6086634,0.000033250963,0.39012638,0.0005186517,0.000090918504,0.00013101798,0.00005081036,0.0000109788125,0.0003745861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831444,0.00004709269,0.00046941204,0.00064113684,0.00020077455,0.00032713596],"domain_scores_gemma":[0.9981465,0.0007325688,0.00012481413,0.00034931832,0.00055190374,0.000094929004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019096604,0.0002208653,0.00019978601,0.0003368173,0.00031040554,0.00011697822,0.0007447747,0.000105994004,0.0000026086655],"category_scores_gemma":[0.000083344356,0.00023134287,0.00015807575,0.0009491158,0.000114448216,0.00054068066,0.0001834359,0.00019348344,0.00002917051],"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":[0.000019573927,0.000059250073,0.000053614192,0.000019017254,0.000016667764,9.0241554e-7,0.00016082305,0.71081656,0.000051495543,0.2312322,0.0009660755,0.056603815],"study_design_scores_gemma":[0.00010587722,0.00004174598,0.0002581774,0.000059948066,0.000008662772,0.0000051290294,0.000007277936,0.6347851,0.00041646822,0.36394292,0.00024762526,0.000121049634],"about_ca_topic_score_codex":0.0000015222654,"about_ca_topic_score_gemma":0.000005332096,"teacher_disagreement_score":0.6076971,"about_ca_system_score_codex":0.00008416339,"about_ca_system_score_gemma":0.00013154675,"threshold_uncertainty_score":0.9433888},"labels":[],"label_agreement":null},{"id":"W4412228426","doi":"","title":"A Simple Latent Variable Model for Graph Learning and Inference","year":2023,"lang":"en","type":"article","venue":"Institutional Research Information System (Università degli Studi di Trento)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latent variable; Inference; Simple (philosophy); Latent variable model; Computer science; Graph; Artificial intelligence; Mathematics; Econometrics; Machine learning; Theoretical computer science; Epistemology; Philosophy","score_opus":0.09614882301960301,"score_gpt":0.3430509645098497,"score_spread":0.24690214149024667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412228426","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":[0.015247628,0.000043471176,0.97962934,0.00022099497,0.00015689456,0.00061999174,0.000041234613,0.00042860722,0.0036118554],"genre_scores_gemma":[0.9946436,0.00006466929,0.0046575284,0.000024416737,0.00002624846,0.0000909139,0.000090934365,0.000005157193,0.0003965118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777406,0.000099519835,0.00029737325,0.0002984872,0.0009442168,0.00058634026],"domain_scores_gemma":[0.99820364,0.00043414393,0.00012427488,0.00023237515,0.0008071034,0.00019848668],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010558701,0.00015451181,0.00019082079,0.0010307507,0.0017515579,0.0002776276,0.0005589663,0.00008667636,0.0000024728515],"category_scores_gemma":[0.00023909633,0.0001548359,0.0000662012,0.0025634996,0.00017439843,0.0041086376,0.000736141,0.00030986592,0.00007126401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031875832,0.000010578901,0.0014781787,0.00012588236,0.000034916804,0.000006724663,0.0010722955,0.31208777,0.000015090527,0.6789586,0.00068113575,0.005496933],"study_design_scores_gemma":[0.00082554494,0.0000796051,0.0024775304,0.000084140425,0.000005198958,0.000009160083,0.001075564,0.9846992,0.000005257355,0.0037222465,0.0068556326,0.00016090428],"about_ca_topic_score_codex":0.000036729245,"about_ca_topic_score_gemma":0.000010553777,"teacher_disagreement_score":0.979396,"about_ca_system_score_codex":0.0002828254,"about_ca_system_score_gemma":0.00027788893,"threshold_uncertainty_score":0.999548},"labels":[],"label_agreement":null},{"id":"W4412377938","doi":"10.1145/3726302.3729889","title":"AdaRPT: An Adaptive Rule Pattern Transfer Model for Fully Inductive Knowledge Graph Reasoning","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Universitas Brawijaya; Central China Normal University; Natural Sciences and Engineering Research Council of Canada; Ministry of Education, India; Natural Science Foundation of Hubei Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Inductive reasoning; Graph; Artificial intelligence; Theoretical computer science","score_opus":0.028842484969888317,"score_gpt":0.28792855757008823,"score_spread":0.2590860726001999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412377938","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":[0.016033651,0.00014328888,0.97911775,0.00024791856,0.00031784675,0.00050603226,0.00001081737,0.00032755747,0.003295129],"genre_scores_gemma":[0.8415048,0.000008730203,0.15635446,0.0006471592,0.000051573476,0.00014025558,0.0000048150905,0.000018081011,0.0012701376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830943,0.00006185408,0.0002543432,0.0007579357,0.00013846872,0.00047796653],"domain_scores_gemma":[0.9989126,0.00013570322,0.000032123986,0.0005678323,0.00022605053,0.00012572933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015445742,0.0002538988,0.00025253845,0.00021592026,0.0002327503,0.0000884524,0.0008898154,0.00012003035,0.0000045860115],"category_scores_gemma":[0.000012985738,0.00022937675,0.00014951252,0.00074271346,0.00007331971,0.0010695247,0.00014642888,0.00025112246,0.0000041699886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010482889,0.00023275455,0.00024854884,0.000023159866,0.000070604634,0.000004978729,0.0030881502,0.028307123,0.00067977386,0.54036605,0.0008303907,0.42604363],"study_design_scores_gemma":[0.00050894305,0.00016923709,0.0004140001,0.000046321635,0.0000134031825,0.0000013826251,0.00010105587,0.8886557,0.0014286141,0.10830795,0.000093279,0.00026007247],"about_ca_topic_score_codex":0.000027933509,"about_ca_topic_score_gemma":0.0002903577,"teacher_disagreement_score":0.86034864,"about_ca_system_score_codex":0.000047470377,"about_ca_system_score_gemma":0.000108439264,"threshold_uncertainty_score":0.9353712},"labels":[],"label_agreement":null},{"id":"W4412470227","doi":"10.1016/j.aei.2025.103655","title":"Large language Models-empowered automatic knowledge graph development based on multi-modal data for building health resilience","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Hong Kong Polytechnic University","keywords":"Modal; Computer science; Resilience (materials science); Knowledge graph; Knowledge management; Graph; Data science; Artificial intelligence; Theoretical computer science","score_opus":0.017481202907824848,"score_gpt":0.3045036570114266,"score_spread":0.2870224541036018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412470227","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":[0.0018920435,0.00045864604,0.9953983,0.00008896498,0.0005636562,0.00070874207,0.000036320907,0.00076536636,0.00008800382],"genre_scores_gemma":[0.16746755,0.000014995487,0.83178365,0.0005031263,0.000011590779,0.000090702466,0.000071696246,0.00002113514,0.000035577512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783987,0.000017867293,0.000774302,0.00036290073,0.00024958584,0.00075544947],"domain_scores_gemma":[0.9976756,0.00035511577,0.00020366354,0.0015381268,0.000076799806,0.00015070461],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000504935,0.0003326601,0.00035621322,0.0004320269,0.00026061814,0.000093756455,0.0018112909,0.00007664252,4.808549e-7],"category_scores_gemma":[0.00015821162,0.00033431145,0.00006204757,0.0009924169,0.000020004432,0.0015148763,0.000529432,0.00027767383,0.000003775582],"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":[0.0000057925567,0.00006281472,0.0000042273064,0.00046497202,0.000016665725,0.0000010247247,0.0013534966,0.93504685,0.000046532514,0.015030702,0.00015830492,0.047808636],"study_design_scores_gemma":[0.0010941969,0.000053253676,0.00006716108,0.0005443915,0.0000043301693,0.000001353819,0.000087367516,0.9922313,0.0004404629,0.0002621831,0.0048959544,0.00031806366],"about_ca_topic_score_codex":8.4615846e-7,"about_ca_topic_score_gemma":0.00000842381,"teacher_disagreement_score":0.1655755,"about_ca_system_score_codex":0.0001868023,"about_ca_system_score_gemma":0.00024188991,"threshold_uncertainty_score":0.9999109},"labels":[],"label_agreement":null},{"id":"W4412535588","doi":"10.1016/j.neucom.2025.131007","title":"Federated graph neural networks in non-IID scenarios—A comprehensive survey","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Advanced Graph Neural Networks","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 Windsor","funders":"Vector Institute","keywords":"Computer science; Artificial neural network; Graph; Artificial intelligence; Data mining; Data science; Machine learning; Theoretical computer science","score_opus":0.016338448874734447,"score_gpt":0.26618859793103256,"score_spread":0.2498501490562981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412535588","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":[0.37603638,0.00022365802,0.62054604,0.00037364414,0.0016186547,0.00047132114,8.909635e-7,0.00038566714,0.00034374194],"genre_scores_gemma":[0.9924577,0.000018733455,0.0036672517,0.0036809582,0.00008673748,0.000012468958,0.000010248165,0.000026726007,0.00003919537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964706,0.00052208855,0.00067366817,0.0010957705,0.000268806,0.00096908334],"domain_scores_gemma":[0.9977467,0.0011374739,0.00020792415,0.0005589078,0.00021545572,0.00013352018],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003456751,0.00041814856,0.0005041251,0.0004725107,0.00043079065,0.00037391583,0.0011789671,0.0001562713,0.0000014806883],"category_scores_gemma":[0.00008012118,0.00044077958,0.00012996269,0.0039998516,0.00008703685,0.00044934926,0.0009824813,0.0010149567,0.0000058973587],"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":[0.000036123878,0.00007867942,0.066251,0.000021238871,0.000017385759,0.00018588663,0.00006815257,0.87112814,0.00032679088,0.0004972505,0.0009736706,0.060415663],"study_design_scores_gemma":[0.0005663764,0.00004314332,0.27261087,0.00006587946,0.0000022633249,0.000014434998,0.0000056613117,0.7259671,0.00005971136,0.00032506484,0.00009193912,0.0002475248],"about_ca_topic_score_codex":0.00016666696,"about_ca_topic_score_gemma":0.00021341654,"teacher_disagreement_score":0.6168788,"about_ca_system_score_codex":0.000051981482,"about_ca_system_score_gemma":0.000044854107,"threshold_uncertainty_score":0.9998044},"labels":[],"label_agreement":null},{"id":"W4412564911","doi":"10.1016/j.knosys.2025.114147","title":"Communication-efficient federated knowledge graph embedding with entity-wise top-K sparsification","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Graph Neural Networks","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":"BC Research (Canada)","funders":"Nanyang Technological University; National Research Foundation Singapore","keywords":"Knowledge graph; Computer science; Embedding; Graph; Theoretical computer science; Information retrieval; Artificial intelligence","score_opus":0.015886634433275506,"score_gpt":0.28033576202184546,"score_spread":0.26444912758857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412564911","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":[0.016740743,0.010858698,0.9590081,0.0003013264,0.0012952735,0.0010844643,0.0000053478766,0.0007673659,0.009938693],"genre_scores_gemma":[0.9913853,0.000038609458,0.006861274,0.00006820181,0.00006723152,0.00030590943,0.000031953685,0.00003052907,0.0012110352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996993,0.0006079833,0.000669695,0.0008660159,0.00029256483,0.00057071995],"domain_scores_gemma":[0.9965669,0.0005718258,0.00031261944,0.001581085,0.0007915516,0.00017604975],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007139044,0.0004006933,0.00045792924,0.000636899,0.00090961927,0.0006310725,0.001576621,0.00016985301,0.0000044249437],"category_scores_gemma":[0.000049471295,0.00035984124,0.00013715906,0.00343417,0.00018933535,0.00034493834,0.00026062797,0.00040680097,0.00012589432],"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":[0.00021834852,0.0033896882,0.006034099,0.0012059894,0.00033564997,0.00003116302,0.0034183522,0.4978571,0.0074607385,0.43751037,0.01757065,0.024967883],"study_design_scores_gemma":[0.00157975,0.00009764737,0.0010044901,0.0012563603,0.000032033764,0.000009104909,0.00017382321,0.96780336,0.004015547,0.0004768959,0.022954695,0.0005962988],"about_ca_topic_score_codex":0.00003326415,"about_ca_topic_score_gemma":0.00010122381,"teacher_disagreement_score":0.97464454,"about_ca_system_score_codex":0.00026993567,"about_ca_system_score_gemma":0.0003237058,"threshold_uncertainty_score":0.9998854},"labels":[],"label_agreement":null},{"id":"W4412620232","doi":"10.1007/978-981-96-9881-3_8","title":"GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","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 Northern British Columbia","funders":"","keywords":"Computer science; Class (philosophy); Graph; Node (physics); Theoretical computer science; Artificial intelligence","score_opus":0.028297756309716926,"score_gpt":0.2743163402482338,"score_spread":0.2460185839385169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412620232","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":[0.00006376304,0.00049460266,0.9909223,0.0020192377,0.003789074,0.0011910524,0.000020404546,0.0003303377,0.0011692265],"genre_scores_gemma":[0.0600866,0.00025827382,0.93247485,0.0049827234,0.0009883039,0.00015464825,0.00007893953,0.00006155449,0.0009140798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99480605,0.000049544517,0.0008085412,0.0025493521,0.0008852269,0.00090131],"domain_scores_gemma":[0.9959546,0.00069080014,0.00053521147,0.0020685843,0.0005681675,0.00018268837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069692667,0.00073462364,0.0006719929,0.0014332712,0.0005328149,0.00059344555,0.003508948,0.0005537243,0.000004141648],"category_scores_gemma":[0.00012563058,0.00071579736,0.000290276,0.0016075539,0.0006240382,0.0009760493,0.0006720518,0.00085629354,0.000009083304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017350283,0.00004465971,0.00006078381,0.000068788446,0.00002610466,0.000013995492,0.00013767443,0.07074238,0.0050633666,0.25630394,0.00061960187,0.66690135],"study_design_scores_gemma":[0.00038972514,0.00012130882,0.00007902406,0.00021212216,0.000012586035,0.000011678577,5.6139033e-8,0.7195725,0.001694742,0.27609292,0.001220509,0.00059286767],"about_ca_topic_score_codex":0.0000045142538,"about_ca_topic_score_gemma":0.00006688709,"teacher_disagreement_score":0.6663085,"about_ca_system_score_codex":0.00030165003,"about_ca_system_score_gemma":0.00045328727,"threshold_uncertainty_score":0.9995293},"labels":[],"label_agreement":null},{"id":"W4412643522","doi":"10.1007/978-981-95-0006-2_16","title":"Decoupled Graph Neural Networks with Hybrid Data Augmentation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial neural network; Graph; Theoretical computer science; Artificial intelligence","score_opus":0.01723756207831407,"score_gpt":0.25706174011811594,"score_spread":0.23982417803980188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412643522","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":[0.00004722002,0.0009212062,0.9945604,0.0006704137,0.0022596223,0.00063747744,0.00001430675,0.0003110065,0.00057835376],"genre_scores_gemma":[0.124543935,0.00025729495,0.86849433,0.005237811,0.00076929247,0.000025494246,0.00018341975,0.000078293684,0.00041009925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99457365,0.000047908805,0.0006059056,0.0027915656,0.0010530889,0.0009278767],"domain_scores_gemma":[0.9946067,0.00071283197,0.00042173915,0.0038079696,0.0002422512,0.00020853741],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005799471,0.0007487435,0.0006296878,0.0009341231,0.00038800633,0.00070750416,0.008047887,0.000216752,0.0000094188745],"category_scores_gemma":[0.000047530793,0.0006379515,0.00009899914,0.0014691693,0.0008022994,0.0017626976,0.0035774948,0.0012566195,0.000004925348],"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":[0.000015908652,0.00001327146,0.000060450944,0.000015911633,0.000015264286,0.0001464889,0.000030434901,0.52834797,0.0000042576676,0.003240734,0.00012049985,0.46798882],"study_design_scores_gemma":[0.00040173548,0.0001617835,0.00007337027,0.00033997407,0.000018962259,0.000107938955,5.7898752e-8,0.9556265,0.00007299522,0.042193286,0.00033790496,0.0006654974],"about_ca_topic_score_codex":0.000016800219,"about_ca_topic_score_gemma":0.00022634535,"teacher_disagreement_score":0.4673233,"about_ca_system_score_codex":0.00017822822,"about_ca_system_score_gemma":0.0003390043,"threshold_uncertainty_score":0.9996072},"labels":[],"label_agreement":null},{"id":"W4412875489","doi":"10.1145/3711896.3736558","title":"Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Data science; Cognitive science; Psychology","score_opus":0.06857409214224364,"score_gpt":0.27597253394409094,"score_spread":0.2073984418018473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412875489","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":[0.0050597345,0.0018960575,0.97642815,0.007075446,0.00017035771,0.000085600936,7.0407175e-8,0.00016439651,0.009120212],"genre_scores_gemma":[0.8783057,0.0003353877,0.1191237,0.00056464673,0.000075976786,0.000015576035,0.0000067616375,0.0000038833978,0.0015683706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993858,0.000044672404,0.00010681985,0.000254468,0.00009373036,0.00011453009],"domain_scores_gemma":[0.9995781,0.00016135222,0.000030898012,0.00015895838,0.000037308775,0.000033378776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070926304,0.00007206795,0.000058566005,0.00010710963,0.00022359227,0.000086377884,0.00014233467,0.000038495637,0.000008891961],"category_scores_gemma":[0.000059777136,0.000065091226,0.000020172698,0.00022184692,0.000035372534,0.0002469837,0.000106634834,0.00014539181,0.0000046391906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.605048e-7,0.0000063576413,0.00020943182,0.0000021036205,0.0000059508707,5.060917e-7,0.00015143157,0.018288588,0.0002593385,0.7458118,0.00006828754,0.23519531],"study_design_scores_gemma":[0.00017231991,0.00003382,0.01459604,0.000007711371,0.0000037548384,0.000007536665,0.000018491124,0.87105787,0.00013930831,0.08703968,0.026802601,0.000120861325],"about_ca_topic_score_codex":0.0000022577233,"about_ca_topic_score_gemma":0.000055328263,"teacher_disagreement_score":0.87324595,"about_ca_system_score_codex":0.000012037668,"about_ca_system_score_gemma":0.000015391768,"threshold_uncertainty_score":0.26543432},"labels":[],"label_agreement":null},{"id":"W4412876892","doi":"10.1145/3711896.3737132","title":"SMA-GNN: A Symbol-Aware Graph Neural Network for Signed Link Prediction in Recommender Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Link (geometry); Symbol (formal); Recommender system; Artificial neural network; Graph; Artificial intelligence; Machine learning; Theoretical computer science; Computer network","score_opus":0.016182289637671154,"score_gpt":0.2561724045905201,"score_spread":0.23999011495284894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412876892","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":[0.0014568565,0.00046761156,0.98854506,0.0031242787,0.0037189217,0.0011179781,0.0000063145208,0.00056136365,0.0010016235],"genre_scores_gemma":[0.97276634,0.000040896113,0.021758532,0.0022443559,0.0005114602,0.0005496782,0.00002911498,0.00002692019,0.002072728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789727,0.00013652282,0.0005208733,0.00066026824,0.00017282422,0.00061224384],"domain_scores_gemma":[0.9986204,0.00046868282,0.000111312416,0.0006010499,0.00011030897,0.000088282235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003480139,0.00024148772,0.00031778697,0.00026692255,0.00018831226,0.00016330242,0.0007371771,0.0001664727,0.0000042872553],"category_scores_gemma":[0.000026553787,0.0002178317,0.00013434577,0.0017371096,0.000032022486,0.00056277955,0.00020826775,0.00027056457,0.0000035970127],"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":[0.00012578013,0.00011992173,0.010845079,0.00018090614,0.00009318591,0.00001744343,0.00019666356,0.64634526,0.000085210406,0.14039806,0.12898724,0.07260522],"study_design_scores_gemma":[0.0008508219,0.00014267235,0.0019944042,0.00013467672,0.00000947082,0.0000053512185,0.00004604535,0.971444,0.00003367895,0.021377003,0.003732657,0.00022918326],"about_ca_topic_score_codex":0.000032327225,"about_ca_topic_score_gemma":0.00008286958,"teacher_disagreement_score":0.9713095,"about_ca_system_score_codex":0.000070872076,"about_ca_system_score_gemma":0.00003776229,"threshold_uncertainty_score":0.88829184},"labels":[],"label_agreement":null},{"id":"W4412876980","doi":"10.1145/3711896.3737599","title":"Training Industry-scale GNNs with GiGL","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; Wilfrid Laurier University; North Carolina State University","keywords":"Scale (ratio); Computer science; Training (meteorology); Geography; Meteorology; Cartography","score_opus":0.018609947950918655,"score_gpt":0.26140003759870434,"score_spread":0.24279008964778567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412876980","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":[0.009421715,0.000029564007,0.9070734,0.001911614,0.0001643481,0.000081128106,2.1187633e-7,0.00030117348,0.08101684],"genre_scores_gemma":[0.87607676,0.0000022072807,0.11532388,0.0021279706,0.0000289491,0.000010572951,3.6615785e-7,0.000004822176,0.0064244857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922734,0.00001675589,0.000101078964,0.00028787367,0.000115702765,0.00025123218],"domain_scores_gemma":[0.9994473,0.00005977028,0.000026259495,0.00037658832,0.000029891788,0.00006022299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055810844,0.00009571518,0.00010274469,0.00007643505,0.000087317196,0.00006828798,0.00052293326,0.00008702792,0.00001545229],"category_scores_gemma":[0.0000055934834,0.000071092254,0.000024183708,0.0008468544,0.000046036123,0.00025142464,0.00013081566,0.0003214753,0.000007893319],"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":[0.000010747675,0.000031768468,0.0064019547,0.0000072408593,0.000028688964,0.000037845715,0.0006657514,0.0049072206,0.0002550835,0.4112513,0.0043761153,0.57202625],"study_design_scores_gemma":[0.0044694007,0.0009857828,0.06955797,0.00064939575,0.00005579476,0.00020613581,0.0018358396,0.5439764,0.015892586,0.18528464,0.17450193,0.0025841184],"about_ca_topic_score_codex":0.000005555633,"about_ca_topic_score_gemma":0.000031567863,"teacher_disagreement_score":0.86665505,"about_ca_system_score_codex":0.00001294015,"about_ca_system_score_gemma":0.000047938316,"threshold_uncertainty_score":0.28990582},"labels":[],"label_agreement":null},{"id":"W4412888926","doi":"10.18653/v1/2025.findings-acl.24","title":"GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Killam Trusts; Georgetown University","keywords":"Salience (neuroscience); SAGE; Computer science; Artificial intelligence; Natural language processing","score_opus":0.018073711360074577,"score_gpt":0.2677527462332645,"score_spread":0.24967903487318993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412888926","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":[0.00095022435,0.0000695944,0.997564,0.00025514423,0.00021471494,0.00035560792,0.00015165485,0.00013954894,0.0002995031],"genre_scores_gemma":[0.34706116,0.00003755987,0.6504359,0.0012977704,0.000047961697,0.0001255745,0.00033222523,0.00000589858,0.0006559452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908805,0.000013286924,0.00014552764,0.0004504011,0.000106922795,0.00019584225],"domain_scores_gemma":[0.9994127,0.00008138376,0.00003932677,0.00039128872,0.000026928292,0.000048375485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014159127,0.000096320386,0.00009860786,0.000072098206,0.00014832859,0.00010621918,0.00046051267,0.00004503693,0.0000010263476],"category_scores_gemma":[0.000034620152,0.00008402291,0.000024546092,0.00042950924,0.000063812884,0.00065303093,0.00029747622,0.00008773454,5.258313e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046815298,0.00041301598,0.0028180836,0.00017132508,0.00005732961,0.0000021621497,0.00015561182,0.003755446,0.0071917344,0.8624838,0.053801946,0.06910271],"study_design_scores_gemma":[0.0010175726,0.00009008558,0.0041668853,0.000021141495,0.000016571235,0.000013113053,0.000021611804,0.9576973,0.0022818272,0.023664193,0.010795276,0.0002144283],"about_ca_topic_score_codex":0.000012756627,"about_ca_topic_score_gemma":0.0000068265012,"teacher_disagreement_score":0.9539418,"about_ca_system_score_codex":0.000013411986,"about_ca_system_score_gemma":0.00001691302,"threshold_uncertainty_score":0.3426355},"labels":[],"label_agreement":null},{"id":"W4412966531","doi":"10.1016/j.inffus.2025.103520","title":"QUARTER: An LLM-enhanced quaternion graph attention network for entity alignment between temporal knowledge graphs","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Quaternion; Quarter (Canadian coin); Knowledge graph; Graph; Artificial intelligence; Theoretical computer science; Mathematics; History; Geometry","score_opus":0.012164317643757175,"score_gpt":0.2747115916877629,"score_spread":0.2625472740440057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412966531","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":[0.12442408,0.00006300218,0.87209934,0.00028215992,0.0014437658,0.0008127184,0.00001100931,0.00034942306,0.0005145052],"genre_scores_gemma":[0.98165405,0.00004765664,0.017182998,0.00030048896,0.00012591257,0.00013315513,0.00046509824,0.000007244812,0.00008340046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815243,0.00010863545,0.0007143993,0.00031963314,0.00028332655,0.00042155056],"domain_scores_gemma":[0.9986322,0.00009199077,0.00035019257,0.000565459,0.00024646524,0.000113697235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005343701,0.00023575431,0.00024222615,0.00036854568,0.0004917148,0.0002678081,0.0006352316,0.0001527283,0.0000045997567],"category_scores_gemma":[0.000016349311,0.00022752423,0.00017606156,0.0010268645,0.000038567698,0.0040776124,0.00019925038,0.00015069732,0.00004007737],"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":[0.00018703839,0.00035013148,0.020027775,0.00033355303,0.00010614131,9.728973e-7,0.0023876599,0.008793476,0.0018167623,0.1584907,0.02236836,0.7851374],"study_design_scores_gemma":[0.006416778,0.0020404696,0.15200645,0.0008193633,0.00011585903,0.0000054115903,0.00042645822,0.40585482,0.007652866,0.35570577,0.06713975,0.0018159971],"about_ca_topic_score_codex":0.00003269251,"about_ca_topic_score_gemma":0.0000650961,"teacher_disagreement_score":0.85722995,"about_ca_system_score_codex":0.00007746869,"about_ca_system_score_gemma":0.000038521568,"threshold_uncertainty_score":0.92781687},"labels":[],"label_agreement":null},{"id":"W4413237958","doi":"10.1007/978-3-031-93257-1_9","title":"KG-ASI: A Knowledge Graph Enhanced Model-Based Retriever for Document Retrieval","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Labrador Retriever; Information retrieval; Computer science; Graph; Medicine; Theoretical computer science; Surgery","score_opus":0.019017872780294647,"score_gpt":0.2598832846963172,"score_spread":0.24086541191602256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413237958","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":[0.000033419266,0.0005848321,0.9952965,0.0007625507,0.00078188936,0.0010896887,0.000065272754,0.00010719238,0.0012786777],"genre_scores_gemma":[0.009287271,0.000106477,0.98987746,0.00032145315,0.000100127974,0.000048187143,0.000037437832,0.000018090688,0.0002034865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983755,0.000011266218,0.0007382294,0.0002752183,0.00025952148,0.0003403065],"domain_scores_gemma":[0.99751234,0.0006792978,0.00051187316,0.0008844709,0.00034977385,0.00006226354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039034797,0.00037908804,0.00047743367,0.0003365318,0.0009919802,0.00022003659,0.0027589363,0.0002896605,2.874284e-7],"category_scores_gemma":[0.00009399142,0.0003183089,0.0003791755,0.0005344897,0.00040387138,0.0004441278,0.0008398822,0.0004632443,1.9892565e-7],"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":[0.000006086955,0.000010547161,1.4645465e-7,0.00020560628,0.000047827358,2.1821625e-8,0.00039225866,0.88375235,0.000013435079,0.10206529,0.00013861022,0.013367795],"study_design_scores_gemma":[0.00037909072,0.00008664242,0.0000017101141,0.00032783794,0.000047887843,0.0000013165958,8.157954e-7,0.97265667,0.00061805814,0.014036815,0.011506112,0.00033706677],"about_ca_topic_score_codex":0.0000029041507,"about_ca_topic_score_gemma":0.000025216215,"teacher_disagreement_score":0.08890428,"about_ca_system_score_codex":0.000106737876,"about_ca_system_score_gemma":0.00038615905,"threshold_uncertainty_score":0.9999269},"labels":[],"label_agreement":null},{"id":"W4413417378","doi":"10.1109/tifs.2025.3598474","title":"Improving Ethereum Mixing Address Linking With Tensor Computation, Neighbor Data Utilization, and Asymmetric Information Modeling","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Computation; Mixing (physics); Tensor (intrinsic definition); Data modeling; Data mining; Theoretical computer science; Algorithm; Mathematics; Database","score_opus":0.024396067251862885,"score_gpt":0.2579054771110968,"score_spread":0.2335094098592339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413417378","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":[0.006860506,0.000076977136,0.99164313,0.0002537145,0.00030251456,0.00034637444,0.000047219874,0.0001743604,0.000295198],"genre_scores_gemma":[0.9650337,0.00016087476,0.03397067,0.0007041108,0.000011806603,0.000013883814,0.000096444615,0.0000056412396,0.000002885766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987571,0.00003402144,0.0004907814,0.00022934568,0.00027200588,0.000216715],"domain_scores_gemma":[0.9987749,0.00013697981,0.00021187663,0.00040141272,0.0003975804,0.000077282144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026764805,0.00019255029,0.00017426613,0.00053946266,0.0005862145,0.00055897416,0.0002960063,0.000110871835,6.0042413e-7],"category_scores_gemma":[0.000020598547,0.00017492828,0.000023650855,0.0010981697,0.00006300161,0.008106329,0.000026770269,0.00031887667,0.0000016583983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048634363,0.00001938913,0.00006545851,0.00018520126,0.00004218768,4.5254546e-7,0.002183925,0.31309518,0.0000033982726,0.017245663,0.000057338846,0.66705316],"study_design_scores_gemma":[0.0006209616,0.000053554733,0.000066211076,0.00010102056,0.000023785346,0.000010735342,0.00024703602,0.9930516,0.00017406038,0.00502149,0.00044141687,0.00018811703],"about_ca_topic_score_codex":0.00005441917,"about_ca_topic_score_gemma":0.00004512365,"teacher_disagreement_score":0.95817316,"about_ca_system_score_codex":0.000034923836,"about_ca_system_score_gemma":0.00006740361,"threshold_uncertainty_score":0.7133368},"labels":[],"label_agreement":null},{"id":"W4413796616","doi":"10.3233/ssw250009","title":"Knowledge Graph Completion for Action Prediction on Situational Graphs – A Case Study on Household Tasks","year":2025,"lang":"en","type":"book-chapter","venue":"Studies on the semantic web","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Knowledge graph; Situational ethics; Computer science; Graph; Machine learning; Artificial intelligence; Data science; Theoretical computer science; Psychology","score_opus":0.12493132450912324,"score_gpt":0.330926895850226,"score_spread":0.20599557134110275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413796616","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15350635,0.01256086,0.2714138,0.02790868,0.0998943,0.083841726,0.0046620592,0.011121937,0.33509028],"genre_scores_gemma":[0.970786,0.0006520387,0.00031652342,0.0010284764,0.0006388177,0.0007028463,0.000046633035,0.00009227238,0.025736438],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970981,0.00015092961,0.00063086906,0.0011145548,0.00063453364,0.0003710108],"domain_scores_gemma":[0.99601233,0.0019672806,0.0004957812,0.001132416,0.0003287439,0.000063473395],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006141885,0.0006665529,0.0006605228,0.00069787947,0.0012251632,0.000087446526,0.00061425904,0.00019693989,0.0000027585304],"category_scores_gemma":[0.000119201315,0.00048861076,0.00041473503,0.0003253054,0.00019683623,0.00013562037,0.0002731497,0.0007451125,0.000028469996],"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":[0.00029424354,0.000737964,0.000031373675,0.00018941858,0.0017230954,0.00025569732,0.0018128846,0.0043412643,0.000028015784,0.9284009,0.050947197,0.011237991],"study_design_scores_gemma":[0.0074480358,0.017654981,0.0012214904,0.00660427,0.0019362838,0.000513346,0.0048665144,0.0509913,0.00018353778,0.87828153,0.026826242,0.00347244],"about_ca_topic_score_codex":0.000005288343,"about_ca_topic_score_gemma":0.00024218089,"teacher_disagreement_score":0.8172796,"about_ca_system_score_codex":0.00019587789,"about_ca_system_score_gemma":0.00006205274,"threshold_uncertainty_score":0.9997566},"labels":[],"label_agreement":null},{"id":"W4414189744","doi":"10.1016/j.inffus.2025.103744","title":"HyIE: An internal-external induced embedding for knowledge hypergraph link prediction","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Graph Neural Networks","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Department of Education of Liaoning Province; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Hypergraph; Tuple; Embedding; Task (project management); Link (geometry); Code (set theory)","score_opus":0.014815556311270461,"score_gpt":0.2930214445258109,"score_spread":0.27820588821454045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414189744","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":[0.08779859,0.000046518344,0.9070483,0.0002677205,0.0019026602,0.00035531863,0.0000051792426,0.00032948947,0.002246234],"genre_scores_gemma":[0.9686125,0.000030620024,0.030178033,0.00068486296,0.00018565472,0.00007443044,0.00004592888,0.0000062079116,0.00018179002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895614,0.00003285419,0.00043525282,0.00018762152,0.00016630441,0.000221848],"domain_scores_gemma":[0.9990615,0.00009667743,0.0001718258,0.00035887907,0.00023550006,0.00007557545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024154365,0.00014040092,0.00012087494,0.00043845727,0.00026070734,0.00022196774,0.00056265696,0.000116230876,0.000004113474],"category_scores_gemma":[0.00007199865,0.0001323294,0.00008005223,0.00059680437,0.000016418911,0.0042743343,0.00021675654,0.00018636008,0.000022259057],"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":[0.00004587468,0.000029522134,0.00036648609,0.000040080875,0.00000906435,3.5378824e-7,0.0011517036,0.0027842538,0.0059202877,0.019758997,0.0010395985,0.9688538],"study_design_scores_gemma":[0.00070806534,0.00017055261,0.004327363,0.00017286596,0.000006374251,0.0000070867236,0.00004736447,0.9586495,0.004529391,0.008267945,0.022964567,0.00014888778],"about_ca_topic_score_codex":0.00000954063,"about_ca_topic_score_gemma":0.0000065377826,"teacher_disagreement_score":0.9687049,"about_ca_system_score_codex":0.000060952538,"about_ca_system_score_gemma":0.000040442603,"threshold_uncertainty_score":0.5396236},"labels":[],"label_agreement":null},{"id":"W4414427902","doi":"10.1145/3749156","title":"A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Management of Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Nanyang Technological University; Ministry of Education - Singapore","keywords":"Benchmarking; Graph; Computation; Benchmark (surveying); Power graph analysis","score_opus":0.028643687810065883,"score_gpt":0.3161488555937306,"score_spread":0.28750516778366475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414427902","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":[0.9807904,0.00024240158,0.00165582,0.004483038,0.00037027226,0.0017874415,0.00003682751,0.00007670745,0.010557108],"genre_scores_gemma":[0.9970779,0.00010249211,0.002417025,0.00031282744,0.000017936802,0.000014061051,0.000002620826,0.0000075086846,0.000047659454],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9984842,0.000029884053,0.0002148003,0.0005953319,0.00042189457,0.00025389486],"domain_scores_gemma":[0.9972139,0.00047708166,0.00022465727,0.0019819788,0.000072414274,0.000029943902],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00045994465,0.00021838803,0.00024717083,0.00013682779,0.00015923174,0.00008182732,0.007680561,0.000030949188,0.0000013551839],"category_scores_gemma":[0.00011198661,0.00011715234,0.00007879357,0.00084026024,0.00018156187,0.0003028534,0.006171436,0.00021813672,0.0000015426584],"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":[0.0019781266,0.0013608112,0.007890954,0.0026077975,0.0013310472,0.0000120095865,0.0004850666,0.014875876,0.0064311195,0.790327,0.040105034,0.13259514],"study_design_scores_gemma":[0.0034331193,0.002992969,0.66943526,0.0051506707,0.0002647027,0.000009011481,0.0005894523,0.05677215,0.042459387,0.21694118,0.0011618285,0.00079024927],"about_ca_topic_score_codex":0.000008027797,"about_ca_topic_score_gemma":2.2259967e-7,"teacher_disagreement_score":0.6615443,"about_ca_system_score_codex":0.000034808123,"about_ca_system_score_gemma":0.000008091544,"threshold_uncertainty_score":0.99768835},"labels":[],"label_agreement":null},{"id":"W4414925507","doi":"10.1016/j.knosys.2025.114537","title":"Efficient cluster-guided key timestamp discovery for temporal knowledge graph completion","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Hubei University; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Timestamp; Cluster analysis; Key (lock); Snapshot (computer storage); Relation (database); Temporal database; Graph; Feature learning","score_opus":0.02323406851963412,"score_gpt":0.292981318411024,"score_spread":0.2697472498913899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414925507","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":[0.010222666,0.003450401,0.9713511,0.00037774318,0.0064406856,0.0024258122,0.000039587358,0.00068560545,0.005006413],"genre_scores_gemma":[0.9863799,0.0000040398645,0.007628093,0.00018304923,0.0003484209,0.0006691117,0.000077454846,0.000050339884,0.0046595745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996095,0.0004991231,0.001011927,0.0011968829,0.0003253822,0.00087169005],"domain_scores_gemma":[0.9963153,0.0010932668,0.00034445463,0.0014198227,0.0006168192,0.00021034063],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010350335,0.0005526695,0.00075648504,0.0007637535,0.000547937,0.0005146078,0.0014818965,0.00023451127,0.0000020210366],"category_scores_gemma":[0.00012565019,0.000507625,0.0004532542,0.0021776247,0.0001810907,0.00032269125,0.0003378389,0.00029805195,0.000095481504],"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":[0.00040683273,0.0027053861,0.0042669913,0.003666614,0.00031924032,0.000026503249,0.0014750609,0.3952289,0.003757511,0.38900265,0.18821263,0.010931698],"study_design_scores_gemma":[0.002714438,0.00020027864,0.000415594,0.0008438525,0.00003905573,0.000009197539,0.000041815558,0.94341654,0.00095490186,0.0016033016,0.04913927,0.00062175427],"about_ca_topic_score_codex":0.000034539156,"about_ca_topic_score_gemma":0.00007653589,"teacher_disagreement_score":0.97615725,"about_ca_system_score_codex":0.0003276903,"about_ca_system_score_gemma":0.0004545626,"threshold_uncertainty_score":0.99973756},"labels":[],"label_agreement":null},{"id":"W4415125331","doi":"10.1109/itc-egypt66095.2025.11186656","title":"Large Language Models in Intent-Based Networking: a Comprehensive Survey Across the Intent Lifecycle","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Adaptation (eye); TRACE (psycholinguistics); Interpretation (philosophy); Perspective (graphical); Taxonomy (biology); Decision support system","score_opus":0.03264229903850841,"score_gpt":0.31279302791893787,"score_spread":0.28015072888042947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415125331","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":[0.121382155,0.0007766931,0.8744985,0.001413212,0.0007335266,0.00037850274,0.0000073086235,0.00021061927,0.0005994712],"genre_scores_gemma":[0.9892446,0.000023173758,0.0022730373,0.008015145,0.000034468852,0.00003841977,0.000010576264,0.000011137819,0.00034946788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788934,0.0003352184,0.00034572516,0.0005168452,0.00021838516,0.00069450296],"domain_scores_gemma":[0.99800676,0.00080354715,0.00008438817,0.00089201744,0.00014410658,0.0000691839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055243704,0.00021839023,0.0002622747,0.00009661445,0.0001785602,0.00016964751,0.0014026784,0.00007978851,0.0000040396008],"category_scores_gemma":[0.00004889478,0.00015155804,0.00010503129,0.0015671258,0.00009096267,0.000306163,0.0008885254,0.00042998092,0.000010264019],"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":[0.00029104194,0.0007314305,0.07268847,0.00006526763,0.00013824143,0.00021396829,0.007547972,0.7222459,0.00041197627,0.111068614,0.013423387,0.07117376],"study_design_scores_gemma":[0.0007367928,0.000027896527,0.024571367,0.000073491945,0.000002035623,0.0000017532237,0.00056134065,0.9690834,0.00017179112,0.0035678141,0.0010144549,0.00018783257],"about_ca_topic_score_codex":0.00039620718,"about_ca_topic_score_gemma":0.0041096252,"teacher_disagreement_score":0.87222546,"about_ca_system_score_codex":0.00006273265,"about_ca_system_score_gemma":0.000046368285,"threshold_uncertainty_score":0.61803573},"labels":[],"label_agreement":null},{"id":"W4415221919","doi":"10.1109/twc.2025.3619100","title":"A Deep Reinforcement Learning With Transformer Integration for Directed Acyclic Graph Scheduling in Edge Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Carleton University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Directed acyclic graph; Reinforcement learning; Scheduling (production processes); Directed graph; Transformer; Job shop scheduling; Fair-share scheduling; Edge computing","score_opus":0.018165249650745268,"score_gpt":0.27414222639855773,"score_spread":0.25597697674781245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415221919","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":[0.002495886,0.00022011694,0.99400246,0.0013476864,0.00019963016,0.00093215477,9.788424e-7,0.0004277686,0.0003733423],"genre_scores_gemma":[0.9279222,0.0009117741,0.06969257,0.00022513443,0.000007918861,0.0010582405,0.00001997892,0.00002137756,0.0001407932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984021,0.00016966747,0.0004790329,0.0004013594,0.00015725856,0.00039055842],"domain_scores_gemma":[0.9976264,0.00070102175,0.00012239207,0.0013140636,0.00016539528,0.00007070281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021006897,0.00025299768,0.0002686604,0.00056489214,0.0008204619,0.00011276343,0.001259694,0.00013190627,0.000002473151],"category_scores_gemma":[0.000007683842,0.0002415864,0.0001383747,0.0023237425,0.00014407921,0.0006251101,0.000007752977,0.00089652935,0.0000017719649],"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":[0.00006227006,0.00014337811,0.000028777047,0.000010070463,0.000042286258,3.9023703e-7,0.00042314164,0.86824244,0.00037185758,0.009795652,0.0000040636623,0.120875664],"study_design_scores_gemma":[0.00097355456,0.00013219545,0.00012579553,0.00028349026,0.000031578456,0.000002534245,0.00013799036,0.99486566,0.0022471026,0.0007818369,0.00017737914,0.0002408797],"about_ca_topic_score_codex":0.00004453863,"about_ca_topic_score_gemma":0.001964836,"teacher_disagreement_score":0.9254263,"about_ca_system_score_codex":0.00013520078,"about_ca_system_score_gemma":0.00006883753,"threshold_uncertainty_score":0.98516077},"labels":[],"label_agreement":null},{"id":"W4415295489","doi":"10.1093/bioinformatics/btag362","title":"Movi 2: Fast and Space-Efficient Queries on Pangenomes","year":2025,"lang":"en","type":"preprint","venue":"Bioinformatics","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Memory footprint; Search engine indexing; Space (punctuation); Footprint; Compression (physics); Variety (cybernetics)","score_opus":0.015049534014978837,"score_gpt":0.24618535397210797,"score_spread":0.23113581995712915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415295489","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":[0.0064841015,0.0013539781,0.9728259,0.001359704,0.0022459463,0.00080701004,0.000104463696,0.00055083464,0.014268055],"genre_scores_gemma":[0.0836134,0.002449799,0.9079783,0.00281299,0.00028271633,0.00013187373,0.00007175787,0.00003725769,0.0026219052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855393,0.000025653713,0.00041957555,0.0003334696,0.0003160144,0.00035138073],"domain_scores_gemma":[0.99836624,0.00014006397,0.00027900736,0.0010275809,0.00007327678,0.00011381804],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012842256,0.00034992213,0.00034352383,0.0002594332,0.00014435856,0.000311779,0.0008929994,0.00021626518,0.0000021067417],"category_scores_gemma":[0.00004159329,0.00030258193,0.00009568595,0.00028157784,0.00011273795,0.00017398025,0.0024892334,0.00050764333,0.00002099645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023059723,0.00009557641,0.00017931449,0.0012053837,0.000107150896,0.000023407592,0.0077897483,0.22672139,0.000003995022,0.25621942,0.004737094,0.50289446],"study_design_scores_gemma":[0.00018377384,0.000079759244,0.00051395525,0.0003938199,0.0000148469435,0.000009340167,0.0001681013,0.98088855,0.00013171336,0.0051927753,0.011932439,0.00049090094],"about_ca_topic_score_codex":0.0000051674683,"about_ca_topic_score_gemma":0.0000038633502,"teacher_disagreement_score":0.7541672,"about_ca_system_score_codex":0.00006333183,"about_ca_system_score_gemma":0.00010440911,"threshold_uncertainty_score":0.9999426},"labels":[],"label_agreement":null},{"id":"W4415507753","doi":"10.1016/j.ipm.2025.104455","title":"DLGTrust: Graph neural network-based trust evaluation using dynamic line graph","year":2025,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Adaptability; Robustness (evolution); Graph; Dynamic network analysis; Global network; Adversarial system; Feature extraction; Artificial neural network; Exploit","score_opus":0.015648993961507193,"score_gpt":0.29236471202700687,"score_spread":0.27671571806549966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415507753","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":[0.009035808,0.00035633604,0.98507077,0.0007442868,0.00084301713,0.0008391652,0.0000013312189,0.00045702007,0.0026522963],"genre_scores_gemma":[0.89767617,0.00002192885,0.098819524,0.0032018956,0.000034353492,0.0001109457,0.000061726605,0.0000109820085,0.000062464824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976527,0.00008147877,0.0007193853,0.00034303844,0.00070911425,0.0004942499],"domain_scores_gemma":[0.99854386,0.00003412435,0.00044920485,0.00056205573,0.0003425752,0.00006819002],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007529503,0.00027741367,0.00020410691,0.0006150115,0.0005595997,0.00065638294,0.0007927428,0.000084057734,0.000007853622],"category_scores_gemma":[0.000022359802,0.0002779083,0.00010477531,0.0030851122,0.00006740432,0.0040815156,0.00026716926,0.00022000479,0.000009672245],"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":[0.00001159591,0.000015330297,0.000109207365,0.00012266832,0.000014958724,0.0000012536963,0.000056090517,0.5831187,0.0000034135792,0.0038060083,0.0001857225,0.41255507],"study_design_scores_gemma":[0.0008288172,0.00002325797,0.0021730217,0.000228525,0.00006363981,0.0000022475883,0.000040621235,0.97113395,0.000035325065,0.023899006,0.0013178558,0.00025375906],"about_ca_topic_score_codex":0.0000043010577,"about_ca_topic_score_gemma":0.0000046363475,"teacher_disagreement_score":0.88864034,"about_ca_system_score_codex":0.00018709141,"about_ca_system_score_gemma":0.000092528266,"threshold_uncertainty_score":0.99996734},"labels":[],"label_agreement":null},{"id":"W4415626332","doi":"10.1016/j.eswa.2026.132374","title":"Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification","year":2025,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Multiplex; Homophily; Node (physics); Graph; Class (philosophy); Directed graph","score_opus":0.07061930209504859,"score_gpt":0.3108450335913657,"score_spread":0.24022573149631715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415626332","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":[0.00028887513,0.0013183705,0.9906461,0.00016587357,0.00021397206,0.0064907246,0.00007263768,0.0004436562,0.00035979383],"genre_scores_gemma":[0.51546633,0.00006224413,0.44734377,0.00009743203,0.00014929505,0.03651899,0.00027100006,0.000032487515,0.000058443544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966134,0.00014662546,0.0007205645,0.0017541734,0.000311897,0.00045333066],"domain_scores_gemma":[0.99671644,0.000118699005,0.00034082486,0.0023230594,0.00035031253,0.00015066715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002469979,0.00047319714,0.0005494159,0.00047346417,0.00026133913,0.00023004097,0.001863251,0.00036968847,1.1392817e-7],"category_scores_gemma":[0.0000089257965,0.00043628673,0.0001301572,0.00082045514,0.00006779083,0.00041759314,0.0003761969,0.00054700667,0.0000013835365],"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":[0.00003746307,0.00019396991,0.000040507846,0.000121592006,0.000033450266,4.3299562e-7,0.0006208252,0.9298205,0.00013195343,0.06674525,0.00006207341,0.0021920002],"study_design_scores_gemma":[0.00041496288,0.000050804825,0.00003556335,0.0002061141,0.000009107038,0.0000045152156,0.0003328523,0.9935341,0.000015241181,0.004598869,0.0003364948,0.0004614],"about_ca_topic_score_codex":0.00038998917,"about_ca_topic_score_gemma":0.00005443911,"teacher_disagreement_score":0.54330236,"about_ca_system_score_codex":0.00020662232,"about_ca_system_score_gemma":0.00019214855,"threshold_uncertainty_score":0.9998089},"labels":[],"label_agreement":null},{"id":"W4415722214","doi":"10.18280/ts.420512","title":"A Collaborative Learning Framework and Optimization Approach for Visual Tasks and Knowledge Graphs in Intelligent Education","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Collaborative learning; Knowledge graph; Task (project management); Collaborative software; Collaborative filtering","score_opus":0.011098715853871888,"score_gpt":0.30082413972688676,"score_spread":0.28972542387301486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415722214","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":[0.013060857,0.009824934,0.97398484,0.0003053004,0.00041383132,0.0021097146,0.0000050086396,0.000047868583,0.00024767648],"genre_scores_gemma":[0.7243853,0.0013539573,0.27336767,0.00022832211,0.00008806359,0.0004168844,0.00003237694,0.000017806366,0.0001095828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736995,0.00034299903,0.00065107556,0.0009777674,0.0001905881,0.00046762536],"domain_scores_gemma":[0.99857455,0.00054376386,0.00024868574,0.00016464041,0.000323554,0.00014481298],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005764872,0.00038803372,0.00040072366,0.000605085,0.0004219259,0.00038035217,0.00027002173,0.00022437402,0.000010250761],"category_scores_gemma":[0.00010446852,0.0004213283,0.00006269659,0.002300672,0.00022789565,0.000571589,0.00023775976,0.00049769756,3.1431352e-7],"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":[0.00026396147,0.0009947415,0.0026999344,0.00033340178,0.00008466497,9.4483346e-7,0.008517642,0.56608635,0.000088269124,0.11995031,0.00015094265,0.3008288],"study_design_scores_gemma":[0.00083832466,0.00057258154,0.0009916144,0.00050121953,0.0000523485,0.0000022678782,0.0016135186,0.9857949,0.00020255937,0.008693549,0.00037673803,0.00036040423],"about_ca_topic_score_codex":0.000005560622,"about_ca_topic_score_gemma":0.000008799954,"teacher_disagreement_score":0.7113245,"about_ca_system_score_codex":0.00011943192,"about_ca_system_score_gemma":0.00039585427,"threshold_uncertainty_score":0.99982387},"labels":[],"label_agreement":null},{"id":"W4416017186","doi":"10.1145/3746252.3761623","title":"QueryBridge: One Million Annotated Questions with SPARQL Queries - Dataset for Question Answering over Knowledge Graphs","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"SPARQL; Question answering; Benchmark (surveying); Knowledge graph; Natural language; Knowledge extraction; Linked data; Mean reciprocal rank","score_opus":0.01907335345570043,"score_gpt":0.3071836583070897,"score_spread":0.2881103048513893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416017186","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":[0.008558723,0.004700872,0.97821045,0.0017807801,0.001974808,0.002149353,0.0015930785,0.0004974985,0.00053445395],"genre_scores_gemma":[0.7653057,0.0037899844,0.21572195,0.0020491579,0.0003960562,0.000886291,0.005267025,0.00015255284,0.0064312983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99510086,0.00032865544,0.0010294012,0.0018898045,0.0004142843,0.0012370072],"domain_scores_gemma":[0.99619055,0.00059106114,0.0003842678,0.0017845824,0.00072038156,0.000329166],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005845629,0.0008575066,0.0007928996,0.000889232,0.001091409,0.00064238074,0.0012115093,0.0003111083,0.000041439394],"category_scores_gemma":[0.00016401544,0.0008229083,0.00020138612,0.004238112,0.0006540689,0.0028671653,0.00060341886,0.0006398333,0.000027849383],"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":[0.00094281323,0.0011677898,0.0020681606,0.0007105527,0.0004735277,0.000039633484,0.00042167018,0.015224816,0.0021009736,0.8505102,0.07034087,0.055999003],"study_design_scores_gemma":[0.005446096,0.0024487434,0.020149328,0.0050650514,0.0006540141,0.000072027375,0.00012746411,0.75856787,0.011515098,0.054105695,0.13863657,0.00321205],"about_ca_topic_score_codex":0.00037092474,"about_ca_topic_score_gemma":0.002229175,"teacher_disagreement_score":0.7964045,"about_ca_system_score_codex":0.00020475237,"about_ca_system_score_gemma":0.00036074733,"threshold_uncertainty_score":0.9994222},"labels":[],"label_agreement":null},{"id":"W4416017410","doi":"10.1145/3746252.3761157","title":"Empirical Study of Over-Squashing in GNNs and Causal Estimation of Rewiring Strategies","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Pairwise comparison; Empirical research; Graph; Uncorrelated; Node (physics); Range (aeronautics); Causal inference","score_opus":0.026987435852803048,"score_gpt":0.3531685401662192,"score_spread":0.3261811043134161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416017410","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":[0.6650862,0.00021352251,0.33363554,0.000103385915,0.00016980177,0.00032355028,4.711093e-7,0.000020115358,0.00044742177],"genre_scores_gemma":[0.99068743,0.00003539932,0.009179294,0.000039152703,0.000009723963,0.0000063325356,3.01587e-7,0.000006003212,0.000036371675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773264,0.00019706455,0.0008938728,0.00055249303,0.0003238789,0.00030007624],"domain_scores_gemma":[0.99860793,0.0005257292,0.00026444797,0.0004675605,0.00008355889,0.000050795334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039950927,0.00021563057,0.00047860402,0.0004255076,0.000075763484,0.00012208495,0.00041867534,0.000100922865,0.0000049670757],"category_scores_gemma":[0.00009221022,0.00021193744,0.000040959785,0.0017262402,0.00014126129,0.0010465215,0.00062288536,0.0003096803,1.8367987e-7],"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":[0.00008975908,0.0008735152,0.22142035,0.00031639758,0.000064919914,0.000027062679,0.010616756,0.6520232,0.0010308926,0.03542995,0.00003606921,0.07807115],"study_design_scores_gemma":[0.0009195242,0.00054072257,0.22729675,0.0003230261,0.000019414088,0.0000015367673,0.002556511,0.7559401,0.0007378821,0.011518309,0.0000010620541,0.0001451564],"about_ca_topic_score_codex":0.00031462318,"about_ca_topic_score_gemma":0.00090956944,"teacher_disagreement_score":0.32560125,"about_ca_system_score_codex":0.00003974245,"about_ca_system_score_gemma":0.0001255269,"threshold_uncertainty_score":0.8642558},"labels":[],"label_agreement":null},{"id":"W4416017620","doi":"10.1145/3746252.3761152","title":"PriviRec: Confidential and Decentralized Graph Filtering for Recommender Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Recommender system; Graph; Filter (signal processing); Aggregate (composite); Overhead (engineering); Collaborative filtering; Adjacency matrix","score_opus":0.011871318607505838,"score_gpt":0.27203517574353275,"score_spread":0.2601638571360269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416017620","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":[0.002972887,0.0004684561,0.9930774,0.0007620721,0.0012814667,0.00041864917,0.000001390835,0.00019585616,0.0008218204],"genre_scores_gemma":[0.8300961,0.00039829814,0.1654459,0.0015610682,0.00006646548,0.00018294729,0.0000048715224,0.000016116075,0.0022282004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991123,0.0000335879,0.0001961765,0.0003227669,0.0000715611,0.00026362075],"domain_scores_gemma":[0.9993934,0.00019083307,0.000046854813,0.00026993852,0.000041721927,0.000057220357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011560752,0.00011019302,0.00015632408,0.000095026335,0.000120263496,0.00020956824,0.00033423144,0.000045750414,0.000004085132],"category_scores_gemma":[0.00001747057,0.000098332544,0.000050088973,0.00024571453,0.000028209399,0.00031645613,0.00017677217,0.00006155819,0.0000010618156],"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":[0.00004449963,0.000037506743,0.00093343545,0.00013554531,0.000102571736,0.00000409411,0.00012073239,0.0009852393,0.002753807,0.90751994,0.021988453,0.065374196],"study_design_scores_gemma":[0.0055601047,0.000191237,0.0019780984,0.00033646528,0.000055002074,0.000054851585,0.00012574925,0.67337096,0.009555307,0.12951453,0.1782328,0.0010248971],"about_ca_topic_score_codex":0.000013689971,"about_ca_topic_score_gemma":0.000011194131,"teacher_disagreement_score":0.82763153,"about_ca_system_score_codex":0.000011822776,"about_ca_system_score_gemma":0.000012245119,"threshold_uncertainty_score":0.4009885},"labels":[],"label_agreement":null},{"id":"W4416026889","doi":"10.48550/arxiv.2511.04557","title":"Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Statistical relational learning; Deep learning; Relational database; Bottleneck; Feature learning; Relational model; Scalability; Graph","score_opus":0.034452809015373204,"score_gpt":0.28395939204992965,"score_spread":0.24950658303455645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416026889","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":[0.4973122,0.0019862864,0.497431,0.0005844344,0.0012206482,0.0011556179,0.000016347207,0.00009180858,0.0002016674],"genre_scores_gemma":[0.9686283,0.0003685599,0.029855246,0.00032435014,0.00016022209,0.0001795654,0.00009981302,0.00003676767,0.00034720165],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99495316,0.0003150008,0.0014261522,0.0018956447,0.00039626178,0.0010138066],"domain_scores_gemma":[0.9970643,0.0013414067,0.0006527625,0.00041226434,0.0002851367,0.00024414307],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00069131213,0.00085693423,0.0009558782,0.00061735144,0.0007381864,0.00020021557,0.0008471065,0.0006443564,0.000019079094],"category_scores_gemma":[0.00037367427,0.0008766256,0.0004419417,0.0010757162,0.00044770207,0.0010937899,0.0006389451,0.0031781709,0.0000027442752],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015157313,0.000019713396,0.7209939,0.00020879379,0.0000925951,0.000012683822,0.0030264815,0.030149292,0.00006811994,0.011728666,0.000006853646,0.23354134],"study_design_scores_gemma":[0.00202075,0.0002785134,0.24373601,0.0011089,0.000058104735,0.000023571538,0.0011900804,0.7287035,0.000111732945,0.021035623,0.0006820714,0.0010511584],"about_ca_topic_score_codex":0.00018396799,"about_ca_topic_score_gemma":0.0010255243,"teacher_disagreement_score":0.6985542,"about_ca_system_score_codex":0.00018423895,"about_ca_system_score_gemma":0.00023932474,"threshold_uncertainty_score":0.9993684},"labels":[],"label_agreement":null},{"id":"W4416119567","doi":"","title":"Generalization and Distributed Learning of GFlowNets","year":2025,"lang":"en","type":"article","venue":"Research Explorer (The University of Manchester)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Engineering and Physical Sciences Research Council; Fundação de Amparo à Pesquisa do Estado de São Paulo; UK Research and Innovation","keywords":"Generalization; Feature (linguistics); Distributed learning; Key (lock); Active learning (machine learning)","score_opus":0.03736599860155435,"score_gpt":0.28325538532192346,"score_spread":0.2458893867203691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416119567","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":[0.3938348,0.00021809876,0.60445565,0.0012188742,0.0000321559,0.00013634808,0.0000010976185,0.000029588524,0.000073401476],"genre_scores_gemma":[0.9964622,0.00038107086,0.0030685405,0.000014666887,0.000006941901,3.8266234e-7,0.000004361783,0.0000026359132,0.00005915843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989841,0.00025891498,0.00009355202,0.000207557,0.00026211125,0.00019375706],"domain_scores_gemma":[0.99911386,0.00027403748,0.000056464647,0.0003154844,0.00020006581,0.000040081704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040192762,0.000060381604,0.00011653702,0.00016939481,0.00023260814,0.00001747271,0.00064229715,0.0000404573,0.000004150469],"category_scores_gemma":[0.00006512448,0.00005391032,0.000035253463,0.0008201835,0.00031891602,0.00029414985,0.0006695577,0.00021939212,0.0000021847568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005098987,0.0004478113,0.13754834,0.000938858,0.0004168913,0.00011916493,0.08986207,0.09553948,0.030346105,0.39352232,0.01451933,0.23622972],"study_design_scores_gemma":[0.0028201928,0.00061584066,0.2543572,0.00073252054,0.000047751917,0.000009732145,0.016743248,0.613826,0.016835222,0.04189151,0.05158796,0.00053288846],"about_ca_topic_score_codex":0.0000054469533,"about_ca_topic_score_gemma":0.000001189399,"teacher_disagreement_score":0.60262746,"about_ca_system_score_codex":0.000029963958,"about_ca_system_score_gemma":0.00003362538,"threshold_uncertainty_score":0.2198399},"labels":[],"label_agreement":null},{"id":"W4416249429","doi":"10.1109/ijcnn64981.2025.11229000","title":"Optimization of Graph Neural Networks Training Using Graph Reordering","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Speedup; Graph; Artificial neural network; Training set; Convergence (economics); Dense graph; Attention network; Deep neural networks; Graph bandwidth","score_opus":0.03112615784146965,"score_gpt":0.2749506316482858,"score_spread":0.24382447380681616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416249429","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":[0.007989971,0.0026812458,0.9831104,0.00029613057,0.0034916291,0.0005545088,0.0000017597034,0.00023470247,0.0016396295],"genre_scores_gemma":[0.74643326,0.0004385953,0.25234523,0.00051160576,0.00011073241,0.0000075916532,0.0000036345798,0.000034824545,0.000114496346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954083,0.00028598588,0.001438401,0.0012344302,0.0004591993,0.0011736819],"domain_scores_gemma":[0.9972329,0.00036651499,0.00066064583,0.0011649446,0.00037018763,0.00020478433],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005138746,0.0006292343,0.00083653827,0.0010051178,0.0005769143,0.00027663424,0.0014489868,0.00034762218,0.000044859047],"category_scores_gemma":[0.00008185136,0.00068225,0.00046685315,0.0075903884,0.00044687308,0.0015478112,0.0008055484,0.00074191653,3.273925e-7],"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":[0.000037280668,0.000050728097,0.0006260363,0.000054272445,0.00008433017,0.000011940105,0.00031876785,0.90526205,0.00013461632,0.017035445,0.00003470528,0.076349825],"study_design_scores_gemma":[0.0006850968,0.000113430244,0.00023031079,0.00044248745,0.00008467969,0.000020327476,0.00016660155,0.9923543,0.00022937988,0.005122502,0.000019595085,0.00053131755],"about_ca_topic_score_codex":0.0000575592,"about_ca_topic_score_gemma":0.0000222996,"teacher_disagreement_score":0.7384433,"about_ca_system_score_codex":0.000061111175,"about_ca_system_score_gemma":0.00013339664,"threshold_uncertainty_score":0.99956286},"labels":[],"label_agreement":null},{"id":"W4416640682","doi":"10.1016/j.procs.2025.10.199","title":"TempHypE-GNN: Hyperbolic Graph Neural ODEs for Hierarchical Temporal Knowledge Graphs","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ode; Ordinary differential equation; Euclidean geometry; Graph; Artificial neural network; Knowledge graph; Hyperbolic geometry","score_opus":0.014853810731522685,"score_gpt":0.2836115977305752,"score_spread":0.2687577869990525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416640682","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":[0.029672861,0.0010054676,0.9621655,0.001680693,0.0033338198,0.00087248575,0.00000455159,0.00074393896,0.00052072835],"genre_scores_gemma":[0.7142137,0.000028560784,0.28405297,0.0012219686,0.00022981783,0.0001539489,0.0000030580027,0.000016432314,0.00007952405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99586016,0.00006776999,0.000549199,0.0017194569,0.0005581751,0.0012452358],"domain_scores_gemma":[0.997358,0.00048669637,0.00015816305,0.0011095945,0.0005125553,0.0003749426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073001697,0.0004436105,0.000449359,0.0010374072,0.0010869759,0.00065026555,0.0046605095,0.00011568202,0.0000010684112],"category_scores_gemma":[0.00012644846,0.00039100111,0.0002533636,0.006271643,0.0011317243,0.0018772533,0.0015492482,0.00041564647,0.000012381666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040732415,0.00031217447,0.006126793,0.00016015179,0.0000280632,0.000014778507,0.00070475927,0.0017865133,0.0014330716,0.6225625,0.0038570699,0.36297333],"study_design_scores_gemma":[0.00076935085,0.0002726056,0.0062162518,0.00008875839,0.000011529101,0.000045696044,0.0000060737793,0.82676476,0.0020351831,0.15869415,0.004510291,0.0005853388],"about_ca_topic_score_codex":0.00000522456,"about_ca_topic_score_gemma":0.00001370992,"teacher_disagreement_score":0.82497823,"about_ca_system_score_codex":0.00006914233,"about_ca_system_score_gemma":0.00045303022,"threshold_uncertainty_score":0.9998542},"labels":[],"label_agreement":null},{"id":"W4416701904","doi":"10.1016/j.inffus.2025.103998","title":"One model connects all graphs: Towards training one unified model for multi-domain graph pre-training using adaptive vector quantization","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Central China Normal University; China Postdoctoral Science Foundation; Natural Science Foundation of Hubei Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Codebook; Vector quantization; Graph; Quantization (signal processing); Linde–Buzo–Gray algorithm; Unified Model; Graph rewriting","score_opus":0.14712635504988855,"score_gpt":0.32211472557866716,"score_spread":0.1749883705287786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416701904","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":[0.0360096,0.000038406833,0.9616159,0.00030104737,0.00031332605,0.0010958471,0.00002761327,0.00037287225,0.00022536151],"genre_scores_gemma":[0.5666453,0.000021742482,0.43242514,0.00074327935,0.000013280717,0.000054324275,0.00007106092,0.000010666089,0.0000151591385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787486,0.000062980376,0.0007605768,0.00036684278,0.00042074567,0.00051400776],"domain_scores_gemma":[0.9985753,0.00012559704,0.00045084188,0.0003791094,0.00035222486,0.000116919946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050667045,0.0002846798,0.00034709656,0.0006886553,0.00045201962,0.00019499677,0.0005186035,0.00020550206,0.0000011094678],"category_scores_gemma":[0.00012006576,0.00032029868,0.00015002232,0.0011239685,0.00006296142,0.0036595643,0.00021571382,0.00024009006,0.0000016757941],"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":[0.000098308956,0.000039555238,0.0000033692077,0.000044605873,0.000032708733,2.0933712e-7,0.012633785,0.76442355,0.0024067317,0.18154316,0.000022613694,0.038751423],"study_design_scores_gemma":[0.0014254441,0.00007654654,0.00012276306,0.00024337947,0.00002435104,0.0000014582552,0.00040806388,0.9240703,0.00058361574,0.07270373,0.000026672635,0.00031369223],"about_ca_topic_score_codex":0.000023158438,"about_ca_topic_score_gemma":0.00003667318,"teacher_disagreement_score":0.5306358,"about_ca_system_score_codex":0.00011776766,"about_ca_system_score_gemma":0.0002609715,"threshold_uncertainty_score":0.9999249},"labels":[],"label_agreement":null},{"id":"W4416829144","doi":"10.1016/j.engappai.2025.113229","title":"Spatiotemporal knowledge graph multi-hop reasoning based on large language models","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Advanced Graph Neural 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":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Knowledge graph; Graph; Natural language; Generalization; Question answering; Natural language understanding; Knowledge representation and reasoning; Reasoning system; Language model","score_opus":0.019350466157026,"score_gpt":0.29547661817445187,"score_spread":0.27612615201742585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416829144","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":[0.0011699212,0.00031499314,0.9972067,0.00012911866,0.00015046168,0.00034153415,0.0000068597756,0.00032277143,0.00035764423],"genre_scores_gemma":[0.77028537,0.000008575375,0.22943784,0.00004429865,0.000027023918,0.00013561499,0.000006222133,0.0000112677035,0.000043771226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877346,0.000022284257,0.00037643805,0.0004081293,0.0001420206,0.00027769507],"domain_scores_gemma":[0.9986777,0.00022644176,0.00009070655,0.000819254,0.00012137422,0.00006454303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022289301,0.00017330257,0.00017636207,0.0003983088,0.00011427615,0.00004409351,0.00081173255,0.00007601486,0.0000030889241],"category_scores_gemma":[0.000066825276,0.00018981098,0.00009156751,0.0016860683,0.00004039108,0.00020194358,0.00011747117,0.00022789599,0.000017022347],"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":[0.0000032008,0.00012718643,0.000012784768,0.00002098959,0.000005259409,6.099833e-7,0.00016001673,0.57830775,0.00095015025,0.38212043,0.000017330081,0.038274266],"study_design_scores_gemma":[0.000030116118,0.000019818423,0.000040734067,0.00008417431,0.000004218722,2.649351e-7,0.000036674614,0.9519888,0.040667973,0.0067162756,0.00026467527,0.00014627927],"about_ca_topic_score_codex":0.000015457597,"about_ca_topic_score_gemma":0.000016308188,"teacher_disagreement_score":0.76911545,"about_ca_system_score_codex":0.000036899517,"about_ca_system_score_gemma":0.00004488974,"threshold_uncertainty_score":0.7740267},"labels":[],"label_agreement":null},{"id":"W4416873377","doi":"10.1109/icdcsw63273.2025.00037","title":"Analysis and Tuning of Knowledge Distillation for Efficient Collaborative Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Hyperparameter; Collaborative learning; Transfer of learning; Set (abstract data type); Collaborative filtering; Knowledge transfer","score_opus":0.0092921168834062,"score_gpt":0.2884298949604294,"score_spread":0.2791377780770232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416873377","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":[0.045990236,0.0024466796,0.94828486,0.00017422112,0.00027137727,0.0004886937,0.0000055297023,0.00004308514,0.0022953337],"genre_scores_gemma":[0.9687997,0.00010032632,0.029473968,0.000018329958,0.000019953113,0.000016431726,0.0000049841433,0.000006055734,0.0015602267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982394,0.00017032825,0.0005140891,0.0006382617,0.00013842911,0.00029949762],"domain_scores_gemma":[0.9974302,0.0011633574,0.0003152076,0.00029531156,0.0007211411,0.00007478309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039139844,0.00021277413,0.0005036316,0.0006920454,0.00038581563,0.00010734441,0.00025673574,0.000093428396,0.0000066074367],"category_scores_gemma":[0.00025492648,0.00020494655,0.00016029597,0.008203152,0.00019462878,0.00017236968,0.00033230014,0.0001600684,5.4367126e-7],"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":[0.000068346795,0.000082468345,0.025824187,0.0001282717,0.0006384395,5.2387094e-7,0.002377927,0.69984925,0.0005931669,0.0779624,0.000035204393,0.19243982],"study_design_scores_gemma":[0.00043652568,0.00015321244,0.016455367,0.000093137234,0.00043266275,1.7025545e-7,0.00026408627,0.9795099,0.001184881,0.0007285608,0.0005691643,0.00017233787],"about_ca_topic_score_codex":0.0000066411994,"about_ca_topic_score_gemma":0.000053528976,"teacher_disagreement_score":0.9228095,"about_ca_system_score_codex":0.000046782356,"about_ca_system_score_gemma":0.00011189373,"threshold_uncertainty_score":0.8357478},"labels":[],"label_agreement":null},{"id":"W4416968605","doi":"10.48550/arxiv.2512.01890","title":"Elastic Weight Consolidation for Knowledge Graph Continual Learning: An Empirical Evaluation","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Athabasca University","keywords":"Forgetting; Embedding; Consolidation (business); Artificial neural network; Task (project management); Graph; Contrast (vision); Relation (database)","score_opus":0.10110134083423716,"score_gpt":0.38533952337047955,"score_spread":0.2842381825362424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416968605","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":[0.36681953,0.0012655103,0.6198303,0.00067570625,0.0067726793,0.00321942,0.00003182899,0.0003901907,0.0009947916],"genre_scores_gemma":[0.98459655,0.00034771452,0.009426529,0.00037862264,0.0013104558,0.0011187637,0.00056342373,0.00007639205,0.0021815689],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9903197,0.0020927754,0.0018010145,0.003453874,0.00096115103,0.0013714381],"domain_scores_gemma":[0.99035734,0.0029319972,0.0013379403,0.001974689,0.002870038,0.0005279901],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002526016,0.0011971069,0.0012739373,0.00082493003,0.0011302732,0.0004265613,0.0024152834,0.0011284875,0.00011586087],"category_scores_gemma":[0.0021529475,0.0012975495,0.0007217178,0.001839659,0.0005203886,0.0013441723,0.001768701,0.0024288183,0.0001566547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090603635,0.0022362275,0.4234502,0.0009237212,0.0008783882,0.000024110623,0.009459187,0.19678836,0.001250994,0.0099349925,0.0048350533,0.34931272],"study_design_scores_gemma":[0.0028863668,0.0010311697,0.076434985,0.00068995915,0.00066399097,0.000014908719,0.00013449996,0.8804853,0.0012391176,0.01698339,0.017912861,0.001523442],"about_ca_topic_score_codex":0.000008695926,"about_ca_topic_score_gemma":0.00006634516,"teacher_disagreement_score":0.6836969,"about_ca_system_score_codex":0.00038941752,"about_ca_system_score_gemma":0.0013833904,"threshold_uncertainty_score":0.9998726},"labels":[],"label_agreement":null},{"id":"W4416968636","doi":"10.48550/arxiv.2512.01878","title":"Graph Distance as Surprise: Free Energy Minimization in Knowledge Graph Reasoning","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; Athabasca University","keywords":"Surprise; Graph; Null graph; Moral graph; Clique-width; Directed graph; Graph theory; Graph rewriting","score_opus":0.021036755373346216,"score_gpt":0.27168736994520704,"score_spread":0.25065061457186083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416968636","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":[0.14019151,0.020246288,0.8174992,0.0009454614,0.009615163,0.0011886121,0.00007571243,0.0006707689,0.00956726],"genre_scores_gemma":[0.96430093,0.013039483,0.014399261,0.000747476,0.0005544792,0.00043621392,0.00014976914,0.00012850919,0.006243887],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9894086,0.0010468262,0.0022949693,0.0043736063,0.0008819173,0.0019940461],"domain_scores_gemma":[0.99084014,0.0010827247,0.0013930724,0.0054036933,0.00073196436,0.0005483789],"candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00069178117,0.0017072536,0.0017086597,0.0017121346,0.0007000382,0.00040656267,0.0066834656,0.0012515069,0.00006135727],"category_scores_gemma":[0.00085567444,0.002002475,0.00084826065,0.00815786,0.0006610165,0.0013130185,0.006531298,0.002551609,0.000059163693],"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":[0.0003798532,0.001167708,0.6249406,0.00067954324,0.0003653774,0.00052555464,0.003895428,0.12881327,0.00012273407,0.17094754,0.00444676,0.06371565],"study_design_scores_gemma":[0.006182802,0.0005563071,0.39132774,0.013963022,0.00035727574,0.00006534099,0.00034787794,0.24014273,0.0033794285,0.28900215,0.046781488,0.007893824],"about_ca_topic_score_codex":0.0006029773,"about_ca_topic_score_gemma":0.0029499521,"teacher_disagreement_score":0.82410944,"about_ca_system_score_codex":0.0004347754,"about_ca_system_score_gemma":0.0007960834,"threshold_uncertainty_score":0.99974954},"labels":[],"label_agreement":null},{"id":"W4417069564","doi":"10.23977/cpcs.2025.090111","title":"Research on Fault Diagnosis and Disposal Suggestions Method of Power Communication Network Based on Dynamic Event Driven Knowledge Graph","year":2025,"lang":"","type":"article","venue":"Computing Performance and Communication systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Timestamp; Asynchronous communication; Telecommunications network; Event (particle physics); Fault (geology); Network topology; Knowledge-based systems; Root cause; Power (physics); Graph","score_opus":0.036703014734517994,"score_gpt":0.3836429242701658,"score_spread":0.3469399095356478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417069564","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":[0.3431443,0.044110328,0.6000197,0.0036910763,0.0014393843,0.0032959897,0.000025001444,0.0002704986,0.0040037585],"genre_scores_gemma":[0.9756916,0.010556682,0.013178637,0.00016826912,0.00003407,0.00018437399,0.000034501056,0.000031556272,0.00012029519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99218106,0.0043169674,0.001309864,0.00086610584,0.0006006342,0.0007253527],"domain_scores_gemma":[0.9875636,0.006885665,0.00072423526,0.0037725845,0.0008743717,0.00017952578],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.004890967,0.0004721303,0.00075575063,0.0009008712,0.0026974229,0.0003849796,0.0024320579,0.00031444855,0.0000021678297],"category_scores_gemma":[0.000114573006,0.00047666102,0.00013937116,0.0033866412,0.0008064792,0.00040070238,0.001970253,0.0016493181,0.000007681107],"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":[0.000119755765,0.00054638507,0.018151566,0.00043797807,0.00008885905,5.671005e-7,0.0015259241,0.7941258,0.000011165948,0.050684128,0.0011495302,0.13315834],"study_design_scores_gemma":[0.0007710099,0.0005991916,0.052165266,0.009418779,0.000037046895,0.000005593935,0.00039661632,0.93371236,0.000022236301,0.0005612892,0.0019627083,0.00034789272],"about_ca_topic_score_codex":0.00005987186,"about_ca_topic_score_gemma":0.00004053024,"teacher_disagreement_score":0.6325473,"about_ca_system_score_codex":0.00019782718,"about_ca_system_score_gemma":0.00018424165,"threshold_uncertainty_score":0.9997685},"labels":[],"label_agreement":null},{"id":"W4417093036","doi":"10.48550/arxiv.2505.10806","title":"RapidGNN: Communication Efficient Large-Scale Distributed Training of Graph Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Cache; Computation; Feature (linguistics); Overhead (engineering); Latency (audio); Training (meteorology); Artificial neural network; Graph; Energy consumption","score_opus":0.03409712801625521,"score_gpt":0.2749098831397769,"score_spread":0.24081275512352168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417093036","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":[0.2420114,0.0020893046,0.75287056,0.0006500511,0.0012174827,0.0005118203,0.00008507805,0.00036358598,0.00020070207],"genre_scores_gemma":[0.98638505,0.0003011318,0.012376927,0.0003443623,0.00009642328,0.00008188938,0.00034313873,0.0000239804,0.00004708581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99659044,0.00038975826,0.0008908657,0.0010046298,0.0003825781,0.00074172736],"domain_scores_gemma":[0.99538094,0.00045085477,0.0007417865,0.0029956887,0.0002697662,0.00016098295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005679154,0.0004995286,0.0007543223,0.00025889656,0.00029801257,0.00008484635,0.0033930147,0.00043505747,0.0000066570333],"category_scores_gemma":[0.00007359831,0.00051202846,0.00043784067,0.0013438975,0.00022110734,0.00018377964,0.003925343,0.0016463184,0.0000027388303],"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":[0.000022809889,0.00015857685,0.025032947,0.00007494519,0.00007632334,0.000008002495,0.000987841,0.96404195,0.00003473349,0.0022482064,0.00039920773,0.006914484],"study_design_scores_gemma":[0.0005074398,0.00004327946,0.036290362,0.0003744894,0.000045779238,0.0000043636355,0.00009398921,0.9598326,0.00017461914,0.0017675442,0.00039664676,0.0004688527],"about_ca_topic_score_codex":0.000020707812,"about_ca_topic_score_gemma":0.000030403013,"teacher_disagreement_score":0.7443737,"about_ca_system_score_codex":0.00007162038,"about_ca_system_score_gemma":0.0000898409,"threshold_uncertainty_score":0.99973315},"labels":[],"label_agreement":null},{"id":"W4417196708","doi":"10.1145/3780098","title":"Enhancing Interpretability of Graph Convolutional Networks for Multi-view Learning","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Interpretability; Graph; Feature learning; Convolutional neural network; Feature (linguistics); Subspace topology; Variety (cybernetics)","score_opus":0.02565939714885769,"score_gpt":0.32000155520011303,"score_spread":0.29434215805125535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417196708","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":[0.00039263204,0.0013061153,0.99596417,0.00094030565,0.00010969575,0.0010001723,0.000013357325,0.00022472756,0.00004880908],"genre_scores_gemma":[0.5121674,0.0005037076,0.4868278,0.000100532576,0.0000105735635,0.00033630402,0.000018013869,0.000007867464,0.00002781913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984223,0.0001603995,0.00059295364,0.0004629905,0.00010264699,0.00025868075],"domain_scores_gemma":[0.9942632,0.0031067478,0.00022226697,0.0020292534,0.00029697453,0.000081614155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041781215,0.00019534113,0.0002859478,0.00024245199,0.001079201,0.000054057105,0.0017262431,0.000105852974,0.0000019727895],"category_scores_gemma":[0.00009334796,0.00021056962,0.00015781313,0.0010115003,0.00037033443,0.00017968129,0.00018565483,0.00050792587,0.0000014150686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013689292,0.0005708679,0.0007330685,0.000085760774,0.00011209752,4.730253e-8,0.00037422037,0.05436524,0.0008795286,0.04136458,0.0000119714505,0.9014889],"study_design_scores_gemma":[0.00052829745,0.00004886343,0.0012458047,0.0001436293,0.000040285642,0.0000019944275,0.00007477405,0.98989534,0.0005022237,0.0042742593,0.0030651074,0.00017941992],"about_ca_topic_score_codex":0.000018824796,"about_ca_topic_score_gemma":0.000048902682,"teacher_disagreement_score":0.9355301,"about_ca_system_score_codex":0.000043080257,"about_ca_system_score_gemma":0.00006359912,"threshold_uncertainty_score":0.858678},"labels":[],"label_agreement":null},{"id":"W4417267046","doi":"10.1007/s10618-025-01180-w","title":"Certifying robustness of graph convolutional networks for node perturbation with polyhedra abstract interpretation","year":2025,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Mitacs; Knut och Alice Wallenbergs Stiftelse","keywords":"Robustness (evolution); Graph; Convolutional neural network; Adversarial system; Polyhedron; Training set","score_opus":0.031970765629140495,"score_gpt":0.29588145799663446,"score_spread":0.263910692367494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417267046","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":[0.037449107,0.0016300231,0.95993054,0.00009015579,0.0004375163,0.00015851673,0.00006549373,0.000040807838,0.0001978542],"genre_scores_gemma":[0.965618,0.000048639868,0.03360411,0.000048324204,0.00006486536,0.00002077352,0.00041621603,0.000008043251,0.00017101958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990012,0.000026238662,0.00024544887,0.00045318238,0.000083728366,0.00019020791],"domain_scores_gemma":[0.9987323,0.0004977957,0.00013778692,0.00049650355,0.00010318249,0.000032401014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019622751,0.00013719373,0.00018072245,0.00014070429,0.00014411115,0.00013040197,0.0005319394,0.000059592396,4.951567e-7],"category_scores_gemma":[0.00006867203,0.00011624211,0.00003233689,0.00038094795,0.00010407533,0.0017608358,0.00029299443,0.00008693375,1.0948555e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016984905,0.0005271618,0.017868387,0.0010763744,0.0005186288,0.0000066331922,0.0029972675,0.24403478,0.000728432,0.07295075,0.009889998,0.6477031],"study_design_scores_gemma":[0.00046127004,0.000049611128,0.007016617,0.00042710768,0.000028930575,0.0000038125597,0.00015042331,0.9910749,0.00006735479,0.00044798426,0.00012465278,0.00014733571],"about_ca_topic_score_codex":0.000004785639,"about_ca_topic_score_gemma":0.00004242464,"teacher_disagreement_score":0.9281689,"about_ca_system_score_codex":0.000015846705,"about_ca_system_score_gemma":0.00008272872,"threshold_uncertainty_score":0.47402155},"labels":[],"label_agreement":null},{"id":"W4417525595","doi":"10.48550/arxiv.2504.21780","title":"MAGNET: an open-source library for mesh agglomeration by Graph Neural Networks","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Discretization; Python (programming language); Cluster analysis; Artificial neural network; Computation; Graph partition; Graph; Convolutional neural network; Partition (number theory)","score_opus":0.028620861627551304,"score_gpt":0.2765868882452868,"score_spread":0.2479660266177355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417525595","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":[0.0626996,0.0023037556,0.92446375,0.0028198424,0.003298279,0.0024446973,0.000107106265,0.0012722303,0.00059076346],"genre_scores_gemma":[0.8590366,0.0014255933,0.09932956,0.019301938,0.002065341,0.0020173022,0.0033590235,0.00030968545,0.013154955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960402,0.000266506,0.00069707865,0.0019121788,0.00027829572,0.0008057325],"domain_scores_gemma":[0.99663496,0.00029435055,0.00047661187,0.002190952,0.00011379008,0.0002893416],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00024880996,0.0006736381,0.0006600139,0.00022560434,0.0004272345,0.0011050246,0.005556594,0.0005534061,0.000026152195],"category_scores_gemma":[0.000032433505,0.00069538056,0.0002595608,0.0008703314,0.00011041408,0.002853983,0.0055469368,0.0011501284,0.000005925113],"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":[0.0003199151,0.000622846,0.05112547,0.0003221148,0.00027915253,0.000051781237,0.0006141417,0.4352946,0.0004196416,0.016563047,0.2825234,0.21186389],"study_design_scores_gemma":[0.0008914568,0.00042300954,0.0046687718,0.00017486654,0.00005561236,0.0000081467415,0.00001540554,0.9501321,0.00047568567,0.013307157,0.028645532,0.0012022586],"about_ca_topic_score_codex":0.000034549244,"about_ca_topic_score_gemma":0.000019314773,"teacher_disagreement_score":0.82513416,"about_ca_system_score_codex":0.0000374526,"about_ca_system_score_gemma":0.00010897825,"threshold_uncertainty_score":0.99993193},"labels":[],"label_agreement":null},{"id":"W6889109823","doi":"10.25384/sage.c.5019014.v1","title":"Comparing Perspectives of Canadian Men Diagnosed With Prostate Cancer and Health Care Professionals About Active Surveillance","year":2020,"lang":"en","type":"other","venue":"Sage Journals Data","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Viewpoints; Focus group; Prostate cancer; Qualitative research; Health professionals; Perspective (graphical); Standardization; Health care; Disease","score_opus":0.045021422897711126,"score_gpt":0.3382906772689971,"score_spread":0.293269254371286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889109823","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037718524,0.93342865,0.0048098788,0.019847289,0.00060618675,0.0033220483,0.013917525,0.00027221336,0.023419045],"genre_scores_gemma":[0.20646012,0.7108114,0.057060942,0.0037850807,0.0018695535,0.00024508746,0.001071199,0.0012531319,0.017443515],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9977764,0.00017574092,0.00032093437,0.0008197655,0.000416922,0.0004902604],"domain_scores_gemma":[0.9974799,0.000079225894,0.0008687757,0.0009868289,0.000099483885,0.00048578173],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022768127,0.00033109987,0.0007305728,0.00048209567,0.00017149062,0.000104029314,0.0015795516,0.00009832844,0.00010354962],"category_scores_gemma":[0.000029491306,0.0002596088,0.000030440551,0.00085652666,0.000117276206,0.00044336703,0.0006341338,0.0006288473,0.0000011355374],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013407393,0.00009336682,0.039518557,0.0008921049,0.0009630332,0.00050427724,0.048806593,0.0001978443,0.00001549676,0.00018755798,0.5725031,0.336184],"study_design_scores_gemma":[0.007491364,0.000987012,0.47785962,0.06930891,0.00014333485,0.00019162,0.04606804,0.006908683,0.00008925247,0.00027828672,0.3862317,0.0044421926],"about_ca_topic_score_codex":0.081843466,"about_ca_topic_score_gemma":0.52507156,"teacher_disagreement_score":0.4432281,"about_ca_system_score_codex":0.00015217636,"about_ca_system_score_gemma":0.0010600213,"threshold_uncertainty_score":0.99998564},"labels":[],"label_agreement":null},{"id":"W6889933543","doi":"10.3205/17dgpraec065","title":"Die Bedeutung von Gleitgewebelappen bei schweren neuropathischen Schmerzsyndromen","year":2017,"lang":"de","type":"article","venue":"German Medical Science (German Research Foundation)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Miller Group (Canada)","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Liquation; Triacetin; Emperipolesis; Demotion","score_opus":0.0803657934816971,"score_gpt":0.4380915166649187,"score_spread":0.3577257231832216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889933543","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":[0.73153216,0.007474207,0.046627834,0.13029493,0.02905313,0.0068952143,0.00005563804,0.0014663799,0.04660053],"genre_scores_gemma":[0.9855263,0.0033016892,0.0028545733,0.001245763,0.0031955664,0.00016123692,0.00005667935,0.0001079733,0.0035501954],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9731178,0.001483241,0.0015079868,0.0036462,0.015212842,0.005031909],"domain_scores_gemma":[0.98506117,0.0011669911,0.0009510756,0.0063711926,0.002041712,0.004407874],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["sts","open_science","insufficient_payload"],"category_scores_codex":[0.019496247,0.0008713009,0.00094549655,0.0016005441,0.016108144,0.009754325,0.023036228,0.0005715087,0.001684678],"category_scores_gemma":[0.009447148,0.0008131388,0.0003139051,0.0034226826,0.01839256,0.011369954,0.013448501,0.005039137,0.014059509],"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":[0.000108264794,0.0006271478,0.003955608,0.0002005563,0.000148104,0.003757184,0.0019395816,0.00003516692,0.0023159373,0.09255066,0.005753281,0.8886085],"study_design_scores_gemma":[0.003536194,0.0016364391,0.14925788,0.0020485774,0.00012411644,0.00028554015,0.00009528194,0.24824037,0.0017991561,0.05068862,0.53915757,0.003130255],"about_ca_topic_score_codex":0.0003275038,"about_ca_topic_score_gemma":0.00016380663,"teacher_disagreement_score":0.88547826,"about_ca_system_score_codex":0.0007639193,"about_ca_system_score_gemma":0.0048160907,"threshold_uncertainty_score":0.99943197},"labels":[],"label_agreement":null},{"id":"W6891669911","doi":"10.4230/lipics.cp.2024.22","title":"Learning Lagrangian Multipliers for the Travelling Salesman Problem","year":2024,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Polytechnique Montréal","funders":"","keywords":"Lagrangian relaxation; Subgradient method; Lagrange multiplier; Travelling salesman problem; Metric (unit); Lagrangian; Relaxation (psychology); Augmented Lagrangian method; Bottleneck traveling salesman problem","score_opus":0.011075608698125449,"score_gpt":0.23553499333388206,"score_spread":0.2244593846357566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891669911","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":[0.0017410821,0.0040105274,0.9825979,0.0074406387,0.00035464173,0.0012653448,0.0000073424603,0.002352557,0.00022996332],"genre_scores_gemma":[0.60634583,0.00038489205,0.3898682,0.0010093547,0.00024531104,0.00088500464,0.000009239235,0.000082707964,0.0011694708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754274,0.00009683777,0.00040328092,0.00072394166,0.00031695096,0.0009162427],"domain_scores_gemma":[0.99789184,0.00092116377,0.000115999326,0.0007817436,0.00007957503,0.0002097015],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008208941,0.00035241703,0.0002563608,0.00032196942,0.0006743803,0.0007103707,0.001388098,0.00020253538,0.000003613903],"category_scores_gemma":[0.000084306455,0.0002725361,0.00031021956,0.0011195261,0.00010747837,0.00067473174,0.00024703713,0.0008308646,0.000014628121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000388122,0.000053782358,0.000898765,0.00011383696,0.000118959164,0.00007697314,0.0016001578,0.20524196,0.0056029097,0.32147256,0.0022054461,0.46257582],"study_design_scores_gemma":[0.00020937019,0.00012953658,0.0006072307,0.00010083209,0.000027302074,0.000077075136,0.00007793805,0.95634556,0.0022830695,0.015644263,0.024144415,0.00035341294],"about_ca_topic_score_codex":0.00031556576,"about_ca_topic_score_gemma":0.00045520643,"teacher_disagreement_score":0.7511036,"about_ca_system_score_codex":0.00018632665,"about_ca_system_score_gemma":0.00011775309,"threshold_uncertainty_score":0.9999727},"labels":[],"label_agreement":null},{"id":"W6892721638","doi":"10.5281/zenodo.12573103","title":"CIRAIG/Regioinvent: v1.0.1","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Coal; Production (economics); Consumption (sociology); Electricity; Energy consumption; Electricity generation","score_opus":0.03245608995530128,"score_gpt":0.2483898152580734,"score_spread":0.21593372530277213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892721638","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010746743,0.0016321811,0.0771116,0.0010709788,0.00064568146,0.00043839499,0.00011467997,0.005601445,0.91338396],"genre_scores_gemma":[0.0012898874,0.0010610434,0.0061864974,0.00093609135,0.0012991452,1.3142419e-7,0.0014102163,0.026692273,0.9611247],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755645,0.0002162075,0.0002476949,0.0009542048,0.0004990826,0.00052638416],"domain_scores_gemma":[0.9982091,0.000014034781,0.00016253123,0.00123606,0.00014902874,0.00022923363],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00026362654,0.00030346157,0.00024591346,0.0006144329,0.00059851544,0.0013432142,0.0032824043,0.00018649553,0.009637028],"category_scores_gemma":[0.00011382587,0.00031200293,0.00012115594,0.0011787228,0.00016938253,0.00023171587,0.0032628556,0.00060939277,0.07386594],"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":[0.0000027776332,0.000027886372,3.2634482e-8,0.00008827217,0.00004253583,0.00007047368,0.00013767235,0.00001090586,0.000032749762,0.034596846,0.89644015,0.06854969],"study_design_scores_gemma":[0.00016011849,0.00009317016,0.0000018432961,0.00019171454,0.000012506374,0.00014694533,0.000011725107,0.00084900955,0.000015580446,0.0037143216,0.9944821,0.00032098326],"about_ca_topic_score_codex":0.0000088921515,"about_ca_topic_score_gemma":5.171856e-7,"teacher_disagreement_score":0.09804193,"about_ca_system_score_codex":0.00010035756,"about_ca_system_score_gemma":0.0000032380572,"threshold_uncertainty_score":0.9999332},"labels":[],"label_agreement":null},{"id":"W6894208205","doi":"10.5281/zenodo.7420702","title":"AAS Birmingham 2022 live streaming online Cheer &amp; Dance free","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Dance; Subject (documents); Work (physics)","score_opus":0.03011730903340559,"score_gpt":0.25073324595732593,"score_spread":0.22061593692392034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894208205","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002486691,0.0023118663,0.10606054,0.0014694569,0.0012167966,0.0015807303,0.0020471755,0.008905379,0.87615937],"genre_scores_gemma":[0.0016424885,0.0022158856,0.046560694,0.0014731821,0.002110275,7.293526e-7,0.0130967,0.02898703,0.903913],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966531,0.00042117506,0.0003293616,0.0011181327,0.00083219184,0.00064604497],"domain_scores_gemma":[0.9969805,0.000048083144,0.00036296528,0.002163821,0.0002122433,0.0002324287],"candidate_categories":["metaepi_narrow","sts","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.0003752949,0.00037073513,0.00032205952,0.0005802613,0.0015780288,0.0006612981,0.005815489,0.00015074744,0.06371189],"category_scores_gemma":[0.00047956023,0.0004164187,0.00011259661,0.0014182247,0.00017511088,0.00031944877,0.00888775,0.0010100754,0.004709812],"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":[0.000009172794,0.00012577148,0.0000018544608,0.00004605407,0.00005129412,0.00004974319,0.00038348537,0.00015324833,0.00016936577,0.0030269634,0.9223377,0.07364536],"study_design_scores_gemma":[0.00035358183,0.00012714925,0.0000422494,0.000068519024,0.000011255103,0.00009503446,0.0000786998,0.0008729344,0.0000089986515,0.0004494473,0.9974708,0.00042135816],"about_ca_topic_score_codex":0.000040906496,"about_ca_topic_score_gemma":0.000010982498,"teacher_disagreement_score":0.075133085,"about_ca_system_score_codex":0.00023613017,"about_ca_system_score_gemma":0.000008224118,"threshold_uncertainty_score":0.99982876},"labels":[],"label_agreement":null},{"id":"W6902125209","doi":"10.6084/m9.figshare.29105695","title":"Additional file 2 of RobusTAD: reference panel based annotation of nested topologically associating domains","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Annotation; Class (philosophy); Scheme (mathematics); Domain (mathematical analysis)","score_opus":0.0445831827471867,"score_gpt":0.2602684865925037,"score_spread":0.21568530384531698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6902125209","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034901946,0.000010356946,0.0027747604,0.00006590201,0.000018280332,0.00010501365,0.9938907,0.00007125492,0.0030288436],"genre_scores_gemma":[0.021565286,2.902393e-7,0.08590522,0.00042415917,0.000022494149,0.00027841938,0.8914244,0.0000053748377,0.00037433283],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991412,0.000061442435,0.00024286986,0.00021281304,0.00019261357,0.0001490601],"domain_scores_gemma":[0.9960434,0.003077111,0.00031285407,0.00022746177,0.0003125732,0.000026602504],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002467489,0.00008629859,0.00014668648,0.00007816795,0.000060043512,0.000016608703,0.00043618222,0.000085099055,0.6279971],"category_scores_gemma":[0.0034072218,0.00008239115,0.00005697439,0.0006657673,0.000012067405,0.00018052668,0.00013321456,0.00012413647,0.000030229146],"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":[0.0000027981719,0.00002399261,0.000009968586,0.00003548765,0.0000050200388,0.0000026170194,0.000009485992,0.0018537077,0.00003520122,0.00048707385,0.99408317,0.0034514796],"study_design_scores_gemma":[0.001126702,0.00043442065,0.18257804,0.011644886,0.000014462511,0.000004550348,0.000077549004,0.38407892,0.001611597,0.012681764,0.40496027,0.000786844],"about_ca_topic_score_codex":0.0000014506954,"about_ca_topic_score_gemma":0.000006511673,"teacher_disagreement_score":0.6279669,"about_ca_system_score_codex":0.000025994348,"about_ca_system_score_gemma":0.00010996396,"threshold_uncertainty_score":0.40790087},"labels":[],"label_agreement":null},{"id":"W6905874244","doi":"10.15468/dl.tugqmp","title":"Occurrence Download","year":2018,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Order (exchange); Data set","score_opus":0.013779079583120283,"score_gpt":0.22259782847285295,"score_spread":0.20881874888973267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6905874244","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003679728,0.00001791885,0.0016725254,0.00016295235,0.0013628566,0.00027742266,0.99616474,0.0002528766,0.00005191379],"genre_scores_gemma":[0.000004287731,0.000056209443,0.00007008832,0.0010114327,0.0000033852334,0.0000031622105,0.99885136,9.851837e-9,5.985163e-8],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99804616,0.00007399126,0.00041771377,0.00040904855,0.00061112683,0.00044193174],"domain_scores_gemma":[0.9977523,0.00002927898,0.00040798195,0.0012292686,0.0003632207,0.00021793589],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021786353,0.0003539945,0.00029017986,0.000113872746,0.00036914367,0.00031639752,0.0022450995,0.00036532147,0.000260393],"category_scores_gemma":[0.000099043194,0.00035694137,0.00016847409,0.00076793,0.00026057623,0.0031479928,0.0012684357,0.00035329463,0.1255143],"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":[0.000013041398,0.000018101375,0.00012037102,0.000042926094,0.000014697935,0.0000031818736,0.00001888408,0.000018211562,1.2865743e-8,9.561921e-7,0.99729705,0.0024525458],"study_design_scores_gemma":[0.00020653257,0.00005445652,0.000036403857,0.0000029093208,0.000013755527,0.000009910616,0.000006643307,0.000004761254,0.0000027447686,0.0000150645155,0.99928963,0.00035717236],"about_ca_topic_score_codex":0.00008849422,"about_ca_topic_score_gemma":0.000009423805,"teacher_disagreement_score":0.1252539,"about_ca_system_score_codex":0.00023071085,"about_ca_system_score_gemma":0.000116710085,"threshold_uncertainty_score":0.99988824},"labels":[],"label_agreement":null},{"id":"W6906063737","doi":"10.15468/dl.zgpsm8","title":"Occurrence Download","year":2024,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Download; Matching (statistics); Range (aeronautics); Data collection; Missing data","score_opus":0.01253783352573161,"score_gpt":0.22297756534163457,"score_spread":0.21043973181590298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906063737","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014468647,0.00008016582,0.000880456,0.0003517032,0.0019532326,0.00026972784,0.9959737,0.00040141642,0.00007512817],"genre_scores_gemma":[0.0000046983073,0.000103172366,0.00003382882,0.0008226457,0.000002359586,0.000004153429,0.99902904,1.2493152e-8,1.0069372e-7],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99810684,0.000054266886,0.00040835413,0.00043207803,0.0005962986,0.00040218144],"domain_scores_gemma":[0.9983328,0.00002927408,0.00023192489,0.0010190572,0.0001908471,0.00019608978],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019161534,0.0003587716,0.00027845276,0.00014320831,0.00022985182,0.0005068927,0.0018699253,0.0003426053,0.00018083744],"category_scores_gemma":[0.000061030456,0.0003511187,0.0002056198,0.0009561986,0.00014335054,0.002698472,0.0013982034,0.00054515206,0.49346265],"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":[0.00000750014,0.000013207695,0.00004754837,0.00012287381,0.000020562024,0.000010941911,0.000023319522,0.000056146877,1.2407649e-8,0.0000025086315,0.99509835,0.0045970534],"study_design_scores_gemma":[0.00012146045,0.000029665298,0.00001142529,0.0000055470405,0.000023639202,0.00001429605,0.000011940092,0.000009031384,0.0000014331789,0.000035667803,0.9993906,0.0003452875],"about_ca_topic_score_codex":0.00005814751,"about_ca_topic_score_gemma":0.00000715568,"teacher_disagreement_score":0.4932818,"about_ca_system_score_codex":0.00025978588,"about_ca_system_score_gemma":0.00012638583,"threshold_uncertainty_score":0.9998941},"labels":[],"label_agreement":null},{"id":"W6906380410","doi":"10.17605/osf.io/xney5","title":"Graph EquiJoin Dataset","year":2020,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Graph; Identification (biology); Graph theory; Field (mathematics)","score_opus":0.025439081970539356,"score_gpt":0.2649035579312501,"score_spread":0.23946447596071074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906380410","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":[0.0057542007,0.0000057223633,0.8196456,0.007995791,0.0006950829,0.0011668497,0.00022601178,0.0010864018,0.16342436],"genre_scores_gemma":[0.7453404,0.00046117493,0.20011526,0.023920676,0.00082181394,0.0005732245,0.0009515943,0.00023374028,0.027582098],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947012,0.00042909713,0.00057780236,0.0030587208,0.00054974493,0.0006834441],"domain_scores_gemma":[0.9933311,0.00047117905,0.00025481218,0.0052765687,0.00008711451,0.00057926157],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014154541,0.00037471202,0.00040459086,0.000121210745,0.00021951618,0.00025188972,0.004465776,0.00016328863,0.0861894],"category_scores_gemma":[0.0014956449,0.00040935856,0.00019800187,0.00093447673,0.0001333795,0.0011600488,0.0053306674,0.0006761021,0.6937334],"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":[0.00016859565,0.00038599406,0.007047596,0.00013079678,0.00022277303,0.0004192063,0.0020526217,0.021561703,0.019114055,0.06354764,0.78964806,0.09570099],"study_design_scores_gemma":[0.0013590535,0.0000074988766,0.0038407675,0.00007949883,0.000054866465,0.00013155382,0.000059698195,0.06547588,0.02597108,0.055541534,0.84593153,0.0015470388],"about_ca_topic_score_codex":0.000032018226,"about_ca_topic_score_gemma":0.000009140704,"teacher_disagreement_score":0.73958623,"about_ca_system_score_codex":0.00006118287,"about_ca_system_score_gemma":0.00007930689,"threshold_uncertainty_score":0.99983585},"labels":[],"label_agreement":null},{"id":"W6912148640","doi":"10.5281/zenodo.15799038","title":"Proceedings of the First International Workshop on Trends in Knowledge Representation and Reasoning (TKR'25)","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"CLARITY; Representation (politics); Knowledge representation and reasoning; Field (mathematics); Process (computing)","score_opus":0.024116787702002988,"score_gpt":0.2728595405845455,"score_spread":0.2487427528825425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912148640","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012528058,0.00014002546,0.0029930123,0.0012615366,0.00029605595,0.00026875574,0.00002733367,0.00037607315,0.9945119],"genre_scores_gemma":[0.1817847,0.0012715072,0.005254202,0.00057434425,0.00078059296,7.6223245e-7,0.00055799435,0.005894564,0.80388135],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99878275,0.000057642355,0.00020510572,0.00049743365,0.00026874203,0.00018831612],"domain_scores_gemma":[0.9991893,0.000032508207,0.00020730418,0.00033042047,0.00019572623,0.000044742832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021161999,0.00014561745,0.00014949183,0.0007022593,0.00033764122,0.00032878615,0.0016759572,0.00009764247,0.00075917254],"category_scores_gemma":[0.0003085089,0.00012983213,0.000045570767,0.0014947775,0.00011216151,0.00018337142,0.001895426,0.00033409114,0.00008577624],"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":[0.000021078808,0.000080519145,0.00003733993,0.000046833004,0.000030771953,0.0000027508584,0.0005928663,0.00006715172,0.00004185424,0.056465995,0.7039321,0.2386807],"study_design_scores_gemma":[0.00033320911,0.00003684746,0.0019007083,0.00059475086,0.0000048986535,0.000012008458,0.000048350645,0.0026440374,0.00005288456,0.00033939435,0.99390215,0.00013077869],"about_ca_topic_score_codex":0.0000097748025,"about_ca_topic_score_gemma":0.0000033805318,"teacher_disagreement_score":0.28997,"about_ca_system_score_codex":0.00008237865,"about_ca_system_score_gemma":0.0000026316852,"threshold_uncertainty_score":0.83124065},"labels":[],"label_agreement":null},{"id":"W6912526499","doi":"10.5281/zenodo.3590121","title":"Saner 2020: Cross-Dataset Design Discussion Mining: Replication Package","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Python (programming language); Download; R package; Directory; Replication (statistics); JavaScript; Package design; Root (linguistics)","score_opus":0.0638414889590741,"score_gpt":0.28876346346519377,"score_spread":0.22492197450611967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912526499","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":[0.0011148462,0.000055018332,0.97983235,0.013159055,0.000103379396,0.0005726603,0.00034674155,0.0016272041,0.00318876],"genre_scores_gemma":[0.8971556,0.00017130462,0.08085594,0.0049892967,0.00072928047,3.9074885e-7,0.012749076,0.0025279103,0.0008211904],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976635,0.00039612263,0.00028459498,0.0008726265,0.0004008216,0.0003823124],"domain_scores_gemma":[0.998039,0.00005342542,0.00015191307,0.0012046782,0.00023683636,0.00031412448],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00051973626,0.00016478132,0.00013978794,0.00008300792,0.0015148548,0.0011618196,0.0023247395,0.000063272026,0.0013564866],"category_scores_gemma":[0.0009853962,0.00013551221,0.00004663956,0.0010740597,0.0001378659,0.00090445514,0.0021092745,0.00026174993,0.0030902978],"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":[0.00006421072,0.00005938986,0.000008024429,0.000022756138,0.000013756781,0.00003674378,0.000999922,0.0013890606,0.007550921,0.0025476022,0.84802115,0.13928644],"study_design_scores_gemma":[0.00037040454,0.0003238696,0.0003803775,0.000015803633,0.0000046596833,0.000064802545,0.000045526347,0.024222948,0.0011610315,0.0004676962,0.97271866,0.00022422217],"about_ca_topic_score_codex":9.598176e-7,"about_ca_topic_score_gemma":2.9362836e-8,"teacher_disagreement_score":0.8989764,"about_ca_system_score_codex":0.000056385576,"about_ca_system_score_gemma":0.0000034007512,"threshold_uncertainty_score":0.99987507},"labels":[],"label_agreement":null},{"id":"W6912991590","doi":"10.5281/zenodo.7998401","title":"Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets","year":2024,"lang":"en","type":"preprint","venue":"RWTH Publications (RWTH Aachen)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Canadian Institute for Advanced Research; McGill University; Université de Montréal","funders":"","keywords":"Supervised learning; Training set; Graph; Labeled data; Data point; Range (aeronautics); Data type; Code (set theory)","score_opus":0.04311897580672169,"score_gpt":0.32654904923392114,"score_spread":0.28343007342719945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6912991590","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":[0.00030959523,0.00093636516,0.98327637,0.008140351,0.0009972826,0.0015647223,0.0020546115,0.0012018104,0.0015188676],"genre_scores_gemma":[0.14970139,0.00011407179,0.8197646,0.0019518902,0.00050339807,0.003907863,0.021478254,0.00019909476,0.00237943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952432,0.00019013038,0.0007545492,0.0021618777,0.00076944183,0.000880794],"domain_scores_gemma":[0.99591434,0.00028816264,0.00044110944,0.0024960795,0.00051008194,0.0003502007],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00078239467,0.00063335436,0.000492336,0.00077734434,0.0005147412,0.0011410165,0.0029885494,0.00047436397,0.000023302917],"category_scores_gemma":[0.00031249633,0.00066363637,0.00041542843,0.0011941381,0.00009881157,0.0009824577,0.0035162885,0.0019657938,0.0001616744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012913103,0.00039354982,0.000025797832,0.00019600459,0.00018461992,0.0000058630367,0.0004703153,0.3567063,0.00006689616,0.59976935,0.014178496,0.027989937],"study_design_scores_gemma":[0.00036821267,0.000049985676,0.00009373815,0.00009139386,0.000050868683,0.000007121371,0.000011603148,0.7728502,0.00007522774,0.14765283,0.07821312,0.0005356806],"about_ca_topic_score_codex":0.00001407036,"about_ca_topic_score_gemma":0.00002232693,"teacher_disagreement_score":0.4521165,"about_ca_system_score_codex":0.00028085423,"about_ca_system_score_gemma":0.00065644627,"threshold_uncertainty_score":0.9998959},"labels":[],"label_agreement":null},{"id":"W6920813311","doi":"10.60692/n2jjj-3bs23","title":"InductiveQE Datasets","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Disjoint sets; Graph; Graph database; Training set; Set (abstract data type)","score_opus":0.030938026627628276,"score_gpt":0.21394666475156426,"score_spread":0.18300863812393597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920813311","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":[0.06521067,0.000004610169,0.9286541,0.0001696617,0.0016546801,0.0004371128,0.00037878315,0.00079926004,0.0026911325],"genre_scores_gemma":[0.9962916,1.787254e-8,0.003025781,0.00044685096,0.000035049627,0.00010482012,0.0000558967,0.000004431721,0.000035600988],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876463,0.000092654045,0.00033489114,0.0001759143,0.00040195195,0.00022995904],"domain_scores_gemma":[0.99897885,0.000006158275,0.00022558626,0.0006768613,0.00004307102,0.00006944843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024524168,0.00011806468,0.000121006415,0.0002203561,0.00038880474,0.0001561796,0.0008174661,0.000025834463,0.000016731248],"category_scores_gemma":[0.0000051438533,0.00010925954,0.00004233228,0.0006209004,0.000014921666,0.0021634481,0.00067029597,0.00017669909,0.00025932054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024008768,0.000028976721,0.09992349,0.00057984865,0.00027386827,0.00022211511,0.44918114,0.13373503,0.000019150828,0.22773492,0.039650578,0.048410777],"study_design_scores_gemma":[0.007471761,0.0008600137,0.09330336,0.00016572469,0.000057184618,0.0031713443,0.056021374,0.6492607,0.0016383219,0.0004672059,0.18397315,0.0036098752],"about_ca_topic_score_codex":0.0000014819697,"about_ca_topic_score_gemma":1.654928e-8,"teacher_disagreement_score":0.9310809,"about_ca_system_score_codex":0.00011206309,"about_ca_system_score_gemma":0.000021609518,"threshold_uncertainty_score":0.4455475},"labels":[],"label_agreement":null},{"id":"W6921019684","doi":"10.60692/ybpez-88h20","title":"InductiveQE Datasets","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Disjoint sets; Graph; Graph database; Training set; Set (abstract data type)","score_opus":0.030938026627628276,"score_gpt":0.21394666475156426,"score_spread":0.18300863812393597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921019684","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":[0.06521067,0.000004610169,0.9286541,0.0001696617,0.0016546801,0.0004371128,0.00037878315,0.00079926004,0.0026911325],"genre_scores_gemma":[0.9962916,1.787254e-8,0.003025781,0.00044685096,0.000035049627,0.00010482012,0.0000558967,0.000004431721,0.000035600988],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876463,0.000092654045,0.00033489114,0.0001759143,0.00040195195,0.00022995904],"domain_scores_gemma":[0.99897885,0.000006158275,0.00022558626,0.0006768613,0.00004307102,0.00006944843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024524168,0.00011806468,0.000121006415,0.0002203561,0.00038880474,0.0001561796,0.0008174661,0.000025834463,0.000016731248],"category_scores_gemma":[0.0000051438533,0.00010925954,0.00004233228,0.0006209004,0.000014921666,0.0021634481,0.00067029597,0.00017669909,0.00025932054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024008768,0.000028976721,0.09992349,0.00057984865,0.00027386827,0.00022211511,0.44918114,0.13373503,0.000019150828,0.22773492,0.039650578,0.048410777],"study_design_scores_gemma":[0.007471761,0.0008600137,0.09330336,0.00016572469,0.000057184618,0.0031713443,0.056021374,0.6492607,0.0016383219,0.0004672059,0.18397315,0.0036098752],"about_ca_topic_score_codex":0.0000014819697,"about_ca_topic_score_gemma":1.654928e-8,"teacher_disagreement_score":0.9310809,"about_ca_system_score_codex":0.00011206309,"about_ca_system_score_gemma":0.000021609518,"threshold_uncertainty_score":0.4455475},"labels":[],"label_agreement":null},{"id":"W6921910029","doi":"10.1016/j.eswa.2025.129026","title":"DECTUIL: Cross-social network user identity linkage via dynamic embedding and clustering model driven by three-way decision","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Science and Technology Service Network Plan; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Organization Department of Sichuan Provincial Party Committee; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Cluster analysis; Embedding; Robustness (evolution); Entropy (arrow of time); Smoothing; Linkage (software); Word embedding; Constrained clustering","score_opus":0.008314776252196229,"score_gpt":0.3051760184642313,"score_spread":0.2968612422120351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921910029","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":[0.008539137,0.0016533415,0.9880318,0.00013688387,0.0002239614,0.0009767985,0.000006406117,0.00031387794,0.00011779267],"genre_scores_gemma":[0.8566049,0.000058981695,0.14168493,0.00019932119,0.00013321167,0.0010548984,0.000011392239,0.000028938879,0.00022345081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808455,0.00004024495,0.00041215218,0.0007435949,0.00029626605,0.00042321117],"domain_scores_gemma":[0.99868935,0.00017685688,0.00018284742,0.00071714225,0.000121262616,0.000112525675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017976681,0.0002512478,0.00029672054,0.00009961877,0.0009788503,0.0005244448,0.000866759,0.00014818358,6.849014e-7],"category_scores_gemma":[0.000007276448,0.0002282019,0.000056628673,0.00092002127,0.000108943816,0.00090159196,0.0004990976,0.00022729982,0.000006386467],"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":[0.000027633809,0.000057988756,0.0038093284,0.00005603337,0.00006479494,0.0000035238675,0.000419629,0.94022316,0.0014317159,0.03564935,0.0020303135,0.016226508],"study_design_scores_gemma":[0.0003398001,0.000013050391,0.0010919081,0.00011192527,0.000006688241,0.000011575831,0.000013439184,0.990766,0.000012689172,0.0055493256,0.0018453377,0.00023824752],"about_ca_topic_score_codex":0.000042435742,"about_ca_topic_score_gemma":0.00024565426,"teacher_disagreement_score":0.84806573,"about_ca_system_score_codex":0.000123723,"about_ca_system_score_gemma":0.000032168497,"threshold_uncertainty_score":0.9305803},"labels":[],"label_agreement":null},{"id":"W6930447306","doi":"10.5281/zenodo.12733954","title":"GlucoTrust USA: Reviews, Work, Benefits, Order, Price &amp; Ingredients?","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Limiting; Population; Work (physics); Filter (signal processing); Noise (video)","score_opus":0.049953219162798844,"score_gpt":0.26567583107432846,"score_spread":0.2157226119115296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930447306","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008685281,0.018218858,0.10822769,0.00074646354,0.0014520605,0.0013636748,0.00013912417,0.006836285,0.8630071],"genre_scores_gemma":[0.00019521156,0.018056283,0.032675426,0.00088177016,0.0015359989,5.416266e-7,0.0016033287,0.027723543,0.9173279],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99661183,0.00035007644,0.00045297804,0.0012505808,0.0006190015,0.0007155575],"domain_scores_gemma":[0.9974063,0.000024751651,0.00033529906,0.0016229664,0.000311948,0.00029871849],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00058838184,0.00044303414,0.0004241284,0.0006479893,0.0006104115,0.0015083527,0.0037816358,0.00023993268,0.010499444],"category_scores_gemma":[0.00047966652,0.00043398616,0.00013177449,0.0031313514,0.00016563981,0.00033311284,0.004361737,0.00087563205,0.07556921],"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":[0.000005632434,0.000043125845,5.1463616e-7,0.00015530256,0.00003818469,0.000016373342,0.00014193081,0.000031311392,0.000010354787,0.008544056,0.8521822,0.13883099],"study_design_scores_gemma":[0.00017781951,0.00006485339,0.000012403308,0.0006685089,0.00001959595,0.00007192304,0.000004995067,0.00011292909,0.0000040292607,0.00034052157,0.99808806,0.000434355],"about_ca_topic_score_codex":0.000013711958,"about_ca_topic_score_gemma":0.0000028305817,"teacher_disagreement_score":0.14590584,"about_ca_system_score_codex":0.00015387255,"about_ca_system_score_gemma":0.0000057046987,"threshold_uncertainty_score":0.9998112},"labels":[],"label_agreement":null},{"id":"W6931768603","doi":"10.5281/zenodo.5710811","title":"SESYNC-ci/gis-abm-lesson: Handouts - Zenodo","year":2021,"lang":"fr","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Identification (biology); Interpretation (philosophy); Perspective (graphical); Set (abstract data type)","score_opus":0.0484813716483236,"score_gpt":0.2557657984214514,"score_spread":0.2072844267731278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931768603","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023502161,0.0050086607,0.19141306,0.0032768105,0.0018081552,0.0011027663,0.00043112616,0.0025304172,0.794194],"genre_scores_gemma":[0.023770468,0.0048682983,0.018709017,0.0027745236,0.004083917,3.2991397e-7,0.008134029,0.029237969,0.90842146],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932851,0.0013158087,0.00075408287,0.0020649298,0.001122963,0.0014570958],"domain_scores_gemma":[0.9951243,0.00011117325,0.0005621588,0.0022623849,0.0012104353,0.0007295557],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00073901477,0.0007868666,0.0007611963,0.0005731529,0.0036955492,0.0040786546,0.005117892,0.0005166984,0.19955906],"category_scores_gemma":[0.00078183506,0.00088929356,0.00032194259,0.0024073813,0.00068685535,0.0006400745,0.007267697,0.0014650404,0.039429586],"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":[0.000048931284,0.0003092222,0.000002342708,0.00028647066,0.00019568039,0.00056839705,0.0013541454,0.0006563374,0.0012362944,0.050529104,0.5138217,0.43099138],"study_design_scores_gemma":[0.00090294,0.00030326168,0.00012561161,0.0006083273,0.00004914472,0.00091736316,0.00009480243,0.005020344,0.00029238782,0.0011620471,0.98963493,0.0008888126],"about_ca_topic_score_codex":0.000033519907,"about_ca_topic_score_gemma":0.00000311049,"teacher_disagreement_score":0.47581327,"about_ca_system_score_codex":0.00040635327,"about_ca_system_score_gemma":0.000021789701,"threshold_uncertainty_score":0.9993558},"labels":[],"label_agreement":null},{"id":"W6931846142","doi":"10.5683/sp3/kc4irp","title":"Travel Survey of Residents of Canada, 2012: Person File, October","year":2012,"lang":"en","type":"dataset","venue":"Borealis","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"TRIPS architecture; Tourism; Travel survey; Accommodation; Travel behavior; Survey data collection; Domestic tourism; Destinations; Survey methodology","score_opus":0.029332730768319527,"score_gpt":0.25230688705884613,"score_spread":0.2229741562905266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931846142","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000063110656,0.0007162721,0.0005979528,0.00003767972,0.0003969845,0.00015962668,0.9978888,0.000010410453,0.00018598163],"genre_scores_gemma":[0.00010235359,0.0001134997,0.0007662108,0.000112075926,0.00007700109,0.000010320218,0.9985392,0.000016700955,0.00026260017],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99766445,0.00021750787,0.00046379896,0.00041251277,0.0007501898,0.00049154204],"domain_scores_gemma":[0.9969362,0.0004509391,0.0006569624,0.0015018268,0.0002588777,0.00019517534],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003364008,0.00030545043,0.0005871482,0.00014750619,0.00005932953,0.000016296934,0.0018239452,0.0002357084,0.0002711411],"category_scores_gemma":[0.0001937899,0.0002849079,0.00009512278,0.00045079004,0.00010080824,0.00026700032,0.0002451744,0.0003201558,0.0000017378604],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000105144845,0.000030563257,0.00012258999,0.000057766734,0.00003330951,0.0000120980885,0.000016484406,0.00001762729,0.0000021080778,0.000030339825,0.9990532,0.00061344256],"study_design_scores_gemma":[0.00016727972,0.00004430244,0.86289454,0.00012598504,0.00002551293,0.000008338189,0.0000046710325,0.00007333313,0.000084572435,0.000037827172,0.13619359,0.00034006048],"about_ca_topic_score_codex":0.9942207,"about_ca_topic_score_gemma":0.98673207,"teacher_disagreement_score":0.86285955,"about_ca_system_score_codex":0.00006769281,"about_ca_system_score_gemma":0.00047515056,"threshold_uncertainty_score":0.9999603},"labels":[],"label_agreement":null},{"id":"W6939194279","doi":"10.60692/c9eaj-3xs44","title":"InductiveQE Datasets","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Disjoint sets; Graph; Graph database; Missing data; Statistical inference","score_opus":0.030938026627628276,"score_gpt":0.21394666475156426,"score_spread":0.18300863812393597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939194279","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":[0.06521067,0.000004610169,0.9286541,0.0001696617,0.0016546801,0.0004371128,0.00037878315,0.00079926004,0.0026911325],"genre_scores_gemma":[0.9962916,1.787254e-8,0.003025781,0.00044685096,0.000035049627,0.00010482012,0.0000558967,0.000004431721,0.000035600988],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876463,0.000092654045,0.00033489114,0.0001759143,0.00040195195,0.00022995904],"domain_scores_gemma":[0.99897885,0.000006158275,0.00022558626,0.0006768613,0.00004307102,0.00006944843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024524168,0.00011806468,0.000121006415,0.0002203561,0.00038880474,0.0001561796,0.0008174661,0.000025834463,0.000016731248],"category_scores_gemma":[0.0000051438533,0.00010925954,0.00004233228,0.0006209004,0.000014921666,0.0021634481,0.00067029597,0.00017669909,0.00025932054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024008768,0.000028976721,0.09992349,0.00057984865,0.00027386827,0.00022211511,0.44918114,0.13373503,0.000019150828,0.22773492,0.039650578,0.048410777],"study_design_scores_gemma":[0.007471761,0.0008600137,0.09330336,0.00016572469,0.000057184618,0.0031713443,0.056021374,0.6492607,0.0016383219,0.0004672059,0.18397315,0.0036098752],"about_ca_topic_score_codex":0.0000014819697,"about_ca_topic_score_gemma":1.654928e-8,"teacher_disagreement_score":0.9310809,"about_ca_system_score_codex":0.00011206309,"about_ca_system_score_gemma":0.000021609518,"threshold_uncertainty_score":0.4455475},"labels":[],"label_agreement":null},{"id":"W6939608453","doi":"10.6084/m9.figshare.29105695.v1","title":"Additional file 2 of RobusTAD: reference panel based annotation of nested topologically associating domains","year":2025,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Annotation; Class (philosophy); Scheme (mathematics); Domain (mathematical analysis)","score_opus":0.0445831827471867,"score_gpt":0.2602684865925037,"score_spread":0.21568530384531698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939608453","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000034901946,0.000010356946,0.0027747604,0.00006590201,0.000018280332,0.00010501365,0.9938907,0.00007125492,0.0030288436],"genre_scores_gemma":[0.021565286,2.902393e-7,0.08590522,0.00042415917,0.000022494149,0.00027841938,0.8914244,0.0000053748377,0.00037433283],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991412,0.000061442435,0.00024286986,0.00021281304,0.00019261357,0.0001490601],"domain_scores_gemma":[0.9960434,0.003077111,0.00031285407,0.00022746177,0.0003125732,0.000026602504],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002467489,0.00008629859,0.00014668648,0.00007816795,0.000060043512,0.000016608703,0.00043618222,0.000085099055,0.6279971],"category_scores_gemma":[0.0034072218,0.00008239115,0.00005697439,0.0006657673,0.000012067405,0.00018052668,0.00013321456,0.00012413647,0.000030229146],"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":[0.0000027981719,0.00002399261,0.000009968586,0.00003548765,0.0000050200388,0.0000026170194,0.000009485992,0.0018537077,0.00003520122,0.00048707385,0.99408317,0.0034514796],"study_design_scores_gemma":[0.001126702,0.00043442065,0.18257804,0.011644886,0.000014462511,0.000004550348,0.000077549004,0.38407892,0.001611597,0.012681764,0.40496027,0.000786844],"about_ca_topic_score_codex":0.0000014506954,"about_ca_topic_score_gemma":0.000006511673,"teacher_disagreement_score":0.6279669,"about_ca_system_score_codex":0.000025994348,"about_ca_system_score_gemma":0.00010996396,"threshold_uncertainty_score":0.40790087},"labels":[],"label_agreement":null},{"id":"W6968975868","doi":"10.5281/zenodo.6936226","title":"OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"OpenAlex","funders":"","keywords":"Transparency (behavior); Microsoft excel; Open data; Semantic Web; Index (typography); Graph","score_opus":0.05845903935785887,"score_gpt":0.3004857062332492,"score_spread":0.24202666687539034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6968975868","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":[0.16142106,0.007961457,0.6964101,0.012149421,0.0018500924,0.0059529576,0.0009543545,0.0035342784,0.10976625],"genre_scores_gemma":[0.9958173,0.00011632781,0.0029698175,0.0002820965,0.000041126426,2.492193e-7,0.00019133044,0.00028059183,0.00030112878],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981007,0.0005175987,0.00024073035,0.00044773467,0.00041939114,0.00027385686],"domain_scores_gemma":[0.9988021,0.000029088302,0.00016224835,0.0005763084,0.00027647917,0.00015376387],"candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0008641749,0.00011021275,0.00015206546,0.00020490406,0.0027739338,0.0018492406,0.0032467041,0.00003742461,0.0009015113],"category_scores_gemma":[0.00028879067,0.00012603603,0.000029667997,0.0012086954,0.00020168448,0.0021524483,0.009106253,0.0005420246,0.0001324239],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016641589,0.00027517235,0.00009194497,0.000045722383,0.00006514367,0.000079732556,0.0027375882,0.0040522474,0.002207717,0.23556471,0.0953395,0.6593741],"study_design_scores_gemma":[0.0006511626,0.00026015245,0.0011164313,0.000036593527,0.0000047361846,0.00016882524,0.00016958558,0.0024213262,0.00007661117,0.002241659,0.9926875,0.00016537943],"about_ca_topic_score_codex":0.000015891488,"about_ca_topic_score_gemma":3.4479285e-7,"teacher_disagreement_score":0.89734805,"about_ca_system_score_codex":0.000097561984,"about_ca_system_score_gemma":0.000013595405,"threshold_uncertainty_score":0.99918693},"labels":[],"label_agreement":null},{"id":"W6976866797","doi":"10.60692/hk0kf-nrc03","title":"InductiveQE Datasets","year":2022,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Disjoint sets; Graph; Graph database; Training set; Set (abstract data type)","score_opus":0.030938026627628276,"score_gpt":0.21394666475156426,"score_spread":0.18300863812393597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976866797","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":[0.06521067,0.000004610169,0.9286541,0.0001696617,0.0016546801,0.0004371128,0.00037878315,0.00079926004,0.0026911325],"genre_scores_gemma":[0.9962916,1.787254e-8,0.003025781,0.00044685096,0.000035049627,0.00010482012,0.0000558967,0.000004431721,0.000035600988],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876463,0.000092654045,0.00033489114,0.0001759143,0.00040195195,0.00022995904],"domain_scores_gemma":[0.99897885,0.000006158275,0.00022558626,0.0006768613,0.00004307102,0.00006944843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024524168,0.00011806468,0.000121006415,0.0002203561,0.00038880474,0.0001561796,0.0008174661,0.000025834463,0.000016731248],"category_scores_gemma":[0.0000051438533,0.00010925954,0.00004233228,0.0006209004,0.000014921666,0.0021634481,0.00067029597,0.00017669909,0.00025932054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024008768,0.000028976721,0.09992349,0.00057984865,0.00027386827,0.00022211511,0.44918114,0.13373503,0.000019150828,0.22773492,0.039650578,0.048410777],"study_design_scores_gemma":[0.007471761,0.0008600137,0.09330336,0.00016572469,0.000057184618,0.0031713443,0.056021374,0.6492607,0.0016383219,0.0004672059,0.18397315,0.0036098752],"about_ca_topic_score_codex":0.0000014819697,"about_ca_topic_score_gemma":1.654928e-8,"teacher_disagreement_score":0.9310809,"about_ca_system_score_codex":0.00011206309,"about_ca_system_score_gemma":0.000021609518,"threshold_uncertainty_score":0.4455475},"labels":[],"label_agreement":null},{"id":"W6979365150","doi":"","title":"FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Graph; Asynchronous communication; Robustness (evolution); Knowledge graph; Feature learning; Federated learning","score_opus":0.012700115150304125,"score_gpt":0.2486766224215193,"score_spread":0.2359765072712152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979365150","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":[0.48551074,0.00051162636,0.51269627,0.0005407319,0.00012775074,0.00017772133,3.688291e-7,0.00029799444,0.0001368127],"genre_scores_gemma":[0.9808408,0.000088333334,0.01762966,0.0009716829,0.00006666743,0.000026984773,0.0000067601777,0.000024935414,0.00034418653],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808604,0.00017168996,0.00028037865,0.0007419115,0.00023429812,0.00048568845],"domain_scores_gemma":[0.9987214,0.00047177,0.0002001734,0.0003343359,0.00015508174,0.00011724666],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001891625,0.00029846263,0.00029554288,0.00018212423,0.0007939941,0.00028457213,0.00035475867,0.00016058808,0.0000067578057],"category_scores_gemma":[0.0001600497,0.00026812495,0.000063090745,0.0013975867,0.00015030053,0.0006006079,0.00027058527,0.00070022914,0.0000088906645],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017683126,0.00007838961,0.89520967,0.00012985198,0.0001559864,0.00011788115,0.00218226,0.017277265,0.00063083123,0.0035361454,0.00020057308,0.080304325],"study_design_scores_gemma":[0.006311401,0.0011440644,0.22671407,0.0044358415,0.00015285495,0.00032482814,0.0015849656,0.7392558,0.0038429555,0.01132976,0.0025916512,0.0023117904],"about_ca_topic_score_codex":0.000027196662,"about_ca_topic_score_gemma":0.000027312622,"teacher_disagreement_score":0.72197855,"about_ca_system_score_codex":0.000058853653,"about_ca_system_score_gemma":0.00007083899,"threshold_uncertainty_score":0.9999771},"labels":[],"label_agreement":null},{"id":"W6987952452","doi":"","title":"On Using Embeddings for Ownership Verification of Graph Neural Networks","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Embedding; Graph; Artificial neural network; Benchmark (surveying); Suspect; Node (physics); Pattern recognition (psychology); Feature extraction","score_opus":0.02383654630530054,"score_gpt":0.24197872543077387,"score_spread":0.21814217912547332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987952452","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":[0.9546364,0.000060265185,0.043184433,0.00019871244,0.0011618108,0.00053931755,0.000017583114,0.00017623662,0.000025264304],"genre_scores_gemma":[0.88249534,0.00017062467,0.04460601,0.000059420494,0.00014701312,0.0000038488733,0.0008543518,0.00012924867,0.07153416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985259,0.000055986002,0.0001970508,0.0005675069,0.00029940787,0.00035414196],"domain_scores_gemma":[0.9982808,0.00019324539,0.0006335814,0.00055883103,0.00025061695,0.000082910825],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001574179,0.00026517242,0.00043188187,0.00049013266,0.0002036017,0.000024957328,0.0010062507,0.0003081869,0.00000418749],"category_scores_gemma":[0.000023125673,0.00030408727,0.0003116634,0.000821965,0.00008172974,0.0003936591,0.00007909055,0.00027029292,0.0000026098323],"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":[0.0022706015,0.00032019182,0.0004657915,0.0017004175,0.0005541698,0.00007703344,0.1092925,0.7651705,0.015451193,0.043979865,0.0042583686,0.056459337],"study_design_scores_gemma":[0.00096030935,0.00048097872,0.0022449803,0.0004983331,0.00015629097,0.0000022128977,0.014541351,0.9716621,0.0019388922,0.0068152878,0.000038978153,0.0006603044],"about_ca_topic_score_codex":0.0016573848,"about_ca_topic_score_gemma":0.0023828319,"teacher_disagreement_score":0.20649154,"about_ca_system_score_codex":0.000051873893,"about_ca_system_score_gemma":0.000032308246,"threshold_uncertainty_score":0.9999411},"labels":[],"label_agreement":null},{"id":"W6992927739","doi":"","title":"Neural Graph Reasoning:A Survey on Complex Logical Query Answering","year":2024,"lang":"en","type":"article","venue":"VU Research Portal","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Graph; Question answering; Logical consequence; Task (project management); Graph database; Taxonomy (biology); Modalities; Field (mathematics)","score_opus":0.17289153470890456,"score_gpt":0.41016587679040795,"score_spread":0.2372743420815034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992927739","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":[0.847622,0.0022078494,0.112550095,0.003755548,0.0032626372,0.0013104624,0.00009556791,0.0029613432,0.02623454],"genre_scores_gemma":[0.9964219,0.000064320375,0.002595182,0.00016045533,0.0002167543,0.000024492605,0.000038138518,0.000028678176,0.00045009947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9959157,0.0005564893,0.00029419782,0.000944556,0.0012310656,0.0010579914],"domain_scores_gemma":[0.9974819,0.0012135677,0.000030862935,0.0007743048,0.00015903368,0.00034032582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018710344,0.00022176292,0.00022571965,0.0004961992,0.0003110372,0.0005765045,0.0012659376,0.000106460604,0.00010463034],"category_scores_gemma":[0.00036790682,0.00018202842,0.00013806492,0.0024743068,0.00029558552,0.00054501975,0.0006117886,0.0013063466,0.0001471304],"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":[0.0002123031,0.00039425917,0.030465929,0.000114749724,0.00014266041,0.015909225,0.00048661808,0.008824698,0.0045644403,0.6159756,0.21041344,0.11249605],"study_design_scores_gemma":[0.00037228945,0.0011841921,0.5139367,0.00025244156,0.0000035942187,0.00021325139,0.00004084209,0.45215112,0.00052140787,0.017950669,0.012576597,0.0007969117],"about_ca_topic_score_codex":0.00017283937,"about_ca_topic_score_gemma":0.0001824208,"teacher_disagreement_score":0.59802496,"about_ca_system_score_codex":0.000035483048,"about_ca_system_score_gemma":0.000085236185,"threshold_uncertainty_score":0.7422904},"labels":[],"label_agreement":null},{"id":"W6996530909","doi":"","title":"Semi-supervised Learning Based on Graph Stochastic Co-Training","year":2023,"lang":"en","type":"article","venue":"Electronic Institutional Repository of the National Aviation University of Ukraine (National Aviation University, Ukraine)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Chen; Graph; Feature (linguistics); Feature selection; Artificial neural network; Set (abstract data type); Eleventh","score_opus":0.01658121056133718,"score_gpt":0.22285778968929412,"score_spread":0.20627657912795694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996530909","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":[0.10666365,0.000081008395,0.8498601,0.012174617,0.0010929409,0.0014473046,0.00024693419,0.00092765596,0.027505826],"genre_scores_gemma":[0.9963157,0.000020539235,0.0011657537,0.00021412876,0.000116469964,0.0000010136072,0.00025076975,0.000013003545,0.0019026193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99591196,0.00033519472,0.00038945198,0.00056877744,0.0023912988,0.0004033353],"domain_scores_gemma":[0.99644125,0.00081098254,0.00072601676,0.00023691445,0.0016776155,0.000107235806],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00084932585,0.00026993587,0.0002812506,0.0011321508,0.0016195518,0.000030232935,0.0011883052,0.00017657773,0.000025310788],"category_scores_gemma":[0.00047738125,0.00030589596,0.00034555764,0.0026197685,0.00041062594,0.0009049138,0.00021700379,0.0005295953,0.000023190305],"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":[0.00010548136,0.0001021836,0.00035639966,0.000016764361,0.00007483278,0.0000054851785,0.00040736,0.7094418,0.0017153568,0.28713745,0.0004432793,0.00019366102],"study_design_scores_gemma":[0.0032978929,0.0002968889,0.03787063,0.0001676262,0.000060059978,0.000035442987,0.00025992564,0.92718184,0.0007235051,0.020538315,0.008992281,0.0005755814],"about_ca_topic_score_codex":0.000032136522,"about_ca_topic_score_gemma":0.00001822408,"teacher_disagreement_score":0.8896521,"about_ca_system_score_codex":0.0013761217,"about_ca_system_score_gemma":0.0018346538,"threshold_uncertainty_score":0.9999393},"labels":[],"label_agreement":null},{"id":"W7000716831","doi":"","title":"Graph-based machine learning algorithms for predicting disease outcomes","year":2019,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Graph; Autoencoder; Artificial neural network; Convolutional neural network; Deep learning; Software deployment; Task (project management)","score_opus":0.018372980468401554,"score_gpt":0.2626573381445462,"score_spread":0.24428435767614468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000716831","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":[0.8961517,0.005256815,0.014331473,0.00052157464,0.032747936,0.016209364,0.008446677,0.011077466,0.015256998],"genre_scores_gemma":[0.9409341,0.00013949884,0.04367376,0.0009812852,0.00012198796,0.0008355489,0.003567081,0.00051581924,0.00923092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99353224,0.0003878793,0.0011510694,0.002315269,0.0012352598,0.001378302],"domain_scores_gemma":[0.99462354,0.0012984186,0.0011886832,0.001631212,0.0005420245,0.0007161399],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00084199215,0.0012697345,0.0011592782,0.0007836653,0.0018404531,0.00028732413,0.0024564865,0.0006555128,0.000023572398],"category_scores_gemma":[0.0012930614,0.0012757273,0.0011051256,0.001175198,0.00005938874,0.0016808814,0.0003054696,0.0024556872,0.00007649607],"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":[0.00093690184,0.00068931305,0.007410409,0.0021209503,0.0007893792,0.00028756488,0.000025236362,0.041190412,0.0031203823,0.15132467,0.000013821673,0.79209095],"study_design_scores_gemma":[0.013068975,0.0020923717,0.022017233,0.003592193,0.0014279844,0.000035682675,0.00019605274,0.67149025,0.021347357,0.17653139,0.07698742,0.011213095],"about_ca_topic_score_codex":0.00005849785,"about_ca_topic_score_gemma":0.00015085276,"teacher_disagreement_score":0.7808779,"about_ca_system_score_codex":0.0003363077,"about_ca_system_score_gemma":0.0001067775,"threshold_uncertainty_score":0.9998457},"labels":[],"label_agreement":null},{"id":"W7010018281","doi":"","title":"Finding and explaining relations in a biographical knowledge graph based on life events : Case BiographySampo","year":2023,"lang":"en","type":"article","venue":"Aaltodoc (Aalto University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"China Scholarship Council","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Demotion","score_opus":0.027197129583931404,"score_gpt":0.24915462378873013,"score_spread":0.2219574942047987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010018281","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":[0.9302243,0.0001042651,0.063352406,0.0013347352,0.00045176194,0.0004905575,0.000017002487,0.00094959635,0.0030754034],"genre_scores_gemma":[0.99587315,0.00009474631,0.0037037325,0.00014036217,0.000023114859,0.0000032505216,0.0000091773145,0.000017027396,0.00013542292],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982117,0.00020482276,0.00020560515,0.0006726021,0.0001870371,0.0005182225],"domain_scores_gemma":[0.99846596,0.0006470347,0.0000954835,0.00044789005,0.000045076977,0.00029855038],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025717457,0.00022813222,0.0002205608,0.004425881,0.00044695742,0.000045168334,0.00047295453,0.00013563204,0.000005665301],"category_scores_gemma":[0.000066086475,0.0002602846,0.00015302052,0.011834167,0.0001159194,0.0006062078,0.00029691434,0.00039312989,0.000028578273],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028388857,0.00078049774,0.5175179,0.00011195999,0.00017803606,0.022706525,0.0040968503,0.03689318,0.00028508328,0.3859279,0.003622392,0.027595751],"study_design_scores_gemma":[0.008719344,0.001186852,0.24578632,0.00094076636,0.00010148994,0.00032063673,0.0018482412,0.6938357,0.00011239877,0.01958865,0.024872264,0.0026873774],"about_ca_topic_score_codex":0.00005238535,"about_ca_topic_score_gemma":0.00017941705,"teacher_disagreement_score":0.6569425,"about_ca_system_score_codex":0.00004683704,"about_ca_system_score_gemma":0.00005926512,"threshold_uncertainty_score":0.9999849},"labels":[],"label_agreement":null},{"id":"W7015712645","doi":"","title":"Towards Machine Learning on Temporal Graphs","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; McGill University; Canadian Institute for Advanced Research","keywords":"Constructive; Database transaction; Graph; Uncountable set","score_opus":0.015968479930179722,"score_gpt":0.25081264000087033,"score_spread":0.23484416007069062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7015712645","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":[0.5002001,0.0027845013,0.00018896772,0.00025710306,0.015606434,0.00303377,0.0009937685,0.0056384923,0.47129688],"genre_scores_gemma":[0.9576721,0.0006517914,0.010516355,0.0010801798,0.000074069176,0.00020984578,0.0014643499,0.00023128671,0.028099999],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.993412,0.00060960697,0.0010921425,0.002307793,0.0013619555,0.0012164759],"domain_scores_gemma":[0.9962021,0.00039622528,0.0008428273,0.0016970354,0.00041461803,0.00044719374],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00075248105,0.0012946567,0.0010902642,0.0011925125,0.0017892977,0.00028033627,0.003058124,0.0010017628,0.00006360439],"category_scores_gemma":[0.00063979294,0.0013309203,0.00074001646,0.0025090063,0.0000554271,0.0013443638,0.0005325244,0.00465573,0.00019345885],"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":[0.00015525907,0.00015561897,0.000048766142,0.00018284112,0.0001525629,0.00020874874,0.000006931927,0.001144679,0.0013222736,0.48191255,0.000017157823,0.5146926],"study_design_scores_gemma":[0.0031841488,0.0016823052,0.0021838977,0.0028944872,0.00030807205,0.000083702915,0.0001031836,0.008054954,0.05620555,0.58735794,0.33208072,0.005861052],"about_ca_topic_score_codex":0.00023994551,"about_ca_topic_score_gemma":0.0009000552,"teacher_disagreement_score":0.50883156,"about_ca_system_score_codex":0.00045479965,"about_ca_system_score_gemma":0.00010224435,"threshold_uncertainty_score":0.9999805},"labels":[],"label_agreement":null},{"id":"W7019398766","doi":"","title":"Geometric deep learning: features, graphs, and affinity supervision","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stability (learning theory); Identification (biology); Artificial neural network; Work (physics)","score_opus":0.01303046553968749,"score_gpt":0.2302943370639032,"score_spread":0.21726387152421572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019398766","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":[0.9437412,0.012195012,0.000104981846,0.00016872212,0.0045781885,0.0018629881,0.00019407386,0.00314042,0.034014408],"genre_scores_gemma":[0.9806695,0.0042264233,0.011273364,0.0005683123,0.000059121907,0.00008423192,0.0007333698,0.000230471,0.0021551992],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936753,0.00058407214,0.00087888585,0.0024348178,0.0013124659,0.00111445],"domain_scores_gemma":[0.9962154,0.00068607944,0.00068842317,0.0011710698,0.0004082393,0.00083077955],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006184231,0.0011630323,0.0010668014,0.0012680469,0.0018353267,0.0004098519,0.0022761181,0.0011226707,0.000038860577],"category_scores_gemma":[0.0013192217,0.0012088105,0.00046356354,0.004716697,0.000083091654,0.0019550421,0.00080879737,0.0046318993,0.00012176231],"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":[0.00014907899,0.00013377327,0.00010044364,0.00031075763,0.00015277983,0.0003150422,0.000025856241,0.00028491896,0.00461554,0.19570728,0.000024426334,0.7981801],"study_design_scores_gemma":[0.006684078,0.0036630256,0.05422738,0.0017938797,0.0008260952,0.00072088407,0.0006622115,0.00596411,0.08331897,0.49264523,0.33697465,0.01251949],"about_ca_topic_score_codex":0.00006872154,"about_ca_topic_score_gemma":0.0003453208,"teacher_disagreement_score":0.7856606,"about_ca_system_score_codex":0.00021999265,"about_ca_system_score_gemma":0.00003600308,"threshold_uncertainty_score":0.99946415},"labels":[],"label_agreement":null},{"id":"W7024864190","doi":"","title":"the_villageboy_a.k.a._Rich.vom.Dorf._-_minimalfrickeltech_mix_2.mp3","year":2006,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Anticipation (artificial intelligence); World War II; Period (music); Sound (geography)","score_opus":0.005079242722596964,"score_gpt":0.18300485860802967,"score_spread":0.1779256158854327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024864190","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000066990815,0.0010405792,0.00032108586,0.00021681775,0.00067650934,0.00062020594,0.00009606709,0.0008897006,0.9961323],"genre_scores_gemma":[0.00029521153,0.0003196875,0.02710036,0.00045441932,0.00030530058,0.000028205019,0.000118539865,0.00019627527,0.971182],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99679285,0.00012662771,0.0010026855,0.00056558766,0.000800789,0.0007114293],"domain_scores_gemma":[0.99698174,0.00020617504,0.0011497629,0.0012817362,0.00018638802,0.00019418592],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025653123,0.000703366,0.0007286761,0.00026151948,0.00012603331,0.0001514757,0.0018612175,0.0006611284,0.043940894],"category_scores_gemma":[0.00007486867,0.00067214755,0.00034408903,0.000056257533,0.00020943406,0.0000010640638,0.0004867137,0.00055010035,0.008862525],"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":[0.000019548073,0.000048293627,0.0000014513893,0.00018066072,0.000047506823,0.00005221103,0.000048511483,0.0007243027,6.600979e-7,0.00058471336,0.9911488,0.007143333],"study_design_scores_gemma":[0.00046839015,0.00016446033,0.000006645822,0.00023372319,0.0000332551,0.000112517184,0.0000140640805,0.00035045855,0.000023386592,0.00023145956,0.99766237,0.0006992729],"about_ca_topic_score_codex":0.0022847697,"about_ca_topic_score_gemma":0.0011895748,"teacher_disagreement_score":0.03507837,"about_ca_system_score_codex":0.00006752894,"about_ca_system_score_gemma":0.000045092045,"threshold_uncertainty_score":0.999573},"labels":[],"label_agreement":null},{"id":"W7025150176","doi":"","title":"Trump win in 2024 could harm fight against climate change -Canada PM","year":2023,"lang":"en","type":"other","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Climate change; Harm; Government (linguistics); Global warming","score_opus":0.024553779217756925,"score_gpt":0.25304290429357373,"score_spread":0.2284891250758168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025150176","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009996713,0.0018894987,0.0045088455,0.0063906964,0.009678674,0.0012489896,0.00016916686,0.0025947955,0.9735093],"genre_scores_gemma":[0.00028566964,0.0052193617,0.006042817,0.014715595,0.0012213708,0.00024374924,0.000039135994,0.0011660871,0.97106624],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99761313,0.000045319928,0.00030397016,0.00082492415,0.00044175083,0.00077090727],"domain_scores_gemma":[0.998724,0.00009693269,0.00016395554,0.0008508518,0.000016224367,0.00014807486],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010322659,0.00040939235,0.00042828487,0.00035954505,0.000043844684,0.00007134336,0.0012505859,0.00026407486,0.00048419688],"category_scores_gemma":[0.00001151639,0.0003653474,0.0000795565,0.0011728985,0.0000327842,0.00019614758,0.0005162824,0.00051670434,0.00026755573],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016461279,0.000011127318,0.00010349015,0.000036052028,0.000012104245,0.00037751297,0.000021535208,0.000051151947,0.0000014061997,0.002693913,0.97224987,0.024440173],"study_design_scores_gemma":[0.00025591912,0.000019100637,0.00027430468,0.00041558951,0.0000035990772,0.0000030954761,0.0000070048545,0.011451347,0.000010938615,0.0002662496,0.9867373,0.00055551535],"about_ca_topic_score_codex":0.04123746,"about_ca_topic_score_gemma":0.8069318,"teacher_disagreement_score":0.7656943,"about_ca_system_score_codex":0.0001443989,"about_ca_system_score_gemma":0.000096149,"threshold_uncertainty_score":0.99987984},"labels":[],"label_agreement":null},{"id":"W7030399226","doi":"","title":"NAIT and the Future of Automotive Training | EV Life","year":2023,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Apprenticeship; Automotive industry; Training (meteorology); Technician; Service (business)","score_opus":0.00645751075267161,"score_gpt":0.18570880050849134,"score_spread":0.17925128975581972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030399226","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007460824,0.0011964781,0.00004100822,0.000791668,0.00053364784,0.0003792949,0.000031148324,0.00037545274,0.99664384],"genre_scores_gemma":[0.00072357256,0.0009756206,0.003837867,0.00047899463,0.000240281,0.000013949206,0.000008887613,0.000108712244,0.9936121],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853706,0.00009775034,0.00051849545,0.00021425557,0.00038313173,0.000249293],"domain_scores_gemma":[0.9982463,0.0003735655,0.0007207067,0.00044387454,0.00012338016,0.0000921527],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029256934,0.00026542696,0.00046401343,0.00007813612,0.000066659806,0.00004436494,0.00067946024,0.0002595242,0.007233497],"category_scores_gemma":[0.00014306122,0.00019693411,0.00013831625,0.000027110102,0.00030460063,5.090711e-7,0.00017455424,0.00031733318,0.00045556072],"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":[0.000030943494,0.0000057102707,4.773822e-7,0.000112674265,0.000053222942,0.0000063760285,0.0007600177,0.0006821148,2.3155145e-8,0.0013778678,0.985582,0.011388553],"study_design_scores_gemma":[0.00066153565,0.000061500796,0.000010573836,0.00020087793,0.000021398415,0.000031600477,0.00020999268,0.00057649356,0.0000014766877,0.00018364358,0.99785215,0.00018873913],"about_ca_topic_score_codex":0.00037965563,"about_ca_topic_score_gemma":0.00050597335,"teacher_disagreement_score":0.012270149,"about_ca_system_score_codex":0.000012368642,"about_ca_system_score_gemma":0.00003178462,"threshold_uncertainty_score":0.99367404},"labels":[],"label_agreement":null},{"id":"W7030429921","doi":"","title":"\"The Miroir des dames in Fifteenth-Century England\"","year":2021,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Advanced Graph Neural 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":"University of British Columbia","funders":"","keywords":"Feature (linguistics); Work (physics); Power (physics)","score_opus":0.01324472743148445,"score_gpt":0.2403076137135178,"score_spread":0.22706288628203336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030429921","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":[0.9301494,0.015595295,0.051302534,0.0008480986,0.00082954206,0.0002610499,0.00006815398,0.00025447126,0.0006914598],"genre_scores_gemma":[0.9652622,0.011554781,0.021752387,0.0006244807,0.0003208356,0.000020283518,0.000336452,0.000028595668,0.00009999819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977518,0.00030989593,0.00031245733,0.00077888323,0.00022663626,0.00062033394],"domain_scores_gemma":[0.9971333,0.00091163855,0.000099476485,0.0016855443,0.000026546126,0.00014346809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045833015,0.00023158985,0.00021682325,0.00005341095,0.0005477891,0.0003828831,0.0025501444,0.00004883867,0.0000017143143],"category_scores_gemma":[0.000023359722,0.00017240857,0.00003696371,0.00065794965,0.0001582089,0.0008337149,0.0025893121,0.00035156106,0.0000042393854],"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":[0.00008784814,0.0001523417,0.1030709,0.00021901993,0.000095992626,0.00047667822,0.011963893,0.0072540366,0.00096000376,0.006280029,0.0006414805,0.8687978],"study_design_scores_gemma":[0.0007819806,0.000043224834,0.1109034,0.00040779795,0.00001607212,0.00013093182,0.00065223104,0.78123367,0.00010007254,0.011051333,0.094193846,0.00048544665],"about_ca_topic_score_codex":0.000089344234,"about_ca_topic_score_gemma":0.006393998,"teacher_disagreement_score":0.8683123,"about_ca_system_score_codex":0.000015461443,"about_ca_system_score_gemma":0.000029796456,"threshold_uncertainty_score":0.70306176},"labels":[],"label_agreement":null},{"id":"W7033541256","doi":"","title":"Press Release (1964-02-28) UMD spring quarter application submission deadline arriving","year":2018,"lang":"en","type":"other","venue":"University of Minnesota Digital Conservancy (University of Minnesota)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Spring (device); Quarter (Canadian coin); Press release; Hydrology (agriculture)","score_opus":0.009332199771230993,"score_gpt":0.18307181098618422,"score_spread":0.17373961121495324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033541256","genre_codex":"methods","genre_gemma":"other","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":[0.03031937,0.0016919131,0.7692512,0.0018064893,0.0008623379,0.0019840356,0.0005590466,0.0011389493,0.19238666],"genre_scores_gemma":[0.29255566,0.0035894841,0.14497618,0.00016795757,0.00061976886,0.0000012015003,0.00041450918,0.0007771411,0.5568981],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969708,0.00011421425,0.00034597085,0.0012844986,0.00065834983,0.00062616874],"domain_scores_gemma":[0.9960721,0.0002130216,0.0012630285,0.0016752429,0.0003975822,0.00037900446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016004404,0.0006190869,0.0009237795,0.00064556417,0.0003084466,0.000056267836,0.0028689927,0.00057804986,0.00022516881],"category_scores_gemma":[0.00004445641,0.0008543692,0.00044897324,0.0008019735,0.00081608025,0.0014103797,0.0012700747,0.00036598896,0.00015994416],"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":[0.0013582467,0.0020429112,0.018126184,0.0028513866,0.0013654667,0.0016676963,0.003915363,0.0003214297,0.0027975964,0.016410567,0.82203865,0.12710452],"study_design_scores_gemma":[0.00212191,0.0003399477,0.0016475577,0.0012543392,0.00019559954,0.0000346757,0.0011073366,0.009801285,0.00016805378,0.00037105283,0.9816487,0.0013095707],"about_ca_topic_score_codex":0.0010857052,"about_ca_topic_score_gemma":0.001240868,"teacher_disagreement_score":0.624275,"about_ca_system_score_codex":0.00012332434,"about_ca_system_score_gemma":0.00015549295,"threshold_uncertainty_score":0.9993907},"labels":[],"label_agreement":null},{"id":"W7034269136","doi":"","title":"Time course of corticospinal excitability in a simple reaction time task","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Ottawa","funders":"","keywords":"Stimulus (psychology); Transcranial magnetic stimulation; Task (project management); Stimulation; Context (archaeology); Electroencephalography","score_opus":0.01748998912153622,"score_gpt":0.26900831238324335,"score_spread":0.25151832326170714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034269136","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":[0.92422754,0.000051356794,0.07282709,0.0003825312,0.00011146563,0.0002305544,0.0000021629653,0.0001652217,0.0020020902],"genre_scores_gemma":[0.99238914,0.0000013939184,0.007287383,0.000086268374,0.000019527184,0.0000055217647,0.0000031692111,0.000005038572,0.00020257637],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989729,0.00006418351,0.00026535633,0.00028005856,0.0002017129,0.00021579297],"domain_scores_gemma":[0.99912393,0.00008043003,0.00010007216,0.00047749598,0.000107207095,0.000110867564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029365928,0.00009686609,0.0001791014,0.000054740533,0.00001576518,0.000013003358,0.0003367028,0.000047857127,0.0000109452885],"category_scores_gemma":[0.000057886664,0.000084313,0.000038799473,0.0004984558,0.000077458484,0.00053154334,0.00013086217,0.00011001475,0.0000924639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086558814,0.0046292455,0.03568365,0.00009746493,0.000120456825,0.00022454992,0.004179664,0.013628561,0.5850663,0.028515704,0.017688911,0.30929986],"study_design_scores_gemma":[0.00090383174,0.0004937051,0.16324672,0.0000176472,0.000005641875,0.000030578143,0.000041350144,0.7940943,0.0005315093,0.03908467,0.0012490036,0.00030100613],"about_ca_topic_score_codex":0.000045763456,"about_ca_topic_score_gemma":0.000022270011,"teacher_disagreement_score":0.7804658,"about_ca_system_score_codex":0.000051389725,"about_ca_system_score_gemma":0.000062991596,"threshold_uncertainty_score":0.34381843},"labels":[],"label_agreement":null},{"id":"W7034758931","doi":"","title":"Understanding how family values shape the health-seeking behaviors of older adults in rural China","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto; China Scholarship Council; McGill University Health Centre; Renmin University of China; National Natural Science Foundation of China; McGill University","keywords":"China; Population; Family values; Rural area; Family life","score_opus":0.029276094009003873,"score_gpt":0.2664954388128802,"score_spread":0.2372193448038763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034758931","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":[0.9874897,0.0019228406,0.00026656498,0.0003003651,0.00331962,0.0018839529,0.00019548318,0.00039410533,0.004227362],"genre_scores_gemma":[0.9960012,0.0006481482,0.0018359751,0.00037759816,0.000030790565,0.00012086181,0.00017304867,0.000095038275,0.0007173353],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9945478,0.0006862857,0.0011753095,0.0013150242,0.0011417888,0.0011338221],"domain_scores_gemma":[0.9964818,0.00051974546,0.0012242454,0.0013371406,0.00021361592,0.00022349754],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0011063095,0.00087780727,0.0010616087,0.00085797464,0.001278038,0.00019786961,0.0026439703,0.00058616797,0.000008418305],"category_scores_gemma":[0.00024713264,0.0007818363,0.00046103317,0.0024395005,0.000099051045,0.0014884331,0.00046855913,0.0024269824,0.0000045580787],"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":[0.00040303828,0.00046737166,0.0005297557,0.0013221,0.00020380916,0.00012433444,0.0006133701,0.00087196915,0.0044459137,0.31963602,0.00003476999,0.67134756],"study_design_scores_gemma":[0.015240224,0.0029950489,0.26093674,0.063368924,0.00072084344,0.00015732081,0.030153671,0.060416896,0.022379993,0.5282012,0.002974566,0.0124545535],"about_ca_topic_score_codex":0.0004200007,"about_ca_topic_score_gemma":0.0018000187,"teacher_disagreement_score":0.658893,"about_ca_system_score_codex":0.0011152977,"about_ca_system_score_gemma":0.0001267532,"threshold_uncertainty_score":0.9998745},"labels":[],"label_agreement":null},{"id":"W7038331441","doi":"","title":"Government of Canada Invests $16.7 Million to Support Ontario's Power Grid Operator With Delivering Reliable, Affordable and Clean Energy","year":2024,"lang":"en","type":"other","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Operator (biology); Electricity; Power (physics); Clean energy; Renewable energy","score_opus":0.004298751643616399,"score_gpt":0.1753533632370587,"score_spread":0.1710546115934423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038331441","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024525537,0.00079708145,0.0691447,0.00034438848,0.0023625856,0.0005830882,0.00005829723,0.00029385783,0.9239634],"genre_scores_gemma":[0.019546011,0.00014759174,0.043007232,0.0022836043,0.00008966033,0.000029229954,0.000005495464,0.00029065754,0.93460053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800086,0.000011271788,0.0002361995,0.0006665111,0.0006964436,0.00038874187],"domain_scores_gemma":[0.9990177,0.000012435621,0.00010561942,0.0005623139,0.00003131599,0.00027059024],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000060370516,0.00033057138,0.00035137177,0.00009289633,0.000034174333,0.00007033192,0.00048420226,0.00009957944,0.0003811433],"category_scores_gemma":[0.0000025557663,0.00025673606,0.000030289866,0.00033515543,0.000031181426,0.00014202835,0.0004755276,0.00017263627,0.0000025362897],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001684143,0.000013488309,0.00030336445,0.000054336495,0.00006140927,0.00013599305,0.000072997675,0.00075347757,0.00003252557,0.0054832026,0.99068195,0.0023904284],"study_design_scores_gemma":[0.00018384687,0.00037983566,0.00008089322,0.0004911313,0.000014314915,0.00002948401,0.000031963405,0.00028850892,0.0015479586,0.00010898731,0.996372,0.00047108353],"about_ca_topic_score_codex":0.83321047,"about_ca_topic_score_gemma":0.9948955,"teacher_disagreement_score":0.16168502,"about_ca_system_score_codex":0.0004770822,"about_ca_system_score_gemma":0.00047664676,"threshold_uncertainty_score":0.9999885},"labels":[],"label_agreement":null},{"id":"W7038977132","doi":"","title":"kutte kamine betichod!!!===== I877288-2779 Gmail customer support number usa===Gmail customer support number usa canada ===!!","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phone; Service (business); Service desk; Service provider; Telephone number","score_opus":0.009929774591186338,"score_gpt":0.257385942899275,"score_spread":0.2474561683080887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038977132","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003495411,0.0000024881242,0.021143204,0.00059349544,0.0042959,0.0020057135,0.00030606502,0.0007967493,0.97050685],"genre_scores_gemma":[0.008788007,0.0004445642,0.01273166,0.002007793,0.00096697337,0.0006053709,0.00015330044,0.0010472994,0.97325504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9829016,0.0010894095,0.0021927892,0.007857116,0.002828511,0.0031305815],"domain_scores_gemma":[0.9817834,0.0010326679,0.0019187722,0.013063964,0.00051484845,0.0016863464],"candidate_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0029549666,0.0023263586,0.0023084243,0.0005848869,0.00045842168,0.00047257938,0.008637856,0.0015404054,0.9329678],"category_scores_gemma":[0.00086992694,0.0022970852,0.0008530829,0.0013224614,0.00070411334,0.0011690906,0.0068943854,0.0023931344,0.9591257],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000634334,0.0002940669,0.034147017,0.00017870501,0.0005409544,0.0008008692,0.00017371493,0.00015579081,0.00026118255,0.00323908,0.9445554,0.015589764],"study_design_scores_gemma":[0.0012702513,0.000003608992,0.0036017469,0.0003464206,0.00018146493,0.0005654116,0.000015535894,0.00020669562,0.0010158587,0.00078986835,0.989393,0.0026101172],"about_ca_topic_score_codex":0.051579844,"about_ca_topic_score_gemma":0.09208776,"teacher_disagreement_score":0.044837598,"about_ca_system_score_codex":0.0013547286,"about_ca_system_score_gemma":0.002411391,"threshold_uncertainty_score":0.9999084},"labels":[],"label_agreement":null},{"id":"W7039030730","doi":"","title":"La paradiplomacia de los gobiernos subnacionales en América del Norte","year":2018,"lang":"en","type":"article","venue":"Dialnet (Universidad de la Rioja)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Public policy; Conceptual framework; International relations; Qualitative research; The Conceptual Framework; Work (physics); State (computer science)","score_opus":0.008294440681341692,"score_gpt":0.258458711079855,"score_spread":0.2501642703985133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039030730","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":[0.44790813,0.00019307307,0.5136038,0.00080815697,0.0003153869,0.00026187833,0.000021893451,0.0005844376,0.036303233],"genre_scores_gemma":[0.9527886,0.00018211262,0.0455132,0.0006312076,0.00028517435,0.000008054295,0.000007292011,0.00002926516,0.0005551171],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978894,0.00046161583,0.00016388435,0.0005437237,0.0003012114,0.0006401546],"domain_scores_gemma":[0.99802375,0.0008329604,0.0001328289,0.00062614185,0.00008377518,0.0003005279],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003537714,0.00025347323,0.00024021878,0.00024426484,0.0002846579,0.00012727118,0.0013728848,0.00025812432,0.000030474721],"category_scores_gemma":[0.00006503759,0.00028117682,0.00014676103,0.0008467396,0.00042667415,0.0006897221,0.00036916704,0.00040444024,0.00014000757],"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":[0.00019259489,0.0003527106,0.039004486,0.000053675692,0.00024709952,0.0026205196,0.008700816,0.002683108,0.007602265,0.7365908,0.027693583,0.17425834],"study_design_scores_gemma":[0.0031143622,0.0005546051,0.3177341,0.00018083937,0.00011531129,0.0010398066,0.00041207904,0.14346519,0.0021838567,0.101209834,0.4280058,0.001984224],"about_ca_topic_score_codex":0.00004387113,"about_ca_topic_score_gemma":0.00016418859,"teacher_disagreement_score":0.635381,"about_ca_system_score_codex":0.00019217905,"about_ca_system_score_gemma":0.00016945468,"threshold_uncertainty_score":0.99996406},"labels":[],"label_agreement":null},{"id":"W7039115838","doi":"","title":"Le plan intercommunal de sauvegarde : « de la conception à la mise en œuvre »","year":2024,"lang":"en","type":"dissertation","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Context (archaeology); Face (sociological concept); Work (physics)","score_opus":0.007585132879235562,"score_gpt":0.24171623878617993,"score_spread":0.23413110590694436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039115838","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":[0.1435279,0.0034225208,0.5488044,0.0065769893,0.0010680014,0.00079075765,0.00005550154,0.0015956053,0.2941583],"genre_scores_gemma":[0.8815889,0.0011025226,0.09169104,0.00019874334,0.00007400007,0.00012503177,0.0009002531,0.00010620482,0.024213335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.98975587,0.0079324385,0.00051523035,0.00083842984,0.0004164791,0.00054154947],"domain_scores_gemma":[0.993841,0.0031475883,0.0003702234,0.0018168956,0.00058234524,0.00024197953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0042558475,0.00043417243,0.0003809012,0.00030000086,0.00040436076,0.0008961001,0.002731567,0.000558452,0.00003654067],"category_scores_gemma":[0.00065947394,0.0004869854,0.00028302148,0.00062907714,0.00023405346,0.00040578513,0.0005427113,0.0012226593,0.000059288624],"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":[0.000059507078,0.0005989469,0.00048320895,0.0003807926,0.00017381863,0.00010348184,0.086673304,0.000380337,0.014751573,0.6778673,0.008420644,0.2101071],"study_design_scores_gemma":[0.003271295,0.000009088074,0.015258567,0.015834639,0.00032295389,0.00061460875,0.0062970384,0.29756305,0.102500364,0.16915339,0.38513532,0.004039692],"about_ca_topic_score_codex":0.00052633067,"about_ca_topic_score_gemma":0.0025468818,"teacher_disagreement_score":0.73806095,"about_ca_system_score_codex":0.00022231751,"about_ca_system_score_gemma":0.0007173243,"threshold_uncertainty_score":0.9997582},"labels":[],"label_agreement":null},{"id":"W7043375682","doi":"","title":"From social networks to biological graphs: Addressing design issues and exploring practical applications in graph representation learning","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Graph; Representation (politics); Knowledge representation and reasoning; Social network (sociolinguistics); Feature learning","score_opus":0.12167938411064906,"score_gpt":0.3474987738074528,"score_spread":0.22581938969680376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043375682","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":[0.9356329,0.0021726105,0.050418183,0.00036853907,0.002286092,0.0042242478,0.000118539625,0.0017128352,0.0030660583],"genre_scores_gemma":[0.91791874,0.0014015004,0.077710755,0.00022708,0.00017080395,0.0015808218,0.0006316768,0.0000944951,0.00026410236],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9946462,0.001141463,0.00090466184,0.001975765,0.0005411784,0.0007907341],"domain_scores_gemma":[0.9966477,0.0017162836,0.0005133835,0.00060909474,0.0002422181,0.00027129022],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007374529,0.0006791288,0.00078841514,0.00080383575,0.0015864945,0.00036344203,0.00089502253,0.0007609991,0.000007963329],"category_scores_gemma":[0.0008492077,0.0007249532,0.00021345876,0.002714631,0.00006846548,0.0018407782,0.00044786875,0.0024513067,0.0000088217275],"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":[0.00043032345,0.00028593442,0.00074664486,0.00010043073,0.0002082598,0.00014298396,0.00026189542,0.024516253,0.007164134,0.2198084,0.000023341168,0.7463114],"study_design_scores_gemma":[0.002576505,0.00064852805,0.036408912,0.0026032282,0.00034508176,0.00002995682,0.004128167,0.020796964,0.022597892,0.89376986,0.010862597,0.0052322815],"about_ca_topic_score_codex":0.00023914238,"about_ca_topic_score_gemma":0.00027273013,"teacher_disagreement_score":0.7410791,"about_ca_system_score_codex":0.00020369019,"about_ca_system_score_gemma":0.000035278546,"threshold_uncertainty_score":0.9998501},"labels":[],"label_agreement":null},{"id":"W7043807940","doi":"","title":"Triangle count estimation and label prediction over uncertain streaming graphs","year":2024,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"","keywords":"Probabilistic logic; Graph; Uncertain data; Scope (computer science); Enhanced Data Rates for GSM Evolution; Graph theory; Dynamic network analysis","score_opus":0.010007076613353545,"score_gpt":0.21949615933815558,"score_spread":0.20948908272480204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043807940","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":[0.9925583,0.0006918485,0.004734049,0.00032680682,0.00088030443,0.00034412267,0.00004726335,0.00026148168,0.00015585058],"genre_scores_gemma":[0.5241632,0.0022254966,0.10097889,0.00008966414,0.00016914406,0.0000061175974,0.0021745635,0.0001389314,0.370054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871206,0.00004634328,0.0001432417,0.0005451196,0.0003246279,0.00022861433],"domain_scores_gemma":[0.9992101,0.00006198184,0.00021788747,0.0003263215,0.00009959026,0.00008408276],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012471348,0.00023758372,0.0003088856,0.0004061579,0.00018908635,0.00007860092,0.00038744157,0.00024694382,0.00001121813],"category_scores_gemma":[0.000010019325,0.00026873595,0.00009825755,0.00051426823,0.0000625853,0.00079296407,0.00010556213,0.00028960092,0.000009535484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008061253,0.0004270779,0.0014615131,0.003733593,0.0010053543,0.00068708905,0.37020686,0.012651298,0.011497895,0.070923224,0.015980868,0.5106191],"study_design_scores_gemma":[0.0020491767,0.0005189373,0.010127423,0.0014454436,0.00039108103,0.000019080895,0.025434894,0.92857105,0.00064636185,0.02926013,0.0006446414,0.00089176145],"about_ca_topic_score_codex":0.0036482017,"about_ca_topic_score_gemma":0.0071671237,"teacher_disagreement_score":0.9159198,"about_ca_system_score_codex":0.00007954745,"about_ca_system_score_gemma":0.00005091282,"threshold_uncertainty_score":0.99997646},"labels":[],"label_agreement":null},{"id":"W7065870066","doi":"","title":"Exploring the Role of ABCF1 in Mucosal Immunity of Human Airway Epithelial Cells","year":2024,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; University of Waterloo","keywords":"In silico; Immune system; Transcriptome; Gene silencing; Function (biology); Receptor; Mucosal immunology; Immunity; Signal transduction; Innate immune system","score_opus":0.019825617184072285,"score_gpt":0.21564546848078298,"score_spread":0.1958198512967107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7065870066","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":[0.7272543,0.0006800952,0.0015530813,0.000039133094,0.0018580847,0.00062118587,0.000035382403,0.00015896113,0.26779982],"genre_scores_gemma":[0.9166784,0.0001726644,0.0009060062,0.000010500832,0.000071180206,0.0000035056662,0.000034668737,0.000046062276,0.08207695],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99845016,0.00016470723,0.00034669432,0.00043590076,0.00030389184,0.00029862026],"domain_scores_gemma":[0.99882007,0.00011538638,0.00029757034,0.0006242876,0.00008941762,0.00005329169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012530417,0.0002891956,0.0003935644,0.0003593717,0.00010169707,0.00003617516,0.0018071926,0.0001415804,0.00062345434],"category_scores_gemma":[0.0000046497994,0.0002656603,0.00021890333,0.001391452,0.00008922495,0.000572242,0.00041201076,0.00070552673,0.000012701638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020471204,0.0001937178,0.00035184436,0.00057452655,0.00016695481,0.00015659475,0.013063658,0.0024984668,0.083752684,0.060198266,0.000056446035,0.83878213],"study_design_scores_gemma":[0.0029732308,0.0011482253,0.009239382,0.0034078173,0.00044392698,0.000009900243,0.045314483,0.011461244,0.79925126,0.029806515,0.09423618,0.0027078518],"about_ca_topic_score_codex":0.00038624028,"about_ca_topic_score_gemma":0.0015231487,"teacher_disagreement_score":0.8360743,"about_ca_system_score_codex":0.00007687023,"about_ca_system_score_gemma":0.00006749596,"threshold_uncertainty_score":0.99997956},"labels":[],"label_agreement":null},{"id":"W7066303043","doi":"","title":"~Help Usa@ +1-800-882-0652...((( avg tech support","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Phone; Service (business); Mobile phone; Smart phone; Phone call","score_opus":0.012637548418814995,"score_gpt":0.2617222552413493,"score_spread":0.2490847068225343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066303043","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026356653,0.0000069263656,0.16018125,0.0004273208,0.0016989441,0.0013106477,0.00004870462,0.0012601586,0.8350397],"genre_scores_gemma":[0.00048455378,0.0005104448,0.023237543,0.0005562237,0.000541753,0.00037252536,0.000029082259,0.00056816253,0.9736997],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9901942,0.00061688205,0.0010378213,0.005511844,0.0011802836,0.0014589602],"domain_scores_gemma":[0.98616165,0.0006579982,0.0010448295,0.011323056,0.00017487934,0.00063760526],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0023398728,0.0011423226,0.0011205284,0.0007820623,0.00023635592,0.00031148212,0.007634339,0.0011171666,0.76465505],"category_scores_gemma":[0.00089778675,0.0010843492,0.0005854986,0.00074369385,0.00036325533,0.00064565294,0.0063965367,0.0013025805,0.96400166],"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":[0.000025808884,0.00018945447,0.00219853,0.000100333695,0.00018356548,0.00020422506,0.00012213571,0.000083477695,0.00073473016,0.009630709,0.9358697,0.050657332],"study_design_scores_gemma":[0.000563371,0.0000020478697,0.00044586926,0.00033305955,0.00005001071,0.00015604182,0.0000061116716,0.0002842188,0.0018795456,0.009603502,0.98542106,0.001255161],"about_ca_topic_score_codex":0.00012419364,"about_ca_topic_score_gemma":0.000107600965,"teacher_disagreement_score":0.19934665,"about_ca_system_score_codex":0.00033281688,"about_ca_system_score_gemma":0.0003450191,"threshold_uncertainty_score":0.99916065},"labels":[],"label_agreement":null},{"id":"W7089239549","doi":"10.1109/rew66121.2025.00043","title":"Model-Driven Integration of Domain Knowledge into Machine Learning Workflows: A Case for Multidisciplinary System Design","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hitachi (Canada)","funders":"","keywords":"Workflow; Domain (mathematical analysis); Domain knowledge; Multidisciplinary approach; Domain engineering; Knowledge engineering; Expert system; Subject-matter expert; Knowledge-based systems","score_opus":0.03192924940363819,"score_gpt":0.30524717099899357,"score_spread":0.2733179215953554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7089239549","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":[0.0054252413,0.00031307834,0.9925039,0.00018167318,0.00019132855,0.0006892545,0.0000014040598,0.00027861746,0.00041554915],"genre_scores_gemma":[0.50404906,0.0000033498095,0.49547747,0.000010113491,0.000010664874,0.00007545365,0.0000024006638,0.000006471771,0.00036502938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873096,0.00016184685,0.00037791952,0.0004121305,0.00008971398,0.00022742951],"domain_scores_gemma":[0.9987957,0.00047646114,0.00012646908,0.00037597425,0.00016794768,0.00005743354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036320434,0.00017844012,0.00025676138,0.00022130311,0.00025777015,0.000041695195,0.00045061667,0.00008724872,5.77917e-7],"category_scores_gemma":[0.000039898427,0.00014638882,0.00010916639,0.0006966767,0.000043611584,0.00036273393,0.0003202027,0.00018533567,0.0000019719637],"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":[0.00008000224,0.00007247539,0.000056313125,0.00016023406,0.0000276977,0.00004753272,0.0022723798,0.7963272,0.004800232,0.15069498,0.00018927603,0.04527173],"study_design_scores_gemma":[0.00048226526,0.00012025488,0.000003028054,0.00022556787,0.000010627713,0.000045055855,0.00029691204,0.9839813,0.0018334087,0.012825063,0.000041924388,0.00013457933],"about_ca_topic_score_codex":0.00002426017,"about_ca_topic_score_gemma":0.00020563856,"teacher_disagreement_score":0.49862382,"about_ca_system_score_codex":0.00010418851,"about_ca_system_score_gemma":0.000060365433,"threshold_uncertainty_score":0.5969563},"labels":[],"label_agreement":null},{"id":"W7092205149","doi":"10.1145/3725843.3756105","title":"TransFusion: End-to-End Transformer Acceleration via Graph Fusion and Pipelining","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Transformer; Graph; Fusion; Acceleration; Minification","score_opus":0.014496647724907052,"score_gpt":0.26406587171164675,"score_spread":0.2495692239867397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7092205149","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":[0.03839409,0.0014479951,0.94056624,0.008004995,0.00212824,0.0011514387,0.000004565196,0.00023223585,0.008070193],"genre_scores_gemma":[0.9560694,0.0036027294,0.03435198,0.004057728,0.00016350517,0.000048943017,0.000007356834,0.000028546612,0.001669789],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995698,0.00017762433,0.0010040494,0.0016090618,0.0005996326,0.0009116497],"domain_scores_gemma":[0.99820405,0.0002975166,0.00011083478,0.0007577913,0.00021099432,0.00041880162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005072166,0.00062388607,0.0005695462,0.00074851967,0.0010437705,0.00052660826,0.00081899454,0.00033999473,0.00046906472],"category_scores_gemma":[0.000020175463,0.0005899317,0.00024430826,0.0034160158,0.00020250147,0.0017463447,0.00020053734,0.0006780901,0.000026712893],"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":[0.00016399997,0.000093173796,0.00027792848,0.000059365877,0.00003524444,0.000015404947,0.0014798373,0.002009579,0.021559604,0.01306832,0.00041253035,0.960825],"study_design_scores_gemma":[0.0056906147,0.0023945896,0.011518214,0.0013038472,0.0003168309,0.00012786593,0.00050957577,0.79162,0.10273966,0.05644996,0.024319163,0.0030096502],"about_ca_topic_score_codex":0.0000919472,"about_ca_topic_score_gemma":0.0004949104,"teacher_disagreement_score":0.95781535,"about_ca_system_score_codex":0.000051214603,"about_ca_system_score_gemma":0.00011831714,"threshold_uncertainty_score":0.9996552},"labels":[],"label_agreement":null},{"id":"W7095314690","doi":"","title":"Estimation of population size from biased samples using non-parametric binary regression. Statistica Sinica","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Estimator; Sampling (signal processing); Covariate; Abundance estimation; Population; Smoothing; Population size; Sample size determination; Kernel (algebra)","score_opus":0.05778569613823359,"score_gpt":0.3013149440289047,"score_spread":0.2435292478906711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095314690","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":[0.2782099,0.0000918727,0.72118866,0.00008209459,0.0001542706,0.00012393434,0.000010689122,0.00007564162,0.00006296694],"genre_scores_gemma":[0.5408969,0.000011628582,0.45899114,0.00005074151,0.000014140419,0.0000015457532,0.000011256869,0.000005872218,0.000016748872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986074,0.00007820476,0.00041759582,0.00037386623,0.0003208949,0.00020206062],"domain_scores_gemma":[0.99725825,0.0017834121,0.00028799623,0.0005327157,0.000059658334,0.000077988734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008605182,0.00014595097,0.00023066287,0.00018962275,0.00010747547,0.000048208745,0.00036077565,0.00007188671,0.000112810514],"category_scores_gemma":[0.0005412072,0.00012193576,0.00005526706,0.001312273,0.00004321971,0.00061962934,0.00012368902,0.000103863305,0.000010809227],"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":[0.00003232558,0.0002916842,0.0063897106,0.000031142685,0.00003236472,0.000026394948,0.0002138852,0.62610084,0.007833259,0.0061352816,0.0018330725,0.35108006],"study_design_scores_gemma":[0.0002424949,0.000063073276,0.035341844,0.000062693616,0.0000093086455,0.0000021491599,0.0000041927483,0.953013,0.0009345602,0.010178223,0.000010441653,0.00013798998],"about_ca_topic_score_codex":0.00033634302,"about_ca_topic_score_gemma":0.000005675018,"teacher_disagreement_score":0.35094208,"about_ca_system_score_codex":0.000035988163,"about_ca_system_score_gemma":0.000008582972,"threshold_uncertainty_score":0.4972396},"labels":[],"label_agreement":null},{"id":"W7097747698","doi":"","title":"QA Sys t em Met i s Based on Semant i c Gr aph Mat chi ng","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Knowledge graph; Graph; Semantic similarity; Interrogative; Matching (statistics); Semantic matching; Question answering","score_opus":0.02804918809838306,"score_gpt":0.2510899998782033,"score_spread":0.22304081177982024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097747698","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":[0.010905089,0.000059943774,0.9740886,0.0010442728,0.00081243186,0.00021467036,0.0000013728919,0.0006053068,0.012268305],"genre_scores_gemma":[0.8465208,0.0000027142796,0.14875133,0.0037447936,0.000105987114,0.000015160518,0.0000029271346,0.00001731817,0.00083900697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984752,0.00007428047,0.00019226389,0.00046059082,0.00042427977,0.00037336448],"domain_scores_gemma":[0.99869287,0.0000968528,0.000068665366,0.0008379635,0.00006362959,0.00023999656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023733724,0.00018441938,0.00018516454,0.00011692596,0.00007371133,0.00011817152,0.0008212547,0.00005853391,0.00001587023],"category_scores_gemma":[0.000031522035,0.00013954709,0.00007659313,0.0005313,0.000027278926,0.00039554015,0.00020534058,0.00017516637,0.00021536107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012946919,0.00053100835,0.0021612279,0.000060042472,0.000066159824,0.0005396118,0.0011636672,0.30297637,0.0008346855,0.48024604,0.09724104,0.11405069],"study_design_scores_gemma":[0.0006388887,0.00032421824,0.00057399034,0.000039403072,0.0000053235976,0.000019633666,0.000043269065,0.97931796,0.0024061413,0.0120550655,0.004220009,0.00035608327],"about_ca_topic_score_codex":0.000009240438,"about_ca_topic_score_gemma":0.000017597904,"teacher_disagreement_score":0.8356157,"about_ca_system_score_codex":0.000040164654,"about_ca_system_score_gemma":0.000033436114,"threshold_uncertainty_score":0.5690565},"labels":[],"label_agreement":null},{"id":"W7098697210","doi":"","title":"Empirically Estimated “True ” PPP Indexes","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Purchasing power parity; Price index; Index (typography); Measure (data warehouse); Axiom; Purchasing power; Reliability (semiconductor); Key (lock)","score_opus":0.050115698916507245,"score_gpt":0.2791068964436274,"score_spread":0.22899119752712016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098697210","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":[0.069926,0.00008866549,0.92160016,0.0011205492,0.00024118138,0.00009759717,4.2629907e-7,0.0007995479,0.0061258967],"genre_scores_gemma":[0.77356434,0.000030259846,0.22377537,0.0016424904,0.000032628657,0.0000059076538,0.0000011925713,0.0000078077155,0.0009399944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898416,0.000024081073,0.00016467678,0.00032578595,0.0002077992,0.00029348145],"domain_scores_gemma":[0.99922186,0.00008757437,0.000042682706,0.00047970287,0.000053054406,0.00011514086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000043187847,0.00012129355,0.00012435974,0.000069345355,0.00014925853,0.000033910652,0.0007119,0.000043766817,0.000039818915],"category_scores_gemma":[0.000028761326,0.000097757285,0.000048252867,0.0006456948,0.000076040735,0.00048347897,0.00022374293,0.00014207298,0.00014606997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054528653,0.0005873663,0.21404174,0.000023934888,0.00009994262,0.0029103798,0.0013953501,0.04017826,0.0052729864,0.35036644,0.17866902,0.20640005],"study_design_scores_gemma":[0.0013648381,0.00043570693,0.21310231,0.0000419796,0.0000075254256,0.0016869081,0.000008311628,0.68301547,0.010540926,0.06561285,0.022764143,0.0014190589],"about_ca_topic_score_codex":0.000008753206,"about_ca_topic_score_gemma":0.000006590119,"teacher_disagreement_score":0.7036383,"about_ca_system_score_codex":0.000013839364,"about_ca_system_score_gemma":0.000029925506,"threshold_uncertainty_score":0.39864263},"labels":[],"label_agreement":null},{"id":"W7098895584","doi":"","title":"CENTRE FOR THE STUDY OF LIVING STANDARDS MOVING FROM A GDP-BASED TO A WELL-BEING BASED METRIC OF ECONOMIC PERFORMANCE AND SOCIAL PROGRESS: RESULTS FROM THE INDEX OF ECONOMIC WELL-BEING FOR OECD COUNTRIES,","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Index (typography); Economic indicator; Economic impact analysis; Economic analysis; Standard of living; Ninth; Economic data; Economic sector; Economic forecasting","score_opus":0.01361307569009377,"score_gpt":0.2460716826577858,"score_spread":0.23245860696769205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098895584","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":[0.8380325,0.000104846375,0.15862809,0.00034565298,0.0002655207,0.002177433,0.00028447463,0.000042469153,0.00011905633],"genre_scores_gemma":[0.9839987,0.00001017844,0.015616839,0.00015620803,0.00009356749,0.00008245499,0.0000052167834,0.000025637548,0.000011153219],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780107,0.000106301006,0.00084606686,0.0005919813,0.00026465446,0.00038993557],"domain_scores_gemma":[0.9932684,0.0051257173,0.0007418629,0.0006395631,0.00016620778,0.00005825732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011568259,0.00025933143,0.0005097135,0.000181551,0.00033431023,0.00007938057,0.0010939467,0.00007985677,0.000023287681],"category_scores_gemma":[0.00010907464,0.00018186284,0.00013376458,0.00019699782,0.00015701356,0.00038001308,0.000295549,0.00013071105,7.564792e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010984436,0.0011967296,0.5802207,0.00059053424,0.0016054935,0.0000043095683,0.11008922,0.17645049,0.00021696334,0.0127110155,0.0035087222,0.10242141],"study_design_scores_gemma":[0.0032528625,0.00080518395,0.031547524,0.00023779704,0.000117272306,1.9962702e-7,0.0022774322,0.9551993,0.0046225567,0.0011237371,0.00048131304,0.00033482688],"about_ca_topic_score_codex":0.0010894726,"about_ca_topic_score_gemma":0.0015495155,"teacher_disagreement_score":0.7787488,"about_ca_system_score_codex":0.00014221636,"about_ca_system_score_gemma":0.00025718458,"threshold_uncertainty_score":0.7416151},"labels":[],"label_agreement":null},{"id":"W7099272442","doi":"","title":"COLLABORATION IN DISASTER MITIGATION 1 Suggestions for Canadian Inter-Organizational Collaboration in Disaster Mitigation","year":2001,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Disaster mitigation; Emergency management; Key (lock); Risk management; Disaster response; Climate change mitigation","score_opus":0.007525727055850006,"score_gpt":0.2613602399115461,"score_spread":0.2538345128556961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099272442","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":[0.27904636,0.000038747112,0.6986178,0.018907502,0.0006804582,0.0014192499,0.000043837106,0.00011461711,0.0011314282],"genre_scores_gemma":[0.9695446,0.000012481214,0.028247273,0.0009671701,0.00010893704,0.00019888802,0.00043548323,0.000021062679,0.0004641123],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982736,0.000107666914,0.0005056309,0.0004984952,0.00022409028,0.0003904889],"domain_scores_gemma":[0.99882114,0.00016306245,0.00012440546,0.0003027008,0.00042886124,0.0001598213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020971136,0.00019203032,0.00016637152,0.0005886194,0.0001649826,0.0003165141,0.00030308068,0.00014477328,0.000042968895],"category_scores_gemma":[0.00018316797,0.00020798435,0.000029510087,0.0034617053,0.00004869946,0.0025040237,0.000042751,0.00013174843,0.000020366933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008934163,0.000333772,0.29567155,0.00004530778,0.000023999133,0.000026656153,0.01158002,0.07498636,0.0036614763,0.5879204,0.0074142674,0.018246792],"study_design_scores_gemma":[0.0035967804,0.00027817587,0.10051436,0.00022417246,0.000014519754,0.000025765068,0.0026407146,0.7709014,0.0017390728,0.10696155,0.011975037,0.0011284772],"about_ca_topic_score_codex":0.001295008,"about_ca_topic_score_gemma":0.5022535,"teacher_disagreement_score":0.695915,"about_ca_system_score_codex":0.0005307407,"about_ca_system_score_gemma":0.00034426904,"threshold_uncertainty_score":0.8481356},"labels":[],"label_agreement":null},{"id":"W7099591334","doi":"","title":"A profile of income assistance recipients in Winnipeg&amp;apos;s inner city","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inner city; Poverty; Inner Cities; Population","score_opus":0.05487229303393986,"score_gpt":0.31435445735791556,"score_spread":0.2594821643239757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099591334","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":[0.63829607,0.00007556608,0.35679093,0.00046831102,0.0003085028,0.0002457239,0.0000023865823,0.00013307128,0.0036794539],"genre_scores_gemma":[0.8273364,0.000007511585,0.17181958,0.00022564839,0.000018036284,0.000017482955,0.000001568843,0.000007305231,0.00056645845],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848974,0.000041659496,0.00037853813,0.00043249447,0.00031247726,0.00034507224],"domain_scores_gemma":[0.9989572,0.00005852129,0.00013955144,0.00068100134,0.000082985374,0.00008071624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018680363,0.00015024326,0.00023036607,0.00017019361,0.000041855095,0.000026803438,0.0008223948,0.00007592642,0.000024310239],"category_scores_gemma":[0.00005176954,0.0001303847,0.00005774567,0.001550978,0.00008203895,0.0006509289,0.0002999202,0.0002195579,0.000026524487],"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":[0.00021732692,0.0015979744,0.5498417,0.00012161924,0.000057998204,0.0001454625,0.0025332663,0.032097127,0.011845096,0.3264847,0.0013749844,0.073682725],"study_design_scores_gemma":[0.003774454,0.0003116925,0.7875229,0.00048857904,0.000005715125,0.000031618452,0.000021242031,0.0051808483,0.02010127,0.17833586,0.003189958,0.0010358776],"about_ca_topic_score_codex":0.00007467585,"about_ca_topic_score_gemma":0.00048310313,"teacher_disagreement_score":0.23768118,"about_ca_system_score_codex":0.00009631824,"about_ca_system_score_gemma":0.000049580904,"threshold_uncertainty_score":0.53169334},"labels":[],"label_agreement":null},{"id":"W7099777008","doi":"","title":"AUTHENTICITY IN NINETEENTH-CENTURY BACH INTERPRETATION","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interpretation (philosophy); Promotion (chess); Work (physics)","score_opus":0.006566258521323059,"score_gpt":0.22692349706451515,"score_spread":0.22035723854319209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099777008","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":[0.061127678,0.000054853826,0.9351596,0.0009508208,0.00032017476,0.000090651745,2.963735e-7,0.00013845063,0.0021574378],"genre_scores_gemma":[0.98088557,0.000032812302,0.018447872,0.00028302753,0.00001819937,0.0000070409073,2.8311587e-7,0.000003971461,0.00032122774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999216,0.000041776893,0.00015737317,0.00025999785,0.00012001865,0.00020479901],"domain_scores_gemma":[0.99948406,0.00011649034,0.00003960576,0.0002912297,0.000021858325,0.000046750294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087435124,0.00007816846,0.00007868122,0.00008806546,0.000018897417,0.000025954094,0.00041358772,0.00003198438,0.00003843243],"category_scores_gemma":[0.000031210766,0.000048577698,0.000032079788,0.00028233376,0.000026409229,0.0006186309,0.0001478309,0.000057665635,0.00007003536],"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":[0.00002248284,0.00008153347,0.012342999,0.0000052324103,0.0000071190316,0.000018693738,0.00048687187,0.00021912978,0.005892825,0.16456191,0.00092654803,0.81543463],"study_design_scores_gemma":[0.0038699743,0.00040091833,0.1977113,0.00044624828,0.0000089285195,0.000039775725,0.00006271379,0.6039632,0.019859174,0.1613622,0.010868827,0.0014067353],"about_ca_topic_score_codex":0.0000051169854,"about_ca_topic_score_gemma":0.000021243895,"teacher_disagreement_score":0.9197579,"about_ca_system_score_codex":0.000033025844,"about_ca_system_score_gemma":0.00000749481,"threshold_uncertainty_score":0.1980941},"labels":[],"label_agreement":null},{"id":"W7102416460","doi":"10.1016/j.ipm.2025.104460","title":"Text-free inductive knowledge graph embedding via meta graph-based prompt learning","year":2025,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Advanced Graph Neural Networks","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":"York University","funders":"Central China Normal University; China Postdoctoral Science Foundation; National Social Science Fund of China; Natural Science Foundation of Hubei Province; Ministry of Education of the People's Republic of China; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Embedding; Graph; Knowledge graph; Node (physics); Inductive bias; Construct (python library); Bridge (graph theory); Entity linking","score_opus":0.013638960100838081,"score_gpt":0.2767357352978942,"score_spread":0.2630967751970561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7102416460","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":[0.00042733946,0.0005504582,0.980009,0.00070005486,0.00034091255,0.0005821279,6.6215625e-7,0.00070336135,0.016686091],"genre_scores_gemma":[0.7791265,0.000043774686,0.21805738,0.0015978555,0.00003451382,0.00046452586,0.000024762952,0.000019646599,0.00063100166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809796,0.00007808366,0.0005989728,0.00037994352,0.0004015774,0.00044347998],"domain_scores_gemma":[0.9985432,0.00006226667,0.00040440235,0.00064350205,0.00027445104,0.00007215956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047446776,0.00030464403,0.00028247805,0.0011377578,0.00062713306,0.000635245,0.0013662574,0.00008073502,0.0000070056885],"category_scores_gemma":[0.00003712722,0.00027945492,0.0001572667,0.0029699851,0.00008529184,0.0049253954,0.0007211422,0.0003795154,0.000030679974],"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":[0.000016663289,0.00006504763,0.000062112085,0.0005009265,0.00025132723,0.0000036257072,0.0011285393,0.07999313,0.000018291892,0.041675467,0.001642125,0.8746427],"study_design_scores_gemma":[0.0017863279,0.00009006985,0.0013746743,0.00053010584,0.00031350448,0.0000041763783,0.0004346938,0.7905546,0.0010743735,0.12826543,0.07473745,0.0008345998],"about_ca_topic_score_codex":0.0000032404748,"about_ca_topic_score_gemma":0.000001962391,"teacher_disagreement_score":0.87380815,"about_ca_system_score_codex":0.00009401307,"about_ca_system_score_gemma":0.000051131043,"threshold_uncertainty_score":0.9999658},"labels":[],"label_agreement":null},{"id":"W7103204158","doi":"","title":"Tractable Shapley Values and Interactions via Tensor Networks","year":2025,"lang":"","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Multilinear map; Computation; Key (lock); Enumeration; Tensor (intrinsic definition); Scheme (mathematics)","score_opus":0.03514825441037215,"score_gpt":0.19567274841162163,"score_spread":0.1605244940012495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7103204158","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":[0.061032463,0.0009802717,0.93152004,0.00047530152,0.0019337459,0.00039244845,0.0000055619344,0.00023695195,0.0034232019],"genre_scores_gemma":[0.9788123,0.0015539252,0.0017021785,0.00065417215,0.00013257285,0.0000011003609,0.000003611081,0.000023043394,0.017117146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967786,0.0002491991,0.00039240188,0.0016123613,0.00008497303,0.00088247197],"domain_scores_gemma":[0.9974479,0.00060316926,0.00026824677,0.0010961761,0.00024515518,0.0003393454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022360601,0.0005051184,0.0004939881,0.000491124,0.0009229669,0.00029314702,0.001209057,0.0002489573,0.00008793463],"category_scores_gemma":[0.000049903556,0.0006041736,0.00024195749,0.0031717955,0.000480027,0.0022681165,0.0010115501,0.00094208185,0.000039681356],"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":[0.00021503044,0.0003308997,0.009319805,0.00005916818,0.00030991665,0.00047562667,0.00025241319,0.8096412,0.00016680261,0.13983154,0.0027834557,0.036614157],"study_design_scores_gemma":[0.0007720912,0.00011237883,0.0043288576,0.00019033461,0.00017631153,0.0000273334,0.00012077428,0.9496919,0.0000610941,0.041919727,0.0020906802,0.00050854444],"about_ca_topic_score_codex":0.00008129171,"about_ca_topic_score_gemma":0.00008859063,"teacher_disagreement_score":0.92981786,"about_ca_system_score_codex":0.00018008568,"about_ca_system_score_gemma":0.00009252823,"threshold_uncertainty_score":0.99964094},"labels":[],"label_agreement":null},{"id":"W7114821263","doi":"","title":"Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Graph; Node (physics); Training set; Generalization; Benchmark (surveying); Bridge (graph theory); Feature (linguistics); Feature learning","score_opus":0.025778387898292104,"score_gpt":0.26439843270514785,"score_spread":0.23862004480685575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114821263","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":[0.0867674,0.00044039713,0.89981186,0.009317706,0.0015447704,0.0014610447,0.00003234905,0.0002606485,0.00036385018],"genre_scores_gemma":[0.97002983,0.00014615673,0.025376962,0.0027440412,0.00018751799,0.0002473234,0.00008980163,0.00005763199,0.0011207056],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99605495,0.00014991146,0.00081745087,0.0015620806,0.00042505094,0.0009905868],"domain_scores_gemma":[0.99642867,0.00036642095,0.0005486265,0.0017144533,0.0006879363,0.00025387292],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000262925,0.0006047541,0.00056187314,0.00042985022,0.0009788235,0.0002556073,0.0014972119,0.00026195415,0.000016181646],"category_scores_gemma":[0.00007622975,0.00058929913,0.00021890888,0.003501041,0.00043913713,0.0006923182,0.00031447178,0.00052337506,0.000026642372],"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":[0.00021754183,0.00034333344,0.802028,0.0002284733,0.0003335897,0.000021579051,0.00059365656,0.021577077,0.009875604,0.14355184,0.0010981674,0.020131135],"study_design_scores_gemma":[0.0026591516,0.00048310874,0.50798965,0.00044736153,0.00025236362,0.000016935111,0.00008939948,0.45994458,0.0046397164,0.01781189,0.00447832,0.0011875177],"about_ca_topic_score_codex":0.00004507139,"about_ca_topic_score_gemma":0.0002658511,"teacher_disagreement_score":0.88326246,"about_ca_system_score_codex":0.00014536711,"about_ca_system_score_gemma":0.0003106334,"threshold_uncertainty_score":0.99965584},"labels":[],"label_agreement":null},{"id":"W7115001468","doi":"10.1007/s13278-025-01514-y","title":"An LLM-guided framework for link prediction in homogeneous graphs","year":2025,"lang":"en","type":"article","venue":"Social Network Analysis and Mining","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University; Ontario Forest Research Institute; University of Windsor","funders":"","keywords":"Homogeneous; Intersection (aeronautics); Graph; Link (geometry); Social network (sociolinguistics); Similarity (geometry); Social network analysis","score_opus":0.01413249501823728,"score_gpt":0.29955431168627084,"score_spread":0.28542181666803357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115001468","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":[0.07683783,0.00072616764,0.92122215,0.0004461732,0.00033257782,0.00017735579,0.0000034719853,0.00011008974,0.00014417924],"genre_scores_gemma":[0.8358579,0.000108849905,0.16292132,0.00052663096,0.0004424121,0.000052044026,0.000021223155,0.000009049779,0.0000605293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841917,0.00010490045,0.0003719351,0.0005455857,0.00013042112,0.00042797474],"domain_scores_gemma":[0.9991576,0.00028554443,0.00013488714,0.00028236635,0.000071873095,0.000067735666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041171417,0.00016586749,0.00039164242,0.0003626546,0.00050865026,0.00014571834,0.0003515508,0.00019792093,0.0000021971848],"category_scores_gemma":[0.00003667929,0.00017345168,0.00022391965,0.0042040288,0.000058800357,0.00022877056,0.00009542169,0.00019126128,2.0917876e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042508465,0.00008731976,0.11656113,0.000020836787,0.000778742,0.000012180454,0.0020014718,0.15861192,0.00003815306,0.29972383,0.001387214,0.42073467],"study_design_scores_gemma":[0.00040247288,0.00011070369,0.0402064,0.00005940711,0.00039783173,0.0000010068761,0.00015903417,0.6401159,0.0000142061745,0.3174269,0.00082544313,0.00028067303],"about_ca_topic_score_codex":0.000017162909,"about_ca_topic_score_gemma":0.00023048987,"teacher_disagreement_score":0.7590201,"about_ca_system_score_codex":0.000027893138,"about_ca_system_score_gemma":0.00002747499,"threshold_uncertainty_score":0.70731544},"labels":[],"label_agreement":null},{"id":"W7115813723","doi":"","title":"DOMAIN-SPECIFIC ADAPTATION AND MULTI-HOP REASONING IN CHEMISTRY AND BIOMEDICINE","year":2025,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Mitacs; McMaster University","keywords":"Biomedicine; Adaptation (eye); Pipeline (software); Bridge (graph theory); Embedding; Domain (mathematical analysis)","score_opus":0.013363919174536894,"score_gpt":0.21094524971601417,"score_spread":0.19758133054147728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115813723","genre_codex":"methods","genre_gemma":"other","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":[0.089522354,0.00947964,0.70176935,0.0004075936,0.0011515212,0.0010674539,0.000019711697,0.00041018432,0.19617216],"genre_scores_gemma":[0.028718444,0.0045606256,0.29893276,0.00016278184,0.00015225542,0.000006646961,0.00033421876,0.0000689878,0.6670633],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986646,0.00004977618,0.00018563583,0.00068808644,0.00015444472,0.00025744538],"domain_scores_gemma":[0.99935466,0.000074401716,0.00015738545,0.0002566731,0.00005842456,0.00009843288],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006527372,0.00027460215,0.00029096028,0.00016154468,0.00011331072,0.00007342035,0.00038980637,0.00024463166,0.0003068966],"category_scores_gemma":[0.0000048876573,0.00031585846,0.000037451326,0.0010064207,0.000056799665,0.0003794716,0.00017295286,0.0003706254,0.0000013076783],"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":[0.00011165722,0.00003378894,0.0004955572,0.00023944471,0.000029505623,0.00027499293,0.0018703192,0.00012824993,0.001762757,0.0051477384,0.00007912823,0.98982686],"study_design_scores_gemma":[0.010689801,0.00021423682,0.019263694,0.0049746395,0.00012159992,0.000060799335,0.026936868,0.066922516,0.0018265119,0.0027726667,0.86371684,0.0024998442],"about_ca_topic_score_codex":0.000033720084,"about_ca_topic_score_gemma":0.00042315436,"teacher_disagreement_score":0.98732704,"about_ca_system_score_codex":0.000090775495,"about_ca_system_score_gemma":0.000046494642,"threshold_uncertainty_score":0.99992937},"labels":[],"label_agreement":null},{"id":"W7116390015","doi":"10.1016/j.asoc.2025.114506","title":"Contrastive learning with transformers for meta-path-free heterogeneous graph embedding","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Graph Neural Networks","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":"Mount Royal University","funders":"","keywords":"Graph; Feature learning; Embedding; Cluster analysis; Graph embedding; Transformer; Clustering coefficient; Artificial neural network","score_opus":0.011827145870331337,"score_gpt":0.2519593064691699,"score_spread":0.24013216059883857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116390015","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":[0.0065011107,0.0005423174,0.9902497,0.00021077847,0.0002035698,0.00078043534,0.000002425317,0.0005983108,0.0009113784],"genre_scores_gemma":[0.77552253,0.000008154584,0.22369362,0.00055421353,0.000037979586,0.00012744595,0.000004360522,0.000026112331,0.00002555922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977457,0.00004780663,0.0003851161,0.0008208625,0.0002544363,0.0007460811],"domain_scores_gemma":[0.9981426,0.0010782516,0.00019032677,0.00037100245,0.00011018212,0.00010760978],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035201278,0.00037407715,0.0005667648,0.00019492519,0.00067476084,0.00016371961,0.0008902502,0.000098326054,0.0000016970848],"category_scores_gemma":[0.00003133307,0.00032058838,0.000254664,0.00086804904,0.00011000907,0.00019321029,0.00022289257,0.00042188665,0.0000015675772],"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":[0.00014186122,0.000049994935,0.00010501499,0.000070701346,0.0011683125,0.000019915227,0.00077198684,0.7057138,0.0012689442,0.07314187,0.00022081952,0.21732679],"study_design_scores_gemma":[0.0032036735,0.00029872765,0.000077522745,0.00011524344,0.00044037154,0.000036411562,0.00021666802,0.95242417,0.0061594015,0.033011686,0.0031347948,0.0008813311],"about_ca_topic_score_codex":0.000003080287,"about_ca_topic_score_gemma":0.00000445106,"teacher_disagreement_score":0.76902145,"about_ca_system_score_codex":0.000043223416,"about_ca_system_score_gemma":0.00004417398,"threshold_uncertainty_score":0.9999246},"labels":[],"label_agreement":null},{"id":"W7117357404","doi":"10.1016/j.jprocont.2025.103614","title":"Robust soft sensing with causal and injectivity-preserving Graph Neural Network","year":2025,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Centre for Clean Coal/Carbon and Mineral Processing Technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Graph; A priori and a posteriori; Benchmark (surveying); Artificial neural network; Noise (video); Graph theory; Pattern recognition (psychology)","score_opus":0.009426928147155785,"score_gpt":0.23622733935786902,"score_spread":0.22680041121071323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117357404","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":[0.09778803,0.0015801486,0.8982681,0.0017093288,0.00037382107,0.00013970201,3.4713798e-7,0.00004367157,0.000096850905],"genre_scores_gemma":[0.9806293,0.000020404228,0.018180883,0.00089600036,0.00023890518,0.0000014744614,7.661438e-8,0.000010661407,0.000022251128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985424,0.0001166983,0.0003778829,0.00026272406,0.00031271015,0.00038754914],"domain_scores_gemma":[0.998379,0.0004089852,0.0004368213,0.00023588006,0.0004172904,0.00012204266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003883456,0.0001924553,0.00040213077,0.00020783715,0.00022884106,0.00022623762,0.00053947675,0.000064136206,7.2222906e-7],"category_scores_gemma":[0.000119417266,0.00014322757,0.000070312366,0.0009145186,0.000083581115,0.0012578865,0.00012035715,0.00055829284,1.262732e-7],"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":[0.00035575725,0.000037129783,0.02245979,0.000076888544,0.00016666001,0.00027926007,0.00021666192,0.92228496,0.00023545307,0.0014341864,0.00050505385,0.05194817],"study_design_scores_gemma":[0.003324216,0.00047475073,0.01041066,0.0005384633,0.0000987785,0.0008600795,0.00007689377,0.95247626,0.00014444694,0.031115798,0.00015696193,0.0003227032],"about_ca_topic_score_codex":0.0000042055362,"about_ca_topic_score_gemma":0.000036016198,"teacher_disagreement_score":0.8828413,"about_ca_system_score_codex":0.000022610773,"about_ca_system_score_gemma":0.000113997405,"threshold_uncertainty_score":0.5840651},"labels":[],"label_agreement":null},{"id":"W7117726295","doi":"10.1145/3773274.3774662","title":"A Hybrid GraphRAG Framework for Geospatial Contextualization in Decision Support Systems","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Contextualization; Geospatial analysis; Flexibility (engineering); Decision support system; Architecture; Graph; Knowledge representation and reasoning; Face (sociological concept); Bayesian network","score_opus":0.015071684943805595,"score_gpt":0.3036364563265237,"score_spread":0.2885647713827181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117726295","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":[0.0041748616,0.0015630875,0.9812174,0.0005349174,0.009037475,0.002753363,0.000026828884,0.00018513972,0.0005069353],"genre_scores_gemma":[0.87235713,0.0003724948,0.12435076,0.0014763047,0.00019809265,0.0002868976,0.000025889038,0.00003287031,0.0008995388],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99515235,0.00020624622,0.0015826578,0.0014794141,0.0005535904,0.0010257616],"domain_scores_gemma":[0.99534744,0.0023632648,0.00040638508,0.0012382524,0.00045307053,0.00019160652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008136277,0.00053419947,0.0007981434,0.0008788646,0.00032799804,0.00058650837,0.0014521431,0.00037931546,0.000045230718],"category_scores_gemma":[0.00072490063,0.0005347242,0.0003048289,0.0025162043,0.00014152643,0.0009744823,0.0005160194,0.00051417615,0.000020620191],"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":[0.00026062684,0.00016645716,0.0016031059,0.00012299085,0.00003675334,0.00003293263,0.000202369,0.030813511,0.000011558911,0.76243055,0.0048870463,0.19943209],"study_design_scores_gemma":[0.0014363767,0.0003051345,0.0007732665,0.0010692552,0.000023791099,0.000011881566,0.00006375152,0.71876585,0.00017407228,0.27247804,0.004467303,0.00043126883],"about_ca_topic_score_codex":0.00010735951,"about_ca_topic_score_gemma":0.00017489487,"teacher_disagreement_score":0.8681823,"about_ca_system_score_codex":0.00016073915,"about_ca_system_score_gemma":0.00028068313,"threshold_uncertainty_score":0.99971044},"labels":[],"label_agreement":null},{"id":"W7117985678","doi":"10.1145/3712285.3787177","title":"10.1145/3712285.3787177","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Training (meteorology); Artificial neural network; Graph","score_opus":0.00477333206998846,"score_gpt":0.17575072235871814,"score_spread":0.17097739028872969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117985678","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034814025,0.000081798156,0.0018407066,0.00081266946,0.000010221933,0.00018397688,0.0000020356176,0.00052242813,0.996198],"genre_scores_gemma":[0.00049497763,6.0018976e-7,0.008446221,0.0003574943,0.000099299155,0.000014226862,0.0000020809389,0.000015036106,0.99057007],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988084,0.00003620937,0.00016157753,0.00039470522,0.00021458551,0.00038451396],"domain_scores_gemma":[0.9990147,0.00006400029,0.000028013586,0.00067194604,0.000034124136,0.00018721327],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008198028,0.00015099284,0.00014516964,0.000073250376,0.00009464093,0.00008128224,0.00092820247,0.00004990106,0.8858256],"category_scores_gemma":[0.000012473345,0.00014576079,0.000062007544,0.0006205717,0.00002857264,0.0003743806,0.00014596376,0.00012396158,0.9555983],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011258476,0.000020303265,1.4574385e-7,0.0000010532126,0.000004390624,0.000015244632,0.00001241593,0.0018053605,0.000046452238,0.00005989732,0.0798843,0.91813916],"study_design_scores_gemma":[0.00012694934,0.00010785877,0.000033697746,0.00000883911,0.0000023482705,0.000015186103,8.1568146e-8,0.011008872,0.000113097776,0.0002707144,0.9881198,0.00019255807],"about_ca_topic_score_codex":0.0000033386946,"about_ca_topic_score_gemma":7.692909e-8,"teacher_disagreement_score":0.91794664,"about_ca_system_score_codex":0.00002092461,"about_ca_system_score_gemma":0.000014495703,"threshold_uncertainty_score":0.5943953},"labels":[],"label_agreement":null},{"id":"W7124274261","doi":"10.65109/uxfg1018","title":"Sum-Product-Max Networks for Tractable Decision Making: (Extended Abstract)","year":2016,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Context (archaeology); Set (abstract data type); Stability (learning theory); Decision theory; Feature (linguistics)","score_opus":0.024526551018238387,"score_gpt":0.29098619774469187,"score_spread":0.2664596467264535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124274261","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":[0.0045179115,0.0027780621,0.9781532,0.0030707456,0.00704632,0.0018383833,0.000020208547,0.0004776192,0.0020975403],"genre_scores_gemma":[0.84994215,0.000891671,0.14177208,0.0009450605,0.001565395,0.00010512427,0.000003142802,0.00011131707,0.004664051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99169016,0.0000965862,0.0016604463,0.0029885778,0.00093324395,0.0026309807],"domain_scores_gemma":[0.9912713,0.0037079959,0.0008060654,0.0029538148,0.00068632065,0.00057450146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012779013,0.0010162359,0.00095821486,0.00032336908,0.00080250134,0.0006694083,0.0030153631,0.0004961623,0.00069855456],"category_scores_gemma":[0.000617459,0.0007067444,0.0006615832,0.0014096656,0.00037058722,0.0035610844,0.0008490193,0.00060298434,0.00021806169],"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":[0.0004871117,0.00027466868,0.00017820961,0.000027930011,0.000061871244,0.000039401417,0.000057587284,0.010756511,0.0010492068,0.01712909,0.02263023,0.9473082],"study_design_scores_gemma":[0.0063570226,0.0014405898,0.018604998,0.0022506493,0.00015665499,0.00023615768,0.000041736937,0.65420556,0.006293447,0.20249501,0.10398719,0.0039310018],"about_ca_topic_score_codex":0.000007159465,"about_ca_topic_score_gemma":0.00006504231,"teacher_disagreement_score":0.9433772,"about_ca_system_score_codex":0.00021816284,"about_ca_system_score_gemma":0.00019964666,"threshold_uncertainty_score":0.99953836},"labels":[],"label_agreement":null},{"id":"W7125482623","doi":"10.1109/healthcom60686.2025.11343158","title":"CHARM: Leveraging Reused Medical Knowledge Graphs and LLMs for Community Health Resource Recommendation","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","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 Guelph; Algoma University","funders":"","keywords":"Resource (disambiguation); Community health; Health care; Key (lock); Public health; The Internet","score_opus":0.049249382275130295,"score_gpt":0.35348972798559075,"score_spread":0.30424034571046044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125482623","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":[0.0052396953,0.0037021122,0.91672784,0.06728118,0.0015748793,0.0013463612,0.000011291372,0.0003662299,0.0037503848],"genre_scores_gemma":[0.9118237,0.0045111687,0.04150111,0.038040295,0.00019514898,0.00017931269,0.00012328943,0.000069300884,0.0035567156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950524,0.0016873555,0.0010576572,0.00091701315,0.0003176803,0.00096791086],"domain_scores_gemma":[0.99480945,0.002843935,0.00034836412,0.0012227307,0.00021420023,0.0005613006],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.004231041,0.00045095285,0.0006718937,0.00048167427,0.0024525332,0.0002823882,0.0016302547,0.00028287419,0.000060706752],"category_scores_gemma":[0.0005757796,0.00045614646,0.0001706333,0.0018337892,0.0003655118,0.00060603116,0.0017642638,0.0016130247,0.0000041373582],"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":[0.00007480132,0.00031641623,0.000402074,0.0003634887,0.00006432203,0.0000011230162,0.0028732703,0.000041096137,0.000011371792,0.08954658,0.044268634,0.8620368],"study_design_scores_gemma":[0.0034365016,0.0006468165,0.0017967945,0.0011562371,0.000029041996,0.000021374299,0.00079422264,0.79124355,0.00016420214,0.07048604,0.1295998,0.0006254268],"about_ca_topic_score_codex":0.00023846589,"about_ca_topic_score_gemma":0.000844209,"teacher_disagreement_score":0.90658396,"about_ca_system_score_codex":0.00018350725,"about_ca_system_score_gemma":0.00050910184,"threshold_uncertainty_score":0.999789},"labels":[],"label_agreement":null},{"id":"W7125612209","doi":"10.1109/cascon66301.2025.00043","title":"Intrinsic Defenses Against Backdoor Attacks in High-Order Graph Neural Networks via Semantic and Outlier-Guided Subgraph Policies","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Backdoor; Camouflage; Leverage (statistics); Cluster analysis; Embedding; Graph; Exploit; Artificial neural network","score_opus":0.012110788314365177,"score_gpt":0.25928912104783297,"score_spread":0.2471783327334678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125612209","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":[0.5180361,0.008628781,0.4611998,0.005130241,0.0039185886,0.0016039445,0.000007129484,0.0004904646,0.0009849475],"genre_scores_gemma":[0.9766129,0.0049889036,0.007873633,0.0094367,0.0002624907,0.00007174744,0.000016985383,0.00008766882,0.00064896024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99108714,0.00069577526,0.0021885063,0.0026217008,0.000682276,0.0027245774],"domain_scores_gemma":[0.99519926,0.0010539173,0.00054089696,0.0020817057,0.00055449834,0.0005697468],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00064203126,0.0015328998,0.0017042151,0.0022887674,0.00078311603,0.0009881504,0.0021021755,0.0007235552,0.000035562713],"category_scores_gemma":[0.0001728381,0.0014902741,0.00043640012,0.011332237,0.0013950878,0.0016444911,0.0023426923,0.0017960672,0.000015968357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029017645,0.0008165029,0.17989556,0.00045366987,0.0005353832,0.00060238637,0.0012548935,0.33674645,0.00039089148,0.06536163,0.009665416,0.40398702],"study_design_scores_gemma":[0.0030776632,0.00029558467,0.13323896,0.00048211458,0.00012163455,0.00006235847,0.00015281144,0.8428958,0.00017787529,0.017425317,0.0003719638,0.0016979332],"about_ca_topic_score_codex":0.00089156925,"about_ca_topic_score_gemma":0.002848102,"teacher_disagreement_score":0.50614935,"about_ca_system_score_codex":0.00013211358,"about_ca_system_score_gemma":0.00015379446,"threshold_uncertainty_score":0.999742},"labels":[],"label_agreement":null},{"id":"W7125614367","doi":"10.1109/cascon66301.2025.00106","title":"Generating Labeled Graphs Using Conditional Wasserstein GANs","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Discriminator; Graph; Adjacency matrix; Generator (circuit theory); Adjacency list; Class (philosophy); Node (physics); Source code; Feature (linguistics)","score_opus":0.022151605167094995,"score_gpt":0.28992080758811356,"score_spread":0.26776920242101854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125614367","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":[0.1079936,0.0010879013,0.88498396,0.0010761274,0.0020463618,0.0003805971,0.000010200835,0.00021714359,0.0022041276],"genre_scores_gemma":[0.7976839,0.0000627415,0.19568925,0.00409065,0.00015398195,0.000012727046,0.000013341008,0.000020928444,0.0022724813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963138,0.00024568298,0.00083100883,0.0011685106,0.00049973733,0.00094127347],"domain_scores_gemma":[0.9980235,0.00033453072,0.0002585762,0.0008538581,0.00032772095,0.00020178044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031764884,0.0004887427,0.00046765126,0.0004700287,0.0010631976,0.0005379814,0.0011111574,0.00023274876,0.00020234227],"category_scores_gemma":[0.0000655355,0.00050864887,0.00028175814,0.00318692,0.00027490966,0.0011637663,0.0006420765,0.00056824053,0.000025429339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015430574,0.0001260792,0.0013831636,0.000051206207,0.0001646852,0.000066068555,0.00012586289,0.24604739,0.046387278,0.69295174,0.0019326819,0.010748394],"study_design_scores_gemma":[0.00074461376,0.000059417194,0.00019890274,0.00016458506,0.000049020997,0.000023187122,0.00005456088,0.9333065,0.007742393,0.056744296,0.000435607,0.00047689225],"about_ca_topic_score_codex":0.000040243765,"about_ca_topic_score_gemma":0.000066633715,"teacher_disagreement_score":0.6896903,"about_ca_system_score_codex":0.00013679826,"about_ca_system_score_gemma":0.00034525915,"threshold_uncertainty_score":0.9997365},"labels":[],"label_agreement":null},{"id":"W7125647762","doi":"10.1109/cascon66301.2025.00055","title":"GrIIM: Graph-Based IED Impact Assessment Using MCDM","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Multiple-criteria decision analysis; Risk assessment; Work (physics); Matching (statistics); Process (computing); Fuzzy logic","score_opus":0.025683518980690094,"score_gpt":0.366773346043733,"score_spread":0.3410898270630429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125647762","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":[0.039493125,0.000704196,0.94974005,0.0018829473,0.003049181,0.0007884356,0.00000753013,0.0003528857,0.0039816615],"genre_scores_gemma":[0.8323043,0.000052967996,0.16348055,0.0034763082,0.00010928688,0.00001576774,0.0000051407565,0.000030224248,0.0005254191],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947945,0.00038437606,0.0009908074,0.0015714418,0.00076778437,0.0014910895],"domain_scores_gemma":[0.99635917,0.00049159105,0.00037904363,0.0020184529,0.00033293836,0.0004187763],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055157323,0.0008122099,0.00076463644,0.00090123655,0.0006048452,0.0007666429,0.0019711682,0.0003025106,0.00024393403],"category_scores_gemma":[0.000039417984,0.000718224,0.0007745988,0.0057452973,0.0003336756,0.0011538486,0.0007663283,0.000896924,0.000017590519],"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":[0.00015349613,0.00094851764,0.016058201,0.00017229923,0.00054508285,0.00020814329,0.00013807544,0.7432892,0.012005864,0.108241156,0.006450318,0.11178965],"study_design_scores_gemma":[0.0014026442,0.00029393836,0.012039875,0.00030614052,0.000090023,0.000009437894,0.000017172852,0.96437496,0.0022482204,0.017828327,0.0006612732,0.0007279905],"about_ca_topic_score_codex":0.00023898343,"about_ca_topic_score_gemma":0.000050844406,"teacher_disagreement_score":0.7928112,"about_ca_system_score_codex":0.0005332927,"about_ca_system_score_gemma":0.0013724368,"threshold_uncertainty_score":0.99952686},"labels":[],"label_agreement":null},{"id":"W7125934732","doi":"10.1109/smc58881.2025.11343641","title":"GEE: Graphormer-Enhanced Encoder Model for Anomaly Detection in Weighted Signed Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"New York Institute of Technology","funders":"","keywords":"Encoder; Embedding; Autoencoder; Encoding (memory); Anomaly detection; Enhanced Data Rates for GSM Evolution","score_opus":0.010632243236059618,"score_gpt":0.2554995147253244,"score_spread":0.24486727148926477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125934732","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":[0.010149031,0.0017152622,0.9801571,0.0005370877,0.0029828714,0.0024744752,0.0000052619134,0.0003643552,0.0016145948],"genre_scores_gemma":[0.92008233,0.0004973217,0.073073864,0.0015391117,0.0001338371,0.00049006793,0.000008029107,0.00004986257,0.0041255574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99400294,0.00021393124,0.0014675875,0.0021322933,0.00040304763,0.0017802157],"domain_scores_gemma":[0.9970334,0.0005400179,0.00038979773,0.0013445145,0.00043728363,0.00025497784],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058683514,0.00084857957,0.000886432,0.001103552,0.00059613795,0.00034292415,0.0015809224,0.00072072,0.000022369086],"category_scores_gemma":[0.000064683605,0.00087567937,0.00051316776,0.0058910325,0.00024083735,0.0016311283,0.00051740685,0.0008636623,0.000006628645],"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":[0.000511367,0.00025137106,0.00009096116,0.0000680176,0.00008492444,0.000006284145,0.00025277943,0.6727996,0.0070826085,0.015322613,0.0005214147,0.30300808],"study_design_scores_gemma":[0.002340188,0.00029999943,0.00028441334,0.00018502986,0.000052588228,0.000002624172,0.000028615104,0.92057246,0.0163077,0.059018306,0.00010613116,0.000801953],"about_ca_topic_score_codex":0.00005408904,"about_ca_topic_score_gemma":0.0013321502,"teacher_disagreement_score":0.9099333,"about_ca_system_score_codex":0.00026107125,"about_ca_system_score_gemma":0.0002421766,"threshold_uncertainty_score":0.9993694},"labels":[],"label_agreement":null},{"id":"W7125961461","doi":"10.21428/594757db.9a835c96","title":"Fast Graph Neural Network for Image Classification","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Preprocessor; Voronoi diagram; Convolutional neural network; Contextual image classification; Pattern recognition (psychology); Graph; Delaunay triangulation; Pixel; Benchmark (surveying)","score_opus":0.01708352924429125,"score_gpt":0.27772869100038944,"score_spread":0.2606451617560982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125961461","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":[0.00074535224,0.000099874305,0.9885291,0.0034448954,0.00083194335,0.00032670907,0.0000011007736,0.0003521899,0.0056688664],"genre_scores_gemma":[0.3273828,0.000024295334,0.66519487,0.003734391,0.00020688957,0.00015121429,0.000011683149,0.000012696008,0.0032811176],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989748,0.000027979106,0.00019267108,0.00038304838,0.00009028383,0.00033120823],"domain_scores_gemma":[0.99912107,0.00018049403,0.00005814725,0.00050436286,0.00008761955,0.000048291004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001085231,0.000117375625,0.00011653892,0.00008131169,0.00018184245,0.00011938073,0.0006656039,0.000049229566,0.000003611303],"category_scores_gemma":[0.000018840796,0.000103242906,0.000097453034,0.00091748993,0.000047865025,0.00047950825,0.00012797701,0.00009958732,0.0000069949842],"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":[0.000011050207,0.000020834006,0.00050527335,0.000009891035,0.00001096484,0.0000014646308,0.000015579035,0.006434374,0.00087767036,0.8236871,0.053962745,0.114463076],"study_design_scores_gemma":[0.00032407994,0.00004397527,0.007066418,0.000012869636,0.0000062898634,0.0000025721645,0.000010673631,0.7985277,0.00043511382,0.18429963,0.009097262,0.0001734149],"about_ca_topic_score_codex":0.0000021565938,"about_ca_topic_score_gemma":0.000011951784,"teacher_disagreement_score":0.79209334,"about_ca_system_score_codex":0.000015004999,"about_ca_system_score_gemma":0.000016916818,"threshold_uncertainty_score":0.42101234},"labels":[],"label_agreement":null},{"id":"W7126407239","doi":"10.21428/594757db.d853176e","title":"SAGNN+CS: A Resilient Graph Neural Network Framework Against Backdoor Threats","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Backdoor; Leverage (statistics); Adversarial system; Vulnerability (computing); Resilience (materials science); Graph; Robustness (evolution); Vulnerability assessment","score_opus":0.0122206421476452,"score_gpt":0.2719084881466722,"score_spread":0.259687845999027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126407239","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":[0.01040602,0.0029607138,0.9571895,0.0044334303,0.0027338532,0.0005093373,0.0000013739177,0.0010191007,0.020746646],"genre_scores_gemma":[0.6545344,0.0005929435,0.31823227,0.02197322,0.00045760162,0.00008847842,0.000006427159,0.00003635657,0.004078298],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99705535,0.00013568942,0.00047649778,0.000987215,0.00037392424,0.00097132014],"domain_scores_gemma":[0.99743176,0.0005561775,0.00011984484,0.0015841475,0.000101540376,0.0002065595],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022427933,0.0003775282,0.0003727092,0.00020442219,0.00037713724,0.00025157377,0.001775626,0.00021168488,0.000027658409],"category_scores_gemma":[0.00006241983,0.00032170906,0.00024365644,0.0030289388,0.0001382895,0.0005908849,0.0008188691,0.00062791427,0.000060203616],"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":[0.00004120521,0.00009177943,0.0062395385,0.000019949584,0.00006237356,0.000082688304,0.00010007546,0.11021374,0.000052273914,0.6748276,0.041616615,0.1666522],"study_design_scores_gemma":[0.0006272528,0.0001565412,0.009825911,0.00033198533,0.000021519078,0.000012182549,0.000036910344,0.18313928,0.00033116032,0.7931976,0.011522974,0.000796698],"about_ca_topic_score_codex":0.000007314239,"about_ca_topic_score_gemma":0.000038286093,"teacher_disagreement_score":0.6441284,"about_ca_system_score_codex":0.000056288598,"about_ca_system_score_gemma":0.00006242537,"threshold_uncertainty_score":0.9999235},"labels":[],"label_agreement":null},{"id":"W7126423241","doi":"10.21428/594757db.269457de","title":"GCE: Confidence Calibration Error for ImprovedTrustworthiness of Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Toronto Metropolitan University","funders":"","keywords":"Graph; Artificial neural network; Confidence interval; Calibration; Homogeneous; Pattern recognition (psychology); Code (set theory); Training set; Error function","score_opus":0.01637108806119735,"score_gpt":0.2768592316950903,"score_spread":0.26048814363389294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126423241","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":[0.002762721,0.00091819913,0.9925809,0.0009823951,0.0017425019,0.0004387957,0.0000039428833,0.00045792625,0.00011263119],"genre_scores_gemma":[0.96703637,0.000025503077,0.031975992,0.0004135405,0.00017000858,0.00006345626,0.000008191088,0.000019072533,0.00028786727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985716,0.000031800028,0.00036326668,0.0005051862,0.00019275723,0.00033540372],"domain_scores_gemma":[0.99890953,0.00034993322,0.000086737266,0.00045713614,0.0001116457,0.00008500089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018728769,0.00018106609,0.00020679332,0.00012489992,0.00007959886,0.00016407597,0.0006489923,0.00009531632,0.0000097003685],"category_scores_gemma":[0.000025998936,0.00014685268,0.00015948346,0.00095668033,0.00008442389,0.0012343442,0.00014243906,0.0001795547,9.165791e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028241688,0.000024640256,0.00016743813,0.0001168606,0.00003156813,0.000015144309,0.00012591419,0.17949054,0.001301402,0.7409091,0.0028197945,0.07496935],"study_design_scores_gemma":[0.00013000479,0.000104608676,0.000112324764,0.000032331944,0.000008740957,0.000013717229,0.0000077246195,0.9660096,0.00108079,0.03202791,0.00030290388,0.00016930881],"about_ca_topic_score_codex":0.00001673623,"about_ca_topic_score_gemma":0.000026565293,"teacher_disagreement_score":0.96427363,"about_ca_system_score_codex":0.000010952636,"about_ca_system_score_gemma":0.000033752418,"threshold_uncertainty_score":0.5988478},"labels":[],"label_agreement":null},{"id":"W7126465581","doi":"10.21428/594757db.c800bcad","title":"How Robust Are Higher-Order Graph Neural Networks to Backdoor Attacks?","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Backdoor; Adversarial system; Artificial neural network; Software deployment; Guard (computer science); Deep neural networks","score_opus":0.017841941291305514,"score_gpt":0.2470336044121141,"score_spread":0.2291916631208086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126465581","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":[0.0018679131,0.00041004198,0.9574233,0.034216397,0.002505938,0.00037648677,0.0000012131925,0.0006663591,0.002532293],"genre_scores_gemma":[0.75765216,0.000051287418,0.18452936,0.0265263,0.00042325057,0.00010034356,0.000006229912,0.000043492244,0.030667575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766576,0.000081416714,0.0002674995,0.0009132542,0.00028166053,0.00079039705],"domain_scores_gemma":[0.99810934,0.00017329973,0.00009981617,0.0011586376,0.00020505887,0.00025386052],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010499466,0.00035179523,0.0003362503,0.00026405274,0.00023868511,0.00056230504,0.0016314782,0.00014755287,0.0000303819],"category_scores_gemma":[0.000032819655,0.00030260894,0.00015194772,0.0035440456,0.00007365573,0.00088720425,0.00079430506,0.00039344523,0.00002041544],"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":[0.000028511657,0.0000825481,0.0050563845,0.000019520228,0.000056519882,0.00007605514,0.000030569885,0.6697171,0.000054808526,0.107393935,0.15669346,0.060790613],"study_design_scores_gemma":[0.0008543755,0.0001583522,0.023631273,0.00009760974,0.000021481419,0.000013044422,0.000046835874,0.91141236,0.0001954267,0.008473533,0.054100934,0.0009948032],"about_ca_topic_score_codex":0.0000091410475,"about_ca_topic_score_gemma":0.0000898816,"teacher_disagreement_score":0.772894,"about_ca_system_score_codex":0.000039159626,"about_ca_system_score_gemma":0.000019107434,"threshold_uncertainty_score":0.9999426},"labels":[],"label_agreement":null},{"id":"W7127992935","doi":"10.32628/cseit25113676","title":"Real-Time Enterprise Data Harmonization Using Graph Neural Networks for Cross-System Integration and Customer Intelligence","year":2024,"lang":"","type":"article","venue":"International Journal of Scientific Research in Computer Science Engineering and Information Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Enterprise information system; Enterprise private network; Enterprise integration; Enterprise life cycle; Data integration; Enterprise software; Graph; Enterprise data management; Big data","score_opus":0.04446078706468331,"score_gpt":0.3548634606905654,"score_spread":0.3104026736258821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127992935","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":[0.071509264,0.0006768369,0.91812575,0.0007466143,0.008453502,0.00037241547,0.000022831486,0.00008561554,0.000007148053],"genre_scores_gemma":[0.8932698,0.00063947577,0.10575103,0.000011790331,0.0002881196,0.000007646536,0.000014246294,0.000011849241,0.000006083879],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955753,0.00007120083,0.0013968574,0.0007457006,0.0015109797,0.00069997617],"domain_scores_gemma":[0.99544907,0.0004399039,0.00038917293,0.0006151369,0.0028936216,0.000213093],"candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.008313977,0.00025583213,0.0003027124,0.008889699,0.00047711795,0.0070874426,0.0037918834,0.00020253267,0.0000018964163],"category_scores_gemma":[0.0004087859,0.00023756544,0.000060030852,0.006277852,0.0017253348,0.019088825,0.0021797398,0.0009864424,0.000004314479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031003754,0.000019815374,0.00011611338,0.00008402075,0.000025080762,0.00003078717,0.0004918252,0.487409,0.0015621699,0.022669753,0.000060023052,0.48750043],"study_design_scores_gemma":[0.00022662083,0.00016289543,0.00012972539,0.0010476022,0.00000590214,0.0006598409,0.000071523886,0.9952104,0.00085565075,0.0009456073,0.000491415,0.00019278053],"about_ca_topic_score_codex":0.000009971576,"about_ca_topic_score_gemma":7.947188e-7,"teacher_disagreement_score":0.8217605,"about_ca_system_score_codex":0.00045951354,"about_ca_system_score_gemma":0.00038268574,"threshold_uncertainty_score":0.9946307},"labels":[],"label_agreement":null},{"id":"W7128052105","doi":"10.22260/crc-csce-2025/0146","title":"Enhancing Job Hazard Analysis Knowledge Retrieval Through Knowledge Graphs and Large Language Models","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Knowledge graph; Job analysis; Knowledge-based systems; Knowledge representation and reasoning; Knowledge base; Domain knowledge; Hazard; Knowledge extraction","score_opus":0.015841441701998398,"score_gpt":0.3033742773309586,"score_spread":0.2875328356289602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128052105","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":[0.068103276,0.072180696,0.8309264,0.0003388772,0.0014382575,0.00057103013,0.000023220422,0.00047211978,0.02594609],"genre_scores_gemma":[0.95755965,0.0031103885,0.023116872,0.0006691308,0.00011550748,0.000017092323,0.000010926654,0.000044241213,0.015356216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933499,0.0005546314,0.0013488967,0.0024973261,0.00047216986,0.0017770411],"domain_scores_gemma":[0.995759,0.0008487817,0.00031888872,0.0021524697,0.0005266139,0.0003942153],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000943911,0.00097520184,0.0015207685,0.0014217604,0.0010061691,0.0006777437,0.0017879669,0.00053283863,0.000119465934],"category_scores_gemma":[0.0001361055,0.0009452691,0.0009072841,0.016620247,0.00040039048,0.0025104894,0.0027108549,0.0010721404,0.000059903272],"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":[0.00016363293,0.000760559,0.0032808012,0.0004515565,0.0031674977,0.00010642402,0.027486473,0.003761509,0.002542909,0.92668355,0.002502958,0.02909216],"study_design_scores_gemma":[0.0014529076,0.00014916029,0.0011289052,0.00031457192,0.0013992764,0.000009324411,0.0010651969,0.9201757,0.008743945,0.06287916,0.001556432,0.0011254365],"about_ca_topic_score_codex":0.000050495917,"about_ca_topic_score_gemma":0.0030479287,"teacher_disagreement_score":0.9164142,"about_ca_system_score_codex":0.00015951862,"about_ca_system_score_gemma":0.00031895295,"threshold_uncertainty_score":0.99929976},"labels":[],"label_agreement":null},{"id":"W7129401639","doi":"10.1109/icipw68931.2025.11386014","title":"Lightweight Transformer for Image Interpolation Via Unrolling of Multiple Learned Graph Laplacian Regularizers","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laplacian matrix; Graph; Pattern recognition (psychology); Pixel; Minification; Transformer; Quadratic equation","score_opus":0.013084271294059894,"score_gpt":0.26776023530257004,"score_spread":0.25467596400851017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129401639","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":[0.0028952095,0.0005819774,0.9878482,0.002532377,0.0016327688,0.0016222113,0.000015729853,0.00016564879,0.0027058476],"genre_scores_gemma":[0.75446373,0.00017131168,0.24301895,0.00041893544,0.000064390384,0.000055693345,0.000015819523,0.000032486994,0.0017586831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962547,0.00014343331,0.0012761744,0.0011184039,0.00036582083,0.0008414784],"domain_scores_gemma":[0.99732906,0.000727198,0.0004156915,0.0009052535,0.00045363113,0.00016915194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005188928,0.0005275662,0.0007281999,0.00076278485,0.00041366386,0.00017282023,0.0011474513,0.00034446857,0.000045493474],"category_scores_gemma":[0.00009872469,0.0005073717,0.00071407674,0.0023815942,0.00035649736,0.0015472029,0.00012464635,0.00042782965,0.0000060403645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023889185,0.00063164503,0.0018091826,0.0010038669,0.0007305645,0.000012823626,0.002926257,0.007884841,0.3391801,0.14479707,0.0015039316,0.49713078],"study_design_scores_gemma":[0.0026623467,0.00033687765,0.00021165962,0.00034383388,0.00009799694,0.000003274944,0.00008877264,0.79572046,0.13892409,0.059300467,0.0018420112,0.00046819795],"about_ca_topic_score_codex":0.000035533758,"about_ca_topic_score_gemma":0.00014430658,"teacher_disagreement_score":0.7878356,"about_ca_system_score_codex":0.000061143815,"about_ca_system_score_gemma":0.00011901058,"threshold_uncertainty_score":0.9997378},"labels":[],"label_agreement":null},{"id":"W7131417874","doi":"10.1109/icdm65498.2025.00107","title":"Federated Graph Out-of-Distribution Generalization via Representation Propagation and Scattering","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Generalization; Robustness (evolution); Graph; Representation (politics); Feature learning; Upper and lower bounds; Feature (linguistics); Topology (electrical circuits); Generalization error","score_opus":0.018530117092676813,"score_gpt":0.2835511549845922,"score_spread":0.2650210378919154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131417874","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":[0.053901233,0.0003751433,0.94195867,0.0009660027,0.00175834,0.00067078415,0.0000053800313,0.00012884603,0.00023558078],"genre_scores_gemma":[0.99205154,0.00035012295,0.006837695,0.0001653501,0.00006657474,0.00002498553,0.00012472857,0.000010385416,0.00036863683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976591,0.00021782896,0.0007547416,0.0007667342,0.000285645,0.0003159899],"domain_scores_gemma":[0.9986225,0.000074078525,0.00039366423,0.00035068538,0.00048313377,0.00007594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024103321,0.00026238273,0.00028625887,0.00023985788,0.00040414926,0.00030534327,0.00021636135,0.00015974113,0.0000100440875],"category_scores_gemma":[0.000064054155,0.00027501548,0.00007418749,0.0020308718,0.00018156371,0.0012718977,0.00026561177,0.00015707509,0.0000027680233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018608992,0.00024091262,0.029236713,0.0005697545,0.00019417827,0.000009682319,0.00074486504,0.049451165,0.18514535,0.0717442,0.0014451334,0.66103196],"study_design_scores_gemma":[0.0005279798,0.000089751615,0.018016497,0.00021576627,0.00003906415,0.000004064235,0.000021861379,0.89476943,0.074744135,0.011270176,0.00005500426,0.00024624326],"about_ca_topic_score_codex":0.0000669398,"about_ca_topic_score_gemma":0.00005230629,"teacher_disagreement_score":0.9381503,"about_ca_system_score_codex":0.00006895857,"about_ca_system_score_gemma":0.00005499818,"threshold_uncertainty_score":0.9999702},"labels":[],"label_agreement":null},{"id":"W7131619484","doi":"10.1109/ickg66886.2025.00021","title":"Quantifying Informativeness in Knowledge Graph-Augmented In-Context Learning for Multiple Choice Query Answering","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Leverage (statistics); Key (lock); Knowledge graph; Selection (genetic algorithm); Embedding; Question answering; Pipeline (software)","score_opus":0.04087843100487856,"score_gpt":0.32742858056182517,"score_spread":0.2865501495569466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131619484","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":[0.21768583,0.0032263217,0.77418834,0.00020661828,0.0017610926,0.0015714251,0.000001923008,0.00018530412,0.0011731584],"genre_scores_gemma":[0.99215233,0.0003267682,0.0060979705,0.0003476584,0.000049632607,0.00027600653,0.000008156349,0.00003238968,0.0007090653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99526256,0.00034437803,0.001620006,0.0011062721,0.00023933628,0.0014274401],"domain_scores_gemma":[0.9943373,0.004232418,0.00037212824,0.0006448619,0.00026501963,0.00014827833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001086441,0.0006444682,0.0008760072,0.0019065473,0.00048003366,0.00035087406,0.001335539,0.00030493372,0.000009793388],"category_scores_gemma":[0.0010627452,0.0007036355,0.00029260106,0.0056060664,0.00016788399,0.0031437366,0.0009687559,0.001252535,0.000011676637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022063112,0.00030275664,0.26762772,0.0008418411,0.00006164134,0.000012704738,0.0060923127,0.18995608,0.00081975386,0.013440485,0.00004121364,0.52058285],"study_design_scores_gemma":[0.0043330467,0.00013846411,0.051190365,0.002382242,0.000010748522,0.000002406859,0.0018780283,0.93025887,0.0023035998,0.0006473139,0.006158309,0.00069663284],"about_ca_topic_score_codex":0.00043175725,"about_ca_topic_score_gemma":0.019915283,"teacher_disagreement_score":0.7744665,"about_ca_system_score_codex":0.0003186995,"about_ca_system_score_gemma":0.0002412604,"threshold_uncertainty_score":0.99954146},"labels":[],"label_agreement":null},{"id":"W7131654614","doi":"10.1109/ickg66886.2025.00028","title":"Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"JSON; Inference; Industry 4.0; Natural language understanding; Knowledge graph; Natural language; Knowledge representation and reasoning; Automation","score_opus":0.013635065804805552,"score_gpt":0.27153228312723826,"score_spread":0.2578972173224327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131654614","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":[0.08507095,0.009149469,0.8799931,0.0005900256,0.0016216028,0.00056444766,0.000008639603,0.00043668906,0.022565113],"genre_scores_gemma":[0.9649576,0.00066483475,0.027403537,0.00093429926,0.000045161625,0.000026636862,0.0000045746897,0.000028827022,0.005934559],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964337,0.00019657069,0.0007118748,0.0013185457,0.00023941527,0.0010999187],"domain_scores_gemma":[0.99809486,0.00035345752,0.00018087565,0.0009802522,0.00012687755,0.00026365655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036068563,0.000607142,0.0005757957,0.0005687095,0.000581243,0.00051465735,0.0012125476,0.00023687576,0.00004377353],"category_scores_gemma":[0.000049615635,0.0005446719,0.0002363663,0.001029315,0.00020606208,0.0015393246,0.0024294388,0.0009632544,0.000021122312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002451687,0.00017663949,0.0007262546,0.00018742698,0.00013149994,0.000062384315,0.005863454,0.0015073,0.0005196032,0.68909186,0.0010694242,0.30063963],"study_design_scores_gemma":[0.0007665402,0.00007911239,0.0003528263,0.00065349136,0.00003959737,0.00001760415,0.0014173911,0.93156457,0.0048935935,0.05880938,0.0008762286,0.00052968593],"about_ca_topic_score_codex":0.000065827466,"about_ca_topic_score_gemma":0.00067935436,"teacher_disagreement_score":0.9300572,"about_ca_system_score_codex":0.000081219194,"about_ca_system_score_gemma":0.00007696216,"threshold_uncertainty_score":0.9997005},"labels":[],"label_agreement":null},{"id":"W7132676662","doi":"","title":"Pre-training molecular graph representation with 3D geometry: rethinking self-supervised learning on structured data","year":2021,"lang":"en","type":"article","venue":"NPARC","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Discriminative model; Graph; Molecular graph; Feature learning; Representation (politics); Consistency (knowledge bases); Graph property; External Data Representation; Encoder","score_opus":0.032018628159558325,"score_gpt":0.27779923183646804,"score_spread":0.2457806036769097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132676662","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":[0.2883185,0.00009347777,0.7075886,0.00061072037,0.0002472783,0.00021027909,0.0000034636373,0.000592904,0.0023347687],"genre_scores_gemma":[0.59529704,0.000018004703,0.4041601,0.00035973868,0.00004721324,0.000005805159,0.00006487243,0.000019835265,0.00002737448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974496,0.0002484821,0.0002269759,0.0010332054,0.0006348882,0.00040687394],"domain_scores_gemma":[0.9977514,0.00020940199,0.00014642718,0.0016629234,0.000121366196,0.00010844308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025479303,0.000209028,0.00022549019,0.0001515858,0.0002490214,0.00023817673,0.0011384804,0.00008830446,0.0000238842],"category_scores_gemma":[0.00018288565,0.0001930703,0.000045158988,0.0016994422,0.00004142148,0.00079814414,0.00060864876,0.0006060515,0.0000030959914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014714224,0.0001401409,0.008136278,0.00009228052,0.00040884237,0.002307328,0.013293943,0.23982212,0.17455402,0.054430097,0.00045926793,0.50620854],"study_design_scores_gemma":[0.0014312074,0.00033543073,0.0050379746,0.00023731455,0.000057108417,0.00021929952,0.00031612857,0.8889272,0.022643,0.07925211,0.0007469842,0.0007962195],"about_ca_topic_score_codex":0.0000031961176,"about_ca_topic_score_gemma":0.000008551949,"teacher_disagreement_score":0.64910513,"about_ca_system_score_codex":0.000026901525,"about_ca_system_score_gemma":0.00007876294,"threshold_uncertainty_score":0.7873178},"labels":[],"label_agreement":null},{"id":"W7132859330","doi":"","title":"Knowledge Translation","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Schema (genetic algorithms); Domain knowledge; Knowledge base; Knowledge acquisition; Domain (mathematical analysis); Set (abstract data type); Knowledge-based systems; Graph; Subject-matter expert","score_opus":0.06459505587284078,"score_gpt":0.38787454202951394,"score_spread":0.3232794861566731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132859330","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":[0.046468247,0.08361295,0.5836008,0.0027823863,0.03706234,0.0044437526,0.000026686772,0.0017517166,0.24025111],"genre_scores_gemma":[0.7299313,0.0040001315,0.03864659,0.0005060833,0.0014295157,0.00067461096,0.0015963305,0.00041554286,0.22279991],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9959981,0.00039540156,0.0006239443,0.0014506164,0.00072716654,0.00080475817],"domain_scores_gemma":[0.99733365,0.00050787587,0.0005487307,0.0011789975,0.00018562602,0.000245096],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033936478,0.0007006012,0.00058646407,0.0004306047,0.0009120171,0.00022437732,0.0017953598,0.00035901606,0.002057283],"category_scores_gemma":[0.00004228101,0.0008446506,0.00040778075,0.0024786438,0.00007269124,0.00062654156,0.00021034686,0.0015263796,0.00028440956],"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":[0.00019408193,0.00035206208,0.000032522003,0.00030899103,0.00011207857,0.00007584484,0.14676663,0.011899235,0.0021221724,0.020093651,0.0031720896,0.81487066],"study_design_scores_gemma":[0.0018989885,0.0009793976,0.0012197567,0.0003907075,0.00025188053,0.000061588704,0.012201892,0.44780162,0.0027744945,0.007775953,0.5213154,0.003328343],"about_ca_topic_score_codex":0.00004796404,"about_ca_topic_score_gemma":0.0001984025,"teacher_disagreement_score":0.8115423,"about_ca_system_score_codex":0.00018173788,"about_ca_system_score_gemma":0.00031700166,"threshold_uncertainty_score":0.99940044},"labels":[],"label_agreement":null},{"id":"W7133394521","doi":"","title":"Decimated Framelet System on Graphs and Fast <i>G</i>-Framelet Transforms","year":2022,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Shanghai Jiao Tong University; European Commission; City University of Hong Kong; National Science Foundation","keywords":"External Data Representation; Graph; Pattern recognition (psychology); Orthonormal basis; Voltage graph; Graph theory; Representation (politics); Artificial neural network","score_opus":0.008474589290824047,"score_gpt":0.21758256318807503,"score_spread":0.209107973897251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133394521","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":[0.9161989,0.00051807065,0.07850712,0.00070674653,0.00097095186,0.0005540084,0.00004047343,0.0010417267,0.0014619946],"genre_scores_gemma":[0.9925029,0.00003423084,0.005544689,0.0017153596,0.000021539203,0.00009277253,0.000010028312,0.000029054807,0.000049376584],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760425,0.00017889483,0.00031378507,0.00073718664,0.0006282651,0.0005376319],"domain_scores_gemma":[0.9987069,0.00013689639,0.000118198855,0.0007605376,0.0000537551,0.00022373624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042813853,0.0002712047,0.00030077665,0.000267341,0.0008316432,0.0003670283,0.0011797884,0.00009065263,0.000014746364],"category_scores_gemma":[0.000026368598,0.00026030376,0.00011602756,0.001314168,0.00008421986,0.0008805882,0.00037739298,0.0011778945,0.000018104849],"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":[0.00021820469,0.0003709539,0.005045823,0.00015492247,0.0001386221,0.0007276473,0.0015099066,0.015805526,0.005400882,0.7330738,0.0033834877,0.23417024],"study_design_scores_gemma":[0.019718444,0.008592632,0.06545159,0.0011479754,0.0002335505,0.0046415026,0.004350983,0.40594465,0.019096224,0.22070873,0.24013023,0.009983509],"about_ca_topic_score_codex":0.000007700954,"about_ca_topic_score_gemma":0.0000030603228,"teacher_disagreement_score":0.51236504,"about_ca_system_score_codex":0.00010576701,"about_ca_system_score_gemma":0.000029184566,"threshold_uncertainty_score":0.9999849},"labels":[],"label_agreement":null},{"id":"W7134193716","doi":"10.1109/bigdata66926.2025.11401973","title":"Enhanced ESG Data Processing Using Retrieval-Augmented AI","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","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":"Brock University","funders":"","keywords":"Data processing; Feature (linguistics); Signal processing; Data processing system; Identification (biology)","score_opus":0.051229893386244715,"score_gpt":0.35537674837825034,"score_spread":0.3041468549920056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134193716","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":[0.0034963184,0.003879705,0.982936,0.0024424081,0.0026057025,0.0007381948,0.000009062699,0.00042343125,0.0034691659],"genre_scores_gemma":[0.9002838,0.00027961592,0.08871691,0.0057460736,0.00024034745,0.0000035278217,0.000021064257,0.000042532694,0.0046660965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99398005,0.00022127388,0.0011538444,0.002626901,0.0007072242,0.0013106882],"domain_scores_gemma":[0.99437755,0.00020722601,0.00044546096,0.004210151,0.0005052335,0.00025440205],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0005331338,0.0006605777,0.0006663434,0.00045025302,0.00093862996,0.0011449112,0.0054550846,0.00030529802,0.000071843606],"category_scores_gemma":[0.00026888758,0.0006570519,0.00012523566,0.0065247775,0.00035240498,0.004821126,0.0059366585,0.0009351043,0.000025742498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032300068,0.0004115852,0.00023449575,0.00048238403,0.00020045378,0.00013733491,0.00037219987,0.017491724,0.031800423,0.011334921,0.0041444264,0.933067],"study_design_scores_gemma":[0.00086227583,0.000063946725,0.00009343589,0.0010829847,0.00009161768,0.000020054098,0.000044323268,0.96140856,0.029578421,0.0043360977,0.0017851153,0.0006331716],"about_ca_topic_score_codex":0.000021935297,"about_ca_topic_score_gemma":0.000015771651,"teacher_disagreement_score":0.94391686,"about_ca_system_score_codex":0.00020668228,"about_ca_system_score_gemma":0.00091013947,"threshold_uncertainty_score":0.99992585},"labels":[],"label_agreement":null},{"id":"W7134973199","doi":"10.1109/icdmw69685.2025.00120","title":"Smooth Transitions in Graph Self-Supervision: Mitigating Feature Twist Across Abstraction Levels","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Feature (linguistics); Twist; Abstraction; Graph; Context (archaeology)","score_opus":0.016515769884600916,"score_gpt":0.2965794799860514,"score_spread":0.2800637101014505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7134973199","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":[0.16961598,0.0022863199,0.7855532,0.02827008,0.0042567975,0.0013716397,0.00007959146,0.00087094127,0.0076954295],"genre_scores_gemma":[0.92807883,0.00019871125,0.06811599,0.0022187734,0.00015077459,0.00004641636,0.000011737678,0.000028736229,0.0011500183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99530214,0.0002467092,0.0009902471,0.0015811393,0.00056211493,0.0013176675],"domain_scores_gemma":[0.9977977,0.00033715065,0.00022059366,0.001133722,0.00026154873,0.00024927937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057024165,0.0006483192,0.00059577834,0.00043337123,0.0010574071,0.00073034636,0.0012052365,0.00057695876,0.000080627775],"category_scores_gemma":[0.00004473918,0.00067530636,0.00040396833,0.0056440155,0.0002521006,0.0023414833,0.00032767907,0.0018324114,0.000029503788],"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":[0.00011476028,0.0016799002,0.0057620956,0.00068520143,0.0002572679,0.00057059794,0.01997517,0.073704444,0.008725419,0.076848134,0.0050531607,0.8066239],"study_design_scores_gemma":[0.005139077,0.00039009607,0.24460703,0.0030146474,0.00011951366,0.00015835383,0.004577566,0.6588287,0.005830671,0.06345988,0.011141072,0.0027334376],"about_ca_topic_score_codex":0.00007903636,"about_ca_topic_score_gemma":0.00111399,"teacher_disagreement_score":0.8038904,"about_ca_system_score_codex":0.00020542553,"about_ca_system_score_gemma":0.00019782098,"threshold_uncertainty_score":0.99956983},"labels":[],"label_agreement":null},{"id":"W7135252846","doi":"","title":"Utilizing Specialized Graph Partitioning and Adaptive GNNs: A Comparative Study for Large-Scale Traffic Forecasting","year":2025,"lang":"","type":"dissertation","venue":"ResearchSpace (University of Auckland)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Graph; Graph partition; Intelligent transportation system; Artificial neural network; Convolutional neural network; Baseline (sea); Graph theory; Deep learning","score_opus":0.1006540449161022,"score_gpt":0.32789799728863234,"score_spread":0.22724395237253014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7135252846","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":[0.8020903,0.0011016411,0.18964429,0.000305839,0.000410084,0.004891812,0.0002203306,0.000116403055,0.0012193308],"genre_scores_gemma":[0.97035,0.0006570697,0.026004009,0.000008004295,0.00008061598,0.000016078213,0.00015628297,0.000028145283,0.0026997502],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950203,0.0007382728,0.00046157744,0.0016021664,0.000975692,0.0012020003],"domain_scores_gemma":[0.9952467,0.0017909281,0.0007661274,0.00061709905,0.0012071076,0.00037202198],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0012494392,0.000595201,0.0013540494,0.0012241609,0.002515291,0.00022111324,0.001200476,0.00033270917,0.00003335675],"category_scores_gemma":[0.00015681543,0.00075812713,0.00042907437,0.002466022,0.00034465877,0.00094883103,0.00068571256,0.0010283312,0.0000027391113],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.029916989,0.0048529347,0.0076305396,0.0040270532,0.004633951,0.00058575143,0.7897871,0.04237224,0.0005434948,0.032918412,0.005999655,0.07673184],"study_design_scores_gemma":[0.006731554,0.002550804,0.005301942,0.0016834635,0.0002964622,0.000004348155,0.36112252,0.61926115,0.00006635294,0.0017543762,0.00047939186,0.0007476788],"about_ca_topic_score_codex":0.00029619355,"about_ca_topic_score_gemma":0.025685407,"teacher_disagreement_score":0.57688886,"about_ca_system_score_codex":0.00015773649,"about_ca_system_score_gemma":0.00045023084,"threshold_uncertainty_score":0.999487},"labels":[],"label_agreement":null},{"id":"W7136144675","doi":"10.1145/3787330.3787359","title":"Resume-Job Compatibility Scoring Using Graph Neural Networks and Large Language Models","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"ENCODE; Compatibility (geochemistry); Graph; Artificial neural network; Disk formatting","score_opus":0.027484263782413896,"score_gpt":0.29563723222559957,"score_spread":0.26815296844318565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7136144675","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":[0.30093026,0.00715388,0.6884787,0.00021162466,0.001455593,0.00054010417,0.0000068040836,0.00024150318,0.0009815504],"genre_scores_gemma":[0.9752963,0.00027568414,0.022602525,0.0013428025,0.00015867317,0.000008444599,0.0000034764153,0.000031194984,0.00028087632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99464643,0.0004495004,0.0010122514,0.0018826223,0.00044303067,0.0015661365],"domain_scores_gemma":[0.99691683,0.00044928025,0.00027370433,0.0018095439,0.00018838256,0.00036227406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086086977,0.00069336983,0.00079755014,0.00040744242,0.0009814567,0.0006524209,0.0012465188,0.00030153443,0.000017527993],"category_scores_gemma":[0.00005040511,0.0006913803,0.00026006828,0.002751566,0.00038549135,0.0022059267,0.0023695258,0.0011221995,0.0000012145026],"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":[0.00008426022,0.00012191531,0.018161813,0.000113167654,0.00007567974,0.00007458676,0.00064468634,0.8699212,0.000132804,0.05639262,0.00012835511,0.05414889],"study_design_scores_gemma":[0.00095257745,0.00006228022,0.004387733,0.0002705835,0.000057346235,0.00001925579,0.00016552152,0.9769277,0.0000796058,0.016469382,0.00001899114,0.00058901065],"about_ca_topic_score_codex":0.00037443492,"about_ca_topic_score_gemma":0.0004991173,"teacher_disagreement_score":0.67436606,"about_ca_system_score_codex":0.00010422949,"about_ca_system_score_gemma":0.00007473593,"threshold_uncertainty_score":0.99955374},"labels":[],"label_agreement":null},{"id":"W7138864149","doi":"10.1109/globecom59602.2025.11432441","title":"Graph Integrated Transformers for Community Detection in Social Networks","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Transformer; Cluster analysis; Graph; Hidden Markov model; Benchmark (surveying); Clustering coefficient","score_opus":0.02099661305245954,"score_gpt":0.285468170040531,"score_spread":0.26447155698807145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138864149","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":[0.012126461,0.0003050819,0.9804231,0.001045488,0.0022442194,0.0014048924,0.000006846177,0.00021848985,0.0022254235],"genre_scores_gemma":[0.9942709,0.00019852823,0.0035448163,0.0012454686,0.00009805501,0.00016507327,0.0000137313145,0.000022166394,0.00044126704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657744,0.00074263586,0.00086951384,0.00060497946,0.00019030132,0.0010151569],"domain_scores_gemma":[0.998371,0.0007342957,0.00015955017,0.00039108683,0.00023588946,0.00010818083],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010571877,0.00047620447,0.0005685318,0.0007011636,0.0016203551,0.00022908411,0.001090076,0.00054704293,0.00001224554],"category_scores_gemma":[0.000057397814,0.00048761631,0.00042275162,0.005895756,0.00034078961,0.0008399538,0.0001823109,0.0022139088,0.0000014721346],"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":[0.00037179602,0.0002581245,0.00020018291,0.00007189709,0.00007107236,0.0000020085577,0.0006850825,0.01323513,0.0002468856,0.014201758,0.00062445406,0.9700316],"study_design_scores_gemma":[0.002196851,0.00034731659,0.002745234,0.00014804021,0.000040966173,0.0000022871297,0.00071018323,0.9506378,0.0015569753,0.03955537,0.0015619163,0.000497068],"about_ca_topic_score_codex":0.00046754532,"about_ca_topic_score_gemma":0.00870494,"teacher_disagreement_score":0.9821444,"about_ca_system_score_codex":0.0002472318,"about_ca_system_score_gemma":0.00013015396,"threshold_uncertainty_score":0.9997575},"labels":[],"label_agreement":null},{"id":"W7139048563","doi":"10.1109/globecom59602.2025.11431685","title":"Early-Exit Strategies for Dynamic Graph CNNs to Accelerate Inference of Point Clouds","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Scalability; Graph; Convolutional neural network; Point (geometry); Point cloud; Virtual reality","score_opus":0.02197882508928876,"score_gpt":0.3167243111566917,"score_spread":0.2947454860674029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139048563","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":[0.09843968,0.0003766906,0.8916872,0.0024132163,0.0016916454,0.0015553449,0.00003260146,0.00018569388,0.0036179062],"genre_scores_gemma":[0.92827946,0.00034710477,0.06845469,0.0013757218,0.00003905909,0.00013510621,0.0000038342073,0.000026607726,0.0013383981],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957524,0.0001218005,0.0012251979,0.0013757473,0.00036086244,0.0011639828],"domain_scores_gemma":[0.9963171,0.00071293453,0.00036296417,0.0015487848,0.0007624954,0.00029573866],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039003382,0.0006397307,0.0008262901,0.0010359928,0.000335623,0.0006252509,0.002609661,0.0002600895,0.00005525505],"category_scores_gemma":[0.00010939956,0.000617284,0.00041233742,0.0057570725,0.00033002623,0.0016662931,0.0011991354,0.0004541166,0.000019320092],"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":[0.00024942128,0.00022671305,0.0007280486,0.0003002757,0.00017064766,0.0000064427254,0.0010142938,0.043811336,0.0066854493,0.87039673,0.001438703,0.07497192],"study_design_scores_gemma":[0.0013113422,0.0016051229,0.0146073,0.0006370621,0.00007518491,0.0000029325256,0.00042894037,0.29834175,0.0066176862,0.67459345,0.0007665398,0.0010126982],"about_ca_topic_score_codex":0.00014399485,"about_ca_topic_score_gemma":0.00037906948,"teacher_disagreement_score":0.8298398,"about_ca_system_score_codex":0.00006264045,"about_ca_system_score_gemma":0.0005365745,"threshold_uncertainty_score":0.9996278},"labels":[],"label_agreement":null},{"id":"W7147212645","doi":"10.1109/iccsm66818.2025.00011","title":"HeuGAT: Integrating Heuristic and Graph Attention Network for Improved Link Prediction and Breakup Prediction in Social Network Structures","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Attention network; Breakup; Graph; Link (geometry); Social network (sociolinguistics); Heuristic; Network structure; Network science","score_opus":0.008024598306539243,"score_gpt":0.2497511939309358,"score_spread":0.24172659562439658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7147212645","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":[0.02462524,0.0034012548,0.9625489,0.001835655,0.0047614207,0.002261254,0.00006604894,0.00028756037,0.00021267899],"genre_scores_gemma":[0.93406236,0.0010484876,0.05942396,0.00077155477,0.0038339237,0.0002293277,0.00011061077,0.00005209355,0.00046768144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954704,0.00030913224,0.0012887546,0.0015447342,0.00024273698,0.0011442491],"domain_scores_gemma":[0.9981033,0.00060885923,0.0004749369,0.00039345128,0.00024804872,0.00017137745],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00087209564,0.0006362818,0.000704402,0.00034370972,0.0015587093,0.00060482335,0.00038330085,0.0006028812,0.0000049785617],"category_scores_gemma":[0.00013324106,0.0006360486,0.0001918784,0.002175994,0.0003752326,0.0010393162,0.00048654672,0.00092992705,2.7161084e-7],"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":[0.0006736121,0.00007962824,0.060721006,0.00047177172,0.00024445998,0.000005014334,0.00054496084,0.037450977,0.00045697653,0.14521837,0.009268878,0.74486434],"study_design_scores_gemma":[0.001523336,0.00034055207,0.13205798,0.00039496282,0.000101139405,0.000013280079,0.00006184343,0.6355122,0.000007012434,0.22929774,0.0003900549,0.00029994748],"about_ca_topic_score_codex":0.0000675505,"about_ca_topic_score_gemma":0.00047193805,"teacher_disagreement_score":0.9094371,"about_ca_system_score_codex":0.00012088663,"about_ca_system_score_gemma":0.00009439773,"threshold_uncertainty_score":0.99974114},"labels":[],"label_agreement":null},{"id":"W7162471993","doi":"10.65521/ijeecs.v14i2.2102","title":"A Comprehensive Review of Graph Neural Networks for Malware Classification Pipelines: Architectures, Robustness, and Intelligent Security Applications","year":2025,"lang":"","type":"article","venue":"International Journal of Electrical Electronics and Computer Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Malware; Adversarial system; Convolutional neural network; Robustness (evolution); Graph; Feature learning; Deep learning; Artificial neural network","score_opus":0.014402124589512596,"score_gpt":0.2866829854489121,"score_spread":0.27228086085939945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162471993","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":[0.0008346301,0.34016314,0.6551351,0.0015158075,0.0013852965,0.00093451556,0.000011544428,0.000013997593,0.0000059699214],"genre_scores_gemma":[0.7650821,0.2258988,0.0057481835,0.0014794498,0.0015952592,0.000119718694,0.000026690175,0.00003167572,0.000018080193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957122,0.0003308177,0.0020989878,0.0006566829,0.00065684837,0.0005444428],"domain_scores_gemma":[0.9934046,0.0009972542,0.0018146699,0.00038443503,0.0031916853,0.0002073055],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000612749,0.00044588136,0.0010386313,0.00062368327,0.00017362574,0.00029350273,0.0014260602,0.00021658593,9.262942e-7],"category_scores_gemma":[0.00006231542,0.00039968407,0.00039638355,0.0011542322,0.00018966704,0.00022896801,0.00031920953,0.00096410967,8.447059e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052042096,0.0005207228,0.00032315138,0.004292269,0.0013487379,0.000020925432,0.000099178535,0.14097464,0.00007002995,0.14764276,0.0033644785,0.70082265],"study_design_scores_gemma":[0.00085499167,0.00080472825,0.00018592954,0.0035224224,0.00016580091,0.0005739266,0.000008728218,0.96937644,0.000025293317,0.005258507,0.01893573,0.00028751435],"about_ca_topic_score_codex":0.000008898937,"about_ca_topic_score_gemma":0.0000035234996,"teacher_disagreement_score":0.8284018,"about_ca_system_score_codex":0.00018532813,"about_ca_system_score_gemma":0.00031535898,"threshold_uncertainty_score":0.9998455},"labels":[],"label_agreement":null}]}