{"meta":{"query_hash":"b128c1e2c194","filters":{"venue":"Optimization and Engineering"},"cohort_total":78,"direct_labels_cover":0,"predictions_cover":78,"exported":78,"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/b128c1e2c194","api":"https://metacan.xera.ac/api/v1/cohort?venue=Optimization+and+Engineering"},"results":[{"id":"W1028453535","doi":"10.1007/s11081-015-9282-1","title":"Constrained problem formulations for power optimization of aircraft electro-thermal anti-icing systems","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":36,"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; Group for Research in Decision Analysis; McGill University","funders":"","keywords":"Icing; Mathematical optimization; Computer science; Convergence (economics); Optimization problem; Power (physics); Mathematics","score_opus":0.00951036878113416,"score_gpt":0.196660483267986,"score_spread":0.18715011448685184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1028453535","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":[0.015334412,0.00011757052,0.98268133,0.00002729042,0.0001789195,0.00028311333,0.000007681837,0.00075821916,0.00061148795],"genre_scores_gemma":[0.7996716,0.000027889027,0.20016673,0.000003275009,0.000021118905,0.00002623094,0.000028843799,0.00003560883,0.000018695462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993256,0.00000494113,0.00026344505,0.000115011324,0.00008714657,0.00020386829],"domain_scores_gemma":[0.9996282,0.00004166717,0.00004386503,0.00011206771,0.00012052967,0.00005366128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014267617,0.00014125122,0.00020075329,0.00019456517,0.000044755598,0.00003934949,0.00005814332,0.00010965054,0.0000024666328],"category_scores_gemma":[0.00007115951,0.00014855659,0.000027840199,0.00021277473,0.0000183979,0.00017614913,0.000013060308,0.000072222334,3.566278e-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":[0.0000031940983,0.0000058003147,0.0001072854,0.000116860625,0.000024757272,2.4217613e-7,0.00017733451,0.9966982,0.0012670881,0.0013209839,0.00008706266,0.00019116461],"study_design_scores_gemma":[0.00046436663,0.000050783725,0.000031206037,0.000082074475,0.000018258288,0.000007647927,0.00010607132,0.9952366,0.0037116578,0.000008153062,0.00012229566,0.00016089511],"about_ca_topic_score_codex":0.0000050825042,"about_ca_topic_score_gemma":1.9116158e-7,"teacher_disagreement_score":0.78433716,"about_ca_system_score_codex":0.000040341863,"about_ca_system_score_gemma":0.000017082459,"threshold_uncertainty_score":0.6057962},"labels":[],"label_agreement":null},{"id":"W1518867187","doi":"10.1023/a:1016086220943","title":"An Introduction to the Space Mapping Technique","year":2001,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Computer science; Space mapping; Preprocessor; Mathematical optimization; Space (punctuation); Algorithm; Function (biology); Mathematics; Artificial intelligence","score_opus":0.006047019999597958,"score_gpt":0.2198307084523365,"score_spread":0.21378368845273854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518867187","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":[0.0061549167,0.000053362095,0.9914667,0.0012905038,0.0002143788,0.00014390217,2.3560125e-7,0.0003635481,0.000312449],"genre_scores_gemma":[0.43092352,0.00014034577,0.56807613,0.00010675141,0.0005406256,0.00005771507,0.000006998009,0.000038507562,0.00010943317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962217,0.000013001226,0.00008944307,0.000100679106,0.00005247371,0.00012220828],"domain_scores_gemma":[0.99976337,0.000019756963,0.0000063968,0.0001236152,0.000017741553,0.000069107744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012954081,0.000079091646,0.000063110245,0.000083410305,0.000045345103,0.00003766698,0.000048273563,0.000035039953,0.00006316616],"category_scores_gemma":[0.000033785163,0.00006898178,0.000010037872,0.00036744212,0.000004463121,0.00008716495,0.0000067997626,0.00007627083,0.0000027081207],"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":[0.0000010884088,0.0000023932928,0.000026847887,0.000005052104,0.0000027987226,2.748749e-7,0.000081349914,0.978915,0.01629503,0.00022896874,0.00020801643,0.004233227],"study_design_scores_gemma":[0.000047372057,0.000024410185,0.00048171793,0.0000040408063,0.0000025010806,0.000014508647,0.000018727875,0.9509751,0.0012158413,0.0000048416696,0.04711884,0.00009210222],"about_ca_topic_score_codex":0.0000014109002,"about_ca_topic_score_gemma":3.222439e-7,"teacher_disagreement_score":0.4247686,"about_ca_system_score_codex":0.000019202344,"about_ca_system_score_gemma":0.0000015273303,"threshold_uncertainty_score":0.28129956},"labels":[],"label_agreement":null},{"id":"W1550542145","doi":"10.1023/a:1011860702585","title":"Mixed Variable Optimization of the Number and Composition of Heat Intercepts in a Thermal Insulation System","year":2001,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":71,"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","funders":"","keywords":"Categorical variable; Mathematical optimization; Mathematics; Discrete optimization; Variable (mathematics); Continuous optimization; Parametric statistics; Continuous variable; Reduction (mathematics); Function (biology); Optimization problem; Computer science; Statistics; Mathematical analysis","score_opus":0.006937388061216975,"score_gpt":0.19937026656132575,"score_spread":0.1924328785001088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550542145","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":[0.039142862,0.00002463004,0.96022475,0.000029020212,0.00012128725,0.00017030313,0.0000015350586,0.000044956414,0.00024063536],"genre_scores_gemma":[0.59775877,0.00002329103,0.4021836,0.0000056861054,0.000005857291,0.0000056500385,0.0000040488694,0.0000068988656,0.0000062105155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933946,0.00004083098,0.00025843424,0.00015983147,0.000106758314,0.00009468726],"domain_scores_gemma":[0.99960226,0.00003838488,0.00008424169,0.00014494869,0.0001019998,0.000028187296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011592597,0.00009556597,0.00013972698,0.00010514844,0.000039872637,0.00002600514,0.00009962347,0.000053888532,0.0000061886863],"category_scores_gemma":[0.000024244828,0.0000862823,0.000015477266,0.0005497303,0.000022810964,0.0004466279,0.00007999147,0.000057372574,1.4464881e-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":[0.0000054478724,0.000018964029,0.0015643958,0.000039932256,0.0000048754277,3.1612808e-7,0.00028800822,0.99319017,0.002573739,0.0020215525,2.2121786e-7,0.00029234894],"study_design_scores_gemma":[0.0004777192,0.000009846744,0.008440634,0.0001305982,0.000004354543,0.000018769582,0.000042549407,0.98999846,0.00079224556,0.0000036142546,0.0000023021346,0.00007890709],"about_ca_topic_score_codex":0.000017549537,"about_ca_topic_score_gemma":4.0346208e-7,"teacher_disagreement_score":0.5586159,"about_ca_system_score_codex":0.000048613183,"about_ca_system_score_gemma":0.000011654663,"threshold_uncertainty_score":0.35184902},"labels":[],"label_agreement":null},{"id":"W1560742679","doi":"10.1023/a:1010000106286","title":"Review of the Space Mapping Approach to Engineering Optimization and Modeling","year":2000,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Space mapping; Computer science; Iterated function; Space (punctuation); Mathematical optimization; Optimization problem; Uniqueness; Algorithm; Mathematics","score_opus":0.010403090540044223,"score_gpt":0.1725521408456413,"score_spread":0.16214905030559706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560742679","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":[0.001095689,0.005854806,0.9915049,0.00007508413,0.00009732005,0.00030944293,0.0000027942303,0.00019440171,0.0008655369],"genre_scores_gemma":[0.13553639,0.06189949,0.8017212,0.00032127736,0.00010619692,0.00006446989,0.000038751754,0.00014508808,0.00016710002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924004,0.000010205232,0.00027687184,0.00017512841,0.00011192376,0.0001858254],"domain_scores_gemma":[0.9996609,0.000015069437,0.000022216098,0.00016505155,0.000046327288,0.00009041923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013527469,0.00018652047,0.00020935985,0.0001107548,0.000054364253,0.00003545278,0.000088775705,0.00006991347,0.00003620718],"category_scores_gemma":[0.000040142364,0.00017130144,0.00003528793,0.0004891806,0.000009511746,0.00018653207,0.000026389678,0.000106618594,8.4961164e-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":[0.0000010481185,0.000004635996,0.000009624033,0.0012734825,0.00001269283,8.875616e-8,0.0001582497,0.99786,0.00018626334,0.00008739814,0.00006418173,0.00034231678],"study_design_scores_gemma":[0.00012811962,0.0000051725715,0.000019368577,0.0015375254,0.000019044352,0.000009888364,0.000018547476,0.9975847,0.00003863319,4.7665694e-7,0.00044894332,0.00018960575],"about_ca_topic_score_codex":0.0000028771324,"about_ca_topic_score_gemma":6.638368e-8,"teacher_disagreement_score":0.18978369,"about_ca_system_score_codex":0.000027172666,"about_ca_system_score_gemma":0.000006210974,"threshold_uncertainty_score":0.698547},"labels":[],"label_agreement":null},{"id":"W158002236","doi":"10.1023/a:1023985013315","title":"A Global Optimization Approach to Laser Design","year":2003,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Solid State Laser Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Dalhousie University","funders":"","keywords":"Flatness (cosmology); Extrapolation; Computer science; Laser; Range (aeronautics); Field (mathematics); Mathematical optimization; Mathematics; Optics; Physics; Engineering","score_opus":0.010979905762078409,"score_gpt":0.185961991662973,"score_spread":0.1749820859008946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W158002236","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":[0.0009772456,0.00010793937,0.99360955,0.000013547906,0.00013601597,0.00019003014,0.0000036498168,0.0011338202,0.0038281807],"genre_scores_gemma":[0.18956296,0.000092457645,0.81020314,0.000021498126,0.000013860268,0.000043328255,0.000008574829,0.000031512427,0.000022683054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994093,0.000009219603,0.00013084165,0.00015784615,0.000077931974,0.0002148297],"domain_scores_gemma":[0.9997295,0.000015272904,0.000009212906,0.00013778188,0.000024042594,0.000084166284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007332982,0.0001535798,0.00011737036,0.000102700455,0.000035202636,0.00005969722,0.00006782179,0.000086799446,0.000010270541],"category_scores_gemma":[0.00007917815,0.00017028063,0.000015529153,0.00049086113,0.000008177524,0.00013855452,0.000014788924,0.000054138894,0.0000034555987],"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":[0.000001262251,0.0000070024894,0.000043540793,0.000021641754,0.000011911637,7.217665e-7,0.000044998236,0.9989367,0.000021342987,0.00051362085,0.00023832597,0.00015892144],"study_design_scores_gemma":[0.00017111872,0.000011212423,0.00003122135,0.000009331297,0.0000070890055,0.0000110441715,0.00004703577,0.99866647,0.00047181445,0.00000713087,0.0003742827,0.00019227195],"about_ca_topic_score_codex":8.411097e-7,"about_ca_topic_score_gemma":1.2741194e-7,"teacher_disagreement_score":0.18858571,"about_ca_system_score_codex":0.00006631425,"about_ca_system_score_gemma":0.000005570648,"threshold_uncertainty_score":0.6943842},"labels":[],"label_agreement":null},{"id":"W1863391138","doi":"10.1007/s11081-015-9285-y","title":"Announcement: Inaugural Howard Rosenbrock Prize","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Financial engineering; Computer science; Mathematical economics; Applied mathematics; Mathematics; Economics; Financial economics","score_opus":0.012194430913530322,"score_gpt":0.19757900221927605,"score_spread":0.18538457130574573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1863391138","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":[0.001474553,0.00009455814,0.9968923,0.00016312182,0.00024171024,0.000042815915,5.602704e-7,0.00015547212,0.0009349163],"genre_scores_gemma":[0.25715044,0.000059420607,0.74164945,0.00013770036,0.0001272125,0.000010057235,0.00000920392,0.000013125963,0.00084338407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996029,0.000008034997,0.00007629368,0.00011578369,0.00008710267,0.000109880064],"domain_scores_gemma":[0.9997404,0.00000914927,0.000016989661,0.00010814751,0.000033977158,0.0000912896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010902789,0.000066142486,0.000059926264,0.000046601228,0.000031747808,0.000099992576,0.00011619013,0.000023847088,0.0000049490654],"category_scores_gemma":[0.000025113952,0.000063703286,0.000010522153,0.00015560775,0.000005497484,0.00035054388,0.00006713622,0.000040774372,0.0000053415474],"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":[0.0000011451104,0.000006129228,0.000030508341,0.000008391243,0.0000034057166,0.0000021494586,0.00028245398,0.9806164,0.00006720397,0.017372666,0.00017049685,0.0014390865],"study_design_scores_gemma":[0.00020694788,0.000018570392,0.000039430968,0.000006989686,0.0000013152717,0.000009752855,0.00001533997,0.99449825,0.00014657709,0.0001093089,0.0048580677,0.00008944869],"about_ca_topic_score_codex":0.0000011074496,"about_ca_topic_score_gemma":4.1874706e-8,"teacher_disagreement_score":0.2556759,"about_ca_system_score_codex":0.000014263361,"about_ca_system_score_gemma":0.000011927642,"threshold_uncertainty_score":0.25977448},"labels":[],"label_agreement":null},{"id":"W1925813256","doi":"10.1007/s11081-015-9283-0","title":"Dynamic scaling in the mesh adaptive direct search algorithm for blackbox optimization","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convergence (economics); Algorithm; Benchmark (surveying); Mathematical optimization; Adaptive mesh refinement; Scale (ratio); Computational science; Mathematics","score_opus":0.047452642848286604,"score_gpt":0.323116341158649,"score_spread":0.2756636983103624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1925813256","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":[0.00015303366,0.000104593324,0.99814445,0.00018043359,0.00008565674,0.0007439996,0.000015189683,0.0001156547,0.00045698835],"genre_scores_gemma":[0.006845132,0.00016453148,0.9924545,0.000034194403,0.00004513442,0.00013822266,0.00006200292,0.00006061607,0.00019561208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866605,0.00007636919,0.00029799953,0.00027845593,0.00034657223,0.00033452897],"domain_scores_gemma":[0.9989243,0.00043914025,0.00005348928,0.00019904104,0.00026835786,0.00011563435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092871505,0.00018369322,0.00021564288,0.0002596577,0.00010051758,0.000106168496,0.0001672454,0.00009866381,0.000016027268],"category_scores_gemma":[0.00055385265,0.00015927631,0.000037997277,0.0006071398,0.000036674483,0.0003138495,0.00005212882,0.00018364353,0.0000013071144],"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":[0.000011649639,0.00003428194,0.0000048871534,0.000026089312,0.000012659432,0.0000021194087,0.0010280154,0.99487454,0.000002492186,0.00074916193,0.00005464783,0.0031994337],"study_design_scores_gemma":[0.00092070666,0.00005089762,0.0000058610053,0.000040243343,0.0000123223,0.000008499769,0.0010010729,0.9974958,0.000023320566,0.00016963648,0.00008813206,0.00018353846],"about_ca_topic_score_codex":0.0000050701296,"about_ca_topic_score_gemma":0.0000020485595,"teacher_disagreement_score":0.0066920985,"about_ca_system_score_codex":0.00013888266,"about_ca_system_score_gemma":0.00005174293,"threshold_uncertainty_score":0.64950997},"labels":[],"label_agreement":null},{"id":"W1969784127","doi":"10.1007/s11081-009-9082-6","title":"Benchmarking multidisciplinary design optimization algorithms","year":2009,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":134,"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 Toronto","funders":"","keywords":"Benchmarking; Multidisciplinary design optimization; Computer science; Modular design; Python (programming language); Multidisciplinary approach; Algorithm; Programming language","score_opus":0.011916215872024604,"score_gpt":0.2317326145440032,"score_spread":0.2198163986719786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969784127","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":[0.00001784264,0.00012308064,0.99826986,0.0002552694,0.0002825751,0.00027033754,0.0000010890302,0.00053122506,0.00024870332],"genre_scores_gemma":[0.007978588,0.0002422978,0.99146783,0.000098809505,0.000087614455,0.00001646563,0.000019227167,0.000022788869,0.000066364766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987094,0.000035939018,0.00027584095,0.0004708925,0.00020160402,0.0003063594],"domain_scores_gemma":[0.999265,0.00007337108,0.00009341603,0.00028286772,0.00013413938,0.00015118648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020227439,0.00024043593,0.00018533239,0.00026211486,0.00021084187,0.00019594686,0.00025266985,0.00009634271,0.000021032041],"category_scores_gemma":[0.00006936035,0.00026097268,0.000036666966,0.00065553817,0.000017724125,0.0012214821,0.000094781746,0.00013295458,0.0000027891163],"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":[0.0000027467165,0.000029255167,0.000009650037,0.0000044089606,0.0000065484664,0.000006370304,0.00022475795,0.9857853,0.00008896067,0.00085307885,0.000012508865,0.012976429],"study_design_scores_gemma":[0.00046790097,0.00008441225,0.0002820342,0.000029148472,0.000005741778,0.000030397889,0.000013273997,0.99849814,0.00020016037,0.000039169143,0.000039665945,0.00030994826],"about_ca_topic_score_codex":0.0000010647044,"about_ca_topic_score_gemma":5.2262454e-8,"teacher_disagreement_score":0.012712862,"about_ca_system_score_codex":0.000075153584,"about_ca_system_score_gemma":0.000026909462,"threshold_uncertainty_score":0.99998426},"labels":[],"label_agreement":null},{"id":"W1972320167","doi":"10.1007/s11081-008-9036-4","title":"Multi-objective aerodynamic shape optimization for unsteady viscous flows","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Aerodynamics; Shape optimization; Mach number; Computer science; Flow (mathematics); Rotor (electric); Mathematical optimization; Domain (mathematical analysis); Helicopter rotor; Control theory (sociology); Mathematics; Applied mathematics; Mechanics; Mechanical engineering; Mathematical analysis; Physics; Geometry; Engineering; Structural engineering; Finite element method","score_opus":0.007895143781932337,"score_gpt":0.1958947832367592,"score_spread":0.18799963945482687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972320167","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":[0.094551556,0.00013695676,0.90425134,0.000016686936,0.00029926418,0.00026915353,0.000028896156,0.0003715502,0.000074584044],"genre_scores_gemma":[0.5348944,0.00042332118,0.4641964,0.000017927843,0.00006650089,0.000048333062,0.00022238476,0.000076078046,0.00005462214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992337,0.000005365103,0.00022997448,0.00021034355,0.000098872064,0.00022169953],"domain_scores_gemma":[0.9996402,0.000059152546,0.000023645307,0.00010082029,0.00009218932,0.00008398132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006055416,0.00020526718,0.00017634429,0.00015286503,0.00012998011,0.000040112256,0.00006861683,0.00008890336,0.00002563475],"category_scores_gemma":[0.000031153904,0.00023907294,0.000050023908,0.00020658434,0.000014888863,0.00019973167,0.000020324267,0.00009200236,0.0000017845717],"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":[0.0000039089355,0.000014102431,0.000046901583,0.000046866564,0.000028108112,0.0000017362665,0.00011700544,0.9986564,0.0005110623,0.0001343279,0.000021356373,0.00041823433],"study_design_scores_gemma":[0.00058997836,0.000021849317,0.0008408087,0.000021013066,0.000013550417,0.00002439446,0.000014953172,0.99813724,0.000008463521,0.0000023722737,0.000054587632,0.0002708019],"about_ca_topic_score_codex":0.0000033963267,"about_ca_topic_score_gemma":0.0000057868733,"teacher_disagreement_score":0.44034287,"about_ca_system_score_codex":0.00008705087,"about_ca_system_score_gemma":0.000015433794,"threshold_uncertainty_score":0.97491115},"labels":[],"label_agreement":null},{"id":"W1975069415","doi":"10.1007/s11081-014-9271-9","title":"A new algorithm using front prediction and NSGA-II for solving two and three-objective optimization problems","year":2014,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Sorting; Multi-objective optimization; Mathematical optimization; Algorithm; Genetic algorithm; Evolutionary algorithm; Computer science; Mathematics","score_opus":0.008871635243027535,"score_gpt":0.21655461054962633,"score_spread":0.2076829753065988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975069415","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":[0.00016846259,0.00021903096,0.9984213,0.00007168141,0.00026427937,0.0005463123,0.000006599106,0.0002647035,0.000037618396],"genre_scores_gemma":[0.00422977,0.0001411686,0.99530643,0.000035753066,0.00013526858,0.00003795176,0.000019925745,0.000039610608,0.00005409889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879414,0.000018268762,0.00026245287,0.0005214588,0.00013931481,0.00026439523],"domain_scores_gemma":[0.99927807,0.00009051955,0.00011766614,0.00017200342,0.00016851563,0.0001732293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023064345,0.00023866595,0.00022050212,0.00021798878,0.0003536545,0.00022349521,0.00009887458,0.0000889369,0.0000045617544],"category_scores_gemma":[0.00012579678,0.00025922147,0.000024686527,0.0002397887,0.000026531286,0.001185002,0.00016032389,0.00010018187,1.5866047e-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":[0.000003154399,0.000009828571,0.000069338144,0.000025757547,0.000017499293,1.9094092e-7,0.00054833497,0.9733542,0.00015142621,0.0005725787,0.000004717188,0.025242971],"study_design_scores_gemma":[0.0014146063,0.00010198614,0.00014852271,0.00007015937,0.000022323045,0.000029833078,0.000021024955,0.99757606,0.000108701206,0.00015195365,0.00008961714,0.00026519797],"about_ca_topic_score_codex":0.000028245897,"about_ca_topic_score_gemma":0.0000044987564,"teacher_disagreement_score":0.024977773,"about_ca_system_score_codex":0.00007240361,"about_ca_system_score_gemma":0.000031237905,"threshold_uncertainty_score":0.999986},"labels":[],"label_agreement":null},{"id":"W1979973936","doi":"10.1023/b:opte.0000042036.04931.49","title":"Rapid, Embeddable Design Method for Spiral Magnetic Resonance Image Reconstruction Resampling Kernels","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Resampling; Piecewise; Mathematics; Algorithm; Mathematical optimization; Bessel function; Kernel (algebra); Iterative reconstruction; Norm (philosophy); Applied mathematics; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.02212336073985096,"score_gpt":0.2911690377505036,"score_spread":0.2690456770106527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979973936","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":[0.00024181184,0.00052662654,0.9981873,0.00020984365,0.00003470515,0.00046962054,0.0000036165275,0.00017498403,0.0001514709],"genre_scores_gemma":[0.0011515283,0.000578119,0.99784607,0.000057438825,0.000060306986,0.00013170362,0.000013040969,0.000024699058,0.00013708035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995169,0.0000035372989,0.00014103617,0.0001654693,0.000043385513,0.0001296793],"domain_scores_gemma":[0.9997225,0.000031723594,0.000027948168,0.00010419028,0.000056852437,0.000056827055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011299429,0.000085838285,0.00011594043,0.00006182151,0.000066492335,0.0000182629,0.000022336875,0.000054827793,0.00002044248],"category_scores_gemma":[0.00005397009,0.00008845541,0.000026217884,0.00012653712,0.000012619835,0.000103140526,0.000008553507,0.00006391586,6.905984e-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":[0.000020073014,0.000010171532,0.0000044213652,0.000030609328,0.0000016271515,5.632784e-7,0.000029643434,0.94727737,0.009542668,0.001077179,0.000022942231,0.041982718],"study_design_scores_gemma":[0.00083523785,0.00011611399,0.00005862961,0.00010857354,0.000024332643,0.00007313453,0.000024295889,0.9817788,0.01269011,0.00045565047,0.0037192602,0.000115852104],"about_ca_topic_score_codex":0.0000034612776,"about_ca_topic_score_gemma":1.0629309e-7,"teacher_disagreement_score":0.041866865,"about_ca_system_score_codex":0.00004039315,"about_ca_system_score_gemma":0.000016919103,"threshold_uncertainty_score":0.36071068},"labels":[],"label_agreement":null},{"id":"W1983035488","doi":"10.1007/s11081-008-9068-9","title":"The hybrid-adjoint method: a semi-analytic gradient evaluation technique applied to composite cure cycle optimization","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Epoxy Resin Curing Processes","field":"Engineering","cited_by":14,"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 Toronto","funders":"","keywords":"Finite element method; Thermosetting polymer; Sensitivity (control systems); Temperature gradient; Composite number; Residual stress; Autoclave; Beam (structure); Optimal design; Mathematical optimization; Gradient method; Materials science; Computer science; Structural engineering; Mathematics; Composite material; Engineering; Physics","score_opus":0.01135768420048941,"score_gpt":0.23446428023617283,"score_spread":0.22310659603568342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983035488","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":[0.0035480673,0.0003377381,0.9939848,0.00010175511,0.0001366778,0.0005990131,0.0000029438318,0.00049448333,0.00079449225],"genre_scores_gemma":[0.58432615,0.00067730166,0.41437677,0.00003656729,0.00007073896,0.0003462573,0.000039449147,0.000083945764,0.000042837542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989459,0.00002191589,0.00028603227,0.00023199958,0.00026089727,0.00025329107],"domain_scores_gemma":[0.99942714,0.00008243419,0.000039290222,0.00022346519,0.00010815324,0.00011952611],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003893171,0.000211037,0.00018187422,0.00017107933,0.0002352107,0.000083216,0.00012031238,0.000062536674,0.000016845659],"category_scores_gemma":[0.00006714725,0.00019529613,0.000033899607,0.000421993,0.0000171725,0.00013314885,0.000038966795,0.00014583014,0.00000389088],"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":[0.0000042127604,0.0000066851444,0.000016273927,0.000058330676,0.000029287612,0.0000013609365,0.00018158474,0.99722624,0.0016671175,0.00007070977,0.0001430971,0.00059512514],"study_design_scores_gemma":[0.0001879845,0.000011752745,0.00010905276,0.000043695512,0.000038206992,0.00005157643,0.000015276037,0.990858,0.0078357905,0.000012980307,0.0006060687,0.00022962589],"about_ca_topic_score_codex":0.000003766086,"about_ca_topic_score_gemma":0.0000010547526,"teacher_disagreement_score":0.58077806,"about_ca_system_score_codex":0.00012453798,"about_ca_system_score_gemma":0.000018681265,"threshold_uncertainty_score":0.7963945},"labels":[],"label_agreement":null},{"id":"W1985059812","doi":"10.1023/b:opte.0000038891.11414.04","title":"An Integrated Optimization Model for Inventory and Quality Control Problems","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":12,"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 New Brunswick","funders":"","keywords":"Computer science; Control chart; Statistical process control; Process (computing); Variance (accounting); Common cause and special cause; Quality (philosophy); Work in process; False alarm; Schedule; Sampling (signal processing); Production (economics); Process control; State (computer science); Control (management); Reliability engineering; Mathematical optimization; Statistics; Mathematics; Algorithm; Operations management; Engineering; Artificial intelligence; Detector","score_opus":0.08435243216666226,"score_gpt":0.3656640375634263,"score_spread":0.28131160539676403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985059812","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":[0.001486413,0.00007162293,0.9979402,0.00007145267,0.000086595406,0.00021868615,0.000019809442,0.00008375976,0.000021472548],"genre_scores_gemma":[0.48608738,0.00002177382,0.513759,0.000027109325,0.000020264426,0.00002657508,0.0000133444955,0.000012793166,0.0000317549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897933,0.000018494004,0.00034297863,0.00029147472,0.00021979885,0.00014792477],"domain_scores_gemma":[0.99924845,0.00018059574,0.00008112796,0.0001248252,0.00023148942,0.00013353684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005588101,0.000112901835,0.00016983942,0.0001112797,0.00010661587,0.00016166366,0.00009050479,0.000059774622,0.000005450486],"category_scores_gemma":[0.0013684985,0.00009954136,0.000015336127,0.00019564899,0.000031189367,0.0005957498,0.000014711642,0.00006660109,3.4023964e-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":[0.000012308043,0.0000123584805,0.0001394803,0.000018818924,0.0000034271372,1.04175356e-7,0.00022002216,0.99504054,0.00013638254,0.003270596,0.0000016388963,0.001144339],"study_design_scores_gemma":[0.00080800673,0.0000311248,0.000059217964,0.000020247144,0.000006104547,7.8522686e-7,0.00007697016,0.99720645,0.000022238508,0.0016119066,0.000030992567,0.00012593852],"about_ca_topic_score_codex":0.0000063441653,"about_ca_topic_score_gemma":0.0000027423084,"teacher_disagreement_score":0.48460096,"about_ca_system_score_codex":0.000038558213,"about_ca_system_score_gemma":0.000024417515,"threshold_uncertainty_score":0.40591788},"labels":[],"label_agreement":null},{"id":"W1985753777","doi":"10.1007/s11081-008-9043-5","title":"Editorial—surrogate modeling and space mapping for engineering optimization","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Surrogate model; Computer science; Financial engineering; Multidisciplinary design optimization; Engineering optimization; Systems engineering; Exploit; Optimization problem; Multidisciplinary approach; Kriging; Industrial engineering; Uncertainty quantification; Machine learning; Engineering","score_opus":0.01166669951559834,"score_gpt":0.20358950946015764,"score_spread":0.1919228099445593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985753777","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":[0.0001621808,0.00016501908,0.99453247,0.000108628024,0.00429612,0.000295716,0.0000029062924,0.00040624588,0.000030714586],"genre_scores_gemma":[0.00798536,0.0005667561,0.98970276,0.000023567614,0.0015663801,0.000052056388,0.000016228501,0.00004267087,0.000044190572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989005,0.000009466521,0.000245372,0.0004083071,0.00015730507,0.00027907832],"domain_scores_gemma":[0.9993278,0.00008362613,0.00006547947,0.00018297978,0.00020348755,0.00013662729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014882509,0.00021963565,0.00019928203,0.00024270493,0.0002209779,0.00011639836,0.00013578216,0.00010515996,0.0000022814277],"category_scores_gemma":[0.0002000066,0.0002565137,0.000032180506,0.00036749075,0.000015443391,0.0009842025,0.00010040929,0.000110623536,5.07461e-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":[0.0000023484986,0.0000074995983,0.000010616829,0.000030179108,0.000011168623,9.607338e-7,0.00032995385,0.9984209,0.000118751566,0.0007678244,0.000062886094,0.00023691094],"study_design_scores_gemma":[0.0006639495,0.000019784404,0.000007168391,0.000035365585,0.000005010553,0.000026232434,0.00001829142,0.99770117,0.00006874499,0.000007513832,0.0011635349,0.00028320984],"about_ca_topic_score_codex":0.0000030958831,"about_ca_topic_score_gemma":1.0846044e-7,"teacher_disagreement_score":0.0078231795,"about_ca_system_score_codex":0.000054723936,"about_ca_system_score_gemma":0.000024854493,"threshold_uncertainty_score":0.99998873},"labels":[],"label_agreement":null},{"id":"W2007267362","doi":"10.1007/s11081-008-9046-2","title":"An asymmetric suboptimization approach to aerostructural optimization","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":66,"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 Toronto","funders":"","keywords":"Multidisciplinary design optimization; Aerodynamics; Solver; Computer science; Sensitivity (control systems); Mathematical optimization; Multidisciplinary approach; Optimization problem; Mathematics; Algorithm; Engineering; Aerospace engineering","score_opus":0.04735350519812372,"score_gpt":0.26526903577634636,"score_spread":0.21791553057822263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007267362","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":[0.0037011788,0.000054054522,0.9943695,0.000050929724,0.00026816363,0.00024704315,0.000004964177,0.00024268607,0.0010614559],"genre_scores_gemma":[0.38875946,0.000030374116,0.6108557,0.000055771026,0.00007160657,0.000015489428,0.000036926645,0.000026174335,0.00014847355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981435,0.000047337307,0.00045652402,0.00051452394,0.000558735,0.000279402],"domain_scores_gemma":[0.9988291,0.00013495958,0.00007715396,0.00038807947,0.00025987034,0.00031080653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005349759,0.0002191504,0.00025642366,0.0006827543,0.00022439954,0.00019712048,0.00030630812,0.00011901935,0.000053788175],"category_scores_gemma":[0.0010733482,0.00019080241,0.000040881416,0.0019040832,0.000032791493,0.000648634,0.000050953062,0.0001102027,0.000010441081],"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":[0.0000071096892,0.000024674873,0.00026884736,0.000005381204,0.0000052454434,0.0000012452467,0.0002707006,0.9976063,0.000056005658,0.0010620361,0.0002115572,0.00048091626],"study_design_scores_gemma":[0.00023880301,0.000049166323,0.0016648675,0.0000057346947,0.0000073114943,0.00005410881,0.000046924724,0.9974655,0.00004228569,0.00001688593,0.00015557403,0.00025282433],"about_ca_topic_score_codex":0.0000051978445,"about_ca_topic_score_gemma":1.157703e-7,"teacher_disagreement_score":0.38505828,"about_ca_system_score_codex":0.000050640705,"about_ca_system_score_gemma":0.000029636349,"threshold_uncertainty_score":0.7780697},"labels":[],"label_agreement":null},{"id":"W2008533091","doi":"10.1007/s11081-006-0351-3","title":"Variable-fidelity optimization: Efficiency and robustness","year":2006,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":26,"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","funders":"","keywords":"Robustness (evolution); Solver; Fidelity; Computer science; Mathematical optimization; Algorithm; Mathematics","score_opus":0.004538927013477352,"score_gpt":0.19152137529082455,"score_spread":0.1869824482773472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008533091","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":[0.000115376235,0.00020698742,0.99810123,0.00011300169,0.00019175342,0.00015553353,0.0000022946092,0.0003523079,0.0007614817],"genre_scores_gemma":[0.010505766,0.00008542327,0.98911136,0.000039458715,0.000055512213,0.000016280399,0.000017073156,0.000020593237,0.00014852166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893516,0.000020158966,0.00024897663,0.0004139469,0.0001507963,0.00023094505],"domain_scores_gemma":[0.9993926,0.00005812097,0.00007001597,0.00023109984,0.00015666967,0.000091447284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015450236,0.00018319784,0.00015780552,0.00015413579,0.00017049874,0.00020926197,0.00015760463,0.000076624114,0.000021498381],"category_scores_gemma":[0.00006674236,0.00019740286,0.00001644931,0.0006253688,0.000033170647,0.00080342835,0.00012527856,0.000092296126,7.100797e-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":[0.0000010436825,0.000020534428,0.00005119681,0.000014964099,0.0000035009584,0.0000018224193,0.000026880796,0.9833694,0.000027779006,0.01608523,0.000012238258,0.00038541667],"study_design_scores_gemma":[0.00041933867,0.0000134596685,0.00020212283,0.000016647171,0.0000051651627,0.000028923807,0.0000071095856,0.99882656,0.00004214115,0.00004686707,0.00016128895,0.00023036788],"about_ca_topic_score_codex":0.000011046651,"about_ca_topic_score_gemma":4.0686206e-7,"teacher_disagreement_score":0.016038362,"about_ca_system_score_codex":0.000040439692,"about_ca_system_score_gemma":0.000023913344,"threshold_uncertainty_score":0.80498546},"labels":[],"label_agreement":null},{"id":"W2009394266","doi":"10.1007/s11081-008-9072-0","title":"Sensor Network Localization, Euclidean Distance Matrix completions, and graph realization","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":87,"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 Waterloo","funders":"","keywords":"Mathematics; Semidefinite programming; Euclidean distance; Mathematical optimization; Euclidean distance matrix; Relaxation (psychology); Euclidean geometry; Graph; Matrix (chemical analysis); Realization (probability); Stability (learning theory); Quadratic equation; Low-rank approximation; Approximation algorithm; Algorithm; Computer science; Combinatorics; Tensor (intrinsic definition); Geometry","score_opus":0.008924183446798219,"score_gpt":0.19450713792148394,"score_spread":0.18558295447468573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009394266","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":[0.0045118853,0.00083994464,0.9933132,0.00002398261,0.00010518589,0.00009630953,0.000003463756,0.00079450494,0.0003115109],"genre_scores_gemma":[0.8938655,0.006218076,0.099521786,0.000053016076,0.000120368175,0.0000103520715,0.0000897828,0.000060467293,0.00006060014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947196,0.00000967913,0.00016549834,0.00013161913,0.00007227349,0.00014896398],"domain_scores_gemma":[0.99974245,0.000020929478,0.000021966613,0.000106872234,0.000045705165,0.00006210057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040131104,0.00013087149,0.00012616359,0.00007947867,0.00014552621,0.000034419787,0.000031698288,0.00005877457,0.000009198863],"category_scores_gemma":[0.000013104989,0.00014842943,0.000015132919,0.00024197607,0.000029398536,0.00012064734,0.00001541185,0.0000610546,7.6219544e-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":[0.0000014401462,0.000002562378,0.0011283403,0.000019632642,0.000009595735,0.0000030626975,0.00007149635,0.9952657,0.000101469675,0.0017743362,0.001543676,0.00007866994],"study_design_scores_gemma":[0.00011783942,0.0000071927275,0.0010301099,0.000055161894,0.000009015888,0.000043811575,0.000009751677,0.9930088,0.00011514801,0.0000566578,0.0053762165,0.0001703146],"about_ca_topic_score_codex":0.0000054690922,"about_ca_topic_score_gemma":0.0000014797134,"teacher_disagreement_score":0.89379144,"about_ca_system_score_codex":0.000014800173,"about_ca_system_score_gemma":0.0000028157165,"threshold_uncertainty_score":0.6052777},"labels":[],"label_agreement":null},{"id":"W2013789196","doi":"10.1007/s11081-011-9174-y","title":"Dynamic vaccination games and hybrid dynamical systems","year":2011,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":6,"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":"Dynamical systems theory; Hybrid system; Interval (graph theory); Track (disk drive); Computer science; Set (abstract data type); Population; Mathematical optimization; Class (philosophy); Dynamical system (definition); Mathematics; Artificial intelligence; Physics; Machine learning","score_opus":0.011683882119262207,"score_gpt":0.22286874368244153,"score_spread":0.2111848615631793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013789196","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":[0.091697864,0.00018188551,0.9054035,0.00014676565,0.00006259814,0.00010125667,8.864283e-7,0.000060113467,0.002345138],"genre_scores_gemma":[0.9822146,0.00018192118,0.017393727,0.000052656942,0.000007869348,0.000009423295,0.000008184861,0.0000069582097,0.0001246503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964315,0.00001035297,0.00012382804,0.00010328568,0.000023339888,0.00009605467],"domain_scores_gemma":[0.9997858,0.000052323936,0.000015784399,0.000048515656,0.000017122464,0.00008046679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001227732,0.000064212254,0.00015050611,0.00004085962,0.00002684539,0.0000045690804,0.000012052493,0.00006136097,0.00012279213],"category_scores_gemma":[0.00010839238,0.00005110768,0.000012884875,0.000027107691,0.00002416933,0.00004067914,0.0000135825,0.000069307804,0.0000024948154],"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":[0.000102237056,0.00015011875,0.0026599981,0.00085144036,0.00011202973,0.000028532333,0.00042433682,0.16290449,0.00015964305,0.8302393,0.00003967463,0.0023282182],"study_design_scores_gemma":[0.00023910613,0.00004622526,0.004149926,0.00003305989,0.000027893633,0.00010130111,0.000014878162,0.994697,0.000010479891,0.0006162089,0.000010000591,0.00005395703],"about_ca_topic_score_codex":0.0000015153141,"about_ca_topic_score_gemma":8.146785e-8,"teacher_disagreement_score":0.89051676,"about_ca_system_score_codex":0.000007859926,"about_ca_system_score_gemma":0.0000027142298,"threshold_uncertainty_score":0.20841107},"labels":[],"label_agreement":null},{"id":"W2027515466","doi":"10.1007/s11081-009-9095-1","title":"A two-stage stochastic mixed-integer programming approach to the index tracking problem","year":2009,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":36,"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 Toronto","funders":"","keywords":"Portfolio; Index (typography); Stochastic programming; Computer science; Mathematical optimization; Integer programming; Set (abstract data type); Integer (computer science); Tracking (education); Dynamic programming; Mathematics; Finance","score_opus":0.031243170206080174,"score_gpt":0.2926003849621429,"score_spread":0.2613572147560627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027515466","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":[0.0015694443,0.00007688527,0.99531883,0.00042118013,0.00013408382,0.00041089783,0.00000248209,0.000115842464,0.0019503261],"genre_scores_gemma":[0.85903585,0.000022704106,0.14009665,0.00015803768,0.00009613775,0.000030192508,0.000010758496,0.000017333341,0.0005323329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984777,0.000035271918,0.00038723758,0.00034724784,0.00048658732,0.00026593293],"domain_scores_gemma":[0.9992456,0.000099145385,0.00009543084,0.00026764828,0.00015535497,0.0001368491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009399498,0.0001647508,0.00017626035,0.00027585178,0.00018664016,0.00057949027,0.00025532226,0.000058431582,0.000016514483],"category_scores_gemma":[0.0003587804,0.000112664144,0.000046010697,0.0010454116,0.000014362036,0.0003595384,0.00003894156,0.00014134478,0.000007139624],"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":[0.0000061288265,0.000020665926,0.00010461603,0.000001822,0.000004040759,5.149588e-7,0.00067383295,0.96655303,0.000010245014,0.0016192987,0.0001346041,0.030871179],"study_design_scores_gemma":[0.00020016126,0.000028235287,0.00063104555,0.000015110402,0.0000071312675,0.000010216268,0.000336439,0.9936058,0.000008645417,0.00003900597,0.0049644387,0.00015376619],"about_ca_topic_score_codex":0.00000777923,"about_ca_topic_score_gemma":0.0000025426982,"teacher_disagreement_score":0.8574664,"about_ca_system_score_codex":0.000021755315,"about_ca_system_score_gemma":0.000019082032,"threshold_uncertainty_score":0.5588038},"labels":[],"label_agreement":null},{"id":"W2028262885","doi":"10.1007/s11081-008-9035-5","title":"The optimal control of unsteady flows with a discrete adjoint method","year":2008,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Adjoint equation; Minification; Laminar flow; Flow (mathematics); Optimal control; Mathematical optimization; Mathematics; Applied mathematics; Drag; Cylinder; Set (abstract data type); Computer science; Control theory (sociology); Control (management); Mathematical analysis; Mechanics; Partial differential equation; Geometry; Physics","score_opus":0.002950179326890712,"score_gpt":0.17353561290098812,"score_spread":0.17058543357409742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028262885","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":[0.0298688,0.00018066131,0.96954936,0.000034934623,0.00006546876,0.00008079681,0.000007744356,0.00007894213,0.00013328361],"genre_scores_gemma":[0.8029472,0.00022625703,0.19673981,0.0000050069057,0.000018505063,0.000008074008,0.000010250608,0.000022393875,0.00002250474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956816,0.000006345508,0.00014554794,0.000076424854,0.00009018641,0.000113333284],"domain_scores_gemma":[0.9997371,0.000085321044,0.00001688035,0.000078246485,0.000040905543,0.000041574443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007265001,0.00009756173,0.00011255849,0.000039530853,0.00006955943,0.000016827993,0.000047079116,0.000025826634,0.0000030195476],"category_scores_gemma":[0.00001038928,0.000072073635,0.000022116912,0.00011391523,0.000016335864,0.00006164709,0.000009711091,0.00006644433,3.0286765e-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":[0.000006402735,0.000002297976,0.00005140023,0.000014047057,0.000032336673,0.000001547083,0.00006276788,0.99790734,0.00025824504,0.0013554163,0.0000047991975,0.00030341808],"study_design_scores_gemma":[0.00033341287,0.000021228394,0.0005605951,0.000014804794,0.00001002363,0.000027016624,0.000012686375,0.9987351,0.000024616023,0.000002320657,0.0001638219,0.00009439797],"about_ca_topic_score_codex":0.0000025487923,"about_ca_topic_score_gemma":0.0000020870998,"teacher_disagreement_score":0.7730784,"about_ca_system_score_codex":0.000015221289,"about_ca_system_score_gemma":0.0000086965865,"threshold_uncertainty_score":0.29390776},"labels":[],"label_agreement":null},{"id":"W2029387956","doi":"10.1007/s11081-011-9147-1","title":"Filter allocation and replacement strategies in fluid power system under uncertainty: a fuzzy robust nonlinear programming approach","year":2011,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":6,"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 Regina","funders":"","keywords":"Fuzzy logic; Mathematical optimization; Nonlinear programming; Computer science; Robust optimization; Nonlinear system; Linearization; Filter (signal processing); Control theory (sociology); Mathematics; Artificial intelligence","score_opus":0.014510710739055686,"score_gpt":0.17531413852241653,"score_spread":0.16080342778336085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029387956","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":[0.042578757,0.00017684,0.9520268,0.000011028678,0.000091867754,0.0003525294,0.0000011952317,0.0003795535,0.0043814345],"genre_scores_gemma":[0.8309885,0.000090371446,0.16869709,0.000007532505,0.000022132166,0.0000525302,0.00006100643,0.000038450664,0.000042371794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931365,0.000010063168,0.00021216982,0.00018861983,0.00008506065,0.00019042661],"domain_scores_gemma":[0.9997898,0.0000059709805,0.000023252791,0.00010893077,0.000021103104,0.00005097555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012239472,0.00016248682,0.00012881224,0.000187042,0.000036276793,0.00011789835,0.000048604194,0.00006763932,0.00001025247],"category_scores_gemma":[0.0000033406695,0.00016595027,0.0000139405765,0.00018842345,0.000012575527,0.00030761474,0.000029395056,0.000070879105,8.5738117e-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":[0.000008258508,0.000013125832,0.00012799914,0.00022597883,0.00002090985,0.0000012587155,0.0011170781,0.997228,0.000034594796,0.0010334157,0.000007705558,0.00018162152],"study_design_scores_gemma":[0.0003488263,0.000023397786,0.0002959298,0.000066688604,0.000013422566,0.0000048166758,0.0016259558,0.99724245,0.000026710426,0.0000018904049,0.00016508673,0.00018480002],"about_ca_topic_score_codex":0.000018413162,"about_ca_topic_score_gemma":0.0000035643889,"teacher_disagreement_score":0.78840977,"about_ca_system_score_codex":0.0000546814,"about_ca_system_score_gemma":0.0000036541092,"threshold_uncertainty_score":0.67672557},"labels":[],"label_agreement":null},{"id":"W2037983258","doi":"10.1007/s11081-007-9024-0","title":"Neuro-space mapping technique for semiconductor device modeling","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Semiconductor Quantum Structures and Devices","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Space mapping; Computer science; MESFET; Artificial neural network; Semiconductor device; Representation (politics); Electronic engineering; Sensitivity (control systems); Voltage; Simulation; Transistor; Artificial intelligence; Field-effect transistor; Algorithm; Electrical engineering; Engineering","score_opus":0.015695362114316398,"score_gpt":0.23487795646068133,"score_spread":0.21918259434636492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037983258","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":[0.10097678,0.000069311995,0.8982764,0.00003135977,0.00013402509,0.00018858987,0.00000558563,0.000055126373,0.00026282103],"genre_scores_gemma":[0.87740856,0.000005013547,0.12224977,0.000040734245,0.00020213124,0.00001905673,0.000025541593,0.000024994384,0.000024202502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944717,0.000002675459,0.00015083022,0.00016296106,0.000046745634,0.00018959116],"domain_scores_gemma":[0.9997192,0.000055541386,0.00003618267,0.0000814259,0.000042966367,0.000064661464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011224946,0.00011802021,0.00010679215,0.00008145956,0.00007377409,0.00004339925,0.00005049862,0.000037924667,0.000030911877],"category_scores_gemma":[0.000010785031,0.00011990254,0.00003364119,0.000108684544,0.0000051406337,0.00013592672,0.000018481674,0.00007441154,3.4080847e-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":[0.0000031859015,0.0000040822747,0.0005116649,0.000035897192,0.000014237797,2.1727573e-7,0.00013730345,0.90778697,0.08639409,0.0048592337,0.000023058847,0.00023003959],"study_design_scores_gemma":[0.00018850621,0.0000073649767,0.000030537933,0.000027452577,0.000008847793,0.000002294951,0.00022083767,0.98721623,0.010776894,0.000116292766,0.0012395994,0.00016515942],"about_ca_topic_score_codex":0.000018239514,"about_ca_topic_score_gemma":2.5978247e-7,"teacher_disagreement_score":0.7764318,"about_ca_system_score_codex":0.000008844247,"about_ca_system_score_gemma":0.0000074155264,"threshold_uncertainty_score":0.48894837},"labels":[],"label_agreement":null},{"id":"W2047959283","doi":"10.1007/s11081-013-9227-5","title":"A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing","year":2013,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematical optimization; Computer science; Pareto principle; Metaheuristic; Multi-objective optimization; Toll; Network planning and design; Heuristic; Mathematics","score_opus":0.020200121759238288,"score_gpt":0.24345002852494832,"score_spread":0.22324990676571002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047959283","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":[0.009144485,0.00005045386,0.9899536,0.0001237219,0.00006932141,0.00043314695,0.0000040208683,0.00012702002,0.000094236944],"genre_scores_gemma":[0.67114645,0.000029847053,0.32850024,0.000032819586,0.000036852798,0.00004720896,0.00007931383,0.000012375564,0.00011491232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994276,0.00002020111,0.00014982828,0.00015218875,0.000094399584,0.00015574641],"domain_scores_gemma":[0.99948597,0.00019909673,0.00005862812,0.000039990973,0.00014613762,0.000070151036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019415637,0.00008701725,0.000098647826,0.00007641914,0.00029804866,0.00011002027,0.000024372936,0.00006900083,0.000012563349],"category_scores_gemma":[0.00015117227,0.000095852025,0.0000174918,0.00016089891,0.0000203578,0.00035353715,0.0000026394173,0.000040627747,4.1343276e-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":[0.0000057574644,0.0000055947,0.0007392905,0.00001939487,0.0000062178133,4.7239773e-8,0.0011431689,0.996949,0.000007182487,0.0005864279,0.000014946285,0.00052296475],"study_design_scores_gemma":[0.00035738095,0.000009517281,0.0016182563,0.00002551552,0.000024227142,6.544152e-8,0.000151698,0.99762297,0.0000035073128,0.000025682966,0.000049908296,0.000111276175],"about_ca_topic_score_codex":0.00010576393,"about_ca_topic_score_gemma":0.000044436587,"teacher_disagreement_score":0.66200197,"about_ca_system_score_codex":0.000030256313,"about_ca_system_score_gemma":0.000029190149,"threshold_uncertainty_score":0.39087322},"labels":[],"label_agreement":null},{"id":"W2052379828","doi":"10.1007/s11081-006-6590-5","title":"Hydro energy management optimization in a deregulated electricity market","year":2006,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":18,"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 Waterloo","funders":"","keywords":"Inflow; Mathematical optimization; Electricity market; Discretization; Moment (physics); Computer science; Optimization problem; State variable; Electricity; Mathematics","score_opus":0.001848695843473466,"score_gpt":0.13405906730831485,"score_spread":0.13221037146484138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052379828","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":[0.004639287,0.00022422278,0.97947586,0.000022623666,0.00008685424,0.00014659269,0.0000010638284,0.00045609786,0.014947429],"genre_scores_gemma":[0.89396405,0.0010430935,0.10324892,0.000030212354,0.00007203025,0.00005557583,0.0001906445,0.000090647365,0.0013048234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992003,0.000011133051,0.00024479948,0.0001829057,0.00010960986,0.00025125066],"domain_scores_gemma":[0.99980617,0.000009535431,0.000023084709,0.00010702023,0.00001502438,0.000039147104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073417374,0.00018100745,0.00013401425,0.00041816782,0.000037315254,0.00008117711,0.000067924964,0.00007458145,0.000083466075],"category_scores_gemma":[0.0000025608076,0.00021007839,0.00002181251,0.00057170953,0.0000074310165,0.00021956392,0.000027003789,0.000059557977,9.4717933e-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":[0.000004775593,0.000014187369,0.0002526355,0.00006458509,0.000015489697,0.0000056684935,0.000017522658,0.99820113,0.000028330129,0.0004952471,0.00037835896,0.00052205985],"study_design_scores_gemma":[0.00048321014,0.0000064868173,0.0011000866,0.00003326438,0.000014242892,0.0000022379575,0.000005473783,0.9966035,0.0001347302,0.000013047641,0.0013752977,0.00022844513],"about_ca_topic_score_codex":0.000024352636,"about_ca_topic_score_gemma":0.0000076629785,"teacher_disagreement_score":0.8893248,"about_ca_system_score_codex":0.00006909589,"about_ca_system_score_gemma":0.0000013437478,"threshold_uncertainty_score":0.85667485},"labels":[],"label_agreement":null},{"id":"W2054148923","doi":"10.1023/b:opte.0000048536.47956.62","title":"A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":256,"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 Toronto","funders":"Air Force Office of Scientific Research; Fundação para a Ciência e a Tecnologia","keywords":"Sensitivity (control systems); Aerodynamics; Parameterized complexity; Mathematical optimization; Computer science; Mathematics; Applied mathematics; Topology (electrical circuits); Algorithm; Aerospace engineering; Engineering","score_opus":0.04790198100555463,"score_gpt":0.309288998847244,"score_spread":0.26138701784168933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054148923","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":[0.0034865588,0.000045752775,0.9956725,0.00018895937,0.00020278148,0.00025332582,0.000013701811,0.00012859363,0.000007781816],"genre_scores_gemma":[0.41528586,0.0000056800613,0.5845792,0.000025155958,0.000044019995,0.00001288472,0.000010115839,0.000010290299,0.000026790363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852705,0.00006038227,0.00041951062,0.00042122393,0.00032940853,0.00024241039],"domain_scores_gemma":[0.99827725,0.000975547,0.00008897523,0.0002852453,0.0002368134,0.00013617185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002327839,0.00018226,0.00038272102,0.00032302548,0.00013332049,0.00016531482,0.000113970185,0.000095386145,0.000026779677],"category_scores_gemma":[0.0020632253,0.00014919112,0.000109799126,0.0009592862,0.00002347221,0.00020649268,0.00004017346,0.00008889999,0.0000023628945],"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":[0.000011935979,0.0000059504796,0.000029281155,0.00000848384,0.00007495268,0.0000020496057,0.000083231644,0.99535644,0.00069030304,0.003251309,0.000018747294,0.00046731872],"study_design_scores_gemma":[0.00040950754,0.000027668268,0.001720637,0.000007857365,0.00012963475,0.000008832145,0.000021018179,0.9956575,0.00028055682,0.001523017,0.000023249038,0.00019054666],"about_ca_topic_score_codex":0.000050717204,"about_ca_topic_score_gemma":0.00000491644,"teacher_disagreement_score":0.41179928,"about_ca_system_score_codex":0.00007068683,"about_ca_system_score_gemma":0.000037151895,"threshold_uncertainty_score":0.6083838},"labels":[],"label_agreement":null},{"id":"W2054928060","doi":"10.1007/s11081-007-9031-1","title":"Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Turbomachinery Performance and Optimization","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Surrogate model; Turbomachinery; Computer science; Aerodynamics; Transonic; Computational fluid dynamics; Artificial neural network; Mathematical optimization; Airfoil; Genetic algorithm; Control theory (sociology); Mathematics; Engineering; Artificial intelligence; Mechanical engineering; Aerospace engineering; Machine learning","score_opus":0.009775469113704088,"score_gpt":0.24418924786043228,"score_spread":0.2344137787467282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054928060","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":[0.07069758,0.0009162541,0.9276356,0.000008335824,0.00016826614,0.00013218523,0.000008231884,0.00024243875,0.00019107995],"genre_scores_gemma":[0.48888582,0.00027319771,0.510707,0.000009490188,0.00002699333,0.000002589798,0.000051542407,0.000037921924,0.0000054286206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990601,0.00001928233,0.0003669328,0.00019035274,0.00012120549,0.00024213971],"domain_scores_gemma":[0.9995612,0.00007465202,0.000061688566,0.00013410398,0.0000710946,0.000097248834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003375476,0.00022417189,0.0002280797,0.00036887627,0.000077521196,0.000031399788,0.000058357076,0.00013345867,0.000015307705],"category_scores_gemma":[0.000025271436,0.00024183864,0.000035440138,0.0003860295,0.000037291513,0.00053000875,0.000023520923,0.00012719902,1.4346347e-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":[0.000011849015,0.000010462281,0.00038424777,0.00014492086,0.00002110617,8.985608e-7,0.00008884597,0.99546105,0.002958688,0.000078152436,0.000003321979,0.0008364808],"study_design_scores_gemma":[0.00040033687,0.00001851555,0.00093940145,0.00006413224,0.000032593598,0.0000122699685,0.000021876274,0.99752057,0.0007281211,0.000007928479,0.000010203413,0.0002440262],"about_ca_topic_score_codex":0.000011125024,"about_ca_topic_score_gemma":0.0000018856311,"teacher_disagreement_score":0.41818824,"about_ca_system_score_codex":0.000055599976,"about_ca_system_score_gemma":0.000014053117,"threshold_uncertainty_score":0.98618937},"labels":[],"label_agreement":null},{"id":"W2055133561","doi":"10.1007/s11081-007-9030-2","title":"Spent potliner treatment process optimization using a MADS algorithm","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":42,"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; Group for Research in Decision Analysis","funders":"Air Force Office of Scientific Research","keywords":"Mathematical optimization; Computer science; Algorithm; Set (abstract data type); Nonlinear system; Process (computing); Function (biology); Reduction (mathematics); Differentiable function; Mathematics","score_opus":0.03924008691482914,"score_gpt":0.3460628235775633,"score_spread":0.30682273666273413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055133561","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":[0.0019201492,0.00007329644,0.9968754,0.00004037609,0.00012112084,0.000391451,0.0000062660924,0.00024953464,0.00032238802],"genre_scores_gemma":[0.004521444,0.00019060604,0.9946069,0.000018902258,0.00013501634,0.000017267934,0.000040657997,0.00008619666,0.00038298522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986309,0.000015677731,0.00037582155,0.0003058611,0.00028506853,0.0003866711],"domain_scores_gemma":[0.9992164,0.00010659562,0.00009542019,0.00018814029,0.0002042931,0.00018919153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027402243,0.00024683448,0.00023216645,0.00030800517,0.00013628708,0.00007768684,0.000080693695,0.00011214948,0.00016196181],"category_scores_gemma":[0.00014970517,0.00023873158,0.000041989217,0.0005194252,0.000027248063,0.00032352554,0.000035452274,0.00010623764,0.000001991323],"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":[0.000009254363,0.000068807996,0.000043801174,0.000038538998,0.00002617606,0.000009085344,0.00021626605,0.9955248,0.00003715404,0.00022268768,0.0000049059486,0.0037985183],"study_design_scores_gemma":[0.00075819885,0.00004800342,0.000013009839,0.000038063663,0.00002709544,0.00003672397,0.00013494988,0.99785554,0.00064922543,0.00005734675,0.0001297017,0.00025216577],"about_ca_topic_score_codex":0.0000054813427,"about_ca_topic_score_gemma":0.000001114327,"teacher_disagreement_score":0.0035463525,"about_ca_system_score_codex":0.00022805306,"about_ca_system_score_gemma":0.00003854324,"threshold_uncertainty_score":0.97351915},"labels":[],"label_agreement":null},{"id":"W2057817947","doi":"10.1007/s11081-013-9213-y","title":"An adaptive geometry parametrization for aerodynamic shape optimization","year":2013,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Mitacs; Canada Research Chairs","keywords":"Parametrization (atmospheric modeling); Mathematics; Airfoil; Shape optimization; Aerodynamics; Geometry; Mathematical optimization; Computer science; Applied mathematics; Physics; Finite element method","score_opus":0.00527573231310666,"score_gpt":0.20509726660380206,"score_spread":0.1998215342906954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057817947","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":[0.012359275,0.00008780794,0.9861922,0.000016843107,0.00006015068,0.00034281117,0.0000056277854,0.00087227,0.00006298391],"genre_scores_gemma":[0.48590764,0.00014904542,0.5136181,0.00001723036,0.000029434183,0.000118121374,0.00010825344,0.000043553875,0.000008641752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993018,0.000006940024,0.00021317578,0.00019606558,0.00008486986,0.00019715237],"domain_scores_gemma":[0.9995851,0.000047227568,0.0000321862,0.00013447543,0.00009890904,0.00010210073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005507395,0.00017029695,0.00017089066,0.000265655,0.00005557028,0.00007696378,0.00007472033,0.00009538963,0.00011708356],"category_scores_gemma":[0.00005018387,0.00018383986,0.00003373725,0.0005271556,0.000012727007,0.0006862399,0.000010844945,0.0000738389,0.000002584421],"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":[0.0000019823856,0.00000975225,0.00003421986,0.000023387323,0.000017771956,8.14398e-8,0.000016823271,0.9920691,0.0012543128,0.0001630419,0.000023915898,0.006385569],"study_design_scores_gemma":[0.00014433025,0.000057375135,0.00021474587,0.00001316441,0.000017593764,0.0000011854567,0.000020672956,0.99882877,0.00040270263,0.000029632187,0.000047006804,0.00022283962],"about_ca_topic_score_codex":0.000003981137,"about_ca_topic_score_gemma":3.6370955e-7,"teacher_disagreement_score":0.47354835,"about_ca_system_score_codex":0.000055798064,"about_ca_system_score_gemma":0.0000029279631,"threshold_uncertainty_score":0.7496772},"labels":[],"label_agreement":null},{"id":"W2060517976","doi":"10.1007/s11081-011-9148-0","title":"An interactive multi-objective algorithm for decentralized decision making in product design","year":2011,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":8,"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; Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Process (computing); Product (mathematics); New product development; Multi-objective optimization; Order (exchange); Autonomy; Mathematical optimization; Algorithm; Industrial engineering; Operations research; Machine learning; Marketing; Mathematics","score_opus":0.022758667370807388,"score_gpt":0.23510372655545694,"score_spread":0.21234505918464955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060517976","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":[0.004385766,0.000038452057,0.9946304,0.000009432932,0.00028213896,0.0004733203,5.612614e-7,0.00010505132,0.0000748759],"genre_scores_gemma":[0.33606923,0.000019595773,0.6636458,0.0000429148,0.000117037365,0.00005211492,0.000022029239,0.00002376458,0.000007470051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933773,0.000005424859,0.00017238321,0.00024926703,0.00006267406,0.00017253855],"domain_scores_gemma":[0.9997156,0.000022488372,0.00006731064,0.00007855584,0.00010597889,0.000010017407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002420043,0.00012540515,0.000116824645,0.00028569752,0.00006400671,0.00009801136,0.0000691311,0.000036245063,0.000029664707],"category_scores_gemma":[0.00013417567,0.00012769396,0.000015442882,0.00030060022,0.0000069070406,0.0013343828,0.000024868774,0.0000500721,0.0000023730483],"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":[0.00017997627,0.00014081277,0.0028116696,0.00005451616,0.000023199169,0.0000028161692,0.0012708072,0.8563765,0.00027468498,0.00032635822,0.000045326262,0.13849331],"study_design_scores_gemma":[0.0007994208,0.000007029927,0.006869263,0.00006784026,0.000011862918,9.225811e-7,0.00010571769,0.99144506,0.00032028602,0.00007261537,0.0001237172,0.00017625572],"about_ca_topic_score_codex":0.000012115962,"about_ca_topic_score_gemma":0.0000042369606,"teacher_disagreement_score":0.3316835,"about_ca_system_score_codex":0.000034797908,"about_ca_system_score_gemma":0.000009331946,"threshold_uncertainty_score":0.52072084},"labels":[],"label_agreement":null},{"id":"W2063927119","doi":"10.1023/b:opte.0000013632.20417.13","title":"Simultaneous Solution Strategies for Inclusion of Input Saturation in the Optimal Design of Dynamically Operable Plants","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Mathematical optimization; Bilinear interpolation; Saturation (graph theory); Computer science; Complementarity (molecular biology); Optimization problem; Mathematics","score_opus":0.03238951122103703,"score_gpt":0.27441901317422435,"score_spread":0.24202950195318731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063927119","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":[0.021855531,0.00008315322,0.9776059,0.00007809189,0.000061700724,0.0002724589,0.0000056002914,0.000014527167,0.000023050232],"genre_scores_gemma":[0.7649987,0.000030506764,0.23492937,0.000006713806,0.000008903819,0.000008773653,0.0000067242545,0.000005047814,0.0000052875334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910975,0.00002886367,0.00036133578,0.00013604938,0.00025583155,0.0001081904],"domain_scores_gemma":[0.99899685,0.0006797062,0.000077914854,0.00011846438,0.00010649617,0.000020542402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093570823,0.00008288657,0.0001523171,0.00014357701,0.000069749774,0.000042852054,0.00017287637,0.000066392546,0.0000031508903],"category_scores_gemma":[0.0008046213,0.000057131536,0.00002196738,0.00024968013,0.000025601417,0.0002071444,0.000046879934,0.000056036766,1.8999344e-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":[0.000031051644,0.000021615042,0.0000037182656,0.000019760706,0.0000029867542,6.5898865e-7,0.0011594613,0.9906124,0.005151083,0.0025652256,0.000004457224,0.000427579],"study_design_scores_gemma":[0.0003805178,0.00008709069,0.000059831335,0.00004537679,0.0000043367727,0.0000044860385,0.00026684973,0.99791384,0.0003503837,0.00081125187,0.000011471398,0.000064558844],"about_ca_topic_score_codex":0.000012091549,"about_ca_topic_score_gemma":0.000002201332,"teacher_disagreement_score":0.74314314,"about_ca_system_score_codex":0.000026951211,"about_ca_system_score_gemma":0.000053461958,"threshold_uncertainty_score":0.23297563},"labels":[],"label_agreement":null},{"id":"W2070890570","doi":"10.1007/s11081-007-9032-0","title":"Quality assessment of coarse models and surrogates for space mapping optimization","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Space mapping; Computer science; Convergence (economics); Mathematical optimization; Quality (philosophy); Space (punctuation); Similarity (geometry); Surrogate model; Data mining; Algorithm; Mathematics; Machine learning; Artificial intelligence","score_opus":0.031161423987999773,"score_gpt":0.29837259838241836,"score_spread":0.2672111743944186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070890570","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":[0.0003719687,0.00006814069,0.9987042,0.00008004079,0.00011558265,0.0002886357,0.0000046378555,0.00012209403,0.0002446944],"genre_scores_gemma":[0.08456199,0.0001377251,0.915193,0.000018051132,0.000015732872,0.000012582382,0.000014773271,0.000015771608,0.000030361354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990796,0.000015844986,0.00030650862,0.0002768685,0.00013404845,0.00018712603],"domain_scores_gemma":[0.99919754,0.000193519,0.00013691398,0.00016023952,0.00022445459,0.00008731549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005106785,0.00013648749,0.00018787554,0.00018884672,0.000082957486,0.000060350038,0.000098792836,0.00006233202,0.0000023770317],"category_scores_gemma":[0.00009090812,0.00015113095,0.00002558803,0.00031552068,0.000025509196,0.00071706716,0.00007528165,0.000057280373,4.148207e-8],"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":[0.0000025677098,0.000016755133,0.00012864514,0.000049233768,0.000010280081,2.570347e-7,0.00023680003,0.98143244,0.00024301124,0.016356245,0.0000012762397,0.0015224686],"study_design_scores_gemma":[0.0005807936,0.000022879103,0.00048047854,0.000025746715,0.0000038596986,0.0000035276062,0.00008069903,0.9982299,0.00030901338,0.00007787722,0.000023169,0.00016201852],"about_ca_topic_score_codex":0.00000470246,"about_ca_topic_score_gemma":0.0000010035355,"teacher_disagreement_score":0.08419002,"about_ca_system_score_codex":0.00004329241,"about_ca_system_score_gemma":0.000020646514,"threshold_uncertainty_score":0.61629415},"labels":[],"label_agreement":null},{"id":"W2070922270","doi":"10.1023/b:opte.0000033370.66768.a9","title":"Convergence Results for Generalized Pattern Search Algorithms are Tight","year":2004,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":64,"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; Group for Research in Decision Analysis","funders":"","keywords":"Differentiable function; Limit (mathematics); Mathematics; Convergence (economics); Limit point; Stationary point; Zero (linguistics); Mathematical optimization; Algorithm; Mathematical proof; Function (biology); Applied mathematics; Mathematical analysis","score_opus":0.04541959430167648,"score_gpt":0.31634148635927795,"score_spread":0.2709218920576015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070922270","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":[0.0013570342,0.00005357379,0.99729145,0.00046848095,0.00013066566,0.00040265193,0.000047889116,0.00016755158,0.00008073193],"genre_scores_gemma":[0.012230518,0.00032739944,0.9864584,0.00005072495,0.00010949466,0.00007645491,0.00007568598,0.000058943002,0.0006123317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989218,0.000013912883,0.00027884834,0.0002771434,0.00021574255,0.00029254952],"domain_scores_gemma":[0.9992874,0.00012300552,0.000060473893,0.00018714988,0.00020776641,0.00013418857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022304442,0.00015393022,0.00018177218,0.0001362478,0.0001148283,0.000058348294,0.000104223574,0.00008209331,0.000036473728],"category_scores_gemma":[0.00034306856,0.00015599943,0.00003787777,0.00024279974,0.000025058165,0.00018405895,0.000047381556,0.00010962512,0.0000037730044],"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":[0.000014345764,0.00002356195,0.000012278429,0.00009480005,0.000015213088,0.000002980441,0.00019454237,0.99757123,0.00008133607,0.0012285477,0.00011125916,0.00064993097],"study_design_scores_gemma":[0.0021976014,0.000030564617,0.00003497909,0.00005498089,0.000009537885,0.000007158299,0.000052856252,0.995323,0.0013689816,0.00026621972,0.00045914226,0.00019499926],"about_ca_topic_score_codex":0.000006346571,"about_ca_topic_score_gemma":0.0000018478012,"teacher_disagreement_score":0.010873484,"about_ca_system_score_codex":0.00007354008,"about_ca_system_score_gemma":0.00002818282,"threshold_uncertainty_score":0.6361472},"labels":[],"label_agreement":null},{"id":"W2071646657","doi":"10.1007/s11081-007-9009-z","title":"Magnetic resonance tissue quantification using optimal bSSFP pulse-sequence design","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Sequence (biology); Pulse sequence; Contrast (vision); Computer science; Mathematical optimization; Pulse (music); Algorithm; Noise (video); Nonlinear system; Mathematics; Physics; Nuclear magnetic resonance; Artificial intelligence; Chemistry; Image (mathematics)","score_opus":0.04154157018541573,"score_gpt":0.31113985743365025,"score_spread":0.26959828724823454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071646657","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":[0.004323358,0.0006246557,0.9943966,0.00008377935,0.000027005068,0.0002753204,0.0000015756697,0.00015706758,0.000110633904],"genre_scores_gemma":[0.17250155,0.00028726083,0.826945,0.000032930766,0.00005047438,0.000012415377,0.0000135918635,0.000019722524,0.00013703134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945027,0.0000033478832,0.00016024233,0.00016267257,0.0000754858,0.00014797212],"domain_scores_gemma":[0.99968565,0.000024418116,0.000029809022,0.00013545458,0.000048126814,0.00007656643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001351371,0.00008753602,0.000093835864,0.00007881002,0.000061655046,0.000015489253,0.000030970063,0.00005737046,0.000025323772],"category_scores_gemma":[0.00003205,0.00009298585,0.000011786051,0.00022317095,0.000021541733,0.0000890851,0.000011051066,0.0000727367,0.0000016853643],"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":[0.000011562515,0.000013373947,0.000041255156,0.000014532151,7.9070776e-7,0.0000038048101,0.000030025765,0.89190507,0.09520305,0.00070545764,0.0000116135525,0.012059481],"study_design_scores_gemma":[0.00016540449,0.000049639908,0.0005632181,0.000054073716,0.000014964071,0.00006070405,0.00001702461,0.96700704,0.028674182,0.0000063494563,0.0032815828,0.00010583497],"about_ca_topic_score_codex":0.000005000573,"about_ca_topic_score_gemma":1.686205e-7,"teacher_disagreement_score":0.1681782,"about_ca_system_score_codex":0.00004086482,"about_ca_system_score_gemma":0.000013004951,"threshold_uncertainty_score":0.3791853},"labels":[],"label_agreement":null},{"id":"W2077800636","doi":"10.1007/s11081-006-0348-y","title":"Non-linear game models for large-scale network bandwidth management","year":2006,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Optical Network Technologies","field":"Engineering","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 Waterloo; University of Guelph","funders":"","keywords":"Multiprotocol Label Switching; Computer science; Bandwidth (computing); Computer network; Path (computing); Context (archaeology); Game theory; Bandwidth allocation; Mathematical optimization; Distributed computing; Quality of service; Mathematics","score_opus":0.00488879095109895,"score_gpt":0.1856045410795135,"score_spread":0.18071575012841454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077800636","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":[0.00097246654,0.00031009768,0.9956602,0.000022457734,0.00016267477,0.00023820256,0.0000046417613,0.0007702964,0.0018589525],"genre_scores_gemma":[0.13008022,0.0004283184,0.8689443,0.000014725461,0.000152326,0.00008147394,0.00004023092,0.00005279283,0.0002056126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993047,9.629695e-7,0.00015954344,0.00014973592,0.00005877792,0.00032628558],"domain_scores_gemma":[0.99978876,0.000019711923,0.000011950808,0.00012566702,0.000017533335,0.00003635443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051832423,0.00014633543,0.00013704837,0.000057448888,0.00004317051,0.000023966695,0.00006528369,0.00008286651,0.0000067242763],"category_scores_gemma":[0.0000023684415,0.00015857739,0.000029093722,0.00017680725,0.000008309884,0.00014318264,0.000033232456,0.000074659605,0.0000017883654],"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":[0.0000024071053,0.0000056413824,0.00001597697,0.000070221904,0.000012167768,8.871604e-7,0.000008679866,0.99296147,0.000013167523,0.0059534316,0.0005566921,0.00039923217],"study_design_scores_gemma":[0.00035817636,0.00000946471,0.000045738845,0.000033168093,0.000013156316,0.0000011457068,0.000012059662,0.99476695,0.000035660454,0.0006493579,0.0038906753,0.00018446954],"about_ca_topic_score_codex":2.606732e-7,"about_ca_topic_score_gemma":7.839252e-7,"teacher_disagreement_score":0.12910776,"about_ca_system_score_codex":0.000026388729,"about_ca_system_score_gemma":7.8474164e-7,"threshold_uncertainty_score":0.6466598},"labels":[],"label_agreement":null},{"id":"W2080638133","doi":"10.1007/s11081-011-9155-1","title":"Planned tournament selection","year":2011,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":4,"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":"Tournament; Tournament selection; Selection (genetic algorithm); Mathematical optimization; Computer science; Population; Variety (cybernetics); Fitness proportionate selection; Fitness function; Mathematics; Artificial intelligence; Genetic algorithm","score_opus":0.010765354826782527,"score_gpt":0.18214762195242906,"score_spread":0.17138226712564653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080638133","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":[0.0008343246,0.00002443135,0.99788177,0.00009166006,0.000051481333,0.0000338354,2.1302255e-7,0.000115795905,0.00096646923],"genre_scores_gemma":[0.18273805,0.000042164582,0.8170529,0.000028816825,0.000022612834,0.000012363627,0.0000018779851,0.0000034812267,0.000097725795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997628,0.000002242198,0.000054514912,0.000083100225,0.00003508138,0.00006230677],"domain_scores_gemma":[0.9998796,0.000003977278,0.000012201069,0.000053666667,0.000015946429,0.000034603574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000029670528,0.000036873982,0.000027876953,0.00003830589,0.00005278996,0.000021799819,0.000058009937,0.000015864476,0.000019637868],"category_scores_gemma":[0.0000025372933,0.000037653015,0.0000070639753,0.00012317153,0.000002713301,0.00018308569,0.00002054694,0.000026823433,0.0000028318477],"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":[8.6881334e-7,0.000027509004,0.00025969962,0.000004364745,0.0000065325244,5.760797e-7,0.00034560362,0.9372227,0.00019845812,0.059474524,0.00015174641,0.0023073931],"study_design_scores_gemma":[0.000056263107,0.000016041722,0.0021813102,0.000003059862,0.0000010761044,0.000009908647,0.0000051696757,0.9967233,0.00017857841,0.000055293596,0.00072152645,0.00004845653],"about_ca_topic_score_codex":0.0000034024797,"about_ca_topic_score_gemma":1.9478426e-7,"teacher_disagreement_score":0.18190372,"about_ca_system_score_codex":0.000010079303,"about_ca_system_score_gemma":0.000004375545,"threshold_uncertainty_score":0.15354455},"labels":[],"label_agreement":null},{"id":"W2097546193","doi":"10.1007/s11081-007-9004-4","title":"Modeling leakage power reduction in VLSI as optimization problems","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Low-power high-performance VLSI design","field":"Engineering","cited_by":7,"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 Waterloo; University of Guelph","funders":"","keywords":"Subthreshold conduction; Very-large-scale integration; Computer science; CMOS; Leakage (economics); Reduction (mathematics); Power optimization; Electronic engineering; Dissipation; Transistor; Cluster analysis; Sizing; Power (physics); Computer engineering; Electrical engineering; Embedded system; Engineering; Voltage; Mathematics","score_opus":0.005268158353263225,"score_gpt":0.18004443015768595,"score_spread":0.17477627180442273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097546193","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":[0.0854639,0.00023562547,0.911542,0.000016840862,0.00042846572,0.0002067447,7.111158e-7,0.00047547644,0.001630265],"genre_scores_gemma":[0.9334368,0.00042720832,0.06589938,0.000009874539,0.00006176385,0.000015447844,0.000024348277,0.000072749935,0.000052432562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897903,0.000006133873,0.00033767425,0.00020309418,0.00014354577,0.000330494],"domain_scores_gemma":[0.99969834,0.000012196792,0.000019867799,0.00013685564,0.000041101717,0.00009161838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030415796,0.00020014819,0.00015528832,0.00041603792,0.000048728358,0.000062551575,0.00006805609,0.00013847867,0.00005421334],"category_scores_gemma":[0.000022130356,0.00023224302,0.000022901893,0.000497463,0.00000988298,0.0005970874,0.000016726242,0.00018698949,0.000008091603],"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":[0.0000046433343,0.000009864494,0.000035302455,0.00005329794,0.000008587819,0.000002802622,0.0005087271,0.9967108,0.002245984,0.00011843365,0.000011223413,0.00029036804],"study_design_scores_gemma":[0.00036498008,0.000020414976,0.000046958336,0.00006597063,0.0000060221037,0.000021843358,0.00009075891,0.9980228,0.0009935104,0.000004277279,0.00009958137,0.00026289365],"about_ca_topic_score_codex":0.0000113849965,"about_ca_topic_score_gemma":0.000002415197,"teacher_disagreement_score":0.8479729,"about_ca_system_score_codex":0.00012341126,"about_ca_system_score_gemma":0.00000898627,"threshold_uncertainty_score":0.9470596},"labels":[],"label_agreement":null},{"id":"W2139604219","doi":"10.1007/s11081-007-9003-5","title":"Application of a sensitivity equation method to the k–ε model of turbulence","year":2007,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"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","funders":"Air Force Office of Scientific Research","keywords":"Sensitivity (control systems); Turbulence; Closure (psychology); K-epsilon turbulence model; Turbulence modeling; K-omega turbulence model; Flow (mathematics); Applied mathematics; Mathematics; Mathematical optimization; Mechanics; Physics; Engineering","score_opus":0.057841064846744845,"score_gpt":0.3137366843848266,"score_spread":0.25589561953808176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139604219","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":[0.0017806687,0.000020125672,0.9978201,0.00010213034,0.00003564461,0.00013367803,0.0000033273795,0.00001849814,0.00008583303],"genre_scores_gemma":[0.55152464,0.000002397782,0.4484292,0.000010759328,0.000008958011,0.0000023609239,7.9726885e-7,0.0000033128642,0.000017581766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991877,0.000022441991,0.00029694053,0.0001357415,0.0002736546,0.000083533494],"domain_scores_gemma":[0.99891394,0.00059960416,0.00008080997,0.00018842044,0.00017425447,0.000042938264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032250837,0.00005918623,0.00011713318,0.00012884445,0.000026511208,0.00001248851,0.0000920813,0.00003615571,0.0000023788941],"category_scores_gemma":[0.00092308753,0.00004140512,0.000021876323,0.00045864034,0.000013003838,0.00007465793,0.000031635285,0.00003973917,9.93589e-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":[0.00000482725,0.0000054752595,0.000032622993,0.000007609315,0.0000023559792,5.2591798e-8,0.0002363219,0.9819292,0.0060332897,0.0045301383,0.000010405426,0.0072077336],"study_design_scores_gemma":[0.000046849822,0.000009073753,0.0004862685,0.000008521551,0.0000050158988,0.0000013038982,0.000032573833,0.9965602,0.002589455,0.00016784115,0.000047279755,0.00004557709],"about_ca_topic_score_codex":0.000008183668,"about_ca_topic_score_gemma":8.988268e-7,"teacher_disagreement_score":0.54974395,"about_ca_system_score_codex":0.000010953899,"about_ca_system_score_gemma":0.000010725476,"threshold_uncertainty_score":0.16884518},"labels":[],"label_agreement":null},{"id":"W2221306040","doi":"10.1007/s11081-015-9301-2","title":"Use of a biobjective direct search algorithm in the process design of material science applications","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; McGill University; Polytechnique Montréal","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Search algorithm; Mathematical optimization; Software; Algorithm; Mathematics; Programming language","score_opus":0.03504315994626296,"score_gpt":0.2741445349296213,"score_spread":0.23910137498335837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2221306040","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":[0.00046955195,0.000017633103,0.9989817,0.00002005856,0.000047232395,0.00037046638,0.000005251574,0.000034821536,0.00005328784],"genre_scores_gemma":[0.14433056,0.000023463503,0.8555585,0.0000079013425,0.000009990625,0.000056124114,0.00000236173,0.0000065849677,0.000004502889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.000036202844,0.00017479758,0.00020244977,0.00024755741,0.00013260299],"domain_scores_gemma":[0.999264,0.00008383675,0.00006104778,0.00019077375,0.00035175987,0.0000485725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004584228,0.00008029152,0.000112600275,0.00026274953,0.000042418953,0.00006903722,0.00029206267,0.0000259762,8.9640196e-7],"category_scores_gemma":[0.00012129964,0.000067189816,0.000009386663,0.0014831836,0.00009624567,0.000755489,0.00006830368,0.00005088172,2.0926214e-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":[0.0000027936583,0.000028577912,0.000023198161,0.000009063866,0.0000021086441,3.6042582e-7,0.0013731944,0.994842,0.00046454964,0.00036853412,6.2413807e-7,0.0028850315],"study_design_scores_gemma":[0.00017400656,0.000032860426,0.00013298914,0.000013327768,0.000001888818,0.0000050263393,0.0001367187,0.9920692,0.00733363,0.000021584508,0.0000090409385,0.000069757596],"about_ca_topic_score_codex":0.00001313257,"about_ca_topic_score_gemma":1.8051173e-7,"teacher_disagreement_score":0.14386101,"about_ca_system_score_codex":0.000038807797,"about_ca_system_score_gemma":0.00010960354,"threshold_uncertainty_score":0.27399212},"labels":[],"label_agreement":null},{"id":"W2298933805","doi":"10.1007/s11081-016-9311-8","title":"Chaos oscillator differential search combined with Pontryagin’s minimum principle for simultaneous power management and component sizing of PHEVs","year":2016,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":12,"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 Waterloo","funders":"","keywords":"Powertrain; Component (thermodynamics); Sizing; Computer science; Automotive engineering; Automotive industry; Optimal control; Mathematical optimization; Controller (irrigation); Control engineering; Control theory (sociology); Torque; Engineering; Control (management); Mathematics","score_opus":0.004620744560633036,"score_gpt":0.18227258428980594,"score_spread":0.17765183972917292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298933805","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":[0.48934034,0.00014391131,0.5098987,0.000041374868,0.00003834076,0.00024190955,0.000006498359,0.0002324162,0.00005651001],"genre_scores_gemma":[0.9812941,0.00034217362,0.01822353,0.000002789208,0.000009188681,0.00002101879,0.0000036021017,0.000028114382,0.00007547024],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945587,0.0000025102881,0.00013378376,0.00013059127,0.00008516035,0.0001920668],"domain_scores_gemma":[0.99973637,0.0000746153,0.00001725435,0.0000955695,0.000030296555,0.000045873643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003707124,0.00012587533,0.00015702232,0.0001071512,0.000034913846,0.000017911454,0.00005237059,0.000042490447,0.0000071275767],"category_scores_gemma":[0.000010127985,0.00009446262,0.000015245958,0.000081118924,0.000025382951,0.00005812746,0.0000337241,0.000040468418,2.3326963e-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":[0.00007489876,0.00002082887,0.0005721024,0.0003238494,0.00011784984,0.000004686359,0.00007650056,0.98341113,0.0093942825,0.0018942447,0.000013890486,0.004095709],"study_design_scores_gemma":[0.0012236018,0.00014112545,0.00087146036,0.00008963711,0.000020231138,0.0000057503116,0.000024662559,0.9915142,0.005668461,0.0000042545234,0.00027003573,0.00016659148],"about_ca_topic_score_codex":8.037826e-7,"about_ca_topic_score_gemma":3.613421e-7,"teacher_disagreement_score":0.49195376,"about_ca_system_score_codex":0.000026727119,"about_ca_system_score_gemma":0.00000271832,"threshold_uncertainty_score":0.38520738},"labels":[],"label_agreement":null},{"id":"W2315126152","doi":"10.1007/s11081-016-9314-5","title":"Factor-based robust index tracking","year":2016,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":20,"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 Toronto","funders":"","keywords":"Tracking error; Benchmark (surveying); Computer science; Conic section; Tracking (education); Portfolio; Efficient frontier; Portfolio optimization; Mathematical optimization; Robust optimization; Sharpe ratio; Mathematics; Artificial intelligence; Finance; Control (management)","score_opus":0.0582568812469636,"score_gpt":0.28799536107110624,"score_spread":0.22973847982414264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2315126152","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":[0.014156065,0.000039284427,0.9845805,0.00034306603,0.0002033962,0.00006208051,0.00000507602,0.0000930827,0.0005174303],"genre_scores_gemma":[0.9665068,0.00016032816,0.032748677,0.00005486322,0.00005503506,0.000004316034,0.0000032889625,0.000016596583,0.0004500922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989675,0.000020753492,0.00027935245,0.00023618678,0.0003415008,0.00015465273],"domain_scores_gemma":[0.9992701,0.00023787147,0.00007706074,0.00018800609,0.00012915749,0.000097833974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029371353,0.00010724694,0.00012515817,0.00025708284,0.00007134664,0.00016587492,0.00012300575,0.00007028146,0.0003814457],"category_scores_gemma":[0.00053804787,0.000069095746,0.000034379565,0.00038521664,0.000018020468,0.0004354064,0.000019816087,0.00004090795,0.000016788317],"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":[0.0000036273093,0.0000048781803,0.0121951355,9.85632e-7,0.0000023218192,8.376752e-7,0.000033409673,0.9651198,0.00017737075,0.00013615341,0.00009838142,0.02222713],"study_design_scores_gemma":[0.00033640864,0.000011078216,0.013493526,0.000018282964,0.0000025294878,0.0000021292,0.00001330921,0.98150355,0.00029224664,0.000023710203,0.0041748015,0.00012841998],"about_ca_topic_score_codex":0.0000025633376,"about_ca_topic_score_gemma":0.0000011163123,"teacher_disagreement_score":0.95235074,"about_ca_system_score_codex":0.000019036223,"about_ca_system_score_gemma":0.000019044248,"threshold_uncertainty_score":0.41765627},"labels":[],"label_agreement":null},{"id":"W2339991536","doi":"10.1007/s11081-012-9191-5","title":"Computing the lowest equilibrium pose of a cable-suspended rigid body","year":2012,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Rigid body; Robotics; Upper and lower bounds; Rigid body dynamics; Computer science; Towing; Robot; Euler's formula; Space (punctuation); MATLAB; Mathematical optimization; Mathematics; Mathematical analysis; Artificial intelligence; Classical mechanics; Physics; Structural engineering; Engineering","score_opus":0.005450412846199315,"score_gpt":0.18068853438855953,"score_spread":0.1752381215423602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339991536","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":[0.0143601755,0.00025916487,0.9833295,0.00001838196,0.0004761188,0.00008271221,0.00000198065,0.00014109844,0.001330881],"genre_scores_gemma":[0.8622559,0.0000531486,0.13750792,0.0000108517315,0.00009931594,0.0000019635866,0.0000091264355,0.000028393913,0.00003340494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995046,0.0000052059345,0.00015617652,0.000059895214,0.000074270305,0.00019982227],"domain_scores_gemma":[0.99974465,0.000036634447,0.00002114995,0.000113675334,0.000017252087,0.0000666408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012421247,0.00010107283,0.0001122623,0.00004424838,0.00002934228,0.000022790422,0.000060047336,0.000045340836,0.000045262248],"category_scores_gemma":[0.000016443793,0.00008471434,0.000022376673,0.000117763586,0.000009568713,0.00012635694,0.000029357157,0.00007594873,0.0000016569425],"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":[8.315596e-7,0.000005929077,0.00011118832,0.000048257007,0.000013051313,2.6337224e-7,0.00014355931,0.9948124,0.001397824,0.0033134697,0.000033421013,0.00011979845],"study_design_scores_gemma":[0.00012233572,0.0000070469773,0.0002897075,0.000021702464,0.0000116345345,0.000009790937,0.000028686523,0.99898916,0.0003329379,0.0000066367506,0.00008306253,0.00009730878],"about_ca_topic_score_codex":0.000005621334,"about_ca_topic_score_gemma":2.3466387e-7,"teacher_disagreement_score":0.8478957,"about_ca_system_score_codex":0.000015018115,"about_ca_system_score_gemma":0.0000041991643,"threshold_uncertainty_score":0.34545502},"labels":[],"label_agreement":null},{"id":"W2591485304","doi":"10.1007/s11081-017-9370-5","title":"Locally weighted regression models for surrogate-assisted design optimization","year":2017,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University; Group for Research in Decision Analysis","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Mathematical optimization; Metric (unit); Surrogate model; Optimization problem; Computer science; Smoothing; Continuous optimization; Mathematics; Multi-swarm optimization; Statistics; Engineering","score_opus":0.027546568764959138,"score_gpt":0.25657014413999807,"score_spread":0.22902357537503892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591485304","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":[0.000012102111,0.00006833045,0.99826545,0.00026114943,0.0003499199,0.00049399637,0.0000041238022,0.00034239484,0.00020253213],"genre_scores_gemma":[0.013023105,0.00022075139,0.98632544,0.000042128646,0.000041209474,0.00008121417,0.000028360842,0.00004087242,0.00019689652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880093,0.000027288901,0.00026228192,0.00046086757,0.00017262796,0.00027599922],"domain_scores_gemma":[0.99867564,0.00009775886,0.00021479731,0.00054501626,0.00032611532,0.00014069617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002254675,0.00023338085,0.00021357271,0.00016730349,0.00058931205,0.0004896891,0.00044753597,0.00012320517,0.00000778564],"category_scores_gemma":[0.00019363202,0.00022859826,0.000045444205,0.0001664586,0.000035469304,0.0019920205,0.00015308433,0.000088719826,0.0000010712021],"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":[0.0000127707,0.00002183305,0.0000053444996,0.00001733441,0.000013152224,0.0000021027297,0.00008486383,0.99178785,0.00008346831,0.0024306204,0.000028179138,0.005512471],"study_design_scores_gemma":[0.001106875,0.000041120093,0.00005007888,0.00006899093,0.00000953133,0.000010193456,0.000006779076,0.99775755,0.00047538304,0.00012413308,0.00007156245,0.0002777779],"about_ca_topic_score_codex":0.0000035722642,"about_ca_topic_score_gemma":4.4104803e-7,"teacher_disagreement_score":0.013011003,"about_ca_system_score_codex":0.00006627594,"about_ca_system_score_gemma":0.000041417763,"threshold_uncertainty_score":0.9321966},"labels":[],"label_agreement":null},{"id":"W2594545404","doi":"10.1007/s11081-017-9347-4","title":"Dynamic portfolio choice: a simulation-and-regression approach","year":2017,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Computer science; Portfolio; State variable; Mathematical optimization; Context (archaeology); Discrete choice; Representation (politics); Econometrics; Regression; Variable (mathematics); Mathematics; Economics; Finance; Machine learning","score_opus":0.020087521378608177,"score_gpt":0.2320731528265739,"score_spread":0.21198563144796573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594545404","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":[0.09768112,0.00078089366,0.8884461,0.00013363113,0.0002468466,0.00011849811,0.000018942495,0.000054628938,0.012519364],"genre_scores_gemma":[0.9775003,0.0002961602,0.021447513,0.000018197281,0.000030097364,0.0000065491913,0.000013164688,0.000016451095,0.0006715341],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952424,0.0000010802125,0.00017425645,0.0001861947,0.000008867601,0.0001053823],"domain_scores_gemma":[0.9996142,0.000014950165,0.00012221039,0.0001870134,0.000007748151,0.000053906057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010518256,0.000085259286,0.00014907001,0.000069314156,0.00018047821,0.00015155357,0.00006798744,0.00005611569,0.0000516538],"category_scores_gemma":[0.00005984233,0.00009449023,0.000020405516,0.000017313674,0.000019930623,0.0002954558,0.000049508908,0.00004908428,0.0000038806415],"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":[0.0000012684435,0.0000048842285,0.0026334063,0.000015789483,0.0000084796775,1.7581583e-7,0.000068181726,0.9618378,0.0000017785092,0.035131592,0.00000613771,0.0002904605],"study_design_scores_gemma":[0.00024567937,0.0000050517897,0.00692608,0.000009738724,0.000002302676,0.0000016961188,0.0000096127715,0.99033904,9.2826616e-7,0.00040828562,0.0019352793,0.000116305404],"about_ca_topic_score_codex":0.000022000608,"about_ca_topic_score_gemma":6.217183e-7,"teacher_disagreement_score":0.8798192,"about_ca_system_score_codex":0.000016580874,"about_ca_system_score_gemma":0.000002215763,"threshold_uncertainty_score":0.38531998},"labels":[],"label_agreement":null},{"id":"W2735702858","doi":"10.1007/s11081-017-9361-6","title":"A stochastic optimization formulation for the transition from open pit to underground mining","year":2017,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; AngloGold Ashanti; Newmont Corporation; Barrick Gold Corporation","keywords":"Open-pit mining; Cash flow; Net present value; Computer science; Financial engineering; Set (abstract data type); Mathematical optimization; Production (economics); Mining engineering; Present value; Underground mining (soft rock); Operations research; Geology; Mathematics; Engineering; Economics","score_opus":0.02405759604799163,"score_gpt":0.236973812695526,"score_spread":0.21291621664753438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735702858","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":[0.004449507,0.000030882973,0.9942172,0.000262147,0.00021765525,0.0004542298,0.000017593928,0.00014737132,0.00020341174],"genre_scores_gemma":[0.64817333,0.0000488285,0.35142696,0.00004072304,0.000088468885,0.00010838032,0.000057061112,0.00003636971,0.000019864003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995573,0.000002026535,0.00014693162,0.0001312692,0.000035270747,0.0001271586],"domain_scores_gemma":[0.9996152,0.00006525245,0.000034487963,0.000212321,0.000022791673,0.000049946575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109821856,0.00010823184,0.000108335946,0.000046241894,0.00028877737,0.0004919222,0.0001937117,0.000059208345,0.000024083649],"category_scores_gemma":[0.00004299674,0.00010633858,0.000020649719,0.000030724637,0.000004699143,0.00044695853,0.000043713422,0.000040457537,7.089952e-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":[0.0000075916932,0.0000020020211,0.0000043626687,0.00000937932,0.000014636836,6.693661e-8,0.00041676618,0.99791545,0.000059305235,0.000522104,0.00010226785,0.00094605744],"study_design_scores_gemma":[0.00028401983,0.000017522752,0.0001401767,0.000043075866,0.000022210312,9.277468e-7,0.0000490651,0.9988565,0.000052032236,0.00003184025,0.00036230622,0.00014034187],"about_ca_topic_score_codex":0.00002907194,"about_ca_topic_score_gemma":0.000012022781,"teacher_disagreement_score":0.64372385,"about_ca_system_score_codex":0.00004210495,"about_ca_system_score_gemma":0.000004790904,"threshold_uncertainty_score":0.47436175},"labels":[],"label_agreement":null},{"id":"W2754127623","doi":"10.1007/s11081-017-9366-1","title":"Best practices for comparing optimization algorithms","year":2017,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":189,"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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Benchmarking; Best practice; Process (computing); Task (project management); Financial engineering; Optimization algorithm","score_opus":0.056139760934673856,"score_gpt":0.2990190674639251,"score_spread":0.24287930652925124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754127623","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":[0.000058915593,0.000053581945,0.99759686,0.0004025373,0.00032968193,0.0002784219,0.000001588433,0.00031440204,0.0009640133],"genre_scores_gemma":[0.02902005,0.00010475069,0.97054714,0.000025527257,0.00006278026,0.00006673351,0.000012685463,0.00001920399,0.00014114715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922276,0.000006771187,0.0001816479,0.00028870575,0.000116428695,0.00018368174],"domain_scores_gemma":[0.99896675,0.00006903446,0.0003274887,0.00041866777,0.00013664215,0.00008140705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018707401,0.00013391358,0.00014283445,0.00012412107,0.00043019204,0.0007685301,0.00043371928,0.000064030566,0.0000048615916],"category_scores_gemma":[0.0005426955,0.00014656554,0.000027236914,0.000083391154,0.000019560017,0.0013498614,0.00015076116,0.000060794802,9.4346524e-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":[0.0000016787154,0.000014013768,0.00008574624,0.000020688583,0.000008840594,4.4935837e-7,0.00007060734,0.98717827,0.000019274155,0.010601511,0.000027667827,0.001971285],"study_design_scores_gemma":[0.00032595877,0.00003701278,0.000048923714,0.000039963616,0.000010583464,0.0000081959,0.000008808262,0.9987237,0.00016961795,0.000034642922,0.00042158918,0.00017101792],"about_ca_topic_score_codex":0.0000188879,"about_ca_topic_score_gemma":9.991131e-7,"teacher_disagreement_score":0.028961133,"about_ca_system_score_codex":0.000024757273,"about_ca_system_score_gemma":0.000015655216,"threshold_uncertainty_score":0.74109536},"labels":[],"label_agreement":null},{"id":"W2756195360","doi":"10.1007/s11081-017-9365-2","title":"Optimization methods for petroleum fields development and production systems: a review","year":2017,"lang":"en","type":"review","venue":"Optimization and Engineering","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":48,"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 Waterloo","funders":"","keywords":"Metaheuristic; Computer science; Mathematical optimization; Heuristics; Nonlinear programming; Stochastic programming; Nonlinear system; Financial engineering; Continuous optimization; Linear programming; Discrete optimization; Integer programming; Optimization problem; Algorithm; Multi-swarm optimization; Mathematics","score_opus":0.08199887581398814,"score_gpt":0.3773771439904614,"score_spread":0.2953782681764733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756195360","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.854333e-8,0.49981004,0.49895218,0.0000065677114,0.00049156806,0.00050940114,0.0000030025499,0.0001833766,0.000043763044],"genre_scores_gemma":[2.4777904e-7,0.56670386,0.4325396,0.000002014166,0.00009715269,0.00031519274,0.00009599089,0.000072350565,0.00017358243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986041,0.000059469905,0.00063576206,0.00035439982,0.00009720561,0.00024906296],"domain_scores_gemma":[0.9991571,0.00016285476,0.00014408525,0.00034011874,0.000074499505,0.00012138782],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00083528354,0.0004409622,0.0011139319,0.00026555918,0.0001466995,0.0001762907,0.00013260863,0.00029856252,0.0000054790576],"category_scores_gemma":[0.00040520803,0.0004277652,0.0001017787,0.00013428662,0.0000125041515,0.00021198852,0.00003848865,0.00022607006,6.606331e-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":[3.6849215e-7,0.0000017777347,4.4589903e-8,0.056240175,0.000064594235,2.2558474e-7,0.000015101308,0.71004444,7.959784e-8,0.000015000475,0.0000733849,0.23354478],"study_design_scores_gemma":[0.000065457396,0.0000047361846,6.694998e-8,0.010123058,0.00013484909,0.00001431298,0.0000011937525,0.5300407,6.9461703e-7,2.077272e-7,0.4593793,0.0002354453],"about_ca_topic_score_codex":6.5374417e-7,"about_ca_topic_score_gemma":9.003509e-8,"teacher_disagreement_score":0.4593059,"about_ca_system_score_codex":0.000082523504,"about_ca_system_score_gemma":0.00003607949,"threshold_uncertainty_score":0.99981743},"labels":[],"label_agreement":null},{"id":"W2922013388","doi":"10.1007/s11081-019-09426-5","title":"Multistage stochastic capacity planning of partially upgraded bitumen production with hybrid solution method","year":2019,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Energy, Environment, and Transportation Policies","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Resources Canada","keywords":"Variable (mathematics); Mathematical optimization; Asphalt; Computer science; Set (abstract data type); Production (economics); Production planning; Random variable; Stochastic modelling; Mathematics; Statistics","score_opus":0.01025784337057726,"score_gpt":0.20626731908464382,"score_spread":0.19600947571406657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922013388","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":[0.26574367,0.00002212774,0.7338282,0.000025932117,0.00007817014,0.00008424377,0.0000030717226,0.000056183686,0.00015840335],"genre_scores_gemma":[0.92938197,0.000014744304,0.070315935,0.00000853897,0.00002637894,0.0000112024045,0.000044574906,0.0000195029,0.00017715481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944603,0.000013439082,0.00015592987,0.00015808562,0.00010442953,0.00012205375],"domain_scores_gemma":[0.9997528,0.000018673694,0.00006940128,0.000100055404,0.000019253088,0.000039809962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081150356,0.00010317555,0.0001251889,0.00008416997,0.00003627523,0.000011020102,0.00002742881,0.00003063492,0.000030292742],"category_scores_gemma":[0.000012654077,0.00009931209,0.000016735337,0.00008938513,0.00001772922,0.00015567652,0.000004641578,0.000054198477,0.000001252977],"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":[0.000013683387,0.00001388564,0.0005265149,0.000049309652,0.000025191885,4.064823e-7,0.0004248814,0.98621833,0.010954073,0.0016572378,0.0000020979087,0.00011439517],"study_design_scores_gemma":[0.00043529255,0.00004230453,0.005402969,0.00005839434,0.000029365152,0.000006084534,0.00006144053,0.9802112,0.013417263,0.000008300117,0.0001797996,0.00014757052],"about_ca_topic_score_codex":0.00013122684,"about_ca_topic_score_gemma":0.000012622649,"teacher_disagreement_score":0.6636383,"about_ca_system_score_codex":0.000018303459,"about_ca_system_score_gemma":0.0000057063617,"threshold_uncertainty_score":0.40498295},"labels":[],"label_agreement":null},{"id":"W2968737012","doi":"10.1007/s11081-019-09461-2","title":"Adjustable Robust Optimization for multi-tasking scheduling with reprocessing due to imperfect tasks","year":2019,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":9,"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 Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Job shop scheduling; Benchmark (surveying); Mathematical optimization; Dynamic priority scheduling; Distributed computing; Schedule; Mathematics","score_opus":0.011496370735835053,"score_gpt":0.2112320773505336,"score_spread":0.19973570661469853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968737012","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":[0.0077629355,0.00022788162,0.9902497,0.000036779573,0.00021911485,0.0005748643,0.000007345648,0.00057252235,0.0003488651],"genre_scores_gemma":[0.20208256,0.000089669826,0.79727715,0.00006880555,0.00005759237,0.00010088616,0.00010677265,0.00010354717,0.00011299503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989416,0.0000065381823,0.00027528044,0.00033600605,0.00012694011,0.00031363338],"domain_scores_gemma":[0.9994316,0.000032655582,0.000048178565,0.00017051447,0.0001858367,0.00013120782],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015497713,0.0002585295,0.00024746024,0.0002628282,0.000114263705,0.00021123615,0.00008867921,0.00011663639,0.00007746275],"category_scores_gemma":[0.000083702434,0.0002578828,0.000030869047,0.00048651712,0.000008028628,0.0006855946,0.000019767966,0.00012628267,0.000004747187],"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":[0.000016503902,0.000009829779,0.000096486474,0.0002650542,0.000022195667,5.617187e-7,0.0002343229,0.9976185,0.0008124502,0.00008108148,0.000020783895,0.0008222533],"study_design_scores_gemma":[0.0007668727,0.000056367615,0.000033912802,0.00021788574,0.000021757449,0.000013527423,0.00014416467,0.99639976,0.0017681376,4.771948e-7,0.00021463961,0.00036250352],"about_ca_topic_score_codex":0.0000035128085,"about_ca_topic_score_gemma":0.0000023330956,"teacher_disagreement_score":0.19431962,"about_ca_system_score_codex":0.00008287484,"about_ca_system_score_gemma":0.000025172083,"threshold_uncertainty_score":0.99998736},"labels":[],"label_agreement":null},{"id":"W2978807476","doi":"10.1007/s11081-020-09507-w","title":"How to catch a lion in the desert: on the solution of the coverage directed generation (CDG) problem","year":2020,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":6,"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 British Columbia","funders":"","keywords":"Noise (video); Function (biology); Reliability (semiconductor); Software; Financial engineering; Optimization problem; Quadratic equation","score_opus":0.030665663267915158,"score_gpt":0.2170810003309102,"score_spread":0.18641533706299507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978807476","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":[0.013988536,0.000014516559,0.9751423,0.010363873,0.000078140845,0.00028125924,5.808912e-7,0.000038874983,0.00009191174],"genre_scores_gemma":[0.7637652,0.000018408042,0.23571825,0.00041875857,0.00003115245,0.000031288113,0.0000018050794,0.000004478967,0.0000106648995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950594,0.000079805264,0.00009798956,0.00011369578,0.00012824575,0.000074304335],"domain_scores_gemma":[0.9996936,0.000040691146,0.000040025403,0.00017481382,0.000032472955,0.000018445133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027390252,0.00005870959,0.000048804035,0.000031176813,0.00006750674,0.00010286051,0.0002457233,0.000026213394,8.666299e-7],"category_scores_gemma":[0.00020302866,0.000033199707,0.0000137410825,0.0005848216,0.000006823438,0.00018518597,0.00004551167,0.00007697983,4.544223e-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":[0.0000019296956,0.0000060586003,0.00003053091,0.000009006935,0.0000015056602,7.120686e-8,0.0018737869,0.98079425,0.006769132,0.008853902,0.00010559309,0.0015542571],"study_design_scores_gemma":[0.00005791113,0.000025669431,0.0013696621,0.000014514235,0.0000015107669,8.4527636e-7,0.000016133037,0.9926259,0.005564373,0.000010335257,0.000271755,0.000041370313],"about_ca_topic_score_codex":0.000005625518,"about_ca_topic_score_gemma":0.000001985199,"teacher_disagreement_score":0.74977666,"about_ca_system_score_codex":0.000019090954,"about_ca_system_score_gemma":0.00000906555,"threshold_uncertainty_score":0.13538447},"labels":[],"label_agreement":null},{"id":"W2990689883","doi":"10.1007/s11081-019-09476-9","title":"Robust principal component analysis using facial reduction","year":2019,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"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 Waterloo","funders":"University of California, Davis; Natural Sciences and Engineering Research Council of Canada; Kungliga Tekniska Högskolan","keywords":"Robust principal component analysis; Outlier; Sparse PCA; Principal component analysis; Computer science; Norm (philosophy); Heuristic; Mathematical optimization; Matrix (chemical analysis); Algorithm; Matrix norm; Reduction (mathematics); Mathematics; Artificial intelligence; Eigenvalues and eigenvectors","score_opus":0.01743263055725297,"score_gpt":0.20196143938077804,"score_spread":0.18452880882352507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990689883","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":[0.468529,0.00005622667,0.53051704,0.0000030314689,0.0001793966,0.000063824555,0.0000010766257,0.00037808414,0.00027237082],"genre_scores_gemma":[0.91711503,0.00006830415,0.08271107,0.000003157391,0.000044242934,0.000001837881,0.000016170727,0.000019670322,0.00002052132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957585,0.0000044858966,0.00011584586,0.00011576671,0.00007016301,0.00011785922],"domain_scores_gemma":[0.99981314,0.000004873714,0.000015387903,0.00010581597,0.000021627719,0.000039183586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035358247,0.00010159806,0.00013469167,0.0002012187,0.000027701426,0.00003837446,0.00003372831,0.000052986587,0.000042041866],"category_scores_gemma":[0.000002386213,0.00011448244,0.000037553564,0.0002832034,0.000005609306,0.0001092837,0.000016476984,0.000068469584,0.000002225833],"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":[0.0000012565451,0.0000027769931,0.00020860316,0.000009633879,0.000073400166,5.230666e-7,0.00004991393,0.985021,0.014425059,0.000041641808,0.0000080331265,0.00015816647],"study_design_scores_gemma":[0.0000829075,0.000005041299,0.00058789755,0.0000149707475,0.00006181803,0.0000065898175,0.000014872051,0.9962687,0.0025838204,9.4153063e-7,0.00023872612,0.00013369917],"about_ca_topic_score_codex":0.0000069207763,"about_ca_topic_score_gemma":3.0381716e-7,"teacher_disagreement_score":0.44858605,"about_ca_system_score_codex":0.00003564094,"about_ca_system_score_gemma":0.0000024873632,"threshold_uncertainty_score":0.46684584},"labels":[],"label_agreement":null},{"id":"W3009274308","doi":"10.1007/s11081-020-09493-z","title":"Convex optimization techniques in compliant assembly simulation","year":2020,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematical optimization; Hessian matrix; Active set method; Interior point method; Quadratic programming; Computer science; Convex optimization; Optimization problem; Linear programming; Matrix (chemical analysis); Algorithm; Mathematics; Regular polygon; Nonlinear programming; Applied mathematics","score_opus":0.01283247509068126,"score_gpt":0.20982433295061034,"score_spread":0.1969918578599291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009274308","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":[0.002277619,0.00010319441,0.9959776,0.000121992154,0.000066552544,0.0001762656,0.0000024459257,0.00067472295,0.0005996047],"genre_scores_gemma":[0.8663398,0.0002904412,0.13310713,0.000092649636,0.00005428068,0.000017784841,0.000048265872,0.000044084383,0.0000055777446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933654,0.0000070843676,0.00023671532,0.00016923751,0.0000918256,0.00015857094],"domain_scores_gemma":[0.99976,0.000025203879,0.000026272022,0.00006774033,0.000032418247,0.00008840158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000055488374,0.00015770763,0.00016193023,0.00012312368,0.000032047654,0.00006959055,0.00005762512,0.00009264774,0.00005176679],"category_scores_gemma":[0.000034289205,0.0001808105,0.000017221231,0.00026680963,0.0000075540256,0.00030657384,0.000018375757,0.00011284783,0.0000019896202],"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":[0.0000036000406,0.000004409026,0.00009392943,0.00012540816,0.000004969139,0.0000013225767,0.00015135789,0.99864113,0.00009608825,0.000050882878,0.000012843256,0.00081406155],"study_design_scores_gemma":[0.00021106334,0.000015293812,0.00015493533,0.000040813426,0.000005439713,0.0000010616975,0.000016436636,0.9977068,0.0012072597,0.0000015697345,0.0004392476,0.00020011795],"about_ca_topic_score_codex":0.000002211946,"about_ca_topic_score_gemma":4.0958955e-7,"teacher_disagreement_score":0.8640622,"about_ca_system_score_codex":0.000032646916,"about_ca_system_score_gemma":0.000005061898,"threshold_uncertainty_score":0.7373238},"labels":[],"label_agreement":null},{"id":"W3009526543","doi":"10.1007/s11081-020-09492-0","title":"Optimization of covered calls under uncertainty","year":2020,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","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":"IHS Markit (Canada); University of Toronto","funders":"","keywords":"Expected utility hypothesis; Portfolio; Mathematical optimization; Portfolio optimization; Computer science; Quadratic equation; Convex optimization; Optimization problem; Financial engineering; Matching (statistics); Econometrics; Regular polygon; Mathematics; Mathematical economics; Economics; Finance; Statistics","score_opus":0.018676566790180187,"score_gpt":0.18383828315075001,"score_spread":0.16516171636056984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009526543","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":[0.0004966457,0.00028955683,0.9978382,0.00044685026,0.000038766528,0.00007957044,0.00003737761,0.000033114542,0.00073992705],"genre_scores_gemma":[0.8730002,0.00022232525,0.12633482,0.0002807545,0.000056536494,0.000016703763,0.000046143778,0.000019444755,0.000023076493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995406,5.5855065e-7,0.00022625412,0.00013917623,0.000016503594,0.00007690668],"domain_scores_gemma":[0.9997606,0.000014301114,0.00008666251,0.000058459336,0.00002621414,0.0000537113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000037211023,0.00006349486,0.00014063779,0.000042453885,0.00002792451,0.000016052936,0.00004851651,0.00004304887,0.000066329594],"category_scores_gemma":[0.00006786498,0.00007846823,0.000020483903,0.00021585508,0.000011833044,0.00008020628,0.000018348017,0.000036971098,0.000005378268],"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":[0.000002396758,0.0000057424973,0.00005716841,0.00002210239,0.0000055061387,4.4812158e-8,0.000067718494,0.8154919,0.000017663891,0.18428566,0.000009426113,0.000034645203],"study_design_scores_gemma":[0.00019994499,0.000016947297,0.00022084427,0.0000061635915,0.0000029532705,4.1872227e-7,0.000015454158,0.9982154,0.000015312751,0.00065337,0.00056739623,0.00008578912],"about_ca_topic_score_codex":0.00001632342,"about_ca_topic_score_gemma":2.0884504e-7,"teacher_disagreement_score":0.8725036,"about_ca_system_score_codex":0.000012149814,"about_ca_system_score_gemma":0.0000065949957,"threshold_uncertainty_score":0.31998414},"labels":[],"label_agreement":null},{"id":"W3012349450","doi":"10.1007/s11081-020-09495-x","title":"Joint stochastic short-term production scheduling and fleet management optimization for mining complexes","year":2020,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"AngloGold Ashanti; Natural Sciences and Engineering Research Council of Canada; Newmont Corporation; IAMGOLD; Canada Research Chairs; McGill University; Barrick Gold Corporation","keywords":"Haulage; Relocation; Shovel; Truck; Computer science; Fleet management; Scheduling (production processes); Schedule; Open-pit mining; Metaheuristic; Time horizon; Operations research; Production schedule; Production (economics); Mathematical optimization; Engineering; Rope; Automotive engineering; Algorithm; Mathematics; Mining engineering","score_opus":0.025318823114117706,"score_gpt":0.20661812831851026,"score_spread":0.18129930520439255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012349450","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":[0.039200485,0.000110056455,0.9596814,0.00009471238,0.00012626541,0.0002805879,0.0000039977717,0.00043851405,0.00006399395],"genre_scores_gemma":[0.42554522,0.00023162916,0.5739909,0.000019913448,0.00008703324,0.000043133958,0.00003870863,0.00003843129,0.0000050418635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994496,0.0000021306198,0.00018550851,0.00018622556,0.000035839712,0.0001406665],"domain_scores_gemma":[0.99981403,0.000009707683,0.000017888584,0.00006510278,0.000017037351,0.00007625162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006654322,0.00013166557,0.00013765827,0.000075485776,0.00006089438,0.00006812618,0.00003296035,0.000046022484,0.0000099897225],"category_scores_gemma":[0.000019011337,0.00015976815,0.000017641929,0.00007392503,0.000008622573,0.00015500342,0.000028567722,0.000048240647,2.3306472e-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":[0.0000037495242,0.0000022196364,0.000024669256,0.00025603265,0.000016925453,2.7906293e-7,0.00025192392,0.9977543,0.00042144916,0.00016331252,0.000052675416,0.0010524903],"study_design_scores_gemma":[0.00014340409,0.000022396938,0.000046362744,0.00006510037,0.00002296165,0.0000042713054,0.000085177075,0.9990952,0.0002709323,0.000002423929,0.000069412374,0.00017234567],"about_ca_topic_score_codex":2.1305007e-7,"about_ca_topic_score_gemma":1.286601e-7,"teacher_disagreement_score":0.38634473,"about_ca_system_score_codex":0.000022534894,"about_ca_system_score_gemma":0.0000016766135,"threshold_uncertainty_score":0.65151566},"labels":[],"label_agreement":null},{"id":"W3081977267","doi":"10.1007/s11081-020-09554-3","title":"Aerodynamic optimisation of a high-speed train head shape using an advanced hybrid surrogate-based nonlinear model representation method","year":2020,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Aerodynamics and Fluid Dynamics Research","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"","keywords":"Surrogate model; Latin hypercube sampling; Computational fluid dynamics; Computer science; Aerodynamics; Radial basis function; Particle swarm optimization; Nonlinear system; Mathematical optimization; Polynomial chaos; Algorithm; Mathematics; Artificial intelligence; Engineering; Monte Carlo method; Artificial neural network; Machine learning","score_opus":0.03310849930653781,"score_gpt":0.2877674344126649,"score_spread":0.2546589351061271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081977267","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":[0.3979133,0.00002739712,0.6016386,0.00004994538,0.000033955323,0.00014634963,0.000037450227,0.00013941932,0.000013561371],"genre_scores_gemma":[0.5648837,0.00004731946,0.43476188,0.000014855523,0.000018450706,0.0000041758453,0.00021794082,0.000048922953,0.000002705364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890167,0.000029260378,0.0003439873,0.0002824492,0.00021059862,0.00023203935],"domain_scores_gemma":[0.9994648,0.000040917654,0.000048335034,0.00016730245,0.00010518975,0.00017344208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016660232,0.00020027957,0.00026883718,0.00015994262,0.000049844795,0.00004804729,0.00010746719,0.000078444675,0.00001879509],"category_scores_gemma":[0.000054288525,0.00023930919,0.000047463338,0.00035410005,0.00002188111,0.0003383071,0.000024820325,0.00015902556,4.7366626e-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":[0.000021306392,0.00001629021,0.000011195533,0.000104206614,0.000016755343,0.0000016111322,0.0001390187,0.868965,0.12918122,0.00011908529,8.0209844e-7,0.0014234631],"study_design_scores_gemma":[0.00075068296,0.000046100584,0.00010612661,0.000027796463,0.000018091321,0.0000023448158,0.000036526963,0.9972378,0.0015433877,0.000006798049,0.0000020732127,0.00022224365],"about_ca_topic_score_codex":0.000021869873,"about_ca_topic_score_gemma":0.000006356115,"teacher_disagreement_score":0.16697045,"about_ca_system_score_codex":0.00007071779,"about_ca_system_score_gemma":0.000031547734,"threshold_uncertainty_score":0.97587454},"labels":[],"label_agreement":null},{"id":"W3099707780","doi":"10.1007/s11081-020-09578-9","title":"A decomposition method for a class of convex generalized Nash equilibrium problems","year":2020,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":6,"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":"Nash equilibrium; Convexity; Mathematical optimization; Class (philosophy); Decomposition; Extension (predicate logic); Convergence (economics); Computer science; Constraint (computer-aided design); Set (abstract data type); Regular polygon; Decomposition method (queueing theory); Mathematics; Applied mathematics; Discrete mathematics","score_opus":0.015587014739055402,"score_gpt":0.24932601145714306,"score_spread":0.23373899671808765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099707780","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":[0.00017893841,0.00003892615,0.99733084,0.0021019469,0.00004354645,0.00013990063,0.0000046533173,0.00008804182,0.00007320696],"genre_scores_gemma":[0.037038803,0.000023380231,0.96245193,0.00036949545,0.000030544514,0.00002041962,0.000036507387,0.000008474061,0.000020421474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994367,0.000016943275,0.00019840903,0.0001732999,0.000087868495,0.00008681589],"domain_scores_gemma":[0.99961776,0.000049418002,0.00006971454,0.000074107054,0.00011448534,0.00007451962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010224807,0.000077260745,0.00014619388,0.00007154249,0.000028395742,0.00005828759,0.00010695433,0.000037121677,0.000016683342],"category_scores_gemma":[0.00004180337,0.00007908126,0.000046333837,0.0003299544,0.000004526591,0.00024198172,0.000041592706,0.000027217462,5.1793666e-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":[0.0000032443181,0.000007893459,0.000014607821,0.000048799455,0.00002104698,7.664141e-8,0.00021051256,0.97428024,0.0044537485,0.02066373,0.000052865722,0.00024323567],"study_design_scores_gemma":[0.0004449638,0.0000319885,0.0000262615,0.000008314614,0.000014588792,0.0000012421904,0.000003885238,0.9976021,0.001047694,0.00004115183,0.0006892101,0.00008861603],"about_ca_topic_score_codex":0.0000025357563,"about_ca_topic_score_gemma":1.3465058e-7,"teacher_disagreement_score":0.036859863,"about_ca_system_score_codex":0.000008477462,"about_ca_system_score_gemma":0.000015250198,"threshold_uncertainty_score":0.32248405},"labels":[],"label_agreement":null},{"id":"W3118562502","doi":"10.1007/s11081-020-09575-y","title":"Pump scheduling in drinking water distribution networks with an LP/NLP-based branch and bound","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":32,"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","funders":"","keywords":"Integer programming; Branch and bound; Mathematical optimization; Linear programming relaxation; Scheduling (production processes); Computer science; Regular polygon; Heuristic; Relaxation (psychology); Linear programming; Integer (computer science); Upper and lower bounds; Branch and cut; Mathematics","score_opus":0.004095670486794863,"score_gpt":0.1627631810331442,"score_spread":0.15866751054634934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118562502","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":[0.28163213,0.00017217999,0.717848,0.000019877129,0.00009781474,0.00006534937,0.0000013951096,0.00012339982,0.000039859337],"genre_scores_gemma":[0.9837694,0.00008600301,0.015689587,0.00001281683,0.00005909409,0.000013298887,0.00031511125,0.000036941547,0.000017723232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934393,0.000012767151,0.00017210677,0.00018197308,0.00007194736,0.0002172459],"domain_scores_gemma":[0.9997654,0.000010650837,0.000012005511,0.0000996799,0.000039263832,0.00007301658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009685701,0.00014480646,0.00014253006,0.00006734563,0.00005792743,0.0001757601,0.000027083857,0.00008711347,0.000010618088],"category_scores_gemma":[0.0000054565867,0.00013067528,0.000010531237,0.00017086414,0.000009740851,0.0003124507,0.000013526139,0.00010818245,2.933919e-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":[0.0000041067196,0.0000066949347,0.0018800293,0.00006883492,0.000008424377,0.0000070005553,0.00013654787,0.99740803,0.00030362382,0.000038690334,0.0000015488388,0.00013645494],"study_design_scores_gemma":[0.00047636064,0.000015477975,0.0013094781,0.00012499769,0.000008697915,0.000015543948,0.000024634624,0.9960882,0.0016474836,0.0000018008272,0.00009283113,0.00019447375],"about_ca_topic_score_codex":0.000009531941,"about_ca_topic_score_gemma":0.00008331524,"teacher_disagreement_score":0.7021584,"about_ca_system_score_codex":0.000039051873,"about_ca_system_score_gemma":0.000006831498,"threshold_uncertainty_score":0.53287834},"labels":[],"label_agreement":null},{"id":"W3138160650","doi":"10.1007/s11081-021-09612-4","title":"State of the art methods for combined water and energy systems optimisation in Kraft pulp mills","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Kraft process; Energy consumption; Environmental science; Greenhouse gas; Kraft paper; Process engineering; Computer science; Biofuel; Waste management; Engineering; Pulp and paper industry","score_opus":0.0067033822572543774,"score_gpt":0.21759502228322103,"score_spread":0.21089164002596666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138160650","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":[0.003189299,0.00056926423,0.9956111,0.0000840915,0.00023231054,0.00009807,0.0000059477325,0.00005317421,0.00015674687],"genre_scores_gemma":[0.66878766,0.0029876253,0.32617158,0.00008770288,0.000048342645,0.00017719016,0.00037485638,0.00006464025,0.0013003926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995159,0.00002180154,0.00020193498,0.00010306722,0.00004796593,0.00010936198],"domain_scores_gemma":[0.99975234,0.00004337125,0.000021431042,0.000070581154,0.00008213163,0.000030154888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013250414,0.00009750498,0.00013528208,0.00006180302,0.000026001411,0.000044089156,0.00003343175,0.00004910099,0.000008465258],"category_scores_gemma":[0.000039187136,0.00007025053,0.000019082881,0.00014728398,0.000008352441,0.0001431743,0.00001613146,0.000044499448,8.06659e-8],"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":[0.0000026558134,0.0000051301745,0.00002204518,0.00010530207,0.000011182766,9.886913e-8,0.00014393248,0.9848611,0.012497583,0.00022320589,0.000033565615,0.00209424],"study_design_scores_gemma":[0.0002887047,0.000008107652,0.00004528878,0.000049756858,0.000007452264,0.0000029951527,0.000027368145,0.9585981,0.038501315,0.000011569468,0.0023683247,0.00009106484],"about_ca_topic_score_codex":0.0000026966347,"about_ca_topic_score_gemma":0.000003984171,"teacher_disagreement_score":0.6694395,"about_ca_system_score_codex":0.000022189874,"about_ca_system_score_gemma":0.000006838607,"threshold_uncertainty_score":0.28647336},"labels":[],"label_agreement":null},{"id":"W3154921582","doi":"10.1007/s11081-021-09630-2","title":"The use of intensity-dependent weight functions to “Weberize” $$L^2$$-based methods of signal and image approximation","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"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 Waterloo","funders":"","keywords":"Intensity (physics); Weight function; Mathematics; Metric (unit); Exponent; Uniqueness; Function (biology); Approximations of π; Image (mathematics); Mathematical analysis; Physics; Computer science; Optics; Artificial intelligence","score_opus":0.024158060434771818,"score_gpt":0.25533874253697947,"score_spread":0.23118068210220766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154921582","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":[0.0013254965,0.00008458675,0.9980939,0.00025329454,0.000115970965,0.00005941637,0.0000015257267,0.000025260208,0.000040573428],"genre_scores_gemma":[0.01271201,0.000030256277,0.98708636,0.000054419084,0.0000096386575,0.0000038810063,0.000003430201,0.00000567829,0.000094352166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994471,0.0000749991,0.0001687303,0.00013440111,0.00009292636,0.00008182563],"domain_scores_gemma":[0.9992907,0.00025192392,0.000044905682,0.00015822535,0.0002101747,0.000044036242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034382276,0.000066128414,0.000116778305,0.00007547607,0.000068192945,0.00010843162,0.00006546747,0.00002740266,0.0000052642013],"category_scores_gemma":[0.00019444921,0.000055345914,0.000022200056,0.00028032524,0.000019484312,0.00023620603,0.00007268302,0.00005243566,2.3665355e-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":[0.000011330743,0.000014242042,0.000020378802,0.000037579426,0.000015243997,0.0000020243472,0.0001918658,0.8583705,0.12383466,0.0010272503,0.00005088776,0.016424028],"study_design_scores_gemma":[0.00015463516,0.000019519392,0.00022659474,0.000022510638,0.000009074863,0.0000071515674,0.000012670866,0.92116284,0.07786118,0.000014108941,0.0004536389,0.000056094355],"about_ca_topic_score_codex":0.000005613064,"about_ca_topic_score_gemma":4.3353117e-7,"teacher_disagreement_score":0.062792316,"about_ca_system_score_codex":0.00000794877,"about_ca_system_score_gemma":0.000022085394,"threshold_uncertainty_score":0.22569409},"labels":[],"label_agreement":null},{"id":"W3158339412","doi":"10.1007/s11081-021-09629-9","title":"A matheuristic approach for optimizing mineral value chains under uncertainty","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; McGill University","funders":"","keywords":"Heuristics; Computer science; Reinforcement learning; Mathematical optimization; Artificial neural network; Value (mathematics); Relaxation (psychology); Decomposition; Artificial intelligence; Machine learning; Mathematics","score_opus":0.012057757233954832,"score_gpt":0.1945331721275355,"score_spread":0.18247541489358066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158339412","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":[0.005047811,0.00016825381,0.99313766,0.00002876582,0.000120269215,0.00010080125,0.000010028158,0.00037668349,0.0010097394],"genre_scores_gemma":[0.13849269,0.00025616138,0.8607344,0.000044846136,0.000085643434,0.000053134885,0.00011133196,0.00005647613,0.00016528244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999487,0.0000031569507,0.0001467838,0.0001490117,0.00003106206,0.00018300136],"domain_scores_gemma":[0.9997642,0.00002652432,0.000013393799,0.00011132031,0.00002364342,0.000060934944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007079893,0.0001258244,0.00014195679,0.00005439235,0.000042591786,0.000059444534,0.000043296663,0.00007200481,0.000015070785],"category_scores_gemma":[0.000019099061,0.00014679783,0.000037546695,0.00008123553,0.0000065681975,0.00007127217,0.00002013277,0.000066797445,3.5840964e-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":[0.0000012879203,0.000005914082,0.0000046054274,0.00009818011,0.000015846594,6.6894586e-7,0.000081143546,0.994865,0.0004931184,0.004119055,0.0001788779,0.00013630453],"study_design_scores_gemma":[0.00020092216,0.000007677805,0.000009564725,0.00001834913,0.000012800498,0.000015742458,0.000061436804,0.99859643,0.00020810058,0.000018468134,0.0006765029,0.00017403216],"about_ca_topic_score_codex":0.0000014686772,"about_ca_topic_score_gemma":2.847579e-7,"teacher_disagreement_score":0.13344489,"about_ca_system_score_codex":0.000043824,"about_ca_system_score_gemma":0.0000083169325,"threshold_uncertainty_score":0.5986242},"labels":[],"label_agreement":null},{"id":"W3183136893","doi":"10.1007/s11081-021-09659-3","title":"Norm induced polyhedral uncertainty sets for robust linear optimization","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":15,"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 Alberta","funders":"","keywords":"Robust optimization; Intersection (aeronautics); Uncertain data; Mathematical optimization; Uncertainty theory; Constraint (computer-aided design); Mathematics; Set (abstract data type); Norm (philosophy); Uncertainty quantification; Uncertainty analysis; Computer science; Data mining","score_opus":0.06890294975717556,"score_gpt":0.3245371272210113,"score_spread":0.25563417746383577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183136893","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":[0.0076460605,0.00010967494,0.9903453,0.0005004586,0.00052282907,0.00021049783,0.000021196578,0.00010686617,0.00053714245],"genre_scores_gemma":[0.117644235,0.00070952845,0.8796879,0.0002408053,0.00022785427,0.000041347077,0.00040509884,0.000055191715,0.0009880654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983656,0.000039055158,0.00050630583,0.00043975952,0.00038992925,0.0002593905],"domain_scores_gemma":[0.9985773,0.00025287658,0.00013769243,0.00029210284,0.0005866117,0.0001534165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005411946,0.00018577793,0.0002524826,0.0002423372,0.00019276922,0.00028216193,0.00014753098,0.00015126752,0.00019687766],"category_scores_gemma":[0.0012399147,0.00017438679,0.000079013545,0.00087487063,0.00001593251,0.00049072097,0.000060115945,0.00009710586,0.000005869772],"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":[0.000012487435,0.000017890354,0.0002835049,0.0000059428994,0.000011332275,0.0000021430074,0.00014326663,0.99541897,0.00011099312,0.0003095119,0.00027835713,0.0034056047],"study_design_scores_gemma":[0.0005414446,0.000029709685,0.00023360668,0.000014201594,0.000016941092,0.000014647156,0.00013093498,0.99644494,0.0002905929,0.0000319011,0.0020369538,0.0002140994],"about_ca_topic_score_codex":0.000008994135,"about_ca_topic_score_gemma":0.00000556329,"teacher_disagreement_score":0.1106574,"about_ca_system_score_codex":0.00003674094,"about_ca_system_score_gemma":0.000082728555,"threshold_uncertainty_score":0.71112865},"labels":[],"label_agreement":null},{"id":"W3198051614","doi":"10.1007/s11081-021-09673-5","title":"A data-driven approach for mixed-case palletization with support","year":2021,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":9,"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 Waterloo","funders":"","keywords":"Pallet; Bin packing problem; Packing problems; Computer science; Bin; Mathematical optimization; Layering; Layer (electronics); Algorithm; Computational science; Mathematics; Mechanical engineering; Materials science; Engineering","score_opus":0.020149346295971614,"score_gpt":0.21080464655756295,"score_spread":0.19065530026159133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198051614","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":[0.00037663436,0.00009597878,0.9975623,0.000035572706,0.000116579504,0.00017215086,0.0000664456,0.00033504795,0.001239312],"genre_scores_gemma":[0.10431193,0.00028270952,0.89230055,0.000041012183,0.00006905591,0.000047800022,0.0026759342,0.000078260055,0.00019274003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937314,0.000007038088,0.00015913053,0.00022515036,0.0000728,0.00016273702],"domain_scores_gemma":[0.99955106,0.000027094053,0.000020325373,0.00024104066,0.00008210266,0.0000783857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000785089,0.00013921048,0.00013345317,0.000068193716,0.00006587847,0.00010202,0.00006311146,0.000067554276,0.000052894993],"category_scores_gemma":[0.000026709102,0.00014310831,0.000015041434,0.00022331717,0.000009367651,0.00023069016,0.000030637668,0.0000662257,9.854565e-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":[0.0000024648127,0.000011643824,0.00003657087,0.00017198,0.000033930595,0.00001930436,0.000084769985,0.9983425,0.000042286225,0.00023963538,0.0005621598,0.00045274536],"study_design_scores_gemma":[0.0004721505,0.000014872431,0.000010735513,0.000017717102,0.000029376395,0.00037125178,0.00006209545,0.99277645,0.000089231115,7.286987e-7,0.005960068,0.00019529152],"about_ca_topic_score_codex":5.5975715e-7,"about_ca_topic_score_gemma":0.0000029020994,"teacher_disagreement_score":0.10526172,"about_ca_system_score_codex":0.000014812681,"about_ca_system_score_gemma":0.000017577398,"threshold_uncertainty_score":0.5835787},"labels":[],"label_agreement":null},{"id":"W4206075549","doi":"10.1007/s11081-021-09701-4","title":"Short-term open-pit production scheduling optimizing multiple objectives accounting for shovel allocation in stockpiles","year":2022,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":15,"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","funders":"","keywords":"Shovel; Computer science; Maximization; Scheduling (production processes); Open-pit mining; Production (economics); Relocation; Term (time); Operations research; Operations management; Mathematical optimization; Engineering; Mining engineering; Mathematics","score_opus":0.017901291763007605,"score_gpt":0.2275146174433093,"score_spread":0.2096133256803017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206075549","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":[0.30731836,0.000113770555,0.6913324,0.00003140696,0.00030467383,0.0005322541,0.000007384683,0.0002866498,0.00007308038],"genre_scores_gemma":[0.7620873,0.00010269473,0.2372656,0.0000070148,0.000051924588,0.00037407523,0.00005395334,0.000045248154,0.0000121895455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928355,0.0000066901935,0.00023875627,0.00022655433,0.000050487455,0.00019396485],"domain_scores_gemma":[0.9997706,0.00003545782,0.0000289273,0.000113538365,0.000021103282,0.000030398876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030511615,0.00013317917,0.00015059808,0.00018763417,0.00015440166,0.00011272887,0.00013031928,0.000043396343,0.000012241251],"category_scores_gemma":[0.00005254563,0.00017902175,0.000022116805,0.00015976746,0.0000055000723,0.00044189455,0.00011283849,0.00014094978,1.1995932e-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":[0.000008320622,0.000011727526,0.0009803652,0.000054186672,0.000008747204,2.3398285e-7,0.00041620494,0.99303645,0.0036924488,0.00016382235,0.000018199513,0.0016092813],"study_design_scores_gemma":[0.00022921567,0.00001728332,0.0008958057,0.000036684727,0.0000059872364,0.000005766321,0.00033203923,0.9969671,0.0011137057,0.0000070117912,0.0001830508,0.00020635841],"about_ca_topic_score_codex":0.0000075337357,"about_ca_topic_score_gemma":0.0000053193457,"teacher_disagreement_score":0.45476893,"about_ca_system_score_codex":0.0001557412,"about_ca_system_score_gemma":0.000008747155,"threshold_uncertainty_score":0.7300295},"labels":[],"label_agreement":null},{"id":"W4322775628","doi":"10.1007/s11081-022-09780-x","title":"An optimization model for the real-time truck dispatching problem in open-pit mining operations","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":32,"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 Alberta","funders":"","keywords":"Shovel; Truck; Computer science; Heuristic; Open-pit mining; Mathematical optimization; Operations research; Engineering; Automotive engineering; Mathematics","score_opus":0.01629608742879384,"score_gpt":0.23524395990695787,"score_spread":0.21894787247816402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322775628","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":[0.011973647,0.000014086995,0.9865803,0.00007605011,0.000061559054,0.0004500536,0.000013546009,0.0005561983,0.0002745641],"genre_scores_gemma":[0.10425417,0.0008217575,0.8940858,0.000018314357,0.000047401212,0.00034063618,0.00018426684,0.00008536716,0.00016227452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936897,0.0000054142947,0.00022558625,0.00016598226,0.0000366705,0.00019738116],"domain_scores_gemma":[0.9997079,0.000051591738,0.000015434593,0.00015898573,0.00001806179,0.000048046462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027367016,0.00012642448,0.00013173818,0.00013068074,0.00012107223,0.00023066535,0.00017191813,0.0000675845,0.000015482841],"category_scores_gemma":[0.00001793008,0.0001235577,0.000017770275,0.00021683301,0.00000616851,0.00047965697,0.00004721877,0.000063831205,0.0000010766947],"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":[0.0000023925984,0.0000040800737,0.00003517779,0.000024954707,0.000005980989,2.0266215e-7,0.00075656734,0.9979647,0.00021393022,0.0005049629,0.00011488924,0.00037218505],"study_design_scores_gemma":[0.00021916174,0.000012800258,0.00003711027,0.000035211073,0.000007379743,0.0000010034859,0.00008401509,0.999363,0.000021842128,0.000014381428,0.00004327521,0.00016084009],"about_ca_topic_score_codex":0.000020501104,"about_ca_topic_score_gemma":0.000015641368,"teacher_disagreement_score":0.09249449,"about_ca_system_score_codex":0.000040861687,"about_ca_system_score_gemma":0.000010748254,"threshold_uncertainty_score":0.5038537},"labels":[],"label_agreement":null},{"id":"W4378374288","doi":"10.1007/s11081-023-09809-9","title":"Topology optimized infill compliant mechanisms for improved output displacements","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":5,"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 Alberta; University of Waterloo","funders":"","keywords":"Topology optimization; Compliant mechanism; Topology (electrical circuits); Stiffness; Displacement (psychology); Constraint (computer-aided design); Infill; Volume (thermodynamics); Computer science; Mathematical optimization; Maximization; Control theory (sociology); Mathematics; Structural engineering; Finite element method; Engineering; Geometry","score_opus":0.010769760703897343,"score_gpt":0.22124517242312805,"score_spread":0.2104754117192307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378374288","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":[0.00079490046,0.000057549907,0.9950617,0.0002485145,0.0012001309,0.0004279793,0.000041950512,0.0018823299,0.00028493485],"genre_scores_gemma":[0.10279553,0.00068427535,0.8935732,0.0001250533,0.00022675276,0.00054106355,0.000606228,0.00029841007,0.00114949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989116,0.000007544789,0.00031502222,0.00024963857,0.00007078572,0.0004453898],"domain_scores_gemma":[0.9995152,0.000094367475,0.000030680123,0.00018384906,0.000049623057,0.00012630568],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014369296,0.00026132143,0.000278869,0.00033258504,0.000100506346,0.000049382306,0.00011846754,0.00016556603,0.000054515654],"category_scores_gemma":[0.000055279725,0.00030109,0.000053378288,0.0003639302,0.000024986512,0.00017487573,0.00005143818,0.0001182746,0.0000114163595],"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":[0.000007477064,0.0000045600923,0.0000060387633,0.00008365866,0.00005439397,9.935095e-7,0.00008057535,0.9953836,0.0016964837,0.0015367923,0.0008793848,0.00026599737],"study_design_scores_gemma":[0.0010502747,0.0000290399,0.00007563592,0.000019921015,0.000022209417,0.000008403384,0.00004534777,0.9945597,0.0004346305,0.00004426401,0.0034003255,0.0003102622],"about_ca_topic_score_codex":0.0000015142356,"about_ca_topic_score_gemma":3.5452388e-7,"teacher_disagreement_score":0.10200063,"about_ca_system_score_codex":0.000056755438,"about_ca_system_score_gemma":0.000007668901,"threshold_uncertainty_score":0.99994415},"labels":[],"label_agreement":null},{"id":"W4386579208","doi":"10.1007/s11081-023-09839-3","title":"A taxonomy of constraints in black-box simulation-based optimization","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; American Institute of Mathematics; Advanced Scientific Computing Research; U.S. Department of Energy","keywords":"Computer science; Taxonomy (biology); Mathematical optimization; Black box; Context (archaeology); Dialog box; Nonlinear programming; Theoretical computer science; Nonlinear system; Artificial intelligence; Mathematics","score_opus":0.07016011307618368,"score_gpt":0.3312541449984584,"score_spread":0.2610940319222747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386579208","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":[0.010229224,0.000005348935,0.98868084,0.00014121314,0.000029582327,0.00026069343,0.000008760102,0.00012642606,0.0005179237],"genre_scores_gemma":[0.9029069,0.000010832496,0.09691451,0.000025408825,0.000010532025,0.0000362257,0.000029470055,0.000009805849,0.00005631645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989928,0.000020021442,0.00043582305,0.00020048856,0.00024050706,0.00011035149],"domain_scores_gemma":[0.998801,0.0007209044,0.00010262453,0.00018129266,0.00014716372,0.000047050708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049510255,0.00008248166,0.00014345352,0.00056522834,0.000035477533,0.000052348852,0.000106529675,0.00006172246,0.00024553857],"category_scores_gemma":[0.000695845,0.00008140955,0.00003025419,0.001541443,0.000049170274,0.00015207299,0.000024331235,0.000047655114,0.000008589885],"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":[0.0000021832807,0.000009417941,0.0022462595,0.000005233797,0.0000014685005,2.7075257e-7,0.00005151594,0.9954343,0.000045761033,0.00056687044,0.000029413812,0.0016073245],"study_design_scores_gemma":[0.00025783412,0.000007642152,0.0018535147,0.000017355635,0.0000021468252,1.2593704e-7,0.000071295945,0.9970212,0.00014458869,0.000053853622,0.00048978836,0.00008065203],"about_ca_topic_score_codex":0.0000045317493,"about_ca_topic_score_gemma":6.7834674e-7,"teacher_disagreement_score":0.89267766,"about_ca_system_score_codex":0.00001679375,"about_ca_system_score_gemma":0.000024753048,"threshold_uncertainty_score":0.3319785},"labels":[],"label_agreement":null},{"id":"W4386988712","doi":"10.1007/s11081-023-09836-6","title":"A fast non-monotone line search for stochastic gradient descent","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":2,"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 Lethbridge","funders":"","keywords":"Stochastic gradient descent; Monotone polygon; Line search; Descent direction; Convergence (economics); Mathematical optimization; Descent (aeronautics); Mathematics; Line (geometry); Interpolation (computer graphics); Gradient descent; Computer science; Regular polygon; Applied mathematics; Algorithm; Artificial intelligence; Path (computing)","score_opus":0.017242471805317332,"score_gpt":0.2414483519281693,"score_spread":0.22420588012285197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386988712","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":[0.0009789275,0.000018477043,0.9972527,0.00027335278,0.00022180165,0.00045595,0.000004353349,0.0007677964,0.000026646376],"genre_scores_gemma":[0.29940045,0.00005538368,0.70003945,0.000045560275,0.00005669493,0.00021114066,0.000043313306,0.000034084536,0.00011393641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991031,0.00000584892,0.00018060033,0.00028314823,0.00014372097,0.00028353345],"domain_scores_gemma":[0.9994718,0.00008053849,0.000029829163,0.00020365596,0.00009790824,0.00011627854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018904083,0.00013229808,0.00012785649,0.00033749794,0.00010191059,0.00011035587,0.00019970936,0.000048988026,0.000003307273],"category_scores_gemma":[0.00005841871,0.00014007858,0.000032532604,0.00070313574,0.000012210519,0.00019454892,0.00011778481,0.00006321605,0.0000047004546],"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":[0.0000022678882,0.000012167555,0.0000055145774,0.000027510736,0.0000072176886,8.7892533e-7,0.00030214823,0.99070734,0.00017693614,0.0074064913,0.0000731142,0.0012784307],"study_design_scores_gemma":[0.00030241444,0.00007245153,0.000076668504,0.000039949453,0.000004652187,0.0000052661926,0.000016207694,0.99863446,0.00062421424,0.000039197006,0.000034862176,0.00014967112],"about_ca_topic_score_codex":0.0000023827492,"about_ca_topic_score_gemma":1.9404334e-7,"teacher_disagreement_score":0.29842153,"about_ca_system_score_codex":0.000038928873,"about_ca_system_score_gemma":0.000018483111,"threshold_uncertainty_score":0.5712239},"labels":[],"label_agreement":null},{"id":"W4387020807","doi":"10.1007/s11081-023-09846-4","title":"A multi-vendor multi-buyer integrated production-inventory model with greenhouse gas emissions","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Greenhouse gas; Supply chain; Solver; Simulated annealing; Mathematical optimization; Computer science; Integer programming; Vendor; Production (economics); Linear programming; Metaheuristic; Total cost; Supply chain optimization; Nonlinear programming; Operations research; Supply chain management; Nonlinear system; Algorithm; Mathematics; Economics; Business; Microeconomics","score_opus":0.025362698491343216,"score_gpt":0.20997448738862431,"score_spread":0.1846117888972811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387020807","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":[0.08838248,0.00008369624,0.9048433,0.0014741567,0.00084176735,0.0009095712,0.000005177441,0.002458179,0.0010016843],"genre_scores_gemma":[0.7830115,0.000500603,0.18598811,0.0011984913,0.0010835401,0.0004474199,0.00063520577,0.00037287828,0.026762228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991656,0.0000036316658,0.00016752268,0.0002825403,0.00014135278,0.00023937122],"domain_scores_gemma":[0.9996664,0.00000548659,0.000056361416,0.00016591443,0.000075380965,0.0000304436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014003666,0.00018309866,0.00012865485,0.0004026193,0.00015366192,0.00013589564,0.00008995685,0.000045197194,0.000078589306],"category_scores_gemma":[0.000069412985,0.00015918267,0.000030789764,0.00069343345,0.000022612232,0.0005943481,0.00008134261,0.000099680765,0.000040478382],"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":[0.000008756232,0.0000504115,0.00083990966,0.00011204306,0.000023838033,0.0000051340053,0.0000994941,0.99520385,0.00032588962,0.00021392766,0.0028263177,0.00029044156],"study_design_scores_gemma":[0.0005969788,0.0000051368665,0.00026382893,0.000083241925,0.000027888507,0.000001164097,0.00022789923,0.98867,0.000024739418,0.0000030128338,0.009885205,0.00021092215],"about_ca_topic_score_codex":0.000041164527,"about_ca_topic_score_gemma":0.000018036517,"teacher_disagreement_score":0.71885514,"about_ca_system_score_codex":0.000029532705,"about_ca_system_score_gemma":0.000011209976,"threshold_uncertainty_score":0.64912814},"labels":[],"label_agreement":null},{"id":"W4387639549","doi":"10.1007/s11081-023-09856-2","title":"Distributionally robust optimization using optimal transport for Gaussian mixture models","year":2023,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":4,"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 Alberta","funders":"","keywords":"Ambiguity; Robust optimization; Mathematical optimization; Mixture model; Gaussian; Probability distribution; Mathematics; Set (abstract data type); Portfolio; Computer science; Portfolio optimization; Optimization problem; Statistics","score_opus":0.06790315755395122,"score_gpt":0.301513552620018,"score_spread":0.2336103950660668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387639549","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":[0.0047760387,0.000063017345,0.99360937,0.00029695433,0.0003297877,0.00033899056,0.00012911164,0.0002430684,0.000213676],"genre_scores_gemma":[0.17457578,0.00080120104,0.82194173,0.000061513645,0.00022403235,0.000059255977,0.0014707434,0.00007578829,0.00078992517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99808955,0.000024796273,0.00056709576,0.00046494757,0.0005151817,0.00033844725],"domain_scores_gemma":[0.99895316,0.00016937961,0.00014495513,0.00023742454,0.00033034696,0.00016470163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008016687,0.00021984984,0.00026486636,0.0004545622,0.00028227933,0.00020621892,0.00019842034,0.00016995665,0.00007714689],"category_scores_gemma":[0.0002528968,0.00020469453,0.00010159726,0.0014078862,0.00003010156,0.0008414406,0.00003342166,0.00009209806,0.0000041480503],"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":[0.000019916972,0.000011207789,0.0002694246,0.000008622359,0.000013307865,0.0000020179125,0.00014648959,0.9972147,0.000030033669,0.001512168,0.00036228,0.00040986083],"study_design_scores_gemma":[0.00042659408,0.000020760868,0.00030399382,0.00001967175,0.000027229511,0.000009879016,0.00009167474,0.9975857,0.000025891819,0.00013065485,0.0011121414,0.00024580306],"about_ca_topic_score_codex":0.0000054270818,"about_ca_topic_score_gemma":9.661043e-7,"teacher_disagreement_score":0.17166759,"about_ca_system_score_codex":0.000042869484,"about_ca_system_score_gemma":0.00005911451,"threshold_uncertainty_score":0.8347201},"labels":[],"label_agreement":null},{"id":"W4389674129","doi":"10.1007/s11081-026-10097-2","title":"A multistage stochastic programming approach for short-term scheduling of batch processes under type II endogenous uncertainty","year":2023,"lang":"en","type":"preprint","venue":"Optimization and Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":0,"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 Waterloo","funders":"Mitacs; Ontario Centre of Innovation","keywords":"Binary number; Computer science; Novelty; Mathematical optimization; Scheduling (production processes); Term (time); Stochastic programming; Mathematics","score_opus":0.04588306683244978,"score_gpt":0.25143629583510346,"score_spread":0.2055532290026537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389674129","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":[0.0026827198,0.0007139578,0.9943726,0.000010399547,0.00038844827,0.0008909166,0.00005751575,0.0008048998,0.000078544246],"genre_scores_gemma":[0.6417223,0.0005796701,0.35577783,0.000004953648,0.00009625674,0.00035129438,0.0012602013,0.00014824106,0.000059219266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986911,0.000006996188,0.0004656496,0.0003714797,0.00016645483,0.00029834965],"domain_scores_gemma":[0.99919045,0.00006405415,0.00008340057,0.00019778297,0.00037066155,0.00009367644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014482717,0.00036522798,0.00040053084,0.00028299468,0.000109129134,0.000108749315,0.00016009851,0.0002978014,0.000008330923],"category_scores_gemma":[0.00020917926,0.00039736883,0.00006362348,0.00039782334,0.000027821236,0.0001488385,0.00011333538,0.00027721399,3.4903456e-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":[0.000011874482,0.000030402049,0.000009890704,0.0036167726,0.000095208175,3.4368242e-7,0.00058592774,0.99436533,0.0005529496,0.00008164404,0.000008682836,0.00064094015],"study_design_scores_gemma":[0.0002476707,0.000040223982,0.000007220301,0.00032818818,0.00007059463,0.00000397298,0.00018176586,0.99830794,0.00040543886,0.000008373383,0.000022596432,0.00037599966],"about_ca_topic_score_codex":0.000007067302,"about_ca_topic_score_gemma":0.0000031248562,"teacher_disagreement_score":0.63903964,"about_ca_system_score_codex":0.00008741662,"about_ca_system_score_gemma":0.00008537297,"threshold_uncertainty_score":0.9998478},"labels":[],"label_agreement":null},{"id":"W4408074130","doi":"10.1007/s11081-024-09952-x","title":"Solar: a solar thermal power plant simulator for blackbox optimization benchmarking","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Photovoltaic System Optimization Techniques","field":"Energy","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Polytechnique Montréal; Concordia University; Group for Research in Decision Analysis","funders":"","keywords":"Benchmarking; Computer science; Simulation; Solar simulator; Environmental science; Solar cell; Electrical engineering; Engineering","score_opus":0.005335356697971348,"score_gpt":0.20680359988806385,"score_spread":0.2014682431900925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408074130","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":[0.0010520314,0.00012333735,0.9952497,0.000070022434,0.0003568585,0.00052089954,0.000019274778,0.00057122606,0.002036633],"genre_scores_gemma":[0.55493873,0.00017291454,0.44342273,0.00028543777,0.00012703644,0.00023131297,0.00031439186,0.00009817164,0.00040930428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988157,0.00002645246,0.00039549483,0.00033396008,0.00013495407,0.00029342886],"domain_scores_gemma":[0.9993288,0.00012164732,0.00009647895,0.00022881299,0.00014487248,0.000079383564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023829065,0.00024734245,0.000264955,0.00031596093,0.00016909602,0.00013665053,0.00012531866,0.00019567924,0.00012694],"category_scores_gemma":[0.0001373389,0.00026444742,0.00007092913,0.0003437271,0.00001904547,0.00029083437,0.000055192657,0.00009704149,0.0000011918072],"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":[0.000024902176,0.00001982254,0.00010605502,0.000074741816,0.000053578227,8.606606e-7,0.0001446756,0.99529415,0.0010331247,0.0026698555,0.00023918034,0.00033906483],"study_design_scores_gemma":[0.00061701686,0.00003458108,0.000045372093,0.00013216124,0.0000325772,0.0000029512685,0.00003750824,0.9887354,0.0035630038,0.000010369356,0.006527598,0.00026145877],"about_ca_topic_score_codex":0.000022856353,"about_ca_topic_score_gemma":0.0000022960337,"teacher_disagreement_score":0.55388665,"about_ca_system_score_codex":0.000087713786,"about_ca_system_score_gemma":0.000038332924,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W4410332066","doi":"10.1007/s11081-025-09971-2","title":"Concurrent print orientation and topology optimization for fiber reinforced additive manufacturing considering mass minimization and compliance minimization problems statements","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minification; Topology optimization; Compliance (psychology); Orientation (vector space); Computer science; Mathematical optimization; Topology (electrical circuits); Mathematics; Engineering; Structural engineering; Finite element method; Geometry; Psychology; Combinatorics","score_opus":0.018194335023465263,"score_gpt":0.2593193543400766,"score_spread":0.24112501931661132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410332066","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":[0.023369381,0.00016760857,0.9748044,0.00008681808,0.000250315,0.00060412375,0.000028967772,0.00050918607,0.0001791962],"genre_scores_gemma":[0.7132807,0.0016045003,0.28416628,0.000036405036,0.00003409894,0.000240854,0.0003624258,0.000055837732,0.00021894282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990229,0.000013432396,0.0003347758,0.00031763793,0.00007187179,0.00023938279],"domain_scores_gemma":[0.9995521,0.0001284534,0.00008041193,0.000110065026,0.00007646421,0.000052554293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000086846456,0.0002403893,0.00022209606,0.00024442305,0.00015954325,0.00011216714,0.000051572057,0.00013010751,0.00002658551],"category_scores_gemma":[0.000108654516,0.00027318092,0.000019199357,0.000115285126,0.000064185006,0.0003226259,0.00006137267,0.00011186324,4.1179527e-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":[0.000005466983,0.0000031856075,0.00005336014,0.00041986402,0.000046538786,3.4064075e-7,0.00016741398,0.98538643,0.00014697685,0.0008071468,0.00004429639,0.012918988],"study_design_scores_gemma":[0.0007790225,0.00002928587,0.00047027937,0.0002423717,0.00003439631,0.0000031931238,0.00014906164,0.98349816,0.013362208,0.000052312083,0.0011252926,0.00025441],"about_ca_topic_score_codex":0.0000031081433,"about_ca_topic_score_gemma":9.4680314e-7,"teacher_disagreement_score":0.6906381,"about_ca_system_score_codex":0.00006555801,"about_ca_system_score_gemma":0.000009390244,"threshold_uncertainty_score":0.99997205},"labels":[],"label_agreement":null},{"id":"W4411012885","doi":"10.1007/s11081-025-09982-z","title":"Optimal dynamic commodity liquidation by joint spot and forward contracts","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Commodity; Spot contract; Financial engineering; Joint (building); Computer science; Economics; Finance; Futures contract","score_opus":0.007125457320398345,"score_gpt":0.17815357961069817,"score_spread":0.17102812229029982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411012885","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":[0.21516941,0.0008522056,0.7813531,0.00045215286,0.00017715168,0.0000986889,0.000059861548,0.000040801722,0.0017965998],"genre_scores_gemma":[0.9866664,0.0009185993,0.011736798,0.00009789343,0.000011677709,0.00001004698,0.000059505313,0.0000114184995,0.00048765808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994591,0.0000023862187,0.00023923602,0.00017465014,0.0000074662116,0.00011716911],"domain_scores_gemma":[0.9997775,0.000019646142,0.00006150536,0.00008623556,0.000010554486,0.00004456706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015695322,0.000087817076,0.00017889452,0.00009253397,0.000059935282,0.00006988565,0.00003412701,0.00006133679,0.000032428183],"category_scores_gemma":[0.000033889526,0.00011185553,0.000019582667,0.00005321475,0.000016705168,0.00019176576,0.00003256555,0.00005981795,0.000002962878],"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":[0.000007903641,0.000012867578,0.00039585726,0.00003440108,0.000026944372,2.0278945e-7,0.000092471986,0.8924118,0.000091669885,0.10630797,0.00028571035,0.000332204],"study_design_scores_gemma":[0.00038116061,0.000013775939,0.0010646315,0.000014666773,0.0000043046653,0.0000012107135,0.000020691037,0.9906475,0.00008115761,0.0004487393,0.0072009815,0.00012118134],"about_ca_topic_score_codex":0.000017004677,"about_ca_topic_score_gemma":0.0000016076068,"teacher_disagreement_score":0.771497,"about_ca_system_score_codex":0.000039027953,"about_ca_system_score_gemma":0.000004125895,"threshold_uncertainty_score":0.4561336},"labels":[],"label_agreement":null},{"id":"W4413202532","doi":"10.1007/s11081-025-10008-x","title":"Proximal methods for equilibrium problems over the set of fixed points of enriched nonexpansive mappings","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Set (abstract data type); Fixed point; Mathematics; Computer science; Applied mathematics; Financial engineering; Mathematical optimization; Mathematical analysis; Economics; Finance","score_opus":0.011741232856187132,"score_gpt":0.2752185028452058,"score_spread":0.2634772699890187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413202532","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":[0.00040293878,0.000067428984,0.99829715,0.0008057964,0.00007206742,0.00020599921,0.000003078775,0.00002830391,0.000117231226],"genre_scores_gemma":[0.055128507,0.000026516725,0.94454604,0.0000928122,0.000010705765,0.00003144058,0.000014218909,0.000005411853,0.0001443268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994444,0.000025797952,0.00023661261,0.0001366312,0.00007145117,0.00008505639],"domain_scores_gemma":[0.99936897,0.0001779454,0.00009882909,0.00014162326,0.00019252038,0.00002010004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029165306,0.000074658004,0.00014522567,0.00014355325,0.000032566204,0.000033594926,0.00016626058,0.000037177033,0.0000091908605],"category_scores_gemma":[0.0001507659,0.000059535512,0.000048790596,0.0005731401,0.0000146507255,0.00017717539,0.000075099124,0.000032715663,7.468482e-8],"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":[0.000002445754,0.00000919081,0.00006330323,0.000076929115,0.000045039887,1.5015496e-8,0.0004061208,0.959253,0.0034051347,0.036095537,0.00003662869,0.00060665625],"study_design_scores_gemma":[0.00027892127,0.000013973955,0.00066297996,0.000019508927,0.000020245698,3.1174642e-7,0.00001723056,0.9969956,0.0013110925,0.00026184853,0.00036111457,0.00005714439],"about_ca_topic_score_codex":0.0000047412577,"about_ca_topic_score_gemma":1.6184153e-7,"teacher_disagreement_score":0.05472557,"about_ca_system_score_codex":0.000009851171,"about_ca_system_score_gemma":0.00002909213,"threshold_uncertainty_score":0.24277878},"labels":[],"label_agreement":null},{"id":"W4414183174","doi":"10.1007/s11081-025-10032-x","title":"Green hydrogen viability in the transition to a fully-renewable energy grid","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Renewable energy; Profitability index; Hydrogen; Grid energy storage; Wind power; Investment (military); Grid; Electricity; Electricity generation; Intermittent energy source","score_opus":0.004463893678310726,"score_gpt":0.18560341331613422,"score_spread":0.1811395196378235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414183174","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":[0.061781794,0.00024498627,0.9205214,0.001574168,0.0004912845,0.0002612388,0.000007191085,0.00022521602,0.014892757],"genre_scores_gemma":[0.9959378,0.00003108918,0.002411231,0.0004884551,0.00012020883,0.00010627217,0.000041752373,0.000019440733,0.0008437043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920046,0.00005816772,0.00022451683,0.00021371806,0.00010805743,0.00019504908],"domain_scores_gemma":[0.9996489,0.000048310038,0.000017296683,0.00020957191,0.000025387371,0.00005051364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023889748,0.00012956256,0.00014898943,0.00019357211,0.000056986286,0.000039412975,0.00012049579,0.00006525633,0.000029034887],"category_scores_gemma":[0.000030347566,0.00011221201,0.000031638996,0.0006243226,0.000009010365,0.00010135173,0.000021112573,0.000052237327,0.0000015165239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065320705,0.000015606936,0.0000493632,0.00003062549,0.000011805813,0.0000025006905,0.000296007,0.99669945,0.0009920918,0.0012119118,0.00012633407,0.00055775937],"study_design_scores_gemma":[0.00028688443,0.000014616985,0.00015123302,0.000051837666,0.000009986955,0.0000056396207,0.00009663412,0.96481,0.0021837559,0.000046780482,0.032203093,0.00013950809],"about_ca_topic_score_codex":0.01282196,"about_ca_topic_score_gemma":0.0024608576,"teacher_disagreement_score":0.93415606,"about_ca_system_score_codex":0.000063811174,"about_ca_system_score_gemma":0.000019348528,"threshold_uncertainty_score":0.99375176},"labels":[],"label_agreement":null},{"id":"W4415517558","doi":"10.1007/s11081-025-10044-7","title":"Inverse problems and machine learning","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"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":"","score_opus":0.008059836759119058,"score_gpt":0.18338180466018514,"score_spread":0.17532196790106608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415517558","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":[0.001317416,0.00017035042,0.9973865,0.0005441583,0.000033584958,0.00004199324,1.0914635e-7,0.000093982424,0.00041186626],"genre_scores_gemma":[0.6375067,0.0012393916,0.35954735,0.0003064869,0.000026996868,0.00002707209,0.0000068784466,0.000008851651,0.0013302184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997887,0.0000028943384,0.000046211626,0.00008967813,0.000019360086,0.000053156444],"domain_scores_gemma":[0.99989784,0.000012912505,0.00000841448,0.00004987454,0.000008312351,0.000022635679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002969783,0.000037084144,0.00003451301,0.000037547143,0.000058028025,0.00006256868,0.000042828455,0.00001403564,0.0000016756634],"category_scores_gemma":[0.0000054980887,0.000036039342,0.0000044821927,0.00016151693,0.0000045374436,0.00010189632,0.000051722745,0.00004639682,3.3000293e-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":[8.6358405e-8,0.0000015897433,0.00021116571,0.0000089275845,0.0000016406624,1.1958775e-7,0.00003255781,0.9862358,0.00012458887,0.010356751,0.00003111722,0.0029956289],"study_design_scores_gemma":[0.000065973516,0.000003291331,0.0001889606,0.000012238651,0.0000012493546,0.0000013461878,0.0000014706075,0.9925,0.00003200095,0.00003512788,0.0071224794,0.000035860965],"about_ca_topic_score_codex":0.0000030615654,"about_ca_topic_score_gemma":7.012852e-7,"teacher_disagreement_score":0.6378392,"about_ca_system_score_codex":0.0000029919827,"about_ca_system_score_gemma":0.000002102796,"threshold_uncertainty_score":0.14696416},"labels":[],"label_agreement":null},{"id":"W4416933128","doi":"10.1007/s11081-025-10031-y","title":"Scheduling of Drilling Machines in Open-Pit Mines: Stochastic and Non-Probabilistic CP Approaches","year":2025,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scheduling (production processes); Drill; Constraint programming; Drilling; Constraint (computer-aided design); Stochastic programming; Representation (politics); Job shop scheduling","score_opus":0.014852991557567348,"score_gpt":0.2066839415045628,"score_spread":0.19183094994699545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416933128","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":[0.19564189,0.0002851871,0.8032385,0.000015164658,0.00007425124,0.00014973665,0.000001999784,0.00007405169,0.00051921344],"genre_scores_gemma":[0.844983,0.00012397938,0.1548206,0.0000043788687,0.000009895714,0.000020405654,0.000006144953,0.000016646612,0.000014929265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995249,0.0000025853803,0.00021381676,0.0001288587,0.000020502805,0.00010933699],"domain_scores_gemma":[0.99981385,0.00004053849,0.000018524759,0.00009046193,0.000009349554,0.000027269563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011694098,0.00010912653,0.00018522673,0.00016694378,0.00001996091,0.000049612056,0.00007938064,0.000058078946,0.0000045259244],"category_scores_gemma":[0.00004123496,0.0001226453,0.000010995642,0.00014703342,0.000012345336,0.000118758944,0.00006710931,0.000063794745,8.417082e-8],"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":[0.0000023945759,0.0000047028357,0.0003868544,0.0002455857,0.000008004465,2.1894165e-7,0.00013012315,0.9973666,0.00019078761,0.0009261213,0.000004317331,0.0007342994],"study_design_scores_gemma":[0.00021238186,0.000006559436,0.00028500566,0.0002068294,0.000008215274,0.0000013672851,0.0000493572,0.9989261,0.000117879,0.00006453482,0.000012541751,0.000109197856],"about_ca_topic_score_codex":0.00001218603,"about_ca_topic_score_gemma":0.0000059423883,"teacher_disagreement_score":0.6493411,"about_ca_system_score_codex":0.000020954965,"about_ca_system_score_gemma":0.000006157374,"threshold_uncertainty_score":0.50013304},"labels":[],"label_agreement":null},{"id":"W79965191","doi":"10.1023/a:1015354615402","title":"Reloading Nuclear Reactor Fuel Using Mixed-Integer Nonlinear Optimization","year":2001,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematical optimization; Integer (computer science); Heuristic; Nonlinear system; Nonlinear programming; Extension (predicate logic); Computer science; Optimization problem; Power (physics); Algorithm; Mathematics; Physics","score_opus":0.008267538046388766,"score_gpt":0.18145525879331498,"score_spread":0.1731877207469262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W79965191","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":[0.040616035,0.00025285795,0.9535436,0.000019160198,0.00069317926,0.0001294624,0.000006250508,0.0011159654,0.0036234665],"genre_scores_gemma":[0.8266154,0.0013164673,0.17133872,0.000028284527,0.000345217,0.000005264457,0.000045241122,0.00027493152,0.000030498071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999076,0.0000056167746,0.00024992396,0.00021135555,0.0001322764,0.00032483245],"domain_scores_gemma":[0.99957055,0.000020589148,0.000029665815,0.00018065775,0.00004482985,0.00015372306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007150036,0.00026120068,0.00021358645,0.00018833815,0.00006220212,0.00012550551,0.00009129227,0.00012022192,0.00009309817],"category_scores_gemma":[0.000022658856,0.00030194348,0.000048357582,0.00037946866,0.000012948689,0.0004499423,0.000036062866,0.00020311837,0.00000908584],"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":[0.0000028632517,0.000008908975,0.000039662373,0.00006316652,0.00002453594,0.0000057526095,0.00010296459,0.99389905,0.0051808087,0.00030567535,0.00004671597,0.00031987182],"study_design_scores_gemma":[0.00022032527,0.000009364936,0.000035869405,0.000073846786,0.000020595067,0.0000384985,0.000040164985,0.9882196,0.00008654038,0.0000019042324,0.010914225,0.0003390198],"about_ca_topic_score_codex":0.000008137757,"about_ca_topic_score_gemma":4.1946933e-7,"teacher_disagreement_score":0.78599936,"about_ca_system_score_codex":0.000091579306,"about_ca_system_score_gemma":0.0000059881722,"threshold_uncertainty_score":0.99994326},"labels":[],"label_agreement":null},{"id":"W809203089","doi":"10.1007/s11081-015-9305-y","title":"Creating annual delivery programs of liquefied natural gas","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd","keywords":"Liquefied natural gas; Integer programming; Revenue; Operations research; Fleet management; Schedule; Linear programming; Computer science; Time horizon; Scheduling (production processes); Port (circuit theory); Procurement; Supply chain; Natural gas; Business; Operations management; Finance; Engineering; Waste management; Marketing","score_opus":0.005837351310503501,"score_gpt":0.17980604582758322,"score_spread":0.17396869451707972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W809203089","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92942584,0.00011738955,0.061309963,0.000048042388,0.00008788974,0.0001253762,0.0000023284388,0.00009101175,0.008792136],"genre_scores_gemma":[0.950588,0.000019009838,0.04915505,0.000007588149,0.000008580707,0.0000026166551,0.000010652948,0.0000059679414,0.00020253981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996282,0.000003528271,0.00009758553,0.000086715365,0.0000908493,0.00009310872],"domain_scores_gemma":[0.99983764,0.0000064169076,0.000019105917,0.000048869573,0.000010823374,0.000077132936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008037957,0.000055034594,0.000060811108,0.0000181999,0.000025339748,0.000008360913,0.00003739591,0.000025373674,0.00013286794],"category_scores_gemma":[0.000016779684,0.00004990672,0.000012253232,0.00011732817,0.000020960342,0.00011474821,0.00002309248,0.000040357234,0.0000019287195],"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":[0.000003976269,0.000017590068,0.0046881065,0.000008298248,0.0000016296434,0.0000010031879,0.0004127651,0.99044067,0.00037783256,0.000029822495,0.000038764123,0.0039795283],"study_design_scores_gemma":[0.00017015375,0.000032825348,0.002082991,0.000020938134,0.0000046091777,0.0000041768885,0.00012484266,0.9964652,0.00019319743,0.0000018284359,0.00081708067,0.00008213526],"about_ca_topic_score_codex":0.00006855486,"about_ca_topic_score_gemma":0.0000018271155,"teacher_disagreement_score":0.021162134,"about_ca_system_score_codex":0.000014621007,"about_ca_system_score_gemma":0.0000041081,"threshold_uncertainty_score":0.2035137},"labels":[],"label_agreement":null}]}