{"meta":{"query_hash":"f7b58334fe5f","filters":{"venue":"Open Journal of Mathematical Optimization"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/f7b58334fe5f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Open+Journal+of+Mathematical+Optimization"},"results":[{"id":"W2971016812","doi":"10.5802/ojmo.2","title":"Revisiting a Cutting-Plane Method for Perfect Matchings","year":2020,"lang":"en","type":"preprint","venue":"Open Journal of Mathematical Optimization","topic":"Complexity and Algorithms in Graphs","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; University of Toronto","funders":"","keywords":"Counterexample; Linear programming; Uniqueness; Mathematics; Cutting-plane method; Matching (statistics); Algorithm; Mathematical optimization; Point (geometry); Polynomial; Sequence (biology); Time complexity; Applied mathematics; Discrete mathematics; Integer programming; Mathematical analysis; Geometry","score_opus":0.06268050422370516,"score_gpt":0.3521685173705242,"score_spread":0.28948801314681905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971016812","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":[0.000025817704,0.00010482453,0.99062175,0.0066355257,0.0003270529,0.0007026628,0.000011218329,0.000036863,0.0015342695],"genre_scores_gemma":[0.00059393153,0.00004558173,0.99851346,0.00036349843,0.00037450573,0.000020583564,0.000012613711,0.000028051634,0.000047765494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974601,0.00027688482,0.001152502,0.00040320976,0.00047075195,0.00023651616],"domain_scores_gemma":[0.9963263,0.0010514881,0.001529633,0.00043623373,0.00046904886,0.00018726074],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003229328,0.00027225335,0.0009072928,0.00014998148,0.00017742554,0.0014622988,0.0030152858,0.0001613391,0.000097667566],"category_scores_gemma":[0.0013405619,0.00023286903,0.0003549017,0.00024210192,0.000026523643,0.00060705765,0.0021685904,0.00070413377,0.0000065955774],"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":[0.00013062873,0.00028710737,0.000006269733,0.0028140235,0.0004378123,0.0000852058,0.003550644,0.5277005,0.00011622767,0.37114656,0.0030141298,0.09071092],"study_design_scores_gemma":[0.00035792435,0.00011086106,0.0000012067809,0.0010501577,0.000071279675,0.00018745747,0.000031429692,0.7308016,0.00012354975,0.26682493,0.00025109752,0.00018852345],"about_ca_topic_score_codex":0.0000024397125,"about_ca_topic_score_gemma":7.305916e-8,"teacher_disagreement_score":0.20310113,"about_ca_system_score_codex":0.000058483234,"about_ca_system_score_gemma":0.0002191826,"threshold_uncertainty_score":0.9995743},"labels":[],"label_agreement":null},{"id":"W3097005701","doi":"10.5802/ojmo.6","title":"Trading off 1-norm and sparsity against rank for linear models using mathematical optimization: 1-norm minimizing partially reflexive ah-symmetric generalized inverses","year":2021,"lang":"en","type":"preprint","venue":"Open Journal of Mathematical Optimization","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","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":"Air Force Office of Scientific Research; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Centre de Recherches Mathématiques; Simons Foundation","keywords":"Moore–Penrose pseudoinverse; Mathematics; Norm (philosophy); Rank (graph theory); Inverse; Generalized inverse; Computation; Low-rank approximation; Least-squares function approximation; Matrix norm; Applied mathematics; Mathematical optimization; Algorithm; Combinatorics; Pure mathematics; Eigenvalues and eigenvectors; Statistics","score_opus":0.10382142233305668,"score_gpt":0.31539656471465355,"score_spread":0.21157514238159686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097005701","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":[0.01576827,0.0007932911,0.9800278,0.00011171438,0.00029720276,0.0010951945,0.000019916955,0.00011667944,0.0017699169],"genre_scores_gemma":[0.06717279,0.0015130816,0.93071073,0.00009287161,0.0002646983,0.000026344742,0.00006472395,0.00013406115,0.000020724265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99681413,0.00016966465,0.0016639954,0.00042779307,0.00051858457,0.00040584916],"domain_scores_gemma":[0.99719864,0.00040711736,0.0008739433,0.0004588426,0.000786981,0.0002744932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010862424,0.0005536229,0.0015265362,0.00044390265,0.00020903602,0.0008009006,0.0006469795,0.0005005154,0.00010253039],"category_scores_gemma":[0.00052138756,0.0005324265,0.00034674926,0.00037453743,0.000095898424,0.00091581896,0.0005782202,0.00061762944,5.6082376e-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":[0.00010012448,0.00013402727,0.0000014698938,0.00054755475,0.00034822625,0.00003728517,0.0006272263,0.99575907,0.00023032095,0.0012312934,0.00018463036,0.0007987874],"study_design_scores_gemma":[0.0011667317,0.00007632414,2.742766e-7,0.0026721174,0.0005568727,0.00015511877,0.0002273509,0.97929186,0.0030450835,0.012264115,0.000018484247,0.00052566035],"about_ca_topic_score_codex":0.0000025925615,"about_ca_topic_score_gemma":8.953066e-7,"teacher_disagreement_score":0.05140452,"about_ca_system_score_codex":0.0002426853,"about_ca_system_score_gemma":0.00021694694,"threshold_uncertainty_score":0.9997127},"labels":[],"label_agreement":null},{"id":"W3178931227","doi":"10.5802/ojmo.7","title":"The difference vectors for convex sets and a resolution of the geometry conjecture","year":2021,"lang":"en","type":"article","venue":"Open Journal of Mathematical Optimization","topic":"Point processes and geometric inequalities","field":"Mathematics","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, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Conjecture; Mathematics; Convex geometry; Regular polygon; Convex set; Hilbert space; Geometry; Space (punctuation); Point (geometry); Combinatorics; Pure mathematics; Convex optimization; Computer science","score_opus":0.056714649993269875,"score_gpt":0.3344459630127415,"score_spread":0.2777313130194716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178931227","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":[0.08626069,0.0007726228,0.9079299,0.0032689243,0.00021774785,0.00056939805,0.000023429715,0.0000052382884,0.0009520357],"genre_scores_gemma":[0.7379452,0.00047203328,0.25939223,0.00016852027,0.00010134932,0.00002350139,0.0000042852694,0.000035689132,0.0018572165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878305,0.00011862947,0.0006039523,0.00008293946,0.000282593,0.00012885974],"domain_scores_gemma":[0.9960447,0.002481556,0.0006510582,0.00018893376,0.00058978965,0.00004398575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012050888,0.00009372809,0.0003234725,0.000048355145,0.00018947685,0.00015135766,0.00033254348,0.00006652609,0.000059558304],"category_scores_gemma":[0.007516754,0.00004696227,0.00009187364,0.00036044727,0.00009795329,0.00013865122,0.00015598303,0.00012931913,2.2043831e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002977243,0.0004709051,0.00026809546,0.0020404595,0.00036512135,0.000005583378,0.0026185778,0.0037844016,0.00037125679,0.981191,0.0025873294,0.005999558],"study_design_scores_gemma":[0.0015166233,0.00027223537,0.00030771777,0.0007893203,0.00025011424,0.00028345812,0.0018115176,0.03618108,0.0066826646,0.9512666,0.0004662554,0.00017244543],"about_ca_topic_score_codex":0.0000010359764,"about_ca_topic_score_gemma":0.0000019997774,"teacher_disagreement_score":0.65168446,"about_ca_system_score_codex":0.000022075272,"about_ca_system_score_gemma":0.00013414171,"threshold_uncertainty_score":0.89987993},"labels":[],"label_agreement":null},{"id":"W4396860342","doi":"10.5802/ojmo.27","title":"Cardinality-constrained structured data-fitting problems","year":2024,"lang":"en","type":"article","venue":"Open Journal of Mathematical Optimization","topic":"Machine Learning and Algorithms","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","funders":"","keywords":"Cardinality (data modeling); Dual (grammatical number); Iterated function; Mathematical optimization; Constraint (computer-aided design); Computer science; Simple (philosophy); Algorithm; Identification (biology); Mathematics; Data mining","score_opus":0.041852714837984556,"score_gpt":0.3248309299069888,"score_spread":0.2829782150690042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396860342","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":[0.00014578117,0.00017258956,0.99269116,0.002205466,0.00031720745,0.00014590967,0.0000036368253,0.000044394208,0.0042738756],"genre_scores_gemma":[0.07246377,0.000014400221,0.9270137,0.000055767807,0.00017492013,0.0000015748429,0.000008527138,0.000013122364,0.00025423765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855644,0.00014488045,0.00056283065,0.0002279468,0.00035719958,0.000150734],"domain_scores_gemma":[0.9988898,0.00021561423,0.00022683661,0.00042901628,0.00013654855,0.000102152495],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021656863,0.00011201921,0.00027430063,0.0000977713,0.00008901272,0.0014592189,0.0019142567,0.00004576888,0.00015999828],"category_scores_gemma":[0.00043584316,0.00007922773,0.00006159644,0.00033624607,0.000029865072,0.0016119982,0.00055886555,0.00029871703,0.00001432754],"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":[0.000009816555,0.00011136066,0.000025186573,0.00040983115,0.00022360812,0.00015652215,0.001721244,0.71601766,0.00012030754,0.13039069,0.0019875925,0.14882615],"study_design_scores_gemma":[0.00020189889,0.000063667,0.0000059326717,0.00035867724,0.000029489522,0.00049701217,0.00003230489,0.9776734,0.000034294815,0.019895956,0.0011107088,0.00009664403],"about_ca_topic_score_codex":0.0000025873258,"about_ca_topic_score_gemma":1.605782e-7,"teacher_disagreement_score":0.26165572,"about_ca_system_score_codex":0.000024030347,"about_ca_system_score_gemma":0.00014610041,"threshold_uncertainty_score":0.99957734},"labels":[],"label_agreement":null},{"id":"W4401035172","doi":"10.5802/ojmo.31","title":"Tight analyses for subgradient descent I: Lower bounds","year":2024,"lang":"lv","type":"article","venue":"Open Journal of Mathematical Optimization","topic":"Machine Learning and Algorithms","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":"Subgradient method; Mathematics; Lipschitz continuity; Upper and lower bounds; Differentiable function; Combinatorics; Convex function; Descent (aeronautics); Matching (statistics); Regular polygon; Function (biology); Discrete mathematics; Mathematical optimization; Pure mathematics; Statistics; Mathematical analysis","score_opus":0.06688709976136888,"score_gpt":0.38187943235358673,"score_spread":0.31499233259221787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401035172","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":[0.00027730883,0.0018130803,0.9889627,0.0045381086,0.0017176081,0.00040990088,0.0000069105267,0.000026130285,0.0022482348],"genre_scores_gemma":[0.06325282,0.00026166806,0.9304822,0.00017920375,0.00067279884,0.000012467749,0.00000814654,0.000052478616,0.0050782342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974573,0.00019070509,0.0010846803,0.00034318754,0.0005781215,0.00034597272],"domain_scores_gemma":[0.99793243,0.00053369405,0.00046907796,0.00033179278,0.00046405566,0.00026894233],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0020338458,0.0002616321,0.0005849719,0.00026299563,0.00021988623,0.0034437086,0.0013197156,0.000111121284,0.00067286944],"category_scores_gemma":[0.0004887787,0.000188665,0.00037469692,0.0006021521,0.00007015027,0.0011451966,0.00028562994,0.00038294023,0.00006297145],"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":[0.00017587128,0.0013591757,0.000012460083,0.0011841392,0.00076126267,0.00028773717,0.0032670074,0.7736562,0.000096794516,0.13657206,0.01736605,0.06526125],"study_design_scores_gemma":[0.0005355274,0.0007735094,0.000005997278,0.0015090284,0.0002585387,0.00023285953,0.00003886286,0.9756187,0.00014496953,0.010223974,0.010430557,0.00022745082],"about_ca_topic_score_codex":0.0000035358808,"about_ca_topic_score_gemma":3.819471e-7,"teacher_disagreement_score":0.20196253,"about_ca_system_score_codex":0.0001289136,"about_ca_system_score_gemma":0.00033901786,"threshold_uncertainty_score":0.99759084},"labels":[],"label_agreement":null}]}