{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"bf3384e7a90e","filters":{"venue":"Computing and Informatics / Computers and Artificial Intelligence"}},"results":[{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.03183234500161008,"gpt":0.2880545947611222,"spread":0.2562222497595121,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008357308,0.0002182032,0.0002747661,0.0002429996,0.0004348247,0.0007514489,0.0005854532,0.00008516565,0.00000529442],"category_scores_gemma":[0.00008728653,0.0001608477,0.00003060716,0.0003453259,0.0001687427,0.0008324638,0.0005548996,0.0001223848,0.00001838228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003470563,"about_ca_system_score_gemma":0.00004870531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001477298,"about_ca_topic_score_gemma":6.969144e-7,"domain_scores_codex":[0.9980372,0.0001154862,0.0008103455,0.0003190718,0.0003493785,0.0003684865],"domain_scores_gemma":[0.9984546,0.0004002919,0.0002396846,0.0003936254,0.0002333201,0.000278486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003874185,0.00002696998,0.00001779124,0.00004992009,0.00001131646,0.00000453143,0.001260517,0.06905172,0.000007555405,0.03418592,0.00003876399,0.8953411],"study_design_scores_gemma":[0.00007876111,0.0001586586,0.00001951219,0.0001442286,0.000004496461,0.00005162688,0.0003461635,0.9977393,0.0002991644,0.0007187478,0.0001994192,0.0002399653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002066158,0.00005205243,0.9963664,0.0002973882,0.0005511429,0.000197313,0.000005975824,0.000277593,0.0001860155],"genre_scores_gemma":[0.18989,0.00009023452,0.8096958,0.0001347331,0.0001515907,0.000002744998,0.000007665547,0.00001058327,0.00001664785],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9286875,"threshold_uncertainty_score":0.7246239,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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)","retraction":null,"screen_n_in":null,"score":{"opus":0.07509896457680171,"gpt":0.3029576007569295,"spread":0.2278586361801278,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007329639,0.000291021,0.0003494157,0.0001414549,0.0006047818,0.001231969,0.0008594417,0.00008544207,0.000002659286],"category_scores_gemma":[0.00007170175,0.0002632217,0.00002786015,0.0002924228,0.0001260411,0.0006264779,0.001420894,0.0002281208,0.0000241272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001193093,"about_ca_system_score_gemma":0.00001176957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001496416,"about_ca_topic_score_gemma":0.000002355805,"domain_scores_codex":[0.9979899,0.0000638146,0.0007085014,0.0004747619,0.0002766469,0.0004863986],"domain_scores_gemma":[0.9984893,0.0003209823,0.000241859,0.0006694662,0.00007260102,0.0002058362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007079587,0.0001443264,0.0006572329,0.00025543,0.00004805838,0.00002243267,0.0207247,0.004406301,0.000006247502,0.1040274,0.006171396,0.8635294],"study_design_scores_gemma":[0.00007889443,0.000205511,0.0001681558,0.000314878,0.00000591823,0.00006280676,0.0004848461,0.9909047,0.00005012253,0.001404247,0.005991194,0.0003287291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1510606,0.001282181,0.8449642,0.0006087986,0.00079866,0.0002343045,0.00001672236,0.0002631002,0.0007714407],"genre_scores_gemma":[0.954788,0.0001910794,0.04424425,0.0005382877,0.0001854096,0.000001084864,0.00001775313,0.000009615304,0.00002451684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9864984,"threshold_uncertainty_score":0.999982,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","retraction":null,"screen_n_in":null,"score":{"opus":0.04849255361357132,"gpt":0.3277189366690197,"spread":0.2792263830554484,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006285107,0.0001895493,0.0002853752,0.0002006458,0.0003584317,0.0001550932,0.0001622643,0.00002577418,0.000004723372],"category_scores_gemma":[0.000008415173,0.0001649662,0.00006360609,0.0002920809,0.00007640706,0.0001245581,0.0001303177,0.0001054766,0.000001521648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008991591,"about_ca_system_score_gemma":0.0000121191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009969586,"about_ca_topic_score_gemma":0.00001543576,"domain_scores_codex":[0.9988763,0.00003959614,0.0005534813,0.0002001536,0.00007190305,0.0002585962],"domain_scores_gemma":[0.9990155,0.0003146538,0.0002425953,0.0001802598,0.0001437106,0.0001032899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004923417,0.00002770073,0.0005251102,0.00002570881,0.00004040304,5.057374e-8,0.003761234,0.002413054,0.00003171943,0.06352073,0.00002046449,0.9295846],"study_design_scores_gemma":[0.0001071175,0.00009707246,0.000119294,0.0001134883,0.00003930778,9.990996e-7,0.0004406025,0.9501767,0.005204248,0.04289299,0.0005791988,0.0002290061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03940153,0.00002538569,0.9593928,0.00002229646,0.00006686458,0.0005572268,0.000002346418,0.00007481375,0.0004567638],"genre_scores_gemma":[0.5430478,0.000002094572,0.4567814,0.00006926825,0.00005878718,0.00001990933,0.000009280591,0.000007282699,0.000004196035],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9477636,"threshold_uncertainty_score":0.6727125,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}