{"meta":{"query_hash":"e8956fc1cf8f","filters":{"venue":"Series on computers and operations research"},"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/e8956fc1cf8f","api":"https://metacan.xera.ac/api/v1/cohort?venue=Series+on+computers+and+operations+research"},"results":[{"id":"W1700519216","doi":"10.1142/9789813200012_0007","title":"From Managing Urban Freight to Smart City Logistics Networks","year":2017,"lang":"en","type":"book-chapter","venue":"Series on computers and operations research","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":93,"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":"City logistics; Business; Traffic management; Transport engineering; Engineering","score_opus":0.08240138169467133,"score_gpt":0.27814968315887717,"score_spread":0.19574830146420585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1700519216","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":[0.0002960673,0.0017255268,0.5186758,0.000595624,0.0024052567,0.000876441,0.0007384253,0.000376615,0.47431022],"genre_scores_gemma":[0.33508384,0.0067873364,0.015596498,0.00043292093,0.0053646094,0.00007664566,0.0019672273,0.0003620374,0.6343289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985087,0.000019380548,0.00027401972,0.00043717783,0.0003634165,0.0003972946],"domain_scores_gemma":[0.9987096,0.000113049566,0.000013875549,0.0007278346,0.00017869506,0.00025693112],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019589266,0.00032803946,0.0003728635,0.0002489574,0.00081646716,0.00044863,0.0004408265,0.00028915567,0.00018290097],"category_scores_gemma":[0.0000151104105,0.0003230985,0.00006217147,0.000033522894,0.00038622852,0.000108229535,0.00014289875,0.0009596787,0.00007146113],"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":[0.000077941615,0.000040900464,0.00033070048,0.00013670839,0.00048726908,0.0007474075,0.0008777343,0.45837662,0.000010038188,0.31922883,0.20535484,0.01433102],"study_design_scores_gemma":[0.00039130735,0.0004404036,0.0006918822,0.0006560865,0.000061969055,0.0000105425115,0.000028279565,0.40121758,0.000024667608,0.005657559,0.58969516,0.0011245713],"about_ca_topic_score_codex":0.00028941905,"about_ca_topic_score_gemma":0.0011008037,"teacher_disagreement_score":0.5030793,"about_ca_system_score_codex":0.000094103845,"about_ca_system_score_gemma":0.00004355314,"threshold_uncertainty_score":0.9999221},"labels":[],"label_agreement":null},{"id":"W4401399850","doi":"10.1142/9789811267048_0004","title":"Two Heuristic Methods for Solving Generalized Nash Equilibrium Problems Using a Novel Penalty Function","year":2024,"lang":"en","type":"book-chapter","venue":"Series on computers and operations research","topic":"Optimization and Variational Analysis","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 Guelph","funders":"","keywords":"Penalty method; Mathematical optimization; Nash equilibrium; Heuristic; Function (biology); Computer science; Mathematics; Applied mathematics; Mathematical economics","score_opus":0.1415827206551833,"score_gpt":0.41428727808191906,"score_spread":0.27270455742673577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401399850","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":[0.0000132955665,0.0005743759,0.9895231,0.0019482865,0.0006848753,0.0007155137,0.000047373407,0.00010256708,0.006390593],"genre_scores_gemma":[0.0005398446,0.0001928245,0.8784913,0.00031437233,0.00051495165,0.00009381492,0.00020028236,0.000060001912,0.1195926],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776435,0.00009209945,0.00045688948,0.00084983936,0.00049037085,0.00034644228],"domain_scores_gemma":[0.9980253,0.00035329495,0.000058377285,0.00045535917,0.00095929566,0.00014836037],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012973204,0.00029297438,0.00037053323,0.0007618472,0.0008128712,0.0019247789,0.00044567967,0.0001508614,0.000068804395],"category_scores_gemma":[0.000064111184,0.00026430833,0.00016619769,0.00033588012,0.00012259289,0.00053923274,0.00055320584,0.00043078553,0.000019211435],"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":[0.0000146767015,0.000022526128,2.3274721e-7,0.000080758764,0.00013522168,0.0000012414289,0.0001590262,0.20438723,0.00059701497,0.7902998,0.00038277794,0.003919533],"study_design_scores_gemma":[0.0003405521,0.00023712091,0.0000012191138,0.00017333754,0.000048319063,0.0000149377265,0.000008999663,0.94065666,0.000028612747,0.023377746,0.034841616,0.0002708632],"about_ca_topic_score_codex":0.000081280676,"about_ca_topic_score_gemma":0.00007429388,"teacher_disagreement_score":0.766922,"about_ca_system_score_codex":0.00014923999,"about_ca_system_score_gemma":0.00033963277,"threshold_uncertainty_score":0.9999809},"labels":[],"label_agreement":null},{"id":"W4401399881","doi":"10.1142/9789811267048_0026","title":"On Removing Diverse Data for Training Machine Learning Models","year":2024,"lang":"en","type":"book-chapter","venue":"Series on computers and operations research","topic":"Machine Learning and Algorithms","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":"Polytechnique Montréal; Concordia University; Group for Research in Decision Analysis","funders":"","keywords":"Training (meteorology); Computer science; Machine learning; Artificial intelligence; Training set; Geography","score_opus":0.2136281158318142,"score_gpt":0.36668852896863213,"score_spread":0.15306041313681792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401399881","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":[0.000082287836,0.0015669371,0.8304228,0.008530332,0.0016731349,0.0012804877,0.0005044677,0.00059439795,0.15534516],"genre_scores_gemma":[0.007822576,0.001229176,0.1193545,0.00051821163,0.0012385917,0.000056936202,0.0012369954,0.00016609694,0.8683769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735594,0.000098722965,0.00027879587,0.0011913114,0.00064166746,0.00043354716],"domain_scores_gemma":[0.99793595,0.0006077034,0.000032378364,0.0010657931,0.00019864435,0.00015950619],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013206789,0.00031935773,0.00033452408,0.0005604357,0.001398841,0.001568279,0.0013818335,0.00016444267,0.000024362835],"category_scores_gemma":[0.00011505686,0.00028057751,0.00007608131,0.00012801225,0.00014990059,0.00058843684,0.001747196,0.0016556002,0.000056546665],"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":[0.00001593668,0.000010836774,1.295224e-7,0.00005646448,0.00005144313,0.000041204163,0.00093805674,0.087564476,0.0000022119896,0.7704124,0.0023147413,0.13859214],"study_design_scores_gemma":[0.00018089253,0.00065445283,2.9768822e-7,0.0004182294,0.000007639667,0.000019498197,0.000056757544,0.8558839,0.0000014280807,0.022097934,0.12042283,0.00025614284],"about_ca_topic_score_codex":0.00008506001,"about_ca_topic_score_gemma":0.000096124306,"teacher_disagreement_score":0.7683194,"about_ca_system_score_codex":0.000063687374,"about_ca_system_score_gemma":0.000177309,"threshold_uncertainty_score":0.99996465},"labels":[],"label_agreement":null}]}