{"meta":{"query_hash":"bf3384e7a90e","filters":{"venue":"Computing and Informatics / Computers and Artificial Intelligence"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/bf3384e7a90e","api":"https://metacan.xera.ac/api/v1/cohort?venue=Computing+and+Informatics+%2F+Computers+and+Artificial+Intelligence"},"results":[{"id":"W12814185","doi":"10.1023/a:1023679303322","title":"MULTILEVEL AGGREGATION METHODS FOR SMALL-WORLD GRAPHS WITH APPLICATION TO RANDOM-WALK RANKING","year":2011,"lang":"en","type":"article","venue":"Computing and Informatics / Computers and Artificial Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain; Computer science; Theoretical computer science; Random walk; Graph; Cluster analysis; Mathematics; Algorithm; Machine learning","score_opus":0.04849255361357132,"score_gpt":0.3277189366690197,"score_spread":0.2792263830554484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W12814185","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001292817,0.00017570842,0.9971366,0.000088720815,0.000041562904,0.0000632659,0.00012400666,0.00076755625,0.00030980608],"genre_scores_gemma":[0.088351026,0.0004837366,0.90546703,0.00011585627,0.00018643579,0.0009927504,0.0011779948,0.0005639912,0.0026611956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99626166,0.0020833663,0.00025314142,0.00057555136,0.0005942743,0.00023200382],"domain_scores_gemma":[0.9877061,0.008532156,0.00092486053,0.001242815,0.0011902747,0.00040374545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057228943,0.0017690379,0.0030051,0.0050558206,0.0013175111,0.0023220521,0.00290281,0.0017644948,0.006317453],"category_scores_gemma":[0.021220744,0.0011206116,0.0031245558,0.004407284,0.0012168753,0.002653779,0.0041893497,0.00334555,0.0018802223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103612394,0.000083118204,0.0013902837,0.00029036292,0.00033510246,0.00014996504,0.00016267237,0.75941867,0.0007227301,0.08950719,0.005056995,0.14277923],"study_design_scores_gemma":[0.000009287259,0.000020915593,0.00010520788,0.00000847507,0.00001232992,0.000011785561,0.000009658244,0.9704378,0.000072857096,0.028067352,0.00123469,0.000009649737],"about_ca_topic_score_codex":0.014774762,"about_ca_topic_score_gemma":0.015976122,"teacher_disagreement_score":0.014774762,"about_ca_system_score_codex":0.0018564669,"about_ca_system_score_gemma":0.0022805964,"threshold_uncertainty_score":0.030265927},"labels":[],"label_agreement":null},{"id":"W22316296","doi":"10.1089/jmf.2011.1827","title":"Mining Large Data Sets on Grids: Issues and Prospects","year":2002,"lang":"en","type":"article","venue":"Computing and Informatics / Computers and Artificial Intelligence","topic":"Distributed and Parallel Computing Systems","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":"Queen's University","funders":"","keywords":"Computer science; Computation; Grid; Knowledge extraction; Distributed computing; Data science; Grid computing; Data grid; Data mining; Scale (ratio)","score_opus":0.07509896457680171,"score_gpt":0.3029576007569295,"score_spread":0.2278586361801278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W22316296","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15104742,0.033228613,0.6929386,0.08470788,0.002446783,0.00084210857,0.017300094,0.00790647,0.009582037],"genre_scores_gemma":[0.381497,0.013802556,0.5730766,0.003293256,0.0020898522,0.0007177355,0.0205793,0.0006699253,0.0042737317],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9937888,0.0024245454,0.000614543,0.0016512909,0.0012480608,0.00027281235],"domain_scores_gemma":[0.9399803,0.03678157,0.0020163527,0.013126198,0.005847345,0.0022483126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012750018,0.0013196995,0.0037555082,0.0033879217,0.0012957668,0.006229393,0.006245818,0.0024660516,0.005069372],"category_scores_gemma":[0.0418864,0.001366749,0.0025430927,0.011208441,0.0019472648,0.013527007,0.0037763102,0.0027498624,0.0025925327],"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":[0.0011630257,0.0007648727,0.062962815,0.002303494,0.0014263277,0.0015119112,0.0011546821,0.18242349,0.0024339699,0.06979336,0.08813602,0.585926],"study_design_scores_gemma":[0.00014916564,0.00019104304,0.009049908,0.00039613547,0.00008439892,0.0008540206,0.0025001138,0.6068925,0.0013419494,0.32355702,0.05489558,0.00008818619],"about_ca_topic_score_codex":0.0066331527,"about_ca_topic_score_gemma":0.0077049565,"teacher_disagreement_score":0.012750018,"about_ca_system_score_codex":0.0013917795,"about_ca_system_score_gemma":0.002373466,"threshold_uncertainty_score":0.067429304},"labels":[],"label_agreement":null},{"id":"W2536934950","doi":"","title":"IPO: An Inclined Planes System Optimization Algorithm","year":2016,"lang":"en","type":"article","venue":"Computing and Informatics / Computers and Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Benchmark (surveying); Heuristic; Plane (geometry); Algorithm; Inclined plane; Motion (physics); Computer science; Space (punctuation); Optimization algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Engineering; Geometry; Mechanical engineering","score_opus":0.03183234500161008,"score_gpt":0.28805459476112216,"score_spread":0.2562222497595121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536934950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0097814,0.00041064853,0.9782908,0.00020381802,0.00011188188,0.00016469034,0.00015922396,0.0010901612,0.009787437],"genre_scores_gemma":[0.23932412,0.0004624528,0.74928796,0.000291001,0.00010819688,0.0008152947,0.00070690626,0.00035660784,0.008647525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968517,0.000083240375,0.00001649868,0.000062768326,0.00010971719,0.000042608863],"domain_scores_gemma":[0.9997861,0.000085715765,0.000029154786,0.000025535624,0.000054149623,0.000019347604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053627946,0.0009450484,0.0010873212,0.0007291162,0.0005391473,0.0008808655,0.0014464367,0.0013901395,0.006623357],"category_scores_gemma":[0.0011969832,0.00042658424,0.00068908435,0.00097268296,0.00043476807,0.00085096003,0.0012353385,0.0011653395,0.0014733304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001730072,0.000102626414,0.00086140755,0.00012105825,0.00007623952,0.000090549365,0.0000538758,0.806405,0.0017379699,0.009656088,0.0060014827,0.17472072],"study_design_scores_gemma":[0.000040904524,0.000049893948,0.00011027383,0.00000913197,0.0000090453805,0.000024706856,0.000008200199,0.9942054,0.00036500825,0.002056189,0.0031157753,0.0000053985555],"about_ca_topic_score_codex":0.0030649402,"about_ca_topic_score_gemma":0.0019852188,"teacher_disagreement_score":0.006623357,"about_ca_system_score_codex":0.00040315802,"about_ca_system_score_gemma":0.0014372858,"threshold_uncertainty_score":0.022157311},"labels":[],"label_agreement":null}]}