{"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":"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":[0.039401527,0.000025385694,0.9593928,0.000022296457,0.00006686458,0.0005572268,0.0000023464183,0.000074813746,0.00045676378],"genre_scores_gemma":[0.54304785,0.0000020945722,0.45678136,0.00006926825,0.00005878718,0.000019909332,0.000009280591,0.000007282699,0.0000041960348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988763,0.00003959614,0.00055348134,0.00020015365,0.00007190305,0.00025859618],"domain_scores_gemma":[0.9990155,0.0003146538,0.00024259527,0.00018025983,0.00014371055,0.00010328992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062851066,0.00018954935,0.00028537516,0.00020064584,0.00035843175,0.00015509318,0.00016226426,0.000025774178,0.0000047233716],"category_scores_gemma":[0.000008415173,0.00016496616,0.000063606094,0.00029208086,0.00007640706,0.00012455808,0.0001303177,0.00010547663,0.0000015216478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049234168,0.000027700731,0.0005251102,0.00002570881,0.000040403036,5.057374e-8,0.0037612342,0.0024130538,0.000031719428,0.06352073,0.00002046449,0.92958456],"study_design_scores_gemma":[0.00010711754,0.000097072465,0.000119293974,0.00011348832,0.000039307783,9.990996e-7,0.00044060248,0.95017666,0.005204248,0.042892992,0.0005791988,0.0002290061],"about_ca_topic_score_codex":0.00009969586,"about_ca_topic_score_gemma":0.00001543576,"teacher_disagreement_score":0.9477636,"about_ca_system_score_codex":0.000008991591,"about_ca_system_score_gemma":0.000012119099,"threshold_uncertainty_score":0.6727125},"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":"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":[0.15106057,0.0012821809,0.8449642,0.00060879864,0.00079866004,0.00023430455,0.000016722357,0.00026310017,0.00077144074],"genre_scores_gemma":[0.954788,0.0001910794,0.04424425,0.00053828774,0.00018540958,0.0000010848639,0.000017753133,0.000009615304,0.000024516836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979899,0.0000638146,0.0007085014,0.00047476188,0.0002766469,0.00048639864],"domain_scores_gemma":[0.99848926,0.0003209823,0.00024185897,0.0006694662,0.00007260102,0.00020583623],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007329639,0.00029102102,0.00034941573,0.00014145489,0.0006047818,0.0012319695,0.0008594417,0.00008544207,0.0000026592857],"category_scores_gemma":[0.000071701754,0.00026322168,0.000027860147,0.00029242277,0.00012604108,0.0006264779,0.0014208942,0.00022812079,0.000024127205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007079587,0.00014432638,0.00065723294,0.00025543,0.000048058377,0.000022432669,0.020724697,0.0044063013,0.0000062475024,0.104027405,0.0061713955,0.8635294],"study_design_scores_gemma":[0.00007889443,0.00020551098,0.00016815584,0.000314878,0.00000591823,0.00006280676,0.0004848461,0.9909047,0.000050122533,0.0014042469,0.005991194,0.00032872907],"about_ca_topic_score_codex":0.000014964157,"about_ca_topic_score_gemma":0.0000023558048,"teacher_disagreement_score":0.9864984,"about_ca_system_score_codex":0.000011930926,"about_ca_system_score_gemma":0.00001176957,"threshold_uncertainty_score":0.999982},"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":"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":[0.0020661575,0.000052052434,0.9963664,0.00029738824,0.0005511429,0.00019731304,0.0000059758245,0.00027759298,0.0001860155],"genre_scores_gemma":[0.18988998,0.00009023452,0.80969584,0.0001347331,0.00015159075,0.0000027449985,0.000007665547,0.000010583273,0.00001664785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980372,0.000115486226,0.00081034546,0.0003190718,0.0003493785,0.00036848654],"domain_scores_gemma":[0.9984546,0.0004002919,0.00023968464,0.00039362538,0.00023332007,0.00027848603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008357308,0.00021820319,0.00027476606,0.00024299955,0.00043482467,0.00075144885,0.0005854532,0.00008516565,0.00000529442],"category_scores_gemma":[0.00008728653,0.00016084773,0.000030607156,0.00034532594,0.00016874271,0.0008324638,0.00055489957,0.00012238484,0.000018382276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003874185,0.00002696998,0.000017791235,0.000049920094,0.000011316458,0.0000045314305,0.001260517,0.06905172,0.000007555405,0.03418592,0.000038763992,0.8953411],"study_design_scores_gemma":[0.00007876111,0.00015865863,0.000019512188,0.00014422863,0.000004496461,0.000051626885,0.0003461635,0.99773926,0.0002991644,0.00071874785,0.00019941921,0.00023996527],"about_ca_topic_score_codex":0.00001477298,"about_ca_topic_score_gemma":6.9691436e-7,"teacher_disagreement_score":0.9286875,"about_ca_system_score_codex":0.000034705627,"about_ca_system_score_gemma":0.00004870531,"threshold_uncertainty_score":0.72462386},"labels":[],"label_agreement":null}]}