{"meta":{"query_hash":"4cef4bdf43be","filters":{"venue":"Optimization methods & software"},"cohort_total":31,"direct_labels_cover":0,"predictions_cover":31,"exported":31,"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/4cef4bdf43be","api":"https://metacan.xera.ac/api/v1/cohort?venue=Optimization+methods+%26+software"},"results":[{"id":"W1821374938","doi":"10.1080/10556788.2015.1062891","title":"An efficient optimization approach for a cardinality-constrained index tracking problem","year":2015,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Key Research and Development Program of China; University of Otago","keywords":"Cardinality (data modeling); Mathematical optimization; Computer science; Tracking (education); Tracking error; Portfolio; Regularization (linguistics); Thresholding; Mathematics; Artificial intelligence; Data mining","score_opus":0.05089273698655643,"score_gpt":0.3335022468907675,"score_spread":0.2826095099042111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1821374938","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":[0.00015535388,0.00015146127,0.99580956,0.000011537613,0.00016598657,0.0007844079,0.00002249198,0.0020596446,0.00083953945],"genre_scores_gemma":[0.030685985,0.0000097043,0.9686418,0.000040133662,0.00010629167,0.00015065505,0.00026118872,0.00009067967,0.000013545053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853075,0.00024912867,0.00035393133,0.00035882305,0.00020746131,0.00029993037],"domain_scores_gemma":[0.99875605,0.00010864919,0.00009621532,0.0003788681,0.0004894407,0.00017076684],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000980817,0.00025284424,0.0003129496,0.0001691653,0.00012165607,0.00014823793,0.00019561432,0.00019407229,0.000010585039],"category_scores_gemma":[0.00026447073,0.00026549058,0.00009213643,0.000379877,0.000047748425,0.00023437067,0.000025184618,0.0001450924,2.4842822e-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":[0.000021673322,0.000047719433,0.00009647052,0.00003769207,0.000030557658,6.7553736e-7,0.00037616526,0.9806594,0.00010362783,0.000085778884,0.0001423163,0.018397924],"study_design_scores_gemma":[0.00048382493,0.000059631908,0.000008605908,0.000032123266,0.000050873452,0.000011385874,0.00018042015,0.99636674,0.0021407187,0.00020690654,0.00014254314,0.0003162422],"about_ca_topic_score_codex":0.000008423132,"about_ca_topic_score_gemma":2.8814958e-7,"teacher_disagreement_score":0.030530632,"about_ca_system_score_codex":0.000113341506,"about_ca_system_score_gemma":0.00005858307,"threshold_uncertainty_score":0.99997973},"labels":[],"label_agreement":null},{"id":"W1981321991","doi":"10.1080/10556780412331332024","title":"An adaptive self-regular proximity-based large-update IPM for LO","year":2005,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Canada Research Chairs","keywords":"Interior point method; Mathematics; Value (mathematics); Position (finance); Point (geometry); Combinatorics; Degree (music); Class (philosophy); Algorithm; Upper and lower bounds; Power (physics); Discrete mathematics; Mathematical optimization; Computer science; Statistics; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.05297343988889592,"score_gpt":0.4344821924655114,"score_spread":0.3815087525766155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981321991","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":[0.000025764408,0.00010284406,0.9957147,0.0005131098,0.00011924731,0.0020344462,0.00017448666,0.0012219745,0.00009341665],"genre_scores_gemma":[0.000092754926,0.000034761582,0.997288,0.0006039022,0.00025946676,0.000574208,0.0004177498,0.00021773604,0.00051142176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961162,0.0009986964,0.00070436765,0.0008168182,0.0005951596,0.0007687425],"domain_scores_gemma":[0.99544054,0.0014307161,0.00039289042,0.0009662298,0.0014289644,0.000340662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025098196,0.00043455098,0.00053982594,0.0003384595,0.0005286961,0.000180116,0.0005404846,0.00030606604,0.00073034276],"category_scores_gemma":[0.0030060231,0.00044061747,0.00019013823,0.0008185055,0.00008618709,0.001139055,0.00009202155,0.0003181179,0.000022658189],"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":[0.00015667107,0.00064902386,0.00003293123,0.00011224197,0.000070486036,0.0000018701614,0.00027399586,0.95375866,0.000027407319,0.0076927138,0.0006567577,0.03656723],"study_design_scores_gemma":[0.0020826415,0.00022083487,0.0000033339388,0.00003386303,0.00009590383,0.0000036376148,0.00010776626,0.96751463,0.0027211208,0.012127965,0.014577822,0.0005104929],"about_ca_topic_score_codex":0.000001051654,"about_ca_topic_score_gemma":0.0000041813596,"teacher_disagreement_score":0.036056735,"about_ca_system_score_codex":0.00029791667,"about_ca_system_score_gemma":0.0003085209,"threshold_uncertainty_score":0.99980456},"labels":[],"label_agreement":null},{"id":"W1982277563","doi":"10.1080/1055678021000012480","title":"Discovering the Characteristics of Mathematical Programs via Sampling","year":2002,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Sampling (signal processing); Computer science; Data science; Telecommunications","score_opus":0.26658484846747105,"score_gpt":0.44752384355568237,"score_spread":0.18093899508821132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982277563","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":[0.00064985023,0.00007139386,0.9977897,0.00016257151,0.00040511184,0.00036134414,0.000020849828,0.00016048925,0.000378716],"genre_scores_gemma":[0.0013094493,0.00007398741,0.9976541,0.000057615314,0.00007529866,0.00006438161,0.000023878145,0.00004235244,0.00069896126],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99816,0.0005683327,0.0005979578,0.00021304362,0.00023280134,0.00022784261],"domain_scores_gemma":[0.99366623,0.0051924903,0.00039475015,0.00046541038,0.00021831437,0.0000627859],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015227923,0.00017865337,0.0003656546,0.00006474733,0.00015747934,0.00006614114,0.00025557305,0.000093371586,0.0010387029],"category_scores_gemma":[0.013339973,0.00012688582,0.00009686704,0.0003011167,0.00015070135,0.00008430178,0.00008186537,0.00016448885,0.000011351498],"study_design_candidate":"design_other","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":[0.00002709384,0.00071561144,0.0017457274,0.00096862693,0.000160453,0.0000020721093,0.009185581,0.00672223,0.00027194442,0.073044345,0.0012399417,0.9059164],"study_design_scores_gemma":[0.0010383386,0.00023692031,0.0012443368,0.0005857059,0.0006993534,0.00010262072,0.0032796906,0.38363516,0.003042089,0.5980958,0.006772167,0.0012678272],"about_ca_topic_score_codex":0.0000019150689,"about_ca_topic_score_gemma":2.8106987e-7,"teacher_disagreement_score":0.90464854,"about_ca_system_score_codex":0.000031643383,"about_ca_system_score_gemma":0.000019551473,"threshold_uncertainty_score":0.9998745},"labels":[],"label_agreement":null},{"id":"W1999360615","doi":"10.1080/10556780903239568","title":"Bi-parametric convex quadratic optimization","year":2009,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"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; Direktion für Entwicklung und Zusammenarbeit","keywords":"Quadratic equation; Parametric statistics; Mathematical optimization; Convex optimization; Quadratic function; Regular polygon; Perturbation (astronomy); Mathematics; Convex function; Function (biology); Optimization problem; Invariant (physics); Parametric equation; Quadratic programming; Computer science; Geometry; Physics","score_opus":0.07474502751145072,"score_gpt":0.4498631699427029,"score_spread":0.3751181424312522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999360615","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":[0.000028547369,0.00027795325,0.99508613,0.0005792566,0.00028822472,0.0010677589,0.000016951155,0.001061662,0.0015935116],"genre_scores_gemma":[0.00032208947,0.00032760404,0.9961283,0.00054643024,0.00013525446,0.00008113391,0.00016874663,0.0001269327,0.0021634994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954437,0.0010667295,0.0010537335,0.0008275134,0.0008770941,0.0007312485],"domain_scores_gemma":[0.99522144,0.0017953846,0.0005388715,0.0009848033,0.0011015027,0.00035801795],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016821631,0.00050811045,0.0007300391,0.00092472666,0.00045921266,0.00025523434,0.0005644381,0.00036251036,0.0024660937],"category_scores_gemma":[0.014745334,0.00051205914,0.00019334798,0.0036606395,0.00013342958,0.00084034447,0.00010267363,0.00048296928,0.000048704],"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":[0.00003393718,0.00021037275,0.00006553362,0.000044983684,0.000031525746,0.0000065771933,0.00015769251,0.95091796,0.00001618726,0.002610016,0.00083697855,0.045068208],"study_design_scores_gemma":[0.0010177462,0.00017344802,0.00004253106,0.00005269652,0.00007341918,0.000018290106,0.00010359125,0.97683865,0.00044653405,0.020047497,0.0005929729,0.00059261825],"about_ca_topic_score_codex":0.0000036096335,"about_ca_topic_score_gemma":5.7013335e-7,"teacher_disagreement_score":0.04447559,"about_ca_system_score_codex":0.00027658054,"about_ca_system_score_gemma":0.00020139074,"threshold_uncertainty_score":0.9997331},"labels":[],"label_agreement":null},{"id":"W2035488027","doi":"10.1080/10556780903420135","title":"Optimal scenario tree reduction for stochastic streamflows in power generation planning problems","year":2009,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Water resources management and optimization","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Reduction (mathematics); Autoregressive model; Mathematical optimization; Resampling; A priori and a posteriori; Time horizon; Tree (set theory); Mathematics; Stochastic optimization; Set (abstract data type); Computer science; Algorithm; Statistics","score_opus":0.02767541781681218,"score_gpt":0.2891533921802617,"score_spread":0.26147797436344955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035488027","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":[0.004139343,0.00024577513,0.9934961,0.00007504794,0.00036640308,0.000880294,0.0000053727117,0.0005080194,0.0002836521],"genre_scores_gemma":[0.037721775,0.00002034559,0.9612364,0.000030386656,0.00016127956,0.00010965003,0.00044425396,0.00005704753,0.00021887185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869007,0.000090366004,0.00041877138,0.00032956718,0.00015104578,0.00032019708],"domain_scores_gemma":[0.99950993,0.000046051937,0.000086958404,0.000203754,0.00008978901,0.000063501255],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044822946,0.00024543516,0.0002331613,0.00035195667,0.00012045417,0.00014119693,0.00012813316,0.00014666194,0.00004787911],"category_scores_gemma":[0.00011480433,0.0002666796,0.00006558938,0.00043400197,0.000014998767,0.0004715679,0.00001537728,0.00013365845,0.000002285923],"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":[0.00002270137,0.000032962336,0.0000338672,0.000023289835,0.000014903921,6.2506626e-7,0.0009374671,0.9747776,0.00049066095,0.000042308962,0.00037470367,0.023248944],"study_design_scores_gemma":[0.00068741164,0.00010502714,0.00011434536,0.000058410464,0.000032013828,0.000002736245,0.00007607125,0.9975478,0.0005002491,0.00009623706,0.00048521065,0.00029447343],"about_ca_topic_score_codex":0.0000025262436,"about_ca_topic_score_gemma":0.0000021799376,"teacher_disagreement_score":0.033582434,"about_ca_system_score_codex":0.00013912337,"about_ca_system_score_gemma":0.000012587384,"threshold_uncertainty_score":0.99997854},"labels":[],"label_agreement":null},{"id":"W2055184420","doi":"10.1080/10556788.2012.693928","title":"Sparse interpolatory reduced-order models for simulation of light-induced molecular transformations","year":2012,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; McMaster University; National Science Foundation","keywords":"Robustness (evolution); Interpolation (computer graphics); Algorithm; Computer science; Relaxation (psychology); Excitation; Biological system; Mathematical optimization; Applied mathematics; Mathematics; Chemistry; Artificial intelligence; Physics","score_opus":0.04470793301297124,"score_gpt":0.3512505846530016,"score_spread":0.30654265164003036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055184420","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":[0.0041877055,0.000091004644,0.99387014,0.000076596116,0.00035607547,0.0004947689,0.00002469154,0.00006557672,0.00083342975],"genre_scores_gemma":[0.47105983,0.000002819196,0.5285839,0.00003322919,0.00010457746,0.00005738127,0.0000831358,0.000023363742,0.000051738996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988865,0.00017231563,0.00040465713,0.00017243896,0.00012113211,0.00024293944],"domain_scores_gemma":[0.99901146,0.00017199146,0.00019914356,0.00021713504,0.00028552444,0.00011474695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035387505,0.00016651969,0.00022368682,0.00010510109,0.000119595665,0.000021382211,0.00009571927,0.00008575653,0.00025776186],"category_scores_gemma":[0.000042710082,0.00016599723,0.00015429367,0.00027138696,0.000017702856,0.0006477245,0.000017113714,0.00010997149,0.000001850549],"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":[0.000021819746,0.000081310944,0.000038216596,0.000017189739,0.00003786075,1.0746934e-8,0.00062233064,0.9580732,0.00235629,0.0041575646,0.000056367982,0.03453786],"study_design_scores_gemma":[0.00036518974,0.000024365498,0.000004764715,0.000021647746,0.00006806023,3.1268434e-7,0.00013870416,0.97047126,0.025612859,0.002574898,0.00054083654,0.00017709253],"about_ca_topic_score_codex":0.000004745876,"about_ca_topic_score_gemma":6.510745e-8,"teacher_disagreement_score":0.46687213,"about_ca_system_score_codex":0.000020752928,"about_ca_system_score_gemma":0.000040900733,"threshold_uncertainty_score":0.6769171},"labels":[],"label_agreement":null},{"id":"W2055391493","doi":"10.1080/10556780701661393","title":"Mehrotra-type predictor-corrector algorithm revisited","year":2008,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"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":"Predictor–corrector method; Perspective (graphical); Mathematics; Algorithm; Type (biology); Point (geometry); Interior point method; Upper and lower bounds; Polynomial; Combinatorics; Discrete mathematics; Mathematical analysis; Geometry","score_opus":0.09444654323418775,"score_gpt":0.4244460149172123,"score_spread":0.32999947168302457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055391493","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":[0.00005073875,0.00039655846,0.9960336,0.00007792659,0.0005370307,0.00091058825,0.000056849487,0.0011392909,0.00079739944],"genre_scores_gemma":[0.000017236454,0.0008628267,0.9919701,0.00017519596,0.00027042735,0.00009272228,0.00023593022,0.00018334444,0.006192172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99585325,0.00107339,0.0008283785,0.0007562451,0.00084617623,0.0006425904],"domain_scores_gemma":[0.9950588,0.0018239117,0.00036398452,0.0009230621,0.0014750483,0.00035521734],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012168671,0.0004416592,0.00064734474,0.00037887206,0.0005776031,0.00007131897,0.00049980305,0.00031011793,0.0032768603],"category_scores_gemma":[0.010662939,0.00043555646,0.0001690688,0.0016035658,0.00022053615,0.00057566975,0.00018270938,0.0004994698,0.000070681104],"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":[0.00019508004,0.0007568407,0.0018290285,0.0003455489,0.0003544713,0.00015442517,0.002467129,0.70153797,0.0001838308,0.0011743909,0.031581998,0.25941932],"study_design_scores_gemma":[0.0016241367,0.00020550376,0.00013923722,0.0001239636,0.000096916905,0.00021017676,0.0001059729,0.9763507,0.0016667222,0.007279507,0.011249516,0.00094762625],"about_ca_topic_score_codex":0.000005864567,"about_ca_topic_score_gemma":5.3320434e-7,"teacher_disagreement_score":0.2748128,"about_ca_system_score_codex":0.00023438896,"about_ca_system_score_gemma":0.00026199332,"threshold_uncertainty_score":0.9998096},"labels":[],"label_agreement":null},{"id":"W2077119768","doi":"10.1080/10556780108805808","title":"The Gauss-Newton direction in semidefinite programming","year":2001,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gauss; Computer science; Newton's method; Applied mathematics; Scaling; Nonlinear system; Mathematics; Physics","score_opus":0.07224147369964559,"score_gpt":0.4460038399389775,"score_spread":0.3737623662393319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077119768","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":[0.000079587626,0.0003080866,0.99649143,0.0007756292,0.00027196412,0.00071699056,0.000002569032,0.00043036052,0.00092336495],"genre_scores_gemma":[0.000043572338,0.0012164532,0.993541,0.00005827316,0.000093057395,0.00024726638,0.000029674882,0.00008485532,0.004685863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969694,0.00095937925,0.000624803,0.00043290146,0.00046213108,0.00055141066],"domain_scores_gemma":[0.99550474,0.0031869872,0.00025237934,0.00053197704,0.00040373957,0.00012015457],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0025335448,0.00024307841,0.00028011666,0.0002510989,0.0005185401,0.00019499456,0.00030541944,0.00016706303,0.00019730357],"category_scores_gemma":[0.009757185,0.00019736214,0.00008683981,0.0018087537,0.0001134912,0.00039362913,0.00011903899,0.0003934339,0.000013381397],"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":[0.00005600652,0.00010329757,0.0016420933,0.00003088016,0.000019777284,0.000009898356,0.0002954819,0.5525414,0.00001728995,0.0018416292,0.00037239934,0.44306988],"study_design_scores_gemma":[0.0015176045,0.00010194808,0.0004369312,0.00015633878,0.000042075866,0.000068585,0.00068219926,0.8269869,0.00038587674,0.0351138,0.13378382,0.0007239009],"about_ca_topic_score_codex":0.000023339242,"about_ca_topic_score_gemma":0.000047320398,"teacher_disagreement_score":0.44234598,"about_ca_system_score_codex":0.0002295448,"about_ca_system_score_gemma":0.0000789213,"threshold_uncertainty_score":0.99858403},"labels":[],"label_agreement":null},{"id":"W2081215000","doi":"10.1080/1055678021000039175","title":"A dynamic large-update primal‐dual interior-point method for linear optimization","year":2002,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Duality gap; Duality (order theory); Interior point method; Path (computing); Mathematical optimization; Mathematics; Dual (grammatical number); Function (biology); Simple (philosophy); Optimization problem; Point (geometry); Power (physics); Computer science; Discrete mathematics","score_opus":0.05516734349933614,"score_gpt":0.4375199633420161,"score_spread":0.38235261984267993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081215000","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":[0.000004101797,0.00027528516,0.99503815,0.0008083044,0.00038486603,0.0020470326,0.00022719007,0.0009429613,0.00027208443],"genre_scores_gemma":[0.000011172928,0.0004947693,0.99433404,0.00049980154,0.00012441518,0.00055640546,0.00050327135,0.0003190659,0.0031570836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485433,0.0012078395,0.0012418695,0.0010769443,0.0006486574,0.0009703327],"domain_scores_gemma":[0.9941136,0.0024832143,0.00065718964,0.0009941986,0.0014218612,0.00032992705],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029314228,0.00060577015,0.00084121746,0.0005590385,0.0005812924,0.00019744835,0.0005256149,0.0004137915,0.005531531],"category_scores_gemma":[0.01097087,0.0006214057,0.00032339754,0.0012988617,0.00010987044,0.0008643472,0.00033405158,0.00048781346,0.00004267502],"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":[0.00007902721,0.00022009693,0.000009373057,0.00019824087,0.000097903474,0.000005136492,0.00053480925,0.9578587,0.000042589214,0.0013583744,0.0012854411,0.038310353],"study_design_scores_gemma":[0.0020459825,0.00014854824,0.0000012149546,0.000080458296,0.00012046992,0.000028922817,0.00017482157,0.9863851,0.0004496338,0.005472664,0.004442617,0.0006495508],"about_ca_topic_score_codex":0.000002098364,"about_ca_topic_score_gemma":0.000003353567,"teacher_disagreement_score":0.0376608,"about_ca_system_score_codex":0.00037685796,"about_ca_system_score_gemma":0.00009119398,"threshold_uncertainty_score":0.9996237},"labels":[],"label_agreement":null},{"id":"W2098204196","doi":"10.1080/10556788.2014.936438","title":"Penalty decomposition methods for rank minimization","year":2014,"lang":"en","type":"preprint","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rank (graph theory); Minification; Mathematical optimization; Penalty method; Mathematics; Low-rank approximation; Matrix (chemical analysis); Sequence (biology); Algorithm; Computer science; Combinatorics; Hankel matrix","score_opus":0.03348095355379413,"score_gpt":0.39550123841638335,"score_spread":0.3620202848625892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098204196","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":[0.000031952437,0.0010631084,0.9925906,0.0000625619,0.0014382728,0.0012177775,0.00004955072,0.003121834,0.00042435183],"genre_scores_gemma":[0.00036230544,0.00051493547,0.9962417,0.00017464993,0.0003737672,0.0004422501,0.0015653834,0.00023300978,0.00009202774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710554,0.00096481486,0.0007189937,0.0006623805,0.00017109132,0.00037719775],"domain_scores_gemma":[0.99703354,0.001131601,0.00031980406,0.0008244444,0.0005638808,0.00012671889],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00131417,0.00056622445,0.000771329,0.0003416247,0.00018849105,0.00021450622,0.0004076559,0.00075963285,0.00010652106],"category_scores_gemma":[0.0008537099,0.0006345143,0.0003419416,0.00023688066,0.000048971964,0.00014637188,0.00021843694,0.0004796984,0.0000034894585],"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":[0.00002178785,0.000016292775,0.000007430539,0.00022991018,0.000087815824,3.563497e-7,0.000085229236,0.85551935,0.0005204073,0.000060381808,0.004025195,0.13942581],"study_design_scores_gemma":[0.00032432508,0.000034398738,0.000015795124,0.0002918908,0.00023567505,0.0000061245664,0.000007074763,0.9556706,0.024878431,0.0078842975,0.010003632,0.0006477725],"about_ca_topic_score_codex":0.000006916818,"about_ca_topic_score_gemma":7.7076385e-7,"teacher_disagreement_score":0.13877805,"about_ca_system_score_codex":0.0001657584,"about_ca_system_score_gemma":0.00005930028,"threshold_uncertainty_score":0.9996106},"labels":[],"label_agreement":null},{"id":"W2110069759","doi":"10.1080/10556780902917735","title":"Provably near-optimal solutions for very large single-row facility layout problems","year":2009,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":110,"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":"Natural Sciences and Engineering Research Council of Canada; Princeton University","keywords":"Relaxation (psychology); Semidefinite programming; Mathematical optimization; Facility location problem; Mathematics; Linear programming relaxation; Set (abstract data type); Representation (politics); Linear programming; Matrix (chemical analysis); Quadratic equation; Computer science","score_opus":0.035586968543957785,"score_gpt":0.29406588871605993,"score_spread":0.25847892017210217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110069759","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":[0.00008996957,0.0003035402,0.99626374,0.00009224273,0.00039328748,0.00077494077,0.0002473888,0.0015490064,0.00028590616],"genre_scores_gemma":[0.009804926,0.000043372533,0.98878163,0.00011116792,0.00008246363,0.00011179007,0.0007494231,0.000050763767,0.00026446875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822307,0.000103299106,0.00043698162,0.00040759455,0.00016562831,0.0006634078],"domain_scores_gemma":[0.99890214,0.00023556202,0.000103279635,0.00038392594,0.000244301,0.00013079871],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006279285,0.00031591853,0.00031998887,0.000100007026,0.00049727614,0.00014985977,0.00017589716,0.00022278016,0.0000699523],"category_scores_gemma":[0.00085333333,0.00034195578,0.00012632416,0.0002912564,0.00005378114,0.00040977128,0.000035853885,0.00021619503,0.00000787039],"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":[0.00001887459,0.000088876295,0.00001790181,0.00008558109,0.000022021295,5.2818024e-7,0.00021259194,0.972196,0.0000916922,0.00016449217,0.0005361253,0.026565358],"study_design_scores_gemma":[0.0006398862,0.00013529853,0.000075611984,0.00002949684,0.000055265584,0.000003835806,0.000035584704,0.98680335,0.0007794669,0.0010753191,0.009939649,0.00042724263],"about_ca_topic_score_codex":0.0000018035569,"about_ca_topic_score_gemma":0.0000022753782,"teacher_disagreement_score":0.026138116,"about_ca_system_score_codex":0.00016208664,"about_ca_system_score_gemma":0.000044908385,"threshold_uncertainty_score":0.99990326},"labels":[],"label_agreement":null},{"id":"W2117753887","doi":"10.1080/10556780802414049","title":"An iterative solver-based long-step infeasible primal-dual path-following algorithm for convex QP based on a class of preconditioners","year":2008,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Preconditioner; Solver; Mathematics; Path (computing); Iterative method; Interior point method; Regular polygon; Linear programming; Mathematical optimization; Algorithm; Computer science","score_opus":0.07167003668825579,"score_gpt":0.42188048575709164,"score_spread":0.35021044906883586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117753887","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":[0.00027183758,0.000030677504,0.99646527,0.00007750423,0.00022439577,0.0019947311,0.0004141072,0.00038604654,0.00013544076],"genre_scores_gemma":[0.00092477084,0.000012859798,0.99652195,0.00027629084,0.00008024527,0.0005330462,0.001174266,0.00017031518,0.00030627495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958552,0.0010728702,0.0009161038,0.00076748076,0.0008283692,0.00055994705],"domain_scores_gemma":[0.99322206,0.0037315832,0.00061927416,0.000764984,0.001377244,0.0002848429],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014156044,0.0004679926,0.0007809319,0.0005398953,0.00046350897,0.00007755374,0.0003272926,0.00027502023,0.0005198866],"category_scores_gemma":[0.0036878926,0.0004814629,0.00033726235,0.0009253292,0.0002324463,0.0007074991,0.000042826083,0.00032728125,0.000004070125],"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":[0.0001338146,0.000672259,0.00070615846,0.00016040842,0.00008109023,0.000014941941,0.0004020517,0.97736573,0.00007525196,0.0002005331,0.0002459076,0.019941855],"study_design_scores_gemma":[0.0037314089,0.00063681975,0.00008712736,0.00015473194,0.00008399872,0.000005629958,0.000117011856,0.9858537,0.008077078,0.00069078547,0.00009880348,0.0004629471],"about_ca_topic_score_codex":0.0000042505885,"about_ca_topic_score_gemma":0.0000014797884,"teacher_disagreement_score":0.019478908,"about_ca_system_score_codex":0.00028056375,"about_ca_system_score_gemma":0.00067303627,"threshold_uncertainty_score":0.9997637},"labels":[],"label_agreement":null},{"id":"W2127485070","doi":"10.1080/10556780802079958","title":"The smoothed Monte Carlo method in robustness optimization","year":2008,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Vancouver Island University","keywords":"Robustness (evolution); Monte Carlo method; Mathematical optimization; Classification of discontinuities; Computer science; Nonlinear system; Monte Carlo integration; Robust optimization; Hybrid Monte Carlo; Algorithm; Mathematics; Markov chain Monte Carlo; Statistics; Physics","score_opus":0.0963803541285717,"score_gpt":0.40177578685875887,"score_spread":0.30539543273018716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127485070","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":[0.000081931066,0.00061938626,0.9972782,0.00039050003,0.00066618924,0.00044504783,0.0000069900393,0.00021720008,0.00029456193],"genre_scores_gemma":[0.0009932366,0.0002655431,0.99617237,0.000101288395,0.000075286975,0.00008857934,0.000007059319,0.00004985821,0.0022467887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99455917,0.0022151757,0.0010896455,0.00071594643,0.0009532604,0.00046679602],"domain_scores_gemma":[0.9897303,0.008017059,0.00035951464,0.0010155587,0.0007238888,0.00015365718],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008893084,0.0002969971,0.00048533204,0.00035156534,0.00068962324,0.00022321442,0.0010146721,0.00022299198,0.00023581389],"category_scores_gemma":[0.023358628,0.00019682688,0.00015192907,0.0023055538,0.00018278578,0.0004294939,0.00015148499,0.00030328252,0.000011832649],"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":[0.00003090048,0.000029789693,0.0005201395,0.0000037227912,0.000010443834,0.000008290699,0.0003399069,0.97397643,0.0000029228695,0.00021011556,0.0007451137,0.024122227],"study_design_scores_gemma":[0.00041722995,0.00002448324,0.0005233664,0.000017014288,0.0000143526295,0.000024572177,0.00014514446,0.9960717,0.000036343336,0.00072789687,0.0017404536,0.00025746637],"about_ca_topic_score_codex":0.000032954773,"about_ca_topic_score_gemma":0.000008369197,"teacher_disagreement_score":0.023864761,"about_ca_system_score_codex":0.00013123239,"about_ca_system_score_gemma":0.0002008121,"threshold_uncertainty_score":0.98486805},"labels":[],"label_agreement":null},{"id":"W2131570268","doi":"10.1080/10556780701374633","title":"Limiting behavior of the Alizadeh–Haeberly–Overton weighted paths in semidefinite programming","year":2007,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Semidefinite programming; Mathematics; Interior point method; Semidefinite embedding; Path (computing); Quadratically constrained quadratic program; Positive-definite matrix; Linear programming; Matrix (chemical analysis); Limit point; Mathematical optimization; Limiting; Applied mathematics; Combinatorics; Quadratic programming; Mathematical analysis; Computer science; Eigenvalues and eigenvectors","score_opus":0.06043185546229511,"score_gpt":0.4208278119046966,"score_spread":0.36039595644240147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131570268","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":[0.0022104217,0.0001580931,0.9956643,0.0000697243,0.00018733855,0.0011183484,0.000014273366,0.00019863831,0.0003788886],"genre_scores_gemma":[0.0011547831,0.00005690417,0.99800044,0.000079806094,0.00004352875,0.000105838604,0.000033548393,0.00010383791,0.00042130245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964453,0.0007253576,0.0010910648,0.00045287266,0.0006858975,0.000599502],"domain_scores_gemma":[0.995392,0.0026308731,0.0006029648,0.0006652965,0.00058350794,0.00012534515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033342561,0.0002896701,0.00044508654,0.00035908958,0.000209921,0.00005204214,0.00045792767,0.00025890503,0.00020422832],"category_scores_gemma":[0.007005816,0.00024319855,0.00015894855,0.0021479507,0.00015729495,0.00031458566,0.0002463714,0.0004671688,0.0000021399158],"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":[0.00011249652,0.00088806095,0.04337154,0.00040100963,0.0000619855,0.000037638554,0.0028975466,0.2904308,0.00068181363,0.0054453993,0.0000909386,0.65558076],"study_design_scores_gemma":[0.007975701,0.00043627838,0.009022817,0.0020067682,0.0005318376,0.00014985529,0.0044387737,0.8573525,0.07819389,0.029159006,0.00766582,0.0030667342],"about_ca_topic_score_codex":0.000023989443,"about_ca_topic_score_gemma":0.00003058839,"teacher_disagreement_score":0.65251404,"about_ca_system_score_codex":0.0002071542,"about_ca_system_score_gemma":0.0001170175,"threshold_uncertainty_score":0.9917349},"labels":[],"label_agreement":null},{"id":"W2152778795","doi":"10.1080/10556788.2012.732074","title":"A new family of high-order directions for unconstrained optimization inspired by Chebyshev and Shamanskii methods","year":2012,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Iterative Methods for Nonlinear Equations","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chebyshev filter; Newton's method; Convergence (economics); Newton's method in optimization; Chebyshev iteration; Mathematical optimization; Displacement (psychology); Computer science; Acceleration; Order (exchange); Applied mathematics; Mathematics; Iterative method; Algorithm; Local convergence; Nonlinear system; Mathematical analysis","score_opus":0.07117554534218135,"score_gpt":0.42126790628190786,"score_spread":0.3500923609397265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152778795","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":[0.00014548788,0.00096627505,0.99605006,0.00021114695,0.000538313,0.0012611381,0.00023307692,0.0003081639,0.0002863185],"genre_scores_gemma":[0.00006794634,0.00011794185,0.9973997,0.00011167492,0.00018694207,0.00021287419,0.00041139478,0.00013647367,0.0013550872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612504,0.0017110924,0.000957955,0.00047607557,0.00025893477,0.00047087146],"domain_scores_gemma":[0.9930121,0.004758814,0.0006221485,0.0004910613,0.0008424345,0.00027345767],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035574927,0.00041577636,0.0007416049,0.00032661876,0.0003468497,0.00007775055,0.00019834905,0.00031021668,0.00037421455],"category_scores_gemma":[0.011368378,0.0004134654,0.0001667213,0.0011199925,0.00015105102,0.0006456324,0.00009353038,0.00020214177,9.4002644e-7],"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":[0.00015978265,0.0008163994,0.00074148376,0.00050274824,0.00061907235,2.8838264e-7,0.005325571,0.2518337,0.008607098,0.02804951,0.0038091673,0.6995352],"study_design_scores_gemma":[0.007750436,0.0006158291,0.0003611156,0.00035387656,0.0021262153,0.00003459166,0.0012120047,0.7869993,0.056585684,0.11937595,0.02202765,0.0025573513],"about_ca_topic_score_codex":0.000060488306,"about_ca_topic_score_gemma":0.000004149562,"teacher_disagreement_score":0.6969778,"about_ca_system_score_codex":0.000096629985,"about_ca_system_score_gemma":0.0001823157,"threshold_uncertainty_score":0.99983174},"labels":[],"label_agreement":null},{"id":"W2155945500","doi":"10.1080/10556780903334682","title":"Robust portfolio selection based on a joint ellipsoidal uncertainty set","year":2009,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Portfolio; Robust optimization; Selection (genetic algorithm); Ellipsoid; Mathematics; Portfolio optimization; Mathematical optimization; Modern portfolio theory; Set (abstract data type); Project portfolio management; Computer science; Artificial intelligence; Economics; Financial economics","score_opus":0.13396194156147348,"score_gpt":0.40509937454343903,"score_spread":0.2711374329819656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155945500","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":[0.00048124758,0.00005412141,0.9932135,0.000879578,0.0005629333,0.00049571716,0.000029892193,0.00045245307,0.0038306103],"genre_scores_gemma":[0.011171122,0.00013092726,0.9835339,0.002294883,0.00022578568,0.000028552688,0.00022917712,0.000044758945,0.002340874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939399,0.0015685799,0.00127151,0.0010901857,0.0016026674,0.00052715215],"domain_scores_gemma":[0.9957004,0.001134661,0.0007768781,0.0008892422,0.0011879853,0.00031081465],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0057773483,0.00043244482,0.0006002192,0.0009938475,0.0005500369,0.0005183629,0.0005299623,0.00033454003,0.0025883554],"category_scores_gemma":[0.007013357,0.00035927564,0.00030491283,0.0033119677,0.00007387428,0.0006159252,0.000039329534,0.0003663658,0.0001193171],"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":[0.00010102922,0.00010032174,0.0011210358,0.0000015961084,0.0000070398687,0.0000039249,0.000092614995,0.8478164,0.000022430902,0.0001676868,0.009294136,0.14127176],"study_design_scores_gemma":[0.0006592015,0.00034724636,0.0018242883,0.000024382785,0.000037055517,0.000012598179,0.000078783036,0.98205316,0.0003917558,0.0031785953,0.010968456,0.00042448696],"about_ca_topic_score_codex":0.000025025243,"about_ca_topic_score_gemma":0.0000051747093,"teacher_disagreement_score":0.14084727,"about_ca_system_score_codex":0.00018373388,"about_ca_system_score_gemma":0.0003084163,"threshold_uncertainty_score":0.9998859},"labels":[],"label_agreement":null},{"id":"W2162546528","doi":"10.1080/10556788.2014.968158","title":"A derivative-free comirror algorithm for convex optimization","year":2014,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Mathematics; Mathematical optimization; Convex function; Rate of convergence; Interpolation (computer graphics); Convex optimization; Convex combination; Convergence (economics); Algorithm; Regular polygon; Computer science; Key (lock)","score_opus":0.021308854748996735,"score_gpt":0.3018796195636025,"score_spread":0.28057076481460574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162546528","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":[0.000013586647,0.00012475102,0.9963741,0.00005712953,0.00037039595,0.00048123897,0.000028491953,0.0020312082,0.0005191214],"genre_scores_gemma":[0.00022242728,0.00005830525,0.99888426,0.00023492458,0.000118363096,0.00013013971,0.00015650189,0.00010711569,0.00008793565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988048,0.00019060764,0.00031747977,0.00028258597,0.00013387341,0.00027061958],"domain_scores_gemma":[0.99847835,0.00052765,0.000095944306,0.00050361396,0.00030870567,0.000085705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004300859,0.00024843626,0.00032680482,0.00013704084,0.00015193848,0.00008119379,0.00027585824,0.00017482073,0.000120616496],"category_scores_gemma":[0.0007753563,0.00026562164,0.00009433501,0.0002766034,0.00004977751,0.00019877731,0.000062171726,0.00012093826,0.0000025734998],"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":[0.0000048931875,0.000011581109,0.000013304652,0.000017635008,0.000027442951,2.566782e-7,0.00006143774,0.85341734,0.00014436884,0.00013334605,0.002954914,0.14321345],"study_design_scores_gemma":[0.00055181794,0.000051877112,0.000014427705,0.000044773788,0.000039473714,0.0000030841866,0.000016191743,0.9801378,0.010754833,0.0016211271,0.0064534815,0.00031110132],"about_ca_topic_score_codex":0.000003966532,"about_ca_topic_score_gemma":6.6521164e-7,"teacher_disagreement_score":0.14290236,"about_ca_system_score_codex":0.00005280679,"about_ca_system_score_gemma":0.000016254358,"threshold_uncertainty_score":0.9999796},"labels":[],"label_agreement":null},{"id":"W2619018971","doi":"10.1080/10556788.2017.1322080","title":"ARC<sub>q</sub>: a new adaptive regularization by cubics","year":2017,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Arc (geometry); Corollary; Regularization (linguistics); Convergence (economics); Factorization; Computer science; Algorithm; Key (lock); Mathematics; Discrete mathematics; Geometry; Artificial intelligence","score_opus":0.02612210389689576,"score_gpt":0.29507553039938456,"score_spread":0.26895342650248877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619018971","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":[0.00031444422,0.0003516766,0.9960309,0.00006209393,0.00033567354,0.00022527018,0.000012710329,0.0013673105,0.0012999142],"genre_scores_gemma":[0.010383118,0.0003761269,0.98862064,0.00008548171,0.0001289269,0.000016137952,0.000081462196,0.00008944453,0.00021864475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989417,0.00011706825,0.00024259284,0.00028408284,0.0001718684,0.0002426587],"domain_scores_gemma":[0.99874854,0.00009297591,0.0001567337,0.0007215695,0.00015765388,0.00012250483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022266447,0.00023578237,0.00024574023,0.000086326465,0.00033195197,0.0002179279,0.00031573576,0.00019932489,0.00003595254],"category_scores_gemma":[0.00040167777,0.00026125571,0.0000743077,0.00012782538,0.00005968378,0.00040907905,0.00008485427,0.00018200208,0.0000087922945],"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":[0.000014523033,0.000015465903,0.000088664325,0.000009392312,0.00005239295,0.000003549051,0.00012922486,0.7246254,0.021117756,0.00033653624,0.020217681,0.23338944],"study_design_scores_gemma":[0.0002783459,0.000032301286,0.00009888022,0.00008814092,0.000050306862,0.0000059826248,0.000012929762,0.66134816,0.33040768,0.004488491,0.0028139446,0.00037485233],"about_ca_topic_score_codex":0.000014568578,"about_ca_topic_score_gemma":0.0000022084093,"teacher_disagreement_score":0.30928993,"about_ca_system_score_codex":0.00006466404,"about_ca_system_score_gemma":0.000037704434,"threshold_uncertainty_score":0.99998397},"labels":[],"label_agreement":null},{"id":"W2755905518","doi":"10.1080/10556788.2017.1374385","title":"Computationally relevant generalized derivatives: theory, evaluation and applications","year":2017,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Process Optimization and Integration","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"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; Novartis-MIT Center for Continuous Manufacturing; Science and Engineering Research Council; Statoil","keywords":"Extension (predicate logic); Automatic differentiation; Mathematics; Context (archaeology); Mathematical optimization; Lexicographical order; Inverse; Computer science; Calculus (dental); Algorithm","score_opus":0.029733858417360495,"score_gpt":0.36173984780559043,"score_spread":0.33200598938822995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755905518","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":[0.00013581438,0.00064812816,0.99482536,0.00012944061,0.00014657454,0.0006390884,0.000018295968,0.00041188524,0.0030453869],"genre_scores_gemma":[0.006844117,0.00055236643,0.99143255,0.00013764997,0.00009535395,0.00039353172,0.0003140908,0.000050606184,0.00017975846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870753,0.0002637797,0.00033631205,0.00028747407,0.00024497198,0.00015990782],"domain_scores_gemma":[0.9985051,0.00022455426,0.0001893435,0.0004080712,0.00058300025,0.000089922985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010979562,0.00019950372,0.00019919599,0.00012193206,0.00066164025,0.00034383463,0.00023026262,0.00011835746,0.00032314903],"category_scores_gemma":[0.001213564,0.000200267,0.000041768402,0.00013749229,0.00011086403,0.00068986166,0.000046207642,0.00011932738,0.000008847194],"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":[0.0000072114294,0.000011886285,0.00012937853,0.000029517942,0.000029510804,1.01954726e-7,0.0001812472,0.82737654,0.00013397454,0.006399236,0.00013630789,0.16556509],"study_design_scores_gemma":[0.00057005684,0.0000123062,0.0011168519,0.000024982293,0.000056231653,0.0000036601666,0.00006225093,0.9815185,0.0006640231,0.013357966,0.0023745887,0.00023859003],"about_ca_topic_score_codex":0.000002602929,"about_ca_topic_score_gemma":0.0000021823494,"teacher_disagreement_score":0.1653265,"about_ca_system_score_codex":0.00007417895,"about_ca_system_score_gemma":0.000059610746,"threshold_uncertainty_score":0.8166651},"labels":[],"label_agreement":null},{"id":"W2760492644","doi":"10.1080/10556788.2019.1692344","title":"mts: a light framework for parallelizing tree search codes","year":2019,"lang":"en","type":"preprint","venue":"Optimization methods & software","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":2,"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":"Japan Society for the Promotion of Science","keywords":"Computer science; Backtracking; Enumeration; Satisfiability; Debugging; Search tree; Parallel computing; Vertex (graph theory); Theoretical computer science; Branch and bound; Search algorithm; Algorithm; Graph; Programming language; Discrete mathematics; Mathematics","score_opus":0.0714005721263264,"score_gpt":0.40162440456002696,"score_spread":0.33022383243370057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760492644","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":[0.0000065440936,0.0011729297,0.97879916,0.0010560013,0.00140877,0.0013988055,0.000033238168,0.01593336,0.00019119073],"genre_scores_gemma":[0.00014890256,0.00018782943,0.9976665,0.00062854233,0.0002687142,0.0005451979,0.00014218176,0.00012000469,0.00029217824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956126,0.0008915589,0.0007199845,0.0015536926,0.0005376637,0.0006844703],"domain_scores_gemma":[0.99031436,0.005696697,0.00051333604,0.0024356202,0.00084869494,0.0001913066],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029953076,0.000606189,0.0008696755,0.00043527217,0.00033880217,0.0008831605,0.0024766629,0.00095633004,0.000026139956],"category_scores_gemma":[0.008078913,0.0006074924,0.00038196545,0.0006342307,0.0000617115,0.00031934687,0.0020697438,0.0010568228,0.000013792391],"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":[0.00002637981,0.000092261704,0.0009452505,0.00066654163,0.000103159386,0.0000056015842,0.0013005967,0.7556555,0.000006975278,0.0075377338,0.009641236,0.22401875],"study_design_scores_gemma":[0.00022339246,0.000102547216,0.000043639102,0.0009603516,0.00005097028,0.000009673669,0.000009206802,0.7527859,0.0005723734,0.24270868,0.0017823374,0.00075088674],"about_ca_topic_score_codex":0.0000339855,"about_ca_topic_score_gemma":8.9539867e-7,"teacher_disagreement_score":0.23517096,"about_ca_system_score_codex":0.0001883518,"about_ca_system_score_gemma":0.0004910196,"threshold_uncertainty_score":0.99963766},"labels":[],"label_agreement":null},{"id":"W3020649264","doi":"10.1080/10556788.2020.1751154","title":"Exponential augmented Lagrangian methods for equilibrium problems","year":2020,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Lagrangian; Applied mathematics; Augmented Lagrangian method; Exponential function; Mathematics; Mathematical optimization; Mathematical economics; Computer science; Mathematical analysis","score_opus":0.048229573428117165,"score_gpt":0.3630258536378706,"score_spread":0.3147962802097534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020649264","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":[0.0000020241205,0.00017050478,0.99232054,0.0056854533,0.00035331128,0.0005848153,0.000018468145,0.00067378744,0.00019112007],"genre_scores_gemma":[0.00006566908,0.000025819001,0.99666137,0.0023649887,0.0001544787,0.00013828526,0.00023004855,0.00003850892,0.00032083975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971769,0.00088329805,0.0005824039,0.00074065255,0.00026948008,0.00034725634],"domain_scores_gemma":[0.9977443,0.00065658666,0.0003138903,0.00042545554,0.0005715029,0.00028822324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012671509,0.00026381257,0.000383753,0.00017866988,0.00023968863,0.00034895452,0.0007930374,0.00014045277,0.00039434354],"category_scores_gemma":[0.0015741802,0.0002593888,0.0002719926,0.0015694186,0.000034129906,0.0008542847,0.00025314867,0.00012263632,0.000016122453],"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":[0.000018144021,0.000055077453,0.000041736253,0.000049733553,0.000085869826,4.055838e-7,0.00065110676,0.91820496,0.0010782205,0.004691908,0.00074735185,0.07437549],"study_design_scores_gemma":[0.0007095484,0.00007950629,0.0000210819,0.000009896809,0.00007363155,0.0000017685338,0.000019967509,0.98050815,0.0016813973,0.0012353993,0.015353562,0.0003060971],"about_ca_topic_score_codex":0.0000060583716,"about_ca_topic_score_gemma":4.0700772e-7,"teacher_disagreement_score":0.074069396,"about_ca_system_score_codex":0.00004659566,"about_ca_system_score_gemma":0.00011736429,"threshold_uncertainty_score":0.9999858},"labels":[],"label_agreement":null},{"id":"W3021430041","doi":"10.1080/10556788.2020.1864370","title":"Sparktope: linear programs from algorithms","year":2021,"lang":"en","type":"preprint","venue":"Optimization methods & software","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; McGill University","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Compiler; Computer science; Algorithm; Polynomial; Matching (statistics); Bipartite graph; Extension (predicate logic); TRACE (psycholinguistics); Time complexity; Linear programming; Exponential function; Graph; Programming language; Theoretical computer science; Mathematics","score_opus":0.06472395234384083,"score_gpt":0.3537934815381213,"score_spread":0.2890695291942805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021430041","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":[0.000039133352,0.0021410326,0.9910559,0.0005355574,0.0039735427,0.0006137078,0.00006244987,0.0013719567,0.00020670691],"genre_scores_gemma":[0.000019067234,0.00051281304,0.99655807,0.00039205176,0.00053879723,0.00018126558,0.0014559763,0.00007122213,0.00027071315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948321,0.0012554326,0.00079492014,0.0018661388,0.0006899954,0.0005614193],"domain_scores_gemma":[0.9954471,0.0005629772,0.0005163986,0.002449766,0.0007432008,0.00028058418],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001153813,0.0006436518,0.00085349026,0.0002394739,0.00033385816,0.00134913,0.0026060487,0.00066201686,0.0003591493],"category_scores_gemma":[0.00067680393,0.00069169933,0.0004756084,0.0011025942,0.0001373508,0.0005435194,0.0043492867,0.0012161417,0.000016193331],"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":[0.0000033030162,0.00016954982,0.00015743157,0.000066412606,0.00012264997,0.000041367763,0.0007042774,0.2696024,0.0000041223107,0.00029291908,0.00018394916,0.72865164],"study_design_scores_gemma":[0.0002960733,0.00003913993,0.0001111327,0.00030123268,0.00006692334,0.000011405801,0.000056063513,0.9799572,0.00040380633,0.011780197,0.006143112,0.0008337041],"about_ca_topic_score_codex":0.00024556278,"about_ca_topic_score_gemma":0.000010993032,"teacher_disagreement_score":0.72781795,"about_ca_system_score_codex":0.00011211041,"about_ca_system_score_gemma":0.00045118824,"threshold_uncertainty_score":0.99968755},"labels":[],"label_agreement":null},{"id":"W3085334717","doi":"10.1080/10556788.2020.1817447","title":"Improving dynamic programming for travelling salesman with precedence constraints: parallel Morin–Marsten bounding","year":2020,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":6,"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":"Bounding overwatch; Travelling salesman problem; Morin; Computer science; Mathematical optimization; Mathematics; Algorithm; Artificial intelligence","score_opus":0.025209316645392842,"score_gpt":0.301731733635452,"score_spread":0.2765224169900592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085334717","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":[0.00029435116,0.00021211029,0.9956567,0.00013631127,0.0002542092,0.0012948599,0.000029967498,0.002005277,0.00011618955],"genre_scores_gemma":[0.0056107156,0.00006187521,0.9934047,0.00012420726,0.00011673535,0.00026649472,0.00013196618,0.0002398658,0.0000434774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973738,0.00030964767,0.00068135903,0.0006810733,0.00028435243,0.00066974823],"domain_scores_gemma":[0.99798596,0.0008214994,0.00025461466,0.00033512534,0.00029410512,0.00030870535],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012157093,0.0004667733,0.0005298492,0.00014877711,0.00036654033,0.00030173306,0.00036551227,0.00022567691,0.00006535485],"category_scores_gemma":[0.001505348,0.000488814,0.00012923204,0.00080741436,0.00014998345,0.00051402947,0.00005690551,0.00037291175,0.0000031070513],"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":[0.000037328307,0.000011845857,0.00030759387,0.00038335862,0.00006461933,0.0000026222865,0.00087691174,0.8145658,0.00051422295,0.00006773654,0.0000075055027,0.18316048],"study_design_scores_gemma":[0.00095907424,0.00012687064,0.00002870076,0.00014157266,0.00012131044,0.000019298754,0.00041186195,0.99632955,0.0008795582,0.000044535296,0.00033466012,0.00060300087],"about_ca_topic_score_codex":0.0000027604967,"about_ca_topic_score_gemma":0.0000021036894,"teacher_disagreement_score":0.18255748,"about_ca_system_score_codex":0.00017487248,"about_ca_system_score_gemma":0.000104857645,"threshold_uncertainty_score":0.99975634},"labels":[],"label_agreement":null},{"id":"W4367182219","doi":"10.1080/10556788.2023.2189716","title":"Properties of semi-conjugate gradient methods for solving unsymmetric positive definite linear systems","year":2023,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"National Natural Science Foundation of China","keywords":"Conjugate gradient method; Orthogonalization; Conjugate; Derivation of the conjugate gradient method; Mathematics; Conjugate residual method; Linear system; Nonlinear conjugate gradient method; Krylov subspace; Positive-definite matrix; Mathematical analysis; Applied mathematics; Algorithm; Computer science; Gradient descent; Physics","score_opus":0.04614607138990487,"score_gpt":0.34296162124064855,"score_spread":0.29681554985074365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367182219","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":[0.00014835419,0.0012536793,0.9961274,0.000087456145,0.000999282,0.00072130415,0.000027806958,0.00056278333,0.00007192065],"genre_scores_gemma":[0.0007669631,0.00014905503,0.9981841,0.00006745084,0.000080752114,0.00015459965,0.000033432178,0.00003889493,0.00052477285],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699944,0.0012239383,0.0006067002,0.00053818914,0.00021906586,0.00041266065],"domain_scores_gemma":[0.9966099,0.0017695827,0.00037427078,0.0005353213,0.00059360516,0.000117313306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004907103,0.0002463439,0.0004714418,0.0005860605,0.0003010341,0.00014760558,0.00064959686,0.00012972235,0.000006101759],"category_scores_gemma":[0.0027036085,0.00021405975,0.00017796808,0.0021940472,0.00007058319,0.00047067547,0.0002796085,0.00013373385,0.000007971228],"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":[0.000031421656,0.00005086456,0.000038712245,0.00041151553,0.000089732945,0.0000025786132,0.001668563,0.8217518,0.0017839713,0.012724222,0.00006455518,0.1613821],"study_design_scores_gemma":[0.0003344243,0.00011253931,0.000018048755,0.00015976762,0.000034925066,0.00000917886,0.000116240495,0.9661632,0.030147377,0.0021233256,0.00051549694,0.00026546954],"about_ca_topic_score_codex":0.00002420484,"about_ca_topic_score_gemma":1.8557006e-7,"teacher_disagreement_score":0.16111663,"about_ca_system_score_codex":0.000050613067,"about_ca_system_score_gemma":0.00009642467,"threshold_uncertainty_score":0.8729103},"labels":[],"label_agreement":null},{"id":"W4390339149","doi":"10.1080/10556788.2023.2285492","title":"An ADMM based method for underdetermined box-constrained integer least squares problems","year":2023,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Underdetermined system; Solver; Integer (computer science); Mathematical optimization; Computer science; Least-squares function approximation; Integer programming; Algorithm; Tree (set theory); Mathematics; Statistics","score_opus":0.03514432945428824,"score_gpt":0.35674848051052077,"score_spread":0.3216041510562325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390339149","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":[0.0001090169,0.000055158664,0.9931631,0.00008802578,0.00028702963,0.0006340793,0.000048985006,0.0054477113,0.00016687311],"genre_scores_gemma":[0.006083064,0.00001632407,0.99286425,0.00015103226,0.00008773359,0.00023329201,0.0003399353,0.00013403402,0.00009032888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984225,0.00030678674,0.00036287587,0.00038371028,0.00014960395,0.0003745448],"domain_scores_gemma":[0.9983961,0.000703151,0.0000810442,0.0004697233,0.00022862712,0.00012138442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00080666225,0.000290082,0.00033978233,0.0003203457,0.0001487809,0.00011714136,0.00024632175,0.00019550786,0.00009836109],"category_scores_gemma":[0.00042295572,0.0002941785,0.0001341567,0.0006080022,0.000045076722,0.00022399004,0.000028154742,0.00015060874,0.0000053417716],"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":[0.000015232813,0.000017440114,0.000032202865,0.0000693022,0.000028167267,0.0000023762386,0.00012688215,0.91483694,0.0054472075,0.00007112125,0.0010525885,0.07830055],"study_design_scores_gemma":[0.00040015072,0.00009551016,0.000024991852,0.000094774055,0.00004540219,0.000004473323,0.00007565718,0.9651379,0.030457139,0.0015709706,0.0017444915,0.00034851301],"about_ca_topic_score_codex":0.000007837021,"about_ca_topic_score_gemma":0.0000046247565,"teacher_disagreement_score":0.07795204,"about_ca_system_score_codex":0.000052323172,"about_ca_system_score_gemma":0.000051000323,"threshold_uncertainty_score":0.99995106},"labels":[],"label_agreement":null},{"id":"W4391532996","doi":"10.1080/10556788.2023.2296443","title":"Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods","year":2024,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"Ministry of Science and Higher Education of the Russian Federation; Deutsche Forschungsgemeinschaft; National Science Foundation","keywords":"Mathematics; Tensor (intrinsic definition); Dual (grammatical number); Norm (philosophy); Regular polygon; Convex function; Mathematical optimization; Applied mathematics; Pure mathematics; Geometry; Political science","score_opus":0.045805575752337714,"score_gpt":0.37152345829503497,"score_spread":0.32571788254269723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391532996","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":[0.00093610835,0.0036507498,0.9920572,0.0001629488,0.00076583354,0.00075480534,0.00004656991,0.0014037295,0.00022204073],"genre_scores_gemma":[0.0011928199,0.00028527467,0.9977149,0.00008878136,0.00010959083,0.00018410316,0.00005812643,0.00012999968,0.00023644198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769074,0.00073705014,0.0005897029,0.0004674473,0.00013803988,0.00037702254],"domain_scores_gemma":[0.99608755,0.0028475523,0.00011544161,0.00044303408,0.0004008322,0.00010556454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001866698,0.00036268384,0.00053333584,0.00021173917,0.0003125479,0.00026088275,0.00019185427,0.00022571726,0.0001005038],"category_scores_gemma":[0.0009898377,0.00028622168,0.00019351872,0.0006990114,0.00020037367,0.00024350721,0.00010299558,0.0003298096,0.0000012210465],"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":[0.000047392736,0.000026536785,0.00004319092,0.00019757787,0.00031092056,0.0000026451119,0.00091929326,0.6028715,0.012616736,0.00029649056,0.0017611706,0.38090658],"study_design_scores_gemma":[0.00024335107,0.000102551894,0.00008541126,0.00012266237,0.00031392588,0.000041231506,0.00025765406,0.92710286,0.049481887,0.0004016055,0.021516206,0.00033064515],"about_ca_topic_score_codex":0.000015492667,"about_ca_topic_score_gemma":8.583255e-7,"teacher_disagreement_score":0.38057593,"about_ca_system_score_codex":0.0000658816,"about_ca_system_score_gemma":0.000068446476,"threshold_uncertainty_score":0.999959},"labels":[],"label_agreement":null},{"id":"W4392808390","doi":"10.1080/10556788.2024.2322095","title":"Decentralized gradient tracking with local steps","year":2024,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Tracking (education); Computer science; Psychology","score_opus":0.014664457297787563,"score_gpt":0.28191548322002674,"score_spread":0.2672510259222392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392808390","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":[0.0002231413,0.001394959,0.996113,0.000048285332,0.00053670234,0.00019880757,0.00000814625,0.0012587862,0.00021817254],"genre_scores_gemma":[0.019392407,0.00033299456,0.97984904,0.00005500984,0.000051687763,0.000018142351,0.000115253344,0.00010502073,0.00008046069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998896,0.00012018751,0.00026175092,0.00026170342,0.00018883127,0.00027153306],"domain_scores_gemma":[0.99939734,0.00017792091,0.000022777496,0.00019555315,0.00009546132,0.0001109331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026599015,0.00020563514,0.00019603861,0.00014104081,0.000083167564,0.00018808836,0.00008858065,0.000104558436,0.00020641793],"category_scores_gemma":[0.000063831685,0.00017972942,0.000060864993,0.00058213447,0.000042025153,0.00020776791,0.000010837119,0.0001553167,0.000011535825],"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":[0.000008291304,0.000009587888,0.00007354576,0.00011907082,0.00004412156,0.000014990601,0.00020851768,0.90383655,0.000051203053,0.00056785106,0.00023560456,0.09483068],"study_design_scores_gemma":[0.00023755623,0.000030856652,0.000037339927,0.00013568961,0.000062399755,0.000015916708,0.000059356564,0.992247,0.0015860026,0.00012147993,0.005219753,0.00024669192],"about_ca_topic_score_codex":0.0000048352413,"about_ca_topic_score_gemma":0.0000035195146,"teacher_disagreement_score":0.09458399,"about_ca_system_score_codex":0.00012072552,"about_ca_system_score_gemma":0.000033944012,"threshold_uncertainty_score":0.73291534},"labels":[],"label_agreement":null},{"id":"W4398183091","doi":"10.1080/10556788.2024.2346641","title":"Computing subgradients of convex relaxations for solutions of parametric ordinary differential equations","year":2024,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Optimization Algorithms Research","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Ordinary differential equation; Parametric statistics; Regular polygon; Applied mathematics; Mathematical optimization; Subderivative; Differential (mechanical device); Convex optimization; Mathematical analysis; Differential equation; Statistics; Geometry","score_opus":0.14334393503401258,"score_gpt":0.4705291716964854,"score_spread":0.3271852366624728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398183091","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":[0.00016658497,0.00041733123,0.9972161,0.00008442465,0.00041837618,0.0009841162,0.0003714152,0.0002728882,0.00006876578],"genre_scores_gemma":[0.010072422,0.00006998765,0.9887226,0.000006644984,0.00005715628,0.00009931327,0.00027897634,0.00007751795,0.0006153434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974609,0.00044390588,0.00094563136,0.0003946682,0.00040534252,0.0003495412],"domain_scores_gemma":[0.9886378,0.009308412,0.00041419518,0.00041215392,0.0011237221,0.00010370492],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0010117777,0.00021000182,0.00047299644,0.0008343852,0.00027995926,0.00003982211,0.0002571897,0.00016243558,0.00031189632],"category_scores_gemma":[0.014102086,0.00021318784,0.00023653507,0.0023137631,0.0001818862,0.00028563352,0.00013809156,0.00020807973,0.0000025760387],"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":[0.000030081912,0.00031829256,0.00007961413,0.0008367103,0.00019699408,8.2279195e-7,0.0006288207,0.9080796,0.0002076647,0.056555234,0.00074633484,0.032319795],"study_design_scores_gemma":[0.0004755427,0.00009940121,0.000028587729,0.00017054814,0.0001588628,0.000003150629,0.00012038188,0.97416866,0.00081695575,0.023596616,0.00017471981,0.00018660446],"about_ca_topic_score_codex":0.000007273466,"about_ca_topic_score_gemma":0.0000012106832,"teacher_disagreement_score":0.066089,"about_ca_system_score_codex":0.0001165101,"about_ca_system_score_gemma":0.00021548936,"threshold_uncertainty_score":0.99420255},"labels":[],"label_agreement":null},{"id":"W4413812281","doi":"10.1080/10556788.2025.2531478","title":"A general framework for floating point error analysis of first-order simplex derivatives","year":2025,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Numerical Methods and Algorithms","field":"Computer Science","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Simplex; Order (exchange); Point (geometry); Mathematical optimization; Simplex algorithm; Algorithm; Applied mathematics; Combinatorics; Linear programming; Geometry; Economics","score_opus":0.035310281368347456,"score_gpt":0.39890740731886803,"score_spread":0.36359712595052057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413812281","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":[0.00005298055,0.00015947699,0.99804074,0.00090356846,0.0002908363,0.0003109029,0.000018340612,0.00016761363,0.00005555049],"genre_scores_gemma":[0.00007576203,0.000018570845,0.99906504,0.00056785316,0.000038752336,0.00006200405,0.000021341864,0.000015525944,0.00013513722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978712,0.0005430434,0.0005625377,0.0005416143,0.00018403743,0.0002975763],"domain_scores_gemma":[0.99490327,0.0035449232,0.0003251129,0.0005935407,0.0005547314,0.000078428115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011964438,0.00020488324,0.0005829681,0.00044086183,0.00025598955,0.00010897641,0.0005922957,0.00013212493,0.00008196735],"category_scores_gemma":[0.006647006,0.00018664188,0.00028275774,0.00495326,0.00006374895,0.00027857922,0.00024822517,0.0001308767,2.836585e-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":[0.000009488794,0.000057575155,0.0009857024,0.00005249131,0.00039021386,3.498028e-7,0.000493608,0.67473334,0.00005664668,0.025729612,0.000049920647,0.29744104],"study_design_scores_gemma":[0.00019243587,0.000042104148,0.0005900576,0.000041246978,0.00021808752,2.038167e-7,0.00006539712,0.95865184,0.0011704428,0.03777865,0.0010609363,0.00018862168],"about_ca_topic_score_codex":0.000022759139,"about_ca_topic_score_gemma":0.000001670962,"teacher_disagreement_score":0.29725242,"about_ca_system_score_codex":0.000041203955,"about_ca_system_score_gemma":0.00007412476,"threshold_uncertainty_score":0.79575676},"labels":[],"label_agreement":null},{"id":"W4413826392","doi":"10.1080/10556788.2025.2541095","title":"Blackbox optimization for origami-inspired bistable structures","year":2025,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Advanced Materials and Mechanics","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":"Group for Research in Decision Analysis; McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bistability; Mathematics; Mathematical optimization","score_opus":0.012790296069541095,"score_gpt":0.3155962837775207,"score_spread":0.3028059877079796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413826392","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":[0.00006495909,0.00040202937,0.99601436,0.000028494314,0.001643354,0.0005256538,0.000064784064,0.0008791182,0.0003772334],"genre_scores_gemma":[0.0003235259,0.0002960079,0.99820757,0.00012207462,0.000079962796,0.00013221534,0.00023961063,0.00006938346,0.0005296284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898076,0.000063064246,0.00033718444,0.0002623336,0.000084240666,0.00027243153],"domain_scores_gemma":[0.9992363,0.00016264214,0.00006831488,0.00027833812,0.0001966698,0.000057727335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027147352,0.00021893422,0.0002837866,0.00013976738,0.00015845106,0.00008508522,0.00014647267,0.00017095866,0.0002367303],"category_scores_gemma":[0.00044921774,0.00022654203,0.00006765163,0.00038232555,0.000017966076,0.00022651,0.000033474655,0.00007280627,0.0000011947308],"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":[0.000020533435,0.00000753267,0.0000030434915,0.00019958046,0.000029247409,2.467918e-7,0.000038788363,0.97438633,0.001512868,0.0035037568,0.0006041611,0.019693935],"study_design_scores_gemma":[0.0005526068,0.000020177815,0.0000024574972,0.000038971477,0.000058259706,7.796861e-7,0.00003358122,0.94538724,0.032475,0.005945203,0.015243375,0.00024234192],"about_ca_topic_score_codex":0.0000018851376,"about_ca_topic_score_gemma":8.0652086e-7,"teacher_disagreement_score":0.03096213,"about_ca_system_score_codex":0.00009271751,"about_ca_system_score_gemma":0.000044811623,"threshold_uncertainty_score":0.9238116},"labels":[],"label_agreement":null},{"id":"W4414845944","doi":"10.1080/10556788.2025.2545846","title":"Near-optimal algorithm with complexity separation for strongly convex-strongly concave composite saddle point problems","year":2025,"lang":"en","type":"article","venue":"Optimization methods & software","topic":"Optimization and Variational Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Optech (Canada)","funders":"Ministry of Science and Higher Education of the Russian Federation","keywords":"Saddle point; Separation (statistics); Point (geometry); Composite number; Computational complexity theory; Separation method","score_opus":0.02667676483093195,"score_gpt":0.33738743854053665,"score_spread":0.3107106737096047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414845944","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":[0.000017769598,0.00008888775,0.9961523,0.0010917725,0.00019754894,0.0009683821,0.000084846986,0.0006740179,0.00072450016],"genre_scores_gemma":[0.0004287802,0.000012119754,0.9959012,0.00045843015,0.00004326015,0.00020007168,0.000632296,0.000043120293,0.0022806672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748003,0.00043711122,0.000576328,0.0007615262,0.0003574652,0.00038756293],"domain_scores_gemma":[0.99751085,0.00044149402,0.00040315106,0.0005696308,0.0009257347,0.00014911141],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007802424,0.00034182423,0.0004958263,0.0002719198,0.0006037888,0.0005975144,0.00056166097,0.00015104198,0.00012870696],"category_scores_gemma":[0.0001554069,0.00031925717,0.00015497433,0.0013128074,0.00016547473,0.00079937244,0.0001630377,0.00015950808,0.000006102866],"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":[0.000032486423,0.00010953734,0.0004918662,0.0000407203,0.00016804504,6.5010295e-7,0.0002400101,0.9707047,0.00001668295,0.018789146,0.0009871909,0.008418981],"study_design_scores_gemma":[0.0015326354,0.00015139107,0.00027375369,0.000049201146,0.00011468812,0.0000046428067,0.00004946433,0.99369425,0.00024181134,0.0008624662,0.0026773724,0.00034833406],"about_ca_topic_score_codex":0.00005807124,"about_ca_topic_score_gemma":0.000013994356,"teacher_disagreement_score":0.022989556,"about_ca_system_score_codex":0.0001345199,"about_ca_system_score_gemma":0.00032010252,"threshold_uncertainty_score":0.999926},"labels":[],"label_agreement":null}]}